diff --git a/CHANGELOG.md b/CHANGELOG.md index 8faba3d1..39c7fb1e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,6 @@ # @vladmandic/human - Version: **3.3.2** + Version: **3.3.3** Description: **Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition** Author: **Vladimir Mandic ** @@ -9,8 +9,13 @@ ## Changelog -### **HEAD -> main** 2024/09/11 mandic00@live.com +### **3.3.3** 2024/10/14 mandic00@live.com +- add loaded property to model stats and mark models not loaded correctly. + +### **origin/main** 2024/09/11 mandic00@live.com + +- release build ### **3.3.2** 2024/09/11 mandic00@live.com @@ -21,9 +26,6 @@ - add config.face.detector.square option - human 3.3 alpha test run - human 3.3 alpha with new build environment - -### **origin/main** 2024/04/17 mandic00@live.com - - release rebuild - fix flazeface tensor scale and update build platform diff --git a/demo/faceid/index.js b/demo/faceid/index.js index 587efa04..2bd9ee4a 100644 --- a/demo/faceid/index.js +++ b/demo/faceid/index.js @@ -4,369 +4,6 @@ author: ' */ - -// demo/faceid/index.ts -import * as H from "../../dist/human.esm.js"; - -// demo/faceid/indexdb.ts -var db; -var database = "human"; -var table = "person"; -var log = (...msg) => console.log("indexdb", ...msg); -async function open() { - if (db) return true; - return new Promise((resolve) => { - const request = indexedDB.open(database, 1); - request.onerror = (evt) => log("error:", evt); - request.onupgradeneeded = (evt) => { - log("create:", evt.target); - db = evt.target.result; - db.createObjectStore(table, { keyPath: "id", autoIncrement: true }); - }; - request.onsuccess = (evt) => { - db = evt.target.result; - log("open:", db); - resolve(true); - }; - }); -} -async function load() { - const faceDB = []; - if (!db) await open(); - return new Promise((resolve) => { - const cursor = db.transaction([table], "readwrite").objectStore(table).openCursor(null, "next"); - cursor.onerror = (evt) => log("load error:", evt); - cursor.onsuccess = (evt) => { - if (evt.target.result) { - faceDB.push(evt.target.result.value); - evt.target.result.continue(); - } else { - resolve(faceDB); - } - }; - }); -} -async function count() { - if (!db) await open(); - return new Promise((resolve) => { - const store = db.transaction([table], "readwrite").objectStore(table).count(); - store.onerror = (evt) => log("count error:", evt); - store.onsuccess = () => resolve(store.result); - }); -} -async function save(faceRecord) { - if (!db) await open(); - const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; - db.transaction([table], "readwrite").objectStore(table).put(newRecord); - log("save:", newRecord); -} -async function remove(faceRecord) { - if (!db) await open(); - db.transaction([table], "readwrite").objectStore(table).delete(faceRecord.id); - log("delete:", faceRecord); -} - -// demo/faceid/index.ts -var humanConfig = { - // user configuration for human, used to fine-tune behavior - cacheSensitivity: 0.01, - modelBasePath: "../../models", - filter: { enabled: true, equalization: true }, - // lets run with histogram equilizer - debug: true, - face: { - enabled: true, - detector: { rotation: true, return: true, mask: false }, - // return tensor is used to get detected face image - description: { enabled: true }, - // default model for face descriptor extraction is faceres - // mobilefacenet: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model - // insightface: { enabled: true, modelPath: 'https://vladmandic.github.io/insightface/models/insightface-mobilenet-swish.json' }, // alternative model - iris: { enabled: true }, - // needed to determine gaze direction - emotion: { enabled: false }, - // not needed - antispoof: { enabled: true }, - // enable optional antispoof module - liveness: { enabled: true } - // enable optional liveness module - }, - body: { enabled: false }, - hand: { enabled: false }, - object: { enabled: false }, - gesture: { enabled: true } - // parses face and iris gestures -}; -var matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; -var options = { - minConfidence: 0.6, - // overal face confidence for box, face, gender, real, live - minSize: 224, - // min input to face descriptor model before degradation - maxTime: 3e4, - // max time before giving up - blinkMin: 10, - // minimum duration of a valid blink - blinkMax: 800, - // maximum duration of a valid blink - threshold: 0.5, - // minimum similarity - distanceMin: 0.4, - // closest that face is allowed to be to the cammera in cm - distanceMax: 1, - // farthest that face is allowed to be to the cammera in cm - mask: humanConfig.face.detector.mask, - rotation: humanConfig.face.detector.rotation, - ...matchOptions -}; -var ok = { - // must meet all rules - faceCount: { status: false, val: 0 }, - faceConfidence: { status: false, val: 0 }, - facingCenter: { status: false, val: 0 }, - lookingCenter: { status: false, val: 0 }, - blinkDetected: { status: false, val: 0 }, - faceSize: { status: false, val: 0 }, - antispoofCheck: { status: false, val: 0 }, - livenessCheck: { status: false, val: 0 }, - distance: { status: false, val: 0 }, - age: { status: false, val: 0 }, - gender: { status: false, val: 0 }, - timeout: { status: true, val: 0 }, - descriptor: { status: false, val: 0 }, - elapsedMs: { status: void 0, val: 0 }, - // total time while waiting for valid face - detectFPS: { status: void 0, val: 0 }, - // mark detection fps performance - drawFPS: { status: void 0, val: 0 } - // mark redraw fps performance -}; -var allOk = () => ok.faceCount.status && ok.faceSize.status && ok.blinkDetected.status && ok.facingCenter.status && ok.lookingCenter.status && ok.faceConfidence.status && ok.antispoofCheck.status && ok.livenessCheck.status && ok.distance.status && ok.descriptor.status && ok.age.status && ok.gender.status; -var current = { face: null, record: null }; -var blink = { - // internal timers for blink start/end/duration - start: 0, - end: 0, - time: 0 -}; -var human = new H.Human(humanConfig); -human.env.perfadd = false; -human.draw.options.font = 'small-caps 18px "Lato"'; -human.draw.options.lineHeight = 20; -var dom = { - // grab instances of dom objects so we dont have to look them up later - video: document.getElementById("video"), - canvas: document.getElementById("canvas"), - log: document.getElementById("log"), - fps: document.getElementById("fps"), - match: document.getElementById("match"), - name: document.getElementById("name"), - save: document.getElementById("save"), - delete: document.getElementById("delete"), - retry: document.getElementById("retry"), - source: document.getElementById("source"), - ok: document.getElementById("ok") -}; -var timestamp = { detect: 0, draw: 0 }; -var startTime = 0; -var log2 = (...msg) => { - dom.log.innerText += msg.join(" ") + "\n"; - console.log(...msg); -}; -async function webCam() { - const cameraOptions = { audio: false, video: { facingMode: "user", resizeMode: "none", width: { ideal: document.body.clientWidth } } }; - const stream = await navigator.mediaDevices.getUserMedia(cameraOptions); - const ready = new Promise((resolve) => { - dom.video.onloadeddata = () => resolve(true); - }); - dom.video.srcObject = stream; - void dom.video.play(); - await ready; - dom.canvas.width = dom.video.videoWidth; - dom.canvas.height = dom.video.videoHeight; - dom.canvas.style.width = "50%"; - dom.canvas.style.height = "50%"; - if (human.env.initial) log2("video:", dom.video.videoWidth, dom.video.videoHeight, "|", stream.getVideoTracks()[0].label); - dom.canvas.onclick = () => { - if (dom.video.paused) void dom.video.play(); - else dom.video.pause(); - }; -} -async function detectionLoop() { - var _a; - if (!dom.video.paused) { - if ((_a = current.face) == null ? void 0 : _a.tensor) human.tf.dispose(current.face.tensor); - await human.detect(dom.video); - const now = human.now(); - ok.detectFPS.val = Math.round(1e4 / (now - timestamp.detect)) / 10; - timestamp.detect = now; - requestAnimationFrame(detectionLoop); - } -} -function drawValidationTests() { - let y = 32; - for (const [key, val] of Object.entries(ok)) { - let el = document.getElementById(`ok-${key}`); - if (!el) { - el = document.createElement("div"); - el.id = `ok-${key}`; - el.innerText = key; - el.className = "ok"; - el.style.top = `${y}px`; - dom.ok.appendChild(el); - } - if (typeof val.status === "boolean") el.style.backgroundColor = val.status ? "lightgreen" : "lightcoral"; - const status = val.status ? "ok" : "fail"; - el.innerText = `${key}: ${val.val === 0 ? status : val.val}`; - y += 28; - } -} -async function validationLoop() { - var _a; - const interpolated = human.next(human.result); - human.draw.canvas(dom.video, dom.canvas); - await human.draw.all(dom.canvas, interpolated); - const now = human.now(); - ok.drawFPS.val = Math.round(1e4 / (now - timestamp.draw)) / 10; - timestamp.draw = now; - ok.faceCount.val = human.result.face.length; - ok.faceCount.status = ok.faceCount.val === 1; - if (ok.faceCount.status) { - const gestures = Object.values(human.result.gesture).map((gesture) => gesture.gesture); - if (gestures.includes("blink left eye") || gestures.includes("blink right eye")) blink.start = human.now(); - if (blink.start > 0 && !gestures.includes("blink left eye") && !gestures.includes("blink right eye")) blink.end = human.now(); - ok.blinkDetected.status = ok.blinkDetected.status || Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax; - if (ok.blinkDetected.status && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start); - ok.facingCenter.status = gestures.includes("facing center"); - ok.lookingCenter.status = gestures.includes("looking center"); - ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0; - ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence; - ok.antispoofCheck.val = human.result.face[0].real || 0; - ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence; - ok.livenessCheck.val = human.result.face[0].live || 0; - ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence; - ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]); - ok.faceSize.status = ok.faceSize.val >= options.minSize; - ok.distance.val = human.result.face[0].distance || 0; - ok.distance.status = ok.distance.val >= options.distanceMin && ok.distance.val <= options.distanceMax; - ok.descriptor.val = ((_a = human.result.face[0].embedding) == null ? void 0 : _a.length) || 0; - ok.descriptor.status = ok.descriptor.val > 0; - ok.age.val = human.result.face[0].age || 0; - ok.age.status = ok.age.val > 0; - ok.gender.val = human.result.face[0].genderScore || 0; - ok.gender.status = ok.gender.val >= options.minConfidence; - } - ok.timeout.status = ok.elapsedMs.val <= options.maxTime; - drawValidationTests(); - if (allOk() || !ok.timeout.status) { - dom.video.pause(); - return human.result.face[0]; - } - ok.elapsedMs.val = Math.trunc(human.now() - startTime); - return new Promise((resolve) => { - setTimeout(async () => { - await validationLoop(); - resolve(human.result.face[0]); - }, 30); - }); -} -async function saveRecords() { - var _a, _b, _c, _d; - if (dom.name.value.length > 0) { - const image = (_a = dom.canvas.getContext("2d")) == null ? void 0 : _a.getImageData(0, 0, dom.canvas.width, dom.canvas.height); - const rec = { id: 0, name: dom.name.value, descriptor: (_b = current.face) == null ? void 0 : _b.embedding, image }; - await save(rec); - log2("saved face record:", rec.name, "descriptor length:", (_d = (_c = current.face) == null ? void 0 : _c.embedding) == null ? void 0 : _d.length); - log2("known face records:", await count()); - } else { - log2("invalid name"); - } -} -async function deleteRecord() { - if (current.record && current.record.id > 0) { - await remove(current.record); - } -} -async function detectFace() { - var _a, _b, _c, _d; - dom.canvas.style.height = ""; - (_a = dom.canvas.getContext("2d")) == null ? void 0 : _a.clearRect(0, 0, options.minSize, options.minSize); - if (!((_b = current == null ? void 0 : current.face) == null ? void 0 : _b.tensor) || !((_c = current == null ? void 0 : current.face) == null ? void 0 : _c.embedding)) return false; - console.log("face record:", current.face); - log2(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${100 * (current.face.distance || 0)}cm/${Math.round(100 * (current.face.distance || 0) / 2.54)}in`); - await human.tf.browser.draw(current.face.tensor, dom.canvas); - if (await count() === 0) { - log2("face database is empty: nothing to compare face with"); - document.body.style.background = "black"; - dom.delete.style.display = "none"; - return false; - } - const db2 = await load(); - const descriptors = db2.map((rec) => rec.descriptor).filter((desc) => desc.length > 0); - const res = human.match.find(current.face.embedding, descriptors, matchOptions); - current.record = db2[res.index] || null; - if (current.record) { - log2(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1e3 * res.similarity) / 10}%`); - dom.name.value = current.record.name; - dom.source.style.display = ""; - (_d = dom.source.getContext("2d")) == null ? void 0 : _d.putImageData(current.record.image, 0, 0); - } - document.body.style.background = res.similarity > options.threshold ? "darkgreen" : "maroon"; - return res.similarity > options.threshold; -} -async function main() { - var _a, _b, _c, _d; - ok.faceCount.status = false; - ok.faceConfidence.status = false; - ok.facingCenter.status = false; - ok.blinkDetected.status = false; - ok.faceSize.status = false; - ok.antispoofCheck.status = false; - ok.livenessCheck.status = false; - ok.age.status = false; - ok.gender.status = false; - ok.elapsedMs.val = 0; - dom.match.style.display = "none"; - dom.retry.style.display = "none"; - dom.source.style.display = "none"; - dom.canvas.style.height = "50%"; - document.body.style.background = "black"; - await webCam(); - await detectionLoop(); - startTime = human.now(); - current.face = await validationLoop(); - dom.canvas.width = ((_b = (_a = current.face) == null ? void 0 : _a.tensor) == null ? void 0 : _b.shape[1]) || options.minSize; - dom.canvas.height = ((_d = (_c = current.face) == null ? void 0 : _c.tensor) == null ? void 0 : _d.shape[0]) || options.minSize; - dom.source.width = dom.canvas.width; - dom.source.height = dom.canvas.height; - dom.canvas.style.width = ""; - dom.match.style.display = "flex"; - dom.save.style.display = "flex"; - dom.delete.style.display = "flex"; - dom.retry.style.display = "block"; - if (!allOk()) { - log2("did not find valid face"); - return false; - } - return detectFace(); -} -async function init() { - var _a, _b; - log2("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]); - log2("options:", JSON.stringify(options).replace(/{|}|"|\[|\]/g, "").replace(/,/g, " ")); - log2("initializing webcam..."); - await webCam(); - log2("loading human models..."); - await human.load(); - log2("initializing human..."); - log2("face embedding model:", humanConfig.face.description.enabled ? "faceres" : "", ((_a = humanConfig.face["mobilefacenet"]) == null ? void 0 : _a.enabled) ? "mobilefacenet" : "", ((_b = humanConfig.face["insightface"]) == null ? void 0 : _b.enabled) ? "insightface" : ""); - log2("loading face database..."); - log2("known face records:", await count()); - dom.retry.addEventListener("click", main); - dom.save.addEventListener("click", saveRecords); - dom.delete.addEventListener("click", deleteRecord); - await human.warmup(); - await main(); -} -window.onload = init; +import*as S from"../../dist/human.esm.js";var l,L="human",f="person",v=(...a)=>console.log("indexdb",...a);async function h(){return l?!0:new Promise(a=>{let n=indexedDB.open(L,1);n.onerror=o=>v("error:",o),n.onupgradeneeded=o=>{v("create:",o.target),l=o.target.result,l.createObjectStore(f,{keyPath:"id",autoIncrement:!0})},n.onsuccess=o=>{l=o.target.result,v("open:",l),a(!0)}})}async function C(){let a=[];return l||await h(),new Promise(n=>{let o=l.transaction([f],"readwrite").objectStore(f).openCursor(null,"next");o.onerror=i=>v("load error:",i),o.onsuccess=i=>{i.target.result?(a.push(i.target.result.value),i.target.result.continue()):n(a)}})}async function b(){return l||await h(),new Promise(a=>{let n=l.transaction([f],"readwrite").objectStore(f).count();n.onerror=o=>v("count error:",o),n.onsuccess=()=>a(n.result)})}async function x(a){l||await h();let n={name:a.name,descriptor:a.descriptor,image:a.image};l.transaction([f],"readwrite").objectStore(f).put(n),v("save:",n)}async function D(a){l||await h(),l.transaction([f],"readwrite").objectStore(f).delete(a.id),v("delete:",a)}var g={cacheSensitivity:.01,modelBasePath:"../../models",filter:{enabled:!0,equalization:!0},debug:!0,face:{enabled:!0,detector:{rotation:!0,return:!0,mask:!1},description:{enabled:!0},iris:{enabled:!0},emotion:{enabled:!1},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},gesture:{enabled:!0}},B={order:2,multiplier:25,min:.2,max:.8},r={minConfidence:.6,minSize:224,maxTime:3e4,blinkMin:10,blinkMax:800,threshold:.5,distanceMin:.4,distanceMax:1,mask:g.face.detector.mask,rotation:g.face.detector.rotation,...B},e={faceCount:{status:!1,val:0},faceConfidence:{status:!1,val:0},facingCenter:{status:!1,val:0},lookingCenter:{status:!1,val:0},blinkDetected:{status:!1,val:0},faceSize:{status:!1,val:0},antispoofCheck:{status:!1,val:0},livenessCheck:{status:!1,val:0},distance:{status:!1,val:0},age:{status:!1,val:0},gender:{status:!1,val:0},timeout:{status:!0,val:0},descriptor:{status:!1,val:0},elapsedMs:{status:void 0,val:0},detectFPS:{status:void 0,val:0},drawFPS:{status:void 0,val:0}},E=()=>e.faceCount.status&&e.faceSize.status&&e.blinkDetected.status&&e.facingCenter.status&&e.lookingCenter.status&&e.faceConfidence.status&&e.antispoofCheck.status&&e.livenessCheck.status&&e.distance.status&&e.descriptor.status&&e.age.status&&e.gender.status,c={face:null,record:null},u={start:0,end:0,time:0},s=new S.Human(g);s.env.perfadd=!1;s.draw.options.font='small-caps 18px "Lato"';s.draw.options.lineHeight=20;var t={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("fps"),match:document.getElementById("match"),name:document.getElementById("name"),save:document.getElementById("save"),delete:document.getElementById("delete"),retry:document.getElementById("retry"),source:document.getElementById("source"),ok:document.getElementById("ok")},y={detect:0,draw:0},I=0,d=(...a)=>{t.log.innerText+=a.join(" ")+` +`,console.log(...a)};async function H(){let a={audio:!1,video:{facingMode:"user",resizeMode:"none",width:{ideal:document.body.clientWidth}}},n=await navigator.mediaDevices.getUserMedia(a),o=new Promise(i=>{t.video.onloadeddata=()=>i(!0)});t.video.srcObject=n,t.video.play(),await o,t.canvas.width=t.video.videoWidth,t.canvas.height=t.video.videoHeight,t.canvas.style.width="50%",t.canvas.style.height="50%",s.env.initial&&d("video:",t.video.videoWidth,t.video.videoHeight,"|",n.getVideoTracks()[0].label),t.canvas.onclick=()=>{t.video.paused?t.video.play():t.video.pause()}}async function T(){var a;if(!t.video.paused){(a=c.face)!=null&&a.tensor&&s.tf.dispose(c.face.tensor),await s.detect(t.video);let n=s.now();e.detectFPS.val=Math.round(1e4/(n-y.detect))/10,y.detect=n,requestAnimationFrame(T)}}function P(){let a=32;for(let[n,o]of Object.entries(e)){let i=document.getElementById(`ok-${n}`);i||(i=document.createElement("div"),i.id=`ok-${n}`,i.innerText=n,i.className="ok",i.style.top=`${a}px`,t.ok.appendChild(i)),typeof o.status=="boolean"&&(i.style.backgroundColor=o.status?"lightgreen":"lightcoral");let m=o.status?"ok":"fail";i.innerText=`${n}: ${o.val===0?m:o.val}`,a+=28}}async function R(){var o;let a=s.next(s.result);s.draw.canvas(t.video,t.canvas),await s.draw.all(t.canvas,a);let n=s.now();if(e.drawFPS.val=Math.round(1e4/(n-y.draw))/10,y.draw=n,e.faceCount.val=s.result.face.length,e.faceCount.status=e.faceCount.val===1,e.faceCount.status){let i=Object.values(s.result.gesture).map(m=>m.gesture);(i.includes("blink left eye")||i.includes("blink right eye"))&&(u.start=s.now()),u.start>0&&!i.includes("blink left eye")&&!i.includes("blink right eye")&&(u.end=s.now()),e.blinkDetected.status=e.blinkDetected.status||Math.abs(u.end-u.start)>r.blinkMin&&Math.abs(u.end-u.start)=r.minConfidence,e.antispoofCheck.val=s.result.face[0].real||0,e.antispoofCheck.status=e.antispoofCheck.val>=r.minConfidence,e.livenessCheck.val=s.result.face[0].live||0,e.livenessCheck.status=e.livenessCheck.val>=r.minConfidence,e.faceSize.val=Math.min(s.result.face[0].box[2],s.result.face[0].box[3]),e.faceSize.status=e.faceSize.val>=r.minSize,e.distance.val=s.result.face[0].distance||0,e.distance.status=e.distance.val>=r.distanceMin&&e.distance.val<=r.distanceMax,e.descriptor.val=((o=s.result.face[0].embedding)==null?void 0:o.length)||0,e.descriptor.status=e.descriptor.val>0,e.age.val=s.result.face[0].age||0,e.age.status=e.age.val>0,e.gender.val=s.result.face[0].genderScore||0,e.gender.status=e.gender.val>=r.minConfidence}return e.timeout.status=e.elapsedMs.val<=r.maxTime,P(),E()||!e.timeout.status?(t.video.pause(),s.result.face[0]):(e.elapsedMs.val=Math.trunc(s.now()-I),new Promise(i=>{setTimeout(async()=>{await R(),i(s.result.face[0])},30)}))}async function F(){var a,n,o,i;if(t.name.value.length>0){let m=(a=t.canvas.getContext("2d"))==null?void 0:a.getImageData(0,0,t.canvas.width,t.canvas.height),p={id:0,name:t.name.value,descriptor:(n=c.face)==null?void 0:n.embedding,image:m};await x(p),d("saved face record:",p.name,"descriptor length:",(i=(o=c.face)==null?void 0:o.embedding)==null?void 0:i.length),d("known face records:",await b())}else d("invalid name")}async function j(){c.record&&c.record.id>0&&await D(c.record)}async function $(){var i,m,p,k;if(t.canvas.style.height="",(i=t.canvas.getContext("2d"))==null||i.clearRect(0,0,r.minSize,r.minSize),!((m=c==null?void 0:c.face)!=null&&m.tensor)||!((p=c==null?void 0:c.face)!=null&&p.embedding))return!1;if(console.log("face record:",c.face),d(`detected face: ${c.face.gender} ${c.face.age||0}y distance ${100*(c.face.distance||0)}cm/${Math.round(100*(c.face.distance||0)/2.54)}in`),await s.tf.browser.draw(c.face.tensor,t.canvas),await b()===0)return d("face database is empty: nothing to compare face with"),document.body.style.background="black",t.delete.style.display="none",!1;let a=await C(),n=a.map(w=>w.descriptor).filter(w=>w.length>0),o=s.match.find(c.face.embedding,n,B);return c.record=a[o.index]||null,c.record&&(d(`best match: ${c.record.name} | id: ${c.record.id} | similarity: ${Math.round(1e3*o.similarity)/10}%`),t.name.value=c.record.name,t.source.style.display="",(k=t.source.getContext("2d"))==null||k.putImageData(c.record.image,0,0)),document.body.style.background=o.similarity>r.threshold?"darkgreen":"maroon",o.similarity>r.threshold}async function M(){var a,n,o,i;return e.faceCount.status=!1,e.faceConfidence.status=!1,e.facingCenter.status=!1,e.blinkDetected.status=!1,e.faceSize.status=!1,e.antispoofCheck.status=!1,e.livenessCheck.status=!1,e.age.status=!1,e.gender.status=!1,e.elapsedMs.val=0,t.match.style.display="none",t.retry.style.display="none",t.source.style.display="none",t.canvas.style.height="50%",document.body.style.background="black",await H(),await T(),I=s.now(),c.face=await R(),t.canvas.width=((n=(a=c.face)==null?void 0:a.tensor)==null?void 0:n.shape[1])||r.minSize,t.canvas.height=((i=(o=c.face)==null?void 0:o.tensor)==null?void 0:i.shape[0])||r.minSize,t.source.width=t.canvas.width,t.source.height=t.canvas.height,t.canvas.style.width="",t.match.style.display="flex",t.save.style.display="flex",t.delete.style.display="flex",t.retry.style.display="block",E()?$():(d("did not find valid face"),!1)}async function q(){var a,n;d("human version:",s.version,"| tfjs version:",s.tf.version["tfjs-core"]),d("options:",JSON.stringify(r).replace(/{|}|"|\[|\]/g,"").replace(/,/g," ")),d("initializing webcam..."),await H(),d("loading human models..."),await s.load(),d("initializing human..."),d("face embedding model:",g.face.description.enabled?"faceres":"",(a=g.face.mobilefacenet)!=null&&a.enabled?"mobilefacenet":"",(n=g.face.insightface)!=null&&n.enabled?"insightface":""),d("loading face database..."),d("known face records:",await b()),t.retry.addEventListener("click",M),t.save.addEventListener("click",F),t.delete.addEventListener("click",j),await s.warmup(),await M()}window.onload=q; //# sourceMappingURL=index.js.map diff --git a/demo/faceid/index.js.map b/demo/faceid/index.js.map index f397e7b5..82eb9f20 100644 --- a/demo/faceid/index.js.map +++ b/demo/faceid/index.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["index.ts", "indexdb.ts"], "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\nimport * as indexDb from './indexdb'; // methods to deal with indexdb\n\nconst humanConfig = { // user configuration for human, used to fine-tune behavior\n cacheSensitivity: 0.01,\n modelBasePath: '../../models',\n filter: { enabled: true, equalization: true }, // lets run with histogram equilizer\n debug: true,\n face: {\n enabled: true,\n detector: { rotation: true, return: true, mask: false }, // return tensor is used to get detected face image\n description: { enabled: true }, // default model for face descriptor extraction is faceres\n // mobilefacenet: { enabled: true, modelPath: 'https://vladmandic.github.io/human-models/models/mobilefacenet.json' }, // alternative model\n // insightface: { enabled: true, modelPath: 'https://vladmandic.github.io/insightface/models/insightface-mobilenet-swish.json' }, // alternative model\n iris: { enabled: true }, // needed to determine gaze direction\n emotion: { enabled: false }, // not needed\n antispoof: { enabled: true }, // enable optional antispoof module\n liveness: { enabled: true }, // enable optional liveness module\n },\n body: { enabled: false },\n hand: { enabled: false },\n object: { enabled: false },\n gesture: { enabled: true }, // parses face and iris gestures\n};\n\n// const matchOptions = { order: 2, multiplier: 1000, min: 0.0, max: 1.0 }; // for embedding model\nconst matchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }; // for faceres model\n\nconst options = {\n minConfidence: 0.6, // overal face confidence for box, face, gender, real, live\n minSize: 224, // min input to face descriptor model before degradation\n maxTime: 30000, // max time before giving up\n blinkMin: 10, // minimum duration of a valid blink\n blinkMax: 800, // maximum duration of a valid blink\n threshold: 0.5, // minimum similarity\n distanceMin: 0.4, // closest that face is allowed to be to the cammera in cm\n distanceMax: 1.0, // farthest that face is allowed to be to the cammera in cm\n mask: humanConfig.face.detector.mask,\n rotation: humanConfig.face.detector.rotation,\n ...matchOptions,\n};\n\nconst ok: Record = { // must meet all rules\n faceCount: { status: false, val: 0 },\n faceConfidence: { status: false, val: 0 },\n facingCenter: { status: false, val: 0 },\n lookingCenter: { status: false, val: 0 },\n blinkDetected: { status: false, val: 0 },\n faceSize: { status: false, val: 0 },\n antispoofCheck: { status: false, val: 0 },\n livenessCheck: { status: false, val: 0 },\n distance: { status: false, val: 0 },\n age: { status: false, val: 0 },\n gender: { status: false, val: 0 },\n timeout: { status: true, val: 0 },\n descriptor: { status: false, val: 0 },\n elapsedMs: { status: undefined, val: 0 }, // total time while waiting for valid face\n detectFPS: { status: undefined, val: 0 }, // mark detection fps performance\n drawFPS: { status: undefined, val: 0 }, // mark redraw fps performance\n};\n\nconst allOk = () => ok.faceCount.status\n && ok.faceSize.status\n && ok.blinkDetected.status\n && ok.facingCenter.status\n && ok.lookingCenter.status\n && ok.faceConfidence.status\n && ok.antispoofCheck.status\n && ok.livenessCheck.status\n && ok.distance.status\n && ok.descriptor.status\n && ok.age.status\n && ok.gender.status;\n\nconst current: { face: H.FaceResult | null, record: indexDb.FaceRecord | null } = { face: null, record: null }; // current face record and matched database record\n\nconst blink = { // internal timers for blink start/end/duration\n start: 0,\n end: 0,\n time: 0,\n};\n\n// let db: Array<{ name: string, source: string, embedding: number[] }> = []; // holds loaded face descriptor database\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('fps') as HTMLPreElement,\n match: document.getElementById('match') as HTMLDivElement,\n name: document.getElementById('name') as HTMLInputElement,\n save: document.getElementById('save') as HTMLSpanElement,\n delete: document.getElementById('delete') as HTMLSpanElement,\n retry: document.getElementById('retry') as HTMLDivElement,\n source: document.getElementById('source') as HTMLCanvasElement,\n ok: document.getElementById('ok') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0 }; // holds information used to calculate performance and possible memory leaks\nlet startTime = 0;\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\n\nasync function webCam() { // initialize webcam\n // @ts-ignore resizeMode is not yet defined in tslib\n const cameraOptions: MediaStreamConstraints = { audio: false, video: { facingMode: 'user', resizeMode: 'none', width: { ideal: document.body.clientWidth } } };\n const stream: MediaStream = await navigator.mediaDevices.getUserMedia(cameraOptions);\n const ready = new Promise((resolve) => { dom.video.onloadeddata = () => resolve(true); });\n dom.video.srcObject = stream;\n void dom.video.play();\n await ready;\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n dom.canvas.style.width = '50%';\n dom.canvas.style.height = '50%';\n if (human.env.initial) log('video:', dom.video.videoWidth, dom.video.videoHeight, '|', stream.getVideoTracks()[0].label);\n dom.canvas.onclick = () => { // pause when clicked on screen and resume on next click\n if (dom.video.paused) void dom.video.play();\n else dom.video.pause();\n };\n}\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (current.face?.tensor) human.tf.dispose(current.face.tensor); // dispose previous tensor\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const now = human.now();\n ok.detectFPS.val = Math.round(10000 / (now - timestamp.detect)) / 10;\n timestamp.detect = now;\n requestAnimationFrame(detectionLoop); // start new frame immediately\n }\n}\n\nfunction drawValidationTests() {\n let y = 32;\n for (const [key, val] of Object.entries(ok)) {\n let el = document.getElementById(`ok-${key}`);\n if (!el) {\n el = document.createElement('div');\n el.id = `ok-${key}`;\n el.innerText = key;\n el.className = 'ok';\n el.style.top = `${y}px`;\n dom.ok.appendChild(el);\n }\n if (typeof val.status === 'boolean') el.style.backgroundColor = val.status ? 'lightgreen' : 'lightcoral';\n const status = val.status ? 'ok' : 'fail';\n el.innerText = `${key}: ${val.val === 0 ? status : val.val}`;\n y += 28;\n }\n}\n\nasync function validationLoop(): Promise { // main screen refresh loop\n const interpolated = human.next(human.result); // smoothen result using last-known results\n human.draw.canvas(dom.video, dom.canvas); // draw canvas to screen\n await human.draw.all(dom.canvas, interpolated); // draw labels, boxes, lines, etc.\n const now = human.now();\n ok.drawFPS.val = Math.round(10000 / (now - timestamp.draw)) / 10;\n timestamp.draw = now;\n ok.faceCount.val = human.result.face.length;\n ok.faceCount.status = ok.faceCount.val === 1; // must be exactly detected face\n if (ok.faceCount.status) { // skip the rest if no face\n const gestures: string[] = Object.values(human.result.gesture).map((gesture: H.GestureResult) => gesture.gesture); // flatten all gestures\n if (gestures.includes('blink left eye') || gestures.includes('blink right eye')) blink.start = human.now(); // blink starts when eyes get closed\n if (blink.start > 0 && !gestures.includes('blink left eye') && !gestures.includes('blink right eye')) blink.end = human.now(); // if blink started how long until eyes are back open\n ok.blinkDetected.status = ok.blinkDetected.status || (Math.abs(blink.end - blink.start) > options.blinkMin && Math.abs(blink.end - blink.start) < options.blinkMax);\n if (ok.blinkDetected.status && blink.time === 0) blink.time = Math.trunc(blink.end - blink.start);\n ok.facingCenter.status = gestures.includes('facing center');\n ok.lookingCenter.status = gestures.includes('looking center'); // must face camera and look at camera\n ok.faceConfidence.val = human.result.face[0].faceScore || human.result.face[0].boxScore || 0;\n ok.faceConfidence.status = ok.faceConfidence.val >= options.minConfidence;\n ok.antispoofCheck.val = human.result.face[0].real || 0;\n ok.antispoofCheck.status = ok.antispoofCheck.val >= options.minConfidence;\n ok.livenessCheck.val = human.result.face[0].live || 0;\n ok.livenessCheck.status = ok.livenessCheck.val >= options.minConfidence;\n ok.faceSize.val = Math.min(human.result.face[0].box[2], human.result.face[0].box[3]);\n ok.faceSize.status = ok.faceSize.val >= options.minSize;\n ok.distance.val = human.result.face[0].distance || 0;\n ok.distance.status = (ok.distance.val >= options.distanceMin) && (ok.distance.val <= options.distanceMax);\n ok.descriptor.val = human.result.face[0].embedding?.length || 0;\n ok.descriptor.status = ok.descriptor.val > 0;\n ok.age.val = human.result.face[0].age || 0;\n ok.age.status = ok.age.val > 0;\n ok.gender.val = human.result.face[0].genderScore || 0;\n ok.gender.status = ok.gender.val >= options.minConfidence;\n }\n // run again\n ok.timeout.status = ok.elapsedMs.val <= options.maxTime;\n drawValidationTests();\n if (allOk() || !ok.timeout.status) { // all criteria met\n dom.video.pause();\n return human.result.face[0];\n }\n ok.elapsedMs.val = Math.trunc(human.now() - startTime);\n return new Promise((resolve) => {\n setTimeout(async () => {\n await validationLoop(); // run validation loop until conditions are met\n resolve(human.result.face[0]); // recursive promise resolve\n }, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n });\n}\n\nasync function saveRecords() {\n if (dom.name.value.length > 0) {\n const image = dom.canvas.getContext('2d')?.getImageData(0, 0, dom.canvas.width, dom.canvas.height) as ImageData;\n const rec = { id: 0, name: dom.name.value, descriptor: current.face?.embedding as number[], image };\n await indexDb.save(rec);\n log('saved face record:', rec.name, 'descriptor length:', current.face?.embedding?.length);\n log('known face records:', await indexDb.count());\n } else {\n log('invalid name');\n }\n}\n\nasync function deleteRecord() {\n if (current.record && current.record.id > 0) {\n await indexDb.remove(current.record);\n }\n}\n\nasync function detectFace() {\n dom.canvas.style.height = '';\n dom.canvas.getContext('2d')?.clearRect(0, 0, options.minSize, options.minSize);\n if (!current?.face?.tensor || !current?.face?.embedding) return false;\n console.log('face record:', current.face); // eslint-disable-line no-console\n log(`detected face: ${current.face.gender} ${current.face.age || 0}y distance ${100 * (current.face.distance || 0)}cm/${Math.round(100 * (current.face.distance || 0) / 2.54)}in`);\n await human.tf.browser.draw(current.face.tensor, dom.canvas);\n if (await indexDb.count() === 0) {\n log('face database is empty: nothing to compare face with');\n document.body.style.background = 'black';\n dom.delete.style.display = 'none';\n return false;\n }\n const db = await indexDb.load();\n const descriptors = db.map((rec) => rec.descriptor).filter((desc) => desc.length > 0);\n const res = human.match.find(current.face.embedding, descriptors, matchOptions);\n current.record = db[res.index] || null;\n if (current.record) {\n log(`best match: ${current.record.name} | id: ${current.record.id} | similarity: ${Math.round(1000 * res.similarity) / 10}%`);\n dom.name.value = current.record.name;\n dom.source.style.display = '';\n dom.source.getContext('2d')?.putImageData(current.record.image, 0, 0);\n }\n document.body.style.background = res.similarity > options.threshold ? 'darkgreen' : 'maroon';\n return res.similarity > options.threshold;\n}\n\nasync function main() { // main entry point\n ok.faceCount.status = false;\n ok.faceConfidence.status = false;\n ok.facingCenter.status = false;\n ok.blinkDetected.status = false;\n ok.faceSize.status = false;\n ok.antispoofCheck.status = false;\n ok.livenessCheck.status = false;\n ok.age.status = false;\n ok.gender.status = false;\n ok.elapsedMs.val = 0;\n dom.match.style.display = 'none';\n dom.retry.style.display = 'none';\n dom.source.style.display = 'none';\n dom.canvas.style.height = '50%';\n document.body.style.background = 'black';\n await webCam();\n await detectionLoop(); // start detection loop\n startTime = human.now();\n current.face = await validationLoop(); // start validation loop\n dom.canvas.width = current.face?.tensor?.shape[1] || options.minSize;\n dom.canvas.height = current.face?.tensor?.shape[0] || options.minSize;\n dom.source.width = dom.canvas.width;\n dom.source.height = dom.canvas.height;\n dom.canvas.style.width = '';\n dom.match.style.display = 'flex';\n dom.save.style.display = 'flex';\n dom.delete.style.display = 'flex';\n dom.retry.style.display = 'block';\n if (!allOk()) { // is all criteria met?\n log('did not find valid face');\n return false;\n }\n return detectFace();\n}\n\nasync function init() {\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('options:', JSON.stringify(options).replace(/{|}|\"|\\[|\\]/g, '').replace(/,/g, ' '));\n log('initializing webcam...');\n await webCam(); // start webcam\n log('loading human models...');\n await human.load(); // preload all models\n log('initializing human...');\n log('face embedding model:', humanConfig.face.description.enabled ? 'faceres' : '', humanConfig.face['mobilefacenet']?.enabled ? 'mobilefacenet' : '', humanConfig.face['insightface']?.enabled ? 'insightface' : '');\n log('loading face database...');\n log('known face records:', await indexDb.count());\n dom.retry.addEventListener('click', main);\n dom.save.addEventListener('click', saveRecords);\n dom.delete.addEventListener('click', deleteRecord);\n await human.warmup(); // warmup function to initialize backend for future faster detection\n await main();\n}\n\nwindow.onload = init;\n", "let db: IDBDatabase; // instance of indexdb\n\nconst database = 'human';\nconst table = 'person';\n\nexport interface FaceRecord { id: number, name: string, descriptor: number[], image: ImageData }\n\nconst log = (...msg) => console.log('indexdb', ...msg); // eslint-disable-line no-console\n\nexport async function open() {\n if (db) return true;\n return new Promise((resolve) => {\n const request: IDBOpenDBRequest = indexedDB.open(database, 1);\n request.onerror = (evt) => log('error:', evt);\n request.onupgradeneeded = (evt: IDBVersionChangeEvent) => { // create if doesnt exist\n log('create:', evt.target);\n db = (evt.target as IDBOpenDBRequest).result;\n db.createObjectStore(table, { keyPath: 'id', autoIncrement: true });\n };\n request.onsuccess = (evt) => { // open\n db = (evt.target as IDBOpenDBRequest).result;\n log('open:', db);\n resolve(true);\n };\n });\n}\n\nexport async function load(): Promise {\n const faceDB: FaceRecord[] = [];\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const cursor: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).openCursor(null, 'next');\n cursor.onerror = (evt) => log('load error:', evt);\n cursor.onsuccess = (evt) => {\n if ((evt.target as IDBRequest).result) {\n faceDB.push((evt.target as IDBRequest).result.value);\n (evt.target as IDBRequest).result.continue();\n } else {\n resolve(faceDB);\n }\n };\n });\n}\n\nexport async function count(): Promise {\n if (!db) await open(); // open or create if not already done\n return new Promise((resolve) => {\n const store: IDBRequest = db.transaction([table], 'readwrite').objectStore(table).count();\n store.onerror = (evt) => log('count error:', evt);\n store.onsuccess = () => resolve(store.result);\n });\n}\n\nexport async function save(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n const newRecord = { name: faceRecord.name, descriptor: faceRecord.descriptor, image: faceRecord.image }; // omit id as its autoincrement\n db.transaction([table], 'readwrite').objectStore(table).put(newRecord);\n log('save:', newRecord);\n}\n\nexport async function remove(faceRecord: FaceRecord) {\n if (!db) await open(); // open or create if not already done\n db.transaction([table], 'readwrite').objectStore(table).delete(faceRecord.id); // delete based on id\n log('delete:', faceRecord);\n}\n"], - "mappings": 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+ "names": ["H", "db", "database", "table", "log", "msg", "open", "resolve", "request", "evt", "load", "faceDB", "cursor", "count", "store", "save", "faceRecord", "newRecord", "remove", "humanConfig", "matchOptions", "options", "ok", "allOk", "current", "blink", "human", "dom", "timestamp", "startTime", "log", "msg", "webCam", "cameraOptions", "stream", "ready", "resolve", "detectionLoop", "_a", "now", "drawValidationTests", "y", "key", "val", "el", "status", "validationLoop", "interpolated", "gestures", "gesture", "saveRecords", "_b", "_c", "_d", "image", "rec", "save", "count", "deleteRecord", "remove", "detectFace", "db", "load", "descriptors", "desc", "res", "main", "init"] } diff --git a/demo/tracker/index.js b/demo/tracker/index.js index fc85d705..00f362dc 100644 --- a/demo/tracker/index.js +++ b/demo/tracker/index.js @@ -4,1353 +4,7 @@ author: ' */ - -// demo/tracker/index.ts -import * as H from "../../dist/human.esm.js"; - -// demo/tracker/tracker.js -var __defProp = Object.defineProperty; -var __getOwnPropNames = Object.getOwnPropertyNames; -var __name = (target, value) => __defProp(target, "name", { value, configurable: true }); -var __commonJS = (cb, mod) => function __require() { - return mod || (0, cb[__getOwnPropNames(cb)[0]])((mod = { exports: {} }).exports, mod), mod.exports; -}; -var require_rng_browser = __commonJS({ - "node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/rng-browser.js"(exports, module) { - var getRandomValues = typeof crypto != "undefined" && crypto.getRandomValues.bind(crypto) || typeof msCrypto != "undefined" && msCrypto.getRandomValues.bind(msCrypto); - if (getRandomValues) { - rnds8 = new Uint8Array(16); - module.exports = __name(function whatwgRNG() { - getRandomValues(rnds8); - return rnds8; - }, "whatwgRNG"); - } else { - rnds = new Array(16); - module.exports = __name(function mathRNG() { - for (var i = 0, r; i < 16; i++) { - if ((i & 3) === 0) - r = Math.random() * 4294967296; - rnds[i] = r >>> ((i & 3) << 3) & 255; - } - return rnds; - }, "mathRNG"); - } - var rnds8; - var rnds; - } -}); -var require_bytesToUuid = __commonJS({ - "node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/bytesToUuid.js"(exports, module) { - var byteToHex = []; - for (i = 0; i < 256; ++i) { - byteToHex[i] = (i + 256).toString(16).substr(1); - } - function bytesToUuid(buf, offset) { - var i2 = offset || 0; - var bth = byteToHex; - return bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + "-" + bth[buf[i2++]] + bth[buf[i2++]] + "-" + bth[buf[i2++]] + bth[buf[i2++]] + "-" + bth[buf[i2++]] + bth[buf[i2++]] + "-" + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]]; - } - __name(bytesToUuid, "bytesToUuid"); - module.exports = bytesToUuid; - var i; - } -}); -var require_v4 = __commonJS({ - "node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/v4.js"(exports, module) { - var rng = require_rng_browser(); - var bytesToUuid = require_bytesToUuid(); - function v4(options, buf, offset) { - var i = buf && offset || 0; - if (typeof options == "string") { - buf = options === "binary" ? new Array(16) : null; - options = null; - } - options = options || {}; - var rnds = options.random || (options.rng || rng)(); - rnds[6] = rnds[6] & 15 | 64; - rnds[8] = rnds[8] & 63 | 128; - if (buf) { - for (var ii = 0; ii < 16; ++ii) { - buf[i + ii] = rnds[ii]; - } - } - return buf || bytesToUuid(rnds); - } - __name(v4, "v4"); - module.exports = v4; - } -}); -var require_utils = __commonJS({ - "utils.js"(exports) { - exports.isDetectionTooLarge = (detections, largestAllowed) => { - if (detections.w >= largestAllowed) { - return true; - } else { - return false; - } - }; - var isInsideArea = __name((area, point) => { - const xMin = area.x - area.w / 2; - const xMax = area.x + area.w / 2; - const yMin = area.y - area.h / 2; - const yMax = area.y + area.h / 2; - if (point.x >= xMin && point.x <= xMax && point.y >= yMin && point.y <= yMax) { - return true; - } else { - return false; - } - }, "isInsideArea"); - exports.isInsideArea = isInsideArea; - exports.isInsideSomeAreas = (areas, point) => { - const isInside = areas.some((area) => isInsideArea(area, point)); - return isInside; - }; - exports.ignoreObjectsNotToDetect = (detections, objectsToDetect) => { - return detections.filter((detection) => objectsToDetect.indexOf(detection.name) > -1); - }; - var getRectangleEdges = __name((item) => { - return { - x0: item.x - item.w / 2, - y0: item.y - item.h / 2, - x1: item.x + item.w / 2, - y1: item.y + item.h / 2 - }; - }, "getRectangleEdges"); - exports.getRectangleEdges = getRectangleEdges; - exports.iouAreas = (item1, item2) => { - var rect1 = getRectangleEdges(item1); - var rect2 = getRectangleEdges(item2); - var overlap_x0 = Math.max(rect1.x0, rect2.x0); - var overlap_y0 = Math.max(rect1.y0, rect2.y0); - var overlap_x1 = Math.min(rect1.x1, rect2.x1); - var overlap_y1 = Math.min(rect1.y1, rect2.y1); - if (overlap_x1 - overlap_x0 <= 0 || overlap_y1 - overlap_y0 <= 0) { - return 0; - } else { - const area_rect1 = item1.w * item1.h; - const area_rect2 = item2.w * item2.h; - const area_intersection = (overlap_x1 - overlap_x0) * (overlap_y1 - overlap_y0); - const area_union = area_rect1 + area_rect2 - area_intersection; - return area_intersection / area_union; - } - }; - exports.computeVelocityVector = (item1, item2, nbFrame) => { - return { - dx: (item2.x - item1.x) / nbFrame, - dy: (item2.y - item1.y) / nbFrame - }; - }; - exports.computeBearingIn360 = function(dx, dy) { - var angle = Math.atan(dx / dy) / (Math.PI / 180); - if (angle > 0) { - if (dy > 0) - return angle; - else - return 180 + angle; - } else { - if (dx > 0) - return 180 + angle; - else - return 360 + angle; - } - }; - } -}); -var require_ItemTracked = __commonJS({ - "ItemTracked.js"(exports) { - var uuidv4 = require_v4(); - var computeBearingIn360 = require_utils().computeBearingIn360; - var computeVelocityVector = require_utils().computeVelocityVector; - exports.ITEM_HISTORY_MAX_LENGTH = 15; - var idDisplay = 0; - exports.ItemTracked = function(properties, frameNb, unMatchedFramesTolerance, fastDelete) { - var DEFAULT_UNMATCHEDFRAMES_TOLERANCE = unMatchedFramesTolerance; - var itemTracked = {}; - itemTracked.available = true; - itemTracked.delete = false; - itemTracked.fastDelete = fastDelete; - itemTracked.frameUnmatchedLeftBeforeDying = unMatchedFramesTolerance; - itemTracked.isZombie = false; - itemTracked.appearFrame = frameNb; - itemTracked.disappearFrame = null; - itemTracked.disappearArea = {}; - itemTracked.nameCount = {}; - itemTracked.nameCount[properties.name] = 1; - itemTracked.x = properties.x; - itemTracked.y = properties.y; - itemTracked.w = properties.w; - itemTracked.h = properties.h; - itemTracked.name = properties.name; - itemTracked.confidence = properties.confidence; - itemTracked.itemHistory = []; - itemTracked.itemHistory.push({ - x: properties.x, - y: properties.y, - w: properties.w, - h: properties.h, - confidence: properties.confidence - }); - if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) { - itemTracked.itemHistory.shift(); - } - itemTracked.velocity = { - dx: 0, - dy: 0 - }; - itemTracked.nbTimeMatched = 1; - itemTracked.id = uuidv4(); - itemTracked.idDisplay = idDisplay; - idDisplay++; - itemTracked.update = function(properties2, frameNb2) { - if (this.disappearFrame) { - this.disappearFrame = null; - this.disappearArea = {}; - } - this.isZombie = false; - this.nbTimeMatched += 1; - this.x = properties2.x; - this.y = properties2.y; - this.w = properties2.w; - this.h = properties2.h; - this.confidence = properties2.confidence; - this.itemHistory.push({ - x: this.x, - y: this.y, - w: this.w, - h: this.h, - confidence: this.confidence - }); - if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) { - itemTracked.itemHistory.shift(); - } - this.name = properties2.name; - if (this.nameCount[properties2.name]) { - this.nameCount[properties2.name]++; - } else { - this.nameCount[properties2.name] = 1; - } - this.frameUnmatchedLeftBeforeDying = DEFAULT_UNMATCHEDFRAMES_TOLERANCE; - this.velocity = this.updateVelocityVector(); - }; - itemTracked.makeAvailable = function() { - this.available = true; - return this; - }; - itemTracked.makeUnavailable = function() { - this.available = false; - return this; - }; - itemTracked.countDown = function(frameNb2) { - if (this.disappearFrame === null) { - this.disappearFrame = frameNb2; - this.disappearArea = { - x: this.x, - y: this.y, - w: this.w, - h: this.h - }; - } - this.frameUnmatchedLeftBeforeDying--; - this.isZombie = true; - if (this.fastDelete && this.nbTimeMatched <= 1) { - this.frameUnmatchedLeftBeforeDying = -1; - } - }; - itemTracked.updateTheoricalPositionAndSize = function() { - this.itemHistory.push({ - x: this.x, - y: this.y, - w: this.w, - h: this.h, - confidence: this.confidence - }); - if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) { - itemTracked.itemHistory.shift(); - } - this.x = this.x + this.velocity.dx; - this.y = this.y + this.velocity.dy; - }; - itemTracked.predictNextPosition = function() { - return { - x: this.x + this.velocity.dx, - y: this.y + this.velocity.dy, - w: this.w, - h: this.h - }; - }; - itemTracked.isDead = function() { - return this.frameUnmatchedLeftBeforeDying < 0; - }; - itemTracked.updateVelocityVector = function() { - if (exports.ITEM_HISTORY_MAX_LENGTH <= 2) { - return { dx: void 0, dy: void 0 }; - } - if (this.itemHistory.length <= exports.ITEM_HISTORY_MAX_LENGTH) { - const start = this.itemHistory[0]; - const end = this.itemHistory[this.itemHistory.length - 1]; - return computeVelocityVector(start, end, this.itemHistory.length); - } else { - const start = this.itemHistory[this.itemHistory.length - exports.ITEM_HISTORY_MAX_LENGTH]; - const end = this.itemHistory[this.itemHistory.length - 1]; - return computeVelocityVector(start, end, exports.ITEM_HISTORY_MAX_LENGTH); - } - }; - itemTracked.getMostlyMatchedName = function() { - var nameMostlyMatchedOccurences = 0; - var nameMostlyMatched = ""; - Object.keys(this.nameCount).map((name) => { - if (this.nameCount[name] > nameMostlyMatchedOccurences) { - nameMostlyMatched = name; - nameMostlyMatchedOccurences = this.nameCount[name]; - } - }); - return nameMostlyMatched; - }; - itemTracked.toJSONDebug = function(roundInt = true) { - return { - id: this.id, - idDisplay: this.idDisplay, - x: roundInt ? parseInt(this.x, 10) : this.x, - y: roundInt ? parseInt(this.y, 10) : this.y, - w: roundInt ? parseInt(this.w, 10) : this.w, - h: roundInt ? parseInt(this.h, 10) : this.h, - confidence: Math.round(this.confidence * 100) / 100, - // Here we negate dy to be in "normal" carthesian coordinates - bearing: parseInt(computeBearingIn360(this.velocity.dx, -this.velocity.dy)), - name: this.getMostlyMatchedName(), - isZombie: this.isZombie, - appearFrame: this.appearFrame, - disappearFrame: this.disappearFrame - }; - }; - itemTracked.toJSON = function(roundInt = true) { - return { - id: this.idDisplay, - x: roundInt ? parseInt(this.x, 10) : this.x, - y: roundInt ? parseInt(this.y, 10) : this.y, - w: roundInt ? parseInt(this.w, 10) : this.w, - h: roundInt ? parseInt(this.h, 10) : this.h, - confidence: Math.round(this.confidence * 100) / 100, - // Here we negate dy to be in "normal" carthesian coordinates - bearing: parseInt(computeBearingIn360(this.velocity.dx, -this.velocity.dy), 10), - name: this.getMostlyMatchedName(), - isZombie: this.isZombie - }; - }; - itemTracked.toMOT = function(frameIndex) { - return `${frameIndex},${this.idDisplay},${this.x - this.w / 2},${this.y - this.h / 2},${this.w},${this.h},${this.confidence / 100},-1,-1,-1`; - }; - itemTracked.toJSONGenericInfo = function() { - return { - id: this.id, - idDisplay: this.idDisplay, - appearFrame: this.appearFrame, - disappearFrame: this.disappearFrame, - disappearArea: this.disappearArea, - nbActiveFrame: this.disappearFrame - this.appearFrame, - name: this.getMostlyMatchedName() - }; - }; - return itemTracked; - }; - exports.reset = function() { - idDisplay = 0; - }; - } -}); -var require_kdTree_min = __commonJS({ - "lib/kdTree-min.js"(exports) { - (function(root, factory) { - if (typeof define === "function" && define.amd) { - define(["exports"], factory); - } else if (typeof exports === "object") { - factory(exports); - } else { - factory(root); - } - })(exports, function(exports2) { - function Node(obj, dimension, parent) { - this.obj = obj; - this.left = null; - this.right = null; - this.parent = parent; - this.dimension = dimension; - } - __name(Node, "Node"); - function kdTree(points, metric, dimensions) { - var self = this; - function buildTree(points2, depth, parent) { - var dim = depth % dimensions.length, median, node; - if (points2.length === 0) { - return null; - } - if (points2.length === 1) { - return new Node(points2[0], dim, parent); - } - points2.sort(function(a, b) { - return a[dimensions[dim]] - b[dimensions[dim]]; - }); - median = Math.floor(points2.length / 2); - node = new Node(points2[median], dim, parent); - node.left = buildTree(points2.slice(0, median), depth + 1, node); - node.right = buildTree(points2.slice(median + 1), depth + 1, node); - return node; - } - __name(buildTree, "buildTree"); - function loadTree(data) { - self.root = data; - function restoreParent(root) { - if (root.left) { - root.left.parent = root; - restoreParent(root.left); - } - if (root.right) { - root.right.parent = root; - restoreParent(root.right); - } - } - __name(restoreParent, "restoreParent"); - restoreParent(self.root); - } - __name(loadTree, "loadTree"); - if (!Array.isArray(points)) - loadTree(points, metric, dimensions); - else - this.root = buildTree(points, 0, null); - this.toJSON = function(src) { - if (!src) - src = this.root; - var dest = new Node(src.obj, src.dimension, null); - if (src.left) - dest.left = self.toJSON(src.left); - if (src.right) - dest.right = self.toJSON(src.right); - return dest; - }; - this.insert = function(point) { - function innerSearch(node, parent) { - if (node === null) { - return parent; - } - var dimension2 = dimensions[node.dimension]; - if (point[dimension2] < node.obj[dimension2]) { - return innerSearch(node.left, node); - } else { - return innerSearch(node.right, node); - } - } - __name(innerSearch, "innerSearch"); - var insertPosition = innerSearch(this.root, null), newNode, dimension; - if (insertPosition === null) { - this.root = new Node(point, 0, null); - return; - } - newNode = new Node(point, (insertPosition.dimension + 1) % dimensions.length, insertPosition); - dimension = dimensions[insertPosition.dimension]; - if (point[dimension] < insertPosition.obj[dimension]) { - insertPosition.left = newNode; - } else { - insertPosition.right = newNode; - } - }; - this.remove = function(point) { - var node; - function nodeSearch(node2) { - if (node2 === null) { - return null; - } - if (node2.obj === point) { - return node2; - } - var dimension = dimensions[node2.dimension]; - if (point[dimension] < node2.obj[dimension]) { - return nodeSearch(node2.left, node2); - } else { - return nodeSearch(node2.right, node2); - } - } - __name(nodeSearch, "nodeSearch"); - function removeNode(node2) { - var nextNode, nextObj, pDimension; - function findMin(node3, dim) { - var dimension, own, left, right, min; - if (node3 === null) { - return null; - } - dimension = dimensions[dim]; - if (node3.dimension === dim) { - if (node3.left !== null) { - return findMin(node3.left, dim); - } - return node3; - } - own = node3.obj[dimension]; - left = findMin(node3.left, dim); - right = findMin(node3.right, dim); - min = node3; - if (left !== null && left.obj[dimension] < own) { - min = left; - } - if (right !== null && right.obj[dimension] < min.obj[dimension]) { - min = right; - } - return min; - } - __name(findMin, "findMin"); - if (node2.left === null && node2.right === null) { - if (node2.parent === null) { - self.root = null; - return; - } - pDimension = dimensions[node2.parent.dimension]; - if (node2.obj[pDimension] < node2.parent.obj[pDimension]) { - node2.parent.left = null; - } else { - node2.parent.right = null; - } - return; - } - if (node2.right !== null) { - nextNode = findMin(node2.right, node2.dimension); - nextObj = nextNode.obj; - removeNode(nextNode); - node2.obj = nextObj; - } else { - nextNode = findMin(node2.left, node2.dimension); - nextObj = nextNode.obj; - removeNode(nextNode); - node2.right = node2.left; - node2.left = null; - node2.obj = nextObj; - } - } - __name(removeNode, "removeNode"); - node = nodeSearch(self.root); - if (node === null) { - return; - } - removeNode(node); - }; - this.nearest = function(point, maxNodes, maxDistance) { - var i, result, bestNodes; - bestNodes = new BinaryHeap( - function(e) { - return -e[1]; - } - ); - function nearestSearch(node) { - var bestChild, dimension = dimensions[node.dimension], ownDistance = metric(point, node.obj), linearPoint = {}, linearDistance, otherChild, i2; - function saveNode(node2, distance) { - bestNodes.push([node2, distance]); - if (bestNodes.size() > maxNodes) { - bestNodes.pop(); - } - } - __name(saveNode, "saveNode"); - for (i2 = 0; i2 < dimensions.length; i2 += 1) { - if (i2 === node.dimension) { - linearPoint[dimensions[i2]] = point[dimensions[i2]]; - } else { - linearPoint[dimensions[i2]] = node.obj[dimensions[i2]]; - } - } - linearDistance = metric(linearPoint, node.obj); - if (node.right === null && node.left === null) { - if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) { - saveNode(node, ownDistance); - } - return; - } - if (node.right === null) { - bestChild = node.left; - } else if (node.left === null) { - bestChild = node.right; - } else { - if (point[dimension] < node.obj[dimension]) { - bestChild = node.left; - } else { - bestChild = node.right; - } - } - nearestSearch(bestChild); - if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) { - saveNode(node, ownDistance); - } - if (bestNodes.size() < maxNodes || Math.abs(linearDistance) < bestNodes.peek()[1]) { - if (bestChild === node.left) { - otherChild = node.right; - } else { - otherChild = node.left; - } - if (otherChild !== null) { - nearestSearch(otherChild); - } - } - } - __name(nearestSearch, "nearestSearch"); - if (maxDistance) { - for (i = 0; i < maxNodes; i += 1) { - bestNodes.push([null, maxDistance]); - } - } - if (self.root) - nearestSearch(self.root); - result = []; - for (i = 0; i < Math.min(maxNodes, bestNodes.content.length); i += 1) { - if (bestNodes.content[i][0]) { - result.push([bestNodes.content[i][0].obj, bestNodes.content[i][1]]); - } - } - return result; - }; - this.balanceFactor = function() { - function height(node) { - if (node === null) { - return 0; - } - return Math.max(height(node.left), height(node.right)) + 1; - } - __name(height, "height"); - function count(node) { - if (node === null) { - return 0; - } - return count(node.left) + count(node.right) + 1; - } - __name(count, "count"); - return height(self.root) / (Math.log(count(self.root)) / Math.log(2)); - }; - } - __name(kdTree, "kdTree"); - function BinaryHeap(scoreFunction) { - this.content = []; - this.scoreFunction = scoreFunction; - } - __name(BinaryHeap, "BinaryHeap"); - BinaryHeap.prototype = { - push: function(element) { - this.content.push(element); - this.bubbleUp(this.content.length - 1); - }, - pop: function() { - var result = this.content[0]; - var end = this.content.pop(); - if (this.content.length > 0) { - this.content[0] = end; - this.sinkDown(0); - } - return result; - }, - peek: function() { - return this.content[0]; - }, - remove: function(node) { - var len = this.content.length; - for (var i = 0; i < len; i++) { - if (this.content[i] == node) { - var end = this.content.pop(); - if (i != len - 1) { - this.content[i] = end; - if (this.scoreFunction(end) < this.scoreFunction(node)) - this.bubbleUp(i); - else - this.sinkDown(i); - } - return; - } - } - throw new Error("Node not found."); - }, - size: function() { - return this.content.length; - }, - bubbleUp: function(n) { - var element = this.content[n]; - while (n > 0) { - var parentN = Math.floor((n + 1) / 2) - 1, parent = this.content[parentN]; - if (this.scoreFunction(element) < this.scoreFunction(parent)) { - this.content[parentN] = element; - this.content[n] = parent; - n = parentN; - } else { - break; - } - } - }, - sinkDown: function(n) { - var length = this.content.length, element = this.content[n], elemScore = this.scoreFunction(element); - while (true) { - var child2N = (n + 1) * 2, child1N = child2N - 1; - var swap = null; - if (child1N < length) { - var child1 = this.content[child1N], child1Score = this.scoreFunction(child1); - if (child1Score < elemScore) - swap = child1N; - } - if (child2N < length) { - var child2 = this.content[child2N], child2Score = this.scoreFunction(child2); - if (child2Score < (swap == null ? elemScore : child1Score)) { - swap = child2N; - } - } - if (swap != null) { - this.content[n] = this.content[swap]; - this.content[swap] = element; - n = swap; - } else { - break; - } - } - } - }; - exports2.kdTree = kdTree; - exports2.BinaryHeap = BinaryHeap; - }); - } -}); -var require_munkres = __commonJS({ - "node_modules/.pnpm/munkres-js@1.2.2/node_modules/munkres-js/munkres.js"(exports, module) { - var MAX_SIZE = parseInt(Number.MAX_SAFE_INTEGER / 2) || (1 << 26) * (1 << 26); - var DEFAULT_PAD_VALUE = 0; - function Munkres() { - this.C = null; - this.row_covered = []; - this.col_covered = []; - this.n = 0; - this.Z0_r = 0; - this.Z0_c = 0; - this.marked = null; - this.path = null; - } - __name(Munkres, "Munkres"); - Munkres.prototype.pad_matrix = function(matrix, pad_value) { - pad_value = pad_value || DEFAULT_PAD_VALUE; - var max_columns = 0; - var total_rows = matrix.length; - var i; - for (i = 0; i < total_rows; ++i) - if (matrix[i].length > max_columns) - max_columns = matrix[i].length; - total_rows = max_columns > total_rows ? max_columns : total_rows; - var new_matrix = []; - for (i = 0; i < total_rows; ++i) { - var row = matrix[i] || []; - var new_row = row.slice(); - while (total_rows > new_row.length) - new_row.push(pad_value); - new_matrix.push(new_row); - } - return new_matrix; - }; - Munkres.prototype.compute = function(cost_matrix, options) { - options = options || {}; - options.padValue = options.padValue || DEFAULT_PAD_VALUE; - this.C = this.pad_matrix(cost_matrix, options.padValue); - this.n = this.C.length; - this.original_length = cost_matrix.length; - this.original_width = cost_matrix[0].length; - var nfalseArray = []; - while (nfalseArray.length < this.n) - nfalseArray.push(false); - this.row_covered = nfalseArray.slice(); - this.col_covered = nfalseArray.slice(); - this.Z0_r = 0; - this.Z0_c = 0; - this.path = this.__make_matrix(this.n * 2, 0); - this.marked = this.__make_matrix(this.n, 0); - var step = 1; - var steps = { - 1: this.__step1, - 2: this.__step2, - 3: this.__step3, - 4: this.__step4, - 5: this.__step5, - 6: this.__step6 - }; - while (true) { - var func = steps[step]; - if (!func) - break; - step = func.apply(this); - } - var results = []; - for (var i = 0; i < this.original_length; ++i) - for (var j = 0; j < this.original_width; ++j) - if (this.marked[i][j] == 1) - results.push([i, j]); - return results; - }; - Munkres.prototype.__make_matrix = function(n, val) { - var matrix = []; - for (var i = 0; i < n; ++i) { - matrix[i] = []; - for (var j = 0; j < n; ++j) - matrix[i][j] = val; - } - return matrix; - }; - Munkres.prototype.__step1 = function() { - for (var i = 0; i < this.n; ++i) { - var minval = Math.min.apply(Math, this.C[i]); - for (var j = 0; j < this.n; ++j) - this.C[i][j] -= minval; - } - return 2; - }; - Munkres.prototype.__step2 = function() { - for (var i = 0; i < this.n; ++i) { - for (var j = 0; j < this.n; ++j) { - if (this.C[i][j] === 0 && !this.col_covered[j] && !this.row_covered[i]) { - this.marked[i][j] = 1; - this.col_covered[j] = true; - this.row_covered[i] = true; - break; - } - } - } - this.__clear_covers(); - return 3; - }; - Munkres.prototype.__step3 = function() { - var count = 0; - for (var i = 0; i < this.n; ++i) { - for (var j = 0; j < this.n; ++j) { - if (this.marked[i][j] == 1 && this.col_covered[j] == false) { - this.col_covered[j] = true; - ++count; - } - } - } - return count >= this.n ? 7 : 4; - }; - Munkres.prototype.__step4 = function() { - var done = false; - var row = -1, col = -1, star_col = -1; - while (!done) { - var z = this.__find_a_zero(); - row = z[0]; - col = z[1]; - if (row < 0) - return 6; - this.marked[row][col] = 2; - star_col = this.__find_star_in_row(row); - if (star_col >= 0) { - col = star_col; - this.row_covered[row] = true; - this.col_covered[col] = false; - } else { - this.Z0_r = row; - this.Z0_c = col; - return 5; - } - } - }; - Munkres.prototype.__step5 = function() { - var count = 0; - this.path[count][0] = this.Z0_r; - this.path[count][1] = this.Z0_c; - var done = false; - while (!done) { - var row = this.__find_star_in_col(this.path[count][1]); - if (row >= 0) { - count++; - this.path[count][0] = row; - this.path[count][1] = this.path[count - 1][1]; - } else { - done = true; - } - if (!done) { - var col = this.__find_prime_in_row(this.path[count][0]); - count++; - this.path[count][0] = this.path[count - 1][0]; - this.path[count][1] = col; - } - } - this.__convert_path(this.path, count); - this.__clear_covers(); - this.__erase_primes(); - return 3; - }; - Munkres.prototype.__step6 = function() { - var minval = this.__find_smallest(); - for (var i = 0; i < this.n; ++i) { - for (var j = 0; j < this.n; ++j) { - if (this.row_covered[i]) - this.C[i][j] += minval; - if (!this.col_covered[j]) - this.C[i][j] -= minval; - } - } - return 4; - }; - Munkres.prototype.__find_smallest = function() { - var minval = MAX_SIZE; - for (var i = 0; i < this.n; ++i) - for (var j = 0; j < this.n; ++j) - if (!this.row_covered[i] && !this.col_covered[j]) { - if (minval > this.C[i][j]) - minval = this.C[i][j]; - } - return minval; - }; - Munkres.prototype.__find_a_zero = function() { - for (var i = 0; i < this.n; ++i) - for (var j = 0; j < this.n; ++j) - if (this.C[i][j] === 0 && !this.row_covered[i] && !this.col_covered[j]) - return [i, j]; - return [-1, -1]; - }; - Munkres.prototype.__find_star_in_row = function(row) { - for (var j = 0; j < this.n; ++j) - if (this.marked[row][j] == 1) - return j; - return -1; - }; - Munkres.prototype.__find_star_in_col = function(col) { - for (var i = 0; i < this.n; ++i) - if (this.marked[i][col] == 1) - return i; - return -1; - }; - Munkres.prototype.__find_prime_in_row = function(row) { - for (var j = 0; j < this.n; ++j) - if (this.marked[row][j] == 2) - return j; - return -1; - }; - Munkres.prototype.__convert_path = function(path, count) { - for (var i = 0; i <= count; ++i) - this.marked[path[i][0]][path[i][1]] = this.marked[path[i][0]][path[i][1]] == 1 ? 0 : 1; - }; - Munkres.prototype.__clear_covers = function() { - for (var i = 0; i < this.n; ++i) { - this.row_covered[i] = false; - this.col_covered[i] = false; - } - }; - Munkres.prototype.__erase_primes = function() { - for (var i = 0; i < this.n; ++i) - for (var j = 0; j < this.n; ++j) - if (this.marked[i][j] == 2) - this.marked[i][j] = 0; - }; - function make_cost_matrix(profit_matrix, inversion_function) { - var i, j; - if (!inversion_function) { - var maximum = -1 / 0; - for (i = 0; i < profit_matrix.length; ++i) - for (j = 0; j < profit_matrix[i].length; ++j) - if (profit_matrix[i][j] > maximum) - maximum = profit_matrix[i][j]; - inversion_function = __name(function(x) { - return maximum - x; - }, "inversion_function"); - } - var cost_matrix = []; - for (i = 0; i < profit_matrix.length; ++i) { - var row = profit_matrix[i]; - cost_matrix[i] = []; - for (j = 0; j < row.length; ++j) - cost_matrix[i][j] = inversion_function(profit_matrix[i][j]); - } - return cost_matrix; - } - __name(make_cost_matrix, "make_cost_matrix"); - function format_matrix(matrix) { - var columnWidths = []; - var i, j; - for (i = 0; i < matrix.length; ++i) { - for (j = 0; j < matrix[i].length; ++j) { - var entryWidth = String(matrix[i][j]).length; - if (!columnWidths[j] || entryWidth >= columnWidths[j]) - columnWidths[j] = entryWidth; - } - } - var formatted = ""; - for (i = 0; i < matrix.length; ++i) { - for (j = 0; j < matrix[i].length; ++j) { - var s = String(matrix[i][j]); - while (s.length < columnWidths[j]) - s = " " + s; - formatted += s; - if (j != matrix[i].length - 1) - formatted += " "; - } - if (i != matrix[i].length - 1) - formatted += "\n"; - } - return formatted; - } - __name(format_matrix, "format_matrix"); - function computeMunkres(cost_matrix, options) { - var m = new Munkres(); - return m.compute(cost_matrix, options); - } - __name(computeMunkres, "computeMunkres"); - computeMunkres.version = "1.2.2"; - computeMunkres.format_matrix = format_matrix; - computeMunkres.make_cost_matrix = make_cost_matrix; - computeMunkres.Munkres = Munkres; - if (typeof module !== "undefined" && module.exports) { - module.exports = computeMunkres; - } - } -}); -var require_tracker = __commonJS({ - "tracker.js"(exports) { - var itemTrackedModule = require_ItemTracked(); - var ItemTracked = itemTrackedModule.ItemTracked; - var kdTree = require_kdTree_min().kdTree; - var iouAreas = require_utils().iouAreas; - var munkres = require_munkres(); - var DEBUG_MODE = false; - var iouDistance = __name(function(item1, item2) { - var iou = iouAreas(item1, item2); - var distance = 1 - iou; - if (distance > 1 - params.iouLimit) { - distance = params.distanceLimit + 1; - } - return distance; - }, "iouDistance"); - var params = { - // DEFAULT_UNMATCHEDFRAMES_TOLERANCE - // This the number of frame we wait when an object isn't matched before considering it gone - unMatchedFramesTolerance: 5, - // DEFAULT_IOU_LIMIT, exclude things from beeing matched if their IOU is lower than this - // 1 means total overlap whereas 0 means no overlap - iouLimit: 0.05, - // Remove new objects fast if they could not be matched in the next frames. - // Setting this to false ensures the object will stick around at least - // unMatchedFramesTolerance frames, even if they could neven be matched in - // subsequent frames. - fastDelete: true, - // The function to use to determine the distance between to detected objects - distanceFunc: iouDistance, - // The distance limit for matching. If values need to be excluded from - // matching set their distance to something greater than the distance limit - distanceLimit: 1e4, - // The algorithm used to match tracks with new detections. Can be either - // 'kdTree' or 'munkres'. - matchingAlgorithm: "kdTree" - }; - var mapOfItemsTracked = /* @__PURE__ */ new Map(); - var mapOfAllItemsTracked = /* @__PURE__ */ new Map(); - var keepAllHistoryInMemory = false; - exports.computeDistance = iouDistance; - exports.updateTrackedItemsWithNewFrame = function(detectionsOfThisFrame, frameNb) { - var treeItemsTracked = new kdTree(Array.from(mapOfItemsTracked.values()), params.distanceFunc, ["x", "y", "w", "h"]); - var treeDetectionsOfThisFrame = new kdTree(detectionsOfThisFrame, params.distanceFunc, ["x", "y", "w", "h"]); - if (mapOfItemsTracked.size === 0) { - detectionsOfThisFrame.forEach(function(itemDetected) { - var newItemTracked = new ItemTracked(itemDetected, frameNb, params.unMatchedFramesTolerance, params.fastDelete); - mapOfItemsTracked.set(newItemTracked.id, newItemTracked); - treeItemsTracked.insert(newItemTracked); - }); - } else { - var matchedList = new Array(detectionsOfThisFrame.length); - matchedList.fill(false); - if (detectionsOfThisFrame.length > 0) { - if (params.matchingAlgorithm === "munkres") { - var trackedItemIds = Array.from(mapOfItemsTracked.keys()); - var costMatrix = Array.from(mapOfItemsTracked.values()).map((itemTracked) => { - var predictedPosition = itemTracked.predictNextPosition(); - return detectionsOfThisFrame.map( - (detection) => params.distanceFunc(predictedPosition, detection) - ); - }); - mapOfItemsTracked.forEach(function(itemTracked) { - itemTracked.makeAvailable(); - }); - munkres(costMatrix).filter((m) => costMatrix[m[0]][m[1]] <= params.distanceLimit).forEach((m) => { - var itemTracked = mapOfItemsTracked.get(trackedItemIds[m[0]]); - var updatedTrackedItemProperties = detectionsOfThisFrame[m[1]]; - matchedList[m[1]] = { idDisplay: itemTracked.idDisplay }; - itemTracked.makeUnavailable().update(updatedTrackedItemProperties, frameNb); - }); - matchedList.forEach(function(matched, index) { - if (!matched) { - if (Math.min(...costMatrix.map((m) => m[index])) > params.distanceLimit) { - var newItemTracked = ItemTracked(detectionsOfThisFrame[index], frameNb, params.unMatchedFramesTolerance, params.fastDelete); - mapOfItemsTracked.set(newItemTracked.id, newItemTracked); - newItemTracked.makeUnavailable(); - costMatrix.push(detectionsOfThisFrame.map( - (detection) => params.distanceFunc(newItemTracked, detection) - )); - } - } - }); - } else if (params.matchingAlgorithm === "kdTree") { - mapOfItemsTracked.forEach(function(itemTracked) { - var predictedPosition = itemTracked.predictNextPosition(); - itemTracked.makeAvailable(); - var treeSearchResult = treeDetectionsOfThisFrame.nearest(predictedPosition, 1, params.distanceLimit)[0]; - var treeSearchResultWithoutPrediction = treeDetectionsOfThisFrame.nearest(itemTracked, 1, params.distanceLimit)[0]; - var treeSearchMultipleResults = treeDetectionsOfThisFrame.nearest(predictedPosition, 2, params.distanceLimit); - if (treeSearchResult) { - var indexClosestNewDetectedItem = detectionsOfThisFrame.indexOf(treeSearchResult[0]); - if (!matchedList[indexClosestNewDetectedItem]) { - matchedList[indexClosestNewDetectedItem] = { - idDisplay: itemTracked.idDisplay - }; - var updatedTrackedItemProperties = detectionsOfThisFrame[indexClosestNewDetectedItem]; - mapOfItemsTracked.get(itemTracked.id).makeUnavailable().update(updatedTrackedItemProperties, frameNb); - } else { - } - } - }); - } else { - throw `Unknown matching algorithm "${params.matchingAlgorithm}"`; - } - } else { - if (DEBUG_MODE) { - console.log("[Tracker] Nothing detected for frame n\xBA" + frameNb); - } - mapOfItemsTracked.forEach(function(itemTracked) { - itemTracked.makeAvailable(); - }); - } - if (params.matchingAlgorithm === "kdTree") { - if (mapOfItemsTracked.size > 0) { - treeItemsTracked = new kdTree(Array.from(mapOfItemsTracked.values()), params.distanceFunc, ["x", "y", "w", "h"]); - matchedList.forEach(function(matched, index) { - if (!matched) { - var treeSearchResult = treeItemsTracked.nearest(detectionsOfThisFrame[index], 1, params.distanceLimit)[0]; - if (!treeSearchResult) { - var newItemTracked = ItemTracked(detectionsOfThisFrame[index], frameNb, params.unMatchedFramesTolerance, params.fastDelete); - mapOfItemsTracked.set(newItemTracked.id, newItemTracked); - treeItemsTracked.insert(newItemTracked); - newItemTracked.makeUnavailable(); - } else { - } - } - }); - } - } - mapOfItemsTracked.forEach(function(itemTracked) { - if (itemTracked.available) { - itemTracked.countDown(frameNb); - itemTracked.updateTheoricalPositionAndSize(); - if (itemTracked.isDead()) { - mapOfItemsTracked.delete(itemTracked.id); - treeItemsTracked.remove(itemTracked); - if (keepAllHistoryInMemory) { - mapOfAllItemsTracked.set(itemTracked.id, itemTracked); - } - } - } - }); - } - }; - exports.reset = function() { - mapOfItemsTracked = /* @__PURE__ */ new Map(); - mapOfAllItemsTracked = /* @__PURE__ */ new Map(); - itemTrackedModule.reset(); - }; - exports.setParams = function(newParams) { - Object.keys(newParams).forEach((key) => { - params[key] = newParams[key]; - }); - }; - exports.enableKeepInMemory = function() { - keepAllHistoryInMemory = true; - }; - exports.disableKeepInMemory = function() { - keepAllHistoryInMemory = false; - }; - exports.getJSONOfTrackedItems = function(roundInt = true) { - return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) { - return itemTracked.toJSON(roundInt); - }); - }; - exports.getJSONDebugOfTrackedItems = function(roundInt = true) { - return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) { - return itemTracked.toJSONDebug(roundInt); - }); - }; - exports.getTrackedItemsInMOTFormat = function(frameNb) { - return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) { - return itemTracked.toMOT(frameNb); - }); - }; - exports.getAllTrackedItems = function() { - return mapOfAllItemsTracked; - }; - exports.getJSONOfAllTrackedItems = function() { - return Array.from(mapOfAllItemsTracked.values()).map(function(itemTracked) { - return itemTracked.toJSONGenericInfo(); - }); - }; - } -}); -var tracker_default = require_tracker(); - -// demo/tracker/index.ts -var humanConfig = { - // user configuration for human, used to fine-tune behavior - debug: true, - backend: "webgl", - // cacheSensitivity: 0, - // cacheModels: false, - // warmup: 'none', - modelBasePath: "https://vladmandic.github.io/human-models/models", - filter: { enabled: true, equalization: false, flip: false }, - face: { - enabled: true, - detector: { rotation: false, maxDetected: 10, minConfidence: 0.3 }, - mesh: { enabled: true }, - attention: { enabled: false }, - iris: { enabled: false }, - description: { enabled: false }, - emotion: { enabled: false }, - antispoof: { enabled: false }, - liveness: { enabled: false } - }, - body: { enabled: false, maxDetected: 6, modelPath: "movenet-multipose.json" }, - hand: { enabled: false }, - object: { enabled: false, maxDetected: 10 }, - segmentation: { enabled: false }, - gesture: { enabled: false } -}; -var trackerConfig = { - unMatchedFramesTolerance: 100, - iouLimit: 0.05, - fastDelete: false, - distanceLimit: 1e4, - matchingAlgorithm: "kdTree" -}; -var human = new H.Human(humanConfig); -var dom = { - // grab instances of dom objects so we dont have to look them up later - video: document.getElementById("video"), - canvas: document.getElementById("canvas"), - log: document.getElementById("log"), - fps: document.getElementById("status"), - tracker: document.getElementById("tracker"), - interpolation: document.getElementById("interpolation"), - config: document.getElementById("config"), - ctx: document.getElementById("canvas").getContext("2d") -}; -var timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; -var fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; -var log = (...msg) => { - dom.log.innerText += msg.join(" ") + "\n"; - console.log(...msg); -}; -var status = (msg) => dom.fps.innerText = msg; -async function detectionLoop() { - if (!dom.video.paused && dom.video.readyState >= 2) { - if (timestamp.start === 0) timestamp.start = human.now(); - await human.detect(dom.video, humanConfig); - const tensors = human.tf.memory().numTensors; - if (tensors - timestamp.tensors !== 0) log("allocated tensors:", tensors - timestamp.tensors); - timestamp.tensors = tensors; - fps.detectFPS = Math.round(1e3 * 1e3 / (human.now() - timestamp.detect)) / 1e3; - fps.frames++; - fps.averageMs = Math.round(1e3 * (human.now() - timestamp.start) / fps.frames) / 1e3; - } - timestamp.detect = human.now(); - requestAnimationFrame(detectionLoop); -} -function drawLoop() { - if (!dom.video.paused && dom.video.readyState >= 2) { - const res = dom.interpolation.checked ? human.next(human.result) : human.result; - let tracking = []; - if (human.config.face.enabled) tracking = res.face; - else if (human.config.body.enabled) tracking = res.body; - else if (human.config.object.enabled) tracking = res.object; - else log("unknown object type"); - let data = []; - if (dom.tracker.checked) { - const items = tracking.map((obj) => ({ - x: obj.box[0] + obj.box[2] / 2, - y: obj.box[1] + obj.box[3] / 2, - w: obj.box[2], - h: obj.box[3], - name: obj.label || (human.config.face.enabled ? "face" : "body"), - confidence: obj.score - })); - tracker_default.updateTrackedItemsWithNewFrame(items, fps.frames); - data = tracker_default.getJSONOfTrackedItems(true); - } - human.draw.canvas(dom.video, dom.canvas); - for (let i = 0; i < tracking.length; i++) { - const name = tracking[i].label || (human.config.face.enabled ? "face" : "body"); - dom.ctx.strokeRect(tracking[i].box[0], tracking[i].box[1], tracking[i].box[1], tracking[i].box[2]); - dom.ctx.fillText(`id: ${tracking[i].id} ${Math.round(100 * tracking[i].score)}% ${name}`, tracking[i].box[0] + 4, tracking[i].box[1] + 16); - if (data[i]) { - dom.ctx.fillText(`t: ${data[i].id} ${Math.round(100 * data[i].confidence)}% ${data[i].name} ${data[i].isZombie ? "zombie" : ""}`, tracking[i].box[0] + 4, tracking[i].box[1] + 34); - } - } - } - const now = human.now(); - fps.drawFPS = Math.round(1e3 * 1e3 / (now - timestamp.draw)) / 1e3; - timestamp.draw = now; - status(dom.video.paused ? "paused" : `fps: ${fps.detectFPS.toFixed(1).padStart(5, " ")} detect | ${fps.drawFPS.toFixed(1).padStart(5, " ")} draw`); - setTimeout(drawLoop, 30); -} -async function handleVideo(file) { - const url = URL.createObjectURL(file); - dom.video.src = url; - await dom.video.play(); - log("loaded video:", file.name, "resolution:", [dom.video.videoWidth, dom.video.videoHeight], "duration:", dom.video.duration); - dom.canvas.width = dom.video.videoWidth; - dom.canvas.height = dom.video.videoHeight; - dom.ctx.strokeStyle = "white"; - dom.ctx.fillStyle = "white"; - dom.ctx.font = "16px Segoe UI"; - dom.video.playbackRate = 0.25; -} -function initInput() { - document.body.addEventListener("dragenter", (evt) => evt.preventDefault()); - document.body.addEventListener("dragleave", (evt) => evt.preventDefault()); - document.body.addEventListener("dragover", (evt) => evt.preventDefault()); - document.body.addEventListener("drop", async (evt) => { - var _a, _b; - evt.preventDefault(); - if (evt.dataTransfer) evt.dataTransfer.dropEffect = "copy"; - const file = (_b = (_a = evt.dataTransfer) == null ? void 0 : _a.files) == null ? void 0 : _b[0]; - if (file) await handleVideo(file); - log(dom.video.readyState); - }); - document.getElementById("inputvideo").onchange = async (evt) => { - var _a, _b; - evt.preventDefault(); - const file = (_b = (_a = evt.target) == null ? void 0 : _a["files"]) == null ? void 0 : _b[0]; - if (file) await handleVideo(file); - }; - dom.config.onchange = () => { - trackerConfig.distanceLimit = document.getElementById("distanceLimit").valueAsNumber; - trackerConfig.iouLimit = document.getElementById("iouLimit").valueAsNumber; - trackerConfig.unMatchedFramesTolerance = document.getElementById("unMatchedFramesTolerance").valueAsNumber; - trackerConfig.unMatchedFramesTolerance = document.getElementById("unMatchedFramesTolerance").valueAsNumber; - trackerConfig.matchingAlgorithm = document.getElementById("matchingAlgorithm-kdTree").checked ? 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c(a,e.distanceFunc,["x","y","w","h"]);if(n.size===0)a.forEach(function(v){var b=new f(v,o,e.unMatchedFramesTolerance,e.fastDelete);n.set(b.id,b),m.insert(b)});else{var d=new Array(a.length);if(d.fill(!1),a.length>0)if(e.matchingAlgorithm==="munkres"){var I=Array.from(n.keys()),p=Array.from(n.values()).map(v=>{var b=v.predictNextPosition();return a.map(k=>e.distanceFunc(b,k))});n.forEach(function(v){v.makeAvailable()}),i(p).filter(v=>p[v[0]][v[1]]<=e.distanceLimit).forEach(v=>{var b=n.get(I[v[0]]),k=a[v[1]];d[v[1]]={idDisplay:b.idDisplay},b.makeUnavailable().update(k,o)}),d.forEach(function(v,b){if(!v&&Math.min(...p.map(T=>T[b]))>e.distanceLimit){var k=f(a[b],o,e.unMatchedFramesTolerance,e.fastDelete);n.set(k.id,k),k.makeUnavailable(),p.push(a.map(T=>e.distanceFunc(k,T)))}})}else if(e.matchingAlgorithm==="kdTree")n.forEach(function(v){var b=v.predictNextPosition();v.makeAvailable();var k=u.nearest(b,1,e.distanceLimit)[0],T=u.nearest(v,1,e.distanceLimit)[0],O=u.nearest(b,2,e.distanceLimit);if(k){var x=a.indexOf(k[0]);if(!d[x]){d[x]={idDisplay:v.idDisplay};var E=a[x];n.get(v.id).makeUnavailable().update(E,o)}}});else throw`Unknown matching algorithm "${e.matchingAlgorithm}"`;else g&&console.log("[Tracker] Nothing detected for frame n\xBA"+o),n.forEach(function(v){v.makeAvailable()});e.matchingAlgorithm==="kdTree"&&n.size>0&&(m=new c(Array.from(n.values()),e.distanceFunc,["x","y","w","h"]),d.forEach(function(v,b){if(!v){var k=m.nearest(a[b],1,e.distanceLimit)[0];if(!k){var T=f(a[b],o,e.unMatchedFramesTolerance,e.fastDelete);n.set(T.id,T),m.insert(T),T.makeUnavailable()}}})),n.forEach(function(v){v.available&&(v.countDown(o),v.updateTheoricalPositionAndSize(),v.isDead()&&(n.delete(v.id),m.remove(v),r&&t.set(v.id,v)))})}},l.reset=function(){n=new Map,t=new Map,y.reset()},l.setParams=function(a){Object.keys(a).forEach(o=>{e[o]=a[o]})},l.enableKeepInMemory=function(){r=!0},l.disableKeepInMemory=function(){r=!1},l.getJSONOfTrackedItems=function(a=!0){return Array.from(n.values()).map(function(o){return o.toJSON(a)})},l.getJSONDebugOfTrackedItems=function(a=!0){return Array.from(n.values()).map(function(o){return o.toJSONDebug(a)})},l.getTrackedItemsInMOTFormat=function(a){return Array.from(n.values()).map(function(o){return o.toMOT(a)})},l.getAllTrackedItems=function(){return t},l.getJSONOfAllTrackedItems=function(){return Array.from(t.values()).map(function(a){return a.toJSONGenericInfo()})}}}),D=Q();var R={debug:!0,backend:"webgl",modelBasePath:"https://vladmandic.github.io/human-models/models",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!1,maxDetected:10,minConfidence:.3},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!1},description:{enabled:!1},emotion:{enabled:!1},antispoof:{enabled:!1},liveness:{enabled:!1}},body:{enabled:!1,maxDetected:6,modelPath:"movenet-multipose.json"},hand:{enabled:!1},object:{enabled:!1,maxDetected:10},segmentation:{enabled:!1},gesture:{enabled:!1}},F={unMatchedFramesTolerance:100,iouLimit:.05,fastDelete:!1,distanceLimit:1e4,matchingAlgorithm:"kdTree"},M=new U.Human(R),_={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),tracker:document.getElementById("tracker"),interpolation:document.getElementById("interpolation"),config:document.getElementById("config"),ctx:document.getElementById("canvas").getContext("2d")},A={detect:0,draw:0,tensors:0,start:0},N={detectFPS:0,drawFPS:0,frames:0,averageMs:0},H=(...l)=>{_.log.innerText+=l.join(" ")+` +`,console.log(...l)},B=l=>_.fps.innerText=l;async function V(){if(!_.video.paused&&_.video.readyState>=2){A.start===0&&(A.start=M.now()),await M.detect(_.video,R);let l=M.tf.memory().numTensors;l-A.tensors!==0&&H("allocated tensors:",l-A.tensors),A.tensors=l,N.detectFPS=Math.round(1e3*1e3/(M.now()-A.detect))/1e3,N.frames++,N.averageMs=Math.round(1e3*(M.now()-A.start)/N.frames)/1e3}A.detect=M.now(),requestAnimationFrame(V)}function Z(){if(!_.video.paused&&_.video.readyState>=2){let y=_.interpolation.checked?M.next(M.result):M.result,f=[];M.config.face.enabled?f=y.face:M.config.body.enabled?f=y.body:M.config.object.enabled?f=y.object:H("unknown object type");let c=[];if(_.tracker.checked){let s=f.map(i=>({x:i.box[0]+i.box[2]/2,y:i.box[1]+i.box[3]/2,w:i.box[2],h:i.box[3],name:i.label||(M.config.face.enabled?"face":"body"),confidence:i.score}));D.updateTrackedItemsWithNewFrame(s,N.frames),c=D.getJSONOfTrackedItems(!0)}M.draw.canvas(_.video,_.canvas);for(let s=0;sl.preventDefault()),document.body.addEventListener("dragleave",l=>l.preventDefault()),document.body.addEventListener("dragover",l=>l.preventDefault()),document.body.addEventListener("drop",async l=>{var f,c;l.preventDefault(),l.dataTransfer&&(l.dataTransfer.dropEffect="copy");let y=(c=(f=l.dataTransfer)==null?void 0:f.files)==null?void 0:c[0];y&&await P(y),H(_.video.readyState)}),document.getElementById("inputvideo").onchange=async l=>{var f,c;l.preventDefault();let y=(c=(f=l.target)==null?void 0:f.files)==null?void 0:c[0];y&&await P(y)},_.config.onchange=()=>{F.distanceLimit=document.getElementById("distanceLimit").valueAsNumber,F.iouLimit=document.getElementById("iouLimit").valueAsNumber,F.unMatchedFramesTolerance=document.getElementById("unMatchedFramesTolerance").valueAsNumber,F.unMatchedFramesTolerance=document.getElementById("unMatchedFramesTolerance").valueAsNumber,F.matchingAlgorithm=document.getElementById("matchingAlgorithm-kdTree").checked?"kdTree":"munkres",D.setParams(F),document.getElementById("keepInMemory").checked?D.enableKeepInMemory():D.disableKeepInMemory(),D.reset(),H("tracker config change",JSON.stringify(F)),R.face.enabled=document.getElementById("box-face").checked,R.body.enabled=document.getElementById("box-body").checked,R.object.enabled=document.getElementById("box-object").checked},_.tracker.onchange=l=>{H("tracker",l.target.checked?"enabled":"disabled"),D.setParams(F),D.reset()}}async function te(){H("human version:",M.version,"| tfjs version:",M.tf.version["tfjs-core"]),H("platform:",M.env.platform,"| agent:",M.env.agent),B("loading..."),await M.load(),H("backend:",M.tf.getBackend(),"| available:",M.env.backends),H("models loaded:",M.models.loaded()),B("initializing..."),await M.warmup(),ee(),await V(),Z()}window.onload=te; //# sourceMappingURL=index.js.map diff --git a/demo/tracker/index.js.map b/demo/tracker/index.js.map index 101e394d..d23e1158 100644 --- a/demo/tracker/index.js.map +++ b/demo/tracker/index.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["index.ts", "tracker.js"], "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\nimport tracker from './tracker.js';\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n debug: true,\n backend: 'webgl',\n // cacheSensitivity: 0,\n // cacheModels: false,\n // warmup: 'none',\n modelBasePath: 'https://vladmandic.github.io/human-models/models',\n filter: { enabled: true, equalization: false, flip: false },\n face: {\n enabled: true,\n detector: { rotation: false, maxDetected: 10, minConfidence: 0.3 },\n mesh: { enabled: true },\n attention: { enabled: false },\n iris: { enabled: false },\n description: { enabled: false },\n emotion: { enabled: false },\n antispoof: { enabled: false },\n liveness: { enabled: false },\n },\n body: { enabled: false, maxDetected: 6, modelPath: 'movenet-multipose.json' },\n hand: { enabled: false },\n object: { enabled: false, maxDetected: 10 },\n segmentation: { enabled: false },\n gesture: { enabled: false },\n};\n\ninterface TrackerConfig {\n unMatchedFramesTolerance: number, // number of frame when an object is not matched before considering it gone; ignored if fastDelete is set\n iouLimit: number, // exclude things from beeing matched if their IOU less than; 1 means total overlap; 0 means no overlap\n fastDelete: boolean, // remove new objects immediately if they could not be matched in the next frames; if set, ignores unMatchedFramesTolerance\n distanceLimit: number, // distance limit for matching; if values need to be excluded from matching set their distance to something greater than the distance limit\n matchingAlgorithm: 'kdTree' | 'munkres', // algorithm used to match tracks with new detections\n}\n\ninterface TrackerResult {\n id: number,\n confidence: number,\n bearing: number,\n isZombie: boolean,\n name: string,\n x: number,\n y: number,\n w: number,\n h: number,\n}\n\nconst trackerConfig: TrackerConfig = {\n unMatchedFramesTolerance: 100,\n iouLimit: 0.05,\n fastDelete: false,\n distanceLimit: 1e4,\n matchingAlgorithm: 'kdTree',\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('status') as HTMLPreElement,\n tracker: document.getElementById('tracker') as HTMLInputElement,\n interpolation: document.getElementById('interpolation') as HTMLInputElement,\n config: document.getElementById('config') as HTMLFormElement,\n ctx: (document.getElementById('canvas') as HTMLCanvasElement).getContext('2d') as CanvasRenderingContext2D,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused && dom.video.readyState >= 2) {\n if (timestamp.start === 0) timestamp.start = human.now();\n // log('profiling data:', await human.profile(dom.video));\n await human.detect(dom.video, humanConfig); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n timestamp.tensors = tensors;\n fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n fps.frames++;\n fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n }\n timestamp.detect = human.now();\n requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nfunction drawLoop() { // main screen refresh loop\n if (!dom.video.paused && dom.video.readyState >= 2) {\n const res: H.Result = dom.interpolation.checked ? human.next(human.result) : human.result; // interpolate results if enabled\n let tracking: H.FaceResult[] | H.BodyResult[] | H.ObjectResult[] = [];\n if (human.config.face.enabled) tracking = res.face;\n else if (human.config.body.enabled) tracking = res.body;\n else if (human.config.object.enabled) tracking = res.object;\n else log('unknown object type');\n let data: TrackerResult[] = [];\n if (dom.tracker.checked) {\n const items = tracking.map((obj) => ({\n x: obj.box[0] + obj.box[2] / 2,\n y: obj.box[1] + obj.box[3] / 2,\n w: obj.box[2],\n h: obj.box[3],\n name: obj.label || (human.config.face.enabled ? 'face' : 'body'),\n confidence: obj.score,\n }));\n tracker.updateTrackedItemsWithNewFrame(items, fps.frames);\n data = tracker.getJSONOfTrackedItems(true) as TrackerResult[];\n }\n human.draw.canvas(dom.video, dom.canvas); // copy input video frame to output canvas\n for (let i = 0; i < tracking.length; i++) {\n // @ts-ignore\n const name = tracking[i].label || (human.config.face.enabled ? 'face' : 'body');\n dom.ctx.strokeRect(tracking[i].box[0], tracking[i].box[1], tracking[i].box[1], tracking[i].box[2]);\n dom.ctx.fillText(`id: ${tracking[i].id} ${Math.round(100 * tracking[i].score)}% ${name}`, tracking[i].box[0] + 4, tracking[i].box[1] + 16);\n if (data[i]) {\n dom.ctx.fillText(`t: ${data[i].id} ${Math.round(100 * data[i].confidence)}% ${data[i].name} ${data[i].isZombie ? 'zombie' : ''}`, tracking[i].box[0] + 4, tracking[i].box[1] + 34);\n }\n }\n }\n const now = human.now();\n fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n timestamp.draw = now;\n status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function handleVideo(file: File) {\n const url = URL.createObjectURL(file);\n dom.video.src = url;\n await dom.video.play();\n log('loaded video:', file.name, 'resolution:', [dom.video.videoWidth, dom.video.videoHeight], 'duration:', dom.video.duration);\n dom.canvas.width = dom.video.videoWidth;\n dom.canvas.height = dom.video.videoHeight;\n dom.ctx.strokeStyle = 'white';\n dom.ctx.fillStyle = 'white';\n dom.ctx.font = '16px Segoe UI';\n dom.video.playbackRate = 0.25;\n}\n\nfunction initInput() {\n document.body.addEventListener('dragenter', (evt) => evt.preventDefault());\n document.body.addEventListener('dragleave', (evt) => evt.preventDefault());\n document.body.addEventListener('dragover', (evt) => evt.preventDefault());\n document.body.addEventListener('drop', async (evt) => {\n evt.preventDefault();\n if (evt.dataTransfer) evt.dataTransfer.dropEffect = 'copy';\n const file = evt.dataTransfer?.files?.[0];\n if (file) await handleVideo(file);\n log(dom.video.readyState);\n });\n (document.getElementById('inputvideo') as HTMLInputElement).onchange = async (evt) => {\n evt.preventDefault();\n const file = evt.target?.['files']?.[0];\n if (file) await handleVideo(file);\n };\n dom.config.onchange = () => {\n trackerConfig.distanceLimit = (document.getElementById('distanceLimit') as HTMLInputElement).valueAsNumber;\n trackerConfig.iouLimit = (document.getElementById('iouLimit') as HTMLInputElement).valueAsNumber;\n trackerConfig.unMatchedFramesTolerance = (document.getElementById('unMatchedFramesTolerance') as HTMLInputElement).valueAsNumber;\n trackerConfig.unMatchedFramesTolerance = (document.getElementById('unMatchedFramesTolerance') as HTMLInputElement).valueAsNumber;\n trackerConfig.matchingAlgorithm = (document.getElementById('matchingAlgorithm-kdTree') as HTMLInputElement).checked ? 'kdTree' : 'munkres';\n tracker.setParams(trackerConfig);\n if ((document.getElementById('keepInMemory') as HTMLInputElement).checked) tracker.enableKeepInMemory();\n else tracker.disableKeepInMemory();\n tracker.reset();\n log('tracker config change', JSON.stringify(trackerConfig));\n humanConfig.face!.enabled = (document.getElementById('box-face') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion\n humanConfig.body!.enabled = (document.getElementById('box-body') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion\n humanConfig.object!.enabled = (document.getElementById('box-object') as HTMLInputElement).checked; // eslint-disable-line @typescript-eslint/no-non-null-assertion\n };\n dom.tracker.onchange = (evt) => {\n log('tracker', (evt.target as HTMLInputElement).checked ? 'enabled' : 'disabled');\n tracker.setParams(trackerConfig);\n tracker.reset();\n };\n}\n\nasync function main() { // main entry point\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('platform:', human.env.platform, '| agent:', human.env.agent);\n status('loading...');\n await human.load(); // preload all models\n log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n log('models loaded:', human.models.loaded());\n status('initializing...');\n await human.warmup(); // warmup function to initialize backend for future faster detection\n initInput(); // initialize input\n await detectionLoop(); // start detection loop\n drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n", "/* eslint-disable */\nvar __defProp = Object.defineProperty;\nvar __getOwnPropNames = Object.getOwnPropertyNames;\nvar __name = (target, value) => __defProp(target, \"name\", { value, configurable: true });\nvar __commonJS = (cb, mod) => function __require() {\n return mod || (0, cb[__getOwnPropNames(cb)[0]])((mod = { exports: {} }).exports, mod), mod.exports;\n};\n\n// node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/rng-browser.js\nvar require_rng_browser = __commonJS({\n \"node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/rng-browser.js\"(exports, module) {\n var getRandomValues = typeof crypto != \"undefined\" && crypto.getRandomValues.bind(crypto) || typeof msCrypto != \"undefined\" && msCrypto.getRandomValues.bind(msCrypto);\n if (getRandomValues) {\n rnds8 = new Uint8Array(16);\n module.exports = /* @__PURE__ */ __name(function whatwgRNG() {\n getRandomValues(rnds8);\n return rnds8;\n }, \"whatwgRNG\");\n } else {\n rnds = new Array(16);\n module.exports = /* @__PURE__ */ __name(function mathRNG() {\n for (var i = 0, r; i < 16; i++) {\n if ((i & 3) === 0)\n r = Math.random() * 4294967296;\n rnds[i] = r >>> ((i & 3) << 3) & 255;\n }\n return rnds;\n }, \"mathRNG\");\n }\n var rnds8;\n var rnds;\n }\n});\n\n// node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/bytesToUuid.js\nvar require_bytesToUuid = __commonJS({\n \"node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/lib/bytesToUuid.js\"(exports, module) {\n var byteToHex = [];\n for (i = 0; i < 256; ++i) {\n byteToHex[i] = (i + 256).toString(16).substr(1);\n }\n function bytesToUuid(buf, offset) {\n var i2 = offset || 0;\n var bth = byteToHex;\n return bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + \"-\" + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]] + bth[buf[i2++]];\n }\n __name(bytesToUuid, \"bytesToUuid\");\n module.exports = bytesToUuid;\n var i;\n }\n});\n\n// node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/v4.js\nvar require_v4 = __commonJS({\n \"node_modules/.pnpm/uuid@3.2.1/node_modules/uuid/v4.js\"(exports, module) {\n var rng = require_rng_browser();\n var bytesToUuid = require_bytesToUuid();\n function v4(options, buf, offset) {\n var i = buf && offset || 0;\n if (typeof options == \"string\") {\n buf = options === \"binary\" ? new Array(16) : null;\n options = null;\n }\n options = options || {};\n var rnds = options.random || (options.rng || rng)();\n rnds[6] = rnds[6] & 15 | 64;\n rnds[8] = rnds[8] & 63 | 128;\n if (buf) {\n for (var ii = 0; ii < 16; ++ii) {\n buf[i + ii] = rnds[ii];\n }\n }\n return buf || bytesToUuid(rnds);\n }\n __name(v4, \"v4\");\n module.exports = v4;\n }\n});\n\n// utils.js\nvar require_utils = __commonJS({\n \"utils.js\"(exports) {\n exports.isDetectionTooLarge = (detections, largestAllowed) => {\n if (detections.w >= largestAllowed) {\n return true;\n } else {\n return false;\n }\n };\n var isInsideArea = /* @__PURE__ */ __name((area, point) => {\n const xMin = area.x - area.w / 2;\n const xMax = area.x + area.w / 2;\n const yMin = area.y - area.h / 2;\n const yMax = area.y + area.h / 2;\n if (point.x >= xMin && point.x <= xMax && point.y >= yMin && point.y <= yMax) {\n return true;\n } else {\n return false;\n }\n }, \"isInsideArea\");\n exports.isInsideArea = isInsideArea;\n exports.isInsideSomeAreas = (areas, point) => {\n const isInside = areas.some((area) => isInsideArea(area, point));\n return isInside;\n };\n exports.ignoreObjectsNotToDetect = (detections, objectsToDetect) => {\n return detections.filter((detection) => objectsToDetect.indexOf(detection.name) > -1);\n };\n var getRectangleEdges = /* @__PURE__ */ __name((item) => {\n return {\n x0: item.x - item.w / 2,\n y0: item.y - item.h / 2,\n x1: item.x + item.w / 2,\n y1: item.y + item.h / 2\n };\n }, \"getRectangleEdges\");\n exports.getRectangleEdges = getRectangleEdges;\n exports.iouAreas = (item1, item2) => {\n var rect1 = getRectangleEdges(item1);\n var rect2 = getRectangleEdges(item2);\n var overlap_x0 = Math.max(rect1.x0, rect2.x0);\n var overlap_y0 = Math.max(rect1.y0, rect2.y0);\n var overlap_x1 = Math.min(rect1.x1, rect2.x1);\n var overlap_y1 = Math.min(rect1.y1, rect2.y1);\n if (overlap_x1 - overlap_x0 <= 0 || overlap_y1 - overlap_y0 <= 0) {\n return 0;\n } else {\n const area_rect1 = item1.w * item1.h;\n const area_rect2 = item2.w * item2.h;\n const area_intersection = (overlap_x1 - overlap_x0) * (overlap_y1 - overlap_y0);\n const area_union = area_rect1 + area_rect2 - area_intersection;\n return area_intersection / area_union;\n }\n };\n exports.computeVelocityVector = (item1, item2, nbFrame) => {\n return {\n dx: (item2.x - item1.x) / nbFrame,\n dy: (item2.y - item1.y) / nbFrame\n };\n };\n exports.computeBearingIn360 = function(dx, dy) {\n var angle = Math.atan(dx / dy) / (Math.PI / 180);\n if (angle > 0) {\n if (dy > 0)\n return angle;\n else\n return 180 + angle;\n } else {\n if (dx > 0)\n return 180 + angle;\n else\n return 360 + angle;\n }\n };\n }\n});\n\n// ItemTracked.js\nvar require_ItemTracked = __commonJS({\n \"ItemTracked.js\"(exports) {\n var uuidv4 = require_v4();\n var computeBearingIn360 = require_utils().computeBearingIn360;\n var computeVelocityVector = require_utils().computeVelocityVector;\n exports.ITEM_HISTORY_MAX_LENGTH = 15;\n var idDisplay = 0;\n exports.ItemTracked = function(properties, frameNb, unMatchedFramesTolerance, fastDelete) {\n var DEFAULT_UNMATCHEDFRAMES_TOLERANCE = unMatchedFramesTolerance;\n var itemTracked = {};\n itemTracked.available = true;\n itemTracked.delete = false;\n itemTracked.fastDelete = fastDelete;\n itemTracked.frameUnmatchedLeftBeforeDying = unMatchedFramesTolerance;\n itemTracked.isZombie = false;\n itemTracked.appearFrame = frameNb;\n itemTracked.disappearFrame = null;\n itemTracked.disappearArea = {};\n itemTracked.nameCount = {};\n itemTracked.nameCount[properties.name] = 1;\n itemTracked.x = properties.x;\n itemTracked.y = properties.y;\n itemTracked.w = properties.w;\n itemTracked.h = properties.h;\n itemTracked.name = properties.name;\n itemTracked.confidence = properties.confidence;\n itemTracked.itemHistory = [];\n itemTracked.itemHistory.push({\n x: properties.x,\n y: properties.y,\n w: properties.w,\n h: properties.h,\n confidence: properties.confidence\n });\n if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) {\n itemTracked.itemHistory.shift();\n }\n itemTracked.velocity = {\n dx: 0,\n dy: 0\n };\n itemTracked.nbTimeMatched = 1;\n itemTracked.id = uuidv4();\n itemTracked.idDisplay = idDisplay;\n idDisplay++;\n itemTracked.update = function(properties2, frameNb2) {\n if (this.disappearFrame) {\n this.disappearFrame = null;\n this.disappearArea = {};\n }\n this.isZombie = false;\n this.nbTimeMatched += 1;\n this.x = properties2.x;\n this.y = properties2.y;\n this.w = properties2.w;\n this.h = properties2.h;\n this.confidence = properties2.confidence;\n this.itemHistory.push({\n x: this.x,\n y: this.y,\n w: this.w,\n h: this.h,\n confidence: this.confidence\n });\n if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) {\n itemTracked.itemHistory.shift();\n }\n this.name = properties2.name;\n if (this.nameCount[properties2.name]) {\n this.nameCount[properties2.name]++;\n } else {\n this.nameCount[properties2.name] = 1;\n }\n this.frameUnmatchedLeftBeforeDying = DEFAULT_UNMATCHEDFRAMES_TOLERANCE;\n this.velocity = this.updateVelocityVector();\n };\n itemTracked.makeAvailable = function() {\n this.available = true;\n return this;\n };\n itemTracked.makeUnavailable = function() {\n this.available = false;\n return this;\n };\n itemTracked.countDown = function(frameNb2) {\n if (this.disappearFrame === null) {\n this.disappearFrame = frameNb2;\n this.disappearArea = {\n x: this.x,\n y: this.y,\n w: this.w,\n h: this.h\n };\n }\n this.frameUnmatchedLeftBeforeDying--;\n this.isZombie = true;\n if (this.fastDelete && this.nbTimeMatched <= 1) {\n this.frameUnmatchedLeftBeforeDying = -1;\n }\n };\n itemTracked.updateTheoricalPositionAndSize = function() {\n this.itemHistory.push({\n x: this.x,\n y: this.y,\n w: this.w,\n h: this.h,\n confidence: this.confidence\n });\n if (itemTracked.itemHistory.length >= exports.ITEM_HISTORY_MAX_LENGTH) {\n itemTracked.itemHistory.shift();\n }\n this.x = this.x + this.velocity.dx;\n this.y = this.y + this.velocity.dy;\n };\n itemTracked.predictNextPosition = function() {\n return {\n x: this.x + this.velocity.dx,\n y: this.y + this.velocity.dy,\n w: this.w,\n h: this.h\n };\n };\n itemTracked.isDead = function() {\n return this.frameUnmatchedLeftBeforeDying < 0;\n };\n itemTracked.updateVelocityVector = function() {\n if (exports.ITEM_HISTORY_MAX_LENGTH <= 2) {\n return { dx: void 0, dy: void 0 };\n }\n if (this.itemHistory.length <= exports.ITEM_HISTORY_MAX_LENGTH) {\n const start = this.itemHistory[0];\n const end = this.itemHistory[this.itemHistory.length - 1];\n return computeVelocityVector(start, end, this.itemHistory.length);\n } else {\n const start = this.itemHistory[this.itemHistory.length - exports.ITEM_HISTORY_MAX_LENGTH];\n const end = this.itemHistory[this.itemHistory.length - 1];\n return computeVelocityVector(start, end, exports.ITEM_HISTORY_MAX_LENGTH);\n }\n };\n itemTracked.getMostlyMatchedName = function() {\n var nameMostlyMatchedOccurences = 0;\n var nameMostlyMatched = \"\";\n Object.keys(this.nameCount).map((name) => {\n if (this.nameCount[name] > nameMostlyMatchedOccurences) {\n nameMostlyMatched = name;\n nameMostlyMatchedOccurences = this.nameCount[name];\n }\n });\n return nameMostlyMatched;\n };\n itemTracked.toJSONDebug = function(roundInt = true) {\n return {\n id: this.id,\n idDisplay: this.idDisplay,\n x: roundInt ? parseInt(this.x, 10) : this.x,\n y: roundInt ? parseInt(this.y, 10) : this.y,\n w: roundInt ? parseInt(this.w, 10) : this.w,\n h: roundInt ? parseInt(this.h, 10) : this.h,\n confidence: Math.round(this.confidence * 100) / 100,\n // Here we negate dy to be in \"normal\" carthesian coordinates\n bearing: parseInt(computeBearingIn360(this.velocity.dx, -this.velocity.dy)),\n name: this.getMostlyMatchedName(),\n isZombie: this.isZombie,\n appearFrame: this.appearFrame,\n disappearFrame: this.disappearFrame\n };\n };\n itemTracked.toJSON = function(roundInt = true) {\n return {\n id: this.idDisplay,\n x: roundInt ? parseInt(this.x, 10) : this.x,\n y: roundInt ? parseInt(this.y, 10) : this.y,\n w: roundInt ? parseInt(this.w, 10) : this.w,\n h: roundInt ? parseInt(this.h, 10) : this.h,\n confidence: Math.round(this.confidence * 100) / 100,\n // Here we negate dy to be in \"normal\" carthesian coordinates\n bearing: parseInt(computeBearingIn360(this.velocity.dx, -this.velocity.dy), 10),\n name: this.getMostlyMatchedName(),\n isZombie: this.isZombie\n };\n };\n itemTracked.toMOT = function(frameIndex) {\n return `${frameIndex},${this.idDisplay},${this.x - this.w / 2},${this.y - this.h / 2},${this.w},${this.h},${this.confidence / 100},-1,-1,-1`;\n };\n itemTracked.toJSONGenericInfo = function() {\n return {\n id: this.id,\n idDisplay: this.idDisplay,\n appearFrame: this.appearFrame,\n disappearFrame: this.disappearFrame,\n disappearArea: this.disappearArea,\n nbActiveFrame: this.disappearFrame - this.appearFrame,\n name: this.getMostlyMatchedName()\n };\n };\n return itemTracked;\n };\n exports.reset = function() {\n idDisplay = 0;\n };\n }\n});\n\n// lib/kdTree-min.js\nvar require_kdTree_min = __commonJS({\n \"lib/kdTree-min.js\"(exports) {\n (function(root, factory) {\n if (typeof define === \"function\" && define.amd) {\n define([\"exports\"], factory);\n } else if (typeof exports === \"object\") {\n factory(exports);\n } else {\n factory(root);\n }\n })(exports, function(exports2) {\n function Node(obj, dimension, parent) {\n this.obj = obj;\n this.left = null;\n this.right = null;\n this.parent = parent;\n this.dimension = dimension;\n }\n __name(Node, \"Node\");\n function kdTree(points, metric, dimensions) {\n var self = this;\n function buildTree(points2, depth, parent) {\n var dim = depth % dimensions.length, median, node;\n if (points2.length === 0) {\n return null;\n }\n if (points2.length === 1) {\n return new Node(points2[0], dim, parent);\n }\n points2.sort(function(a, b) {\n return a[dimensions[dim]] - b[dimensions[dim]];\n });\n median = Math.floor(points2.length / 2);\n node = new Node(points2[median], dim, parent);\n node.left = buildTree(points2.slice(0, median), depth + 1, node);\n node.right = buildTree(points2.slice(median + 1), depth + 1, node);\n return node;\n }\n __name(buildTree, \"buildTree\");\n function loadTree(data) {\n self.root = data;\n function restoreParent(root) {\n if (root.left) {\n root.left.parent = root;\n restoreParent(root.left);\n }\n if (root.right) {\n root.right.parent = root;\n restoreParent(root.right);\n }\n }\n __name(restoreParent, \"restoreParent\");\n restoreParent(self.root);\n }\n __name(loadTree, \"loadTree\");\n if (!Array.isArray(points))\n loadTree(points, metric, dimensions);\n else\n this.root = buildTree(points, 0, null);\n this.toJSON = function(src) {\n if (!src)\n src = this.root;\n var dest = new Node(src.obj, src.dimension, null);\n if (src.left)\n dest.left = self.toJSON(src.left);\n if (src.right)\n dest.right = self.toJSON(src.right);\n return dest;\n };\n this.insert = function(point) {\n function innerSearch(node, parent) {\n if (node === null) {\n return parent;\n }\n var dimension2 = dimensions[node.dimension];\n if (point[dimension2] < node.obj[dimension2]) {\n return innerSearch(node.left, node);\n } else {\n return innerSearch(node.right, node);\n }\n }\n __name(innerSearch, \"innerSearch\");\n var insertPosition = innerSearch(this.root, null), newNode, dimension;\n if (insertPosition === null) {\n this.root = new Node(point, 0, null);\n return;\n }\n newNode = new Node(point, (insertPosition.dimension + 1) % dimensions.length, insertPosition);\n dimension = dimensions[insertPosition.dimension];\n if (point[dimension] < insertPosition.obj[dimension]) {\n insertPosition.left = newNode;\n } else {\n insertPosition.right = newNode;\n }\n };\n this.remove = function(point) {\n var node;\n function nodeSearch(node2) {\n if (node2 === null) {\n return null;\n }\n if (node2.obj === point) {\n return node2;\n }\n var dimension = dimensions[node2.dimension];\n if (point[dimension] < node2.obj[dimension]) {\n return nodeSearch(node2.left, node2);\n } else {\n return nodeSearch(node2.right, node2);\n }\n }\n __name(nodeSearch, \"nodeSearch\");\n function removeNode(node2) {\n var nextNode, nextObj, pDimension;\n function findMin(node3, dim) {\n var dimension, own, left, right, min;\n if (node3 === null) {\n return null;\n }\n dimension = dimensions[dim];\n if (node3.dimension === dim) {\n if (node3.left !== null) {\n return findMin(node3.left, dim);\n }\n return node3;\n }\n own = node3.obj[dimension];\n left = findMin(node3.left, dim);\n right = findMin(node3.right, dim);\n min = node3;\n if (left !== null && left.obj[dimension] < own) {\n min = left;\n }\n if (right !== null && right.obj[dimension] < min.obj[dimension]) {\n min = right;\n }\n return min;\n }\n __name(findMin, \"findMin\");\n if (node2.left === null && node2.right === null) {\n if (node2.parent === null) {\n self.root = null;\n return;\n }\n pDimension = dimensions[node2.parent.dimension];\n if (node2.obj[pDimension] < node2.parent.obj[pDimension]) {\n node2.parent.left = null;\n } else {\n node2.parent.right = null;\n }\n return;\n }\n if (node2.right !== null) {\n nextNode = findMin(node2.right, node2.dimension);\n nextObj = nextNode.obj;\n removeNode(nextNode);\n node2.obj = nextObj;\n } else {\n nextNode = findMin(node2.left, node2.dimension);\n nextObj = nextNode.obj;\n removeNode(nextNode);\n node2.right = node2.left;\n node2.left = null;\n node2.obj = nextObj;\n }\n }\n __name(removeNode, \"removeNode\");\n node = nodeSearch(self.root);\n if (node === null) {\n return;\n }\n removeNode(node);\n };\n this.nearest = function(point, maxNodes, maxDistance) {\n var i, result, bestNodes;\n bestNodes = new BinaryHeap(\n function(e) {\n return -e[1];\n }\n );\n function nearestSearch(node) {\n var bestChild, dimension = dimensions[node.dimension], ownDistance = metric(point, node.obj), linearPoint = {}, linearDistance, otherChild, i2;\n function saveNode(node2, distance) {\n bestNodes.push([node2, distance]);\n if (bestNodes.size() > maxNodes) {\n bestNodes.pop();\n }\n }\n __name(saveNode, \"saveNode\");\n for (i2 = 0; i2 < dimensions.length; i2 += 1) {\n if (i2 === node.dimension) {\n linearPoint[dimensions[i2]] = point[dimensions[i2]];\n } else {\n linearPoint[dimensions[i2]] = node.obj[dimensions[i2]];\n }\n }\n linearDistance = metric(linearPoint, node.obj);\n if (node.right === null && node.left === null) {\n if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n saveNode(node, ownDistance);\n }\n return;\n }\n if (node.right === null) {\n bestChild = node.left;\n } else if (node.left === null) {\n bestChild = node.right;\n } else {\n if (point[dimension] < node.obj[dimension]) {\n bestChild = node.left;\n } else {\n bestChild = node.right;\n }\n }\n nearestSearch(bestChild);\n if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {\n saveNode(node, ownDistance);\n }\n if (bestNodes.size() < maxNodes || Math.abs(linearDistance) < bestNodes.peek()[1]) {\n if (bestChild === node.left) {\n otherChild = node.right;\n } else {\n otherChild = node.left;\n }\n if (otherChild !== null) {\n nearestSearch(otherChild);\n }\n }\n }\n __name(nearestSearch, \"nearestSearch\");\n if (maxDistance) {\n for (i = 0; i < maxNodes; i += 1) {\n bestNodes.push([null, maxDistance]);\n }\n }\n if (self.root)\n nearestSearch(self.root);\n result = [];\n for (i = 0; i < Math.min(maxNodes, bestNodes.content.length); i += 1) {\n if (bestNodes.content[i][0]) {\n result.push([bestNodes.content[i][0].obj, bestNodes.content[i][1]]);\n }\n }\n return result;\n };\n this.balanceFactor = function() {\n function height(node) {\n if (node === null) {\n return 0;\n }\n return Math.max(height(node.left), height(node.right)) + 1;\n }\n __name(height, \"height\");\n function count(node) {\n if (node === null) {\n return 0;\n }\n return count(node.left) + count(node.right) + 1;\n }\n __name(count, \"count\");\n return height(self.root) / (Math.log(count(self.root)) / Math.log(2));\n };\n }\n __name(kdTree, \"kdTree\");\n function BinaryHeap(scoreFunction) {\n this.content = [];\n this.scoreFunction = scoreFunction;\n }\n __name(BinaryHeap, \"BinaryHeap\");\n BinaryHeap.prototype = {\n push: function(element) {\n this.content.push(element);\n this.bubbleUp(this.content.length - 1);\n },\n pop: function() {\n var result = this.content[0];\n var end = this.content.pop();\n if (this.content.length > 0) {\n this.content[0] = end;\n this.sinkDown(0);\n }\n return result;\n },\n peek: function() {\n return this.content[0];\n },\n remove: function(node) {\n var len = this.content.length;\n for (var i = 0; i < len; i++) {\n if (this.content[i] == node) {\n var end = this.content.pop();\n if (i != len - 1) {\n this.content[i] = end;\n if (this.scoreFunction(end) < this.scoreFunction(node))\n this.bubbleUp(i);\n else\n this.sinkDown(i);\n }\n return;\n }\n }\n throw new Error(\"Node not found.\");\n },\n size: function() {\n return this.content.length;\n },\n bubbleUp: function(n) {\n var element = this.content[n];\n while (n > 0) {\n var parentN = Math.floor((n + 1) / 2) - 1, parent = this.content[parentN];\n if (this.scoreFunction(element) < this.scoreFunction(parent)) {\n this.content[parentN] = element;\n this.content[n] = parent;\n n = parentN;\n } else {\n break;\n }\n }\n },\n sinkDown: function(n) {\n var length = this.content.length, element = this.content[n], elemScore = this.scoreFunction(element);\n while (true) {\n var child2N = (n + 1) * 2, child1N = child2N - 1;\n var swap = null;\n if (child1N < length) {\n var child1 = this.content[child1N], child1Score = this.scoreFunction(child1);\n if (child1Score < elemScore)\n swap = child1N;\n }\n if (child2N < length) {\n var child2 = this.content[child2N], child2Score = this.scoreFunction(child2);\n if (child2Score < (swap == null ? elemScore : child1Score)) {\n swap = child2N;\n }\n }\n if (swap != null) {\n this.content[n] = this.content[swap];\n this.content[swap] = element;\n n = swap;\n } else {\n break;\n }\n }\n }\n };\n exports2.kdTree = kdTree;\n exports2.BinaryHeap = BinaryHeap;\n });\n }\n});\n\n// node_modules/.pnpm/munkres-js@1.2.2/node_modules/munkres-js/munkres.js\nvar require_munkres = __commonJS({\n \"node_modules/.pnpm/munkres-js@1.2.2/node_modules/munkres-js/munkres.js\"(exports, module) {\n var MAX_SIZE = parseInt(Number.MAX_SAFE_INTEGER / 2) || (1 << 26) * (1 << 26);\n var DEFAULT_PAD_VALUE = 0;\n function Munkres() {\n this.C = null;\n this.row_covered = [];\n this.col_covered = [];\n this.n = 0;\n this.Z0_r = 0;\n this.Z0_c = 0;\n this.marked = null;\n this.path = null;\n }\n __name(Munkres, \"Munkres\");\n Munkres.prototype.pad_matrix = function(matrix, pad_value) {\n pad_value = pad_value || DEFAULT_PAD_VALUE;\n var max_columns = 0;\n var total_rows = matrix.length;\n var i;\n for (i = 0; i < total_rows; ++i)\n if (matrix[i].length > max_columns)\n max_columns = matrix[i].length;\n total_rows = max_columns > total_rows ? max_columns : total_rows;\n var new_matrix = [];\n for (i = 0; i < total_rows; ++i) {\n var row = matrix[i] || [];\n var new_row = row.slice();\n while (total_rows > new_row.length)\n new_row.push(pad_value);\n new_matrix.push(new_row);\n }\n return new_matrix;\n };\n Munkres.prototype.compute = function(cost_matrix, options) {\n options = options || {};\n options.padValue = options.padValue || DEFAULT_PAD_VALUE;\n this.C = this.pad_matrix(cost_matrix, options.padValue);\n this.n = this.C.length;\n this.original_length = cost_matrix.length;\n this.original_width = cost_matrix[0].length;\n var nfalseArray = [];\n while (nfalseArray.length < this.n)\n nfalseArray.push(false);\n this.row_covered = nfalseArray.slice();\n this.col_covered = nfalseArray.slice();\n this.Z0_r = 0;\n this.Z0_c = 0;\n this.path = this.__make_matrix(this.n * 2, 0);\n this.marked = this.__make_matrix(this.n, 0);\n var step = 1;\n var steps = {\n 1: this.__step1,\n 2: this.__step2,\n 3: this.__step3,\n 4: this.__step4,\n 5: this.__step5,\n 6: this.__step6\n };\n while (true) {\n var func = steps[step];\n if (!func)\n break;\n step = func.apply(this);\n }\n var results = [];\n for (var i = 0; i < this.original_length; ++i)\n for (var j = 0; j < this.original_width; ++j)\n if (this.marked[i][j] == 1)\n results.push([i, j]);\n return results;\n };\n Munkres.prototype.__make_matrix = function(n, val) {\n var matrix = [];\n for (var i = 0; i < n; ++i) {\n matrix[i] = [];\n for (var j = 0; j < n; ++j)\n matrix[i][j] = val;\n }\n return matrix;\n };\n Munkres.prototype.__step1 = function() {\n for (var i = 0; i < this.n; ++i) {\n var minval = Math.min.apply(Math, this.C[i]);\n for (var j = 0; j < this.n; ++j)\n this.C[i][j] -= minval;\n }\n return 2;\n };\n Munkres.prototype.__step2 = function() {\n for (var i = 0; i < this.n; ++i) {\n for (var j = 0; j < this.n; ++j) {\n if (this.C[i][j] === 0 && !this.col_covered[j] && !this.row_covered[i]) {\n this.marked[i][j] = 1;\n this.col_covered[j] = true;\n this.row_covered[i] = true;\n break;\n }\n }\n }\n this.__clear_covers();\n return 3;\n };\n Munkres.prototype.__step3 = function() {\n var count = 0;\n for (var i = 0; i < this.n; ++i) {\n for (var j = 0; j < this.n; ++j) {\n if (this.marked[i][j] == 1 && this.col_covered[j] == false) {\n this.col_covered[j] = true;\n ++count;\n }\n }\n }\n return count >= this.n ? 7 : 4;\n };\n Munkres.prototype.__step4 = function() {\n var done = false;\n var row = -1, col = -1, star_col = -1;\n while (!done) {\n var z = this.__find_a_zero();\n row = z[0];\n col = z[1];\n if (row < 0)\n return 6;\n this.marked[row][col] = 2;\n star_col = this.__find_star_in_row(row);\n if (star_col >= 0) {\n col = star_col;\n this.row_covered[row] = true;\n this.col_covered[col] = false;\n } else {\n this.Z0_r = row;\n this.Z0_c = col;\n return 5;\n }\n }\n };\n Munkres.prototype.__step5 = function() {\n var count = 0;\n this.path[count][0] = this.Z0_r;\n this.path[count][1] = this.Z0_c;\n var done = false;\n while (!done) {\n var row = this.__find_star_in_col(this.path[count][1]);\n if (row >= 0) {\n count++;\n this.path[count][0] = row;\n this.path[count][1] = this.path[count - 1][1];\n } else {\n done = true;\n }\n if (!done) {\n var col = this.__find_prime_in_row(this.path[count][0]);\n count++;\n this.path[count][0] = this.path[count - 1][0];\n this.path[count][1] = col;\n }\n }\n this.__convert_path(this.path, count);\n this.__clear_covers();\n this.__erase_primes();\n return 3;\n };\n Munkres.prototype.__step6 = function() {\n var minval = this.__find_smallest();\n for (var i = 0; i < this.n; ++i) {\n for (var j = 0; j < this.n; ++j) {\n if (this.row_covered[i])\n this.C[i][j] += minval;\n if (!this.col_covered[j])\n this.C[i][j] -= minval;\n }\n }\n return 4;\n };\n Munkres.prototype.__find_smallest = function() {\n var minval = MAX_SIZE;\n for (var i = 0; i < this.n; ++i)\n for (var j = 0; j < this.n; ++j)\n if (!this.row_covered[i] && !this.col_covered[j]) {\n if (minval > this.C[i][j])\n minval = this.C[i][j];\n }\n return minval;\n };\n Munkres.prototype.__find_a_zero = function() {\n for (var i = 0; i < this.n; ++i)\n for (var j = 0; j < this.n; ++j)\n if (this.C[i][j] === 0 && !this.row_covered[i] && !this.col_covered[j])\n return [i, j];\n return [-1, -1];\n };\n Munkres.prototype.__find_star_in_row = function(row) {\n for (var j = 0; j < this.n; ++j)\n if (this.marked[row][j] == 1)\n return j;\n return -1;\n };\n Munkres.prototype.__find_star_in_col = function(col) {\n for (var i = 0; i < this.n; ++i)\n if (this.marked[i][col] == 1)\n return i;\n return -1;\n };\n Munkres.prototype.__find_prime_in_row = function(row) {\n for (var j = 0; j < this.n; ++j)\n if (this.marked[row][j] == 2)\n return j;\n return -1;\n };\n Munkres.prototype.__convert_path = function(path, count) {\n for (var i = 0; i <= count; ++i)\n this.marked[path[i][0]][path[i][1]] = this.marked[path[i][0]][path[i][1]] == 1 ? 0 : 1;\n };\n Munkres.prototype.__clear_covers = function() {\n for (var i = 0; i < this.n; ++i) {\n this.row_covered[i] = false;\n this.col_covered[i] = false;\n }\n };\n Munkres.prototype.__erase_primes = function() {\n for (var i = 0; i < this.n; ++i)\n for (var j = 0; j < this.n; ++j)\n if (this.marked[i][j] == 2)\n this.marked[i][j] = 0;\n };\n function make_cost_matrix(profit_matrix, inversion_function) {\n var i, j;\n if (!inversion_function) {\n var maximum = -1 / 0;\n for (i = 0; i < profit_matrix.length; ++i)\n for (j = 0; j < profit_matrix[i].length; ++j)\n if (profit_matrix[i][j] > maximum)\n maximum = profit_matrix[i][j];\n inversion_function = /* @__PURE__ */ __name(function(x) {\n return maximum - x;\n }, \"inversion_function\");\n }\n var cost_matrix = [];\n for (i = 0; i < profit_matrix.length; ++i) {\n var row = profit_matrix[i];\n cost_matrix[i] = [];\n for (j = 0; j < row.length; ++j)\n cost_matrix[i][j] = inversion_function(profit_matrix[i][j]);\n }\n return cost_matrix;\n }\n __name(make_cost_matrix, \"make_cost_matrix\");\n function format_matrix(matrix) {\n var columnWidths = [];\n var i, j;\n for (i = 0; i < matrix.length; ++i) {\n for (j = 0; j < matrix[i].length; ++j) {\n var entryWidth = String(matrix[i][j]).length;\n if (!columnWidths[j] || entryWidth >= columnWidths[j])\n columnWidths[j] = entryWidth;\n }\n }\n var formatted = \"\";\n for (i = 0; i < matrix.length; ++i) {\n for (j = 0; j < matrix[i].length; ++j) {\n var s = String(matrix[i][j]);\n while (s.length < columnWidths[j])\n s = \" \" + s;\n formatted += s;\n if (j != matrix[i].length - 1)\n formatted += \" \";\n }\n if (i != matrix[i].length - 1)\n formatted += \"\\n\";\n }\n return formatted;\n }\n __name(format_matrix, \"format_matrix\");\n function computeMunkres(cost_matrix, options) {\n var m = new Munkres();\n return m.compute(cost_matrix, options);\n }\n __name(computeMunkres, \"computeMunkres\");\n computeMunkres.version = \"1.2.2\";\n computeMunkres.format_matrix = format_matrix;\n computeMunkres.make_cost_matrix = make_cost_matrix;\n computeMunkres.Munkres = Munkres;\n if (typeof module !== \"undefined\" && module.exports) {\n module.exports = computeMunkres;\n }\n }\n});\n\n// tracker.js\nvar require_tracker = __commonJS({\n \"tracker.js\"(exports) {\n var itemTrackedModule = require_ItemTracked();\n var ItemTracked = itemTrackedModule.ItemTracked;\n var kdTree = require_kdTree_min().kdTree;\n var iouAreas = require_utils().iouAreas;\n var munkres = require_munkres();\n var DEBUG_MODE = false;\n var iouDistance = /* @__PURE__ */ __name(function(item1, item2) {\n var iou = iouAreas(item1, item2);\n var distance = 1 - iou;\n if (distance > 1 - params.iouLimit) {\n distance = params.distanceLimit + 1;\n }\n return distance;\n }, \"iouDistance\");\n var params = {\n // DEFAULT_UNMATCHEDFRAMES_TOLERANCE\n // This the number of frame we wait when an object isn't matched before considering it gone\n unMatchedFramesTolerance: 5,\n // DEFAULT_IOU_LIMIT, exclude things from beeing matched if their IOU is lower than this\n // 1 means total overlap whereas 0 means no overlap\n iouLimit: 0.05,\n // Remove new objects fast if they could not be matched in the next frames.\n // Setting this to false ensures the object will stick around at least\n // unMatchedFramesTolerance frames, even if they could neven be matched in\n // subsequent frames.\n fastDelete: true,\n // The function to use to determine the distance between to detected objects\n distanceFunc: iouDistance,\n // The distance limit for matching. If values need to be excluded from\n // matching set their distance to something greater than the distance limit\n distanceLimit: 1e4,\n // The algorithm used to match tracks with new detections. Can be either\n // 'kdTree' or 'munkres'.\n matchingAlgorithm: \"kdTree\"\n };\n var mapOfItemsTracked = /* @__PURE__ */ new Map();\n var mapOfAllItemsTracked = /* @__PURE__ */ new Map();\n var keepAllHistoryInMemory = false;\n exports.computeDistance = iouDistance;\n exports.updateTrackedItemsWithNewFrame = function(detectionsOfThisFrame, frameNb) {\n var treeItemsTracked = new kdTree(Array.from(mapOfItemsTracked.values()), params.distanceFunc, [\"x\", \"y\", \"w\", \"h\"]);\n var treeDetectionsOfThisFrame = new kdTree(detectionsOfThisFrame, params.distanceFunc, [\"x\", \"y\", \"w\", \"h\"]);\n if (mapOfItemsTracked.size === 0) {\n detectionsOfThisFrame.forEach(function(itemDetected) {\n var newItemTracked = new ItemTracked(itemDetected, frameNb, params.unMatchedFramesTolerance, params.fastDelete);\n mapOfItemsTracked.set(newItemTracked.id, newItemTracked);\n treeItemsTracked.insert(newItemTracked);\n });\n } else {\n var matchedList = new Array(detectionsOfThisFrame.length);\n matchedList.fill(false);\n if (detectionsOfThisFrame.length > 0) {\n if (params.matchingAlgorithm === \"munkres\") {\n var trackedItemIds = Array.from(mapOfItemsTracked.keys());\n var costMatrix = Array.from(mapOfItemsTracked.values()).map((itemTracked) => {\n var predictedPosition = itemTracked.predictNextPosition();\n return detectionsOfThisFrame.map(\n (detection) => params.distanceFunc(predictedPosition, detection)\n );\n });\n mapOfItemsTracked.forEach(function(itemTracked) {\n itemTracked.makeAvailable();\n });\n munkres(costMatrix).filter((m) => costMatrix[m[0]][m[1]] <= params.distanceLimit).forEach((m) => {\n var itemTracked = mapOfItemsTracked.get(trackedItemIds[m[0]]);\n var updatedTrackedItemProperties = detectionsOfThisFrame[m[1]];\n matchedList[m[1]] = { idDisplay: itemTracked.idDisplay };\n itemTracked.makeUnavailable().update(updatedTrackedItemProperties, frameNb);\n });\n matchedList.forEach(function(matched, index) {\n if (!matched) {\n if (Math.min(...costMatrix.map((m) => m[index])) > params.distanceLimit) {\n var newItemTracked = ItemTracked(detectionsOfThisFrame[index], frameNb, params.unMatchedFramesTolerance, params.fastDelete);\n mapOfItemsTracked.set(newItemTracked.id, newItemTracked);\n newItemTracked.makeUnavailable();\n costMatrix.push(detectionsOfThisFrame.map(\n (detection) => params.distanceFunc(newItemTracked, detection)\n ));\n }\n }\n });\n } else if (params.matchingAlgorithm === \"kdTree\") {\n mapOfItemsTracked.forEach(function(itemTracked) {\n var predictedPosition = itemTracked.predictNextPosition();\n itemTracked.makeAvailable();\n var treeSearchResult = treeDetectionsOfThisFrame.nearest(predictedPosition, 1, params.distanceLimit)[0];\n var treeSearchResultWithoutPrediction = treeDetectionsOfThisFrame.nearest(itemTracked, 1, params.distanceLimit)[0];\n var treeSearchMultipleResults = treeDetectionsOfThisFrame.nearest(predictedPosition, 2, params.distanceLimit);\n if (treeSearchResult) {\n var indexClosestNewDetectedItem = detectionsOfThisFrame.indexOf(treeSearchResult[0]);\n if (!matchedList[indexClosestNewDetectedItem]) {\n matchedList[indexClosestNewDetectedItem] = {\n idDisplay: itemTracked.idDisplay\n };\n var updatedTrackedItemProperties = detectionsOfThisFrame[indexClosestNewDetectedItem];\n mapOfItemsTracked.get(itemTracked.id).makeUnavailable().update(updatedTrackedItemProperties, frameNb);\n } else {\n }\n }\n });\n } else {\n throw `Unknown matching algorithm \"${params.matchingAlgorithm}\"`;\n }\n } else {\n if (DEBUG_MODE) {\n console.log(\"[Tracker] Nothing detected for frame n\\xBA\" + frameNb);\n }\n mapOfItemsTracked.forEach(function(itemTracked) {\n itemTracked.makeAvailable();\n });\n }\n if (params.matchingAlgorithm === \"kdTree\") {\n if (mapOfItemsTracked.size > 0) {\n treeItemsTracked = new kdTree(Array.from(mapOfItemsTracked.values()), params.distanceFunc, [\"x\", \"y\", \"w\", \"h\"]);\n matchedList.forEach(function(matched, index) {\n if (!matched) {\n var treeSearchResult = treeItemsTracked.nearest(detectionsOfThisFrame[index], 1, params.distanceLimit)[0];\n if (!treeSearchResult) {\n var newItemTracked = ItemTracked(detectionsOfThisFrame[index], frameNb, params.unMatchedFramesTolerance, params.fastDelete);\n mapOfItemsTracked.set(newItemTracked.id, newItemTracked);\n treeItemsTracked.insert(newItemTracked);\n newItemTracked.makeUnavailable();\n } else {\n }\n }\n });\n }\n }\n mapOfItemsTracked.forEach(function(itemTracked) {\n if (itemTracked.available) {\n itemTracked.countDown(frameNb);\n itemTracked.updateTheoricalPositionAndSize();\n if (itemTracked.isDead()) {\n mapOfItemsTracked.delete(itemTracked.id);\n treeItemsTracked.remove(itemTracked);\n if (keepAllHistoryInMemory) {\n mapOfAllItemsTracked.set(itemTracked.id, itemTracked);\n }\n }\n }\n });\n }\n };\n exports.reset = function() {\n mapOfItemsTracked = /* @__PURE__ */ new Map();\n mapOfAllItemsTracked = /* @__PURE__ */ new Map();\n itemTrackedModule.reset();\n };\n exports.setParams = function(newParams) {\n Object.keys(newParams).forEach((key) => {\n params[key] = newParams[key];\n });\n };\n exports.enableKeepInMemory = function() {\n keepAllHistoryInMemory = true;\n };\n exports.disableKeepInMemory = function() {\n keepAllHistoryInMemory = false;\n };\n exports.getJSONOfTrackedItems = function(roundInt = true) {\n return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toJSON(roundInt);\n });\n };\n exports.getJSONDebugOfTrackedItems = function(roundInt = true) {\n return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toJSONDebug(roundInt);\n });\n };\n exports.getTrackedItemsInMOTFormat = function(frameNb) {\n return Array.from(mapOfItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toMOT(frameNb);\n });\n };\n exports.getAllTrackedItems = function() {\n return mapOfAllItemsTracked;\n };\n exports.getJSONOfAllTrackedItems = function() {\n return Array.from(mapOfAllItemsTracked.values()).map(function(itemTracked) {\n return itemTracked.toJSONGenericInfo();\n });\n };\n }\n});\nexport default require_tracker();\n/**\n * k-d Tree JavaScript - V 1.01\n *\n * https://github.com/ubilabs/kd-tree-javascript\n *\n * @author Mircea Pricop , 2012\n * @author Martin Kleppe , 2012\n * @author Ubilabs http://ubilabs.net, 2012\n * @license MIT License \n */\n"], - "mappings": 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"tracking", "data", "items", "obj", "tracker_default", "i", "name", "now", "handleVideo", "file", "url", "initInput", "evt", "_a", "_b", "main"] } diff --git a/demo/typescript/index.js b/demo/typescript/index.js index 94d4c63f..0aded5ab 100644 --- a/demo/typescript/index.js +++ b/demo/typescript/index.js @@ -4,105 +4,6 @@ author: ' */ - -// demo/typescript/index.ts -import * as H from "../../dist/human.esm.js"; -var width = 1920; -var humanConfig = { - // user configuration for human, used to fine-tune behavior - debug: true, - backend: "webgl", - // cacheSensitivity: 0, - // cacheModels: false, - // warmup: 'none', - // modelBasePath: '../../models', - modelBasePath: "https://vladmandic.github.io/human-models/models/", - filter: { enabled: true, equalization: false, flip: false }, - face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } }, - body: { enabled: false }, - hand: { enabled: false }, - object: { enabled: false }, - segmentation: { enabled: false }, - gesture: { enabled: true } -}; -var human = new H.Human(humanConfig); -human.env.perfadd = false; -human.draw.options.font = 'small-caps 18px "Lato"'; -human.draw.options.lineHeight = 20; -human.draw.options.drawPoints = true; -var dom = { - // grab instances of dom objects so we dont have to look them up later - video: document.getElementById("video"), - canvas: document.getElementById("canvas"), - log: document.getElementById("log"), - fps: document.getElementById("status"), - perf: document.getElementById("performance") -}; -var timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; -var fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; -var log = (...msg) => { - dom.log.innerText += msg.join(" ") + "\n"; - console.log(...msg); -}; -var status = (msg) => dom.fps.innerText = msg; -var perf = (msg) => dom.perf.innerText = "tensors:" + human.tf.memory().numTensors.toString() + " | performance: " + JSON.stringify(msg).replace(/"|{|}/g, "").replace(/,/g, " | "); -async function detectionLoop() { - if (!dom.video.paused) { - if (timestamp.start === 0) timestamp.start = human.now(); - await human.detect(dom.video); - const tensors = human.tf.memory().numTensors; - if (tensors - timestamp.tensors !== 0) log("allocated tensors:", tensors - timestamp.tensors); - timestamp.tensors = tensors; - fps.detectFPS = Math.round(1e3 * 1e3 / (human.now() - timestamp.detect)) / 1e3; - fps.frames++; - fps.averageMs = Math.round(1e3 * (human.now() - timestamp.start) / fps.frames) / 1e3; - if (fps.frames % 100 === 0 && !dom.video.paused) log("performance", { ...fps, tensors: timestamp.tensors }); - } - timestamp.detect = human.now(); - requestAnimationFrame(detectionLoop); -} -async function drawLoop() { - var _a, _b, _c; - if (!dom.video.paused) { - const interpolated = human.next(human.result); - const processed = await human.image(dom.video); - human.draw.canvas(processed.canvas, dom.canvas); - const opt = { bodyLabels: `person confidence [score] and ${(_c = (_b = (_a = human.result) == null ? void 0 : _a.body) == null ? void 0 : _b[0]) == null ? void 0 : _c.keypoints.length} keypoints` }; - await human.draw.all(dom.canvas, interpolated, opt); - perf(interpolated.performance); - } - const now = human.now(); - fps.drawFPS = Math.round(1e3 * 1e3 / (now - timestamp.draw)) / 1e3; - timestamp.draw = now; - status(dom.video.paused ? "paused" : `fps: ${fps.detectFPS.toFixed(1).padStart(5, " ")} detect | ${fps.drawFPS.toFixed(1).padStart(5, " ")} draw`); - setTimeout(drawLoop, 30); -} -async function webCam() { - const devices = await human.webcam.enumerate(); - const id = devices[0].deviceId; - const webcamStatus = await human.webcam.start({ element: dom.video, crop: false, width, id }); - log(webcamStatus); - dom.canvas.width = human.webcam.width; - dom.canvas.height = human.webcam.height; - dom.canvas.onclick = async () => { - if (human.webcam.paused) await human.webcam.play(); - else human.webcam.pause(); - }; -} -async function main() { - log("human version:", human.version, "| tfjs version:", human.tf.version["tfjs-core"]); - log("platform:", human.env.platform, "| agent:", human.env.agent); - status("loading..."); - await human.load(); - log("backend:", human.tf.getBackend(), "| available:", human.env.backends); - log("models stats:", human.models.stats()); - log("models loaded:", human.models.loaded()); - log("environment", human.env); - status("initializing..."); - await human.warmup(); - await webCam(); - await detectionLoop(); - await drawLoop(); -} -window.onload = main; +import*as m from"../../dist/human.esm.js";var v=1920,b={debug:!0,backend:"webgl",modelBasePath:"https://vladmandic.github.io/human-models/models/",filter:{enabled:!0,equalization:!1,flip:!1},face:{enabled:!0,detector:{rotation:!1},mesh:{enabled:!0},attention:{enabled:!1},iris:{enabled:!0},description:{enabled:!0},emotion:{enabled:!0},antispoof:{enabled:!0},liveness:{enabled:!0}},body:{enabled:!1},hand:{enabled:!1},object:{enabled:!1},segmentation:{enabled:!1},gesture:{enabled:!0}},e=new m.Human(b);e.env.perfadd=!1;e.draw.options.font='small-caps 18px "Lato"';e.draw.options.lineHeight=20;e.draw.options.drawPoints=!0;var a={video:document.getElementById("video"),canvas:document.getElementById("canvas"),log:document.getElementById("log"),fps:document.getElementById("status"),perf:document.getElementById("performance")},n={detect:0,draw:0,tensors:0,start:0},s={detectFPS:0,drawFPS:0,frames:0,averageMs:0},o=(...t)=>{a.log.innerText+=t.join(" ")+` +`,console.log(...t)},i=t=>a.fps.innerText=t,g=t=>a.perf.innerText="tensors:"+e.tf.memory().numTensors.toString()+" | performance: "+JSON.stringify(t).replace(/"|{|}/g,"").replace(/,/g," | ");async function f(){if(!a.video.paused){n.start===0&&(n.start=e.now()),await e.detect(a.video);let t=e.tf.memory().numTensors;t-n.tensors!==0&&o("allocated tensors:",t-n.tensors),n.tensors=t,s.detectFPS=Math.round(1e3*1e3/(e.now()-n.detect))/1e3,s.frames++,s.averageMs=Math.round(1e3*(e.now()-n.start)/s.frames)/1e3,s.frames%100===0&&!a.video.paused&&o("performance",{...s,tensors:n.tensors})}n.detect=e.now(),requestAnimationFrame(f)}async function u(){var d,r,c;if(!a.video.paused){let l=e.next(e.result),w=await e.image(a.video);e.draw.canvas(w.canvas,a.canvas);let p={bodyLabels:`person confidence [score] and ${(c=(r=(d=e.result)==null?void 0:d.body)==null?void 0:r[0])==null?void 0:c.keypoints.length} keypoints`};await e.draw.all(a.canvas,l,p),g(l.performance)}let t=e.now();s.drawFPS=Math.round(1e3*1e3/(t-n.draw))/1e3,n.draw=t,i(a.video.paused?"paused":`fps: ${s.detectFPS.toFixed(1).padStart(5," ")} detect | ${s.drawFPS.toFixed(1).padStart(5," ")} draw`),setTimeout(u,30)}async function h(){let d=(await e.webcam.enumerate())[0].deviceId,r=await e.webcam.start({element:a.video,crop:!1,width:v,id:d});o(r),a.canvas.width=e.webcam.width,a.canvas.height=e.webcam.height,a.canvas.onclick=async()=>{e.webcam.paused?await e.webcam.play():e.webcam.pause()}}async function y(){o("human version:",e.version,"| tfjs version:",e.tf.version["tfjs-core"]),o("platform:",e.env.platform,"| agent:",e.env.agent),i("loading..."),await e.load(),o("backend:",e.tf.getBackend(),"| available:",e.env.backends),o("models stats:",e.models.stats()),o("models loaded:",e.models.loaded()),o("environment",e.env),i("initializing..."),await e.warmup(),await h(),await f(),await u()}window.onload=y; //# sourceMappingURL=index.js.map diff --git a/demo/typescript/index.js.map b/demo/typescript/index.js.map index ad82ba87..0d0679e0 100644 --- a/demo/typescript/index.js.map +++ b/demo/typescript/index.js.map @@ -2,6 +2,6 @@ "version": 3, "sources": ["index.ts"], "sourcesContent": ["/**\n * Human demo for browsers\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\nimport * as H from '../../dist/human.esm.js'; // equivalent of @vladmandic/Human\n\nconst width = 1920; // used by webcam config as well as human maximum resultion // can be anything, but resolutions higher than 4k will disable internal optimizations\n\nconst humanConfig: Partial = { // user configuration for human, used to fine-tune behavior\n debug: true,\n backend: 'webgl',\n // cacheSensitivity: 0,\n // cacheModels: false,\n // warmup: 'none',\n // modelBasePath: '../../models',\n modelBasePath: 'https://vladmandic.github.io/human-models/models/',\n filter: { enabled: true, equalization: false, flip: false },\n face: { enabled: true, detector: { rotation: false }, mesh: { enabled: true }, attention: { enabled: false }, iris: { enabled: true }, description: { enabled: true }, emotion: { enabled: true }, antispoof: { enabled: true }, liveness: { enabled: true } },\n body: { enabled: false },\n hand: { enabled: false },\n object: { enabled: false },\n segmentation: { enabled: false },\n gesture: { enabled: true },\n};\n\nconst human = new H.Human(humanConfig); // create instance of human with overrides from user configuration\n\nhuman.env.perfadd = false; // is performance data showing instant or total values\nhuman.draw.options.font = 'small-caps 18px \"Lato\"'; // set font used to draw labels when using draw methods\nhuman.draw.options.lineHeight = 20;\nhuman.draw.options.drawPoints = true; // draw points on face mesh\n// human.draw.options.fillPolygons = true;\n\nconst dom = { // grab instances of dom objects so we dont have to look them up later\n video: document.getElementById('video') as HTMLVideoElement,\n canvas: document.getElementById('canvas') as HTMLCanvasElement,\n log: document.getElementById('log') as HTMLPreElement,\n fps: document.getElementById('status') as HTMLPreElement,\n perf: document.getElementById('performance') as HTMLDivElement,\n};\nconst timestamp = { detect: 0, draw: 0, tensors: 0, start: 0 }; // holds information used to calculate performance and possible memory leaks\nconst fps = { detectFPS: 0, drawFPS: 0, frames: 0, averageMs: 0 }; // holds calculated fps information for both detect and screen refresh\n\nconst log = (...msg) => { // helper method to output messages\n dom.log.innerText += msg.join(' ') + '\\n';\n console.log(...msg); // eslint-disable-line no-console\n};\nconst status = (msg) => dom.fps.innerText = msg; // print status element\nconst perf = (msg) => dom.perf.innerText = 'tensors:' + human.tf.memory().numTensors.toString() + ' | performance: ' + JSON.stringify(msg).replace(/\"|{|}/g, '').replace(/,/g, ' | '); // print performance element\n\nasync function detectionLoop() { // main detection loop\n if (!dom.video.paused) {\n if (timestamp.start === 0) timestamp.start = human.now();\n // log('profiling data:', await human.profile(dom.video));\n await human.detect(dom.video); // actual detection; were not capturing output in a local variable as it can also be reached via human.result\n const tensors = human.tf.memory().numTensors; // check current tensor usage for memory leaks\n if (tensors - timestamp.tensors !== 0) log('allocated tensors:', tensors - timestamp.tensors); // printed on start and each time there is a tensor leak\n timestamp.tensors = tensors;\n fps.detectFPS = Math.round(1000 * 1000 / (human.now() - timestamp.detect)) / 1000;\n fps.frames++;\n fps.averageMs = Math.round(1000 * (human.now() - timestamp.start) / fps.frames) / 1000;\n if (fps.frames % 100 === 0 && !dom.video.paused) log('performance', { ...fps, tensors: timestamp.tensors });\n }\n timestamp.detect = human.now();\n requestAnimationFrame(detectionLoop); // start new frame immediately\n}\n\nasync function drawLoop() { // main screen refresh loop\n if (!dom.video.paused) {\n const interpolated = human.next(human.result); // smoothen result using last-known results\n const processed = await human.image(dom.video); // get current video frame, but enhanced with human.filters\n human.draw.canvas(processed.canvas as HTMLCanvasElement, dom.canvas);\n\n const opt: Partial = { bodyLabels: `person confidence [score] and ${human.result?.body?.[0]?.keypoints.length} keypoints` };\n await human.draw.all(dom.canvas, interpolated, opt); // draw labels, boxes, lines, etc.\n perf(interpolated.performance); // write performance data\n }\n const now = human.now();\n fps.drawFPS = Math.round(1000 * 1000 / (now - timestamp.draw)) / 1000;\n timestamp.draw = now;\n status(dom.video.paused ? 'paused' : `fps: ${fps.detectFPS.toFixed(1).padStart(5, ' ')} detect | ${fps.drawFPS.toFixed(1).padStart(5, ' ')} draw`); // write status\n setTimeout(drawLoop, 30); // use to slow down refresh from max refresh rate to target of 30 fps\n}\n\nasync function webCam() {\n const devices = await human.webcam.enumerate();\n const id = devices[0].deviceId; // use first available video source\n const webcamStatus = await human.webcam.start({ element: dom.video, crop: false, width, id }); // use human webcam helper methods and associate webcam stream with a dom element\n log(webcamStatus);\n dom.canvas.width = human.webcam.width;\n dom.canvas.height = human.webcam.height;\n dom.canvas.onclick = async () => { // pause when clicked on screen and resume on next click\n if (human.webcam.paused) await human.webcam.play();\n else human.webcam.pause();\n };\n}\n\nasync function main() { // main entry point\n log('human version:', human.version, '| tfjs version:', human.tf.version['tfjs-core']);\n log('platform:', human.env.platform, '| agent:', human.env.agent);\n status('loading...');\n await human.load(); // preload all models\n log('backend:', human.tf.getBackend(), '| available:', human.env.backends);\n log('models stats:', human.models.stats());\n log('models loaded:', human.models.loaded());\n log('environment', human.env);\n status('initializing...');\n await human.warmup(); // warmup function to initialize backend for future faster detection\n await webCam(); // start webcam\n await detectionLoop(); // start detection loop\n await drawLoop(); // start draw loop\n}\n\nwindow.onload = main;\n"], - 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-// dist/tfjs.esm.js -var tfjs_esm_exports = {}; -__export(tfjs_esm_exports, { - node: () => node, - version: () => version7 -}); -__reExport(tfjs_esm_exports, dist_star); -__reExport(tfjs_esm_exports, dist_star2); -__reExport(tfjs_esm_exports, dist_star3); -__reExport(tfjs_esm_exports, dist_star4); -__reExport(tfjs_esm_exports, dist_star5); -__reExport(tfjs_esm_exports, dist_star6); -import * as dist_star from "@tensorflow/tfjs-core/dist/index.js"; -import * as dist_star2 from "@tensorflow/tfjs-converter/dist/index.js"; -import * as dist_star3 from "@tensorflow/tfjs-backend-cpu/dist/index.js"; -import * as dist_star4 from "@tensorflow/tfjs-backend-webgl/dist/index.js"; -import * as dist_star5 from "@tensorflow/tfjs-backend-wasm/dist/index.js"; -import * as dist_star6 from "@tensorflow/tfjs-backend-webgpu/dist/index.js"; -var version = "4.21.0"; -var version2 = "4.21.0"; -var version3 = "4.21.0"; -var version4 = "4.21.0"; -var version5 = "4.21.0"; -var version6 = "4.21.0"; -var version7 = { - tfjs: version, - "tfjs-core": version, - // 'tfjs-data': tfjsDataVersion, - // 'tfjs-layers': tfjsLayersVersion, - "tfjs-converter": version2, - "tfjs-backend-cpu": version3, - "tfjs-backend-webgl": version4, - "tfjs-backend-wasm": version5, - "tfjs-backend-webgpu": version6 -}; -var node = void 0; - -// src/util/util.ts -function log(...msg) { - const dt = /* @__PURE__ */ new Date(); - const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; - if (msg) console.log(ts, "Human:", ...msg); -} -function join(folder, file) { - const separator = folder.endsWith("/") ? "" : "/"; - const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); - const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; - if (!path.toLocaleLowerCase().includes(".json")) throw new Error(`modelpath error: expecting json file: ${path}`); - return path; -} -var now = () => { - if (typeof performance !== "undefined") return performance.now(); - return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); -}; -function validate(defaults, config3, parent = "config", msgs = []) { - for (const key of Object.keys(config3)) { - if (typeof config3[key] === "object") { - validate(defaults[key], config3[key], key, msgs); - } else { - const defined = defaults && typeof defaults[key] !== "undefined"; - if (!defined) msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); - const same = defaults && typeof defaults[key] === typeof config3[key]; - if (defined && !same) msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); - } - } - if (config3.debug && parent === "config" && msgs.length > 0) log("invalid configuration", msgs); - return msgs; -} -function mergeDeep(...objects) { - const isObject = (obj) => obj && typeof obj === "object"; - return objects.reduce((prev, obj) => { - Object.keys(obj || {}).forEach((key) => { - const pVal = prev[key]; - const oVal = obj[key]; - if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal); - else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal); - else prev[key] = oVal; - }); - return prev; - }, {}); -} - -// src/config.ts -var config = { - backend: "", - modelBasePath: "", - cacheModels: true, - validateModels: true, - wasmPath: "", - wasmPlatformFetch: false, - debug: false, - async: true, - warmup: "full", - cacheSensitivity: 0.7, - skipAllowed: false, - deallocate: false, - flags: {}, - softwareKernels: false, - filter: { - enabled: true, - equalization: false, - width: 0, - height: 0, - flip: false, - return: true, - autoBrightness: true, - brightness: 0, - contrast: 0, - sharpness: 0, - blur: 0, - saturation: 0, - hue: 0, - negative: false, - sepia: false, - vintage: false, - kodachrome: false, - technicolor: false, - polaroid: false, - pixelate: 0 - }, - gesture: { - enabled: true - }, - face: { - enabled: true, - detector: { - modelPath: "blazeface.json", - rotation: false, - maxDetected: 1, - skipFrames: 99, - skipTime: 2500, - minConfidence: 0.2, - minSize: 0, - iouThreshold: 0.1, - scale: 1.4, - mask: false, - return: false - }, - mesh: { - enabled: true, - modelPath: "facemesh.json", - keepInvalid: false - }, - attention: { - enabled: false, - modelPath: "facemesh-attention.json" - }, - iris: { - enabled: true, - scale: 2.3, - modelPath: "iris.json" - }, - emotion: { - enabled: true, - minConfidence: 0.1, - skipFrames: 99, - skipTime: 1500, - modelPath: "emotion.json" - }, - description: { - enabled: true, - modelPath: "faceres.json", - skipFrames: 99, - skipTime: 3e3, - minConfidence: 0.1 - }, - antispoof: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "antispoof.json" - }, - liveness: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "liveness.json" - } - }, - body: { - enabled: true, - modelPath: "movenet-lightning.json", - maxDetected: -1, - minConfidence: 0.3, - skipFrames: 1, - skipTime: 200 - }, - hand: { - enabled: true, - rotation: true, - skipFrames: 99, - skipTime: 1e3, - minConfidence: 0.5, - iouThreshold: 0.2, - maxDetected: -1, - landmarks: true, - detector: { - modelPath: "handtrack.json" - }, - skeleton: { - modelPath: "handlandmark-lite.json" - } - }, - object: { - enabled: false, - modelPath: "centernet.json", - minConfidence: 0.2, - iouThreshold: 0.4, - maxDetected: 10, - skipFrames: 99, - skipTime: 2e3 - }, - segmentation: { - enabled: false, - modelPath: "rvm.json", - ratio: 0.5, - mode: "default" - } -}; - -// src/image/imagefxshaders.ts -var vertexIdentity = ` +var at=Object.defineProperty;var _n=Object.getOwnPropertyDescriptor;var $n=Object.getOwnPropertyNames;var eo=Object.prototype.hasOwnProperty;var q5=e=>{throw TypeError(e)};var to=(e,t,n)=>t in e?at(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var re=(e,t)=>{for(var n in t)at(e,n,{get:t[n],enumerable:!0})},X5=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let s of $n(t))!eo.call(e,s)&&s!==n&&at(e,s,{get:()=>t[s],enumerable:!(o=_n(t,s))||o.enumerable});return e},E=(e,t,n)=>(X5(e,t,"default"),n&&X5(n,t,"default"));var z=(e,t,n)=>to(e,typeof t!="symbol"?t+"":t,n),U5=(e,t,n)=>t.has(e)||q5("Cannot "+n);var E0=(e,t,n)=>(U5(e,t,"read from private field"),n?n.call(e):t.get(e)),q0=(e,t,n)=>t.has(e)?q5("Cannot add the same private member more than once"):t instanceof WeakSet?t.add(e):t.set(e,n),_0=(e,t,n,o)=>(U5(e,t,"write to private field"),o?o.call(e,n):t.set(e,n),n);var r={};re(r,{node:()=>it,version:()=>Je});E(r,RA);E(r,MA);E(r,PA);E(r,kA);E(r,wA);E(r,EA);import*as RA from"@tensorflow/tfjs-core/dist/index.js";import*as MA from"@tensorflow/tfjs-converter/dist/index.js";import*as PA from"@tensorflow/tfjs-backend-cpu/dist/index.js";import*as kA from"@tensorflow/tfjs-backend-webgl/dist/index.js";import*as wA from"@tensorflow/tfjs-backend-wasm/dist/index.js";import*as EA from"@tensorflow/tfjs-backend-webgpu/dist/index.js";var Y5="4.21.0",no="4.21.0",oo="4.21.0",ro="4.21.0",so="4.21.0",Ao="4.21.0",Je={tfjs:Y5,"tfjs-core":Y5,"tfjs-converter":no,"tfjs-backend-cpu":oo,"tfjs-backend-webgl":ro,"tfjs-backend-wasm":so,"tfjs-backend-webgpu":Ao},it=void 0;function g(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function K5(e,t){let n=e.endsWith("/")?"":"/",s=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!s.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${s}`);return s}var R=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function lt(e,t,n="config",o=[]){for(let s of Object.keys(t))if(typeof t[s]=="object")lt(e[s],t[s],s,o);else{let A=e&&typeof e[s]!="undefined";A||o.push({reason:"unknown property",where:`${n}.${s} = ${t[s]}`});let a=e&&typeof e[s]==typeof t[s];A&&!a&&o.push({reason:"property type mismatch",where:`${n}.${s} = ${t[s]}`,expected:typeof e[s]})}return t.debug&&n==="config"&&o.length>0&&g("invalid configuration",o),o}function Q(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(s=>{let A=n[s],a=o[s];Array.isArray(A)&&Array.isArray(a)?n[s]=A.concat(...a):t(A)&&t(a)?n[s]=Q(A,a):n[s]=a}),n),{})}var Ce={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,scale:1.4,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,scale:2.3,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var J5=` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -262,8 +14,7 @@ var vertexIdentity = ` vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`; -var colorMatrixWithAlpha = ` +`;var Q5=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -275,8 +26,7 @@ var colorMatrixWithAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`; -var colorMatrixWithoutAlpha = ` +`,_5=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -288,8 +38,7 @@ var colorMatrixWithoutAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`; -var pixelate = ` +`,$5=` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -302,8 +51,7 @@ var pixelate = ` vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`; -var blur = ` +`,e1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -326,8 +74,7 @@ var blur = ` gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`; -var convolution = ` +`,t1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -349,6022 +96,20 @@ var convolution = ` c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`; - -// src/image/imagefx.ts -var collect = (source, prefix, collection) => { - const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); - source.replace(r, (match2, name) => { - collection[name] = 0; - return match2; - }); -}; -var GLProgram = class { - constructor(gl, vertexSource, fragmentSource) { - __publicField(this, "uniform", {}); - __publicField(this, "attribute", {}); - __publicField(this, "gl"); - __publicField(this, "id"); - __publicField(this, "compile", (source, type) => { - const shader = this.gl.createShader(type); - if (!shader) { - log("filter: could not create shader"); - return null; - } - this.gl.shaderSource(shader, source); - this.gl.compileShader(shader); - if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { - log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); - return null; - } - return shader; - }); - this.gl = gl; - const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); - const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); - this.id = this.gl.createProgram(); - if (!vertexShader || !fragmentShader) return; - if (!this.id) { - log("filter: could not create webgl program"); - return; - } - this.gl.attachShader(this.id, vertexShader); - this.gl.attachShader(this.id, fragmentShader); - this.gl.linkProgram(this.id); - if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { - log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); - return; - } - this.gl.useProgram(this.id); - collect(vertexSource, "attribute", this.attribute); - for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a); - collect(vertexSource, "uniform", this.uniform); - collect(fragmentSource, "uniform", this.uniform); - for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u); - } -}; -function GLImageFilter() { - let drawCount = 0; - let sourceTexture = null; - let lastInChain = false; - let currentFramebufferIndex = -1; - let tempFramebuffers = [null, null]; - let filterChain = []; - let vertexBuffer = null; - let currentProgram = null; - const fxcanvas = canvas(100, 100); - const shaderProgramCache = {}; - const DRAW = { INTERMEDIATE: 1 }; - const gl = fxcanvas.getContext("webgl"); - if (!gl) { - log("filter: cannot get webgl context"); - return; - } - this.gl = gl; - function resize(width, height) { - if (width === fxcanvas.width && height === fxcanvas.height) return; - fxcanvas.width = width; - fxcanvas.height = height; - if (!vertexBuffer) { - const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); - vertexBuffer = gl.createBuffer(); - gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); - gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); - gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); - } - gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); - tempFramebuffers = [null, null]; - } - function createFramebufferTexture(width, height) { - const fbo = gl.createFramebuffer(); - gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); - const renderbuffer = gl.createRenderbuffer(); - gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); - const texture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, texture); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); - gl.bindTexture(gl.TEXTURE_2D, null); - gl.bindFramebuffer(gl.FRAMEBUFFER, null); - return { fbo, texture }; - } - function getTempFramebuffer(index2) { - tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); - return tempFramebuffers[index2]; - } - function draw(flags = 0) { - if (!currentProgram) return; - let source = null; - let target = null; - let flipY = false; - if (drawCount === 0) source = sourceTexture; - else source = getTempFramebuffer(currentFramebufferIndex).texture || null; - drawCount++; - if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { - target = null; - flipY = drawCount % 2 === 0; - } else { - currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; - target = getTempFramebuffer(currentFramebufferIndex).fbo || null; - } - gl.bindTexture(gl.TEXTURE_2D, source); - gl.bindFramebuffer(gl.FRAMEBUFFER, target); - gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); - gl.drawArrays(gl.TRIANGLES, 0, 6); - } - function compileShader(fragmentSource) { - if (shaderProgramCache[fragmentSource]) { - currentProgram = shaderProgramCache[fragmentSource]; - gl.useProgram((currentProgram ? currentProgram.id : null) || null); - return currentProgram; - } - currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); - if (!currentProgram) { - log("filter: could not get webgl program"); - return null; - } - const floatSize = Float32Array.BYTES_PER_ELEMENT; - const vertSize = 4 * floatSize; - gl.enableVertexAttribArray(currentProgram.attribute["pos"]); - gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); - gl.enableVertexAttribArray(currentProgram.attribute["uv"]); - gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); - shaderProgramCache[fragmentSource] = currentProgram; - return currentProgram; - } - const filter = { - colorMatrix: (matrix) => { - const m = new Float32Array(matrix); - m[4] /= 255; - m[9] /= 255; - m[14] /= 255; - m[19] /= 255; - const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; - const program = compileShader(shader); - if (!program) return; - gl.uniform1fv(program.uniform["m"], m); - draw(); - }, - brightness: (brightness) => { - const b = (brightness || 0) + 1; - filter.colorMatrix([ - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - saturation: (amount) => { - const x = (amount || 0) * 2 / 3 + 1; - const y = (x - 1) * -0.5; - filter.colorMatrix([ - x, - y, - y, - 0, - 0, - y, - x, - y, - 0, - 0, - y, - y, - x, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturate: () => { - filter.saturation(-1); - }, - contrast: (amount) => { - const v = (amount || 0) + 1; - const o = -128 * (v - 1); - filter.colorMatrix([ - v, - 0, - 0, - 0, - o, - 0, - v, - 0, - 0, - o, - 0, - 0, - v, - 0, - o, - 0, - 0, - 0, - 1, - 0 - ]); - }, - negative: () => { - filter.contrast(-2); - }, - hue: (rotation) => { - rotation = (rotation || 0) / 180 * Math.PI; - const cos = Math.cos(rotation); - const sin = Math.sin(rotation); - const lumR = 0.213; - const lumG = 0.715; - const lumB = 0.072; - filter.colorMatrix([ - lumR + cos * (1 - lumR) + sin * -lumR, - lumG + cos * -lumG + sin * -lumG, - lumB + cos * -lumB + sin * (1 - lumB), - 0, - 0, - lumR + cos * -lumR + sin * 0.143, - lumG + cos * (1 - lumG) + sin * 0.14, - lumB + cos * -lumB + sin * -0.283, - 0, - 0, - lumR + cos * -lumR + sin * -(1 - lumR), - lumG + cos * -lumG + sin * lumG, - lumB + cos * (1 - lumB) + sin * lumB, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturateLuminance: () => { - filter.colorMatrix([ - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0, - 0, - 0, - 1, - 0 - ]); - }, - sepia: () => { - filter.colorMatrix([ - 0.393, - 0.7689999, - 0.18899999, - 0, - 0, - 0.349, - 0.6859999, - 0.16799999, - 0, - 0, - 0.272, - 0.5339999, - 0.13099999, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - brownie: () => { - filter.colorMatrix([ - 0.5997023498159715, - 0.34553243048391263, - -0.2708298674538042, - 0, - 47.43192855600873, - -0.037703249837783157, - 0.8609577587992641, - 0.15059552388459913, - 0, - -36.96841498319127, - 0.24113635128153335, - -0.07441037908422492, - 0.44972182064877153, - 0, - -7.562075277591283, - 0, - 0, - 0, - 1, - 0 - ]); - }, - vintagePinhole: () => { - filter.colorMatrix([ - 0.6279345635605994, - 0.3202183420819367, - -0.03965408211312453, - 0, - 9.651285835294123, - 0.02578397704808868, - 0.6441188644374771, - 0.03259127616149294, - 0, - 7.462829176470591, - 0.0466055556782719, - -0.0851232987247891, - 0.5241648018700465, - 0, - 5.159190588235296, - 0, - 0, - 0, - 1, - 0 - ]); - }, - kodachrome: () => { - filter.colorMatrix([ - 1.1285582396593525, - -0.3967382283601348, - -0.03992559172921793, - 0, - 63.72958762196502, - -0.16404339962244616, - 1.0835251566291304, - -0.05498805115633132, - 0, - 24.732407896706203, - -0.16786010706155763, - -0.5603416277695248, - 1.6014850761964943, - 0, - 35.62982807460946, - 0, - 0, - 0, - 1, - 0 - ]); - }, - technicolor: () => { - filter.colorMatrix([ - 1.9125277891456083, - -0.8545344976951645, - -0.09155508482755585, - 0, - 11.793603434377337, - -0.3087833385928097, - 1.7658908555458428, - -0.10601743074722245, - 0, - -70.35205161461398, - -0.231103377548616, - -0.7501899197440212, - 1.847597816108189, - 0, - 30.950940869491138, - 0, - 0, - 0, - 1, - 0 - ]); - }, - polaroid: () => { - filter.colorMatrix([ - 1.438, - -0.062, - -0.062, - 0, - 0, - -0.122, - 1.378, - -0.122, - 0, - 0, - -0.016, - -0.016, - 1.483, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - shiftToBGR: () => { - filter.colorMatrix([ - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - convolution: (matrix) => { - const m = new Float32Array(matrix); - const pixelSizeX = 1 / fxcanvas.width; - const pixelSizeY = 1 / fxcanvas.height; - const program = compileShader(convolution); - if (!program) return; - gl.uniform1fv(program.uniform["m"], m); - gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); - draw(); - }, - detectEdges: () => { - filter.convolution.call(this, [ - 0, - 1, - 0, - 1, - -4, - 1, - 0, - 1, - 0 - ]); - }, - sobelX: () => { - filter.convolution.call(this, [ - -1, - 0, - 1, - -2, - 0, - 2, - -1, - 0, - 1 - ]); - }, - sobelY: () => { - filter.convolution.call(this, [ - -1, - -2, - -1, - 0, - 0, - 0, - 1, - 2, - 1 - ]); - }, - sharpen: (amount) => { - const a = amount || 1; - filter.convolution.call(this, [ - 0, - -1 * a, - 0, - -1 * a, - 1 + 4 * a, - -1 * a, - 0, - -1 * a, - 0 - ]); - }, - emboss: (size2) => { - const s = size2 || 1; - filter.convolution.call(this, [ - -2 * s, - -1 * s, - 0, - -1 * s, - 1, - 1 * s, - 0, - 1 * s, - 2 * s - ]); - }, - blur: (size2) => { - const blurSizeX = size2 / 7 / fxcanvas.width; - const blurSizeY = size2 / 7 / fxcanvas.height; - const program = compileShader(blur); - if (!program) return; - gl.uniform2f(program.uniform["px"], 0, blurSizeY); - draw(DRAW.INTERMEDIATE); - gl.uniform2f(program.uniform["px"], blurSizeX, 0); - draw(); - }, - pixelate: (size2) => { - const blurSizeX = size2 / fxcanvas.width; - const blurSizeY = size2 / fxcanvas.height; - const program = compileShader(pixelate); - if (!program) return; - gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); - draw(); - } - }; - this.add = function(name) { - const args = Array.prototype.slice.call(arguments, 1); - const func = filter[name]; - filterChain.push({ func, args }); - }; - this.reset = function() { - filterChain = []; - }; - this.get = function() { - return filterChain; - }; - this.apply = function(image28) { - resize(image28.width, image28.height); - drawCount = 0; - if (!sourceTexture) sourceTexture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, sourceTexture); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); - for (let i = 0; i < filterChain.length; i++) { - lastInChain = i === filterChain.length - 1; - const f = filterChain[i]; - f.func.apply(this, f.args || []); - } - return fxcanvas; - }; - this.draw = function(image28) { - this.add("brightness", 0); - return this.apply(image28); - }; -} - -// src/image/enhance.ts -async function histogramEqualization(inputImage) { - const squeeze14 = inputImage.shape.length === 4 ? tfjs_esm_exports.squeeze(inputImage) : inputImage; - const rgb3 = tfjs_esm_exports.split(squeeze14, 3, 2); - const min2 = [tfjs_esm_exports.min(rgb3[0]), tfjs_esm_exports.min(rgb3[1]), tfjs_esm_exports.min(rgb3[2])]; - const max5 = [tfjs_esm_exports.max(rgb3[0]), tfjs_esm_exports.max(rgb3[1]), tfjs_esm_exports.max(rgb3[2])]; - const absMax = await Promise.all(max5.map((channel) => channel.data())); - const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); - const maxRange = maxValue > 1 ? 255 : 1; - const factor = maxRange / maxValue; - let final; - if (factor > 1) { - const sub11 = [tfjs_esm_exports.sub(rgb3[0], min2[0]), tfjs_esm_exports.sub(rgb3[1], min2[1]), tfjs_esm_exports.sub(rgb3[2], min2[2])]; - const range = [tfjs_esm_exports.sub(max5[0], min2[0]), tfjs_esm_exports.sub(max5[1], min2[1]), tfjs_esm_exports.sub(max5[2], min2[2])]; - const enh = [tfjs_esm_exports.mul(sub11[0], factor), tfjs_esm_exports.mul(sub11[1], factor), tfjs_esm_exports.mul(sub11[2], factor)]; - const stack5 = tfjs_esm_exports.stack([enh[0], enh[1], enh[2]], 2); - final = tfjs_esm_exports.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); - tfjs_esm_exports.dispose([...sub11, ...range, ...enh, stack5]); - } else { - final = tfjs_esm_exports.expandDims(squeeze14, 0); - } - tfjs_esm_exports.dispose([...rgb3, ...min2, ...max5, rgb3, squeeze14, inputImage]); - return final; -} - -// src/image/image.ts -var maxSize = 3840; -var inCanvas = null; -var outCanvas = null; -var tmpCanvas = null; -var fx; -var last = { - inputSum: 0, - cacheDiff: 1, - sumMethod: 0, - inputTensor: void 0 -}; -function reset() { - last.inputSum = 0; - last.cacheDiff = 1; - last.sumMethod = 0; - last.inputTensor = void 0; -} -function canvas(width, height) { - let c; - if (env.browser) { - if (env.worker) { - if (typeof OffscreenCanvas === "undefined") throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); - c = new OffscreenCanvas(width, height); - } else { - if (typeof document !== "undefined") { - c = document.createElement("canvas"); - c.width = width; - c.height = height; - } else if (typeof navigator !== "undefined" && navigator.product === "ReactNative") { - if (typeof env.Canvas !== "undefined") c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") c = new globalThis.Canvas(width, height); - else throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed"); - } else { - throw new Error("canvas error: attempted to run in browser but DOM is not defined"); - } - } - } else { - if (typeof env.Canvas !== "undefined") c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") c = new globalThis.Canvas(width, height); - } - return c; -} -function copy(input, output) { - const outputCanvas = output || canvas(input.width, input.height); - const ctx = outputCanvas.getContext("2d"); - ctx.drawImage(input, 0, 0); - return outputCanvas; -} -async function process2(input, config3, getTensor = true) { - var _a, _b, _c; - if (!input) { - if (config3.debug) log("input error: input is missing"); - return { tensor: null, canvas: null }; - } - if (!(input instanceof tfjs_esm_exports.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { - throw new Error("input error: type not recognized"); - } - if (input instanceof tfjs_esm_exports.Tensor) { - let tensor7 = null; - if (input["isDisposedInternal"]) throw new Error("input error: attempted to use tensor but it is disposed"); - if (!input.shape) throw new Error("input error: attempted to use tensor without a shape"); - if (input.shape.length === 3) { - if (input.shape[2] === 3) { - tensor7 = tfjs_esm_exports.expandDims(input, 0); - } else if (input.shape[2] === 4) { - const rgb3 = tfjs_esm_exports.slice3d(input, [0, 0, 0], [-1, -1, 3]); - tensor7 = tfjs_esm_exports.expandDims(rgb3, 0); - tfjs_esm_exports.dispose(rgb3); - } - } else if (input.shape.length === 4) { - if (input.shape[3] === 3) { - tensor7 = tfjs_esm_exports.clone(input); - } else if (input.shape[3] === 4) { - tensor7 = tfjs_esm_exports.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); - } - } - if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); - if (tensor7.dtype === "int32") { - const cast8 = tfjs_esm_exports.cast(tensor7, "float32"); - tfjs_esm_exports.dispose(tensor7); - tensor7 = cast8; - } - return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; - } - if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { - if (config3.debug) log("input stream is not ready"); - return { tensor: null, canvas: inCanvas }; - } - const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; - const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; - if (!originalWidth || !originalHeight) { - if (config3.debug) log("cannot determine input dimensions"); - return { tensor: null, canvas: inCanvas }; - } - let targetWidth = originalWidth; - let targetHeight = originalHeight; - if (targetWidth > maxSize) { - targetWidth = maxSize; - targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); - } - if (targetHeight > maxSize) { - targetHeight = maxSize; - targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); - } - if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) targetWidth = config3.filter.width; - else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); - if ((config3.filter.height || 0) > 0) targetHeight = config3.filter.height; - else if ((config3.filter.width || 0) > 0) targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); - if (!targetWidth || !targetHeight) throw new Error("input error: cannot determine dimension"); - if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) inCanvas = canvas(targetWidth, targetHeight); - const inCtx = inCanvas.getContext("2d"); - if (typeof ImageData !== "undefined" && input instanceof ImageData) { - inCtx.putImageData(input, 0, 0); - } else { - if (config3.filter.flip && typeof inCtx.translate !== "undefined") { - inCtx.translate(originalWidth, 0); - inCtx.scale(-1, 1); - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - inCtx.setTransform(1, 0, 0, 1, 0, 0); - } else { - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - } - } - if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) outCanvas = canvas(inCanvas.width, inCanvas.height); - if (config3.filter.enabled && env.webgl.supported) { - if (!fx) fx = env.browser ? new GLImageFilter() : null; - env.filter = !!fx; - if (!(fx == null ? void 0 : fx.add)) { - if (config3.debug) log("input process error: cannot initialize filters"); - env.webgl.supported = false; - config3.filter.enabled = false; - copy(inCanvas, outCanvas); - } else { - fx.reset(); - if (config3.filter.brightness !== 0) fx.add("brightness", config3.filter.brightness); - if (config3.filter.contrast !== 0) fx.add("contrast", config3.filter.contrast); - if (config3.filter.sharpness !== 0) fx.add("sharpen", config3.filter.sharpness); - if (config3.filter.blur !== 0) fx.add("blur", config3.filter.blur); - if (config3.filter.saturation !== 0) fx.add("saturation", config3.filter.saturation); - if (config3.filter.hue !== 0) fx.add("hue", config3.filter.hue); - if (config3.filter.negative) fx.add("negative"); - if (config3.filter.sepia) fx.add("sepia"); - if (config3.filter.vintage) fx.add("brownie"); - if (config3.filter.sepia) fx.add("sepia"); - if (config3.filter.kodachrome) fx.add("kodachrome"); - if (config3.filter.technicolor) fx.add("technicolor"); - if (config3.filter.polaroid) fx.add("polaroid"); - if (config3.filter.pixelate !== 0) fx.add("pixelate", config3.filter.pixelate); - if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) outCanvas = fx.apply(inCanvas); - else outCanvas = fx.draw(inCanvas); - } - } else { - copy(inCanvas, outCanvas); - if (fx) fx = null; - env.filter = !!fx; - } - if (!getTensor) return { tensor: null, canvas: outCanvas }; - if (!outCanvas) throw new Error("canvas error: cannot create output"); - let pixels; - let depth = 3; - if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && tfjs_esm_exports.browser) { - pixels = tfjs_esm_exports.browser ? tfjs_esm_exports.browser.fromPixels(input) : null; - } else { - depth = input.data.length / input.height / input.width; - const arr = new Uint8Array(input.data.buffer); - pixels = tfjs_esm_exports.tensor(arr, [input.height, input.width, depth], "int32"); - } - } else { - if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (tfjs_esm_exports.browser && env.browser) { - if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = tfjs_esm_exports.browser.fromPixels(outCanvas); - } else { - tmpCanvas = copy(outCanvas); - pixels = tfjs_esm_exports.browser.fromPixels(tmpCanvas); - } - } else { - const tempCanvas = copy(outCanvas); - const tempCtx = tempCanvas.getContext("2d"); - const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); - depth = tempData.data.length / targetWidth / targetHeight; - const arr = new Uint8Array(tempData.data.buffer); - pixels = tfjs_esm_exports.tensor(arr, [targetWidth, targetHeight, depth]); - } - } - if (depth === 4) { - const rgb3 = tfjs_esm_exports.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); - tfjs_esm_exports.dispose(pixels); - pixels = rgb3; - } - if (!pixels) throw new Error("input error: cannot create tensor"); - const casted = tfjs_esm_exports.cast(pixels, "float32"); - const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tfjs_esm_exports.expandDims(casted, 0); - tfjs_esm_exports.dispose([pixels, casted]); - if (config3.filter.autoBrightness) { - const max5 = tfjs_esm_exports.max(tensor6); - const maxVal = await max5.data(); - config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; - tfjs_esm_exports.dispose(max5); - } - return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; -} -async function skip(config3, input) { - let skipFrame = false; - if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; - if (!last.inputTensor) { - last.inputTensor = tfjs_esm_exports.clone(input); - } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { - tfjs_esm_exports.dispose(last.inputTensor); - last.inputTensor = tfjs_esm_exports.clone(input); - } else { - const t2 = {}; - t2.diff = tfjs_esm_exports.sub(input, last.inputTensor); - t2.squared = tfjs_esm_exports.mul(t2.diff, t2.diff); - t2.sum = tfjs_esm_exports.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; - tfjs_esm_exports.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); - last.inputTensor = tfjs_esm_exports.clone(input); - skipFrame = diffRelative <= (config3.cacheSensitivity || 0); - } - return skipFrame; -} -async function compare(config3, input1, input2) { - const t2 = {}; - if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { - if (!config3.debug) log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); - return 0; - } - if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { - if (!config3.debug) log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); - return 0; - } - t2.input1 = tfjs_esm_exports.clone(input1); - t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tfjs_esm_exports.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tfjs_esm_exports.clone(input2); - t2.diff = tfjs_esm_exports.sub(t2.input1, t2.input2); - t2.squared = tfjs_esm_exports.mul(t2.diff, t2.diff); - t2.sum = tfjs_esm_exports.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; - tfjs_esm_exports.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); - return diffRelative; -} - -// src/util/env.ts -var _canvas, _image, _imageData; -var Env = class { - constructor() { - /** Running in Browser */ - __publicField(this, "browser"); - /** Running in NodeJS */ - __publicField(this, "node"); - /** Running in WebWorker thread */ - __publicField(this, "worker"); - /** Detected platform */ - __publicField(this, "platform", ""); - /** Detected agent */ - __publicField(this, "agent", ""); - /** List of supported backends */ - __publicField(this, "backends", []); - /** Has any work been performed so far */ - __publicField(this, "initial"); - /** Are image filters supported? */ - __publicField(this, "filter"); - /** TFJS instance details */ - __publicField(this, "tfjs"); - /** Is offscreenCanvas supported? */ - __publicField(this, "offscreen"); - /** Are performance counter instant values or additive */ - __publicField(this, "perfadd", false); - /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */ - __publicField(this, "tensorflow", { - version: void 0, - gpu: void 0 - }); - /** WASM detected capabilities */ - __publicField(this, "wasm", { - supported: void 0, - backend: void 0, - simd: void 0, - multithread: void 0 - }); - /** WebGL detected capabilities */ - __publicField(this, "webgl", { - supported: void 0, - backend: void 0, - version: void 0, - renderer: void 0, - shader: void 0, - vendor: void 0 - }); - /** WebGPU detected capabilities */ - __publicField(this, "webgpu", { - supported: void 0, - backend: void 0, - adapter: void 0 - }); - /** CPU info */ - __publicField(this, "cpu", { - model: void 0, - flags: [] - }); - /** List of supported kernels for current backend */ - __publicField(this, "kernels", []); - /** MonkeyPatch for Canvas/Image/ImageData */ - __privateAdd(this, _canvas); - __privateAdd(this, _image); - __privateAdd(this, _imageData); - this.browser = typeof navigator !== "undefined" && typeof navigator.appVersion !== "undefined"; - this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; - this.tfjs = { version: version7["tfjs-core"] }; - this.offscreen = typeof OffscreenCanvas !== "undefined"; - this.initial = true; - this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; - if (typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined") { - const agent = navigator.userAgent || ""; - const raw = agent.match(/\(([^()]+)\)/g); - if (raw == null ? void 0 : raw[0]) { - const platformMatch = raw[0].match(/\(([^()]+)\)/g); - this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; - this.agent = agent.replace(raw[0], ""); - if (this.platform[1]) this.agent = this.agent.replace(raw[1], ""); - this.agent = this.agent.replace(/ /g, " "); - } - } else if (typeof process !== "undefined") { - this.platform = `${process.platform} ${process.arch}`; - this.agent = `NodeJS ${process.version}`; - } - } - get Canvas() { - return __privateGet(this, _canvas); - } - set Canvas(val) { - __privateSet(this, _canvas, val); - globalThis.Canvas = val; - } - get Image() { - return __privateGet(this, _image); - } - // @ts-ignore monkey-patch; - set Image(val) { - __privateSet(this, _image, val); - globalThis.Image = val; - } - get ImageData() { - return __privateGet(this, _imageData); - } - // @ts-ignore monkey-patch; - set ImageData(val) { - __privateSet(this, _imageData, val); - globalThis.ImageData = val; - } - /** update backend information */ - async updateBackend() { - this.backends = Object.keys(tfjs_esm_exports.engine().registryFactory); - try { - this.tensorflow = { - version: tfjs_esm_exports.backend()["binding"] ? tfjs_esm_exports.backend()["binding"].TF_Version : void 0, - gpu: tfjs_esm_exports.backend()["binding"] ? tfjs_esm_exports.backend()["binding"].isUsingGpuDevice() : void 0 - }; - } catch (e) { - } - this.wasm.supported = typeof WebAssembly !== "undefined"; - this.wasm.backend = this.backends.includes("wasm"); - if (this.wasm.supported && this.wasm.backend) { - this.wasm.simd = await tfjs_esm_exports.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - this.wasm.multithread = await tfjs_esm_exports.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - } - const c = canvas(100, 100); - const gl = c ? c.getContext("webgl2") : void 0; - this.webgl.supported = typeof gl !== "undefined"; - this.webgl.backend = this.backends.includes("webgl"); - if (this.webgl.supported && this.webgl.backend && gl) { - this.webgl.version = gl.getParameter(gl.VERSION); - this.webgl.vendor = gl.getParameter(gl.VENDOR); - this.webgl.renderer = gl.getParameter(gl.RENDERER); - this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); - } - this.webgpu.supported = this.browser && typeof navigator !== "undefined" && typeof navigator.gpu !== "undefined"; - this.webgpu.backend = this.backends.includes("webgpu"); - try { - if (this.webgpu.supported) { - const adapter = await navigator.gpu.requestAdapter(); - this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); - } - } catch (e) { - this.webgpu.supported = false; - } - try { - this.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); - } catch (e) { - } - } - /** update cpu information */ - updateCPU() { - const cpu = { model: "", flags: [] }; - if (this.node && this.platform.startsWith("linux")) { - } - if (!this.cpu) Object.defineProperty(this, "cpu", { value: cpu }); - else this.cpu = cpu; - } -}; -_canvas = new WeakMap(); -_image = new WeakMap(); -_imageData = new WeakMap(); -var env = new Env(); - -// src/util/webcam.ts -var WebCam = class { - constructor() { - // eslint-disable-line @typescript-eslint/no-extraneous-class - /** current webcam configuration */ - __publicField(this, "config"); - /** instance of dom element associated with webcam stream */ - __publicField(this, "element"); - /** active webcam stream */ - __publicField(this, "stream"); - /** enumerated video devices */ - __publicField(this, "devices", []); - __publicField(this, "enumerate", async () => { - try { - const devices = await navigator.mediaDevices.enumerateDevices(); - this.devices = devices.filter((device) => device.kind === "videoinput"); - } catch (e) { - this.devices = []; - } - return this.devices; - }); - /** start method initializizes webcam stream and associates it with a dom video element */ - __publicField(this, "start", async (webcamConfig) => { - var _a, _b; - if (webcamConfig == null ? void 0 : webcamConfig.debug) this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; - if (webcamConfig == null ? void 0 : webcamConfig.crop) this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; - if (webcamConfig == null ? void 0 : webcamConfig.mode) this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; - if (webcamConfig == null ? void 0 : webcamConfig.width) this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; - if (webcamConfig == null ? void 0 : webcamConfig.height) this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; - if (webcamConfig == null ? void 0 : webcamConfig.id) this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; - if (webcamConfig == null ? void 0 : webcamConfig.element) { - if (typeof webcamConfig.element === "string") { - const el = document.getElementById(webcamConfig.element); - if (el && el instanceof HTMLVideoElement) { - this.element = el; - } else { - if (this.config.debug) log("webcam", "cannot get dom element", webcamConfig.element); - return `webcam error: cannot get dom element: ${webcamConfig.element}`; - } - } else if (webcamConfig.element instanceof HTMLVideoElement) { - this.element = webcamConfig.element; - } else { - if (this.config.debug) log("webcam", "unknown dom element", webcamConfig.element); - return `webcam error: unknown dom element: ${webcamConfig.element}`; - } - } else { - this.element = document.createElement("video"); - } - const requestedConstraints = { - audio: false, - video: { - facingMode: this.config.mode === "front" ? "user" : "environment", - // @ts-ignore // resizeMode is still not defined in tslib - resizeMode: this.config.crop ? "crop-and-scale" : "none" - } - }; - if (((_a = this.config) == null ? void 0 : _a.width) > 0) requestedConstraints.video.width = { ideal: this.config.width }; - if (((_b = this.config) == null ? void 0 : _b.height) > 0) requestedConstraints.video.height = { ideal: this.config.height }; - if (this.config.id) requestedConstraints.video.deviceId = this.config.id; - this.element.addEventListener("play", () => { - if (this.config.debug) log("webcam", "play"); - }); - this.element.addEventListener("pause", () => { - if (this.config.debug) log("webcam", "pause"); - }); - this.element.addEventListener("click", async () => { - if (!this.element || !this.stream) return; - if (this.element.paused) await this.element.play(); - else this.element.pause(); - }); - if (!(navigator == null ? void 0 : navigator.mediaDevices)) { - if (this.config.debug) log("webcam error", "no devices"); - return "webcam error: no devices"; - } - try { - this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); - } catch (err) { - log("webcam", err); - return `webcam error: ${err}`; - } - if (!this.stream) { - if (this.config.debug) log("webcam error", "no stream"); - return "webcam error no stream"; - } - this.element.srcObject = this.stream; - const ready4 = new Promise((resolve) => { - if (!this.element) resolve(false); - else this.element.onloadeddata = () => resolve(true); - }); - await ready4; - await this.element.play(); - if (this.config.debug) { - log("webcam", { - width: this.width, - height: this.height, - label: this.label, - stream: this.stream, - track: this.track, - settings: this.settings, - constraints: this.constraints, - capabilities: this.capabilities - }); - } - return `webcam: ${this.label}`; - }); - /** pause webcam video method */ - __publicField(this, "pause", () => { - if (this.element) this.element.pause(); - }); - /** play webcam video method */ - __publicField(this, "play", async () => { - if (this.element) await this.element.play(); - }); - /** stop method stops active webcam stream track and disconnects webcam */ - __publicField(this, "stop", () => { - if (this.config.debug) log("webcam", "stop"); - if (this.track) this.track.stop(); - }); - this.config = { - element: void 0, - debug: true, - mode: "front", - crop: false, - width: 0, - height: 0 - }; - } - /** get active webcam stream track */ - get track() { - if (!this.stream) return void 0; - return this.stream.getVideoTracks()[0]; - } - /** get webcam capabilities */ - get capabilities() { - if (!this.track) return void 0; - return this.track.getCapabilities ? this.track.getCapabilities() : void 0; - } - /** get webcam constraints */ - get constraints() { - if (!this.track) return void 0; - return this.track.getConstraints ? this.track.getConstraints() : void 0; - } - /** get webcam settings */ - get settings() { - if (!this.stream) return void 0; - const track = this.stream.getVideoTracks()[0]; - return track.getSettings ? track.getSettings() : void 0; - } - /** get webcam label */ - get label() { - if (!this.track) return ""; - return this.track.label; - } - /** is webcam paused */ - get paused() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.paused) || false; - } - /** webcam current width */ - get width() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; - } - /** webcam current height */ - get height() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; - } -}; - -// models/models.json -var models_exports = {}; -__export(models_exports, { - "affectnet-mobilenet": () => affectnet_mobilenet, - age: () => age, - "anti-spoofing": () => anti_spoofing, - antispoof: () => antispoof, - blazeface: () => blazeface, - "blazeface-back": () => blazeface_back, - "blazeface-front": () => blazeface_front, - "blazepose-detector": () => blazepose_detector, - "blazepose-full": () => blazepose_full, - "blazepose-heavy": () => blazepose_heavy, - "blazepose-lite": () => blazepose_lite, - centernet: () => centernet, - default: () => models_default, - efficientpose: () => efficientpose, - "efficientpose-i-lite": () => efficientpose_i_lite, - "efficientpose-ii-lite": () => efficientpose_ii_lite, - "efficientpose-iv": () => efficientpose_iv, - emotion: () => emotion, - faceboxes: () => faceboxes, - facemesh: () => facemesh, - "facemesh-attention": () => facemesh_attention, - "facemesh-attention-pinto": () => facemesh_attention_pinto, - "facemesh-detection-full": () => facemesh_detection_full, - "facemesh-detection-short": () => facemesh_detection_short, - faceres: () => faceres, - "faceres-deep": () => faceres_deep, - gear: () => gear, - "gear-e1": () => gear_e1, - "gear-e2": () => gear_e2, - gender: () => gender, - "gender-ssrnet-imdb": () => gender_ssrnet_imdb, - handdetect: () => handdetect, - "handlandmark-full": () => handlandmark_full, - "handlandmark-lite": () => handlandmark_lite, - "handlandmark-sparse": () => handlandmark_sparse, - handskeleton: () => handskeleton, - handtrack: () => handtrack, - "insightface-efficientnet-b0": () => insightface_efficientnet_b0, - "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, - "insightface-mobilenet-emore": () => insightface_mobilenet_emore, - "insightface-mobilenet-swish": () => insightface_mobilenet_swish, - iris: () => iris, - liveness: () => liveness, - meet: () => meet, - mobileface: () => mobileface, - mobilefacenet: () => mobilefacenet, - models: () => models, - "movenet-lightning": () => movenet_lightning, - "movenet-multipose": () => movenet_multipose, - "movenet-thunder": () => movenet_thunder, - nanodet: () => nanodet, - "nanodet-e": () => nanodet_e, - "nanodet-g": () => nanodet_g, - "nanodet-m": () => nanodet_m, - "nanodet-t": () => nanodet_t, - posenet: () => posenet, - rvm: () => rvm, - selfie: () => selfie -}); -var antispoof = 853098; -var blazeface = 538928; -var centernet = 4030290; -var emotion = 820516; -var facemesh = 1477958; -var faceres = 6978814; -var handlandmark_lite = 2023432; -var handtrack = 2964837; -var iris = 2599092; -var liveness = 592976; -var models = 0; -var movenet_lightning = 4650216; -var affectnet_mobilenet = 6920630; -var age = 161240; -var blazeface_back = 538928; -var blazeface_front = 402048; -var blazepose_detector = 5928856; -var blazepose_full = 6339202; -var blazepose_heavy = 27502466; -var blazepose_lite = 2726402; -var efficientpose = 5651240; -var faceboxes = 2013002; -var facemesh_attention_pinto = 2387598; -var facemesh_attention = 2382414; -var facemesh_detection_full = 1026192; -var facemesh_detection_short = 201268; -var faceres_deep = 13957620; -var gear_e1 = 112438; -var gear_e2 = 112438; -var gear = 1498916; -var gender_ssrnet_imdb = 161236; -var gender = 201808; -var handdetect = 3515612; -var handlandmark_full = 5431368; -var handlandmark_sparse = 5286322; -var handskeleton = 5502280; -var meet = 372228; -var mobileface = 2183192; -var mobilefacenet = 5171976; -var movenet_multipose = 9448838; -var movenet_thunder = 12477112; -var nanodet = 7574558; -var posenet = 5032780; -var rvm = 3739355; -var selfie = 212886; -var anti_spoofing = 853098; -var efficientpose_i_lite = 2269064; -var efficientpose_ii_lite = 5651240; -var efficientpose_iv = 25643252; -var insightface_efficientnet_b0 = 13013224; -var insightface_ghostnet_strides1 = 8093408; -var insightface_ghostnet_strides2 = 8049584; -var insightface_mobilenet_emore = 6938536; -var insightface_mobilenet_swish = 12168584; -var nanodet_e = 12319156; -var nanodet_g = 7574558; -var nanodet_m = 1887474; -var nanodet_t = 5294216; -var models_default = { - antispoof, - blazeface, - centernet, - emotion, - facemesh, - faceres, - "handlandmark-lite": handlandmark_lite, - handtrack, - iris, - liveness, - models, - "movenet-lightning": movenet_lightning, - "affectnet-mobilenet": affectnet_mobilenet, - age, - "blazeface-back": blazeface_back, - "blazeface-front": blazeface_front, - "blazepose-detector": blazepose_detector, - "blazepose-full": blazepose_full, - "blazepose-heavy": blazepose_heavy, - "blazepose-lite": blazepose_lite, - efficientpose, - faceboxes, - "facemesh-attention-pinto": facemesh_attention_pinto, - "facemesh-attention": facemesh_attention, - "facemesh-detection-full": facemesh_detection_full, - "facemesh-detection-short": facemesh_detection_short, - "faceres-deep": faceres_deep, - "gear-e1": gear_e1, - "gear-e2": gear_e2, - gear, - "gender-ssrnet-imdb": gender_ssrnet_imdb, - gender, - handdetect, - "handlandmark-full": handlandmark_full, - "handlandmark-sparse": handlandmark_sparse, - handskeleton, - meet, - mobileface, - mobilefacenet, - "movenet-multipose": movenet_multipose, - "movenet-thunder": movenet_thunder, - nanodet, - posenet, - rvm, - selfie, - "anti-spoofing": anti_spoofing, - "efficientpose-i-lite": efficientpose_i_lite, - "efficientpose-ii-lite": efficientpose_ii_lite, - "efficientpose-iv": efficientpose_iv, - "insightface-efficientnet-b0": insightface_efficientnet_b0, - "insightface-ghostnet-strides1": insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": insightface_ghostnet_strides2, - "insightface-mobilenet-emore": insightface_mobilenet_emore, - "insightface-mobilenet-swish": insightface_mobilenet_swish, - "nanodet-e": nanodet_e, - "nanodet-g": nanodet_g, - "nanodet-m": nanodet_m, - "nanodet-t": nanodet_t -}; - -// src/tfjs/load.ts -var options = { - cacheModels: true, - cacheSupported: true, - verbose: true, - debug: false, - modelBasePath: "" -}; -var modelStats = {}; -async function httpHandler(url, init4) { - if (options.debug) log("load model fetch:", url, init4); - return fetch(url, init4); -} -function setModelLoadOptions(config3) { - options.cacheModels = config3.cacheModels; - options.verbose = config3.debug; - options.modelBasePath = config3.modelBasePath; -} -async function loadModel(modelPath) { - var _a, _b, _c, _d, _e, _f; - let modelUrl = join(options.modelBasePath, modelPath || ""); - if (!modelUrl.toLowerCase().endsWith(".json")) modelUrl += ".json"; - const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); - const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); - const cachedModelName = "indexeddb://" + shortModelName; - modelStats[shortModelName] = { - name: shortModelName, - loaded: false, - sizeFromManifest: 0, - sizeLoadedWeights: 0, - sizeDesired: models_exports[shortModelName], - inCache: false, - url: "" - }; - options.cacheSupported = typeof indexedDB !== "undefined"; - let cachedModels = {}; - try { - cachedModels = options.cacheSupported && options.cacheModels ? await tfjs_esm_exports.io.listModels() : {}; - } catch (e) { - options.cacheSupported = false; - } - modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); - modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; - const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; - let model23 = new tfjs_esm_exports.GraphModel(modelStats[shortModelName].url, tfLoadOptions); - modelStats[shortModelName].loaded = false; - try { - model23.findIOHandler(); - if (options.debug) log("model load handler:", model23["handler"]); - } catch (err) { - log("error finding model i/o handler:", modelUrl, err); - } - try { - const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; - modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; - if (artifacts) model23.loadSync(artifacts); - else model23 = await tfjs_esm_exports.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); - modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || ((_f = (_e = model23.artifacts) == null ? void 0 : _e.weightData) == null ? void 0 : _f[0].byteLength) || 0; - if (options.verbose) log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); - modelStats[shortModelName].loaded = true; - } catch (err) { - log("error loading model:", modelUrl, err); - } - if (modelStats[shortModelName].loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { - try { - const saveResult = await model23.save(cachedModelName); - if (options.debug) log("model saved:", cachedModelName, saveResult); - } catch (err) { - log("error saving model:", modelUrl, err); - } - } - return model23; -} - -// package.json -var version8 = "3.3.2"; - -// src/tfjs/humangl.ts -var config2 = { - name: "humangl", - priority: 999, - canvas: null, - gl: null, - extensions: [], - webGLattr: { - // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2 - alpha: false, - antialias: false, - premultipliedAlpha: false, - preserveDrawingBuffer: false, - depth: false, - stencil: false, - failIfMajorPerformanceCaveat: false, - // default=true - desynchronized: true - // default=undefined - } -}; -function extensions() { - const gl = config2.gl; - if (!gl) return; - config2.extensions = gl.getSupportedExtensions(); -} -function register(instance) { - var _a; - if (instance.config.backend !== "humangl") return; - if (config2.name in tfjs_esm_exports.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { - log("humangl error: backend invalid context"); - instance.models.reset(); - } - if (!tfjs_esm_exports.findBackend(config2.name)) { - try { - config2.canvas = canvas(100, 100); - } catch (err) { - log("humangl error: cannot create canvas:", err); - return; - } - try { - config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); - if (!config2.gl) { - log("humangl error: cannot get webgl context"); - return; - } - const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); - if (!glv2) { - log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); - instance.config.backend = "webgl"; - return; - } - if (config2.canvas) { - config2.canvas.addEventListener("webglcontextlost", (e) => { - log("humangl error:", e.type); - log("possible browser memory leak using webgl or conflict with multiple backend registrations"); - instance.emit("error"); - throw new Error("backend error: webgl context lost"); - }); - config2.canvas.addEventListener("webglcontextrestored", (e) => { - log("humangl error: context restored:", e); - }); - config2.canvas.addEventListener("webglcontextcreationerror", (e) => { - log("humangl error: context create:", e); - }); - } - } catch (err) { - log("humangl error: cannot get webgl context:", err); - return; - } - try { - tfjs_esm_exports.setWebGLContext(2, config2.gl); - } catch (err) { - log("humangl error: cannot set webgl context:", err); - return; - } - try { - const ctx = new tfjs_esm_exports.GPGPUContext(config2.gl); - tfjs_esm_exports.registerBackend(config2.name, () => new tfjs_esm_exports.MathBackendWebGL(ctx), config2.priority); - } catch (err) { - log("humangl error: cannot register webgl backend:", err); - return; - } - try { - const kernels = tfjs_esm_exports.getKernelsForBackend("webgl"); - kernels.forEach((kernelConfig) => { - const newKernelConfig = { ...kernelConfig, backendName: config2.name }; - tfjs_esm_exports.registerKernel(newKernelConfig); - }); - } catch (err) { - log("humangl error: cannot update webgl backend registration:", err); - return; - } - try { - if (tfjs_esm_exports.env().flagRegistry.WEBGL_VERSION) tfjs_esm_exports.env().set("WEBGL_VERSION", 2); - } catch (err) { - log("humangl error: cannot set WebGL backend flags:", err); - return; - } - extensions(); - const backend4 = tfjs_esm_exports.backend(); - const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; - if (current) { - if (instance.config.debug) log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); - } else { - log("humangl error: no current gl context:", current, config2.gl); - } - } -} - -// src/tfjs/constants.ts -var constants = { - tf255: 255, - tf1: 1, - tf2: 2, - tf05: 0.5, - tf127: 127.5, - rgb: [0.2989, 0.587, 0.114] -}; -function init() { - constants.tf255 = tfjs_esm_exports.scalar(255, "float32"); - constants.tf1 = tfjs_esm_exports.scalar(1, "float32"); - constants.tf2 = tfjs_esm_exports.scalar(2, "float32"); - constants.tf05 = tfjs_esm_exports.scalar(0.5, "float32"); - constants.tf127 = tfjs_esm_exports.scalar(127.5, "float32"); - constants.rgb = tfjs_esm_exports.tensor1d([0.2989, 0.587, 0.114], "float32"); -} - -// src/tfjs/backend.ts -async function getBestBackend() { - var _a; - await env.updateBackend(); - if ((_a = env.tensorflow) == null ? void 0 : _a.version) return "tensorflow"; - if (env.webgpu.supported && env.webgpu.backend) return "webgpu"; - if (env.webgl.supported && env.webgl.backend) return "webgl"; - if (env.wasm.supported && env.wasm.backend) return "wasm"; - return "cpu"; -} -function registerCustomOps(config3) { - const newKernels = []; - if (!env.kernels.includes("mod")) { - const kernelMod = { - kernelName: "Mod", - backendName: tfjs_esm_exports.getBackend(), - kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.sub(op.inputs.a, tfjs_esm_exports.mul(tfjs_esm_exports.div(op.inputs.a, op.inputs.b), op.inputs.b))) - }; - tfjs_esm_exports.registerKernel(kernelMod); - env.kernels.push("mod"); - newKernels.push("mod"); - } - if (!env.kernels.includes("floormod")) { - const kernelFloorMod = { - kernelName: "FloorMod", - backendName: tfjs_esm_exports.getBackend(), - kernelFunc: (op) => tfjs_esm_exports.tidy(() => tfjs_esm_exports.add(tfjs_esm_exports.mul(tfjs_esm_exports.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tfjs_esm_exports.mod(op.inputs.a, op.inputs.b))) - }; - tfjs_esm_exports.registerKernel(kernelFloorMod); - env.kernels.push("floormod"); - newKernels.push("floormod"); - } - if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { - const kernelRotateWithOffset = { - kernelName: "RotateWithOffset", - backendName: tfjs_esm_exports.getBackend(), - kernelFunc: (op) => tfjs_esm_exports.tidy(() => { - const backend4 = tfjs_esm_exports.getBackend(); - tfjs_esm_exports.setBackend("cpu"); - const t2 = tfjs_esm_exports.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); - tfjs_esm_exports.setBackend(backend4); - return t2; - }) - }; - tfjs_esm_exports.registerKernel(kernelRotateWithOffset); - env.kernels.push("rotatewithoffset"); - newKernels.push("rotatewithoffset"); - } - if (newKernels.length > 0 && config3.debug) log("registered kernels:", newKernels); -} -var defaultFlags = {}; -async function check(instance, force = false) { - var _a, _b; - instance.state = "backend"; - if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) instance.config.backend = await getBestBackend(); - if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tfjs_esm_exports.getBackend() !== instance.config.backend) { - const timeStamp = now(); - if (instance.config.backend && instance.config.backend.length > 0) { - if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { - if (instance.config.debug) log("running inside web worker"); - } - if (typeof navigator !== "undefined" && ((_b = navigator == null ? void 0 : navigator.userAgent) == null ? void 0 : _b.toLowerCase().includes("electron"))) { - if (instance.config.debug) log("running inside electron"); - } - let available = Object.keys(tfjs_esm_exports.engine().registryFactory); - if (instance.config.backend === "humangl" && !available.includes("humangl")) { - register(instance); - available = Object.keys(tfjs_esm_exports.engine().registryFactory); - } - if (instance.config.debug) log("available backends:", available); - if (env.browser && !env.node && instance.config.backend === "tensorflow" && available.includes("webgl")) { - if (instance.config.debug) log("override: backend set to tensorflow while running in browser"); - instance.config.backend = "webgl"; - } - if (env.node && !env.browser && (instance.config.backend === "webgl" || instance.config.backend === "humangl") && available.includes("tensorflow")) { - if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`); - instance.config.backend = "tensorflow"; - } - if (env.browser && instance.config.backend === "webgpu") { - if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { - log("override: backend set to webgpu but browser does not support webgpu"); - instance.config.backend = "webgl"; - } else { - const adapter = await navigator.gpu.requestAdapter(); - if (instance.config.debug) log("enumerated webgpu adapter:", adapter); - if (!adapter) { - log("override: backend set to webgpu but browser reports no available gpu"); - instance.config.backend = "webgl"; - } else { - const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; - log("webgpu adapter info:", adapterInfo); - } - } - } - if (!available.includes(instance.config.backend)) { - log(`error: backend ${instance.config.backend} not found in registry`); - instance.config.backend = env.node ? "tensorflow" : "webgl"; - if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`); - } - if (instance.config.debug) log("setting backend:", [instance.config.backend]); - if (instance.config.backend === "wasm") { - if (tfjs_esm_exports.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tfjs_esm_exports.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); - if (instance.config.debug) log("wasm path:", instance.config.wasmPath); - if (typeof tfjs_esm_exports.setWasmPaths !== "undefined") tfjs_esm_exports.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); - else throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); - let mt = false; - let simd = false; - try { - mt = await tfjs_esm_exports.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - simd = await tfjs_esm_exports.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - if (instance.config.debug) log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); - if (instance.config.debug && !simd) log("warning: wasm simd support is not enabled"); - } catch (e) { - log("wasm detection failed"); - } - } - try { - await tfjs_esm_exports.setBackend(instance.config.backend); - await tfjs_esm_exports.ready(); - } catch (err) { - log("error: cannot set backend:", instance.config.backend, err); - return false; - } - if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tfjs_esm_exports.env().flags)); - } - if (tfjs_esm_exports.getBackend() === "humangl" || tfjs_esm_exports.getBackend() === "webgl") { - if (tfjs_esm_exports.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tfjs_esm_exports.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); - if (tfjs_esm_exports.env().flagRegistry.WEBGL_EXP_CONV) tfjs_esm_exports.env().set("WEBGL_EXP_CONV", true); - if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { - log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); - tfjs_esm_exports.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); - } - } - if (tfjs_esm_exports.getBackend() === "webgpu") { - } - if (instance.config.debug) { - const newFlags = tfjs_esm_exports.env().flags; - const updatedFlags = {}; - for (const key of Object.keys(newFlags)) { - if (defaultFlags[key] === newFlags[key]) continue; - updatedFlags[key] = newFlags[key]; - } - if (instance.config.debug && Object.keys(updatedFlags).length > 0) log("backend:", tfjs_esm_exports.getBackend(), "flags:", updatedFlags); - } - if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { - if (instance.config.debug) log("flags:", instance.config["flags"]); - for (const [key, val] of Object.entries(instance.config.flags)) { - tfjs_esm_exports.env().set(key, val); - } - } - tfjs_esm_exports.enableProdMode(); - init(); - instance.performance.initBackend = Math.trunc(now() - timeStamp); - instance.config.backend = tfjs_esm_exports.getBackend(); - await env.updateBackend(); - registerCustomOps(instance.config); - } - return true; -} -function fakeOps(kernelNames, config3) { - for (const kernelName of kernelNames) { - const kernelConfig = { - kernelName, - backendName: config3.backend, - kernelFunc: (param) => { - var _a; - if (config3.debug) log("kernelFunc", kernelName, config3.backend, param); - return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; - } - // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, - // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, - }; - tfjs_esm_exports.registerKernel(kernelConfig); - } - env.kernels = tfjs_esm_exports.getKernelsForBackend(tfjs_esm_exports.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); -} - -// src/draw/draw.ts -var draw_exports = {}; -__export(draw_exports, { - all: () => all, - body: () => body, - canvas: () => canvas2, - face: () => face, - gesture: () => gesture, - hand: () => hand, - init: () => init2, - object: () => object, - options: () => options2, - person: () => person -}); - -// src/draw/primitives.ts -var getCanvasContext = (input) => { - if (!input) log("draw error: invalid canvas"); - else if (!input.getContext) log("draw error: canvas context not defined"); - else { - const ctx = input.getContext("2d", { willReadFrequently: true }); - if (!ctx) log("draw error: cannot get canvas context"); - else return ctx; - } - return null; -}; -var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); -var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); -var colorDepth = (z, opt) => { - if (!opt.useDepth || typeof z === "undefined") return opt.color; - const rgb3 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); - return `rgba(${rgb3[0]}, ${rgb3[1]}, ${rgb3[2]}, ${opt.alpha})`; -}; -function labels(ctx, str, startX, startY, localOptions2) { - const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); - const x = Math.max(0, startX); - for (let i = line.length - 1; i >= 0; i--) { - const y = i * localOptions2.lineHeight + startY; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(line[i], x + 5, y + 16); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(line[i], x + 4, y + 15); - } -} -function point(ctx, x, y, z, localOptions2) { - ctx.fillStyle = colorDepth(z, localOptions2); - ctx.beginPath(); - ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); - ctx.fill(); -} -function rect(ctx, x, y, width, height, localOptions2) { - ctx.beginPath(); - ctx.lineWidth = localOptions2.lineWidth; - if (localOptions2.useCurves) { - const cx = (x + x + width) / 2; - const cy = (y + y + height) / 2; - ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); - } else { - ctx.moveTo(x + localOptions2.roundRect, y); - ctx.lineTo(x + width - localOptions2.roundRect, y); - ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); - ctx.lineTo(x + width, y + height - localOptions2.roundRect); - ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); - ctx.lineTo(x + localOptions2.roundRect, y + height); - ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); - ctx.lineTo(x, y + localOptions2.roundRect); - ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); - ctx.closePath(); - } - ctx.stroke(); -} -function lines(ctx, points, localOptions2) { - if (points.length < 2) return; - ctx.beginPath(); - ctx.moveTo(points[0][0], points[0][1]); - for (const pt of points) { - ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); - ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); - } - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function curves(ctx, points, localOptions2) { - if (points.length < 2) return; - ctx.lineWidth = localOptions2.lineWidth; - if (!localOptions2.useCurves || points.length <= 2) { - lines(ctx, points, localOptions2); - return; - } - ctx.moveTo(points[0][0], points[0][1]); - for (let i = 0; i < points.length - 2; i++) { - const xc = (points[i][0] + points[i + 1][0]) / 2; - const yc = (points[i][1] + points[i + 1][1]) / 2; - ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); - } - ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function arrow(ctx, from, to, radius = 5) { - let angle; - let x; - let y; - ctx.beginPath(); - ctx.moveTo(from[0], from[1]); - ctx.lineTo(to[0], to[1]); - angle = Math.atan2(to[1] - from[1], to[0] - from[0]); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.moveTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - ctx.closePath(); - ctx.stroke(); - ctx.fill(); -} - -// src/draw/options.ts -var options2 = { - color: "rgba(173, 216, 230, 0.6)", - // 'lightblue' with light alpha channel - labelColor: "rgba(173, 216, 230, 1)", - // 'lightblue' with dark alpha channel - shadowColor: "black", - alpha: 0.5, - font: 'small-caps 16px "Segoe UI"', - lineHeight: 18, - lineWidth: 4, - pointSize: 2, - roundRect: 8, - drawPoints: false, - drawLabels: true, - drawBoxes: true, - drawAttention: true, - drawGestures: true, - drawPolygons: true, - drawGaze: true, - fillPolygons: false, - useDepth: true, - useCurves: false, - faceLabels: "", - bodyLabels: "", - bodyPartLabels: "", - objectLabels: "", - handLabels: "", - fingerLabels: "", - gestureLabels: "" -}; - -// src/face/facemeshcoords.ts -var meshAnnotations = { - silhouette: [ - 10, - 338, - 297, - 332, - 284, - 251, - 389, - 356, - 454, - 323, - 361, - 288, - 397, - 365, - 379, - 378, - 400, - 377, - 152, - 148, - 176, - 149, - 150, - 136, - 172, - 58, - 132, - 93, - 234, - 127, - 162, - 21, - 54, - 103, - 67, - 109 - ], - // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11 - // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10 - // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11 - // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11 - lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], - lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], - lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], - lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], - lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], - lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], - lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], - lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], - rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], - // 7 - rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], - // 9 - rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], - // 7 - rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], - // 9 - rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], - // 7 - rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], - // 9 - rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], - // 9 - rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], - // 8 - rightEyebrowLower: [35, 124, 46, 53, 52, 65], - // 6 - rightEyeIris: [473, 474, 475, 476, 477], - // 5 - leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], - leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], - leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], - leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], - leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], - leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], - leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], - leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], - leftEyebrowLower: [265, 353, 276, 283, 282, 295], - leftEyeIris: [468, 469, 470, 471, 472], - midwayBetweenEyes: [168], - noseTip: [1], - noseBottom: [2], - noseRightCorner: [98], - noseLeftCorner: [327], - rightCheek: [205], - leftCheek: [425] -}; -var meshLandmarks = { - count: 468, - mouth: 13, - symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] -}; -var blazeFaceLandmarks = { - leftEye: 0, - rightEye: 1, - nose: 2, - mouth: 3, - leftEar: 4, - rightEar: 5, - symmetryLine: [3, 2] -}; -var irisIndices = [ - // A mapping from facemesh model keypoints to iris model keypoints. - { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, - // 7 x 3d - { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, - // 7 x 3d - { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, - // 7 x 3d - { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, - // 7 x 3d - { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, - // 9 x 3d - { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, - // 9 x 3d - { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, - // 9 x 3d - { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, - // 8 x 3d - { key: "EyebrowLower", indices: [48, 49, 50, 51, 52, 53] } - // 6 x 3d -]; -var UV468 = [ - [0.499976992607117, 0.652534008026123], - [0.500025987625122, 0.547487020492554], - [0.499974012374878, 0.602371990680695], - [0.482113003730774, 0.471979022026062], - [0.500150978565216, 0.527155995368958], - [0.499909996986389, 0.498252987861633], - [0.499523013830185, 0.40106201171875], - [0.289712011814117, 0.380764007568359], - [0.499954998493195, 0.312398016452789], - [0.499987006187439, 0.269918978214264], - [0.500023007392883, 0.107050001621246], - [0.500023007392883, 0.666234016418457], - [0.5000159740448, 0.679224014282227], - [0.500023007392883, 0.692348003387451], - [0.499976992607117, 0.695277988910675], - [0.499976992607117, 0.70593398809433], - [0.499976992607117, 0.719385027885437], - [0.499976992607117, 0.737019002437592], - 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301, - 389, - 385, - 380, - 386, - 394, - 395, - 379, - 399, - 412, - 419, - 410, - 436, - 322, - 387, - 373, - 388, - 326, - 2, - 393, - 354, - 370, - 461, - 393, - 164, - 267, - 268, - 302, - 12, - 386, - 374, - 387, - 312, - 268, - 13, - 298, - 293, - 301, - 265, - 446, - 340, - 380, - 385, - 381, - 280, - 330, - 425, - 322, - 426, - 391, - 420, - 429, - 437, - 393, - 391, - 326, - 344, - 440, - 438, - 458, - 459, - 461, - 364, - 434, - 394, - 428, - 396, - 262, - 274, - 354, - 457, - 317, - 316, - 402, - 316, - 315, - 403, - 315, - 314, - 404, - 314, - 313, - 405, - 313, - 421, - 406, - 323, - 366, - 361, - 292, - 306, - 407, - 306, - 291, - 408, - 291, - 287, - 409, - 287, - 432, - 410, - 427, - 434, - 411, - 372, - 264, - 383, - 459, - 309, - 457, - 366, - 352, - 401, - 1, - 274, - 4, - 418, - 421, - 262, - 331, - 294, - 358, - 435, - 433, - 367, - 392, - 289, - 439, - 328, - 462, - 326, - 94, - 2, - 370, - 289, - 305, - 455, - 339, - 254, - 448, - 359, - 255, - 446, - 254, - 253, - 449, - 253, - 252, - 450, - 252, - 256, - 451, - 256, - 341, - 452, - 414, - 413, - 463, - 286, - 441, - 414, - 286, - 258, - 441, - 258, - 257, - 442, - 257, - 259, - 443, - 259, - 260, - 444, - 260, - 467, - 445, - 309, - 459, - 250, - 305, - 289, - 290, - 305, - 290, - 460, - 401, - 376, - 435, - 309, - 250, - 392, - 376, - 411, - 433, - 453, - 341, - 464, - 357, - 453, - 465, - 343, - 357, - 412, - 437, - 343, - 399, - 344, - 360, - 440, - 420, - 437, - 456, - 360, - 420, - 363, - 361, - 401, - 288, - 265, - 372, - 353, - 390, - 339, - 249, - 339, - 448, - 255 -]; -var VTX68 = [ - /* cont */ - 127, - 234, - 132, - 58, - 172, - 150, - 149, - 148, - 152, - 377, - 378, - 379, - 397, - 288, - 361, - 454, - 356, - /* brows */ - 70, - 63, - 105, - 66, - 107, - 336, - 296, - 334, - 293, - 300, - /* nose */ - 168, - 6, - 195, - 4, - 98, - 97, - 2, - 326, - 327, - /* eyes */ - 33, - 160, - 158, - 133, - 153, - 144, - 362, - 385, - 387, - 263, - 373, - 380, - /* lip */ - 57, - 40, - 37, - 0, - 267, - 270, - 287, - 321, - 314, - 17, - 84, - 91, - /* mouth */ - 78, - 81, - 13, - 311, - 308, - 402, - 14, - 178 -]; -var VTX33 = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152]; -var VTX7 = [33, 133, 362, 263, 1, 78, 308]; -var UV68 = VTX68.map((x) => UV468[x]); -var UV33 = VTX33.map((x) => UV468[x]); -var UV7 = VTX7.map((x) => UV468[x]); -function connectionsToIndices(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var pairsLips = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var pairsFaceContour = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -var contourKeypoints = { - lips: connectionsToIndices(pairsLips), - leftEye: connectionsToIndices(pairsLeftEye), - leftEyebrow: connectionsToIndices(pairsLeftEyebrow), - leftIris: connectionsToIndices(pairsLeftIris), - rightEye: connectionsToIndices(pairsRightEye), - rightEyebrow: connectionsToIndices(pairsRightEyebrow), - rightIris: connectionsToIndices(pairsRightIris), - faceOval: connectionsToIndices(pairsFaceContour) -}; - -// src/face/constants.ts -var LIPS_CONNECTIONS = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var FACE_OVAL_CONNECTIONS = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -function connectionsToIndices2(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { - lips: connectionsToIndices2(LIPS_CONNECTIONS), - leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), - leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), - leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), - rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), - rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), - rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), - faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) -}; -var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); -var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); -var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ - 61, - 146, - 91, - 181, - 84, - 17, - 314, - 405, - 321, - 375, - 291, - // Lower outer. - 185, - 40, - 39, - 37, - 0, - 267, - 269, - 270, - 409, - // Upper outer(excluding corners). - 78, - 95, - 88, - 178, - 87, - 14, - 317, - 402, - 318, - 324, - 308, - // Lower inner. - 191, - 80, - 81, - 82, - 13, - 312, - 311, - 310, - 415, - // Upper inner(excluding corners). - 76, - 77, - 90, - 180, - 85, - 16, - 315, - 404, - 320, - 307, - 306, - // Lower semi - outer. - 184, - 74, - 73, - 72, - 11, - 302, - 303, - 304, - 408, - // Upper semi - outer(excluding corners). - 62, - 96, - 89, - 179, - 86, - 15, - 316, - 403, - 319, - 325, - 292, - // Lower semi - inner. - 183, - 42, - 41, - 38, - 12, - 268, - 271, - 272, - 407 - // Upper semi - inner(excluding corners). -]; -var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ - 33, - 7, - 163, - 144, - 145, - 153, - 154, - 155, - 133, - // Lower contour. - 246, - 161, - 160, - 159, - 158, - 157, - 173, - // upper contour (excluding corners). - 130, - 25, - 110, - 24, - 23, - 22, - 26, - 112, - 243, - // Halo x2 lower contour. - 247, - 30, - 29, - 27, - 28, - 56, - 190, - // Halo x2 upper contour (excluding corners). - 226, - 31, - 228, - 229, - 230, - 231, - 232, - 233, - 244, - // Halo x3 lower contour. - 113, - 225, - 224, - 223, - 222, - 221, - 189, - // Halo x3 upper contour (excluding corners). - 35, - 124, - 46, - 53, - 52, - 65, - // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour. - 143, - 111, - 117, - 118, - 119, - 120, - 121, - 128, - 245, - // Halo x5 lower contour. - 156, - 70, - 63, - 105, - 66, - 107, - 55, - 193 - // Halo x5 upper contour (excluding corners) or eyebrow outer contour. -]; -var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ - 263, - 249, - 390, - 373, - 374, - 380, - 381, - 382, - 362, - // Lower contour. - 466, - 388, - 387, - 386, - 385, - 384, - 398, - // Upper contour (excluding corners). - 359, - 255, - 339, - 254, - 253, - 252, - 256, - 341, - 463, - // Halo x2 lower contour. - 467, - 260, - 259, - 257, - 258, - 286, - 414, - // Halo x2 upper contour (excluding corners). - 446, - 261, - 448, - 449, - 450, - 451, - 452, - 453, - 464, - // Halo x3 lower contour. - 342, - 445, - 444, - 443, - 442, - 441, - 413, - // Halo x3 upper contour (excluding corners). - 265, - 353, - 276, - 283, - 282, - 295, - // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour. - 372, - 340, - 346, - 347, - 348, - 349, - 350, - 357, - 465, - // Halo x5 lower contour. - 383, - 300, - 293, - 334, - 296, - 336, - 285, - 417 - // Halo x5 upper contour (excluding corners) or eyebrow outer contour. -]; - -// src/draw/face.ts -var localOptions; -function drawLabels(f, ctx) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) return; - let l = localOptions.faceLabels.slice(); - l = replace(l, "[id]", f.id.toFixed(0)); - if (f.score) l = replace(l, "[score]", 100 * f.score); - if (f.gender) l = replace(l, "[gender]", f.gender); - if (f.genderScore) l = replace(l, "[genderScore]", 100 * f.genderScore); - if (f.age) l = replace(l, "[age]", f.age); - if (f.distance) l = replace(l, "[distance]", 100 * f.distance); - if (f.real) l = replace(l, "[real]", 100 * f.real); - if (f.live) l = replace(l, "[live]", 100 * f.live); - if (f.emotion && f.emotion.length > 0) { - const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); - if (emotion2.length > 3) emotion2.length = 3; - l = replace(l, "[emotions]", emotion2.join(" ")); - } - if ((_c = (_b = f.rotation) == null ? void 0 : _b.angle) == null ? void 0 : _c.roll) l = replace(l, "[roll]", rad2deg(f.rotation.angle.roll)); - if ((_e = (_d = f.rotation) == null ? void 0 : _d.angle) == null ? void 0 : _e.yaw) l = replace(l, "[yaw]", rad2deg(f.rotation.angle.yaw)); - if ((_g = (_f = f.rotation) == null ? void 0 : _f.angle) == null ? void 0 : _g.pitch) l = replace(l, "[pitch]", rad2deg(f.rotation.angle.pitch)); - if ((_i = (_h = f.rotation) == null ? void 0 : _h.gaze) == null ? void 0 : _i.bearing) l = replace(l, "[gaze]", rad2deg(f.rotation.gaze.bearing)); - labels(ctx, l, f.box[0], f.box[1], localOptions); -} -function drawIrisElipse(f, ctx) { - var _a, _b, _c, _d; - if (((_a = f.annotations) == null ? void 0 : _a.leftEyeIris) && ((_b = f.annotations) == null ? void 0 : _b.leftEyeIris[0])) { - ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; - ctx.beginPath(); - const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2; - const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2; - ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); - ctx.stroke(); - if (localOptions.fillPolygons) { - ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color; - ctx.fill(); - } - } - if (((_c = f.annotations) == null ? void 0 : _c.rightEyeIris) && ((_d = f.annotations) == null ? void 0 : _d.rightEyeIris[0])) { - ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color; - ctx.beginPath(); - const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2; - const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2; - ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI); - ctx.stroke(); - if (localOptions.fillPolygons) { - ctx.fillStyle = localOptions.useDepth ? 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Ae(e){let t=e.map(n=>n[0]);return t.push(e[e.length-1][1]),t}var Vr={lips:Ae(Or),leftEye:Ae(Cr),leftEyebrow:Ae(Wr),leftIris:Ae(Dr),rightEye:Ae(Fr),rightEyebrow:Ae(Br),rightIris:Ae(Hr),faceOval:Ae(Gr)},Zr=Object.entries(Vr).map(([e,t])=>t.map(n=>[n,e])).flat(),$A=new 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n=B.faceLabels.slice();if(n=H(n,"[id]",e.id.toFixed(0)),e.score&&(n=H(n,"[score]",100*e.score)),e.gender&&(n=H(n,"[gender]",e.gender)),e.genderScore&&(n=H(n,"[genderScore]",100*e.genderScore)),e.age&&(n=H(n,"[age]",e.age)),e.distance&&(n=H(n,"[distance]",100*e.distance)),e.real&&(n=H(n,"[real]",100*e.real)),e.live&&(n=H(n,"[live]",100*e.live)),e.emotion&&e.emotion.length>0){let f=e.emotion.map(y=>`${Math.trunc(100*y.score)}% ${y.emotion}`);f.length>3&&(f.length=3),n=H(n,"[emotions]",f.join(" "))}(A=(s=e.rotation)==null?void 0:s.angle)!=null&&A.roll&&(n=H(n,"[roll]",ge(e.rotation.angle.roll))),(i=(a=e.rotation)==null?void 0:a.angle)!=null&&i.yaw&&(n=H(n,"[yaw]",ge(e.rotation.angle.yaw))),(d=(c=e.rotation)==null?void 0:c.angle)!=null&&d.pitch&&(n=H(n,"[pitch]",ge(e.rotation.angle.pitch))),(l=(x=e.rotation)==null?void 0:x.gaze)!=null&&l.bearing&&(n=H(n,"[gaze]",ge(e.rotation.gaze.bearing))),C0(t,n,e.box[0],e.box[1],B)}function qr(e,t){var n,o,s,A;if((n=e.annotations)!=null&&n.leftEyeIris&&((o=e.annotations)!=null&&o.leftEyeIris[0])){t.strokeStyle=B.useDepth?"rgba(255, 200, 255, 0.3)":B.color,t.beginPath();let a=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,i=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],a,i,0,0,2*Math.PI),t.stroke(),B.fillPolygons&&(t.fillStyle=B.useDepth?"rgba(255, 255, 200, 0.3)":B.color,t.fill())}if((s=e.annotations)!=null&&s.rightEyeIris&&((A=e.annotations)!=null&&A.rightEyeIris[0])){t.strokeStyle=B.useDepth?"rgba(255, 200, 255, 0.3)":B.color,t.beginPath();let a=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,i=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],a,i,0,0,2*Math.PI),t.stroke(),B.fillPolygons&&(t.fillStyle=B.useDepth?"rgba(255, 255, 200, 0.3)":B.color,t.fill())}}function Ur(e,t){var n;if(B.drawGaze&&((n=e.rotation)!=null&&n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*ge(e.rotation.angle.yaw)/90,s=e.box[1]+e.box[3]/2+e.box[2]*ge(e.rotation.angle.pitch)/90,A=new Path2D(` + M ${e.box[0]+e.box[2]/2} ${e.box[1]} C - ${valX} ${f.box[1]}, - ${valX} ${f.box[1] + f.box[3]}, - ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} - `); - const pathH = new Path2D(` - M ${f.box[0]} ${f.box[1] + f.box[3] / 2} + ${o} ${e.box[1]}, + ${o} ${e.box[1]+e.box[3]}, + ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} + `),a=new Path2D(` + M ${e.box[0]} ${e.box[1]+e.box[3]/2} C - ${f.box[0]} ${valY}, - ${f.box[0] + f.box[2]} ${valY}, - ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} - `); - ctx.stroke(pathH); - ctx.stroke(pathV); - } -} -function drawGazeArrows(f, ctx) { - var _a; - if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { - ctx.strokeStyle = "pink"; - ctx.fillStyle = "pink"; - const leftGaze = [ - f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); - const rightGaze = [ - f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); - } -} -function drawFacePolygons(f, ctx) { - if (localOptions.drawPolygons && f.mesh.length >= 468) { - ctx.lineWidth = 1; - for (let i = 0; i < TRI468.length / 3; i++) { - const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); - lines(ctx, points, localOptions); - } - drawIrisElipse(f, ctx); - } -} -function drawFacePoints(f, ctx) { - if (localOptions.drawPoints) { - if ((f == null ? void 0 : f.mesh.length) >= 468) { - for (let i = 0; i < f.mesh.length; i++) { - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); - if (localOptions.drawAttention) { - if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); - if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - } - } - } else { - for (const [k, v] of Object.entries((f == null ? void 0 : f.annotations) || {})) { - if (!(v == null ? void 0 : v[0])) continue; - const pt = v[0]; - point(ctx, pt[0], pt[1], 0, localOptions); - if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions); - } - } - } -} -function drawFaceBoxes(f, ctx) { - if (localOptions.drawBoxes) { - rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); - } -} -function face(inCanvas2, result, drawOptions) { - localOptions = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.font = localOptions.font; - ctx.strokeStyle = localOptions.color; - ctx.fillStyle = localOptions.color; - for (const f of result) { - drawFaceBoxes(f, ctx); - drawLabels(f, ctx); - if (f.mesh && f.mesh.length > 0) { - drawFacePoints(f, ctx); - drawFacePolygons(f, ctx); - drawGazeSpheres(f, ctx); - drawGazeArrows(f, ctx); - } - } -} - -// src/draw/body.ts -function body(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - for (let i = 0; i < result.length; i++) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - ctx.lineWidth = localOptions2.lineWidth; - ctx.font = localOptions2.font; - if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.bodyLabels.slice(); - l = replace(l, "[id]", result[i].id.toFixed(0)); - l = replace(l, "[score]", 100 * result[i].score); - labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); - } - } - if (localOptions2.drawPoints && result[i].keypoints) { - for (let pt = 0; pt < result[i].keypoints.length; pt++) { - if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) continue; - ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); - point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); - } - } - if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { - ctx.font = localOptions2.font; - for (const pt of result[i].keypoints) { - if (!pt.score || pt.score === 0) continue; - let l = localOptions2.bodyPartLabels.slice(); - l = replace(l, "[label]", pt.part); - l = replace(l, "[score]", 100 * pt.score); - labels(ctx, l, pt.position[0], pt.position[1], localOptions2); - } - } - if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { - for (const part of Object.values(result[i].annotations)) { - for (const connected4 of part) curves(ctx, connected4, localOptions2); - } - } - } -} - -// src/draw/hand.ts -function hand(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.handLabels.slice(); - l = replace(l, "[id]", h.id.toFixed(0)); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - if (localOptions2.drawPoints) { - if (h.keypoints && h.keypoints.length > 0) { - for (const pt of h.keypoints) { - ctx.fillStyle = colorDepth(pt[2], localOptions2); - point(ctx, pt[0], pt[1], 0, localOptions2); - } - } - } - if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { - for (const [part, pt] of Object.entries(h.annotations)) { - let l = localOptions2.fingerLabels.slice(); - l = replace(l, "[label]", part); - labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); - } - } - if (localOptions2.drawPolygons && h.annotations) { - const addHandLine = (part) => { - if (!part || part.length === 0 || !part[0]) return; - for (let i = 0; i < part.length; i++) { - ctx.beginPath(); - const z = part[i][2] || 0; - ctx.strokeStyle = colorDepth(i * z, localOptions2); - ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); - ctx.lineTo(part[i][0], part[i][1]); - ctx.stroke(); - } - }; - ctx.lineWidth = localOptions2.lineWidth; - addHandLine(h.annotations.index); - addHandLine(h.annotations.middle); - addHandLine(h.annotations.ring); - addHandLine(h.annotations.pinky); - addHandLine(h.annotations.thumb); - } - } -} - -// src/draw/object.ts -function object(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.objectLabels.slice(); - l = replace(l, "[id]", h.id.toFixed(0)); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - } -} - -// src/draw/gesture.ts -function gesture(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.font = localOptions2.font; - ctx.fillStyle = localOptions2.color; - let i = 1; - for (let j = 0; j < result.length; j++) { - const [where, what] = Object.entries(result[j]); - if (what.length > 1 && what[1].length > 0) { - const who = where[1] > 0 ? `#${where[1]}` : ""; - let l = localOptions2.gestureLabels.slice(); - l = replace(l, "[where]", where[0]); - l = replace(l, "[who]", who); - l = replace(l, "[what]", what[1]); - labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); - i += 1; - } - } - } -} - -// src/draw/labels.ts -var defaultLabels = { - face: `face + ${e.box[0]} ${s}, + ${e.box[0]+e.box[2]} ${s}, + ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} + `);t.stroke(a),t.stroke(A)}}function Yr(e,t){var n;if(B.drawGaze&&((n=e.rotation)!=null&&n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];pt(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let s=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];pt(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[s[0],s[1]],4)}}function Kr(e,t){if(B.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;ne.mesh[s]);mt(t,o,B)}qr(e,t)}}function Jr(e,t){if(B.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let n=0;n0&&(Jr(s,o),Kr(s,o),Ur(s,o),Yr(s,o))}}function g2(e,t,n){var A,a;let o=Q(n0,n);if(!t||!e)return;let s=O0(e);if(s){s.lineJoin="round";for(let i=0;i0)){let c=o.bodyLabels.slice();c=H(c,"[id]",t[i].id.toFixed(0)),c=H(c,"[score]",100*t[i].score),C0(s,c,t[i].box[0],t[i].box[1],o)}if(o.drawPoints&&t[i].keypoints)for(let c=0;c0&&t[i].keypoints){s.font=o.font;for(let c of t[i].keypoints){if(!c.score||c.score===0)continue;let d=o.bodyPartLabels.slice();d=H(d,"[label]",c.part),d=H(d,"[score]",100*c.score),C0(s,d,c.position[0],c.position[1],o)}}if(o.drawPolygons&&t[i].keypoints&&t[i].annotations)for(let c of Object.values(t[i].annotations))for(let d of c)c1(s,d,o)}}}function T2(e,t,n){var A,a;let o=Q(n0,n);if(!t||!e)return;let s=O0(e);if(s){s.lineJoin="round",s.font=o.font;for(let i of t){if(o.drawBoxes){if(s.strokeStyle=o.color,s.fillStyle=o.color,Y0(s,i.box[0],i.box[1],i.box[2],i.box[3],o),o.drawLabels&&((A=o.handLabels)==null?void 0:A.length)>0){let c=o.handLabels.slice();c=H(c,"[id]",i.id.toFixed(0)),c=H(c,"[label]",i.label),c=H(c,"[score]",100*i.score),C0(s,c,i.box[0],i.box[1],o)}s.stroke()}if(o.drawPoints&&i.keypoints&&i.keypoints.length>0)for(let c of i.keypoints)s.fillStyle=Te(c[2],o),U0(s,c[0],c[1],0,o);if(o.drawLabels&&i.annotations&&((a=o.fingerLabels)==null?void 0:a.length)>0)for(let[c,d]of Object.entries(i.annotations)){let x=o.fingerLabels.slice();x=H(x,"[label]",c),C0(s,x,d[d.length-1][0],d[d.length-1][1],o)}if(o.drawPolygons&&i.annotations){let c=d=>{if(!(!d||d.length===0||!d[0]))for(let x=0;x0?x-1:0][0],d[x>0?x-1:0][1]),s.lineTo(d[x][0],d[x][1]),s.stroke()}};s.lineWidth=o.lineWidth,c(i.annotations.index),c(i.annotations.middle),c(i.annotations.ring),c(i.annotations.pinky),c(i.annotations.thumb)}}}}function v2(e,t,n){var A;let o=Q(n0,n);if(!t||!e)return;let s=O0(e);if(s){s.lineJoin="round",s.font=o.font;for(let a of t)if(o.drawBoxes){if(s.strokeStyle=o.color,s.fillStyle=o.color,Y0(s,a.box[0],a.box[1],a.box[2],a.box[3],o),o.drawLabels&&((A=o.objectLabels)==null?void 0:A.length)>0){let i=o.objectLabels.slice();i=H(i,"[id]",a.id.toFixed(0)),i=H(i,"[label]",a.label),i=H(i,"[score]",100*a.score),C0(s,i,a.box[0],a.box[1],o)}s.stroke()}}}function R2(e,t,n){var s;let o=Q(n0,n);if(!(!t||!e)&&o.drawGestures&&((s=o.gestureLabels)==null?void 0:s.length)>0){let A=O0(e);if(!A)return;A.font=o.font,A.fillStyle=o.color;let a=1;for(let i=0;i1&&d[1].length>0){let x=c[1]>0?`#${c[1]}`:"",l=o.gestureLabels.slice();l=H(l,"[where]",c[0]),l=H(l,"[who]",x),l=H(l,"[what]",d[1]),C0(A,l,8,2+a*o.lineHeight,o),a+=1}}}}var ae={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -6373,7400 +118,7 @@ var defaultLabels = { live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`, - body: "body [score]%", - bodyPart: "[label] [score]%", - object: "[label] [score]%", - hand: "[label] [score]%", - finger: "[label]", - gesture: "[where] [who]: [what]" -}; - -// src/draw/draw.ts -var drawTime = 0; -function person(inCanvas2, result, drawOptions) { - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (let i = 0; i < result.length; i++) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels) { - const label = `person #${i}`; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.stroke(); - } - } -} -function canvas2(input, output) { - if (!input || !output) return; - const ctx = getCanvasContext(output); - if (!ctx) return; - ctx.drawImage(input, 0, 0); -} -async function all(inCanvas2, result, drawOptions) { - if (!(result == null ? void 0 : result.performance) || !inCanvas2) return null; - const timeStamp = now(); - const localOptions2 = mergeDeep(options2, drawOptions); - const promise = Promise.all([ - face(inCanvas2, result.face, localOptions2), - body(inCanvas2, result.body, localOptions2), - hand(inCanvas2, result.hand, localOptions2), - object(inCanvas2, result.object, localOptions2), - gesture(inCanvas2, result.gesture, localOptions2) - // gestures do not have buffering - // person(inCanvas, result.persons, localOptions); // already included above - ]); - drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); - result.performance.draw = drawTime; - return promise; -} -function init2() { - options2.faceLabels = defaultLabels.face; - options2.bodyLabels = defaultLabels.body; - options2.bodyPartLabels = defaultLabels.bodyPart; - options2.handLabels = defaultLabels.hand; - options2.fingerLabels = defaultLabels.finger; - options2.objectLabels = defaultLabels.object; - options2.gestureLabels = defaultLabels.gesture; -} - -// src/body/blazeposecoords.ts -var blazeposecoords_exports = {}; -__export(blazeposecoords_exports, { - connected: () => connected, - kpt: () => kpt -}); -var kpt = [ - "nose", - // 0 - "leftEyeInside", - // 1 - "leftEye", - // 2 - "leftEyeOutside", - // 3 - "rightEyeInside", - // 4 - "rightEye", - // 5 - "rightEyeOutside", - // 6 - "leftEar", - // 7 - "rightEar", - // 8 - "leftMouth", - // 9 - "rightMouth", - // 10 - "leftShoulder", - // 11 - "rightShoulder", - // 12 - "leftElbow", - // 13 - "rightElbow", - // 14 - "leftWrist", - // 15 - "rightWrist", - // 16 - "leftPinky", - // 17 - "rightPinky", - // 18 - "leftIndex", - // 19 - "rightIndex", - // 20 - "leftThumb", - // 21 - "rightThumb", - // 22 - "leftHip", - // 23 - "rightHip", - // 24 - "leftKnee", - // 25 - "rightKnee", - // 26 - "leftAnkle", - // 27 - "rightAnkle", - // 28 - "leftHeel", - // 29 - "rightHeel", - // 30 - "leftFoot", - // 31 - "rightFoot", - // 32 - "bodyCenter", - // 33 - "bodyTop", - // 34 - "leftPalm", - // 35 // z-coord not ok - "leftHand", - // 36 // similar to wrist but z-coord not ok - "rightPalm", - // 37 // z-coord not ok - "rightHand" - // 38 // similar to wrist but z-coord not ok -]; -var connected = { - shoulders: ["leftShoulder", "rightShoulder"], - hips: ["rightHip", "leftHip"], - mouth: ["leftMouth", "rightMouth"], - leftLegUpper: ["leftHip", "leftKnee"], - leftLegLower: ["leftKnee", "leftAnkle"], - leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], - leftTorso: ["leftShoulder", "leftHip"], - leftArmUpper: ["leftShoulder", "leftElbow"], - leftArmLower: ["leftElbow", "leftWrist"], - leftHand: ["leftWrist", "leftPalm"], - leftHandPinky: ["leftPalm", "leftPinky"], - leftHandIndex: ["leftPalm", "leftIndex"], - leftHandThumb: ["leftPalm", "leftThumb"], - leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], - rightLegUpper: ["rightHip", "rightKnee"], - rightLegLower: ["rightKnee", "rightAnkle"], - rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], - rightTorso: ["rightShoulder", "rightHip"], - rightArmUpper: ["rightShoulder", "rightElbow"], - rightArmLower: ["rightElbow", "rightWrist"], - rightHand: ["rightWrist", "rightPalm"], - rightHandPinky: ["rightPalm", "rightPinky"], - rightHandIndex: ["rightPalm", "rightIndex"], - rightHandThumb: ["rightPalm", "rightThumb"], - rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] -}; - -// src/body/blazeposedetector.ts -var model; -var inputSize = 224; -var anchorTensor; -var numLayers = 5; -var strides = [8, 16, 32, 32, 32]; -function createAnchors() { - const anchors3 = []; - let layerId = 0; - while (layerId < numLayers) { - let anchorCount = 0; - let lastSameStrideLayer = layerId; - while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { - anchorCount += 2; - lastSameStrideLayer++; - } - const stride = strides[layerId]; - const featureMapHeight = Math.ceil(inputSize / stride); - const featureMapWidth = Math.ceil(inputSize / stride); - for (let y = 0; y < featureMapHeight; ++y) { - for (let x = 0; x < featureMapWidth; ++x) { - for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { - anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); - } - } - } - layerId = lastSameStrideLayer; - } - anchorTensor = { x: tfjs_esm_exports.tensor1d(anchors3.map((a) => a.x)), y: tfjs_esm_exports.tensor1d(anchors3.map((a) => a.y)) }; -} -async function loadDetector(config3) { - if (env.initial) model = null; - if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { - model = await loadModel(config3.body["detector"].modelPath); - const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; - inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug && model) log("cached model:", model["modelUrl"]); - createAnchors(); - return model; -} -var cropFactor = [5, 5]; -function decodeBoxes(boxesTensor, anchor) { - return tfjs_esm_exports.tidy(() => { - const split6 = tfjs_esm_exports.split(boxesTensor, 12, 1); - let xCenter = tfjs_esm_exports.squeeze(split6[0]); - let yCenter = tfjs_esm_exports.squeeze(split6[1]); - let width = tfjs_esm_exports.squeeze(split6[2]); - let height = tfjs_esm_exports.squeeze(split6[3]); - xCenter = tfjs_esm_exports.add(tfjs_esm_exports.div(xCenter, inputSize), anchor.x); - yCenter = tfjs_esm_exports.add(tfjs_esm_exports.div(yCenter, inputSize), anchor.y); - width = tfjs_esm_exports.mul(tfjs_esm_exports.div(width, inputSize), cropFactor[0]); - height = tfjs_esm_exports.mul(tfjs_esm_exports.div(height, inputSize), cropFactor[1]); - const xMin = tfjs_esm_exports.sub(xCenter, tfjs_esm_exports.div(width, 2)); - const yMin = tfjs_esm_exports.sub(yCenter, tfjs_esm_exports.div(height, 2)); - const xMax = tfjs_esm_exports.add(xMin, width); - const yMax = tfjs_esm_exports.add(yMin, height); - const boxes = tfjs_esm_exports.stack([xMin, yMin, xMax, yMax], 1); - return boxes; - }); -} -async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { - var _a, _b; - const detectedBoxes = []; - const t2 = {}; - t2.boxes = decodeBoxes(boxesTensor, anchorTensor); - t2.scores = tfjs_esm_exports.sigmoid(logitsTensor); - t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); - const nms = await t2.nms.data(); - const scores = await t2.scores.data(); - const boxes = await t2.boxes.array(); - for (const i of Array.from(nms)) { - const score = scores[i]; - const boxRaw = boxes[i]; - const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; - const detectedBox = { score, boxRaw, box }; - detectedBoxes.push(detectedBox); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return detectedBoxes; -} -async function detectBoxes(input, config3, outputSize2) { - const t2 = {}; - t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); - t2.logitsRaw = tfjs_esm_exports.slice(t2.res, [0, 0, 0], [1, -1, 1]); - t2.boxesRaw = tfjs_esm_exports.slice(t2.res, [0, 0, 1], [1, -1, -1]); - t2.logits = tfjs_esm_exports.squeeze(t2.logitsRaw); - t2.boxes = tfjs_esm_exports.squeeze(t2.boxesRaw); - const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return boxes; -} - -// src/util/box.ts -function calc(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function square(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; - const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); - const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function scale(box, scaleFact) { - const dist = [box[2] * scaleFact, box[3] * scaleFact]; - const newBox = [ - box[0] - (dist[0] - box[2]) / 2, - box[1] - (dist[1] - box[3]) / 2, - dist[0], - dist[1] - ]; - return newBox; -} - -// src/body/blazepose.ts -var model2; -var inputSize2 = 256; -var skipped = Number.MAX_SAFE_INTEGER; -var outputNodes = { - landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], - detector: [] -}; -var cache = []; -var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; -var lastTime = 0; -var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); -var loadDetect = (config3) => loadDetector(config3); -async function loadPose(config3) { - if (env.initial) model2 = null; - if (!model2) { - model2 = await loadModel(config3.body.modelPath); - const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; - inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug) log("cached model:", model2["modelUrl"]); - return model2; -} -function prepareImage(input, size2, cropBox) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) return input; - let final; - if (cropBox) { - t2.cropped = tfjs_esm_exports.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); - } - if (input.shape[1] !== input.shape[2]) { - const height = [ - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 - ]; - const width = [ - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 - ]; - padding = [ - [0, 0], - // dont touch batch - height, - // height before&after - width, - // width before&after - [0, 0] - // dont touch rbg - ]; - t2.pad = tfjs_esm_exports.pad(t2.cropped || input, padding); - t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.pad, [size2, size2]); - final = tfjs_esm_exports.div(t2.resize, constants.tf255); - } else if (input.shape[1] !== size2) { - t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.cropped || input, [size2, size2]); - final = tfjs_esm_exports.div(t2.resize, constants.tf255); - } else { - final = tfjs_esm_exports.div(t2.cropped || input, constants.tf255); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return final; -} -function rescaleKeypoints(keypoints, outputSize2, cropBox) { - for (const kpt4 of keypoints) { - kpt4.position = [ - Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), - Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), - kpt4.position[2] - ]; - kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; - } - if (cropBox) { - const width = cropBox[2] - cropBox[0]; - const height = cropBox[3] - cropBox[1]; - for (const kpt4 of keypoints) { - kpt4.positionRaw = [ - kpt4.positionRaw[0] / height + cropBox[1], - // correct offset due to crop - kpt4.positionRaw[1] / width + cropBox[0], - // correct offset due to crop - kpt4.positionRaw[2] - ]; - kpt4.position = [ - Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), - Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), - kpt4.positionRaw[2] - ]; - } - } - return keypoints; -} -function fixKeypoints(keypoints) { - const leftPalm = keypoints.find((k) => k.part === "leftPalm"); - const leftWrist = keypoints.find((k) => k.part === "leftWrist"); - const leftIndex = keypoints.find((k) => k.part === "leftIndex"); - leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; - const rightPalm = keypoints.find((k) => k.part === "rightPalm"); - const rightWrist = keypoints.find((k) => k.part === "rightWrist"); - const rightIndex = keypoints.find((k) => k.part === "rightIndex"); - rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; -} -async function detectLandmarks(input, config3, outputSize2) { - if (!(model2 == null ? void 0 : model2["executor"])) return null; - const t2 = {}; - [ - t2.ld, - t2.segmentation, - t2.heatmap, - t2.world, - t2.poseflag - /* 1,1 */ - ] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); - const poseScore = (await t2.poseflag.data())[0]; - const points = await t2.ld.data(); - const distances = await t2.world.data(); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - const keypointsRelative = []; - const depth = 5; - for (let i = 0; i < points.length / depth; i++) { - const score = sigmoid2(points[depth * i + 3]); - const presence = sigmoid2(points[depth * i + 4]); - const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; - const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; - const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; - const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; - keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); - } - if (poseScore < (config3.body.minConfidence || 0)) return null; - fixKeypoints(keypointsRelative); - const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); - const kpts = keypoints.map((k) => k.position); - const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; - return body4; -} -async function predict(input, config3) { - var _a, _b, _c; - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime; - const skipFrame = skipped < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { - skipped++; - } else { - let boxes = []; - if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { - const preparedImage = prepareImage(input, 224); - boxes = await detectBoxes(preparedImage, config3, outputSize2); - tfjs_esm_exports.dispose(preparedImage); - } else { - boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; - } - for (let i = 0; i < boxes.length; i++) { - const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); - cache.length = 0; - const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); - tfjs_esm_exports.dispose(preparedBox); - if (!bodyResult) continue; - bodyResult.id = i; - cache.push(bodyResult); - } - lastTime = now(); - skipped = 0; - } - return cache; -} - -// src/object/labels.ts -var labels2 = [ - { class: 1, label: "person" }, - { class: 2, label: "bicycle" }, - { class: 3, label: "car" }, - { class: 4, label: "motorcycle" }, - { class: 5, label: "airplane" }, - { class: 6, label: "bus" }, - { class: 7, label: "train" }, - { class: 8, label: "truck" }, - { class: 9, label: "boat" }, - { class: 10, label: "traffic light" }, - { class: 11, label: "fire hydrant" }, - { class: 12, label: "stop sign" }, - { class: 13, label: "parking meter" }, - { class: 14, label: "bench" }, - { class: 15, label: "bird" }, - { class: 16, label: "cat" }, - { class: 17, label: "dog" }, - { class: 18, label: "horse" }, - { class: 19, label: "sheep" }, - { class: 20, label: "cow" }, - { class: 21, label: "elephant" }, - { class: 22, label: "bear" }, - { class: 23, label: "zebra" }, - { class: 24, label: "giraffe" }, - { class: 25, label: "backpack" }, - { class: 26, label: "umbrella" }, - { class: 27, label: "handbag" }, - { class: 28, label: "tie" }, - { class: 29, label: "suitcase" }, - { class: 30, label: "frisbee" }, - { class: 31, label: "skis" }, - { class: 32, label: "snowboard" }, - { class: 33, label: "sports ball" }, - { class: 34, label: "kite" }, - { class: 35, label: "baseball bat" }, - { class: 36, label: "baseball glove" }, - { class: 37, label: "skateboard" }, - { class: 38, label: "surfboard" }, - { class: 39, label: "tennis racket" }, - { class: 40, label: "bottle" }, - { class: 41, label: "wine glass" }, - { class: 42, label: "cup" }, - { class: 43, label: "fork" }, - { class: 44, label: "knife" }, - { class: 45, label: "spoon" }, - { class: 46, label: "bowl" }, - { class: 47, label: "banana" }, - { class: 48, label: "apple" }, - { class: 49, label: "sandwich" }, - { class: 50, label: "orange" }, - { class: 51, label: "broccoli" }, - { class: 52, label: "carrot" }, - { class: 53, label: "hot dog" }, - { class: 54, label: "pizza" }, - { class: 55, label: "donut" }, - { class: 56, label: "cake" }, - { class: 57, label: "chair" }, - { class: 58, label: "couch" }, - { class: 59, label: "potted plant" }, - { class: 60, label: "bed" }, - { class: 61, label: "dining table" }, - { class: 62, label: "toilet" }, - { class: 63, label: "tv" }, - { class: 64, label: "laptop" }, - { class: 65, label: "mouse" }, - { class: 66, label: "remote" }, - { class: 67, label: "keyboard" }, - { class: 68, label: "cell phone" }, - { class: 69, label: "microwave" }, - { class: 70, label: "oven" }, - { class: 71, label: "toaster" }, - { class: 72, label: "sink" }, - { class: 73, label: "refrigerator" }, - { class: 74, label: "book" }, - { class: 75, label: "clock" }, - { class: 76, label: "vase" }, - { class: 77, label: "scissors" }, - { class: 78, label: "teddy bear" }, - { class: 79, label: "hair drier" }, - { class: 80, label: "toothbrush" } -]; - -// src/object/centernet.ts -var model3; -var inputSize3 = 0; -var last2 = []; -var lastTime2 = 0; -var skipped2 = Number.MAX_SAFE_INTEGER; -async function load(config3) { - if (env.initial) model3 = null; - if (!model3) { - model3 = await loadModel(config3.object.modelPath); - const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; - inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", model3["modelUrl"]); - return model3; -} -async function process3(res, outputShape, config3) { - if (!res) return []; - const t2 = {}; - const results = []; - const detections = await res.array(); - t2.squeeze = tfjs_esm_exports.squeeze(res); - const arr = tfjs_esm_exports.split(t2.squeeze, 6, 1); - t2.stack = tfjs_esm_exports.stack([arr[1], arr[0], arr[3], arr[2]], 1); - t2.boxes = tfjs_esm_exports.squeeze(t2.stack); - t2.scores = tfjs_esm_exports.squeeze(arr[4]); - t2.classes = tfjs_esm_exports.squeeze(arr[5]); - tfjs_esm_exports.dispose([res, ...arr]); - t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); - const nms = await t2.nms.data(); - let i = 0; - for (const id of Array.from(nms)) { - const score = Math.trunc(100 * detections[0][id][4]) / 100; - const classVal = detections[0][id][5]; - if (Number.isNaN(classVal)) continue; - const label = labels2[classVal].label; - const [x, y] = [ - detections[0][id][0] / inputSize3, - detections[0][id][1] / inputSize3 - ]; - const boxRaw = [ - x, - y, - detections[0][id][2] / inputSize3 - x, - detections[0][id][3] / inputSize3 - y - ]; - const box = [ - Math.trunc(boxRaw[0] * outputShape[0]), - Math.trunc(boxRaw[1] * outputShape[1]), - Math.trunc(boxRaw[2] * outputShape[0]), - Math.trunc(boxRaw[3] * outputShape[1]) - ]; - results.push({ id: i++, score, class: classVal, label, box, boxRaw }); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return results; -} -async function predict2(input, config3) { - if (!(model3 == null ? void 0 : model3["executor"])) return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; - const skipFrame = skipped2 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { - skipped2++; - return last2; - } - skipped2 = 0; - return new Promise(async (resolve) => { - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const resize = tfjs_esm_exports.image.resizeBilinear(input, [inputSize3, inputSize3]); - const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; - lastTime2 = now(); - tfjs_esm_exports.dispose(resize); - const obj = await process3(objectT, outputSize2, config3); - last2 = obj; - resolve(obj); - }); -} - -// src/body/efficientposecoords.ts -var efficientposecoords_exports = {}; -__export(efficientposecoords_exports, { - connected: () => connected2, - kpt: () => kpt2 -}); -var kpt2 = [ - "head", - "neck", - "rightShoulder", - "rightElbow", - "rightWrist", - "chest", - "leftShoulder", - "leftElbow", - "leftWrist", - "bodyCenter", - "rightHip", - "rightKnee", - "rightAnkle", - "leftHip", - "leftKnee", - "leftAnkle" -]; -var connected2 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/body/efficientpose.ts -var model4; -var lastTime3 = 0; -var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; -var skipped3 = Number.MAX_SAFE_INTEGER; -async function load2(config3) { - if (env.initial) model4 = null; - if (!model4) model4 = await loadModel(config3.body.modelPath); - else if (config3.debug) log("cached model:", model4["modelUrl"]); - return model4; -} -async function max2d(inputs, minScore) { - const [width, height] = inputs.shape; - const reshaped = tfjs_esm_exports.reshape(inputs, [height * width]); - const max5 = tfjs_esm_exports.max(reshaped, 0); - const newScore = (await max5.data())[0]; - if (newScore > minScore) { - const coordinates = tfjs_esm_exports.argMax(reshaped, 0); - const mod3 = tfjs_esm_exports.mod(coordinates, width); - const x = (await mod3.data())[0]; - const div15 = tfjs_esm_exports.div(coordinates, width); - const y = (await div15.data())[0]; - tfjs_esm_exports.dispose([reshaped, max5, coordinates, mod3, div15]); - return [x, y, newScore]; - } - tfjs_esm_exports.dispose([reshaped, max5]); - return [0, 0, newScore]; -} -async function predict3(image28, config3) { - if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) return []; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; - const skipFrame = skipped3 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { - skipped3++; - return [cache2]; - } - skipped3 = 0; - return new Promise(async (resolve) => { - const tensor6 = tfjs_esm_exports.tidy(() => { - var _a, _b; - const resize = tfjs_esm_exports.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); - const enhance2 = tfjs_esm_exports.mul(resize, constants.tf2); - const norm = tfjs_esm_exports.sub(enhance2, constants.tf1); - return norm; - }); - let resT; - if (config3.body.enabled) resT = model4 == null ? void 0 : model4.execute(tensor6); - lastTime3 = now(); - tfjs_esm_exports.dispose(tensor6); - if (resT) { - cache2.keypoints.length = 0; - const squeeze14 = tfjs_esm_exports.squeeze(resT); - tfjs_esm_exports.dispose(resT); - const stack5 = tfjs_esm_exports.unstack(squeeze14, 2); - tfjs_esm_exports.dispose(squeeze14); - for (let id = 0; id < stack5.length; id++) { - const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); - if (partScore > (config3.body.minConfidence || 0)) { - cache2.keypoints.push({ - score: Math.round(100 * partScore) / 100, - part: kpt2[id], - positionRaw: [ - // normalized to 0..1 - // @ts-ignore model is not undefined here - x2 / model4.inputs[0].shape[2], - y2 / model4.inputs[0].shape[1] - ], - position: [ - // normalized to input image size - // @ts-ignore model is not undefined here - Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), - Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) - ] - }); - } - } - stack5.forEach((s) => tfjs_esm_exports.dispose(s)); - } - cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const x = cache2.keypoints.map((a) => a.position[0]); - const y = cache2.keypoints.map((a) => a.position[1]); - cache2.box = [ - Math.min(...x), - Math.min(...y), - Math.max(...x) - Math.min(...x), - Math.max(...y) - Math.min(...y) - ]; - const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); - const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); - cache2.boxRaw = [ - Math.min(...xRaw), - Math.min(...yRaw), - Math.max(...xRaw) - Math.min(...xRaw), - Math.max(...yRaw) - Math.min(...yRaw) - ]; - for (const [name, indexes] of Object.entries(connected2)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - cache2.annotations[name] = pt; - } - resolve([cache2]); - }); -} - -// src/face/facemeshutil.ts -var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; -var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; -var clampBox = (box, input) => box ? [ - Math.trunc(Math.max(0, box.startPoint[0])), - Math.trunc(Math.max(0, box.startPoint[1])), - Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), - Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) -] : [0, 0, 0, 0]; -var getRawBox = (box, input) => box ? [ - box.startPoint[0] / (input.shape[2] || 0), - box.startPoint[1] / (input.shape[1] || 0), - (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), - (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) -] : [0, 0, 0, 0]; -var scaleBoxCoordinates = (box, factor, anchor) => { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]); - return { startPoint, endPoint, landmarks, confidence: box.confidence }; -}; -var cutAndResize = (box, image28, cropSize) => { - const h = image28.shape[1]; - const w = image28.shape[2]; - const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; - const crop = tfjs_esm_exports.image.cropAndResize(image28, [cutBox], [0], cropSize); - const norm = tfjs_esm_exports.div(crop, constants.tf255); - tfjs_esm_exports.dispose(crop); - return norm; -}; -var enlargeBox = (box, factor) => { - const center = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - return { - startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], - endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], - landmarks: box.landmarks, - confidence: box.confidence, - size: size2 - }; -}; -var squarifyBox = (box) => { - const centers = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = Math.max(...size2) / 2; - return { - startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], - endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], - landmarks: box.landmarks, - confidence: box.confidence, - size: [Math.round(size2[0]), Math.round(size2[1])] - }; -}; -var calculateLandmarksBoundingBox = (landmarks) => { - const x = landmarks.map((d) => d[0]); - const y = landmarks.map((d) => d[1]); - return { - startPoint: [Math.min(...x), Math.min(...y)], - endPoint: [Math.max(...x), Math.max(...y)], - landmarks - }; -}; -var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; -var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); -var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -var dot = (v1, v2) => { - let product = 0; - for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i]; - return product; -}; -var getColumnFrom2DArr = (arr, columnIndex) => { - const column = []; - for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]); - return column; -}; -var multiplyTransformMatrices = (mat1, mat2) => { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); - } - return product; -}; -var buildRotationMatrix = (rotation, center) => { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); - return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); -}; -var invertTransformMatrix = (matrix) => { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; - return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; -}; -var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; -function generateAnchors(inputSize10) { - const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; - const anchors3 = []; - for (let i = 0; i < spec.strides.length; i++) { - const stride = spec.strides[i]; - const gridRows = Math.floor((inputSize10 + stride - 1) / stride); - const gridCols = Math.floor((inputSize10 + stride - 1) / stride); - const anchorsNum = spec.anchors[i]; - for (let gridY = 0; gridY < gridRows; gridY++) { - const anchorY = stride * (gridY + 0.5); - for (let gridX = 0; gridX < gridCols; gridX++) { - const anchorX = stride * (gridX + 0.5); - for (let n = 0; n < anchorsNum; n++) anchors3.push([anchorX, anchorY]); - } - } - } - return anchors3; -} -function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { - const boxSize = getBoxSize(box); - const coordsScaled = coordsRaw.map((coord) => [ - // scaled around zero-point - boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), - boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), - coord[2] || 0 - ]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; - const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; - const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; - const boxCenter = getBoxCenter(box); - const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + offsets[0]), - Math.trunc(coord[1] + offsets[1]), - Math.trunc(coord[2] || 0) - ]); -} -function correctFaceRotation(rotate, box, input, inputSize10) { - const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; - let angle = 0; - let rotationMatrix = fixedRotationMatrix; - let face4; - if (rotate && env.kernels.includes("rotatewithoffset")) { - angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - if (largeAngle) { - const center = getBoxCenter(box); - const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; - const rotated = tfjs_esm_exports.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); - rotationMatrix = buildRotationMatrix(-angle, center); - face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); - tfjs_esm_exports.dispose(rotated); - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - return [angle, rotationMatrix, face4]; -} -var findFaceCenter = (mesh) => { - const x = mesh.map((m) => m[0]); - const y = mesh.map((m) => m[1]); - return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; -}; -var calculateFaceBox = (mesh, previousBox) => { - const center = findFaceCenter(mesh); - const boxSize = getBoxSize(previousBox); - const calculatedBox = { - startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], - endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] - }; - return calculatedBox; -}; - -// src/face/blazeface.ts -var keypointsCount = 6; -var model5; -var anchors = null; -var inputSize4 = 0; -var inputSizeT = null; -var size = () => inputSize4; -async function load3(config3) { - var _a; - if (env.initial) model5 = null; - if (!model5) model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model5["modelUrl"]); - inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; - inputSizeT = tfjs_esm_exports.scalar(inputSize4, "int32"); - anchors = tfjs_esm_exports.tensor2d(generateAnchors(inputSize4)); - return model5; -} -function decodeBoxes2(boxOutputs) { - if (!anchors || !inputSizeT) return tfjs_esm_exports.zeros([0, 0]); - const t2 = {}; - t2.boxStarts = tfjs_esm_exports.slice(boxOutputs, [0, 1], [-1, 2]); - t2.centers = tfjs_esm_exports.add(t2.boxStarts, anchors); - t2.boxSizes = tfjs_esm_exports.slice(boxOutputs, [0, 3], [-1, 2]); - t2.boxSizesNormalized = tfjs_esm_exports.div(t2.boxSizes, inputSizeT); - t2.centersNormalized = tfjs_esm_exports.div(t2.centers, inputSizeT); - t2.halfBoxSize = tfjs_esm_exports.div(t2.boxSizesNormalized, constants.tf2); - t2.starts = tfjs_esm_exports.sub(t2.centersNormalized, t2.halfBoxSize); - t2.ends = tfjs_esm_exports.add(t2.centersNormalized, t2.halfBoxSize); - t2.startNormalized = tfjs_esm_exports.mul(t2.starts, inputSizeT); - t2.endNormalized = tfjs_esm_exports.mul(t2.ends, inputSizeT); - const boxes = tfjs_esm_exports.concat2d([t2.startNormalized, t2.endNormalized], 1); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return boxes; -} -async function getBoxes(inputImage, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) return []; - const t2 = {}; - let pad4 = [0, 0]; - let scale2 = [1, 1]; - if ((_b = (_a = config3 == null ? void 0 : config3.face) == null ? void 0 : _a.detector) == null ? void 0 : _b.square) { - const xy = Math.max(inputImage.shape[2], inputImage.shape[1]); - pad4 = [Math.floor((xy - inputImage.shape[2]) / 2), Math.floor((xy - inputImage.shape[1]) / 2)]; - t2.padded = tfjs_esm_exports.pad(inputImage, [[0, 0], [pad4[1], pad4[1]], [pad4[0], pad4[0]], [0, 0]]); - scale2 = [inputImage.shape[2] / xy, inputImage.shape[1] / xy]; - pad4 = [pad4[0] / inputSize4, pad4[1] / inputSize4]; - } else { - t2.padded = inputImage.clone(); - } - t2.resized = tfjs_esm_exports.image.resizeBilinear(t2.padded, [inputSize4, inputSize4]); - t2.div = tfjs_esm_exports.div(t2.resized, constants.tf127); - t2.normalized = tfjs_esm_exports.sub(t2.div, constants.tf1); - const res = model5 == null ? void 0 : model5.execute(t2.normalized); - if (Array.isArray(res) && res.length > 2) { - const sorted = res.sort((a, b) => a.size - b.size); - t2.concat384 = tfjs_esm_exports.concat([sorted[0], sorted[2]], 2); - t2.concat512 = tfjs_esm_exports.concat([sorted[1], sorted[3]], 2); - t2.concat = tfjs_esm_exports.concat([t2.concat512, t2.concat384], 1); - t2.batch = tfjs_esm_exports.squeeze(t2.concat, [0]); - } else if (Array.isArray(res)) { - t2.batch = tfjs_esm_exports.squeeze(res[0]); - } else { - t2.batch = tfjs_esm_exports.squeeze(res); - } - tfjs_esm_exports.dispose(res); - t2.boxes = decodeBoxes2(t2.batch); - t2.logits = tfjs_esm_exports.slice(t2.batch, [0, 0], [-1, 1]); - t2.sigmoid = tfjs_esm_exports.sigmoid(t2.logits); - t2.scores = tfjs_esm_exports.squeeze(t2.sigmoid); - t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_c = config3.face.detector) == null ? void 0 : _c.maxDetected) || 0, ((_d = config3.face.detector) == null ? void 0 : _d.iouThreshold) || 0, ((_e = config3.face.detector) == null ? void 0 : _e.minConfidence) || 0); - const nms = await t2.nms.array(); - const boxes = []; - const scores = await t2.scores.data(); - for (let i = 0; i < nms.length; i++) { - const confidence = scores[nms[i]]; - if (confidence > (((_f = config3.face.detector) == null ? void 0 : _f.minConfidence) || 0)) { - const b = {}; - b.bbox = tfjs_esm_exports.slice(t2.boxes, [nms[i], 0], [1, -1]); - b.slice = tfjs_esm_exports.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); - b.squeeze = tfjs_esm_exports.squeeze(b.slice); - b.landmarks = tfjs_esm_exports.reshape(b.squeeze, [keypointsCount, -1]); - const points = await b.bbox.data(); - const unpadded = [ - // TODO fix this math - points[0] * scale2[0] - pad4[0], - points[1] * scale2[1] - pad4[1], - points[2] * scale2[0] - pad4[0], - points[3] * scale2[1] - pad4[1] - ]; - const rawBox = { - startPoint: [unpadded[0], unpadded[1]], - endPoint: [unpadded[2], unpadded[3]], - landmarks: await b.landmarks.array(), - confidence - }; - b.anchor = tfjs_esm_exports.slice(anchors, [nms[i], 0], [1, 2]); - const anchor = await b.anchor.data(); - const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4], anchor); - const enlargedBox = enlargeBox(scaledBox, ((_g = config3.face.detector) == null ? void 0 : _g.scale) || 1.4); - const squaredBox = squarifyBox(enlargedBox); - if (squaredBox.size[0] > (((_h = config3.face.detector) == null ? void 0 : _h["minSize"]) || 0) && squaredBox.size[1] > (((_i = config3.face.detector) == null ? void 0 : _i["minSize"]) || 0)) boxes.push(squaredBox); - Object.keys(b).forEach((tensor6) => tfjs_esm_exports.dispose(b[tensor6])); - } - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return boxes; -} - -// src/face/iris.ts -var model6; -var inputSize5 = 0; -var leftOutline = meshAnnotations.leftEyeLower0; -var rightOutline = meshAnnotations.rightEyeLower0; -var eyeLandmarks = { - leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], - rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] -}; -var irisLandmarks = { - upperCenter: 3, - lowerCenter: 4, - index: 71, - numCoordinates: 76 -}; -async function load4(config3) { - var _a, _b; - if (env.initial) model6 = null; - if (!model6) model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model6["modelUrl"]); - inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; - if (inputSize5 === -1) inputSize5 = 64; - return model6; -} -function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { - for (let i = 0; i < irisIndices.length; i++) { - const { key, indices } = irisIndices[i]; - const originalIndices = meshAnnotations[`${prefix}${key}`]; - if (!keys || keys.includes(key)) { - for (let j = 0; j < indices.length; j++) { - const index2 = indices[j]; - rawCoords[originalIndices[j]] = [ - newCoords[index2][0], - newCoords[index2][1], - (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 - ]; - } - } - } -} -var getLeftToRightEyeDepthDifference = (rawCoords) => { - const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; - const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; - return leftEyeZ - rightEyeZ; -}; -var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false, scale2 = 2.3) => { - const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), scale2)); - const boxSize = getBoxSize(box); - let crop = tfjs_esm_exports.image.cropAndResize(face4, [[ - box.startPoint[1] / meshSize, - box.startPoint[0] / meshSize, - box.endPoint[1] / meshSize, - box.endPoint[0] / meshSize - ]], [0], [inputSize5, inputSize5]); - if (flip && env.kernels.includes("flipleftright")) { - const flipped = tfjs_esm_exports.image.flipLeftRight(crop); - tfjs_esm_exports.dispose(crop); - crop = flipped; - } - return { box, boxSize, crop }; -}; -var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { - const eyeRawCoords = []; - for (let i = 0; i < irisLandmarks.numCoordinates; i++) { - const x = eyeData[i * 3]; - const y = eyeData[i * 3 + 1]; - const z = eyeData[i * 3 + 2]; - eyeRawCoords.push([ - (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], - y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], - z - ]); - } - return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; -}; -var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { - const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; - const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; - const averageZ = (upperCenterZ + lowerCenterZ) / 2; - return irisCoords.map((coord, i) => { - let z = averageZ; - if (i === 2) { - z = upperCenterZ; - } else if (i === 4) { - z = lowerCenterZ; - } - return [coord[0], coord[1], z]; - }); -}; -async function augmentIris(rawCoords, face4, meshSize, config3) { - var _a, _b; - if (!(model6 == null ? void 0 : model6["executor"])) return rawCoords; - const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true, ((_a = config3.face.iris) == null ? void 0 : _a.scale) || 2.3); - const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true, ((_b = config3.face.iris) == null ? void 0 : _b.scale) || 2.3); - const combined = tfjs_esm_exports.concat([leftEyeCrop, rightEyeCrop]); - tfjs_esm_exports.dispose(leftEyeCrop); - tfjs_esm_exports.dispose(rightEyeCrop); - const eyePredictions = model6.execute(combined); - tfjs_esm_exports.dispose(combined); - const eyePredictionsData = await eyePredictions.data(); - tfjs_esm_exports.dispose(eyePredictions); - const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); - const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); - const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); - const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); - const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); - if (Math.abs(leftToRightEyeDepthDifference) < 30) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); - } else if (leftToRightEyeDepthDifference < 1) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); - } else { - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); - } - const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); - const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); - const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); - return newCoords; -} - -// src/face/attention.ts -async function augment(rawCoords, results) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const t2 = { - // all attention models produce 2d results so it needs to be later augmented with correct z-coords - // mesh: results[0], // already have it in rawCoords // output_mesh_identity - // flag: results[1], // already processed in parent // conv_faceflag - lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), - // 80 x 2d = 160 // output_lips - irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), - // 5 x 2d = 10 // output_right_iris - eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), - // 71 x 2d = 142 // output_right_eye - irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), - // 5 x 2d = 10 // output_left_iris - eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) - // 71 x 2d = 142// output_left_eye - }; - for (const val of Object.values(t2)) { - if (!val) return rawCoords; - } - const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisL.length / 2; i++) rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); - const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisR.length / 2; i++) rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); - for (let i = 0; i < t2.eyeL.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.eyeR.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.lips.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; - return rawCoords; -} - -// src/face/facemesh.ts -var cache3 = { - boxes: [], - skipped: Number.MAX_SAFE_INTEGER, - timestamp: 0 -}; -var model7 = null; -var inputSize6 = 0; -async function predict4(input, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; - const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); - if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { - cache3.boxes = await getBoxes(input, config3); - cache3.timestamp = now(); - cache3.skipped = 0; - } else { - cache3.skipped++; - } - const faces = []; - const newCache = []; - let id = 0; - const size2 = inputSize6; - for (let i = 0; i < cache3.boxes.length; i++) { - const box = cache3.boxes[i]; - let angle = 0; - let rotationMatrix; - const face4 = { - // init face result - id: id++, - mesh: [], - meshRaw: [], - box: [0, 0, 0, 0], - boxRaw: [0, 0, 0, 0], - score: 0, - boxScore: 0, - faceScore: 0, - size: [0, 0], - // contoursRaw: [], - // contours: [], - annotations: {} - }; - [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); - if (config3.filter.equalization) { - const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; - tfjs_esm_exports.dispose(face4.tensor); - if (equilized) face4.tensor = equilized; - } - face4.boxScore = Math.round(100 * box.confidence) / 100; - if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled) || !(model7 == null ? void 0 : model7["executor"])) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.size = box.size; - face4.mesh = box.landmarks; - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } else if (!model7) { - if (config3.debug) log("face mesh detection requested, but model is not loaded"); - } else { - if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { - config3.face.attention.enabled = false; - tfjs_esm_exports.dispose(face4.tensor); - return faces; - } - const results = model7.execute(face4.tensor); - const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); - const faceConfidence = await confidenceT.data(); - face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; - if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { - box.confidence = face4.faceScore; - if (config3.face.mesh["keepInvalid"]) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.size = box.size; - face4.score = face4.boxScore; - face4.mesh = box.landmarks; - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } - } else { - const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); - const coordsReshaped = tfjs_esm_exports.reshape(meshT, [-1, 3]); - let rawCoords = await coordsReshaped.array(); - tfjs_esm_exports.dispose(coordsReshaped); - if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { - rawCoords = await augment(rawCoords, results); - } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { - rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6, config3); - } - face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(meshAnnotations)) face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); - face4.score = face4.faceScore; - const calculatedBox = { - ...calculateFaceBox(face4.mesh, box), - confidence: box.confidence, - landmarks: box.landmarks, - size: box.size - }; - face4.box = clampBox(calculatedBox, input); - face4.boxRaw = getRawBox(calculatedBox, input); - face4.size = calculatedBox.size; - newCache.push(calculatedBox); - } - tfjs_esm_exports.dispose(results); - } - if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) faces.push(face4); - else tfjs_esm_exports.dispose(face4.tensor); - } - cache3.boxes = newCache; - return faces; -} -async function load5(config3) { - var _a, _b, _c, _d, _e, _f; - if (env.initial) model7 = null; - if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { - if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) model7 = null; - } - if (!model7) { - if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) model7 = await loadModel(config3.face.attention.modelPath); - else model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); - } else if (config3.debug) { - log("cached model:", model7["modelUrl"]); - } - inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; - return model7; -} -var triangulation = TRI468; -var uvmap = UV468; - -// src/gear/emotion.ts -var annotations = []; -var model8; -var last3 = []; -var lastCount = 0; -var lastTime4 = 0; -var skipped4 = Number.MAX_SAFE_INTEGER; -var rgb = false; -async function load6(config3) { - var _a, _b, _c; - if (env.initial) model8 = null; - if (!model8) { - model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); - rgb = ((_c = (_b = model8 == null ? void 0 : model8.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[3]) === 3; - if (!rgb) annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; - else annotations = ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]; - } else if (config3.debug) { - log("cached model:", model8["modelUrl"]); - } - return model8; -} -async function predict5(image28, config3, idx, count2) { - var _a, _b; - if (!model8) return []; - const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; - if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { - skipped4++; - return last3[idx]; - } - skipped4 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c; - const obj = []; - if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { - const t2 = {}; - const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; - if (((_b2 = config3.face.emotion) == null ? void 0 : _b2["crop"]) > 0) { - const crop = (_c = config3.face.emotion) == null ? void 0 : _c["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tfjs_esm_exports.image.cropAndResize(image28, box, [0], [inputSize10, inputSize10]); - } else { - t2.resize = tfjs_esm_exports.image.resizeBilinear(image28, [inputSize10, inputSize10], false); - } - if (rgb) { - t2.mul = tfjs_esm_exports.mul(t2.resize, 255); - t2.normalize = tfjs_esm_exports.sub(t2.mul, [103.939, 116.779, 123.68]); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.normalize); - } else { - t2.channels = tfjs_esm_exports.mul(t2.resize, constants.rgb); - t2.grayscale = tfjs_esm_exports.sum(t2.channels, 3, true); - t2.grayscaleSub = tfjs_esm_exports.sub(t2.grayscale, constants.tf05); - t2.grayscaleMul = tfjs_esm_exports.mul(t2.grayscaleSub, constants.tf2); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); - } - lastTime4 = now(); - const data = await t2.emotion.data(); - for (let i = 0; i < data.length; i++) { - if (data[i] > (config3.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); - } - obj.sort((a, b) => b.score - a.score); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - } - last3[idx] = obj; - lastCount = count2; - resolve(obj); - }); -} - -// src/face/faceres.ts -var model9; -var last4 = []; -var lastTime5 = 0; -var lastCount2 = 0; -var skipped5 = Number.MAX_SAFE_INTEGER; -async function load7(config3) { - var _a; - if (env.initial) model9 = null; - if (!model9) model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model9["modelUrl"]); - return model9; -} -function enhance(input, config3) { - var _a, _b; - const tensor6 = input.image || input.tensor || input; - if (!(model9 == null ? void 0 : model9.inputs[0].shape)) return tensor6; - let crop; - if (((_a = config3.face.description) == null ? void 0 : _a["crop"]) > 0) { - const cropval = (_b = config3.face.description) == null ? void 0 : _b["crop"]; - const box = [[cropval, cropval, 1 - cropval, 1 - cropval]]; - crop = tfjs_esm_exports.image.cropAndResize(tensor6, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]); - } else { - crop = tfjs_esm_exports.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); - } - const norm = tfjs_esm_exports.mul(crop, constants.tf255); - tfjs_esm_exports.dispose(crop); - return norm; -} -async function predict6(image28, config3, idx, count2) { - var _a, _b, _c, _d; - const obj = { - age: 0, - gender: "unknown", - genderScore: 0, - descriptor: [] - }; - if (!(model9 == null ? void 0 : model9["executor"])) return obj; - const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; - if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped5++; - return last4[idx]; - } - skipped5 = 0; - return new Promise(async (resolve) => { - var _a2; - if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { - const enhanced = enhance(image28, config3); - const resT = model9 == null ? void 0 : model9.execute(enhanced); - lastTime5 = now(); - tfjs_esm_exports.dispose(enhanced); - const genderT = resT.find((t2) => t2.shape[1] === 1); - const gender2 = await genderT.data(); - const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; - if (confidence > (config3.face.description.minConfidence || 0)) { - obj.gender = gender2[0] <= 0.5 ? "female" : "male"; - obj.genderScore = Math.min(0.99, confidence); - } - const argmax = tfjs_esm_exports.argMax(resT.find((t2) => t2.shape[1] === 100), 1); - const ageIdx = (await argmax.data())[0]; - tfjs_esm_exports.dispose(argmax); - const ageT = resT.find((t2) => t2.shape[1] === 100); - const all2 = await ageT.data(); - obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; - if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) log("faceres error:", { model: model9, result: resT }); - const desc = resT.find((t2) => t2.shape[1] === 1024); - const descriptor = desc ? await desc.data() : []; - obj.descriptor = Array.from(descriptor); - resT.forEach((t2) => tfjs_esm_exports.dispose(t2)); - } - last4[idx] = obj; - lastCount2 = count2; - resolve(obj); - }); -} - -// src/face/mask.ts -var expandFact = 0.1; -var alpha = 0.5; -function insidePoly(x, y, polygon) { - let inside = false; - let j = polygon.length - 1; - for (let i = 0; i < polygon.length; j = i++) { - if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) inside = !inside; - } - return inside; -} -async function mask(face4) { - if (!face4.tensor) return face4.tensor; - if (!face4.mesh || face4.mesh.length < 100) return face4.tensor; - const width = face4.tensor.shape[2] || 0; - const height = face4.tensor.shape[1] || 0; - const buffer = await face4.tensor.buffer(); - let silhouette = []; - for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); - if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); - for (let x = 0; x < width; x++) { - for (let y = 0; y < height; y++) { - const inside = insidePoly(x / width, y / width, silhouette); - if (!inside) { - buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); - buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); - buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); - } - } - } - const output = buffer.toTensor(); - return output; -} - -// src/face/antispoof.ts -var model10; -var cached = []; -var skipped6 = Number.MAX_SAFE_INTEGER; -var lastCount3 = 0; -var lastTime6 = 0; -async function load8(config3) { - var _a; - if (env.initial) model10 = null; - if (!model10) model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model10["modelUrl"]); - return model10; -} -async function predict7(image28, config3, idx, count2) { - var _a, _b; - if (!(model10 == null ? void 0 : model10["executor"])) return 0; - const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; - const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { - skipped6++; - return cached[idx]; - } - skipped6 = 0; - return new Promise(async (resolve) => { - const resize = tfjs_esm_exports.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); - const res = model10 == null ? void 0 : model10.execute(resize); - const num = (await res.data())[0]; - cached[idx] = Math.round(100 * num) / 100; - lastCount3 = count2; - lastTime6 = now(); - tfjs_esm_exports.dispose([resize, res]); - resolve(cached[idx]); - }); -} - -// src/face/liveness.ts -var model11; -var cached2 = []; -var skipped7 = Number.MAX_SAFE_INTEGER; -var lastCount4 = 0; -var lastTime7 = 0; -async function load9(config3) { - var _a; - if (env.initial) model11 = null; - if (!model11) model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model11["modelUrl"]); - return model11; -} -async function predict8(image28, config3, idx, count2) { - var _a, _b; - if (!(model11 == null ? void 0 : model11["executor"])) return 0; - const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; - const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { - skipped7++; - return cached2[idx]; - } - skipped7 = 0; - return new Promise(async (resolve) => { - const resize = tfjs_esm_exports.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); - const res = model11 == null ? void 0 : model11.execute(resize); - const num = (await res.data())[0]; - cached2[idx] = Math.round(100 * num) / 100; - lastCount4 = count2; - lastTime7 = now(); - tfjs_esm_exports.dispose([resize, res]); - resolve(cached2[idx]); - }); -} - -// src/gear/gear.ts -var model12; -var last5 = []; -var raceNames = ["white", "black", "asian", "indian", "other"]; -var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; -var lastCount5 = 0; -var lastTime8 = 0; -var skipped8 = Number.MAX_SAFE_INTEGER; -async function load10(config3) { - var _a; - if (env.initial) model12 = null; - if (!model12) model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model12["modelUrl"]); - return model12; -} -async function predict9(image28, config3, idx, count2) { - var _a, _b; - if (!model12) return { age: 0, gender: "unknown", genderScore: 0, race: [] }; - const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; - if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { - skipped8++; - return last5[idx]; - } - skipped8 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c, _d; - if (!(model12 == null ? void 0 : model12.inputs[0].shape)) return; - const t2 = {}; - let box = [[0, 0.1, 0.9, 0.9]]; - if (((_a2 = config3.face.gear) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face.gear) == null ? void 0 : _b2["crop"]; - box = [[crop, crop, 1 - crop, 1 - crop]]; - } - t2.resize = tfjs_esm_exports.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); - const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; - if ((_c = config3.face.gear) == null ? void 0 : _c.enabled) [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); - const gender2 = await t2.gender.data(); - obj.gender = gender2[0] > gender2[1] ? "male" : "female"; - obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; - const race = await t2.race.data(); - for (let i = 0; i < race.length; i++) { - if (race[i] > (((_d = config3.face.gear) == null ? void 0 : _d.minConfidence) || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); - } - obj.race.sort((a, b) => b.score - a.score); - const ageDistribution = Array.from(await t2.age.data()); - const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); - let age2 = ageSorted[0][0]; - for (let i = 1; i < ageSorted.length; i++) age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); - obj.age = Math.round(10 * age2) / 10; - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - last5[idx] = obj; - lastCount5 = count2; - lastTime8 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-age.ts -var model13; -var last6 = []; -var lastCount6 = 0; -var lastTime9 = 0; -var skipped9 = Number.MAX_SAFE_INTEGER; -async function load11(config3) { - if (env.initial) model13 = null; - if (!model13) model13 = await loadModel(config3.face["ssrnet"].modelPathAge); - else if (config3.debug) log("cached model:", model13["modelUrl"]); - return model13; -} -async function predict10(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model13) return { age: 0 }; - const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; - if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { - skipped9++; - return last6[idx]; - } - skipped9 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c2; - if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) return; - const t2 = {}; - if (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tfjs_esm_exports.image.cropAndResize(image28, box, [0], [model13.inputs[0].shape[2], model13.inputs[0].shape[1]]); - } else { - t2.resize = tfjs_esm_exports.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); - } - t2.enhance = tfjs_esm_exports.mul(t2.resize, constants.tf255); - const obj = { age: 0 }; - if ((_c2 = config3.face["ssrnet"]) == null ? void 0 : _c2.enabled) t2.age = model13.execute(t2.enhance); - if (t2.age) { - const data = await t2.age.data(); - obj.age = Math.trunc(10 * data[0]) / 10; - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - last6[idx] = obj; - lastCount6 = count2; - lastTime9 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-gender.ts -var model14; -var last7 = []; -var lastCount7 = 0; -var lastTime10 = 0; -var skipped10 = Number.MAX_SAFE_INTEGER; -var rgb2 = [0.2989, 0.587, 0.114]; -async function load12(config3) { - var _a; - if (env.initial) model14 = null; - if (!model14) model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); - else if (config3.debug) log("cached model:", model14["modelUrl"]); - return model14; -} -async function predict11(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model14) return { gender: "unknown", genderScore: 0 }; - const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; - if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped10++; - return last7[idx]; - } - skipped10 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c2; - if (!(model14 == null ? void 0 : model14.inputs[0].shape)) return; - const t2 = {}; - if (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tfjs_esm_exports.image.cropAndResize(image28, box, [0], [model14.inputs[0].shape[2], model14.inputs[0].shape[1]]); - } else { - t2.resize = tfjs_esm_exports.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); - } - t2.enhance = tfjs_esm_exports.tidy(() => { - var _a3, _b3; - let normalize2; - if (((_b3 = (_a3 = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a3[0].shape) == null ? void 0 : _b3[3]) === 1) { - const [red, green, blue] = tfjs_esm_exports.split(t2.resize, 3, 3); - const redNorm = tfjs_esm_exports.mul(red, rgb2[0]); - const greenNorm = tfjs_esm_exports.mul(green, rgb2[1]); - const blueNorm = tfjs_esm_exports.mul(blue, rgb2[2]); - const grayscale = tfjs_esm_exports.addN([redNorm, greenNorm, blueNorm]); - normalize2 = tfjs_esm_exports.mul(tfjs_esm_exports.sub(grayscale, constants.tf05), 2); - } else { - normalize2 = tfjs_esm_exports.mul(tfjs_esm_exports.sub(t2.resize, constants.tf05), 2); - } - return normalize2; - }); - const obj = { gender: "unknown", genderScore: 0 }; - if ((_c2 = config3.face["ssrnet"]) == null ? void 0 : _c2.enabled) t2.gender = model14.execute(t2.enhance); - const data = await t2.gender.data(); - obj.gender = data[0] > data[1] ? "female" : "male"; - obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - last7[idx] = obj; - lastCount7 = count2; - lastTime10 = now(); - resolve(obj); - }); -} - -// src/face/mobilefacenet.ts -var model15; -var last8 = []; -var lastCount8 = 0; -var lastTime11 = 0; -var skipped11 = Number.MAX_SAFE_INTEGER; -async function load13(config3) { - var _a; - if (env.initial) model15 = null; - if (!model15) model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model15["modelUrl"]); - return model15; -} -async function predict12(input, config3, idx, count2) { - var _a, _b; - if (!(model15 == null ? void 0 : model15["executor"])) return []; - const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; - if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { - skipped11++; - return last8[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { - const t2 = {}; - t2.crop = tfjs_esm_exports.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); - t2.data = model15.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - } - last8[idx] = data; - lastCount8 = count2; - lastTime11 = now(); - resolve(data); - }); -} - -// src/face/insightface.ts -var model16; -var last9 = []; -var lastCount9 = 0; -var lastTime12 = 0; -var skipped12 = Number.MAX_SAFE_INTEGER; -async function load14(config3) { - if (env.initial) model16 = null; - if (!model16) model16 = await loadModel(config3.face["insightface"].modelPath); - else if (config3.debug) log("cached model:", model16["modelUrl"]); - return model16; -} -async function predict13(input, config3, idx, count2) { - var _a, _b; - if (!(model16 == null ? void 0 : model16["executor"])) return []; - const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; - if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { - skipped12++; - return last9[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { - const t2 = {}; - t2.crop = tfjs_esm_exports.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); - t2.data = model16.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - } - last9[idx] = data; - lastCount9 = count2; - lastTime12 = now(); - resolve(data); - }); -} - -// src/face/angles.ts -var calculateGaze = (face4) => { - const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); - if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) return { bearing: 0, strength: 0 }; - const offsetIris = [0, -0.1]; - const eyeRatio = 1; - const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); - const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; - const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; - const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; - const eyeDiff = [ - // x distance between extreme point and center point normalized with eye size - (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], - eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] - ]; - let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); - strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); - const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; - return { bearing, strength }; -}; -var calculateFaceAngle = (face4, imageSize) => { - const normalize2 = (v) => { - const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); - v[0] /= length; - v[1] /= length; - v[2] /= length; - return v; - }; - const subVectors = (a, b) => { - const x = a[0] - b[0]; - const y = a[1] - b[1]; - const z = a[2] - b[2]; - return [x, y, z]; - }; - const crossVectors = (a, b) => { - const x = a[1] * b[2] - a[2] * b[1]; - const y = a[2] * b[0] - a[0] * b[2]; - const z = a[0] * b[1] - a[1] * b[0]; - return [x, y, z]; - }; - const rotationMatrixToEulerAngle = (r) => { - const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; - let thetaX; - let thetaY; - let thetaZ; - if (r10 < 1) { - if (r10 > -1) { - const cosThetaZ = Math.sqrt(r00 * r00 + r20 * r20); - thetaZ = Math.atan2(r10, cosThetaZ); - thetaY = Math.atan2(-r20, r00); - thetaX = Math.atan2(-r12, r11); - } else { - thetaZ = -Math.PI / 2; - thetaY = -Math.atan2(r21, r22); - thetaX = 0; - } - } else { - thetaZ = Math.PI / 2; - thetaY = Math.atan2(r21, r22); - thetaX = 0; - } - if (Number.isNaN(thetaX)) thetaX = 0; - if (Number.isNaN(thetaY)) thetaY = 0; - if (Number.isNaN(thetaZ)) thetaZ = 0; - return { pitch: -thetaX, yaw: -thetaY, roll: -thetaZ }; - }; - const mesh = face4.meshRaw; - if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; - const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; - const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); - const yAxis = normalize2(subVectors(pts[1], pts[0])); - let xAxis = normalize2(subVectors(pts[3], pts[2])); - const zAxis = normalize2(crossVectors(xAxis, yAxis)); - xAxis = crossVectors(yAxis, zAxis); - const matrix = [ - xAxis[0], - xAxis[1], - xAxis[2], - yAxis[0], - yAxis[1], - yAxis[2], - zAxis[0], - zAxis[1], - zAxis[2] - ]; - const angle = rotationMatrixToEulerAngle(matrix); - const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; - return { angle, matrix, gaze }; -}; - -// src/face/anthropometry.ts -function calculateCameraDistance(face4, width) { - const f = face4 == null ? void 0 : face4.annotations; - if (!(f == null ? void 0 : f.leftEyeIris) || !(f == null ? void 0 : f.rightEyeIris)) return 0; - const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; - const cameraDistance = Math.round(1.17 / irisSize) / 100; - return cameraDistance; -} - -// src/face/face.ts -var detectFace = async (instance, input) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - let timeStamp = now(); - let ageRes; - let gearRes; - let genderRes; - let emotionRes; - let mobilefacenetRes; - let insightfaceRes; - let antispoofRes; - let livenessRes; - let descRes; - const faceRes = []; - instance.state = "run:face"; - const faces = await predict4(input, instance.config); - instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - if (!input.shape || input.shape.length !== 4) return []; - if (!faces) return []; - for (let i = 0; i < faces.length; i++) { - instance.analyze("Get Face"); - if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { - log("Face object is disposed:", faces[i].tensor); - continue; - } - if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { - const masked = await mask(faces[i]); - tfjs_esm_exports.dispose(faces[i].tensor); - if (masked) faces[i].tensor = masked; - } - const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; - instance.analyze("Start Emotion:"); - if (instance.config.async) { - emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : []; - } else { - instance.state = "run:emotion"; - timeStamp = now(); - emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : []; - instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Emotion:"); - instance.analyze("Start AntiSpoof:"); - if (instance.config.async) { - antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:antispoof"; - timeStamp = now(); - antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End AntiSpoof:"); - instance.analyze("Start Liveness:"); - if (instance.config.async) { - livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:liveness"; - timeStamp = now(); - livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Liveness:"); - instance.analyze("Start GEAR:"); - if (instance.config.async) { - gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:gear"; - timeStamp = now(); - gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - instance.performance.gear = Math.trunc(now() - timeStamp); - } - instance.analyze("End GEAR:"); - instance.analyze("Start SSRNet:"); - if (instance.config.async) { - ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:ssrnet"; - timeStamp = now(); - ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - instance.performance.ssrnet = Math.trunc(now() - timeStamp); - } - instance.analyze("End SSRNet:"); - instance.analyze("Start MobileFaceNet:"); - if (instance.config.async) { - mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End MobileFaceNet:"); - instance.analyze("Start InsightFace:"); - if (instance.config.async) { - insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End InsightFace:"); - instance.analyze("Start Description:"); - if (instance.config.async) { - descRes = predict6(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length); - } else { - instance.state = "run:description"; - timeStamp = now(); - descRes = await predict6(faces[i].tensor || tfjs_esm_exports.tensor([]), instance.config, i, faces.length); - instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Description:"); - if (instance.config.async) { - [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); - } - instance.analyze("Finish Face:"); - if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { - descRes = { - ...descRes, - age: ageRes.age, - gender: genderRes.gender, - genderScore: genderRes.genderScore - }; - } - if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { - descRes = { - ...descRes, - age: gearRes.age, - gender: gearRes.gender, - genderScore: gearRes.genderScore, - race: gearRes.race - }; - } - if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { - descRes.descriptor = mobilefacenetRes; - } - if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { - descRes.descriptor = insightfaceRes; - } - const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; - const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tfjs_esm_exports.squeeze(faces[i].tensor) : null; - tfjs_esm_exports.dispose(faces[i].tensor); - if (faces[i].tensor) delete faces[i].tensor; - const res = { - ...faces[i], - id: i - }; - if (descRes.age) res.age = descRes.age; - if (descRes.gender) res.gender = descRes.gender; - if (descRes.genderScore) res.genderScore = descRes.genderScore; - if (descRes.descriptor) res.embedding = descRes.descriptor; - if (descRes.race) res.race = descRes.race; - if (emotionRes) res.emotion = emotionRes; - if (antispoofRes) res.real = antispoofRes; - if (livenessRes) res.live = livenessRes; - if (irisSize > 0) res.distance = irisSize; - if (rotation) res.rotation = rotation; - if (tensor6) res.tensor = tensor6; - faceRes.push(res); - instance.analyze("End Face"); - } - instance.analyze("End FaceMesh:"); - if (instance.config.async) { - if (instance.performance.face) delete instance.performance.face; - if (instance.performance.age) delete instance.performance.age; - if (instance.performance.gender) delete instance.performance.gender; - if (instance.performance.emotion) delete instance.performance.emotion; - } - return faceRes; -}; - -// src/hand/fingerdef.ts -var Finger = { - thumb: 0, - index: 1, - middle: 2, - ring: 3, - pinky: 4, - all: [0, 1, 2, 3, 4], - // just for convenience - nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, - // Describes mapping of joints based on the 21 points returned by handpose. - // [0] Palm - // [1-4] Thumb - // [5-8] Index - // [9-12] Middle - // [13-16] Ring - // [17-20] Pinky - pointsMapping: { - 0: [[0, 1], [1, 2], [2, 3], [3, 4]], - 1: [[0, 5], [5, 6], [6, 7], [7, 8]], - 2: [[0, 9], [9, 10], [10, 11], [11, 12]], - 3: [[0, 13], [13, 14], [14, 15], [15, 16]], - 4: [[0, 17], [17, 18], [18, 19], [19, 20]] - }, - getName: (value) => Finger.nameMapping[value], - getPoints: (value) => Finger.pointsMapping[value] -}; -var FingerCurl = { - none: 0, - half: 1, - full: 2, - nameMapping: { 0: "none", 1: "half", 2: "full" }, - getName: (value) => FingerCurl.nameMapping[value] -}; -var FingerDirection = { - verticalUp: 0, - verticalDown: 1, - horizontalLeft: 2, - horizontalRight: 3, - diagonalUpRight: 4, - diagonalUpLeft: 5, - diagonalDownRight: 6, - diagonalDownLeft: 7, - nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, - getName: (value) => FingerDirection.nameMapping[value] -}; -var FingerGesture = class { - constructor(name) { - __publicField(this, "name"); - __publicField(this, "curls"); - __publicField(this, "directions"); - __publicField(this, "weights"); - __publicField(this, "weightsRelative"); - this.name = name; - this.curls = {}; - this.directions = {}; - this.weights = [1, 1, 1, 1, 1]; - this.weightsRelative = [1, 1, 1, 1, 1]; - } - curl(finger, curl, confidence) { - if (typeof this.curls[finger] === "undefined") this.curls[finger] = []; - this.curls[finger].push([curl, confidence]); - } - direction(finger, position, confidence) { - if (!this.directions[finger]) this.directions[finger] = []; - this.directions[finger].push([position, confidence]); - } - weight(finger, weight) { - this.weights[finger] = weight; - const total = this.weights.reduce((a, b) => a + b, 0); - this.weightsRelative = this.weights.map((el) => el * 5 / total); - } - matchAgainst(detectedCurls, detectedDirections) { - let confidence = 0; - for (const fingerIdx in detectedCurls) { - const detectedCurl = detectedCurls[fingerIdx]; - const expectedCurls = this.curls[fingerIdx]; - if (typeof expectedCurls === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedCurl, score] of expectedCurls) { - if (detectedCurl === expectedCurl) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - for (const fingerIdx in detectedDirections) { - const detectedDirection = detectedDirections[fingerIdx]; - const expectedDirections = this.directions[fingerIdx]; - if (typeof expectedDirections === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedDirection, score] of expectedDirections) { - if (detectedDirection === expectedDirection) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - return confidence / 10; - } -}; - -// src/hand/fingergesture.ts -var { thumb, index, middle, ring, pinky } = Finger; -var { none, half, full } = FingerCurl; -var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; -var ThumbsUp = new FingerGesture("thumbs up"); -ThumbsUp.curl(thumb, none, 1); -ThumbsUp.direction(thumb, verticalUp, 1); -ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); -ThumbsUp.direction(thumb, diagonalUpRight, 0.25); -for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { - ThumbsUp.curl(finger, full, 1); - ThumbsUp.direction(finger, horizontalLeft, 1); - ThumbsUp.direction(finger, horizontalRight, 1); -} -var Victory = new FingerGesture("victory"); -Victory.curl(thumb, half, 0.5); -Victory.curl(thumb, none, 0.5); -Victory.direction(thumb, verticalUp, 1); -Victory.direction(thumb, diagonalUpLeft, 1); -Victory.curl(index, none, 1); -Victory.direction(index, verticalUp, 0.75); -Victory.direction(index, diagonalUpLeft, 1); -Victory.curl(middle, none, 1); -Victory.direction(middle, verticalUp, 1); -Victory.direction(middle, diagonalUpLeft, 0.75); -Victory.curl(ring, full, 1); -Victory.direction(ring, verticalUp, 0.2); -Victory.direction(ring, diagonalUpLeft, 1); -Victory.direction(ring, horizontalLeft, 0.2); -Victory.curl(pinky, full, 1); -Victory.direction(pinky, verticalUp, 0.2); -Victory.direction(pinky, diagonalUpLeft, 1); -Victory.direction(pinky, horizontalLeft, 0.2); -Victory.weight(index, 2); -Victory.weight(middle, 2); -var Point = new FingerGesture("point"); -Point.curl(thumb, full, 1); -Point.curl(index, none, 0.5); -Point.curl(middle, full, 0.5); -Point.curl(ring, full, 0.5); -Point.curl(pinky, full, 0.5); -Point.weight(index, 2); -Point.weight(middle, 2); -var MiddleFinger = new FingerGesture("middle finger"); -MiddleFinger.curl(thumb, none, 1); -MiddleFinger.curl(index, full, 0.5); -MiddleFinger.curl(middle, full, 0.5); -MiddleFinger.curl(ring, full, 0.5); -MiddleFinger.curl(pinky, full, 0.5); -MiddleFinger.weight(index, 2); -MiddleFinger.weight(middle, 2); -var OpenPalm = new FingerGesture("open palm"); -OpenPalm.curl(thumb, none, 0.75); -OpenPalm.curl(index, none, 0.75); -OpenPalm.curl(middle, none, 0.75); -OpenPalm.curl(ring, none, 0.75); -OpenPalm.curl(pinky, none, 0.75); -var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; - -// src/hand/fingerpose.ts -var minConfidence = 0.7; -var options3 = { - // curl estimation - HALF_CURL_START_LIMIT: 60, - NO_CURL_START_LIMIT: 130, - // direction estimation - DISTANCE_VOTE_POWER: 1.1, - SINGLE_ANGLE_VOTE_POWER: 0.9, - TOTAL_ANGLE_VOTE_POWER: 1.6 -}; -function calculateSlope(point1x, point1y, point2x, point2y) { - const value = (point1y - point2y) / (point1x - point2x); - let slope = Math.atan(value) * 180 / Math.PI; - if (slope <= 0) slope = -slope; - else if (slope > 0) slope = 180 - slope; - return slope; -} -function getSlopes(point1, point2) { - if (!point1 || !point2) return [0, 0]; - const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); - if (point1.length === 2) return slopeXY; - const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); - return [slopeXY, slopeYZ]; -} -function angleOrientationAt(angle, weightageAt = 1) { - let isVertical = 0; - let isDiagonal = 0; - let isHorizontal = 0; - if (angle >= 75 && angle <= 105) isVertical = 1 * weightageAt; - else if (angle >= 25 && angle <= 155) isDiagonal = 1 * weightageAt; - else isHorizontal = 1 * weightageAt; - return [isVertical, isDiagonal, isHorizontal]; -} -function estimateFingerCurl(startPoint, midPoint, endPoint) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const start_mid_z_dist = startPoint[2] - midPoint[2]; - const start_end_z_dist = startPoint[2] - endPoint[2]; - const mid_end_z_dist = midPoint[2] - endPoint[2]; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); - let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); - if (cos_in > 1) cos_in = 1; - else if (cos_in < -1) cos_in = -1; - let angleOfCurve = Math.acos(cos_in); - angleOfCurve = 57.2958 * angleOfCurve % 180; - let fingerCurl; - if (angleOfCurve > options3.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none; - else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half; - else fingerCurl = FingerCurl.full; - return fingerCurl; -} -function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - if (max_dist_x === Math.abs(start_end_x_dist)) { - if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } else if (max_dist_x === Math.abs(start_mid_x_dist)) { - if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } else { - if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } - return estimatedDirection; -} -function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { - let estimatedDirection; - if (max_dist_y === Math.abs(start_end_y_dist)) { - if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } else if (max_dist_y === Math.abs(start_mid_y_dist)) { - if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } else { - if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } - return estimatedDirection; -} -function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - if (reqd_vertical_direction === FingerDirection.verticalUp) { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft; - else estimatedDirection = FingerDirection.diagonalUpRight; - } else { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft; - else estimatedDirection = FingerDirection.diagonalDownRight; - } - return estimatedDirection; -} -function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); - const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); - let voteVertical = 0; - let voteDiagonal = 0; - let voteHorizontal = 0; - const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); - if (start_end_x_y_dist_ratio > 1.5) voteVertical += options3.DISTANCE_VOTE_POWER; - else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options3.DISTANCE_VOTE_POWER; - else voteHorizontal += options3.DISTANCE_VOTE_POWER; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); - const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); - let calc_start_point_x = startPoint[0]; - let calc_start_point_y = startPoint[1]; - let calc_end_point_x = endPoint[0]; - let calc_end_point_y = endPoint[1]; - if (max_dist === start_mid_dist) { - calc_end_point_x = endPoint[0]; - calc_end_point_y = endPoint[1]; - } else if (max_dist === mid_end_dist) { - calc_start_point_x = midPoint[0]; - calc_start_point_y = midPoint[1]; - } - const calcStartPoint = [calc_start_point_x, calc_start_point_y]; - const calcEndPoint = [calc_end_point_x, calc_end_point_y]; - const totalAngle = getSlopes(calcStartPoint, calcEndPoint); - const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); - voteVertical += votes[0]; - voteDiagonal += votes[1]; - voteHorizontal += votes[2]; - for (const fingerSlope of fingerSlopes) { - const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); - voteVertical += fingerVotes[0]; - voteDiagonal += fingerVotes[1]; - voteHorizontal += fingerVotes[2]; - } - let estimatedDirection; - if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } else { - estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } - return estimatedDirection; -} -function estimate(landmarks) { - const slopesXY = []; - const slopesYZ = []; - const fingerCurls = []; - const fingerDirections = []; - if (!landmarks) return { curls: fingerCurls, directions: fingerDirections }; - for (const finger of Finger.all) { - const points = Finger.getPoints(finger); - const slopeAtXY = []; - const slopeAtYZ = []; - for (const point2 of points) { - const point1 = landmarks[point2[0]]; - const point22 = landmarks[point2[1]]; - const slopes = getSlopes(point1, point22); - const slopeXY = slopes[0]; - const slopeYZ = slopes[1]; - slopeAtXY.push(slopeXY); - slopeAtYZ.push(slopeYZ); - } - slopesXY.push(slopeAtXY); - slopesYZ.push(slopeAtYZ); - } - for (const finger of Finger.all) { - const pointIndexAt = finger === Finger.thumb ? 1 : 0; - const fingerPointsAt = Finger.getPoints(finger); - const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; - const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; - const endPoint = landmarks[fingerPointsAt[3][1]]; - const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); - const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); - fingerCurls[finger] = fingerCurled; - fingerDirections[finger] = fingerPosition; - } - return { curls: fingerCurls, directions: fingerDirections }; -} -function analyze(keypoints) { - if (!keypoints || keypoints.length === 0) return null; - const estimatorRes = estimate(keypoints); - const landmarks = {}; - for (const fingerIdx of Finger.all) { - landmarks[Finger.getName(fingerIdx)] = { - curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), - direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) - }; - } - return landmarks; -} -function match(keypoints) { - const poses = []; - if (!keypoints || keypoints.length === 0) return poses; - const estimatorRes = estimate(keypoints); - for (const gesture2 of fingergesture_default) { - const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); - if (confidence >= minConfidence) poses.push({ name: gesture2.name, confidence }); - } - return poses; -} - -// src/gesture/gesture.ts -var body2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); - const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); - const nose = res[i].keypoints.find((a) => a.part === "nose"); - if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "i give up" }); - else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "raise left hand" }); - else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "raise right hand" }); - const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); - const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); - if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { - gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); - } - } - return gestures; -}; -var face2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (res[i].mesh && res[i].mesh.length > 450) { - const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); - const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; - if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: "facing center" }); - else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); - const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); - if (openLeft < 0.2) gestures.push({ face: i, gesture: "blink left eye" }); - const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); - if (openRight < 0.2) gestures.push({ face: i, gesture: "blink right eye" }); - const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); - if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); - const chinDepth = res[i].mesh[152][2] || 0; - if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); - } - } - return gestures; -}; -var iris2 = (res) => { - var _a, _b, _c, _d; - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) continue; - const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; - const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; - const areaLeft = Math.abs(sizeXLeft * sizeYLeft); - const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; - const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; - const areaRight = Math.abs(sizeXRight * sizeYRight); - let center = false; - const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); - if (difference < 0.25) { - center = true; - gestures.push({ iris: i, gesture: "facing center" }); - } - const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; - const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; - if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false; - if (leftIrisCenterX > rightIrisCenterX) { - if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: "looking right" }); - } else { - if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: "looking left" }); - } - const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; - const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: "looking down" }); - if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: "looking up" }); - if (center) gestures.push({ iris: i, gesture: "looking center" }); - } - return gestures; -}; -var hand2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const fingers = []; - if (res[i].annotations) { - for (const [finger, pos] of Object.entries(res[i].annotations)) { - if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); - } - } - if (fingers && fingers.length > 0) { - const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); - gestures.push({ hand: i, gesture: `${closest.name} forward` }); - const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); - gestures.push({ hand: i, gesture: `${highest.name} up` }); - } - if (res[i].keypoints) { - const poses = match(res[i].keypoints); - for (const pose of poses) gestures.push({ hand: i, gesture: pose.name }); - } - } - return gestures; -}; - -// src/hand/handposeutil.ts -function getBoxSize2(box) { - return [ - Math.abs(box.endPoint[0] - box.startPoint[0]), - Math.abs(box.endPoint[1] - box.startPoint[1]) - ]; -} -function getBoxCenter2(box) { - return [ - box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, - box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 - ]; -} -function cutBoxFromImageAndResize(box, image28, cropSize) { - const h = image28.shape[1]; - const w = image28.shape[2]; - const boxes = [[ - box.startPoint[1] / h, - box.startPoint[0] / w, - box.endPoint[1] / h, - box.endPoint[0] / w - ]]; - return tfjs_esm_exports.image.cropAndResize(image28, boxes, [0], cropSize); -} -function scaleBoxCoordinates2(box, factor) { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const palmLandmarks = box.palmLandmarks.map((coord) => { - const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; - return scaledCoord; - }); - return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; -} -function enlargeBox2(box, factor = 1.5) { - const center = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; - const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function squarifyBox2(box) { - const centers = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const maxEdge = Math.max(...size2); - const halfSize = maxEdge / 2; - const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; - const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function normalizeRadians2(angle) { - return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -} -function computeRotation2(point1, point2) { - const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); - return normalizeRadians2(radians); -} -var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -function dot2(v1, v2) { - let product = 0; - for (let i = 0; i < v1.length; i++) { - product += v1[i] * v2[i]; - } - return product; -} -function getColumnFrom2DArr2(arr, columnIndex) { - const column = []; - for (let i = 0; i < arr.length; i++) { - column.push(arr[i][columnIndex]); - } - return column; -} -function multiplyTransformMatrices2(mat1, mat2) { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) { - product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); - } - } - return product; -} -function buildRotationMatrix2(rotation, center) { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix2(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); - return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); -} -function invertTransformMatrix2(matrix) { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [ - -dot2(rotationComponent[0], translationComponent), - -dot2(rotationComponent[1], translationComponent) - ]; - return [ - rotationComponent[0].concat(invertedTranslation[0]), - rotationComponent[1].concat(invertedTranslation[1]), - [0, 0, 1] - ]; -} -function rotatePoint2(homogeneousCoordinate, rotationMatrix) { - return [ - dot2(homogeneousCoordinate, rotationMatrix[0]), - dot2(homogeneousCoordinate, rotationMatrix[1]) - ]; -} - -// src/hand/handposeanchors.ts -var anchors2 = [ - { x: 0.015625, y: 0.015625 }, - { x: 0.015625, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - 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{ x: 0.71875, y: 0.96875 }, - { x: 0.78125, y: 0.96875 }, - { x: 0.78125, y: 0.96875 }, - { x: 0.84375, y: 0.96875 }, - { x: 0.84375, y: 0.96875 }, - { x: 0.90625, y: 0.96875 }, - { x: 0.90625, y: 0.96875 }, - { x: 0.96875, y: 0.96875 }, - { x: 0.96875, y: 0.96875 }, - { x: 0.0625, y: 0.0625 }, - { x: 0.0625, y: 0.0625 }, - { x: 0.0625, y: 0.0625 }, - { x: 0.0625, y: 0.0625 }, - { x: 0.0625, y: 0.0625 }, - { x: 0.0625, y: 0.0625 }, - { x: 0.1875, y: 0.0625 }, - { x: 0.1875, y: 0.0625 }, - { x: 0.1875, y: 0.0625 }, - { x: 0.1875, y: 0.0625 }, - { x: 0.1875, y: 0.0625 }, - { x: 0.1875, y: 0.0625 }, - { x: 0.3125, y: 0.0625 }, - { x: 0.3125, y: 0.0625 }, - { x: 0.3125, y: 0.0625 }, - { x: 0.3125, y: 0.0625 }, - { x: 0.3125, y: 0.0625 }, - { x: 0.3125, y: 0.0625 }, - { x: 0.4375, y: 0.0625 }, - { x: 0.4375, y: 0.0625 }, - { x: 0.4375, y: 0.0625 }, - { x: 0.4375, y: 0.0625 }, - { x: 0.4375, y: 0.0625 }, - { x: 0.4375, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.5625, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.6875, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.8125, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.9375, y: 0.0625 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.0625, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.1875, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.3125, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.4375, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.5625, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.6875, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.8125, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.9375, y: 0.1875 }, - { x: 0.0625, y: 0.3125 }, - { x: 0.0625, y: 0.3125 }, - { x: 0.0625, y: 0.3125 }, - { x: 0.0625, y: 0.3125 }, - { x: 0.0625, y: 0.3125 }, - { x: 0.0625, y: 0.3125 }, - { x: 0.1875, y: 0.3125 }, - { x: 0.1875, y: 0.3125 }, - { x: 0.1875, y: 0.3125 }, - { x: 0.1875, y: 0.3125 }, - { x: 0.1875, y: 0.3125 }, - { x: 0.1875, y: 0.3125 }, - { x: 0.3125, y: 0.3125 }, - { x: 0.3125, y: 0.3125 }, - { x: 0.3125, y: 0.3125 }, - { x: 0.3125, y: 0.3125 }, - { x: 0.3125, y: 0.3125 }, - { x: 0.3125, y: 0.3125 }, - { x: 0.4375, y: 0.3125 }, - { x: 0.4375, y: 0.3125 }, - { x: 0.4375, y: 0.3125 }, - { x: 0.4375, y: 0.3125 }, - { x: 0.4375, y: 0.3125 }, - { x: 0.4375, y: 0.3125 }, - { x: 0.5625, y: 0.3125 }, - { x: 0.5625, y: 0.3125 }, - { x: 0.5625, y: 0.3125 }, - { x: 0.5625, y: 0.3125 }, - { x: 0.5625, y: 0.3125 }, - { x: 0.5625, y: 0.3125 }, - { x: 0.6875, y: 0.3125 }, - { x: 0.6875, y: 0.3125 }, - { x: 0.6875, y: 0.3125 }, - { x: 0.6875, y: 0.3125 }, - { x: 0.6875, y: 0.3125 }, - { x: 0.6875, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.8125, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.9375, y: 0.3125 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.0625, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.1875, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.3125, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.4375, y: 0.4375 }, - { x: 0.5625, y: 0.4375 }, - { x: 0.5625, y: 0.4375 }, - { x: 0.5625, y: 0.4375 }, - { x: 0.5625, y: 0.4375 }, - { x: 0.5625, y: 0.4375 }, - { x: 0.5625, y: 0.4375 }, - { x: 0.6875, y: 0.4375 }, - { x: 0.6875, y: 0.4375 }, - { x: 0.6875, y: 0.4375 }, - { x: 0.6875, y: 0.4375 }, - { x: 0.6875, y: 0.4375 }, - { x: 0.6875, y: 0.4375 }, - { x: 0.8125, y: 0.4375 }, - { x: 0.8125, y: 0.4375 }, - { x: 0.8125, y: 0.4375 }, - { x: 0.8125, y: 0.4375 }, - { x: 0.8125, y: 0.4375 }, - { x: 0.8125, y: 0.4375 }, - { x: 0.9375, y: 0.4375 }, - { x: 0.9375, y: 0.4375 }, - { x: 0.9375, y: 0.4375 }, - { x: 0.9375, y: 0.4375 }, - { x: 0.9375, y: 0.4375 }, - { x: 0.9375, y: 0.4375 }, - { x: 0.0625, y: 0.5625 }, - { x: 0.0625, y: 0.5625 }, - { x: 0.0625, y: 0.5625 }, - { x: 0.0625, y: 0.5625 }, - { x: 0.0625, y: 0.5625 }, - { x: 0.0625, y: 0.5625 }, - { x: 0.1875, y: 0.5625 }, - { x: 0.1875, y: 0.5625 }, - { x: 0.1875, y: 0.5625 }, - { x: 0.1875, y: 0.5625 }, - { x: 0.1875, y: 0.5625 }, - { x: 0.1875, y: 0.5625 }, - { x: 0.3125, y: 0.5625 }, - { x: 0.3125, y: 0.5625 }, - { x: 0.3125, y: 0.5625 }, - { x: 0.3125, y: 0.5625 }, - { x: 0.3125, y: 0.5625 }, - { x: 0.3125, y: 0.5625 }, - { x: 0.4375, y: 0.5625 }, - { x: 0.4375, y: 0.5625 }, - { x: 0.4375, y: 0.5625 }, - { x: 0.4375, y: 0.5625 }, - { x: 0.4375, y: 0.5625 }, - { x: 0.4375, y: 0.5625 }, - { x: 0.5625, y: 0.5625 }, - { x: 0.5625, y: 0.5625 }, - { x: 0.5625, y: 0.5625 }, - { x: 0.5625, y: 0.5625 }, - { x: 0.5625, y: 0.5625 }, - { x: 0.5625, y: 0.5625 }, - { x: 0.6875, y: 0.5625 }, - { x: 0.6875, y: 0.5625 }, - { x: 0.6875, y: 0.5625 }, - { x: 0.6875, y: 0.5625 }, - { x: 0.6875, y: 0.5625 }, - { x: 0.6875, y: 0.5625 }, - { x: 0.8125, y: 0.5625 }, - { x: 0.8125, y: 0.5625 }, - { x: 0.8125, y: 0.5625 }, - { x: 0.8125, y: 0.5625 }, - { x: 0.8125, y: 0.5625 }, - { x: 0.8125, y: 0.5625 }, - { x: 0.9375, y: 0.5625 }, - { x: 0.9375, y: 0.5625 }, - { x: 0.9375, y: 0.5625 }, - { x: 0.9375, y: 0.5625 }, - { x: 0.9375, y: 0.5625 }, - { x: 0.9375, y: 0.5625 }, - { x: 0.0625, y: 0.6875 }, - { x: 0.0625, y: 0.6875 }, - { x: 0.0625, y: 0.6875 }, - { x: 0.0625, y: 0.6875 }, - { x: 0.0625, y: 0.6875 }, - { x: 0.0625, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.1875, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.3125, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.4375, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.5625, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.6875, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.8125, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.9375, y: 0.6875 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.0625, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.1875, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.3125, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.4375, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.5625, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.6875, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.8125, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.9375, y: 0.8125 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.0625, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.1875, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.3125, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.4375, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.5625, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.6875, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.8125, y: 0.9375 }, - { x: 0.9375, y: 0.9375 }, - { x: 0.9375, y: 0.9375 }, - { x: 0.9375, y: 0.9375 }, - { x: 0.9375, y: 0.9375 }, - { x: 0.9375, y: 0.9375 }, - { x: 0.9375, y: 0.9375 } -]; - -// src/hand/handposedetector.ts -var HandDetector = class { - constructor(model23) { - __publicField(this, "model"); - __publicField(this, "anchors"); - __publicField(this, "anchorsTensor"); - __publicField(this, "inputSize"); - __publicField(this, "inputSizeTensor"); - __publicField(this, "doubleInputSizeTensor"); - var _a, _b, _c, _d; - this.model = model23; - this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); - this.anchorsTensor = tfjs_esm_exports.tensor2d(this.anchors); - this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; - this.inputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize, this.inputSize]); - this.doubleInputSizeTensor = tfjs_esm_exports.tensor1d([this.inputSize * 2, this.inputSize * 2]); - } - normalizeBoxes(boxes) { - const t2 = {}; - t2.boxOffsets = tfjs_esm_exports.slice(boxes, [0, 0], [-1, 2]); - t2.boxSizes = tfjs_esm_exports.slice(boxes, [0, 2], [-1, 2]); - t2.div = tfjs_esm_exports.div(t2.boxOffsets, this.inputSizeTensor); - t2.boxCenterPoints = tfjs_esm_exports.add(t2.div, this.anchorsTensor); - t2.halfBoxSizes = tfjs_esm_exports.div(t2.boxSizes, this.doubleInputSizeTensor); - t2.sub = tfjs_esm_exports.sub(t2.boxCenterPoints, t2.halfBoxSizes); - t2.startPoints = tfjs_esm_exports.mul(t2.sub, this.inputSizeTensor); - t2.add = tfjs_esm_exports.add(t2.boxCenterPoints, t2.halfBoxSizes); - t2.endPoints = tfjs_esm_exports.mul(t2.add, this.inputSizeTensor); - const res = tfjs_esm_exports.concat2d([t2.startPoints, t2.endPoints], 1); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return res; - } - normalizeLandmarks(rawPalmLandmarks, index2) { - const t2 = {}; - t2.reshape = tfjs_esm_exports.reshape(rawPalmLandmarks, [-1, 7, 2]); - t2.div = tfjs_esm_exports.div(t2.reshape, this.inputSizeTensor); - t2.landmarks = tfjs_esm_exports.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); - const res = tfjs_esm_exports.mul(t2.landmarks, this.inputSizeTensor); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return res; - } - async predict(input, config3) { - var _a; - const t2 = {}; - t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [this.inputSize, this.inputSize]); - t2.div = tfjs_esm_exports.div(t2.resize, constants.tf127); - t2.image = tfjs_esm_exports.sub(t2.div, constants.tf1); - t2.batched = this.model.execute(t2.image); - t2.predictions = tfjs_esm_exports.squeeze(t2.batched); - t2.slice = tfjs_esm_exports.slice(t2.predictions, [0, 0], [-1, 1]); - t2.sigmoid = tfjs_esm_exports.sigmoid(t2.slice); - t2.scores = tfjs_esm_exports.squeeze(t2.sigmoid); - const scores = await t2.scores.data(); - t2.boxes = tfjs_esm_exports.slice(t2.predictions, [0, 1], [-1, 4]); - t2.norm = this.normalizeBoxes(t2.boxes); - t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); - const nms = await t2.nms.array(); - const hands = []; - for (const index2 of nms) { - const p = {}; - p.box = tfjs_esm_exports.slice(t2.norm, [index2, 0], [1, -1]); - p.slice = tfjs_esm_exports.slice(t2.predictions, [index2, 5], [1, 14]); - p.norm = this.normalizeLandmarks(p.slice, index2); - p.palmLandmarks = tfjs_esm_exports.reshape(p.norm, [-1, 2]); - const box = await p.box.data(); - const startPoint = box.slice(0, 2); - const endPoint = box.slice(2, 4); - const palmLandmarks = await p.palmLandmarks.array(); - const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; - const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); - hands.push(scaled); - Object.keys(p).forEach((tensor6) => tfjs_esm_exports.dispose(p[tensor6])); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return hands; - } -}; - -// src/hand/handposepipeline.ts -var palmBoxEnlargeFactor = 5; -var handBoxEnlargeFactor = 1.65; -var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; -var palmLandmarksPalmBase = 0; -var palmLandmarksMiddleFingerBase = 2; -var lastTime13 = 0; -var HandPipeline = class { - constructor(handDetector, handPoseModel2) { - __publicField(this, "handDetector"); - __publicField(this, "handPoseModel"); - __publicField(this, "inputSize"); - __publicField(this, "storedBoxes"); - __publicField(this, "skipped"); - __publicField(this, "detectedHands"); - var _a, _b, _c; - this.handDetector = handDetector; - this.handPoseModel = handPoseModel2; - this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; - this.storedBoxes = []; - this.skipped = Number.MAX_SAFE_INTEGER; - this.detectedHands = 0; - } - calculateLandmarksBoundingBox(landmarks) { - const xs = landmarks.map((d) => d[0]); - const ys = landmarks.map((d) => d[1]); - const startPoint = [Math.min(...xs), Math.min(...ys)]; - const endPoint = [Math.max(...xs), Math.max(...ys)]; - return { startPoint, endPoint }; - } - getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { - const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); - const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); - return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); - } - getBoxForHandLandmarks(landmarks) { - const boundingBox = this.calculateLandmarksBoundingBox(landmarks); - const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); - boxAroundHand.palmLandmarks = []; - for (let i = 0; i < palmLandmarkIds.length; i++) { - boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); - } - return boxAroundHand; - } - transformRawCoords(rawCoords, box2, angle, rotationMatrix) { - const boxSize = getBoxSize2(box2); - const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; - const coordsScaled = rawCoords.map((coord) => [ - scaleFactor[0] * (coord[0] - this.inputSize / 2), - scaleFactor[1] * (coord[1] - this.inputSize / 2), - scaleFactor[2] * coord[2] - ]); - const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); - const coordsRotated = coordsScaled.map((coord) => { - const rotated = rotatePoint2(coord, coordsRotationMatrix); - return [...rotated, coord[2]]; - }); - const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); - const boxCenter = [...getBoxCenter2(box2), 1]; - const originalBoxCenter = [ - dot2(boxCenter, inverseRotationMatrix[0]), - dot2(boxCenter, inverseRotationMatrix[1]) - ]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + originalBoxCenter[0]), - Math.trunc(coord[1] + originalBoxCenter[1]), - Math.trunc(coord[2]) - ]); - } - async estimateHands(image28, config3) { - let useFreshBox = false; - let boxes; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; - const skipFrame = this.skipped < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - this.skipped++; - } else { - boxes = await this.handDetector.predict(image28, config3); - this.skipped = 0; - } - if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { - this.detectedHands = 0; - this.storedBoxes = [...boxes]; - if (this.storedBoxes.length > 0) useFreshBox = true; - } - const hands = []; - for (let i = 0; i < this.storedBoxes.length; i++) { - const currentBox = this.storedBoxes[i]; - if (!currentBox) continue; - if (config3.hand.landmarks) { - const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; - const palmCenter = getBoxCenter2(currentBox); - const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; - const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tfjs_esm_exports.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); - const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); - const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; - const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); - const handImage = tfjs_esm_exports.div(croppedInput, constants.tf255); - tfjs_esm_exports.dispose(croppedInput); - tfjs_esm_exports.dispose(rotatedImage); - const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); - lastTime13 = now(); - tfjs_esm_exports.dispose(handImage); - const confidence = (await confidenceT.data())[0]; - tfjs_esm_exports.dispose(confidenceT); - if (confidence >= config3.hand.minConfidence / 4) { - const keypointsReshaped = tfjs_esm_exports.reshape(keypoints, [-1, 3]); - const rawCoords = await keypointsReshaped.array(); - tfjs_esm_exports.dispose(keypoints); - tfjs_esm_exports.dispose(keypointsReshaped); - const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); - const nextBoundingBox = this.getBoxForHandLandmarks(coords); - this.storedBoxes[i] = { ...nextBoundingBox, confidence }; - const result = { - landmarks: coords, - confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: confidence, - box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } - }; - hands.push(result); - } else { - this.storedBoxes[i] = null; - } - tfjs_esm_exports.dispose(keypoints); - } else { - const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); - const result = { - confidence: currentBox.confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: 0, - box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, - landmarks: [] - }; - hands.push(result); - } - } - this.storedBoxes = this.storedBoxes.filter((a) => a !== null); - this.detectedHands = hands.length; - if (hands.length > config3.hand.maxDetected) hands.length = config3.hand.maxDetected; - return hands; - } -}; - -// src/hand/handpose.ts -var meshAnnotations2 = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - palm: [0] -}; -var handDetectorModel; -var handPoseModel; -var handPipeline; -function initPipeline() { - const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; - if (handDetector && handPoseModel) handPipeline = new HandPipeline(handDetector, handPoseModel); -} -async function predict14(input, config3) { - if (!handPipeline) initPipeline(); - const predictions = await handPipeline.estimateHands(input, config3); - if (!predictions) return []; - const hands = []; - for (let i = 0; i < predictions.length; i++) { - const annotations2 = {}; - if (predictions[i].landmarks) { - for (const key of Object.keys(meshAnnotations2)) { - annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); - } - } - const keypoints = predictions[i].landmarks; - let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; - let boxRaw = [0, 0, 0, 0]; - if (keypoints && keypoints.length > 0) { - for (const pt of keypoints) { - if (pt[0] < box[0]) box[0] = pt[0]; - if (pt[1] < box[1]) box[1] = pt[1]; - if (pt[0] > box[2]) box[2] = pt[0]; - if (pt[1] > box[3]) box[3] = pt[1]; - } - box[2] -= box[0]; - box[3] -= box[1]; - boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; - } else { - box = predictions[i].box ? [ - Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), - Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) - ] : [0, 0, 0, 0]; - boxRaw = [ - predictions[i].box.topLeft[0] / (input.shape[2] || 0), - predictions[i].box.topLeft[1] / (input.shape[1] || 0), - (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), - (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) - ]; - } - const landmarks = analyze(keypoints); - hands.push({ - id: i, - score: Math.round(100 * predictions[i].confidence) / 100, - boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, - fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, - label: "hand", - box, - boxRaw, - keypoints, - annotations: annotations2, - landmarks - }); - } - return hands; -} -async function loadDetect2(config3) { - var _a; - if (env.initial) handDetectorModel = null; - if (!handDetectorModel) handDetectorModel = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", handDetectorModel["modelUrl"]); - return handDetectorModel; -} -async function loadSkeleton(config3) { - var _a; - if (env.initial) handPoseModel = null; - if (!handPoseModel) handPoseModel = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", handPoseModel["modelUrl"]); - return handPoseModel; -} - -// src/hand/handtrack.ts -var models2 = [null, null]; -var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; -var inputSize7 = [[0, 0], [0, 0]]; -var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; -var faceIndex = 4; -var boxExpandFact = 1.6; -var maxDetectorResolution = 512; -var detectorExpandFact = 1.4; -var skipped13 = Number.MAX_SAFE_INTEGER; -var lastTime14 = 0; -var outputSize = [0, 0]; -var cache4 = { - boxes: [], - hands: [] -}; -var fingerMap = { - /* - thumb: [0, 1, 2, 3, 4], - index: [0, 5, 6, 7, 8], - middle: [0, 9, 10, 11, 12], - ring: [0, 13, 14, 15, 16], - pinky: [0, 17, 18, 19, 20], - palm: [0], - */ - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - base: [0], - palm: [0, 17, 13, 9, 5, 1, 0] -}; -async function loadDetect3(config3) { - var _a; - if (env.initial) models2[0] = null; - if (!models2[0]) { - fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); - models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; - inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", models2[0]["modelUrl"]); - return models2[0]; -} -async function loadSkeleton2(config3) { - var _a; - if (env.initial) models2[1] = null; - if (!models2[1]) { - models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; - inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", models2[1]["modelUrl"]); - return models2[1]; -} -async function detectHands(input, config3) { - const hands = []; - if (!input || !models2[0]) return hands; - const t2 = {}; - const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); - const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); - const width = Math.round(height * ratio2 / 8) * 8; - t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [height, width]); - t2.cast = tfjs_esm_exports.cast(t2.resize, "int32"); - [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); - t2.boxes = tfjs_esm_exports.squeeze(t2.rawBoxes, [0, 2]); - t2.scores = tfjs_esm_exports.squeeze(t2.rawScores, [0]); - const classScores = tfjs_esm_exports.unstack(t2.scores, 1); - tfjs_esm_exports.dispose(classScores[faceIndex]); - classScores.splice(faceIndex, 1); - t2.filtered = tfjs_esm_exports.stack(classScores, 1); - tfjs_esm_exports.dispose(classScores); - t2.max = tfjs_esm_exports.max(t2.filtered, 1); - t2.argmax = tfjs_esm_exports.argMax(t2.filtered, 1); - let id = 0; - t2.nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); - const nms = await t2.nms.data(); - const scores = await t2.max.data(); - const classNum = await t2.argmax.data(); - for (const nmsIndex of Array.from(nms)) { - const boxSlice = tfjs_esm_exports.slice(t2.boxes, nmsIndex, 1); - const boxYX = await boxSlice.data(); - tfjs_esm_exports.dispose(boxSlice); - const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; - const boxRaw = scale(boxData, detectorExpandFact); - const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; - const score = scores[nmsIndex]; - const label = classes[classNum[nmsIndex]]; - const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; - hands.push(hand3); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - hands.sort((a, b) => b.score - a.score); - if (hands.length > (config3.hand.maxDetected || 1)) hands.length = config3.hand.maxDetected || 1; - return hands; -} -async function detectFingers(input, h, config3) { - const hand3 = { - // initial values inherited from hand detect - id: h.id, - score: Math.round(100 * h.score) / 100, - boxScore: Math.round(100 * h.score) / 100, - fingerScore: 0, - box: h.box, - boxRaw: h.boxRaw, - label: h.label, - keypoints: [], - landmarks: {}, - annotations: {} - }; - if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { - const t2 = {}; - const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; - t2.crop = tfjs_esm_exports.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); - t2.div = tfjs_esm_exports.div(t2.crop, constants.tf255); - [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); - const rawScore = (await t2.score.data())[0]; - const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; - if (score >= (config3.hand.minConfidence || 0)) { - hand3.fingerScore = score; - t2.reshaped = tfjs_esm_exports.reshape(t2.keypoints, [-1, 3]); - const coordsData = await t2.reshaped.array(); - const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); - const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); - hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); - hand3.landmarks = analyze(hand3.keypoints); - for (const key of Object.keys(fingerMap)) { - hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); - } - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - } - return hand3; -} -async function predict15(input, config3) { - var _a, _b; - if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) return []; - outputSize = [input.shape[2] || 0, input.shape[1] || 0]; - skipped13++; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrame = skipped13 < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache4.hands; - } - return new Promise(async (resolve) => { - const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); - if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else { - cache4.boxes = await detectHands(input, config3); - lastTime14 = now(); - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - skipped13 = 0; - } - const oldCache = [...cache4.boxes]; - cache4.boxes.length = 0; - if (config3.cacheSensitivity > 0) { - for (let i = 0; i < cache4.hands.length; i++) { - const boxKpt = square(cache4.hands[i].keypoints, outputSize); - if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { - const boxScale = scale(boxKpt.box, boxExpandFact); - const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); - cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); - } - } - } - for (let i = 0; i < cache4.hands.length; i++) { - const bbox = calc(cache4.hands[i].keypoints, outputSize); - cache4.hands[i].box = bbox.box; - cache4.hands[i].boxRaw = bbox.boxRaw; - } - resolve(cache4.hands); - }); -} - -// src/result.ts -var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); - -// src/body/movenetcoords.ts -var movenetcoords_exports = {}; -__export(movenetcoords_exports, { - connected: () => connected3, - horizontal: () => horizontal, - kpt: () => kpt3, - relative: () => relative, - vertical: () => vertical -}); -var kpt3 = [ - // used to create part labels - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var horizontal = [ - // used to fix left vs right - ["leftEye", "rightEye"], - ["leftEar", "rightEar"], - ["leftShoulder", "rightShoulder"], - ["leftElbow", "rightElbow"], - ["leftWrist", "rightWrist"], - ["leftHip", "rightHip"], - ["leftKnee", "rightKnee"], - ["leftAnkle", "rightAnkle"] -]; -var vertical = [ - // used to remove unlikely keypoint positions - ["leftKnee", "leftShoulder"], - ["rightKnee", "rightShoulder"], - ["leftAnkle", "leftKnee"], - ["rightAnkle", "rightKnee"] -]; -var relative = [ - // used to match relative body parts - [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], - [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] -]; -var connected3 = { - // used to create body outline in annotations - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/util/interpolate.ts -var bufferedResult = empty(); -var interpolateTime = 0; -function calc2(newResult, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z; - const t0 = now(); - if (!newResult) return empty(); - const elapsed = Date.now() - newResult.timestamp; - const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; - if (newResult.canvas) bufferedResult.canvas = newResult.canvas; - if (newResult.error) bufferedResult.error = newResult.error; - if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { - bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); - } else { - for (let i = 0; i < newResult.body.length; i++) { - const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); - const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); - const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; - return { - score: newKpt.score, - part: newKpt.part, - position: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] - ], - positionRaw: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] - ], - distance: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] - ] - }; - }); - const annotations2 = {}; - let coords = { connected: {} }; - if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) coords = efficientposecoords_exports; - else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) coords = blazeposecoords_exports; - else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) coords = movenetcoords_exports; - for (const [name, indexes] of Object.entries(coords.connected)) { - const pt = []; - for (let j = 0; j < indexes.length - 1; j++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[j]); - const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); - if (pt0 && pt1) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { - bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); - } else { - for (let i = 0; i < newResult.hand.length; i++) { - const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); - if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; - const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; - let annotations2 = {}; - if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { - bufferedResult.hand[i].annotations = newResult.hand[i].annotations; - annotations2 = bufferedResult.hand[i].annotations; - } else if (newResult.hand[i].annotations) { - for (const key of Object.keys(newResult.hand[i].annotations)) { - annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { - bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); - } else { - for (let i = 0; i < newResult.face.length; i++) { - const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); - let annotations2 = newResult.face[i].annotations; - if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) { - bufferedResult.face[i].annotations = newResult.face[i].annotations; - annotations2 = bufferedResult.face[i].annotations; - } else if (newResult.face[i].annotations) { - for (const key of Object.keys(newResult.face[i].annotations)) { - annotations2[key] = ((_i = (_h = (_g = newResult.face[i]) == null ? void 0 : _g.annotations) == null ? void 0 : _h[key]) == null ? void 0 : _i[0]) ? newResult.face[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - if (newResult.face[i].rotation) { - const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; - rotation.matrix = (_j = newResult.face[i].rotation) == null ? void 0 : _j.matrix; - rotation.angle = { - roll: ((bufferedFactor - 1) * (((_l = (_k = bufferedResult.face[i].rotation) == null ? void 0 : _k.angle) == null ? void 0 : _l.roll) || 0) + (((_n = (_m = newResult.face[i].rotation) == null ? void 0 : _m.angle) == null ? void 0 : _n.roll) || 0)) / bufferedFactor, - yaw: ((bufferedFactor - 1) * (((_p = (_o = bufferedResult.face[i].rotation) == null ? void 0 : _o.angle) == null ? void 0 : _p.yaw) || 0) + (((_r = (_q = newResult.face[i].rotation) == null ? void 0 : _q.angle) == null ? void 0 : _r.yaw) || 0)) / bufferedFactor, - pitch: ((bufferedFactor - 1) * (((_t = (_s = bufferedResult.face[i].rotation) == null ? void 0 : _s.angle) == null ? void 0 : _t.pitch) || 0) + (((_v = (_u = newResult.face[i].rotation) == null ? void 0 : _u.angle) == null ? void 0 : _v.pitch) || 0)) / bufferedFactor - }; - rotation.gaze = { - // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive - bearing: ((bufferedFactor - 1) * (((_w = bufferedResult.face[i].rotation) == null ? void 0 : _w.gaze.bearing) || 0) + (((_x = newResult.face[i].rotation) == null ? void 0 : _x.gaze.bearing) || 0)) / bufferedFactor, - strength: ((bufferedFactor - 1) * (((_y = bufferedResult.face[i].rotation) == null ? void 0 : _y.gaze.strength) || 0) + (((_z = newResult.face[i].rotation) == null ? void 0 : _z.gaze.strength) || 0)) / bufferedFactor - }; - bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations: annotations2 }; - } else { - bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations: annotations2 }; - } - } - } - if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { - bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); - } else { - for (let i = 0; i < newResult.object.length; i++) { - const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); - bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; - } - } - if (newResult.persons) { - const newPersons = newResult.persons; - if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { - bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); - } else { - for (let i = 0; i < newPersons.length; i++) { - bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); - } - } - } - if (newResult.gesture) bufferedResult.gesture = newResult.gesture; - bufferedResult.width = newResult.width; - bufferedResult.height = newResult.height; - const t1 = now(); - interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); - if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; - return bufferedResult; -} - -// src/segmentation/meet.ts -var model17; -async function load15(config3) { - if (!model17 || env.initial) model17 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model17["modelUrl"]); - return model17; -} -async function predict16(input, config3) { - var _a; - if (!model17) model17 = await load15(config3); - if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) return null; - const t2 = {}; - t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); - t2.norm = tfjs_esm_exports.div(t2.resize, constants.tf255); - t2.res = model17.execute(t2.norm); - t2.squeeze = tfjs_esm_exports.squeeze(t2.res, [0]); - [t2.bgRaw, t2.fgRaw] = tfjs_esm_exports.unstack(t2.squeeze, 2); - t2.fg = tfjs_esm_exports.softmax(t2.fgRaw); - t2.mul = tfjs_esm_exports.mul(t2.fg, constants.tf255); - t2.expand = tfjs_esm_exports.expandDims(t2.mul, 2); - t2.output = tfjs_esm_exports.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tfjs_esm_exports.squeeze(input); - t2.concat = tfjs_esm_exports.concat([t2.input, t2.output], -1); - rgba = tfjs_esm_exports.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tfjs_esm_exports.cast(t2.output, "int32"); - break; - default: - rgba = tfjs_esm_exports.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return rgba; -} - -// src/face/match.ts -var match_exports = {}; -__export(match_exports, { - distance: () => distance, - find: () => find, - similarity: () => similarity -}); -function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { - if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER; - let sum3 = 0; - for (let i = 0; i < descriptor1.length; i++) { - const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); - sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; - } - const dist = Math.round(100 * (options4.multiplier || 20) * sum3) / 100; - return dist; -} -var normalizeDistance = (dist, order, min2, max5) => { - if (dist === 0) return 1; - const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); - const norm = (1 - root / 100 - min2) / (max5 - min2); - const clamp2 = Math.round(100 * Math.max(Math.min(norm, 1), 0)) / 100; - return clamp2; -}; -function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { - const dist = distance(descriptor1, descriptor2, options4); - return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); -} -function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { - if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { - return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; - } - let lowestDistance = Number.MAX_SAFE_INTEGER; - let index2 = -1; - for (let i = 0; i < descriptors.length; i++) { - const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; - if (res < lowestDistance) { - lowestDistance = res; - index2 = i; - } - if (lowestDistance < (options4.threshold || 0)) break; - } - const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); - return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; -} - -// src/models.ts -var models_exports2 = {}; -__export(models_exports2, { - Models: () => Models, - validateModel: () => validateModel -}); - -// src/body/movenetfix.ts -var maxJitter = 5e-3; -var cache5 = { - keypoints: [], - padding: [[0, 0], [0, 0], [0, 0], [0, 0]] -}; -function bodyParts(body4) { - for (const pair of horizontal) { - const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); - if (body4.keypoints[left] && body4.keypoints[right]) { - if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } - } - for (const pair of vertical) { - const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - if (body4.keypoints[lower] && body4.keypoints[higher]) { - if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { - body4.keypoints.splice(lower, 1); - } - } - } - for (const [pair, compare2] of relative) { - const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); - const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); - if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) continue; - const distanceLeft = body4.keypoints[left] ? [ - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) - ] : [0, 0]; - const distanceRight = body4.keypoints[right] ? [ - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) - ] : [0, 0]; - if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } -} -function jitter(keypoints) { - for (let i = 0; i < keypoints.length; i++) { - if (keypoints[i] && cache5.keypoints[i]) { - const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; - if (diff[0] < maxJitter && diff[1] < maxJitter) { - keypoints[i] = cache5.keypoints[i]; - } else { - cache5.keypoints[i] = keypoints[i]; - } - } else { - cache5.keypoints[i] = keypoints[i]; - } - } - return keypoints; -} -function padInput(input, inputSize10) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) return input; - cache5.padding = [ - [0, 0], - // dont touch batch - [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], - // height before&after - [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], - // width before&after - [0, 0] - // dont touch rbg - ]; - t2.pad = tfjs_esm_exports.pad(input, cache5.padding); - t2.resize = tfjs_esm_exports.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); - const final = tfjs_esm_exports.cast(t2.resize, "int32"); - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return final; -} -function rescaleBody(body4, outputSize2) { - body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); - for (const kpt4 of body4.keypoints) { - kpt4.position = [ - kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], - kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] - ]; - kpt4.positionRaw = [ - kpt4.position[0] / outputSize2[0], - kpt4.position[1] / outputSize2[1] - ]; - } - const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); - body4.box = rescaledBoxes.box; - body4.boxRaw = rescaledBoxes.boxRaw; - return body4; -} - -// src/body/movenet.ts -var model18; -var inputSize8 = 0; -var skipped14 = Number.MAX_SAFE_INTEGER; -var cache6 = { - boxes: [], - bodies: [], - last: 0 -}; -async function load16(config3) { - var _a; - if (env.initial) model18 = null; - if (!model18) { - fakeOps(["size"], config3); - model18 = await loadModel(config3.body.modelPath); - } else if (config3.debug) log("cached model:", model18["modelUrl"]); - inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; - if (inputSize8 < 64) inputSize8 = 256; - if (tfjs_esm_exports.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tfjs_esm_exports.env().set("WEBGL_USE_SHAPES_UNIFORMS", false); - return model18; -} -function parseSinglePose(res, config3, image28) { - const kpt4 = res[0][0]; - const keypoints = []; - let score = 0; - for (let id = 0; id < kpt4.length; id++) { - score = kpt4[id][2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[id][1], kpt4[id][0]]; - keypoints.push({ - score: Math.round(100 * score) / 100, - part: kpt3[id], - positionRaw, - position: [ - // normalized to input image size - Math.round((image28.shape[2] || 0) * positionRaw[0]), - Math.round((image28.shape[1] || 0) * positionRaw[1]) - ] - }); - } - } - score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const bodies = []; - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - return bodies; -} -function parseMultiPose(res, config3, image28) { - const bodies = []; - for (let id = 0; id < res[0].length; id++) { - const kpt4 = res[0][id]; - const boxScore = Math.round(100 * kpt4[51 + 4]) / 100; - if (boxScore > config3.body.minConfidence) { - const keypoints = []; - for (let i = 0; i < 17; i++) { - const score = kpt4[3 * i + 2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; - keypoints.push({ - part: kpt3[i], - score: Math.round(100 * score) / 100, - positionRaw, - position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] - }); - } - } - const boxRaw = [kpt4[51 + 1], kpt4[51 + 0], kpt4[51 + 3] - kpt4[51 + 1], kpt4[51 + 2] - kpt4[51 + 0]]; - const boxNorm = [Math.trunc(boxRaw[0] * (image28.shape[2] || 0)), Math.trunc(boxRaw[1] * (image28.shape[1] || 0)), Math.trunc(boxRaw[2] * (image28.shape[2] || 0)), Math.trunc(boxRaw[3] * (image28.shape[1] || 0))]; - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - } - } - bodies.sort((a, b) => b.score - a.score); - if (bodies.length > config3.body.maxDetected) bodies.length = config3.body.maxDetected; - return bodies; -} -async function predict17(input, config3) { - var _a; - if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) return []; - if (!config3.skipAllowed) cache6.boxes.length = 0; - skipped14++; - const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; - const skipFrame = skipped14 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache6.bodies; - } - return new Promise(async (resolve) => { - const t2 = {}; - skipped14 = 0; - t2.input = padInput(input, inputSize8); - t2.res = model18 == null ? void 0 : model18.execute(t2.input); - cache6.last = now(); - const res = await t2.res.array(); - cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); - for (const body4 of cache6.bodies) { - rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); - jitter(body4.keypoints); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - resolve(cache6.bodies); - }); -} - -// src/object/nanodet.ts -var model19; -var last10 = []; -var lastTime15 = 0; -var skipped15 = Number.MAX_SAFE_INTEGER; -var inputSize9 = 0; -var scaleBox = 2.5; -async function load17(config3) { - if (!model19 || env.initial) { - model19 = await loadModel(config3.object.modelPath); - const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; - inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; - } else if (config3.debug) log("cached model:", model19["modelUrl"]); - return model19; -} -async function process4(res, outputShape, config3) { - var _a, _b; - let id = 0; - let results = []; - const size2 = inputSize9; - for (const strideSize of [1, 2, 4]) { - const baseSize = strideSize * 13; - const scoresT = tfjs_esm_exports.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); - const scores = await scoresT.array(); - const featuresT = tfjs_esm_exports.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); - const boxesMaxT = tfjs_esm_exports.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); - const boxIdxT = tfjs_esm_exports.argMax(boxesMaxT, 2); - const boxIdx = await boxIdxT.array(); - for (let i = 0; i < scoresT.shape[0]; i++) { - for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { - const score = scores[i][j]; - if (score > (config3.object.minConfidence || 0) && j !== 61) { - const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; - const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; - const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); - const [x, y] = [ - cx - scaleBox / strideSize * boxOffset[0], - cy - scaleBox / strideSize * boxOffset[1] - ]; - const [w, h] = [ - cx + scaleBox / strideSize * boxOffset[2] - x, - cy + scaleBox / strideSize * boxOffset[3] - y - ]; - let boxRaw = [x, y, w, h]; - boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); - const box = [ - // results normalized to input image pixels - boxRaw[0] * outputShape[0], - boxRaw[1] * outputShape[1], - boxRaw[2] * outputShape[0], - boxRaw[3] * outputShape[1] - ]; - const result = { - id: id++, - // strideSize, - score: Math.round(100 * score) / 100, - class: j + 1, - label: labels2[j].label, - // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)], - // centerRaw: [cx, cy], - box: box.map((a) => Math.trunc(a)), - boxRaw - }; - results.push(result); - } - } - } - tfjs_esm_exports.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); - } - const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); - const nmsScores = results.map((a) => a.score); - let nmsIdx = []; - if (nmsBoxes && nmsBoxes.length > 0) { - const nms = await tfjs_esm_exports.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); - nmsIdx = Array.from(await nms.data()); - tfjs_esm_exports.dispose(nms); - } - results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); - return results; -} -async function predict18(image28, config3) { - if (!(model19 == null ? void 0 : model19["executor"])) return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; - const skipFrame = skipped15 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { - skipped15++; - return last10; - } - skipped15 = 0; - if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) return last10; - return new Promise(async (resolve) => { - const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; - const resizeT = tfjs_esm_exports.image.resizeBilinear(image28, [inputSize9, inputSize9], false); - const normT = tfjs_esm_exports.div(resizeT, constants.tf255); - const transposeT = tfjs_esm_exports.transpose(normT, [0, 3, 1, 2]); - let objectT; - if (config3.object.enabled) objectT = model19.execute(transposeT); - lastTime15 = now(); - const obj = await process4(objectT, outputSize2, config3); - last10 = obj; - tfjs_esm_exports.dispose([resizeT, normT, transposeT, ...objectT]); - resolve(obj); - }); -} - -// src/body/posenetutils.ts -var partNames = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var count = partNames.length; -var partIds = partNames.reduce((result, jointName, i) => { - result[jointName] = i; - return result; -}, {}); -var connectedPartNames = [ - ["leftHip", "leftShoulder"], - ["leftElbow", "leftShoulder"], - ["leftElbow", "leftWrist"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["rightHip", "rightShoulder"], - ["rightElbow", "rightShoulder"], - ["rightElbow", "rightWrist"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"], - ["leftShoulder", "rightShoulder"], - ["leftHip", "rightHip"] -]; -var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); -var poseChain = [ - ["nose", "leftEye"], - ["leftEye", "leftEar"], - ["nose", "rightEye"], - ["rightEye", "rightEar"], - ["nose", "leftShoulder"], - ["leftShoulder", "leftElbow"], - ["leftElbow", "leftWrist"], - ["leftShoulder", "leftHip"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["nose", "rightShoulder"], - ["rightShoulder", "rightElbow"], - ["rightElbow", "rightWrist"], - ["rightShoulder", "rightHip"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"] -]; -function getBoundingBox(keypoints) { - const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ - maxX: Math.max(maxX, x), - maxY: Math.max(maxY, y), - minX: Math.min(minX, x), - minY: Math.min(minY, y) - }), { - maxX: Number.NEGATIVE_INFINITY, - maxY: Number.NEGATIVE_INFINITY, - minX: Number.POSITIVE_INFINITY, - minY: Number.POSITIVE_INFINITY - }); - return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; -} -function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { - const scaleY = height / inputResolutionHeight; - const scaleX = width / inputResolutionWidth; - const scalePose = (pose, i) => ({ - id: i, - score: pose.score, - boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], - box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], - keypoints: pose.keypoints.map(({ score, part, position }) => ({ - score, - part, - position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], - positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] - })), - annotations: {} - }); - const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); - return scaledPoses; -} -var MaxHeap = class { - // function call - constructor(maxSize2, getElementValue) { - __publicField(this, "priorityQueue"); - // don't touch - __publicField(this, "numberOfElements"); - __publicField(this, "getElementValue"); - this.priorityQueue = new Array(maxSize2); - this.numberOfElements = -1; - this.getElementValue = getElementValue; - } - enqueue(x) { - this.priorityQueue[++this.numberOfElements] = x; - this.swim(this.numberOfElements); - } - dequeue() { - const max5 = this.priorityQueue[0]; - this.exchange(0, this.numberOfElements--); - this.sink(0); - this.priorityQueue[this.numberOfElements + 1] = null; - return max5; - } - empty() { - return this.numberOfElements === -1; - } - size() { - return this.numberOfElements + 1; - } - all() { - return this.priorityQueue.slice(0, this.numberOfElements + 1); - } - max() { - return this.priorityQueue[0]; - } - swim(k) { - while (k > 0 && this.less(Math.floor(k / 2), k)) { - this.exchange(k, Math.floor(k / 2)); - k = Math.floor(k / 2); - } - } - sink(k) { - while (2 * k <= this.numberOfElements) { - let j = 2 * k; - if (j < this.numberOfElements && this.less(j, j + 1)) j++; - if (!this.less(k, j)) break; - this.exchange(k, j); - k = j; - } - } - getValueAt(i) { - return this.getElementValue(this.priorityQueue[i]); - } - less(i, j) { - return this.getValueAt(i) < this.getValueAt(j); - } - exchange(i, j) { - const t2 = this.priorityQueue[i]; - this.priorityQueue[i] = this.priorityQueue[j]; - this.priorityQueue[j] = t2; - } -}; -function getOffsetPoint(y, x, keypoint, offsets) { - return { - y: offsets.get(y, x, keypoint), - x: offsets.get(y, x, keypoint + count) - }; -} -function getImageCoords(part, outputStride2, offsets) { - const { heatmapY, heatmapX, id: keypoint } = part; - const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); - return { - x: part.heatmapX * outputStride2 + x, - y: part.heatmapY * outputStride2 + y - }; -} -function clamp(a, min2, max5) { - if (a < min2) return min2; - if (a > max5) return max5; - return a; -} -function squaredDistance(y1, x1, y2, x2) { - const dy = y2 - y1; - const dx = x2 - x1; - return dy * dy + dx * dx; -} -function addVectors(a, b) { - return { x: a.x + b.x, y: a.y + b.y }; -} - -// src/body/posenet.ts -var model20; -var poseNetOutputs = [ - "MobilenetV1/offset_2/BiasAdd", - "MobilenetV1/heatmap_2/BiasAdd", - "MobilenetV1/displacement_fwd_2/BiasAdd", - "MobilenetV1/displacement_bwd_2/BiasAdd" - /* displacementBwd */ -]; -var localMaximumRadius = 1; -var outputStride = 16; -var squaredNmsRadius = 50 ** 2; -function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { - const getDisplacement = (point2) => ({ - y: displacements.get(point2.y, point2.x, edgeId), - x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) - }); - const getStridedIndexNearPoint = (point2, height2, width2) => ({ - y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), - x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) - }); - const [height, width] = scores.shape; - const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); - const displacement = getDisplacement(sourceKeypointIndices); - const displacedPoint = addVectors(sourceKeypoint.position, displacement); - let targetKeypoint = displacedPoint; - for (let i = 0; i < offsetRefineStep; i++) { - const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); - targetKeypoint = addVectors( - { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, - { x: offsetPoint.x, y: offsetPoint.y } - ); - } - const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); - return { position: targetKeypoint, part: partNames[targetId], score }; -} -function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { - const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); - const edgesFwd = tuples.map(([, childJointId]) => childJointId); - const edgesBwd = tuples.map(([parentJointId]) => parentJointId); - const numParts = scores.shape[2]; - const numEdges = edgesFwd.length; - const keypoints = new Array(numParts); - const rootPoint = getImageCoords(root.part, outputStride, offsets); - keypoints[root.part.id] = { - score: root.score, - part: partNames[root.part.id], - position: rootPoint - }; - for (let edge = numEdges - 1; edge >= 0; --edge) { - const sourceId = edgesFwd[edge]; - const targetId = edgesBwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); - } - } - for (let edge = 0; edge < numEdges; ++edge) { - const sourceId = edgesBwd[edge]; - const targetId = edgesFwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); - } - } - return keypoints; -} -function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { - const [height, width] = scores.shape; - let localMaximum = true; - const yStart = Math.max(heatmapY - localMaximumRadius, 0); - const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); - for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { - const xStart = Math.max(heatmapX - localMaximumRadius, 0); - const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); - for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { - if (scores.get(yCurrent, xCurrent, keypointId) > score) { - localMaximum = false; - break; - } - } - if (!localMaximum) break; - } - return localMaximum; -} -function buildPartWithScoreQueue(minConfidence2, scores) { - const [height, width, numKeypoints] = scores.shape; - const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); - for (let heatmapY = 0; heatmapY < height; ++heatmapY) { - for (let heatmapX = 0; heatmapX < width; ++heatmapX) { - for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { - const score = scores.get(heatmapY, heatmapX, keypointId); - if (score < minConfidence2) continue; - if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); - } - } - } - return queue; -} -function withinRadius(poses, { x, y }, keypointId) { - return poses.some(({ keypoints }) => { - var _a; - const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; - if (!correspondingKeypoint) return false; - return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; - }); -} -function getInstanceScore(existingPoses, keypoints) { - const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { - if (!withinRadius(existingPoses, position, keypointId)) result += score; - return result; - }, 0); - return notOverlappedKeypointScores / keypoints.length; -} -function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { - const poses = []; - const queue = buildPartWithScoreQueue(minConfidence2, scores); - while (poses.length < maxDetected && !queue.empty()) { - const root = queue.dequeue(); - const rootImageCoords = getImageCoords(root.part, outputStride, offsets); - if (withinRadius(poses, rootImageCoords, root.part.id)) continue; - let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); - keypoints = keypoints.filter((a) => a.score > minConfidence2); - const score = getInstanceScore(poses, keypoints); - const box = getBoundingBox(keypoints); - if (score > minConfidence2) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); - } - return poses; -} -async function predict19(input, config3) { - if (!(model20 == null ? void 0 : model20["executor"])) return []; - const res = tfjs_esm_exports.tidy(() => { - if (!model20.inputs[0].shape) return []; - const resized = tfjs_esm_exports.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - const normalized = tfjs_esm_exports.sub(tfjs_esm_exports.div(tfjs_esm_exports.cast(resized, "float32"), 127.5), 1); - const results = model20.execute(normalized, poseNetOutputs); - const results3d = results.map((y) => tfjs_esm_exports.squeeze(y, [0])); - results3d[1] = tfjs_esm_exports.sigmoid(results3d[1]); - return results3d; - }); - const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); - for (const t2 of res) tfjs_esm_exports.dispose(t2); - const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); - if (!model20.inputs[0].shape) return []; - const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - return scaled; -} -async function load18(config3) { - if (!model20 || env.initial) model20 = await loadModel(config3.body.modelPath); - else if (config3.debug) log("cached model:", model20["modelUrl"]); - return model20; -} - -// src/segmentation/rvm.ts -var model21; -var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; -var t = {}; -var ratio = 0; -function init3(config3) { - tfjs_esm_exports.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); - t.r1i = tfjs_esm_exports.tensor(0); - t.r2i = tfjs_esm_exports.tensor(0); - t.r3i = tfjs_esm_exports.tensor(0); - t.r4i = tfjs_esm_exports.tensor(0); - ratio = config3.segmentation.ratio || 0.5; - t.downsample_ratio = tfjs_esm_exports.tensor(ratio); -} -async function load19(config3) { - if (!model21 || env.initial) model21 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model21["modelUrl"]); - init3(config3); - return model21; -} -var normalize = (r) => tfjs_esm_exports.tidy(() => { - const squeeze14 = tfjs_esm_exports.squeeze(r, [0]); - const mul15 = tfjs_esm_exports.mul(squeeze14, constants.tf255); - const cast8 = tfjs_esm_exports.cast(mul15, "int32"); - return cast8; -}); -function getRGBA(fgr, pha) { - const rgb3 = fgr ? normalize(fgr) : tfjs_esm_exports.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); - const a = pha ? normalize(pha) : tfjs_esm_exports.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); - const rgba = tfjs_esm_exports.concat([rgb3, a], -1); - tfjs_esm_exports.dispose([rgb3, a]); - return rgba; -} -function getState(state) { - return tfjs_esm_exports.tidy(() => { - const r = {}; - r.unstack = tfjs_esm_exports.unstack(state, -1); - r.concat = tfjs_esm_exports.concat(r.unstack, 1); - r.split = tfjs_esm_exports.split(r.concat, 4, 1); - r.stack = tfjs_esm_exports.concat(r.split, 2); - r.squeeze = tfjs_esm_exports.squeeze(r.stack, [0]); - r.expand = tfjs_esm_exports.expandDims(r.squeeze, -1); - r.add = tfjs_esm_exports.add(r.expand, 1); - r.mul = tfjs_esm_exports.mul(r.add, 127.5); - r.cast = tfjs_esm_exports.cast(r.mul, "int32"); - r.tile = tfjs_esm_exports.tile(r.cast, [1, 1, 3]); - r.alpha = tfjs_esm_exports.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); - return tfjs_esm_exports.concat([r.tile, r.alpha], -1); - }); -} -async function predict20(input, config3) { - if (!model21) model21 = await load19(config3); - if (!(model21 == null ? void 0 : model21["executor"])) return null; - t.src = tfjs_esm_exports.div(input, 255); - if (ratio !== config3.segmentation.ratio) init3(config3); - const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - rgba = getRGBA(fgr, pha); - break; - case "alpha": - rgba = getRGBA(null, pha); - break; - case "foreground": - rgba = getRGBA(fgr, null); - break; - case "state": - rgba = getState(r1o); - break; - default: - rgba = tfjs_esm_exports.tensor(0); - } - tfjs_esm_exports.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); - [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; - return rgba; -} - -// src/segmentation/selfie.ts -var model22; -async function load20(config3) { - if (!model22 || env.initial) model22 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model22["modelUrl"]); - return model22; -} -async function predict21(input, config3) { - var _a; - if (!model22) model22 = await load20(config3); - if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) return null; - const t2 = {}; - t2.resize = tfjs_esm_exports.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); - t2.norm = tfjs_esm_exports.div(t2.resize, constants.tf255); - t2.res = model22.execute(t2.norm); - t2.squeeze = tfjs_esm_exports.squeeze(t2.res, [0]); - t2.alpha = tfjs_esm_exports.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); - t2.mul = tfjs_esm_exports.mul(t2.alpha, constants.tf255); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tfjs_esm_exports.squeeze(input); - t2.concat = tfjs_esm_exports.concat([t2.input, t2.mul], -1); - rgba = tfjs_esm_exports.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tfjs_esm_exports.cast(t2.mul, "int32"); - break; - default: - rgba = tfjs_esm_exports.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tfjs_esm_exports.dispose(t2[tensor6])); - return rgba; -} - -// src/models.ts -function validateModel(instance, model23, name) { - var _a, _b; - if (!model23) return null; - if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) return null; - const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; - const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; - const ops = []; - const missing = []; - const url = model23["modelUrl"]; - const executor = model23["executor"]; - if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { - for (const kernel of Object.values(executor.graph.nodes)) { - const op = kernel.op.toLowerCase(); - if (!ops.includes(op)) ops.push(op); - } - } else { - if (!executor && instance.config.debug) { - log("model not loaded", name); - } - } - for (const op of ops) { - if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { - missing.push(op); - } - } - if (instance.config.debug && missing.length > 0) log("model validation failed:", name, missing); - return missing.length > 0 ? { name, missing, ops, url } : null; -} -var Models = class { - constructor(currentInstance) { - __publicField(this, "instance"); - __publicField(this, "models", {}); - this.models = {}; - this.instance = currentInstance; - } - stats() { - let totalSizeFromManifest = 0; - let totalSizeWeights = 0; - let totalSizeLoading = 0; - for (const m of Object.values(modelStats)) { - totalSizeFromManifest += Number.isNaN(+m.sizeFromManifest) ? 0 : m.sizeFromManifest; - totalSizeWeights += Number.isNaN(+m.sizeLoadedWeights) ? 0 : m.sizeLoadedWeights; - totalSizeLoading += Number.isNaN(+m.sizeDesired) ? 0 : m.sizeDesired; - } - const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; - return { - numLoadedModels: Object.values(modelStats).filter((m) => m == null ? void 0 : m.loaded).length, - numDefinedModels: Object.keys(this.models).length, - percentageLoaded, - totalSizeFromManifest, - totalSizeWeights, - totalSizeLoading, - modelStats: Object.values(modelStats) - }; - } - reset() { - for (const model23 of Object.keys(this.models)) this.models[model23] = null; - } - async load(instance) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; - if (env.initial) this.reset(); - if (instance) this.instance = instance; - const m = {}; - m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; - m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; - m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; - m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; - m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; - m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; - m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; - m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; - m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; - m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; - m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; - m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; - m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; - m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; - m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; - m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load16(this.instance.config) : null; - m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load18(this.instance.config) : null; - m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect3(this.instance.config) : null; - m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton2(this.instance.config) : null; - if (this.instance.config.hand.enabled && !this.models.handdetect && ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect"))) { - m.handdetect = loadDetect2(this.instance.config); - m.handskeleton = loadSkeleton(this.instance.config); - } - m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; - m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load17(this.instance.config) : null; - m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load20(this.instance.config) : null; - m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load15(this.instance.config) : null; - m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load19(this.instance.config) : null; - for (const [model23, promise] of Object.entries(m)) { - if (promise == null ? void 0 : promise["then"]) promise["then"]((val) => this.models[model23] = val); - } - await Promise.all(Object.values(m)); - } - list() { - const models3 = Object.keys(this.models).map((model23) => { - var _a; - return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; - }); - for (const m of models3) { - const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); - if (!stats) continue; - m.size = modelStats[stats].sizeLoadedWeights; - m.url = modelStats[stats].url; - } - return models3; - } - loaded() { - const list = this.list(); - const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); - return loaded; - } - validate() { - const missing = []; - for (const defined of Object.keys(this.models)) { - const model23 = this.models[defined]; - if (!model23) continue; - const res = validateModel(this.instance, model23, defined); - if (res) missing.push(res); - } - return missing; - } -}; - -// src/util/persons.ts -function join2(faces, bodies, hands, gestures, shape) { - var _a, _b, _c, _d, _e, _f; - let id = 0; - const persons = []; - for (const face4 of faces) { - const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; - for (const body4 of bodies) { - if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { - person2.body = body4; - } - } - if (person2.body) { - for (const hand3 of hands) { - if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) person2.hands.left = hand3; - } - if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) person2.hands.right = hand3; - } - } - } - for (const gesture2 of gestures) { - if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) person2.gestures.push(gesture2); - else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) person2.gestures.push(gesture2); - else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) person2.gestures.push(gesture2); - } - const x = []; - const y = []; - const extractXY = (box) => { - if (box && box.length === 4) { - x.push(box[0], box[0] + box[2]); - y.push(box[1], box[1] + box[3]); - } - }; - extractXY(person2.face.box); - extractXY((_d = person2.body) == null ? void 0 : _d.box); - extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); - extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); - const minX = Math.min(...x); - const minY = Math.min(...y); - person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; - if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; - persons.push(person2); - } - return persons; -} - -// src/sample.ts -var face3 = ` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var gt=0;function _r(e,t,n){let o=Q(n0,n);if(!t||!e)return;let s=O0(e);if(s){s.lineJoin="round",s.font=o.font;for(let A=0;AMt,kpt:()=>Rt});var Rt=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],Mt={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var W0,ke=224,y1,ts=5,P2=[8,16,32,32,32];function ns(){let e=[],t=0;for(;tn.x)),y:r.tensor1d(e.map(n=>n.y))}}async function f1(e){if(M.initial&&(W0=null),!W0&&e.body.detector&&e.body.detector.modelPath){W0=await L(e.body.detector.modelPath);let t=W0!=null&&W0.executor?Object.values(W0.modelSignature.inputs):void 0;ke=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&W0&&g("cached model:",W0.modelUrl);return ns(),W0}var x1=[5,5];function os(e,t){return r.tidy(()=>{let n=r.split(e,12,1),o=r.squeeze(n[0]),s=r.squeeze(n[1]),A=r.squeeze(n[2]),a=r.squeeze(n[3]);o=r.add(r.div(o,ke),t.x),s=r.add(r.div(s,ke),t.y),A=r.mul(r.div(A,ke),x1[0]),a=r.mul(r.div(a,ke),x1[1]);let i=r.sub(o,r.div(A,2)),c=r.sub(s,r.div(a,2)),d=r.add(i,A),x=r.add(c,a);return r.stack([i,c,d,x],1)})}async function rs(e,t,n,o){var d,x;let s=[],A={};A.boxes=os(e,y1),A.scores=r.sigmoid(t),A.nms=await r.image.nonMaxSuppressionAsync(A.boxes,A.scores,1,((d=n.body.detector)==null?void 0:d.minConfidence)||.1,((x=n.body.detector)==null?void 0:x.iouThreshold)||.1);let a=await A.nms.data(),i=await A.scores.data(),c=await A.boxes.array();for(let l of Array.from(a)){let f=i[l],y=c[l],p=[Math.round(y[0]*o[0]),Math.round(y[1]*o[1]),Math.round(y[2]*o[0]),Math.round(y[3]*o[1])],m={score:f,boxRaw:y,box:p};s.push(m)}return Object.keys(A).forEach(l=>r.dispose(A[l])),s}async function m1(e,t,n){let o={};o.res=W0==null?void 0:W0.execute(e,["Identity"]),o.logitsRaw=r.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=r.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=r.squeeze(o.logitsRaw),o.boxes=r.squeeze(o.boxesRaw);let s=await rs(o.boxes,o.logits,t,n);return Object.keys(o).forEach(A=>r.dispose(o[A])),s}function ie(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],o=[Math.min(...n[0]),Math.min(...n[1])],s=[Math.max(...n[0]),Math.max(...n[1])],A=[o[0],o[1],s[0]-o[0],s[1]-o[1]],a=[A[0]/t[0],A[1]/t[1],A[2]/t[0],A[3]/t[1]];return{box:A,boxRaw:a}}function p1(e,t=[1,1]){let n=[e.map(d=>d[0]),e.map(d=>d[1])],o=[Math.min(...n[0]),Math.min(...n[1])],s=[Math.max(...n[0]),Math.max(...n[1])],A=[(o[0]+s[0])/2,(o[1]+s[1])/2],a=Math.max(A[0]-o[0],A[1]-o[1],-A[0]+s[0],-A[1]+s[1]),i=[Math.trunc(A[0]-a),Math.trunc(A[1]-a),Math.trunc(2*a),Math.trunc(2*a)],c=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:c}}function k2(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var P0,kt=256,Pt=Number.MAX_SAFE_INTEGER,ss={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},E2=[],le=[[0,0],[0,0],[0,0],[0,0]],u1=0,h1=e=>1-1/(1+Math.exp(e)),g1=e=>f1(e);async function T1(e){if(M.initial&&(P0=null),P0)e.debug&&g("cached model:",P0.modelUrl);else{P0=await L(e.body.modelPath);let t=P0!=null&&P0.executor?Object.values(P0.modelSignature.inputs):void 0;kt=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return P0}function b1(e,t,n){var A,a;let o={};if(!((A=e==null?void 0:e.shape)!=null&&A[1])||!((a=e==null?void 0:e.shape)!=null&&a[2]))return e;let s;if(n&&(o.cropped=r.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let i=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],c=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];le=[[0,0],i,c,[0,0]],o.pad=r.pad(o.cropped||e,le),o.resize=r.image.resizeBilinear(o.pad,[t,t]),s=r.div(o.resize,O.tf255)}else e.shape[1]!==t?(o.resize=r.image.resizeBilinear(o.cropped||e,[t,t]),s=r.div(o.resize,O.tf255)):s=r.div(o.cropped||e,O.tf255);return Object.keys(o).forEach(i=>r.dispose(o[i])),s}function As(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+le[2][0]+le[2][1])/t[0]-le[2][0]),Math.trunc(o.position[1]*(t[1]+le[1][0]+le[1][1])/t[1]-le[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(n){let o=n[2]-n[0],s=n[3]-n[1];for(let A of e)A.positionRaw=[A.positionRaw[0]/s+n[1],A.positionRaw[1]/o+n[0],A.positionRaw[2]],A.position=[Math.trunc(A.positionRaw[0]*t[0]),Math.trunc(A.positionRaw[1]*t[1]),A.positionRaw[2]]}return e}function as(e){let t=e.find(i=>i.part==="leftPalm"),n=e.find(i=>i.part==="leftWrist"),o=e.find(i=>i.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let s=e.find(i=>i.part==="rightPalm"),A=e.find(i=>i.part==="rightWrist"),a=e.find(i=>i.part==="rightIndex");s.position[2]=((A.position[2]||0)+(a.position[2]||0))/2}async function is(e,t,n){if(!(P0!=null&&P0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=P0==null?void 0:P0.execute(e,ss.landmarks);let s=(await o.poseflag.data())[0],A=await o.ld.data(),a=await o.world.data();Object.keys(o).forEach(p=>r.dispose(o[p]));let i=[],c=5;for(let p=0;pp.position),l=ie(x,[n[0],n[1]]),f={};for(let[p,m]of Object.entries(Mt)){let b=[];for(let v=0;vh.part===m[v]),u=d.find(h=>h.part===m[v+1]);T&&u&&b.push([T.position,u.position])}f[p]=b}return{id:0,score:Math.trunc(100*s)/100,box:l.box,boxRaw:l.boxRaw,keypoints:d,annotations:f}}async function wt(e,t){var A,a,i;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>R()-u1,s=Pt<(t.body.skipFrames||0);if(t.skipAllowed&&o&&s&&E2!==null)Pt++;else{let c=[];if((a=(A=t.body)==null?void 0:A.detector)!=null&&a.enabled){let d=b1(e,224);c=await m1(d,t,n),r.dispose(d)}else c=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let d=0;dr.dispose(o[d])),s}async function St(e,t){if(!(k0!=null&&k0.executor))return[];let n=(t.object.skipTime||0)>R()-R1,o=zt<(t.object.skipFrames||0);return t.skipAllowed&&n&&o&&Et.length>0?(zt++,Et):(zt=0,new Promise(async s=>{let A=[e.shape[2]||0,e.shape[1]||0],a=r.image.resizeBilinear(e,[we,we]),i=t.object.enabled?k0==null?void 0:k0.execute(a,["tower_0/detections"]):null;R1=R(),r.dispose(a);let c=await ls(i,A,t);Et=c,s(c)}))}var z2={};re(z2,{connected:()=>Nt,kpt:()=>jt});var jt=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],Nt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var _,k1=0,v0={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},It=Number.MAX_SAFE_INTEGER;async function w1(e){return M.initial&&(_=null),_?e.debug&&g("cached model:",_.modelUrl):_=await L(e.body.modelPath),_}async function cs(e,t){let[n,o]=e.shape,s=r.reshape(e,[o*n]),A=r.max(s,0),a=(await A.data())[0];if(a>t){let i=r.argMax(s,0),c=r.mod(i,n),d=(await c.data())[0],x=r.div(i,n),l=(await x.data())[0];return r.dispose([s,A,i,c,x]),[d,l,a]}return r.dispose([s,A]),[0,0,a]}async function Lt(e,t){if(!(_!=null&&_.executor)||!(_!=null&&_.inputs[0].shape))return[];let n=(t.body.skipTime||0)>R()-k1,o=It<(t.body.skipFrames||0);return t.skipAllowed&&n&&o&&Object.keys(v0.keypoints).length>0?(It++,[v0]):(It=0,new Promise(async s=>{let A=r.tidy(()=>{var p,m;let l=r.image.resizeBilinear(e,[((p=_==null?void 0:_.inputs[0].shape)==null?void 0:p[2])||0,((m=_==null?void 0:_.inputs[0].shape)==null?void 0:m[1])||0],!1),f=r.mul(l,O.tf2);return r.sub(f,O.tf1)}),a;if(t.body.enabled&&(a=_==null?void 0:_.execute(A)),k1=R(),r.dispose(A),a){v0.keypoints.length=0;let l=r.squeeze(a);r.dispose(a);let f=r.unstack(l,2);r.dispose(l);for(let y=0;y(t.body.minConfidence||0)&&v0.keypoints.push({score:Math.round(100*b)/100,part:jt[y],positionRaw:[p/_.inputs[0].shape[2],m/_.inputs[0].shape[1]],position:[Math.round(e.shape[2]*p/_.inputs[0].shape[2]),Math.round(e.shape[1]*m/_.inputs[0].shape[1])]})}f.forEach(y=>r.dispose(y))}v0.score=v0.keypoints.reduce((l,f)=>f.score>l?f.score:l,0);let i=v0.keypoints.map(l=>l.position[0]),c=v0.keypoints.map(l=>l.position[1]);v0.box=[Math.min(...i),Math.min(...c),Math.max(...i)-Math.min(...i),Math.max(...c)-Math.min(...c)];let d=v0.keypoints.map(l=>l.positionRaw[0]),x=v0.keypoints.map(l=>l.positionRaw[1]);v0.boxRaw=[Math.min(...d),Math.min(...x),Math.max(...d)-Math.min(...d),Math.max(...x)-Math.min(...x)];for(let[l,f]of Object.entries(Nt)){let y=[];for(let p=0;pv.part===f[p]),b=v0.keypoints.find(v=>v.part===f[p+1]);m&&b&&m.score>(t.body.minConfidence||0)&&b.score>(t.body.minConfidence||0)&&y.push([m.position,b.position])}v0.annotations[l]=y}s([v0])}))}var Fe=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],S2=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2,1],j2=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],N2=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],j1=(e,t,n)=>{let o=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],A=e.landmarks.map(a=>[(a[0]+n[0])*t[0],(a[1]+n[1])*t[1]]);return{startPoint:o,endPoint:s,landmarks:A,confidence:e.confidence}},Ot=(e,t,n)=>{let o=t.shape[1],s=t.shape[2],A=[e.startPoint[1]/o,e.startPoint[0]/s,e.endPoint[1]/o,e.endPoint[0]/s],a=r.image.cropAndResize(t,[A],[0],n),i=r.div(a,O.tf255);return r.dispose(a),i},I2=(e,t)=>{let n=S2(e),o=Fe(e),s=[t*o[0]/2,t*o[1]/2];return{startPoint:[n[0]-s[0],n[1]-s[1]],endPoint:[n[0]+s[0],n[1]+s[1]],landmarks:e.landmarks,confidence:e.confidence,size:o}},L2=e=>{let t=S2(e),n=Fe(e),o=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-o),Math.round(t[1]-o)],endPoint:[Math.round(t[0]+o),Math.round(t[1]+o)],landmarks:e.landmarks,confidence:e.confidence,size:[Math.round(n[0]),Math.round(n[1])]}},N1=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},Ct=[[1,0,0],[0,1,0],[0,0,1]],ds=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),xs=(e,t)=>ds(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var z1=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Ee=(e,t)=>{let n=0;for(let o=0;o{let n=[];for(let o=0;o{let n=[],o=e.length;for(let s=0;s{let n=Math.cos(e),o=Math.sin(e),s=[[n,-o,0],[o,n,0],[0,0,1]],A=z1(t[0],t[1]),a=S1(A,s),i=z1(-t[0],-t[1]);return S1(a,i)},fs=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],o=[-Ee(t[0],n),-Ee(t[1],n)];return[t[0].concat(o[0]),t[1].concat(o[1]),[0,0,1]]},ms=(e,t)=>[Ee(e,t[0]),Ee(e,t[1])];function L1(e){let t=e===192?{strides:[4],anchors:[1]}:{strides:[e/16,e/8],anchors:[2,6]},n=[];for(let o=0;o[A[0]/s*(y[0]-s/2),A[1]/s*(y[1]-s/2),y[2]||0]),i=n&&n!==0&&Math.abs(n)>.2,c=i?I1(n,[0,0]):Ct,d=i?a.map(y=>[...ms(y,c),y[2]]):a,x=i?fs(o):Ct,l=S2(t),f=[Ee(l,x[0]),Ee(l,x[1])];return d.map(y=>[Math.trunc(y[0]+f[0]),Math.trunc(y[1]+f[1]),Math.trunc(y[2]||0)])}function C1(e,t,n,o){let s=t.landmarks.length>=ut.count?ut.symmetryLine:ve.symmetryLine,A=0,a=Ct,i;if(e&&M.kernels.includes("rotatewithoffset"))if(A=xs(t.landmarks[s[0]],t.landmarks[s[1]]),A&&A!==0&&Math.abs(A)>.2){let d=S2(t),x=[d[0]/n.shape[2],d[1]/n.shape[1]],l=r.image.rotateWithOffset(n,A,0,[x[0],x[1]]);a=I1(-A,d),i=Ot(t,l,[o,o]),r.dispose(l)}else i=Ot(t,n,[o,o]);else i=Ot(t,n,[o,o]);return[A,a,i]}var ps=e=>{let t=e.map(o=>o[0]),n=e.map(o=>o[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},W1=(e,t)=>{let n=ps(e),o=Fe(t);return{startPoint:[n[0]-o[0]/2,n[1]-o[1]/2],endPoint:[n[0]+o[0]/2,n[1]+o[1]/2]}};var D1=6,G0,O2=null,K0=0,Be=null,F1=()=>K0;async function B1(e){var t;return M.initial&&(G0=null),G0?e.debug&&g("cached model:",G0.modelUrl):G0=await L((t=e.face.detector)==null?void 0:t.modelPath),K0=G0.executor&&G0.inputs[0].shape?G0.inputs[0].shape[2]:256,Be=r.scalar(K0,"int32"),O2=r.tensor2d(L1(K0)),G0}function us(e){if(!O2||!Be)return r.zeros([0,0]);let t={};t.boxStarts=r.slice(e,[0,1],[-1,2]),t.centers=r.add(t.boxStarts,O2),t.boxSizes=r.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=r.div(t.boxSizes,Be),t.centersNormalized=r.div(t.centers,Be),t.halfBoxSize=r.div(t.boxSizesNormalized,O.tf2),t.starts=r.sub(t.centersNormalized,t.halfBoxSize),t.ends=r.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=r.mul(t.starts,Be),t.endNormalized=r.mul(t.ends,Be);let n=r.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>r.dispose(t[o])),n}async function H1(e,t){var d,x,l,f,y,p,m,b,v;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={},o=[0,0],s=[1,1];if((x=(d=t==null?void 0:t.face)==null?void 0:d.detector)!=null&&x.square){let T=Math.max(e.shape[2],e.shape[1]);o=[Math.floor((T-e.shape[2])/2),Math.floor((T-e.shape[1])/2)],n.padded=r.pad(e,[[0,0],[o[1],o[1]],[o[0],o[0]],[0,0]]),s=[e.shape[2]/T,e.shape[1]/T],o=[o[0]/K0,o[1]/K0]}else n.padded=e.clone();n.resized=r.image.resizeBilinear(n.padded,[K0,K0]),n.div=r.div(n.resized,O.tf127),n.normalized=r.sub(n.div,O.tf1);let A=G0==null?void 0:G0.execute(n.normalized);if(Array.isArray(A)&&A.length>2){let T=A.sort((u,h)=>u.size-h.size);n.concat384=r.concat([T[0],T[2]],2),n.concat512=r.concat([T[1],T[3]],2),n.concat=r.concat([n.concat512,n.concat384],1),n.batch=r.squeeze(n.concat,[0])}else Array.isArray(A)?n.batch=r.squeeze(A[0]):n.batch=r.squeeze(A);r.dispose(A),n.boxes=us(n.batch),n.logits=r.slice(n.batch,[0,0],[-1,1]),n.sigmoid=r.sigmoid(n.logits),n.scores=r.squeeze(n.sigmoid),n.nms=await r.image.nonMaxSuppressionAsync(n.boxes,n.scores,((l=t.face.detector)==null?void 0:l.maxDetected)||0,((f=t.face.detector)==null?void 0:f.iouThreshold)||0,((y=t.face.detector)==null?void 0:y.minConfidence)||0);let a=await n.nms.array(),i=[],c=await n.scores.data();for(let T=0;T(((p=t.face.detector)==null?void 0:p.minConfidence)||0)){let h={};h.bbox=r.slice(n.boxes,[a[T],0],[1,-1]),h.slice=r.slice(n.batch,[a[T],D1-1],[1,-1]),h.squeeze=r.squeeze(h.slice),h.landmarks=r.reshape(h.squeeze,[D1,-1]);let w=await h.bbox.data(),k=[w[0]*s[0]-o[0],w[1]*s[1]-o[1],w[2]*s[0]-o[0],w[3]*s[1]-o[1]],I={startPoint:[k[0],k[1]],endPoint:[k[2],k[3]],landmarks:await h.landmarks.array(),confidence:u};h.anchor=r.slice(O2,[a[T],0],[1,2]);let C=await h.anchor.data(),V=j1(I,[(e.shape[2]||0)/K0,(e.shape[1]||0)/K0],C),W=I2(V,((m=t.face.detector)==null?void 0:m.scale)||1.4),G=L2(W);G.size[0]>(((b=t.face.detector)==null?void 0:b.minSize)||0)&&G.size[1]>(((v=t.face.detector)==null?void 0:v.minSize)||0)&&i.push(G),Object.keys(h).forEach(X=>r.dispose(h[X]))}}return Object.keys(n).forEach(T=>r.dispose(n[T])),i}var z0,ce=0,Dt=F0.leftEyeLower0,Ft=F0.rightEyeLower0,He={leftBounds:[Dt[0],Dt[Dt.length-1]],rightBounds:[Ft[0],Ft[Ft.length-1]]},Ge={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function q1(e){var t,n;return M.initial&&(z0=null),z0?e.debug&&g("cached model:",z0.modelUrl):z0=await L((t=e.face.iris)==null?void 0:t.modelPath),ce=z0!=null&&z0.executor&&((n=z0.inputs)!=null&&n[0].shape)?z0.inputs[0].shape[2]:0,ce===-1&&(ce=64),z0}function C2(e,t,n,o){for(let s=0;s{let t=e[He.leftBounds[0]][2],n=e[He.rightBounds[0]][2];return t-n},V1=(e,t,n,o,s,A=!1,a=2.3)=>{let i=L2(I2(N1([e[n],e[o]]),a)),c=Fe(i),d=r.image.cropAndResize(t,[[i.startPoint[1]/s,i.startPoint[0]/s,i.endPoint[1]/s,i.endPoint[0]/s]],[0],[ce,ce]);if(A&&M.kernels.includes("flipleftright")){let x=r.image.flipLeftRight(d);r.dispose(d),d=x}return{box:i,boxSize:c,crop:d}},Z1=(e,t,n,o=!1)=>{let s=[];for(let A=0;A{let o=e[F0[`${n}EyeUpper0`][Ge.upperCenter]][2],s=e[F0[`${n}EyeLower0`][Ge.lowerCenter]][2],A=(o+s)/2;return t.map((a,i)=>{let c=A;return i===2?c=o:i===4&&(c=s),[a[0],a[1],c]})};async function U1(e,t,n,o){var I,C;if(!(z0!=null&&z0.executor))return e;let{box:s,boxSize:A,crop:a}=V1(e,t,He.leftBounds[0],He.leftBounds[1],n,!0,((I=o.face.iris)==null?void 0:I.scale)||2.3),{box:i,boxSize:c,crop:d}=V1(e,t,He.rightBounds[0],He.rightBounds[1],n,!0,((C=o.face.iris)==null?void 0:C.scale)||2.3),x=r.concat([a,d]);r.dispose(a),r.dispose(d);let l=z0.execute(x);r.dispose(x);let f=await l.data();r.dispose(l);let y=f.slice(0,Ge.numCoordinates*3),{rawCoords:p,iris:m}=Z1(y,s,A,!0),b=f.slice(Ge.numCoordinates*3),{rawCoords:v,iris:T}=Z1(b,i,c,!1),u=hs(e);Math.abs(u)<30?(C2(e,p,"left",null),C2(e,v,"right",null)):u<1?C2(e,p,"left",["EyeUpper0","EyeLower0"]):C2(e,v,"right",["EyeUpper0","EyeLower0"]);let h=X1(e,m,"left"),w=X1(e,T,"right");return e.concat(h).concat(w)}async function K1(e,t){var A,a,i,c,d,x,l,f,y,p;let n={lips:await((a=(A=t.filter(m=>m.size===160))==null?void 0:A[0])==null?void 0:a.data()),irisL:await((c=(i=t.filter(m=>m.size===10))==null?void 0:i[0])==null?void 0:c.data()),eyeL:await((x=(d=t.filter(m=>m.size===142))==null?void 0:d[0])==null?void 0:x.data()),irisR:await((f=(l=t.filter(m=>m.size===10))==null?void 0:l[1])==null?void 0:f.data()),eyeR:await((p=(y=t.filter(m=>m.size===142))==null?void 0:y[1])==null?void 0:p.data())};for(let m of Object.values(n))if(!m)return e;let o=Me.reduce((m,b)=>m+=e[b][2],0)/Me.length;for(let m=0;mm+=e[b][2],0)/Pe.length;for(let m=0;mR()-J0.timestamp,o=J0.skipped<(((d=t.face.detector)==null?void 0:d.skipFrames)||0);!t.skipAllowed||!n||!o||J0.boxes.length===0?(J0.boxes=await H1(e,t),J0.timestamp=R(),J0.skipped=0):J0.skipped++;let s=[],A=[],a=0,i=o2;for(let T=0;T[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/i]);for(let I of Object.keys(ve))k.annotations[I]=[k.mesh[ve[I]]]}else if(!J)t.debug&&g("face mesh detection requested, but model is not loaded");else{if((y=t.face.attention)!=null&&y.enabled&&!M.kernels.includes("atan2"))return t.face.attention.enabled=!1,r.dispose(k.tensor),s;let I=J.execute(k.tensor),V=await I.find(W=>W.shape[W.shape.length-1]===1).data();if(k.faceScore=Math.round(100*V[0])/100,k.faceScore<(((p=t.face.detector)==null?void 0:p.minConfidence)||1)){if(u.confidence=k.faceScore,t.face.mesh.keepInvalid){k.box=j2(u,e),k.boxRaw=N2(u,e),k.size=u.size,k.score=k.boxScore,k.mesh=u.landmarks,k.meshRaw=k.mesh.map(W=>[W[0]/(e.shape[2]||1),W[1]/(e.shape[1]||1),(W[2]||0)/i]);for(let W of Object.keys(ve))k.annotations[W]=[k.mesh[ve[W]]]}}else{let W=I.find(Y=>Y.shape[Y.shape.length-1]===1404),G=r.reshape(W,[-1,3]),X=await G.array();r.dispose(G),(m=t.face.attention)!=null&&m.enabled?X=await K1(X,I):(b=t.face.iris)!=null&&b.enabled&&(X=await U1(X,k.tensor,o2,t)),k.mesh=O1(X,u,h,w,o2),k.meshRaw=k.mesh.map(Y=>[Y[0]/(e.shape[2]||0),Y[1]/(e.shape[1]||0),(Y[2]||0)/i]);for(let Y of Object.keys(F0))k.annotations[Y]=F0[Y].map(p0=>k.mesh[p0]);k.score=k.faceScore;let U={...W1(k.mesh,u),confidence:u.confidence,landmarks:u.landmarks,size:u.size};k.box=j2(U,e),k.boxRaw=N2(U,e),k.size=U.size,A.push(U)}r.dispose(I)}k.score>(((v=t.face.detector)==null?void 0:v.minConfidence)||1)?s.push(k):r.dispose(k.tensor)}return J0.boxes=A,s}async function Q1(e){var t,n,o,s,A,a;return M.initial&&(J=null),(t=e.face.attention)!=null&&t.enabled&&(J!=null&&J.signature)&&Object.keys(((n=J==null?void 0:J.signature)==null?void 0:n.outputs)||{}).length<6&&(J=null),J?e.debug&&g("cached model:",J.modelUrl):(o=e.face.attention)!=null&&o.enabled?J=await L(e.face.attention.modelPath):J=await L((s=e.face.mesh)==null?void 0:s.modelPath),o2=J.executor&&((A=J==null?void 0:J.inputs)!=null&&A[0].shape)?(a=J==null?void 0:J.inputs)==null?void 0:a[0].shape[2]:256,J}var _1=Re,$1=t2;var Gt=[],x0,W2=[],e3=0,t3=0,Ht=Number.MAX_SAFE_INTEGER,Vt=!1;async function n3(e){var t,n,o;return M.initial&&(x0=null),x0?e.debug&&g("cached model:",x0.modelUrl):(x0=await L((t=e.face.emotion)==null?void 0:t.modelPath),Vt=((o=(n=x0==null?void 0:x0.inputs)==null?void 0:n[0].shape)==null?void 0:o[3])===3,Vt?Gt=["angry","disgust","fear","happy","neutral","sad","surprise"]:Gt=["angry","disgust","fear","happy","sad","surprise","neutral"]),x0}async function Zt(e,t,n,o){var a,i;if(!x0)return[];let s=Ht<(((a=t.face.emotion)==null?void 0:a.skipFrames)||0),A=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>R()-t3;return t.skipAllowed&&A&&s&&e3===o&&W2[n]&&W2[n].length>0?(Ht++,W2[n]):(Ht=0,new Promise(async c=>{var x,l,f;let d=[];if((x=t.face.emotion)!=null&&x.enabled){let y={},p=x0!=null&&x0.inputs[0].shape?x0.inputs[0].shape[2]:0;if(((l=t.face.emotion)==null?void 0:l.crop)>0){let b=(f=t.face.emotion)==null?void 0:f.crop,v=[[b,b,1-b,1-b]];y.resize=r.image.cropAndResize(e,v,[0],[p,p])}else y.resize=r.image.resizeBilinear(e,[p,p],!1);Vt?(y.mul=r.mul(y.resize,255),y.normalize=r.sub(y.mul,[103.939,116.779,123.68]),y.emotion=x0==null?void 0:x0.execute(y.normalize)):(y.channels=r.mul(y.resize,O.rgb),y.grayscale=r.sum(y.channels,3,!0),y.grayscaleSub=r.sub(y.grayscale,O.tf05),y.grayscaleMul=r.mul(y.grayscaleSub,O.tf2),y.emotion=x0==null?void 0:x0.execute(y.grayscaleMul)),t3=R();let m=await y.emotion.data();for(let b=0;b(t.face.emotion.minConfidence||0)&&d.push({score:Math.min(.99,Math.trunc(100*m[b])/100),emotion:Gt[b]});d.sort((b,v)=>v.score-b.score),Object.keys(y).forEach(b=>r.dispose(y[b]))}W2[n]=d,e3=o,c(d)}))}var y0,de=[],r3=0,s3=0,Xt=Number.MAX_SAFE_INTEGER;async function A3(e){var t;return M.initial&&(y0=null),y0?e.debug&&g("cached model:",y0.modelUrl):y0=await L((t=e.face.description)==null?void 0:t.modelPath),y0}function gs(e,t){var A,a;let n=e.image||e.tensor||e;if(!(y0!=null&&y0.inputs[0].shape))return n;let o;if(((A=t.face.description)==null?void 0:A.crop)>0){let i=(a=t.face.description)==null?void 0:a.crop,c=[[i,i,1-i,1-i]];o=r.image.cropAndResize(n,c,[0],[y0.inputs[0].shape[2],y0.inputs[0].shape[1]])}else o=r.image.resizeBilinear(n,[y0.inputs[0].shape[2],y0.inputs[0].shape[1]],!1);let s=r.mul(o,O.tf255);return r.dispose(o),s}async function qt(e,t,n,o){var i,c,d,x;let s={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(y0!=null&&y0.executor))return s;let A=Xt<(((i=t.face.description)==null?void 0:i.skipFrames)||0),a=(((c=t.face.description)==null?void 0:c.skipTime)||0)>R()-r3;return t.skipAllowed&&A&&a&&s3===o&&((d=de==null?void 0:de[n])==null?void 0:d.age)>0&&((x=de==null?void 0:de[n])==null?void 0:x.genderScore)>0?(Xt++,de[n]):(Xt=0,new Promise(async l=>{var f;if((f=t.face.description)!=null&&f.enabled){let y=gs(e,t),p=y0==null?void 0:y0.execute(y);r3=R(),r.dispose(y);let b=await p.find(C=>C.shape[1]===1).data(),v=Math.trunc(200*Math.abs(b[0]-.5))/100;v>(t.face.description.minConfidence||0)&&(s.gender=b[0]<=.5?"female":"male",s.genderScore=Math.min(.99,v));let T=r.argMax(p.find(C=>C.shape[1]===100),1),u=(await T.data())[0];r.dispose(T);let w=await p.find(C=>C.shape[1]===100).data();s.age=Math.round(w[u-1]>w[u+1]?10*u-100*w[u-1]:10*u+100*w[u+1])/10,(Number.isNaN(b[0])||Number.isNaN(w[0]))&&g("faceres error:",{model:y0,result:p});let k=p.find(C=>C.shape[1]===1024),I=k?await k.data():[];s.descriptor=Array.from(I),p.forEach(C=>r.dispose(C))}de[n]=s,s3=o,l(s)}))}var Ve=.1,Ut=.5;function Ts(e,t,n){let o=!1,s=n.length-1;for(let A=0;At!=n[s].y>t&&e<(n[s].x-n[A].x)*(t-n[A].y)/(n[s].y-n[A].y)+n[A].x&&(o=!o);return o}async function i3(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,o=await e.tensor.buffer(),s=[];for(let a of F0.silhouette)s.push({x:(e.mesh[a][0]-e.box[0])/e.box[2],y:(e.mesh[a][1]-e.box[1])/e.box[3]});Ve&&Ve>0&&(s=s.map(a=>({x:a.x>.5?a.x+Ve:a.x-Ve,y:a.y>.5?a.y+Ve:a.y-Ve})));for(let a=0;aR()-c3,A=Yt<(((i=t.face.antispoof)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&l3===o&&D2[n]?(Yt++,D2[n]):(Yt=0,new Promise(async c=>{let d=r.image.resizeBilinear(e,[f0!=null&&f0.inputs[0].shape?f0.inputs[0].shape[2]:0,f0!=null&&f0.inputs[0].shape?f0.inputs[0].shape[1]:0],!1),x=f0==null?void 0:f0.execute(d),l=(await x.data())[0];D2[n]=Math.round(100*l)/100,l3=o,c3=R(),r.dispose([d,x]),c(D2[n])}))}var m0,F2=[],Jt=Number.MAX_SAFE_INTEGER,y3=0,f3=0;async function m3(e){var t;return M.initial&&(m0=null),m0?e.debug&&g("cached model:",m0.modelUrl):m0=await L((t=e.face.liveness)==null?void 0:t.modelPath),m0}async function Qt(e,t,n,o){var a,i;if(!(m0!=null&&m0.executor))return 0;let s=(((a=t.face.liveness)==null?void 0:a.skipTime)||0)>R()-f3,A=Jt<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&s&&A&&y3===o&&F2[n]?(Jt++,F2[n]):(Jt=0,new Promise(async c=>{let d=r.image.resizeBilinear(e,[m0!=null&&m0.inputs[0].shape?m0.inputs[0].shape[2]:0,m0!=null&&m0.inputs[0].shape?m0.inputs[0].shape[1]:0],!1),x=m0==null?void 0:m0.execute(d),l=(await x.data())[0];F2[n]=Math.round(100*l)/100,y3=o,f3=R(),r.dispose([d,x]),c(F2[n])}))}var B0,_t=[],Rs=["white","black","asian","indian","other"],Ms=[15,23,28,35.5,45.5,55.5,65],u3=0,h3=0,$t=Number.MAX_SAFE_INTEGER;async function b3(e){var t;return M.initial&&(B0=null),B0?e.debug&&g("cached model:",B0.modelUrl):B0=await L((t=e.face.gear)==null?void 0:t.modelPath),B0}async function e5(e,t,n,o){var a,i;if(!B0)return{age:0,gender:"unknown",genderScore:0,race:[]};let s=$t<(((a=t.face.gear)==null?void 0:a.skipFrames)||0),A=(((i=t.face.gear)==null?void 0:i.skipTime)||0)>R()-h3;return t.skipAllowed&&A&&s&&u3===o&&_t[n]?($t++,_t[n]):($t=0,new Promise(async c=>{var v,T,u,h;if(!(B0!=null&&B0.inputs[0].shape))return;let d={},x=[[0,.1,.9,.9]];if(((v=t.face.gear)==null?void 0:v.crop)>0){let w=(T=t.face.gear)==null?void 0:T.crop;x=[[w,w,1-w,1-w]]}d.resize=r.image.cropAndResize(e,x,[0],[B0.inputs[0].shape[2],B0.inputs[0].shape[1]]);let l={age:0,gender:"unknown",genderScore:0,race:[]};(u=t.face.gear)!=null&&u.enabled&&([d.age,d.gender,d.race]=B0.execute(d.resize,["age_output","gender_output","race_output"]));let f=await d.gender.data();l.gender=f[0]>f[1]?"male":"female",l.genderScore=Math.round(100*(f[0]>f[1]?f[0]:f[1]))/100;let y=await d.race.data();for(let w=0;w(((h=t.face.gear)==null?void 0:h.minConfidence)||.2)&&l.race.push({score:Math.round(100*y[w])/100,race:Rs[w]});l.race.sort((w,k)=>k.score-w.score);let m=Array.from(await d.age.data()).map((w,k)=>[Ms[k],w]).sort((w,k)=>k[1]-w[1]),b=m[0][0];for(let w=1;wr.dispose(d[w])),_t[n]=l,u3=o,h3=R(),c(l)}))}var R0,B2=[],T3=0,v3=0,t5=Number.MAX_SAFE_INTEGER;async function R3(e){return M.initial&&(R0=null),R0?e.debug&&g("cached model:",R0.modelUrl):R0=await L(e.face.ssrnet.modelPathAge),R0}async function n5(e,t,n,o){var a,i,c,d;if(!R0)return{age:0};let s=t5<(((a=t.face.ssrnet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>R()-v3;return t.skipAllowed&&s&&A&&T3===o&&((c=B2[n])!=null&&c.age)&&((d=B2[n])==null?void 0:d.age)>0?(t5++,B2[n]):(t5=0,new Promise(async x=>{var y,p,m;if(!(R0!=null&&R0.inputs)||!R0.inputs[0]||!R0.inputs[0].shape)return;let l={};if(((y=t.face.ssrnet)==null?void 0:y.crop)>0){let b=(p=t.face.ssrnet)==null?void 0:p.crop,v=[[b,b,1-b,1-b]];l.resize=r.image.cropAndResize(e,v,[0],[R0.inputs[0].shape[2],R0.inputs[0].shape[1]])}else l.resize=r.image.resizeBilinear(e,[R0.inputs[0].shape[2],R0.inputs[0].shape[1]],!1);l.enhance=r.mul(l.resize,O.tf255);let f={age:0};if((m=t.face.ssrnet)!=null&&m.enabled&&(l.age=R0.execute(l.enhance)),l.age){let b=await l.age.data();f.age=Math.trunc(10*b[0])/10}Object.keys(l).forEach(b=>r.dispose(l[b])),B2[n]=f,T3=o,v3=R(),x(f)}))}var u0,H2=[],P3=0,k3=0,o5=Number.MAX_SAFE_INTEGER,r5=[.2989,.587,.114];async function w3(e){var t;return M.initial&&(u0=null),u0?e.debug&&g("cached model:",u0.modelUrl):u0=await L((t=e.face.ssrnet)==null?void 0:t.modelPathGender),u0}async function s5(e,t,n,o){var a,i,c,d;if(!u0)return{gender:"unknown",genderScore:0};let s=o5<(((a=t.face.ssrnet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.ssrnet)==null?void 0:i.skipTime)||0)>R()-k3;return t.skipAllowed&&s&&A&&P3===o&&((c=H2[n])!=null&&c.gender)&&((d=H2[n])==null?void 0:d.genderScore)>0?(o5++,H2[n]):(o5=0,new Promise(async x=>{var p,m,b;if(!(u0!=null&&u0.inputs[0].shape))return;let l={};if(((p=t.face.ssrnet)==null?void 0:p.crop)>0){let v=(m=t.face.ssrnet)==null?void 0:m.crop,T=[[v,v,1-v,1-v]];l.resize=r.image.cropAndResize(e,T,[0],[u0.inputs[0].shape[2],u0.inputs[0].shape[1]])}else l.resize=r.image.resizeBilinear(e,[u0.inputs[0].shape[2],u0.inputs[0].shape[1]],!1);l.enhance=r.tidy(()=>{var T,u;let v;if(((u=(T=u0==null?void 0:u0.inputs)==null?void 0:T[0].shape)==null?void 0:u[3])===1){let[h,w,k]=r.split(l.resize,3,3),I=r.mul(h,r5[0]),C=r.mul(w,r5[1]),V=r.mul(k,r5[2]),W=r.addN([I,C,V]);v=r.mul(r.sub(W,O.tf05),2)}else v=r.mul(r.sub(l.resize,O.tf05),2);return v});let f={gender:"unknown",genderScore:0};(b=t.face.ssrnet)!=null&&b.enabled&&(l.gender=u0.execute(l.enhance));let y=await l.gender.data();f.gender=y[0]>y[1]?"female":"male",f.genderScore=y[0]>y[1]?Math.trunc(100*y[0])/100:Math.trunc(100*y[1])/100,Object.keys(l).forEach(v=>r.dispose(l[v])),H2[n]=f,P3=o,k3=R(),x(f)}))}var S0,A5=[],z3=0,S3=0,j3=Number.MAX_SAFE_INTEGER;async function N3(e){var t;return M.initial&&(S0=null),S0?e.debug&&g("cached model:",S0.modelUrl):S0=await L((t=e.face.mobilefacenet)==null?void 0:t.modelPath),S0}async function a5(e,t,n,o){var a,i;if(!(S0!=null&&S0.executor))return[];let s=j3<(((a=t.face.mobilefacenet)==null?void 0:a.skipFrames)||0),A=(((i=t.face.mobilefacenet)==null?void 0:i.skipTime)||0)>R()-S3;return t.skipAllowed&&A&&s&&z3===o&&A5[n]?(j3++,A5[n]):new Promise(async c=>{var x;let d=[];if((x=t.face.mobilefacenet)!=null&&x.enabled&&(S0!=null&&S0.inputs[0].shape)){let l={};l.crop=r.image.resizeBilinear(e,[S0.inputs[0].shape[2],S0.inputs[0].shape[1]],!1),l.data=S0.execute(l.crop);let f=await l.data.data();d=Array.from(f),Object.keys(l).forEach(y=>r.dispose(l[y]))}A5[n]=d,z3=o,S3=R(),c(d)})}var j0,i5=[],L3=0,O3=0,C3=Number.MAX_SAFE_INTEGER;async function W3(e){return M.initial&&(j0=null),j0?e.debug&&g("cached model:",j0.modelUrl):j0=await L(e.face.insightface.modelPath),j0}async function l5(e,t,n,o){var a,i;if(!(j0!=null&&j0.executor))return[];let s=C3<(((a=t.face.insightface)==null?void 0:a.skipFrames)||0),A=(((i=t.face.insightface)==null?void 0:i.skipTime)||0)>R()-O3;return t.skipAllowed&&A&&s&&L3===o&&i5[n]?(C3++,i5[n]):new Promise(async c=>{var x;let d=[];if((x=t.face.insightface)!=null&&x.enabled&&(j0!=null&&j0.inputs[0].shape)){let l={};l.crop=r.image.resizeBilinear(e,[j0.inputs[0].shape[2],j0.inputs[0].shape[1]],!1),l.data=j0.execute(l.crop);let f=await l.data.data();d=Array.from(f),Object.keys(l).forEach(y=>r.dispose(l[y]))}i5[n]=d,L3=o,O3=R(),c(d)})}var Ps=e=>{let t=(l,f)=>Math.atan2(l[1]-f[1],l[0]-f[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],o=1,s=(e.mesh[33][2]||0)>(e.mesh[263][2]||0),A=s?e.mesh[473]:e.mesh[468],a=s?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=s?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],c=[(a[0]-A[0])/i[0]-n[0],o*(A[1]-a[1])/i[1]-n[1]],d=Math.sqrt(c[0]*c[0]+c[1]*c[1]);return d=Math.min(d,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],c)+Math.PI/2)%Math.PI,strength:d}},F3=(e,t)=>{let n=m=>{let b=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=b,m[1]/=b,m[2]/=b,m},o=(m,b)=>{let v=m[0]-b[0],T=m[1]-b[1],u=m[2]-b[2];return[v,T,u]},s=(m,b)=>{let v=m[1]*b[2]-m[2]*b[1],T=m[2]*b[0]-m[0]*b[2],u=m[0]*b[1]-m[1]*b[0];return[v,T,u]},A=m=>{let[b,v,T,u,h,w,k,I,C]=m,V,W,G;if(u<1)if(u>-1){let X=Math.sqrt(b*b+k*k);G=Math.atan2(u,X),W=Math.atan2(-k,b),V=Math.atan2(-w,h)}else G=-Math.PI/2,W=-Math.atan2(I,C),V=0;else G=Math.PI/2,W=Math.atan2(I,C),V=0;return Number.isNaN(V)&&(V=0),Number.isNaN(W)&&(W=0),Number.isNaN(G)&&(G=0),{pitch:-V,yaw:-W,roll:-G}},a=e.meshRaw;if(!a||a.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let i=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,c=[a[10],a[152],a[234],a[454]].map(m=>[m[0]*t[0]/i,m[1]*t[1]/i,m[2]]),d=n(o(c[1],c[0])),x=n(o(c[3],c[2])),l=n(s(x,d));x=s(d,l);let f=[x[0],x[1],x[2],d[0],d[1],d[2],l[0],l[1],l[2]],y=A(f),p=a.length===478?Ps(e):{bearing:0,strength:0};return{angle:y,matrix:f,gaze:p}};function B3(e,t){let n=e==null?void 0:e.annotations;if(!(n!=null&&n.leftEyeIris)||!(n!=null&&n.rightEyeIris))return 0;let o=Math.max(Math.abs(n.leftEyeIris[3][0]-n.leftEyeIris[1][0]),Math.abs(n.rightEyeIris[3][0]-n.rightEyeIris[1][0]))/t;return Math.round(1.17/o)/100}var c5=async(e,t)=>{var p,m,b,v,T,u,h,w,k,I,C,V,W,G,X,U,Y,p0,P,i0,g0,e0,Z;let n=R(),o,s,A,a,i,c,d,x,l,f=[];e.state="run:face";let y=await J1(t,e.config);if(e.performance.face=M.perfadd?(e.performance.face||0)+Math.trunc(R()-n):Math.trunc(R()-n),!t.shape||t.shape.length!==4)return[];if(!y)return[];for(let S=0;S200?F3(y[S],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?a=(m=e.config.face.emotion)!=null&&m.enabled?Zt(y[S].tensor||r.tensor([]),e.config,S,y.length):[]:(e.state="run:emotion",n=R(),a=(b=e.config.face.emotion)!=null&&b.enabled?await Zt(y[S].tensor||r.tensor([]),e.config,S,y.length):[],e.performance.emotion=M.perfadd?(e.performance.emotion||0)+Math.trunc(R()-n):Math.trunc(R()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?d=(v=e.config.face.antispoof)!=null&&v.enabled?Kt(y[S].tensor||r.tensor([]),e.config,S,y.length):0:(e.state="run:antispoof",n=R(),d=(T=e.config.face.antispoof)!=null&&T.enabled?await Kt(y[S].tensor||r.tensor([]),e.config,S,y.length):0,e.performance.antispoof=M.perfadd?(e.performance.antispoof||0)+Math.trunc(R()-n):Math.trunc(R()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?x=(u=e.config.face.liveness)!=null&&u.enabled?Qt(y[S].tensor||r.tensor([]),e.config,S,y.length):0:(e.state="run:liveness",n=R(),x=(h=e.config.face.liveness)!=null&&h.enabled?await Qt(y[S].tensor||r.tensor([]),e.config,S,y.length):0,e.performance.liveness=M.perfadd?(e.performance.antispoof||0)+Math.trunc(R()-n):Math.trunc(R()-n)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?s=(w=e.config.face.gear)!=null&&w.enabled?e5(y[S].tensor||r.tensor([]),e.config,S,y.length):null:(e.state="run:gear",n=R(),s=(k=e.config.face.gear)!=null&&k.enabled?await e5(y[S].tensor||r.tensor([]),e.config,S,y.length):null,e.performance.gear=Math.trunc(R()-n)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(o=(I=e.config.face.ssrnet)!=null&&I.enabled?n5(y[S].tensor||r.tensor([]),e.config,S,y.length):null,A=(C=e.config.face.ssrnet)!=null&&C.enabled?s5(y[S].tensor||r.tensor([]),e.config,S,y.length):null):(e.state="run:ssrnet",n=R(),o=(V=e.config.face.ssrnet)!=null&&V.enabled?await n5(y[S].tensor||r.tensor([]),e.config,S,y.length):null,A=(W=e.config.face.ssrnet)!=null&&W.enabled?await s5(y[S].tensor||r.tensor([]),e.config,S,y.length):null,e.performance.ssrnet=Math.trunc(R()-n)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=(G=e.config.face.mobilefacenet)!=null&&G.enabled?a5(y[S].tensor||r.tensor([]),e.config,S,y.length):null:(e.state="run:mobilefacenet",n=R(),i=(X=e.config.face.mobilefacenet)!=null&&X.enabled?await a5(y[S].tensor||r.tensor([]),e.config,S,y.length):null,e.performance.mobilefacenet=Math.trunc(R()-n)),e.analyze("End MobileFaceNet:"),e.analyze("Start InsightFace:"),e.config.async?c=(U=e.config.face.insightface)!=null&&U.enabled?l5(y[S].tensor||r.tensor([]),e.config,S,y.length):null:(e.state="run:mobilefacenet",n=R(),c=(Y=e.config.face.insightface)!=null&&Y.enabled?await l5(y[S].tensor||r.tensor([]),e.config,S,y.length):null,e.performance.mobilefacenet=Math.trunc(R()-n)),e.analyze("End InsightFace:"),e.analyze("Start Description:"),e.config.async?l=qt(y[S].tensor||r.tensor([]),e.config,S,y.length):(e.state="run:description",n=R(),l=await qt(y[S].tensor||r.tensor([]),e.config,S,y.length),e.performance.description=M.perfadd?(e.performance.description||0)+Math.trunc(R()-n):Math.trunc(R()-n)),e.analyze("End Description:"),e.config.async&&([o,A,a,i,c,l,s,d,x]=await Promise.all([o,A,a,i,c,l,s,d,x])),e.analyze("Finish Face:"),(p0=e.config.face.ssrnet)!=null&&p0.enabled&&o&&A&&(l={...l,age:o.age,gender:A.gender,genderScore:A.genderScore}),(P=e.config.face.gear)!=null&&P.enabled&&s&&(l={...l,age:s.age,gender:s.gender,genderScore:s.genderScore,race:s.race}),(i0=e.config.face.mobilefacenet)!=null&&i0.enabled&&i&&(l.descriptor=i),(g0=e.config.face.insightface)!=null&&g0.enabled&&c&&(l.descriptor=c);let F=(e0=e.config.face.iris)!=null&&e0.enabled?B3(y[S],t.shape[2]):0,w0=(Z=e.config.face.detector)!=null&&Z.return?r.squeeze(y[S].tensor):null;r.dispose(y[S].tensor),y[S].tensor&&delete y[S].tensor;let t0={...y[S],id:S};l.age&&(t0.age=l.age),l.gender&&(t0.gender=l.gender),l.genderScore&&(t0.genderScore=l.genderScore),l.descriptor&&(t0.embedding=l.descriptor),l.race&&(t0.race=l.race),a&&(t0.emotion=a),d&&(t0.real=d),x&&(t0.live=x),F>0&&(t0.distance=F),D&&(t0.rotation=D),w0&&(t0.tensor=w0),f.push(t0),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),f};var 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dn={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},Ne,Ie,m5;function Vs(){let e=Ne?new q2(Ne):void 0;e&&Ie&&(m5=new U2(e,Ie))}async function p5(e,t){m5||Vs();let n=await m5.estimateHands(e,t);if(!n)return[];let o=[];for(let s=0;sn[s].landmarks[l]);let a=n[s].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],c=[0,0,0,0];if(a&&a.length>0){for(let x of a)x[0]i[2]&&(i[2]=x[0]),x[1]>i[3]&&(i[3]=x[1]);i[2]-=i[0],i[3]-=i[1],c=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[s].box?[Math.trunc(Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.max(0,n[s].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[s].box.bottomRight[0])-Math.max(0,n[s].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[s].box.bottomRight[1])-Math.max(0,n[s].box.topLeft[1]))]:[0,0,0,0],c=[n[s].box.topLeft[0]/(e.shape[2]||0),n[s].box.topLeft[1]/(e.shape[1]||0),(n[s].box.bottomRight[0]-n[s].box.topLeft[0])/(e.shape[2]||0),(n[s].box.bottomRight[1]-n[s].box.topLeft[1])/(e.shape[1]||0)];let d=G2(a);o.push({id:s,score:Math.round(100*n[s].confidence)/100,boxScore:Math.round(100*n[s].boxConfidence)/100,fingerScore:Math.round(100*n[s].fingerConfidence)/100,label:"hand",box:i,boxRaw:c,keypoints:a,annotations:A,landmarks:d})}return o}async function xn(e){var t;return M.initial&&(Ne=null),Ne?e.debug&&g("cached model:",Ne.modelUrl):Ne=await L((t=e.hand.detector)==null?void 0:t.modelPath),Ne}async function yn(e){var t;return M.initial&&(Ie=null),Ie?e.debug&&g("cached model:",Ie.modelUrl):Ie=await L((t=e.hand.skeleton)==null?void 0:t.modelPath),Ie}var r0=[null,null],Zs=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],he=[[0,0],[0,0]],Xs=["hand","fist","pinch","point","face","tip","pinchtip"],mn=4,pn=1.6,qs=512,Us=1.4,Y2=Number.MAX_SAFE_INTEGER,u5=0,te=[0,0],o0={boxes:[],hands:[]},un={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function hn(e){var t;if(M.initial&&(r0[0]=null),r0[0])e.debug&&g("cached model:",r0[0].modelUrl);else{h2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),r0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=r0[0].executor?Object.values(r0[0].modelSignature.inputs):void 0;he[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[0]}async function bn(e){var t;if(M.initial&&(r0[1]=null),r0[1])e.debug&&g("cached model:",r0[1].modelUrl);else{r0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=r0[1].executor?Object.values(r0[1].modelSignature.inputs):void 0;he[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,he[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return r0[1]}async function Ys(e,t){let n=[];if(!e||!r0[0])return n;let o={},s=(e.shape[2]||1)/(e.shape[1]||1),A=Math.min(Math.round((e.shape[1]||0)/8)*8,qs),a=Math.round(A*s/8)*8;o.resize=r.image.resizeBilinear(e,[A,a]),o.cast=r.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await r0[0].executeAsync(o.cast,Zs),o.boxes=r.squeeze(o.rawBoxes,[0,2]),o.scores=r.squeeze(o.rawScores,[0]);let i=r.unstack(o.scores,1);r.dispose(i[mn]),i.splice(mn,1),o.filtered=r.stack(i,1),r.dispose(i),o.max=r.max(o.filtered,1),o.argmax=r.argMax(o.filtered,1);let c=0;o.nms=await r.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let d=await o.nms.data(),x=await o.max.data(),l=await o.argmax.data();for(let f of Array.from(d)){let y=r.slice(o.boxes,f,1),p=await y.data();r.dispose(y);let m=[p[1],p[0],p[3]-p[1],p[2]-p[0]],b=k2(m,Us),v=[Math.trunc(m[0]*te[0]),Math.trunc(m[1]*te[1]),Math.trunc(m[2]*te[0]),Math.trunc(m[3]*te[1])],T=x[f],u=Xs[l[f]],h={id:c++,score:T,box:v,boxRaw:b,label:u};n.push(h)}return Object.keys(o).forEach(f=>r.dispose(o[f])),n.sort((f,y)=>y.score-f.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function h5(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&r0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let s={},A=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];s.crop=r.image.cropAndResize(e,[A],[0],[he[1][0],he[1][1]],"bilinear"),s.div=r.div(s.crop,O.tf255),[s.score,s.keypoints]=r0[1].execute(s.div,["Identity_1","Identity"]);let a=(await s.score.data())[0],i=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(i>=(n.hand.minConfidence||0)){o.fingerScore=i,s.reshaped=r.reshape(s.keypoints,[-1,3]);let x=(await s.reshaped.array()).map(l=>[l[0]/he[1][1],l[1]/he[1][0],l[2]||0]).map(l=>[l[0]*t.boxRaw[2],l[1]*t.boxRaw[3],l[2]||0]);o.keypoints=x.map(l=>[te[0]*(l[0]+t.boxRaw[0]),te[1]*(l[1]+t.boxRaw[1]),l[2]||0]),o.landmarks=G2(o.keypoints);for(let l of Object.keys(un))o.annotations[l]=un[l].map(f=>o.landmarks&&o.keypoints[f]?o.keypoints[f]:null)}Object.keys(s).forEach(c=>r.dispose(s[c]))}return o}async function b5(e,t){var s,A;if(!((s=r0[0])!=null&&s.executor)||!((A=r0[1])!=null&&A.executor)||!r0[0].inputs[0].shape||!r0[1].inputs[0].shape)return[];te=[e.shape[2]||0,e.shape[1]||0],Y2++;let n=(t.hand.skipTime||0)>R()-u5,o=Y2<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?o0.hands:new Promise(async a=>{let i=3*(t.hand.skipTime||0)>R()-u5,c=Y2<3*(t.hand.skipFrames||0);t.skipAllowed&&o0.hands.length===t.hand.maxDetected?o0.hands=await Promise.all(o0.boxes.map(x=>h5(e,x,t))):t.skipAllowed&&i&&c&&o0.hands.length>0?o0.hands=await Promise.all(o0.boxes.map(x=>h5(e,x,t))):(o0.boxes=await Ys(e,t),u5=R(),o0.hands=await Promise.all(o0.boxes.map(x=>h5(e,x,t))),Y2=0);let d=[...o0.boxes];if(o0.boxes.length=0,t.cacheSensitivity>0)for(let x=0;x.05&&l.box[3]/(e.shape[1]||1)>.05&&o0.hands[x].fingerScore&&o0.hands[x].fingerScore>(t.hand.minConfidence||0)){let f=k2(l.box,pn),y=k2(l.boxRaw,pn);o0.boxes.push({...d[x],box:f,boxRaw:y})}}for(let x=0;x({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var s2={};re(s2,{connected:()=>J2,horizontal:()=>g5,kpt:()=>K2,relative:()=>v5,vertical:()=>T5});var K2=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],g5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],T5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],v5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],J2={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var j=ne(),R5=0;function Tn(e,t){var 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0:oe[0],j.body[P].keypoints[F]?((s-1)*(((Ke=j.body[P].keypoints[F].distance)==null?void 0:Ke[1])||0)+(((Oe=D.distance)==null?void 0:Oe[1])||0))/s:(H5=D.distance)==null?void 0:H5[1],j.body[P].keypoints[F]?((s-1)*(((G5=j.body[P].keypoints[F].distance)==null?void 0:G5[2])||0)+(((V5=D.distance)==null?void 0:V5[2])||0))/s:(Z5=D.distance)==null?void 0:Z5[2]]}}),Z={},S={connected:{}};(a=t.body.modelPath)!=null&&a.includes("efficientpose")?S=z2:(i=t.body.modelPath)!=null&&i.includes("blazepose")?S=M2:(c=t.body.modelPath)!=null&&c.includes("movenet")&&(S=s2);for(let[D,F]of Object.entries(S.connected)){let w0=[];for(let t0=0;t0Oe.part===F[t0]),Ke=e0.find(Oe=>Oe.part===F[t0+1]);oe&&Ke&&w0.push([oe.position,Ke.position])}Z[D]=w0}j.body[P]={...e.body[P],box:i0,boxRaw:g0,keypoints:e0,annotations:Z}}if(!j.hand||e.hand.length!==j.hand.length)j.hand=JSON.parse(JSON.stringify(e.hand));else for(let P=0;P((s-1)*j.hand[P].box[D]+S)/s),g0=e.hand[P].boxRaw.map((S,D)=>((s-1)*j.hand[P].boxRaw[D]+S)/s);j.hand[P].keypoints.length!==e.hand[P].keypoints.length&&(j.hand[P].keypoints=e.hand[P].keypoints);let e0=e.hand[P].keypoints&&e.hand[P].keypoints.length>0?e.hand[P].keypoints.map((S,D)=>S.map((F,w0)=>((s-1)*(j.hand[P].keypoints[D][w0]||1)+(F||0))/s)):[],Z={};if(Object.keys(j.hand[P].annotations).length!==Object.keys(e.hand[P].annotations).length)j.hand[P].annotations=e.hand[P].annotations,Z=j.hand[P].annotations;else if(e.hand[P].annotations)for(let S of Object.keys(e.hand[P].annotations))Z[S]=(l=(x=(d=e.hand[P])==null?void 0:d.annotations)==null?void 0:x[S])!=null&&l[0]?e.hand[P].annotations[S].map((D,F)=>D.map((w0,t0)=>((s-1)*j.hand[P].annotations[S][F][t0]+w0)/s)):null;j.hand[P]={...e.hand[P],box:i0,boxRaw:g0,keypoints:e0,annotations:Z}}if(!j.face||e.face.length!==j.face.length)j.face=JSON.parse(JSON.stringify(e.face));else for(let P=0;P((s-1)*j.face[P].box[S]+Z)/s),g0=e.face[P].boxRaw.map((Z,S)=>((s-1)*j.face[P].boxRaw[S]+Z)/s),e0=e.face[P].annotations;if(Object.keys(j.face[P].annotations).length!==Object.keys(e.face[P].annotations).length)j.face[P].annotations=e.face[P].annotations,e0=j.face[P].annotations;else if(e.face[P].annotations)for(let Z of Object.keys(e.face[P].annotations))e0[Z]=(p=(y=(f=e.face[P])==null?void 0:f.annotations)==null?void 0:y[Z])!=null&&p[0]?e.face[P].annotations[Z].map((S,D)=>S.map((F,w0)=>((s-1)*j.face[P].annotations[Z][D][w0]+F)/s)):null;if(e.face[P].rotation){let Z={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Z.matrix=(m=e.face[P].rotation)==null?void 0:m.matrix,Z.angle={roll:((s-1)*(((v=(b=j.face[P].rotation)==null?void 0:b.angle)==null?void 0:v.roll)||0)+(((u=(T=e.face[P].rotation)==null?void 0:T.angle)==null?void 0:u.roll)||0))/s,yaw:((s-1)*(((w=(h=j.face[P].rotation)==null?void 0:h.angle)==null?void 0:w.yaw)||0)+(((I=(k=e.face[P].rotation)==null?void 0:k.angle)==null?void 0:I.yaw)||0))/s,pitch:((s-1)*(((V=(C=j.face[P].rotation)==null?void 0:C.angle)==null?void 0:V.pitch)||0)+(((G=(W=e.face[P].rotation)==null?void 0:W.angle)==null?void 0:G.pitch)||0))/s},Z.gaze={bearing:((s-1)*(((X=j.face[P].rotation)==null?void 0:X.gaze.bearing)||0)+(((U=e.face[P].rotation)==null?void 0:U.gaze.bearing)||0))/s,strength:((s-1)*(((Y=j.face[P].rotation)==null?void 0:Y.gaze.strength)||0)+(((p0=e.face[P].rotation)==null?void 0:p0.gaze.strength)||0))/s},j.face[P]={...e.face[P],rotation:Z,box:i0,boxRaw:g0,annotations:e0}}else j.face[P]={...e.face[P],box:i0,boxRaw:g0,annotations:e0}}if(!j.object||e.object.length!==j.object.length)j.object=JSON.parse(JSON.stringify(e.object));else for(let P=0;P((s-1)*j.object[P].box[Z]+e0)/s),g0=e.object[P].boxRaw.map((e0,Z)=>((s-1)*j.object[P].boxRaw[Z]+e0)/s);j.object[P]={...e.object[P],box:i0,boxRaw:g0}}if(e.persons){let P=e.persons;if(!j.persons||P.length!==j.persons.length)j.persons=JSON.parse(JSON.stringify(P));else for(let i0=0;i0((s-1)*j.persons[i0].box[e0]+g0)/s)}e.gesture&&(j.gesture=e.gesture),j.width=e.width,j.height=e.height;let A=R();return R5=M.perfadd?R5+Math.round(A-n):Math.round(A-n),e.performance&&(j.performance={...e.performance,interpolate:R5}),j}var h0;async function M5(e){return!h0||M.initial?h0=await L(e.segmentation.modelPath):e.debug&&g("cached model:",h0.modelUrl),h0}async function vn(e,t){var s;if(h0||(h0=await M5(t)),!(h0!=null&&h0.executor)||!((s=h0==null?void 0:h0.inputs)!=null&&s[0].shape))return null;let n={};n.resize=r.image.resizeBilinear(e,[h0.inputs[0].shape?h0.inputs[0].shape[1]:0,h0.inputs[0].shape?h0.inputs[0].shape[2]:0],!1),n.norm=r.div(n.resize,O.tf255),n.res=h0.execute(n.norm),n.squeeze=r.squeeze(n.res,[0]),[n.bgRaw,n.fgRaw]=r.unstack(n.squeeze,2),n.fg=r.softmax(n.fgRaw),n.mul=r.mul(n.fg,O.tf255),n.expand=r.expandDims(n.mul,2),n.output=r.image.resizeBilinear(n.expand,[e.shape[1]||0,e.shape[2]||0]);let o;switch(t.segmentation.mode||"default"){case"default":n.input=r.squeeze(e),n.concat=r.concat([n.input,n.output],-1),o=r.cast(n.concat,"int32");break;case"alpha":o=r.cast(n.output,"int32");break;default:o=r.tensor(0)}return Object.keys(n).forEach(A=>r.dispose(n[A])),o}var k5={};re(k5,{distance:()=>P5,find:()=>Qs,similarity:()=>Js});function P5(e,t,n={order:2,multiplier:25}){if(!e||!e)return Number.MAX_SAFE_INTEGER;let o=0;for(let A=0;A{if(e===0)return 1;let A=(1-(t===2?Math.sqrt(e):e**(1/t))/100-n)/(o-n);return Math.round(100*Math.max(Math.min(A,1),0))/100};function Js(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let o=P5(e,t,n);return Mn(o,n.order||2,n.min||0,n.max||1)}function Qs(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let o=Number.MAX_SAFE_INTEGER,s=-1;for(let a=0;ai2,validateModel:()=>ot});var Pn=.005,N0={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function w5(e){for(let t of g5){let n=e.keypoints.findIndex(s=>s.part===t[0]),o=e.keypoints.findIndex(s=>s.part===t[1]);if(e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[0]s&&s.part===t[0]),o=e.keypoints.findIndex(s=>s&&s.part===t[1]);e.keypoints[n]&&e.keypoints[o]&&e.keypoints[n].position[1]d&&d.part===t[0]),s=e.keypoints.findIndex(d=>d&&d.part===t[1]),A=e.keypoints.findIndex(d=>d&&d.part===n[0]),a=e.keypoints.findIndex(d=>d&&d.part===n[1]);if(!e.keypoints[A]||!e.keypoints[a])continue;let 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n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?B1(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)!=null&&o.enabled)&&!this.models.antispoof?d3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((s=this.instance.config.face.liveness)!=null&&s.enabled)&&!this.models.liveness?m3(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((A=this.instance.config.face.description)!=null&&A.enabled)&&!this.models.faceres?A3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((a=this.instance.config.face.emotion)!=null&&a.enabled)&&!this.models.emotion?n3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((i=this.instance.config.face.iris)!=null&&i.enabled)&&!((c=this.instance.config.face.attention)!=null&&c.enabled)&&!this.models.iris?q1(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((d=this.instance.config.face.mesh)!=null&&d.enabled)&&!this.models.facemesh?Q1(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((x=this.instance.config.face.gear)!=null&&x.enabled)&&!this.models.gear?b3(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((l=this.instance.config.face.ssrnet)!=null&&l.enabled)&&!this.models.ssrnetage?R3(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((f=this.instance.config.face.ssrnet)!=null&&f.enabled)&&!this.models.ssrnetgender?w3(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((y=this.instance.config.face.mobilefacenet)!=null&&y.enabled)&&!this.models.mobilefacenet?N3(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((p=this.instance.config.face.insightface)!=null&&p.enabled)&&!this.models.insightface?W3(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((m=this.instance.config.body.modelPath)!=null&&m.includes("blazepose"))?T1(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?g1(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((b=this.instance.config.body.modelPath)!=null&&b.includes("efficientpose"))?w1(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((v=this.instance.config.body.modelPath)!=null&&v.includes("movenet"))?zn(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((T=this.instance.config.body.modelPath)!=null&&T.includes("posenet"))?Bn(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((h=(u=this.instance.config.hand.detector)==null?void 0:u.modelPath)!=null&&h.includes("handtrack"))?hn(this.instance.config):null,n.handskeleton=this.instance.config.hand.enabled&&this.instance.config.hand.landmarks&&!this.models.handskeleton&&((k=(w=this.instance.config.hand.detector)==null?void 0:w.modelPath)!=null&&k.includes("handtrack"))?bn(this.instance.config):null,this.instance.config.hand.enabled&&!this.models.handdetect&&((C=(I=this.instance.config.hand.detector)==null?void 0:I.modelPath)!=null&&C.includes("handdetect"))&&(n.handdetect=xn(this.instance.config),n.handskeleton=yn(this.instance.config)),n.centernet=this.instance.config.object.enabled&&!this.models.centernet&&((V=this.instance.config.object.modelPath)!=null&&V.includes("centernet"))?M1(this.instance.config):null,n.nanodet=this.instance.config.object.enabled&&!this.models.nanodet&&((W=this.instance.config.object.modelPath)!=null&&W.includes("nanodet"))?Nn(this.instance.config):null,n.selfie=this.instance.config.segmentation.enabled&&!this.models.selfie&&((G=this.instance.config.segmentation.modelPath)!=null&&G.includes("selfie"))?B5(this.instance.config):null,n.meet=this.instance.config.segmentation.enabled&&!this.models.meet&&((X=this.instance.config.segmentation.modelPath)!=null&&X.includes("meet"))?M5(this.instance.config):null,n.rvm=this.instance.config.segmentation.enabled&&!this.models.rvm&&((U=this.instance.config.segmentation.modelPath)!=null&&U.includes("rvm"))?F5(this.instance.config):null;for(let[Y,p0]of Object.entries(n))p0!=null&&p0.then&&p0.then(P=>this.models[Y]=P);await Promise.all(Object.values(n))}list(){let t=Object.keys(this.models).map(n=>{var o;return{name:n,loaded:this.models[n]!==null,size:0,url:this.models[n]?(o=this.models[n])==null?void 0:o.modelUrl:null}});for(let n of t){let o=Object.keys(l0).find(s=>s.startsWith(n.name));o&&(n.size=l0[o].sizeLoadedWeights,n.url=l0[o].url)}return t}loaded(){return this.list().filter(o=>o.loaded).map(o=>o.name)}validate(){let t=[];for(let n of Object.keys(this.models)){let o=this.models[n];if(!o)continue;let s=ot(this.instance,o,n);s&&t.push(s)}return t}};function Kn(e,t,n,o,s){var i,c,d,x,l,f;let A=0,a=[];for(let y of e){let p={id:A++,face:y,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let h of t)y.box[0]>h.box[0]&&y.box[0]h.box[1]&&y.box[1]+y.box[3]p.body.box[0]&&h.box[0]+h.box[2]p.body.box[1]&&h.box[1]+h.box[3]p.body.box[0]&&h.box[1]+h.box[3]>p.body.box[1]&&h.box[1]+h.box[3]{h&&h.length===4&&(m.push(h[0],h[0]+h[2]),b.push(h[1],h[1]+h[3]))};v(p.face.box),v((x=p.body)==null?void 0:x.box),v((l=p.hands.left)==null?void 0:l.box),v((f=p.hands.right)==null?void 0:f.box);let T=Math.min(...m),u=Math.min(...b);p.box=[T,u,Math.max(...m)-T,Math.max(...b)-u],s!=null&&s[1]&&(s!=null&&s[2])&&(p.boxRaw=[p.box[0]/s[2],p.box[1]/s[1],p.box[2]/s[2],p.box[3]/s[1]]),a.push(p)}return a}var rt=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -13917,8 +269,7 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l c6oaM5TUJ8EgGsG4kLNUHT0M64OaqMMikSRsuKbnFMRLG3zVehOaGNE445NNlnVFpDMu6uie9Vo1 8z5mOAOST2pDK91cNN+5tsrH3PrW54a06KxT7fdrlh/q1Pc+tJ6IUdZGvHPLezMcnBOWbsPap5r3 ylFtbdT1xUWNWzU0/Zbwlgfmx8zGsHWtRHmMqE59aAMyNifvHPc1f0gtPdqkY5JosJHeNci2tktY -euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`; -var body3 = ` +euPnNY+oXWZEVJNrZ9aun8SIq/CzodHuriIokhDIR1ronbKZr0o6o8ipoz//2Q==`,st=` /9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAsICAoIBwsKCQoNDAsNERwSEQ8PESIZGhQcKSQrKigk JyctMkA3LTA9MCcnOEw5PUNFSElIKzZPVU5GVEBHSEX/2wBDAQwNDREPESESEiFFLicuRUVFRUVF RUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUX/wAARCASwBLADASIA @@ -14486,607 +837,4 @@ AAAAAAJAAAAAAAAAAAAAABAJEAAAAAAAAAAAAAAAIEoBKAAAAAAAAAAAAAAABAlAAAAAAAIAAAAA BAkBAkBAkBAlACEgMZjdjbFW8bWrEx8YWANb6Fp+bfwab+vLDKMFK9qxH5L0bAr8OPRPKz2AY7J2 SbAjYZAI2E7AIEgIEgIEgMdkSy2NgY7MdlmyNoBXsxmFuyNgVTVjNV3KjlBRNTlXTVHKCrlIqt5T lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/ -2Q==`; - -// src/warmup.ts -async function warmupBitmap(instance) { - const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); - let blob; - let res; - switch (instance.config.warmup) { - case "face": - blob = await b64toBlob(face3); - break; - case "body": - case "full": - blob = await b64toBlob(body3); - break; - default: - blob = null; - } - if (blob) { - const bitmap = await createImageBitmap(blob); - res = await instance.detect(bitmap, instance.config); - bitmap.close(); - } - return res; -} -async function warmupCanvas(instance) { - return new Promise((resolve) => { - let src; - switch (instance.config.warmup) { - case "face": - src = "data:image/jpeg;base64," + face3; - break; - case "full": - case "body": - src = "data:image/jpeg;base64," + body3; - break; - default: - src = ""; - } - let img; - if (typeof Image !== "undefined") img = new Image(); - else if (env.Image) img = new env.Image(); - else { - resolve(void 0); - return; - } - img.onload = async () => { - const canvas3 = canvas(img.naturalWidth, img.naturalHeight); - if (!canvas3) { - log("Warmup: Canvas not found"); - resolve(void 0); - } else { - const ctx = canvas3.getContext("2d"); - if (ctx) ctx.drawImage(img, 0, 0); - const tensor6 = await instance.image(canvas3, true); - const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; - resolve(res); - } - }; - if (src) img.src = src; - else resolve(void 0); - }); -} -async function warmupNode(instance) { - const atob = (str) => Buffer.from(str, "base64"); - let img; - if (instance.config.warmup === "face") img = atob(face3); - else img = atob(body3); - let res; - if ("node" in tfjs_esm_exports && tfjs_esm_exports.getBackend() === "tensorflow") { - const data = node.decodeJpeg(img); - const expanded = tfjs_esm_exports.expandDims(data, 0); - instance.tf.dispose(data); - res = await instance.detect(expanded, instance.config); - instance.tf.dispose(expanded); - } else { - if (instance.config.debug) log("Warmup tfjs-node not loaded"); - } - return res; -} -async function runInference(instance) { - let res; - if (typeof createImageBitmap === "function") res = await warmupBitmap(instance); - else if (typeof Image !== "undefined" || env.Canvas !== void 0) res = await warmupCanvas(instance); - else res = await warmupNode(instance); - return res; -} -async function runCompile(instance) { - var _a, _b, _c, _d; - if (!tfjs_esm_exports.env().flagRegistry.ENGINE_COMPILE_ONLY) return; - const backendType = tfjs_esm_exports.getBackend(); - const webGLBackend = tfjs_esm_exports.backend(); - if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { - return; - } - tfjs_esm_exports.env().set("ENGINE_COMPILE_ONLY", true); - const numTensorsStart = tfjs_esm_exports.engine().state.numTensors; - const compiledModels = []; - for (const [modelName, model23] of Object.entries(instance.models.models)) { - if (!model23) continue; - const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; - const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; - for (let dim = 0; dim < shape.length; dim++) { - if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; - } - const tensor6 = tfjs_esm_exports.zeros(shape, dtype); - try { - const res = model23.execute(tensor6); - compiledModels.push(modelName); - if (Array.isArray(res)) res.forEach((t2) => tfjs_esm_exports.dispose(t2)); - else tfjs_esm_exports.dispose(res); - } catch (e) { - if (instance.config.debug) log("compile fail model:", modelName); - } - tfjs_esm_exports.dispose(tensor6); - } - const kernels = await webGLBackend["checkCompileCompletionAsync"](); - webGLBackend["getUniformLocations"](); - if (instance.config.debug) log("compile pass:", { models: compiledModels, kernels: kernels.length }); - tfjs_esm_exports.env().set("ENGINE_COMPILE_ONLY", false); - const numTensorsEnd = tfjs_esm_exports.engine().state.numTensors; - if (numTensorsEnd - numTensorsStart > 0) log("tensor leak:", numTensorsEnd - numTensorsStart); -} -async function warmup(instance, userConfig) { - await check(instance, false); - const t0 = now(); - instance.state = "warmup"; - if (userConfig) instance.config = mergeDeep(instance.config, userConfig); - if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { - return empty(); - } - return new Promise(async (resolve) => { - await instance.models.load(); - await tfjs_esm_exports.ready(); - await runCompile(instance); - const res = await runInference(instance); - const t1 = now(); - if (instance.config.debug) log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); - instance.emit("warmup"); - resolve(res); - }); -} - -// src/human.ts -var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; -var Human = class { - // definition end - /** Constructor for **Human** library that is futher used for all operations - * @param userConfig - user configuration object {@link Config} - */ - constructor(userConfig) { - /** Current version of Human library in *semver* format */ - __publicField(this, "version"); - /** Current configuration - * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) - */ - __publicField(this, "config"); - /** Last known result of detect run - * - Can be accessed anytime after initial detection - */ - __publicField(this, "result"); - /** Current state of Human library - * - Can be polled to determine operations that are currently executed - * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle' - */ - __publicField(this, "state"); - /** currenty processed image tensor and canvas */ - __publicField(this, "process"); - /** Instance of TensorFlow/JS used by Human - * - Can be embedded or externally provided - * [TFJS API](https://js.tensorflow.org/api/latest/) - */ - __publicField(this, "tf"); - /** Object containing environment information used for diagnostics */ - __publicField(this, "env", env); - /** Draw helper classes that can draw detected objects on canvas using specified draw - * - canvas: draws input to canvas - * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions} - * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas - */ - // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions }; - __publicField(this, "draw", draw_exports); - /** Face Matching - * - similarity: compare two face descriptors and return similarity index - * - distance: compare two face descriptors and return raw calculated differences - * - find: compare face descriptor to array of face descriptors and return best match - */ - __publicField(this, "match", match_exports); - /** Currently loaded models - * @internal - * {@link models#Models} - */ - __publicField(this, "models"); - /** Container for events dispatched by Human - * Possible events: - * - `create`: triggered when Human object is instantiated - * - `load`: triggered when models are loaded (explicitly or on-demand) - * - `image`: triggered when input image is processed - * - `result`: triggered when detection is complete - * - `warmup`: triggered when warmup is complete - * - `error`: triggered on some errors - */ - __publicField(this, "events"); - /** Reference face triangualtion array of 468 points, used for triangle references between points */ - __publicField(this, "faceTriangulation"); - /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */ - __publicField(this, "faceUVMap"); - /** Performance object that contains values for all recently performed operations */ - __publicField(this, "performance"); - // perf members are dynamically defined as needed - __privateAdd(this, _numTensors); - __privateAdd(this, _analyzeMemoryLeaks); - __privateAdd(this, _checkSanity); - /** internal function to measure tensor leaks */ - __publicField(this, "analyze", (...msg) => { - if (!__privateGet(this, _analyzeMemoryLeaks)) return; - const currentTensors = this.tf.engine().state.numTensors; - const previousTensors = __privateGet(this, _numTensors); - __privateSet(this, _numTensors, currentTensors); - const leaked = currentTensors - previousTensors; - if (leaked !== 0) log(...msg, leaked); - }); - /** internal function for quick sanity check on inputs @hidden */ - __privateAdd(this, _sanity, (input) => { - if (!__privateGet(this, _checkSanity)) return null; - if (!input) return "input is not defined"; - if (this.env.node && !(input instanceof tfjs_esm_exports.Tensor)) return "input must be a tensor"; - try { - this.tf.getBackend(); - } catch (e) { - return "backend not loaded"; - } - return null; - }); - /** WebCam helper methods - * - */ - __publicField(this, "webcam", new WebCam()); - /** emit event */ - __publicField(this, "emit", (event) => { - var _a; - if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) this.events.dispatchEvent(new Event(event)); - }); - /** internal structure that keeps track of processed videos @hidden */ - __privateAdd(this, _loops, {}); - const tfVersion = (version7.tfjs || tfjs_esm_exports.version_core).replace(/-(.*)/, ""); - config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; - config.modelBasePath = env.browser ? "../models/" : "file://models/"; - this.version = version8; - Object.defineProperty(this, "version", { value: version8 }); - this.config = JSON.parse(JSON.stringify(config)); - Object.seal(this.config); - this.config.cacheModels = typeof indexedDB !== "undefined"; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - setModelLoadOptions(this.config); - this.tf = tfjs_esm_exports; - this.state = "idle"; - __privateSet(this, _numTensors, 0); - __privateSet(this, _analyzeMemoryLeaks, false); - __privateSet(this, _checkSanity, false); - this.performance = {}; - this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; - this.models = new Models(this); - init2(); - this.result = empty(); - this.process = { tensor: null, canvas: null }; - this.faceTriangulation = triangulation; - this.faceUVMap = uvmap; - validateModel(this, null, ""); - this.emit("create"); - if (this.config.debug || this.env.browser) log(`version: ${this.version}`); - if (this.config.debug) log(`tfjs version: ${this.tf.version["tfjs-core"]}`); - const envTemp = JSON.parse(JSON.stringify(this.env)); - delete envTemp.kernels; - delete envTemp.initial; - delete envTemp.perfadd; - if (this.config.debug) log("environment:", envTemp); - } - /** Reset configuration to default values */ - reset() { - const currentBackend = this.config.backend; - this.config = JSON.parse(JSON.stringify(config)); - this.config.backend = currentBackend; - reset(); - env.initial = true; - } - /** Validate current configuration schema */ - validate(userConfig) { - const msgs = validate(config, userConfig || this.config); - if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig); - return msgs; - } - /** Utility wrapper for performance.now() */ - now() { - return now(); - } - /** Process input as return canvas and tensor - * - * @param input - any input {@link Input} - * @param getTensor - should image processing also return tensor or just canvas - * Returns object with `tensor` and `canvas` - */ - image(input, getTensor = false) { - return process2(input, this.config, getTensor); - } - /** Segmentation method takes any input and returns RGBA tensor - * Note: Segmentation is not triggered as part of detect process - * - * @param input - {@link Input} - * Returns tensor which contains image data in RGBA format - */ - async segmentation(input, userConfig) { - var _a, _b, _c; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - if (!this.config.segmentation.enabled) return null; - const processed = await process2(input, this.config); - if (!processed.tensor) return null; - let tensor6 = null; - if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) tensor6 = await predict20(processed.tensor, this.config); - if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) tensor6 = await predict16(processed.tensor, this.config); - if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) tensor6 = await predict21(processed.tensor, this.config); - tfjs_esm_exports.dispose(processed.tensor); - return tensor6; - } - /** Compare two input tensors for pixel similarity - * - use `human.image` to process any valid input and get a tensor that can be used for compare - * - when passing manually generated tensors: - * - both input tensors must be in format [1, height, width, 3] - * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor - * - return value is pixel similarity score normalized by input resolution and rgb channels - */ - compare(firstImageTensor, secondImageTensor) { - return compare(this.config, firstImageTensor, secondImageTensor); - } - /** Explicit backend initialization - * - Normally done implicitly during initial load phase - * - Call to explictly register and initialize TFJS backend without any other operations - * - Use when changing backend during runtime - */ - async init() { - await check(this, true); - await this.tf.ready(); - reset(); - } - /** Load method preloads all configured models on-demand - * - Not explicitly required as any required model is load implicitly on it's first run - * - * @param userConfig - {@link Config} - */ - async load(userConfig) { - this.state = "load"; - const timeStamp = now(); - const count2 = Object.values(this.models.models).filter((model23) => model23).length; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - if (this.env.initial) { - if (!await check(this, false)) log("error: backend check failed"); - await tfjs_esm_exports.ready(); - if (this.env.browser) { - if (this.config.debug) log("configuration:", this.config); - if (this.config.debug) log("tf flags:", this.tf.ENV.flags); - } - } - await this.models.load(this); - if (this.env.initial && this.config.debug) log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); - this.env.initial = false; - const loaded = Object.values(this.models.models).filter((model23) => model23).length; - if (loaded !== count2) { - this.models.validate(); - this.emit("load"); - } - const current = Math.trunc(now() - timeStamp); - if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; - } - /** Runs interpolation using last known result and returns smoothened result - * Interpolation is based on time since last known result so can be called independently - * - * @param result - {@link Result} optional use specific result set to run interpolation on - * @returns result - {@link Result} - */ - next(result = this.result) { - return calc2(result, this.config); - } - /** Warmup method pre-initializes all configured models for faster inference - * - can take significant time on startup - * - only used for `webgl` and `humangl` backends - * @param userConfig - {@link Config} - * @returns result - {@link Result} - */ - async warmup(userConfig) { - const t0 = now(); - const res = await warmup(this, userConfig); - const t1 = now(); - this.performance.warmup = Math.trunc(t1 - t0); - return res; - } - /** Run detect with tensorflow profiling - * - result object will contain total exeuction time information for top-20 kernels - * - actual detection object can be accessed via `human.result` - */ - async profile(input, userConfig) { - const profile = await this.tf.profile(() => this.detect(input, userConfig)); - const kernels = {}; - let total = 0; - for (const kernel of profile.kernels) { - const ms = Number(kernel.kernelTimeMs) || 0; - if (kernels[kernel.name]) kernels[kernel.name] += ms; - else kernels[kernel.name] = ms; - total += ms; - } - const kernelArr = []; - Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); - for (const kernel of kernelArr) { - kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; - kernel.time = Math.round(1e3 * kernel.time) / 1e3; - } - kernelArr.sort((a, b) => b.time - a.time); - kernelArr.length = 20; - return kernelArr; - } - /** Main detection method - * - Analyze configuration: {@link Config} - * - Pre-process input: {@link Input} - * - Run inference for all configured models - * - Process and return result: {@link Result} - * - * @param input - {@link Input} - * @param userConfig - {@link Config} - * @returns result - {@link Result} - */ - async detect(input, userConfig) { - this.state = "detect"; - return new Promise(async (resolve) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; - this.state = "config"; - let timeStamp; - this.config = mergeDeep(this.config, userConfig); - this.state = "check"; - const error = __privateGet(this, _sanity).call(this, input); - if (error) { - log(error, input); - this.emit("error"); - resolve(empty(error)); - } - const timeStart = now(); - await this.load(); - timeStamp = now(); - this.state = "image"; - const img = await process2(input, this.config); - this.process = img; - this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Get Image:"); - if (!img.tensor) { - if (this.config.debug) log("could not convert input to tensor"); - this.emit("error"); - resolve(empty("could not convert input to tensor")); - return; - } - this.emit("image"); - timeStamp = now(); - this.config.skipAllowed = await skip(this.config, img.tensor); - this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; - if (!this.performance.totalFrames) this.performance.totalFrames = 0; - if (!this.performance.cachedFrames) this.performance.cachedFrames = 0; - this.performance.totalFrames++; - if (this.config.skipAllowed) this.performance.cachedFrames++; - this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Check Changed:"); - let faceRes = []; - let bodyRes = []; - let handRes = []; - let objectRes = []; - this.state = "detect:face"; - if (this.config.async) { - faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; - if (this.performance.face) delete this.performance.face; - } else { - timeStamp = now(); - faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; - this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; - this.analyze("Start Body:"); - this.state = "detect:body"; - const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; - else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; - else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; - else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; - if (this.performance.body) delete this.performance.body; - } else { - timeStamp = now(); - if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; - else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; - else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; - else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; - this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Body:"); - this.analyze("Start Hand:"); - this.state = "detect:hand"; - const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; - else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; - if (this.performance.hand) delete this.performance.hand; - } else { - timeStamp = now(); - if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; - else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; - this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Hand:"); - this.analyze("Start Object:"); - this.state = "detect:object"; - if (this.config.async) { - if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; - else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; - if (this.performance.object) delete this.performance.object; - } else { - timeStamp = now(); - if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; - else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; - this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Object:"); - this.state = "detect:await"; - if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); - this.state = "detect:gesture"; - let gestureRes = []; - if (this.config.gesture.enabled) { - timeStamp = now(); - gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; - if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - else if (this.performance.gesture) delete this.performance.gesture; - } - this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); - const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; - this.result = { - face: faceRes, - body: bodyRes, - hand: handRes, - gesture: gestureRes, - object: objectRes, - performance: this.performance, - canvas: this.process.canvas, - timestamp: Date.now(), - error: null, - width: shape[2], - height: shape[1], - get persons() { - return join2(faceRes, bodyRes, handRes, gestureRes, shape); - } - }; - tfjs_esm_exports.dispose(img.tensor); - this.emit("detect"); - this.state = "idle"; - resolve(this.result); - }); - } - /** Helper function - * @param ms - sleep time in miliseconds - */ - async sleep(ms) { - return new Promise((resolve) => { - setTimeout(resolve, ms); - }); - } - /** Continously detect video frames - * @param element - HTMLVideoElement input - * @param run - boolean run continously or stop if already running, default true - * @param delay - number delay detection between frames for number of miliseconds, default 0 - */ - async video(element, run = true, delay = 0) { - if (run) { - if (!__privateGet(this, _loops)[element.id]) { - if (this.config.debug) log("video start", element.id); - __privateGet(this, _loops)[element.id] = true; - } - if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) await this.detect(element); - if (delay > 0) await this.sleep(delay); - if (__privateGet(this, _loops)[element.id]) requestAnimationFrame(() => this.video(element, run, delay)); - } else { - if (this.config.debug) log("video stop", element.id); - __privateGet(this, _loops)[element.id] = false; - } - } -}; -_numTensors = new WeakMap(); -_analyzeMemoryLeaks = new WeakMap(); -_checkSanity = new WeakMap(); -_sanity = new WeakMap(); -_loops = new WeakMap(); -export { - Env, - Human, - Human as default, - config as defaults, - draw_exports as draw, - empty, - env, - match_exports as match, - models_exports2 as models -}; +2Q==`;async function pA(e){let t=(s,A="application/octet-stream")=>fetch(`data:${A};base64,${s}`).then(a=>a.blob()),n,o;switch(e.config.warmup){case"face":n=await t(rt);break;case"body":case"full":n=await t(st);break;default:n=null}if(n){let s=await createImageBitmap(n);o=await e.detect(s,e.config),s.close()}return o}async function uA(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+rt;break;case"full":case"body":n="data:image/jpeg;base64,"+st;break;default:n=""}let o;if(typeof Image!="undefined")o=new Image;else if(M.Image)o=new M.Image;else{t(void 0);return}o.onload=async()=>{let s=D0(o.naturalWidth,o.naturalHeight);if(!s)g("Warmup: Canvas not found"),t(void 0);else{let A=s.getContext("2d");A&&A.drawImage(o,0,0);let a=await e.image(s,!0),i=a.tensor?await e.detect(a.tensor,e.config):void 0;t(i)}},n?o.src=n:t(void 0)})}async function hA(e){let t=s=>Buffer.from(s,"base64"),n;e.config.warmup==="face"?n=t(rt):n=t(st);let o;if("node"in r&&r.getBackend()==="tensorflow"){let s=it.decodeJpeg(n),A=r.expandDims(s,0);e.tf.dispose(s),o=await e.detect(A,e.config),e.tf.dispose(A)}else e.config.debug&&g("Warmup tfjs-node not loaded");return o}async function bA(e){let t;return typeof createImageBitmap=="function"?t=await pA(e):typeof Image!="undefined"||M.Canvas!==void 0?t=await uA(e):t=await hA(e),t}async function gA(e){var i,c,d,x;if(!r.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=r.getBackend(),n=r.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;r.env().set("ENGINE_COMPILE_ONLY",!0);let o=r.engine().state.numTensors,s=[];for(let[l,f]of Object.entries(e.models.models)){if(!f)continue;let y=f!=null&&f.modelSignature&&((c=(i=f==null?void 0:f.inputs)==null?void 0:i[0])!=null&&c.shape)?[...f.inputs[0].shape]:[1,64,64,3],p=f!=null&&f.modelSignature&&((x=(d=f==null?void 0:f.inputs)==null?void 0:d[0])!=null&&x.dtype)?f.inputs[0].dtype:"float32";for(let b=0;br.dispose(v)):r.dispose(b)}catch(b){e.config.debug&&g("compile fail model:",l)}r.dispose(m)}let A=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&g("compile pass:",{models:s,kernels:A.length}),r.env().set("ENGINE_COMPILE_ONLY",!1);let a=r.engine().state.numTensors;a-o>0&&g("tensor leak:",a-o)}async function Jn(e,t){await e2(e,!1);let n=R();return e.state="warmup",t&&(e.config=Q(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?ne():new Promise(async o=>{await e.models.load(),await r.ready(),await gA(e);let s=await bA(e),A=R();e.config.debug&&g("warmup",e.config.warmup,Math.round(A-n),"ms"),e.emit("warmup"),o(s)})}var Ye,l2,c2,At,be,Qn=class{constructor(t){z(this,"version");z(this,"config");z(this,"result");z(this,"state");z(this,"process");z(this,"tf");z(this,"env",M);z(this,"draw",vt);z(this,"match",k5);z(this,"models");z(this,"events");z(this,"faceTriangulation");z(this,"faceUVMap");z(this,"performance");q0(this,Ye);q0(this,l2);q0(this,c2);z(this,"analyze",(...t)=>{if(!E0(this,l2))return;let n=this.tf.engine().state.numTensors,o=E0(this,Ye);_0(this,Ye,n);let s=n-o;s!==0&&g(...t,s)});q0(this,At,t=>{if(!E0(this,c2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof r.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});z(this,"webcam",new u2);z(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});q0(this,be,{});let n=(Je.tfjs||r.version_core).replace(/-(.*)/,"");Ce.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ce.modelBasePath=M.browser?"../models/":"file://models/",this.version=ft,Object.defineProperty(this,"version",{value:ft}),this.config=JSON.parse(JSON.stringify(Ce)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=Q(this.config,t)),s1(this.config),this.tf=r,this.state="idle",_0(this,Ye,0),_0(this,l2,!1),_0(this,c2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new i2(this),Tt(),this.result=ne(),this.process={tensor:null,canvas:null},this.faceTriangulation=_1,this.faceUVMap=$1,ot(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&g(`version: ${this.version}`),this.config.debug&&g(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&g("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ce)),this.config.backend=t,xt(),M.initial=!0}validate(t){let n=lt(Ce,t||this.config);return n.length===0&&(this.config=Q(this.config,t)),n}now(){return R()}image(t,n=!1){return f2(t,this.config,n)}async segmentation(t,n){var A,a,i;if(n&&(this.config=Q(this.config,n)),!this.config.segmentation.enabled)return null;let o=await f2(t,this.config);if(!o.tensor)return null;let s=null;return(A=this.config.segmentation.modelPath)!=null&&A.includes("rvm")&&(s=await Zn(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("meet")&&(s=await vn(o.tensor,this.config)),(i=this.config.segmentation.modelPath)!=null&&i.includes("selfie")&&(s=await qn(o.tensor,this.config)),r.dispose(o.tensor),s}compare(t,n){return r1(this.config,t,n)}async init(){await e2(this,!0),await this.tf.ready(),xt()}async load(t){this.state="load";let n=R(),o=Object.values(this.models.models).filter(a=>a).length;t&&(this.config=Q(this.config,t)),this.env.initial&&(await e2(this,!1)||g("error: backend check failed"),await r.ready(),this.env.browser&&(this.config.debug&&g("configuration:",this.config),this.config.debug&&g("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&g("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(a=>a).length!==o&&(this.models.validate(),this.emit("load"));let A=Math.trunc(R()-n);A>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+A:A)}next(t=this.result){return Tn(t,this.config)}async warmup(t){let n=R(),o=await Jn(this,t),s=R();return this.performance.warmup=Math.trunc(s-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),s={},A=0;for(let i of o.kernels){let c=Number(i.kernelTimeMs)||0;s[i.name]?s[i.name]+=c:s[i.name]=c,A+=c}let a=[];Object.entries(s).forEach(i=>a.push({kernel:i[0],time:i[1],perc:0}));for(let i of a)i.perc=Math.round(1e3*i.time/A)/1e3,i.time=Math.round(1e3*i.time)/1e3;return a.sort((i,c)=>c.time-i.time),a.length=20,a}async detect(t,n){return this.state="detect",new Promise(async o=>{var b,v,T,u,h,w,k,I,C,V,W,G,X,U,Y,p0,P,i0,g0,e0,Z;this.state="config";let s;this.config=Q(this.config,n),this.state="check";let A=E0(this,At).call(this,t);A&&(g(A,t),this.emit("error"),o(ne(A)));let a=R();await this.load(),s=R(),this.state="image";let i=await f2(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(R()-s):Math.trunc(R()-s),this.analyze("Get Image:"),!i.tensor){this.config.debug&&g("could not convert input to tensor"),this.emit("error"),o(ne("could not convert input to tensor"));return}this.emit("image"),s=R(),this.config.skipAllowed=await o1(this.config,i.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(R()-s):Math.trunc(R()-s),this.analyze("Check Changed:");let c=[],d=[],x=[],l=[];this.state="detect:face",this.config.async?(c=this.config.face.enabled?c5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(s=R(),c=this.config.face.enabled?await c5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(c=await c),this.analyze("Start Body:"),this.state="detect:body";let f=this.config.body.maxDetected===-1?Q(this.config,{body:{maxDetected:this.config.face.enabled?1*c.length:1}}):this.config;this.config.async?((b=this.config.body.modelPath)!=null&&b.includes("posenet")?d=this.config.body.enabled?C5(i.tensor,f):[]:(v=this.config.body.modelPath)!=null&&v.includes("blazepose")?d=this.config.body.enabled?wt(i.tensor,f):[]:(T=this.config.body.modelPath)!=null&&T.includes("efficientpose")?d=this.config.body.enabled?Lt(i.tensor,f):[]:(u=this.config.body.modelPath)!=null&&u.includes("movenet")&&(d=this.config.body.enabled?z5(i.tensor,f):[]),this.performance.body&&delete this.performance.body):(s=R(),(h=this.config.body.modelPath)!=null&&h.includes("posenet")?d=this.config.body.enabled?await C5(i.tensor,f):[]:(w=this.config.body.modelPath)!=null&&w.includes("blazepose")?d=this.config.body.enabled?await wt(i.tensor,f):[]:(k=this.config.body.modelPath)!=null&&k.includes("efficientpose")?d=this.config.body.enabled?await Lt(i.tensor,f):[]:(I=this.config.body.modelPath)!=null&&I.includes("movenet")&&(d=this.config.body.enabled?await z5(i.tensor,f):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let y=this.config.hand.maxDetected===-1?Q(this.config,{hand:{maxDetected:this.config.face.enabled?2*c.length:1}}):this.config;this.config.async?((V=(C=this.config.hand.detector)==null?void 0:C.modelPath)!=null&&V.includes("handdetect")?x=this.config.hand.enabled?p5(i.tensor,y):[]:(G=(W=this.config.hand.detector)==null?void 0:W.modelPath)!=null&&G.includes("handtrack")&&(x=this.config.hand.enabled?b5(i.tensor,y):[]),this.performance.hand&&delete this.performance.hand):(s=R(),(U=(X=this.config.hand.detector)==null?void 0:X.modelPath)!=null&&U.includes("handdetect")?x=this.config.hand.enabled?await p5(i.tensor,y):[]:(p0=(Y=this.config.hand.detector)==null?void 0:Y.modelPath)!=null&&p0.includes("handtrack")&&(x=this.config.hand.enabled?await b5(i.tensor,y):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((P=this.config.object.modelPath)!=null&&P.includes("nanodet")?l=this.config.object.enabled?j5(i.tensor,this.config):[]:(i0=this.config.object.modelPath)!=null&&i0.includes("centernet")&&(l=this.config.object.enabled?St(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(s=R(),(g0=this.config.object.modelPath)!=null&&g0.includes("nanodet")?l=this.config.object.enabled?await j5(i.tensor,this.config):[]:(e0=this.config.object.modelPath)!=null&&e0.includes("centernet")&&(l=this.config.object.enabled?await St(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([c,d,x,l]=await Promise.all([c,d,x,l])),this.state="detect:gesture";let p=[];this.config.gesture.enabled&&(s=R(),p=[...J3(c),...K3(d),..._3(x),...Q3(c)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(R()-s):Math.trunc(R()-s)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(R()-a):Math.trunc(R()-a);let m=((Z=this.process.tensor)==null?void 0:Z.shape)||[0,0,0,0];this.result={face:c,body:d,hand:x,gesture:p,object:l,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:m[2],height:m[1],get persons(){return Kn(c,d,x,p,m)}},r.dispose(i.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(E0(this,be)[t.id]||(this.config.debug&&g("video start",t.id),E0(this,be)[t.id]=!0),!t.paused&&E0(this,be)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),E0(this,be)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&g("video stop",t.id),E0(this,be)[t.id]=!1)}};Ye=new WeakMap,l2=new WeakMap,c2=new WeakMap,At=new WeakMap,be=new WeakMap;export{p2 as Env,Qn as Human,Qn as default,Ce as defaults,vt as draw,ne as empty,M as env,k5 as match,Yn as models}; diff --git a/dist/human.esm.js b/dist/human.esm.js index 317d78aa..4ac3a401 100644 --- a/dist/human.esm.js +++ b/dist/human.esm.js @@ -536,7 +536,6 @@ var AG = (r15, e, t10, o) => { }; var zp = (r15, e, t10) => (t10 = r15 != null ? _G(RG(r15)) : {}, AG(e || !r15 || !r15.__esModule ? QC(t10, "default", { value: r15, enumerable: true }) : t10, r15)); var U0 = Kt((ple, W0) => { - "use strict"; W0.exports = kt; var ko = null; try { @@ -827,13 +826,10 @@ var U0 = Kt((ple, W0) => { }; }); var Ek = Kt(() => { - "use strict"; }); var $k = Kt(() => { - "use strict"; }); var o1 = Kt((r1, Ww) => { - "use strict"; (function(r15, e, t10) { function o(i) { var p = this, u = a(); @@ -873,7 +869,6 @@ var o1 = Kt((r1, Ww) => { })(r1, typeof Ww == "object" && Ww, typeof define == "function" && define); }); var s1 = Kt((n1, Uw) => { - "use strict"; (function(r15, e, t10) { function o(a) { var i = this, p = ""; @@ -905,7 +900,6 @@ var s1 = Kt((n1, Uw) => { })(n1, typeof Uw == "object" && Uw, typeof define == "function" && define); }); var i1 = Kt((a1, Gw) => { - "use strict"; (function(r15, e, t10) { function o(a) { var i = this, p = ""; @@ -937,7 +931,6 @@ var i1 = Kt((a1, Gw) => { })(a1, typeof Gw == "object" && Gw, typeof define == "function" && define); }); var p1 = Kt((u1, Hw) => { - "use strict"; (function(r15, e, t10) { function o(a) { var i = this; @@ -978,7 +971,6 @@ var p1 = Kt((u1, Hw) => { })(u1, typeof Hw == "object" && Hw, typeof define == "function" && define); }); var l1 = Kt((c1, Kw) => { - "use strict"; (function(r15, e, t10) { function o(a) { var i = this; @@ -1017,7 +1009,6 @@ var l1 = Kt((c1, Kw) => { })(c1, typeof Kw == "object" && Kw, typeof define == "function" && define); }); var d1 = Kt((m1, qw) => { - "use strict"; (function(r15, e, t10) { function o(a) { var i = this, p = ""; @@ -1049,10 +1040,8 @@ var d1 = Kt((m1, qw) => { })(m1, typeof qw == "object" && qw, typeof define == "function" && define); }); var f1 = Kt(() => { - "use strict"; }); var g1 = Kt((h1, Md) => { - "use strict"; (function(r15, e, t10) { var o = 256, n = 6, s = 52, a = "random", i = t10.pow(o, n), p = t10.pow(2, s), u = p * 2, c = o - 1, l; function m(C, S, k) { @@ -1121,7 +1110,6 @@ var g1 = Kt((h1, Md) => { })(typeof self != "undefined" ? self : h1, [], Math); }); var jw = Kt((s1e, x1) => { - "use strict"; var Dq = o1(), Aq = s1(), Fq = i1(), Pq = p1(), Oq = l1(), Mq = d1(), Ku = g1(); Ku.alea = Dq; Ku.xor128 = Aq; @@ -1132,22 +1120,16 @@ var jw = Kt((s1e, x1) => { x1.exports = Ku; }); var Vv = Kt(() => { - "use strict"; }); var Wv = Kt(() => { - "use strict"; }); var LB = Kt(() => { - "use strict"; }); var BB = Kt(() => { - "use strict"; }); var zB = Kt(() => { - "use strict"; }); var VB = Kt((Wg, Gv) => { - "use strict"; var Uv = (() => { var r15 = typeof document != "undefined" && document.currentScript ? document.currentScript.src : void 0; return typeof __filename != "undefined" && (r15 = r15 || __filename), function(e) { @@ -2106,12 +2088,10 @@ var VB = Kt((Wg, Gv) => { }) : typeof Wg == "object" && (Wg.WasmBackendModuleThreadedSimd = Uv); }); var UB = Kt((e3t, WB) => { - "use strict"; WB.exports.wasmWorkerContents = `"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof 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"../src/tfjs/load.ts", "../package.json", "../src/tfjs/humangl.ts", "../src/tfjs/constants.ts", "../src/tfjs/backend.ts", "../src/draw/draw.ts", "../src/draw/primitives.ts", "../src/draw/options.ts", "../src/face/facemeshcoords.ts", "../src/face/constants.ts", "../src/draw/face.ts", "../src/draw/body.ts", "../src/draw/hand.ts", "../src/draw/object.ts", "../src/draw/gesture.ts", "../src/draw/labels.ts", "../src/body/blazeposecoords.ts", "../src/body/blazeposedetector.ts", "../src/util/box.ts", "../src/body/blazepose.ts", "../src/object/labels.ts", "../src/object/centernet.ts", "../src/body/efficientposecoords.ts", "../src/body/efficientpose.ts", "../src/face/facemeshutil.ts", "../src/face/blazeface.ts", "../src/face/iris.ts", "../src/face/attention.ts", "../src/face/facemesh.ts", "../src/gear/emotion.ts", "../src/face/faceres.ts", "../src/face/mask.ts", "../src/face/antispoof.ts", "../src/face/liveness.ts", "../src/gear/gear.ts", "../src/gear/ssrnet-age.ts", "../src/gear/ssrnet-gender.ts", "../src/face/mobilefacenet.ts", "../src/face/insightface.ts", "../src/face/angles.ts", "../src/face/anthropometry.ts", "../src/face/face.ts", "../src/hand/fingerdef.ts", "../src/hand/fingergesture.ts", "../src/hand/fingerpose.ts", "../src/gesture/gesture.ts", "../src/hand/handposeutil.ts", "../src/hand/handposeanchors.ts", "../src/hand/handposedetector.ts", "../src/hand/handposepipeline.ts", "../src/hand/handpose.ts", "../src/hand/handtrack.ts", "../src/result.ts", "../src/body/movenetcoords.ts", "../src/util/interpolate.ts", "../src/segmentation/meet.ts", "../src/face/match.ts", "../src/models.ts", "../src/body/movenetfix.ts", "../src/body/movenet.ts", "../src/object/nanodet.ts", "../src/body/posenetutils.ts", "../src/body/posenet.ts", "../src/segmentation/rvm.ts", "../src/segmentation/selfie.ts", "../src/util/persons.ts", "../src/sample.ts", "../src/warmup.ts", "../src/human.ts"], - "sourcesContent": ["/*\n Human\n homepage: \n author: '\n*/\n\nvar 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yw;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(yw||(yw={}));var bw;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(bw||(bw={}));var Cw;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(Cw||(Cw={}));var f4={float32:bw,int32:xw,bool:yw,complex64:Cw};function dt(r,e){if(r===\"string\"||e===\"string\"){if(r===\"string\"&&e===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${e}`)}return f4[r][e]}function oi(r){return dt(r,\"int32\")}function rd(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function od(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Oe(r,e){if(r.dtype===e.dtype)return[r,e];let t=dt(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function ww(r,e){E(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function h4(r,e){return e.some(t=>t.id===r.id)}function Cl(r){let e=[];return tk(r,e,new Set),e}function tk(r,e,t){if(r==null)return;if(r instanceof mt){e.push(r);return}if(!g4(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),tk(s,e,t))}}function g4(r){return Array.isArray(r)||typeof r==\"object\"}function Sw(r){return r.kernelName!=null}var nd=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},wl=class r{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new nd}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Ym(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof ao)&&typeof o.then==\"function\"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return r.nextTensorId++}nextVariableId(){return r.nextVariableId++}clone(e){let t=T.runKernel(Co,{x:e}),o={x:e},n=a=>({x:()=>{let i=\"float32\",p={x:a},u={dtype:i};return T.runKernel(yo,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(this.backendName==null&&this.backend,!(Xp(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=Sw(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Sw(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Xp(f,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let S=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,S);o=this.saveTensorsForBackwardMode(k)}return S}}else{let{forwardFunc:f}=e,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Sw(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=iw(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(E(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");o=o||\"float32\",n=n||this.backend;let s=e;o===\"string\"&&zo(e[0])&&(s=e.map(p=>Ji(p)));let a=n.write(s,t,o),i=new mt(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o===\"string\"){let p=this.state.tensorInfo.get(a),u=ow(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||\"float32\";let s={dataId:e,shape:t,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(e,t){let{dataId:o,shape:n,dtype:s}=e,a=new mt(n,s,o,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new ri(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let o=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(o=e.size*Wp(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof ri||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let o=e.size*Wp(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},p=iw(e);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=Gp(m.size,m.dtype);return this.makeTensor(d,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Cl(e),o=new Set(t.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if(E(t.length>0,()=>\"gradients() received an empty list of xs.\"),o!=null&&o.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",e));E(s instanceof mt,()=>\"The result y returned by f() must be a tensor.\");let a=q0(this.state.activeTape,t,s);if(!n&&a.length===0&&t.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let i={};i[s.id]=o==null?x4(s.shape):o,j0(i,a,u=>this.tidy(u),y4);let p=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(e){return E(qs(e),()=>\"The f passed in customGrad(f) must be a function.\"),(...t)=>{E(t.every(i=>i instanceof mt),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let o,n={};t.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=e(...t,p),E(o.value instanceof mt,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),E(qs(o.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];E(c.length===t.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),E(c.every(m=>m instanceof mt),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let l={};return c.forEach((m,d)=>{l[d]=()=>m}),l};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=Mu(),o=await this.backend.time(e);return o.wallMs=Mu()-t,o}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new nd;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};wl.nextTensorId=0;wl.nextVariableId=0;function x4(r){let e=ml(ze(r),\"float32\");return T.makeTensor(e,r,\"float32\")}function Iw(){let r=aw();if(r._tfengine==null){let e=new dl(r);r._tfengine=new wl(e)}return $0(r._tfengine.ENV),Z0(()=>r._tfengine),r._tfengine}var T=Iw();function y4(r,e){let t={a:r,b:e};return T.runKernel(uo,t)}var eu={};qe(eu,{isBrowser:()=>kw,isMobile:()=>w4,mockIsMobile:()=>C4});function b4(){return typeof navigator!=\"undefined\"&&navigator!=null}var vw;function C4(r){vw=r}function w4(r){if(vw!==void 0)return vw;if(r||b4()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let e=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!e){let t=r;return 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a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),nu(a,i,p,c,u,s)}var Xk=N({batchNorm3d_:kH});function NH(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),nu(a,i,p,c,u,s)}var Yk=N({batchNorm4d_:NH});function TH(r,e,t){let o=v(r,\"x\",\"bincount\"),n=v(e,\"weights\",\"bincount\");E(o.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);let s={x:o,weights:n},a={size:t};return T.runKernel(Jo,s,a)}var hd=N({bincount_:TH});function _H(r,e){let t=v(r,\"x\",\"bitwiseAnd\"),o=v(e,\"y\",\"bitwiseAnd\");if(!br(t.shape,o.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${t.shape}, y: ${o.shape}`);if(t.dtype!==\"int32\"||o.dtype!==\"int32\")throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${t.dtype} and type of y: ${o.dtype}`);let n={a:t,b:o};return T.runKernel(qa,n)}var Qk=N({bitwiseAnd_:_H});function EH(r,e){let t=v(r,\"s0\",\"broadcastArgs\",\"int32\"),o=v(e,\"s1\",\"broadcastArgs\",\"int32\");if(t.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t.rank}`);if(o.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${o.rank}`);let n={s0:t,s1:o};return T.runKernel(ea,n)}var Zk=N({broadcastArgs_:EH});function $H(r,e){let t=v(r,\"broadcastTo\",\"x\"),o=t.shape;if(Ct(e),e.lengtht.rank){let u=t.shape.slice();for(;u.length=0;u--)if(n[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Ur(t);let i={x:t},p={reps:s};return T.runKernel(po,i,p)}var su=N({broadcastTo_:$H});function RH(r){let t={x:v(r,\"x\",\"ceil\",\"float32\")};return T.runKernel(en,t)}var Jk=N({ceil_:RH});function $a(r,e,t){Ct(r),t=t||Ei(e);let o={shape:r,value:e,dtype:t};return T.runKernel(sa,{},o)}function DH(r,e,t){let o=v(r,\"x\",\"clipByValue\");if(E(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`),e===t)return $a(o.shape,e,o.dtype);let n={x:o},s={clipValueMin:e,clipValueMax:t};return T.runKernel(bo,n,s)}var e2=N({clipByValue_:DH});function AH(r){return yt(r,0)}var t2=N({concat1d_:AH});function FH(r,e){return yt(r,e)}var r2=N({concat2d_:FH});function PH(r,e){return yt(r,e)}var o2=N({concat3d_:PH});function OH(r,e){return yt(r,e)}var n2=N({concat4d_:OH});function MH(r,e,t,o,n=\"NHWC\",s=[1,1],a){let i=v(r,\"x\",\"conv2d\",\"float32\"),p=v(e,\"filter\",\"conv2d\",\"float32\"),u=i,c=!1;i.rank===3&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`),Lt(\"conv2d\",o,a);let l=n===\"NHWC\"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`),E(gr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),E(Ta(s),()=>\"Error in conv2D: Dilated rates should be larger than 0.\"),E(Ta(t),()=>\"Error in conv2D: Strides should be larger than 0.\");let m={x:u,filter:p},d={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=T.runKernel(tn,m,d);return c?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var au=N({conv2d_:MH});function LH(r,e,t,o,n=\"NWC\",s=1,a){let i=v(r,\"x\",\"conv1d\"),p=v(e,\"filter\",\"conv1d\"),u=i,c=!1;i.rank===2&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1]])),E(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),E(p.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`),Lt(\"conv1d\",o,a),E(u.shape[2]===p.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`),E(gr(t,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${t} and dilation '${s}'`),E(Ta(s),()=>\"Error in conv1D: Dilated rates should be larger than 0.\"),E(Ta(t),()=>\"Error in conv1D: Stride should be larger than 0.\"),E(n===\"NWC\",()=>`Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`);let l=W(p,[1,p.shape[0],p.shape[1],p.shape[2]]),m=W(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=au(m,l,[1,t],o,\"NHWC\",[1,s],a);return c?W(g,[g.shape[2],g.shape[3]]):W(g,[g.shape[0],g.shape[2],g.shape[3]])}var s2=N({conv1d_:LH});function BH(r,e,t,o,n,s=\"NHWC\",a){E(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let i=r,p=e,u=!1;e.rank===3&&(u=!0,p=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]),i=[1,r[0],r[1],r[2]]),E(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),E(p.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`),E(t.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${t.rank}`);let c=s===\"NHWC\"?i[3]:i[1],l=s===\"NHWC\"?p.shape[3]:p.shape[1];E(c===t.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t.shape[2]}.`),E(l===t.shape[3],()=>`Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${t.shape[3]}.`),Lt(\"conv2dDerInput\",n,a);let m={dy:p,filter:t},d={strides:o,pad:n,dataFormat:s,dimRoundingMode:a,inputShape:i},f=T.runKernel(rn,m,d);return u?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var gd=N({conv2DBackpropInput_:BH});function zH(r,e,t,o,n,s){let a=v(r,\"x\",\"conv2dTranspose\"),i=v(e,\"filter\",\"conv2dTranspose\");return gd(t,a,i,o,n,\"NHWC\",s)}var a2=N({conv2dTranspose_:zH});function VH(r,e,t,o,n=\"NDHWC\",s=[1,1,1]){let a=v(r,\"x\",\"conv3d\"),i=v(e,\"filter\",\"conv3d\"),p=a,u=!1;a.rank===4&&(u=!0,p=W(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),E(p.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${p.rank}.`),E(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),E(p.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`),E(gr(t,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),E(n===\"NDHWC\",()=>`Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`),E(Ta(s),()=>\"Error in conv3D: Dilated rates should be larger than 0.\"),E(Ta(t),()=>\"Error in conv3D: Strides should be larger than 0.\");let c={x:p,filter:i},l={strides:t,pad:o,dataFormat:n,dilations:s},m=T.runKernel(on,c,l);return u?W(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var i2=N({conv3d_:VH});function WH(r,e,t,o,n){E(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let s=r,a=e,i=!1;e.rank===4&&(i=!0,a=W(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let p=s[4],u=a.shape[4];E(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),E(a.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`),E(t.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${t.rank}`),E(p===t.shape[3],()=>`Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t.shape[3]}.`),E(u===t.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t.shape[4]}.`);let c={dy:a,filter:t},l={pad:n,strides:o,inputShape:s},m=T.runKernel(nn,c,l);return i?W(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var u2=N({conv3DBackpropInput_:WH});function UH(r,e,t,o,n){let s=v(r,\"x\",\"conv3dTranspose\"),a=v(e,\"filter\",\"conv3dTranspose\");return u2(t,s,a,o,n)}var p2=N({conv3dTranspose_:UH});function GH(r){let t={x:v(r,\"x\",\"cos\",\"float32\")};return T.runKernel(sn,t)}var c2=N({cos_:GH});function HH(r){let t={x:v(r,\"x\",\"cosh\",\"float32\")};return T.runKernel(an,t)}var l2=N({cosh_:HH});function KH(r,e=0,t=!1,o=!1){let s={x:v(r,\"x\",\"cumprod\")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(un,s,a)}var m2=N({cumprod_:KH});function qH(r,e=0,t=!1,o=!1){let s={x:v(r,\"x\",\"cumsum\")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(pn,s,a)}var d2=N({cumsum_:qH});function jH(r,e,t,o=!1){let n=v(r,\"x\",\"denseBincount\"),s=v(e,\"weights\",\"denseBincount\");E(n.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${n.dtype}`),E(n.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(s.size===n.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);let a={x:n,weights:s},i={size:t,binaryOutput:o};return T.runKernel(ra,a,i)}var f2=N({denseBincount_:jH});function XH(r,e,t=\"NHWC\"){let o=v(r,\"x\",\"depthToSpace\",\"float32\"),n=t===\"NHWC\"?o.shape[1]:o.shape[2],s=t===\"NHWC\"?o.shape[2]:o.shape[3],a=t===\"NHWC\"?o.shape[3]:o.shape[1];E(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`),E(n*e>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${n} and ${e} for depthToSpace with input shape\n ${o.shape}`),E(s*e>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${s} and ${e} for depthToSpace with input shape\n ${o.shape}`),E(a%(e*e)===0,()=>`Dimension size must be evenly divisible by ${e*e} but is ${a} for depthToSpace with input shape ${o.shape}`);let i={x:o},p={blockSize:e,dataFormat:t};return T.runKernel(ln,i,p)}var h2=N({depthToSpace_:XH});function YH(r,e,t,o,n=\"NHWC\",s=[1,1],a){let i=v(r,\"x\",\"depthwiseConv2d\",\"float32\"),p=v(e,\"filter\",\"depthwiseConv2d\",\"float32\"),u=i,c=!1;i.rank===3&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`);let l=n===\"NHWC\"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`),Lt(\"depthwiseConv2d\",o,a);let m={x:u,filter:p},d={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=T.runKernel(mn,m,d);return c?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var sc=N({depthwiseConv2d_:YH});function QH(r){let t={x:v(r,\"x\",\"diag\")};return T.runKernel(oa,t)}var g2=N({diag_:QH});function ZH(r,e,t,o,n=[1,1],s=\"NHWC\"){let a=v(r,\"x\",\"dilation2d\"),i=v(e,\"filter\",\"dilation2d\");E(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),E(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),E(s===\"NHWC\",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let p=a,u=!1;a.rank===3&&(p=W(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0),E(p.shape[3]===i.shape[2],()=>`Error in dilation2d: input and filter must have the same depth: ${p.shape[3]} vs 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Eo(r,e,t,o,n,s){o==null&&(o=.5),n==null&&(n=Number.NEGATIVE_INFINITY),s==null&&(s=0);let a=r.shape[0];return t=Math.min(t,a),E(0<=o&&o<=1,()=>`iouThreshold must be in [0, 1], but was '${o}'`),E(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),E(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),E(e.rank===1,()=>\"scores must be a 1D tensor\"),E(e.shape[0]===a,()=>`scores has incompatible shape with boxes. 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g=0;gn&&u.push({score:e[g],boxIndex:g,suppressBeginIndex:0});u.sort(yN);let c=s>0?-.5/s:0,l=[],m=[];for(;l.length0;){let g=u.pop(),{score:x,boxIndex:b,suppressBeginIndex:C}=g;if(x=C;--k){let _=yj(r,b,l[k]);if(_>=o){S=!0;break}if(g.score=g.score*bj(o,c,_),g.score<=n)break}g.suppressBeginIndex=l.length,S||(g.score===x?(l.push(b),m.push(g.score)):g.score>n&&xN(u,g,yN))}let d=l.length,f=t-d;i&&f>0&&(l.push(...new Array(f).fill(0)),m.push(...new Array(f).fill(0)));let h={selectedIndices:l};return a&&(h.selectedScores=m),p&&(h.validOutputs=d),h}function yj(r,e,t){let o=r.subarray(e*4,e*4+4),n=r.subarray(t*4,t*4+4),s=Math.min(o[0],o[2]),a=Math.min(o[1],o[3]),i=Math.max(o[0],o[2]),p=Math.max(o[1],o[3]),u=Math.min(n[0],n[2]),c=Math.min(n[1],n[3]),l=Math.max(n[0],n[2]),m=Math.max(n[1],n[3]),d=(i-s)*(p-a),f=(l-u)*(m-c);if(d<=0||f<=0)return 0;let h=Math.max(s,u),g=Math.max(a,c),x=Math.min(i,l),b=Math.min(p,m),C=Math.max(x-h,0)*Math.max(b-g,0);return C/(d+f-C)}function bj(r,e,t){let o=Math.exp(e*t*t);return t<=r?o:0}function yN(r,e){return r.score-e.score||r.score===e.score&&e.boxIndex-r.boxIndex}async function Cj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY){let s=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=v(e,\"scores\",\"nonMaxSuppressionAsync\"),i=Eo(s,a,t,o,n);t=i.maxOutputSize,o=i.iouThreshold,n=i.scoreThreshold;let p=await Promise.all([s.data(),a.data()]),u=p[0],c=p[1],{selectedIndices:l}=Jd(u,c,t,o,n);return s!==r&&s.dispose(),a!==e&&a.dispose(),Jt(l,\"int32\")}var bN=Cj;function wj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,\"boxes\",\"nonMaxSuppression\"),i=v(e,\"scores\",\"nonMaxSuppression\"),p=Eo(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u={boxes:a,scores:i},c={maxOutputSize:t,iouThreshold:o,scoreThreshold:n,softNmsSigma:s},l=T.runKernel(Zn,u,c);return{selectedIndices:l[0],selectedScores:l[1]}}var CN=N({nonMaxSuppressionWithScore_:wj});async function Sj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=v(e,\"scores\",\"nonMaxSuppressionAsync\"),p=Eo(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u=await Promise.all([a.data(),i.data()]),c=u[0],l=u[1],{selectedIndices:m,selectedScores:d}=tf(c,l,t,o,n,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Jt(m,\"int32\"),selectedScores:Jt(d)}}var wN=Sj;function Ij(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,\"boxes\",\"nonMaxSuppression\"),i=v(e,\"scores\",\"nonMaxSuppression\"),p=Eo(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,m={boxes:a,scores:i},d={maxOutputSize:u,iouThreshold:c,scoreThreshold:l,padToMaxOutputSize:s},f=T.runKernel(Qa,m,d);return{selectedIndices:f[0],validOutputs:f[1]}}var SN=N({nonMaxSuppressionPadded_:Ij});async function vj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=v(e,\"scores\",\"nonMaxSuppressionAsync\"),p=Eo(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,[m,d]=await Promise.all([a.data(),i.data()]),{selectedIndices:f,validOutputs:h}=ef(m,d,u,c,l,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Jt(f,\"int32\"),validOutputs:ke(h,\"int32\")}}var IN=vj;function kj(r,e,t=!1,o=!1){let n=v(r,\"images\",\"resizeBilinear\");E(n.rank===3||n.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${e}.`),E(o===!1||t===!1,()=>\"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.\");let s=n,a=!1;n.rank===3&&(a=!0,s=W(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(is,i,p);return a?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var vN=N({resizeBilinear_:kj});function Nj(r,e,t=!1,o=!1){let n=v(r,\"images\",\"resizeNearestNeighbor\");E(n.rank===3||n.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`),E(n.dtype===\"float32\"||n.dtype===\"int32\",()=>\"`images` must have `int32` or `float32` as dtype\"),E(o===!1||t===!1,()=>\"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.\");let s=n,a=!1;n.rank===3&&(a=!0,s=W(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(as,i,p);return a?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var kN=N({resizeNearestNeighbor_:Nj});function Tj(r,e=\"binary\",t=!1,o=.5){let n=v(r,\"image\",\"threshold\"),s=.2989,a=.587,i=.114,p=n.shape[0]*n.shape[1],u=se(Jt([o]),255),c,l,m,d;if(E(n.rank===3,()=>`Error in threshold: image must be rank 3,but got rank 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p={image:a,transforms:i},u={interpolation:t,fillMode:o,fillValue:n,outputShape:s};return T.runKernel(Rs,p,u)}var TN=N({transform_:Ej});function $j(r,e,t){let o=v(r,\"a\",\"bandPart\");E(o.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${o.rank}.`);let n=o.shape,[s,a]=o.shape.slice(-2),i,p;typeof e==\"number\"?(E(e%1===0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(e<=s,()=>`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`),i=v(e<0?s:e,\"numLower\",\"bandPart\")):(E(e.dtype===\"int32\",()=>\"bandPart(): numLower's dtype must be an int32.\"),i=lo(Tl(e,0),s,Hu(e,s))),typeof t==\"number\"?(E(t%1===0,()=>`bandPart(): numUpper must be an integer, got ${t}.`),E(t<=a,()=>`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`),p=v(t<0?a:t,\"numUpper\",\"bandPart\")):(E(t.dtype===\"int32\",()=>\"bandPart(): numUpper's dtype must be an int32.\"),p=lo(Tl(t,0),a,Hu(t,a)));let u=W(cu(0,s,1,\"int32\"),[-1,1]),c=cu(0,a,1,\"int32\"),l=Te(u,c),m=Uu(ac(l,i),Id(l,pr(p))),d=Gr([s,a],o.dtype);return W(vr(fo(W(o,[-1,s,a])).map(f=>lo(m,f,d))),n)}var _N=N({bandPart_:$j});function Rj(r){let e;if(Array.isArray(r)){e=!1,E(r!=null&&r.length>0,()=>\"Gram-Schmidt process: input must not be null, undefined, or empty\");let n=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`)}else e=!0,r=li(r,r.shape[0],0).map(n=>cc(n,[0]));E(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let t=[],o=r;for(let n=0;n{let s=o[n];if(n>0)for(let a=0;a=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return $N(r,e);{let t=r.shape.slice(0,r.shape.length-2).reduce((p,u)=>p*u),o=fo(W(r,[t,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),n=[],s=[];o.forEach(p=>{let[u,c]=$N(p,e);n.push(u),s.push(c)});let a=W(vr(n,0),r.shape),i=W(vr(s,0),r.shape);return[a,i]}}function $N(r,e=!1){return T.tidy(()=>{E(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let t=r.shape[0],o=r.shape[1],n=Cd(t),s=Ur(r),a=mu([[1]],[1,1]),i=Ur(a),p=t>=o?o:t;for(let u=0;u{let d=Xe(s,[u,u],[t-u,1]),f=Vu(d),h=Xe(s,[u,u],[1,1]),g=lo(Wu(h,0),mu([[-1]]),mu([[1]])),x=Te(h,se(g,f)),b=je(d,x);b.shape[0]===1?i=Ur(a):i=yt([a,Xe(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=pr(je(Ze(g,x),f)),S=Xe(s,[u,0],[t-u,o]),k=se(C,i),_=mc(i);if(u===0)s=Te(S,Ze(k,Ze(_,S)));else{let D=Te(S,Ze(k,Ze(_,S)));s=yt([Xe(s,[0,0],[u,o]),D],0)}let $=mc(k),R=Xe(n,[0,u],[t,n.shape[1]-u]);if(u===0)n=Te(R,Ze(Ze(R,i),$));else{let D=Te(R,Ze(Ze(R,i),$));n=yt([Xe(n,[0,0],[t,u]),D],1)}return[i,s,n]}),Ot([c,l,m])}return!e&&t>o&&(n=Xe(n,[0,0],[t,o]),s=Xe(s,[0,0],[o,o])),[n,s]})}var RN=N({qr_:Dj});var $t;(function(r){r[r.NONE=0]=\"NONE\",r[r.MEAN=1]=\"MEAN\",r[r.SUM=2]=\"SUM\",r[r.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"})($t||($t={}));function Aj(r,e,t=$t.SUM_BY_NONZERO_WEIGHTS){let o=v(r,\"losses\",\"computeWeightedLoss\"),n=null;e!=null&&(n=v(e,\"weights\",\"computeWeightedLoss\"));let s=n==null?o:se(o,n);if(t===$t.NONE)return s;if(t===$t.SUM)return ot(s);if(t===$t.MEAN){if(n==null)return Gu(s);{let a=o.size/n.size,i=je(ot(s),ot(n));return a>1?je(i,ke(a)):i}}if(t===$t.SUM_BY_NONZERO_WEIGHTS){if(n==null)return je(ot(s),ke(o.size));{let a=se(n,Da(o.shape)),i=Ue(ot(Fd(a,ke(0))),\"float32\");return je(ot(s),i)}}throw Error(`Unknown reduction: ${t}`)}var cr=N({computeWeightedLoss_:Aj});function Fj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"absoluteDifference\"),s=v(e,\"predictions\",\"absoluteDifference\"),a=null;t!=null&&(a=v(t,\"weights\",\"absoluteDifference\")),xt(n.shape,s.shape,\"Error in absoluteDifference: \");let i=Qt(Te(n,s));return cr(i,a,o)}var DN=N({absoluteDifference_:Fj});function Pj(r,e,t,o,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"cosineDistance\"),a=v(e,\"predictions\",\"cosineDistance\"),i=null;o!=null&&(i=v(o,\"weights\",\"cosineDistance\")),xt(s.shape,a.shape,\"Error in cosineDistance: \");let p=ke(1),u=Te(p,ot(se(s,a),t,!0));return cr(u,i,n)}var AN=N({cosineDistance_:Pj});function Oj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"hingeLoss\"),s=v(e,\"predictions\",\"hingeLoss\"),a=null;t!=null&&(a=v(t,\"weights\",\"hingeLoss\")),xt(n.shape,s.shape,\"Error in hingeLoss: \");let i=ke(1);n=Te(se(ke(2),n),i);let p=lu(Te(i,se(n,s)));return cr(p,a,o)}var FN=N({hingeLoss_:Oj});function Mj(r,e,t,o=1,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"huberLoss\"),a=v(e,\"predictions\",\"huberLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"huberLoss\")),xt(s.shape,a.shape,\"Error in huberLoss: \");let p=ke(o),u=Qt(Te(a,s)),c=Hu(u,p),l=Te(u,c),m=Ce(se(ke(.5),Zt(c)),se(p,l));return cr(m,i,n)}var PN=N({huberLoss_:Mj});function Lj(r,e,t,o=1e-7,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"logLoss\"),a=v(e,\"predictions\",\"logLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"logLoss\")),xt(s.shape,a.shape,\"Error in logLoss: \");let p=ke(1),u=ke(o),c=pr(se(s,pi(Ce(a,u)))),l=se(Te(p,s),pi(Ce(Te(p,a),u))),m=Te(c,l);return cr(m,i,n)}var ON=N({logLoss_:Lj});function Bj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"meanSquaredError\"),s=v(e,\"predictions\",\"meanSquaredError\"),a=null;t!=null&&(a=v(t,\"weights\",\"meanSquaredError\")),xt(n.shape,s.shape,\"Error in meanSquaredError: \");let i=Kd(n,s);return cr(i,a,o)}var MN=N({meanSquaredError_:Bj});function zj(r,e){let t=v(r,\"labels\",\"sigmoidCrossEntropyWithLogits\"),o=v(e,\"logits\",\"sigmoidCrossEntropyWithLogits\");xt(t.shape,o.shape,\"Error in sigmoidCrossEntropyWithLogits: \");let n=lu(o),s=se(o,t),a=kd(_o(pr(Qt(o))));return Ce(Te(n,s),a)}function Vj(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let 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Labels / logits was rank ${e.rank} and dim was ${t}`);return Ir((n,s,a)=>{let p=_d(s,[t],!0),u=Te(Ue(s,\"float32\"),p);a([n,u]);let c=pr(se(u,n));return{value:ot(c,[t]),gradFunc:(d,f)=>{let[h,g]=f,x=ii(d.shape,[t]);return[se(W(d,x),Te(Ue(h,\"float32\"),_o(g))),se(W(d,x),Te(_o(g),Ue(h,\"float32\")))]}}})(r,e)}function Uj(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"onehotLabels\",\"softmaxCrossEntropy\"),a=v(e,\"logits\",\"softmaxCrossEntropy\"),i=null;if(t!=null&&(i=v(t,\"weights\",\"softmaxCrossEntropy\")),xt(s.shape,a.shape,\"Error in softmaxCrossEntropy: \"),o>0){let u=ke(o),c=ke(1),l=ke(s.shape[1]);s=Ce(se(s,Te(c,u)),je(u,l))}let p=Wj(s,a);return cr(p,i,n)}var BN=N({softmaxCrossEntropy_:Uj});function Gj(r,e,t,o){let n=v(r,\"indices\",\"sparseFillEmptyRows\",\"int32\"),s=v(e,\"values\",\"sparseFillEmptyRows\"),a=v(t,\"denseShape\",\"sparseFillEmptyRows\",\"int32\"),i=v(o,\"defaultValue\",\"sparseFillEmptyRows\",s.dtype);if(n.rank!==2)throw new Error(`Indices should be Tensor2D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let p={indices:n,values:s,denseShape:a,defaultValue:i},u=T.runKernel(Ki,p);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var zN=N({sparseFillEmptyRows_:Gj});function Hj(r,e,t){let o=v(r,\"inputIndices\",\"sparseReshape\",\"int32\"),n=v(e,\"inputShape\",\"sparseReshape\",\"int32\"),s=v(t,\"newShape\",\"sparseReshape\",\"int32\");if(o.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape\n ${o.shape}`);if(n.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(ei,a);return{outputIndices:i[0],outputShape:i[1]}}var VN=N({sparseReshape_:Hj});function Kj(r,e,t){let o=v(r,\"data\",\"sparseSegmentMean\"),n=v(e,\"indices\",\"sparseSegmentMean\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ya,a)}var WN=N({sparseSegmentMean_:Kj});function qj(r,e,t){let o=v(r,\"data\",\"sparseSegmentSum\"),n=v(e,\"indices\",\"sparseSegmentSum\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ba,a)}var UN=N({sparseSegmentSum_:qj});function jj(r,e,t,o,n,s,a,i){let p=v(r,\"data\",\"stringNGrams\",\"string\");if(p.dtype!==\"string\")throw new Error(\"Data must be of datatype string\");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,\"dataSplits\",\"stringNGrams\");if(u.dtype!==\"int32\")throw new Error(\"Data splits must be of datatype int32\");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(Ca,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var GN=N({stringNGrams_:jj});function Xj(r,e,t=!0){let o=v(r,\"input\",\"stringSplit\",\"string\"),n=v(e,\"delimiter\",\"stringSplit\",\"string\");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(ji,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var HN=N({stringSplit_:Xj});function Yj(r,e){let t=v(r,\"input\",\"stringToHashBucketFast\",\"string\"),o={numBuckets:e};if(e<=0)throw new Error(\"Number of buckets must be at least 1\");let n={input:t};return T.runKernel(Xi,n,o)}var KN=N({stringToHashBucketFast_:Yj});function Qj(r,e,t,o=!0){let n=v(r,\"input\",\"staticRegexReplace\",\"string\"),s={pattern:e,rewrite:t,replaceGlobal:o};return T.runKernel(Ru,{x:n},s)}var qN=N({staticRegexReplace_:Qj});var Zj={fft:uc,ifft:ju,rfft:pc,irfft:Hd},Jj={hammingWindow:pN,hannWindow:Qd,frame:Zd,stft:cN},eX={flipLeftRight:mN,grayscaleToRGB:dN,resizeNearestNeighbor:kN,resizeBilinear:vN,rgbToGrayscale:fN,rotateWithOffset:hN,cropAndResize:lN,nonMaxSuppression:gN,nonMaxSuppressionAsync:bN,nonMaxSuppressionWithScore:CN,nonMaxSuppressionWithScoreAsync:wN,nonMaxSuppressionPadded:SN,nonMaxSuppressionPaddedAsync:IN,threshold:NN,transform:TN},tX={bandPart:_N,gramSchmidt:EN,qr:RN},rX={absoluteDifference:DN,computeWeightedLoss:cr,cosineDistance:AN,hingeLoss:FN,huberLoss:PN,logLoss:ON,meanSquaredError:MN,sigmoidCrossEntropy:LN,softmaxCrossEntropy:BN},oX={sparseFillEmptyRows:zN,sparseReshape:VN,sparseSegmentMean:WN,sparseSegmentSum:UN},nX={stringNGrams:GN,stringSplit:HN,stringToHashBucketFast:KN,staticRegexReplace:qN};var jN={};qe(jN,{Serializable:()=>Rl,SerializationMap:()=>rf,getRegisteredName:()=>aX,registerClass:()=>rS});var sX=new Map,tS=new Map,Rl=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},rf=class r{constructor(){this.classNameMap={}}static getMap(){return r.instance==null&&(r.instance=new r),r.instance}static register(e){r.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function rS(r,e,t){E(r.className!=null,()=>\"Class being registered does not have the static className property defined.\"),E(typeof r.className==\"string\",()=>\"className is required to be a string, but got type \"+typeof r.className),E(r.className.length>0,()=>\"Class being registered has an empty-string as its className, which is disallowed.\"),typeof e==\"undefined\"&&(e=\"Custom\"),typeof t==\"undefined\"&&(t=r.className);let o=t,n=e+\">\"+o;return rf.register(r),sX.set(n,r),tS.set(r,n),r}function aX(r){return tS.has(r)?tS.get(r):r.className}var kr=class extends Rl{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ot(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Vw(e,t)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:ke(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(kr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ju=class extends kr{static get className(){return\"Adadelta\"}constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=Ce(se(p,this.rho),se(Zt(i),1-this.rho)),l=se(je(Rr(Ce(u,this.epsilon)),Rr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(Zt(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var ep=class extends kr{static get className(){return\"Adagrad\"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>$a(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,Zt(a));i.assign(p);let u=Ce(se(je(a,Rr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var tp=class extends kr{static get className(){return\"Adam\"}constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Gt(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Gt(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(Zt(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Rr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(ui(this.beta1,this.iterations_+1)),this.accBeta2.assign(ui(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var rp=class extends kr{static get className(){return\"Adamax\"}constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Gt(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Gt(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Qt(u),h=Ad(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var mi=class extends kr{static get className(){return\"SGD\"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=$r(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var op=class extends mi{static get className(){return\"Momentum\"}constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var np=class extends kr{static get className(){return\"RMSProp\"}constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=Ce(se(p,this.decay),se(Zt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=Ce(se(l,this.decay),se(i,1-this.decay)),d=je(se(i,this.learningRate),Rr(Te(c,Ce(Zt(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=Ce(se(p,this.decay),se(Zt(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Rr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ot(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};var iX=[Ju,ep,tp,rp,op,np,mi];function XN(){for(let r of iX)rS(r)}var 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o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=I(\"numElements\",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case\"TensorListFromTensor\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=$T(o,n,s);return t.addTensorList(a),[a.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id),s=I(\"dtype\",r,e,t),a=I(\"elementShape\",r,e,t);return[n.concat(s,a)]}case\"TensorListPushBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"tensor\",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case\"TensorListPopBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case\"TensorListSplit\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"lengths\",r,e,t),a=AT(o,s,n);return t.addTensorList(a),[a.idTensor]}case\"TensorListLength\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id);return[ke(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,e,t),n=I(\"size\",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PT(r,e,t){let[o,n]=I(\"fusedOps\",r,e,t),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,e,t);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,e,t),l=Pl(r,e,t),m=I(\"dataFormat\",r,e,t).toUpperCase(),d=I(\"dilations\",r,e,t),[f,h]=I(\"args\",r,e,t);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var OT=(r,e,t,o=Je)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilation\",r,e,t);return[o.conv1d(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,e,t),s=Pl(r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PT(r,e,t);return[o.fused.conv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PT(r,e,t);return[o.fused.depthwiseConv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let n=I(\"outputShape\",r,e,t),s=I(\"strides\",r,e,t),a=Pl(r,e,t);return[o.conv2dTranspose(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,e,t),s=Pl(r,e,t),a=I(\"dilations\",r,e,t),i=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv3d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t),i=I(\"includeBatchInIndex\",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dilations\",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(r,e,t,o=Je)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,e,t),s=I(\"dtype\",r,e,t),a=I(\"value\",r,e,t);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"num\",r,e,t);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,e,t),s=I(\"numSamples\",r,e,t),a=I(\"seed\",r,e,t);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,e,t),s=I(\"depth\",r,e,t),a=I(\"onValue\",r,e,t),i=I(\"offValue\",r,e,t),p=I(\"dtype\",r,e,t);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,e,t))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,e,t),I(\"dtype\",r,e,t),I(\"seed\",r,e,t))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"dtype\",r,e,t))];case\"RandomUniformInt\":return[o.randomUniformInt(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"seed\",r,e,t))];case\"Range\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"step\",r,e,t);return[o.range(n,s,a,I(\"dtype\",r,e,t))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,e,t),s=I(\"mean\",r,e,t),a=I(\"stdDev\",r,e,t),i=I(\"seed\",r,e,t);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,e,t),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function OS(r,e,t){let o=I(\"boxes\",r,e,t),n=I(\"scores\",r,e,t),s=I(\"maxOutputSize\",r,e,t),a=I(\"iouThreshold\",r,e,t),i=I(\"scoreThreshold\",r,e,t),p=I(\"softNmsSigma\",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var LT=async(r,e,t,o,n=Je)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=OS(r,e,t),l=await 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n=I(\"x\",r,e,t),s=I(\"k\",r,e,t),a=I(\"sorted\",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,e,t),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,e,t),s=I(\"axis\",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zT=(r,e,t,o=Je)=>{switch(r.op){case\"Const\":return e[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,e,t);return[Bt(r.name,e,t)||n];case\"Placeholder\":return[Bt(r.name,e,t)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,e,t);return[Bs(c)]}case\"IdentityN\":return I(\"x\",r,e,t).map(c=>Bs(c));case\"Snapshot\":let s=I(\"x\",r,e,t);return[Bs(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,e,t).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,e,t).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,e,t).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,e,t).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,e,t),i=I(\"data\",r,e,t),p=I(\"message\",r,e,t),u=I(\"summarize\",r,e,t);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=fo(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,e,t),a=I(\"valueDType\",r,e,t),i=new vf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"values\",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"defaultValue\",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var WT=(r,e,t,o=Je)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UT=(r,e,t,o=Je)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"BitwiseAnd\":return[o.bitwiseAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GT=(r,e,t,o=Je)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,e,t);return[o.fused.matMul({a:I(\"a\",r,e,t),b:I(\"b\",r,e,t),transposeA:I(\"transposeA\",r,e,t),transposeB:I(\"transposeB\",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case\"MatrixBandPart\":return[o.linalg.bandPart(I(\"a\",r,e,t),I(\"numLower\",r,e,t),I(\"numUpper\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HT=(r,e,t,o=Je)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KT=(r,e,t,o=Je)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qT=(r,e,t,o=Je)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.sum(I(\"x\",r,e,t),i,p)]}case\"All\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.all(I(\"x\",r,e,t),i,p)]}case\"Any\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.any(I(\"x\",r,e,t),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,e,t);return[o.argMax(I(\"x\",r,e,t),i)]}case\"ArgMin\":{let i=I(\"axis\",r,e,t);return[o.argMin(I(\"x\",r,e,t),i)]}case\"Prod\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.prod(I(\"x\",r,e,t),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumprod(I(\"x\",r,e,t),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumsum(I(\"x\",r,e,t),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,e,t),s=I(\"weights\",r,e,t),a=I(\"size\",r,e,t);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,e,t),p=I(\"weights\",r,e,t),u=I(\"size\",r,e,t),c=I(\"binaryOutput\",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jT=(r,e,t,o=Je)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,e,t),s=I(\"axis\",r,e,t),a=I(\"tensors\",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,e,t),s=I(\"batchDims\",r,e,t),a=I(\"x\",r,e,t),i=I(\"indices\",r,e,t);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,e,t),s=[];for(let i=0;i{let n=I(\"axis\",r,e,t),s=I(\"tensors\",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,e,t),s=I(\"tensor\",r,e,t);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,e,t);return[o.tile(I(\"x\",r,e,t),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,e,t),s=I(\"numOrSizeSplits\",r,e,t),a=I(\"x\",r,e,t);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"shape\",r,e,t);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,e,t),s=I(\"outputShape\",r,e,t),a=I(\"sparseValues\",r,e,t),i=I(\"defaultValue\",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}case\"TensorScatterUpdate\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"tensor\",r,e,t);return[o.tensorScatterUpdate(a,n,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XT=(r,e,t,o=Je)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YT=(r,e,t,o=Je)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QT=(r,e,t,o=Je)=>{switch(r.op){case\"StaticRegexReplace\":return[o.string.staticRegexReplace(I(\"input\",r,e,t),I(\"pattern\",r,e,t),I(\"rewrite\",r,e,t),I(\"replaceGlobal\",r,e,t))];case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,e,t),I(\"dataSplits\",r,e,t),I(\"separator\",r,e,t),I(\"nGramWidths\",r,e,t),I(\"leftPad\",r,e,t),I(\"rightPad\",r,e,t),I(\"padWidth\",r,e,t),I(\"preserveShortSequences\",r,e,t));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,e,t),I(\"delimiter\",r,e,t),I(\"skipEmpty\",r,e,t));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,e,t),I(\"numBuckets\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZT=(r,e,t,o=Je)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,e,t),I(\"dtype\",r,e,t))];case\"ExpandDims\":{let n=I(\"axis\",r,e,t);return[o.expandDims(I(\"x\",r,e,t),n)]}case\"Squeeze\":{let n=I(\"axis\",r,e,t);return[o.squeeze(I(\"x\",r,e,t),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"EnsureShape\":return[o.ensureShape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"mode\",r,e,t))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"constantValue\",r,e,t))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,e,t),s=I(\"paddings\",r,e,t);return[o.spaceToBatchND(I(\"x\",r,e,t),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,e,t),s=I(\"crops\",r,e,t);return[o.batchToSpaceND(I(\"x\",r,e,t),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,e,t),s=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthToSpace(I(\"x\",r,e,t),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,e,t),I(\"s1\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function MS(r,e,t,o,n=De){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>TT(a,i,p));case\"basic_math\":return n(()=>_T(a,i,p));case\"control\":return FT(a,i,p);case\"convolution\":return n(()=>OT(a,i,p));case\"creation\":return n(()=>MT(a,i,p));case\"dynamic\":return LT(a,i,p);case\"evaluation\":return n(()=>BT(a,i,p));case\"image\":return n(()=>WT(a,i,p));case\"graph\":return n(()=>zT(a,i,p));case\"logical\":return n(()=>UT(a,i,p));case\"matrices\":return n(()=>GT(a,i,p));case\"normalization\":return n(()=>HT(a,i,p));case\"ragged\":return n(()=>KT(a,i,p));case\"reduction\":return n(()=>qT(a,i,p));case\"slice_join\":return n(()=>jT(a,i,p));case\"sparse\":return n(()=>XT(a,i,p));case\"spectral\":return n(()=>YT(a,i,p));case\"string\":return n(()=>QT(a,i,p));case\"transformation\":return n(()=>ZT(a,i,p));case\"hash_table\":return VT(a,i,p,o);case\"custom\":let u=pf(a.op);if(u&&u.customExecutor)return u.customExecutor(new wf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Ml=class{constructor(e={},t={},o={},n={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function LS(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Nr(m)[0]));o=o||[];let c=new Set(o.map(m=>Nr(m.name)[0])),l=[...e];for(;l.length>0;){let m=l.pop();if((fu(m)||A8(m)||F8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function JT(r,e){let{usedNodes:t,inputs:o}=e,n=Object.keys(o).map(g=>Nr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>t.has(typeof g==\"string\"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=_8(f,p);return E8(h,p),h}function _8(r,e){let t=new Map(r.map(a=>[a.name,a])),o=e.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=t.get(a);for(let p of i.children)!t.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var gc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function E8(r,e){let t=new Map(r.map((i,p)=>[i.name,p])),o=new Set(e.map(i=>i.name)),n=i=>o.has(typeof i==\"string\"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i==\"string\"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!t.has(p.name))throw new gc(`Child ${p.name} of node ${i.name} is unreachable.`);if(t.get(i.name)>t.get(p.name))throw new gc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!t.has(p.name))throw new gc(`Input ${p.name} of node ${i.name} is unreachable.`);if(t.get(p.name)>t.get(i.name))throw new gc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function e_(r){let e=new Map(r.map((i,p)=>[i.name,p])),t=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>fu(i)?t:p),n=i=>{let p=o[e.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;ie[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new r(e.functions[o],this)})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPARATOR)+\"--\"+n.join(this.SEPARATOR)}compile(e,t){let o=LS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=t.map(l=>l.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=JT(this.graph,o),p=e_(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return $r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(m=>this.graph.nodes[Nr(m)[0]]),s=t.map(m=>Nr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(e,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Ml(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(x=>{let[b,C]=Nr(x,m),S=[];S[C]=e[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=MS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),t.map(x=>Bt(x,d,m))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){if(!(fu(t)||a.has(e))){for(let p of o[e])p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length);for(let p of t.inputs){if(fu(p))continue;let u=hS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,o,n,s,a){function i(p){return fu(p)||s.has(p.name)}if(!(fu(e)||a==null))for(let p of a){if(i(p))continue;let u=hS(p.name,t,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Ml(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Bt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(S=>this.graph.nodes[Nr(S)[0]]),i=o.map(S=>Nr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=LS(e,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(S=>{let[k,_]=Nr(S),$=[];$[_]=e[S],h[k]=$});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,t,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let C=u.filter(S=>!fu(S)&&!Bt(S.name,h,t)).map(S=>S.name);if(C.length>0){let S=\"\";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=Ls(l.node.name,o)),n[l.node.name]==null){let d=MS(l.node,n,o,this._resourceManager);m||([m]=Ls(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=Ls(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Nr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Nr(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Nr(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var kf=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let o=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=n,this.version=`${o.versions.producer}.${o.versions.minConsumer}`,this.executor=new Ll(Ol.Instance.transformGraph(o,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Ol.Instance.transformGraph(e.modelInitializer);this.initializer=new Ll(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e==\"string\"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof mt?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof mt)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let 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this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return 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FY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Q(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat===\"channelsLast\",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),$=k[0],R=C?k[1]:k[2],D=C?k[2]:1,P=C?1:k[1],O=S.strides[0],M=C?S.strides[1]:S.strides[2],L=C?S.strides[2]:1,B=C?1:S.strides[1],z=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let le=oe*_[0],be=Y+ie*R;for(let _e=0;_e=m.inWidth)continue;let ct=le+Pe*_[1],He=be+st*D,lt=ct;for(let it=0;it=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=q+ee*D[1],le=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let st=ie+Fe*D[2],ct=le+Pe*u.inChannels,He=st;for(let lt=0;ltMath.cos(r)),kE={kernelName:sn,backendName:\"cpu\",kernelFunc:XY};var YY=Ie(an,r=>Math.cosh(r)),NE={kernelName:an,backendName:\"cpu\",kernelFunc:YY};function QY(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],\"float32\"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,S=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let $=0;$=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. 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Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p),l;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))l=lr({inputs:{x:n},backend:t});else{let m=t.data.get(n.dataId).values,d=y.computeStrides(n.shape),f=vc(m,n.shape,n.dtype,d,c,\"max\");l=t.makeTensorInfo(c.outShape,n.dtype,f.values)}return l}var c$={kernelName:Wn,backendName:\"cpu\",kernelFunc:LQ};function BQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o;Q(n,\"maxPool3d\");let c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.data.get(n.dataId).values,m=zf(l,n.shape,n.dtype,y.computeStrides(n.shape),c,\"max\");return t.makeTensorInfo(m.shape,\"float32\",m.values)}var l$={kernelName:ia,backendName:\"cpu\",kernelFunc:BQ};function zQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o;Q([n,s],\"maxPool3DGrad\");let c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=t.bufferSync(s),m=aE(l,c),d=c.strideDepth,f=c.strideHeight,h=c.strideWidth,g=c.dilationDepth,x=c.dilationHeight,b=c.dilationWidth,C=c.effectiveFilterDepth,S=c.effectiveFilterHeight,k=c.effectiveFilterWidth,_=C-1-c.padInfo.front,$=k-1-c.padInfo.left,R=S-1-c.padInfo.top,D=me(s.shape,\"float32\"),P=t.bufferSync(n);for(let O=0;O=c.outDepth||Math.floor(re)!==re))for(let ne=0;ne=c.outHeight||Math.floor(ee)!==ee))for(let oe=0;oe=c.outWidth||Math.floor(ie)!==ie)continue;let le=C*S*k-1-m.get(O,re,ee,ie,M),be=J*S*k+ne*k+oe,_e=le===be?1:0;if(_e===0)continue;let ve=P.get(O,re,ee,ie,M);Y+=ve*_e}}}D.set(Y,O,L,B,z,M)}return t.makeTensorInfo(D.shape,D.dtype,D.values)}var m$={kernelName:Gi,backendName:\"cpu\",kernelFunc:zQ};function VQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Q([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=t.data.get(i.dataId).values,f=me(m.outShape,i.dtype,Bf(d,i.shape,i.dtype,m).values),h=m.strideHeight,g=m.strideWidth,x=m.dilationHeight,b=m.dilationWidth,C=m.effectiveFilterHeight,S=m.effectiveFilterWidth,k=S-1-m.padInfo.left,_=C-1-m.padInfo.top,$=me(i.shape,\"float32\"),R=t.data.get(n.dataId).values,D=me(n.shape,\"float32\",R);for(let P=0;P=m.outHeight||Math.floor(q)!==q))for(let Y=0;Y=m.outWidth||Math.floor(J)!==J)continue;let re=C*S-1-f.get(P,q,J,O),ne=j*S+Y,ee=re===ne?1:0;if(ee===0)continue;let oe=D.get(P,q,J,O);U+=oe*ee}}$.set(U,P,M,L,O)}return t.makeTensorInfo($.shape,$.dtype,$.values)}var d$={kernelName:Ui,backendName:\"cpu\",kernelFunc:VQ};function f$(r,e,t,o,n){let s=y.computeStrides(e),a=vc(r,e,t,s,n,\"max\"),i=Bf(r,e,t,n,!0,o);return[a.values,i.values]}var h$={kernelName:ua,backendName:\"cpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;Q(o,\"MaxPoolWithArgmax\");let u=p.data.get(o.dataId).values,c=w.computePool2DInfo(o.shape,n,s,[1,1],a),[l,m]=f$(u,o.shape,o.dtype,i,c),d=p.write(l,c.outShape,o.dtype),f=p.write(m,c.outShape,o.dtype);return[{dataId:d,shape:c.outShape,dtype:o.dtype},{dataId:f,shape:c.outShape,dtype:\"int32\"}]}};function WQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=y.parseAxisParam(s,n.shape),u=w.computeOutAndReduceShapes(n.shape,i)[1],c=y.sizeFromShape(u),l=[],m=t.makeTensorInfo([],\"float32\",new Float32Array([c]));l.push(m);let d=Ro({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}});l.push(d);let f=Ul({inputs:{a:d,b:m},backend:t});l.push(f);let h=fi({inputs:{x:f},backend:t,attrs:{axis:s,keepDims:a}});return l.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}var g$={kernelName:Un,backendName:\"cpu\",kernelFunc:WQ};function UQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;Q(n,\"min\");let i=y.parseAxisParam(s,n.shape),p=i,u=w.getAxesPermutation(p,n.shape.length),c=n;u!=null&&(c=St({inputs:{x:n},backend:t,attrs:{perm:u}}),p=w.getInnerMostAxes(p.length,n.shape.length)),w.assertAxesAreInnerMostDims(\"min\",p,c.shape.length);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=y.sizeFromShape(m),f=y.makeZerosTypedArray(y.sizeFromShape(l),c.dtype),h=t.data.get(c.dataId).values;for(let x=0;xC[0]+n.shape[S]+C[1]),p=s.map(C=>C[0]),u=s.map((C,S)=>C[0]+n.shape[S]),c=a===\"reflect\"?0:1,l=t.data.get(n.dataId).values,m=n.shape.length,d=y.computeStrides(n.shape),f=y.sizeFromShape(i),h=i.length,g=y.computeStrides(i),x=y.getTypedArrayFromDType(n.dtype,f);for(let C=0;C=u[_]&&(S[_]=(u[_]-1)*2-S[_]+c);S=S.map((_,$)=>_-p[$]);let k=y.locToIndex(S,m,d);x[C]=l[k]}return{dataId:t.write(x,i,n.dtype),shape:i,dtype:n.dtype}}var y$={kernelName:Kn,backendName:\"cpu\",kernelFunc:GQ};var HQ=Ve((r,e)=>{let t=r%e;return r<0&&e<0||r>=0&&e>=0?t:(t+e)%e}),KQ=Ye(qn,HQ),b$={kernelName:qn,backendName:\"cpu\",kernelFunc:KQ};var w$=zp(jw());function vI(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=n.shape.length,i=s;if(i===-1&&(i=a-1),i!==a-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${a} and dim was ${i}`);let p=y.parseAxisParam([i],n.shape),u=II({inputs:{x:n},backend:t,attrs:{reductionIndices:p,keepDims:!1}}),c=w.expandShapeToKeepDim(u.shape,p),l=We({inputs:{x:u},backend:t,attrs:{shape:c}}),m=Vl({inputs:{a:n,b:l},backend:t}),d=qS({inputs:{x:m},backend:t}),f=fi({inputs:{x:d},backend:t,attrs:{axis:p,keepDims:!1}}),h=We({inputs:{x:f},backend:t,attrs:{shape:c}}),g=Ul({inputs:{a:d,b:h},backend:t});return t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var C$={kernelName:Is,backendName:\"cpu\",kernelFunc:vI};function qQ(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o;Q(n,\"multinomial\");let p=i?n:vI({inputs:{logits:n},backend:t,attrs:{dim:-1}}),u=p.shape[0],c=p.shape[1],l=t.data.get(p.dataId).values,m=[u,s],d=y.makeZerosTypedArray(y.sizeFromShape(m),\"int32\");for(let f=0;f=0&&l[m]{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=kc({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=hu({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var $$={kernelName:la,backendName:\"cpu\",kernelFunc:kI};function t7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;Q(n,\"pad\");let i=s.map((b,C)=>b[0]+n.shape[C]+b[1]),p=s.map(b=>b[0]),u=t.data.get(n.dataId).values,c=y.sizeFromShape(n.shape),l=n.shape.length,m=y.computeStrides(n.shape),d=y.sizeFromShape(i),f=i.length,h=y.computeStrides(i),g=y.getTypedArrayFromDType(n.dtype,d);a!==0&&g.fill(a);for(let 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0:return AR();case 1:return TZ(r,e,t);case 2:return PZ(r,e,t);case 3:return EZ(r,e,t);case 4:return RZ(r,e,t);case 5:return DZ(r,e);case 6:return AZ(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function yZ(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function bZ(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function CZ(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function wZ(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int 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Zf(r,e,t){let{newShape:o,keptDims:n}=y.squeezeShape(e),s=e.length,a=r&&s===3&&e[0]===1,i=a?e.slice(1):o,p=!r&&s>1&&!y.arraysEqual(e,t)&&o.lengthr[t]).join(\", \")}function PR(r,e,t,o){let n=t.map((c,l)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:e.variableNames[l],shapeInfo:m}}),s=n.map(c=>c.shapeInfo),a={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},i=RR(n,a,e),p=RI(r.gl,i),u=r.createProgram(p);return A().get(\"ENGINE_COMPILE_ONLY\")?{program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(u),Object.assign({program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a},XI(r,e,u)))}function XI(r,e,t){let o=[],n=[],s,a,i,p=null,u=null;u=r.getUniformLocation(t,\"NAN\",!1),A().getNumber(\"WEBGL_VERSION\")===1&&(p=r.getUniformLocation(t,\"INFINITY\",!1));let c=!1;for(let l of e.variableNames){let 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Shape ${i} and ${p} must match`)})}function OR(r,e,t,o,n){e.program.enableShapeUniforms||(FR(e.inShapeInfos,t),FR([e.outShapeInfo],[o]));let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,a[0],a[1]):r.setOutputMatrixTexture(s.texture,a[0],a[1]),r.setProgram(e.webGLProgram),r.bindVertexArray(e.webGLProgram.vao),A().getNumber(\"WEBGL_VERSION\")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,1/0),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN);for(let p=0;p{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!a.isUniform){let p=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:l}=Zf(r.packedInputs,a.shape,p),m=\"\",d=\"\",f=\"\";if(c.length===1&&r.packedInputs){let k=[Math.ceil(p[0]/2),Math.ceil(p[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)d=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=w.getBroadcastDims(a.shape,t.shape),C=!r.packedInputs&&h===t.shape.length&&y.arraysEqual(p,t.texData.texShape),S=r.packedInputs||c.length>2?\"\":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`}else{let p=a.isUniform?\"uniform\":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+=\"_\"+o+\"_\"+n+`${A().getNumber(\"WEBGL_VERSION\")}`,s}function ut(r){return A().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Jf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=gu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=It();this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}};var th=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=mr.DOWNLOAD;let t=It();this.outputShape=e,this.userCode=`\n ${Qf}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}};var rh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=mr.DOWNLOAD;let t=It();this.outputShape=e,this.userCode=`\n ${Qf}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${t.output} = encode_float(x);\n }\n `}};var ZZ={R:0,G:1,B:2,A:3},Zl=class{constructor(e,t=!1,o=\"RGBA\"){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=It();this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\");let a=\"\";for(let i=0;inv,createBufferFromOutputTexture:()=>iv,createFloat16MatrixTexture:()=>ev,createFloat16PackedMatrixTexture:()=>ov,createFloat32MatrixTexture:()=>JI,createIndexBuffer:()=>ZI,createPackedMatrixTexture:()=>rv,createUnsignedBytesMatrixTexture:()=>tv,createVertexBuffer:()=>QI,createVertexShader:()=>YI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>pv,downloadFloat32MatrixFromBuffer:()=>uv,downloadMatrixFromPackedOutputTexture:()=>lv,downloadPackedMatrixFromBuffer:()=>cv,getInternalFormatForFloat16MatrixTexture:()=>sh,getInternalFormatForFloat16PackedMatrixTexture:()=>uh,getInternalFormatForFloat32MatrixTexture:()=>nh,getInternalFormatForPackedMatrixTexture:()=>ih,getInternalFormatForUnsignedBytesMatrixTexture:()=>ah,uploadDenseMatrixToTexture:()=>sv,uploadPixelDataToTexture:()=>av});function YI(r){let e=It(),t=`${e.version}\n precision highp float;\n ${e.attribute} vec3 clipSpacePos;\n ${e.attribute} vec2 uv;\n ${e.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = 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Jl(r,n,s,sh(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function ah(r){return r.downloadTextureFormat}function tv(r,e,t,o){let[n,s]=gp(e,t);return Jl(r,n,s,ah(o),r.RGBA,r.UNSIGNED_BYTE)}function ih(r){return r.internalFormatPackedFloat}function rv(r,e,t,o){let[n,s]=Ma(e,t);return Jl(r,n,s,ih(o),r.RGBA,r.FLOAT)}function uh(r){return r.internalFormatPackedHalfFloat}function ov(r,e,t,o){let[n,s]=Ma(e,t);return Jl(r,n,s,uh(o),r.RGBA,o.textureTypeHalfFloat)}function nv(r,e,t){return ce(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),jf(r,e,\"clipSpacePos\",t,3,20,0)&&jf(r,e,\"uv\",t,2,20,12)}function sv(r,e,t,o,n,s){ce(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,p;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,p=r.RGBA):(a=new 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ce(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),ce(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function uv(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function pv(r,e,t,o){let[n,s]=gp(e,t),a=4,i=new Uint8Array(IR(e*t,a));return ce(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function cv(r,e,t,o,n,s,a,i){let p=r,u=new Float32Array(vR(s,a));return p.bindBuffer(p.PIXEL_PACK_BUFFER,e),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,u),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),u}function lv(r,e,t){let o=new Float32Array(e*t*4);return ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var bp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=A().getNumber(\"WEBGL_VERSION\");if(e!=null?(this.gl=e,NI(t,e)):this.gl=Kr(t),e=this.gl,A().getNumber(\"WEBGL_VERSION\")===2){let s=e;this.createVertexArray=()=>ce(s,()=>s.createVertexArray()),this.bindVertexArray=a=>ce(s,()=>s.bindVertexArray(a)),this.deleteVertexArray=a=>ce(s,()=>s.deleteVertexArray(a)),this.getVertexArray=()=>ce(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(e!=null){let s=e.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ce(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>ce(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>ce(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>ce(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),A().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=Nc(this.gl,s),qr(this.gl,a))this.textureHalfFloatExtension=Nc(this.gl,a);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(o),qr(this.gl,n))this.colorBufferHalfFloatExtension=Nc(this.gl,n);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(o=\"EXT_color_buffer_float\",qr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(qr(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=QI(this.gl),this.indexBuffer=ZI(this.gl),this.framebuffer=LI(this.gl),this.textureConfig=Xl(this.gl,this.textureHalfFloatExtension)}get debug(){return A().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),JI(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),ev(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),tv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),av(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),sv(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),ov(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),rv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Xf(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>pv(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return cv(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return uv(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=iv(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(A().getBool(\"WEBGL_FENCE_API_ENABLED\")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>lv(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=YI(t));let o=DI(t);ce(t,()=>t.attachShader(o,this.vertexShader)),ce(t,()=>t.attachShader(o,e)),AI(t,o);let n=Object.assign(o,{vao:this.createVertexArray()});return this.debug&&Yl(t,n),n}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);let t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),nv(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Yl(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?BI(this.gl,e,t):zI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),VI(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Ma(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&Yl(this.gl,this.program),Tc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Nc(this.gl,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(e,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=JZ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in A().platform&&(o=A().platform.setTimeoutCustom.bind(A().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Ql(this.gl,e,this.framebuffer),this.debug&&Tc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Ql(this.gl,this.outputTexture,this.framebuffer),this.debug&&Tc(this.gl)):Xf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Ql(n,e,this.framebuffer),this.debug&&Tc(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function JZ(r){let e=0;for(;e`${r}.${t}`)}function Rt(r,e){return e===1?[r]:dv(r,e)}function ED(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}};var Mc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${e9(t,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Rc():$c(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function e9(r,e){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${e?ER([\"r\",\"c\",\"d\"],\"inputShape\"):Ws([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var dh=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,o){let n=RD(t,o),s=DD(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=$D(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===er.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===er.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===er.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=RD(o,n),a=DD(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=$D(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().getNumber(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(e);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function t9(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function $D(r,e,t,o,n){let s=r9(e,o),a;if(n){let[p,u]=Ma(r[0],r[1]);a=p*u}else{let[p,u]=gp(r[0],r[1]);a=p*u}let i=t9(t,s);return a*i}function r9(r,e){switch(r){case er.PACKED_2X2_FLOAT32:return ih(e);case er.PACKED_2X2_FLOAT16:return uh(e);case er.UNPACKED_FLOAT32:return nh(e);case er.UNPACKED_FLOAT16:return sh(e);case er.PACKED_4X1_UNSIGNED_BYTE:return ah(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function o9(r){return A().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?er.PACKED_2X2_FLOAT32:er.UNPACKED_FLOAT32:r?er.PACKED_2X2_FLOAT16:er.UNPACKED_FLOAT16}function RD(r,e){if(r===mr.UPLOAD)return er.PACKED_2X2_FLOAT32;if(r===mr.RENDER||r==null)return o9(e);if(r===mr.DOWNLOAD||r===mr.PIXELS)return er.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function DD(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var tr=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},Wt=\"if (isnan(x)) return x;\",AD=\"return x;\",fv=\"return abs(x);\";var FD=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",PD=Wt+`\n return (x < 0.0) ? 0.0 : x;\n`,OD=Wt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,La=\"return x;\",MD=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var BD=\"return x;\",zD=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,VD=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,WD=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,UD=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Fr=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var fh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.length,o=Rt(\"rc\",t),n=Re(t),s=ED(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var s9=Vt.whereImpl,a9=1e-7,i9=1e-4,hh={};function u9(r){return r in hh||(hh[r]={}),hh[r]}var p9=A().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),c9=600;function l9(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*c9/1024/1024}var Lc=class r extends ao{nextDataId(){return r.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof bp)t=e;else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"),e);t=new bp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"));t=new bp(o),this.binaryCache=u9(A().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new dh(this.gpgpu),this.numMBBeforeWarning=l9(),this.texData=new Bo(this,ur())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=_c(t),c=new Zl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((A().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||A().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:mr.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(A().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:o,dtype:n,values:t,usage:mr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Fr(i,La):m=new tr(i,La);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=w.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Fr(n,La):f=new tr(n,La);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(A().getBool(\"DEBUG\")&&!A().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&A().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&A().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...jl(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=w.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ur().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Fr(s,La):d=new tr(s,La);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(e,t.customTexShape),l=ur().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=p9){return A().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new fh(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new mh(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){let o=[gi(e.shape),...xi(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[gi(t),...xi(t)],a=new Mc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=_c(s),p;n?p=new eh(i):p=new Jf(i);let u=!0,c=[t!=null?t:jl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===gu.DENSE){let x=a!=null?a:jl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!xu(b.shape,x.shape)){let C=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),C.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=MR(e,c,l),d=this.getAndSaveBinary(m,()=>PR(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get(\"ENGINE_COMPILE_ONLY\")||OR(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=A().getNumber(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!A().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!A().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=A().getBool(\"DEBUG\");A().set(\"DEBUG\",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(A().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?a9:i9}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=WI(o,p),t.texShape=l),s!=null){let m=_c(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ma(l[0],l[1])),p?d=new oh(m,g):d=new Zl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=mr.PIXELS:C.usage=mr.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],_=this.runWebGLProgram(d,[b],n,S,!0),$=this.texData.get(_.dataId);t.texShape=$.texShape,t.isPacked=$.isPacked,t.usage=$.usage,A().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=$.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=m9(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await cS(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(qf(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=XI(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.outShapeLocation=a,e.outShapeStridesLocation=i,e.outTexShapeLocation=p}}createTensorFromGPUData(e,t,o){e.channels=e.channels||\"RGBA\";let{texture:n,height:s,width:a,channels:i}=e,p=ur().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return ur().makeTensorFromDataId(u,t,o,p)}};Lc.nextDataId=0;function m9(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Lc,2);var $at={forceHalfFloat:GD};var Bc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var Pr=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Xr=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var jr=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=ut(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${Re(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=Rt(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function Dt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var HD={kernelName:Co,backendName:\"webgl\",kernelFunc:Dt};function Or(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var KD={kernelName:Di,backendName:\"webgl\",kernelFunc:Or};var hv=\"return (a < 0.) ? b * a : a;\",gv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function f9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(gv,n.shape,a.shape):new Pr(hv,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var qD={kernelName:$n,backendName:\"webgl\",kernelFunc:f9};var xv=\"return (a < 0.) ? b * a : a;\",yv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function h9(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(yv,o.shape,n.shape):new Pr(xv,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var jD={kernelName:rs,backendName:\"webgl\",kernelFunc:h9};var Fo=\"if (isnan(x)) return x;\";function xe({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Fr(a.shape,e):c=new tr(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[S,k]=C,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},$={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new Pr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,$],dt(S.dtype,k.dtype))}),b=Or({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?w.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?w.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(C,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new jr(e,p.shape,u.shape,t):d=new Pr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function yi(r,e=!1){if(r===\"linear\")return e?BD:AD;if(r===\"relu\")return e?VD:PD;if(r===\"elu\")return e?zD:FD;if(r===\"relu6\")return e?WD:OD;if(r===\"prelu\")return e?yv:xv;if(r===\"leakyrelu\")return e?gv:hv;if(r===\"sigmoid\")return e?UD:MD;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var zc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ut(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",S=\"rc.x\";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!xu(n.shape,p)&&!(c.texture!==null&&xu(c.shape,p))?QD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var ZD={kernelName:da,backendName:\"webgl\",kernelFunc:te};var rm=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var gh=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${t===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${t===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${t===\"min\"} || ${t===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";t===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function x9(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=w.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Yr(r,e,t,o){let n=x9(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=dv(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=te({inputs:{x:r},backend:n,attrs:{shape:k}}),R=te({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[$,R],P=Math.max(x,b),O=t?$.shape[1]:$.shape[2],M=s!=null,L=a!=null,B=p===\"leakyrelu\",z=p!=null?yi(p,!0):null,U=M||L||B||z!=null,j;if((d===1||f===1)&&O>Cv&&U===!1){let Y=$,J=R;t&&(Y=bt({inputs:{x:$},backend:n,attrs:{perm:[0,2,1]}}),D.push(Y)),o&&(J=bt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),D.push(J));let re=f!==1,ne=f===1,ee=Y;re&&(ee=te({inputs:{x:Y},backend:n,attrs:{shape:[P,O,1]}}),D.push(ee));let oe=f===1?2:1,ie=J;ne&&(ie=te({inputs:{x:J},backend:n,attrs:{shape:[P,1,O]}}),D.push(ie));let le=tm({inputs:{a:ee,b:ie},backend:n});j=wp({inputs:{x:le},backend:n,attrs:{axis:oe,keepDims:!0}}),D.push(le)}else{let Y=dt(r.dtype,e.dtype),J=new zc(k,_,[P,d,f],t,o,M,z,L,B),re=[$,R];if(s!=null&&re.push(s),L&&re.push(a),B){let ne=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,Y)}let q=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let Y of D)n.disposeIntermediateTensorInfo(Y);return q}function b9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Sp({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var rA={kernelName:So,backendName:\"webgl\",kernelFunc:b9};var oA=\"return abs(x);\";function C9(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=t.texData.get(o.dataId),a=ch(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Fr(o.shape,oA):n=new tr(o.shape,oA),t.runWebGLProgram(n,[o],o.dtype)}var nA={kernelName:Xs,backendName:\"webgl\",kernelFunc:C9};var w9=Wt+`\n if 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o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function wh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});if(o.length>A().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=wh({inputs:o.slice(0,p),backend:t}),c=wh({inputs:o.slice(p),backend:t});return wh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool(\"WEBGL_PACK\")?new Ch(o[0].shape,s):new bh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var pA={kernelName:Uo,backendName:\"webgl\",kernelFunc:wh};function N9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,\"all\",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var cA={kernelName:Go,backendName:\"webgl\",kernelFunc:N9};function 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i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${n}; i++) {\n int inIdx = ${p};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var Ih=class{constructor(e,t,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=Re(p),c=Rt(\"coords\",p),l,m;if(a===1){m=p+1;let R=Re(m);l=`\n ${R} sourceLocR = 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n=[t];if(w.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!A().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=w.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=mA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return dA(r,e,o)}function _9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=vh(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var fA={kernelName:Ys,backendName:\"webgl\",kernelFunc:_9};function E9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=vh(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var hA={kernelName:Qs,backendName:\"webgl\",kernelFunc:E9};var $9=Wt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,R9=xe({opSnippet:$9}),gA={kernelName:Ko,backendName:\"webgl\",kernelFunc:R9};var D9=Wt+\"return log(x + sqrt(x * x + 1.0));\",A9=xe({opSnippet:D9}),xA={kernelName:qo,backendName:\"webgl\",kernelFunc:A9};var F9=Wt+`\n return atan(x);\n`,P9=xe({opSnippet:F9}),yA={kernelName:jo,backendName:\"webgl\",kernelFunc:P9};var O9=Bc+`\n return atan(a, b);\n`,M9=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Xr+`\n return result;\n`,L9=nt({opSnippet:O9,packedOpSnippet:M9}),bA={kernelName:Yo,backendName:\"webgl\",kernelFunc:L9};var B9=Wt+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelName:Xo,backendName:\"webgl\",kernelFunc:z9};var Us=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t===\"avg\",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),o){let R=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${R} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let C=\"max\",S=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(S=\"avgValue / max(count, 1.0)\");let k=Math.floor(a/4)*4,_=a%4,$=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${C}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${k}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${$}\n }\n\n int xC = xCCorner + ${k};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${$}\n }\n }\n setOutput(${S});\n }\n `}},bu=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t===\"avg\",S=\"0.0\";if(C||(S=\"-1.0 / 1e-20\"),o){let P=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${P} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let $=Math.floor(a/4)*4,R=a%4,D=`\n if (${C}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${S};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${S});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${$}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${D}\n }\n\n int xC = xCCorner + ${$};\n if (${R===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${D}\n }\n }\n }\n setOutput(${_});\n }\n `}};function V9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Vs(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Us(c,\"avg\",!1);return t.runWebGLProgram(l,[n],\"float32\")}var wA={kernelName:Qo,backendName:\"webgl\",kernelFunc:V9};function W9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new bu(l,\"avg\",!1);return t.runWebGLProgram(m,[n],\"float32\")}var SA={kernelName:Zs,backendName:\"webgl\",kernelFunc:W9};var kh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${l});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},Nh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function U9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Nh(m);return t.runWebGLProgram(d,[n],a.dtype)}var IA={kernelName:Ri,backendName:\"webgl\",kernelFunc:U9};function G9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Vs([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new kh(c);return t.runWebGLProgram(l,[n],a.dtype)}var vA={kernelName:$i,backendName:\"webgl\",kernelFunc:G9};function H9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Sp({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var kA={kernelName:Zo,backendName:\"webgl\",kernelFunc:H9};var Th=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i=\"0.0\";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${p};\n float inv = scale * inversesqrt(variance + float(${a}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var _h=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i=\"vec4(0.0)\";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${p};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var K9=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new _h(o.shape,n.shape,s.shape,c,l,p):new Th(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},NA={kernelName:In,backendName:\"webgl\",kernelFunc:K9};var Eh=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let t=Re(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let o=q9(this.rank),n,s=e.map((a,i)=>`sourceLoc.${wv[i]} = start[${i}] + coords.${wv[i]};`);n=`\n ${t} sourceLoc;\n ${t} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${n}\n setOutput(getSource(${o}));\n }\n `}},wv=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function q9(r){if(r===1)return\"sourceLoc\";if(r<=6)return wv.slice(0,r).map(e=>\"sourceLoc.\"+e).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var $h=class{constructor(e){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let t=Re(this.rank),o=Rt(\"coords\",this.rank),n=Rt(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`\n result.x = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.y = ${a};\n --${n[this.rank-1]};\n }\n `,p=this.rank===1?\"\":`\n --${o[this.rank-1]};\n if (++${o[this.rank-2]} < ${e[this.rank-2]}) {\n ++${n[this.rank-2]};\n result.z = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.w = ${a};\n }\n }\n `,u=this.rank<=4?`sourceLoc = coords +\n ${t}(${e.map((c,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(`\n`);this.userCode=`\n void main() {\n ${t} coords = getOutputCoords();\n ${t} sourceLoc;\n ${u}\n vec4 result = vec4(0.);\n ${i}\n ${p}\n setOutput(result);\n }\n `}};function j9(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=pt.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Gs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.texData.get(n.dataId),m=gD(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=pt.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new $h(p):new Eh(p),m=[i];return t.runWebGLProgram(l,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),j9(n,i,p,t)}var TA={kernelName:ha,backendName:\"webgl\",kernelFunc:Gs};var X9=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:t,attrs:{shape:p}}),h=bt({inputs:{x:f},backend:t,attrs:{perm:u}}),g=te({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},_A={kernelName:Js,backendName:\"webgl\",kernelFunc:X9};function Y9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=ph(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var EA={kernelName:Jo,backendName:\"webgl\",kernelFunc:Y9};var Q9=`\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`,Z9=`\n return float(int(a.r) & int(b.r));\n`;function J9(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"),a=A().getNumber(\"WEBGL_VERSION\");if(t.shouldExecuteOnCPU([o,n])||a===1){let p=t.texData.get(o.dataId).values,u=t.texData.get(n.dataId).values,[c,l]=zR(o.shape,n.shape,p,u,o.dtype),m=t.makeTensorInfo(l,o.dtype),d=t.texData.get(m.dataId);return d.values=c,m}let i;return s?i=new jr(Q9,o.shape,n.shape,!1):i=new Pr(Z9,o.shape,n.shape),t.runWebGLProgram(i,[o,n],o.dtype)}var $A={kernelName:qa,backendName:\"webgl\",kernelFunc:J9};function eJ(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var RA={kernelName:ea,backendName:\"webgl\",kernelFunc:eJ};var tJ=\"return float(a != b);\",Sv=nt({opSnippet:tJ,cpuKernelImpl:iD,dtype:\"bool\"}),DA={kernelName:Yn,backendName:\"webgl\",kernelFunc:Sv};function bi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var AA={kernelName:Hi,backendName:\"webgl\",kernelFunc:bi};var rJ=\"return float(int(x));\";function FA(r,e){let t=new tr(r.shape,rJ),o=e.runWebGLProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Iv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Dt({inputs:{x:n},backend:t});let a=Gr(n.shape),i=Iv({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=Or({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),p}if(n.dtype===\"complex64\"){let a=bi({inputs:{input:n},backend:t}),i=Iv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=VR(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return FA(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=Sv({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var PA={kernelName:yo,backendName:\"webgl\",kernelFunc:Iv};var OA=\"return ceil(x);\",oJ=xe({opSnippet:OA,packedOpSnippet:OA,cpuKernelImpl:WR}),MA={kernelName:en,backendName:\"webgl\",kernelFunc:oJ};var Rh=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var Dh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function nJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;A().getBool(\"WEBGL_PACK_CLIP\")?i=new Dh(n.shape):i=new Rh(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var LA={kernelName:bo,backendName:\"webgl\",kernelFunc:nJ};var Ah=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). 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1;\n if (${a[n-2]} < ${o[n-2]} &&\n ${a[n-1]} < ${o[n-1]}) {\n result.b = getValue(${a});\n }\n setOutput(result);\n }\n `}};function Ph(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function Ip(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var VA={kernelName:Wi,backendName:\"webgl\",kernelFunc:Ip};function Vc(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>bi({inputs:{input:b},backend:t})),f=r.map(b=>Ip({inputs:{input:b},backend:t})),h=Vc(d,e,t),g=Vc(f,e,t),x=Or({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(S=>{let _=[-1,y.sizeFromShape(S.shape.slice(e))];return te({inputs:{x:S},backend:t,attrs:{shape:_}})}),f=d.map(S=>({vals:t.readSync(S.dataId),shape:S.shape})),h=w.computeOutShape(d.map(S=>S.shape),1),g=d[0].shape[0]===1,x=UR(f,h,o,g),b=w.computeOutShape(r.map(S=>S.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(S=>t.disposeIntermediateTensorInfo(S)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new tr(r[0].shape,La):new Fr(r[0].shape,La);return t.runWebGLProgram(d,r,o)}let i=A().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=aJ(s,e,t),c=new Fh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function aJ(r,e,t){let o=w.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function vv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:t}):Vc(p,s,t)}var WA={kernelName:ta,backendName:\"webgl\",kernelFunc:vv};var Wc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,C=g?3:1,S=\"\",k=\"\";o&&(n?S=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?S=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:S=`\n float activation(float x) {\n ${o}\n }\n `,k=\"result = activation(result);\");let _=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${S}\n\n const ivec2 strides = ivec2(${p}, ${u});\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${C}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Mh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${t}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Uc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function Bh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function zh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let S=Bh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Bh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>Cv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(xu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let $=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push($);let R=Sp({a:k,b:$,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,D.shape=t.outShape,g=Dt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}else{let S=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,S,t.inChannels]:[t.batchSize,t.inChannels,S]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),$=Sp({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:$},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push($)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Vh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,S=[];if(s!=null){let q=Bh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Bh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});S.push(k);let _=new Lh(x,t),$=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],R=o.runWebGLProgram(_,[r],\"float32\",$),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let P=n!=null,O=s!=null,M=i===\"leakyrelu\",L=i?yi(i,!0):null,B=new zc(f?D.shape:k.shape,f?k.shape:D.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,P,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let q=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));z.push(q),S.push(q)}let U=o.runWebGLProgram(B,z,\"float32\"),j=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function iJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=zh({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let h=new Uc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))d=Vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new Wc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var UA={kernelName:tn,backendName:\"webgl\",kernelFunc:iJ};var Wh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n ${a?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Uh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Gh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${s};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Hh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function uJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Wh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var GA={kernelName:Fi,backendName:\"webgl\",kernelFunc:uJ};var Kh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=e.inShape,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.filterHeight,o=e.filterWidth,n=t-1-e.padInfo.top,s=o-1-e.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${n}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n int wCPerm = ${o} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function pJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(A().getBool(\"WEBGL_PACK_CONV2DTRANSPOSE\")&&l===\"channelsLast\"){let d=[[m.strideHeight,m.strideWidth]],f=new Kh(m);return t.runWebGLProgram(f,[n,s],\"float32\",d)}else{let d=new Uh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}}var HA={kernelName:rn,backendName:\"webgl\",kernelFunc:pJ};function cJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Mh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var KA={kernelName:on,backendName:\"webgl\",kernelFunc:cJ};function lJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Gh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var qA={kernelName:ja,backendName:\"webgl\",kernelFunc:lJ};function mJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,i,1,a),c=new Hh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var jA={kernelName:nn,backendName:\"webgl\",kernelFunc:mJ};var dJ=Fo+`\n return cos(x);\n`,fJ=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,hJ=xe({opSnippet:dJ,packedOpSnippet:fJ}),XA={kernelName:sn,backendName:\"webgl\",kernelFunc:hJ};var gJ=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,xJ=xe({opSnippet:gJ}),YA={kernelName:an,backendName:\"webgl\",kernelFunc:xJ};var qh=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${C});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${S};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var yJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new qh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},QA={kernelName:cn,backendName:\"webgl\",kernelFunc:yJ};var vp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(vp||(vp={}));var om=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===vp.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${ZA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${Re(s)} coords = getOutputCoords();\n int end = ${JA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${JA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${ZA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function ZA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function JA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function jh(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=bt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new om(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new om(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=bt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function bJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return jh(vp.Prod,n,t,s,a,i)}var eF={kernelName:un,backendName:\"webgl\",kernelFunc:bJ};function CJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return jh(vp.Sum,n,t,s,a,i)}var tF={kernelName:pn,backendName:\"webgl\",kernelFunc:CJ};function wJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=ph(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=BR(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var rF={kernelName:ra,backendName:\"webgl\",kernelFunc:wJ};var Xh=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function SJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Xh(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var oF={kernelName:ln,backendName:\"webgl\",kernelFunc:SJ};var Gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var Hc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):C===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${C};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=w.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;A().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new Hc(l):m=new Gc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var nF={kernelName:mn,backendName:\"webgl\",kernelFunc:IJ};var Yh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Qh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function vJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Yh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var sF={kernelName:Pi,backendName:\"webgl\",kernelFunc:vJ};function kJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Qh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var aF={kernelName:Oi,backendName:\"webgl\",kernelFunc:kJ};var Zh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function NJ(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Zh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var iF={kernelName:oa,backendName:\"webgl\",kernelFunc:NJ};var Jh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function TJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Jh(u);c=t.runWebGLProgram(l,[n,s],\"float32\");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var uF={kernelName:dn,backendName:\"webgl\",kernelFunc:TJ};function _J(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=wp({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var pF={kernelName:Bi,backendName:\"webgl\",kernelFunc:_J};var EJ=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",$J=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,RJ=xe({opSnippet:EJ,packedOpSnippet:$J}),cF={kernelName:hn,backendName:\"webgl\",kernelFunc:RJ};var DJ=\"return (b >= 0.0) ? a : a * (b + 1.0);\",AJ=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,FJ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(AJ,o.shape,n.shape):new Pr(DJ,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},lF={kernelName:Xa,backendName:\"webgl\",kernelFunc:FJ};var PJ=`\n return vec4(equal(a, b));\n`,OJ=\"return float(a == b);\",MJ=nt({opSnippet:OJ,packedOpSnippet:PJ,dtype:\"bool\",cpuKernelImpl:GR}),mF={kernelName:xn,backendName:\"webgl\",kernelFunc:MJ};var LJ=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${w.ERF_P};\n float a1 = ${w.ERF_A1};\n float a2 = ${w.ERF_A2};\n float a3 = ${w.ERF_A3};\n float a4 = ${w.ERF_A4};\n float a5 = ${w.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,BJ=xe({opSnippet:LJ}),dF={kernelName:gn,backendName:\"webgl\",kernelFunc:BJ};var zJ=Fo+`\n return exp(x);\n`,VJ=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,kv=xe({opSnippet:zJ,packedOpSnippet:VJ,cpuKernelImpl:HR,dtype:\"float32\"}),fF={kernelName:yn,backendName:\"webgl\",kernelFunc:kv};function eg(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var hF={kernelName:na,backendName:\"webgl\",kernelFunc:eg};var gF=\"return exp(x) - 1.0;\",WJ=xe({opSnippet:gF,packedOpSnippet:gF,cpuKernelImpl:KR}),xF={kernelName:bn,backendName:\"webgl\",kernelFunc:WJ};var nm=class{constructor(e,t,o){this.variableNames=[\"real\",\"imag\"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${n}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${a};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function tg(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new nm(\"real\",p,e),c=new nm(\"imag\",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Or({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function UJ(r){let{inputs:e,backend:t}=r,{input:o}=e;return tg(o,!1,t)}var yF={kernelName:zi,backendName:\"webgl\",kernelFunc:UJ};var rg=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ci(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new rg(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var bF={kernelName:sa,backendName:\"webgl\",kernelFunc:Ci};var og=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var CF={kernelName:Cn,backendName:\"webgl\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new og(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var wF=\"return floor(x);\",GJ=xe({opSnippet:wF,packedOpSnippet:wF,cpuKernelImpl:qR}),SF={kernelName:wn,backendName:\"webgl\",kernelFunc:GJ};var HJ=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,KJ=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,qJ=nt({opSnippet:HJ,packedOpSnippet:KJ,dtype:\"int32\"}),IF={kernelName:Sn,backendName:\"webgl\",kernelFunc:qJ};var ng=class{constructor(e){this.variableNames=[\"A\"];let t=It(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var sg=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let t=It(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}};var vF={kernelName:Du,backendName:\"webgl\",kernelFunc:jJ},Kc,Nv=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function jJ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Kc==null||h!==Nv)&&(Nv=h,Kc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Nv})),Kc.canvas.width=p,Kc.canvas.height=u,Kc.drawImage(n,0,0,p,u),n=Kc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=mr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=A().getBool(\"WEBGL_PACK\")?new sg(l):new ng(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function XJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,S=i!=null,k=d===\"leakyrelu\",_=()=>{let R=[n,s],D=(P,O)=>{if(O===\"NCHW\"&&P.shape.length===1&&P.shape[0]!==1){let M=te({inputs:{x:P},backend:t,attrs:{shape:[P.shape[0],1,1]}});return b.push(M),M}return P};if(C&&R.push(D(a,c)),S&&R.push(D(i,c)),k){let P=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));R.push(P),b.push(P)}return R};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=zh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let R=d?yi(d,!0):null,D=new Uc(g,C,R,S,k),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],O=_();x=t.runWebGLProgram(D,O,\"float32\",P)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))x=Vh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let R=d?yi(d,!1):null,D=new Wc(g,C,R,S,k),P=_();x=t.runWebGLProgram(D,P,\"float32\")}let $=te({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(R=>t.disposeIntermediateTensorInfo(R)),$}var kF={kernelName:Io,backendName:\"webgl\",kernelFunc:XJ};function YJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${a}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function QJ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=te({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=te({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let x=t.readSync(n.dataId),b=t.bufferSync(o),C=jR(x,b,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,C.values)}let f=new ag(a,l,[u,c],o.shape),h=t.runWebGLProgram(f,[d,m],d.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:p}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var TF={kernelName:vn,backendName:\"webgl\",kernelFunc:QJ};var ig=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=Re(this.rank),n=ZJ(e,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function ZJ(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),C=t.bufferSync(m),S=XR(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,S.dtype,S.values)}let h=new ig(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var _F={kernelName:aa,backendName:\"webgl\",kernelFunc:Tv};var JJ=\"return float(a > b);\",eee=`\n return vec4(greaterThan(a, b));\n`,tee=nt({opSnippet:JJ,packedOpSnippet:eee,cpuKernelImpl:YR,dtype:\"bool\"}),EF={kernelName:kn,backendName:\"webgl\",kernelFunc:tee};var ree=\"return float(a >= b);\",oee=`\n return vec4(greaterThanEqual(a, b));\n`,nee=nt({opSnippet:ree,packedOpSnippet:oee,dtype:\"bool\",cpuKernelImpl:QR}),$F={kernelName:Nn,backendName:\"webgl\",kernelFunc:nee};function see(r){let{inputs:e,backend:t}=r,{input:o}=e;return tg(o,!0,t)}var RF={kernelName:Vi,backendName:\"webgl\",kernelFunc:see};var aee=\"return float(!isnan(x) && !isinf(x));\",iee=xe({opSnippet:aee,dtype:\"bool\"}),DF={kernelName:Tn,backendName:\"webgl\",kernelFunc:iee};var uee=\"return float(isinf(x));\",pee=xe({opSnippet:uee,dtype:\"bool\"}),AF={kernelName:_n,backendName:\"webgl\",kernelFunc:pee};var cee=\"return float(isnan(x));\",lee=xe({opSnippet:cee,dtype:\"bool\"}),FF={kernelName:En,backendName:\"webgl\",kernelFunc:lee};var mee=\"return float(a < b);\",dee=`\n return vec4(lessThan(a, b));\n`,fee=nt({opSnippet:mee,packedOpSnippet:dee,cpuKernelImpl:ZR,dtype:\"bool\"}),PF={kernelName:Rn,backendName:\"webgl\",kernelFunc:fee};var hee=\"return float(a <= b);\",gee=`\n return vec4(lessThanEqual(a, b));\n`,xee=nt({opSnippet:hee,packedOpSnippet:gee,cpuKernelImpl:JR,dtype:\"bool\"}),OF={kernelName:Dn,backendName:\"webgl\",kernelFunc:xee};function yee(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=eD(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var MF={kernelName:An,backendName:\"webgl\",kernelFunc:yee};var bee=Fo+`\n return x < 0.0 ? 0./0. : log(x);\n`,Cee=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,wee=xe({opSnippet:bee,packedOpSnippet:Cee,cpuKernelImpl:tD}),LF={kernelName:Fn,backendName:\"webgl\",kernelFunc:wee};var See=Fo+`\n return log(1.0 + x);\n`,Iee=xe({opSnippet:See}),BF={kernelName:Pn,backendName:\"webgl\",kernelFunc:Iee};var vee=\"return float(a >= 1.0 && b >= 1.0);\",kee=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,Nee=nt({opSnippet:vee,packedOpSnippet:kee,dtype:\"bool\"}),zF={kernelName:On,backendName:\"webgl\",kernelFunc:Nee};var Tee=\"return float(!(x >= 1.0));\",_ee=xe({opSnippet:Tee}),VF={kernelName:Mn,backendName:\"webgl\",kernelFunc:_ee};var Eee=\"return float(a >= 1.0 || b >= 1.0);\",$ee=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,Ree=nt({opSnippet:Eee,packedOpSnippet:$ee,dtype:\"bool\"}),WF={kernelName:Ln,backendName:\"webgl\",kernelFunc:Ree};var ug=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var pg=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var Dee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new pg(n.shape,s,a,i,p):new ug(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},UF={kernelName:Bn,backendName:\"webgl\",kernelFunc:Dee};var cg=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var Aee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new cg(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},GF={kernelName:Ya,backendName:\"webgl\",kernelFunc:Aee};function HF(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,r.dtype,\"max\",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function _v(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,S=new Array(i);for(let $=0;$`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Us(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var jF={kernelName:Wn,backendName:\"webgl\",kernelFunc:Mee};function Lee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new bu(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var XF={kernelName:ia,backendName:\"webgl\",kernelFunc:Lee};var lg=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},mg=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Bee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new bu(m,\"max\",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new mg(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var YF={kernelName:Gi,backendName:\"webgl\",kernelFunc:Bee};function zee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Vs([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new Us(m,\"max\",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new lg(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var QF={kernelName:Ui,backendName:\"webgl\",kernelFunc:zee};function ZF(r,e,t,o){let n=new Us(t,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new Us(t,\"max\",!0,!0,e);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var JF={kernelName:ua,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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yg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Re(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=Rt(\"rc\",n),u=Rt(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Ci({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new yg(n.shape,s,a):new xg(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},C3={kernelName:es,backendName:\"webgl\",kernelFunc:Dv};var pte=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,cte=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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f=w.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var S3={kernelName:os,backendName:\"webgl\",kernelFunc:mte};function dte(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=pD(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var I3={kernelName:Hp,backendName:\"webgl\",kernelFunc:dte};function fte(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=cD(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var v3={kernelName:Kp,backendName:\"webgl\",kernelFunc:fte};function hte(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=lD(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var k3={kernelName:qp,backendName:\"webgl\",kernelFunc:hte};var Av=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=mD(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},N3={kernelName:ma,backendName:\"webgl\",kernelFunc:Av};var gte=\"return 1.0 / x;\",xte=xe({opSnippet:gte}),T3={kernelName:ns,backendName:\"webgl\",kernelFunc:xte};var yte=Wt+`\n return (x < 0.0) ? 0.0 : x;\n`,bte=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Cte=xe({opSnippet:yte,packedOpSnippet:bte}),_3={kernelName:ss,backendName:\"webgl\",kernelFunc:Cte};var wte=Wt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ste=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Ite=xe({opSnippet:wte,packedOpSnippet:Ste}),E3={kernelName:us,backendName:\"webgl\",kernelFunc:Ite};var bg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var Cg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function vte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Cg(n.shape,p,u,s,a):new bg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var $3={kernelName:is,backendName:\"webgl\",kernelFunc:vte};var wg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function kte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new wg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var R3={kernelName:Ja,backendName:\"webgl\",kernelFunc:kte};var Sg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var Ig=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function Nte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Ig(n.shape,p,u,s,a):new Sg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var D3={kernelName:as,backendName:\"webgl\",kernelFunc:Nte};var vg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function Tte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new vg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var A3={kernelName:Za,backendName:\"webgl\",kernelFunc:Tte};var kg=class{constructor(e,t){this.variableNames=[\"x\"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(\",\"),a=Re(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var Ng=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Rt(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function _te(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Dt({inputs:{x:n},backend:t});let p=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new Ng(n.shape,i):new kg(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var F3={kernelName:ps,backendName:\"webgl\",kernelFunc:_te};var Tg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n vec3 fill = vec3(${t.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var P3={kernelName:Ds,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new Tg(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var Ete=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,$te=xe({opSnippet:Ete}),O3={kernelName:cs,backendName:\"webgl\",kernelFunc:$te};var Rte=\"return inversesqrt(x);\",Dte=xe({opSnippet:Rte,cpuKernelImpl:dD}),M3={kernelName:ls,backendName:\"webgl\",kernelFunc:Dte};var Cu=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${e}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${t}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${f};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var _g=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\",b=t>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${e}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${t}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${t}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${f};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function Ate(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g;A().getBool(\"WEBGL_PACK\")?g=new _g(p,i,d.shape.length,f.shape.length,c,m):g=new Cu(p,i,d.shape.length,f.shape.length,c,m);let x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var L3={kernelName:ms,backendName:\"webgl\",kernelFunc:Ate};var Eg=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=A().getNumber(\"WEBGL_VERSION\")===2?s:a,p=n===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${i}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${p} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function Fte(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new Eg(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return t.runWebGLProgram(i,[n,s],\"int32\",p)}var B3={kernelName:fs,backendName:\"webgl\",kernelFunc:Fte};var $g=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s=\"resRC\",n=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],p=[],u=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function Pte(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new $g(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var z3={kernelName:fa,backendName:\"webgl\",kernelFunc:Pte};var Ote=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${w.SELU_SCALEALPHA};\n float scale = ${w.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Mte=xe({opSnippet:Ote}),V3={kernelName:hs,backendName:\"webgl\",kernelFunc:Mte};var Lte=Fo+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,Bte=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,zte=xe({opSnippet:Lte,packedOpSnippet:Bte,cpuKernelImpl:hD}),W3={kernelName:bs,backendName:\"webgl\",kernelFunc:zte};var Vte=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,Wte=xe({opSnippet:Vte}),U3={kernelName:ys,backendName:\"webgl\",kernelFunc:Wte};var Ute=Fo+`\n return sin(x);\n`,Gte=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,Hte=xe({opSnippet:Ute,packedOpSnippet:Gte}),G3={kernelName:gs,backendName:\"webgl\",kernelFunc:Hte};var Kte=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,qte=xe({opSnippet:Kte}),H3={kernelName:xs,backendName:\"webgl\",kernelFunc:qte};var jte=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Xte=xe({opSnippet:jte}),K3={kernelName:Cs,backendName:\"webgl\",kernelFunc:Xte};var Yte=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeIntermediateTensorInfo(x)),g},q3={kernelName:ga,backendName:\"webgl\",kernelFunc:Yte};function Qte(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${a.shape}`);let i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=xD(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var j3={kernelName:Ki,backendName:\"webgl\",kernelFunc:Qte};function Zte(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=yD(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var X3={kernelName:ei,backendName:\"webgl\",kernelFunc:Zte};function Jte(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var Y3={kernelName:ya,backendName:\"webgl\",kernelFunc:Jte};function ere(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p);return t.makeTensorInfo(c,o.dtype,u)}var Q3={kernelName:ba,backendName:\"webgl\",kernelFunc:ere};function tre(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let x=t.bufferSync(n),b=t.bufferSync(s),C=y.decodeString(t.readSync(a.dataId)[0]),S=fD(x,b,i,m,c,u,p,l,C,d);return t.makeTensorInfo(i,S.dtype,S.values)}let f=new Cu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=t.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(h),g}var Z3={kernelName:vs,backendName:\"webgl\",kernelFunc:tre};function rre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Gs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var J3={kernelName:xa,backendName:\"webgl\",kernelFunc:rre};var eP=\"return sqrt(x);\",ore=xe({opSnippet:eP,packedOpSnippet:eP,cpuKernelImpl:bD}),tP={kernelName:ws,backendName:\"webgl\",kernelFunc:ore};var nre=\"return x * x;\",sre=xe({opSnippet:nre}),rP={kernelName:qi,backendName:\"webgl\",kernelFunc:sre};var oP=\"return (a - b) * (a - b);\",are=nt({opSnippet:oP,packedOpSnippet:oP}),nP={kernelName:ks,backendName:\"webgl\",kernelFunc:are};function ire(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;if(n.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=t.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=CD(a,\"string\",o);return t.makeTensorInfo(n.shape,\"string\",i)}var sP={kernelName:Ru,backendName:\"webgl\",kernelFunc:ire};function ure({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=Wt+`\n return x > 0.0 ? 1.0 : float(${e.alpha});\n `,s=new tr(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var aP={kernelName:wo,backendName:\"webgl\",kernelFunc:ure};var Rg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i=\"\";if(n===1)i=\"coords * strides + begin\";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${e});\n ${s} strides = ${s}(${t});\n\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${i}));\n }\n `}};function pre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=te({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let $=pt.computeOutShape(b,C,S),R=Gs({inputs:{x:n},backend:t,attrs:{begin:b,size:$}});k=te({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeIntermediateTensorInfo(R)}else if(t.shouldExecuteOnCPU([n])){let R=t.readSync(n.dataId),D=me(n.shape,n.dtype,R),P=wD(d,D,S,b);k=t.makeTensorInfo(f,n.dtype,P.values)}else{let R=new Rg(b,S,d);k=t.runWebGLProgram(R,[n],n.dtype)}let _=te({inputs:{x:k},backend:t,attrs:{shape:f}});return t.disposeIntermediateTensorInfo(k),_}var iP={kernelName:Ns,backendName:\"webgl\",kernelFunc:pre};function cre(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=SD(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var uP={kernelName:Ca,backendName:\"webgl\",kernelFunc:cre};function lre(r){let{inputs:e,backend:t,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.readSync(s.dataId),p=t.readSync(a.dataId)[0],[u,c,l]=ID(i,p,n),m=c.length;return[t.makeTensorInfo([m,2],\"int32\",u),t.makeTensorInfo([m],\"string\",c),t.makeTensorInfo([2],\"int32\",new Int32Array(l))]}var pP={kernelName:ji,backendName:\"webgl\",kernelFunc:lre};function mre(r){let{inputs:e,backend:t,attrs:o}=r,{numBuckets:n}=o,{input:s}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(n<=0)throw new Error(\"Number of buckets must be at least 1\");let a=t.readSync(s.dataId),i=vD(a,n);return t.makeTensorInfo(s.shape,\"int32\",i)}var cP={kernelName:Xi,backendName:\"webgl\",kernelFunc:mre};var dre=\"return tan(x);\",fre=xe({opSnippet:dre}),lP={kernelName:_s,backendName:\"webgl\",kernelFunc:fre};var hre=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,gre=xe({opSnippet:hre}),mP={kernelName:Es,backendName:\"webgl\",kernelFunc:gre};function xre(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=te({inputs:{x:s},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:a},backend:t,attrs:{shape:[p,u]}}),h=te({inputs:{x:n},backend:t,attrs:{shape:m}}),g=new Cu(p,i,d.shape.length,f.shape.length,c,m,!1,!0),x=t.runWebGLProgram(g,[f,d,h],h.dtype),b=te({inputs:{x},backend:t,attrs:{shape:n.shape}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(x),b}var dP={kernelName:ds,backendName:\"webgl\",kernelFunc:xre};var Dg=class{constructor(e,t){this.variableNames=[\"A\"];let o=new Array(e.length);for(let a=0;a5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],o=[];for(let n=0;n5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=ND(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Dg(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var fP={kernelName:po,backendName:\"webgl\",kernelFunc:Fv};var Ag=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},Fg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function kp(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function hP(r){let e=1;for(;ep){let P=t.readSync(n.dataId),[O,M]=TD(P,u,n.dtype,s,a);return[t.makeTensorInfo(O.shape,O.dtype,O.values),t.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,n.dtype,[]),t.makeTensorInfo(u,\"int32\",[])];if(c===1)return[n,Ci({attrs:{shape:u,dtype:\"int32\",value:0},backend:t})];let l=t.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?t.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:t});m&&kp(t,d);let x=hP(s),b=hP(c),C=null,S=()=>C===null?[g,g]:[g,C],k=(P,O,M)=>{let L=S(),B=new Ag(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[P],[O]],j=C;C=t.runWebGLProgram(B,L,\"int32\",U),kp(t,j)};for(let P=1;P=1;M/=2)k(O,M,[h,b])}for(let P=b;P>x;P/=2){let O=S(),M=new Fg([h,P/2]),B=[[c],[C===null?1:0],[x]],z=C;C=t.runWebGLProgram(M,O,\"int32\",B),kp(t,z);let U=x/2,j=U*2;for(let q=U;q>=1;q/=2)k(j,q,C.shape)}let _=C;C=Gs({inputs:{x:C},backend:t,attrs:{begin:0,size:[h,s]}}),kp(t,_);let $=Tv({inputs:{x:g,indices:C},backend:t,attrs:{axis:1,batchDims:1}});kp(t,g);let R=u.slice(0,-1);R.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:R},backend:t}),kp(t,_);let D=$;return $=te({inputs:{x:$},attrs:{shape:R},backend:t}),kp(t,D),[$,C]}var gP={kernelName:$s,backendName:\"webgl\",kernelFunc:bre};var Pg=class{constructor(e,t,o,n,s,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=o===\"nearest\"?1:2,p;switch(n){case\"constant\":p=1;break;case\"reflect\":p=2;break;case\"wrap\":p=3;break;case\"nearest\":p=4;break;default:p=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${p} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = 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valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function Cre(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Pg(l,m,a,i,p,g);return t.runWebGLProgram(x,[n,s],\"float32\")}var xP={kernelName:Rs,backendName:\"webgl\",kernelFunc:Cre};function wre(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;Vs(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=_D(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],\"int32\",u)]}var yP={kernelName:Yi,backendName:\"webgl\",kernelFunc:wre};function Sre(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var bP={kernelName:wa,backendName:\"webgl\",kernelFunc:Sre};var Og=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${a})) * float(${o}));\n int currentSeg = int(mod(float(outIdx), float(${a})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function Ire(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=te({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=oi(n.dtype),g=(S,k,_,$,R)=>{let D=S.shape[0],P=S.shape[1],O=w.segment_util.segOpComputeOptimalWindowSize(P,R),M={windowSize:O,inSize:P,batchSize:D,numSegments:R},L=new Og(M,k),B=t.compileAndRun(L,[S,_],$);if(p.push(B),B.shape[1]===R)return B;let z=Av({backend:t,attrs:{start:0,stop:R,step:1,dtype:\"float32\"}}),U=Fv({inputs:{x:z},backend:t,attrs:{reps:[P/O]}});return p.push(z),p.push(U),g(B,k,U,$,R)},x=g(f,\"unsortedSegmentSum\",s,h,a),b=te({inputs:{x},backend:t,attrs:{shape:m}}),C=b;if(c!=null){p.push(b);let S=w.getUndoAxesPermutation(c);C=bt({inputs:{x:C},backend:t,attrs:{perm:S}})}return p.forEach(S=>t.disposeIntermediateTensorInfo(S)),C}var CP={kernelName:Qi,backendName:\"webgl\",kernelFunc:Ire};var vre=[rA,nA,sA,aA,uA,pA,cA,lA,fA,hA,gA,xA,yA,bA,CA,wA,SA,IA,vA,kA,NA,_A,EA,$A,RA,PA,MA,LA,KD,zA,WA,UA,GA,HA,KA,qA,jA,XA,YA,QA,eF,tF,rF,oF,nF,sF,aF,iF,uF,pF,cF,lF,mF,dF,fF,hF,xF,yF,bF,CF,SF,IF,vF,kF,NF,TF,_F,EF,$F,HD,RF,VA,DF,AF,FF,qD,PF,OF,MF,LF,BF,zF,VF,WF,UF,GF,KF,qF,jF,XF,YF,QF,JF,t3,r3,o3,n3,s3,c3,YD,l3,m3,d3,f3,DA,h3,y3,b3,C3,w3,jD,S3,I3,v3,k3,N3,AA,a3,T3,_3,E3,ZD,$3,R3,D3,A3,F3,P3,O3,M3,L3,B3,z3,V3,W3,U3,G3,H3,TA,p3,K3,q3,j3,X3,Y3,Q3,Z3,J3,tP,rP,nP,sP,aP,iP,uP,pP,cP,u3,eA,lP,mP,dP,fP,gP,xP,tA,yP,bP,CP,g3];for(let r of vre)ti(r);var we;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(we||(we={}));var wu;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(wu||(wu={}));var wP;function kre(r){wP=r.wasm.cwrap(So,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Nre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=wu[c];if(g==null)throw new Error(`${c} 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voe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=t,m=u==null?[1,1]:u,d=w.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,C=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,$=d.strideWidth,R=d.inChannels,D=d.outChannels,P=d.padInfo.type===\"SAME\"?1:0;if(d.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(s.shape,s.dtype);return BO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var zO={kernelName:Li,backendName:\"wasm\",setupFunc:Eoe,kernelFunc:$oe};var VO;function Roe(r){VO=r.wasm.cwrap(Mi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Doe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(n.shape,n.dtype);return VO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var WO={kernelName:Mi,backendName:\"wasm\",setupFunc:Roe,kernelFunc:Doe};var UO=he(hn);var GO;function Aoe(r){GO=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\"])}function Foe(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=t.makeOutput(n.shape,\"float32\"),a=i=>t.dataIdMap.get(i.dataId).id;return GO(a(n),a(o),a(s)),s}var HO={kernelName:Xa,backendName:\"wasm\",setupFunc:Aoe,kernelFunc:Foe};var Poe=!1,KO=Ge(xn,Poe,\"bool\");var qO=he(gn);var jO=he(yn,\"float32\");function Lg(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var XO={kernelName:na,backendName:\"wasm\",kernelFunc:Lg};var YO=he(bn,\"float32\");function Mv(r){let{attrs:{shape:e,value:t},backend:o}=r,{attrs:{dtype:n}}=r;n=n||y.inferDtype(t);let s=o.makeOutput(e,n);return o.typedArrayFromHeap(s).fill(t),s}var QO={kernelName:sa,backendName:\"wasm\",kernelFunc:Mv};var ZO;function Ooe(r){ZO=r.wasm.cwrap(Cn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Moe(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return ZO(s,i,p,u,c,a),n}var JO={kernelName:Cn,backendName:\"wasm\",kernelFunc:Moe,setupFunc:Ooe};var eM=he(wn);var Loe=!1,tM=Ge(Sn,Loe);var rM;function Boe(r){rM=r.wasm.cwrap(In,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function zoe(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,l=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return rM(c,l,m,d,f,n,g),h}var oM={kernelName:In,backendName:\"wasm\",setupFunc:Boe,kernelFunc:zoe};var nM;function Voe(r){nM=r.wasm.cwrap(Io,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Woe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=wu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return nM(x,j,q,Y,b,k,_,S,$,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var sM={kernelName:Io,backendName:\"wasm\",setupFunc:Voe,kernelFunc:Woe};var aM;function Uoe(r){aM=r.wasm.cwrap(vo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Goe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!0),g=wu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return aM(x,j,q,Y,b,k,_,S,$,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var iM={kernelName:vo,backendName:\"wasm\",setupFunc:Uoe,kernelFunc:Goe};var uM;function Hoe(r){uM=r.wasm.cwrap(vn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Koe(r){let{backend:e,inputs:t}=r,{params:o,indices:n}=t,[s,a,i,p]=af.prepareAndValidate(o,n),u=e.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,l=c[c.length-1],d=e.dataIdMap.get(o.dataId).id,h=e.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=e.dataIdMap.get(u.dataId).id;return uM(d,we[o.dtype],h,a,l,i,g,x),u}var pM={kernelName:vn,backendName:\"wasm\",setupFunc:Hoe,kernelFunc:Koe};var cM;function qoe(r){cM=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function joe(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:e}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,_=e.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return cM(C,we[n.dtype],$,x,k,l.batchSize,R,_),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=l.outputShape,g}var lM={kernelName:aa,backendName:\"wasm\",setupFunc:qoe,kernelFunc:joe};var Xoe=!1,mM=Ge(kn,Xoe,\"bool\");var Yoe=!1,dM=Ge(Nn,Yoe,\"bool\");var fM=he(Tn,\"bool\");var hM=he(_n,\"bool\");var gM=he(En,\"bool\");var xM;function Qoe(r){xM=r.wasm.cwrap($n,null,[\"number\",\"number\",\"number\",\"number\"])}function Zoe(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,\"float32\");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;xM(n,we[e.dtype],t,a)}return s}var yM={kernelName:$n,backendName:\"wasm\",setupFunc:Qoe,kernelFunc:Zoe};var Joe=!1,bM=Ge(Rn,Joe,\"bool\");var ene=!1,CM=Ge(Dn,ene,\"bool\");var wM;function tne(r){wM=r.wasm.cwrap(An,null,[\"number\",\"number\",\"number\",\"number\"])}function rne(r){let{attrs:e,backend:t}=r,{start:o,stop:n,num:s}=e,a=Math.floor(s),i=t.makeOutput([a],\"float32\");return wM(t.dataIdMap.get(i.dataId).id,o,n,a),i}var SM={kernelName:An,backendName:\"wasm\",setupFunc:tne,kernelFunc:rne};var IM=he(Fn);var vM=he(Pn);var one=!1,kM=Ge(On,one,\"bool\");var NM=he(Mn);var nne=!1,TM=Ge(Ln,nne,\"bool\");var sne=!1,_M=Ge(R0,sne,\"bool\");var EM;function ane(r){EM=r.wasm.cwrap(Bn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ine(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let u=t.makeOutput(n.shape,n.dtype);return EM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var $M={kernelName:Bn,backendName:\"wasm\",setupFunc:ane,kernelFunc:ine};var RM;function une(r){RM=r.wasm.cwrap(Ya,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function pne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!==\"float32\"||s.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let l=t.makeOutput(n.shape,n.dtype);return RM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var DM={kernelName:Ya,backendName:\"wasm\",setupFunc:une,kernelFunc:pne};var AM;function cne(r){AM=r.wasm.cwrap(zn,null,[\"number\",\"number\",\"number\",\"number\"])}function lne(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,p=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims(\"max\",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;AM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var FM={kernelName:zn,backendName:\"wasm\",setupFunc:cne,kernelFunc:lne};var mne=!1,PM=Ge(Vn,mne);var OM;function dne(r){OM=r.wasm.cwrap(Wn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function fne(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=t,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=o.makeOutput(c.outShape,\"float32\"),R=o.dataIdMap.get($.dataId).id;return OM(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),$}var MM={kernelName:Wn,backendName:\"wasm\",setupFunc:dne,kernelFunc:fne};var LM;function hne(r){LM=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.makeOutput(c.outShape,n.dtype);return LM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var BM={kernelName:ia,backendName:\"wasm\",setupFunc:hne,kernelFunc:gne};var zM;function xne(r){zM=r.wasm.cwrap(\"MaxPool3DGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function yne(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return zM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var VM={kernelName:Gi,backendName:\"wasm\",setupFunc:xne,kernelFunc:yne};var WM;function bne(r){WM=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Cne(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool2DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return WM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),l}var UM={kernelName:Ui,backendName:\"wasm\",setupFunc:bne,kernelFunc:Cne};var GM;function wne(r){GM=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Sne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,includeBatchInIndex:p}=o;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=t.makeOutput(c.outShape,n.dtype),m=t.makeOutput(c.outShape,\"int32\");return GM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,t.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var HM={kernelName:ua,backendName:\"wasm\",setupFunc:wne,kernelFunc:Sne};var KM;function Ine(r){KM=r.wasm.cwrap(Un,null,[\"number, number, number\"])}function vne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let S=e.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"mean\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!==\"float32\"&&(b=Mr({backend:e,inputs:{x:u},attrs:{dtype:\"float32\"}}),p=e.dataIdMap.get(b.dataId).id);let C=e.makeOutput(h,\"float32\");if(y.sizeFromShape(u.shape)!==0){let S=e.dataIdMap.get(C.dataId).id;KM(p,x,S)}if(d&&e.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!==\"float32\"&&e.disposeData(b.dataId),C}var qM={kernelName:Un,backendName:\"wasm\",setupFunc:Ine,kernelFunc:vne};var jM;function kne(r){jM=r.wasm.cwrap(Gn,null,[\"number\",\"number\",\"number\",\"number\"])}function Nne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let 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CL={kernelName:rs,backendName:\"wasm\",setupFunc:jne,kernelFunc:Xne};var wL;function Yne(r){wL=r.wasm.cwrap(os,null,[\"number\",\"number\",\"number\",\"number\"])}function Qne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"prod\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;wL(p,x,we[b.dtype],C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var SL={kernelName:os,backendName:\"wasm\",setupFunc:Yne,kernelFunc:Qne};var Zne=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=up(o,n,s,a),p=e.makeOutput([i.length],a);return 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PL={kernelName:Za,backendName:\"wasm\",setupFunc:ase,kernelFunc:ise};var OL;function use(r){OL=r.wasm.cwrap(ps,null,[\"number\",\"array\",\"number\",\"array\",\"number\",\"number\"])}function pse(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return Np({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),p=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);OL(p,c,a.length,l,n.shape.length,u);let m=zt({inputs:{x:i},attrs:{shape:n.shape},backend:t});return t.disposeData(i.dataId),m}var ML={kernelName:ps,backendName:\"wasm\",kernelFunc:pse,setupFunc:use};var LL;function cse(r){LL=r.wasm.cwrap(Ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function 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find the buffer in buffer manager\");a[i]=a[a.length-1],a.pop(),this.numUsedBuffers--,this.numBytesUsed-=o,t?(this.freeBuffers.get(s).push(e),this.numFreeBuffers++):(e.destroy(),this.numBytesAllocated-=o)}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function YB(r,e){return`${r}_${e}`}var qg=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,o,n){let s=ZB(o),a=e*t*s,i=QB(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e){if(this.freeTextures.size===0)return;let t=e.width,o=e.height,n=e.format,s=e.usage,a=QB(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");i.splice(p,1);let u=ZB(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function QB(r,e,t,o){return`${r}_${e}_${t}_${o}`}function ZB(r){if(r===\"rgba8unorm\")return 16;throw new Error(`${r} is not supported!`)}function JB(r,e){if(Math.max(...r)>5)throw new Error(\"Cannot symbolically compute strides for rank > 6 tensor.\");let t=r.length,o=\"xyzwuv\",n=r.map(a=>`${e}.${o[a]}`),s=new Array(t-1);s[t-2]=n[t-1];for(let a=t-3;a>=0;--a)s[a]=`(${s[a+1]} * ${n[a+1]})`;return s}var Qr=(r,e,t)=>t===\"int32\"?`atomicAdd(${r}, bitcast(${e}));`:`\n {\n var oldValue = 0;\n loop {\n let newValueF32 = bitcast(oldValue) + (${e});\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);\n if res.exchanged {\n break;\n }\n oldValue = res.old_value;\n }\n }`;var wi;(function(r){r[r.FROM_PIXELS=0]=\"FROM_PIXELS\",r[r.DRAW=1]=\"DRAW\"})(wi||(wi={}));var oz=(r,e,t,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=cae(t,s,e),i=r.createShaderModule({code:a,label:e.constructor.name}),p=A().get(\"WEBGPU_PRINT_SHADER\");if(p!==\"\"){p=p.toLowerCase();let u=p.split(\",\");(p===\"all\"||u.some(c=>e.shaderKey.toLowerCase().includes(c)))&&(console.group(e.shaderKey),console.debug(a),console.groupEnd())}return n?r.createComputePipelineAsync({compute:{module:i,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"}):r.createComputePipeline({compute:{module:i,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"})},Ae=(r,e=\"f32\")=>{switch(r){case 1:return`${e}`;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${r}-component ${e} is not supported.`)}};function ft(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Oo(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function G(...r){let e;switch(r.length){case 0:e=`\n fn main()\n `;break;case 1:e=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return e}function ez(r,e){let t;return t=`\n ${pae(e)}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,t}function pae(r){return`\n @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})\n`}function cae(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(t.outputComponent=t.outputComponent?t.outputComponent:1,o.push(`\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${sz(t)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +\n localIndex);\n `}\n }\n `),t.pixelsOpType!=null){let f=t.pixelsOpType===wi.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Su(e.dtype,t.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Su(r[0].dtype,t.outputComponent)}>;`,h=e.shape.length===3?\"vec2\":\"i32\";o.push(`\n struct Uniform {\n outShapeStrides : ${h},\n size : i32,\n numChannels : i32,\n alpha : f32,\n };\n\n ${f}\n @group(0) @binding(2) var uniforms: Uniform;\n `);let g=rz(t);return[tz,o.join(`\n`),cm(e.shape),t.getUserCode(),ez(g,t)].join(`\n`)}let s,a,i=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=ft(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ft(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ft(e.shape.length);i+=`outShape : ${p}, `,s=e.shape.length-1,a=ft(s),i+=`\n outShapeStrides: ${a}, `,t.size&&(i+=\"size : i32, \"),t.uniforms&&(i+=t.uniforms),i+=\"};\",i=yae(i),o.push(i),t.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${Su(e.dtype,t.outputComponent)}>;\n `),t.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${t.variableComponents?Su(r[h].dtype,t.variableComponents[h]):Su(r[h].dtype,t.outputComponent)}>;\n `)}),i!==\"\"&&o.push(`\n @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms;\n `);let u=hae(e.shape,t.dispatchLayout),c=[tz,o.join(`\n`)+lae,cm(e.shape),u,gae(e.shape.length)];t.atomic||c.push(xae(e.shape,e.dtype,t.outputComponent)),t.variableNames.forEach((f,h)=>{c.push(`${cm(r[h].shape,f)}`)});let l=r.map((f,h)=>fae(f,e.shape,t.variableComponents?t.variableComponents[h]:t.outputComponent,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=rz(t);return c.push(ez(m,t)),c.join(`\n`)}function nz(r,e,t){let o=r.shaderKey;if(r.pixelsOpType!=null)return o;let n=[],s=[];e.forEach(c=>{n.push(c.shape),s.push(c.dtype)}),n.push(t.shape),s.push(t.dtype);let a=e.map(c=>w.getBroadcastDims(c.shape,t.shape)),i=e.map(c=>y.arraysEqual(c.shape,t.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=sz(r)?\"flatDispatch\":\"\";return o+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+n.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,o}var tz=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n let floatToUint: vec4 = bitcast>(val);\n return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u);\n }\n`,lae=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function cm(r,e=\"\"){let t=r.length,o=e!==\"\"?`get${e.charAt(0).toUpperCase()+e.slice(1)}CoordsFromIndex`:\"getCoordsFromIndex\",n=e!==\"\"?`${e.charAt(0).toLowerCase()+e.slice(1)}ShapeStrides`:\"outShapeStrides\";if(t<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=ft(t),i=[];for(let u=0;u vec2 {\n let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n};\n return vec2(d0, d1);\n }`;let p;return p=\"var index2 = index;\"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Oo(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`;return`${l}; ${m};`}).join(\"\"),`\n fn ${o}(index : i32) -> ${a} {\n ${p}\n return ${a}(${i.join(\",\")});\n }\n `}function mae(r,e){let t=r.name,o=r.shape.length,n=ft(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return`\n fn ${s}() -> ${Ae(e)} {\n return ${Ae(e)}(${t}[0]);\n }\n `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),`\n fn ${s}(${i}) -> ${Ae(e)} {\n return ${Ae(e)}(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})${e===1?\"\":` / ${e}`}]);\n }\n `}function dae(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=ft(p);if(y.arraysEqual(r.shape,e)&&o)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}${t===1?\"\":` / ${t}`}]);\n }\n `;let c=w.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)}{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)}{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${Oo(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=ft(i),x=r.shape.map((b,C)=>`coords.${Oo(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> ${Ae(t)} {\n var coords = coordsIn;\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n`}function fae(r,e,t,o){let n=mae(r,t);return r.shape.length<=e.length&&(n+=dae(r,e,t,o)),n}function hae(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${ft(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[t,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function gae(r){let e=\"\";switch(r){case 0:case 1:e+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:e+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:e+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:e+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:e+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:e+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function sz(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function Su(r,e=1){if(r===\"float32\")return Ae(e,\"f32\");if(r===\"int32\"||r===\"bool\")return Ae(e,\"i32\");throw new Error(`type ${r} is not supported.`)}function xae(r,e,t){let o=r.length,n=Su(e,t),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t)}) {\n result[flatIndex] = ${n}(value);\n }\n\n fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t,\"i32\")}) {\n result[flatIndex] = ${n}(value);\n }\n `;if(o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=ft(o);s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t)}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t,\"i32\")}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n `}return s}function yae(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function rz(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var Zv={};qe(Zv,{GPUBytesPerElement:()=>jg,MatMulProgramType:()=>Mo,assertNotComplex:()=>fm,computeDispatch:()=>H,computeWorkPerThreadForConv2d:()=>mm,computeWorkgroupInfoForMatMul:()=>Qv,computeWorkgroupSizeForConv2d:()=>lm,flatDispatchLayout:()=>X,isWebGPUSupported:()=>dm,tilesFitEvenlyIntoShape:()=>Cae});var Tp=r=>{let e=1;for(let t=0;tt%r[o]===0)}function H(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Tp(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Tp(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Tp(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function Qv(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function lm(r,e,t=!1){if(t)return[8,8,1];let o=Tp(r.x.map(s=>e[s])),n=Tp(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function mm(r,e,t=!1){if(t)return[4,4,1];let o=Tp(r.x.map(s=>e[s])),n=Tp(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((e,t)=>t)}}function jg(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function dm(){return!!(typeof globalThis!=\"undefined\"&&globalThis.navigator&&globalThis.navigator.gpu)}function fm(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Mo;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Mo||(Mo={}));var wae=A().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Sae=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},jc=class r extends ao{nextDataId(){return r.nextDataId++}constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!dm())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new Hg(t),this.supportTimestampQuery=this.device.features.has(\"timestamp-query\"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Kg(this.device),this.textureManager=new qg(this.device),this.tensorMap=new Bo(this,ur()),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(e,t=!1){if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);return t?o.refCount=0:o.refCount--,o.refCount>0?!1:(o.complexTensorInfos!=null&&(this.disposeData(o.complexTensorInfos.real.dataId),this.disposeData(o.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(e)?(this.tensorDataPendingDisposal.push(e),!0):(this.releaseResource(e),this.tensorMap.delete(e),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resource)){if(t.external){t.resource=null;return}t.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(t.resource):t.resource instanceof GPUTexture&&this.textureManager.releaseTexture(t.resource),t.resource=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let e;try{e=await Promise.all(Object.values(this.pipelineCache))}catch(t){throw new Error(t.message)}Object.keys(this.pipelineCache).map((t,o)=>{this.pipelineCache[t]=e[o]})}async getBufferData(e){if(A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\"))return console.warn(\"The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false\"),null;let t=e.size,o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o,complexTensorInfos:n}=t;if(o!=null||t.dtype===\"string\")return o;if(t.dtype===\"complex64\"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h,g).buffer,\"float32\");return this.convertAndCacheOnCPU(e,x),x}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn(\"The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead.\"));let s=[\"opaque\",\"premultiplied\"],a=t.resource,i=a.size;y.assert(i%4===0,()=>\"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.\");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.endComputePassEncoder(),m.map((h,g)=>{let x=h.getContext(\"webgpu\");return x.configure({device:this.device,format:\"bgra8unorm\",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:P},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext(\"2d\",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,P,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=$%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,t.dtype);return this.convertAndCacheOnCPU(e,f),f}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return o;let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=w.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(t.resource);n=y.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e){let t=e.size,o=e.usage,n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. \");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=jg(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.sizey.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}async time(e){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --enable-dawn-features=allow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled.\"),this.hasTimestampQueryWarned=!0);let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let o=this.tensorMap.get(e.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resource!=null)return;let o=jg(t.dtype)*y.sizeFromShape(t.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(t.values){if(n=this.bufferManager.acquireBuffer(o,s,!0),n.mapState===\"unmapped\"){let a=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=a.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(i).set(t.values):new Float32Array(i).set(t.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(o,s);t.resource=n}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),u>s&&(s=u),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((p,u)=>{let c=n[u];p.type===\"int32\"?new Int32Array(a,c,p.data.length).set(p.data):p.type===\"uint32\"?new Uint32Array(a,c,p.data.length).set(p.data):new Float32Array(a,c,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,t),this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=Sae(this.device,e);let a=t.map((p,u)=>{if(p.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(p.dataId),{dtype:this.tensorMap.get(p.dataId).dtype,shape:p.shape,name:e.variableNames[u]}});e.shaderKey=nz(e,a,s);let i=A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\");return e.shaderKey in this.pipelineCache||(this.pipelineCache[e.shaderKey]=oz(this.device,e,a,s,i)),e.pipeline=this.pipelineCache[e.shaderKey],i||this.recordAndSubmit(e,s,t,n),s}recordAndSubmit(e,t,o,n){if(e.pipeline instanceof Promise)throw new Error(\"Please call checkCompileCompletionAsync to ensure parallel compilation is done!\");let s=[],a=[],i=\"int32\";if(e.pixelsOpType==null){s.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),a=o.concat(t).map(d=>d.shape);let m=\"int32\";a.map(d=>{s.push({type:m,data:d});let f=y.computeStrides(d);s.push({type:m,data:f})})}else{let m=y.computeStrides(t.shape);s.push({type:i,data:m})}if(e.size){let m=y.sizeFromShape(e.outputShape);s.push({type:i,data:[e.outputComponent?m/e.outputComponent:m]})}n&&(s=[...s,...n]);let p=[this.tensorToBinding(t),...o.map(m=>this.tensorToBinding(m)),this.makeUniforms(s)];o.forEach(m=>{this.commandQueueOwnedIds.add(m.dataId)}),this.commandQueueOwnedIds.add(t.dataId);let u=this.device.createBindGroup({layout:e.pipeline.getBindGroupLayout(0),entries:p.map((m,d)=>({binding:d,resource:m}))}),c=this.activeTimers!=null;this.ensureCommandEncoderReady();let l={};c&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:this.querySetCount})),l.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1},this.computePassEncoder=this.commandEncoder.beginComputePass(l)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(l)),this.computePassEncoder.setPipeline(e.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),this.dispatchCountInPass++,(c||A().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchCountInPass||e.pixelsOpType===wi.DRAW)&&(this.endComputePassEncoder(),c?this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let e=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.querySetCount*8),this.submitQueue(),await e.mapAsync(GPUMapMode.READ);let t=new BigUint64Array(e.getMappedRange()),o=Number(t[1]-t[0])/1e6;return e.unmap(),this.bufferManager.releaseBuffer(e),o}shouldExecuteOnCPU(e,t=wae){return A().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resource==null&&y.sizeFromShape(o.shape){let r={powerPreference:A().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={},o=[];e.features.has(\"timestamp-query\")&&o.push(\"timestamp-query\"),e.features.has(\"bgra8unorm-storage\")&&o.push([\"bgra8unorm-storage\"]),t.requiredFeatures=o;let n=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let s=await e.requestDevice(t),a=await e.requestAdapterInfo();return new jc(s,a)},3);var fe;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.ELU_DER=5]=\"ELU_DER\",r[r.EQUAL=6]=\"EQUAL\",r[r.FLOOR_DIV=7]=\"FLOOR_DIV\",r[r.GREATER=8]=\"GREATER\",r[r.GREATER_EQUAL=9]=\"GREATER_EQUAL\",r[r.LESS=10]=\"LESS\",r[r.LESS_EQUAL=11]=\"LESS_EQUAL\",r[r.LOGICAL_AND=12]=\"LOGICAL_AND\",r[r.LOGICAL_OR=13]=\"LOGICAL_OR\",r[r.MAX=14]=\"MAX\",r[r.MIN=15]=\"MIN\",r[r.MOD=16]=\"MOD\",r[r.MUL=17]=\"MUL\",r[r.NOT_EQUAL=18]=\"NOT_EQUAL\",r[r.POW=19]=\"POW\",r[r.PRELU=20]=\"PRELU\",r[r.SQUARED_DIFFERENCE=21]=\"SQUARED_DIFFERENCE\",r[r.SUB=22]=\"SUB\"})(fe||(fe={}));var Iae=\"let resultTemp = a + b;\",vae=\"let resultTemp = atan2(a, b);\",kae=\"let resultTemp = areal * breal - aimag * bimag;\",Nae=\"let resultTemp = areal * bimag + aimag * breal;\",Tae=\"let resultTemp = a / b;\",_ae=\"let resultTemp = select(a * (b + 1.0), a, b >= b - b);\",Eae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a == b);\n`,$ae=`\n let remainder =\n select(a % b, round(a % b), (round(a) == a) & (round(b) == b));\n let quotient = (a - remainder) / b;\n let resultTemp =\n round(select(quotient, quotient - 1, sign(remainder) == -sign(b)));\n`,Rae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a > b);\n`,Dae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a >= b);\n`,Aae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a < b);\n`,Fae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a <= b);\n`,Pae=\"return f32(a >= 1.0 && b >= 1.0);\",Oae=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,Mae=\"return f32(a >= 1.0 || b >= 1.0);\",Lae=`return min(vec4(a >= vec4(1.0)) +\n vec4(b >= vec4(1.0)), vec4(1.0));`,Bae=\"let resultTemp = max(a, b);\",zae=\"let resultTemp = min(a, b);\",Vae=`\n let isNaN = b == 0.;\n var resultTemp = a % b;\n resultTemp = select((resultTemp + b) % b, resultTemp,\n (a < 0. && b < 0.) || (a >= 0. && b > 0.));\n`,Wae=`\n let isNaN = !vec4(b);\n var resultTemp = vec4(a % b);\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n`,Uae=\"let resultTemp = a * b;\",Gae=`\n var resultTemp = f32(a != b);\n let valueForNaN = 1.0;\n`,Hae=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n`,Kae=`\n let isNaN = a < 0.0 && floor(b) < b;\n if (b == 0.0) {\n return 1.0;\n }\n var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b),\n round(abs(b) % 2.0) != 1.0);\n`,qae=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n`,jae=\"if (a < 0.0) { return b * a; } return a;\",Xae=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Yae=\"let resultTemp = (a - b) * (a - b);\",Qae=\"let resultTemp = a - b;\";function Xc(r,e){let t;do{switch(r){case fe.ATAN2:t=vae;break;case fe.MAX:t=Bae;break;case fe.MIN:t=zae;break;case fe.MOD:t=e?Wae:Vae;break;case fe.NOT_EQUAL:t=e?Hae:Gae;break;case fe.POW:t=e?qae:Kae;break;default:continue}let o,n,s;return e?(o=\"isnanVec4\",n=\"vec4\",s=\"vec4\"):(o=\"isnan\",n=\"f32\",s=\"bool\"),`\n let aIsNaN = ${o}(a);\n let aPostLegalization = select(a, ${n}(42), aIsNaN);\n let bIsNaN = ${o}(b);\n let bPostLegalization = select(b, ${n}(42), bIsNaN);\n let isNaN = false;\n let valueForNaN = uniforms.NAN;\n {\n let a = aPostLegalization;\n let b = bPostLegalization;\n ${t}\n return select(\n resultTemp, ${n}(valueForNaN),\n ${s}(isNaN) | aIsNaN | bIsNaN);\n }\n `}while(!1);switch(r){case fe.ADD:t=Iae;break;case fe.COMPLEX_MULTIPLY_IMAG:t=Nae;break;case fe.COMPLEX_MULTIPLY_REAL:t=kae;break;case fe.DIV:t=Tae;break;case fe.ELU_DER:t=_ae;break;case fe.EQUAL:t=Eae;break;case fe.FLOOR_DIV:t=$ae;break;case fe.GREATER:t=Rae;break;case fe.GREATER_EQUAL:t=Dae;break;case fe.LESS:t=Aae;break;case fe.LESS_EQUAL:t=Fae;break;case fe.LOGICAL_AND:return e?Oae:Pae;case fe.LOGICAL_OR:return e?Lae:Mae;case fe.MUL:t=Uae;break;case fe.PRELU:return e?Xae:jae;case fe.SQUARED_DIFFERENCE:t=Yae;break;case fe.SUB:t=Qae;break;default:}return`\n ${t}\n return resultTemp;\n `}var Z;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.ROUND=27]=\"ROUND\",r[r.RSQRT=28]=\"RSQRT\",r[r.SELU=29]=\"SELU\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SIGN=31]=\"SIGN\",r[r.SIN=32]=\"SIN\",r[r.SINH=33]=\"SINH\",r[r.SOFTPLUS=34]=\"SOFTPLUS\",r[r.SQRT=35]=\"SQRT\",r[r.SQUARE=36]=\"SQUARE\",r[r.STEP=37]=\"STEP\",r[r.TAN=38]=\"TAN\",r[r.TANH=39]=\"TANH\",r[r.TO_INT=40]=\"TO_INT\"})(Z||(Z={}));var Zae=\"return abs(a);\",Jae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,eie=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,tie=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,rie=\"return asinh(a);\",oie=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,nie=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,sie=\"return ceil(a);\",aie=\"return cos(a);\",iie=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,uie=\"return exp(a) - 1.0;\",pie=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",cie=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,lie=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${w.ERF_P};\n let a1 = ${w.ERF_A1};\n let a2 = ${w.ERF_A2};\n let a3 = ${w.ERF_A3};\n let a4 = ${w.ERF_A4};\n let a5 = ${w.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,mie=\"return exp(a);\",die=\"return floor(a);\",fie=\"return f32(!isnan(a) && !isinf(a));\",hie=\"return f32(isinf(a));\",gie=\"return f32(isnan(a));\",xie=\"return a;\",yie=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,bie=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,Cie=\"return f32(!(a >= 1.0));\",wie=\"return -a;\",Sie=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Iie=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,vie=\"return 1.0 / a;\",kie=\"return select(a, 0.0, a < 0.0);\",Nie=\"return clamp(a, 0.0, 6.0);\",Tie=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",_ie=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,Eie=\"return round(a);\",$ie=\"return inverseSqrt(a);\",Rie=`\n if (a >= 0.0) {\n return ${w.SELU_SCALE} * a;\n } else {\n return ${w.SELU_SCALEALPHA} * (exp(a) - 1.0);\n }\n`,Die=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Aie=\"return sign(a);\",Fie=\"return sin(a);\",Pie=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Oie=`\n let epsilon = 1.1920928955078125e-7;\n let threshold = log(epsilon) + 2.0;\n\n let too_large = a > -threshold;\n let too_small = a < threshold;\n let exp_a = exp(a);\n\n if (too_large) {\n return a;\n } else if (too_small) {\n return exp_a;\n } else {\n return log(exp_a + 1.0);\n }\n`,Mie=\"return sqrt(a);\",Lie=\"return a * a;\",Bie=`\n if (isnan(a)) {\n return a;\n }\n\n return select(uniforms.stepAlpha, 1.0, a > 0.0);\n`,zie=\"return tan(a);\",Vie=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Wie=\"return f32(i32((a)));\";function Si(r,e){switch(r){case Z.ABS:return Zae;case Z.ACOS:return Jae;case Z.ACOSH:return eie;case Z.ASIN:return tie;case Z.ASINH:return rie;case Z.ATAN:return oie;case Z.ATANH:return nie;case Z.COS:return aie;case Z.COSH:return iie;case Z.CEIL:return sie;case Z.ELU:return e?cie:pie;case Z.ERF:return lie;case Z.EXP:return mie;case Z.EXPM1:return uie;case Z.FLOOR:return die;case Z.IS_FINITE:return fie;case Z.IS_INF:return hie;case Z.IS_NAN:return gie;case Z.LINEAR:return xie;case Z.LOG:return yie;case Z.LOG1P:return bie;case Z.LOGICAL_NOT:return Cie;case Z.NEG:return wie;case Z.LEAKYRELU:return e?Iie:Sie;case Z.RECIPROCAL:return vie;case Z.RELU:return e?_ie:kie;case Z.RELU6:return e?Tie:Nie;case Z.ROUND:return Eie;case Z.RSQRT:return $ie;case Z.SELU:return Rie;case Z.SIGMOID:return Die;case Z.SIGN:return Aie;case Z.SIN:return Fie;case Z.SINH:return Pie;case Z.SOFTPLUS:return Oie;case Z.SQRT:return Mie;case Z.SQUARE:return Lie;case Z.STEP:return Bie;case Z.TAN:return zie;case Z.TANH:return Vie;case Z.TO_INT:return Wie;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function dr(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=Si(Z.LINEAR);else if(r===\"relu\")n=Si(Z.RELU,t);else if(r===\"elu\")n=Si(Z.ELU,t);else if(r===\"relu6\")n=Si(Z.RELU6,t);else if(r===\"prelu\")n=Xc(fe.PRELU,t);else if(r===\"sigmoid\")n=Si(Z.SIGMOID,t);else if(r===\"leakyrelu\")n=Si(Z.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(t?4:1),i=\"\";return e?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function Zr(r,e){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${e?\"value = activation(value, coords);\":\"\"}\n `}function Jv(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=`\n ${r?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,i=e?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${t&&n?a:`\n ${r?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${a}\n }\n `}\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${i}\n return value;\n }\n `}function hm(r,e,t,o,n=!1,s=!1,a=!1,i=1){return`\n ${Jv(t,o,n,s,a,i)}\n fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Ae(i)}) {\n ${n&&s?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${Zr(r,e)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var Uie=(r,e)=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol * ${e});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRow + innerRow,\n kStart + inputCol * ${e});\n `,Gie=(r,e,t,o)=>{if(r)return`\n for (var k = 0; k < ${o}; k++) {\n let BCached0 = mm_Bsub[k][tileCol];\n let ACached0 = mm_Asub[k][localRow];\n for (var i = 0; i < ${t}; i++) {\n acc[i] = fma(BCached0, vec4(ACached0[i]), acc[i]);\n }\n }`;{let n=\"\",s=\"\";for(let a=0;a(ACached[${a}]), acc[i]);`;return`\n for (var k = 0; k < ${o/e}; k++) {\n ${n}\n for (var i = 0; i < ${t}; i++) {\n let ACached = mm_Asub[tileRow + i][k];\n ${s}\n }\n }`}};function _p(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1],d=r[1],f=r[0];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${l} must be 3 or 4.\n tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${u/l}>, ${c}>;\n var mm_Bsub : array, ${p/r[0]}>, ${o}>;\n\n ${G()} {\n let localRow = i32(localId.y);\n let tileRow = localRow * ${d};\n let tileCol = i32(localId.x);\n\n let globalRow = i32(globalId.y) * ${d};\n let globalCol = i32(globalId.x) * ${f};\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!a?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!a?\"batch\":\"batch % uniforms.bShape[0]\"};\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, ${d}>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${m};\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${Uie(t,l)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n ${Gie(t,l,d,o)}\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var az=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,Hie=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Ep(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${p};\n let globalColStart = i32(workgroupId.x) * ${u};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {\n ${az(t)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let gCol = globalColStart + localCol + innerCol * ${e[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * ${h};\n let tileCol = i32(localId.x) * ${g};\n\n let globalRow = i32(globalId.y) * ${h};\n let globalCol = i32(globalId.x) * ${g};\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let tileRowA = i32(localId.y) * ${m};\n let tileColA = i32(localId.x) * ${d};\n let tileRowB = i32(localId.y) * ${f};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n for (var innerCol = 0; innerCol < ${d}; innerCol++) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${az(t)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n ${Hie(t)}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${l}>;\n var mm_Bsub : array, ${o}>;\n\n ${G()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, ${h}>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${x}\n }\n `}var Kie=r=>r?`\n mm_readA(batchA, colA, globalRow),\n mm_readA(batchA, colA + 1, globalRow),\n mm_readA(batchA, colA + 2, globalRow),\n mm_readA(batchA, colA + 3, globalRow)\n `:`\n mm_readA(batchA, globalRow, colA),\n mm_readA(batchA, globalRow, colA + 1),\n mm_readA(batchA, globalRow, colA + 2),\n mm_readA(batchA, globalRow, colA + 3)\n `;function qie(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return`\n var mm_Asub : array, ${r[0]}>;\n\n ${G()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / ${t} + 1;\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n let colA = t * ${t} + tileCol * 4;\n mm_Asub[tileCol] = vec4(${Kie(e)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${t/4}; k++) {\n let rowB = t * ${t} + k * 4;\n let BCached = vec4(mm_readB(batchB, rowB, globalCol),\n mm_readB(batchB, rowB + 1, globalCol),\n mm_readB(batchB, rowB + 2, globalCol),\n mm_readB(batchB, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Xg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Qv(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${hm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?_p(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?qie(this.workgroupSize,this.transposeA):Ep(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}\n `}};function jie(r){return`\n var sumValues : array;\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + ${r}) {\n let dataA = mm_readA(batchA, row, k);\n let dataB = mm_readB(batchB, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = ${r/2}u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var Yg=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${jie(this.workgroupSize[0])}\n `}};function Xie(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n var mm_Asub : array, ${e}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${G()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batchA, globalRow, globalColA);\n var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batchA, globalRow, globalColA);\n regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Qg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Xie(this.workgroupSize)}\n `}};var Zg=class{constructor(e,t,o=!1,n=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=H(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return`\n ${Jv(!1,this.transposeB,!1,!1,!1,e)}\n fn mm_write(batch: i32, row : i32, col : i32, value : ${Ae(e)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n for (var i = 0; i < ${e}; i = i + 1) {\n ${Qr(\"&result[flatIndex + i]\",`${e>1?\"value[i]\":\"value\"}`,\"float32\")}\n }\n }\n }\n ${e===4?_p(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Ep(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Jg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${Zr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var ex=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function vt(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new ex(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var iz={kernelName:sa,backendName:\"webgpu\",kernelFunc:vt};function pe(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var uz={kernelName:da,backendName:\"webgpu\",kernelFunc:pe};function $p({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Sr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[$,R],P=Math.max(x,b),O=[$,R],M=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],L,B,z=[P,d,f],U=A().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(U<0){let q=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),Y=q>0?q:n.thresholdToIncreaseWorkgroups,J=P*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?P*d*f<=128?U=Mo.MatMulReduceProgram:P===1&&m>=2e3?U=Mo.MatMulSplitKProgram:U=Mo.MatMulSmallOutputSizeProgram:U=Mo.MatMulPackedProgram}switch(U){case Mo.MatMulReduceProgram:L=new Yg(z,t,o,s,p,a);break;case Mo.MatMulSplitKProgram:{if(B=vt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Zg(z,m,t,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Jg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p===\"leakyrelu\"&&(J=[{type:\"float32\",data:[i]}],Y.uniforms+=\" alpha : f32,\");let ne=n.runWebGPUProgram(Y,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Mo.MatMulSmallOutputSizeProgram:L=new Qg(k,_,z,t,o,s,p,a);break;case Mo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new Xg(k,z,t,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p===\"leakyrelu\"&&(M.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Yie(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return $p({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var pz={kernelName:So,backendName:\"webgpu\",kernelFunc:Yie};var gm=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${Xc(this.op,!1)}\n }\n\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var Ii=class{constructor(e,t,o){if(this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type=\"shared\",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type=\"nonshared\",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n ${Xc(this.op,this.outputComponent===4)}\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;e=`\n ${o}\n var sharedBuf : array;\n ${G(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else e=`\n ${o}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index * ${this.outputComponent});\n let a = ${t}(getAByOutputCoords(coords));\n let b = ${t}(getBByOutputCoords(coords));\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return e}};function At(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var cz={kernelName:Co,backendName:\"webgpu\",kernelFunc:At};function xo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var lz={kernelName:Di,backendName:\"webgpu\",kernelFunc:xo};var Jr=class{constructor(e,t,o=\"\"){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==\"\"&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${Si(this.op,!1)}\n }\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function ye({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Jr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Ii(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new gm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new gm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=xo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?w.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?w.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Ii(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:mz,castImpl:dz,ceilImpl:fz,concatImpl:hz,equalImpl:gz,expImpl:xz,expm1Impl:yz,floorImpl:bz,floorDivImpl:Cz,gatherNdImpl:wz,gatherV2Impl:Sz,greaterEqualImpl:Iz,greaterImpl:vz,lessEqualImpl:kz,lessImpl:Nz,logImpl:Tz,maxImpl:_z,maximumImpl:Ez,minimumImpl:$z,multiplyImpl:Rz,negImpl:Dz,notEqualImpl:Az,prodImpl:Fz,rangeImpl:Pz,rsqrtImpl:Oz,scatterImpl:Mz,simpleAbsImpl:Lz,sliceImpl:Bz,stridedSliceImpl:zz,stringNGramsImpl:Vz,subImpl:Wz,tileImpl:Uz,topKImpl:Gz,transposeImpl:Hz,uniqueImpl:rOt}=Ic;var Qie=ye({opType:Z.ABS,cpuKernelImpl:Lz}),Kz={kernelName:Xs,backendName:\"webgpu\",kernelFunc:Qie};var Zie=ye({opType:Z.ACOS}),qz={kernelName:Vo,backendName:\"webgpu\",kernelFunc:Zie};var Jie=ye({opType:Z.ACOSH}),jz={kernelName:Wo,backendName:\"webgpu\",kernelFunc:Jie};var eue=et({opType:fe.ADD,cpuKernelImpl:mz,supportsComplex:!0}),Xz={kernelName:uo,backendName:\"webgpu\",kernelFunc:eue};var tx=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${G(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${e.join(`\n `)}\n setOutputAtIndex(flatIndex, ${t});\n }\n }\n }\n `}};function tue(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new tx(s);return t.runWebGPUProgram(a,o,n)}var Yz={kernelName:Uo,backendName:\"webgpu\",kernelFunc:tue};var rx=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return`\n var tile : array, ${this.workgroupSize[0]}>;\n ${G()} {\n var x = i32(workgroupId.x) * ${e} + i32(localId.x);\n var y = i32(workgroupId.y) * ${e} + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * ${e} + i32(localId.x);\n y = i32(workgroupId.x) * ${e} + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var ox=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\",o=this.workgroupSize[0];this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n if (isnan(candidate)) {\n bestValue = uniforms.NAN;\n } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n { bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${G(\"index\")} {\n let outputIndex = index / ${o};\n let offset = getOffset(outputIndex);\n var bestValue = ${t};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + ${o}) {\n let candidate = f32(x[offset + k]);\n ${e}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), ${o}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${e}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${n}\n }\n }\n `}};var rue={mean:\"float32\",all:\"bool\",any:\"bool\"};function eo(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=w.getAxesPermutation(p,s),c=r;u!=null&&(c=xr({inputs:{x:r},attrs:{perm:u},backend:n}),p=w.getInnerMostAxes(p.length,s),a.push(c)),w.assertAxesAreInnerMostDims(o,p,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=w.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=_z(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=Fz(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=rue[o]||oi(r.dtype),S=[{type:\"int32\",data:[h]}],k=new nx(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],C,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function oue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"all\",t)}var Zz={kernelName:Go,backendName:\"webgpu\",kernelFunc:oue};function nue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"any\",t)}var Jz={kernelName:Ho,backendName:\"webgpu\",kernelFunc:nue};var Yc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=w.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32?(this.type=\"plain\",this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${Oo(this.inputShape.length-1)}`,o=()=>{let n=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+=\"outputCoords,\");else for(let s=0;s u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${G(\"index\")} {\n let outputIndex = index / ${e};\n let reduceLength = ${t()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + ${e}) {\n let candidate = getX(${o()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), ${e}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${o()} 0);\n let reduceLength = ${t()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${o()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function sue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new Yc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var eV={kernelName:Ys,backendName:\"webgpu\",kernelFunc:sue};function aue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new Yc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var tV={kernelName:Qs,backendName:\"webgpu\",kernelFunc:aue};var iue=ye({opType:Z.ASIN}),rV={kernelName:Ko,backendName:\"webgpu\",kernelFunc:iue};var uue=ye({opType:Z.ASINH}),oV={kernelName:qo,backendName:\"webgpu\",kernelFunc:uue};var pue=ye({opType:Z.ATAN}),nV={kernelName:jo,backendName:\"webgpu\",kernelFunc:pue};var cue=et({opType:fe.ATAN2}),sV={kernelName:Yo,backendName:\"webgpu\",kernelFunc:cue};var lue=ye({opType:Z.ATANH}),aV={kernelName:Xo,backendName:\"webgpu\",kernelFunc:lue};var sx=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"strides : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.strides;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};var Ba=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue = resultValue + value; count = count + 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"wR * uniforms.filterDims.y + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n let xRCCorner = vec2(coords.yz) * uniforms.strides - uniforms.pads;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilations.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilations.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, d);\n ${e}\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}},Iu=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue += value; count += 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let xCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xDCorner = xCorner.x;\n let xRCorner = xCorner.y;\n let xCCorner = xCorner.z;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wD = 0; wD < uniforms.filterDims.x; wD++) {\n let xD = xDCorner + wD;\n if (xD < 0 || xD >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims.y; wR++) {\n let xR = xRCorner + wR;\n if (xR < 0 || xR >= uniforms.convDims.y) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.z; wC++) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.z) {\n continue;\n }\n\n let value = getX(batch, xD, xR, xC, ch);\n ${e}\n }\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}};function t0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return eo(n,s,a,\"max\",t)}var iV={kernelName:zn,backendName:\"webgpu\",kernelFunc:t0};function r0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"mean\",t)}var uV={kernelName:Un,backendName:\"webgpu\",kernelFunc:r0};function ax(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return At({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=r0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=t0({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new sx(e):(t===\"avg\"?n=new Ba(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Ba(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function mue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,c=w.computePool2DInfo(n.shape,s,a,1,i,p);return ax(n,c,\"avg\",t)}var pV={kernelName:Qo,backendName:\"webgpu\",kernelFunc:mue};function due(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,\"avg\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var cV={kernelName:Zs,backendName:\"webgpu\",kernelFunc:due};var ix=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n\n dotProd = dotProd + dyValue * uniforms.avgMultiplier;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},ux=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n dotProd += dyValue * uniforms.avgMultiplier;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function fue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=w.computePool3DInfo(a.shape,i,p,1,u,c),m=new ux(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:\"int32\",data:[l.outDepth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"float32\",data:[d]}];return t.runWebGPUProgram(m,[n],a.dtype,f)}var lV={kernelName:Ri,backendName:\"webgpu\",kernelFunc:fue};function hue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;fm([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new ix(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:\"int32\",data:[c.outHeight]},{type:\"int32\",data:[c.outWidth]},{type:\"float32\",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var mV={kernelName:$i,backendName:\"webgpu\",kernelFunc:hue};function gue(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return $p({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var dV={kernelName:Zo,backendName:\"webgpu\",kernelFunc:gue};var px=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ft(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=ft(this.rank),t=xue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${o0[a]} = uniforms.start.${Oo(a)} + coords.${o0[a]};`),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${e};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${t}));\n }\n }\n `}},o0=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function xue(r){if(r===1)return\"sourceLoc\";if(r<=6)return o0.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function Hs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=Bz(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new px(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var fV={kernelName:ha,backendName:\"webgpu\",kernelFunc:Hs};var yue=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),h=xr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Hs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},hV={kernelName:Js,backendName:\"webgpu\",kernelFunc:yue};var bue=`\n fn bincount_write(index: i32, value: f32) {\n ${Qr(\"&result[index]\",\"value\",\"float32\")}\n }\n`,Cue=`\n fn bincount_write(index: i32, value: f32) {\n atomicStore(&result[index], bitcast(value));\n }\n`,Qc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?Cue:bue}\n ${G(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(index)\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(coord[0], coord[1])\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function wue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=vt({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new Qc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var gV={kernelName:Jo,backendName:\"webgpu\",kernelFunc:wue};var cx=class{constructor(e){this.outputShape=[],this.variableNames=[\"s0\",\"s1\"],this.uniforms=\"s0Size : i32, s1Size : i32, \",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"broadcastArgs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var s0 = 1.0;\n var s1 = 1.0;\n let indexS0 = index - uniforms.size + uniforms.s0Size;\n let indexS1 = index - uniforms.size + uniforms.s1Size;\n if (indexS0 >= 0) {\n s0 = getS0(indexS0);\n }\n if (indexS1 >= 0) {\n s1 = getS1(indexS1);\n }\n\n if (s0 == 1.0) {\n setOutputAtIndex(index, s1);\n } else if (s1 == 1.0) {\n setOutputAtIndex(index, s0);\n } else if (s0 != s1) {\n setOutputAtIndex(index, uniforms.NAN);\n } else {\n setOutputAtIndex(index, s0);\n }\n }\n }\n `}};function Sue(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e;if(t.shouldExecuteOnCPU([o,n])){let c=t.tensorMap.get(o.dataId),l=t.tensorMap.get(n.dataId),m=c.values,d=l.values,f=w.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return t.makeTensorInfo([f.length],\"int32\",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new cx(i),u=[{type:\"int32\",data:[s]},{type:\"int32\",data:[a]}];return t.runWebGPUProgram(p,[o,n],\"int32\",u)}var xV={kernelName:ea,backendName:\"webgpu\",kernelFunc:Sue};var n0=et({opType:fe.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:Az}),yV={kernelName:Yn,backendName:\"webgpu\",kernelFunc:n0};function vi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:t})}var bV={kernelName:Hi,backendName:\"webgpu\",kernelFunc:vi};function CV(r,e){let t=new Jr(r.shape,Z.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function s0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return At({inputs:{x:n},backend:t});let a=Gr(n.shape),i=s0({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=xo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=vi({inputs:{input:n},backend:t}),i=s0({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=dz(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return CV(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=n0({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var wV={kernelName:yo,backendName:\"webgpu\",kernelFunc:s0};var Iue=ye({opType:Z.CEIL,cpuKernelImpl:fz}),SV={kernelName:en,backendName:\"webgpu\",kernelFunc:Iue};var lx=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue = clamp(\n value, vec4(uniforms.minVal), vec4(uniforms.maxVal));\n clampedValue = select(clampedValue, value, isnanVec4(value));\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var mx=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function vue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new lx(n.shape):i=new mx(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var IV={kernelName:bo,backendName:\"webgpu\",kernelFunc:vue};var dx=class{constructor(e){this.outputShape=[],this.variableNames=[\"real\",\"imag\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"complexAbs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let re = abs(getRealByOutputIndex(index));\n let im = abs(getImagByOutputIndex(index));\n let mx = max(re, im);\n\n // The length function in wgsl may be not underflow-safe on some GPUs.\n // So the safe solution is to ensure underflow-safety in all cases.\n setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0));\n }\n }\n `}};function vV(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function kue(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.tensorMap.get(o.dataId),s=new dx(o.shape),a=[vV(o,n.complexTensorInfos.real),vV(o,n.complexTensorInfos.imag)];return t.runWebGPUProgram(s,a,a[0].dtype)}var kV={kernelName:Ai,backendName:\"webgpu\",kernelFunc:kue};var fx=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=w.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;svi({inputs:{input:C},backend:t})),h=r.map(C=>Rp({inputs:{input:C},backend:t})),g=Zc(f,e,t),x=Zc(h,e,t),b=xo({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let $=[-1,y.sizeFromShape(k.shape.slice(e))];return pe({inputs:{x:k},backend:t,attrs:{shape:$}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=w.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=hz(h,g,o,x),C=w.computeOutShape(r.map(k=>k.shape),e),S=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),S}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new fx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function Nue(r,e,t){let o=w.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function a0(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):Zc(p,s,t)}var TV={kernelName:ta,backendName:\"webgpu\",kernelFunc:a0};function Tue(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return\"resData = f32(x[xIndex]);\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = vec4(x[xIndex / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return\"return f32(W[row * uniforms.wShape[3] + col]);\";case 4:return\"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1];\n let xCh = ${x} % inChannels;\n var resData = ${Ae(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,C=r?e&&o?`\n ${b}`:`\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${Ae(i)}(0.0);`:o&&t?`\n ${b}`:`\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${Ae(i)}(0.0);`,S=`${l(p)}`,k=Ae(u),_=r?Ae(i):Ae(p),$=r?Ae(p):Ae(i);return`\n ${dr(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${_} {\n ${r?C:S}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${$} {\n ${r?S:C}\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueIn : ${k}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${Zr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var hx=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push(\"bias\"),this.variableComponents.push(4)),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?_p(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Ep(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${Tue(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n ${e}\n `}};var gx=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${G(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * row - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * col - uniforms.pads[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var xx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${G(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0];\n let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.strides[1] -\n uniforms.pads[1];\n let xCol = offsetX + uniforms.dilations[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function yx(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function _ue({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=yx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=yx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=$p({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function Eue({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b===\"channelsLast\",S=p*u*c,k=h*f,_=C?[t.batchSize,k,S]:[t.batchSize,S,k],$=new xx(_,C),R=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],D=o.runWebGPUProgram($,[r],r.dtype,R),P=[];P.push(D);let O=pe({inputs:{x:e},backend:o,attrs:{shape:[1,S,-1]}});if(P.push(O),s!=null){let U=yx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=yx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=$p({a:C?D:O,b:C?O:D,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:t.outShape}});P.push(B);for(let U of P)o.disposeData(U.dataId);return z}function bx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return _ue({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>-1?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(A().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return Eue({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new gx(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,$=c?t.outChannels:t.outHeight*t.outWidth,R=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[$]},{type:\"int32\",data:[R]});let D=o.adapterInfo.isIntel();g=new hx(t,_,$,R,p,i,u,D)}let C=[],S=[r,e];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),S.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function $ue(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return bx({x:n,filter:s,convInfo:m,backend:o})}var _V={kernelName:tn,backendName:\"webgpu\",kernelFunc:$ue};var Cx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=`\n ${G()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n\n let dyCorner = vec2(r, c) - uniforms.pads;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = f32(dyCorner.x + wR) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) ||\n fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = f32(dyCorner.y + wC) / f32(uniforms.strides.y);\n let dyC2 = f32(dyCorner.y + 1 + wC) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n var bDyCVal = true;\n var bDyCVal2 = true;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= f32(uniforms.outBackprop[2]) ||\n fract(dyC2) > 0.0) {\n bDyCVal2 = false;\n }\n\n let idyC = i32(dyC);\n let idyC2 = i32(dyC2);\n if (bDyCVal && bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n xValue = getDy(batch, idyR, idyC2, d2);\n dotProd[1] = dotProd[1] + vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n }\n } else if (bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC2, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[1] = dotProd[1] + tmpval;\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n }\n }\n }\n `;return this.isVec4?`\n ${n}\n `:`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n let xValue = ${this.isChannelsLast?\"getDy(batch, idyR, idyC, d2)\":\"getDy(batch, d2, idyR, idyC)\"};\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},wx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=\"pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let d2 = coords[3];\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b = b + 1) {\n for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n if (${this.isChannelsLast}) {\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd = dotProd + xValue * dyValue;\n } else {\n let dyValue = getDy(b, d2, yR, yC);\n let xValue = getX(b, d1, xR, xC);\n dotProd = dotProd + xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Sx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32,\n outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wF = coords.x;\n let wR = coords.y;\n let wC = coords.z;\n let d1 = coords.w;\n let d2 = coords.u;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yF = 0; yF < uniforms.outDepth; yF++) {\n let xF = wF + yF * uniforms.strides[0] - uniforms.pads[0];\n if (xF < 0 || xF >= uniforms.inDepth) {\n continue;\n }\n\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[1] - uniforms.pads[1];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[2] - uniforms.pads[2];\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yF, yR, yC, d2);\n let xValue = getX(b, xF, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Ix=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let d1 = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyFCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let dyF = f32(dyFCorner + wF) / f32(uniforms.strides[0]);\n if (dyF < 0.0 || dyF >= f32(uniforms.outDepth) || fract(dyF) > 0.0) {\n continue;\n }\n let idyF = i32(dyF);\n\n let wFPerm = uniforms.filterDims[0] - 1 - wF;\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n let wRPerm = uniforms.filterDims[1] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let wCPerm = uniforms.filterDims[2] - 1 - wC;\n\n for (var d2 = 0; d2 < uniforms.outChannels; d2++) {\n let xValue = getDy(batch, idyF, idyR, idyC, d2);\n let wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Rue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new wx(m),f=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]},{type:\"int32\",data:[m.inHeight]},{type:\"int32\",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var EV={kernelName:Fi,backendName:\"webgpu\",kernelFunc:Rue};function Due(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${Ae(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${e(r)}\n }\n return ${Ae(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${Ae(r)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var vx=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?_p(this.elementsPerThread,this.workgroupSize):Ep(this.elementsPerThread,this.workgroupSize);return`\n ${Due(this.isVec4?4:1)}\n ${e}\n `}};function Aue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.dataFormat!==\"channelsLast\")f=new Cx(m);else{f=new vx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var $V={kernelName:rn,backendName:\"webgpu\",kernelFunc:Aue};var kx=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3dnaive\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords.x;\n let d2 = coords.u;\n\n let xFRCCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xFCorner = xFRCCorner.x;\n let xRCorner = xFRCCorner.y;\n let xCCorner = xFRCCorner.z;\n\n let inputDepthNearestVec4 = (uniforms.xShape.u / 4) * 4;\n let inputDepthVec4Remainder = uniforms.xShape.u % 4;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let xF = xFCorner + wF * uniforms.dilations[0];\n if (xF < 0 || xF >= uniforms.xShape.y) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let xR = xRCorner + wR * uniforms.dilations[1];\n if (xR < 0 || xR >= uniforms.xShape.z) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let xC = xCCorner + wC * uniforms.dilations[2];\n if (xC < 0 || xC >= uniforms.xShape.w) {\n continue;\n }\n\n for (var d1 = 0; d1 < inputDepthNearestVec4; d1 += 4) {\n let xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n let wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (inputDepthVec4Remainder == 1) {\n dotProd += getX(batch, xF, xR, xC, inputDepthNearestVec4) *\n getW(wF, wR, wC, inputDepthNearestVec4, d2);\n } else if (inputDepthVec4Remainder == 2) {\n let xValues = vec2(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)\n );\n let wValues = vec2(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (inputDepthVec4Remainder == 3) {\n let xValues = vec3(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)\n );\n let wValues = vec3(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }`}};function Fue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new kx(u),d=dt(n.dtype,s.dtype);return t.runWebGPUProgram(m,[n,s],d,l)}var RV={kernelName:on,backendName:\"webgpu\",kernelFunc:Fue};function Pue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Sx(u),l=[{type:\"int32\",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.batchSize]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.inDepth]},{type:\"int32\",data:[u.inHeight]},{type:\"int32\",data:[u.inWidth]}];return t.runWebGPUProgram(c,[n,s],s.dtype,l)}var DV={kernelName:ja,backendName:\"webgpu\",kernelFunc:Pue};function Oue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new Ix(u),l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.outChannels]}];return t.runWebGPUProgram(c,[n,s],n.dtype,l)}var AV={kernelName:nn,backendName:\"webgpu\",kernelFunc:Oue};var Mue=ye({opType:Z.COS}),FV={kernelName:sn,backendName:\"webgpu\",kernelFunc:Mue};var Lue=ye({opType:Z.COSH}),PV={kernelName:an,backendName:\"webgpu\",kernelFunc:Lue};var Nx=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Bue=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Nx(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},OV={kernelName:cn,backendName:\"webgpu\",kernelFunc:Bue};var Dp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Dp||(Dp={}));var xm=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Dp.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${MV(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${LV(e,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${LV(e,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${MV(e,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function MV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function LV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Tx(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=xr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new xm(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new xm(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=xr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function zue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Tx(Dp.Prod,n,t,s,a,i)}var BV={kernelName:un,backendName:\"webgpu\",kernelFunc:zue};function Vue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Tx(Dp.Sum,n,t,s,a,i)}var zV={kernelName:pn,backendName:\"webgpu\",kernelFunc:Vue};function Wue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=vt({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new Qc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var VV={kernelName:ra,backendName:\"webgpu\",kernelFunc:Wue};var _x=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Uue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new _x(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var WV={kernelName:ln,backendName:\"webgpu\",kernelFunc:Uue};var Ex=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pads : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pads;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${e, inDims : vec2, virtualWidth : i32,\",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=H(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return`\n ${dr(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n ${G(\"index\")} {\n let width0 = uniforms.outShape[3] / ${this.outputComponent};\n let d1 = (index % width0) * ${this.outputComponent};\n var index1 = index / width0;\n let width1 = uniforms.virtualWidth / ${this.workPerThread};\n let c = (index1 % width1) * ${this.workPerThread};\n index1 = index1 / width1;\n let r = index1 % uniforms.outShape[1];\n let batch = index1 / uniforms.outShape[1];\n\n let xRCCorner = vec2(r, c) * vec2(${t}, ${o}) - uniforms.pads;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${e}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${e}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var el=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.strides - uniforms.pads;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilations[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilations[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function Gue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new Ex(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?(g=new Jc(d),f.push({type:\"int32\",data:[g.virtualWidth]})):(g=new el(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var UV={kernelName:mn,backendName:\"webgpu\",kernelFunc:Gue};var $x=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32,\n outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_filter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let dm = coords[3];\n let d2 = d1 * uniforms.channelMul + dm;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Rx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_input\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[3];\n let dyCorner = coords.yz - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n\n let idyR = i32(dyR);\n let wRPerm = uniforms.filterDims[0] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n\n let idyC = i32(dyC);\n let wCPerm = uniforms.filterDims[1] - 1 - wC;\n\n for (var dm = 0; dm < uniforms.channelMul; dm++) {\n let d2 = d1 * uniforms.channelMul + dm;\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Hue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new $x(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.inHeight]},{type:\"int32\",data:[l.inWidth]},{type:\"int32\",data:[l.batchSize]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],\"float32\",d)}var GV={kernelName:Pi,backendName:\"webgpu\",kernelFunc:Hue};function Kue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Rx(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],n.dtype,d)}var HV={kernelName:Oi,backendName:\"webgpu\",kernelFunc:Kue};var Dx=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"diag\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);\n setOutputAtIndex(index, value);\n }\n }\n `}};function que(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Dx(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var KV={kernelName:oa,backendName:\"webgpu\",kernelFunc:que};var Ax=class{constructor(e){this.variableNames=[\"x\",\"w\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"dilation2d\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let neg_infinity = -3.4e38;\n let coords = getOutputCoords();\n let batch = coords.x;\n let d1 = coords.w;\n let outTopLeftCorner = coords.yz * uniforms.strides - uniforms.pads;\n let hBeg = outTopLeftCorner.x;\n let wBeg = outTopLeftCorner.y;\n\n var curVal = neg_infinity;\n for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {\n let hIn = hBeg + h * uniforms.dilations[0];\n\n if (hIn >= 0 && hIn < uniforms.xShape[1]) {\n for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {\n let wIn = wBeg + w * uniforms.dilations[1];\n\n if (wIn >= 0 && wIn < uniforms.xShape[2]) {\n let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n setOutputAtIndex(index, curVal);\n }\n }\n `}};function jue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationHeight,u.dilationWidth]}],m=new Ax(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var qV={kernelName:dn,backendName:\"webgpu\",kernelFunc:jue};var Fx=class{constructor(e,t){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=X(e.outShape),this.dispatch=H(this.dispatchLayout,e.outShape,this.workgroupSize),t!==\"float32\"&&t!==\"int32\")throw new Error(`Dilation2DBackpropInput only supports float32 and int32\n types, does not support ${t} type.`);this.type=t,this.shaderKey=\"dilation2DBackpropInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var xRMax = 0;\n var xCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n xRMax = xR;\n xCMax = xC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.xShape[3] *\n (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b));\n let value = getDy(b, r, c, d);\n ${Qr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}},Px=class{constructor(e,t,o){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(e.outShape),this.dispatch=H(this.dispatchLayout,e.outShape,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"dilation2DBackpropFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var wRMax = 0;\n var wCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n wRMax = wR;\n wCMax = wC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]);\n let value = getDy(b, r, c, d);\n ${Qr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}};function Xue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=s.dtype,m=new Px(c,s.shape,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:t,attrs:{shape:s.shape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var jV={kernelName:Li,backendName:\"webgpu\",kernelFunc:Xue};function Yue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=n.dtype,m=new Fx(c,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:t,attrs:{shape:c.inShape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var XV={kernelName:Mi,backendName:\"webgpu\",kernelFunc:Yue};var Ox=class{constructor(e,t,o){this.variableNames=[\"Image\"],this.uniforms=\"alpha: f32,\",this.workgroupSize=[64,1,1],this.pixelsOpType=wi.DRAW,this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=t,this.textureFormat=o,this.shaderKey=`draw_${t}_${o}`}getUserCode(){let e,t=this.type===\"float32\"?\"value\":\"value / 255.0\";return e=`\n if (uniforms.numChannels == 1) {\n rgba[0] = ${t};\n rgba[1] = ${t};\n rgba[2] = ${t};\n } else {\n rgba[d] = ${t};\n }`,`\n @group(0) @binding(0) var outImage : texture_storage_2d<${this.textureFormat}, write>;\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var rgba = vec4(0.0, 0.0, 0.0, uniforms.alpha);\n for (var d = 0; d < uniforms.numChannels; d = d + 1) {\n let value = f32(inBuf[index * uniforms.numChannels + d]);\n ${e}\n }\n rgba.x = rgba.x * rgba.w;\n rgba.y = rgba.y * rgba.w;\n rgba.z = rgba.z * rgba.w;\n let coords = getCoordsFromIndex(index);\n textureStore(outImage, vec2(coords.yx), rgba);\n }\n }\n `}};function Que(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},c=(u==null?void 0:u.alpha)||1,l=t.device.features.has(\"bgra8unorm-storage\")?\"bgra8unorm\":\"rgba8unorm\",m=[i,p],d=new Ox(m,n.dtype,l);s.width=p,s.height=i;let f=\"webgpu\",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:t.device,format:l,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:\"premultiplied\"});let b=\"int32\",C=t.makeTensorInfo(m,b),S=t.tensorMap.get(C.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:\"uint32\",data:[x]},{type:\"float32\",data:[c]}];if(t.runWebGPUProgram(d,[n],b,k,C),g){let _=s.getContext(\"2d\");if(!_)throw new Error(\"Please make sure this canvas has only been used for 2d or webgpu context!\");_.drawImage(g,0,0)}return t.disposeData(C.dataId),n}var YV={kernelName:$u,backendName:\"webgpu\",kernelFunc:Que};var i0=et({opType:fe.MUL,cpuKernelImpl:Rz,supportsComplex:!0}),QV={kernelName:Xn,backendName:\"webgpu\",kernelFunc:i0};function u0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"sum\",t)}var ZV={kernelName:Ss,backendName:\"webgpu\",kernelFunc:u0};function Zue(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=u0({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var JV={kernelName:Bi,backendName:\"webgpu\",kernelFunc:Zue};var Jue=ye({opType:Z.ELU}),eW={kernelName:hn,backendName:\"webgpu\",kernelFunc:Jue};var epe=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=new Ii(fe.ELU_DER,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],o.dtype)},tW={kernelName:Xa,backendName:\"webgpu\",kernelFunc:epe};var tpe=et({opType:fe.EQUAL,dtype:\"bool\",cpuKernelImpl:gz}),rW={kernelName:xn,backendName:\"webgpu\",kernelFunc:tpe};var rpe=ye({opType:Z.ERF}),oW={kernelName:gn,backendName:\"webgpu\",kernelFunc:rpe};var ope=ye({opType:Z.EXP,cpuKernelImpl:xz,dtype:\"float32\"}),nW={kernelName:yn,backendName:\"webgpu\",kernelFunc:ope};function Mx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var sW={kernelName:na,backendName:\"webgpu\",kernelFunc:Mx};var npe=ye({opType:Z.EXPM1,cpuKernelImpl:yz}),aW={kernelName:bn,backendName:\"webgpu\",kernelFunc:npe};var ym=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function Lx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ym(\"real\",u),l=new ym(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=xo({inputs:{real:g,imag:x},backend:t});i.push(b);let C=pe({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(S=>t.disposeData(S.dataId)),C}function spe(r){let{inputs:e,backend:t}=r,{input:o}=e;return Lx(o,!1,t)}var iW={kernelName:zi,backendName:\"webgpu\",kernelFunc:spe};var Bx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var uW={kernelName:Cn,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bx(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var ape=ye({opType:Z.FLOOR,cpuKernelImpl:bz}),pW={kernelName:wn,backendName:\"webgpu\",kernelFunc:ape};var ipe=et({opType:fe.FLOOR_DIV,cpuKernelImpl:Cz,dtype:\"int32\"}),cW={kernelName:Sn,backendName:\"webgpu\",kernelFunc:ipe};var zx=class{constructor(e,t,o=!1){this.pixelsOpType=wi.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${G(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${e};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var lW={kernelName:Du,backendName:\"webgpu\",kernelFunc:upe},tl,p0=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function upe(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!=\"undefined\"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!=\"undefined\"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=A().getBool(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\")&&a,f=a||i;if(u||p||f){let b;if(d)b=t.device.importExternalTexture({source:n});else{if(f){let L=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(tl==null||L!==p0)&&(p0=L,tl=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:p0})),tl.canvas.width=c,tl.canvas.height=l,tl.drawImage(n,0,0,c,l),n=tl.canvas}let P=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,M=t.textureManager.acquireTexture(m[1],m[0],\"rgba8unorm\",P);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b=M}let C=y.sizeFromShape(m),S=y.computeStrides(m),k=new zx(m,s,d),_=[{type:\"uint32\",data:[C]},{type:\"uint32\",data:[s]},{type:\"uint32\",data:[...S]}],$=t.makeTensorInfo([l,c],\"int32\"),R=t.tensorMap.get($.dataId);R.resource=b;let D=t.runWebGPUProgram(k,[$],\"int32\",_);return t.disposeData($.dataId),D}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0)));\n }\n }\n `}};var mW={kernelName:In,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new Vx(o.shape,a.shape,i.shape,l,m),f=[{type:\"float32\",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function ppe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return bx({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var dW={kernelName:Io,backendName:\"webgpu\",kernelFunc:ppe};function cpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=w.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:\"int32\",data:[h.padInfo.top,h.padInfo.left]},{type:\"int32\",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(S=new Jc(h,x,m,b),C.push({type:\"int32\",data:[S.virtualWidth]})):(S=new el(h,x,m,b),C.push({type:\"int32\",data:[h.filterHeight]},{type:\"int32\",data:[h.filterWidth]},{type:\"int32\",data:[h.strideHeight,h.strideWidth]},{type:\"int32\",data:[h.dilationHeight,h.dilationWidth]})),m===\"leakyrelu\"&&(C.push({type:\"float32\",data:[d]}),S.uniforms+=\" alpha : f32,\"),t.runWebGPUProgram(S,g,\"float32\",C)}var fW={kernelName:vo,backendName:\"webgpu\",kernelFunc:cpe};var Wx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${ft(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${e};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function lpe(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let b=t.readSync(n.dataId),C=t.bufferSync(o),S=wz(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,S.values)}let f=new Wx(a,[u,c]),h=[{type:\"int32\",data:[a]},{type:\"int32\",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var hW={kernelName:vn,backendName:\"webgpu\",kernelFunc:lpe};var Ux=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=mpe(this.aShape);return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${e}));\n }\n }\n `}};function mpe(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;ot.disposeData(D.dataId)),t.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new Ux(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var gW={kernelName:aa,backendName:\"webgpu\",kernelFunc:c0};var dpe=et({opType:fe.GREATER,cpuKernelImpl:vz,dtype:\"bool\"}),xW={kernelName:kn,backendName:\"webgpu\",kernelFunc:dpe};var fpe=et({opType:fe.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:Iz}),yW={kernelName:Nn,backendName:\"webgpu\",kernelFunc:fpe};function hpe(r){let{inputs:e,backend:t}=r,{input:o}=e;return Lx(o,!0,t)}var bW={kernelName:Vi,backendName:\"webgpu\",kernelFunc:hpe};var gpe=ye({opType:Z.IS_FINITE,dtype:\"bool\"}),CW={kernelName:Tn,backendName:\"webgpu\",kernelFunc:gpe};var xpe=ye({opType:Z.IS_INF,dtype:\"bool\"}),wW={kernelName:_n,backendName:\"webgpu\",kernelFunc:xpe};var ype=ye({opType:Z.IS_NAN,dtype:\"bool\"}),SW={kernelName:En,backendName:\"webgpu\",kernelFunc:ype};function bpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Jr(n.shape,Z.LEAKYRELU,\"alpha : f32,\");return t.runWebGPUProgram(i,[n],\"float32\",a)}var IW={kernelName:$n,backendName:\"webgpu\",kernelFunc:bpe};var Cpe=et({opType:fe.LESS,dtype:\"bool\",cpuKernelImpl:Nz}),vW={kernelName:Rn,backendName:\"webgpu\",kernelFunc:Cpe};var wpe=et({opType:fe.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:kz}),kW={kernelName:Dn,backendName:\"webgpu\",kernelFunc:wpe};var Gx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"start : f32, step : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"linSpace\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);\n }\n }\n `}};function Spe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new Gx(s),p=[{type:\"float32\",data:[o]},{type:\"float32\",data:[a]}];return e.runWebGPUProgram(i,[],\"float32\",p)}var NW={kernelName:An,backendName:\"webgpu\",kernelFunc:Spe};var Ipe=ye({opType:Z.LOG,cpuKernelImpl:Tz}),TW={kernelName:Fn,backendName:\"webgpu\",kernelFunc:Ipe};var vpe=ye({opType:Z.LOG1P}),_W={kernelName:Pn,backendName:\"webgpu\",kernelFunc:vpe};var kpe=et({opType:fe.LOGICAL_AND,dtype:\"bool\"}),EW={kernelName:On,backendName:\"webgpu\",kernelFunc:kpe};var Npe=ye({opType:Z.LOGICAL_NOT}),$W={kernelName:Mn,backendName:\"webgpu\",kernelFunc:Npe};var Tpe=et({opType:fe.LOGICAL_OR}),RW={kernelName:Ln,backendName:\"webgpu\",kernelFunc:Tpe};var DW=`\n var powValue = 0.0;\n let basis = uniforms.bias + uniforms.alpha * sum;\n if (uniforms.beta == 0.5) {\n powValue = inverseSqrt(basis);\n } else if (uniforms.beta == 1.0) {\n powValue = 1.0 / basis;\n } else {\n powValue = exp(log(basis) * (-uniforms.beta));\n }\n`,Hx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let x = getX(b, r, c, d);\n var sum = 0.0;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let idx = d + i;\n if (idx >= 0 && idx < uniforms.xShape[3]) {\n let z = getX(b, r, c, idx);\n sum = sum + z * z;\n }\n }\n ${DW}\n\n setOutputAtIndex(index, x * powValue);\n }\n }\n `}},Kx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey=\"lrn_shared\"}getUserCode(){return`\n var lrnSub: array;\n const elementsPerWorkgroup = ${this.elementsPerWorkgroup};\n const maxAllowRadius = ${this.maxAllowRadius};\n\n ${G()} {\n let localDepth = i32(localId.x);\n let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;\n let xDepth = workgroupDepth + localDepth - maxAllowRadius;\n let b = i32(globalId.z) / uniforms.xShape[1];\n let r = i32(globalId.z) - b * uniforms.xShape[1];\n let c = i32(globalId.y);\n let d = workgroupDepth + localDepth;\n\n var x = 0.0;\n if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {\n x = getX(b, r, c, xDepth);\n }\n lrnSub[localDepth] = x;\n workgroupBarrier();\n\n if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {\n var sum = 0.0;\n let index = localDepth + maxAllowRadius;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let z = lrnSub[index + i];\n sum = sum + z * z;\n }\n ${DW}\n\n setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);\n }\n } `}};function _pe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Hx(n.shape):u=new Kx(n.shape,s);let c=[{type:\"int32\",data:[s]},{type:\"float32\",data:[a]},{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var AW={kernelName:Bn,backendName:\"webgpu\",kernelFunc:_pe};var qx=class{constructor(e){this.outputShape=[],this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.uniforms=\"depthRadius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn_grad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n\n let MIN_DEPTH_BEGIN = 0;\n let MAX_DEPTH_END = uniforms.outShape[3];\n var result = 0.0;\n for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) {\n let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius);\n let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1);\n\n var norm = 0.0;\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n } else {\n break;\n }\n }\n\n norm = uniforms.alpha * norm + uniforms.bias;\n\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n var dyi = -2.0 * uniforms.alpha * uniforms.beta\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * uniforms.beta);\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n } else {\n break;\n }\n }\n }\n\n setOutputAtIndex(index, result);\n }\n }\n `}};function Epe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new qx(n.shape),m=[{type:\"int32\",data:[i]},{type:\"float32\",data:[p]},{type:\"float32\",data:[u]},{type:\"float32\",data:[c]}];return t.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var FW={kernelName:Ya,backendName:\"webgpu\",kernelFunc:Epe};var $pe=et({opType:fe.MAX,cpuKernelImpl:Ez}),PW={kernelName:Vn,backendName:\"webgpu\",kernelFunc:$pe};function Rpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,c=w.computePool2DInfo(n.shape,s,a,1,i,p);return ax(n,c,\"max\",t)}var OW={kernelName:Wn,backendName:\"webgpu\",kernelFunc:Rpe};function Dpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,\"max\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var MW={kernelName:ia,backendName:\"webgpu\",kernelFunc:Dpe};var jx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] - 1;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR += uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC += uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wR * uniforms.filterDims[1] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Xx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] * uniforms.filterDims[2] - 1;\n\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wD * uniforms.filterDims[1] * uniforms.filterDims[2] + wR * uniforms.filterDims[2] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n }\n\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Ape(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Iu(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.inDepth,m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[a],\"int32\",f),g=new Xx(m);f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outDepth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],a.dtype,f);return t.disposeData(h.dataId),x}var LW={kernelName:Gi,backendName:\"webgpu\",kernelFunc:Ape};function Fpe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;fm([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new Ba(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[i],\"int32\",f),g=new jx(m);f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],i.dtype,f);return t.disposeData(h.dataId),x}var BW={kernelName:Ui,backendName:\"webgpu\",kernelFunc:Fpe};function Ppe(r){let{inputs:e,backend:t,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=e;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Ba(c,\"max\",!1),d=t.runWebGPUProgram(m,[p],p.dtype,l);m=new Ba(c,\"max\",!0,!0,i);let f=t.runWebGPUProgram(m,[p],\"int32\",l);return[d,f]}var zW={kernelName:ua,backendName:\"webgpu\",kernelFunc:Ppe};function Ope(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"min\",t)}var VW={kernelName:Gn,backendName:\"webgpu\",kernelFunc:Ope};var Mpe=et({opType:fe.MIN,cpuKernelImpl:$z}),WW={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Mpe};var Yx=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:\"\"}`).join(\",\"),n=e===1?\"start\":\"start[i]\",s=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=ft(e),p=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let start = ${i}(${t});\n let end = ${i}(${o});\n var outC = getCoordsFromIndex(index);\n for (var i = 0; i < ${e}; i = i + 1) {\n if (${a} < ${n}) {\n ${a} = ${n} * 2 - ${a} - ${this.offset};\n } else if(${a} >= ${s}) {\n ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};\n }\n }\n let coords = outC - start;\n setOutputAtIndex(index, getX(${p}));\n }\n }\n `}};var UW={kernelName:Kn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:\"int32\",data:[c[0],c[1]]})),p=new Yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Lpe=et({opType:fe.MOD}),GW={kernelName:qn,backendName:\"webgpu\",kernelFunc:Lpe};var Qx=class{constructor(e,t){this.variableNames=[\"probs\"],this.outputShape=[],this.uniforms=\"seed : f32, numOutcomes: i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"multinomial\"}getUserCode(){return`\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n fn random (seed : f32, resultUV : vec2) -> f32 {\n let HASHSCALE1 = 443.8975;\n let p = resultUV * seed;\n var p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 = p3 + dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n\n let resUV = vec2(f32(coords[1]) / f32(uniforms.outShape[1]),\n f32(coords[0]) / f32(uniforms.outShape[0]));\n let r = random(uniforms.seed, resUV);\n var cdf = 0.0;\n for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) {\n cdf = cdf + getProbs(batch, i);\n\n if (r < cdf) {\n setOutputAtIndexI32(index, i);\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutputAtIndexI32(index, uniforms.numOutcomes - 1);\n }\n }\n `}};var Zx=class{constructor(e){this.variableNames=[\"logits\"],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey=\"softmax\"}getUserCode(){return`\n var buf : array;\n var rowMaxShared : f32;\n var rowSumShared : f32;\n const blockSize = ${this.workgroupSize[0]};\n ${G(\"index\")} {\n let row = index / blockSize;\n let tid = i32(localId.x);\n let cols = uniforms.outShape[1];\n\n var threadMax = -3.402823e+38f;\n for (var col = tid; col < cols; col += blockSize) {\n let value = getLogits(row, col);\n threadMax = max(threadMax, value);\n }\n if (tid < cols) {\n buf[tid] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, blockSize);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (tid < currSize) {\n buf[tid] = max(buf[tid], buf[tid + reduceSize]);\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowMaxShared = buf[0];\n }\n workgroupBarrier();\n\n var threadSum = 0.0;\n for (var col = tid; col < cols; col += blockSize) {\n let subExp = exp(getLogits(row, col) - rowMaxShared);\n threadSum += subExp;\n }\n buf[tid] = threadSum;\n workgroupBarrier();\n\n for (var currSize = blockSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (tid < currSize) {\n buf[tid] = buf[tid] + buf[tid + currSize];\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowSumShared = buf[0];\n }\n workgroupBarrier();\n\n for (var col = tid; col < cols; col += blockSize) {\n let value = exp(getLogits(row, col) - rowMaxShared) / rowSumShared;\n setOutputAtCoords(row, col, value);\n }\n }\n `}};function l0(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=pe({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Zx(a.shape),p=t.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n.shape}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var HW={kernelName:Is,backendName:\"webgpu\",kernelFunc:l0};function Bpe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:l0({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qx(u,s),m=[{type:\"float32\",data:[a]},{type:\"int32\",data:[c]}],d=t.runWebGPUProgram(l,[p],\"int32\",m);return i||t.disposeData(p.dataId),d}var KW={kernelName:jn,backendName:\"webgpu\",kernelFunc:Bpe};function zpe(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=Dz(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Jr(o.shape,Z.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var qW={kernelName:pa,backendName:\"webgpu\",kernelFunc:zpe};function Vpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],\"int32\",new Int32Array(l))}var jW={kernelName:Qn,backendName:\"webgpu\",kernelFunc:Vpe};function Wpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var XW={kernelName:Zn,backendName:\"webgpu\",kernelFunc:Wpe};var Jx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function Upe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Jx(u,a),l=pe({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var YW={kernelName:Jn,backendName:\"webgpu\",kernelFunc:Upe};function bm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=vi({inputs:{input:o},backend:t}),s=bm({inputs:{x:n},backend:t}),a=Rp({inputs:{input:o},backend:t}),i=bm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var QW={kernelName:Sa,backendName:\"webgpu\",kernelFunc:bm};function ZW(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=vi({inputs:{input:o},backend:t}),s=ZW({inputs:{x:n},backend:t}),a=Rp({inputs:{input:o},backend:t}),i=bm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var JW={kernelName:ca,backendName:\"webgpu\",kernelFunc:ZW};function Gpe(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Mx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=Mx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=a0({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var eU={kernelName:la,backendName:\"webgpu\",kernelFunc:Gpe};function m0(r,e=!1){let t=r.length,o=ft(t),n=r.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),s=r.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${t>1?`[${m}]`:\"\"}`).join(\",\"),a=t>1?`${o}(${n})`:`${n}`,i=t>1?`${o}(${s})`:`${s}`,p=t>1?\"any(paddedCoords < start)\":\"paddedCoords < start\",u=t>1?\"any(paddedCoords >= end)\":\"paddedCoords >= end\",c=t>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,t):\"coords\";return`\n let start = ${a};\n let end = ${i};\n if (${p} || ${u}) {\n setOutputAtIndex(index, ${e?0:\"uniforms.constantValue\"});\n } else {\n let coords = paddedCoords - start;\n setOutputAtIndex(index, getX(${c}));\n }\n `}var ey=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let paddedCoords = getCoordsFromIndex(index);\n ${m0(this.xShape)}\n }\n }\n `}};var Hpe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return vt({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new ey(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},tU={kernelName:es,backendName:\"webgpu\",kernelFunc:Hpe};var Kpe=et({opType:fe.POW}),rU={kernelName:ts,backendName:\"webgpu\",kernelFunc:Kpe};function qpe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Ii(fe.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var oU={kernelName:rs,backendName:\"webgpu\",kernelFunc:qpe};function jpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"prod\",t)}var nU={kernelName:os,backendName:\"webgpu\",kernelFunc:jpe};var Xpe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=Pz(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},sU={kernelName:ma,backendName:\"webgpu\",kernelFunc:Xpe};var Ype=et({opType:fe.DIV}),aU={kernelName:fn,backendName:\"webgpu\",kernelFunc:Ype};var Qpe=ye({opType:Z.RECIPROCAL}),iU={kernelName:ns,backendName:\"webgpu\",kernelFunc:Qpe};var Zpe=ye({opType:Z.RELU}),uU={kernelName:ss,backendName:\"webgpu\",kernelFunc:Zpe};var Jpe=ye({opType:Z.RELU6}),pU={kernelName:us,backendName:\"webgpu\",kernelFunc:Jpe};var ty=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function ece(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new ty(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var cU={kernelName:is,backendName:\"webgpu\",kernelFunc:ece};var ry=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32,\n invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(startRLerp - f32(uniforms.winHeight / 2));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(startCLerp - f32(uniforms.winWidth / 2));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let dxR = f32(dyR) * uniforms.heightScale;\n let topDxRIndex = i32(floor(dxR));\n let bottomDxRIndex = i32(min(ceil(dxR), f32(uniforms.outShape[1] - 1)));\n let dxRLerp = dxR - f32(topDxRIndex);\n let inverseDxRLerp = 1.0 - dxRLerp;\n\n let dxC = f32(dyC) * uniforms.widthScale;\n let leftDxCIndex = i32(floor(dxC));\n let rightDxCIndex = i32(min(ceil(dxC), f32(uniforms.outShape[2] - 1)));\n let dxCLerp = dxC - f32(leftDxCIndex);\n let inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function tce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ry(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[d]},{type:\"float32\",data:[f]},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var lU={kernelName:Ja,backendName:\"webgpu\",kernelFunc:tce};var oy=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":e=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${e};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function rce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new oy(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var mU={kernelName:as,backendName:\"webgpu\",kernelFunc:rce};var ny=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32,\n winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(floor(startRLerp - f32(uniforms.winHeight / 2)));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(floor(startCLerp - f32(uniforms.winWidth / 2)));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let sourceFracRow = f32(uniforms.effectiveXSize[0]) *\n (f32(dyR) / f32(uniforms.effectiveYSize[0]));\n\n let sourceFracCol = f32(uniforms.effectiveXSize[1]) *\n (f32(dyC) / f32(uniforms.effectiveYSize[1]));\n\n let sourceNearestRow =\n i32(min(f32(uniforms.outShape[1] - 1),\n ${this.alignCorners?\"floor(sourceFracRow + 0.5)\":\"floor(sourceFracRow)\"}));\n\n let sourceNearestCol =\n i32(min(f32(uniforms.outShape[2] - 1),\n ${this.alignCorners?\"floor(sourceFracCol + 0.5)\":\"floor(sourceFracCol)\"}));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function oce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ny(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var dU={kernelName:Za,backendName:\"webgpu\",kernelFunc:oce};var sy=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function nce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return At({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new sy(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var fU={kernelName:ps,backendName:\"webgpu\",kernelFunc:nce};var ay=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var hU={kernelName:Ds,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new ay(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var sce=ye({opType:Z.ROUND}),gU={kernelName:cs,backendName:\"webgpu\",kernelFunc:sce};var ace=ye({opType:Z.RSQRT,cpuKernelImpl:Oz}),xU={kernelName:ls,backendName:\"webgpu\",kernelFunc:ace};var za=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}_${s.length}`;let u=ft(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(\", \")})`;return`\n ${s}\n ${G(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${t}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${Su(this.type)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${this.sumDupeIndices?Qr(\"&result[flatIndex]\",\"updateValue\",this.type):\"atomicStore(&result[flatIndex], bitcast(updateValue));\"}\n }\n }`}};function ice(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=vt({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new za(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=t.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(S.dataId),k}var yU={kernelName:ms,backendName:\"webgpu\",kernelFunc:ice};var iy=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function uce(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new iy([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var bU={kernelName:fs,backendName:\"webgpu\",kernelFunc:uce};var uy=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${t}));\n } else {\n setOutputAtIndex(index, getB(${t}));\n }\n }\n }\n `}};function pce(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new uy(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var CU={kernelName:fa,backendName:\"webgpu\",kernelFunc:pce};var cce=ye({opType:Z.SELU}),wU={kernelName:hs,backendName:\"webgpu\",kernelFunc:cce};var lce=ye({opType:Z.SIGMOID}),SU={kernelName:bs,backendName:\"webgpu\",kernelFunc:lce};var mce=ye({opType:Z.SIGN}),IU={kernelName:ys,backendName:\"webgpu\",kernelFunc:mce};var dce=ye({opType:Z.SIN}),vU={kernelName:gs,backendName:\"webgpu\",kernelFunc:dce};var fce=ye({opType:Z.SINH}),kU={kernelName:xs,backendName:\"webgpu\",kernelFunc:fce};var hce=ye({opType:Z.SOFTPLUS}),NU={kernelName:Cs,backendName:\"webgpu\",kernelFunc:hce};var py=class{constructor(e,t,o,n,s,a){this.variableNames=[\"x\"],this.outputShape=[],this.uniforms=\"\",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let e=ft(this.outputShape.length),t=e0(this.newDim);return`\n ${cm(this.paddedXShape,\"PaddedX\")}\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t}), uniforms.reshapedPaddedXShape);\n let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex);\n ${m0(this.xShape,!0)}\n }\n }\n `}};var gce=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[C]+b[1]),c=w.getReshaped(u,s,i,!1),l=w.getPermuted(c.length,s.length,!1),m=w.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new py(n.shape,u,p,c,l,d.length),h=[{type:\"int32\",data:c},{type:\"int32\",data:d}];p.map(b=>h.push({type:\"int32\",data:[b[0],b[1]]}));let g=t.runWebGPUProgram(f,[n],n.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeData(g.dataId),x},TU={kernelName:ga,backendName:\"webgpu\",kernelFunc:gce};var cy=class{constructor(e,t,o){this.variableNames=[\"input\",\"indices\",\"segmentIds\"],this.outputShape=[],this.uniforms=\"segmentSize : i32, sparseSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.type=o,this.dispatchLayout=X([t]),this.dispatch=H(this.dispatchLayout,[t],this.workgroupSize),this.shaderKey=\"sparseSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.sparseSize) {\n let indexInSegmentIds = index / uniforms.segmentSize;\n let indexInSegment = index % uniforms.segmentSize;\n let indexInInput = indices[indexInSegmentIds];\n let segmentId = segmentIds[indexInSegmentIds];\n\n let value = input[indexInInput * uniforms.segmentSize + indexInSegment];\n let outIndex = segmentId * uniforms.segmentSize + indexInSegment;\n ${Qr(\"&result[outIndex]\",\"value\",this.type)}\n }\n }\n `}},ly=class{constructor(e,t){this.variableNames=[\"segmentIds\"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[e],this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.shaderKey=\"sparseSegmentIdCountProgram\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.segmentIdsShape) {\n let segmentId = segmentIds[index];\n ${Qr(\"&result[segmentId]\",\"1\",\"int32\")}\n }\n }\n `}},my=class{constructor(e,t){this.variableNames=[\"segmentSum\",\"sameSegmentIdCount\"],this.outputShape=[],this.uniforms=\"segmentSize : i32\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.type=t,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.shaderKey=\"sparseSegmentMean\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let segmentId = index / uniforms.segmentSize;\n let count = sameSegmentIdCount[segmentId];\n if (count != 0) {\n ${this.type===\"float32\"?\"setOutputAtIndex(index, segmentSum[index] / f32(count));\":\"setOutputAtIndexI32(index, segmentSum[index] / count);\"}\n }\n }\n }\n `}};function dy(r,e,t,o=!1,n){let a=y.sizeFromShape(r.shape)/r.shape[0],i=r.dtype,p=y.sizeFromShape(e.shape),u=n.readSync(t.dataId),l=p>0?u[p-1]+1:0,m,d=r.shape.slice();d[0]=l;let f=p*a,h=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new cy(d,f,i);let g=[{type:\"int32\",data:[a]},{type:\"int32\",data:[f]}],x=n.runWebGPUProgram(m,[r,e,t],i,g,h);if(o)return x;let b=vt({backend:n,attrs:{shape:[l],value:0,dtype:\"int32\"}});m=new ly(l,t.shape);let C=n.runWebGPUProgram(m,[t],\"int32\",null,b),S=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new my(d,i),g=[{type:\"int32\",data:[a]}];let k=n.runWebGPUProgram(m,[x,C],i,g,S);return n.disposeData(x.dataId),n.disposeData(C.dataId),k}function xce(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;return dy(o,n,s,!1,t)}var _U={kernelName:ya,backendName:\"webgpu\",kernelFunc:xce};function yce(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;return dy(o,n,s,!0,t)}var EU={kernelName:ba,backendName:\"webgpu\",kernelFunc:yce};var fy=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=Uz(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new fy(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var $U={kernelName:po,backendName:\"webgpu\",kernelFunc:Cm};function Cce(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let R=t.bufferSync(n),D=t.bufferSync(s),P=y.decodeString(t.readSync(a.dataId)[0]),O=Mz(R,D,i,m,c,u,p,l,P,d);return t.makeTensorInfo(i,O.dtype,O.values)}let f=[m/c,c],h=pe({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):At({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=pe({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),S=Cm({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new za([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(R,[g,h],x,_,S)}}let $=pe({inputs:{x:S},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(S.dataId),$}var RU={kernelName:vs,backendName:\"webgpu\",kernelFunc:Cce};function wce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Hs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var DU={kernelName:xa,backendName:\"webgpu\",kernelFunc:wce};var Sce=ye({opType:Z.SQRT}),AU={kernelName:ws,backendName:\"webgpu\",kernelFunc:Sce};var FU={kernelName:qi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Jr(t.shape,Z.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var Ice=et({opType:fe.SQUARED_DIFFERENCE}),PU={kernelName:ks,backendName:\"webgpu\",kernelFunc:Ice};function vce({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new Jr(o.shape,Z.STEP,\"stepAlpha : f32,\"),s=[{type:\"float32\",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var OU={kernelName:wo,backendName:\"webgpu\",kernelFunc:vce};var hy=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=ft(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${t}));\n }\n }\n `}};function kce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=pt.computeOutShape(b,C,S),$=Hs({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=pe({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeData($.dataId)}else if(t.shouldExecuteOnCPU([n])){let $=t.readSync(n.dataId),R=me(n.shape,n.dtype,$),D=zz(d,R,S,b);k=t.makeTensorInfo(f,n.dtype,D.values)}else{let $=new hy(d),R=[{type:\"int32\",data:b},{type:\"int32\",data:S}],D=t.runWebGPUProgram($,[n],n.dtype,R);k=pe({inputs:{x:D},backend:t,attrs:{shape:f}}),t.disposeData(D.dataId)}return k}var MU={kernelName:Ns,backendName:\"webgpu\",kernelFunc:kce};function Nce(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=Vz(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var LU={kernelName:Ca,backendName:\"webgpu\",kernelFunc:Nce};var Tce=et({opType:fe.SUB,cpuKernelImpl:Wz,supportsComplex:!0}),BU={kernelName:Ts,backendName:\"webgpu\",kernelFunc:Tce};var _ce=ye({opType:Z.TAN}),zU={kernelName:_s,backendName:\"webgpu\",kernelFunc:_ce};var Ece=ye({opType:Z.TANH}),VU={kernelName:Es,backendName:\"webgpu\",kernelFunc:Ece};function $ce(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:t,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:t,attrs:{shape:m}});d.push(g);let x=Cm({inputs:{x:g},backend:t,attrs:{reps:Array(m.length).fill(1)}}),b=new za([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[C]}],k=t.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:t,attrs:{shape:n.shape}});return d.forEach($=>t.disposeData($.dataId)),_}var WU={kernelName:ds,backendName:\"webgpu\",kernelFunc:$ce};var gy=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},xy=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function rl(r,e){e!==null&&r.disposeData(e.dataId)}function UU(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,$)=>{let R=h(),D=new gy($),O=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(D,R,\"int32\",O),rl(t,M)};for(let k=1;k=1;$/=2)g(_,$,[c,d])}for(let k=d;k>m;k/=2){let _=h(),$=new xy([c,k/2]),D=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],P=f;f=t.runWebGPUProgram($,_,\"int32\",D),rl(t,P);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=Hs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),rl(t,x);let b=c0({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});rl(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:t}),rl(t,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:t}),rl(t,S),[b,f]}var GU={kernelName:$s,backendName:\"webgpu\",kernelFunc:Rce};var yy=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function Dce(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new yy(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let S=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",S)}var HU={kernelName:Rs,backendName:\"webgpu\",kernelFunc:Dce};function Ace(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var KU={kernelName:wa,backendName:\"webgpu\",kernelFunc:Ace};var by=class{constructor(e,t,o){if(this.outputShape=[],this.variableNames=[\"x\",\"segmentIds\"],this.uniforms=\"numSegments : i32, xSize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`UnsortedSegmentSum only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"unsortedSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.xSize) {\n let coords = getXCoordsFromIndex(index);\n let b = coords[0];\n let inCol = coords[1];\n\n let segmentId = i32(getSegmentIds(inCol));\n if (segmentId >= 0) {\n let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments;\n let value = getX(b, inCol);\n\n ${Qr(\"&result[flatIndex]\",\"value\",this.type)}\n }\n }\n }\n `}};function Fce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=xr({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=vt({backend:t,attrs:{shape:g,value:0,dtype:h}}),b=new by(f.shape,g,h),C=[{type:\"int32\",data:[a]},{type:\"int32\",data:[y.sizeFromShape(f.shape)]}],S=t.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:t,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let $=w.getUndoAxesPermutation(c);_=xr({inputs:{x:_},backend:t,attrs:{perm:$}})}return p.forEach($=>t.disposeData($.dataId)),_}var qU={kernelName:Qi,backendName:\"webgpu\",kernelFunc:Fce};var Pce=[pz,Kz,qz,jz,Xz,Yz,Zz,Jz,eV,tV,rV,oV,nV,sV,aV,pV,cV,lV,mV,dV,hV,gV,xV,wV,SV,IV,lz,kV,TV,_V,EV,$V,RV,DV,AV,FV,PV,OV,BV,zV,VV,WV,GV,HV,UV,KV,qV,jV,XV,YV,JV,eW,tW,rW,oW,nW,sW,aW,iW,iz,uW,lW,pW,cW,mW,dW,fW,hW,gW,xW,yW,cz,bW,NV,CW,wW,SW,IW,vW,kW,NW,_W,TW,EW,$W,RW,AW,FW,iV,PW,OW,BW,MW,LW,zW,uV,VW,WW,UW,GW,KW,QV,qW,jW,XW,yV,YW,JW,eU,tU,rU,oU,nU,sU,bV,aU,iU,uU,pU,uz,cU,lU,mU,dU,fU,hU,gU,xU,yU,bU,CU,wU,SU,IU,vU,kU,fV,OU,MU,LU,HW,NU,TU,_U,EU,RU,DU,AU,FU,PU,BU,ZV,zU,VU,WU,$U,GU,HU,Qz,KU,qU,QW];for(let r of Pce)ti(r);var jU=\"4.21.0\",Oce=\"4.21.0\",Mce=\"4.21.0\",Lce=\"4.21.0\",Bce=\"4.21.0\",zce=\"4.21.0\",Vce={tfjs:jU,\"tfjs-core\":jU,\"tfjs-converter\":Oce,\"tfjs-backend-cpu\":Mce,\"tfjs-backend-webgl\":Lce,\"tfjs-backend-wasm\":Bce,\"tfjs-backend-webgpu\":zce};var EQt=void 0;export{Xs as Abs,Vo as Acos,Wo as Acosh,Ju as AdadeltaOptimizer,ep as AdagradOptimizer,tp as AdamOptimizer,rp as AdamaxOptimizer,uo as Add,Uo as AddN,Go as All,Ho as Any,Ys as ArgMax,Qs as ArgMin,Ko as Asin,qo as Asinh,jo as Atan,Yo as Atan2,Xo as Atanh,Qo as AvgPool,Zs as AvgPool3D,Ri as AvgPool3DGrad,$i as AvgPoolGrad,pm as BackendWasm,Zo as BatchMatMul,Js as BatchToSpaceND,Jo as Bincount,qa as BitwiseAnd,ea as BroadcastArgs,qce as BroadcastTo,yo as Cast,en as Ceil,bo as ClipByValue,Di as Complex,Ai as ComplexAbs,ta as Concat,tn as Conv2D,Fi as Conv2DBackpropFilter,rn as Conv2DBackpropInput,on as Conv3D,ja as Conv3DBackpropFilterV2,nn as Conv3DBackpropInputV2,sn as Cos,an as Cosh,cn as CropAndResize,un as Cumprod,pn as Cumsum,Bo as DataStorage,ra as DenseBincount,ln as DepthToSpace,mn as DepthwiseConv2dNative,Pi as DepthwiseConv2dNativeBackpropFilter,Oi as DepthwiseConv2dNativeBackpropInput,oa as Diag,dn as Dilation2D,Li as Dilation2DBackpropFilter,Mi as Dilation2DBackpropInput,$u as Draw,nw as ENV,Bi as Einsum,hn as Elu,Xa as EluGrad,dl as Environment,xn as Equal,gn as Erf,yn as Exp,na as ExpandDims,bn as Expm1,zi as FFT,sa as Fill,Cn as FlipLeftRight,wn as Floor,Sn as FloorDiv,Du as FromPixels,In as FusedBatchNorm,Io as FusedConv2D,vo as FusedDepthwiseConv2D,bp as GPGPUContext,vn as GatherNd,aa as GatherV2,Bl as GraphModel,kn as Greater,Nn as GreaterEqual,Vi as IFFT,Co as Identity,Wi as Imag,Tn as IsFinite,_n as IsInf,En as IsNan,ao as KernelBackend,Bn as LRN,Ya as LRNGrad,$n as LeakyRelu,Rn as Less,Dn as LessEqual,An as LinSpace,Fn as Log,Pn as Log1p,jce as LogSoftmax,On as LogicalAnd,Mn as LogicalNot,Ln as LogicalOr,R0 as LogicalXor,Xce as LowerBound,xc as MathBackendCPU,Lc as MathBackendWebGL,Yce as MatrixBandPart,zn as Max,Wn as MaxPool,ia as MaxPool3D,Gi as MaxPool3DGrad,Ui as MaxPoolGrad,ua as MaxPoolWithArgmax,Vn as Maximum,Un as Mean,Gn as Min,Hn as Minimum,Kn as MirrorPad,qn as Mod,op as MomentumOptimizer,jn as Multinomial,Xn as Multiply,pa as Neg,Qn as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,Zn as NonMaxSuppressionV5,Yn as NotEqual,Nw as OP_SCOPE_SUFFIX,Jn as OneHot,ca as OnesLike,kr as Optimizer,Fl as OptimizerConstructors,la as Pack,es as PadV2,Qce as Pool,ts as Pow,rs as Prelu,os as Prod,np as RMSPropOptimizer,Hp as RaggedGather,Kp as RaggedRange,qp as RaggedTensorToTensor,ma as Range,gw as Rank,Hi as Real,fn as RealDiv,ns as Reciprocal,$t as Reduction,ss as Relu,us as Relu6,da as Reshape,is as ResizeBilinear,Ja as ResizeBilinearGrad,as as ResizeNearestNeighbor,Za as ResizeNearestNeighborGrad,ps as Reverse,Ds as RotateWithOffset,cs as Round,ls as Rsqrt,mi as SGDOptimizer,ms as ScatterNd,fs as SearchSorted,fa as Select,hs as Selu,bs as Sigmoid,ys as Sign,gs as Sin,xs as Sinh,ha as Slice,Is as Softmax,Cs as Softplus,ga as SpaceToBatchND,Ki as SparseFillEmptyRows,ei as SparseReshape,ya as SparseSegmentMean,ba as SparseSegmentSum,vs as SparseToDense,xa as SplitV,ws as Sqrt,qi as Square,ks as SquaredDifference,Ru as StaticRegexReplace,wo as Step,Ns as StridedSlice,Ca as StringNGrams,ji as StringSplit,Xi as StringToHashBucketFast,Ts as Sub,Ss as Sum,_s as Tan,Es as Tanh,mt as Tensor,tt as TensorBuffer,ds as TensorScatterUpdate,po as Tile,$s as TopK,Rs as Transform,co as Transpose,Yi as Unique,wa as Unpack,Qi as UnsortedSegmentSum,Zce as UpperBound,ri as Variable,jc as WebGPUBackend,Sa as ZerosLike,So as _FusedMatMul,Qt as abs,Rk as acos,Dk as acosh,Ce as add,Ak as addN,Fk as all,Pk as any,Ok as argMax,Mk as argMin,Lk as asin,Bk as asinh,zk as atan,Vk as atan2,Wk as atanh,dd as avgPool,Hk as avgPool3d,ak as backend,w as backend_util,Kk as basicLSTMCell,nu as batchNorm,jk as batchNorm2d,Xk as batchNorm3d,Yk as batchNorm4d,fd as batchToSpaceND,hd as bincount,Qk as bitwiseAnd,L6 as booleanMaskAsync,Zk as broadcastArgs,su as broadcastTo,Sr as broadcast_util,cT as browser,me as buffer,Ue as cast,Jk as ceil,e2 as clipByValue,Ur as clone,Er as complex,yt as concat,t2 as concat1d,r2 as concat2d,o2 as concat3d,n2 as concat4d,s2 as conv1d,au as conv2d,a2 as conv2dTranspose,i2 as conv3d,p2 as conv3dTranspose,ale as copyRegisteredKernels,c2 as cos,l2 as cosh,$l as cosineWindow,m2 as cumprod,d2 as cumsum,Ir as customGrad,f2 as denseBincount,Tw as deprecationWarn,h2 as depthToSpace,sc as depthwiseConv2d,V5 as deregisterOp,eu as device_util,g2 as diag,x2 as dilation2d,xme as disableDeprecationWarnings,Ot as dispose,yme as disposeVariables,je as div,b2 as divNoNan,C2 as dot,Y6 as dropout,iu as einsum,bd as elu,gme as enableDebugMode,hme as enableProdMode,Zw as enclosingPowerOfTwo,ur as engine,w2 as ensureShape,A as env,yd as equal,S2 as erf,k2 as euclideanNorm,_o as exp,Ms as expandDims,N2 as expm1,Cd as eye,uc as fft,$a as fill,kme as findBackend,Nme as findBackendFactory,wd as floor,md as floorDiv,GD as forceHalfFloat,Jw as fused,Sd as gather,j6 as gatherND,af as gather_util,sk as getBackend,iw as getGradient,Xp as getKernel,Ym as getKernelsForBackend,aae as getThreadsCount,mv as gpgpu_util,VK as grad,WK as grads,Wu as greater,Id as greaterEqual,ju as ifft,pu as imag,eX as image,Z6 as inTopKAsync,di as io,Hd as irfft,T2 as isFinite,_2 as isInf,E2 as isNaN,$r as keep,Vt as kernel_impls,vd as leakyRelu,Tl as less,ac as lessEqual,tX as linalg,$2 as linspace,M8 as loadGraphModel,L8 as loadGraphModelSync,R2 as localResponseNormalization,pi as log,kd as log1p,D2 as logSigmoid,A2 as logSoftmax,_d as logSumExp,Uu as logicalAnd,Ed as logicalNot,$d as logicalOr,F2 as logicalXor,rX as losses,P2 as lowerBound,Ze as matMul,aT as math,Ra as max,Dd as maxPool,O2 as maxPool3d,M2 as maxPoolWithArgmax,Ad as maximum,Gu as mean,bme as memory,L2 as meshgrid,Nl as min,Hu as minimum,B2 as mirrorPad,z2 as mod,V2 as moments,V6 as movingAverage,se as mul,W2 as multiRNNCell,U2 as multinomial,pr as neg,cS as nextFrame,EQt as node,Vu as norm,Fd as notEqual,El as oneHot,Da as ones,G2 as onesLike,N as op,H2 as outerProduct,Aa as pad,K2 as pad1d,q2 as pad2d,j2 as pad3d,X2 as pad4d,Y2 as pool,ui as pow,Od as prelu,ld as print,Q2 as prod,Cme as profile,Z2 as raggedGather,J2 as raggedRange,e1 as raggedTensorToTensor,t1 as rand,S1 as randomGamma,Wd as randomNormal,I1 as randomStandardNormal,ic as randomUniform,v1 as randomUniformInt,cu as range,Ime as ready,ci as real,k1 as reciprocal,tu as registerBackend,ole as registerGradient,ti as registerKernel,z5 as registerOp,lu as relu,Ud as relu6,vme as removeBackend,W as reshape,mo as reverse,N1 as reverse1d,T1 as reverse2d,_1 as reverse3d,E1 as reverse4d,pc as rfft,Gd as round,$1 as rsqrt,ke as scalar,U6 as scatterND,du as scatter_util,_l as searchSorted,R1 as selu,D1 as separableConv2d,jN as serialization,Sme as setBackend,Tme as setPlatform,sae as setThreadsCount,oae as setWasmPath,nae as setWasmPaths,NI as setWebGLContext,A1 as setdiff1dAsync,Ic as shared,Ea as sigmoid,F1 as sign,Jj as signal,P1 as sin,O1 as sinh,Xe as slice,M1 as slice1d,L1 as slice2d,B1 as slice3d,z1 as slice4d,pt as slice_util,V1 as softmax,Td as softplus,Pd as spaceToBatchND,oX as sparse,K6 as sparseToDense,Zj as spectral,li as split,Rr as sqrt,Zt as square,Kd as squaredDifference,cc as squeeze,vr as stack,qd as step,W1 as stridedSlice,nX as string,Te as sub,ot as sum,oi as sumOutType,U1 as tan,kl as tanh,ar as tensor,Jt as tensor1d,mu as tensor2d,jd as tensor3d,G1 as tensor4d,H1 as tensor5d,K1 as tensor6d,j1 as tensorScatterUpdate,rk as tensor_util,w1 as test_util,De as tidy,uu as tile,wme as time,X1 as topk,OGe as train,mc as transpose,Y1 as truncatedNormal,Q1 as unique,sle as unregisterGradient,nle as unregisterKernel,Z1 as unsortedSegmentSum,fo as unstack,dt as upcastType,J1 as upperBound,y as util,UK as valueAndGrad,GK as valueAndGrads,eN as variable,Vw as variableGrads,Vce as version,z8 as version_converter,OX as version_core,yY as version_cpu,iae as version_wasm,d9 as version_webgl,$at as webgl,Ec as webgl_util,Zv as webgpu_util,lo as where,Yd as whereAsync,Gr as zeros,Gt as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu' | 'none';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum size in pixels of a detected face box before resutls are discared */\n minSize: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** how much should face box be enlarged over the min/max facial coordinates */\n scale: number,\n /** automatically pad image to square */\n square: boolean,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {\n /** how much should iris box be enlarged over the min/max iris coordinates */\n scale: number,\n}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: false,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n minSize: 0,\n iouThreshold: 0.1,\n scale: 1.4,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n scale: 2.3,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh, stack]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // eslint-disable-line @typescript-eslint/no-redundant-type-constituents\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document !== 'undefined') {\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n } else if (typeof navigator !== 'undefined' && navigator.product === 'ReactNative') {\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n else throw new Error('canvas error: attempted to use canvas in react-native without canvas support installed');\n } else {\n throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n }\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = (typeof navigator !== 'undefined') && (typeof navigator.appVersion !== 'undefined');\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if ((typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined')) { // TBD replace with navigator.userAgentData once in mainline\n const agent = navigator.userAgent || '';\n const raw = agent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = agent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator !== 'undefined' && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n this.webgpu.adapter = await adapter?.requestAdapterInfo();\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return `webcam error: cannot get dom element: ${webcamConfig.element}`;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return `webcam error: unknown dom element: ${webcamConfig.element}`;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam error', 'no devices');\n return 'webcam error: no devices';\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return `webcam error: ${err}`;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam error', 'no stream');\n return 'webcam error no stream';\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n return `webcam: ${this.label}`;\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "{\n \"antispoof\": 853098,\n \"blazeface\": 538928,\n \"centernet\": 4030290,\n \"emotion\": 820516,\n \"facemesh\": 1477958,\n \"faceres\": 6978814,\n \"handlandmark-lite\": 2023432,\n \"handtrack\": 2964837,\n \"iris\": 2599092,\n \"liveness\": 592976,\n \"models\": 0,\n \"movenet-lightning\": 4650216,\n \"affectnet-mobilenet\": 6920630,\n \"age\": 161240,\n \"blazeface-back\": 538928,\n \"blazeface-front\": 402048,\n \"blazepose-detector\": 5928856,\n \"blazepose-full\": 6339202,\n \"blazepose-heavy\": 27502466,\n \"blazepose-lite\": 2726402,\n \"efficientpose\": 5651240,\n \"faceboxes\": 2013002,\n \"facemesh-attention-pinto\": 2387598,\n \"facemesh-attention\": 2382414,\n \"facemesh-detection-full\": 1026192,\n \"facemesh-detection-short\": 201268,\n \"faceres-deep\": 13957620,\n \"gear-e1\": 112438,\n \"gear-e2\": 112438,\n \"gear\": 1498916,\n \"gender-ssrnet-imdb\": 161236,\n \"gender\": 201808,\n \"handdetect\": 3515612,\n \"handlandmark-full\": 5431368,\n \"handlandmark-sparse\": 5286322,\n \"handskeleton\": 5502280,\n \"meet\": 372228,\n \"mobileface\": 2183192,\n \"mobilefacenet\": 5171976,\n \"movenet-multipose\": 9448838,\n \"movenet-thunder\": 12477112,\n \"nanodet\": 7574558,\n \"posenet\": 5032780,\n \"rvm\": 3739355,\n \"selfie\": 212886,\n \"anti-spoofing\": 853098,\n \"efficientpose-i-lite\": 2269064,\n \"efficientpose-ii-lite\": 5651240,\n \"efficientpose-iv\": 25643252,\n \"insightface-efficientnet-b0\": 13013224,\n \"insightface-ghostnet-strides1\": 8093408,\n \"insightface-ghostnet-strides2\": 8049584,\n \"insightface-mobilenet-emore\": 6938536,\n \"insightface-mobilenet-swish\": 12168584,\n \"nanodet-e\": 12319156,\n \"nanodet-g\": 7574558,\n \"nanodet-m\": 1887474,\n \"nanodet-t\": 5294216\n}", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n loaded: boolean,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n loaded: false,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n modelStats[shortModelName].loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || model.artifacts?.weightData?.[0].byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n modelStats[shortModelName].loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (modelStats[shortModelName].loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "{\n \"name\": \"@vladmandic/human\",\n \"version\": \"3.3.2\",\n \"description\": \"Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition\",\n \"sideEffects\": false,\n \"main\": \"dist/human.node.js\",\n \"module\": \"dist/human.esm.js\",\n \"browser\": \"dist/human.esm.js\",\n \"types\": \"types/human.d.ts\",\n \"exports\": {\n \"node\": \"./dist/human.node.js\",\n \"script\": \"./dist/human.js\",\n \"module\": \"./dist/human.esm.js\",\n \"types\": \"./types/human.d.ts\",\n \"dist/human\": \"./dist/human.js\",\n \"dist/human.js\": \"./dist/human.js\",\n \"dist/human.esm\": \"./dist/human.esm.js\",\n \"dist/human.esm.js\": \"./dist/human.esm.js\",\n \"dist/human.esm-nobundle\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.esm-nobundle.js\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.node\": \"./dist/human.node.js\",\n \"dist/human.node.js\": \"./dist/human.node.js\",\n \"dist/human.node-wasm\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-wasm.js\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-gpu\": \"./dist/human.node-gpu.js\",\n \"dist/human.node-gpu.js\": \"./dist/human.node-gpu.js\",\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.esm.js\"\n },\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/human/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/human/demo/index.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/human.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/nodejs/node.js\",\n \"dev\": \"build --profile development\",\n \"clean\": \"build --profile clean\",\n \"build\": \"rimraf test/build.log && node build.js\",\n \"test\": \"node --no-warnings --unhandled-rejections=strict --trace-uncaught test/node.js\",\n \"lint\": \"eslint *.json *.js src demo test models wiki\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"human\",\n \"human-library\",\n \"face-detection\",\n \"faceid\",\n \"face-geometry\",\n \"face-embedding\",\n \"face-recognition\",\n \"face-description\",\n \"face-matching\",\n \"body-tracking\",\n \"body-segmentation\",\n \"hand-tracking\",\n \"iris-tracking\",\n \"age-estimation\",\n \"emotion-detection\",\n \"gender-prediction\",\n \"gesture-recognition\",\n \"gaze-tracking\",\n \"age-gender\",\n \"tensorflowjs\",\n \"tfjs\",\n \"tensorflow\"\n ],\n \"devDependencies\": {\n \"@html-eslint/eslint-plugin\": \"^0.26.0\",\n \"@html-eslint/parser\": \"^0.26.0\",\n \"@microsoft/api-extractor\": \"^7.47.7\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.21.0\",\n \"@tensorflow/tfjs-converter\": \"^4.21.0\",\n \"@tensorflow/tfjs-core\": \"^4.21.0\",\n \"@tensorflow/tfjs-data\": \"^4.21.0\",\n \"@tensorflow/tfjs-layers\": \"^4.21.0\",\n \"@tensorflow/tfjs-node\": \"^4.21.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.21.0\",\n \"@types/emscripten\": \"^1.39.13\",\n \"@types/node\": \"^22.5.4\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^8.5.0\",\n \"@typescript-eslint/parser\": \"^8.5.0\",\n \"@vladmandic/build\": \"^0.10.1\",\n \"@vladmandic/pilogger\": \"^0.5.1\",\n \"@vladmandic/tfjs\": \"github:vladmandic/tfjs\",\n \"canvas\": \"^2.11.2\",\n \"esbuild\": \"^0.23.1\",\n \"eslint\": \"8.57.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-html\": \"^8.1.1\",\n \"eslint-plugin-import\": \"^2.30.0\",\n \"eslint-plugin-json\": \"^4.0.1\",\n \"eslint-plugin-markdown\": \"^5.1.0\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^7.1.0\",\n \"rimraf\": \"^6.0.1\",\n \"tslib\": \"^2.7.0\",\n \"typedoc\": \"0.26.7\",\n \"typescript\": \"5.5.4\"\n }\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n if (typeof navigator !== 'undefined' && navigator?.userAgent?.toLowerCase().includes('electron')) {\n if (instance.config.debug) log('running inside electron');\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n // force browser vs node backend\n if (env.browser && !env.node && (instance.config.backend === 'tensorflow') && available.includes('webgl')) {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && !env.browser && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl') && available.includes('tensorflow')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter = await navigator.gpu.requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n // @ts-ignore requestAdapterInfo is not in tslib\n const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n // env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d', { willReadFrequently: true });\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n l = replace(l, '[id]', f.id.toFixed(0));\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints) {\n if (f?.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n } else {\n for (const [k, v] of Object.entries(f?.annotations || {})) {\n if (!v?.[0]) continue;\n const pt = v[0];\n point(ctx, pt[0], pt[1], 0, localOptions);\n if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[id]', result[i].id.toFixed(0));\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor, anchor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n // const centerPoint = [(startPoint[0] + endPoint[0]) / 2, (startPoint[1] + endPoint[1]) / 2];\n const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]);\n /**\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[1] / blazeface.size()),\n ]);\n */\n\n return { startPoint, endPoint, landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return {\n startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point,\n endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size,\n };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return {\n startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point,\n endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size: [Math.round(size[0]), Math.round(size[1])] as [number, number],\n };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return {\n startPoint: [Math.min(...x), Math.min(...y)] as Point,\n endPoint: [Math.max(...x), Math.max(...y)] as Point,\n landmarks,\n };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\nexport interface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number, size: [number, number] }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config): Promise {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n let pad = [0, 0];\n let scale = [1, 1];\n if (config?.face?.detector?.square) {\n const xy = Math.max(inputImage.shape[2], inputImage.shape[1]);\n pad = [Math.floor((xy - inputImage.shape[2]) / 2), Math.floor((xy - inputImage.shape[1]) / 2)];\n t.padded = tf.pad(inputImage, [[0, 0], [pad[1], pad[1]], [pad[0], pad[0]], [0, 0]]);\n scale = [inputImage.shape[2] / xy, inputImage.shape[1] / xy];\n pad = [pad[0] / inputSize, pad[1] / inputSize];\n } else {\n t.padded = inputImage.clone();\n }\n t.resized = tf.image.resizeBilinear(t.padded as Tensor4D, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf1);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const unpadded = [ // TODO fix this math\n points[0] * scale[0] - pad[0],\n points[1] * scale[1] - pad[1],\n points[2] * scale[0] - pad[0],\n points[3] * scale[1] - pad[1],\n ];\n const rawBox = {\n startPoint: [unpadded[0], unpadded[1]] as Point,\n endPoint: [unpadded[2], unpadded[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n b.anchor = tf.slice(anchors as Tensor, [nms[i], 0], [1, 2]);\n const anchor = await b.anchor.data();\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize], anchor);\n const enlargedBox = util.enlargeBox(scaledBox, config.face.detector?.scale || 1.4);\n const squaredBox = util.squarifyBox(enlargedBox);\n if (squaredBox.size[0] > (config.face.detector?.['minSize'] || 0) && squaredBox.size[1] > (config.face.detector?.['minSize'] || 0)) boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false, scale = 2.3) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), scale));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize, config: Config) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true, config.face.iris?.scale || 2.3);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true, config.face.iris?.scale || 2.3);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\nimport type { DetectBox } from './blazeface';\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n size: [0, 0],\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled || !model?.['executor']) { // mesh not enabled or not loaded, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.size = box.size;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh['keepInvalid']) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.size = box.size;\n face.score = face.boxScore;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize, config); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = {\n ...util.calculateFaceBox(face.mesh, box),\n confidence: box.confidence,\n landmarks: box.landmarks,\n size: box.size,\n };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n face.size = calculatedBox.size;\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nlet annotations: string[] = [];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet rgb = false;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.face.emotion?.modelPath);\n rgb = model?.inputs?.[0].shape?.[3] === 3;\n if (!rgb) annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']; // oarriaga and gear\n else annotations = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']; // affectnet\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (config.face.emotion?.['crop'] > 0) { // optional crop\n const crop = config.face.emotion?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n }\n if (rgb) {\n t.mul = tf.mul(t.resize, 255);\n t.normalize = tf.sub(t.mul, [103.939, 116.779, 123.68]); // affectnet uses specific norm values\n t.emotion = model?.execute(t.normalize) as Tensor; // result is already in range 0..1, no need for additional activation\n } else {\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n }\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input, config: Config): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n let crop: Tensor;\n if (config.face.description?.['crop'] > 0) { // optional crop\n const cropval = config.face.description?.['crop'];\n const box = [[cropval, cropval, 1 - cropval, 1 - cropval]];\n crop = tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image, config);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n let box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n if (config.face.gear?.['crop'] > 0) { // optional crop config value\n const crop = config.face.gear?.['crop'];\n box = [[crop, crop, 1 - crop, 1 - crop]];\n }\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.tidy(() => {\n let normalize: Tensor;\n if (model?.inputs?.[0].shape?.[3] === 1) {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n } else {\n normalize = tf.mul(tf.sub(t.resize, constants.tf05), 2); // range rgb:-1..1\n }\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n // thetaZ = Math.asin(r10);\n const cosThetaZ = Math.sqrt(r00 * r00 + r20 * r20); // \n thetaZ = Math.atan2(r10, cosThetaZ);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n // return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n return { pitch: -thetaX, yaw: -thetaY, roll: -thetaZ };\n };\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n this.skipped++;\n } else {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport function initPipeline() {\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!handPipeline) initPipeline();\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function loadDetect(config: Config): Promise {\n if (env.initial) handDetectorModel = null;\n if (!handDetectorModel) handDetectorModel = await loadModel(config.hand.detector?.modelPath);\n else if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n return handDetectorModel;\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) handPoseModel = null;\n if (!handPoseModel) handPoseModel = await loadModel(config.hand.skeleton?.modelPath);\n else if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n return handPoseModel;\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face box size */\n size: [number, number],\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty, FaceLandmark } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n let annotations: Record = newResult.face[i].annotations;\n if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) {\n bufferedResult.face[i].annotations = newResult.face[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.face[i].annotations;\n } else if (newResult.face[i].annotations) {\n for (const key of Object.keys(newResult.face[i].annotations)) { // update annotations\n annotations[key] = newResult.face[i]?.annotations?.[key]?.[0]\n ? newResult.face[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n const dist = Math.round(100 * (options.multiplier || 20) * sum) / 100;\n return dist;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.round(100 * Math.max(Math.min(norm, 1), 0)) / 100; // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += Number.isNaN(+m.sizeFromManifest) ? 0 : m.sizeFromManifest;\n totalSizeWeights += Number.isNaN(+m.sizeLoadedWeights) ? 0 : m.sizeLoadedWeights;\n totalSizeLoading += Number.isNaN(+m.sizeDesired) ? 0 : m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).filter((m) => m?.loaded).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n // if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n if (this.instance.config.hand.enabled && !this.models.handdetect && this.instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n m.handdetect = handpose.loadDetect(this.instance.config);\n m.handskeleton = handpose.loadSkeleton(this.instance.config);\n }\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded(): string[] {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', false); // default=false \n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const boxScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (boxScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n // const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n const boxNorm: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n // const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n const body: BodyResult = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face: string = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await tf.ready();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], - "mappings": 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yw;(function(r){r.float32=\"float32\",r.int32=\"int32\",r.bool=\"bool\",r.complex64=\"complex64\"})(yw||(yw={}));var bw;(function(r){r.float32=\"float32\",r.int32=\"float32\",r.bool=\"float32\",r.complex64=\"complex64\"})(bw||(bw={}));var Cw;(function(r){r.float32=\"complex64\",r.int32=\"complex64\",r.bool=\"complex64\",r.complex64=\"complex64\"})(Cw||(Cw={}));var f4={float32:bw,int32:xw,bool:yw,complex64:Cw};function dt(r,e){if(r===\"string\"||e===\"string\"){if(r===\"string\"&&e===\"string\")return\"string\";throw new Error(`Can not upcast ${r} with ${e}`)}return f4[r][e]}function oi(r){return dt(r,\"int32\")}function rd(r){return r!=null&&typeof r==\"object\"&&\"texture\"in r&&r.texture instanceof WebGLTexture}function od(r){return typeof GPUBuffer!=\"undefined\"&&r!=null&&typeof r==\"object\"&&\"buffer\"in r&&r.buffer instanceof GPUBuffer}function Oe(r,e){if(r.dtype===e.dtype)return[r,e];let t=dt(r.dtype,e.dtype);return[r.cast(t),e.cast(t)]}function ww(r,e){E(r.dtype===e.dtype,()=>`The dtypes of the first(${r.dtype}) and second(${e.dtype}) input must match`)}function h4(r,e){return e.some(t=>t.id===r.id)}function Cl(r){let e=[];return tk(r,e,new Set),e}function tk(r,e,t){if(r==null)return;if(r instanceof mt){e.push(r);return}if(!g4(r))return;let o=r;for(let n in o){let s=o[n];t.has(s)||(t.add(s),tk(s,e,t))}}function g4(r){return Array.isArray(r)||typeof r==\"object\"}function Sw(r){return r.kernelName!=null}var nd=class{constructor(){this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.gradientDepth=0,this.kernelDepth=0,this.scopeStack=[],this.numDataMovesStack=[],this.nextScopeId=0,this.tensorInfo=new WeakMap,this.profiling=!1,this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null,get kernelNames(){return Array.from(new Set(this.kernels.map(e=>e.name)))}}}dispose(){for(let e in this.registeredVariables)this.registeredVariables[e].dispose()}},wl=class r{constructor(e){this.ENV=e,this.registry={},this.registryFactory={},this.pendingBackendInitId=0,this.state=new nd}async ready(){if(this.pendingBackendInit!=null)return this.pendingBackendInit.then(()=>{});if(this.backendInstance!=null)return;let e=this.getSortedBackends();for(let t=0;t{t.setupFunc!=null&&t.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Ym(e).forEach(o=>{o.disposeFunc!=null&&o.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let o=t.factory();if(o&&!(o instanceof ao)&&typeof o.then==\"function\"){let n=++this.pendingBackendInitId,s=o.then(a=>n(nthis.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;tthis.startScope(o),()=>this.endScope(n),()=>(n=t(),n instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),n))}scopedRun(e,t,o){e();try{let n=o();return t(),n}catch(n){throw t(),n}}nextTensorId(){return r.nextTensorId++}nextVariableId(){return r.nextVariableId++}clone(e){let t=T.runKernel(Co,{x:e}),o={x:e},n=a=>({x:()=>{let i=\"float32\",p={x:a},u={dtype:i};return T.runKernel(yo,p,u)}}),s=[];return this.addTapeNode(this.state.activeScope.name,o,[t],n,s,{}),t}runKernel(e,t,o){if(this.backendName==null&&this.backend,!(Xp(e,this.backendName)!=null))throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:o})}shouldCheckForMemLeaks(){return this.ENV.getBool(\"IS_TEST\")}checkKernelForMemLeak(e,t,o){let n=this.backend.numDataIds(),s=0;o.forEach(p=>{s+=p.dtype===\"complex64\"?3:1});let a=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=n-t-s-a;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,o=[],n=this.isTapeOn(),s=this.state.numBytes,a=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let p,u=Sw(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:\"\";if(Sw(e)){let{kernelName:f,inputs:h,attrs:g}=e;this.backendName==null&&this.backend;let x=Xp(f,this.backendName);E(x!=null,()=>`Cannot find registered kernel '${f}' for backend '${this.backendName}'`),i=()=>{let b=this.backend.numDataIds();p=x.kernelFunc({inputs:h,attrs:g,backend:this.backend});let C=Array.isArray(p)?p:[p];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(f,b,C);let S=C.map(k=>k.rank!=null?k:this.makeTensorFromTensorInfo(k));if(n){let k=this.getTensorsForGradient(f,h,S);o=this.saveTensorsForBackwardMode(k)}return S}}else{let{forwardFunc:f}=e,h=g=>{n&&(o=g.map(x=>this.keep(this.clone(x))))};i=()=>{let g=this.backend.numDataIds();p=this.tidy(()=>f(this.backend,h));let x=Array.isArray(p)?p:[p];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,x),x}}let{inputs:c,attrs:l}=e,m=Sw(e)?null:e.backwardsFunc,d;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool(\"DEBUG\")&&!this.state.profiling?t=i():(d=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool(\"DEBUG\")&&this.profiler.logKernelProfile(d),t=d.outputs)}),n&&this.addTapeNode(u,c,t,m,o,l),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(f=>c[f]!=null?c[f].shape:null),outputShapes:t.map(f=>f.shape),kernelTimeMs:d.timeMs,extraInfo:d.extraInfo}),Array.isArray(p)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=iw(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(E(Array.isArray(t),()=>\"saveAllInputs is true, expected inputs to be an array.\"),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let p=o.filter((u,c)=>a[c]);return i.concat(p)}return[]}makeTensor(e,t,o,n){if(e==null)throw new Error(\"Values passed to engine.makeTensor() are null\");o=o||\"float32\",n=n||this.backend;let s=e;o===\"string\"&&zo(e[0])&&(s=e.map(p=>Ji(p)));let a=n.write(s,t,o),i=new mt(t,o,a,this.nextTensorId());if(this.trackTensor(i,n),o===\"string\"){let p=this.state.tensorInfo.get(a),u=ow(s);this.state.numBytes+=u-p.bytes,p.bytes=u}return i}makeTensorFromDataId(e,t,o,n){o=o||\"float32\";let s={dataId:e,shape:t,dtype:o};return this.makeTensorFromTensorInfo(s,n)}makeTensorFromTensorInfo(e,t){let{dataId:o,shape:n,dtype:s}=e,a=new mt(n,s,o,this.nextTensorId());return this.trackTensor(a,t),a}makeVariable(e,t=!0,o,n){o=o||this.nextVariableId().toString(),n!=null&&n!==e.dtype&&(e=e.cast(n));let s=new ri(e,t,o,this.nextTensorId());if(this.state.registeredVariables[s.name]!=null)throw new Error(`Variable with name ${s.name} was already registered`);return this.state.registeredVariables[s.name]=s,this.incRef(s,this.backend),s}trackTensor(e,t){this.state.numTensors++,e.dtype===\"string\"&&this.state.numStringTensors++;let o=0;e.dtype!==\"complex64\"&&e.dtype!==\"string\"&&(o=e.size*Wp(e.dtype)),this.state.numBytes+=o,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:o})),e instanceof ri||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype===\"string\"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!==\"complex64\"&&e.dtype!==\"string\"){let o=e.size*Wp(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of this.state.activeProfile.kernels)n.kernelTimeMs=await n.kernelTimeMs,n.extraInfo=await n.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,o,n,s,a){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:o,saved:s},p=iw(e);p!=null&&(n=p.gradFunc),n!=null&&(i.gradient=u=>(u=u.map((c,l)=>{if(c==null){let m=o[l],d=Gp(m.size,m.dtype);return this.makeTensor(d,m.shape,m.dtype)}return c}),n(u.length>1?u:u[0],s,a))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:\"unnamed scope\",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=Cl(e),o=new Set(t.map(s=>s.id));for(let s=0;s{!s.kept&&s.scopeId===n.id&&this.track(s)})}gradients(e,t,o,n=!1){if(E(t.length>0,()=>\"gradients() received an empty list of xs.\"),o!=null&&o.dtype!==\"float32\")throw new Error(`dy must have 'float32' dtype, but has '${o.dtype}'`);let s=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy(\"forward\",e));E(s instanceof mt,()=>\"The result y returned by f() must be a tensor.\");let a=q0(this.state.activeTape,t,s);if(!n&&a.length===0&&t.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");return this.tidy(\"backward\",()=>{let i={};i[s.id]=o==null?x4(s.shape):o,j0(i,a,u=>this.tidy(u),y4);let p=t.map(u=>i[u.id]);return this.state.gradientDepth===0&&(this.state.activeTape.forEach(u=>{for(let c of u.saved)c.dispose()}),this.state.activeTape=null),{value:s,grads:p}})}customGrad(e){return E(qs(e),()=>\"The f passed in customGrad(f) must be a function.\"),(...t)=>{E(t.every(i=>i instanceof mt),()=>\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\");let o,n={};t.forEach((i,p)=>{n[p]=i});let s=(i,p)=>(o=e(...t,p),E(o.value instanceof mt,()=>\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),E(qs(o.gradFunc),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),o.value),a=(i,p)=>{let u=o.gradFunc(i,p),c=Array.isArray(u)?u:[u];E(c.length===t.length,()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),E(c.every(m=>m instanceof mt),()=>\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\");let l={};return c.forEach((m,d)=>{l[d]=()=>m}),l};return this.runKernelFunc({forwardFunc:s,backwardsFunc:a,inputs:n})}}readSync(e){return this.state.tensorInfo.get(e).backend.readSync(e)}read(e){return this.state.tensorInfo.get(e).backend.read(e)}readToGPU(e,t){return this.state.tensorInfo.get(e).backend.readToGPU(e,t)}async time(e){let t=Mu(),o=await this.backend.time(e);return o.wallMs=Mu()-t,o}track(e){return this.state.activeScope!=null&&(e.scopeId=this.state.activeScope.id,this.state.activeScope.track.push(e)),e}get registeredVariables(){return this.state.registeredVariables}reset(){this.pendingBackendInitId++,this.state.dispose(),this.ENV.reset(),this.state=new nd;for(let e in this.registry)this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e];this.backendName=null,this.backendInstance=null,this.pendingBackendInit=null}};wl.nextTensorId=0;wl.nextVariableId=0;function x4(r){let e=ml(ze(r),\"float32\");return T.makeTensor(e,r,\"float32\")}function Iw(){let r=aw();if(r._tfengine==null){let e=new dl(r);r._tfengine=new wl(e)}return $0(r._tfengine.ENV),Z0(()=>r._tfengine),r._tfengine}var T=Iw();function y4(r,e){let t={a:r,b:e};return T.runKernel(uo,t)}var eu={};qe(eu,{isBrowser:()=>kw,isMobile:()=>w4,mockIsMobile:()=>C4});function b4(){return typeof navigator!=\"undefined\"&&navigator!=null}var vw;function C4(r){vw=r}function w4(r){if(vw!==void 0)return vw;if(r||b4()){if(r||(r=navigator),r.product===\"ReactNative\")return!0;let e=r.userAgent||r.vendor||(typeof window!=\"undefined\"?window.opera:\"\");if(!e){let t=r;return 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a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${a.rank}.`),E(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),E(p.rank===3||p.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`),nu(a,i,p,c,u,s)}var Xk=N({batchNorm3d_:kH});function NH(r,e,t,o,n,s){let a=v(r,\"x\",\"batchNorm\"),i=v(e,\"mean\",\"batchNorm\"),p=v(t,\"variance\",\"batchNorm\"),u;n!=null&&(u=v(n,\"scale\",\"batchNorm\"));let c;return o!=null&&(c=v(o,\"offset\",\"batchNorm\")),E(a.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${a.rank}.`),E(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),E(p.rank===4||p.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${p.rank}.`),u!=null&&E(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${u.rank}.`),c!=null&&E(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`),nu(a,i,p,c,u,s)}var Yk=N({batchNorm4d_:NH});function TH(r,e,t){let o=v(r,\"x\",\"bincount\"),n=v(e,\"weights\",\"bincount\");E(o.dtype===\"int32\",()=>`Error in bincount: input dtype must be int32, but got ${o.dtype}`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(n.size===o.size||n.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${o.shape}, weights shape: ${n.shape}.`);let s={x:o,weights:n},a={size:t};return T.runKernel(Jo,s,a)}var hd=N({bincount_:TH});function _H(r,e){let t=v(r,\"x\",\"bitwiseAnd\"),o=v(e,\"y\",\"bitwiseAnd\");if(!br(t.shape,o.shape))throw new Error(`BitwiseAnd: Tensors must have the same shape. x: ${t.shape}, y: ${o.shape}`);if(t.dtype!==\"int32\"||o.dtype!==\"int32\")throw new Error(`BitwiseAnd: Only supports 'int32' values in tensor, found type of x: ${t.dtype} and type of y: ${o.dtype}`);let n={a:t,b:o};return T.runKernel(qa,n)}var Qk=N({bitwiseAnd_:_H});function EH(r,e){let t=v(r,\"s0\",\"broadcastArgs\",\"int32\"),o=v(e,\"s1\",\"broadcastArgs\",\"int32\");if(t.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${t.rank}`);if(o.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${o.rank}`);let n={s0:t,s1:o};return T.runKernel(ea,n)}var Zk=N({broadcastArgs_:EH});function $H(r,e){let t=v(r,\"broadcastTo\",\"x\"),o=t.shape;if(Ct(e),e.lengtht.rank){let u=t.shape.slice();for(;u.length=0;u--)if(n[u]===e[u])s[u]=1;else if(t.shape[u]!==1)throw new Error(`broadcastTo(): [${o}] cannot be broadcast to [${e}].`);if(s.map((u,c)=>u>1?c:-1).filter(u=>u>=0).length===0)return Ur(t);let i={x:t},p={reps:s};return T.runKernel(po,i,p)}var su=N({broadcastTo_:$H});function RH(r){let t={x:v(r,\"x\",\"ceil\",\"float32\")};return T.runKernel(en,t)}var Jk=N({ceil_:RH});function $a(r,e,t){Ct(r),t=t||Ei(e);let o={shape:r,value:e,dtype:t};return T.runKernel(sa,{},o)}function DH(r,e,t){let o=v(r,\"x\",\"clipByValue\");if(E(e<=t,()=>`Error in clip: min (${e}) must be less than or equal to max (${t}).`),e===t)return $a(o.shape,e,o.dtype);let n={x:o},s={clipValueMin:e,clipValueMax:t};return T.runKernel(bo,n,s)}var e2=N({clipByValue_:DH});function AH(r){return yt(r,0)}var t2=N({concat1d_:AH});function FH(r,e){return yt(r,e)}var r2=N({concat2d_:FH});function PH(r,e){return yt(r,e)}var o2=N({concat3d_:PH});function OH(r,e){return yt(r,e)}var n2=N({concat4d_:OH});function MH(r,e,t,o,n=\"NHWC\",s=[1,1],a){let i=v(r,\"x\",\"conv2d\",\"float32\"),p=v(e,\"filter\",\"conv2d\",\"float32\"),u=i,c=!1;i.rank===3&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in conv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in conv2d: filter must be rank 4, but got rank ${p.rank}.`),Lt(\"conv2d\",o,a);let l=n===\"NHWC\"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in conv2d: depth of input (${l}) must match input depth for filter ${p.shape[2]}.`),E(gr(t,s),()=>`Error in conv2D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),E(Ta(s),()=>\"Error in conv2D: Dilated rates should be larger than 0.\"),E(Ta(t),()=>\"Error in conv2D: Strides should be larger than 0.\");let m={x:u,filter:p},d={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=T.runKernel(tn,m,d);return c?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var au=N({conv2d_:MH});function LH(r,e,t,o,n=\"NWC\",s=1,a){let i=v(r,\"x\",\"conv1d\"),p=v(e,\"filter\",\"conv1d\"),u=i,c=!1;i.rank===2&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1]])),E(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),E(p.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${p.rank}.`),Lt(\"conv1d\",o,a),E(u.shape[2]===p.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${p.shape[1]}.`),E(gr(t,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${t} and dilation '${s}'`),E(Ta(s),()=>\"Error in conv1D: Dilated rates should be larger than 0.\"),E(Ta(t),()=>\"Error in conv1D: Stride should be larger than 0.\"),E(n===\"NWC\",()=>`Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`);let l=W(p,[1,p.shape[0],p.shape[1],p.shape[2]]),m=W(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=au(m,l,[1,t],o,\"NHWC\",[1,s],a);return c?W(g,[g.shape[2],g.shape[3]]):W(g,[g.shape[0],g.shape[2],g.shape[3]])}var s2=N({conv1d_:LH});function BH(r,e,t,o,n,s=\"NHWC\",a){E(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let i=r,p=e,u=!1;e.rank===3&&(u=!0,p=W(e,[1,e.shape[0],e.shape[1],e.shape[2]]),i=[1,r[0],r[1],r[2]]),E(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),E(p.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${p.rank}`),E(t.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${t.rank}`);let c=s===\"NHWC\"?i[3]:i[1],l=s===\"NHWC\"?p.shape[3]:p.shape[1];E(c===t.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t.shape[2]}.`),E(l===t.shape[3],()=>`Error in conv2dDerInput: depth of output (${l}) must match output depth for filter ${t.shape[3]}.`),Lt(\"conv2dDerInput\",n,a);let m={dy:p,filter:t},d={strides:o,pad:n,dataFormat:s,dimRoundingMode:a,inputShape:i},f=T.runKernel(rn,m,d);return u?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var gd=N({conv2DBackpropInput_:BH});function zH(r,e,t,o,n,s){let a=v(r,\"x\",\"conv2dTranspose\"),i=v(e,\"filter\",\"conv2dTranspose\");return gd(t,a,i,o,n,\"NHWC\",s)}var a2=N({conv2dTranspose_:zH});function VH(r,e,t,o,n=\"NDHWC\",s=[1,1,1]){let a=v(r,\"x\",\"conv3d\"),i=v(e,\"filter\",\"conv3d\"),p=a,u=!1;a.rank===4&&(u=!0,p=W(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),E(p.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${p.rank}.`),E(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),E(p.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${p.shape[4]}) must match input depth for filter ${i.shape[3]}.`),E(gr(t,s),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${t} and dilations '${s}'`),E(n===\"NDHWC\",()=>`Error in conv3d: got dataFormat of ${n} but only NDHWC is currently supported.`),E(Ta(s),()=>\"Error in conv3D: Dilated rates should be larger than 0.\"),E(Ta(t),()=>\"Error in conv3D: Strides should be larger than 0.\");let c={x:p,filter:i},l={strides:t,pad:o,dataFormat:n,dilations:s},m=T.runKernel(on,c,l);return u?W(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var i2=N({conv3d_:VH});function WH(r,e,t,o,n){E(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let s=r,a=e,i=!1;e.rank===4&&(i=!0,a=W(e,[1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]]),s=[1,r[0],r[1],r[2],r[3]]);let p=s[4],u=a.shape[4];E(s.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${s.length}.`),E(a.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${a.rank}`),E(t.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${t.rank}`),E(p===t.shape[3],()=>`Error in conv3dDerInput: depth of input (${p}) must match input depth for filter ${t.shape[3]}.`),E(u===t.shape[4],()=>`Error in conv3dDerInput: depth of output (${u}) must match output depth for filter ${t.shape[4]}.`);let c={dy:a,filter:t},l={pad:n,strides:o,inputShape:s},m=T.runKernel(nn,c,l);return i?W(m,[m.shape[1],m.shape[2],m.shape[3],m.shape[4]]):m}var u2=N({conv3DBackpropInput_:WH});function UH(r,e,t,o,n){let s=v(r,\"x\",\"conv3dTranspose\"),a=v(e,\"filter\",\"conv3dTranspose\");return u2(t,s,a,o,n)}var p2=N({conv3dTranspose_:UH});function GH(r){let t={x:v(r,\"x\",\"cos\",\"float32\")};return T.runKernel(sn,t)}var c2=N({cos_:GH});function HH(r){let t={x:v(r,\"x\",\"cosh\",\"float32\")};return T.runKernel(an,t)}var l2=N({cosh_:HH});function KH(r,e=0,t=!1,o=!1){let s={x:v(r,\"x\",\"cumprod\")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(un,s,a)}var m2=N({cumprod_:KH});function qH(r,e=0,t=!1,o=!1){let s={x:v(r,\"x\",\"cumsum\")},a={axis:e,exclusive:t,reverse:o};return T.runKernel(pn,s,a)}var d2=N({cumsum_:qH});function jH(r,e,t,o=!1){let n=v(r,\"x\",\"denseBincount\"),s=v(e,\"weights\",\"denseBincount\");E(n.dtype===\"int32\",()=>`Error in denseBincount: input dtype must be int32, but got ${n.dtype}`),E(n.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${n.rank}.`),E(t>=0,()=>`size must be non-negative, but got ${t}.`),E(s.size===n.size||s.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${n.shape}, weights shape: ${s.shape}.`);let a={x:n,weights:s},i={size:t,binaryOutput:o};return T.runKernel(ra,a,i)}var f2=N({denseBincount_:jH});function XH(r,e,t=\"NHWC\"){let o=v(r,\"x\",\"depthToSpace\",\"float32\"),n=t===\"NHWC\"?o.shape[1]:o.shape[2],s=t===\"NHWC\"?o.shape[2]:o.shape[3],a=t===\"NHWC\"?o.shape[3]:o.shape[1];E(e>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${e}`),E(n*e>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${n} and ${e} for depthToSpace with input shape\n ${o.shape}`),E(s*e>=0,()=>`Negative dimension size caused by overflow when multiplying\n ${s} and ${e} for depthToSpace with input shape\n ${o.shape}`),E(a%(e*e)===0,()=>`Dimension size must be evenly divisible by ${e*e} but is ${a} for depthToSpace with input shape ${o.shape}`);let i={x:o},p={blockSize:e,dataFormat:t};return T.runKernel(ln,i,p)}var h2=N({depthToSpace_:XH});function YH(r,e,t,o,n=\"NHWC\",s=[1,1],a){let i=v(r,\"x\",\"depthwiseConv2d\",\"float32\"),p=v(e,\"filter\",\"depthwiseConv2d\",\"float32\"),u=i,c=!1;i.rank===3&&(c=!0,u=W(i,[1,i.shape[0],i.shape[1],i.shape[2]])),E(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),E(p.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${p.rank}.`);let l=n===\"NHWC\"?u.shape[3]:u.shape[1];E(l===p.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${l}) must match the inChannels dimension in filter ${p.shape[2]}.`),Lt(\"depthwiseConv2d\",o,a);let m={x:u,filter:p},d={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=T.runKernel(mn,m,d);return c?W(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var sc=N({depthwiseConv2d_:YH});function QH(r){let t={x:v(r,\"x\",\"diag\")};return T.runKernel(oa,t)}var g2=N({diag_:QH});function ZH(r,e,t,o,n=[1,1],s=\"NHWC\"){let a=v(r,\"x\",\"dilation2d\"),i=v(e,\"filter\",\"dilation2d\");E(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),E(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),E(s===\"NHWC\",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let p=a,u=!1;a.rank===3&&(p=W(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0),E(p.shape[3]===i.shape[2],()=>`Error in dilation2d: input and filter must have the same depth: ${p.shape[3]} vs 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Eo(r,e,t,o,n,s){o==null&&(o=.5),n==null&&(n=Number.NEGATIVE_INFINITY),s==null&&(s=0);let a=r.shape[0];return t=Math.min(t,a),E(0<=o&&o<=1,()=>`iouThreshold must be in [0, 1], but was '${o}'`),E(r.rank===2,()=>`boxes must be a 2D tensor, but was of rank '${r.rank}'`),E(r.shape[1]===4,()=>`boxes must have 4 columns, but 2nd dimension was ${r.shape[1]}`),E(e.rank===1,()=>\"scores must be a 1D tensor\"),E(e.shape[0]===a,()=>`scores has incompatible shape with boxes. 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g=0;gn&&u.push({score:e[g],boxIndex:g,suppressBeginIndex:0});u.sort(yN);let c=s>0?-.5/s:0,l=[],m=[];for(;l.length0;){let g=u.pop(),{score:x,boxIndex:b,suppressBeginIndex:C}=g;if(x=C;--k){let _=yj(r,b,l[k]);if(_>=o){S=!0;break}if(g.score=g.score*bj(o,c,_),g.score<=n)break}g.suppressBeginIndex=l.length,S||(g.score===x?(l.push(b),m.push(g.score)):g.score>n&&xN(u,g,yN))}let d=l.length,f=t-d;i&&f>0&&(l.push(...new Array(f).fill(0)),m.push(...new Array(f).fill(0)));let h={selectedIndices:l};return a&&(h.selectedScores=m),p&&(h.validOutputs=d),h}function yj(r,e,t){let o=r.subarray(e*4,e*4+4),n=r.subarray(t*4,t*4+4),s=Math.min(o[0],o[2]),a=Math.min(o[1],o[3]),i=Math.max(o[0],o[2]),p=Math.max(o[1],o[3]),u=Math.min(n[0],n[2]),c=Math.min(n[1],n[3]),l=Math.max(n[0],n[2]),m=Math.max(n[1],n[3]),d=(i-s)*(p-a),f=(l-u)*(m-c);if(d<=0||f<=0)return 0;let h=Math.max(s,u),g=Math.max(a,c),x=Math.min(i,l),b=Math.min(p,m),C=Math.max(x-h,0)*Math.max(b-g,0);return C/(d+f-C)}function bj(r,e,t){let o=Math.exp(e*t*t);return t<=r?o:0}function yN(r,e){return r.score-e.score||r.score===e.score&&e.boxIndex-r.boxIndex}async function Cj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY){let s=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),a=v(e,\"scores\",\"nonMaxSuppressionAsync\"),i=Eo(s,a,t,o,n);t=i.maxOutputSize,o=i.iouThreshold,n=i.scoreThreshold;let p=await Promise.all([s.data(),a.data()]),u=p[0],c=p[1],{selectedIndices:l}=Jd(u,c,t,o,n);return s!==r&&s.dispose(),a!==e&&a.dispose(),Jt(l,\"int32\")}var bN=Cj;function wj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,\"boxes\",\"nonMaxSuppression\"),i=v(e,\"scores\",\"nonMaxSuppression\"),p=Eo(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u={boxes:a,scores:i},c={maxOutputSize:t,iouThreshold:o,scoreThreshold:n,softNmsSigma:s},l=T.runKernel(Zn,u,c);return{selectedIndices:l[0],selectedScores:l[1]}}var CN=N({nonMaxSuppressionWithScore_:wj});async function Sj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=0){let a=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=v(e,\"scores\",\"nonMaxSuppressionAsync\"),p=Eo(a,i,t,o,n,s);t=p.maxOutputSize,o=p.iouThreshold,n=p.scoreThreshold,s=p.softNmsSigma;let u=await Promise.all([a.data(),i.data()]),c=u[0],l=u[1],{selectedIndices:m,selectedScores:d}=tf(c,l,t,o,n,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Jt(m,\"int32\"),selectedScores:Jt(d)}}var wN=Sj;function Ij(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,\"boxes\",\"nonMaxSuppression\"),i=v(e,\"scores\",\"nonMaxSuppression\"),p=Eo(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,m={boxes:a,scores:i},d={maxOutputSize:u,iouThreshold:c,scoreThreshold:l,padToMaxOutputSize:s},f=T.runKernel(Qa,m,d);return{selectedIndices:f[0],validOutputs:f[1]}}var SN=N({nonMaxSuppressionPadded_:Ij});async function vj(r,e,t,o=.5,n=Number.NEGATIVE_INFINITY,s=!1){let a=v(r,\"boxes\",\"nonMaxSuppressionAsync\"),i=v(e,\"scores\",\"nonMaxSuppressionAsync\"),p=Eo(a,i,t,o,n,null),u=p.maxOutputSize,c=p.iouThreshold,l=p.scoreThreshold,[m,d]=await Promise.all([a.data(),i.data()]),{selectedIndices:f,validOutputs:h}=ef(m,d,u,c,l,s);return a!==r&&a.dispose(),i!==e&&i.dispose(),{selectedIndices:Jt(f,\"int32\"),validOutputs:ke(h,\"int32\")}}var IN=vj;function kj(r,e,t=!1,o=!1){let n=v(r,\"images\",\"resizeBilinear\");E(n.rank===3||n.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeBilinear: new shape must 2D, but got shape ${e}.`),E(o===!1||t===!1,()=>\"Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.\");let s=n,a=!1;n.rank===3&&(a=!0,s=W(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(is,i,p);return a?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var vN=N({resizeBilinear_:kj});function Nj(r,e,t=!1,o=!1){let n=v(r,\"images\",\"resizeNearestNeighbor\");E(n.rank===3||n.rank===4,()=>`Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${n.rank}.`),E(e.length===2,()=>`Error in resizeNearestNeighbor: new shape must 2D, but got shape ${e}.`),E(n.dtype===\"float32\"||n.dtype===\"int32\",()=>\"`images` must have `int32` or `float32` as dtype\"),E(o===!1||t===!1,()=>\"Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.\");let s=n,a=!1;n.rank===3&&(a=!0,s=W(n,[1,n.shape[0],n.shape[1],n.shape[2]]));let[]=e,i={images:s},p={alignCorners:t,halfPixelCenters:o,size:e},u=T.runKernel(as,i,p);return a?W(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var kN=N({resizeNearestNeighbor_:Nj});function Tj(r,e=\"binary\",t=!1,o=.5){let n=v(r,\"image\",\"threshold\"),s=.2989,a=.587,i=.114,p=n.shape[0]*n.shape[1],u=se(Jt([o]),255),c,l,m,d;if(E(n.rank===3,()=>`Error in threshold: image must be rank 3,but got rank 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p={image:a,transforms:i},u={interpolation:t,fillMode:o,fillValue:n,outputShape:s};return T.runKernel(Rs,p,u)}var TN=N({transform_:Ej});function $j(r,e,t){let o=v(r,\"a\",\"bandPart\");E(o.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${o.rank}.`);let n=o.shape,[s,a]=o.shape.slice(-2),i,p;typeof e==\"number\"?(E(e%1===0,()=>`bandPart(): numLower must be an integer, got ${e}.`),E(e<=s,()=>`bandPart(): numLower (${e}) must not be greater than the number of rows (${s}).`),i=v(e<0?s:e,\"numLower\",\"bandPart\")):(E(e.dtype===\"int32\",()=>\"bandPart(): numLower's dtype must be an int32.\"),i=lo(Tl(e,0),s,Hu(e,s))),typeof t==\"number\"?(E(t%1===0,()=>`bandPart(): numUpper must be an integer, got ${t}.`),E(t<=a,()=>`bandPart(): numUpper (${t}) must not be greater than the number of columns (${a}).`),p=v(t<0?a:t,\"numUpper\",\"bandPart\")):(E(t.dtype===\"int32\",()=>\"bandPart(): numUpper's dtype must be an int32.\"),p=lo(Tl(t,0),a,Hu(t,a)));let u=W(cu(0,s,1,\"int32\"),[-1,1]),c=cu(0,a,1,\"int32\"),l=Te(u,c),m=Uu(ac(l,i),Id(l,pr(p))),d=Gr([s,a],o.dtype);return W(vr(fo(W(o,[-1,s,a])).map(f=>lo(m,f,d))),n)}var _N=N({bandPart_:$j});function Rj(r){let e;if(Array.isArray(r)){e=!1,E(r!=null&&r.length>0,()=>\"Gram-Schmidt process: input must not be null, undefined, or empty\");let n=r[0].shape[0];for(let s=1;s`Gram-Schmidt: Non-unique lengths found in the input vectors: (${r[s].shape[0]} vs. ${n})`)}else e=!0,r=li(r,r.shape[0],0).map(n=>cc(n,[0]));E(r.length<=r[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${r.length}) exceeds number of dimensions (${r[0].shape[0]}).`);let t=[],o=r;for(let n=0;n{let s=o[n];if(n>0)for(let a=0;a=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${r.rank}`),r.rank===2)return $N(r,e);{let t=r.shape.slice(0,r.shape.length-2).reduce((p,u)=>p*u),o=fo(W(r,[t,r.shape[r.shape.length-2],r.shape[r.shape.length-1]]),0),n=[],s=[];o.forEach(p=>{let[u,c]=$N(p,e);n.push(u),s.push(c)});let a=W(vr(n,0),r.shape),i=W(vr(s,0),r.shape);return[a,i]}}function $N(r,e=!1){return T.tidy(()=>{E(r.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${r.shape.length}D Tensor.`);let t=r.shape[0],o=r.shape[1],n=Cd(t),s=Ur(r),a=mu([[1]],[1,1]),i=Ur(a),p=t>=o?o:t;for(let u=0;u{let d=Xe(s,[u,u],[t-u,1]),f=Vu(d),h=Xe(s,[u,u],[1,1]),g=lo(Wu(h,0),mu([[-1]]),mu([[1]])),x=Te(h,se(g,f)),b=je(d,x);b.shape[0]===1?i=Ur(a):i=yt([a,Xe(b,[1,0],[b.shape[0]-1,b.shape[1]])],0);let C=pr(je(Ze(g,x),f)),S=Xe(s,[u,0],[t-u,o]),k=se(C,i),_=mc(i);if(u===0)s=Te(S,Ze(k,Ze(_,S)));else{let D=Te(S,Ze(k,Ze(_,S)));s=yt([Xe(s,[0,0],[u,o]),D],0)}let $=mc(k),R=Xe(n,[0,u],[t,n.shape[1]-u]);if(u===0)n=Te(R,Ze(Ze(R,i),$));else{let D=Te(R,Ze(Ze(R,i),$));n=yt([Xe(n,[0,0],[t,u]),D],1)}return[i,s,n]}),Ot([c,l,m])}return!e&&t>o&&(n=Xe(n,[0,0],[t,o]),s=Xe(s,[0,0],[o,o])),[n,s]})}var RN=N({qr_:Dj});var $t;(function(r){r[r.NONE=0]=\"NONE\",r[r.MEAN=1]=\"MEAN\",r[r.SUM=2]=\"SUM\",r[r.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"})($t||($t={}));function Aj(r,e,t=$t.SUM_BY_NONZERO_WEIGHTS){let o=v(r,\"losses\",\"computeWeightedLoss\"),n=null;e!=null&&(n=v(e,\"weights\",\"computeWeightedLoss\"));let s=n==null?o:se(o,n);if(t===$t.NONE)return s;if(t===$t.SUM)return ot(s);if(t===$t.MEAN){if(n==null)return Gu(s);{let a=o.size/n.size,i=je(ot(s),ot(n));return a>1?je(i,ke(a)):i}}if(t===$t.SUM_BY_NONZERO_WEIGHTS){if(n==null)return je(ot(s),ke(o.size));{let a=se(n,Da(o.shape)),i=Ue(ot(Fd(a,ke(0))),\"float32\");return je(ot(s),i)}}throw Error(`Unknown reduction: ${t}`)}var cr=N({computeWeightedLoss_:Aj});function Fj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"absoluteDifference\"),s=v(e,\"predictions\",\"absoluteDifference\"),a=null;t!=null&&(a=v(t,\"weights\",\"absoluteDifference\")),xt(n.shape,s.shape,\"Error in absoluteDifference: \");let i=Qt(Te(n,s));return cr(i,a,o)}var DN=N({absoluteDifference_:Fj});function Pj(r,e,t,o,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"cosineDistance\"),a=v(e,\"predictions\",\"cosineDistance\"),i=null;o!=null&&(i=v(o,\"weights\",\"cosineDistance\")),xt(s.shape,a.shape,\"Error in cosineDistance: \");let p=ke(1),u=Te(p,ot(se(s,a),t,!0));return cr(u,i,n)}var AN=N({cosineDistance_:Pj});function Oj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"hingeLoss\"),s=v(e,\"predictions\",\"hingeLoss\"),a=null;t!=null&&(a=v(t,\"weights\",\"hingeLoss\")),xt(n.shape,s.shape,\"Error in hingeLoss: \");let i=ke(1);n=Te(se(ke(2),n),i);let p=lu(Te(i,se(n,s)));return cr(p,a,o)}var FN=N({hingeLoss_:Oj});function Mj(r,e,t,o=1,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"huberLoss\"),a=v(e,\"predictions\",\"huberLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"huberLoss\")),xt(s.shape,a.shape,\"Error in huberLoss: \");let p=ke(o),u=Qt(Te(a,s)),c=Hu(u,p),l=Te(u,c),m=Ce(se(ke(.5),Zt(c)),se(p,l));return cr(m,i,n)}var PN=N({huberLoss_:Mj});function Lj(r,e,t,o=1e-7,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"labels\",\"logLoss\"),a=v(e,\"predictions\",\"logLoss\"),i=null;t!=null&&(i=v(t,\"weights\",\"logLoss\")),xt(s.shape,a.shape,\"Error in logLoss: \");let p=ke(1),u=ke(o),c=pr(se(s,pi(Ce(a,u)))),l=se(Te(p,s),pi(Ce(Te(p,a),u))),m=Te(c,l);return cr(m,i,n)}var ON=N({logLoss_:Lj});function Bj(r,e,t,o=$t.SUM_BY_NONZERO_WEIGHTS){let n=v(r,\"labels\",\"meanSquaredError\"),s=v(e,\"predictions\",\"meanSquaredError\"),a=null;t!=null&&(a=v(t,\"weights\",\"meanSquaredError\")),xt(n.shape,s.shape,\"Error in meanSquaredError: \");let i=Kd(n,s);return cr(i,a,o)}var MN=N({meanSquaredError_:Bj});function zj(r,e){let t=v(r,\"labels\",\"sigmoidCrossEntropyWithLogits\"),o=v(e,\"logits\",\"sigmoidCrossEntropyWithLogits\");xt(t.shape,o.shape,\"Error in sigmoidCrossEntropyWithLogits: \");let n=lu(o),s=se(o,t),a=kd(_o(pr(Qt(o))));return Ce(Te(n,s),a)}function Vj(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let 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Labels / logits was rank ${e.rank} and dim was ${t}`);return Ir((n,s,a)=>{let p=_d(s,[t],!0),u=Te(Ue(s,\"float32\"),p);a([n,u]);let c=pr(se(u,n));return{value:ot(c,[t]),gradFunc:(d,f)=>{let[h,g]=f,x=ii(d.shape,[t]);return[se(W(d,x),Te(Ue(h,\"float32\"),_o(g))),se(W(d,x),Te(_o(g),Ue(h,\"float32\")))]}}})(r,e)}function Uj(r,e,t,o=0,n=$t.SUM_BY_NONZERO_WEIGHTS){let s=v(r,\"onehotLabels\",\"softmaxCrossEntropy\"),a=v(e,\"logits\",\"softmaxCrossEntropy\"),i=null;if(t!=null&&(i=v(t,\"weights\",\"softmaxCrossEntropy\")),xt(s.shape,a.shape,\"Error in softmaxCrossEntropy: \"),o>0){let u=ke(o),c=ke(1),l=ke(s.shape[1]);s=Ce(se(s,Te(c,u)),je(u,l))}let p=Wj(s,a);return cr(p,i,n)}var BN=N({softmaxCrossEntropy_:Uj});function Gj(r,e,t,o){let n=v(r,\"indices\",\"sparseFillEmptyRows\",\"int32\"),s=v(e,\"values\",\"sparseFillEmptyRows\"),a=v(t,\"denseShape\",\"sparseFillEmptyRows\",\"int32\"),i=v(o,\"defaultValue\",\"sparseFillEmptyRows\",s.dtype);if(n.rank!==2)throw new Error(`Indices should be Tensor2D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(a.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${a.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let p={indices:n,values:s,denseShape:a,defaultValue:i},u=T.runKernel(Ki,p);return{outputIndices:u[0],outputValues:u[1],emptyRowIndicator:u[2],reverseIndexMap:u[3]}}var zN=N({sparseFillEmptyRows_:Gj});function Hj(r,e,t){let o=v(r,\"inputIndices\",\"sparseReshape\",\"int32\"),n=v(e,\"inputShape\",\"sparseReshape\",\"int32\"),s=v(t,\"newShape\",\"sparseReshape\",\"int32\");if(o.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape\n ${o.shape}`);if(n.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${n.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let a={inputIndices:o,inputShape:n,newShape:s},i=T.runKernel(ei,a);return{outputIndices:i[0],outputShape:i[1]}}var VN=N({sparseReshape_:Hj});function Kj(r,e,t){let o=v(r,\"data\",\"sparseSegmentMean\"),n=v(e,\"indices\",\"sparseSegmentMean\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentMean\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ya,a)}var WN=N({sparseSegmentMean_:Kj});function qj(r,e,t){let o=v(r,\"data\",\"sparseSegmentSum\"),n=v(e,\"indices\",\"sparseSegmentSum\",\"int32\"),s=v(t,\"segmentIds\",\"sparseSegmentSum\",\"int32\");if(o.rank<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.rank!==1)throw new Error(`Indices should be Tensor1D but received shape\n ${n.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape\n ${s.shape}`);let a={data:o,indices:n,segmentIds:s};return T.runKernel(ba,a)}var UN=N({sparseSegmentSum_:qj});function jj(r,e,t,o,n,s,a,i){let p=v(r,\"data\",\"stringNGrams\",\"string\");if(p.dtype!==\"string\")throw new Error(\"Data must be of datatype string\");if(p.shape.length!==1)throw new Error(`Data must be a vector, saw: ${p.shape}`);let u=v(e,\"dataSplits\",\"stringNGrams\");if(u.dtype!==\"int32\")throw new Error(\"Data splits must be of datatype int32\");let c={separator:t,nGramWidths:o,leftPad:n,rightPad:s,padWidth:a,preserveShortSequences:i},l={data:p,dataSplits:u},m=T.runKernel(Ca,l,c);return{nGrams:m[0],nGramsSplits:m[1]}}var GN=N({stringNGrams_:jj});function Xj(r,e,t=!0){let o=v(r,\"input\",\"stringSplit\",\"string\"),n=v(e,\"delimiter\",\"stringSplit\",\"string\");if(o.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${o.shape}`);if(n.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${n.shape}`);let s={skipEmpty:t},a={input:o,delimiter:n},i=T.runKernel(ji,a,s);return{indices:i[0],values:i[1],shape:i[2]}}var HN=N({stringSplit_:Xj});function Yj(r,e){let t=v(r,\"input\",\"stringToHashBucketFast\",\"string\"),o={numBuckets:e};if(e<=0)throw new Error(\"Number of buckets must be at least 1\");let n={input:t};return T.runKernel(Xi,n,o)}var KN=N({stringToHashBucketFast_:Yj});function Qj(r,e,t,o=!0){let n=v(r,\"input\",\"staticRegexReplace\",\"string\"),s={pattern:e,rewrite:t,replaceGlobal:o};return T.runKernel(Ru,{x:n},s)}var qN=N({staticRegexReplace_:Qj});var Zj={fft:uc,ifft:ju,rfft:pc,irfft:Hd},Jj={hammingWindow:pN,hannWindow:Qd,frame:Zd,stft:cN},eX={flipLeftRight:mN,grayscaleToRGB:dN,resizeNearestNeighbor:kN,resizeBilinear:vN,rgbToGrayscale:fN,rotateWithOffset:hN,cropAndResize:lN,nonMaxSuppression:gN,nonMaxSuppressionAsync:bN,nonMaxSuppressionWithScore:CN,nonMaxSuppressionWithScoreAsync:wN,nonMaxSuppressionPadded:SN,nonMaxSuppressionPaddedAsync:IN,threshold:NN,transform:TN},tX={bandPart:_N,gramSchmidt:EN,qr:RN},rX={absoluteDifference:DN,computeWeightedLoss:cr,cosineDistance:AN,hingeLoss:FN,huberLoss:PN,logLoss:ON,meanSquaredError:MN,sigmoidCrossEntropy:LN,softmaxCrossEntropy:BN},oX={sparseFillEmptyRows:zN,sparseReshape:VN,sparseSegmentMean:WN,sparseSegmentSum:UN},nX={stringNGrams:GN,stringSplit:HN,stringToHashBucketFast:KN,staticRegexReplace:qN};var jN={};qe(jN,{Serializable:()=>Rl,SerializationMap:()=>rf,getRegisteredName:()=>aX,registerClass:()=>rS});var sX=new Map,tS=new Map,Rl=class{getClassName(){return this.constructor.className}static fromConfig(e,t){return new e(t)}},rf=class r{constructor(){this.classNameMap={}}static getMap(){return r.instance==null&&(r.instance=new r),r.instance}static register(e){r.getMap().classNameMap[e.className]=[e,e.fromConfig]}};function rS(r,e,t){E(r.className!=null,()=>\"Class being registered does not have the static className property defined.\"),E(typeof r.className==\"string\",()=>\"className is required to be a string, but got type \"+typeof r.className),E(r.className.length>0,()=>\"Class being registered has an empty-string as its className, which is disallowed.\"),typeof e==\"undefined\"&&(e=\"Custom\"),typeof t==\"undefined\"&&(t=r.className);let o=t,n=e+\">\"+o;return rf.register(r),sX.set(n,r),tS.set(r,n),r}function aX(r){return tS.has(r)?tS.get(r):r.className}var kr=class extends Rl{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Ot(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return Vw(e,t)}dispose(){this.iterations_!=null&&Ot(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:\"iter\",tensor:ke(this.iterations_,\"int32\")}}async getWeights(){throw new Error(\"getWeights() is not implemented for this optimizer yet.\")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(kr,Symbol.hasInstance,{value:r=>r.minimize!=null&&r.computeGradients!=null&&r.applyGradients!=null});var Ju=class extends kr{static get className(){return\"Adadelta\"}constructor(e,t,o=null){super(),this.learningRate=e,this.rho=t,this.epsilon=o,this.accumulatedGrads=[],this.accumulatedUpdates=[],o==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accum_grad`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedUpdates[n]==null&&(this.accumulatedUpdates[n]={originalName:`${o}/accum_var`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedGrads[n].variable,u=this.accumulatedUpdates[n].variable;De(()=>{let c=Ce(se(p,this.rho),se(Zt(i),1-this.rho)),l=se(je(Rr(Ce(u,this.epsilon)),Rr(Ce(p,this.epsilon))),i),m=Ce(se(u,this.rho),se(Zt(l),1-this.rho));p.assign(c),u.assign(m);let d=Ce(se(l,-this.learningRate),s);s.assign(d)})}),this.incrementIterations()}dispose(){this.accumulatedUpdates!=null&&(Ot(this.accumulatedGrads.map(e=>e.variable)),Ot(this.accumulatedUpdates.map(e=>e.variable)))}async getWeights(){let e=[...this.accumulatedGrads,...this.accumulatedUpdates];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};var ep=class extends kr{static get className(){return\"Adagrad\"}constructor(e,t=.1){super(),this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulatedGrads[n]==null&&(this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:De(()=>$a(s.shape,this.initialAccumulatorValue).variable(!1))});let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;De(()=>{let p=Ce(i,Zt(a));i.assign(p);let u=Ce(se(je(a,Rr(Ce(p,T.backend.epsilon()))),-this.learningRate),s);s.assign(u)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&Ot(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};var tp=class extends kr{static get className(){return\"Adam\"}constructor(e,t,o,n=null){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],De(()=>{this.accBeta1=ke(t).variable(),this.accBeta2=ke(o).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=Te(1,this.accBeta2);t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:De(()=>Gt(i).variable(p))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:De(()=>Gt(i).variable(p))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedSecondMoment[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=Ce(se(l,this.beta2),se(Zt(u),1-this.beta2)),f=je(m,o),h=je(d,n);c.assign(m),l.assign(d);let g=Ce(se(je(f,Ce(Rr(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(se(this.accBeta1,this.beta1)),this.accBeta2.assign(se(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ot(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),De(()=>{this.accBeta1.assign(ui(this.beta1,this.iterations_+1)),this.accBeta2.assign(ui(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};var rp=class extends kr{static get className(){return\"Adamax\"}constructor(e,t,o,n=null,s=0){super(),this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],De(()=>{this.iteration=ke(0).variable(),this.accBeta1=ke(t).variable()}),n==null&&(this.epsilon=T.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);De(()=>{let o=Te(1,this.accBeta1),n=je(-this.learningRate,Ce(se(this.iteration,this.decay),1));t.forEach((s,a)=>{let i=T.registeredVariables[s],p=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:Gt(i).variable(p)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${s}/v`,variable:Gt(i).variable(p)});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,l=this.accumulatedWeightedInfNorm[a].variable,m=Ce(se(c,this.beta1),se(u,1-this.beta1)),d=se(l,this.beta2),f=Qt(u),h=Ad(d,f);c.assign(m),l.assign(h);let g=Ce(se(je(n,o),je(m,Ce(h,this.epsilon))),i);i.assign(g)}),this.iteration.assign(Ce(this.iteration,1)),this.accBeta1.assign(se(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ot(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ot(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error(\"getWeights() is not implemented for Adamax yet.\")}async setWeights(e){throw new Error(\"setWeights() is not implemented for Adamax yet.\")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};var mi=class extends kr{static get className(){return\"SGD\"}constructor(e){super(),this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=T.registeredVariables[o];De(()=>{let i=Ce(se(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=$r(ke(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error(\"SGD optimizer does not have settable weights.\")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};var op=class extends mi{static get className(){return\"Momentum\"}constructor(e,t,o=!1){super(e),this.learningRate=e,this.momentum=t,this.useNesterov=o,this.accumulations=[],this.m=ke(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o];this.accumulations[n]==null&&(this.accumulations[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(!1))});let a=this.accumulations[n].variable,i=Array.isArray(e)?e[n].tensor:e[o];i!=null&&De(()=>{let p,u=Ce(se(this.m,a),i);this.useNesterov?p=Ce(se(this.c,Ce(i,se(u,this.m))),s):p=Ce(se(this.c,u),s),a.assign(u),s.assign(p)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&Ot(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(o=>({originalName:o.name,variable:o.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};var np=class extends kr{static get className(){return\"RMSProp\"}constructor(e,t=.9,o=0,n=null,s=!1){if(super(),this.learningRate=e,this.decay=t,this.momentum=o,this.epsilon=n,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=s,n==null&&(this.epsilon=T.backend.epsilon()),e==null)throw new Error(\"learningRate for RMSPropOptimizer must be defined.\")}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=T.registeredVariables[o],a=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${o}/rms`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${o}/momentum`,variable:De(()=>Gt(s).variable(a))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${o}/mg`,variable:De(()=>Gt(s).variable(a))});let i=Array.isArray(e)?e[n].tensor:e[o];if(i==null)return;let p=this.accumulatedMeanSquares[n].variable,u=this.accumulatedMoments[n].variable;De(()=>{let c=Ce(se(p,this.decay),se(Zt(i),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,m=Ce(se(l,this.decay),se(i,1-this.decay)),d=je(se(i,this.learningRate),Rr(Te(c,Ce(Zt(m),this.epsilon)))),f=Ce(se(u,this.momentum),d);p.assign(c),l.assign(m),u.assign(f);let h=Te(s,f);s.assign(h)}else{let l=Ce(se(p,this.decay),se(Zt(i),1-this.decay)),m=Ce(se(u,this.momentum),je(se(i,this.learningRate),Rr(Ce(l,this.epsilon))));p.assign(l),u.assign(m);let d=Te(s,m);s.assign(d)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ot(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ot(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ot(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=this.centered?e.length/3:e.length/2,o=!1;this.accumulatedMeanSquares=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedMoments=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};var iX=[Ju,ep,tp,rp,op,np,mi];function XN(){for(let r of iX)rS(r)}var 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o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=I(\"numElements\",r,e,t);return[t.getTensorList(o.id).stack(n,s,a)]}case\"TensorListFromTensor\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t),a=$T(o,n,s);return t.addTensorList(a),[a.idTensor]}case\"TensorListConcat\":case\"TensorListConcatV2\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id),s=I(\"dtype\",r,e,t),a=I(\"elementShape\",r,e,t);return[n.concat(s,a)]}case\"TensorListPushBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"tensor\",r,e,t),s=t.getTensorList(o.id);return s.pushBack(n),[s.idTensor]}case\"TensorListPopBack\":{let o=I(\"tensorListId\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"elementDType\",r,e,t);return[t.getTensorList(o.id).popBack(n,s)]}case\"TensorListSplit\":{let o=I(\"tensor\",r,e,t),n=I(\"elementShape\",r,e,t),s=I(\"lengths\",r,e,t),a=AT(o,s,n);return t.addTensorList(a),[a.idTensor]}case\"TensorListLength\":{let o=I(\"tensorListId\",r,e,t),n=t.getTensorList(o.id);return[ke(n.size(),\"int32\")]}case\"TensorListResize\":{let o=I(\"tensorListId\",r,e,t),n=I(\"size\",r,e,t),a=t.getTensorList(o.id).resize(n);return t.addTensorList(a),[a.idTensor]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};function PT(r,e,t){let[o,n]=I(\"fusedOps\",r,e,t),s=o===\"biasadd\",a=!s,i=n===\"prelu\",p=o===\"fusedbatchnorm\",u=I(\"numArgs\",r,e,t);if(s){if(i&&u!==2)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&s&&u!==1)throw new Error(\"FusedConv2d and DepthwiseConv2d with BiasAdd must have one extra argument: bias.\")}if(p)throw new Error(\"FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported\");let c=I(\"strides\",r,e,t),l=Pl(r,e,t),m=I(\"dataFormat\",r,e,t).toUpperCase(),d=I(\"dilations\",r,e,t),[f,h]=I(\"args\",r,e,t);a&&(h=f,f=void 0);let g=I(\"leakyreluAlpha\",r,e,t);return{stride:c,pad:l,dataFormat:m,dilations:d,biasArg:f,preluArg:h,activationFunc:n,leakyreluAlpha:g}}var OT=(r,e,t,o=Je)=>{switch(r.op){case\"Conv1D\":{let n=I(\"stride\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilation\",r,e,t);return[o.conv1d(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,s,a,i)]}case\"Conv2D\":{let n=I(\"strides\",r,e,t),s=Pl(r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,a,[i[1],i[2]])]}case\"_FusedConv2D\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PT(r,e,t);return[o.fused.conv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"FusedDepthwiseConv2dNative\":{let{stride:n,pad:s,dataFormat:a,dilations:i,biasArg:p,preluArg:u,activationFunc:c,leakyreluAlpha:l}=PT(r,e,t);return[o.fused.depthwiseConv2d({x:I(\"x\",r,e,t),filter:I(\"filter\",r,e,t),strides:[n[1],n[2]],pad:s,dataFormat:a,dilations:[i[1],i[2]],bias:p,activation:c,preluActivationWeights:u,leakyreluAlpha:l})]}case\"Conv2DBackpropInput\":case\"Conv2dTranspose\":{let n=I(\"outputShape\",r,e,t),s=I(\"strides\",r,e,t),a=Pl(r,e,t);return[o.conv2dTranspose(I(\"x\",r,e,t),I(\"filter\",r,e,t),n,[s[1],s[2]],a)]}case\"DepthwiseConv2dNative\":case\"DepthwiseConv2d\":{let n=I(\"strides\",r,e,t),s=Pl(r,e,t),a=I(\"dilations\",r,e,t),i=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthwiseConv2d(I(\"input\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2]],s,i,[a[1],a[2]])]}case\"Conv3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dataFormat\",r,e,t).toUpperCase(),i=I(\"dilations\",r,e,t);return[o.conv3d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[n[1],n[2],n[3]],s,a,[i[1],i[2],i[3]])]}case\"AvgPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPool\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s)]}case\"MaxPoolWithArgmax\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t),i=I(\"includeBatchInIndex\",r,e,t),{result:p,indexes:u}=o.maxPoolWithArgmax(I(\"x\",r,e,t),[a[1],a[2]],[n[1],n[2]],s,i);return[p,u]}case\"AvgPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.avgPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"MaxPool3D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"kernelSize\",r,e,t);return[o.maxPool3d(I(\"x\",r,e,t),[a[1],a[2],a[3]],[n[1],n[2],n[3]],s)]}case\"Dilation2D\":{let n=I(\"strides\",r,e,t),s=I(\"pad\",r,e,t),a=I(\"dilations\",r,e,t),i=n[1],p=n[2],u=a[1],c=a[2];return[o.dilation2d(I(\"x\",r,e,t),I(\"filter\",r,e,t),[i,p],s,[u,c],\"NHWC\")]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var MT=(r,e,t,o=Je)=>{switch(r.op){case\"Fill\":{let n=I(\"shape\",r,e,t),s=I(\"dtype\",r,e,t),a=I(\"value\",r,e,t);return[o.fill(n,a,s)]}case\"LinSpace\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"num\",r,e,t);return[o.linspace(n,s,a)]}case\"Multinomial\":{let n=I(\"logits\",r,e,t),s=I(\"numSamples\",r,e,t),a=I(\"seed\",r,e,t);return[o.multinomial(n,s,a)]}case\"OneHot\":{let n=I(\"indices\",r,e,t),s=I(\"depth\",r,e,t),a=I(\"onValue\",r,e,t),i=I(\"offValue\",r,e,t),p=I(\"dtype\",r,e,t);return[o.oneHot(n,s,a,i,p)]}case\"Ones\":return[o.ones(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"OnesLike\":return[o.onesLike(I(\"x\",r,e,t))];case\"RandomStandardNormal\":return[o.randomStandardNormal(I(\"shape\",r,e,t),I(\"dtype\",r,e,t),I(\"seed\",r,e,t))];case\"RandomUniform\":return[o.randomUniform(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"dtype\",r,e,t))];case\"RandomUniformInt\":return[o.randomUniformInt(I(\"shape\",r,e,t),I(\"minval\",r,e,t),I(\"maxval\",r,e,t),I(\"seed\",r,e,t))];case\"Range\":{let n=I(\"start\",r,e,t),s=I(\"stop\",r,e,t),a=I(\"step\",r,e,t);return[o.range(n,s,a,I(\"dtype\",r,e,t))]}case\"TruncatedNormal\":{let n=I(\"shape\",r,e,t),s=I(\"mean\",r,e,t),a=I(\"stdDev\",r,e,t),i=I(\"seed\",r,e,t);return[o.truncatedNormal(n,s,a,I(\"dtype\",r,e,t),i)]}case\"Zeros\":return[o.zeros(I(\"shape\",r,e,t),I(\"dtype\",r,e,t))];case\"ZerosLike\":return[o.zerosLike(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function OS(r,e,t){let o=I(\"boxes\",r,e,t),n=I(\"scores\",r,e,t),s=I(\"maxOutputSize\",r,e,t),a=I(\"iouThreshold\",r,e,t),i=I(\"scoreThreshold\",r,e,t),p=I(\"softNmsSigma\",r,e,t);return{boxes:o,scores:n,maxOutputSize:s,iouThreshold:a,scoreThreshold:i,softNmsSigma:p}}var LT=async(r,e,t,o,n=Je)=>{switch(r.op){case\"NonMaxSuppressionV5\":{let{boxes:s,scores:a,maxOutputSize:i,iouThreshold:p,scoreThreshold:u,softNmsSigma:c}=OS(r,e,t),l=await 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n=I(\"x\",r,e,t),s=I(\"k\",r,e,t),a=I(\"sorted\",r,e,t),i=o.topk(n,s,a);return[i.values,i.indices]}case\"UpperBound\":{let n=I(\"sortedSequence\",r,e,t),s=I(\"values\",r,e,t);return[o.upperBound(n,s)]}case\"Unique\":{let n=I(\"x\",r,e,t),s=o.unique(n);return[s.values,s.indices]}case\"UniqueV2\":{let n=I(\"x\",r,e,t),s=I(\"axis\",r,e,t),a=o.unique(n,s);return[a.values,a.indices]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var zT=(r,e,t,o=Je)=>{switch(r.op){case\"Const\":return e[r.name];case\"PlaceholderWithDefault\":let n=I(\"default\",r,e,t);return[Bt(r.name,e,t)||n];case\"Placeholder\":return[Bt(r.name,e,t)];case\"Identity\":case\"StopGradient\":case\"FakeQuantWithMinMaxVars\":{let c=I(\"x\",r,e,t);return[Bs(c)]}case\"IdentityN\":return I(\"x\",r,e,t).map(c=>Bs(c));case\"Snapshot\":let s=I(\"x\",r,e,t);return[Bs(s)];case\"Shape\":return[o.tensor1d(I(\"x\",r,e,t).shape,\"int32\")];case\"ShapeN\":return I(\"x\",r,e,t).map(c=>o.tensor1d(c.shape));case\"Size\":return[o.scalar(I(\"x\",r,e,t).size,\"int32\")];case\"Rank\":return[o.scalar(I(\"x\",r,e,t).rank,\"int32\")];case\"NoOp\":return[o.scalar(1)];case\"Print\":let a=I(\"x\",r,e,t),i=I(\"data\",r,e,t),p=I(\"message\",r,e,t),u=I(\"summarize\",r,e,t);console.warn(\"The graph has a tf.print() operation,usually used for debugging, which slows down performance.\"),console.log(p);for(let c=0;ce.dispose()),this.tensorMap.clear(),this.handle.dispose()}size(){return this.tensorMap.size}tensorSize(){return ke(this.size(),\"int32\")}async import(e,t){this.checkKeyAndValueTensor(e,t);let o=await e.data();return this.tensorMap.forEach(n=>n.dispose()),this.tensorMap.clear(),De(()=>{let n=fo(t),s=o.length,a=n.length;y.assert(s===a,()=>`The number of elements doesn't match, keys has ${s} elements, the values has ${a} elements.`);for(let i=0;i{let n=[];for(let s=0;s{switch(r.op){case\"HashTable\":case\"HashTableV2\":{let n=o.getHashTableHandleByName(r.name);if(n!=null)return[n];{let s=I(\"keyDType\",r,e,t),a=I(\"valueDType\",r,e,t),i=new vf(s,a);return o.addHashTable(r.name,i),[i.handle]}}case\"InitializeTable\":case\"InitializeTableV2\":case\"LookupTableImport\":case\"LookupTableImportV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"values\",r,e,t);return[await o.getHashTableById(n.id).import(s,a)]}case\"LookupTableFind\":case\"LookupTableFindV2\":{let n=I(\"tableHandle\",r,e,t,o),s=I(\"keys\",r,e,t),a=I(\"defaultValue\",r,e,t);return[await o.getHashTableById(n.id).find(s,a)]}case\"LookupTableSize\":case\"LookupTableSizeV2\":{let n=I(\"tableHandle\",r,e,t,o);return[o.getHashTableById(n.id).tensorSize()]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var WT=(r,e,t,o=Je)=>{switch(r.op){case\"ResizeBilinear\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeBilinear(n,[s[0],s[1]],a,i)]}case\"ResizeNearestNeighbor\":{let n=I(\"images\",r,e,t),s=I(\"size\",r,e,t),a=I(\"alignCorners\",r,e,t),i=I(\"halfPixelCenters\",r,e,t);return[o.image.resizeNearestNeighbor(n,[s[0],s[1]],a,i)]}case\"CropAndResize\":{let n=I(\"image\",r,e,t),s=I(\"boxes\",r,e,t),a=I(\"boxInd\",r,e,t),i=I(\"cropSize\",r,e,t),p=I(\"method\",r,e,t),u=I(\"extrapolationValue\",r,e,t);return[o.image.cropAndResize(n,s,a,i,p,u)]}case\"ImageProjectiveTransformV3\":{let n=I(\"images\",r,e,t),s=I(\"transforms\",r,e,t),a=I(\"outputShape\",r,e,t),i=I(\"fillValue\",r,e,t),p=I(\"interpolation\",r,e,t),u=I(\"fillMode\",r,e,t);return[o.image.transform(n,s,p.toLowerCase(),u.toLowerCase(),i,a)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var UT=(r,e,t,o=Je)=>{switch(r.op){case\"Equal\":return[o.equal(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"NotEqual\":return[o.notEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Greater\":return[o.greater(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"GreaterEqual\":return[o.greaterEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Less\":return[o.less(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LessEqual\":return[o.lessEqual(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalAnd\":return[o.logicalAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"LogicalNot\":return[o.logicalNot(I(\"a\",r,e,t))];case\"LogicalOr\":return[o.logicalOr(I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"Select\":case\"SelectV2\":return[o.where(I(\"condition\",r,e,t),I(\"a\",r,e,t),I(\"b\",r,e,t))];case\"BitwiseAnd\":return[o.bitwiseAnd(I(\"a\",r,e,t),I(\"b\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var GT=(r,e,t,o=Je)=>{switch(r.op){case\"BatchMatMul\":case\"BatchMatMulV2\":case\"MatMul\":return[o.matMul(I(\"a\",r,e,t),I(\"b\",r,e,t),I(\"transposeA\",r,e,t),I(\"transposeB\",r,e,t))];case\"Einsum\":return[o.einsum(I(\"equation\",r,e,t),...I(\"tensors\",r,e,t))];case\"Transpose\":return[o.transpose(I(\"x\",r,e,t),I(\"perm\",r,e,t))];case\"_FusedMatMul\":let[n,s]=I(\"fusedOps\",r,e,t),a=n===\"biasadd\",i=s===\"prelu\",p=I(\"numArgs\",r,e,t),u=I(\"leakyreluAlpha\",r,e,t);if(a){if(i&&p!==2)throw new Error(\"Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.\");if(!i&&p!==1)throw new Error(\"Fused MatMul with BiasAdd must have one extra argument: bias.\")}let[c,l]=I(\"args\",r,e,t);return[o.fused.matMul({a:I(\"a\",r,e,t),b:I(\"b\",r,e,t),transposeA:I(\"transposeA\",r,e,t),transposeB:I(\"transposeB\",r,e,t),bias:c,activation:s,preluActivationWeights:l,leakyreluAlpha:u})];case\"MatrixBandPart\":return[o.linalg.bandPart(I(\"a\",r,e,t),I(\"numLower\",r,e,t),I(\"numUpper\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var HT=(r,e,t,o=Je)=>{switch(r.op){case\"EuclideanNorm\":return[o.euclideanNorm(I(\"x\",r,e,t),I(\"axis\",r,e,t),I(\"keepDims\",r,e,t))];case\"FusedBatchNorm\":case\"FusedBatchNormV2\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"FusedBatchNormV3\":return[o.batchNorm(I(\"x\",r,e,t),I(\"mean\",r,e,t),I(\"variance\",r,e,t),I(\"offset\",r,e,t),I(\"scale\",r,e,t),I(\"epsilon\",r,e,t))];case\"LRN\":return[o.localResponseNormalization(I(\"x\",r,e,t),I(\"radius\",r,e,t),I(\"bias\",r,e,t),I(\"alpha\",r,e,t),I(\"beta\",r,e,t))];case\"Softmax\":return[o.softmax(I(\"x\",r,e,t))];case\"LogSoftmax\":return[o.logSoftmax(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var KT=(r,e,t,o=Je)=>{switch(r.op){case\"RaggedGather\":{let{outputNestedSplits:n,outputDenseValues:s}=o.raggedGather(I(\"paramsNestedSplits\",r,e,t),I(\"paramsDenseValues\",r,e,t),I(\"indices\",r,e,t),I(\"outputRaggedRank\",r,e,t));return n.concat(s)}case\"RaggedRange\":{let{rtNestedSplits:n,rtDenseValues:s}=o.raggedRange(I(\"starts\",r,e,t),I(\"limits\",r,e,t),I(\"splits\",r,e,t));return[n,s]}case\"RaggedTensorToTensor\":return[o.raggedTensorToTensor(I(\"shape\",r,e,t),I(\"values\",r,e,t),I(\"defaultValue\",r,e,t),I(\"rowPartitionTensors\",r,e,t),I(\"rowPartitionTypes\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var qT=(r,e,t,o=Je)=>{switch(r.op){case\"Max\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.max(I(\"x\",r,e,t),i,p)]}case\"Mean\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.mean(I(\"x\",r,e,t),i,p)]}case\"Min\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.min(I(\"x\",r,e,t),i,p)]}case\"Sum\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.sum(I(\"x\",r,e,t),i,p)]}case\"All\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.all(I(\"x\",r,e,t),i,p)]}case\"Any\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.any(I(\"x\",r,e,t),i,p)]}case\"ArgMax\":{let i=I(\"axis\",r,e,t);return[o.argMax(I(\"x\",r,e,t),i)]}case\"ArgMin\":{let i=I(\"axis\",r,e,t);return[o.argMin(I(\"x\",r,e,t),i)]}case\"Prod\":{let i=I(\"axis\",r,e,t),p=I(\"keepDims\",r,e,t);return[o.prod(I(\"x\",r,e,t),i,p)]}case\"Cumprod\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumprod(I(\"x\",r,e,t),i,p,u)]}case\"Cumsum\":{let i=I(\"axis\",r,e,t),p=I(\"exclusive\",r,e,t),u=I(\"reverse\",r,e,t);return[o.cumsum(I(\"x\",r,e,t),i,p,u)]}case\"Bincount\":let n=I(\"x\",r,e,t),s=I(\"weights\",r,e,t),a=I(\"size\",r,e,t);return[o.bincount(n,s,a)];case\"DenseBincount\":{let i=I(\"x\",r,e,t),p=I(\"weights\",r,e,t),u=I(\"size\",r,e,t),c=I(\"binaryOutput\",r,e,t);return[o.denseBincount(i,p,u,c)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var jT=(r,e,t,o=Je)=>{switch(r.op){case\"ConcatV2\":case\"Concat\":{let n=I(\"n\",r,e,t),s=I(\"axis\",r,e,t),a=I(\"tensors\",r,e,t);return a=a.slice(0,n),[o.concat(a,s)]}case\"Gather\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gather(n,o.cast(s,\"int32\"),0)]}case\"GatherV2\":{let n=I(\"axis\",r,e,t),s=I(\"batchDims\",r,e,t),a=I(\"x\",r,e,t),i=I(\"indices\",r,e,t);return[o.gather(a,o.cast(i,\"int32\"),n,s)]}case\"Reverse\":{let n=I(\"dims\",r,e,t),s=[];for(let i=0;i{let n=I(\"axis\",r,e,t),s=I(\"tensors\",r,e,t),a=s[0].shape,i=o.squeeze(s[0]).shape,p=s.map(u=>{let c=y.arraysEqual(u.shape,a);if(!c&&!y.arraysEqual(o.squeeze(u).shape,i))throw new Error(\"the input tensors shape does not match\");return c?u:o.reshape(u,a)});return[o.stack(p,n)]});case\"Unpack\":{let n=I(\"axis\",r,e,t),s=I(\"tensor\",r,e,t);return o.unstack(s,n)}case\"Tile\":{let n=I(\"reps\",r,e,t);return[o.tile(I(\"x\",r,e,t),n)]}case\"Split\":case\"SplitV\":{let n=I(\"axis\",r,e,t),s=I(\"numOrSizeSplits\",r,e,t),a=I(\"x\",r,e,t);return o.split(a,s,n)}case\"ScatterNd\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"shape\",r,e,t);return[o.scatterND(n,s,a)]}case\"GatherNd\":{let n=I(\"x\",r,e,t),s=I(\"indices\",r,e,t);return[o.gatherND(n,s)]}case\"SparseToDense\":{let n=I(\"sparseIndices\",r,e,t),s=I(\"outputShape\",r,e,t),a=I(\"sparseValues\",r,e,t),i=I(\"defaultValue\",r,e,t);return[o.sparseToDense(n,a,s,a.dtype===i.dtype?i:o.cast(i,a.dtype))]}case\"TensorScatterUpdate\":{let n=I(\"indices\",r,e,t),s=I(\"values\",r,e,t),a=I(\"tensor\",r,e,t);return[o.tensorScatterUpdate(a,n,s)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var XT=(r,e,t,o=Je)=>{switch(r.op){case\"SparseFillEmptyRows\":{let{outputIndices:n,outputValues:s,emptyRowIndicator:a,reverseIndexMap:i}=o.sparse.sparseFillEmptyRows(I(\"indices\",r,e,t),I(\"values\",r,e,t),I(\"denseShape\",r,e,t),I(\"defaultValue\",r,e,t));return[n,s,a,i]}case\"SparseReshape\":{let{outputIndices:n,outputShape:s}=o.sparse.sparseReshape(I(\"inputIndices\",r,e,t),I(\"inputShape\",r,e,t),I(\"newShape\",r,e,t));return[n,s]}case\"SparseSegmentMean\":return[o.sparse.sparseSegmentMean(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];case\"SparseSegmentSum\":return[o.sparse.sparseSegmentSum(I(\"data\",r,e,t),I(\"indices\",r,e,t),I(\"segmentIds\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var YT=(r,e,t,o=Je)=>{switch(r.op){case\"FFT\":return[o.fft(I(\"x\",r,e,t))];case\"IFFT\":return[o.ifft(I(\"x\",r,e,t))];case\"RFFT\":return[o.rfft(I(\"x\",r,e,t))];case\"IRFFT\":return[o.irfft(I(\"x\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var QT=(r,e,t,o=Je)=>{switch(r.op){case\"StaticRegexReplace\":return[o.string.staticRegexReplace(I(\"input\",r,e,t),I(\"pattern\",r,e,t),I(\"rewrite\",r,e,t),I(\"replaceGlobal\",r,e,t))];case\"StringNGrams\":{let{nGrams:n,nGramsSplits:s}=o.string.stringNGrams(I(\"data\",r,e,t),I(\"dataSplits\",r,e,t),I(\"separator\",r,e,t),I(\"nGramWidths\",r,e,t),I(\"leftPad\",r,e,t),I(\"rightPad\",r,e,t),I(\"padWidth\",r,e,t),I(\"preserveShortSequences\",r,e,t));return[n,s]}case\"StringSplit\":{let{indices:n,values:s,shape:a}=o.string.stringSplit(I(\"input\",r,e,t),I(\"delimiter\",r,e,t),I(\"skipEmpty\",r,e,t));return[n,s,a]}case\"StringToHashBucketFast\":return[o.string.stringToHashBucketFast(I(\"input\",r,e,t),I(\"numBuckets\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var ZT=(r,e,t,o=Je)=>{switch(r.op){case\"Cast\":return[o.cast(I(\"x\",r,e,t),I(\"dtype\",r,e,t))];case\"ExpandDims\":{let n=I(\"axis\",r,e,t);return[o.expandDims(I(\"x\",r,e,t),n)]}case\"Squeeze\":{let n=I(\"axis\",r,e,t);return[o.squeeze(I(\"x\",r,e,t),n)]}case\"Reshape\":return[o.reshape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"EnsureShape\":return[o.ensureShape(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"MirrorPad\":return[o.mirrorPad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"mode\",r,e,t))];case\"PadV2\":case\"Pad\":return[o.pad(I(\"x\",r,e,t),I(\"padding\",r,e,t),I(\"constantValue\",r,e,t))];case\"SpaceToBatchND\":{let n=I(\"blockShape\",r,e,t),s=I(\"paddings\",r,e,t);return[o.spaceToBatchND(I(\"x\",r,e,t),n,s)]}case\"BatchToSpaceND\":{let n=I(\"blockShape\",r,e,t),s=I(\"crops\",r,e,t);return[o.batchToSpaceND(I(\"x\",r,e,t),n,s)]}case\"DepthToSpace\":{let n=I(\"blockSize\",r,e,t),s=I(\"dataFormat\",r,e,t).toUpperCase();return[o.depthToSpace(I(\"x\",r,e,t),n,s)]}case\"BroadcastTo\":return[o.broadcastTo(I(\"x\",r,e,t),I(\"shape\",r,e,t))];case\"BroadcastArgs\":return[o.broadcastArgs(I(\"s0\",r,e,t),I(\"s1\",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function MS(r,e,t,o,n=De){let s=((a,i,p)=>{switch(a.category){case\"arithmetic\":return n(()=>TT(a,i,p));case\"basic_math\":return n(()=>_T(a,i,p));case\"control\":return FT(a,i,p);case\"convolution\":return n(()=>OT(a,i,p));case\"creation\":return n(()=>MT(a,i,p));case\"dynamic\":return LT(a,i,p);case\"evaluation\":return n(()=>BT(a,i,p));case\"image\":return n(()=>WT(a,i,p));case\"graph\":return n(()=>zT(a,i,p));case\"logical\":return n(()=>UT(a,i,p));case\"matrices\":return n(()=>GT(a,i,p));case\"normalization\":return n(()=>HT(a,i,p));case\"ragged\":return n(()=>KT(a,i,p));case\"reduction\":return n(()=>qT(a,i,p));case\"slice_join\":return n(()=>jT(a,i,p));case\"sparse\":return n(()=>XT(a,i,p));case\"spectral\":return n(()=>YT(a,i,p));case\"string\":return n(()=>QT(a,i,p));case\"transformation\":return n(()=>ZT(a,i,p));case\"hash_table\":return VT(a,i,p,o);case\"custom\":let u=pf(a.op);if(u&&u.customExecutor)return u.customExecutor(new wf(a,i,p));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return y.isPromise(s)?s.then(a=>[].concat(a)):[].concat(s)}var Ml=class{constructor(e={},t={},o={},n={},s){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,this.parseNodeNameCache=s,this.rootContext={id:0,frameName:\"\",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;tt.id===0&&t.iterationId===0?\"\":`${t.frameName}-${t.iterationId}`).join(\"/\"):\"\"}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error(\"Cannot exit frame, the context is empty\")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error(\"Cannot increase frame iteration, the context is empty\")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function LS(r,e,t,o){let n=new Set,s=[],a=null,i=null,p=new Set,u=new Set(Object.keys(r).map(m=>Nr(m)[0]));o=o||[];let c=new Set(o.map(m=>Nr(m.name)[0])),l=[...e];for(;l.length>0;){let m=l.pop();if((fu(m)||A8(m)||F8(m))&&a==null&&(a=m,i=a.children.map(d=>d.name).filter(d=>n.has(d))),n.add(m.name),t[m.name]==null&&!u.has(m.name)&&!c.has(m.name)){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(d=>{p.has(d.name)||(p.add(d.name),l.push(d))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function JT(r,e){let{usedNodes:t,inputs:o}=e,n=Object.keys(o).map(g=>Nr(g)[0]).map(g=>r.nodes[g]),s=r.initNodes||[],a=g=>t.has(typeof g==\"string\"?g:g.name);function i(g){return[...new Map(g.map(x=>[x.name,x])).values()]}let p=i([...n,...r.weights,...s]).filter(a),u=i([...p,...Object.values(r.nodes)]).filter(a),c=new Map(u.map(g=>[g.name,g])),l={};for(let g of u){l[g.name]=l[g.name]||0;for(let x of g.children)a(x)||(l[x.name]=Number.POSITIVE_INFINITY),l[x.name]=(l[x.name]||0)+1}let m=Object.entries(l).filter(([,g])=>g===0).map(([g])=>g),d=[...m];for(;m.length>0;){let g=m.pop(),x=c.get(g);for(let b of x.children.filter(a))--l[b.name]===0&&(d.push(b.name),m.push(b.name))}let f=d.map(g=>c.get(g)),h=_8(f,p);return E8(h,p),h}function _8(r,e){let t=new Map(r.map(a=>[a.name,a])),o=e.map(a=>a.name),n=new Set(o);for(;o.length>0;){let a=o.pop(),i=t.get(a);for(let p of i.children)!t.has(p.name)||n.has(p.name)||(n.add(p.name),o.push(p.name))}return r.filter(a=>n.has(a.name))}var gc=class extends Error{constructor(e){super(`NodesExecutionOrderError: ${e}`)}};function E8(r,e){let t=new Map(r.map((i,p)=>[i.name,p])),o=new Set(e.map(i=>i.name)),n=i=>o.has(typeof i==\"string\"?i:i.name),s=new Set(r.map(i=>i.name)),a=i=>s.has(typeof i==\"string\"?i:i.name);for(let i of r){for(let p of i.children.filter(a)){if(!t.has(p.name))throw new gc(`Child ${p.name} of node ${i.name} is unreachable.`);if(t.get(i.name)>t.get(p.name))throw new gc(`Node ${i.name} is scheduled to run after its child ${p.name}.`)}if(!n(i))for(let p of i.inputs){if(!t.has(p.name))throw new gc(`Input ${p.name} of node ${i.name} is unreachable.`);if(t.get(p.name)>t.get(i.name))throw new gc(`Node ${i.name} is scheduled to run before its input ${p.name}.`)}}}function e_(r){let e=new Map(r.map((i,p)=>[i.name,p])),t=Number.MAX_SAFE_INTEGER,o=r.map((i,p)=>fu(i)?t:p),n=i=>{let p=o[e.get(i.name)];return p==null?-1:p},s=r.map((i,p)=>i.children.map(n).reduce((u,c)=>Math.max(u,c),o[p])),a=new Map;for(let i=0;ie[o].map(n=>n.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this.parseNodeNameCache=new Map,this._weightMap={},this.SEPARATOR=\",\",this._functions={},this._functionExecutorMap={},this.keepIntermediateTensors=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(o=>{this._functionExecutorMap[o]=new r(e.functions[o],this)})}getCompilationKey(e,t){let o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPARATOR)+\"--\"+n.join(this.SEPARATOR)}compile(e,t){let o=LS(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let u=t.map(l=>l.name),c=Object.keys(e);throw new Error(`Cannot compute the outputs [${u}] from the provided inputs [${c}]. Missing the following inputs: [${n}]`)}let i=JT(this.graph,o),p=e_(i);return{orderedNodes:i,nodeLiveUntilMap:p}}cloneAndKeepTensor(e){if(e==null)return null;let t=e.clone();return $r(t),t}cloneTensorList(e){return e?e.map(o=>this.cloneAndKeepTensor(o)):null}cloneTensorMap(e){return Object.fromEntries(Object.entries(e).map(([t,o])=>[t,this.cloneTensorList(o)]))}execute(e,t){this.disposeIntermediateTensors(),e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(m=>this.graph.nodes[Nr(m)[0]]),s=t.map(m=>Nr(m)[0]),a=new Set(s),i=s.map(m=>this.graph.nodes[m]);i.length===0&&(i=this._outputs);let p=this.getCompilationKey(n,i),u=this.compiledMap.get(p);u==null&&(u=this.compile(e,i),this.compiledMap.set(p,u));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let c={},l={};return De(()=>{let m=new Ml(this.weightMap,c,l,this.functionExecutorMap,this.parseNodeNameCache),d=Object.assign({},this.weightMap);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap)),Object.keys(e).forEach(x=>{let[b,C]=Nr(x,m),S=[];S[C]=e[x],d[b]=S,this.keepIntermediateTensors&&(this.clonedTensorsMap[b]=this.cloneTensorList(S))});let f=this.getFrozenTensorIds(d),{orderedNodes:h,nodeLiveUntilMap:g}=u;for(let x of h){if(d[x.name])continue;let b=MS(x,d,m,this._resourceManager);if(y.isPromise(b))throw new Error(`The execution of the op '${x.op}' returned a promise. Please use model.executeAsync() instead.`);d[x.name]=b,this.keepIntermediateTensors&&(this.clonedTensorsMap[x.name]=this.cloneTensorList(b)),this.checkTensorForDisposalWithNodeLiveUntilInfo(x,d,m,f,a,g.get(x.name))}return this.parent==null&&m.dispose(f),t.map(x=>Bt(x,d,m))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){if(!(fu(t)||a.has(e))){for(let p of o[e])p!=null&&(i[p.id]=(i[p.id]||0)+t.children.length);for(let p of t.inputs){if(fu(p))continue;let u=hS(p.name,o,n);if(u!=null)for(let c of u){if(!c||c.kept||s.has(c.id))continue;let l=i[c.id];l===1?(c.dispose(),delete i[c.id]):l!=null&&i[c.id]--}}}}checkTensorForDisposalWithNodeLiveUntilInfo(e,t,o,n,s,a){function i(p){return fu(p)||s.has(p.name)}if(!(fu(e)||a==null))for(let p of a){if(i(p))continue;let u=hS(p.name,t,o);for(let c of u)!c||c.kept||n.has(c.id)||c.dispose()}}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){this.clonedTensorsMap&&(Object.values(this.clonedTensorsMap).forEach(e=>{for(let t of e)t&&!t.isDisposed&&t.dispose()}),this.clonedTensorsMap=null)}getIntermediateTensors(){return this.clonedTensorsMap}async _executeAsync(e,t,o=!1,n={},s={}){this.disposeIntermediateTensors(),o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepIntermediateTensors=A().getBool(\"KEEP_INTERMEDIATE_TENSORS\")}catch(m){this.keepIntermediateTensors=!1,console.warn(m.message)}let a=new Ml(this.weightMap,n,s,this.functionExecutorMap,this.parseNodeNameCache);this.keepIntermediateTensors&&(this.clonedTensorsMap=this.cloneTensorMap(this.weightMap));let i=await this.executeWithControlFlow(e,a,t,o),p=t.map(m=>Bt(m,i,a)),u=p.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),l=new Set([...u,...c,...this.weightIds]);return Object.values(i).forEach(m=>{m.forEach(d=>{d&&!d.isDisposed&&!l.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(l),p}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(S=>this.graph.nodes[Nr(S)[0]]),i=o.map(S=>Nr(S)[0]),p=new Set(i),u=i.map(S=>this.graph.nodes[S]);u.length===0&&(u=this._outputs);let{usedNodes:c,missingInputs:l,dynamicNode:m,syncInputs:d}=LS(e,u,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(S=>({node:S,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(S=>{let[k,_]=Nr(S),$=[];$[_]=e[S],h[k]=$});let g={},x=this.getFrozenTensorIds(h),b={};for(;f.length>0;){let S=this.processStack(a,f,t,h,b,x,p,g,c);await Promise.all(S)}m==null&&!n&&console.warn(\"This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.\");let C=u.filter(S=>!fu(S)&&!Bt(S.name,h,t)).map(S=>S.name);if(C.length>0){let S=\"\";throw m!=null&&(S=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${C}] from the provided inputs [${s}]. Consider providing the following inputs: [${l}]. ${S}`)}return h}processStack(e,t,o,n,s,a,i,p,u){let c=[];for(;t.length>0;){let l=t.pop();o.currentContext=l.contexts;let m=\"\";if(l.node.op===\"Enter\"&&I(\"isConstant\",l.node,n,o)&&([m]=Ls(l.node.name,o)),n[l.node.name]==null){let d=MS(l.node,n,o,this._resourceManager);m||([m]=Ls(l.node.name,o));let f=o.currentContext;y.isPromise(d)?c.push(d.then(h=>(n[m]=h,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(h)),o.currentContext=f,this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u),h))):(n[m]=d,this.keepIntermediateTensors&&(this.clonedTensorsMap[m]=this.cloneTensorList(d)),this.checkTensorForDisposal(m,l.node,n,o,a,i,p),this.processChildNodes(l.node,t,o,n,s,u))}else this.processChildNodes(l.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[p]=Ls(i.name,o);s[p]||!a.has(i.name)||(i.op===\"Merge\"?i.inputNames.some(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!Bt(u,n,o))&&(s[p]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=Nr(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((p,u)=>a[u]===-1||a[u]===p);y.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&y.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){var t,o;let n={};for(let s in e){let a=(o=(t=this._signature)===null||t===void 0?void 0:t.inputs)===null||o===void 0?void 0:o[s];a!=null?n[a.name]=e[s]:n[s]=e[s]}return n}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=Nr(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>{var o,n;let s=(n=(o=this._signature)===null||o===void 0?void 0:o.outputs)===null||n===void 0?void 0:n[t];return s!=null?s.name:t},{})}checkOutputs(e){e.forEach(t=>{let[o]=Nr(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var kf=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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this.loadWithWeightMap(e,t)}loadWithWeightMap(e,t){this.artifacts=e;let o=this.artifacts.modelTopology,n=this.artifacts.signature;if(this.artifacts.userDefinedMetadata!=null){let s=this.artifacts.userDefinedMetadata;s.signature!=null&&(n=s.signature),s.structuredOutputKeys!=null&&(this.structuredOutputKeys=s.structuredOutputKeys)}if(this.signature=n,this.version=`${o.versions.producer}.${o.versions.minConsumer}`,this.executor=new Ll(Ol.Instance.transformGraph(o,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(t),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Ol.Instance.transformGraph(e.modelInitializer);this.initializer=new Ll(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializerSignature=e.initializerSignature}return!0}async save(e,t){if(typeof e==\"string\"){let o=this.io.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[0]}if(e.save==null)throw new Error(\"GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.\");return e.save(this.artifacts)}addStructuredOutputNames(e){if(this.structuredOutputKeys){let t=e instanceof mt?[e]:e,o={};return t.forEach((n,s)=>o[this.structuredOutputKeys[s]]=n),o}return e}predict(e,t){let o=this.execute(e,this.outputNodes);return this.addStructuredOutputNames(o)}async predictAsync(e,t){let o=await this.executeAsync(e,this.outputNodes);return this.addStructuredOutputNames(o)}normalizeInputs(e){var t;if(!(e instanceof mt)&&!Array.isArray(e)){let s=(t=this.signature)===null||t===void 0?void 0:t.inputs;if(s!=null)for(let a in s){let i=s[a];i.resourceId!=null&&(e[a]=this.resourceIdToCapturedInput[i.resourceId])}return e}e=Array.isArray(e)?e:[e];let 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this.initializer==null?[]:this.initializerSignature==null?this.initializer.executeAsync({},[]):this.initializer.executeAsync({},Object.keys(this.initializerSignature.outputs))}setResourceIdToCapturedInput(e){if(this.resourceIdToCapturedInput={},this.initializerSignature){let t=this.initializerSignature.outputs,o=Object.keys(t);for(let n=0;n1?o:o[0]}async executeAsync(e,t){this.resourceIdToCapturedInput==null&&this.setResourceIdToCapturedInput(await this.executeInitializerGraphAsync()),e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return 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FY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Q(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. 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l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d=m.filterHeight,f=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,x=m.padInfo.left,b=m.padInfo.top,C=m.dataFormat===\"channelsLast\",S=new tt(m.outShape,n.dtype),k=y.computeStrides(n.shape),_=y.computeStrides(s.shape),$=k[0],R=C?k[1]:k[2],D=C?k[2]:1,P=C?1:k[1],O=S.strides[0],M=C?S.strides[1]:S.strides[2],L=C?S.strides[2]:1,B=C?1:S.strides[1],z=t.data.get(n.dataId).values,U=t.data.get(s.dataId).values,j=S.values;for(let q=0;q=m.inHeight)continue;let le=oe*_[0],be=Y+ie*R;for(let _e=0;_e=m.inWidth)continue;let ct=le+Pe*_[1],He=be+st*D,lt=ct;for(let it=0;it=u.inDepth)continue;let q=U*D[0],Y=O+j*R[1];for(let J=0;J=u.inHeight)continue;let ie=q+ee*D[1],le=Y+oe*R[2];for(let be=0;be=u.inWidth)continue;let st=ie+Fe*D[2],ct=le+Pe*u.inChannels,He=st;for(let lt=0;ltMath.cos(r)),kE={kernelName:sn,backendName:\"cpu\",kernelFunc:XY};var YY=Ie(an,r=>Math.cosh(r)),NE={kernelName:an,backendName:\"cpu\",kernelFunc:YY};function QY(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,[c,l,m,d]=n.shape,f=s.shape[0],[h,g]=i,x=me([f,h,g,d],\"float32\"),b=t.data.get(s.dataId).values,C=t.data.get(a.dataId).values,S=t.data.get(n.dataId).values,k=y.computeStrides(n.shape),_=y.computeStrides(x.shape);for(let $=0;$=c)continue;let B=h>1?(O-D)*(l-1)/(h-1):0,z=g>1?(M-P)*(m-1)/(g-1):0;for(let U=0;U1?D*(l-1)+U*B:.5*(D+O)*(l-1);if(j<0||j>l-1){for(let q=0;q1?P*(m-1)+re*z:.5*(P+M)*(m-1);if(ne<0||ne>m-1){for(let le=0;le1?P*(m-1)+q*z:.5*(P+M)*(m-1);if(Y<0||Y>m-1){for(let ne=0;nex+f-b-1:(x,b)=>x+b;for(let x=0;xx+f-b-1:(x,b)=>x+b;for(let x=0;x`Only NHWC dataFormat supported on CPU for depthToSpace. Got ${a}`);let i=n.shape[0],p=n.shape[1],u=n.shape[2],c=n.shape[3],l=p*s,m=u*s,d=c/(s*s),f=t.data.get(n.dataId).values,h=new Float32Array(i*l*m*d),g=0;for(let x=0;x`Error in depthwiseConv2d: Either strides or dilations must be 1. 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Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p),l;if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))l=lr({inputs:{x:n},backend:t});else{let m=t.data.get(n.dataId).values,d=y.computeStrides(n.shape),f=vc(m,n.shape,n.dtype,d,c,\"max\");l=t.makeTensorInfo(c.outShape,n.dtype,f.values)}return l}var c$={kernelName:Wn,backendName:\"cpu\",kernelFunc:LQ};function BQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o;Q(n,\"maxPool3d\");let c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.data.get(n.dataId).values,m=zf(l,n.shape,n.dtype,y.computeStrides(n.shape),c,\"max\");return t.makeTensorInfo(m.shape,\"float32\",m.values)}var l$={kernelName:ia,backendName:\"cpu\",kernelFunc:BQ};function zQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o;Q([n,s],\"maxPool3DGrad\");let c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=t.bufferSync(s),m=aE(l,c),d=c.strideDepth,f=c.strideHeight,h=c.strideWidth,g=c.dilationDepth,x=c.dilationHeight,b=c.dilationWidth,C=c.effectiveFilterDepth,S=c.effectiveFilterHeight,k=c.effectiveFilterWidth,_=C-1-c.padInfo.front,$=k-1-c.padInfo.left,R=S-1-c.padInfo.top,D=me(s.shape,\"float32\"),P=t.bufferSync(n);for(let O=0;O=c.outDepth||Math.floor(re)!==re))for(let ne=0;ne=c.outHeight||Math.floor(ee)!==ee))for(let oe=0;oe=c.outWidth||Math.floor(ie)!==ie)continue;let le=C*S*k-1-m.get(O,re,ee,ie,M),be=J*S*k+ne*k+oe,_e=le===be?1:0;if(_e===0)continue;let ve=P.get(O,re,ee,ie,M);Y+=ve*_e}}}D.set(Y,O,L,B,z,M)}return t.makeTensorInfo(D.shape,D.dtype,D.values)}var m$={kernelName:Gi,backendName:\"cpu\",kernelFunc:zQ};function VQ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Q([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=t.data.get(i.dataId).values,f=me(m.outShape,i.dtype,Bf(d,i.shape,i.dtype,m).values),h=m.strideHeight,g=m.strideWidth,x=m.dilationHeight,b=m.dilationWidth,C=m.effectiveFilterHeight,S=m.effectiveFilterWidth,k=S-1-m.padInfo.left,_=C-1-m.padInfo.top,$=me(i.shape,\"float32\"),R=t.data.get(n.dataId).values,D=me(n.shape,\"float32\",R);for(let P=0;P=m.outHeight||Math.floor(q)!==q))for(let Y=0;Y=m.outWidth||Math.floor(J)!==J)continue;let re=C*S-1-f.get(P,q,J,O),ne=j*S+Y,ee=re===ne?1:0;if(ee===0)continue;let oe=D.get(P,q,J,O);U+=oe*ee}}$.set(U,P,M,L,O)}return t.makeTensorInfo($.shape,$.dtype,$.values)}var d$={kernelName:Ui,backendName:\"cpu\",kernelFunc:VQ};function f$(r,e,t,o,n){let s=y.computeStrides(e),a=vc(r,e,t,s,n,\"max\"),i=Bf(r,e,t,n,!0,o);return[a.values,i.values]}var h$={kernelName:ua,backendName:\"cpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;Q(o,\"MaxPoolWithArgmax\");let u=p.data.get(o.dataId).values,c=w.computePool2DInfo(o.shape,n,s,[1,1],a),[l,m]=f$(u,o.shape,o.dtype,i,c),d=p.write(l,c.outShape,o.dtype),f=p.write(m,c.outShape,o.dtype);return[{dataId:d,shape:c.outShape,dtype:o.dtype},{dataId:f,shape:c.outShape,dtype:\"int32\"}]}};function WQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=y.parseAxisParam(s,n.shape),u=w.computeOutAndReduceShapes(n.shape,i)[1],c=y.sizeFromShape(u),l=[],m=t.makeTensorInfo([],\"float32\",new Float32Array([c]));l.push(m);let d=Ro({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}});l.push(d);let f=Ul({inputs:{a:d,b:m},backend:t});l.push(f);let h=fi({inputs:{x:f},backend:t,attrs:{axis:s,keepDims:a}});return l.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}var g$={kernelName:Un,backendName:\"cpu\",kernelFunc:WQ};function UQ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;Q(n,\"min\");let i=y.parseAxisParam(s,n.shape),p=i,u=w.getAxesPermutation(p,n.shape.length),c=n;u!=null&&(c=St({inputs:{x:n},backend:t,attrs:{perm:u}}),p=w.getInnerMostAxes(p.length,n.shape.length)),w.assertAxesAreInnerMostDims(\"min\",p,c.shape.length);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=y.sizeFromShape(m),f=y.makeZerosTypedArray(y.sizeFromShape(l),c.dtype),h=t.data.get(c.dataId).values;for(let x=0;xC[0]+n.shape[S]+C[1]),p=s.map(C=>C[0]),u=s.map((C,S)=>C[0]+n.shape[S]),c=a===\"reflect\"?0:1,l=t.data.get(n.dataId).values,m=n.shape.length,d=y.computeStrides(n.shape),f=y.sizeFromShape(i),h=i.length,g=y.computeStrides(i),x=y.getTypedArrayFromDType(n.dtype,f);for(let C=0;C=u[_]&&(S[_]=(u[_]-1)*2-S[_]+c);S=S.map((_,$)=>_-p[$]);let k=y.locToIndex(S,m,d);x[C]=l[k]}return{dataId:t.write(x,i,n.dtype),shape:i,dtype:n.dtype}}var y$={kernelName:Kn,backendName:\"cpu\",kernelFunc:GQ};var HQ=Ve((r,e)=>{let t=r%e;return r<0&&e<0||r>=0&&e>=0?t:(t+e)%e}),KQ=Ye(qn,HQ),b$={kernelName:qn,backendName:\"cpu\",kernelFunc:KQ};var w$=zp(jw());function vI(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=n.shape.length,i=s;if(i===-1&&(i=a-1),i!==a-1)throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${a} and dim was ${i}`);let p=y.parseAxisParam([i],n.shape),u=II({inputs:{x:n},backend:t,attrs:{reductionIndices:p,keepDims:!1}}),c=w.expandShapeToKeepDim(u.shape,p),l=We({inputs:{x:u},backend:t,attrs:{shape:c}}),m=Vl({inputs:{a:n,b:l},backend:t}),d=qS({inputs:{x:m},backend:t}),f=fi({inputs:{x:d},backend:t,attrs:{axis:p,keepDims:!1}}),h=We({inputs:{x:f},backend:t,attrs:{shape:c}}),g=Ul({inputs:{a:d,b:h},backend:t});return t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),g}var C$={kernelName:Is,backendName:\"cpu\",kernelFunc:vI};function qQ(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o;Q(n,\"multinomial\");let p=i?n:vI({inputs:{logits:n},backend:t,attrs:{dim:-1}}),u=p.shape[0],c=p.shape[1],l=t.data.get(p.dataId).values,m=[u,s],d=y.makeZerosTypedArray(y.sizeFromShape(m),\"int32\");for(let f=0;f=0&&l[m]{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=kc({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=hu({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var $$={kernelName:la,backendName:\"cpu\",kernelFunc:kI};function t7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;Q(n,\"pad\");let i=s.map((b,C)=>b[0]+n.shape[C]+b[1]),p=s.map(b=>b[0]),u=t.data.get(n.dataId).values,c=y.sizeFromShape(n.shape),l=n.shape.length,m=y.computeStrides(n.shape),d=y.sizeFromShape(i),f=i.length,h=y.computeStrides(i),g=y.getTypedArrayFromDType(n.dtype,d);a!==0&&g.fill(a);for(let 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0:return AR();case 1:return TZ(r,e,t);case 2:return PZ(r,e,t);case 3:return EZ(r,e,t);case 4:return RZ(r,e,t);case 5:return DZ(r,e);case 6:return AZ(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function yZ(r){return`\n float sampleTexture(sampler2D textureSampler, vec2 uv) {\n return ${r.texture2D}(textureSampler, uv).r;\n }\n `}function bZ(r){return`\n void setOutput(float val) {\n ${r.output} = vec4(val, 0, 0, 0);\n }\n `}function CZ(r){return`\n void setOutput(vec4 val) {\n ${r.output} = val;\n }\n `}function wZ(r){return`${r.version}\n precision highp float;\n precision highp int;\n precision highp sampler2D;\n ${r.varyingFs} vec2 resultUV;\n ${r.defineOutput}\n const vec2 halfCR = vec2(0.5, 0.5);\n\n struct ivec5\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n };\n\n struct ivec6\n {\n int x;\n int y;\n int z;\n int w;\n int u;\n int v;\n };\n\n uniform float NAN;\n ${r.defineSpecialNaN}\n ${r.defineSpecialInf}\n ${r.defineRound}\n\n int 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Zf(r,e,t){let{newShape:o,keptDims:n}=y.squeezeShape(e),s=e.length,a=r&&s===3&&e[0]===1,i=a?e.slice(1):o,p=!r&&s>1&&!y.arraysEqual(e,t)&&o.lengthr[t]).join(\", \")}function PR(r,e,t,o){let n=t.map((c,l)=>{let m={logicalShape:c.shape,texShape:c.isUniform?null:c.texData.texShape,isUniform:c.isUniform,isPacked:c.isUniform?!1:c.texData.isPacked,flatOffset:null};return c.texData!=null&&c.texData.slice!=null&&c.texData.slice.flatOffset>0&&(m.flatOffset=c.texData.slice.flatOffset),{name:e.variableNames[l],shapeInfo:m}}),s=n.map(c=>c.shapeInfo),a={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},i=RR(n,a,e),p=RI(r.gl,i),u=r.createProgram(p);return A().get(\"ENGINE_COMPILE_ONLY\")?{program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a,variablesLocations:null,customUniformLocations:null,infLoc:null,nanLoc:null,outShapeLocation:null,outShapeStridesLocation:null,outTexShapeLocation:null}:(r.buildVao(u),Object.assign({program:e,fragmentShader:p,source:i,webGLProgram:u,inShapeInfos:s,outShapeInfo:a},XI(r,e,u)))}function XI(r,e,t){let o=[],n=[],s,a,i,p=null,u=null;u=r.getUniformLocation(t,\"NAN\",!1),A().getNumber(\"WEBGL_VERSION\")===1&&(p=r.getUniformLocation(t,\"INFINITY\",!1));let c=!1;for(let l of e.variableNames){let 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Shape ${i} and ${p} must match`)})}function OR(r,e,t,o,n){e.program.enableShapeUniforms||(FR(e.inShapeInfos,t),FR([e.outShapeInfo],[o]));let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s.texture,a[0],a[1]):r.setOutputMatrixTexture(s.texture,a[0],a[1]),r.setProgram(e.webGLProgram),r.bindVertexArray(e.webGLProgram.vao),A().getNumber(\"WEBGL_VERSION\")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,1/0),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN);for(let p=0;p{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0;if(r.enableShapeUniforms&&!a.isUniform){let p=a.texData.texShape,{useSqueezeShape:u,uniformShape:c,keptDims:l}=Zf(r.packedInputs,a.shape,p),m=\"\",d=\"\",f=\"\";if(c.length===1&&r.packedInputs){let k=[Math.ceil(p[0]/2),Math.ceil(p[1]/2)];m=`${k[0]>1}_${k[1]>1}`}else if(c.length===2&&!r.packedInputs)d=`${c[0]>1}_${c[1]>1}`;else if(c.length>2&&!r.packedInputs){let k=y.computeStrides(c);f=`${k[0]===p[1]}_${k[k.length-1]===p[1]}`}let h=a.shape.length,g=c.length===2&&y.arraysEqual(a.shape,p),x=y.sizeFromShape(a.shape)===1,b=w.getBroadcastDims(a.shape,t.shape),C=!r.packedInputs&&h===t.shape.length&&y.arraysEqual(p,t.texData.texShape),S=r.packedInputs||c.length>2?\"\":`${p[0]>1}_${p[1]>1}`;o+=`${h}_${C}_${u?l:\"\"}_${c.length}_${x}_${b}_${g}_${m}_${d}_${f}_${S}_${i}`}else{let p=a.isUniform?\"uniform\":a.texData.texShape;o+=`${a.shape}_${p}_${i}`}});let n=r.userCode,s=r.constructor.name;return s+=\"_\"+o+\"_\"+n+`${A().getNumber(\"WEBGL_VERSION\")}`,s}function ut(r){return A().getBool(\"WEBGL_USE_SHAPES_UNIFORMS\")&&r<=4}var Jf=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=gu.DENSE,this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let t=It();this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n ivec3 outCoordsFromFlatIndex(int index) {\n 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texShape[1]));\n int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);\n\n vec4 result = vec4(0.);\n\n for (int i=0; i<4; i++) {\n int flatIndex = index + i;\n ivec3 rc = outCoordsFromFlatIndex(flatIndex);\n result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));\n }\n\n ${t.output} = result;\n }\n `}};var th=class{constructor(e){this.variableNames=[\"A\"],this.outTexUsage=mr.DOWNLOAD;let t=It();this.outputShape=e,this.userCode=`\n ${Qf}\n\n void main() {\n float x = getAAtOutCoords();\n ${t.output} = encode_float(x);\n }\n `}};var rh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=mr.DOWNLOAD;let t=It();this.outputShape=e,this.userCode=`\n ${Qf}\n\n void main() {\n ivec3 coords = getOutputCoords();\n float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));\n ${t.output} = encode_float(x);\n }\n `}};var ZZ={R:0,G:1,B:2,A:3},Zl=class{constructor(e,t=!1,o=\"RGBA\"){this.variableNames=[\"A\"],this.customUniforms=[{name:\"texShape\",type:\"ivec2\"}];let n=It();this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let s=\"result\";t&&(s=\"floor(result * 255. + 0.5)\");let a=\"\";for(let i=0;inv,createBufferFromOutputTexture:()=>iv,createFloat16MatrixTexture:()=>ev,createFloat16PackedMatrixTexture:()=>ov,createFloat32MatrixTexture:()=>JI,createIndexBuffer:()=>ZI,createPackedMatrixTexture:()=>rv,createUnsignedBytesMatrixTexture:()=>tv,createVertexBuffer:()=>QI,createVertexShader:()=>YI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>pv,downloadFloat32MatrixFromBuffer:()=>uv,downloadMatrixFromPackedOutputTexture:()=>lv,downloadPackedMatrixFromBuffer:()=>cv,getInternalFormatForFloat16MatrixTexture:()=>sh,getInternalFormatForFloat16PackedMatrixTexture:()=>uh,getInternalFormatForFloat32MatrixTexture:()=>nh,getInternalFormatForPackedMatrixTexture:()=>ih,getInternalFormatForUnsignedBytesMatrixTexture:()=>ah,uploadDenseMatrixToTexture:()=>sv,uploadPixelDataToTexture:()=>av});function YI(r){let e=It(),t=`${e.version}\n precision highp float;\n ${e.attribute} vec3 clipSpacePos;\n ${e.attribute} vec2 uv;\n ${e.varyingVs} vec2 resultUV;\n\n void main() {\n gl_Position = 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Jl(r,n,s,sh(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function ah(r){return r.downloadTextureFormat}function tv(r,e,t,o){let[n,s]=gp(e,t);return Jl(r,n,s,ah(o),r.RGBA,r.UNSIGNED_BYTE)}function ih(r){return r.internalFormatPackedFloat}function rv(r,e,t,o){let[n,s]=Ma(e,t);return Jl(r,n,s,ih(o),r.RGBA,r.FLOAT)}function uh(r){return r.internalFormatPackedHalfFloat}function ov(r,e,t,o){let[n,s]=Ma(e,t);return Jl(r,n,s,uh(o),r.RGBA,o.textureTypeHalfFloat)}function nv(r,e,t){return ce(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),jf(r,e,\"clipSpacePos\",t,3,20,0)&&jf(r,e,\"uv\",t,2,20,12)}function sv(r,e,t,o,n,s){ce(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,p;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,p=r.RGBA):(a=new 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ce(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),ce(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function uv(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function pv(r,e,t,o){let[n,s]=gp(e,t),a=4,i=new Uint8Array(IR(e*t,a));return ce(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function cv(r,e,t,o,n,s,a,i){let p=r,u=new Float32Array(vR(s,a));return p.bindBuffer(p.PIXEL_PACK_BUFFER,e),p.getBufferSubData(p.PIXEL_PACK_BUFFER,0,u),p.bindBuffer(p.PIXEL_PACK_BUFFER,null),u}function lv(r,e,t){let o=new Float32Array(e*t*4);return ce(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var bp=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.itemsToPoll=[];let t=A().getNumber(\"WEBGL_VERSION\");if(e!=null?(this.gl=e,NI(t,e)):this.gl=Kr(t),e=this.gl,A().getNumber(\"WEBGL_VERSION\")===2){let s=e;this.createVertexArray=()=>ce(s,()=>s.createVertexArray()),this.bindVertexArray=a=>ce(s,()=>s.bindVertexArray(a)),this.deleteVertexArray=a=>ce(s,()=>s.deleteVertexArray(a)),this.getVertexArray=()=>ce(s,()=>s.getParameter(s.VERTEX_ARRAY_BINDING))}else if(e!=null){let s=e.getExtension(\"OES_vertex_array_object\");if(s==null)throw new Error(\"All WebGL1 implementations are expected to offer OES_vertex_array_object.\");this.createVertexArray=()=>ce(e,()=>s.createVertexArrayOES()),this.bindVertexArray=a=>ce(e,()=>s.bindVertexArrayOES(a)),this.deleteVertexArray=a=>ce(e,()=>s.deleteVertexArrayOES(a)),this.getVertexArray=()=>ce(e,()=>e.getParameter(s.VERTEX_ARRAY_BINDING_OES))}let o=\"WEBGL_color_buffer_float\",n=\"EXT_color_buffer_half_float\";if(this.parallelCompilationExtension=this.gl.getExtension(\"KHR_parallel_shader_compile\"),A().getNumber(\"WEBGL_VERSION\")===1){let s=\"OES_texture_float\",a=\"OES_texture_half_float\";if(this.textureFloatExtension=Nc(this.gl,s),qr(this.gl,a))this.textureHalfFloatExtension=Nc(this.gl,a);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\");if(this.colorBufferFloatExtension=this.gl.getExtension(o),qr(this.gl,n))this.colorBufferHalfFloatExtension=Nc(this.gl,n);else if(A().get(\"WEBGL_FORCE_F16_TEXTURES\"))throw new Error(\"GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.\")}else if(o=\"EXT_color_buffer_float\",qr(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(qr(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error(\"GL context does not support color renderable floats\");this.vertexBuffer=QI(this.gl),this.indexBuffer=ZI(this.gl),this.framebuffer=LI(this.gl),this.textureConfig=Xl(this.gl,this.textureHalfFloatExtension)}get debug(){return A().getBool(\"DEBUG\")}dispose(){if(this.disposed)return;this.program!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),this.outputTexture!=null&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");let e=this.gl;ce(e,()=>e.finish()),ce(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ce(e,()=>e.deleteFramebuffer(this.framebuffer)),ce(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ce(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ce(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),JI(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),ev(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),tv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),av(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),sv(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),ov(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),rv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Xf(this.gl,this.framebuffer),this.outputTexture=null),ce(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>pv(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return cv(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return uv(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=iv(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(A().getBool(\"WEBGL_FENCE_API_ENABLED\")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>lv(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=YI(t));let o=DI(t);ce(t,()=>t.attachShader(o,this.vertexShader)),ce(t,()=>t.attachShader(o,e)),AI(t,o);let n=Object.assign(o,{vao:this.createVertexArray()});return this.debug&&Yl(t,n),n}buildVao(e){this.setProgram(e),this.bindVertexArray(e.vao);let t=this.gl;ce(t,()=>t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,this.indexBuffer)),nv(t,e,this.vertexBuffer)}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&(ce(this.gl,()=>this.gl.deleteProgram(e)),this.deleteVertexArray(e.vao))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Yl(this.gl,this.program),ce(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?BI(this.gl,e,t):zI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ce(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),VI(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Ma(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")}debugValidate(){this.program!=null&&Yl(this.gl,this.program),Tc(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;if(this.debug){let t=this.getVertexArray();console.assert(t===this.program.vao,\"VAO changed between setProgram and executeProgram!\"),this.debugValidate()}ce(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ce(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Nc(this.gl,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await y.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))),this.getQueryTime(e,A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=JZ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){if(this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1)return;let o;\"setTimeoutCustom\"in A().platform&&(o=A().platform.setTimeoutCustom.bind(A().platform)),y.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0),()=>0,null,o)}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Ql(this.gl,e,this.framebuffer),this.debug&&Tc(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Ql(this.gl,this.outputTexture,this.framebuffer),this.debug&&Tc(this.gl)):Xf(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Ql(n,e,this.framebuffer),this.debug&&Tc(n),this.outputTexture=e,ce(n,()=>n.viewport(0,0,t,o)),ce(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),ce(this.gl,()=>this.gl.scissor(e,t,o,n))}throwIfDisposed(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")}throwIfNoProgram(){if(this.program==null)throw new Error(\"No GPU program is currently set.\")}};function JZ(r){let e=0;for(;e`${r}.${t}`)}function Rt(r,e){return e===1?[r]:dv(r,e)}function ED(r,e){if(r===1)return\"rc\";let t=\"\";for(let o=0;o ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]}`;let t=\"\";for(let o=this.rank-2;o= ${this.enableShapeUniforms?`outShape[${o}]`:this.outputShape[o]}`,o= ${o};\n bool rEdge = rp1 >= ${n};\n `}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc), (rc + 1 >= ${this.enableShapeUniforms?\"outShape\":this.outputShape[0]} ? 0. : getA(rc + 1)), 0, 0`:`getA(${t[0]}),\n cEdge ? 0. : getA(${t[1]}),\n rEdge ? 0. : getA(${t[2]}),\n rEdge || cEdge ? 0. : getA(${t[3]})`}};var Mc=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"inputShape\",type:\"ivec3\"}],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let o=\"\";for(let n=0;n<4;n++){let s=\"thisRC = rc;\";n%2===1&&(s+=\"thisRC.z += 1;\"),n>1&&(s+=\"thisRC.y += 1;\"),o+=`\n ${s}\n ${n>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\"}\n int flatIndex = getFlatIndex(thisRC);\n\n ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n result[${n}] =\n getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n ${n>0?\"}\":\"\"}\n `}this.userCode=`\n ${e9(t,this.enableShapeUniforms)}\n ${this.enableShapeUniforms?Rc():$c(e)}\n\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0.);\n\n ivec3 thisRC;\n int rows = ${this.enableShapeUniforms?\"outShape[1]\":e[1]};\n int cols = ${this.enableShapeUniforms?\"outShape[2]\":e[2]};\n\n ${o}\n\n setOutput(result);\n }\n `}};function e9(r,e){return`\n ivec3 inputCoordsFromReshapedOutCoords(int index) {\n ${e?ER([\"r\",\"c\",\"d\"],\"inputShape\"):Ws([\"r\",\"c\",\"d\"],r)}\n return ivec3(r, c, d);\n }\n `}var dh=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.usedTextures={},this.logEnabled=!1}acquireTexture(e,t,o){let n=RD(t,o),s=DD(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=$D(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let p=this.freeTextures[s].pop();return this.usedTextures[s].push(p),p}let i;return n===er.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===er.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===er.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===er.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=RD(o,n),a=DD(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=$D(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),p=A().getNumber(\"WEBGL_DELETE_TEXTURE_THRESHOLD\");p!==-1&&this._numBytesAllocated>p?(this.gpgpu.deleteMatrixTexture(e.texture),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u&&u.indexOf(e);if(c==null||c<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");u[c]=u[u.length-1],u.pop(),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t.texture)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function t9(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;if(e===t.RGBA8)return 4;throw new Error(`Unknown internal format ${e}`)}function $D(r,e,t,o,n){let s=r9(e,o),a;if(n){let[p,u]=Ma(r[0],r[1]);a=p*u}else{let[p,u]=gp(r[0],r[1]);a=p*u}let i=t9(t,s);return a*i}function r9(r,e){switch(r){case er.PACKED_2X2_FLOAT32:return ih(e);case er.PACKED_2X2_FLOAT16:return uh(e);case er.UNPACKED_FLOAT32:return nh(e);case er.UNPACKED_FLOAT16:return sh(e);case er.PACKED_4X1_UNSIGNED_BYTE:return ah(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function o9(r){return A().getBool(\"WEBGL_RENDER_FLOAT32_ENABLED\")?r?er.PACKED_2X2_FLOAT32:er.UNPACKED_FLOAT32:r?er.PACKED_2X2_FLOAT16:er.UNPACKED_FLOAT16}function RD(r,e){if(r===mr.UPLOAD)return er.PACKED_2X2_FLOAT32;if(r===mr.RENDER||r==null)return o9(e);if(r===mr.DOWNLOAD||r===mr.PIXELS)return er.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function DD(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var tr=class{constructor(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float unaryOperation(float x) {\n ${t}\n }\n\n void main() {\n float x = getAAtOutCoords();\n float y = unaryOperation(x);\n\n setOutput(y);\n }\n `}},Wt=\"if (isnan(x)) return x;\",AD=\"return x;\",fv=\"return abs(x);\";var FD=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",PD=Wt+`\n return (x < 0.0) ? 0.0 : x;\n`,OD=Wt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,La=\"return x;\",MD=\"return 1.0 / (1.0 + exp(-1.0 * x));\";var BD=\"return x;\",zD=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,VD=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,WD=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,UD=\"return 1.0 / (1.0 + exp(-1.0 * x));\",Fr=class{constructor(e,t){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n vec4 unaryOperation(vec4 x) {\n ${t}\n }\n\n void main() {\n vec4 x = getAAtOutCoords();\n vec4 y = unaryOperation(x);\n\n setOutput(y);\n }\n `}};var fh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.length,o=Rt(\"rc\",t),n=Re(t),s=ED(t,o),a=o.slice(-2),i=t<=1?\"rc\":`vec2(${a.join(\",\")})`;this.userCode=`\n void main() {\n ${n} rc = getOutputCoords();\n vec4 packedInput = getA(${s});\n\n setOutput(getChannel(packedInput, ${i}));\n }\n `}};var s9=Vt.whereImpl,a9=1e-7,i9=1e-4,hh={};function u9(r){return r in hh||(hh[r]={}),hh[r]}var p9=A().getNumber(\"CPU_HANDOFF_SIZE_THRESHOLD\"),c9=600;function l9(){return A().global.screen==null?1024:A().global.screen.height*A().global.screen.width*window.devicePixelRatio*c9/1024/1024}var Lc=class r extends ao{nextDataId(){return r.nextDataId++}constructor(e){if(super(),this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!A().getBool(\"HAS_WEBGL\"))throw new Error(\"WebGL is not supported on this device\");let t;if(e!=null){if(e instanceof bp)t=e;else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"),e);t=new bp(o)}this.binaryCache={},this.gpgpuCreatedLocally=!1}else{let o=Kr(A().getNumber(\"WEBGL_VERSION\"));t=new bp(o),this.binaryCache=u9(A().getNumber(\"WEBGL_VERSION\")),this.gpgpuCreatedLocally=!0}this.gpgpu=t,this.canvas=this.gpgpu.gl.canvas,this.textureManager=new dh(this.gpgpu),this.numMBBeforeWarning=l9(),this.texData=new Bo(this,ur())}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}writeTexture(e,t,o,n,s,a){let i=this.makeTensorInfo(t,o),p=this.texData.get(i.dataId);p.isPacked=!1,p.texture={texture:e,texShape:[n,s]},p.texShape=[n,s];let u=_c(t),c=new Zl(u,!1,a),l=this.runWebGLProgram(c,[i],o,[[n,s]]);return l.shape=t,p.texture=null,this.disposeIntermediateTensorInfo(i),l.dataId}write(e,t,o){if((A().getBool(\"WEBGL_CHECK_NUMERICAL_PROBLEMS\")||A().getBool(\"DEBUG\"))&&this.checkNumericalProblems(e),o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:mr.UPLOAD,refCount:1}),n}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,o,n,s){if(A().getBool(\"DEBUG\")&&this.checkNumericalProblems(t),n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.texData.set(e,{shape:o,dtype:n,values:t,usage:mr.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:p}=t;if(a!=null){let m;p?m=new Fr(i,La):m=new tr(i,La);let d=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),f=this.readSync(d.dataId);return this.disposeIntermediateTensorInfo(d),f}if(o!=null)return this.convertAndCacheOnCPU(e);if(n===\"string\")return o;let u=this.activeTimers!=null,c;u&&(c=y.now());let l;if(n===\"complex64\"){let m=this.readSync(s.real.dataId),d=this.readSync(s.imag.dataId);l=w.mergeRealAndImagArrays(m,d)}else l=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=y.now()-c),this.convertAndCacheOnCPU(e,l)}async read(e){if(this.pendingRead.has(e)){let f=this.pendingRead.get(e);return new Promise(h=>f.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:p}=t;if(s!=null){let f;p?f=new Fr(n,La):f=new tr(n,La);let h=this.runWebGLProgram(f,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(A().getBool(\"DEBUG\")&&!A().getBool(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&A().getNumber(\"WEBGL_VERSION\")===2)throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");let u=null,c;if(a!==\"complex64\"&&A().get(\"WEBGL_BUFFER_SUPPORTED\")){c=this.decode(e);let f=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(f.texture.texture,...jl(n))}this.pendingRead.set(e,[]),a!==\"complex64\"&&await this.gpgpu.createAndWaitForFence();let l;if(a===\"complex64\"){let f=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=f[0],g=f[1];l=w.mergeRealAndImagArrays(h,g)}else if(u==null)l=this.getValuesFromTexture(e);else{let f=y.sizeFromShape(n);l=this.gpgpu.downloadFloat32MatrixFromBuffer(u,f)}if(c!=null&&this.disposeIntermediateTensorInfo(c),u!=null){let f=this.gpgpu.gl;ce(f,()=>f.deleteBuffer(u))}let m=this.convertAndCacheOnCPU(e,l),d=this.pendingRead.get(e);return this.pendingRead.delete(e),d.forEach(f=>f(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&ur().removeDataId(e,this),this.pendingDeletes--),m}readToGPU(e,t={}){let o=this.texData.get(e),{values:n,shape:s,slice:a,dtype:i,isPacked:p,texture:u}=o;if(i===\"complex64\")throw new Error(\"Does not support reading texture for complex64 dtype.\");if(a!=null){let d;p?d=new Fr(s,La):d=new tr(s,La);let f=this.runWebGLProgram(d,[{dataId:e,shape:s,dtype:i}],i),h=this.readToGPU(f,t);return this.disposeIntermediateTensorInfo(f),h}if(u==null)throw n!=null?new Error(\"Data is not on GPU but on CPU.\"):new Error(\"There is no data on GPU or CPU.\");let c=this.decode(e,t.customTexShape),l=ur().makeTensorFromTensorInfo(c),m=this.texData.get(c.dataId);return Object.assign({tensorRef:l},m.texture)}bufferSync(e){let t=this.readSync(e.dataId);if(e.dtype===\"string\")try{let o=t.map(n=>y.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}checkNumericalProblems(e){if(e!=null)for(let t=0;t0}time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(p=>p.query)).filter(p=>p!=null),a=y.flatten(this.activeTimers.map(p=>p.name)).filter(p=>p!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};return(async()=>{if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0){let p=await Promise.all(s);i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \")}else i.kernelMs={error:\"WebGL query timers are not supported in this environment.\"};return this.uploadWaitMs=0,this.downloadWaitMs=0,i})()}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?this.gpgpu.beginQuery():{startMs:y.now(),endMs:null}}endTimer(e){return A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0?(this.gpgpu.endQuery(),e):(e.endMs=y.now(),e)}async getQueryTime(e){if(A().getNumber(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),p=i&&i.origDataId||e,u=this.dataRefCount.get(p);u>1?this.dataRefCount.set(p,u-1):(this.dataRefCount.delete(p),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture.texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=p9){return A().getBool(\"WEBGL_CPU_FORWARD\")&&e.every(o=>this.texData.get(o.dataId).texture==null&&y.sizeFromShape(o.shape)0&&y.isString(o[0])){let s=o.map(a=>y.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){return ur().makeTensorFromTensorInfo(this.makeTensorInfo(e,t,o),this)}unpackTensor(e){let t=new fh(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new mh(e.shape);return this.runWebGLProgram(t,[e],e.dtype,null,!0)}packedReshape(e,t){let o=[gi(e.shape),...xi(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[gi(t),...xi(t)],a=new Mc(s,o),i=!0,p=[o],u=this.runWebGLProgram(a,[n],e.dtype,p,i);return{dataId:u.dataId,shape:t,dtype:u.dtype}}decode(e,t){let o=this.texData.get(e),{isPacked:n,shape:s,dtype:a}=o;if(t!=null){let m=y.sizeFromShape(s),d=t[0]*t[1]*4;y.assert(m<=d,()=>\"customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.\")}let i=_c(s),p;n?p=new eh(i):p=new Jf(i);let u=!0,c=[t!=null?t:jl(i)],l=this.runWebGLProgram(p,[{shape:i,dtype:a,dataId:e}],a,c,u,t);return{dtype:a,shape:s,dataId:l.dataId}}runWebGLProgram(e,t,o,n,s=!1,a){let i=this.makeTensorInfo(e.outputShape,o),p=this.texData.get(i.dataId);if(e.packedOutput&&(p.isPacked=!0),e.outPackingScheme===gu.DENSE){let x=a!=null?a:jl(e.outputShape);p.texShape=x.map(b=>b*2)}if(e.outTexUsage!=null&&(p.usage=e.outTexUsage),y.sizeFromShape(i.shape)===0)return p.values=y.getTypedArrayFromDType(i.dtype,0),i;let u=[],c=t.map(x=>{if(x.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");let b=this.texData.get(x.dataId);if(b.texture==null){if(!e.packedInputs&&y.sizeFromShape(x.shape)<=A().getNumber(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:x.shape,texData:null,isUniform:!0,uniformValues:b.values};e.packedInputs&&(b.isPacked=!0,b.shape=x.shape)}if(this.uploadToGPU(x.dataId),!!b.isPacked!=!!e.packedInputs)x=b.isPacked?this.unpackTensor(x):this.packTensor(x),u.push(x),b=this.texData.get(x.dataId);else if(b.isPacked&&!xu(b.shape,x.shape)){let C=x,S=x.shape;x.shape=b.shape,x=this.packedReshape(x,S),u.push(x),b=this.texData.get(x.dataId),C.shape=S}return{shape:x.shape,texData:b,isUniform:!1}});this.uploadToGPU(i.dataId);let l={shape:i.shape,texData:p,isUniform:!1},m=MR(e,c,l),d=this.getAndSaveBinary(m,()=>PR(this.gpgpu,e,c,l)),f=this.activeTimers!=null,h;f&&(h=this.startTimer()),A().get(\"ENGINE_COMPILE_ONLY\")||OR(this.gpgpu,d,c,l,n),u.forEach(x=>this.disposeIntermediateTensorInfo(x)),f&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let g=A().getNumber(\"WEBGL_FLUSH_THRESHOLD\");if(g>0){let x=y.now();x-this.lastGlFlushTime>g&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=x)}if(!A().getBool(\"WEBGL_LAZILY_UNPACK\")&&p.isPacked&&s===!1){let x=this.unpackTensor(i);return this.disposeIntermediateTensorInfo(i),x}return i}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(A().getBool(\"IS_TEST\")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!=\"undefined\"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=De(()=>{if(!A().get(\"WEBGL_RENDER_FLOAT32_ENABLED\")){let e=A().getBool(\"DEBUG\");A().set(\"DEBUG\",!1);let t=this.abs(ke(1e-8)).dataSync()[0];if(A().set(\"DEBUG\",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?a9:i9}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:p}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=y.now());let l=t.texShape;if(l==null&&(l=WI(o,p),t.texShape=l),s!=null){let m=_c(o),d,f=l[1],h=l[0],g=s instanceof Uint8Array||s instanceof Uint8ClampedArray;(p||!g)&&([f,h]=Ma(l[0],l[1])),p?d=new oh(m,g):d=new Zl(m,g);let x=g?[h,f]:l,b=this.makeTensorInfo(x,n),C=this.texData.get(b.dataId);g?C.usage=mr.PIXELS:C.usage=mr.UPLOAD,C.texShape=x,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(b.dataId),f,h,s);let S=[[h,f]],_=this.runWebGLProgram(d,[b],n,S,!0),$=this.texData.get(_.dataId);t.texShape=$.texShape,t.isPacked=$.isPacked,t.usage=$.usage,A().get(\"ENGINE_COMPILE_ONLY\")?this.disposeData(_.dataId):(t.texture=$.texture,t.values=null,this.texData.delete(_.dataId)),this.disposeIntermediateTensorInfo(b),u&&(this.uploadWaitMs+=y.now()-c)}else{let m=this.acquireTexture(l,i,n,p);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return t!=null&&(o.values=m9(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*y.bytesPerElement(t)}checkCompileCompletion(){for(let[,e]of Object.entries(this.binaryCache))this.checkCompletion_(e)}async checkCompileCompletionAsync(){let e=[];if(this.gpgpu.parallelCompilationExtension){for(let[,t]of Object.entries(this.binaryCache))e.push(this.checkCompletionAsync_(t));return Promise.all(e)}else{for(let[,t]of Object.entries(this.binaryCache)){let o=new Promise(n=>{try{this.checkCompletion_(t),n(!0)}catch(s){throw s}});e.push(o)}return Promise.all(e)}}async checkCompletionAsync_(e){return this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)?this.checkCompletion_(e):(await cS(),this.checkCompletionAsync_(e))}checkCompletion_(e){if(this.gpgpu.gl.getProgramParameter(e.webGLProgram,this.gpgpu.gl.LINK_STATUS)===!1)throw console.log(this.gpgpu.gl.getProgramInfoLog(e.webGLProgram)),this.gpgpu.gl.getShaderParameter(e.fragmentShader,this.gpgpu.gl.COMPILE_STATUS)===!1?(qf(e.source,this.gpgpu.gl.getShaderInfoLog(e.fragmentShader)),new Error(\"Failed to compile fragment shader.\")):new Error(\"Failed to link vertex and fragment shaders.\");return!0}getUniformLocations(){for(let e of Object.values(this.binaryCache)){this.gpgpu.buildVao(e.webGLProgram);let{variablesLocations:t,customUniformLocations:o,infLoc:n,nanLoc:s,outShapeLocation:a,outShapeStridesLocation:i,outTexShapeLocation:p}=XI(this.gpgpu,e.program,e.webGLProgram);e.variablesLocations=t,e.customUniformLocations=o,e.infLoc=n,e.nanLoc=s,e.outShapeLocation=a,e.outShapeStridesLocation=i,e.outTexShapeLocation=p}}createTensorFromGPUData(e,t,o){e.channels=e.channels||\"RGBA\";let{texture:n,height:s,width:a,channels:i}=e,p=ur().backend;if(!p.gpgpu.gl.isTexture(n))throw new Error(\"The texture is invalid. Also, please make sure the texture and the TFJS WebGL backend are using the same canvas. If you want to use your own custom canvas, you have to create and use the custom TFJS WebGL backend created from the canvas through 'new tf.MathBackendWebGL(customCanvas)'.\");let u=p.writeTexture(n,t,o,s,a,i);return ur().makeTensorFromDataId(u,t,o,p)}};Lc.nextDataId=0;function m9(r,e){if(e===\"float32\"||e===\"complex64\")return r;if(e===\"int32\"||e===\"bool\"){let t=e===\"int32\"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;onew Lc,2);var $at={forceHalfFloat:GD};var Bc=`\n if (isnan(a)) return a;\n if (isnan(b)) return b;\n`;var Pr=class{constructor(e,t,o){this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.enableShapeUniforms=ut(this.outputShape.length),this.userCode=`\n float binaryOperation(float a, float b) {\n ${e}\n }\n\n void main() {\n float a = getAAtOutCoords();\n float b = getBAtOutCoords();\n setOutput(binaryOperation(a, b));\n }\n `}};var Xr=`\n result.r = isNaN.r ? NAN : result.r;\n result.g = isNaN.g ? NAN : result.g;\n result.b = isNaN.b ? NAN : result.b;\n result.a = isNaN.a ? NAN : result.a;\n`;var jr=class{constructor(e,t,o,n=!1){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length;this.enableShapeUniforms=ut(s);let a=\"\";if(n)if(s===0||y.sizeFromShape(this.outputShape)===1)a=`\n result.y = 0.;\n result.z = 0.;\n result.w = 0.;\n `;else if(a=`\n ${Re(s)} coords = getOutputCoords();\n `,s===1)this.enableShapeUniforms?a+=`\n result.y = (coords + 1) >= outShape ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `:a+=`\n result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;\n result.z = 0.;\n result.w = 0.;\n `;else{let p=Rt(\"coords\",s);this.enableShapeUniforms?a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= outShape[${s} - 2];\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= outShape[${s} - 1];\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `:a+=`\n bool nextRowOutOfBounds =\n (${p[s-2]} + 1) >= ${this.outputShape[s-2]};\n bool nextColOutOfBounds =\n (${p[s-1]} + 1) >= ${this.outputShape[s-1]};\n result.y = nextColOutOfBounds ? 0. : result.y;\n result.z = nextRowOutOfBounds ? 0. : result.z;\n result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;\n `}this.userCode=`\n vec4 binaryOperation(vec4 a, vec4 b) {\n ${e}\n }\n\n void main() {\n vec4 a = getAAtOutCoords();\n vec4 b = getBAtOutCoords();\n\n vec4 result = binaryOperation(a, b);\n ${a}\n\n setOutput(result);\n }\n `}};function Dt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var HD={kernelName:Co,backendName:\"webgl\",kernelFunc:Dt};function Or(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.texData.get(s.dataId),i=Dt({inputs:{x:o},backend:t}),p=Dt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var KD={kernelName:Di,backendName:\"webgl\",kernelFunc:Or};var hv=\"return (a < 0.) ? b * a : a;\",gv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function f9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],\"float32\",y.createScalarValue(s,\"float32\")),i=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(gv,n.shape,a.shape):new Pr(hv,n.shape,a.shape),p=t.runWebGLProgram(i,[n,a],\"float32\");return t.disposeIntermediateTensorInfo(a),p}var qD={kernelName:$n,backendName:\"webgl\",kernelFunc:f9};var xv=\"return (a < 0.) ? b * a : a;\",yv=`\n vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`;function h9(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(yv,o.shape,n.shape):new Pr(xv,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],\"float32\")}var jD={kernelName:rs,backendName:\"webgl\",kernelFunc:h9};var Fo=\"if (isnan(x)) return x;\";function xe({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,p=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let l=i.texData.get(a.dataId),m=t(l.values,p);return i.makeTensorInfo(a.shape,p,m)}let u=A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")&&e!=null,c;return u?c=new Fr(a.shape,e):c=new tr(a.shape,r),i.runWebGLProgram(c,[a],p)}}function nt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:p,b:u}=a,c=i;if(o&&p.dtype===\"complex64\"){let f=c.texData.get(p.dataId),h=c.texData.get(u.dataId),[g,x]=[[f.complexTensorInfos.real,h.complexTensorInfos.real],[f.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(C=>{let[S,k]=C,_={dataId:S.dataId,dtype:S.dtype,shape:p.shape},$={dataId:k.dataId,dtype:k.dtype,shape:u.shape},R=new Pr(r,p.shape,u.shape);return c.runWebGLProgram(R,[_,$],dt(S.dtype,k.dtype))}),b=Or({inputs:{real:g,imag:x},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(x),b}let l=s||dt(p.dtype,u.dtype);if((p.dtype===\"string\"||u.dtype===\"string\"||c.shouldExecuteOnCPU([p,u]))&&n!=null){let f=c.texData.get(p.dataId).values,h=c.texData.get(u.dataId).values,g=p.dtype===\"string\"?w.fromUint8ToStringArray(f):f,x=p.dtype===\"string\"?w.fromUint8ToStringArray(h):h,[b,C]=n(p.shape,u.shape,g,x,l),S=c.makeTensorInfo(C,l),k=c.texData.get(S.dataId);return k.values=b,S}let m=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")&&e!=null,d;return m?d=new jr(e,p.shape,u.shape,t):d=new Pr(r,p.shape,u.shape),c.runWebGLProgram(d,[p,u],l)}}function yi(r,e=!1){if(r===\"linear\")return e?BD:AD;if(r===\"relu\")return e?VD:PD;if(r===\"elu\")return e?zD:FD;if(r===\"relu6\")return e?WD:OD;if(r===\"prelu\")return e?yv:xv;if(r===\"leakyrelu\")return e?gv:hv;if(r===\"sigmoid\")return e?UD:MD;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var zc=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,p=!1,u=!1){this.variableNames=[\"matrixA\",\"matrixB\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o,this.enableShapeUniforms=ut(this.outputShape.length);let c=n?e[1]:e[2],l=Math.ceil(c/2),m=n?\"i * 2, rc.y\":\"rc.y, i * 2\",d=s?\"rc.z, i * 2\":\"i * 2, rc.z\",f=n?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],h=s?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"],g=\"\",x=\"\";i&&(p?g=`vec4 activation(vec4 a) {\n vec4 b = getPreluActivationWeightsAtOutCoords();\n ${i}\n }`:u?g=`vec4 activation(vec4 a) {\n vec4 b = getLeakyreluAlphaAtOutCoords();\n ${i}\n }`:g=`vec4 activation(vec4 x) {\n ${i}\n }`,x=\"result = activation(result);\");let b=a?\"result += getBiasAtOutCoords();\":\"\";a&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),u&&this.variableNames.push(\"leakyreluAlpha\");let C=\"rc.x\",S=\"rc.x\";e[0]`The new shape (${p}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!xu(n.shape,p)&&!(c.texture!==null&&xu(c.shape,p))?QD(n,p,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:p,dtype:n.dtype})}var ZD={kernelName:da,backendName:\"webgl\",kernelFunc:te};var rm=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,p=o%4,u=\"sumValue += dot(values, ones);\";if(t!=null){let l=1/t;u=`sumValue += dot(values * ${y.isInt(l)?l.toPrecision(2):l}, ones);`}let c=\"\";s%o>0&&(c=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return 0.0;\n }\n `),this.userCode=`\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${c}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${i}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${u}\n }\n\n int inIdx = inOffset + ${i};\n if (${p===1}) {\n vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);\n\n ${u}\n } else if (${p===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1), 0.0, 0.0);\n\n ${u}\n } else if (${p===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2), 0.0);\n\n ${u}\n }\n setOutput(sumValue);\n }\n `}};var gh=class{constructor(e,t){this.variableNames=[\"x\"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=\"0.0\",p=\"\";t===\"prod\"?i=\"1.0\":t===\"min\"?(i=\"1.0 / 1e-20\",p=\"min\"):t===\"max\"&&(i=\"-1.0 / 1e-20\",p=\"max\");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"sum\"?u=\"sumValue\":t===\"prod\"?u=\"prodValue\":t===\"all\"?u=\"allValue\":t===\"any\"&&(u=\"anyValue\");let c=Math.floor(o/4)*4,l=o%4,m=`\n if (${t===\"sum\"}) {\n sumValue += dot(values, ones);\n } else if (${t===\"prod\"}) {\n vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n prodValue *= tmp[0] * tmp[1];\n } else {\n minMaxValue = ${p}(values, minMaxValue);\n if (${t===\"min\"} || ${t===\"max\"}) {\n minMaxValue = ${p}(values, minMaxValue);\n bvec4 isNaN = isnan(values);\n if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {\n minMaxValue = vec4(NAN);\n }\n }\n }\n `,d=\"vec4\";t===\"all\"?(i=\"1.0\",m=`\n bool reducedAllValue = all(values);\n float floatedReducedAllValue = float(reducedAllValue);\n allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n `,d=\"bvec4\"):t===\"any\"&&(i=\"0.0\",m=`\n bool reducedAnyValue = any(values);\n float floatedReducedAnyValue = float(reducedAnyValue);\n anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n `,d=\"bvec4\");let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `),this.userCode=`\n const float initializationValue = ${i};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float getValue(int batch, int inIdx) {\n ${f}\n return getX(batch, inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${o};\n\n vec4 minMaxValue = vec4(${i});\n float prodValue = 1.0;\n float sumValue = 0.0;\n float allValue = 1.0;\n float anyValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===2}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n ${m}\n } else if (${l===3}) {\n ${d} values = ${d}(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function x9(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=w.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function Yr(r,e,t,o){let n=x9(r.shape),s=r;for(let a=0;a6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],o=new Array(e);for(let n=0;n6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Re(this.rank),s=dv(\"rc\",this.rank),a=new Array(this.rank);for(let c=0;c`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=te({inputs:{x:r},backend:n,attrs:{shape:k}}),R=te({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[$,R],P=Math.max(x,b),O=t?$.shape[1]:$.shape[2],M=s!=null,L=a!=null,B=p===\"leakyrelu\",z=p!=null?yi(p,!0):null,U=M||L||B||z!=null,j;if((d===1||f===1)&&O>Cv&&U===!1){let Y=$,J=R;t&&(Y=bt({inputs:{x:$},backend:n,attrs:{perm:[0,2,1]}}),D.push(Y)),o&&(J=bt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),D.push(J));let re=f!==1,ne=f===1,ee=Y;re&&(ee=te({inputs:{x:Y},backend:n,attrs:{shape:[P,O,1]}}),D.push(ee));let oe=f===1?2:1,ie=J;ne&&(ie=te({inputs:{x:J},backend:n,attrs:{shape:[P,1,O]}}),D.push(ie));let le=tm({inputs:{a:ee,b:ie},backend:n});j=wp({inputs:{x:le},backend:n,attrs:{axis:oe,keepDims:!0}}),D.push(le)}else{let Y=dt(r.dtype,e.dtype),J=new zc(k,_,[P,d,f],t,o,M,z,L,B),re=[$,R];if(s!=null&&re.push(s),L&&re.push(a),B){let ne=n.makeTensorInfo([],\"float32\",y.createScalarValue(i,\"float32\"));re.push(ne),D.push(ne)}j=n.runWebGLProgram(J,re,Y)}let q=te({inputs:{x:j},backend:n,attrs:{shape:S}});D.push(j);for(let Y of D)n.disposeIntermediateTensorInfo(Y);return q}function b9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return Sp({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var rA={kernelName:So,backendName:\"webgl\",kernelFunc:b9};var oA=\"return abs(x);\";function C9(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!==\"complex64\"){let s=t.texData.get(o.dataId),a=ch(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return A().getBool(\"WEBGL_PACK_UNARY_OPERATIONS\")?n=new Fr(o.shape,oA):n=new tr(o.shape,oA),t.runWebGLProgram(n,[o],o.dtype)}var nA={kernelName:Xs,backendName:\"webgl\",kernelFunc:C9};var w9=Wt+`\n if 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o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(\" + \");this.userCode=`\n void main() {\n ${o.join(`\n `)}\n\n vec4 result = ${n};\n setOutput(result);\n }\n `}};function wh(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return Dt({inputs:{x:o[0]},backend:t});if(o.length>A().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\")){let p=Math.floor(o.length/2),u=wh({inputs:o.slice(0,p),backend:t}),c=wh({inputs:o.slice(p),backend:t});return wh({inputs:[u,c],backend:t})}let n=o.map(p=>p.dtype).reduce((p,u)=>dt(p,u)),s=o.map(p=>p.shape),i=A().getBool(\"WEBGL_PACK\")?new Ch(o[0].shape,s):new bh(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var pA={kernelName:Uo,backendName:\"webgl\",kernelFunc:wh};function N9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=w.getInnerMostAxes(u.length,i)),w.assertAxesAreInnerMostDims(\"all\",u,i);let[m,d]=w.computeOutAndReduceShapes(l.shape,u),f=y.sizeFromShape(d),h=te({inputs:{x:l},backend:t,attrs:{shape:[-1,f]}}),g=Yr(h,h.dtype,\"all\",t),x;if(a){let b=w.expandShapeToKeepDim(m,p);x=te({inputs:{x:g},backend:t,attrs:{shape:b}})}else x=te({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(l),x}var cA={kernelName:Go,backendName:\"webgl\",kernelFunc:N9};function 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i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = outIdx * ${n};\n\n int bestIndex = inOffset;\n float bestValue = getA(batch, bestIndex);\n\n for (int i = 0; i < ${n}; i++) {\n int inIdx = ${p};\n float candidate = getA(batch, inIdx);\n if (candidate ${i} bestValue) {\n bestValue = candidate;\n bestIndex = inIdx;\n }\n }\n setOutput(float(bestIndex));\n }\n `}};var Ih=class{constructor(e,t,o,n){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,y.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push(\"bestIndicesA\");let i=this.outputShape,p=i.length,u=Re(p),c=Rt(\"coords\",p),l,m;if(a===1){m=p+1;let R=Re(m);l=`\n ${R} sourceLocR = 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n=[t];if(w.assertAxesAreInnerMostDims(\"arg\"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!A().getBool(\"WEBGL_PACK_REDUCE\")||e.shape.length<=2){let s=[],a=r.texData.get(e.dataId),i=a!==null&&a.isPacked,p=e;i&&(p=r.unpackTensor(e),s.push(p));let[u,c]=w.computeOutAndReduceShapes(p.shape,n),l=y.sizeFromShape(c),m=te({inputs:{x:p},backend:r,attrs:{shape:[-1,l]}});s.push(m);let d=mA(r,m,o);s.push(d);let f=te({inputs:{x:d},backend:r,attrs:{shape:u}});return s.forEach(h=>r.disposeIntermediateTensorInfo(h)),f}return dA(r,e,o)}function _9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=vh(t,p,a[0],\"max\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var fA={kernelName:Ys,backendName:\"webgl\",kernelFunc:_9};function E9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=bt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=vh(t,p,a[0],\"min\");return u.forEach(l=>t.disposeIntermediateTensorInfo(l)),c}var hA={kernelName:Qs,backendName:\"webgl\",kernelFunc:E9};var $9=Wt+`\n if (abs(x) > 1.) {\n return NAN;\n }\n return asin(x);\n`,R9=xe({opSnippet:$9}),gA={kernelName:Ko,backendName:\"webgl\",kernelFunc:R9};var D9=Wt+\"return log(x + sqrt(x * x + 1.0));\",A9=xe({opSnippet:D9}),xA={kernelName:qo,backendName:\"webgl\",kernelFunc:A9};var F9=Wt+`\n return atan(x);\n`,P9=xe({opSnippet:F9}),yA={kernelName:jo,backendName:\"webgl\",kernelFunc:P9};var O9=Bc+`\n return atan(a, b);\n`,M9=`\n vec4 result = atan(a, b);\n bvec4 isNaNA = isnan(a);\n bvec4 isNaNB = isnan(b);\n bvec4 isNaN = bvec4(isNaNA.x || isNaNB.x, isNaNA.y || isNaNB.y, isNaNA.z || isNaNB.z, isNaNA.w || isNaNB.w);\n `+Xr+`\n return result;\n`,L9=nt({opSnippet:O9,packedOpSnippet:M9}),bA={kernelName:Yo,backendName:\"webgl\",kernelFunc:L9};var B9=Wt+`\n if ((x < -1.0) || (x > 1.0)) return NAN;\nreturn (log(1.0 + x) - log(1.0 - x)) / 2.0;`,z9=xe({opSnippet:B9}),CA={kernelName:Xo,backendName:\"webgl\",kernelFunc:z9};var Us=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideHeight,p=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterHeight,m=e.effectiveFilterWidth,d=e.padInfo.top,f=e.padInfo.left;this.outputShape=e.outShape;let h=t===\"avg\",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,x=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b=\"0.0\";if(h||(b=\"-1.0 / 1e-20\"),o){let R=\">=\";this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n float avgValue = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${m};\n wC += ${c}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xR, xC, d);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${R} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?g:x:`wR * ${m} + wC`};\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let C=\"max\",S=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(S=\"avgValue / max(count, 1.0)\");let k=Math.floor(a/4)*4,_=a%4,$=`\n if (${h}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${C}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec2 strides = ivec2(${i}, ${p});\n const ivec2 pads = ivec2(${d}, ${f});\n const float initializationValue = ${b};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xR, int xC, int d) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xR, xC, d);\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d = coords[3];\n\n ivec2 xRCCorner = coords.yz * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // max/min x(?, ?, d) to get y(yR, yC, d).\n // ? = to be determined\n vec4 minMaxValue = vec4(${b});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wR = 0; wR < ${l};\n wR += ${u}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${k}; wC += 4) {\n int xC = xCCorner + wC * ${c};\n\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n getValue(batch, xR, xC + 3 * ${c}, d)\n );\n\n ${$}\n }\n\n int xC = xCCorner + ${k};\n if (${_===1}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===2}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n initializationValue,\n initializationValue\n );\n\n ${$}\n } else if (${_===3}) {\n vec4 values = vec4(\n getValue(batch, xR, xC, d),\n getValue(batch, xR, xC + ${c}, d),\n getValue(batch, xR, xC + 2 * ${c}, d),\n initializationValue\n );\n\n ${$}\n }\n }\n setOutput(${S});\n }\n `}},bu=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=[\"x\"],t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");let a=e.filterWidth,i=e.strideDepth,p=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,l=e.dilationHeight,m=e.dilationWidth,d=e.effectiveFilterDepth,f=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,x=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let C=t===\"avg\",S=\"0.0\";if(C||(S=\"-1.0 / 1e-20\"),o){let P=\">=\";this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).\n // ? = to be determined\n float minMaxValue = 0.0;\n float minMaxValueFound = 0.0;\n int minMaxPosition = 0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${h};\n wC += ${m}) {\n int xC = xCCorner + wC;\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float value = getX(batch, xD, xR, xC, ch);\n\n // If a min / max value has already been found, use it. If not,\n // use the current value.\n float currMinMaxValue = mix(\n value, minMaxValue, minMaxValueFound);\n if (value ${P} currMinMaxValue) {\n minMaxValue = value;\n minMaxValueFound = 1.0;\n minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${f} * ${h} +\n wR * ${h} + wC`};\n }\n }\n }\n }\n setOutput(float(minMaxPosition));\n }\n `;return}let k=\"max\",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t===\"avg\"&&(_=\"avgValue / max(count, 1.0)\");let $=Math.floor(a/4)*4,R=a%4,D=`\n if (${C}) {\n avgValue += dot(values, ones);\n } else {\n minMaxValue = ${k}(values, minMaxValue);\n }\n `;this.userCode=`\n const ivec3 strides =\n ivec3(${i}, ${p}, ${u});\n const ivec3 pads = ivec3(${g}, ${x}, ${b});\n const float initializationValue = ${S};\n const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n float count = 0.0;\n\n float getValue(int batch, int xD, int xR, int xC, int ch) {\n if (xC < 0 || xC >= ${e.inWidth}) {\n return initializationValue;\n }\n count += 1.0;\n return getX(batch, xD, xR, xC, ch);\n }\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xDCorner = xCorner.x;\n int xRCorner = xCorner.y;\n int xCCorner = xCorner.z;\n\n // max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).\n // ? = to be determined\n vec4 minMaxValue = vec4(${S});\n float avgValue = 0.0;\n count = 0.0;\n\n for (int wD = 0; wD < ${d};\n wD += ${c}) {\n int xD = xDCorner + wD;\n\n if (xD < 0 || xD >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${f};\n wR += ${l}) {\n int xR = xRCorner + wR;\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${$}; wC += 4) {\n int xC = xCCorner + wC * ${m};\n\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n getValue(batch, xD, xR, xC + 3 * ${m}, ch)\n );\n\n ${D}\n }\n\n int xC = xCCorner + ${$};\n if (${R===1}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===2}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n initializationValue,\n initializationValue\n );\n\n ${D}\n } else if (${R===3}) {\n vec4 values = vec4(\n getValue(batch, xD, xR, xC, ch),\n getValue(batch, xD, xR, xC + ${m}, ch),\n getValue(batch, xD, xR, xC + 2 * ${m}, ch),\n initializationValue\n );\n\n ${D}\n }\n }\n }\n setOutput(${_});\n }\n `}};function V9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;Vs(n,\"avgPool\");let{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,u=1;y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Us(c,\"avg\",!1);return t.runWebGLProgram(l,[n],\"float32\")}var wA={kernelName:Qo,backendName:\"webgl\",kernelFunc:V9};function W9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,p,u),m=new bu(l,\"avg\",!1);return t.runWebGLProgram(m,[n],\"float32\")}var SA={kernelName:Zs,backendName:\"webgl\",kernelFunc:W9};var kh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=p-1-e.padInfo.top,l=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`\n const ivec2 pads = ivec2(${c}, ${l});\n const float avgMultiplier = float(${m});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${p};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${u};\n wC+= ${i}) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n setOutput(dotProd);\n }\n `}},Nh=class{constructor(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.effectiveFilterDepth,m=e.effectiveFilterHeight,d=e.effectiveFilterWidth,f=l-1-e.padInfo.front,h=m-1-e.padInfo.top,g=d-1-e.padInfo.left,x=1/(t*o*n);this.userCode=`\n const ivec3 pads = ivec3(${f}, ${h}, ${g});\n const float avgMultiplier = float(${x});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${l};\n wD += ${p}) {\n float dyD = float(dyDCorner + wD) / ${s}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${m};\n wR += ${u}) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${d};\n wC += ${c}) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n\n dotProd += dyValue * avgMultiplier;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function U9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Nh(m);return t.runWebGLProgram(d,[n],a.dtype)}var IA={kernelName:Ri,backendName:\"webgl\",kernelFunc:U9};function G9(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;Vs([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new kh(c);return t.runWebGLProgram(l,[n],a.dtype)}var vA={kernelName:$i,backendName:\"webgl\",kernelFunc:G9};function H9(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return Sp({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var kA={kernelName:Zo,backendName:\"webgl\",kernelFunc:H9};var Th=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i=\"0.0\";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"1.0\";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n float x = getXAtOutCoords();\n float mean = getMeanAtOutCoords();\n float variance = getVarianceAtOutCoords();\n float offset = ${i};\n float scale = ${p};\n float inv = scale * inversesqrt(variance + float(${a}));\n setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n }\n `}};var _h=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],w.assertAndGetBroadcastShape(e,t),w.assertAndGetBroadcastShape(e,o);let i=\"vec4(0.0)\";n!=null&&(w.assertAndGetBroadcastShape(e,n),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");let p=\"vec4(1.0)\";s!=null&&(w.assertAndGetBroadcastShape(e,s),this.variableNames.push(\"scale\"),p=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=`\n void main() {\n vec4 offset = ${i};\n vec4 scale = ${p};\n\n vec4 x = getXAtOutCoords();\n vec4 mean = getMeanAtOutCoords();\n vec4 variance = getVarianceAtOutCoords();\n\n vec4 inv = scale * inversesqrt(variance + vec4(${a}));\n\n setOutput((x - mean) * inv + offset);\n }\n `}};var K9=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;y.assert(n.shape.length===s.shape.length,()=>\"Batch normalization gradient requires mean and variance to have equal ranks.\"),y.assert(a==null||n.shape.length===a.shape.length,()=>\"Batch normalization gradient requires mean and offset to have equal ranks.\"),y.assert(i==null||n.shape.length===i.shape.length,()=>\"Batch normalization gradient requires mean and scale to have equal ranks.\");let{varianceEpsilon:p}=t;p==null&&(p=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let l=null;i!=null&&(l=i.shape,u.push(i));let m=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new _h(o.shape,n.shape,s.shape,c,l,p):new Th(o.shape,n.shape,s.shape,c,l,p);return e.runWebGLProgram(m,u,u[0].dtype)},NA={kernelName:In,backendName:\"webgl\",kernelFunc:K9};var Eh=class{constructor(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;let t=Re(this.rank);this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let o=q9(this.rank),n,s=e.map((a,i)=>`sourceLoc.${wv[i]} = start[${i}] + coords.${wv[i]};`);n=`\n ${t} sourceLoc;\n ${t} coords = getOutputCoords();\n ${s.join(`\n`)}\n `,this.userCode=`\n void main() {\n ${n}\n setOutput(getSource(${o}));\n }\n `}},wv=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function q9(r){if(r===1)return\"sourceLoc\";if(r<=6)return wv.slice(0,r).map(e=>\"sourceLoc.\"+e).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}var $h=class{constructor(e){this.variableNames=[\"source\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:\"start\",arrayIndex:this.rank,type:\"int\"}];let t=Re(this.rank),o=Rt(\"coords\",this.rank),n=Rt(\"sourceLoc\",this.rank),s=this.rank===1?\"sourceLoc\":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`\n result.x = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.y = ${a};\n --${n[this.rank-1]};\n }\n `,p=this.rank===1?\"\":`\n --${o[this.rank-1]};\n if (++${o[this.rank-2]} < ${e[this.rank-2]}) {\n ++${n[this.rank-2]};\n result.z = ${a};\n if (++${o[this.rank-1]} < ${e[this.rank-1]}) {\n ++${n[this.rank-1]};\n result.w = ${a};\n }\n }\n `,u=this.rank<=4?`sourceLoc = coords +\n ${t}(${e.map((c,l)=>`start[${l}]`).join()});`:e.map((c,l)=>`${n[l]} = ${o[l]} + start[${l}];`).join(`\n`);this.userCode=`\n void main() {\n ${t} coords = getOutputCoords();\n ${t} sourceLoc;\n ${u}\n vec4 result = vec4(0.);\n ${i}\n ${p}\n setOutput(result);\n }\n `}};function j9(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=pt.computeFlatOffset(e,y.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let p=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,p+1),s}function Gs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.texData.get(n.dataId),m=gD(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=pt.isSliceContinous(n.shape,i,p);if(u||!c){let l=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new $h(p):new Eh(p),m=[i];return t.runWebGLProgram(l,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),j9(n,i,p,t)}var TA={kernelName:ha,backendName:\"webgl\",kernelFunc:Gs};var X9=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=te({inputs:{x:n},backend:t,attrs:{shape:p}}),h=bt({inputs:{x:f},backend:t,attrs:{perm:u}}),g=te({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Gs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeIntermediateTensorInfo(b)),x},_A={kernelName:Js,backendName:\"webgl\",kernelFunc:X9};function Y9(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),p=t.readSync(s.dataId),u=ph(i,p,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var EA={kernelName:Jo,backendName:\"webgl\",kernelFunc:Y9};var Q9=`\n int r = int(a.r) & int(b.r);\n int g = int(a.g) & int(b.g);\n int rb = int(a.b) & int(b.b);\n int ra = int(a.a) & int(b.a);\n return vec4(r, g, rb, ra);\n`,Z9=`\n return float(int(a.r) & int(b.r));\n`;function J9(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\"),a=A().getNumber(\"WEBGL_VERSION\");if(t.shouldExecuteOnCPU([o,n])||a===1){let p=t.texData.get(o.dataId).values,u=t.texData.get(n.dataId).values,[c,l]=zR(o.shape,n.shape,p,u,o.dtype),m=t.makeTensorInfo(l,o.dtype),d=t.texData.get(m.dataId);return d.values=c,m}let i;return s?i=new jr(Q9,o.shape,n.shape,!1):i=new Pr(Z9,o.shape,n.shape),t.runWebGLProgram(i,[o,n],o.dtype)}var $A={kernelName:qa,backendName:\"webgl\",kernelFunc:J9};function eJ(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e,s=t.readSync(o.dataId),a=t.readSync(n.dataId),i=w.assertAndGetBroadcastShape(Array.from(s),Array.from(a));return t.makeTensorInfo([i.length],\"int32\",Int32Array.from(i))}var RA={kernelName:ea,backendName:\"webgl\",kernelFunc:eJ};var tJ=\"return float(a != b);\",Sv=nt({opSnippet:tJ,cpuKernelImpl:iD,dtype:\"bool\"}),DA={kernelName:Yn,backendName:\"webgl\",kernelFunc:Sv};function bi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.real},backend:t})}var AA={kernelName:Hi,backendName:\"webgl\",kernelFunc:bi};var rJ=\"return float(int(x));\";function FA(r,e){let t=new tr(r.shape,rJ),o=e.runWebGLProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Iv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return Dt({inputs:{x:n},backend:t});let a=Gr(n.shape),i=Iv({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=Or({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),p}if(n.dtype===\"complex64\"){let a=bi({inputs:{input:n},backend:t}),i=Iv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=Dt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.texData.get(n.dataId).values,[i,p,u]=VR(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return FA(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=Sv({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var PA={kernelName:yo,backendName:\"webgl\",kernelFunc:Iv};var OA=\"return ceil(x);\",oJ=xe({opSnippet:OA,packedOpSnippet:OA,cpuKernelImpl:WR}),MA={kernelName:en,backendName:\"webgl\",kernelFunc:oJ};var Rh=class{constructor(e){this.variableNames=[\"A\"],this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n\n void main() {\n float value = getAAtOutCoords();\n if (isnan(value)) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, minVal, maxVal));\n }\n `}};var Dh=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"minVal\",type:\"float\"},{name:\"maxVal\",type:\"float\"}],this.outputShape=e,this.userCode=`\n void main() {\n vec4 value = getAAtOutCoords();\n\n if (any(isnan(value))) {\n setOutput(value);\n return;\n }\n\n setOutput(clamp(value, vec4(minVal), vec4(maxVal)));\n }\n `}};function nJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;A().getBool(\"WEBGL_PACK_CLIP\")?i=new Dh(n.shape):i=new Rh(n.shape);let p=[[s],[a]];return t.runWebGLProgram(i,[n],n.dtype,p)}var LA={kernelName:bo,backendName:\"webgl\",kernelFunc:nJ};var Ah=class{constructor(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=`\n void main() {\n float re = abs(getRealAtOutCoords());\n float im = abs(getImagAtOutCoords());\n float mx = max(re, im);\n\n // sadly the length function in glsl is not underflow-safe\n // (at least not on Intel GPUs). 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1;\n if (${a[n-2]} < ${o[n-2]} &&\n ${a[n-1]} < ${o[n-1]}) {\n result.b = getValue(${a});\n }\n setOutput(result);\n }\n `}};function Ph(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function Ip(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return Dt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var VA={kernelName:Wi,backendName:\"webgl\",kernelFunc:Ip};function Vc(r,e,t){let o=r[0].dtype;if(o===\"complex64\"){let d=r.map(b=>bi({inputs:{input:b},backend:t})),f=r.map(b=>Ip({inputs:{input:b},backend:t})),h=Vc(d,e,t),g=Vc(f,e,t),x=Or({inputs:{real:h,imag:g},backend:t});return d.forEach(b=>t.disposeIntermediateTensorInfo(b)),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),x}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let d=r.map(S=>{let _=[-1,y.sizeFromShape(S.shape.slice(e))];return te({inputs:{x:S},backend:t,attrs:{shape:_}})}),f=d.map(S=>({vals:t.readSync(S.dataId),shape:S.shape})),h=w.computeOutShape(d.map(S=>S.shape),1),g=d[0].shape[0]===1,x=UR(f,h,o,g),b=w.computeOutShape(r.map(S=>S.shape),e),C=t.makeTensorInfo(b,o,x);return d.forEach(S=>t.disposeIntermediateTensorInfo(S)),C}let s=r.filter(d=>y.sizeFromShape(d.shape)>0),a=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")&&s[0].shape.length>1;if(s.length===1){let d=a?new tr(r[0].shape,La):new Fr(r[0].shape,La);return t.runWebGLProgram(d,r,o)}let i=A().getNumber(\"WEBGL_MAX_TEXTURES_IN_SHADER\");if(s.length>i){let d=[];for(let h=0;hf.shape),e);return t.runWebGLProgram(d,s,o)}let{tensors2D:p,outShape:u}=aJ(s,e,t),c=new Fh(p.map(d=>d.shape)),l=t.runWebGLProgram(c,p,o);p.forEach(d=>t.disposeIntermediateTensorInfo(d));let m=te({inputs:{x:l},attrs:{shape:u},backend:t});return t.disposeIntermediateTensorInfo(l),m}function aJ(r,e,t){let o=w.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>te({inputs:{x:s},attrs:{shape:[-1,y.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function vv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?Dt({inputs:{x:p[0]},backend:t}):Vc(p,s,t)}var WA={kernelName:ta,backendName:\"webgl\",kernelFunc:vv};var Wc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,p=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,l=e.dilationWidth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat===\"channelsLast\",x=g?1:2,b=g?2:3,C=g?3:1,S=\"\",k=\"\";o&&(n?S=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?S=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:S=`\n float activation(float x) {\n ${o}\n }\n `,k=\"result = activation(result);\");let _=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${S}\n\n const ivec2 strides = ivec2(${p}, ${u});\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d2 = coords[${C}];\n\n ivec2 xRCCorner =\n ivec2(coords[${x}], coords[${b}]) * strides - pads;\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${c};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${l};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 wValues = vec4(\n getW(wR, wC, d1, d2),\n getW(wR, wC, d1 + 1, d2),\n getW(wR, wC, d1 + 2, d2),\n getW(wR, wC, d1 + 3, d2)\n );\n\n if (${g}) {\n vec4 xValues = vec4(\n getX(batch, xR, xC, d1),\n getX(batch, xR, xC, d1 + 1),\n getX(batch, xR, xC, d1 + 2),\n getX(batch, xR, xC, d1 + 3)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec4 xValues = vec4(\n getX(batch, d1, xR, xC),\n getX(batch, d1 + 1, xR, xC),\n getX(batch, d1 + 2, xR, xC),\n getX(batch, d1 + 3, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n\n if (${h===1}) {\n\n if (${g}) {\n dotProd +=\n getX(batch, xR, xC, ${f}) *\n getW(wR, wC, ${f}, d2);\n } else {\n dotProd +=\n getX(batch, ${f}, xR, xC) *\n getW(wR, wC, ${f}, d2);\n }\n\n } else if (${h===2}) {\n vec2 wValues = vec2(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2)\n );\n\n if (${g}) {\n vec2 xValues = vec2(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec2 xValues = vec2(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n } else if (${h===3}) {\n vec3 wValues = vec3(\n getW(wR, wC, ${f}, d2),\n getW(wR, wC, ${f} + 1, d2),\n getW(wR, wC, ${f} + 2, d2)\n );\n\n if (${g}) {\n vec3 xValues = vec3(\n getX(batch, xR, xC, ${f}),\n getX(batch, xR, xC, ${f} + 1),\n getX(batch, xR, xC, ${f} + 2)\n );\n dotProd += dot(xValues, wValues);\n } else {\n vec3 xValues = vec3(\n getX(batch, ${f}, xR, xC),\n getX(batch, ${f} + 1, xR, xC),\n getX(batch, ${f} + 2, xR, xC)\n );\n dotProd += dot(xValues, wValues);\n }\n\n }\n }\n }\n\n float result = dotProd;\n ${_}\n ${k}\n setOutput(result);\n }\n `}},Mh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,l=e.filterDepth,m=e.filterHeight,d=e.filterWidth,f=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`\n const ivec3 strides = ivec3(${s}, ${a}, ${i});\n const ivec3 pads = ivec3(${t}, ${o}, ${n});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d2 = coords.u;\n\n ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n int xFCorner = xFRCCorner.x;\n int xRCorner = xFRCCorner.y;\n int xCCorner = xFRCCorner.z;\n\n // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n // y(yF, yR, yC, d2). ? = to be determined. : = across all\n // values in that axis.\n float dotProd = 0.0;\n for (int wF = 0; wF < ${l}; wF++) {\n int xF = xFCorner + wF * ${p};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int wR = 0; wR < ${m}; wR++) {\n int xR = xRCorner + wR * ${u};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int wC = 0; wC < ${d}; wC++) {\n int xC = xCCorner + wC * ${c};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n for (int d1 = 0; d1 < ${f}; d1 += 4) {\n vec4 xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n vec4 wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (${h===1}) {\n dotProd +=\n getX(batch, xF, xR, xC, ${f}) *\n getW(wF, wR, wC, ${f}, d2);\n } else if (${h===2}) {\n vec2 xValues = vec2(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1)\n );\n vec2 wValues = vec2(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (${h===3}) {\n vec3 xValues = vec3(\n getX(batch, xF, xR, xC, ${f}),\n getX(batch, xF, xR, xC, ${f} + 1),\n getX(batch, xF, xR, xC, ${f} + 2)\n );\n vec3 wValues = vec3(\n getW(wF, wR, wC, ${f}, d2),\n getW(wF, wR, wC, ${f} + 1, d2),\n getW(wF, wR, wC, ${f} + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};var Uc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.padInfo.left,i=e.strideWidth,p=e.dilationWidth,u=e.filterHeight,c=e.filterWidth,l=c,m=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g=0 && xR < inDims[0]) {\n `;for(let g=0;g<(l+1)/2;g++){let x=g*2;if(m+=`\n xC = xCCorner + ${x*p};\n `,i===1){if(x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n `,p===1&&x>0?m+=`\n xC${x} = vec4(xTexelC${x-2}.zw, xTexelC${x}.xy);\n `:m+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${x} = vec4(previous.zw, xTexelC${x}.xy);\n } else {\n xC${x} = vec4(0.0, 0.0, xTexelC${x}.xy);\n }\n `):m+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xC${x} = xTexelC${x};\n `,x+1= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n `,p>1?m+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${x+1} = vec4(previous.zw, xTexelC${x+1}.xy);\n } else {\n xC${x+1} = vec4(0.0, 0.0, xTexelC${x+1}.xy);\n }\n `:m+=`\n xC${x+1} = vec4(xTexelC${x}.zw, xTexelC${x+1}.xy);\n `):b===1?m+=`\n xC${x+1} = xTexelC${x};\n `:m+=`\n xCOffset = xC + ${b};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x+1} = xTexelC${x+1};\n `}}else x= 0 && xCOffset < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.0);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(xTexelC${x}.zw, xTexelC${x+1}.zw);\n `,x+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${x+1} = vec4(xTexelC${x+1}.xy, final.xy);\n `)):(m+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${x}Ready == 0) {\n xTexelC${x} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${x}.zw = vec2(0.0);\n }\n xTexelC${x}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${x+1}Ready == 0) {\n xTexelC${x+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${x+1}.zw = vec2(0.);\n }\n xTexelC${x+1}Ready = 1;\n }\n\n xC${x} = vec4(\n xTexelC${x}.xy, xTexelC${x+1}.xy);\n `,x+1= 0) {\n // Use custom imod instead mod. On Intel GPU, mod may generate\n // unexpected value.\n // https://github.com/tensorflow/tfjs/issues/5447\n offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];\n d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /\n inChannels);\n\n if(d1 < inputShape[${i}] && d1 >= 0) {\n\n ch = imod(pos, inChannels);\n\n if (${s}) {\n innerDims = vec2(d1, ch);\n result[${c*2+l}] = getChannel(\n getA(rc.x, d0, int(innerDims.x),\n int(innerDims.y)), innerDims);\n } else {\n innerDims = vec2(d0, d1);\n result[${c*2+l}] = getChannel(\n getA(rc.x, ch, int(innerDims.x),\n int(innerDims.y)), innerDims);\n }\n }\n }\n }\n `;this.userCode=`\n void main() {\n ivec3 rc = getOutputCoords();\n\n vec4 result = vec4(0);\n\n int blockIndex, pos, offsetY, d0, offsetX, d1, ch;\n vec2 innerDims;\n\n ${u}\n\n ${n.output} = result;\n }\n `}};function Bh(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function zh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,l=p[0]*p[1]*p[2],m=t.outChannels,d=t.dataFormat===\"channelsLast\",f=!1,h=!1,g,x=[];if(s!=null){let S=Bh(s.shape,d);S!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:S}}),x.push(s))}if(n!=null){let S=Bh(n.shape,d);S!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:S}}),x.push(n))}if(!((l===1||m===1)&&c>Cv)&&u.isPacked&&d&&u.texture!=null&&p[2]%2!==0&&y.arraysEqual(u.shape.slice(-3),p.slice(-3))){let S=p[0]*p[1]*(p[2]+1),k={dataId:r.dataId,shape:[1,S,t.inChannels],dtype:r.dtype},_=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,y.assert(xu(u.shape,k.shape),()=>`packed reshape ${u.shape} to ${k.shape} isn't free`);let $=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});x.push($);let R=Sp({a:k,b:$,backend:o,transposeA:f,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),D=o.texData.get(R.dataId);y.assert(D.isPacked,()=>\"batchMatMul result is expected to be packed\"),u.shape=_,D.shape=t.outShape,g=Dt({inputs:{x:R},backend:o}),g.shape=t.outShape,x.push(R)}else{let S=t.outHeight*t.outWidth,k=te({inputs:{x:r},backend:o,attrs:{shape:d?[t.batchSize,S,t.inChannels]:[t.batchSize,t.inChannels,S]}}),_=te({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),$=Sp({a:d?k:_,b:d?_:k,transposeA:!d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=te({inputs:{x:$},backend:o,attrs:{shape:t.outShape}}),x.push(k),x.push(_),x.push($)}for(let S of x)o.disposeIntermediateTensorInfo(S);return g}function Vh({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,outWidth:l,outHeight:m,dataFormat:d}=t,f=d===\"channelsLast\",h=p*u*c,g=m*l,x=[t.batchSize,h,g],b=!0,C=!1,S=[];if(s!=null){let q=Bh(s.shape,f);q!=null&&(s=te({inputs:{x:s},backend:o,attrs:{shape:q}}),S.push(s))}if(n!=null){let q=Bh(n.shape,f);q!=null&&(n=te({inputs:{x:n},backend:o,attrs:{shape:q}}),S.push(n))}let k=te({inputs:{x:e},backend:o,attrs:{shape:[1,h,y.sizeFromShape(e.shape)/h]}});S.push(k);let _=new Lh(x,t),$=[r.shape,[t.padInfo.top,t.padInfo.left],[t.strideHeight,t.strideWidth],[t.dilationHeight,t.dilationWidth],[t.inChannels],[t.filterWidth*t.inChannels],[t.outWidth]],R=o.runWebGLProgram(_,[r],\"float32\",$),D=te({inputs:{x:R},backend:o,attrs:{shape:x}});S.push(R),S.push(D);let P=n!=null,O=s!=null,M=i===\"leakyrelu\",L=i?yi(i,!0):null,B=new zc(f?D.shape:k.shape,f?k.shape:D.shape,f?[t.batchSize,g,t.outChannels]:[t.batchSize,t.outChannels,g],b,C,P,L,O,M),z=f?[D,k]:[k,D];if(n&&z.push(n),O&&z.push(s),M){let q=o.makeTensorInfo([],\"float32\",y.createScalarValue(a,\"float32\"));z.push(q),S.push(q)}let U=o.runWebGLProgram(B,z,\"float32\"),j=te({inputs:{x:U},backend:o,attrs:{shape:t.outShape}});S.push(U);for(let q of S)o.disposeIntermediateTensorInfo(q);return j}function iJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l),d;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type===\"SAME\"||m.padInfo.type===\"VALID\"))d=zh({x:n,filter:s,convInfo:m,backend:t});else if(m.strideWidth<=2&&l===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let h=new Uc(m),g=[[m.padInfo.top,m.padInfo.left],[m.strideHeight,m.strideWidth],[m.dilationHeight,m.dilationWidth],[m.inHeight,m.inWidth]];d=t.runWebGLProgram(h,[n,s],\"float32\",g)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))d=Vh({x:n,filter:s,convInfo:m,backend:t});else{let h=new Wc(m);d=t.runWebGLProgram(h,[n,s],\"float32\")}let f=te({inputs:{x:d},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(d),f}var UA={kernelName:tn,backendName:\"webgl\",kernelFunc:iJ};var Wh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.dataFormat===\"channelsLast\";this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n ${a?`float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);`:`float dyValue = getDy(b, d2, yR, yC);\n float xValue = getX(b, d1, xR, xC);\n dotProd += (xValue * dyValue);`}\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Uh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat===\"channelsLast\",i=t-1-e.padInfo.top,p=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,l=a?3:1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[${l}];\n\n ivec2 dyCorner = ivec2(coords[${u}], coords[${c}]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n\n if (${a}) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n } else {\n float xValue = getDy(batch, d2, idyR, idyC);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Gh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=`\n void main() {\n ivec5 coords = getOutputCoords();\n int wF = coords.x;\n int wR = coords.y;\n int wC = coords.z;\n int d1 = coords.w;\n int d2 = coords.u;\n\n float dotProd = 0.0;\n\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yF = 0; yF < ${e.outDepth}; yF++) {\n int xF = wF + yF * ${t} - ${s};\n\n if (xF < 0 || xF >= ${e.inDepth}) {\n continue;\n }\n\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${o} - ${a};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${n} - ${i};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yF, yR, yC, d2);\n float xValue = getX(b, xF, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Hh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,p=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`\n const ivec3 pads = ivec3(${p}, ${u}, ${c});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.u;\n\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyFCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n float dotProd = 0.0;\n for (int wF = 0; wF < ${t}; wF++) {\n float dyF = float(dyFCorner + wF) / ${s}.0;\n\n if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {\n continue;\n }\n int idyF = int(dyF);\n\n int wFPerm = ${t} - 1 - wF;\n\n for (int wR = 0; wR < ${o}; wR++) {\n float dyR = float(dyRCorner + wR) / ${a}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${o} - 1 - wR;\n\n for (int wC = 0; wC < ${n}; wC++) {\n float dyC = float(dyCCorner + wC) / ${i}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${n} - 1 - wC;\n\n for (int d2 = 0; d2 < ${e.outChannels}; d2++) {\n float xValue = getDy(batch, idyF, idyR, idyC, d2);\n float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function uJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new Wh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}var GA={kernelName:Fi,backendName:\"webgl\",kernelFunc:uJ};var Kh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"strides\",type:\"vec2\"}],this.outputShape=e.inShape,this.enableShapeUniforms=ut(this.outputShape.length);let t=e.filterHeight,o=e.filterWidth,n=t-1-e.padInfo.top,s=o-1-e.padInfo.left;this.userCode=`\n const ivec2 pads = ivec2(${n}, ${s});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = ivec2(coords[1], coords[2]) - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n vec4 result = vec4(0.);\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / strides[0];\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n int wCPerm = ${o} - 1 - wC;\n\n float dyC = float(dyCCorner + wC) / strides[1];\n bool idyCVal = (dyC >= 0.0) && (dyC < ${e.outWidth}.0)\n && (fract(dyC) == 0.0);\n int idyC = int(dyC);\n\n float dyC2 = float(dyCCorner + wC + 1) / strides[1];\n bool idyCVal2 = (dyC2 >= 0.0) && (dyC2 < ${e.outWidth}.0)\n && (fract(dyC2) == 0.0);\n int idyC2 = int(dyC2);\n\n if (idyCVal && idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec4 dySample2 = (idyC / 2 == idyC2 / 2) ?\n dySample : getDy(batch, idyR, idyC2, d2);\n\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n\n dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample2.xy : dySample2.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC, d2);\n vec2 dyValue = mod(float(idyC), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.xy += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n } else if (idyCVal2) {\n for (int d2 = 0; d2 < ${e.outChannels}; d2 += 2) {\n vec4 wValue = getW(wRPerm, wCPerm, d1, d2);\n vec4 dySample = getDy(batch, idyR, idyC2, d2);\n vec2 dyValue = mod(float(idyC2), 2.) == 0. ?\n dySample.xy : dySample.zw;\n result.zw += vec2(dot(dyValue, wValue.xy),\n dot(dyValue, wValue.zw));\n }\n }\n }\n }\n setOutput(result);\n }\n `}};function pJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l);if(A().getBool(\"WEBGL_PACK_CONV2DTRANSPOSE\")&&l===\"channelsLast\"){let d=[[m.strideHeight,m.strideWidth]],f=new Kh(m);return t.runWebGLProgram(f,[n,s],\"float32\",d)}else{let d=new Uh(m);return t.runWebGLProgram(d,[n,s],\"float32\")}}var HA={kernelName:rn,backendName:\"webgl\",kernelFunc:pJ};function cJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=new Mh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var KA={kernelName:on,backendName:\"webgl\",kernelFunc:cJ};function lJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Gh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var qA={kernelName:ja,backendName:\"webgl\",kernelFunc:lJ};function mJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,i,1,a),c=new Hh(u);return t.runWebGLProgram(c,[n,s],\"float32\")}var jA={kernelName:nn,backendName:\"webgl\",kernelFunc:mJ};var dJ=Fo+`\n return cos(x);\n`,fJ=`\n vec4 result = cos(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,hJ=xe({opSnippet:dJ,packedOpSnippet:fJ}),XA={kernelName:sn,backendName:\"webgl\",kernelFunc:hJ};var gJ=`\n float e2x = exp(-x);\n return (e2x + 1.0 / e2x) / 2.0;\n`,xJ=xe({opSnippet:gJ}),YA={kernelName:an,backendName:\"webgl\",kernelFunc:xJ};var qh=class{constructor(e,t,o,n,s){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];let[a,i,p,u]=e,[c]=t,[l,m]=o;this.outputShape=[c,l,m,u];let d=n===\"bilinear\"?1:0,[f,h]=[`${i-1}.0`,`${p-1}.0`],[g,x,b]=l>1?[`${(i-1)/(l-1)}`,\"(y2-y1) * height_ratio\",`y1*${f} + float(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${f}`],[C,S,k]=m>1?[`${(p-1)/(m-1)}`,\"(x2-x1) * width_ratio\",`x1*${h} + float(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${h}`];this.userCode=`\n const float height_ratio = float(${g});\n const float width_ratio = float(${C});\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int y = coords[1];\n int x = coords[2];\n int d = coords[3];\n\n // get box vals\n float y1 = getBoxes(b,0);\n float x1 = getBoxes(b,1);\n float y2 = getBoxes(b,2);\n float x2 = getBoxes(b,3);\n\n // get image in batch index\n int bInd = round(getBoxInd(b));\n if(bInd < 0 || bInd >= ${a}) {\n return;\n }\n\n float height_scale = ${x};\n float width_scale = ${S};\n\n float in_y = ${b};\n if( in_y < 0.0 || in_y > ${f} ) {\n setOutput(float(${s}));\n return;\n }\n float in_x = ${k};\n if( in_x < 0.0 || in_x > ${h} ) {\n setOutput(float(${s}));\n return;\n }\n\n vec2 sourceFracIndexCR = vec2(in_x,in_y);\n if(${d} == 1) {\n // Compute the four integer indices.\n ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);\n ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));\n\n float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);\n float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);\n float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);\n float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);\n\n vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n\n float top = topLeft + (topRight - topLeft) * fracCR.x;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n float newValue = top + (bottom - top) * fracCR.y;\n setOutput(newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestCR = ivec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutput(newValue);\n }\n }\n `}};var yJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new qh(n.shape,s.shape,i,p,u);return t.runWebGLProgram(c,[n,s,a],\"float32\")},QA={kernelName:cn,backendName:\"webgl\",kernelFunc:yJ};var vp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(vp||(vp={}));var om=class{constructor(e,t,o,n){this.op=e,this.outputShape=t,this.variableNames=[\"x\"],this.customUniforms=[{name:\"index\",type:\"float\"}];let s=this.outputShape.length,a=this.op===vp.Prod?\"1.0\":\"0.0\",i=o?a:`getX(${ZA(s,\"coords\",this.op)})`,p=this.outputShape[this.outputShape.length-1],u=\"\",c=\"\";o?(u=n?`end != ${p-1}`:\"end != 0\",c=n?\"end + 1\":\"end - 1\"):(u=n?`end + pow2 < ${p}`:\"end >= pow2\",c=n?\"end + pow2\":\"end - pow2\"),this.userCode=`\n void main() {\n ${Re(s)} coords = getOutputCoords();\n int end = ${JA(s,\"coords\",this.op)};\n float val = ${i};\n int pow2 = int(pow(2.0, index));\n if (${u}) {\n int idx = ${c};\n ${JA(s,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${ZA(s,\"coords\",this.op)});\n }\n setOutput(val);\n }\n `}};function ZA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function JA(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw new Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function jh(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=bt({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGL cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=Dt({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new om(r,p.shape,!1,s),f=[[m]],h=l;l=t.runWebGLProgram(d,[l],l.dtype,f),t.disposeIntermediateTensorInfo(h)}if(n){let m=new om(r,p.shape,n,s),d=l;l=t.runWebGLProgram(m,[l],l.dtype),t.disposeIntermediateTensorInfo(d)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=bt({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeIntermediateTensorInfo(l),t.disposeIntermediateTensorInfo(p),d}return l}function bJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return jh(vp.Prod,n,t,s,a,i)}var eF={kernelName:un,backendName:\"webgl\",kernelFunc:bJ};function CJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return jh(vp.Sum,n,t,s,a,i)}var tF={kernelName:pn,backendName:\"webgl\",kernelFunc:CJ};function wJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=ph(p,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let p=t.bufferSync(n),u=t.bufferSync(s),c=BR(p,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var rF={kernelName:ra,backendName:\"webgl\",kernelFunc:wJ};var Xh=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int h = ${this.getHeightCoordString()};\n int w = ${this.getWidthCoordString()};\n int d = ${this.getDepthCoordString()};\n\n int in_h = h / ${t};\n int offset_h = imod(h, ${t});\n int in_w = w / ${t};\n int offset_w = imod(w, ${t});\n int offset_d = (offset_h * ${t} + offset_w) *\n ${this.getOutputDepthSize()};\n int in_d = d + offset_d;\n\n float result = ${this.getInputSamplingString()};\n setOutput(result);\n }\n `}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function SJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=new Xh(f,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var oF={kernelName:ln,backendName:\"webgl\",kernelFunc:SJ};var Gc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.filterHeight,i=e.filterWidth,p=e.outChannels/e.inChannels,u=\"\",c=\"\";o&&(n?u=`float activation(float a) {\n float b = getPreluActivationWeightsAtOutCoords();\n ${o}\n }`:s?u=`float activation(float a) {\n float b = getLeakyreluAlphaAtOutCoords();\n ${o}\n }`:u=`\n float activation(float x) {\n ${o}\n }\n `,c=\"result = activation(result);\");let l=t?\"result += getBiasAtOutCoords();\":\"\";t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),s&&this.variableNames.push(\"leakyreluAlpha\"),this.userCode=`\n ${u}\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n ivec2 xRCCorner = coords.yz * strides - pads;\n int d2 = coords.w;\n int d1 = d2 / ${p};\n int q = d2 - d1 * ${p};\n\n int xRCorner = xRCCorner.x;\n int xCCorner = xRCCorner.y;\n\n // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n // TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.\n for (int wR = 0; wR < ${a}; wR++) {\n int xR = xRCorner + wR * dilations[0];\n\n if (xR < 0 || xR >= inDims[0]) {\n continue;\n }\n\n for (int wC = 0; wC < ${i}; wC++) {\n int xC = xCCorner + wC * dilations[1];\n\n if (xC < 0 || xC >= inDims[1]) {\n continue;\n }\n\n float xVal = getX(batch, xR, xC, d1);\n float wVal = getW(wR, wC, d1, q);\n dotProd += xVal * wVal;\n }\n }\n\n float result = dotProd;\n ${l}\n ${c}\n setOutput(result);\n }\n `}};var Hc=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=[\"x\",\"W\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"pads\",type:\"ivec2\"},{name:\"strides\",type:\"ivec2\"},{name:\"dilations\",type:\"ivec2\"},{name:\"inDims\",type:\"ivec2\"}],this.outputShape=e.outShape,this.enableShapeUniforms=ut(this.outputShape.length);let a=e.outChannels/e.inChannels,i=e.padInfo.left,p=e.strideWidth,u=e.dilationWidth,c=e.filterHeight,l=e.filterWidth,m=l,d=`\n int xR; int xC; int xCOffset;\n vec4 wTexel; vec4 previous; vec4 final;`;for(let x=0;x=0 && xR < inDims[0]) {\n `;for(let x=0;x<(m+1)/2;x++){let b=x*2;if(d+=`\n xC = xCCorner + ${b*u};\n `,p===1){if(b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n `,u===1&&b>0?d+=`\n xC${b} = vec4(xTexelC${b-2}.zw, xTexelC${b}.xy);\n `:d+=`\n xCOffset = xC + 1 - 2;\n\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n previous.zw = vec2(0.0);\n }\n\n xC${b} = vec4(previous.zw, xTexelC${b}.xy);\n } else {\n xC${b} = vec4(0.0, 0.0, xTexelC${b}.xy);\n }\n `):d+=`\n if (xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xC${b} = xTexelC${b};\n `,b+1= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n `,u>1?d+=`\n xCOffset -= 2;\n if (xCOffset >= 0 && xCOffset < inDims[1]) {\n previous = getX(batch, xR, xCOffset, d1);\n xC${b+1} = vec4(previous.zw, xTexelC${b+1}.xy);\n } else {\n xC${b+1} = vec4(0.0, 0.0, xTexelC${b+1}.xy);\n }\n `:d+=`\n xC${b+1} = vec4(xTexelC${b}.zw, xTexelC${b+1}.xy);\n `):C===1?d+=`\n xC${b+1} = xTexelC${b};\n `:d+=`\n xCOffset = xC + ${C};\n\n if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b+1} = xTexelC${b+1};\n `}}else b= 0 && xCOffset < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xCOffset, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xC + 1, d1);\n // Need to manually clear unused channels in case\n // we're reading from recycled texture.\n if (xC + 2 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.0);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(xTexelC${b}.zw, xTexelC${b+1}.zw);\n `,b+1= 0 && xCOffset < inDims[1]) {\n final = getX(batch, xR, xCOffset, d1);\n }\n xC${b+1} = vec4(xTexelC${b+1}.xy, final.xy);\n `)):(d+=`\n if(xC >= 0 && xC < inDims[1] && xTexelC${b}Ready == 0) {\n xTexelC${b} = getX(batch, xR, xC, d1);\n if (xC + 1 >= inDims[1]) {\n xTexelC${b}.zw = vec2(0.0);\n }\n xTexelC${b}Ready = 1;\n }\n\n xCOffset = xC + strides[1];\n if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${b+1}Ready == 0) {\n xTexelC${b+1} = getX(batch, xR, xCOffset, d1);\n if (xCOffset + 1 >= inDims[1]) {\n xTexelC${b+1}.zw = vec2(0.);\n }\n xTexelC${b+1}Ready = 1;\n }\n\n xC${b} = vec4(\n xTexelC${b}.xy, xTexelC${b+1}.xy);\n `,b+1`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let l=w.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;A().getBool(\"WEBGL_PACK_DEPTHWISECONV\")&&l.strideWidth<=2&&l.outChannels/l.inChannels===1?m=new Hc(l):m=new Gc(l);let d=[[l.padInfo.top,l.padInfo.left],[l.strideHeight,l.strideWidth],[l.dilationHeight,l.dilationWidth],[l.inHeight,l.inWidth]];return t.runWebGLProgram(m,[n,s],\"float32\",d)}var nF={kernelName:mn,backendName:\"webgl\",kernelFunc:IJ};var Yh=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int dm = coords.w;\n int d2 = d1 * ${a} + dm;\n\n float dotProd = 0.0;\n\n // TO DO: Vec4 over the batch size\n for (int b = 0; b < ${e.batchSize}; b++) {\n for (int yR = 0; yR < ${e.outHeight}; yR++) {\n int xR = wR + yR * ${t} - ${n};\n\n if (xR < 0 || xR >= ${e.inHeight}) {\n continue;\n }\n\n for (int yC = 0; yC < ${e.outWidth}; yC++) {\n int xC = wC + yC * ${o} - ${s};\n\n if (xC < 0 || xC >= ${e.inWidth}) {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n `}},Qh=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,p=e.outChannels/e.inChannels;this.userCode=`\n const ivec2 pads = ivec2(${a}, ${i});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n float dotProd = 0.0;\n\n for (int wR = 0; wR < ${t}; wR++) {\n float dyR = float(dyRCorner + wR) / ${n}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = ${t} - 1 - wR;\n\n for (int wC = 0; wC < ${o}; wC++) {\n float dyC = float(dyCCorner + wC) / ${s}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = ${o} - 1 - wC;\n\n // TO DO: Vec4 over the channelMul\n for (int dm = 0; dm < ${p}; dm++) {\n int d2 = d1 * ${p} + dm;\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function vJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new Yh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var sF={kernelName:Pi,backendName:\"webgl\",kernelFunc:vJ};function kJ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Qh(l);return t.runWebGLProgram(m,[n,s],\"float32\")}var aF={kernelName:Oi,backendName:\"webgl\",kernelFunc:kJ};var Zh=class{constructor(e){this.variableNames=[\"X\"],this.outputShape=[e,e],this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;\n setOutput(val);\n }\n `}};function NJ(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=te({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Zh(s),p=t.runWebGLProgram(i,[a],a.dtype),u=te({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(p),u}var iF={kernelName:oa,backendName:\"webgl\",kernelFunc:NJ};var Jh=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:p,dilationHeight:u,dilationWidth:c}=e,{top:l,left:m}=n;this.userCode=`\n const ivec2 strides = ivec2(${s}, ${a});\n const ivec2 pads = ivec2(${l}, ${m});\n const float neg_infinity = -3.4e38;\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords.x;\n int d1 = coords.w;\n ivec2 outTopLeftCorner =\n coords.yz * strides - pads;\n int hBeg = outTopLeftCorner.x;\n int wBeg = outTopLeftCorner.y;\n\n float curVal = neg_infinity;\n for (int h = 0; h < ${i}; h++) {\n int hIn = hBeg + h * ${u};\n\n if (hIn >= 0 && hIn < ${t}) {\n for (int w = 0; w < ${p}; w++) {\n int wIn = wBeg + w * ${c};\n\n if (wIn >= 0 && wIn < ${o}) {\n float xVal = getX(batch, hIn, wIn, d1);\n float wVal = getW(h, w, d1);\n\n float val = xVal + wVal;\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n float result = curVal;\n setOutput(result);\n }\n `}};function TJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c,l=new Jh(u);c=t.runWebGLProgram(l,[n,s],\"float32\");let m=te({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var uF={kernelName:dn,backendName:\"webgl\",kernelFunc:TJ};function _J(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=wp({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeIntermediateTensorInfo(h);return m}var pF={kernelName:Bi,backendName:\"webgl\",kernelFunc:_J};var EJ=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",$J=`\n vec4 result;\n\n result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);\n result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);\n result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);\n result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);\n\n return result;\n`,RJ=xe({opSnippet:EJ,packedOpSnippet:$J}),cF={kernelName:hn,backendName:\"webgl\",kernelFunc:RJ};var DJ=\"return (b >= 0.0) ? a : a * (b + 1.0);\",AJ=`\n vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));\n return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));\n`,FJ=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=A().getBool(\"WEBGL_PACK_BINARY_OPERATIONS\")?new jr(AJ,o.shape,n.shape):new Pr(DJ,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},lF={kernelName:Xa,backendName:\"webgl\",kernelFunc:FJ};var PJ=`\n return vec4(equal(a, b));\n`,OJ=\"return float(a == b);\",MJ=nt({opSnippet:OJ,packedOpSnippet:PJ,dtype:\"bool\",cpuKernelImpl:GR}),mF={kernelName:xn,backendName:\"webgl\",kernelFunc:MJ};var LJ=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n float p = ${w.ERF_P};\n float a1 = ${w.ERF_A1};\n float a2 = ${w.ERF_A2};\n float a3 = ${w.ERF_A3};\n float a4 = ${w.ERF_A4};\n float a5 = ${w.ERF_A5};\n\n float sign = sign(x);\n x = abs(x);\n float t = 1.0 / (1.0 + p * x);\n return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));\n`,BJ=xe({opSnippet:LJ}),dF={kernelName:gn,backendName:\"webgl\",kernelFunc:BJ};var zJ=Fo+`\n return exp(x);\n`,VJ=`\n vec4 result = exp(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,kv=xe({opSnippet:zJ,packedOpSnippet:VJ,cpuKernelImpl:HR,dtype:\"float32\"}),fF={kernelName:yn,backendName:\"webgl\",kernelFunc:kv};function eg(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),te({inputs:{x:s},backend:o,attrs:{shape:i}})}var hF={kernelName:na,backendName:\"webgl\",kernelFunc:eg};var gF=\"return exp(x) - 1.0;\",WJ=xe({opSnippet:gF,packedOpSnippet:gF,cpuKernelImpl:KR}),xF={kernelName:bn,backendName:\"webgl\",kernelFunc:WJ};var nm=class{constructor(e,t,o){this.variableNames=[\"real\",\"imag\"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:\"1.0\",i;if(e===\"real\")i=\"return real * expR - imag * expI;\";else if(e===\"imag\")i=\"return real * expI + imag * expR;\";else throw new Error(`FFT component must be either \"real\" or \"imag\", got ${e}.`);this.userCode=`\n const float exponentMultiplier = ${s};\n\n float unaryOpComplex(float real, float expR, float imag, float expI) {\n ${i}\n }\n\n float mulMatDFT(int batch, int index) {\n float indexRatio = float(index) / float(${n});\n float exponentMultiplierTimesIndexRatio =\n exponentMultiplier * indexRatio;\n\n float result = 0.0;\n\n for (int i = 0; i < ${n}; i++) {\n // x = (-2|2 * PI / N) * index * i;\n float x = exponentMultiplierTimesIndexRatio * float(i);\n float expR = cos(x);\n float expI = sin(x);\n float real = getReal(batch, i);\n float imag = getImag(batch, i);\n\n result +=\n unaryOpComplex(real, expR, imag, expI) / ${a};\n }\n\n return result;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n setOutput(mulMatDFT(coords[0], coords[1]));\n }\n `}};function tg(r,e,t){let o=t.texData.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=te({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),p=i.shape,u=new nm(\"real\",p,e),c=new nm(\"imag\",p,e),l=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:p},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:p}],m=t.runWebGLProgram(u,l,\"float32\"),d=t.runWebGLProgram(c,l,\"float32\"),f=Or({inputs:{real:m,imag:d},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d);let h=te({inputs:{x:f},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(f),h}function UJ(r){let{inputs:e,backend:t}=r,{input:o}=e;return tg(o,!1,t)}var yF={kernelName:zi,backendName:\"webgl\",kernelFunc:UJ};var rg=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:\"value\",type:\"float\"}],this.variableNames=[\"x\"],this.outputShape=e,this.userCode=`\n void main() {\n // Input can be obtained from uniform value.\n setOutput(value);\n }\n `}};function Ci(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new rg(o,n),i=[[n]];return e.runWebGLProgram(a,[],s,i)}}var bF={kernelName:sa,backendName:\"webgl\",kernelFunc:Ci};var og=class{constructor(e){this.variableNames=[\"Image\"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n\n int coordX = ${t} - x - 1;\n float outputValue;\n if(coordX >= 0 && coordX < ${t}) {\n outputValue = getImage(coords[0], coords[1], coordX, coords[3]);\n } else {\n outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var CF={kernelName:Cn,backendName:\"webgl\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new og(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var wF=\"return floor(x);\",GJ=xe({opSnippet:wF,packedOpSnippet:wF,cpuKernelImpl:qR}),SF={kernelName:wn,backendName:\"webgl\",kernelFunc:GJ};var HJ=`\n float s = sign(a) * sign(b);\n int ia = round(a);\n int ib = round(b);\n if (ib != 0) {\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n return float(idiv(ia, ib, s));\n } else {\n return NAN;\n }\n`,KJ=`\n ivec4 ia = round(a);\n ivec4 ib = round(b);\n bvec4 cond = notEqual(ib, ivec4(0));\n ivec4 result = ivec4(0);\n vec4 s = sign(a) * sign(b);\n\n // Windows (D3D) wants guaranteed non-zero int division at compile-time.\n if (cond[0]) {\n result[0] = idiv(ia[0], ib[0], s[0]);\n }\n if (cond[1]) {\n result[1] = idiv(ia[1], ib[1], s[1]);\n }\n if (cond[2]) {\n result[2] = idiv(ia[2], ib[2], s[2]);\n }\n if (cond[3]) {\n result[3] = idiv(ia[3], ib[3], s[3]);\n }\n return vec4(result);\n`,qJ=nt({opSnippet:HJ,packedOpSnippet:KJ,dtype:\"int32\"}),IF={kernelName:Sn,backendName:\"webgl\",kernelFunc:qJ};var ng=class{constructor(e){this.variableNames=[\"A\"];let t=It(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${n}.0, ${o}.0);\n\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n setOutput(floor(value * 255.0 + 0.5));\n }\n `}};var sg=class{constructor(e){this.variableNames=[\"A\"],this.packedInputs=!1,this.packedOutput=!0;let t=It(),[o,n]=e;this.outputShape=e,this.userCode=`\n void main() {\n ivec3 coords = getOutputCoords();\n int texR = coords[0];\n int texC = coords[1];\n int depth = coords[2];\n\n vec4 result = vec4(0.);\n\n for(int row=0; row<=1; row++) {\n for(int col=0; col<=1; col++) {\n texC = coords[1] + row;\n depth = coords[2] + col;\n\n vec2 uv = (vec2(texC, texR) + halfCR) /\n vec2(${n}.0, ${o}.0);\n vec4 values = ${t.texture2D}(A, uv);\n float value;\n if (depth == 0) {\n value = values.r;\n } else if (depth == 1) {\n value = values.g;\n } else if (depth == 2) {\n value = values.b;\n } else if (depth == 3) {\n value = values.a;\n }\n\n result[row * 2 + col] = floor(value * 255.0 + 0.5);\n }\n }\n\n ${t.output} = result;\n }\n `}};var vF={kernelName:Du,backendName:\"webgl\",kernelFunc:jJ},Kc,Nv=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function jJ(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,[p,u]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],c=[u,p],l=[u,p,s];if(i||a){let h=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(Kc==null||h!==Nv)&&(Nv=h,Kc=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:Nv})),Kc.canvas.width=p,Kc.canvas.height=u,Kc.drawImage(n,0,0,p,u),n=Kc.canvas}let m=t.makeTensorInfo(c,\"int32\");t.texData.get(m.dataId).usage=mr.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(m.dataId),n);let d=A().getBool(\"WEBGL_PACK\")?new sg(l):new ng(l),f=t.runWebGLProgram(d,[m],\"int32\");return t.disposeData(m.dataId),f}function XJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h),x,b=[],C=a!=null,S=i!=null,k=d===\"leakyrelu\",_=()=>{let R=[n,s],D=(P,O)=>{if(O===\"NCHW\"&&P.shape.length===1&&P.shape[0]!==1){let M=te({inputs:{x:P},backend:t,attrs:{shape:[P.shape[0],1,1]}});return b.push(M),M}return P};if(C&&R.push(D(a,c)),S&&R.push(D(i,c)),k){let P=t.makeTensorInfo([],\"float32\",y.createScalarValue(f,\"float32\"));R.push(P),b.push(P)}return R};if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type===\"SAME\"||g.padInfo.type===\"VALID\"))x=zh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else if(g.strideWidth<=2&&h===\"channelsLast\"&&A().getBool(\"WEBGL_EXP_CONV\")){let R=d?yi(d,!0):null,D=new Uc(g,C,R,S,k),P=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],O=_();x=t.runWebGLProgram(D,O,\"float32\",P)}else if(A().getBool(\"WEBGL_CONV_IM2COL\"))x=Vh({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:d,preluActivationWeights:i,leakyreluAlpha:f});else{let R=d?yi(d,!1):null,D=new Wc(g,C,R,S,k),P=_();x=t.runWebGLProgram(D,P,\"float32\")}let $=te({inputs:{x},backend:t,attrs:{shape:g.outShape}});return b.push(x),b.forEach(R=>t.disposeIntermediateTensorInfo(R)),$}var kF={kernelName:Io,backendName:\"webgl\",kernelFunc:XJ};function YJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=[],h=c;h==null&&(h=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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main() {\n ${s} coords = getOutputCoords();\n int flattenIndex = 0;\n bool out_of_bounds = false;\n\n ${a}\n\n setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));\n }\n `}};function QJ(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=te({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=te({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let x=t.readSync(n.dataId),b=t.bufferSync(o),C=jR(x,b,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,C.values)}let f=new ag(a,l,[u,c],o.shape),h=t.runWebGLProgram(f,[d,m],d.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:p}});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var TF={kernelName:vn,backendName:\"webgl\",kernelFunc:QJ};var ig=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.outputShape=t,this.rank=t.length;let o=Re(this.rank),n=ZJ(e,2);this.userCode=`\n void main() {\n ${o} resRC = getOutputCoords();\n int index = int(getIndices(resRC.x, resRC.z));\n float inBounds = (index >= 0) && (index < ${e[2]}) ? 1.0 : 0.0;\n setOutput(inBounds * getA(${n}));\n }\n `}};function ZJ(r,e){let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=0,()=>`GatherV2: the index value ${k} is not in [0, ${C-1}]`)}}let u=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),c=y.sizeFromShape(s.shape),l=[],m=te({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),d=te({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});l.push(m),l.push(d);let f=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype===\"string\"){let b=t.bufferSync(d),C=t.bufferSync(m),S=XR(C,b,f);return l.forEach(k=>t.disposeIntermediateTensorInfo(k)),t.makeTensorInfo(u.outputShape,S.dtype,S.values)}let h=new ig(m.shape,f),g=t.runWebGLProgram(h,[m,d],m.dtype);l.push(g);let x=te({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeIntermediateTensorInfo(b)),x}var _F={kernelName:aa,backendName:\"webgl\",kernelFunc:Tv};var JJ=\"return float(a > b);\",eee=`\n return vec4(greaterThan(a, b));\n`,tee=nt({opSnippet:JJ,packedOpSnippet:eee,cpuKernelImpl:YR,dtype:\"bool\"}),EF={kernelName:kn,backendName:\"webgl\",kernelFunc:tee};var ree=\"return float(a >= b);\",oee=`\n return vec4(greaterThanEqual(a, b));\n`,nee=nt({opSnippet:ree,packedOpSnippet:oee,dtype:\"bool\",cpuKernelImpl:QR}),$F={kernelName:Nn,backendName:\"webgl\",kernelFunc:nee};function see(r){let{inputs:e,backend:t}=r,{input:o}=e;return tg(o,!0,t)}var RF={kernelName:Vi,backendName:\"webgl\",kernelFunc:see};var aee=\"return float(!isnan(x) && !isinf(x));\",iee=xe({opSnippet:aee,dtype:\"bool\"}),DF={kernelName:Tn,backendName:\"webgl\",kernelFunc:iee};var uee=\"return float(isinf(x));\",pee=xe({opSnippet:uee,dtype:\"bool\"}),AF={kernelName:_n,backendName:\"webgl\",kernelFunc:pee};var cee=\"return float(isnan(x));\",lee=xe({opSnippet:cee,dtype:\"bool\"}),FF={kernelName:En,backendName:\"webgl\",kernelFunc:lee};var mee=\"return float(a < b);\",dee=`\n return vec4(lessThan(a, b));\n`,fee=nt({opSnippet:mee,packedOpSnippet:dee,cpuKernelImpl:ZR,dtype:\"bool\"}),PF={kernelName:Rn,backendName:\"webgl\",kernelFunc:fee};var hee=\"return float(a <= b);\",gee=`\n return vec4(lessThanEqual(a, b));\n`,xee=nt({opSnippet:hee,packedOpSnippet:gee,cpuKernelImpl:JR,dtype:\"bool\"}),OF={kernelName:Dn,backendName:\"webgl\",kernelFunc:xee};function yee(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=eD(o,n,s);return e.makeTensorInfo([a.length],\"float32\",a)}var MF={kernelName:An,backendName:\"webgl\",kernelFunc:yee};var bee=Fo+`\n return x < 0.0 ? 0./0. : log(x);\n`,Cee=`\n vec4 result = log(x);\n bvec4 isNaN = isnan(x);\n result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);\n result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);\n result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);\n result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);\n return result;\n`,wee=xe({opSnippet:bee,packedOpSnippet:Cee,cpuKernelImpl:tD}),LF={kernelName:Fn,backendName:\"webgl\",kernelFunc:wee};var See=Fo+`\n return log(1.0 + x);\n`,Iee=xe({opSnippet:See}),BF={kernelName:Pn,backendName:\"webgl\",kernelFunc:Iee};var vee=\"return float(a >= 1.0 && b >= 1.0);\",kee=`\n return vec4(\n vec4(greaterThanEqual(a, vec4(1.0))) *\n vec4(greaterThanEqual(b, vec4(1.0))));\n`,Nee=nt({opSnippet:vee,packedOpSnippet:kee,dtype:\"bool\"}),zF={kernelName:On,backendName:\"webgl\",kernelFunc:Nee};var Tee=\"return float(!(x >= 1.0));\",_ee=xe({opSnippet:Tee}),VF={kernelName:Mn,backendName:\"webgl\",kernelFunc:_ee};var Eee=\"return float(a >= 1.0 || b >= 1.0);\",$ee=`\n return min(\n vec4(greaterThanEqual(a, vec4(1.0))) +\n vec4(greaterThanEqual(b, vec4(1.0))),\n vec4(1.0));\n`,Ree=nt({opSnippet:Eee,packedOpSnippet:$ee,dtype:\"bool\"}),WF={kernelName:Ln,backendName:\"webgl\",kernelFunc:Ree};var ug=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n int d = coords[3];\n float x = getX(b, r, c, d);\n float sum = 0.0;\n for (int j = -${a}; j <= ${a}; j++) {\n int idx = d + j;\n if (idx >= 0 && idx <= ${i}) {\n float z = getX(b, r, c, idx);\n sum += z * z;\n }\n }\n float val = x * ${p};\n setOutput(val);\n }\n `}};var pg=class{constructor(e,t,o,n,s){this.variableNames=[\"x\"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let p,u=`float(${o}) + float(${n}) * sum`;s===.5?p=`inversesqrt(${u})`:s===1?p=`1.0/(${u})`:p=`exp(log(${u}) * float(-${s}));`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < ${this.outputShape[3]};\n bool hasNextRow = c < ${this.outputShape[2]};\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - ${a};\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - ${a}; j <= ${a}; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * ${p};\n setOutput(result);\n }\n `}};var Dee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u=A().getBool(\"WEBGL_PACK_NORMALIZATION\")?new pg(n.shape,s,a,i,p):new ug(n.shape,s,a,i,p);return t.runWebGLProgram(u,[n],n.dtype)},UF={kernelName:Bn,backendName:\"webgl\",kernelFunc:Dee};var cg=class{constructor(e,t,o,n,s){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int r = coords[1];\n int c = coords[2];\n\n float result = 0.0;\n for (int d = 0; d < ${this.depth}; ++d) {\n int depthBegin = int(max(0.0, float(d - ${t})));\n int depthEnd = int(min(float(${this.depth}),\n float(d + ${t} + 1)));\n\n const int MIN_DEPTH_BEGIN = 0;\n const int MAX_DEPTH_END = ${this.depth};\n\n float norm = 0.0;\n for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n }\n else {\n break;\n }\n }\n\n norm = float(${n}) * norm + float(${o});\n\n for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n if (k < depthBegin){\n continue;\n }\n else if (k >= depthBegin && k < depthEnd){\n float dyi = -2.0 * float(${n})\n * float(${s})\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d)\n / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * ${s});\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n }\n else {\n break;\n }\n }\n }\n setOutput(result);\n }\n `}};var Aee=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new cg(n.shape,i,p,u,c);return t.runWebGLProgram(l,[n,s,a],n.dtype)},GF={kernelName:Ya,backendName:\"webgl\",kernelFunc:Aee};function HF(r,e,t,o){let n=y.sizeFromShape(e),a=y.sizeFromShape(r.shape)/n,i=te({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),p=Yr(i,r.dtype,\"max\",o),u=te({inputs:{x:p},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(p),u}function _v(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,p=y.parseAxisParam(s,n.shape),u=p,c=w.getAxesPermutation(u,i),l=c!=null,m=t.shouldExecuteOnCPU([n]),d=n;if(l){if(m){let C=t.texData.get(d.dataId).values,S=new Array(i);for(let $=0;$`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,u,i,p);if(c.filterWidth===1&&c.filterHeight===1&&y.arraysEqual(c.inShape,c.outShape))return Dt({inputs:{x:n},backend:t});let l=new Us(c,\"max\",!1);return t.runWebGLProgram(l,[n],n.dtype)}var jF={kernelName:Wn,backendName:\"webgl\",kernelFunc:Mee};function Lee(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new bu(l,\"max\",!1);return t.runWebGLProgram(m,[n],n.dtype)}var XF={kernelName:ia,backendName:\"webgl\",kernelFunc:Lee};var lg=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,p=a-1-e.padInfo.left,u=s*a-1;this.userCode=`\n const ivec2 pads = ivec2(${i}, ${p});\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n\n ivec2 dyRCCorner = coords.yz - pads;\n int dyRCorner = dyRCCorner.x;\n int dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < ${s};\n wR += ${n}) {\n float dyR = float(dyRCorner + wR) / ${t}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${a}; wC++) {\n float dyC = float(dyCCorner + wC) / ${o}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(b, idyR, idyC, d);\n int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue = wR * ${a} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n setOutput(dotProd);\n }\n `}},mg=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,p=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,l=p-1-e.padInfo.front,m=u-1-e.padInfo.top,d=c-1-e.padInfo.left,f=p*u*c-1;this.userCode=`\n const ivec3 pads = ivec3(${l}, ${m}, ${d});\n\n void main() {\n ivec5 coords = getOutputCoords();\n int batch = coords.x;\n int ch = coords.u;\n\n ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n int dyDCorner = dyCorner.x;\n int dyRCorner = dyCorner.y;\n int dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int wD = 0; wD < ${p};\n wD += ${s}) {\n float dyD = float(dyDCorner + wD) / ${t}.0;\n\n if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {\n continue;\n }\n int idyD = int(dyD);\n\n for (int wR = 0; wR < ${u};\n wR += ${a}) {\n float dyR = float(dyRCorner + wR) / ${o}.0;\n\n if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||\n fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n for (int wC = 0; wC < ${c};\n wC += ${i}) {\n float dyC = float(dyCCorner + wC) / ${n}.0;\n\n if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n float dyValue = getDy(batch, idyD, idyR, idyC, ch);\n int maxPosValue = ${f} -\n int(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n int curPosValue =\n wD * ${u} * ${c} +\n wR * ${c} + wC;\n float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n dotProd += dyValue * mask;\n }\n }\n }\n setOutput(dotProd);\n }\n `}};function Bee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new bu(m,\"max\",!0),f=t.runWebGLProgram(d,[a],a.dtype),h=new mg(m),g=t.runWebGLProgram(h,[n,f],a.dtype);return t.disposeIntermediateTensorInfo(f),g}var YF={kernelName:Gi,backendName:\"webgl\",kernelFunc:Bee};function zee(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;Vs([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=!0,f=new Us(m,\"max\",d),h=t.runWebGLProgram(f,[i],i.dtype),g=new lg(m),x=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),x}var QF={kernelName:Ui,backendName:\"webgl\",kernelFunc:zee};function ZF(r,e,t,o){let n=new Us(t,\"max\",!1),s=o.runWebGLProgram(n,[r],\"float32\");n=new Us(t,\"max\",!0,!0,e);let a=o.runWebGLProgram(n,[r],\"float32\");return[s,a]}var JF={kernelName:ua,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,p=t;y.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. 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yg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:\"value\",type:\"float\"}],this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Re(n),a=t.map(h=>h[0]).join(\",\"),i=t.map((h,g)=>h[0]+e[g]).join(\",\"),p=Rt(\"rc\",n),u=Rt(\"source\",n),c=`${p[n-1]} < ${this.outputShape[n-1]}`,l=n===1?\"source\":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${p[n-1]} += 1;\n if(${c}) {\n `,n===1?\"\":`}\n rc = outputLoc;\n ${p[n-2]} += 1;\n if(${p[n-2]} < ${this.outputShape[n-2]}) {`,n===1?\"\":` ${p[n-1]} += 1;\n if(${c}) {`],d=n===1?\"rc < start || rc >= end\":\"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))\",f=\"\";for(let h=0,g=n===1?2:4;h{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return Ci({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new yg(n.shape,s,a):new xg(n.shape,s,a),p=[[a]];return t.runWebGLProgram(i,[n],n.dtype,p)},C3={kernelName:es,backendName:\"webgl\",kernelFunc:Dv};var pte=`\n if(a < 0.0 && floor(b) < b){\n return NAN;\n }\n if (b == 0.0) {\n return 1.0;\n }\n return (round(mod(b, 2.0)) != 1) ?\n pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`,cte=`\n // isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.\n vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));\n vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n vec4 result = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n bvec4 isExpZero = equal(b, vec4(0.0));\n result.r = isExpZero.r ? 1.0 : result.r;\n result.g = isExpZero.g ? 1.0 : result.g;\n result.b = isExpZero.b ? 1.0 : result.b;\n result.a = isExpZero.a ? 1.0 : result.a;\n\n bvec4 isNaN1 = lessThan(a, vec4(0.0));\n bvec4 isNaN2 = 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f=w.expandShapeToKeepDim(d.shape,u);d=te({inputs:{x:d},backend:t,attrs:{shape:f}})}return p.forEach(f=>t.disposeIntermediateTensorInfo(f)),d}var S3={kernelName:os,backendName:\"webgl\",kernelFunc:mte};function dte(r){let{inputs:e,backend:t,attrs:o}=r,{paramsNestedSplits:n,paramsDenseValues:s,indices:a}=e,{outputRaggedRank:i}=o,p=n.map(x=>t.readSync(x.dataId)),u=n.map(x=>x.shape),c=t.readSync(s.dataId),l=t.readSync(a.dataId),[m,d,f]=pD(p,u,c,s.shape,s.dtype,l,a.shape,i),h=m.map(x=>t.makeTensorInfo([x.length],\"int32\",x)),g=t.makeTensorInfo(f,s.dtype,d);return h.concat([g])}var I3={kernelName:Hp,backendName:\"webgl\",kernelFunc:dte};function fte(r){let{inputs:e,backend:t}=r,{starts:o,limits:n,deltas:s}=e,a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=cD(a,o.shape,o.dtype,i,n.shape,p,s.shape),l=t.makeTensorInfo([u.length],\"int32\",u),m=t.makeTensorInfo([c.length],o.dtype,c);return[l,m]}var v3={kernelName:Kp,backendName:\"webgl\",kernelFunc:fte};function hte(r){let{inputs:e,backend:t,attrs:o}=r,{shape:n,values:s,defaultValue:a,rowPartitionTensors:i}=e,{rowPartitionTypes:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),l=t.readSync(a.dataId),m=i.map(g=>t.readSync(g.dataId)),d=i.map(g=>g.shape),[f,h]=lD(u,n.shape,c,s.shape,s.dtype,l,a.shape,m,d,p);return t.makeTensorInfo(f,s.dtype,h)}var k3={kernelName:qp,backendName:\"webgl\",kernelFunc:hte};var Av=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=mD(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},N3={kernelName:ma,backendName:\"webgl\",kernelFunc:Av};var gte=\"return 1.0 / x;\",xte=xe({opSnippet:gte}),T3={kernelName:ns,backendName:\"webgl\",kernelFunc:xte};var yte=Wt+`\n return (x < 0.0) ? 0.0 : x;\n`,bte=`\n vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Cte=xe({opSnippet:yte,packedOpSnippet:bte}),_3={kernelName:ss,backendName:\"webgl\",kernelFunc:Cte};var wte=Wt+`\n return (x < 0.0) ? 0.0 : min(6.0, x);\n`,Ste=`\n vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,Ite=xe({opSnippet:wte,packedOpSnippet:Ste}),E3={kernelName:us,backendName:\"webgl\",kernelFunc:Ite};var bg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)\":m=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));\n ivec2 sourceCeilRC = ivec2(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n float top = topLeft + (topRight - topLeft) * fracRC.y;\n float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n float newValue = top + (bottom - top) * fracRC.x;\n\n setOutput(newValue);\n }\n `}};var Cg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m=\"(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)\":m=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${m};\n\n // Compute the four integer indices.\n ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));\n ivec3 sourceCeilRC = ivec3(\n min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n // In parallel, construct four corners for all four components in\n // packed 2x2 cell.\n vec4 topLeft = vec4(\n getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 bottomLeft = vec4(\n getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);\n\n vec4 topRight = vec4(\n getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec4 bottomRight = vec4(\n getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),\n hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);\n\n vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);\n\n vec4 top = mix(topLeft, topRight, fracRC.yyzz);\n vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);\n vec4 newValue = mix(top, bottom, fracRC.x);\n\n setOutput(newValue);\n }\n `}};function vte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Cg(n.shape,p,u,s,a):new bg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],\"float32\")}var $3={kernelName:is,backendName:\"webgl\",kernelFunc:vte};var wg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(startRLerp - float(winHeight / 2));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(startCLerp - float(winWidth / 2));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float dxR = float(dyR) * heightScale;\n int topDxRIndex = int(floor(dxR));\n int bottomDxRIndex = int(min(ceil(dxR), ${n-1}.0));\n float dxRLerp = dxR - float(topDxRIndex);\n float inverseDxRLerp = 1.0 - dxRLerp;\n\n float dxC = float(dyC) * widthScale;\n int leftDxCIndex = int(floor(dxC));\n int rightDxCIndex = int(min(ceil(dxC), ${s-1}.0));\n float dxCLerp = dxC - float(leftDxCIndex);\n float inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function kte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new wg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var R3={kernelName:Ja,backendName:\"webgl\",kernelFunc:kte};var Sg=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":d=\"vec2(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec2 effectiveInputOverOutputRatioRC = vec2(\n ${c[0]/l[0]},\n ${c[1]/l[1]});\n const vec2 inputShapeRC = vec2(${i}.0, ${p}.0);\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n ivec2 yRC = coords.yz;\n\n // Fractional source index.\n vec2 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec2 sourceNearestRC = ivec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutput(newValue);\n }\n `}};var Ig=class{constructor(e,t,o,n,s){this.variableNames=[\"A\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,p,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?p-1:p],l=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?\"0.5\":\"0.0\",d;s?d=\"max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))\":d=\"vec3(yRC) * effectiveInputOverOutputRatioRC\",this.userCode=`\n const vec3 effectiveInputOverOutputRatioRC = vec3(\n ${c[0]/l[0]},\n ${c[1]/l[1]},\n ${c[1]/l[1]});\n const vec3 inputShapeRC = vec3(${i}.0, ${p}.0,\n ${p}.0);\n\n float getAValue(int b, int r, int c, int d) {\n return getChannel(getA(b, r, c, d), vec2(c, d));\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n // Calculate values for next column in yRC.z.\n ivec3 yRC = coords.yzz + ivec3(0, 0, 1);\n\n // Fractional source index.\n vec3 sourceFracIndexRC = ${d};\n\n // Compute the coordinators of nearest neighbor point.\n ivec3 sourceNearestRC = ivec3(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));\n\n // Should we calculate next column and row elements in 2x2 packed cell.\n bool hasNextCol = d < ${u-1};\n bool hasNextRow = coords.z < ${o-1};\n\n vec4 newValue = vec4(\n getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),\n hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)\n : 0.0,\n hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)\n : 0.0,\n (hasNextRow && hasNextCol) ?\n getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);\n\n setOutput(newValue);\n }\n `}};function Nte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=A().getBool(\"WEBGL_PACK_IMAGE_OPERATIONS\")?new Ig(n.shape,p,u,s,a):new Sg(n.shape,p,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var D3={kernelName:as,backendName:\"webgl\",kernelFunc:Nte};var vg=class{constructor(e,t,o){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,p=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=p[0]/u[0],l=p[1]/u[1],m=1/c,d=1/l,f=Math.ceil(m)*2+2,h=Math.ceil(d)*2+2;this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords[0];\n int d = coords[3];\n int r = coords[1];\n int c = coords[2];\n\n float accumulator = 0.0;\n\n const float heightScale = float(${c});\n const float widthScale = float(${l});\n\n const float invHeightScale = float(${m});\n const float invWidthScale = float(${d});\n\n const int winHeight = int(${f});\n const int winWidth = int(${h});\n\n // Compute bounds for where in dy we will look\n float startRLerp = floor(float(r) * invHeightScale);\n int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n float startCLerp = floor(float(c) * invWidthScale);\n int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n // Loop over dy\n for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n int dyR = dyROffset + startDyR;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= ${a}) {\n continue;\n }\n\n for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n int dyC = dyCOffset + startDyC;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= ${i}) {\n continue;\n }\n\n float sourceFracRow =\n float(${p[0]}) *\n (float(dyR) / float(${u[0]}));\n\n float sourceFracCol =\n float(${p[1]}) *\n (float(dyC) / float(${u[1]}));\n\n int sourceNearestRow = int(min(\n float(int(${n}) - 1),\n ${o} ? float(round(sourceFracRow)) :\n float(floor(sourceFracRow))));\n\n int sourceNearestCol = int(min(\n float(int(${s}) - 1),\n ${o} ? float(round(sourceFracCol)) :\n float(floor(sourceFracCol))));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutput(accumulator);\n }\n `}};function Tte(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new vg(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var A3={kernelName:Za,backendName:\"webgl\",kernelFunc:Tte};var kg=class{constructor(e,t){this.variableNames=[\"x\"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`\n void main() {\n int coord = getOutputCoords();\n setOutput(getX(${e[0]} - coord - 1));\n }\n `;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,p)=>n(p)).join(\",\"),a=Re(o);this.userCode=`\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${s}));\n }\n `}};var Ng=class{constructor(e,t){this.variableNames=[\"x\"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Rt(\"rc\",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Re(o);o===1?this.userCode=`\n void main(){\n int rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = getChannel(getX(${e[0]} - rc - 1),\n ${e[0]} - rc - 1);\n if(${s}){\n result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),\n ${e[0]} - (rc + 1) - 1);\n }\n setOutput(result);\n }\n `:this.userCode=`\n void main() {\n ${i} rc = getOutputCoords();\n vec4 result = vec4(0.);\n result.r = ${p(n.slice())};\n if(${s}){\n result.g = ${u(n.slice())};\n }\n if(${a}) {\n result.b = ${c(n.slice())};\n if(${s}) {\n result.a = ${l(n.slice())};\n }\n }\n setOutput(result);\n }\n `;function p(f){return m(f)}function u(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",m(f)}function c(f){return f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function l(f){return f[o-1]=\"(\"+f[o-1]+\" + 1)\",f[o-2]=\"(\"+f[o-2]+\" + 1)\",m(f)}function m(f){let h=e.map((b,C)=>d(C,f)),g=h.join(\",\"),x=h.slice(-2).join(\",\");return`getChannel(getX(${g}), vec2(${x}))`}function d(f,h){return t.indexOf(f)!==-1&&e[f]!==1?`${e[f]} - ${h[f]} - 1`:`${h[f]}`}}};function _te(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=y.parseAxisParam(s,n.shape);if(a===0)return Dt({inputs:{x:n},backend:t});let p=A().getBool(\"WEBGL_PACK_ARRAY_OPERATIONS\")?new Ng(n.shape,i):new kg(n.shape,i);return t.runWebGLProgram(p,[n],n.dtype)}var F3={kernelName:ps,backendName:\"webgl\",kernelFunc:_te};var Tg=class{constructor(e,t){this.variableNames=[\"Image\"],this.outputShape=[],this.customUniforms=[{name:\"params\",type:\"vec4\"}];let o=e[1],n=e[2];this.outputShape=e;let s=\"\";typeof t==\"number\"?s=`float outputValue = ${t.toFixed(2)};`:s=`\n vec3 fill = vec3(${t.join(\",\")});\n float outputValue = fill[coords[3]];`,this.userCode=`\n void main() {\n ivec4 coords = getOutputCoords();\n int x = coords[2];\n int y = coords[1];\n float coordXFloat = (float(x) - params[0]) * params[3] -\n (float(y) - params[1]) * params[2];\n float coordYFloat = (float(x) - params[0]) * params[2] +\n (float(y) - params[1]) * params[3];\n int coordX = int(round(coordXFloat + params[0]));\n int coordY = int(round(coordYFloat + params[1]));\n ${s}\n if(coordX >= 0 && coordX < ${n} && coordY >= 0 && coordY < ${o}) {\n outputValue = getImage(coords[0], coordY, coordX, coords[3]);\n }\n setOutput(outputValue);\n }\n `}};var P3={kernelName:Ds,backendName:\"webgl\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new Tg(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[[u,c,Math.sin(n),Math.cos(n)]];return i.runWebGLProgram(p,[o],o.dtype,l)}};var Ete=`\n // OpenGL ES does not support round function.\n // The algorithm is based on banker's rounding.\n float base = floor(x);\n if ((x - base) < 0.5) {\n return floor(x);\n } else if ((x - base) > 0.5) {\n return ceil(x);\n } else {\n if (mod(base, 2.0) == 0.0) {\n return base;\n } else {\n return base + 1.0;\n }\n }\n`,$te=xe({opSnippet:Ete}),O3={kernelName:cs,backendName:\"webgl\",kernelFunc:$te};var Rte=\"return inversesqrt(x);\",Dte=xe({opSnippet:Rte,cpuKernelImpl:dD}),M3={kernelName:ls,backendName:\"webgl\",kernelFunc:Dte};var Cu=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n float sum = 0.0;\n bool found = false;\n for (int i = 0; i < ${e}; i++) {\n int flattenedIndex = 0;\n for (int j = 0; j < ${t}; j++) {\n int index = round(${m});\n flattenedIndex += index * ${x};\n }\n if (flattenedIndex == coords[0]) {\n sum += ${f};\n found = true;\n }\n }\n setOutput(mix(${g}, sum, float(found)));\n }\n `}};var _g=class{constructor(e,t,o,n,s,a,i=!0,p=!1){this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=a;let u=Re(s.length),c=Re(a.length),l=\"\";o===1?l=\"i\":o===2&&(l=\"i, j\");let m=`getIndices(${l})`,d=\"\";n===1?d=\"i\":n===2&&(d=\"i, coords[1]\");let f=`getUpdates(${d})`,h=\"\";p&&(h=\"coords[0], coords[1]\");let g=`getDefaultValue(${h})`,x=t>1?\"strides[j]\":\"strides\",b=t>1?\"strides[j + 1]\":\"strides\";this.userCode=`\n ${u} strides = ${u}(${s});\n\n void main() {\n ${c} coords = getOutputCoords();\n vec4 sum = vec4(0.);\n vec4 found = vec4(0.);\n for (int i = 0; i < ${e}; i+=2) {\n ivec2 flattenedIndex = ivec2(0);\n for (int j = 0; j < ${t}; j+=2) {\n ivec4 index = round(${m});\n flattenedIndex += index.xz * ${x};\n if (j + 1 < ${t}) {\n flattenedIndex += index.yw * ${b};\n }\n }\n if (flattenedIndex[0] == coords[0] || flattenedIndex[1] == coords[0] ||\n flattenedIndex[0] == coords[0] + 1 || flattenedIndex[1] == coords[0] + 1) {\n vec4 updVals = ${f};\n if (flattenedIndex[0] == coords[0]) {\n sum.xy += updVals.xy;\n found.xy = vec2(1.);\n } else if (flattenedIndex[0] == coords[0] + 1) {\n sum.zw += updVals.xy;\n found.zw = vec2(1.);\n }\n if (flattenedIndex[1] == coords[0]) {\n sum.xy += updVals.zw;\n found.xy = vec2(1.);\n } else if (flattenedIndex[1] == coords[0] + 1) {\n sum.zw += updVals.zw;\n found.zw = vec2(1.);\n }\n }\n }\n setOutput(mix(${g}, sum, found));\n }\n `}};function Ate(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=te({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=t.makeTensorInfo([],\"float32\",new Float32Array([0])),g;A().getBool(\"WEBGL_PACK\")?g=new _g(p,i,d.shape.length,f.shape.length,c,m):g=new Cu(p,i,d.shape.length,f.shape.length,c,m);let x=t.runWebGLProgram(g,[f,d,h],f.dtype),b=te({inputs:{x},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(x),t.disposeIntermediateTensorInfo(h),b}var L3={kernelName:ms,backendName:\"webgl\",kernelFunc:Ate};var Eg=class{constructor(e,t,o,n){this.variableNames=[\"sortedSequence\",\"values\"],this.customUniforms=[{name:\"numInputs\",type:\"int\"}],this.outputShape=[e,o];let s=\"while (left < right) {\",a=`for (int i = 0; i < ${Math.ceil(Math.log2(t+1))}; ++i) { if (left >= right) break;`,i=A().getNumber(\"WEBGL_VERSION\")===2?s:a,p=n===\"left\"?\"<\":\"<=\";this.userCode=`\n int findBound(int batch, float value) {\n int left = 0;\n int right = numInputs;\n int mid;\n ${i}\n mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${p} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int valueIndex = coords[1];\n\n float value = getValues(batch, valueIndex);\n\n setOutput(float(findBound(batch, value)));\n }\n `}};function Fte(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new Eg(n.shape[0],n.shape[1],s.shape[1],a),p=[[n.shape[1]]];return t.runWebGLProgram(i,[n,s],\"int32\",p)}var B3={kernelName:fs,backendName:\"webgl\",kernelFunc:Fte};var $g=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s=\"resRC\",n=\"resRC\";else{let i=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],p=[],u=[];for(let c=0;c= 1.0) {\n setOutput(getA(${s}));\n } else {\n setOutput(getB(${s}));\n }\n }\n `}};function Pte(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new $g(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var z3={kernelName:fa,backendName:\"webgl\",kernelFunc:Pte};var Ote=`\n // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n // see: https://arxiv.org/abs/1706.02515\n float scaleAlpha = ${w.SELU_SCALEALPHA};\n float scale = ${w.SELU_SCALE};\n return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`,Mte=xe({opSnippet:Ote}),V3={kernelName:hs,backendName:\"webgl\",kernelFunc:Mte};var Lte=Fo+`\n return 1.0 / (1.0 + exp(-1.0 * x));\n`,Bte=`\n vec4 result = 1.0 / (1.0 + exp(-1.0 * x));\n bvec4 isNaN = isnan(x);\n\n result.r = isNaN.r ? x.r : result.r;\n result.g = isNaN.g ? x.g : result.g;\n result.b = isNaN.b ? x.b : result.b;\n result.a = isNaN.a ? x.a : result.a;\n\n return result;\n`,zte=xe({opSnippet:Lte,packedOpSnippet:Bte,cpuKernelImpl:hD}),W3={kernelName:bs,backendName:\"webgl\",kernelFunc:zte};var Vte=`\n if (isnan(x)) { return 0.0; }\n return sign(x);\n`,Wte=xe({opSnippet:Vte}),U3={kernelName:ys,backendName:\"webgl\",kernelFunc:Wte};var Ute=Fo+`\n return sin(x);\n`,Gte=`\n vec4 result = sin(x);\n bvec4 isNaN = isnan(x);\n ${Xr}\n return result;\n`,Hte=xe({opSnippet:Ute,packedOpSnippet:Gte}),G3={kernelName:gs,backendName:\"webgl\",kernelFunc:Hte};var Kte=`\n float e2x = exp(x);\n return (e2x - 1.0 / e2x) / 2.0;\n`,qte=xe({opSnippet:Kte}),H3={kernelName:xs,backendName:\"webgl\",kernelFunc:qte};var jte=`\n float epsilon = 1.1920928955078125e-7;\n float threshold = log(epsilon) + 2.0;\n\n bool too_large = x > -threshold;\n bool too_small = x < threshold;\n\n float result;\n float exp_x = exp(x);\n\n if (too_large){\n result = x;\n }\n else if (too_small){\n result = exp_x;\n }\n else{\n result = log(exp_x + 1.0);\n }\n return result;\n`,Xte=xe({opSnippet:jte}),K3={kernelName:Cs,backendName:\"webgl\",kernelFunc:Xte};var Yte=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");let i=s.reduce((x,b)=>x*b),p=[[0,0]];p.push(...a);for(let x=1+s.length;xt.disposeIntermediateTensorInfo(x)),g},q3={kernelName:ga,backendName:\"webgl\",kernelFunc:Yte};function Qte(r){let{inputs:e,backend:t}=r,{indices:o,values:n,denseShape:s,defaultValue:a}=e;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:\n ${s.shape}`);if(o.shape.length!==2)throw new Error(`Indices must be a matrix, saw:\n ${o.shape}`);if(n.shape.length!==1)throw new Error(`Values must be a vector, saw:\n ${n.shape}`);if(a.shape.length!==0)throw new Error(`Default value must be a scalar, saw:\n ${a.shape}`);let i=t.readSync(o.dataId),p=t.readSync(n.dataId),u=t.readSync(s.dataId),c=t.readSync(a.dataId)[0],[l,m,d,f,h]=xD(i,o.shape,o.dtype,p,n.dtype,u,c);return[t.makeTensorInfo(m,o.dtype,l),t.makeTensorInfo([m[0]],n.dtype,d),t.makeTensorInfo([f.length],\"bool\",new Uint8Array(f.map(g=>Number(g)))),t.makeTensorInfo([h.length],o.dtype,new Int32Array(h))]}var j3={kernelName:Ki,backendName:\"webgl\",kernelFunc:Qte};function Zte(r){let{inputs:e,backend:t}=r,{inputIndices:o,inputShape:n,newShape:s}=e;if(o.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${o.shape}`);if(n.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${n.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let a=Array.from(t.readSync(n.dataId)),i=t.readSync(o.dataId),p=Array.from(t.readSync(s.dataId)),[u,c,l]=yD(i,o.shape,o.dtype,a,p);return[t.makeTensorInfo(c,o.dtype,u),t.makeTensorInfo([l.length],s.dtype,new Int32Array(l))]}var X3={kernelName:ei,backendName:\"webgl\",kernelFunc:Zte};function Jte(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p,!0);return t.makeTensorInfo(c,o.dtype,u)}var Y3={kernelName:ya,backendName:\"webgl\",kernelFunc:Jte};function ere(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;if(o.shape.length<1)throw new Error(\"Data should be at least 1 dimensional but received scalar\");if(n.shape.length!==1)throw new Error(`Indices should be a vector but received shape\n ${n.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape\n ${s.shape}`);let a=t.readSync(o.dataId),i=t.readSync(n.dataId),p=t.readSync(s.dataId),[u,c]=lh(a,o.shape,o.dtype,i,p);return t.makeTensorInfo(c,o.dtype,u)}var Q3={kernelName:ba,backendName:\"webgl\",kernelFunc:ere};function tre(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let x=t.bufferSync(n),b=t.bufferSync(s),C=y.decodeString(t.readSync(a.dataId)[0]),S=fD(x,b,i,m,c,u,p,l,C,d);return t.makeTensorInfo(i,S.dtype,S.values)}let f=new Cu(u,p,n.shape.length,s.shape.length,l,[m,1],d),h=t.runWebGLProgram(f,[s,n,a],s.dtype),g=te({inputs:{x:h},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(h),g}var Z3={kernelName:vs,backendName:\"webgl\",kernelFunc:tre};function rre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Gs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var J3={kernelName:xa,backendName:\"webgl\",kernelFunc:rre};var eP=\"return sqrt(x);\",ore=xe({opSnippet:eP,packedOpSnippet:eP,cpuKernelImpl:bD}),tP={kernelName:ws,backendName:\"webgl\",kernelFunc:ore};var nre=\"return x * x;\",sre=xe({opSnippet:nre}),rP={kernelName:qi,backendName:\"webgl\",kernelFunc:sre};var oP=\"return (a - b) * (a - b);\",are=nt({opSnippet:oP,packedOpSnippet:oP}),nP={kernelName:ks,backendName:\"webgl\",kernelFunc:are};function ire(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;if(n.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");let s=t.readSync(n.dataId),a=w.fromUint8ToStringArray(s),i=CD(a,\"string\",o);return t.makeTensorInfo(n.shape,\"string\",i)}var sP={kernelName:Ru,backendName:\"webgl\",kernelFunc:ire};function ure({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=Wt+`\n return x > 0.0 ? 1.0 : float(${e.alpha});\n `,s=new tr(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var aP={kernelName:wo,backendName:\"webgl\",kernelFunc:ure};var Rg=class{constructor(e,t,o){this.variableNames=[\"x\"],this.outputShape=o;let n=o.length,s=Re(o.length),a=Re(o.length),i=\"\";if(n===1)i=\"coords * strides + begin\";else{let p=0;i=o.map((u,c)=>(p++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${p-1}] * strides[${c}] + begin[${c}]`)).join(\",\")}this.userCode=`\n ${s} begin = ${s}(${e});\n ${s} strides = ${s}(${t});\n\n void main() {\n ${a} coords = getOutputCoords();\n setOutput(getX(${i}));\n }\n `}};function pre(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=te({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let $=pt.computeOutShape(b,C,S),R=Gs({inputs:{x:n},backend:t,attrs:{begin:b,size:$}});k=te({inputs:{x:R},backend:t,attrs:{shape:f}}),t.disposeIntermediateTensorInfo(R)}else if(t.shouldExecuteOnCPU([n])){let R=t.readSync(n.dataId),D=me(n.shape,n.dtype,R),P=wD(d,D,S,b);k=t.makeTensorInfo(f,n.dtype,P.values)}else{let R=new Rg(b,S,d);k=t.runWebGLProgram(R,[n],n.dtype)}let _=te({inputs:{x:k},backend:t,attrs:{shape:f}});return t.disposeIntermediateTensorInfo(k),_}var iP={kernelName:Ns,backendName:\"webgl\",kernelFunc:pre};function cre(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=SD(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var uP={kernelName:Ca,backendName:\"webgl\",kernelFunc:cre};function lre(r){let{inputs:e,backend:t,attrs:o}=r,{skipEmpty:n}=o,{input:s,delimiter:a}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(a.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${a.shape}`);let i=t.readSync(s.dataId),p=t.readSync(a.dataId)[0],[u,c,l]=ID(i,p,n),m=c.length;return[t.makeTensorInfo([m,2],\"int32\",u),t.makeTensorInfo([m],\"string\",c),t.makeTensorInfo([2],\"int32\",new Int32Array(l))]}var pP={kernelName:ji,backendName:\"webgl\",kernelFunc:lre};function mre(r){let{inputs:e,backend:t,attrs:o}=r,{numBuckets:n}=o,{input:s}=e;if(s.dtype!==\"string\")throw new Error(\"Input must be of datatype string\");if(n<=0)throw new Error(\"Number of buckets must be at least 1\");let a=t.readSync(s.dataId),i=vD(a,n);return t.makeTensorInfo(s.shape,\"int32\",i)}var cP={kernelName:Xi,backendName:\"webgl\",kernelFunc:mre};var dre=\"return tan(x);\",fre=xe({opSnippet:dre}),lP={kernelName:_s,backendName:\"webgl\",kernelFunc:fre};var hre=`\n float e2x = exp(-2.0 * abs(x));\n return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`,gre=xe({opSnippet:hre}),mP={kernelName:Es,backendName:\"webgl\",kernelFunc:gre};function xre(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=te({inputs:{x:s},backend:t,attrs:{shape:[p,i]}}),f=te({inputs:{x:a},backend:t,attrs:{shape:[p,u]}}),h=te({inputs:{x:n},backend:t,attrs:{shape:m}}),g=new Cu(p,i,d.shape.length,f.shape.length,c,m,!1,!0),x=t.runWebGLProgram(g,[f,d,h],h.dtype),b=te({inputs:{x},backend:t,attrs:{shape:n.shape}});return t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(x),b}var dP={kernelName:ds,backendName:\"webgl\",kernelFunc:xre};var Dg=class{constructor(e,t){this.variableNames=[\"A\"];let o=new Array(e.length);for(let a=0;a5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],o=[];for(let n=0;n5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=ND(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new Dg(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var fP={kernelName:po,backendName:\"webgl\",kernelFunc:Fv};var Ag=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"negativeInf\",type:\"float\"},{name:\"dir\",type:\"int\"},{name:\"inc\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced above,\n // Figure5(a) shows that element[1] is in the\n // second half of the group when group size is 2, but it is in the\n // first half of the group when group size is 4.\n\n bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;\n int i = isFirstInPair ? elemIdx : elemIdx - inc;\n\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));\n float x0 = i0 < n ? getX(batch, i0) : negativeInf;\n float x1 = i1 < n ? getX(batch, i1) : negativeInf;\n\n // Denotes which direction indices are in (ascending or descending).\n bool reverse = imod(elemIdx, 2 * dir) >= dir;\n bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) { // Elements in opposite order of direction\n int iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutput(float(i0));\n } else {\n setOutput(float(i1));\n }\n }\n `}},Fg=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.customUniforms=[{name:\"n\",type:\"int\"},{name:\"firstPass\",type:\"int\"},{name:\"k\",type:\"int\"}],this.outputShape=e,this.userCode=`\n void main() {\n // Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int elemIdx = coords[1];\n\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),\n // we only need to output the indices at positions |, the indices at\n // positions _ can be thrown away, see Figure5(b) After Phase 2\n // (Merge phase) in the Bitonic Top K paper referenced above.\n // For example, the paper shows we only need to output the orange bars.\n // The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back\n // to the previous sequence to find the corresponding value,\n // we need to double the index. When we double the index,\n // we basically interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position\n // of each 2k positions by - elemIdx % k. E.g. for output at\n // index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));\n int i0 = firstPass == 1 ? i : int(getIndices(batch, i));\n int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));\n\n float x0 = getX(batch, i0);\n float x1 = i1 < n ? getX(batch, i1) : x0;\n\n setOutput(x0 >= x1 ? float(i0) : float(i1));\n }\n `}};function kp(r,e){e!==null&&r.disposeIntermediateTensorInfo(e)}function hP(r){let e=1;for(;ep){let P=t.readSync(n.dataId),[O,M]=TD(P,u,n.dtype,s,a);return[t.makeTensorInfo(O.shape,O.dtype,O.values),t.makeTensorInfo(M.shape,M.dtype,M.values)]}if(s===0)return u[u.length-1]=0,[t.makeTensorInfo(u,n.dtype,[]),t.makeTensorInfo(u,\"int32\",[])];if(c===1)return[n,Ci({attrs:{shape:u,dtype:\"int32\",value:0},backend:t})];let l=t.texData.get(n.dataId),m=l!==null&&l.isPacked,d=m?t.unpackTensor(n):n,h=y.sizeFromShape(u)/c,g=te({inputs:{x:d},attrs:{shape:[h,c]},backend:t});m&&kp(t,d);let x=hP(s),b=hP(c),C=null,S=()=>C===null?[g,g]:[g,C],k=(P,O,M)=>{let L=S(),B=new Ag(M),U=[[c],[C===null?1:0],[Number.NEGATIVE_INFINITY],[P],[O]],j=C;C=t.runWebGLProgram(B,L,\"int32\",U),kp(t,j)};for(let P=1;P=1;M/=2)k(O,M,[h,b])}for(let P=b;P>x;P/=2){let O=S(),M=new Fg([h,P/2]),B=[[c],[C===null?1:0],[x]],z=C;C=t.runWebGLProgram(M,O,\"int32\",B),kp(t,z);let U=x/2,j=U*2;for(let q=U;q>=1;q/=2)k(j,q,C.shape)}let _=C;C=Gs({inputs:{x:C},backend:t,attrs:{begin:0,size:[h,s]}}),kp(t,_);let $=Tv({inputs:{x:g,indices:C},backend:t,attrs:{axis:1,batchDims:1}});kp(t,g);let R=u.slice(0,-1);R.push(s),_=C,C=te({inputs:{x:C},attrs:{shape:R},backend:t}),kp(t,_);let D=$;return $=te({inputs:{x:$},attrs:{shape:R},backend:t}),kp(t,D),[$,C]}var gP={kernelName:$s,backendName:\"webgl\",kernelFunc:bre};var Pg=class{constructor(e,t,o,n,s,a){this.variableNames=[\"Image\",\"Transforms\"],this.outputShape=a;let i=o===\"nearest\"?1:2,p;switch(n){case\"constant\":p=1;break;case\"reflect\":p=2;break;case\"wrap\":p=3;break;case\"nearest\":p=4;break;default:p=1;break}this.userCode=`\n float mapCoord(float outCoord, float len) {\n float inCoord = outCoord;\n if(${p} == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * float(int(float(-inCoord / sz2))) +\n inCoord;\n }\n inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz2 = 2.0 * len;\n inCoord -= sz2 * float(int(float(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n float sz = len - 1.0;\n inCoord -= len * float(int(float(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (${p} == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n } else {\n return outCoord;\n }\n }\n\n float readWithFillValue(int batch, int coordY, int coordX,\n int channel) {\n float outputValue;\n if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = float(${s});\n }\n return outputValue;\n }\n\n void main() {\n ivec4 coords = getOutputCoords();\n float outputValue;\n int batch = coords[0];\n int x = coords[2];\n int y = coords[1];\n int channel = coords[3];\n float xf = float(x);\n float yf = float(y);\n float a1 = getTransforms(batch, 0);\n float a2 = getTransforms(batch, 1);\n float a3 = 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valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, int(yCeil), int(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, int(yCeil), int(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutput(outputValue);\n }\n `}};function Cre(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new Pg(l,m,a,i,p,g);return t.runWebGLProgram(x,[n,s],\"float32\")}var xP={kernelName:Rs,backendName:\"webgl\",kernelFunc:Cre};function wre(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;Vs(s,\"unique\"),console.warn(\"WARNING: \",\"UI might be locked temporarily as data is being downloaded\");let a=o.readSync(s.dataId),{outputValues:i,outputShape:p,indices:u}=_D(a,n,s.shape,s.dtype);return[o.makeTensorInfo(p,s.dtype,i),o.makeTensorInfo([u.length],\"int32\",u)]}var yP={kernelName:Yi,backendName:\"webgl\",kernelFunc:wre};function Sre(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeIntermediateTensorInfo(h)),f}var bP={kernelName:wa,backendName:\"webgl\",kernelFunc:Sre};var Og=class{constructor(e,t){this.variableNames=[\"x\",\"segmentIds\"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let p=\"0.0\",u=\"sumValue\",c=Math.floor(o/4)*4,l=o%4,m=`\n sumValue += dot(values, segFilter);\n `,d=\"\";s%o>0&&(d=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return initializationValue;\n }\n `);let f=\"\";s%o>0&&(f=`\n if (inIdx < 0 || inIdx >= ${s}) {\n return -1.0;\n }\n `),this.userCode=`\n const float initializationValue = ${p};\n\n float getValue(int batch, int inIdx) {\n ${d}\n return getX(batch, inIdx);\n }\n\n float getSegmentIdAtIndex(int inIdx) {\n ${f}\n return getSegmentIds(inIdx);\n }\n\n void main() {\n ivec2 coords = getOutputCoords();\n int batch = coords[0];\n int outIdx = coords[1];\n int inOffset = int(floor(float(outIdx) / float(\n ${a})) * float(${o}));\n int currentSeg = int(mod(float(outIdx), float(${a})));\n\n float sumValue = 0.0;\n\n for (int i = 0; i < ${c}; i += 4) {\n int inIdx = inOffset + i;\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n getValue(batch, inIdx + 3)\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n );\n\n ${m}\n }\n\n int inIdx = inOffset + ${c};\n if (${l===1}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n initializationValue,\n initializationValue,\n initializationValue\n );\n\n int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===2}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n initializationValue,\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n 0,\n 0\n );\n\n ${m}\n } else if (${l===3}) {\n vec4 values = vec4(\n getValue(batch, inIdx),\n getValue(batch, inIdx + 1),\n getValue(batch, inIdx + 2),\n initializationValue\n );\n\n vec4 segFilter = vec4(\n int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n 0\n );\n\n ${m}\n }\n setOutput(${u});\n }\n `}};function Ire(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=bt({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=te({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=oi(n.dtype),g=(S,k,_,$,R)=>{let D=S.shape[0],P=S.shape[1],O=w.segment_util.segOpComputeOptimalWindowSize(P,R),M={windowSize:O,inSize:P,batchSize:D,numSegments:R},L=new Og(M,k),B=t.compileAndRun(L,[S,_],$);if(p.push(B),B.shape[1]===R)return B;let z=Av({backend:t,attrs:{start:0,stop:R,step:1,dtype:\"float32\"}}),U=Fv({inputs:{x:z},backend:t,attrs:{reps:[P/O]}});return p.push(z),p.push(U),g(B,k,U,$,R)},x=g(f,\"unsortedSegmentSum\",s,h,a),b=te({inputs:{x},backend:t,attrs:{shape:m}}),C=b;if(c!=null){p.push(b);let S=w.getUndoAxesPermutation(c);C=bt({inputs:{x:C},backend:t,attrs:{perm:S}})}return p.forEach(S=>t.disposeIntermediateTensorInfo(S)),C}var CP={kernelName:Qi,backendName:\"webgl\",kernelFunc:Ire};var vre=[rA,nA,sA,aA,uA,pA,cA,lA,fA,hA,gA,xA,yA,bA,CA,wA,SA,IA,vA,kA,NA,_A,EA,$A,RA,PA,MA,LA,KD,zA,WA,UA,GA,HA,KA,qA,jA,XA,YA,QA,eF,tF,rF,oF,nF,sF,aF,iF,uF,pF,cF,lF,mF,dF,fF,hF,xF,yF,bF,CF,SF,IF,vF,kF,NF,TF,_F,EF,$F,HD,RF,VA,DF,AF,FF,qD,PF,OF,MF,LF,BF,zF,VF,WF,UF,GF,KF,qF,jF,XF,YF,QF,JF,t3,r3,o3,n3,s3,c3,YD,l3,m3,d3,f3,DA,h3,y3,b3,C3,w3,jD,S3,I3,v3,k3,N3,AA,a3,T3,_3,E3,ZD,$3,R3,D3,A3,F3,P3,O3,M3,L3,B3,z3,V3,W3,U3,G3,H3,TA,p3,K3,q3,j3,X3,Y3,Q3,Z3,J3,tP,rP,nP,sP,aP,iP,uP,pP,cP,u3,eA,lP,mP,dP,fP,gP,xP,tA,yP,bP,CP,g3];for(let r of vre)ti(r);var we;(function(r){r[r.float32=0]=\"float32\",r[r.int32=1]=\"int32\",r[r.bool=2]=\"bool\",r[r.string=3]=\"string\",r[r.complex64=4]=\"complex64\"})(we||(we={}));var wu;(function(r){r[r.linear=0]=\"linear\",r[r.relu=1]=\"relu\",r[r.relu6=2]=\"relu6\",r[r.prelu=3]=\"prelu\",r[r.leakyrelu=4]=\"leakyrelu\",r[r.sigmoid=5]=\"sigmoid\",r[r.elu=6]=\"elu\"})(wu||(wu={}));var wP;function kre(r){wP=r.wasm.cwrap(So,null,[\"number\",\"array\",\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Nre(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!==\"float32\"||s.dtype!==\"float32\")throw new Error(\"_FusedMatMul for non non-float32 tensors not yet supported.\");let{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o,m=t.dataIdMap.get(n.dataId).id,d=t.dataIdMap.get(s.dataId).id,f=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);f=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=wu[c];if(g==null)throw new Error(`${c} 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voe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,a=o.dataIdMap.get(n.dataId).id,i=o.dataIdMap.get(s.dataId).id,{strides:p,dilations:u,pad:c,dimRoundingMode:l}=t,m=u==null?[1,1]:u,d=w.computeConv2DInfo(n.shape,s.shape,p,m,c,l,!0),f=d.filterHeight,h=d.filterWidth,g=d.padInfo.top,x=d.padInfo.right,b=d.padInfo.bottom,C=d.padInfo.left,S=d.dilationHeight,k=d.dilationWidth,_=d.strideHeight,$=d.strideWidth,R=d.inChannels,D=d.outChannels,P=d.padInfo.type===\"SAME\"?1:0;if(d.dataFormat!==\"channelsLast\")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${d.dataFormat}'. 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Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(s.shape,s.dtype);return BO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var zO={kernelName:Li,backendName:\"wasm\",setupFunc:Eoe,kernelFunc:$oe};var VO;function Roe(r){VO=r.wasm.cwrap(Mi,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Doe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o;if(n.dtype!==s.dtype||n.dtype!==a.dtype)throw new Error(`Dilation2DBackpropInput error: x must have the same dtype as filter and dy. Got ${n.dtype}, ${s.dtype}, and ${a.dtype}`);let c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=t.makeOutput(n.shape,n.dtype);return VO(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,we[n.dtype],c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.filterHeight,c.filterWidth,c.padInfo.top,c.padInfo.left),l}var WO={kernelName:Mi,backendName:\"wasm\",setupFunc:Roe,kernelFunc:Doe};var UO=he(hn);var GO;function Aoe(r){GO=r.wasm.cwrap(Xa,null,[\"number\",\"number\",\"number\"])}function Foe(r){let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=t.makeOutput(n.shape,\"float32\"),a=i=>t.dataIdMap.get(i.dataId).id;return GO(a(n),a(o),a(s)),s}var HO={kernelName:Xa,backendName:\"wasm\",setupFunc:Aoe,kernelFunc:Foe};var Poe=!1,KO=Ge(xn,Poe,\"bool\");var qO=he(gn);var jO=he(yn,\"float32\");function Lg(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),p=s;return s<0&&(y.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+s+1),i.splice(p,0,1),zt({inputs:{x:n},backend:o,attrs:{shape:i}})}var XO={kernelName:na,backendName:\"wasm\",kernelFunc:Lg};var YO=he(bn,\"float32\");function Mv(r){let{attrs:{shape:e,value:t},backend:o}=r,{attrs:{dtype:n}}=r;n=n||y.inferDtype(t);let s=o.makeOutput(e,n);return o.typedArrayFromHeap(s).fill(t),s}var QO={kernelName:sa,backendName:\"wasm\",kernelFunc:Mv};var ZO;function Ooe(r){ZO=r.wasm.cwrap(Cn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Moe(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,p,u,c]=o.shape;return ZO(s,i,p,u,c,a),n}var JO={kernelName:Cn,backendName:\"wasm\",kernelFunc:Moe,setupFunc:Ooe};var eM=he(wn);var Loe=!1,tM=Ge(Sn,Loe);var rM;function Boe(r){rM=r.wasm.cwrap(In,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function zoe(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:p,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,l=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,d=p!=null?e.dataIdMap.get(p.dataId).id:0,f=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(y.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return rM(c,l,m,d,f,n,g),h}var oM={kernelName:In,backendName:\"wasm\",setupFunc:Boe,kernelFunc:zoe};var nM;function Voe(r){nM=r.wasm.cwrap(Io,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Woe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m),g=wu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return nM(x,j,q,Y,b,k,_,S,$,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var sM={kernelName:Io,backendName:\"wasm\",setupFunc:Voe,kernelFunc:Woe};var aM;function Uoe(r){aM=r.wasm.cwrap(vo,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Goe(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dataFormat:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=t,h=w.computeConv2DInfo(n.shape,s.shape,p,c,u,m,!0),g=wu[d];if(g==null)throw new Error(`${d} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let x=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,C=h.outChannels,S=0;if(a!=null){let ee=o.dataIdMap.get(a.dataId);if(ee.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${ee.shape.length}.`);if(ee.shape[0]!==C)throw new Error(`FusedDepthwiseConv2D bias shape (${ee.shape}) does not match the number of output channels (${C})`);S=ee.id}let k=h.filterHeight,_=h.filterWidth,$=h.padInfo.top,R=h.padInfo.right,D=h.padInfo.bottom,P=h.padInfo.left,O=h.dilationHeight,M=h.dilationWidth,L=h.strideHeight,B=h.strideWidth,z=h.inChannels,U=h.padInfo.type===\"SAME\"?1:0,j=h.batchSize,q=h.inHeight,Y=h.inWidth;if(l!==\"NHWC\")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${l}'. Please use 'NHWC'.`);let J=o.makeOutput(h.outShape,\"float32\"),re=o.dataIdMap.get(J.dataId).id,ne=i==null?0:o.dataIdMap.get(i.dataId).id;return aM(x,j,q,Y,b,k,_,S,$,R,D,P,U,O,M,L,B,z,C,g,ne,f||0,re),J}var iM={kernelName:vo,backendName:\"wasm\",setupFunc:Uoe,kernelFunc:Goe};var uM;function Hoe(r){uM=r.wasm.cwrap(vn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function Koe(r){let{backend:e,inputs:t}=r,{params:o,indices:n}=t,[s,a,i,p]=af.prepareAndValidate(o,n),u=e.makeOutput(s,o.dtype);if(a===0)return u;let c=n.shape,l=c[c.length-1],d=e.dataIdMap.get(o.dataId).id,h=e.dataIdMap.get(n.dataId).id,g=new Uint8Array(new Int32Array(p).buffer),x=e.dataIdMap.get(u.dataId).id;return uM(d,we[o.dtype],h,a,l,i,g,x),u}var pM={kernelName:vn,backendName:\"wasm\",setupFunc:Hoe,kernelFunc:Koe};var cM;function qoe(r){cM=r.wasm.cwrap(\"Gather\",null,[\"number\",\"number\",\"array\",\"number\",\"number\",\"number\",\"array\",\"number\"])}function joe(r){let{backend:e,inputs:t,attrs:o}=r,{x:n,indices:s}=t,{axis:a,batchDims:i}=o,p=y.parseAxisParam(a,n.shape)[0],u=e.readSync(s.dataId),c=n.shape[p];for(let D=0;D=0,()=>`GatherV2: the index value ${P} is not in [0, ${c-1}]`)}let l=w.segment_util.collectGatherOpShapeInfo(n,s,p,i),m=zt({inputs:{x:n},attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]},backend:e}),d=y.sizeFromShape(s.shape),f=zt({inputs:{x:s},attrs:{shape:[l.batchSize,d/l.batchSize]},backend:e}),h=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize],g=e.makeOutput(h,n.dtype);if(y.sizeFromShape(n.shape)===0)return g;let x=m.shape.length-1,C=e.dataIdMap.get(m.dataId).id,k=e.dataIdMap.get(f.dataId).id,_=e.dataIdMap.get(g.dataId).id,$=new Uint8Array(new Int32Array(y.computeStrides(m.shape)).buffer),R=new Uint8Array(new Int32Array(y.computeStrides(h)).buffer);return cM(C,we[n.dtype],$,x,k,l.batchSize,R,_),e.disposeData(m.dataId),e.disposeData(f.dataId),g.shape=l.outputShape,g}var lM={kernelName:aa,backendName:\"wasm\",setupFunc:qoe,kernelFunc:joe};var Xoe=!1,mM=Ge(kn,Xoe,\"bool\");var Yoe=!1,dM=Ge(Nn,Yoe,\"bool\");var fM=he(Tn,\"bool\");var hM=he(_n,\"bool\");var gM=he(En,\"bool\");var xM;function Qoe(r){xM=r.wasm.cwrap($n,null,[\"number\",\"number\",\"number\",\"number\"])}function Zoe(r){let{inputs:{x:e},attrs:{alpha:t},backend:o}=r,n=o.dataIdMap.get(e.dataId).id,s=o.makeOutput(e.shape,\"float32\");if(y.sizeFromShape(e.shape)!==0){let a=o.dataIdMap.get(s.dataId).id;xM(n,we[e.dtype],t,a)}return s}var yM={kernelName:$n,backendName:\"wasm\",setupFunc:Qoe,kernelFunc:Zoe};var Joe=!1,bM=Ge(Rn,Joe,\"bool\");var ene=!1,CM=Ge(Dn,ene,\"bool\");var wM;function tne(r){wM=r.wasm.cwrap(An,null,[\"number\",\"number\",\"number\",\"number\"])}function rne(r){let{attrs:e,backend:t}=r,{start:o,stop:n,num:s}=e,a=Math.floor(s),i=t.makeOutput([a],\"float32\");return wM(t.dataIdMap.get(i.dataId).id,o,n,a),i}var SM={kernelName:An,backendName:\"wasm\",setupFunc:tne,kernelFunc:rne};var IM=he(Fn);var vM=he(Pn);var one=!1,kM=Ge(On,one,\"bool\");var NM=he(Mn);var nne=!1,TM=Ge(Ln,nne,\"bool\");var sne=!1,_M=Ge(R0,sne,\"bool\");var EM;function ane(r){EM=r.wasm.cwrap(Bn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function ine(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o;if(n.dtype!==\"float32\")throw new Error(\"LRN error: x must have dtype float32\");let u=t.makeOutput(n.shape,n.dtype);return EM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(u.dataId).id,n.shape[3],s,a,i,p),u}var $M={kernelName:Bn,backendName:\"wasm\",setupFunc:ane,kernelFunc:ine};var RM;function une(r){RM=r.wasm.cwrap(Ya,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function pne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o;if(n.dtype!==\"float32\"||s.dtype!==\"float32\"||a.dtype!==\"float32\")throw new Error(\"LRNGrad error: x, y, and dy must have dtype float32\");let l=t.makeOutput(n.shape,n.dtype);return RM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(a.dataId).id,t.dataIdMap.get(l.dataId).id,a.shape[3],i,p,u,c),l}var DM={kernelName:Ya,backendName:\"wasm\",setupFunc:une,kernelFunc:pne};var AM;function cne(r){AM=r.wasm.cwrap(zn,null,[\"number\",\"number\",\"number\",\"number\"])}function lne(r){let{backend:e,inputs:t,attrs:o}=r,{reductionIndices:n,keepDims:s}=o,{x:a}=t,p=e.dataIdMap.get(a.dataId).id,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let C=e.dataIdMap.get(c.dataId).id;u=c,p=C}let f=u.shape.length;w.assertAxesAreInnerMostDims(\"max\",l,f);let[h,g]=w.computeOutAndReduceShapes(u.shape,l),x=y.sizeFromShape(g),b=e.makeOutput(h,a.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;AM(p,we[a.dtype],x,C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var FM={kernelName:zn,backendName:\"wasm\",setupFunc:cne,kernelFunc:lne};var mne=!1,PM=Ge(Vn,mne);var OM;function dne(r){OM=r.wasm.cwrap(Wn,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function fne(r){let{inputs:e,attrs:t,backend:o}=r,n=e.x,s=o.dataIdMap.get(n.dataId).id;y.assert(n.dtype===\"float32\",()=>`Error in MaxPool: only float32 input is supported. Got ${n.dtype}.`);let{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=t,c=w.computePool2DInfo(n.shape,a,i,1,p,u),l=c.filterHeight,m=c.filterWidth,d=c.padInfo.top,f=c.padInfo.right,h=c.padInfo.bottom,g=c.padInfo.left,x=c.dilationHeight,b=c.dilationWidth,C=c.strideHeight,S=c.strideWidth,k=c.inChannels,_=c.outChannels;if(c.dataFormat!==\"channelsLast\")throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);let $=o.makeOutput(c.outShape,\"float32\"),R=o.dataIdMap.get($.dataId).id;return OM(s,n.shape[0],n.shape[1],n.shape[2],l,m,d,f,h,g,x,b,C,S,k,_,R),$}var MM={kernelName:Wn,backendName:\"wasm\",setupFunc:dne,kernelFunc:fne};var LM;function hne(r){LM=r.wasm.cwrap(\"MaxPool3D\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function gne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p,dataFormat:u}=o,c=w.computePool3DInfo(n.shape,s,a,1,i,p,u),l=t.makeOutput(c.outShape,n.dtype);return LM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var BM={kernelName:ia,backendName:\"wasm\",setupFunc:hne,kernelFunc:gne};var zM;function xne(r){zM=r.wasm.cwrap(\"MaxPool3DGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function yne(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool3DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return zM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inDepth,c.inHeight,c.inWidth,c.outDepth,c.outHeight,c.outWidth,c.strideDepth,c.strideHeight,c.strideWidth,c.dilationDepth,c.dilationHeight,c.dilationWidth,c.effectiveFilterDepth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.front,c.padInfo.top,c.padInfo.left),l}var VM={kernelName:Gi,backendName:\"wasm\",setupFunc:xne,kernelFunc:yne};var WM;function bne(r){WM=r.wasm.cwrap(\"MaxPoolGrad\",null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Cne(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,{filterSize:a,strides:i,pad:p,dimRoundingMode:u}=o,c=w.computePool2DInfo(s.shape,a,i,1,p,u),l=t.makeOutput(s.shape,s.dtype);return WM(t.dataIdMap.get(s.dataId).id,t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),l}var UM={kernelName:Ui,backendName:\"wasm\",setupFunc:bne,kernelFunc:Cne};var GM;function wne(r){GM=r.wasm.cwrap(\"MaxPoolWithArgmax\",null,[\"number\",\"number\",\"number\",\"number\",\"boolean\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\"])}function Sne(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,includeBatchInIndex:p}=o;y.assert(n.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${n.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=w.computePool2DInfo(n.shape,s,a,[1,1],i),l=t.makeOutput(c.outShape,n.dtype),m=t.makeOutput(c.outShape,\"int32\");return GM(t.dataIdMap.get(n.dataId).id,t.dataIdMap.get(l.dataId).id,t.dataIdMap.get(m.dataId).id,we[n.dtype],p,c.batchSize,c.inChannels,c.inHeight,c.inWidth,c.outHeight,c.outWidth,c.strideHeight,c.strideWidth,c.dilationHeight,c.dilationWidth,c.effectiveFilterHeight,c.effectiveFilterWidth,c.padInfo.top,c.padInfo.left),[l,m]}var HM={kernelName:ua,backendName:\"wasm\",setupFunc:wne,kernelFunc:Sne};var KM;function Ine(r){KM=r.wasm.cwrap(Un,null,[\"number, number, number\"])}function vne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let S=e.dataIdMap.get(c.dataId).id;S!==i&&(u=c,p=S,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"mean\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=u;u.dtype!==\"float32\"&&(b=Mr({backend:e,inputs:{x:u},attrs:{dtype:\"float32\"}}),p=e.dataIdMap.get(b.dataId).id);let C=e.makeOutput(h,\"float32\");if(y.sizeFromShape(u.shape)!==0){let S=e.dataIdMap.get(C.dataId).id;KM(p,x,S)}if(d&&e.disposeData(c.dataId),s){let S=w.expandShapeToKeepDim(C.shape,m);C.shape=S}return u.dtype!==\"float32\"&&e.disposeData(b.dataId),C}var qM={kernelName:Un,backendName:\"wasm\",setupFunc:Ine,kernelFunc:vne};var jM;function kne(r){jM=r.wasm.cwrap(Gn,null,[\"number\",\"number\",\"number\",\"number\"])}function Nne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e);if(d){let 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CL={kernelName:rs,backendName:\"wasm\",setupFunc:jne,kernelFunc:Xne};var wL;function Yne(r){wL=r.wasm.cwrap(os,null,[\"number\",\"number\",\"number\",\"number\"])}function Qne(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,p=i,u=a,{transposed:c,axes:l,originalAxes:m,inputWasTransposed:d}=Tr(a,n,e),f=l;if(d){let C=e.dataIdMap.get(c.dataId).id;C!==i&&(u=c,p=C,f=w.getInnerMostAxes(f.length,u.shape.length))}w.assertAxesAreInnerMostDims(\"prod\",f,u.shape.length);let[h,g]=w.computeOutAndReduceShapes(u.shape,f),x=y.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(y.sizeFromShape(u.shape)!==0){let C=e.dataIdMap.get(b.dataId).id;wL(p,x,we[b.dtype],C)}if(d&&e.disposeData(c.dataId),s){let C=w.expandShapeToKeepDim(b.shape,m);b.shape=C}return b}var SL={kernelName:os,backendName:\"wasm\",setupFunc:Yne,kernelFunc:Qne};var Zne=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=up(o,n,s,a),p=e.makeOutput([i.length],a);return 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PL={kernelName:Za,backendName:\"wasm\",setupFunc:ase,kernelFunc:ise};var OL;function use(r){OL=r.wasm.cwrap(ps,null,[\"number\",\"array\",\"number\",\"array\",\"number\",\"number\"])}function pse(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=y.parseAxisParam(s,n.shape);if(n.shape.length===0)return Np({inputs:{x:n},backend:t});let i=t.makeOutput(n.shape,n.dtype),p=t.dataIdMap.get(n.dataId).id,u=t.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(a).buffer),l=new Uint8Array(new Int32Array(n.shape).buffer);OL(p,c,a.length,l,n.shape.length,u);let m=zt({inputs:{x:i},attrs:{shape:n.shape},backend:t});return t.disposeData(i.dataId),m}var ML={kernelName:ps,backendName:\"wasm\",kernelFunc:pse,setupFunc:use};var LL;function cse(r){LL=r.wasm.cwrap(Ds,null,[\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"number\",\"array\",\"number\",\"number\"])}function 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find the buffer in buffer manager\");a[i]=a[a.length-1],a.pop(),this.numUsedBuffers--,this.numBytesUsed-=o,t?(this.freeBuffers.get(s).push(e),this.numFreeBuffers++):(e.destroy(),this.numBytesAllocated-=o)}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}dispose(){this.freeBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function YB(r,e){return`${r}_${e}`}var qg=class{constructor(e){this.device=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures=new Map,this.usedTextures=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireTexture(e,t,o,n){let s=ZB(o),a=e*t*s,i=QB(e,t,o,n);if(this.freeTextures.has(i)||this.freeTextures.set(i,[]),this.usedTextures.has(i)||this.usedTextures.set(i,[]),this.numBytesUsed+=a,this.numUsedTextures++,this.freeTextures.get(i).length>0){this.numFreeTextures--;let u=this.freeTextures.get(i).shift();return this.usedTextures.get(i).push(u),u}this.numBytesAllocated+=a;let p=this.device.createTexture({size:[e,t],format:o,usage:n});return this.usedTextures.get(i).push(p),p}releaseTexture(e){if(this.freeTextures.size===0)return;let t=e.width,o=e.height,n=e.format,s=e.usage,a=QB(t,o,n,s);this.freeTextures.has(a)||this.freeTextures.set(a,[]),this.freeTextures.get(a).push(e),this.numFreeTextures++,this.numUsedTextures--;let i=this.usedTextures.get(a),p=i.indexOf(e);if(p<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");i.splice(p,1);let u=ZB(n),c=t*o*u;this.numBytesUsed-=c}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){this.freeTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.usedTextures.forEach((e,t)=>{e.forEach(o=>{o.destroy()})}),this.freeTextures=new Map,this.usedTextures=new Map,this.numUsedTextures=0,this.numFreeTextures=0,this.numBytesUsed=0,this.numBytesAllocated=0}};function QB(r,e,t,o){return`${r}_${e}_${t}_${o}`}function ZB(r){if(r===\"rgba8unorm\")return 16;throw new Error(`${r} is not supported!`)}function JB(r,e){if(Math.max(...r)>5)throw new Error(\"Cannot symbolically compute strides for rank > 6 tensor.\");let t=r.length,o=\"xyzwuv\",n=r.map(a=>`${e}.${o[a]}`),s=new Array(t-1);s[t-2]=n[t-1];for(let a=t-3;a>=0;--a)s[a]=`(${s[a+1]} * ${n[a+1]})`;return s}var Qr=(r,e,t)=>t===\"int32\"?`atomicAdd(${r}, bitcast(${e}));`:`\n {\n var oldValue = 0;\n loop {\n let newValueF32 = bitcast(oldValue) + (${e});\n let newValue = bitcast(newValueF32);\n let res = atomicCompareExchangeWeak(${r}, oldValue, newValue);\n if res.exchanged {\n break;\n }\n oldValue = res.old_value;\n }\n }`;var wi;(function(r){r[r.FROM_PIXELS=0]=\"FROM_PIXELS\",r[r.DRAW=1]=\"DRAW\"})(wi||(wi={}));var oz=(r,e,t,o,n)=>{let s={dtype:o.dtype,shape:o.shape},a=cae(t,s,e),i=r.createShaderModule({code:a,label:e.constructor.name}),p=A().get(\"WEBGPU_PRINT_SHADER\");if(p!==\"\"){p=p.toLowerCase();let u=p.split(\",\");(p===\"all\"||u.some(c=>e.shaderKey.toLowerCase().includes(c)))&&(console.group(e.shaderKey),console.debug(a),console.groupEnd())}return n?r.createComputePipelineAsync({compute:{module:i,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"}):r.createComputePipeline({compute:{module:i,entryPoint:\"_start\"},label:e.constructor.name,layout:\"auto\"})},Ae=(r,e=\"f32\")=>{switch(r){case 1:return`${e}`;case 2:return`vec2<${e}>`;case 3:return`vec3<${e}>`;case 4:return`vec4<${e}>`;default:throw new Error(`${r}-component ${e} is not supported.`)}};function ft(r){if(r<=1)return\"i32\";if(r===2)return\"vec2\";if(r===3)return\"vec3\";if(r===4)return\"vec4\";if(r===5)return\"vec5\";if(r===6)return\"vec6\";throw Error(`GPU for rank ${r} is not yet supported`)}function Oo(r){if(r===0)return\"x\";if(r===1)return\"y\";if(r===2)return\"z\";if(r===3)return\"w\";if(r===4)return\"u\";if(r===5)return\"v\";throw Error(`Index ${r} is not yet supported`)}function G(...r){let e;switch(r.length){case 0:e=`\n fn main()\n `;break;case 1:e=`\n fn main(${r[0]} : i32)\n `;break;default:throw Error(\"Unreachable\")}return e}function ez(r,e){let t;return t=`\n ${pae(e)}\n fn _start(@builtin(local_invocation_id) LocalId : vec3,\n @builtin(global_invocation_id) GlobalId : vec3,\n @builtin(local_invocation_index) LocalIndex: u32,\n @builtin(workgroup_id) WorkgroupId : vec3,\n @builtin(num_workgroups) NumWorkgroups : vec3) {\n localId = LocalId;\n localIndex = LocalIndex;\n globalId = GlobalId;\n numWorkgroups = NumWorkgroups;\n workgroupId = WorkgroupId;\n ${r?\"main(getGlobalIndex());\":\"main();\"};\n }\n `,t}function pae(r){return`\n @compute @workgroup_size(${r.workgroupSize[0]}, ${r.workgroupSize[1]}, ${r.workgroupSize[2]})\n`}function cae(r,e,t){let o=[],n=t.workgroupSize[0]*t.workgroupSize[1]*t.workgroupSize[2];if(t.outputComponent=t.outputComponent?t.outputComponent:1,o.push(`\n\n var localId: vec3;\n var localIndex: u32;\n var globalId: vec3;\n var numWorkgroups: vec3;\n var workgroupId: vec3;\n\n // Only used when the y/z dimension of workgroup size is 1.\n fn getGlobalIndex() -> i32 {\n ${sz(t)?\" return i32(globalId.x);\":` return i32((workgroupId.z * numWorkgroups.x * numWorkgroups.y +\n workgroupId.y * numWorkgroups.x + workgroupId.x) * ${n}u +\n localIndex);\n `}\n }\n `),t.pixelsOpType!=null){let f=t.pixelsOpType===wi.FROM_PIXELS?`@group(0) @binding(0) var result: array<${Su(e.dtype,t.outputComponent)}>;`:`@group(0) @binding(1) var inBuf : array<${Su(r[0].dtype,t.outputComponent)}>;`,h=e.shape.length===3?\"vec2\":\"i32\";o.push(`\n struct Uniform {\n outShapeStrides : ${h},\n size : i32,\n numChannels : i32,\n alpha : f32,\n };\n\n ${f}\n @group(0) @binding(2) var uniforms: Uniform;\n `);let g=rz(t);return[tz,o.join(`\n`),cm(e.shape),t.getUserCode(),ez(g,t)].join(`\n`)}let s,a,i=\"struct Uniforms { NAN : f32, INFINITY : f32, \";t.variableNames.forEach((f,h)=>{let g=ft(r[h].shape.length);i+=`${f.charAt(0).toLowerCase()+f.slice(1)}Shape : ${g}, `,s=r[h].shape.length-1,a=ft(s),i+=`${f.charAt(0).toLowerCase()+f.slice(1)}ShapeStrides: ${a}, `});let p=ft(e.shape.length);i+=`outShape : ${p}, `,s=e.shape.length-1,a=ft(s),i+=`\n outShapeStrides: ${a}, `,t.size&&(i+=\"size : i32, \"),t.uniforms&&(i+=t.uniforms),i+=\"};\",i=yae(i),o.push(i),t.atomic?o.push(`\n @group(0) @binding(0) var result: array>;\n `):o.push(`\n @group(0) @binding(0) var result: array<${Su(e.dtype,t.outputComponent)}>;\n `),t.variableNames.forEach((f,h)=>{o.push(`\n @group(0) @binding(${1+h}) var ${f}: array<${t.variableComponents?Su(r[h].dtype,t.variableComponents[h]):Su(r[h].dtype,t.outputComponent)}>;\n `)}),i!==\"\"&&o.push(`\n @group(0) @binding(${1+t.variableNames.length}) var uniforms: Uniforms;\n `);let u=hae(e.shape,t.dispatchLayout),c=[tz,o.join(`\n`)+lae,cm(e.shape),u,gae(e.shape.length)];t.atomic||c.push(xae(e.shape,e.dtype,t.outputComponent)),t.variableNames.forEach((f,h)=>{c.push(`${cm(r[h].shape,f)}`)});let l=r.map((f,h)=>fae(f,e.shape,t.variableComponents?t.variableComponents[h]:t.outputComponent,t.dispatchLayout.x.length===e.shape.length)).join(`\n`);c.push(l),c.push(t.getUserCode());let m=rz(t);return c.push(ez(m,t)),c.join(`\n`)}function nz(r,e,t){let o=r.shaderKey;if(r.pixelsOpType!=null)return o;let n=[],s=[];e.forEach(c=>{n.push(c.shape),s.push(c.dtype)}),n.push(t.shape),s.push(t.dtype);let a=e.map(c=>w.getBroadcastDims(c.shape,t.shape)),i=e.map(c=>y.arraysEqual(c.shape,t.shape)).join(\"_\"),p=a.map(c=>c.join(\"_\")).join(\";\"),u=sz(r)?\"flatDispatch\":\"\";return o+=\"_\"+(r.workgroupSize?r.workgroupSize.join(\",\"):\"\")+n.map(c=>c.length).join(\",\")+s.join(\",\")+r.variableNames.join(\",\")+p+i+u,o}var tz=`\n struct vec5 {x: i32, y: i32, z: i32, w: i32, u: i32};\n struct vec6 {x: i32, y: i32, z: i32, w: i32, u: i32, v: i32};\n\n // Checks whether coordinates lie within the bounds of the shape.\n fn coordsInBounds2D(coord : vec2, shape : vec2) -> bool {\n return all(coord >= vec2(0)) && all(coord < shape);\n }\n fn coordsInBounds3D(coord : vec3, shape : vec3) -> bool {\n return all(coord >= vec3(0)) && all(coord < shape);\n }\n fn coordsInBounds4D(coord : vec4, shape : vec4) -> bool {\n return all(coord >= vec4(0)) && all(coord < shape);\n }\n\n fn getIndexFromCoords1D(coord : i32, shape : i32) -> i32 {\n return coord;\n }\n fn getIndexFromCoords2D(coords : vec2, shape : vec2) -> i32 {\n return dot(coords, vec2(shape.y, 1));\n }\n fn getIndexFromCoords3D(coords : vec3, shape : vec3) -> i32 {\n return dot(coords, vec3(shape.y * shape.z, shape.z, 1));\n }\n fn getIndexFromCoords4D(coords : vec4, shape : vec4) -> i32 {\n return dot(coords, vec4(\n shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));\n }\n fn getIndexFromCoords5D(coords : vec5, shape : vec5) -> i32 {\n let shapeStrides: vec5 = vec5(shape.y * shape.z * shape.w * shape.u, shape.z * shape.w * shape.u, shape.w * shape.u, shape.u, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u;\n }\n fn getIndexFromCoords6D(coords : vec6, shape : vec6) -> i32 {\n let shapeStrides: vec6 = vec6(shape.y * shape.z * shape.w * shape.u * shape.v, shape.z * shape.w * shape.u * shape.v, shape.w * shape.u * shape.v, shape.u * shape.v, shape.v, 1);\n return coords.x*shapeStrides.x + coords.y*shapeStrides.y + coords.z*shapeStrides.z + coords.w*shapeStrides.w + coords.u*shapeStrides.u + coords.v*shapeStrides.v;\n }\n\n // NaN defination in IEEE 754-1985 is :\n // - sign = either 0 or 1.\n // - biased exponent = all 1 bits.\n // - fraction = anything except all 0 bits (since all 0 bits represents infinity).\n // https://en.wikipedia.org/wiki/IEEE_754-1985#Representation_of_non-numbers\n fn isnan(val: f32) -> bool {\n let floatToUint: u32 = bitcast(val);\n return (floatToUint & 0x7fffffffu) > 0x7f800000u;\n }\n fn isnanVec4(val : vec4) -> vec4 {\n let floatToUint: vec4 = bitcast>(val);\n return (floatToUint & vec4(0x7fffffffu)) > vec4(0x7f800000u);\n }\n`,lae=`\n fn isinf(val: f32) -> bool {\n return abs(val) == uniforms.INFINITY;\n }\n`;function cm(r,e=\"\"){let t=r.length,o=e!==\"\"?`get${e.charAt(0).toUpperCase()+e.slice(1)}CoordsFromIndex`:\"getCoordsFromIndex\",n=e!==\"\"?`${e.charAt(0).toLowerCase()+e.slice(1)}ShapeStrides`:\"outShapeStrides\";if(t<=1)return`fn ${o}(index : i32) -> i32 { return index; }`;let s=y.computeStrides(r),a=ft(t),i=[];for(let u=0;u vec2 {\n let d0 = index / uniforms.${n}; let d1 = index - d0 * uniforms.${n};\n return vec2(d0, d1);\n }`;let p;return p=\"var index2 = index;\"+s.map((u,c)=>{let l=`let ${i[c]} = index2 / uniforms.${n}.${Oo(c)}`,m=c===s.length-1?`let ${i[c+1]} = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`:`index2 = index2 - ${i[c]} * uniforms.${n}.${Oo(c)}`;return`${l}; ${m};`}).join(\"\"),`\n fn ${o}(index : i32) -> ${a} {\n ${p}\n return ${a}(${i.join(\",\")});\n }\n `}function mae(r,e){let t=r.name,o=r.shape.length,n=ft(o),s=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=a.map(c=>`${c} : i32`).join(\", \");if(o<1)return`\n fn ${s}() -> ${Ae(e)} {\n return ${Ae(e)}(${t}[0]);\n }\n `;let p=`uniforms.${t.charAt(0).toLowerCase()+t.slice(1)}Shape`,u=`${o}D`;return o===0&&(u=\"1D\"),`\n fn ${s}(${i}) -> ${Ae(e)} {\n return ${Ae(e)}(${t}[getIndexFromCoords${u}(${n}(${a.join(\",\")}),\n ${p})${e===1?\"\":` / ${e}`}]);\n }\n `}function dae(r,e,t,o){let n=r.name,s=n.charAt(0).toUpperCase()+n.slice(1),a=\"get\"+s+\"ByOutput\",i=r.shape.length,p=e.length,u=ft(p);if(y.arraysEqual(r.shape,e)&&o)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[globalIndex]);\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)} {\n return ${Ae(t)}(${n}[${p>1?\"getOutputIndexFromCoords(coords)\":\"coords\"}${t===1?\"\":` / ${t}`}]);\n }\n `;let c=w.getBroadcastDims(r.shape,e),l=p-i,m=\"\";if(i===0)return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)}{\n return get${s}();\n }\n\n fn ${a}Coords(coords : ${u}) -> ${Ae(t)}{\n return get${s}();\n }\n `;p<2&&c.length>=1?m=\"coords = 0;\":m=c.map(g=>`coords.${Oo(g+l)} = 0;`).join(`\n`);let d=\"\";if(p<2&&i>0)d=\"coords\";else if(p>1){let g=ft(i),x=r.shape.map((b,C)=>`coords.${Oo(C+l)}`).join(\", \");d=`${g}(${x})`}else d=\"coords\";let f=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,h=`${i}D`;return`\n fn ${a}Index(globalIndex : i32) -> ${Ae(t)} {\n var coords = getCoordsFromIndex(globalIndex);\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n\n fn ${a}Coords(coordsIn : ${u}) -> ${Ae(t)} {\n var coords = coordsIn;\n ${m}\n return ${Ae(t)}(${n}[getIndexFromCoords${h}(${d}, ${f})${t===1?\"\":` / ${t}`}]);\n }\n`}function fae(r,e,t,o){let n=mae(r,t);return r.shape.length<=e.length&&(n+=dae(r,e,t,o)),n}function hae(r,e){let{x:t,y:o=[],z:n=[]}=e,s=r.length,a=t.length+o.length+n.length;if(a!==s)return\"\";if(t.length===s)return`fn getOutputCoords() -> ${ft(s)}{\n let globalIndex = getGlobalIndex();\n return getCoordsFromIndex(globalIndex);\n }\n `;let i=\"\",p=[t,o,n];for(let m=0;m ${c} {\n ${i}\n`;return u.length===0?l+=`return ${c}(0); }`:l+=`return ${c}(${u.join(\",\")}); }`,l}function gae(r){let e=\"\";switch(r){case 0:case 1:e+=`\n fn getOutputIndexFromCoords(coords : i32) -> i32 {\n return coords;\n }\n `;break;case 2:e+=`\n fn getOutputIndexFromCoords(coords : vec2) -> i32 {\n return dot(coords, vec2(uniforms.outShapeStrides, 1));\n }\n `;break;case 3:e+=`\n fn getOutputIndexFromCoords(coords : vec3) -> i32 {\n return dot(coords, vec3(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));\n }\n `;break;case 4:e+=`\n fn getOutputIndexFromCoords(coords : vec4) -> i32 {\n return dot(coords, vec4(\n uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));\n }\n `;break;case 5:e+=`\n fn getOutputIndexFromCoords(coords : vec5) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u;\n }\n `;break;case 6:e+=`\n fn getOutputIndexFromCoords(coords : vec6) -> i32 {\n return coords.x * uniforms.outShapeStrides.x +\n coords.y * uniforms.outShapeStrides.y +\n coords.z * uniforms.outShapeStrides.z +\n coords.w * uniforms.outShapeStrides.w +\n coords.u * uniforms.outShapeStrides.u +\n coords.v;\n }\n `;break;default:y.assert(!1,()=>`Unsupported ${r}D shape`);break}return e}function sz(r){return r.dispatch[1]===1&&r.dispatch[2]===1}function Su(r,e=1){if(r===\"float32\")return Ae(e,\"f32\");if(r===\"int32\"||r===\"bool\")return Ae(e,\"i32\");throw new Error(`type ${r} is not supported.`)}function xae(r,e,t){let o=r.length,n=Su(e,t),s=`fn setOutputAtIndex(flatIndex : i32, value : ${Ae(t)}) {\n result[flatIndex] = ${n}(value);\n }\n\n fn setOutputAtIndexI32(flatIndex : i32, value : ${Ae(t,\"i32\")}) {\n result[flatIndex] = ${n}(value);\n }\n `;if(o>=2){let a=[\"d0\",\"d1\",\"d2\",\"d3\",\"d4\",\"d5\"].slice(0,o),i=ft(o);s+=`\n fn setOutputAtCoords(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t)}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndex(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n fn setOutputAtCoordsI32(${a.map(p=>`${p} : i32`).join(\", \")}, value : ${Ae(t,\"i32\")}) {\n let flatIndex = getOutputIndexFromCoords(${i}(${a.join(\", \")}));\n setOutputAtIndexI32(flatIndex${t===1?\"\":` / ${t}`}, value);\n }\n `}return s}function yae(r){let e=/(\\w+)\\s*:\\s*vec(5|6)/g;r=r.replace(e,o=>\"@align(16) \"+o);let t=/vec(5|6)\\s*,\\s*(\\w+)/g;return r=r.replace(t,(o,n,s)=>`vec${n}, @align(16) ${s}`),r}function rz(r){return!(r.dispatchLayout.hasOwnProperty(\"y\")&&r.dispatchLayout.y.length!==0||r.dispatchLayout.hasOwnProperty(\"z\")&&r.dispatchLayout.z.length!==0)}var Zv={};qe(Zv,{GPUBytesPerElement:()=>jg,MatMulProgramType:()=>Mo,assertNotComplex:()=>fm,computeDispatch:()=>H,computeWorkPerThreadForConv2d:()=>mm,computeWorkgroupInfoForMatMul:()=>Qv,computeWorkgroupSizeForConv2d:()=>lm,flatDispatchLayout:()=>X,isWebGPUSupported:()=>dm,tilesFitEvenlyIntoShape:()=>Cae});var Tp=r=>{let e=1;for(let t=0;tt%r[o]===0)}function H(r,e,t=[1,1,1],o=[1,1,1]){let[n,s,a]=[Math.ceil(Tp(r.x.map(i=>e[i]))/(t[0]*o[0])),r.y?Math.ceil(Tp(r.y.map(i=>e[i]))/(t[1]*o[1])):1,r.z?Math.ceil(Tp(r.z.map(i=>e[i]))/(t[2]*o[2])):1];return[n,s,a]}function Qv(r,e,t,o=!1){let n=[8,8,1],s=[4,4,1];return o||(r<=8&&(s[1]=1),e<=16&&t<=16&&(n[0]=4)),{workgroupSize:n,elementsPerThread:s}}function lm(r,e,t=!1){if(t)return[8,8,1];let o=Tp(r.x.map(s=>e[s])),n=Tp(r.y.map(s=>e[s]));return o<=4?[4,16,1]:n<=4?[16,4,1]:[16,16,1]}function mm(r,e,t=!1){if(t)return[4,4,1];let o=Tp(r.x.map(s=>e[s])),n=Tp(r.y.map(s=>e[s]));return o<=4?[1,2,1]:n<=4?[2,1,1]:[2,2,1]}function X(r){return{x:r.map((e,t)=>t)}}function jg(r){if(r===\"float32\"||r===\"int32\"||r===\"bool\"||r===\"string\")return 4;if(r===\"complex64\")return 8;throw new Error(`Unknown dtype ${r}`)}function dm(){return!!(typeof globalThis!=\"undefined\"&&globalThis.navigator&&globalThis.navigator.gpu)}function fm(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&y.assert(t.dtype!==\"complex64\",()=>`${e} does not support complex64 tensors in the WebGPU backend.`)})}var Mo;(function(r){r[r.MatMulReduceProgram=0]=\"MatMulReduceProgram\",r[r.MatMulSplitKProgram=1]=\"MatMulSplitKProgram\",r[r.MatMulSmallOutputSizeProgram=2]=\"MatMulSmallOutputSizeProgram\",r[r.MatMulPackedProgram=3]=\"MatMulPackedProgram\",r[r.MatMulMax=4]=\"MatMulMax\"})(Mo||(Mo={}));var wae=A().getNumber(\"WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD\"),Sae=(r,e)=>{let t=r.limits.maxComputeWorkgroupsPerDimension,o=e.dispatchLayout,n=e.dispatch;if(n.every(a=>a<=t))return n;y.assert(n[0]>t&&o.y===void 0&&o.z===void 0,()=>\"Dispatch size exceeds WebGPU limits in Y or Z dimension.\");let s=Math.ceil(Math.sqrt(n[0]));return s>t?(s=Math.ceil(Math.cbrt(n[0])),y.assert(s<=t,()=>\"Total dispatch size exceeds WebGPU maximum.\"),[s,s,s]):[s,s,1]},jc=class r extends ao{nextDataId(){return r.nextDataId++}constructor(e,t){if(super(),this.commandQueueOwnedIds=new WeakSet,this.dispatchCountInPass=0,this.disposed=!1,this.downloadWaitMs=0,this.tensorDataPendingDisposal=[],this.queryResolveBuffer=null,this.querySet=null,this.querySetCount=2,this.stagingPendingDisposal=[],this.uniformPendingDisposal=[],this.uploadWaitMs=0,this.hasReadSyncWarned=!1,this.hasTimestampQueryWarned=!1,!dm())throw new Error(\"WebGPU is not supported on this device\");this.pipelineCache={},this.device=e,this.queue=e.queue,this.commandEncoder=null,this.computePassEncoder=null,this.adapterInfo=new Hg(t),this.supportTimestampQuery=this.device.features.has(\"timestamp-query\"),this.thresholdToIncreaseWorkgroups=this.adapterInfo.intelGPUGeneration>=12?16:8,this.bufferManager=new Kg(this.device),this.textureManager=new qg(this.device),this.tensorMap=new Bo(this,ur()),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(this.dummyCanvas=document.createElement(\"canvas\"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext(\"webgpu\"),this.dummyContext.configure({device:e,format:\"bgra8unorm\"}),document.body.appendChild(this.dummyCanvas))}floatPrecision(){return 32}disposeData(e,t=!1){if(!this.tensorMap.has(e))return!0;let o=this.tensorMap.get(e);return t?o.refCount=0:o.refCount--,o.refCount>0?!1:(o.complexTensorInfos!=null&&(this.disposeData(o.complexTensorInfos.real.dataId),this.disposeData(o.complexTensorInfos.imag.dataId)),this.commandQueueOwnedIds.has(e)?(this.tensorDataPendingDisposal.push(e),!0):(this.releaseResource(e),this.tensorMap.delete(e),!0))}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}releaseResource(e){let t=this.tensorMap.get(e);if(!(!t||!t.resource)){if(t.external){t.resource=null;return}t.resource instanceof GPUBuffer?this.bufferManager.releaseBuffer(t.resource):t.resource instanceof GPUTexture&&this.textureManager.releaseTexture(t.resource),t.resource=null}}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,o){if(o===\"complex64\"&&e!=null)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");let n={id:this.nextDataId()};return this.tensorMap.set(n,{dtype:o,shape:t,values:e,refCount:1}),n}move(e,t,o,n,s){if(n===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");this.tensorMap.set(e,{dtype:n,shape:o,values:t,refCount:s})}submitQueue(){this.queue.submit([this.commandEncoder.finish()]),this.commandEncoder=null,this.dispatchCountInPass=0,this.commandQueueOwnedIds=new WeakSet,this.tensorDataPendingDisposal.forEach(e=>{this.releaseResource(e),this.tensorMap.delete(e)}),this.uniformPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e)),this.stagingPendingDisposal.forEach(e=>this.bufferManager.releaseBuffer(e,!1)),this.tensorDataPendingDisposal=[],this.uniformPendingDisposal=[],this.stagingPendingDisposal=[]}ensureCommandEncoderReady(){this.commandEncoder||(this.commandEncoder=this.device.createCommandEncoder())}endComputePassEncoder(){this.computePassEncoder&&(this.computePassEncoder.end(),this.computePassEncoder=null)}async checkCompileCompletionAsync(){let e;try{e=await Promise.all(Object.values(this.pipelineCache))}catch(t){throw new Error(t.message)}Object.keys(this.pipelineCache).map((t,o)=>{this.pipelineCache[t]=e[o]})}async getBufferData(e){if(A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\"))return console.warn(\"The data may be invalid since WEBGPU_ENGINE_COMPILE_ONLY is true, this can only be called when WEBGPU_ENGINE_COMPILE_ONLY is false\"),null;let t=e.size,o=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,o,0,t),this.submitQueue(),await o.mapAsync(GPUMapMode.READ);let n=o.getMappedRange().slice(0);return o.unmap(),o!=null&&this.bufferManager.releaseBuffer(o),A().getBool(\"WEBGPU_USE_PROFILE_TOOL\")&&(y.assert(this.dummyContext!==void 0,()=>\"Fail to get context for profiling tool\"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let o=this.tensorMap.get(e);return o.values=t,o.values}readSync(e){let t=this.tensorMap.get(e),{values:o,complexTensorInfos:n}=t;if(o!=null||t.dtype===\"string\")return o;if(t.dtype===\"complex64\"){let h=this.readSync(n.real.dataId),g=this.readSync(n.imag.dataId),x=y.convertBackendValuesAndArrayBuffer(w.mergeRealAndImagArrays(h,g).buffer,\"float32\");return this.convertAndCacheOnCPU(e,x),x}this.hasReadSyncWarned||(this.hasReadSyncWarned=!0,console.warn(\"The performance of synchronously reading data from GPU to CPU is poor on the webgpu backend, please use asynchronous APIs instead.\"));let s=[\"opaque\",\"premultiplied\"],a=t.resource,i=a.size;y.assert(i%4===0,()=>\"Because there is 4 bytes for one pixel, buffer size must be multiple of 4.\");let p=i/4,u=new ArrayBuffer(i),c=256,l=256,m=s.map(h=>new OffscreenCanvas(c,l)),d=new OffscreenCanvas(c,l);this.endComputePassEncoder(),m.map((h,g)=>{let x=h.getContext(\"webgpu\");return x.configure({device:this.device,format:\"bgra8unorm\",usage:GPUTextureUsage.COPY_DST,alphaMode:s[g]}),x.getCurrentTexture()}).map((h,g)=>{let x=c*4,b=(R,D,P)=>{this.ensureCommandEncoderReady(),this.commandEncoder.copyBufferToTexture({buffer:a,bytesPerRow:x,offset:P},{texture:h},{width:R,height:D}),this.submitQueue();let O=d.getContext(\"2d\",{willReadFrequently:!0});O.clearRect(0,0,R,D),O.drawImage(m[g],0,0);let M=O.getImageData(0,0,R,D).data,L=s[g],B=new Uint8ClampedArray(u,P,R*D*4);for(let z=0;z0&&(b(S,k,_),_+=k*(c*4)),S=$%c,S>0&&b(S,1,_)});let f=y.convertBackendValuesAndArrayBuffer(u,t.dtype);return this.convertAndCacheOnCPU(e,f),f}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:o}=t;if(o!=null)return o;let n;if(t.dtype===\"complex64\"){let s=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=s[0],i=s[1];n=w.mergeRealAndImagArrays(a,i)}else{let s=await this.getBufferData(t.resource);n=y.convertBackendValuesAndArrayBuffer(s,t.dtype)}return this.convertAndCacheOnCPU(e,n),n}copyBuffer(e){let t=e.size,o=e.usage,n=this.bufferManager.acquireBuffer(t,o);return this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(e,0,n,0,t),this.submitQueue(),n}createTensorFromGPUData(e,t,o){let n=e.buffer;if(o===\"complex64\")throw new Error(\"Cannot write to a complex64 dtype. \");let s={id:this.nextDataId()};this.tensorMap.set(s,{dtype:o,shape:t,values:null,refCount:1,external:e.zeroCopy});let a=this.tensorMap.get(s),i=jg(a.dtype)*y.sizeFromShape(a.shape);if(e.buffer.sizey.decodeString(n));return me(e.shape,e.dtype,o)}catch(o){throw new Error(\"Failed to decode encoded string bytes into utf-8\")}return me(e.shape,e.dtype,t)}async time(e){!this.supportTimestampQuery&&!this.hasTimestampQueryWarned&&(console.warn(\"This device doesn't support timestamp-query extension. Start Chrome browser with flag --enable-dawn-features=allow_unsafe_apis to try it again. Otherwise, zero will be shown for the kernel time when profiling mode is enabled.\"),this.hasTimestampQueryWarned=!0);let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=y.flatten(this.activeTimers.map(u=>u.query)).filter(u=>u!=null),a=y.flatten(this.activeTimers.map(u=>u.name)).filter(u=>u!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},p=await Promise.all(s);return i.kernelMs=y.sum(p),i.getExtraProfileInfo=()=>p.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(\", \"),this.uploadWaitMs=0,this.downloadWaitMs=0,i}makeTensorInfo(e,t,o){return t===\"string\"&&o!=null&&o.length>0&&y.isString(o[0])&&(o=o.map(s=>y.encodeString(s))),{dataId:this.write(o,e,t),shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let o=this.tensorMap.get(e.dataId).resource;return o instanceof GPUBuffer?{buffer:o}:o instanceof GPUTexture?o.createView():o}uploadToGPU(e){let t=this.tensorMap.get(e);if(t.resource!=null)return;let o=jg(t.dtype)*y.sizeFromShape(t.shape),n,s=GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST;if(t.values){if(n=this.bufferManager.acquireBuffer(o,s,!0),n.mapState===\"unmapped\"){let a=this.bufferManager.acquireBuffer(o,GPUBufferUsage.MAP_WRITE|GPUBufferUsage.COPY_SRC,!0,!1),i=a.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(i).set(t.values):new Float32Array(i).set(t.values),a.unmap(),this.ensureCommandEncoderReady(),this.endComputePassEncoder(),this.commandEncoder.copyBufferToBuffer(a,0,n,0,o),this.stagingPendingDisposal.push(a)}else{let a=n.getMappedRange();t.dtype===\"int32\"||t.dtype===\"bool\"?new Int32Array(a).set(t.values):new Float32Array(a).set(t.values),n.unmap()}t.values=null}else n=this.bufferManager.acquireBuffer(o,s);t.resource=n}makeUniforms(e){let t=0,o=0,n=[],s=1;e.forEach(p=>{p.data.length===0&&(p.data=[1]);let u;switch(p.data.length){case 1:u=4;break;case 2:u=8;break;case 3:u=16;break;case 4:u=16;break;case 5:u=16;break;case 6:u=16;break;default:y.assert(!1,()=>`Unsupported ${p.data.length}D shape`)}(o===5||o===6)&&(u=16),u>s&&(s=u),t=Math.ceil(t/u)*u,o=p.data.length,n.push(t),t+=p.data.length*4}),t=Math.ceil(t/s)*s;let a=new ArrayBuffer(t);e.forEach((p,u)=>{let c=n[u];p.type===\"int32\"?new Int32Array(a,c,p.data.length).set(p.data):p.type===\"uint32\"?new Uint32Array(a,c,p.data.length).set(p.data):new Float32Array(a,c,p.data.length).set(p.data)});let i=this.bufferManager.acquireBuffer(t,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(i,0,a,0,t),this.uniformPendingDisposal.push(i),{offset:0,size:t,buffer:i}}runWebGPUProgram(e,t,o,n,s){if(s||(s=this.makeTensorInfo(e.outputShape,o)),y.sizeFromShape(s.shape)===0)return this.tensorMap.get(s.dataId).values=y.getTypedArrayFromDType(s.dtype,0),s;this.uploadToGPU(s.dataId),e.dispatch=Sae(this.device,e);let a=t.map((p,u)=>{if(p.dtype===\"complex64\")throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");return this.uploadToGPU(p.dataId),{dtype:this.tensorMap.get(p.dataId).dtype,shape:p.shape,name:e.variableNames[u]}});e.shaderKey=nz(e,a,s);let i=A().getBool(\"WEBGPU_ENGINE_COMPILE_ONLY\");return e.shaderKey in this.pipelineCache||(this.pipelineCache[e.shaderKey]=oz(this.device,e,a,s,i)),e.pipeline=this.pipelineCache[e.shaderKey],i||this.recordAndSubmit(e,s,t,n),s}recordAndSubmit(e,t,o,n){if(e.pipeline instanceof Promise)throw new Error(\"Please call checkCompileCompletionAsync to ensure parallel compilation is done!\");let s=[],a=[],i=\"int32\";if(e.pixelsOpType==null){s.push({type:\"float32\",data:[NaN]},{type:\"float32\",data:[1/0]}),a=o.concat(t).map(d=>d.shape);let m=\"int32\";a.map(d=>{s.push({type:m,data:d});let f=y.computeStrides(d);s.push({type:m,data:f})})}else{let m=y.computeStrides(t.shape);s.push({type:i,data:m})}if(e.size){let m=y.sizeFromShape(e.outputShape);s.push({type:i,data:[e.outputComponent?m/e.outputComponent:m]})}n&&(s=[...s,...n]);let p=[this.tensorToBinding(t),...o.map(m=>this.tensorToBinding(m)),this.makeUniforms(s)];o.forEach(m=>{this.commandQueueOwnedIds.add(m.dataId)}),this.commandQueueOwnedIds.add(t.dataId);let u=this.device.createBindGroup({layout:e.pipeline.getBindGroupLayout(0),entries:p.map((m,d)=>({binding:d,resource:m}))}),c=this.activeTimers!=null;this.ensureCommandEncoderReady();let l={};c&&this.supportTimestampQuery?(this.endComputePassEncoder(),this.querySet==null&&(this.querySet=this.device.createQuerySet({type:\"timestamp\",count:this.querySetCount})),l.timestampWrites={querySet:this.querySet,beginningOfPassWriteIndex:0,endOfPassWriteIndex:1},this.computePassEncoder=this.commandEncoder.beginComputePass(l)):this.computePassEncoder||(this.computePassEncoder=this.commandEncoder.beginComputePass(l)),this.computePassEncoder.setPipeline(e.pipeline),this.computePassEncoder.setBindGroup(0,u),this.computePassEncoder.dispatchWorkgroups(e.dispatch[0],e.dispatch[1],e.dispatch[2]),this.dispatchCountInPass++,(c||A().get(\"WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE\")<=this.dispatchCountInPass||e.pixelsOpType===wi.DRAW)&&(this.endComputePassEncoder(),c?this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime()}):this.submitQueue())}async getQueryTime(){if(!this.supportTimestampQuery)return 0;this.queryResolveBuffer==null&&(this.queryResolveBuffer=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST|GPUBufferUsage.QUERY_RESOLVE)),this.commandEncoder.resolveQuerySet(this.querySet,0,this.querySetCount,this.queryResolveBuffer,0);let e=this.bufferManager.acquireBuffer(this.querySetCount*8,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.commandEncoder.copyBufferToBuffer(this.queryResolveBuffer,0,e,0,this.querySetCount*8),this.submitQueue(),await e.mapAsync(GPUMapMode.READ);let t=new BigUint64Array(e.getMappedRange()),o=Number(t[1]-t[0])/1e6;return e.unmap(),this.bufferManager.releaseBuffer(e),o}shouldExecuteOnCPU(e,t=wae){return A().getBool(\"WEBGPU_CPU_FORWARD\")&&e.every(o=>this.tensorMap.get(o.dataId).resource==null&&y.sizeFromShape(o.shape){let r={powerPreference:A().get(\"WEBGPU_USE_LOW_POWER_GPU\")?\"low-power\":\"high-performance\"},e=await navigator.gpu.requestAdapter(r),t={},o=[];e.features.has(\"timestamp-query\")&&o.push(\"timestamp-query\"),e.features.has(\"bgra8unorm-storage\")&&o.push([\"bgra8unorm-storage\"]),t.requiredFeatures=o;let n=e.limits;t.requiredLimits={maxComputeWorkgroupStorageSize:n.maxComputeWorkgroupStorageSize,maxComputeWorkgroupsPerDimension:n.maxComputeWorkgroupsPerDimension,maxStorageBufferBindingSize:n.maxStorageBufferBindingSize,maxBufferSize:n.maxBufferSize,maxComputeWorkgroupSizeX:n.maxComputeWorkgroupSizeX,maxComputeInvocationsPerWorkgroup:n.maxComputeInvocationsPerWorkgroup};let s=await e.requestDevice(t),a=await e.requestAdapterInfo();return new jc(s,a)},3);var fe;(function(r){r[r.ADD=0]=\"ADD\",r[r.ATAN2=1]=\"ATAN2\",r[r.COMPLEX_MULTIPLY_IMAG=2]=\"COMPLEX_MULTIPLY_IMAG\",r[r.COMPLEX_MULTIPLY_REAL=3]=\"COMPLEX_MULTIPLY_REAL\",r[r.DIV=4]=\"DIV\",r[r.ELU_DER=5]=\"ELU_DER\",r[r.EQUAL=6]=\"EQUAL\",r[r.FLOOR_DIV=7]=\"FLOOR_DIV\",r[r.GREATER=8]=\"GREATER\",r[r.GREATER_EQUAL=9]=\"GREATER_EQUAL\",r[r.LESS=10]=\"LESS\",r[r.LESS_EQUAL=11]=\"LESS_EQUAL\",r[r.LOGICAL_AND=12]=\"LOGICAL_AND\",r[r.LOGICAL_OR=13]=\"LOGICAL_OR\",r[r.MAX=14]=\"MAX\",r[r.MIN=15]=\"MIN\",r[r.MOD=16]=\"MOD\",r[r.MUL=17]=\"MUL\",r[r.NOT_EQUAL=18]=\"NOT_EQUAL\",r[r.POW=19]=\"POW\",r[r.PRELU=20]=\"PRELU\",r[r.SQUARED_DIFFERENCE=21]=\"SQUARED_DIFFERENCE\",r[r.SUB=22]=\"SUB\"})(fe||(fe={}));var Iae=\"let resultTemp = a + b;\",vae=\"let resultTemp = atan2(a, b);\",kae=\"let resultTemp = areal * breal - aimag * bimag;\",Nae=\"let resultTemp = areal * bimag + aimag * breal;\",Tae=\"let resultTemp = a / b;\",_ae=\"let resultTemp = select(a * (b + 1.0), a, b >= b - b);\",Eae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a == b);\n`,$ae=`\n let remainder =\n select(a % b, round(a % b), (round(a) == a) & (round(b) == b));\n let quotient = (a - remainder) / b;\n let resultTemp =\n round(select(quotient, quotient - 1, sign(remainder) == -sign(b)));\n`,Rae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a > b);\n`,Dae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a >= b);\n`,Aae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a < b);\n`,Fae=`\n let zero = sign(a) * 0 + 0;\n let one = sign(b) * 0 + 1;\n let resultTemp = select(zero, one, a <= b);\n`,Pae=\"return f32(a >= 1.0 && b >= 1.0);\",Oae=`return (vec4(a >= vec4(1.0)) *\n vec4(b >= vec4(1.0)));`,Mae=\"return f32(a >= 1.0 || b >= 1.0);\",Lae=`return min(vec4(a >= vec4(1.0)) +\n vec4(b >= vec4(1.0)), vec4(1.0));`,Bae=\"let resultTemp = max(a, b);\",zae=\"let resultTemp = min(a, b);\",Vae=`\n let isNaN = b == 0.;\n var resultTemp = a % b;\n resultTemp = select((resultTemp + b) % b, resultTemp,\n (a < 0. && b < 0.) || (a >= 0. && b > 0.));\n`,Wae=`\n let isNaN = !vec4(b);\n var resultTemp = vec4(a % b);\n if (!((a[0] < 0. && b[0] < 0.) || (a[0] >= 0. && b[0] > 0.))) {\n resultTemp[0] = (resultTemp[0] + b[0]) % b[0];\n }\n if (!((a[1] < 0. && b[1] < 0.) || (a[1] >= 0. && b[1] > 0.))) {\n resultTemp[1] = (resultTemp[1] + b[1]) % b[1];\n }\n if (!((a[2] < 0. && b[2] < 0.) || (a[2] >= 0. && b[2] > 0.))) {\n resultTemp[2] = (resultTemp[2] + b[2]) % b[2];\n }\n if (!((a[3] < 0. && b[3] < 0.) || (a[3] >= 0. && b[3] > 0.))) {\n resultTemp[3] = (resultTemp[3] + b[3]) % b[3];\n }\n`,Uae=\"let resultTemp = a * b;\",Gae=`\n var resultTemp = f32(a != b);\n let valueForNaN = 1.0;\n`,Hae=`\n var resultTemp = vec4(a != b);\n let valueForNaN = 1.0;\n`,Kae=`\n let isNaN = a < 0.0 && floor(b) < b;\n if (b == 0.0) {\n return 1.0;\n }\n var resultTemp = select(sign(a) * pow(abs(a), b), pow(abs(a), b),\n round(abs(b) % 2.0) != 1.0);\n`,qae=`\n let isModRound1Bool = vec4(round(abs(b) % vec4(2.0))) == vec4(1);\n let isModRound1 = vec4(isModRound1Bool);\n let multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);\n var resultTemp = multiplier * pow(abs(a), b);\n\n // Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS\n let isExpZero = b == vec4(0.0);\n if (isExpZero.r) {\n resultTemp.r = 1.0;\n }\n if (isExpZero.g) {\n resultTemp.g = 1.0;\n }\n if (isExpZero.b) {\n resultTemp.b = 1.0;\n }\n if (isExpZero.a) {\n resultTemp.a = 1.0;\n }\n let isNaN = (a < vec4(0.0)) & (floor(b) < b);\n`,jae=\"if (a < 0.0) { return b * a; } return a;\",Xae=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,Yae=\"let resultTemp = (a - b) * (a - b);\",Qae=\"let resultTemp = a - b;\";function Xc(r,e){let t;do{switch(r){case fe.ATAN2:t=vae;break;case fe.MAX:t=Bae;break;case fe.MIN:t=zae;break;case fe.MOD:t=e?Wae:Vae;break;case fe.NOT_EQUAL:t=e?Hae:Gae;break;case fe.POW:t=e?qae:Kae;break;default:continue}let o,n,s;return e?(o=\"isnanVec4\",n=\"vec4\",s=\"vec4\"):(o=\"isnan\",n=\"f32\",s=\"bool\"),`\n let aIsNaN = ${o}(a);\n let aPostLegalization = select(a, ${n}(42), aIsNaN);\n let bIsNaN = ${o}(b);\n let bPostLegalization = select(b, ${n}(42), bIsNaN);\n let isNaN = false;\n let valueForNaN = uniforms.NAN;\n {\n let a = aPostLegalization;\n let b = bPostLegalization;\n ${t}\n return select(\n resultTemp, ${n}(valueForNaN),\n ${s}(isNaN) | aIsNaN | bIsNaN);\n }\n `}while(!1);switch(r){case fe.ADD:t=Iae;break;case fe.COMPLEX_MULTIPLY_IMAG:t=Nae;break;case fe.COMPLEX_MULTIPLY_REAL:t=kae;break;case fe.DIV:t=Tae;break;case fe.ELU_DER:t=_ae;break;case fe.EQUAL:t=Eae;break;case fe.FLOOR_DIV:t=$ae;break;case fe.GREATER:t=Rae;break;case fe.GREATER_EQUAL:t=Dae;break;case fe.LESS:t=Aae;break;case fe.LESS_EQUAL:t=Fae;break;case fe.LOGICAL_AND:return e?Oae:Pae;case fe.LOGICAL_OR:return e?Lae:Mae;case fe.MUL:t=Uae;break;case fe.PRELU:return e?Xae:jae;case fe.SQUARED_DIFFERENCE:t=Yae;break;case fe.SUB:t=Qae;break;default:}return`\n ${t}\n return resultTemp;\n `}var Z;(function(r){r[r.ABS=0]=\"ABS\",r[r.ACOS=1]=\"ACOS\",r[r.ACOSH=2]=\"ACOSH\",r[r.ASIN=3]=\"ASIN\",r[r.ASINH=4]=\"ASINH\",r[r.ATAN=5]=\"ATAN\",r[r.ATANH=6]=\"ATANH\",r[r.CEIL=7]=\"CEIL\",r[r.COS=8]=\"COS\",r[r.COSH=9]=\"COSH\",r[r.ELU=10]=\"ELU\",r[r.ERF=11]=\"ERF\",r[r.EXP=12]=\"EXP\",r[r.EXPM1=13]=\"EXPM1\",r[r.FLOOR=14]=\"FLOOR\",r[r.IS_FINITE=15]=\"IS_FINITE\",r[r.IS_INF=16]=\"IS_INF\",r[r.IS_NAN=17]=\"IS_NAN\",r[r.LINEAR=18]=\"LINEAR\",r[r.LOG=19]=\"LOG\",r[r.LOG1P=20]=\"LOG1P\",r[r.LOGICAL_NOT=21]=\"LOGICAL_NOT\",r[r.NEG=22]=\"NEG\",r[r.RELU=23]=\"RELU\",r[r.RELU6=24]=\"RELU6\",r[r.LEAKYRELU=25]=\"LEAKYRELU\",r[r.RECIPROCAL=26]=\"RECIPROCAL\",r[r.ROUND=27]=\"ROUND\",r[r.RSQRT=28]=\"RSQRT\",r[r.SELU=29]=\"SELU\",r[r.SIGMOID=30]=\"SIGMOID\",r[r.SIGN=31]=\"SIGN\",r[r.SIN=32]=\"SIN\",r[r.SINH=33]=\"SINH\",r[r.SOFTPLUS=34]=\"SOFTPLUS\",r[r.SQRT=35]=\"SQRT\",r[r.SQUARE=36]=\"SQUARE\",r[r.STEP=37]=\"STEP\",r[r.TAN=38]=\"TAN\",r[r.TANH=39]=\"TANH\",r[r.TO_INT=40]=\"TO_INT\"})(Z||(Z={}));var Zae=\"return abs(a);\",Jae=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return acos(a);\n`,eie=`\n if (a < 1.) {\n return uniforms.NAN;\n }\n return acosh(a);\n`,tie=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n return asin(a);\n`,rie=\"return asinh(a);\",oie=`\n if (isnan(a)) {\n return uniforms.NAN;\n }\n return atan(a);\n`,nie=`\n if (abs(a) > 1.) {\n return uniforms.NAN;\n }\n if (a == 1.) {\n return uniforms.INFINITY;\n }\n if (a == -1.) {\n return -uniforms.INFINITY;\n }\n return atanh(a);\n`,sie=\"return ceil(a);\",aie=\"return cos(a);\",iie=`\n let e2x = exp(-a);\n return (e2x + 1.0 / e2x) / 2.0;\n`,uie=\"return exp(a) - 1.0;\",pie=\"if (a >= 0.0) { return a; } return (exp(a) - 1.0);\",cie=`\n var resFloat = exp(a) - vec4(1.0);\n if (a.r >= 0.0) {\n resFloat.r = a.r;\n }\n if (a.g >= 0.0) {\n resFloat.g = a.g;\n }\n if (a.b >= 0.0) {\n resFloat.b = a.b;\n }\n if (a.a >= 0.0) {\n resFloat.a = a.a;\n }\n return resFloat;\n`,lie=`\n // Error function is calculated approximately with elementary function.\n // See \"Handbook of Mathematical Functions with Formulas,\n // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n let p = ${w.ERF_P};\n let a1 = ${w.ERF_A1};\n let a2 = ${w.ERF_A2};\n let a3 = ${w.ERF_A3};\n let a4 = ${w.ERF_A4};\n let a5 = ${w.ERF_A5};\n\n let sign = sign(a);\n let absA = abs(a);\n let t = 1.0 / (1.0 + p * absA);\n return sign * (1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * exp(-absA * absA));\n`,mie=\"return exp(a);\",die=\"return floor(a);\",fie=\"return f32(!isnan(a) && !isinf(a));\",hie=\"return f32(isinf(a));\",gie=\"return f32(isnan(a));\",xie=\"return a;\",yie=`if (a < 0.0) { return uniforms.NAN; }\n return log(a);`,bie=`\n if (isnan(a)) { return a; }\n return log(1.0 + a);\n`,Cie=\"return f32(!(a >= 1.0));\",wie=\"return -a;\",Sie=\"if (a < 0.0) { return uniforms.alpha * a; } return a;\",Iie=`\n let aLessThanZero = vec4(a < vec4(0.0));\n return (aLessThanZero * (uniforms.alpha * a)) + ((vec4(1.0) - aLessThanZero) * a);\n`,vie=\"return 1.0 / a;\",kie=\"return select(a, 0.0, a < 0.0);\",Nie=\"return clamp(a, 0.0, 6.0);\",Tie=\"return clamp(a, vec4(0.0, 0.0, 0.0, 0.0), vec4(6.0, 6.0, 6.0, 6.0));\",_ie=`\n return select(a, vec4(0.0), a < vec4(0.0));\n`,Eie=\"return round(a);\",$ie=\"return inverseSqrt(a);\",Rie=`\n if (a >= 0.0) {\n return ${w.SELU_SCALE} * a;\n } else {\n return ${w.SELU_SCALEALPHA} * (exp(a) - 1.0);\n }\n`,Die=\"return 1.0 / (1.0 + exp(-1.0 * a));\",Aie=\"return sign(a);\",Fie=\"return sin(a);\",Pie=`\n let e2x = exp(a);\n return (e2x - 1.0 / e2x) / 2.0;\n`,Oie=`\n let epsilon = 1.1920928955078125e-7;\n let threshold = log(epsilon) + 2.0;\n\n let too_large = a > -threshold;\n let too_small = a < threshold;\n let exp_a = exp(a);\n\n if (too_large) {\n return a;\n } else if (too_small) {\n return exp_a;\n } else {\n return log(exp_a + 1.0);\n }\n`,Mie=\"return sqrt(a);\",Lie=\"return a * a;\",Bie=`\n if (isnan(a)) {\n return a;\n }\n\n return select(uniforms.stepAlpha, 1.0, a > 0.0);\n`,zie=\"return tan(a);\",Vie=`\n let e2x = exp(-2.0 * abs(a));\n return sign(a) * (1.0 - e2x) / (1.0 + e2x);\n`,Wie=\"return f32(i32((a)));\";function Si(r,e){switch(r){case Z.ABS:return Zae;case Z.ACOS:return Jae;case Z.ACOSH:return eie;case Z.ASIN:return tie;case Z.ASINH:return rie;case Z.ATAN:return oie;case Z.ATANH:return nie;case Z.COS:return aie;case Z.COSH:return iie;case Z.CEIL:return sie;case Z.ELU:return e?cie:pie;case Z.ERF:return lie;case Z.EXP:return mie;case Z.EXPM1:return uie;case Z.FLOOR:return die;case Z.IS_FINITE:return fie;case Z.IS_INF:return hie;case Z.IS_NAN:return gie;case Z.LINEAR:return xie;case Z.LOG:return yie;case Z.LOG1P:return bie;case Z.LOGICAL_NOT:return Cie;case Z.NEG:return wie;case Z.LEAKYRELU:return e?Iie:Sie;case Z.RECIPROCAL:return vie;case Z.RELU:return e?_ie:kie;case Z.RELU6:return e?Tie:Nie;case Z.ROUND:return Eie;case Z.RSQRT:return $ie;case Z.SELU:return Rie;case Z.SIGMOID:return Die;case Z.SIGN:return Aie;case Z.SIN:return Fie;case Z.SINH:return Pie;case Z.SOFTPLUS:return Oie;case Z.SQRT:return Mie;case Z.SQUARE:return Lie;case Z.STEP:return Bie;case Z.TAN:return zie;case Z.TANH:return Vie;case Z.TO_INT:return Wie;default:throw new Error(`BinaryType ${r} is not implemented!`)}}function dr(r,e=!1,t=!1,o=3){if(r===null)return\"\";let n=\"\";if(r===\"linear\")n=Si(Z.LINEAR);else if(r===\"relu\")n=Si(Z.RELU,t);else if(r===\"elu\")n=Si(Z.ELU,t);else if(r===\"relu6\")n=Si(Z.RELU6,t);else if(r===\"prelu\")n=Xc(fe.PRELU,t);else if(r===\"sigmoid\")n=Si(Z.SIGMOID,t);else if(r===\"leakyrelu\")n=Si(Z.LEAKYRELU,t);else throw new Error(`Activation ${r} has not been implemented for the WebGPU backend.`);let a=Ae(t?4:1),i=\"\";return e?i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n let b = getPreluActivationWeightsByOutputCoords(coords);\n ${n}\n }`:i=`\n fn activation(a : ${a}, coords : vec${o}) -> ${a} {\n ${n}\n }`,i}function Zr(r,e){return`\n ${r?\"value = value + getBiasByOutputCoords(coords);\":\"\"}\n ${e?\"value = activation(value, coords);\":\"\"}\n `}function Jv(r,e,t=!1,o=!1,n=!1,s=1){y.assert(r&&s===1||!r,()=>`transposeA ${r} is not compatible with component size ${s}`);let a=`\n ${r?\"value = getA(batch, col, row);\":\"value = getA(batch, row, col);\"}\n\n `,i=e?\"value = getB(batch, col, row);\":\"value = getB(batch, row, col);\";return`\n fn mm_readA(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${t&&n?a:`\n ${r?\"if(row < uniforms.dimAOuter && col < uniforms.dimInner)\":\"if(row < uniforms.aShape[1] && col < uniforms.aShape[2])\"}\n {\n ${a}\n }\n `}\n return value;\n }\n\n fn mm_readB(batch: i32, row: i32, col: i32) -> ${Ae(s)} {\n var value = ${Ae(s)}(0.0);\n ${i}\n return value;\n }\n `}function hm(r,e,t,o,n=!1,s=!1,a=!1,i=1){return`\n ${Jv(t,o,n,s,a,i)}\n fn mm_write(batch: i32, row: i32, col: i32, valueIn: ${Ae(i)}) {\n ${n&&s?\"\":\"if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\"}\n {\n var value = valueIn;\n let coords = vec3(batch, row, col);\n ${Zr(r,e)}\n setOutputAtCoords(coords[0], coords[1], coords[2], value);\n }\n }\n `}var Uie=(r,e)=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol * ${e});\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRow + innerRow,\n kStart + inputCol * ${e});\n `,Gie=(r,e,t,o)=>{if(r)return`\n for (var k = 0; k < ${o}; k++) {\n let BCached0 = mm_Bsub[k][tileCol];\n let ACached0 = mm_Asub[k][localRow];\n for (var i = 0; i < ${t}; i++) {\n acc[i] = fma(BCached0, vec4(ACached0[i]), acc[i]);\n }\n }`;{let n=\"\",s=\"\";for(let a=0;a(ACached[${a}]), acc[i]);`;return`\n for (var k = 0; k < ${o/e}; k++) {\n ${n}\n for (var i = 0; i < ${t}; i++) {\n let ACached = mm_Asub[tileRow + i][k];\n ${s}\n }\n }`}};function _p(r,e,t=!1,o=32,n=!1,s=32,a=!1){let i=e[1]*r[1],p=e[0]*r[0],u=t?i:o,c=t?o:i,l=u/e[0],m=o/e[1],d=r[1],f=r[0];return y.assert((t&&l===4&&r[1]===4||!t&&(l===3||l===4))&&u%e[0]===0&&o%e[1]===0&&r[0]===4,()=>`If transposeA ${t} is true, innerElementSize ${l} and workPerThread[1] ${r[1]} must be 4.\n Otherwise, innerElementSize ${l} must be 3 or 4.\n tileAWidth ${u} must be divisible by workgroupSize[0]${e[0]}. tileInner ${o} must be divisible by workgroupSize[1] ${e[1]}. colPerThread ${r[0]} must be 4.`),`\n var mm_Asub : array, ${u/l}>, ${c}>;\n var mm_Bsub : array, ${p/r[0]}>, ${o}>;\n\n ${G()} {\n let localRow = i32(localId.y);\n let tileRow = localRow * ${d};\n let tileCol = i32(localId.x);\n\n let globalRow = i32(globalId.y) * ${d};\n let globalCol = i32(globalId.x) * ${f};\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!a?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!a?\"batch\":\"batch % uniforms.bShape[0]\"};\n let globalRowStart = i32(workgroupId.y) * ${i};\n\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc: array, ${d}>;\n\n // Loop over shared dimension.\n let tileRowB = localRow * ${m};\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n let inputRow = tileRow + innerRow;\n let inputCol = tileCol;\n ${Uie(t,l)}\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB, kStart + inputRow, globalCol);\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n ${Gie(t,l,d,o)}\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${d}; innerRow++) {\n mm_write(batch, globalRow + innerRow, globalCol, acc[innerRow]);\n }\n }`}var az=r=>r?`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n kStart + inputRow,\n globalRowStart + inputCol);\n `:`\n mm_Asub[inputRow][inputCol] = mm_readA(batchA,\n globalRowStart + inputRow,\n kStart + inputCol);\n `,Hie=r=>r?\"let ACached = mm_Asub[k][tileRow + innerRow];\":\"let ACached = mm_Asub[tileRow + innerRow][k];\";function Ep(r,e,t=!1,o=32,n=!1,s=32,a=!1,i=!1){let p=r[1]*e[1],u=r[0]*e[0],c=t?p:o,l=t?o:p;y.assert(l%e[1]===0&&c%e[0]===0&&o%e[1]===0,()=>`tileAHight ${l} must be divisible by workgroupSize[1]${e[1]}, tileAWidth ${c} must be divisible by workgroupSize[0]${e[0]}, tileInner ${o} must be divisible by workgroupSize[1]${e[1]}`);let m=l/e[1],d=c/e[0],f=o/e[1],h=r[1],g=r[0],x=a?`\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n let globalRowStart = i32(workgroupId.y) * ${p};\n let globalColStart = i32(workgroupId.x) * ${u};\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var inputRow = localRow; inputRow < ${l}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${c}; inputCol = inputCol + ${e[0]}) {\n ${az(t)}\n }\n }\n // Load one tile of B into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${e[1]}) {\n for (var inputCol = localCol; inputCol < ${u}; inputCol = inputCol + ${e[0]}) {\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalColStart + inputCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][localCol + inner * ${e[0]}];\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let ACached = ${t?`mm_Asub[k][localRow + innerRow * ${e[1]}];`:`mm_Asub[localRow + innerRow * ${e[1]}][k];`}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n workgroupBarrier();\n }\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n let gRow = globalRowStart + localRow + innerRow * ${e[1]};\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let gCol = globalColStart + localCol + innerCol * ${e[0]};\n mm_write(batch, gRow, gCol, acc[innerRow][innerCol]);\n }\n }\n `:`\n let tileRow = i32(localId.y) * ${h};\n let tileCol = i32(localId.x) * ${g};\n\n let globalRow = i32(globalId.y) * ${h};\n let globalCol = i32(globalId.x) * ${g};\n let globalRowStart = i32(workgroupId.y) * ${p};\n\n let tileRowA = i32(localId.y) * ${m};\n let tileColA = i32(localId.x) * ${d};\n let tileRowB = i32(localId.y) * ${f};\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n for (var innerRow = 0; innerRow < ${m}; innerRow++) {\n for (var innerCol = 0; innerCol < ${d}; innerCol++) {\n let inputRow = tileRowA + innerRow;\n let inputCol = tileColA + innerCol;\n ${az(t)}\n }\n }\n\n // Load one tile of B into local memory.\n for (var innerRow = 0; innerRow < ${f}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n let inputRow = tileRowB + innerRow;\n let inputCol = tileCol + innerCol;\n mm_Bsub[inputRow][inputCol] = mm_readB(batchB,\n kStart + inputRow,\n globalCol + innerCol);\n }\n }\n kStart = kStart + ${o};\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n var BCached : array;\n for (var k = 0; k < ${o}; k++) {\n for (var inner = 0; inner < ${g}; inner++) {\n BCached[inner] = mm_Bsub[k][tileCol + inner];\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n ${Hie(t)}\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] =\n fma(ACached, BCached[innerCol], acc[innerRow][innerCol]);\n }\n }\n }\n\n workgroupBarrier();\n }\n\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n mm_write(batch, globalRow + innerRow, globalCol + innerCol,\n acc[innerRow][innerCol]);\n }\n }\n `;return`\n var mm_Asub : array, ${l}>;\n var mm_Bsub : array, ${o}>;\n\n ${G()} {\n let batch = ${n?\"0\":\"i32(globalId.z)\"};\n let batchA = ${n||!i?\"batch\":\"batch % uniforms.aShape[0]\"};\n let batchB = ${n||!i?\"batch\":\"batch % uniforms.bShape[0]\"};\n let numTiles = ${n?`${Math.ceil(s/o)}`:`(uniforms.dimInner - 1) / ${o} + 1`};\n var kStart = ${n?`i32(globalId.z) * ${s}`:\"0\"};\n\n var acc : array, ${h}>;\n\n // Without this initialization strange values show up in acc.\n for (var innerRow = 0; innerRow < ${h}; innerRow++) {\n for (var innerCol = 0; innerCol < ${g}; innerCol++) {\n acc[innerRow][innerCol] = 0.0;\n }\n }\n ${x}\n }\n `}var Kie=r=>r?`\n mm_readA(batchA, colA, globalRow),\n mm_readA(batchA, colA + 1, globalRow),\n mm_readA(batchA, colA + 2, globalRow),\n mm_readA(batchA, colA + 3, globalRow)\n `:`\n mm_readA(batchA, globalRow, colA),\n mm_readA(batchA, globalRow, colA + 1),\n mm_readA(batchA, globalRow, colA + 2),\n mm_readA(batchA, globalRow, colA + 3)\n `;function qie(r,e=!1){y.assert(r[1]===1&&r[2]===1,()=>`A linear work group size is required. But got ${r}.`);let t=r[0]*4;return`\n var mm_Asub : array, ${r[0]}>;\n\n ${G()} {\n let tileCol = i32(localId.x);\n let globalCol = i32(globalId.x);\n let globalRow = i32(globalId.y);\n\n let numTiles = (uniforms.dimInner - 1) / ${t} + 1;\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n // Without this initialization strange values show up in acc.\n var acc = 0.0;\n\n // Loop over shared dimension.\n for (var t = 0; t < numTiles; t++) {\n // Load one tile of A into local memory.\n let colA = t * ${t} + tileCol * 4;\n mm_Asub[tileCol] = vec4(${Kie(e)});\n workgroupBarrier();\n\n // Compute acc values for a single thread.\n for (var k = 0; k < ${t/4}; k++) {\n let rowB = t * ${t} + k * 4;\n let BCached = vec4(mm_readB(batchB, rowB, globalCol),\n mm_readB(batchB, rowB + 1, globalCol),\n mm_readB(batchB, rowB + 2, globalCol),\n mm_readB(batchB, rowB + 3, globalCol));\n\n let ACached = mm_Asub[k];\n acc = acc + dot(ACached, BCached);\n }\n\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Xg=class{constructor(e,t,o=!1,n=!1,s=null,a=null,i=null,p=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let u=o?e[1]:e[2];if(this.isVec4=(u%4===0&&!o||t[1]%4===0&&o)&&t[2]%4===0&&!n,this.outputComponent=this.isVec4?4:1,this.isVectorA=t[1]===1&&!o,!this.isVec4&&this.isVectorA)this.elementsPerThread=[1,1,1],this.workgroupSize=[32,1,1];else{let m=Qv(t[1],u,t[2],o);this.workgroupSize=m.workgroupSize,this.elementsPerThread=m.elementsPerThread}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread);let c=s!=null,l=i!=null;c&&this.variableNames.push(\"bias\"),l&&this.variableNames.push(\"preluActivationWeights\"),this.sequentialAccessByThreads=p,this.transposeA=o,this.transposeB=n,this.addBias=c,this.activation=a,this.hasPreluActivationWeights=l,[this.fitAOuter,this.fitBOuter,this.fitInner]=this.getShapeFit(t[1],t[2],u),this.shaderKey=`matMulPacked_${this.elementsPerThread}_${o}_${n}_${this.activation}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.isVectorA}_${this.sequentialAccessByThreads}`}getShapeFit(e,t,o){let n=this.workgroupSize[1]*this.elementsPerThread[1],s=this.workgroupSize[0]*this.elementsPerThread[0];!this.isVec4&&this.isVectorA?this.tileInner=this.workgroupSize[0]*4:this.tileInner=s;let a=e%n===0,i=t%s===0,p=o%this.tileInner===0;return[a,i,p]}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,this.isVec4)}\n ${hm(this.addBias,this.activation,!1,this.transposeB,this.fitAOuter,this.fitBOuter,this.fitInner,this.isVec4?4:1)}\n ${this.isVec4?_p(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,!0):this.isVectorA?qie(this.workgroupSize,this.transposeA):Ep(this.elementsPerThread,this.workgroupSize,this.transposeA,this.tileInner,!1,null,this.sequentialAccessByThreads,!0)}\n `}};function jie(r){return`\n var sumValues : array;\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n let row = coords[1];\n let col = coords[2];\n var sum = 0.0;\n let Length = uniforms.dimInner;\n for (var k = i32(localId.x); k < Length; k = k + ${r}) {\n let dataA = mm_readA(batchA, row, k);\n let dataB = mm_readB(batchB, k, col);\n sum = sum + dataA * dataB;\n }\n sumValues[localId.x] = sum;\n workgroupBarrier();\n\n for(var currentSize = ${r/2}u; currentSize > 1u;\n currentSize = currentSize / 2u) {\n if (localId.x < currentSize)\n {\n sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];\n }\n workgroupBarrier();\n }\n\n if (localId.x == 0u) {\n sum = sumValues[0] + sumValues[1];\n mm_write(batch, row, col, sum);\n }\n }\n `}var Yg=class{constructor(e,t=!1,o=!1,n=null,s=null,a=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize);let i=n!=null,p=a!=null;i&&this.variableNames.push(\"bias\"),p&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=t,this.transposeB=o,this.addBias=i,this.activation=s,this.hasPreluActivationWeights=p,this.shaderKey=`matMulReduce_${this.activation}_${t}_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${jie(this.workgroupSize[0])}\n `}};function Xie(r){let e=r[1],t=r[0],o=e>t?e:t;return`\n var mm_Asub : array, ${e}>;\n var mm_Bsub : array, ${o}>;\n\n // If the output size is small for matrix multiplication, avoid to use vec4\n // and handle some elements per thread to optimally utilize the ALU.\n // Read data from global memory to registers firstly, then store them into\n // shared memory, so it is instruction-Level parallelism for arithmetic\n // operations and others handle IO operations between barrier api, makes ALU\n // and load/store units work simultaneously, could improves the performance.\n ${G()} {\n let tileRow = i32(localId.y);\n let tileCol = i32(localId.x);\n let globalRow = i32(globalId.y);\n let globalCol = i32(globalId.x);\n let batch = i32(globalId.z);\n let batchA = batch % uniforms.aShape[0];\n let batchB = batch % uniforms.bShape[0];\n\n // uniforms.dimInner should be greater than 0.\n let numTiles = (uniforms.dimInner - 1) / ${o} + 1;\n var acc = 0.0;\n\n var globalColA = tileCol;\n var globalRowB = 0;\n var regA = mm_readA(batchA, globalRow, globalColA);\n var regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n var regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var t = 0; t < numTiles; t = t + 1) {\n mm_Asub[tileRow][tileCol] = regA;\n mm_Bsub[2 * tileRow][tileCol] = regB0;\n mm_Bsub[2 * tileRow + 1][tileCol] = regB1;\n\n workgroupBarrier();\n\n regA = mm_readA(batchA, globalRow, globalColA);\n regB0 = mm_readB(batchB, globalRowB + 2 * tileRow, globalCol);\n regB1 = mm_readB(batchB, globalRowB + 2 * tileRow + 1, globalCol);\n globalColA = globalColA + ${o};\n globalRowB = globalRowB + ${o};\n\n for (var k = 0; k < ${o}; k = k + 1) {\n acc = acc + mm_Asub[tileRow][k] * mm_Bsub[k][tileCol];\n }\n workgroupBarrier();\n }\n\n mm_write(batch, globalRow, globalCol, acc);\n }\n `}var Qg=class{constructor(e,t,o,n=!1,s=!1,a=null,i=null,p=null){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[16,8,1],this.outputShape=o,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(o[2]/this.workgroupSize[0]),Math.ceil(o[1]/this.workgroupSize[1]),o[0]];let u=a!=null;u&&this.variableNames.push(\"bias\");let c=p!=null;c&&this.variableNames.push(\"preluActivationWeights\"),this.transposeA=n,this.transposeB=s,this.addBias=u,this.activation=i,this.hasPreluActivationWeights=c,this.shaderKey=`matMulSmallOutputSize_${this.activation}_${n}_${s}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${hm(this.addBias,this.activation,this.transposeA,this.transposeB)}\n ${Xie(this.workgroupSize)}\n `}};var Zg=class{constructor(e,t,o=!1,n=!1){this.variableNames=[\"A\",\"B\"],this.uniforms=\"dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.workgroupSize=[8,8,1],this.atomic=!0,this.splitedDimInner=128,y.assert(e[0]===1,()=>\"MatMulSplitKProgram only supports batch = 1.\"),this.outputShape=e,this.dispatchLayout={x:[2],y:[1],z:[0,3]};let s=(o&&this.outputShape[1]%4===0||!o&&t%4===0)&&this.outputShape[2]%4===0;this.elementsPerThread=[4,4,this.splitedDimInner],this.outputComponent=s?4:1,s||(this.outputShape[1]<16&&(this.elementsPerThread[1]=1),this.outputShape[2]<16&&(this.elementsPerThread[0]=1)),this.dispatch=H(this.dispatchLayout,[this.outputShape[0],this.outputShape[1],this.outputShape[2],t],this.workgroupSize,this.elementsPerThread),this.transposeA=o,this.transposeB=n,this.shaderKey=`matMulSplitK_${o}_${n}_${this.elementsPerThread}_${this.outputComponent}`}getUserCode(){let e=this.outputComponent;return`\n ${Jv(!1,this.transposeB,!1,!1,!1,e)}\n fn mm_write(batch: i32, row : i32, col : i32, value : ${Ae(e)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n let coords = vec3(batch, row, col);\n let flatIndex = getOutputIndexFromCoords(coords);\n // The problem is that we should initialize output to zero before using.\n // Otherwise, the original value will be added to the result.\n for (var i = 0; i < ${e}; i = i + 1) {\n ${Qr(\"&result[flatIndex + i]\",`${e>1?\"value[i]\":\"value\"}`,\"float32\")}\n }\n }\n }\n ${e===4?_p(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner):Ep(this.elementsPerThread,this.workgroupSize,this.transposeA,32,!0,this.splitedDimInner)}\n `}},Jg=class{constructor(e,t=null,o=null,n=null){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t!=null,this.hasPreluActivationWeights=n!=null,this.activation=o,this.addBias&&this.variableNames.push(\"bias\"),this.hasPreluActivationWeights&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`biasActivation_${o}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights)}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var value = getXByOutputIndex(index);\n ${Zr(this.addBias,this.activation)}\n setOutputAtIndex(index, value);\n }\n }\n `}};var ex=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"value : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"fill\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.value);\n }\n }\n `}};function vt(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||y.inferDtype(n),s===\"string\"){let a=y.getArrayFromDType(s,y.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new ex(o),i=[{type:\"float32\",data:[n]}];return e.runWebGPUProgram(a,[],s,i)}}var iz={kernelName:sa,backendName:\"webgpu\",kernelFunc:vt};function pe(r){let{inputs:e,attrs:t}=r,{x:o}=e,{shape:n}=t,s=y.sizeFromShape(o.shape),a=y.inferFromImplicitShape(n,s),i=y.sizeFromShape(a);return y.assert(s===i,()=>`The new shape (${a}) has ${i} elements and the old shape (${o.shape}) has ${s} elements. The new shape and old shape must have the same number of elements.`),r.backend.incRef(o.dataId),{dataId:o.dataId,shape:a,dtype:o.dtype}}var uz={kernelName:da,backendName:\"webgpu\",kernelFunc:pe};function $p({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:p=null}){let u=r.shape.length,c=e.shape.length,l=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],d=t?r.shape[u-1]:r.shape[u-2],f=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),x=y.sizeFromShape(h),b=y.sizeFromShape(g),S=Sr.assertAndGetBroadcastShape(r.shape.slice(0,-2),e.shape.slice(0,-2)).concat([d,f]);y.assert(l===m,()=>`Error in matMul: inner shapes (${l}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let k=t?[x,l,d]:[x,d,l],_=o?[b,f,m]:[b,m,f],$=pe({inputs:{x:r},backend:n,attrs:{shape:k}}),R=pe({inputs:{x:e},backend:n,attrs:{shape:_}}),D=[$,R],P=Math.max(x,b),O=[$,R],M=[{type:\"int32\",data:[d]},{type:\"int32\",data:[f]},{type:\"int32\",data:[l]}],L,B,z=[P,d,f],U=A().get(\"WEBGPU_MATMUL_PROGRAM_TYPE\");if(U<0){let q=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),Y=q>0?q:n.thresholdToIncreaseWorkgroups,J=P*Math.ceil(d/32)*Math.ceil(f/32);J<=Y||d<=8&&J<=Y*2?P*d*f<=128?U=Mo.MatMulReduceProgram:P===1&&m>=2e3?U=Mo.MatMulSplitKProgram:U=Mo.MatMulSmallOutputSizeProgram:U=Mo.MatMulPackedProgram}switch(U){case Mo.MatMulReduceProgram:L=new Yg(z,t,o,s,p,a);break;case Mo.MatMulSplitKProgram:{if(B=vt({backend:n,attrs:{shape:z,value:0,dtype:r.dtype}}),L=new Zg(z,m,t,o),s||p){B=n.runWebGPUProgram(L,O,r.dtype,M,B);let Y=new Jg(B.shape,s,p,a),J=null,re=[B];s&&re.push(s),a&&re.push(a),p===\"leakyrelu\"&&(J=[{type:\"float32\",data:[i]}],Y.uniforms+=\" alpha : f32,\");let ne=n.runWebGPUProgram(Y,re,B.dtype,J);D.push(B);let ee=pe({inputs:{x:ne},backend:n,attrs:{shape:S}});D.push(ne);for(let oe of D)n.disposeData(oe.dataId);return ee}break}case Mo.MatMulSmallOutputSizeProgram:L=new Qg(k,_,z,t,o,s,p,a);break;case Mo.MatMulPackedProgram:let q=n.adapterInfo.isIntel();L=new Xg(k,z,t,o,s,p,a,q);break;default:throw new Error(`Unsupported MatMulProgramType ${U}.`)}s&&O.push(s),a&&O.push(a),p===\"leakyrelu\"&&(M.push({type:\"float32\",data:[i]}),L.uniforms+=\" alpha : f32,\"),B=n.runWebGPUProgram(L,O,r.dtype,M,B);let j=pe({inputs:{x:B},backend:n,attrs:{shape:S}});D.push(B);for(let q of D)n.disposeData(q.dataId);return j}function Yie(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:p,transposeB:u,activation:c,leakyreluAlpha:l}=o;return $p({a:n,b:s,transposeA:p,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:l,activation:c})}var pz={kernelName:So,backendName:\"webgpu\",kernelFunc:Yie};var gm=class{constructor(e,t,o){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.workgroupSize=[128,1,1],this.size=!0,this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`\n fn binaryOpComplex(\n areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {\n ${Xc(this.op,!1)}\n }\n\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let areal = getARealByOutputIndex(index);\n let aimag = getAImagByOutputIndex(index);\n let breal = getBRealByOutputIndex(index);\n let bimag = getBImagByOutputIndex(index);\n setOutputAtIndex(index, binaryOpComplex(areal, aimag, breal, bimag));\n }\n }\n `}};var Ii=class{constructor(e,t,o){if(this.size=!0,this.variableNames=[\"A\",\"B\"],this.outputShape=w.assertAndGetBroadcastShape(t,o),this.dispatchLayout=X(this.outputShape),this.op=e,this.useSharedMemoryWithA=t.length<=1&&o.length>1&&t[0]<128,this.useSharedMemoryWithB=o.length<=1&&t.length>1&&o[0]<128,this.useSharedMemoryWithA||this.useSharedMemoryWithB)this.outputComponent=1,this.variableComponents=[1,1],this.lastDimensionSize=this.useSharedMemoryWithB?o[0]:t[0],this.shaderKey=`binary_${e}_${this.lastDimensionSize}`,this.type=\"shared\",this.workgroupSize=[256,1,1];else{let n=t.length>0&&t[t.length-1]%4===0,s=o.length>0&&o[o.length-1]%4===0;n&&s?(this.outputComponent=4,this.variableComponents=[4,4]):n&&(y.isScalarShape(o)||o[o.length-1]===1)||s&&(y.isScalarShape(t)||t[t.length-1]===1)?(this.outputComponent=4,this.variableComponents=n?[4,1]:[1,4]):(this.outputComponent=1,this.variableComponents=[1,1]),this.type=\"nonshared\",this.shaderKey=`binary_${e}_${this.variableComponents}`,this.workgroupSize=[128,1,1]}this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.outputComponent,1,1])}getUserCode(){let e,t=this.outputComponent===4?\"vec4\":\"f32\",o=`\n fn binaryOperation(a : ${t}, b : ${t}) -> ${t} {\n ${Xc(this.op,this.outputComponent===4)}\n };\n `;if(this.type===\"shared\"){let n=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:\"0\",s=this.useSharedMemoryWithB?`let a = getAByOutputIndex(index);\n let b = sharedBuf[${n}];`:`let a = sharedBuf[${n}];\n let b = getBByOutputIndex(index);`;e=`\n ${o}\n var sharedBuf : array;\n ${G(\"index\")} {\n // Fill in the shared memory buffer.\n let localIndex = i32(localId.x);\n if(localIndex < ${this.lastDimensionSize}) {\n sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?\"B\":\"A\"}[localIndex]);\n }\n workgroupBarrier();\n\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n ${s}\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `}else e=`\n ${o}\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index * ${this.outputComponent});\n let a = ${t}(getAByOutputCoords(coords));\n let b = ${t}(getBByOutputCoords(coords));\n setOutputAtIndex(index, binaryOperation(a, b));\n }\n }\n `;return e}};function At(r){let{inputs:e}=r,{x:t}=e;return r.backend.incRef(t.dataId),{dataId:t.dataId,shape:t.shape,dtype:t.dtype}}var cz={kernelName:Co,backendName:\"webgpu\",kernelFunc:At};function xo(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,\"complex64\"),a=t.tensorMap.get(s.dataId),i=At({inputs:{x:o},backend:t}),p=At({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:p},s}var lz={kernelName:Di,backendName:\"webgpu\",kernelFunc:xo};var Jr=class{constructor(e,t,o=\"\"){this.variableNames=[\"A\"],this.size=!0;let n=128;this.workgroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.op=t,o!==\"\"&&(this.uniforms=o),this.shaderKey=`unary_${t}`}getUserCode(){return`\n fn unaryOperation(a : f32) -> f32 {\n ${Si(this.op,!1)}\n }\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let a = getAByOutputIndex(index);\n setOutputAtIndex(index, unaryOperation(a));\n }\n }\n `}};function ye({opType:r,cpuKernelImpl:e,dtype:t}){return({inputs:o,backend:n})=>{let{x:s}=o,a=n,i=t||s.dtype;if(a.shouldExecuteOnCPU([s])&&e!=null){let u=a.tensorMap.get(s.dataId),c=e(u.values,i);return a.makeTensorInfo(s.shape,i,c)}let p=new Jr(s.shape,r);return a.runWebGPUProgram(p,[s],i)}}function et({opType:r,cpuKernelImpl:e,supportsComplex:t=!1,dtype:o}){return({inputs:n,backend:s})=>{let{a,b:i}=n,p=s;if(t&&a.dtype===\"complex64\"){let l=p.tensorMap.get(a.dataId),m=p.tensorMap.get(i.dataId),d,f;if(r!==fe.MUL)[d,f]=[[l.complexTensorInfos.real,m.complexTensorInfos.real],[l.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(g=>{let[x,b]=g,C={dataId:x.dataId,dtype:x.dtype,shape:a.shape},S={dataId:b.dataId,dtype:b.dtype,shape:i.shape},k=new Ii(r,a.shape,i.shape);return p.runWebGPUProgram(k,[C,S],dt(x.dtype,b.dtype))});else{let g=new gm(fe.COMPLEX_MULTIPLY_REAL,a.shape,i.shape),x=new gm(fe.COMPLEX_MULTIPLY_IMAG,a.shape,i.shape),b=[{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:a.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:m.complexTensorInfos.real.dataId,dtype:m.complexTensorInfos.real.dtype,shape:i.shape},{dataId:m.complexTensorInfos.imag.dataId,dtype:m.complexTensorInfos.imag.dtype,shape:i.shape}];d=p.runWebGPUProgram(g,b,\"float32\"),f=p.runWebGPUProgram(x,b,\"float32\")}let h=xo({inputs:{real:d,imag:f},backend:p});return p.disposeData(d.dataId),p.disposeData(f.dataId),h}let u=o||dt(a.dtype,i.dtype);if((a.dtype===\"string\"||i.dtype===\"string\"||p.shouldExecuteOnCPU([a,i]))&&e!=null){let l=p.tensorMap.get(a.dataId).values,m=p.tensorMap.get(i.dataId).values,d=a.dtype===\"string\"?w.fromUint8ToStringArray(l):l,f=a.dtype===\"string\"?w.fromUint8ToStringArray(m):m,[h,g]=e(a.shape,i.shape,d,f,u);return p.makeTensorInfo(g,u,h)}let c=new Ii(r,a.shape,i.shape);return p.runWebGPUProgram(c,[a,i],u)}}var{addImpl:mz,castImpl:dz,ceilImpl:fz,concatImpl:hz,equalImpl:gz,expImpl:xz,expm1Impl:yz,floorImpl:bz,floorDivImpl:Cz,gatherNdImpl:wz,gatherV2Impl:Sz,greaterEqualImpl:Iz,greaterImpl:vz,lessEqualImpl:kz,lessImpl:Nz,logImpl:Tz,maxImpl:_z,maximumImpl:Ez,minimumImpl:$z,multiplyImpl:Rz,negImpl:Dz,notEqualImpl:Az,prodImpl:Fz,rangeImpl:Pz,rsqrtImpl:Oz,scatterImpl:Mz,simpleAbsImpl:Lz,sliceImpl:Bz,stridedSliceImpl:zz,stringNGramsImpl:Vz,subImpl:Wz,tileImpl:Uz,topKImpl:Gz,transposeImpl:Hz,uniqueImpl:rOt}=Ic;var Qie=ye({opType:Z.ABS,cpuKernelImpl:Lz}),Kz={kernelName:Xs,backendName:\"webgpu\",kernelFunc:Qie};var Zie=ye({opType:Z.ACOS}),qz={kernelName:Vo,backendName:\"webgpu\",kernelFunc:Zie};var Jie=ye({opType:Z.ACOSH}),jz={kernelName:Wo,backendName:\"webgpu\",kernelFunc:Jie};var eue=et({opType:fe.ADD,cpuKernelImpl:mz,supportsComplex:!0}),Xz={kernelName:uo,backendName:\"webgpu\",kernelFunc:eue};var tx=class{constructor(e){this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"addN\"}getUserCode(){let e=[];this.variableNames.forEach(n=>{e.push(`let v${n} = get${n}ByOutputCoords(coords);`)});let t=this.variableNames.map(n=>`v${n}`).join(\" + \");return`\n ${G(\"index\")} {\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let flatIndex = index * ${this.workPerThread} + i;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n ${e.join(`\n `)}\n setOutputAtIndex(flatIndex, ${t});\n }\n }\n }\n `}};function tue(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return At({inputs:{x:o[0]},backend:t});let n=o.map(i=>i.dtype).reduce((i,p)=>dt(i,p)),s=o.map(i=>i.shape),a=new tx(s);return t.runWebGPUProgram(a,o,n)}var Yz={kernelName:Uo,backendName:\"webgpu\",kernelFunc:tue};var rx=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[16,16,1];let o=new Array(e.length);for(let n=0;n`Must be a square tile, current tile shape is ${this.workgroupSize[0]} x ${this.workgroupSize[1]}`);let e=this.workgroupSize[0];return`\n var tile : array, ${this.workgroupSize[0]}>;\n ${G()} {\n var x = i32(workgroupId.x) * ${e} + i32(localId.x);\n var y = i32(workgroupId.y) * ${e} + i32(localId.y);\n let width = uniforms.outShape[0];\n let height = uniforms.outShape[1];\n if (x < width && y < height) {\n tile[localId.y][localId.x] = f32(A[y * width + x]);\n }\n workgroupBarrier();\n\n x = i32(workgroupId.y) * ${e} + i32(localId.x);\n y = i32(workgroupId.x) * ${e} + i32(localId.y);\n if (x < height && y < width) {\n setOutputAtIndex((y * height + x), tile[localId.x]\n [localId.y]);\n }\n }\n `}};var ox=class{constructor(e,t){this.variableNames=[\"A\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=new Array(e);for(let o=0;o=32768&&o>=512?this.workgroupSize=[512,1,1]:e.inSize>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e=\"\",t=\"0.0\",o=this.workgroupSize[0];this.reduceType===\"min\"||this.reduceType===\"max\"?(e=`\n if (isnan(candidate)) {\n bestValue = uniforms.NAN;\n } else if (!isnan(bestValue) && candidate ${this.reduceType===\"min\"?\"<\":\">\"} bestValue)\n { bestValue = candidate; }`,t=\"f32(x[offset])\"):this.reduceType===\"sum\"||this.reduceType===\"mean\"?e=\" bestValue = bestValue + candidate; \":this.reduceType===\"prod\"?(e=\" bestValue = bestValue * candidate; \",t=\"1.0\"):this.reduceType===\"all\"?(e=\" bestValue = f32(bestValue >= 1.0 && candidate >= 1.0); \",t=\"1.0\"):this.reduceType===\"any\"&&(e=\" bestValue = f32(bestValue >= 1.0 || candidate >= 1.0); \",t=\"0.0\");let n=this.reduceType===\"mean\"?\"setOutputAtIndex(outputIndex, bestValue / f32(uniforms.reduceSize));\":\"setOutputAtIndex(outputIndex, bestValue);\";return`\n fn DIV_CEIL(a : u32, b : u32) -> u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestValues : array;\n `}\n fn getOffset(outputIndex : i32) -> i32 {\n let outputCoords = getCoordsFromIndex(outputIndex);\n let offset = ${this.outputShape.length===1?\"outputCoords\":\"outputCoords[0]\"} * uniforms.reduceSize;\n return offset;\n }\n ${G(\"index\")} {\n let outputIndex = index / ${o};\n let offset = getOffset(outputIndex);\n var bestValue = ${t};\n let Length = uniforms.reduceSize;\n let WorkPerThread = DIV_CEIL(u32(Length), ${o}u);\n for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;\n k = k + ${o}) {\n let candidate = f32(x[offset + k]);\n ${e}\n }\n xBestValues[localId.x] = bestValue;\n workgroupBarrier();\n\n var reduceSize = min(u32(Length), ${o}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n ${e}\n xBestValues[localId.x] = bestValue;\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n ${n}\n }\n }\n `}};var rue={mean:\"float32\",all:\"bool\",any:\"bool\"};function eo(r,e,t,o,n){let s=r.shape.length,a=[],i=y.parseAxisParam(e,r.shape),p=i,u=w.getAxesPermutation(p,s),c=r;u!=null&&(c=xr({inputs:{x:r},attrs:{perm:u},backend:n}),p=w.getInnerMostAxes(p.length,s),a.push(c)),w.assertAxesAreInnerMostDims(o,p,s);let[l,m]=w.computeOutAndReduceShapes(c.shape,p),d=l;t&&(d=w.expandShapeToKeepDim(l,i));let f;if((o===\"max\"||o===\"prod\")&&n.shouldExecuteOnCPU([c])){let h=n.tensorMap.get(c.dataId).values;switch(o){case\"max\":let g=_z(h,y.sizeFromShape(m),d,r.dtype);f=n.makeTensorInfo(d,r.dtype,g);break;case\"prod\":let{outVals:x,outShape:b,outDtype:C}=Fz(c.shape,c.dtype,h,p);f=n.makeTensorInfo(b,C,x);break;default:throw new Error(`${o} CPU implementation is not yet supported.`)}}else{let h=y.sizeFromShape(m),x=y.sizeFromShape(c.shape)/h,b={windowSize:h,inSize:h,batchSize:x,outSize:1},C=rue[o]||oi(r.dtype),S=[{type:\"int32\",data:[h]}],k=new nx(b,o,n.device.limits.maxComputeWorkgroupSizeX),_=n.runWebGPUProgram(k,[c],C,S);a.push(_),f=pe({inputs:{x:_},attrs:{shape:d},backend:n})}return a.forEach(h=>n.disposeData(h.dataId)),f}function oue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"all\",t)}var Zz={kernelName:Go,backendName:\"webgpu\",kernelFunc:oue};function nue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"any\",t)}var Jz={kernelName:Ho,backendName:\"webgpu\",kernelFunc:nue};var Yc=class{constructor(e,t,o){this.workgroupSize=[64,1,1],this.variableNames=[\"x\"],this.uniforms=\"infinityValue : f32,\",this.size=!0;let n=[t];this.op=o===\"min\"?\"<\":\">\";let[s,a]=w.computeOutAndReduceShapes(e,n);this.outputShape=s.length===0?[1]:s,this.dispatchLayout=X(this.outputShape),y.sizeFromShape(a)<32?(this.type=\"plain\",this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)):(this.type=\"shared\",this.dispatch=H(this.dispatchLayout,this.outputShape,[1,1,1])),this.inputShape=e,this.shaderKey=`argMinMax_${this.op}_${this.type}`}getUserCode(){let e=this.workgroupSize[0],t=()=>this.inputShape.length===1?\"uniforms.xShape\":`uniforms.xShape.${Oo(this.inputShape.length-1)}`,o=()=>{let n=\"\";if(this.outputShape.length===1)this.inputShape.length!==1&&(n+=\"outputCoords,\");else for(let s=0;s u32 {\n return ((a - 1u) / b + 1u);\n }\n\n ${`\n var xBestIndices : array;\n var xBestValues : array;\n `}\n\n ${G(\"index\")} {\n let outputIndex = index / ${e};\n let reduceLength = ${t()};\n\n var bestIndex = i32(localId.x);\n var bestValue = uniforms.infinityValue;\n let outputCoords = getCoordsFromIndex(outputIndex);\n for (var k = i32(localId.x); k < reduceLength && outputIndex < uniforms.size;\n k = k + ${e}) {\n let candidate = getX(${o()} k);\n if (!isnan(candidate) && candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = k;\n }\n }\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = bestIndex;\n workgroupBarrier();\n\n var reduceSize = min(u32(reduceLength), ${e}u);\n for (var currentSize = reduceSize / 2u; reduceSize > 1u;\n currentSize = reduceSize / 2u) {\n let interval = DIV_CEIL(reduceSize, 2u);\n if (localId.x < currentSize) {\n let candidate = xBestValues[localId.x + interval];\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n xBestValues[localId.x] = bestValue;\n xBestIndices[localId.x] = xBestIndices[localId.x + interval];\n }\n }\n reduceSize = interval;\n workgroupBarrier();\n }\n\n if (localId.x == 0u && outputIndex < uniforms.size) {\n setOutputAtIndexI32(outputIndex, xBestIndices[localId.x]);\n }\n }\n `:`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outputCoords = getCoordsFromIndex(index);\n var bestIndex = 0;\n var bestValue = getX(${o()} 0);\n let reduceLength = ${t()};\n for (var i = 1; i < reduceLength; i++) {\n let candidate = getX(${o()} i);\n if (candidate ${this.op} bestValue) {\n bestValue = candidate;\n bestIndex = i;\n }\n }\n setOutputAtIndexI32(index, bestIndex);\n }\n }\n `}};function sue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMax\",[a[0]],p.shape.length);let c=new Yc(p.shape,a[0],\"max\"),l=[{type:\"float32\",data:[Number.NEGATIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var eV={kernelName:Ys,backendName:\"webgpu\",kernelFunc:sue};function aue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=y.parseAxisParam(s,n.shape),i=w.getAxesPermutation(a,n.shape.length),p=n,u=[];i!=null&&(p=xr({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(p),a=w.getInnerMostAxes(a.length,p.shape.length)),w.assertAxesAreInnerMostDims(\"argMin\",[a[0]],p.shape.length);let c=new Yc(p.shape,a[0],\"min\"),l=[{type:\"float32\",data:[Number.POSITIVE_INFINITY]}],m=t.runWebGPUProgram(c,[p],\"int32\",l);return u.forEach(d=>t.disposeData(d.dataId)),m}var tV={kernelName:Qs,backendName:\"webgpu\",kernelFunc:aue};var iue=ye({opType:Z.ASIN}),rV={kernelName:Ko,backendName:\"webgpu\",kernelFunc:iue};var uue=ye({opType:Z.ASINH}),oV={kernelName:qo,backendName:\"webgpu\",kernelFunc:uue};var pue=ye({opType:Z.ATAN}),nV={kernelName:jo,backendName:\"webgpu\",kernelFunc:pue};var cue=et({opType:fe.ATAN2}),sV={kernelName:Yo,backendName:\"webgpu\",kernelFunc:cue};var lue=ye({opType:Z.ATANH}),aV={kernelName:Xo,backendName:\"webgpu\",kernelFunc:lue};var sx=class{constructor(e){this.variableNames=[\"x\"],this.uniforms=\"strides : vec2,\",this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"poolWithFilterSizeEqualsOne\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let xRCCorner = coords.yz * uniforms.strides;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n let value = getX(batch, xRCorner, xCCorner, d);\n setOutputAtIndex(index, value);\n }\n }\n `}};var Ba=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec2, pads : vec2, dilations : vec2, convDims : vec2, filterDims : vec2,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool2D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue = resultValue + value; count = count + 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"((batch * uniforms.xShape[1] + xR) * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"(xR * uniforms.xShape[2] + xC) * uniforms.xShape[3] + d\":\"wR * uniforms.filterDims.y + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n let xRCCorner = vec2(coords.yz) * uniforms.strides - uniforms.pads;\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilations.x) {\n let xR = xRCorner + wR;\n\n if (xR < 0 || xR >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilations.y) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.y) {\n continue;\n }\n\n let value = getX(batch, xR, xC, d);\n ${e}\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}},Iu=class{constructor(e,t,o=!1,n=!1,s=!1){if(this.variableNames=[\"x\"],this.uniforms=\"strides : vec3, pads : vec3, convDims : vec3, filterDims : vec3,\",this.workgroupSize=[128,1,1],this.size=!0,t===\"avg\"&&o)throw new Error(\"Cannot compute positions for average pool.\");this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.poolType=t,this.computePositions=o,this.flattenPositions=n,this.includeBatchIndex=s,this.shaderKey=`pool3D_${t}_${o}_${n}_${s}`}getUserCode(){let e;this.poolType===\"avg\"?e=\"resultValue += value; count += 1.0;\":this.computePositions?e=`let currMaxValue = mix(value, maxValue, maxValueFound);\n if (value >= currMaxValue) {\n maxValue = value;\n maxValueFound = 1.0;\n maxPosition = ${this.flattenPositions?this.includeBatchIndex?\"(((batch * uniforms.xShape.y + xD) * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"((xD * uniforms.xShape.z + xR) * uniforms.xShape.w + xC) * uniforms.xShape.u + ch\":\"wD * uniforms.filterDims.y * uniforms.filterDims.y + wR * uniforms.filterDims.z + wC\"};\n }`:e=\"resultValue = max(value, resultValue);\";let t=\"resultValue\";return this.poolType===\"avg\"&&(t=\"resultValue / max(count, 1.0)\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let xCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xDCorner = xCorner.x;\n let xRCorner = xCorner.y;\n let xCCorner = xCorner.z;\n\n ${this.computePositions?`var maxValue = 0.0;\n var maxValueFound = 0.0;\n var maxPosition = 0;`:`var resultValue = ${this.poolType===\"avg\"?\"0.0\":\"-1.0 / pow(10.0, -20.0)\"};`}\n\n var count = 0.0;\n for (var wD = 0; wD < uniforms.filterDims.x; wD++) {\n let xD = xDCorner + wD;\n if (xD < 0 || xD >= uniforms.convDims.x) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims.y; wR++) {\n let xR = xRCorner + wR;\n if (xR < 0 || xR >= uniforms.convDims.y) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims.z; wC++) {\n let xC = xCCorner + wC;\n if (xC < 0 || xC >= uniforms.convDims.z) {\n continue;\n }\n\n let value = getX(batch, xD, xR, xC, ch);\n ${e}\n }\n }\n }\n\n ${this.computePositions?\"setOutputAtIndexI32(index, maxPosition);\":`setOutputAtIndex(index, ${t});`}\n }\n }\n `}};function t0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o;return eo(n,s,a,\"max\",t)}var iV={kernelName:zn,backendName:\"webgpu\",kernelFunc:t0};function r0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{keepDims:s,axis:a}=o;return eo(n,a,s,\"mean\",t)}var uV={kernelName:Un,backendName:\"webgpu\",kernelFunc:r0};function ax(r,e,t,o){if(e.filterWidth===1&&e.filterHeight===1&&y.arraysEqual(e.inShape,e.outShape))return At({inputs:{x:r},backend:o});if(e.filterWidth===e.inWidth&&e.filterHeight===e.inHeight&&e.batchSize===1&&e.padInfo.type===\"VALID\"){let a=r.shape.length,i=pe({inputs:{x:r},backend:o,attrs:{shape:[r.shape[a-3]*r.shape[a-2],r.shape[a-1]]}}),p;t===\"avg\"?p=r0({inputs:{x:i},backend:o,attrs:{axis:0,keepDims:!1}}):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),p=t0({inputs:{x:i},backend:o,attrs:{reductionIndices:0,keepDims:!1}}));let u=pe({inputs:{x:p},backend:o,attrs:{shape:e.outShape}});return o.disposeData(i.dataId),o.disposeData(p.dataId),u}let n,s=[{type:\"int32\",data:[e.strideHeight,e.strideWidth]}];return e.filterHeight===1&&e.filterWidth===1?n=new sx(e):(t===\"avg\"?n=new Ba(e,\"avg\"):(y.assert(t===\"max\",()=>`Invalid pool type ${t}`),n=new Ba(e,\"max\")),s.push({type:\"int32\",data:[e.padInfo.top,e.padInfo.left]},{type:\"int32\",data:[e.dilationHeight,e.dilationWidth]},{type:\"int32\",data:[e.inHeight,e.inWidth]},{type:\"int32\",data:[e.effectiveFilterHeight,e.effectiveFilterWidth]})),o.runWebGPUProgram(n,[r],r.dtype,s)}function mue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,c=w.computePool2DInfo(n.shape,s,a,1,i,p);return ax(n,c,\"avg\",t)}var pV={kernelName:Qo,backendName:\"webgpu\",kernelFunc:mue};function due(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,\"avg\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var cV={kernelName:Zs,backendName:\"webgpu\",kernelFunc:due};var ix=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR = wR + uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC = wC + uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n\n dotProd = dotProd + dyValue * uniforms.avgMultiplier;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},ux=class{constructor(e){this.variableNames=[\"dy\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, avgMultiplier : f32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"avgPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n dotProd += dyValue * uniforms.avgMultiplier;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function fue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=w.computePool3DInfo(a.shape,i,p,1,u,c),m=new ux(l),d=1/(l.filterDepth*l.filterHeight*l.filterWidth),f=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.effectiveFilterDepth-1-l.padInfo.front,l.effectiveFilterHeight-1-l.padInfo.top,l.effectiveFilterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]},{type:\"int32\",data:[l.outDepth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"float32\",data:[d]}];return t.runWebGPUProgram(m,[n],a.dtype,f)}var lV={kernelName:Ri,backendName:\"webgpu\",kernelFunc:fue};function hue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;fm([n,s],\"avgPoolGrad\");let{filterSize:i,strides:p,pad:u}=o,c=w.computePool2DInfo(a.shape,i,p,1,u),l=new ix(c),m=1/(c.filterHeight*c.filterWidth),d=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.effectiveFilterHeight-1-c.padInfo.top,c.effectiveFilterWidth-1-c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]},{type:\"int32\",data:[c.outHeight]},{type:\"int32\",data:[c.outWidth]},{type:\"float32\",data:[m]}];return t.runWebGPUProgram(l,[n],a.dtype,d)}var mV={kernelName:$i,backendName:\"webgpu\",kernelFunc:hue};function gue(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return $p({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var dV={kernelName:Zo,backendName:\"webgpu\",kernelFunc:gue};var px=class{constructor(e,t){this.variableNames=[\"source\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${ft(e.length)}, `,this.shaderKey=\"slice\"}getUserCode(){let e=ft(this.rank),t=xue(this.rank),o;return this.start.length===1?o=this.outputShape.map((s,a)=>\"sourceLoc = uniforms.start + coords;\"):o=this.outputShape.map((s,a)=>`sourceLoc.${o0[a]} = uniforms.start.${Oo(a)} + coords.${o0[a]};`),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var sourceLoc : ${e};\n let coords = getCoordsFromIndex(index);\n ${o.join(`\n`)}\n setOutputAtIndex(index, getSource(${t}));\n }\n }\n `}},o0=[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"];function xue(r){if(r===1)return\"sourceLoc\";if(r<=6)return o0.slice(0,r).map(e=>`sourceLoc.${e}`).join(\",\");throw Error(`Slicing for rank ${r} is not yet supported`)}function Hs(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,p]=pt.parseSliceParams(n,s,a);if(pt.assertParamsValid(n,i,p),t.shouldExecuteOnCPU([n])||n.dtype===\"string\"){let l=t.tensorMap.get(n.dataId),m=Bz(l.values,i,p,n.shape,n.dtype);return t.makeTensorInfo(p,n.dtype,m)}if(y.sizeFromShape(p)===0)return t.makeTensorInfo(p,n.dtype,[]);let u=new px(i,p),c=[{type:\"int32\",data:i}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var fV={kernelName:ha,backendName:\"webgpu\",kernelFunc:Hs};var yue=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;y.assert(n.shape.length<=4,()=>\"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=w.getReshaped(n.shape,s,i),u=w.getPermuted(p.length,s.length),c=w.getReshapedPermuted(n.shape,s,i),l=w.getSliceBeginCoords(a,s.length),m=w.getSliceSize(c,a,s.length),d=[],f=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),h=xr({inputs:{x:f},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),x=Hs({inputs:{x:g},backend:t,attrs:{begin:l,size:m}});return d.push(f),d.push(h),d.push(g),d.forEach(b=>t.disposeData(b.dataId)),x},hV={kernelName:Js,backendName:\"webgpu\",kernelFunc:yue};var bue=`\n fn bincount_write(index: i32, value: f32) {\n ${Qr(\"&result[index]\",\"value\",\"float32\")}\n }\n`,Cue=`\n fn bincount_write(index: i32, value: f32) {\n atomicStore(&result[index], bitcast(value));\n }\n`,Qc=class{constructor(e,t,o=!1){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"binCountSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.hasWeights=!0,this.binaryOutput=!1,this.outputShape=e,this.rank=e.length,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.binaryOutput=o,o&&(this.atomic=!1),this.hasWeights=t,this.hasWeights&&this.variableNames.push(\"w\"),this.shaderKey=`bincount_${this.hasWeights}_${this.binaryOutput}_${this.rank}`}getUserCode(){return`\n ${this.binaryOutput?Cue:bue}\n ${G(\"index\")} {\n ${this.rank===1?`if (index < uniforms.xShape) {\n let indexVal = i32(getX(index));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(index)\":\"1.\"};\n bincount_write(indexVal, value);\n }\n }`:`let coord = getCoordsFromIndex(index);\n if (coordsInBounds2D(coord, uniforms.xShape)) {\n let indexVal = i32(getX(coord[0], coord[1]));\n if (indexVal < uniforms.binCountSize) {\n let value = ${this.binaryOutput?1:this.hasWeights?\"getW(coord[0], coord[1])\":\"1.\"};\n bincount_write(coord.x * uniforms.binCountSize + indexVal, value);\n }\n }`}\n }\n `}};function wue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=y.sizeFromShape(n.shape),u=y.sizeFromShape(s.shape)>0,c=[a],l=s.dtype,m=vt({backend:t,attrs:{shape:c,value:0,dtype:l}}),d=new Qc([i],u),f=[{type:\"int32\",data:[a]}],h=u?[n,s]:[n];return t.runWebGPUProgram(d,h,l,f,m)}var gV={kernelName:Jo,backendName:\"webgpu\",kernelFunc:wue};var cx=class{constructor(e){this.outputShape=[],this.variableNames=[\"s0\",\"s1\"],this.uniforms=\"s0Size : i32, s1Size : i32, \",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"broadcastArgs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var s0 = 1.0;\n var s1 = 1.0;\n let indexS0 = index - uniforms.size + uniforms.s0Size;\n let indexS1 = index - uniforms.size + uniforms.s1Size;\n if (indexS0 >= 0) {\n s0 = getS0(indexS0);\n }\n if (indexS1 >= 0) {\n s1 = getS1(indexS1);\n }\n\n if (s0 == 1.0) {\n setOutputAtIndex(index, s1);\n } else if (s1 == 1.0) {\n setOutputAtIndex(index, s0);\n } else if (s0 != s1) {\n setOutputAtIndex(index, uniforms.NAN);\n } else {\n setOutputAtIndex(index, s0);\n }\n }\n }\n `}};function Sue(r){let{inputs:e,backend:t}=r,{s0:o,s1:n}=e;if(t.shouldExecuteOnCPU([o,n])){let c=t.tensorMap.get(o.dataId),l=t.tensorMap.get(n.dataId),m=c.values,d=l.values,f=w.assertAndGetBroadcastShape(Array.from(m),Array.from(d));return t.makeTensorInfo([f.length],\"int32\",Int32Array.from(f))}let s=y.sizeFromShape(o.shape),a=y.sizeFromShape(n.shape),i=Math.max(s,a),p=new cx(i),u=[{type:\"int32\",data:[s]},{type:\"int32\",data:[a]}];return t.runWebGPUProgram(p,[o,n],\"int32\",u)}var xV={kernelName:ea,backendName:\"webgpu\",kernelFunc:Sue};var n0=et({opType:fe.NOT_EQUAL,dtype:\"bool\",cpuKernelImpl:Az}),yV={kernelName:Yn,backendName:\"webgpu\",kernelFunc:n0};function vi(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.tensorMap.get(o.dataId);return At({inputs:{x:n.complexTensorInfos.real},backend:t})}var bV={kernelName:Hi,backendName:\"webgpu\",kernelFunc:vi};function CV(r,e){let t=new Jr(r.shape,Z.TO_INT),o=e.runWebGPUProgram(t,[r],\"int32\");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function s0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s===\"complex64\"){if(n.dtype===\"complex64\")return At({inputs:{x:n},backend:t});let a=Gr(n.shape),i=s0({inputs:{x:n},backend:t,attrs:{dtype:\"float32\"}}),p=xo({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeData(i.dataId),p}if(n.dtype===\"complex64\"){let a=vi({inputs:{input:n},backend:t}),i=s0({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeData(a.dataId),i}if(!y.hasEncodingLoss(n.dtype,s)){let a=At({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(t.shouldExecuteOnCPU([n])){let a=t.tensorMap.get(n.dataId).values,[i,p,u]=dz(a,n.shape,n.dtype,s);return t.makeTensorInfo(i,p,u)}if(s===\"int32\")return CV(n,t);if(s===\"bool\"){let a=t.makeTensorInfo([],\"bool\",y.getTypedArrayFromDType(\"bool\",1)),p=n0({inputs:{a:n,b:a},backend:t});return t.disposeData(a.dataId),p}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var wV={kernelName:yo,backendName:\"webgpu\",kernelFunc:s0};var Iue=ye({opType:Z.CEIL,cpuKernelImpl:fz}),SV={kernelName:en,backendName:\"webgpu\",kernelFunc:Iue};var lx=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workPerThread=4,this.workgroupSize=[64,1,1],this.outputComponent=4,this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.shaderKey=\"clipVec4\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n var clampedValue = clamp(\n value, vec4(uniforms.minVal), vec4(uniforms.maxVal));\n clampedValue = select(clampedValue, value, isnanVec4(value));\n setOutputAtIndex(index, clampedValue);\n }\n }\n `}};var mx=class{constructor(e){this.variableNames=[\"A\"],this.uniforms=\"minVal : f32, maxVal : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"clip\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let value = getAByOutputIndex(index);\n if (isnan(value)) {\n setOutputAtIndex(index, value);\n return;\n }\n setOutputAtIndex(index, clamp(value, uniforms.minVal, uniforms.maxVal));\n }\n }\n `}};function vue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i,p=[{type:\"float32\",data:[s]},{type:\"float32\",data:[a]}];return y.sizeFromShape(n.shape)%4===0?i=new lx(n.shape):i=new mx(n.shape),t.runWebGPUProgram(i,[n],n.dtype,p)}var IV={kernelName:bo,backendName:\"webgpu\",kernelFunc:vue};var dx=class{constructor(e){this.outputShape=[],this.variableNames=[\"real\",\"imag\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"complexAbs\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let re = abs(getRealByOutputIndex(index));\n let im = abs(getImagByOutputIndex(index));\n let mx = max(re, im);\n\n // The length function in wgsl may be not underflow-safe on some GPUs.\n // So the safe solution is to ensure underflow-safety in all cases.\n setOutputAtIndex(index, select(mx * length(vec2(1, min(re, im)/mx)), 0.0, mx == 0.0));\n }\n }\n `}};function vV(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function kue(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.tensorMap.get(o.dataId),s=new dx(o.shape),a=[vV(o,n.complexTensorInfos.real),vV(o,n.complexTensorInfos.imag)];return t.runWebGPUProgram(s,a,a[0].dtype)}var kV={kernelName:Ai,backendName:\"webgpu\",kernelFunc:kue};var fx=class{constructor(e){this.uniforms=\"\",this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=w.computeOutShape(e,1),this.variableNames=e.map((t,o)=>`T${o}`),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t0){e.push(\"if (yC < uniforms.offset0){ setOutputAtCoords(coords.x, coords.y, getT0(yR, yC)); }\");for(let s=1;svi({inputs:{input:C},backend:t})),h=r.map(C=>Rp({inputs:{input:C},backend:t})),g=Zc(f,e,t),x=Zc(h,e,t),b=xo({inputs:{real:g,imag:x},backend:t});return f.forEach(C=>t.disposeData(C.dataId)),h.forEach(C=>t.disposeData(C.dataId)),t.disposeData(g.dataId),t.disposeData(x.dataId),b}let n=t.shouldExecuteOnCPU(r);if(o===\"string\"&&(n=!0),n){let f=r.map(k=>{let $=[-1,y.sizeFromShape(k.shape.slice(e))];return pe({inputs:{x:k},backend:t,attrs:{shape:$}})}),h=f.map(k=>({vals:t.readSync(k.dataId),shape:k.shape})),g=w.computeOutShape(f.map(k=>k.shape),1),x=f[0].shape[0]===1,b=hz(h,g,o,x),C=w.computeOutShape(r.map(k=>k.shape),e),S=t.makeTensorInfo(C,o,b);return f.forEach(k=>t.disposeData(k.dataId)),S}let s=t.device.limits.maxStorageBuffersPerShaderStage-1;if(r.length>s){let f=[];for(let g=0;gf.shape),u=new fx(p),c=[],l=new Array(p.length-1);if(l.length>0){l[0]=p[0][1],c.push({type:\"int32\",data:[l[0]]});for(let f=1;ft.disposeData(f.dataId));let d=pe({inputs:{x:m},backend:t,attrs:{shape:i}});return t.disposeData(m.dataId),d}function Nue(r,e,t){let o=w.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},backend:t,attrs:{shape:[y.sizeFromShape(s.shape.slice(0,e)),y.sizeFromShape(s.shape.slice(e))]}})),outShape:o}}function a0(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=y.parseAxisParam(n,e[0].shape)[0],a=e.map(u=>u.shape);w.assertParamsConsistent(a,s);let i=w.computeOutShape(e.map(u=>u.shape),s);if(y.sizeFromShape(i)===0)return t.makeTensorInfo(i,e[0].dtype,[]);let p=e.filter(u=>y.sizeFromShape(u.shape)>0);return p.length===1?At({inputs:{x:p[0]},backend:t}):Zc(p,s,t)}var TV={kernelName:ta,backendName:\"webgpu\",kernelFunc:a0};function Tue(r,e,t,o,n=!1,s=null,a=!1,i=4,p=4,u=4){let c=D=>{switch(D){case 1:return\"resData = f32(x[xIndex]);\";case 3:return\"resData = vec3(x[xIndex], x[xIndex + 1], x[xIndex + 2]);\";case 4:return\"resData = vec4(x[xIndex / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},l=D=>{switch(D){case 1:return\"return f32(W[row * uniforms.wShape[3] + col]);\";case 4:return\"return vec4(W[(row * uniforms.wShape[3] + col) / 4]);\";default:throw new Error(`innerElementSize ${D} is not supported.`)}},m=r?`\n let coord = vec4(batch, xRow, xCol, xCh);\n `:`\n let coord = vec4(batch, xCh, xRow, xCol);\n `,d=r?`\n let coords = vec4(\n batch,\n row / outWidth,\n row % outWidth,\n col);\n `:`\n let coords = vec4(\n batch,\n row,\n col / outWidth,\n col % outWidth);\n `,f=r?\"uniforms.xShape[1]\":\"uniforms.xShape[2]\",h=r?\"uniforms.xShape[2]\":\"uniforms.xShape[3]\",g=r?\"row\":\"col\",x=r?\"col\":\"row\",b=`\n let inChannels = uniforms.wShape[2];\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n let outRow = ${g} / outWidth;\n let outCol = ${g} % outWidth;\n\n let WRow = ${x} / (uniforms.filterDims[1] * inChannels);\n let WCol = ${x} / inChannels % uniforms.filterDims[1];\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * WRow - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * WCol - uniforms.pads[1];\n let xCh = ${x} % inChannels;\n var resData = ${Ae(i)}(0.0);\n // The bounds checking is always needed since we use it to pad zero for\n // the 'same' padding type.\n if (xRow >= 0 && xRow < ${f} && xCol >= 0 && xCol < ${h}) {\n ${m}\n let xIndex = getIndexFromCoords4D(coord, uniforms.xShape);\n ${c(i)}\n }\n return resData;`,C=r?e&&o?`\n ${b}`:`\n if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${b}\n }\n return ${Ae(i)}(0.0);`:o&&t?`\n ${b}`:`\n if (row < uniforms.dimInner && col < uniforms.dimBOuter) {\n ${b}\n }\n return ${Ae(i)}(0.0);`,S=`${l(p)}`,k=Ae(u),_=r?Ae(i):Ae(p),$=r?Ae(p):Ae(i);return`\n ${dr(s,a,u===4,4)}\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${_} {\n ${r?C:S}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${$} {\n ${r?S:C}\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueIn : ${k}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter)\n {\n var value = valueIn;\n let outWidth = ${r?\"uniforms.outShape[2]\":\"uniforms.outShape[3]\"};\n ${d}\n ${Zr(n,s)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }`}var hx=class{constructor(e,t,o,n,s=!1,a=null,i=!1,p=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, dilations : vec2, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=((e.inChannels%4===0||e.inChannels%3===0)&&this.isChannelsLast||e.outWidth%4===0&&!this.isChannelsLast)&&e.outChannels%4===0,this.dispatchLayout=this.isChannelsLast?{x:[3],y:[1,2],z:[0]}:{x:[2,3],y:[1],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4?(this.outputComponent=4,this.isChannelsLast&&e.inChannels%4!==0?(this.innerElementSize=3,this.variableComponents=[1,4]):(this.innerElementSize=4,this.variableComponents=[4,4]),s&&(this.variableNames.push(\"bias\"),this.variableComponents.push(4)),i&&(this.variableNames.push(\"preluActivationWeights\"),this.variableComponents.push(4))):(this.innerElementSize=this.elementsPerThread[0],s&&this.variableNames.push(\"bias\"),i&&this.variableNames.push(\"preluActivationWeights\")),this.sequentialAccessByThreads=p,this.addBias=s,this.activation=a,this.hasPreluActivationWeights=i,this.tileAOuter=this.workgroupSize[1]*this.elementsPerThread[1],this.tileBOuter=this.workgroupSize[0]*this.elementsPerThread[0],this.tileInner=Math.max(this.workgroupSize[0]*this.innerElementSize,this.workgroupSize[1]),this.fitAOuter=t%this.tileAOuter===0,this.fitBOuter=o%this.tileBOuter===0,this.fitInner=n%this.tileInner===0,this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}}_${this.fitAOuter}_${this.fitBOuter}_${this.fitInner}_${this.isVec4}_${this.innerElementSize}_${this.isChannelsLast}_${this.sequentialAccessByThreads}`}getUserCode(){let e=this.isVec4?_p(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner):Ep(this.elementsPerThread,this.workgroupSize,!this.isChannelsLast,this.tileInner,!1,null,this.sequentialAccessByThreads),t=this.isVec4?[this.innerElementSize,4,4]:[1,1,1];return`\n ${Tue(this.isChannelsLast,this.fitAOuter,this.fitBOuter,this.fitInner,this.addBias,this.activation,this.hasPreluActivationWeights,t[0],t[1],t[2])}\n ${e}\n `}};var gx=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2,\",this.workgroupSize=[4,4,8],this.outputShape=e.outShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.dispatchLayout=this.isChannelsLast?{x:[2],y:[1],z:[0,3]}:{x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.addBias=t,this.activation=o,this.hasPreluActivationWeights=n,t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.shaderKey=`conv2dnaive_${this.activation}_${this.isChannelsLast}`}getUserCode(){return`\n ${dr(this.activation,this.hasPreluActivationWeights,!1,4)}\n fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32{\n let coords = vec4(batch, row, col, chan);\n if (coordsInBounds4D(coords, uniforms.xShape)) {\n return getX(batch, row, col, chan);\n } else {\n return 0.0;\n }\n }\n fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{\n let coords = vec4(row, col, xChannel, outChannel);\n if(coordsInBounds4D(coords, uniforms.wShape)) {\n return getW(row, col, xChannel, outChannel);\n } else {\n return 0.0;\n }\n }\n fn writeResult(batch : i32, row : i32, col : i32, chan : i32, valueIn : f32) {\n let coords = ${this.isChannelsLast?\"vec4(batch, row, col, chan);\":\"vec4(batch, chan, row, col);\"}\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = valueIn;\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords.x, coords.y, coords.z, coords.w, value);\n }\n }\n ${G(\"index\")} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let outChannel = ${this.isChannelsLast?\"coords[3];\":\"coords[1];\"}\n let outRow = ${this.isChannelsLast?\"coords[1];\":\"coords[2];\"}\n let outCol = ${this.isChannelsLast?\"coords[2];\":\"coords[3];\"}\n var acc : f32 = 0.0;\n for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {\n for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {\n let xRow = outRow * uniforms.strides[0] + uniforms.dilations[0] * row - uniforms.pads[0];\n let xCol = outCol * uniforms.strides[1] + uniforms.dilations[1] * col - uniforms.pads[1];\n for (var xChannel = 0; xChannel < ${this.isChannelsLast?\"uniforms.xShape[3];\":\"uniforms.xShape[1];\"} xChannel = xChannel + 1) {\n ${this.isChannelsLast?\"let v = readInp(batch, xRow, xCol, xChannel);\":\"let v = readInp(batch, xChannel, xRow, xCol);\"}\n let f = readFilt(row, col, xChannel, outChannel);\n acc = acc + v * f;\n }\n }\n }\n writeResult(batch, outRow, outCol, outChannel, acc);\n }\n `}};var xx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=`pads : vec2, strides : vec2, dilations : vec2, outWidth : i32, itemsPerBlockRow : i32,\n inChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?\"coords[1]\":\"coords[2]\",n=this.isChannelsLast?\"coords[2]\":\"coords[1]\",s=this.isChannelsLast?\"getX(batch, xRow, xCol, ch)\":\"getX(batch, ch, xRow, xCol)\";return`\n ${G(\"index\")} {\n let coords = getCoordsFromIndex(index);\n if(index < uniforms.size) {\n let batch = coords[0];\n let row = ${o};\n let col = ${n};\n let offsetY = (row / uniforms.outWidth) * uniforms.strides[0] - uniforms.pads[0];\n let xRow = offsetY + uniforms.dilations[0] * (col / uniforms.itemsPerBlockRow);\n var value = 0.0;\n if(xRow < uniforms.xShape[${e}] && xRow >= 0) {\n let offsetX = (row % uniforms.outWidth) * uniforms.strides[1] -\n uniforms.pads[1];\n let xCol = offsetX + uniforms.dilations[1] * ((col %\n uniforms.itemsPerBlockRow) / uniforms.inChannels);\n let ch = col % uniforms.inChannels;\n if(xCol < uniforms.xShape[${t}] && xCol >= 0) {\n value = ${s};\n }\n }\n setOutputAtIndex(index, value);\n }\n }\n `}};function yx(r,e){let t=r.length;return t>=3?e?[...r.slice(0,-3),r[t-3]*r[t-2],r[t-1]]:[...r.slice(0,-3),r[t-3],r[t-2]*r[t-1]]:!e&&t===1&&r[0]>1?[r[0],1]:null}function _ue({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=t.dataFormat===\"channelsLast\",u=!p,c=!1,l=p&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=[],d,f;if(l){let x=t.inHeight*t.inWidth*t.inChannels;d=pe({inputs:{x:r},backend:o,attrs:{shape:[1,t.batchSize,x]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,x,t.outChannels]}})}else d=pe({inputs:{x:r},backend:o,attrs:{shape:p?[t.batchSize,t.inHeight*t.inWidth,t.inChannels]:[t.batchSize,t.inChannels,t.inHeight*t.inWidth]}}),f=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});if(m.push(d),m.push(f),s!=null){let x=yx(s.shape,p);x!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:x}}),m.push(s))}if(n!=null){let x=yx(n.shape,p);x!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:x}}),m.push(n))}let h=$p({a:p?d:f,b:p?f:d,transposeA:u,transposeB:c,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),g=pe({inputs:{x:h},backend:o,attrs:{shape:t.outShape}});m.push(h);for(let x of m)o.disposeData(x.dataId);return g}function Eue({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:p,filterHeight:u,inChannels:c,strideWidth:l,strideHeight:m,padInfo:d,outWidth:f,outHeight:h,dilationWidth:g,dilationHeight:x,dataFormat:b}=t,C=b===\"channelsLast\",S=p*u*c,k=h*f,_=C?[t.batchSize,k,S]:[t.batchSize,S,k],$=new xx(_,C),R=[{type:\"int32\",data:[d.top,d.left]},{type:\"int32\",data:[m,l]},{type:\"int32\",data:[x,g]},{type:\"int32\",data:[f]},{type:\"int32\",data:[c*p]},{type:\"int32\",data:[c]}],D=o.runWebGPUProgram($,[r],r.dtype,R),P=[];P.push(D);let O=pe({inputs:{x:e},backend:o,attrs:{shape:[1,S,-1]}});if(P.push(O),s!=null){let U=yx(s.shape,C);U!=null&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:U}}),P.push(s))}if(n!=null){let U=yx(n.shape,C);U!=null&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:U}}),P.push(n))}let B=$p({a:C?D:O,b:C?O:D,transposeA:!C,transposeB:!1,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),z=pe({inputs:{x:B},backend:o,attrs:{shape:t.outShape}});P.push(B);for(let U of P)o.disposeData(U.dataId);return z}function bx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let p=n!=null,u=s!=null,c=t.dataFormat===\"channelsLast\",l=c&&t.filterHeight===t.inHeight&&t.filterWidth===t.inWidth&&t.padInfo.type===\"VALID\",m=A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_DEBUG\");if(!m&&(l||t.filterHeight===1&&t.filterWidth===1&&t.dilationHeight===1&&t.dilationWidth===1&&t.strideHeight===1&&t.strideWidth===1&&(t.padInfo.type===\"SAME\"||t.padInfo.type===\"VALID\")))return _ue({x:r,filter:e,convInfo:t,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});let d=A().getNumber(\"WEBGPU_THRESHOLD_TO_INCREASE_WORKGROUPS_FOR_MATMUL\"),f=d>-1?d:o.thresholdToIncreaseWorkgroups,h=t.batchSize*Math.ceil(t.outHeight*t.outWidth/32)*Math.ceil(t.outChannels/32);if(A().getBool(\"WEBGPU_CONV_SEPARATE_IM2COL_SHADER\")||h<=f)return Eue({x:r,filter:e,convInfo:t,backend:o,bias:n,preluActivationWeights:s,leakyreluAlpha:a,activation:i});let g,x=[t.padInfo.top,t.padInfo.left],b=[{type:\"int32\",data:[t.filterHeight,t.filterWidth]},{type:\"int32\",data:[...x]},{type:\"int32\",data:[t.strideHeight,t.strideWidth]},{type:\"int32\",data:[t.dilationHeight,t.dilationWidth]}];if(m)g=new gx(t,p,i,u);else{let _=c?t.outHeight*t.outWidth:t.outChannels,$=c?t.outChannels:t.outHeight*t.outWidth,R=t.filterHeight*t.filterWidth*t.inChannels;b.push({type:\"int32\",data:[_]},{type:\"int32\",data:[$]},{type:\"int32\",data:[R]});let D=o.adapterInfo.isIntel();g=new hx(t,_,$,R,p,i,u,D)}let C=[],S=[r,e];p&&(!c&&n.shape.length===1&&(n=pe({inputs:{x:n},backend:o,attrs:{shape:[n.shape[0],1,1]}}),C.push(n)),S.push(n)),u&&(!c&&s.shape.length===1&&(s=pe({inputs:{x:s},backend:o,attrs:{shape:[s.shape[0],1,1]}}),C.push(s)),S.push(s)),i===\"leakyrelu\"&&(b.push({type:\"float32\",data:[a]}),g.uniforms+=\" alpha : f32,\");let k=o.runWebGPUProgram(g,S,r.dtype,b);for(let _ of C)o.disposeData(_.dataId);return k}function $ue(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=t,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,l);return bx({x:n,filter:s,convInfo:m,backend:o})}var _V={kernelName:tn,backendName:\"webgpu\",kernelFunc:$ue};var Cx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4,\",this.workgroupSize=[64,1,1],this.size=!1,this.isVec4=!1,this.workPerThread=1,this.outputShape=e.inShape,this.isChannelsLast=e.dataFormat===\"channelsLast\",this.isVec4=this.isChannelsLast&&e.outChannels%4===0&&e.inChannels%4===0,this.isVec4?(this.workPerThread=2,this.outputComponent=4,this.workgroupSize=[4,4,4],this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[4,this.workPerThread,1])):(this.size=!0,this.workPerThread=1,this.workgroupSize=[64,1,1],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize)),this.shaderKey=`conv2DDerInput_${this.isChannelsLast}_${this.isVec4}_${this.workPerThread}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,o=this.isChannelsLast?3:1,n=`\n ${G()} {\n let batch = i32(globalId.z) / uniforms.outShape[1];\n let r = i32(globalId.z) % uniforms.outShape[1];\n let c = i32(globalId.y) * ${this.workPerThread};\n let d1 = i32(globalId.x) * 4;\n\n let dyCorner = vec2(r, c) - uniforms.pads;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd: array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = f32(dyCorner.x + wR) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) ||\n fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = f32(dyCorner.y + wC) / f32(uniforms.strides.y);\n let dyC2 = f32(dyCorner.y + 1 + wC) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n var bDyCVal = true;\n var bDyCVal2 = true;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0) {\n bDyCVal = false;\n }\n if (dyC2 < 0.0 || dyC2 >= f32(uniforms.outBackprop[2]) ||\n fract(dyC2) > 0.0) {\n bDyCVal2 = false;\n }\n\n let idyC = i32(dyC);\n let idyC2 = i32(dyC2);\n if (bDyCVal && bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n xValue = getDy(batch, idyR, idyC2, d2);\n dotProd[1] = dotProd[1] + vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n }\n } else if (bDyCVal) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[0] = dotProd[0] + tmpval;\n }\n } else if (bDyCVal2) {\n let d2Length = uniforms.outBackprop[3];\n for (var d2 = 0; d2 < d2Length; d2 = d2 + 4) {\n let wValue0 = getW(wRPerm, wCPerm, d1, d2);\n let wValue1 = getW(wRPerm, wCPerm, d1 + 1, d2);\n let wValue2 = getW(wRPerm, wCPerm, d1 + 2, d2);\n let wValue3 = getW(wRPerm, wCPerm, d1 + 3, d2);\n var xValue = getDy(batch, idyR, idyC2, d2);\n let tmpval = vec4(dot(xValue, wValue0),\n dot(xValue, wValue1),\n dot(xValue, wValue2),\n dot(xValue, wValue3));\n dotProd[1] = dotProd[1] + tmpval;\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);\n }\n }\n }\n `;return this.isVec4?`\n ${n}\n `:`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[${o}];\n\n let dyCorner = vec2(coords[${e}], coords[${t}]) - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {\n let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.strides.x);\n let wRPerm = uniforms.filterDims.x - 1 - wR;\n if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||\n wRPerm < 0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {\n let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.strides.y);\n let wCPerm = uniforms.filterDims.y - 1 - wC;\n if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||\n fract(dyC) > 0.0 || wCPerm < 0) {\n continue;\n }\n let idyC = i32(dyC);\n\n for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {\n let xValue = ${this.isChannelsLast?\"getDy(batch, idyR, idyC, d2)\":\"getDy(batch, d2, idyR, idyC)\"};\n let wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd = dotProd + xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},wx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=\"pads : vec2, strides : vec2, batchSize : i32, outHeight : i32, outWidth : i32, inHeight : i32, inWidth : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",this.shaderKey=`conv2DDerFilter_${this.isChannelsLast}`}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let d2 = coords[3];\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b = b + 1) {\n for (var yR = 0; yR < uniforms.outHeight; yR = yR + 1) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC = yC + 1) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n if (${this.isChannelsLast}) {\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd = dotProd + xValue * dyValue;\n } else {\n let dyValue = getDy(b, d2, yR, yC);\n let xValue = getX(b, d1, xR, xC);\n dotProd = dotProd + xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Sx=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`pads : vec3, strides : vec3, batchSize : i32, outDepth : i32,\n outHeight : i32, outWidth : i32, inDepth : i32, inHeight : i32, inWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wF = coords.x;\n let wR = coords.y;\n let wC = coords.z;\n let d1 = coords.w;\n let d2 = coords.u;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yF = 0; yF < uniforms.outDepth; yF++) {\n let xF = wF + yF * uniforms.strides[0] - uniforms.pads[0];\n if (xF < 0 || xF >= uniforms.inDepth) {\n continue;\n }\n\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[1] - uniforms.pads[1];\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[2] - uniforms.pads[2];\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yF, yR, yC, d2);\n let xValue = getX(b, xF, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Ix=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`filterDims : vec3, pads : vec3, strides : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32, outChannels : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3DDerInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let d1 = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyFCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let dyF = f32(dyFCorner + wF) / f32(uniforms.strides[0]);\n if (dyF < 0.0 || dyF >= f32(uniforms.outDepth) || fract(dyF) > 0.0) {\n continue;\n }\n let idyF = i32(dyF);\n\n let wFPerm = uniforms.filterDims[0] - 1 - wF;\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n let wRPerm = uniforms.filterDims[1] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let wCPerm = uniforms.filterDims[2] - 1 - wC;\n\n for (var d2 = 0; d2 < uniforms.outChannels; d2++) {\n let xValue = getDy(batch, idyF, idyR, idyC, d2);\n let wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Rue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:p,dimRoundingMode:u,filterShape:c}=o,l=w.convertConv2DDataFormat(p),m=w.computeConv2DInfo(n.shape,c,a,1,i,u,!1,l),d=new wx(m),f=[{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]},{type:\"int32\",data:[m.inHeight]},{type:\"int32\",data:[m.inWidth]}];return t.runWebGPUProgram(d,[n,s],n.dtype,f)}var EV={kernelName:Fi,backendName:\"webgpu\",kernelFunc:Rue};function Due(r=4){let e=s=>{switch(s){case 1:return\"return W[getIndexFromCoords4D(coord, uniforms.wShape)];\";case 4:return`\n let coord1 = vec4(coordX, coordY, col + 1, rowInner);\n let coord2 = vec4(coordX, coordY, col + 2, rowInner);\n let coord3 = vec4(coordX, coordY, col + 3, rowInner);\n let v0 = W[getIndexFromCoords4D(coord, uniforms.wShape)];\n let v1 = W[getIndexFromCoords4D(coord1, uniforms.wShape)];\n let v2 = W[getIndexFromCoords4D(coord2, uniforms.wShape)];\n let v3 = W[getIndexFromCoords4D(coord3, uniforms.wShape)];\n return vec4(v0, v1, v2, v3);\n `;default:throw new Error(`innerElementSize ${s} is not supported.`)}},o=`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {\n ${`\n let outRow = row / uniforms.outShape[2];\n let outCol = row % uniforms.outShape[2];\n\n let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];\n let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.strides[0]);\n let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.strides[1]);\n if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {\n return ${Ae(r)}(0.0);\n }\n let coord = vec4(\n batch,\n i32(xR),\n i32(xC),\n col % uniforms.outBackprop[3]);\n return x[getIndexFromCoords4D(coord, uniforms.xShape)/${r}];`}\n }\n return ${Ae(r)}(0.0);`;return`\n fn mm_readA(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n ${o}\n }\n\n fn mm_readB(batch: i32, row : i32, col : i32) -> ${Ae(r)} {\n let coordX = uniforms.filterDims.x - 1 -\n row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);\n let coordY = uniforms.filterDims.y - 1 -\n (row / uniforms.outBackprop[3]) % uniforms.filterDims[1];\n if (row < uniforms.dimInner && col < uniforms.dimBOuter &&\n coordX >= 0 && coordY >= 0) {\n let rowInner = row % uniforms.outBackprop[3];\n let coord = vec4(coordX, coordY, col, rowInner);\n ${e(r)}\n }\n return ${Ae(r)}(0.0);\n }\n\n fn mm_write(batch: i32, row : i32, col : i32, valueInput : ${Ae(r)}) {\n if (row < uniforms.dimAOuter && col < uniforms.dimBOuter) {\n var value = valueInput;\n let outCoord = vec4(\n batch,\n row / uniforms.outShape[2],\n row % uniforms.outShape[2],\n col);\n result[getIndexFromCoords4D(outCoord, uniforms.outShape)/${r}] = value;\n }\n }`}var vx=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims : vec2, pads : vec2, strides : vec2, outBackprop : vec4, dimAOuter : i32, dimBOuter : i32, dimInner : i32,\",this.outputShape=e.inShape,y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),this.isVec4=e.inChannels%4===0&&e.outChannels%4===0,this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workgroupSize=lm(this.dispatchLayout,this.outputShape,this.isVec4),this.elementsPerThread=mm(this.dispatchLayout,this.outputShape,this.isVec4),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,this.elementsPerThread),this.isVec4&&(this.outputComponent=4,this.variableComponents=[4,1]),this.shaderKey=`conv2DDerInputMM_${this.isVec4}_${this.elementsPerThread}`}getUserCode(){let e=this.isVec4?_p(this.elementsPerThread,this.workgroupSize):Ep(this.elementsPerThread,this.workgroupSize);return`\n ${Due(this.isVec4?4:1)}\n ${e}\n `}};function Aue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:p,dataFormat:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(u),m=w.computeConv2DInfo(a,s.shape,i,1,p,c,!1,l),d=[{type:\"int32\",data:[m.filterHeight,m.filterWidth]},{type:\"int32\",data:[m.filterHeight-1-m.padInfo.top,m.filterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.batchSize,m.outHeight,m.outWidth,m.outChannels]}],f;if(A().getBool(\"WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE\")||m.dataFormat!==\"channelsLast\")f=new Cx(m);else{f=new vx(m);let h=m.inHeight*m.inWidth,g=m.inChannels,x=m.filterHeight*m.filterWidth*m.outChannels;d.push({type:\"uint32\",data:[h]},{type:\"uint32\",data:[g]},{type:\"uint32\",data:[x]})}return t.runWebGPUProgram(f,[n,s],\"float32\",d)}var $V={kernelName:rn,backendName:\"webgpu\",kernelFunc:Aue};var kx=class{constructor(e){this.variableNames=[\"x\",\"W\"],this.uniforms=\"filterDims: vec3, pads: vec3, strides: vec3, dilations: vec3,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"conv3dnaive\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords.x;\n let d2 = coords.u;\n\n let xFRCCorner = vec3(coords.y, coords.z, coords.w) * uniforms.strides - uniforms.pads;\n let xFCorner = xFRCCorner.x;\n let xRCorner = xFRCCorner.y;\n let xCCorner = xFRCCorner.z;\n\n let inputDepthNearestVec4 = (uniforms.xShape.u / 4) * 4;\n let inputDepthVec4Remainder = uniforms.xShape.u % 4;\n\n var dotProd = 0.0;\n for (var wF = 0; wF < uniforms.filterDims[0]; wF++) {\n let xF = xFCorner + wF * uniforms.dilations[0];\n if (xF < 0 || xF >= uniforms.xShape.y) {\n continue;\n }\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let xR = xRCorner + wR * uniforms.dilations[1];\n if (xR < 0 || xR >= uniforms.xShape.z) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let xC = xCCorner + wC * uniforms.dilations[2];\n if (xC < 0 || xC >= uniforms.xShape.w) {\n continue;\n }\n\n for (var d1 = 0; d1 < inputDepthNearestVec4; d1 += 4) {\n let xValues = vec4(\n getX(batch, xF, xR, xC, d1),\n getX(batch, xF, xR, xC, d1 + 1),\n getX(batch, xF, xR, xC, d1 + 2),\n getX(batch, xF, xR, xC, d1 + 3)\n );\n let wValues = vec4(\n getW(wF, wR, wC, d1, d2),\n getW(wF, wR, wC, d1 + 1, d2),\n getW(wF, wR, wC, d1 + 2, d2),\n getW(wF, wR, wC, d1 + 3, d2)\n );\n\n dotProd += dot(xValues, wValues);\n }\n\n if (inputDepthVec4Remainder == 1) {\n dotProd += getX(batch, xF, xR, xC, inputDepthNearestVec4) *\n getW(wF, wR, wC, inputDepthNearestVec4, d2);\n } else if (inputDepthVec4Remainder == 2) {\n let xValues = vec2(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1)\n );\n let wValues = vec2(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2)\n );\n dotProd += dot(xValues, wValues);\n } else if (inputDepthVec4Remainder == 3) {\n let xValues = vec3(\n getX(batch, xF, xR, xC, inputDepthNearestVec4),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 1),\n getX(batch, xF, xR, xC, inputDepthNearestVec4 + 2)\n );\n let wValues = vec3(\n getW(wF, wR, wC, inputDepthNearestVec4, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 1, d2),\n getW(wF, wR, wC, inputDepthNearestVec4 + 2, d2)\n );\n dotProd += dot(xValues, wValues);\n }\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }`}};function Fue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeConv3DInfo(n.shape,s.shape,a,p,i),c=[u.padInfo.front,u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationDepth,u.dilationHeight,u.dilationWidth]}],m=new kx(u),d=dt(n.dtype,s.dtype);return t.runWebGPUProgram(m,[n,s],d,l)}var RV={kernelName:on,backendName:\"webgpu\",kernelFunc:Fue};function Pue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:p}=o,u=w.computeConv3DInfo(n.shape,p,a,1,i),c=new Sx(u),l=[{type:\"int32\",data:[u.padInfo.front,u.padInfo.top,u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.batchSize]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.inDepth]},{type:\"int32\",data:[u.inHeight]},{type:\"int32\",data:[u.inWidth]}];return t.runWebGPUProgram(c,[n,s],s.dtype,l)}var DV={kernelName:ja,backendName:\"webgpu\",kernelFunc:Pue};function Oue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,pad:i,inputShape:p}=o,u=w.computeConv3DInfo(p,s.shape,a,1,i),c=new Ix(u),l=[{type:\"int32\",data:[u.filterDepth,u.filterHeight,u.filterWidth]},{type:\"int32\",data:[u.filterDepth-1-u.padInfo.front,u.filterHeight-1-u.padInfo.top,u.filterWidth-1-u.padInfo.left]},{type:\"int32\",data:[u.strideDepth,u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.outDepth]},{type:\"int32\",data:[u.outHeight]},{type:\"int32\",data:[u.outWidth]},{type:\"int32\",data:[u.outChannels]}];return t.runWebGPUProgram(c,[n,s],n.dtype,l)}var AV={kernelName:nn,backendName:\"webgpu\",kernelFunc:Oue};var Mue=ye({opType:Z.COS}),FV={kernelName:sn,backendName:\"webgpu\",kernelFunc:Mue};var Lue=ye({opType:Z.COSH}),PV={kernelName:an,backendName:\"webgpu\",kernelFunc:Lue};var Nx=class{constructor(e,t,o,n){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.uniforms=\"extrapolationValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0;let[s]=t;this.outputShape=[s,o[0],o[1],e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.methodId=n===\"bilinear\"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=[\"f32(uniforms.imageShape[1] - 1)\",\"f32(uniforms.imageShape[2] - 1)\"],[o,n,s]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,\"(y2-y1) * height_ratio\",`y1*${e} + f32(y)*(height_scale)`]:[\"0.0\",\"0.0\",`0.5 * (y1+y2) * ${e}`],[a,i,p]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,\"(x2-x1) * width_ratio\",`x1*${t} + f32(x)*(width_scale)`]:[\"0.0\",\"0.0\",`0.5 * (x1+x2) * ${t}`];return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let height_ratio = f32(${o});\n let width_ratio = f32(${a});\n let b = coords[0];\n let y = coords[1];\n let x = coords[2];\n let d = coords[3];\n // get box vals\n let y1 = getBoxes(b, 0);\n let x1 = getBoxes(b, 1);\n let y2 = getBoxes(b, 2);\n let x2 = getBoxes(b, 3);\n // get image in batch index\n let bInd = i32(round(getBoxInd(b)));\n if(bInd < 0 || bInd >= uniforms.outShape[0]) {\n return;\n }\n let height_scale = ${n};\n let width_scale = ${i};\n let in_y = ${s};\n if( in_y < 0.0 || in_y > ${e} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let in_x = ${p};\n if( in_x < 0.0 || in_x > ${t} ) {\n setOutputAtIndex(index, uniforms.extrapolationValue);\n return;\n }\n let sourceFracIndexCR = vec2(in_x,in_y);\n if(${this.methodId} == 1) {\n // Compute the four integer indices.\n let sourceFloorCR = vec2(sourceFracIndexCR);\n let sourceCeilCR = vec2(ceil(sourceFracIndexCR));\n let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);\n let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);\n let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);\n let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);\n let fracCR = sourceFracIndexCR - vec2(sourceFloorCR);\n let top = topLeft + (topRight - topLeft) * fracCR.x;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;\n let newValue = top + (bottom - top) * fracCR.y;\n setOutputAtIndex(index, newValue);\n } else {\n // Compute the coordinators of nearest neighbor point.\n let sourceNearestCR = vec2(floor(\n sourceFracIndexCR + vec2(0.5,0.5)));\n let newValue = getImage(\n bInd, sourceNearestCR.y, sourceNearestCR.x, d);\n setOutputAtIndex(index, newValue);\n }\n }\n }\n `}};var Bue=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:p,extrapolationValue:u}=o,c=new Nx(n.shape[3],s.shape,i,p),l=[{type:\"float32\",data:[u]}];return t.runWebGPUProgram(c,[n,s,a],\"float32\",l)},OV={kernelName:cn,backendName:\"webgpu\",kernelFunc:Bue};var Dp;(function(r){r.Prod=\"*\",r.Sum=\"+\"})(Dp||(Dp={}));var xm=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"index : f32,\",this.size=!0,this.workgroupSize=[128,1,1],this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.exclusive=o,this.reverse=n,this.op=e,this.shaderKey=`cum_${this.op}_${this.exclusive}_${this.reverse}`}getUserCode(){let e=this.outputShape.length,t=this.op===Dp.Prod?\"1.0\":\"0.0\",o=this.exclusive?t:`getX(${MV(e,\"coords\",this.op)})`,n=this.outputShape[this.outputShape.length-1],s=\"\",a=\"\";return this.exclusive?(s=this.reverse?`end != ${n-1}`:\"end != 0\",a=this.reverse?\"end + 1\":\"end - 1\"):(s=this.reverse?`end + pow2 < ${n}`:\"end >= pow2\",a=this.reverse?\"end + pow2\":\"end - pow2\"),`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var coords = getCoordsFromIndex(index);\n\n let end = ${LV(e,\"coords\",this.op)};\n var val = ${o};\n let pow2 = i32(pow(2.0, uniforms.index));\n if (${s}) {\n let idx = ${a};\n ${LV(e,\"coords\",this.op)} = idx;\n val ${this.op}= getX(${MV(e,\"coords\",this.op)});\n }\n setOutputAtIndex(index, val);\n }\n }\n `}};function MV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function LV(r,e,t){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative ${t} for rank ${r} is not yet supported`)}function Tx(r,e,t,o,n,s){let a=e.shape.length,i=w.getAxesPermutation([o],a),p=e;i!=null&&(p=xr({inputs:{x:e},backend:t,attrs:{perm:i}}));let u=w.getInnerMostAxes(1,a)[0];if(u!==a-1)throw new Error(`WebGPU cumprod shader expects an inner-most axis=${e.shape.length-1} but got axis=${o}`);let c=p.shape[u],l=At({inputs:{x:p},backend:t});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let d=new xm(r,p.shape,!1,s),f=l,h=[{type:\"float32\",data:[m]}];l=t.runWebGPUProgram(d,[l],l.dtype,h),t.disposeData(f.dataId)}if(n){let m=new xm(r,p.shape,n,s),d=l,f=[{type:\"float32\",data:[0]}];l=t.runWebGPUProgram(m,[l],l.dtype,f),t.disposeData(d.dataId)}if(i!=null){let m=w.getUndoAxesPermutation(i),d=xr({inputs:{x:l},backend:t,attrs:{perm:m}});return t.disposeData(l.dataId),t.disposeData(p.dataId),d}return l}function zue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Tx(Dp.Prod,n,t,s,a,i)}var BV={kernelName:un,backendName:\"webgpu\",kernelFunc:zue};function Vue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o;return Tx(Dp.Sum,n,t,s,a,i)}var zV={kernelName:pn,backendName:\"webgpu\",kernelFunc:Vue};function Wue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o,p=n.shape.length===1,c=y.sizeFromShape(s.shape)>0,l=s.dtype,m=p?[n.shape[0]]:[n.shape[0],n.shape[1]],d=p?[a]:[n.shape[0],a],f=vt({backend:t,attrs:{shape:d,value:0,dtype:l}}),h=new Qc(m,c,i),g=[{type:\"int32\",data:[a]}],x=c?[n,s]:[n];return t.runWebGPUProgram(h,x,l,g,f)}var VV={kernelName:ra,backendName:\"webgpu\",kernelFunc:Wue};var _x=class{constructor(e,t){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.uniforms=\"blockSize : i32,\",this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let h = ${this.getHeightCoordString()};\n let w = ${this.getWidthCoordString()};\n let d = ${this.getDepthCoordString()};\n\n let in_h = h / uniforms.blockSize;\n let offset_h = h % uniforms.blockSize;\n let in_w = w / uniforms.blockSize;\n let offset_w = w % uniforms.blockSize;\n let offset_d = (offset_h * uniforms.blockSize + offset_w) *\n ${this.getOutputDepthSize()};\n let in_d = d + offset_d;\n\n let rlt = ${this.getInputSamplingString()};\n setOutputAtIndex(index, rlt);\n }\n }`}getHeightCoordString(){return this.dataFormat===\"NHWC\"?\"coords[1]\":\"coords[2]\"}getWidthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[2]\":\"coords[3]\"}getDepthCoordString(){return this.dataFormat===\"NHWC\"?\"coords[3]\":\"coords[1]\"}getOutputDepthSize(){return this.dataFormat===\"NHWC\"?\"uniforms.outShape[3]\":\"uniforms.outShape[1]\"}getInputSamplingString(){return this.dataFormat===\"NHWC\"?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"}};function Uue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o,i=n.shape[0],p=a===\"NHWC\"?n.shape[1]:n.shape[2],u=a===\"NHWC\"?n.shape[2]:n.shape[3],c=a===\"NHWC\"?n.shape[3]:n.shape[1],l=p*s,m=u*s,d=c/(s*s),f=a===\"NHWC\"?[i,l,m,d]:[i,d,l,m],h=[{type:\"int32\",data:[s]}],g=new _x(f,a);return t.runWebGPUProgram(g,[n],n.dtype,h)}var WV={kernelName:ln,backendName:\"webgpu\",kernelFunc:Uue};var Ex=class{constructor(e,t,o,n=!1,s=null,a=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=\"pads : vec2, inDims : vec2,\",this.workgroupSize=[16,16,1],this.outputShape=e,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),n&&this.variableNames.push(\"bias\"),a&&this.variableNames.push(\"preluActivationWeights\"),this.addBias=n,this.activation=s,this.hasPreluActivation=a,this.filterHeight=t,this.filterWidth=o,this.shaderKey=`depthwiseNCHW_${this.activation}_${this.filterHeight}_${this.filterWidth}`}getUserCode(){let e=this.filterWidth*this.filterHeight,t=this.workgroupSize[0]*this.workgroupSize[1]*this.workgroupSize[2],o=this.workgroupSize[1]+this.filterHeight-1,n=this.workgroupSize[0]+this.filterWidth-1;return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n var mm_Asub : array, ${o}>;\n var mm_Bsub : array, ${this.filterHeight}>;\n fn readX(batch : i32, channel : i32, row : i32, col : i32) -> f32 {\n var value = 0.0;\n if (row >=0 && row < uniforms.inDims[0] && col >=0 && col < uniforms.inDims[1])\n {\n value = getX(batch, channel, row, col);\n }\n return value;\n }\n\n ${G()} {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.zw) - uniforms.pads;\n let channelMul = uniforms.wShape[3];\n let d1 = coords[1] / channelMul;\n let q = coords[1] % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n\n let localRow = i32(localId.y);\n let localCol = i32(localId.x);\n\n // Load one tile of X into local memory.\n for (var inputRow = localRow; inputRow < ${o}; inputRow = inputRow + ${this.workgroupSize[1]}) {\n for (var inputCol = localCol; inputCol < ${n}; inputCol = inputCol + ${this.workgroupSize[0]}) {\n let rowOffset = inputRow - localRow;\n let colOffset = inputCol - localCol;\n mm_Asub[inputRow][inputCol] = readX(batch, d1, inputRowStart + rowOffset, inputColStart + colOffset);\n }\n }\n\n // Load one tile of W into local memory.\n var wIndex = i32(localIndex);\n ${e, inDims : vec2, virtualWidth : i32,\",this.workgroupSize=[64,1,1],this.workPerThread=4,this.outputComponent=4,this.outputShape=e.outShape,this.virtualWidth=Math.ceil(this.outputShape[2]/this.workPerThread)*this.workPerThread;let s=[this.outputShape[0],this.outputShape[1],this.virtualWidth,this.outputShape[3]];this.dispatchLayout=X(s),this.dispatch=H(this.dispatchLayout,s,this.workgroupSize,[this.outputComponent*this.workPerThread,1,1]),y.assert(e.dataFormat===\"channelsLast\",()=>\"TODO: NCHW is unimplemented\"),t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwiseVec4_${o}_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.convInfo.strideHeight}_${this.convInfo.strideWidth}_${this.workPerThread}`}getUserCode(){let e=(this.workPerThread-1)*this.convInfo.strideWidth+this.convInfo.filterWidth,t=this.convInfo.strideHeight,o=this.convInfo.strideWidth;return`\n ${dr(this.activation,this.hasPreluActivation,!0,4)}\n fn readX(batch : i32, row : i32, col : i32, channel : i32) -> vec4 {\n var value = vec4(0.0);\n if (col >=0 && col < uniforms.inDims[1]) {\n value = getX(batch, row, col, channel);\n }\n return value;\n }\n\n ${G(\"index\")} {\n let width0 = uniforms.outShape[3] / ${this.outputComponent};\n let d1 = (index % width0) * ${this.outputComponent};\n var index1 = index / width0;\n let width1 = uniforms.virtualWidth / ${this.workPerThread};\n let c = (index1 % width1) * ${this.workPerThread};\n index1 = index1 / width1;\n let r = index1 % uniforms.outShape[1];\n let batch = index1 / uniforms.outShape[1];\n\n let xRCCorner = vec2(r, c) * vec2(${t}, ${o}) - uniforms.pads;\n\n let xRCorner = xRCCorner.x;\n let xCCorner = xRCCorner.y;\n var xVals : array, ${e}>;\n var dotProd : array, ${this.workPerThread}>;\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = vec4(0.0);\n }\n\n // Use constant instead of uniform can give better performance.\n for (var wR = 0; wR < ${this.convInfo.filterHeight}; wR = wR + 1) {\n let xR = xRCorner + wR;\n if (xR >=0 && xR < uniforms.inDims[0]) {\n for (var i = 0; i < ${e}; i++) {\n xVals[i] = readX(batch, xR, xCCorner + i, d1);\n }\n for (var wC = 0; wC < ${this.convInfo.filterWidth}; wC = wC + 1) {\n let wValue = getW(wR, wC, d1, 0);\n for (var i = 0; i < ${this.workPerThread}; i++) {\n dotProd[i] = fma(xVals[i * ${o} + wC], wValue, dotProd[i]);\n }\n }\n }\n }\n\n for (var i = 0; i < ${this.workPerThread}; i = i + 1) {\n let coords = vec4(batch, r, c + i, d1);\n if (coordsInBounds4D(coords, uniforms.outShape)) {\n var value = dotProd[i];\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n }\n `}};var el=class{constructor(e,t=!1,o=null,n=!1){this.variableNames=[\"x\",\"W\"],this.uniforms=`pads : vec2, inDims : vec2, filterHeight : i32,\n filterWidth : i32, strides : vec2, dilations : vec2,`,this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.isChannelsLast=e.dataFormat===\"channelsLast\",t&&this.variableNames.push(\"bias\"),n&&this.variableNames.push(\"preluActivationWeights\"),this.convInfo=e,this.addBias=t,this.activation=o,this.hasPreluActivation=n,this.shaderKey=`depthwise_${this.activation}_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?\"getX(batch, xR, xC, d1);\":\"getX(batch, d1, xR, xC);\";return`\n ${dr(this.activation,this.hasPreluActivation,!1,4)}\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n let xRCCorner = vec2(coords.${this.isChannelsLast?\"yz\":\"zw\"}) * uniforms.strides - uniforms.pads;\n let d2 = coords[${this.isChannelsLast?3:1}];\n let channelMul = uniforms.wShape[3];\n let d1 = d2 / channelMul;\n let q = d2 % channelMul;\n\n let inputRowStart = xRCCorner.x;\n let inputColStart = xRCCorner.y;\n let inputRowEnd = inputRowStart + uniforms.filterHeight *\n uniforms.dilations[0];\n let inputColEnd = inputColStart + uniforms.filterWidth *\n uniforms.dilations[1];\n\n // Convolve x(?, ?, d1)|x(d1, ?, ?) with w(:, :, d1, q) to get\n // y(yR, yC, d2)|y(d2, yR, yC). ? = to be determined. : = across all\n // values in that axis. x(?, ?, d1) and y(yR, yC, d2) is for NHWC.\n // x(d1, ?, ?) and y(d2, yR, yC) is for NCHW.\n var value = 0.0;\n\n // Extract if checking out of for loop for performance.\n if (inputRowStart >= 0 && inputColStart >= 0 &&\n inputRowEnd < uniforms.inDims[0] &&\n inputColEnd < uniforms.inDims[1]) {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n } else {\n for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {\n let xR = inputRowStart + wR * uniforms.dilations[0];\n\n if (xR < 0 || xR >= uniforms.inDims[0]) {\n continue;\n }\n\n for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {\n let xC = inputColStart + wC * uniforms.dilations[1];\n\n if (xC < 0 || xC >= uniforms.inDims[1]) {\n continue;\n }\n\n let xVal = ${e};\n let wVal = getW(wR, wC, d1, q);\n value = value + xVal * wVal;\n }\n }\n }\n ${Zr(this.addBias,this.activation)}\n setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], value);\n }\n }\n `}};function Gue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:p,dilations:u,dimRoundingMode:c}=o,l=w.convertConv2DDataFormat(p),m=u;m==null&&(m=[1,1]);let d=w.computeConv2DInfo(n.shape,s.shape,a,m,i,c,!0,l),f=[{type:\"int32\",data:[d.padInfo.top,d.padInfo.left]},{type:\"int32\",data:[d.inHeight,d.inWidth]}],h=d.dataFormat===\"channelsLast\",g;return!h&&d.inHeight>16&&d.inWidth>16&&d.strideHeight===1&&d.strideWidth===1&&d.dilationWidth===1&&d.dilationHeight===1&&d.inChannels===d.outChannels?g=new Ex(d.outShape,d.filterHeight,d.filterWidth):h&&d.outHeight>4&&d.outWidth>4&&d.strideWidth<=2&&d.inChannels===d.outChannels&&d.dilationHeight===1&&d.dilationWidth===1&&d.inChannels%4===0?(g=new Jc(d),f.push({type:\"int32\",data:[g.virtualWidth]})):(g=new el(d),f.push({type:\"int32\",data:[d.filterHeight]},{type:\"int32\",data:[d.filterWidth]},{type:\"int32\",data:[d.strideHeight,d.strideWidth]},{type:\"int32\",data:[d.dilationHeight,d.dilationWidth]})),t.runWebGPUProgram(g,[n,s],n.dtype,f)}var UV={kernelName:mn,backendName:\"webgpu\",kernelFunc:Gue};var $x=class{constructor(e){this.variableNames=[\"x\",\"dy\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2, outHeight : i32,\n outWidth : i32, inHeight : i32, inWidth : i32, batchSize : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_filter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let wR = coords[0];\n let wC = coords[1];\n let d1 = coords[2];\n let dm = coords[3];\n let d2 = d1 * uniforms.channelMul + dm;\n\n var dotProd = 0.0;\n for (var b = 0; b < uniforms.batchSize; b++) {\n for (var yR = 0; yR < uniforms.outHeight; yR++) {\n let xR = wR + yR * uniforms.strides[0] - uniforms.pads[0];\n\n if (xR < 0 || xR >= uniforms.inHeight) {\n continue;\n }\n\n for (var yC = 0; yC < uniforms.outWidth; yC++) {\n let xC = wC + yC * uniforms.strides[1] - uniforms.pads[1];\n\n if (xC < 0 || xC >= uniforms.inWidth) {\n continue;\n }\n\n let dyValue = getDy(b, yR, yC, d2);\n let xValue = getX(b, xR, xC, d1);\n dotProd += xValue * dyValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Rx=class{constructor(e){this.variableNames=[\"dy\",\"W\"],this.uniforms=`strides : vec2, pads : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32, channelMul : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"depthwise_conv2d_backprop_input\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d1 = coords[3];\n let dyCorner = coords.yz - uniforms.pads;\n let dyRCorner = dyCorner.x;\n let dyCCorner = dyCorner.y;\n\n var dotProd = 0.0;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n\n let idyR = i32(dyR);\n let wRPerm = uniforms.filterDims[0] - 1 - wR;\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n\n let idyC = i32(dyC);\n let wCPerm = uniforms.filterDims[1] - 1 - wC;\n\n for (var dm = 0; dm < uniforms.channelMul; dm++) {\n let d2 = d1 * uniforms.channelMul + dm;\n let xValue = getDy(batch, idyR, idyC, d2);\n let wValue = getW(wRPerm, wCPerm, d1, dm);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Hue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,filterShape:c}=o,l=w.computeConv2DInfo(n.shape,c,a,i,p,u,!0),m=new $x(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.inHeight]},{type:\"int32\",data:[l.inWidth]},{type:\"int32\",data:[l.batchSize]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],\"float32\",d)}var GV={kernelName:Pi,backendName:\"webgpu\",kernelFunc:Hue};function Kue(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:p,dimRoundingMode:u,inputShape:c}=o,l=w.computeConv2DInfo(c,s.shape,a,i,p,u,!0),m=new Rx(l),d=[{type:\"int32\",data:[l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.filterHeight-1-l.padInfo.top,l.filterWidth-1-l.padInfo.left]},{type:\"int32\",data:[l.filterHeight,l.filterWidth]},{type:\"int32\",data:[l.outHeight]},{type:\"int32\",data:[l.outWidth]},{type:\"int32\",data:[l.outChannels/l.inChannels]}];return t.runWebGPUProgram(m,[n,s],n.dtype,d)}var HV={kernelName:Oi,backendName:\"webgpu\",kernelFunc:Kue};var Dx=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"diag\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let value = select(0.0, getX(coords[0]), coords[0] == coords[1]);\n setOutputAtIndex(index, value);\n }\n }\n `}};function que(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=y.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new Dx(s),p=t.runWebGPUProgram(i,[a],a.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var KV={kernelName:oa,backendName:\"webgpu\",kernelFunc:que};var Ax=class{constructor(e){this.variableNames=[\"x\",\"w\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"dilation2d\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let neg_infinity = -3.4e38;\n let coords = getOutputCoords();\n let batch = coords.x;\n let d1 = coords.w;\n let outTopLeftCorner = coords.yz * uniforms.strides - uniforms.pads;\n let hBeg = outTopLeftCorner.x;\n let wBeg = outTopLeftCorner.y;\n\n var curVal = neg_infinity;\n for (var h = 0; h < uniforms.filterDims[0]; h = h + 1) {\n let hIn = hBeg + h * uniforms.dilations[0];\n\n if (hIn >= 0 && hIn < uniforms.xShape[1]) {\n for (var w = 0; w < uniforms.filterDims[1]; w = w + 1) {\n let wIn = wBeg + w * uniforms.dilations[1];\n\n if (wIn >= 0 && wIn < uniforms.xShape[2]) {\n let val = getX(batch, hIn, wIn, d1) + getW(h, w, d1);\n if (val > curVal) {\n curVal = val;\n }\n }\n }\n }\n }\n\n setOutputAtIndex(index, curVal);\n }\n }\n `}};function jue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:p}=o,u=w.computeDilation2DInfo(n.shape,s.shape,a,i,\"NHWC\",p),c=[u.padInfo.top,u.padInfo.left],l=[{type:\"int32\",data:[u.filterHeight,u.filterWidth]},{type:\"int32\",data:[...c]},{type:\"int32\",data:[u.strideHeight,u.strideWidth]},{type:\"int32\",data:[u.dilationHeight,u.dilationWidth]}],m=new Ax(u);return t.runWebGPUProgram(m,[n,s],n.dtype,l)}var qV={kernelName:dn,backendName:\"webgpu\",kernelFunc:jue};var Fx=class{constructor(e,t){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.inShape,this.dispatchLayout=X(e.outShape),this.dispatch=H(this.dispatchLayout,e.outShape,this.workgroupSize),t!==\"float32\"&&t!==\"int32\")throw new Error(`Dilation2DBackpropInput only supports float32 and int32\n types, does not support ${t} type.`);this.type=t,this.shaderKey=\"dilation2DBackpropInput\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var xRMax = 0;\n var xCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n xRMax = xR;\n xCMax = xC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.xShape[3] *\n (xCMax + uniforms.xShape[2] * (xRMax + uniforms.xShape[1] * b));\n let value = getDy(b, r, c, d);\n ${Qr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}},Px=class{constructor(e,t,o){if(this.variableNames=[\"x\",\"w\",\"dy\"],this.uniforms=\"filterDims: vec2, pads: vec2, strides: vec2, dilations: vec2, dySize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e.filterShape,this.dispatchLayout=X(e.outShape),this.dispatch=H(this.dispatchLayout,e.outShape,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`Dilation2DBackpropFilter only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"dilation2DBackpropFilter\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.dySize) {\n let coords = getDyCoordsFromIndex(index);\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let dyCorner = vec2(r, c) * uniforms.strides - uniforms.pads;\n var curVal = -3.4e38; // neg_infinity\n var wRMax = 0;\n var wCMax = 0;\n\n // In the case of multiple argmax branches, we only back-propagate\n // along the last branch, i.e., the one with largest value of\n // 'wR * uniforms.filterDims[1] + wC', similarly to the max-pooling\n // backward routines.\n for (var wR = 0; wR < uniforms.filterDims[0]; wR++) {\n let xR = dyCorner.x + wR * uniforms.dilations[0];\n\n if (xR >= 0 && xR < uniforms.xShape[1]) {\n for (var wC = 0; wC < uniforms.filterDims[1]; wC++) {\n let xC = dyCorner.y + wC * uniforms.dilations[1];\n\n if (xC >= 0 && xC < uniforms.xShape[2]) {\n let val = getX(b, xR, xC, d) + getW(wR, wC, d);\n if (val > curVal) {\n curVal = val;\n wRMax = wR;\n wCMax = wC;\n }\n }\n }\n }\n }\n\n let flatIndexIn = d + uniforms.wShape[2] * (wCMax + wRMax * uniforms.wShape[1]);\n let value = getDy(b, r, c, d);\n ${Qr(\"&result[flatIndexIn]\",\"value\",this.type)}\n }\n }\n `}};function Xue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=s.dtype,m=new Px(c,s.shape,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:t,attrs:{shape:s.shape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var jV={kernelName:Li,backendName:\"webgpu\",kernelFunc:Xue};function Yue(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,dy:a}=e,{strides:i,pad:p,dilations:u}=o,c=w.computeDilation2DInfo(n.shape,s.shape,i,p,\"NHWC\",u),l=n.dtype,m=new Fx(c,l),d=[{type:\"int32\",data:[c.filterHeight,c.filterWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[y.sizeFromShape(c.outShape)]}],f=vt({backend:t,attrs:{shape:c.inShape,value:0,dtype:l}});return t.runWebGPUProgram(m,[n,s,a],l,d,f)}var XV={kernelName:Mi,backendName:\"webgpu\",kernelFunc:Yue};var Ox=class{constructor(e,t,o){this.variableNames=[\"Image\"],this.uniforms=\"alpha: f32,\",this.workgroupSize=[64,1,1],this.pixelsOpType=wi.DRAW,this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.type=t,this.textureFormat=o,this.shaderKey=`draw_${t}_${o}`}getUserCode(){let e,t=this.type===\"float32\"?\"value\":\"value / 255.0\";return e=`\n if (uniforms.numChannels == 1) {\n rgba[0] = ${t};\n rgba[1] = ${t};\n rgba[2] = ${t};\n } else {\n rgba[d] = ${t};\n }`,`\n @group(0) @binding(0) var outImage : texture_storage_2d<${this.textureFormat}, write>;\n ${G(\"index\")} {\n if (index < uniforms.size) {\n var rgba = vec4(0.0, 0.0, 0.0, uniforms.alpha);\n for (var d = 0; d < uniforms.numChannels; d = d + 1) {\n let value = f32(inBuf[index * uniforms.numChannels + d]);\n ${e}\n }\n rgba.x = rgba.x * rgba.w;\n rgba.y = rgba.y * rgba.w;\n rgba.z = rgba.z * rgba.w;\n let coords = getCoordsFromIndex(index);\n textureStore(outImage, vec2(coords.yx), rgba);\n }\n }\n `}};function Que(r){let{inputs:e,backend:t,attrs:o}=r,{image:n}=e,{canvas:s,options:a}=o,[i,p]=n.shape.slice(0,2),{imageOptions:u}=a||{},c=(u==null?void 0:u.alpha)||1,l=t.device.features.has(\"bgra8unorm-storage\")?\"bgra8unorm\":\"rgba8unorm\",m=[i,p],d=new Ox(m,n.dtype,l);s.width=p,s.height=i;let f=\"webgpu\",h=s.getContext(f),g;h||(g=new OffscreenCanvas(p,i),h=g.getContext(f));let x=n.shape.length===3?n.shape[2]:1;h.configure({device:t.device,format:l,usage:GPUTextureUsage.STORAGE_BINDING,alphaMode:\"premultiplied\"});let b=\"int32\",C=t.makeTensorInfo(m,b),S=t.tensorMap.get(C.dataId);S.resource=h.getCurrentTexture(),S.external=!0;let k=[{type:\"uint32\",data:[x]},{type:\"float32\",data:[c]}];if(t.runWebGPUProgram(d,[n],b,k,C),g){let _=s.getContext(\"2d\");if(!_)throw new Error(\"Please make sure this canvas has only been used for 2d or webgpu context!\");_.drawImage(g,0,0)}return t.disposeData(C.dataId),n}var YV={kernelName:$u,backendName:\"webgpu\",kernelFunc:Que};var i0=et({opType:fe.MUL,cpuKernelImpl:Rz,supportsComplex:!0}),QV={kernelName:Xn,backendName:\"webgpu\",kernelFunc:i0};function u0(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"sum\",t)}var ZV={kernelName:Ss,backendName:\"webgpu\",kernelFunc:u0};function Zue(r){let{inputs:e,backend:t,attrs:o}=r,{equation:n}=o,s=e,{allDims:a,summedDims:i,idDims:p}=w.decodeEinsumEquation(n,s.length);w.checkEinsumDimSizes(a.length,p,s);let{path:u,steps:c}=w.getEinsumComputePath(i,p),l=c.length,m=null,d=a.length,f=[];for(let h=0;h=0&&(m=u0({inputs:{x:m},backend:t,attrs:{axis:u[h]-(a.length-d),keepDims:!1}}),f.push(m)),d--)}for(let h of f)h!==m&&t.disposeData(h.dataId);return m}var JV={kernelName:Bi,backendName:\"webgpu\",kernelFunc:Zue};var Jue=ye({opType:Z.ELU}),eW={kernelName:hn,backendName:\"webgpu\",kernelFunc:Jue};var epe=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=new Ii(fe.ELU_DER,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],o.dtype)},tW={kernelName:Xa,backendName:\"webgpu\",kernelFunc:epe};var tpe=et({opType:fe.EQUAL,dtype:\"bool\",cpuKernelImpl:gz}),rW={kernelName:xn,backendName:\"webgpu\",kernelFunc:tpe};var rpe=ye({opType:Z.ERF}),oW={kernelName:gn,backendName:\"webgpu\",kernelFunc:rpe};var ope=ye({opType:Z.EXP,cpuKernelImpl:xz,dtype:\"float32\"}),nW={kernelName:yn,backendName:\"webgpu\",kernelFunc:ope};function Mx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),p=n;return n<0&&(y.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),p=a+n+1),i.splice(p,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var sW={kernelName:na,backendName:\"webgpu\",kernelFunc:Mx};var npe=ye({opType:Z.EXPM1,cpuKernelImpl:yz}),aW={kernelName:bn,backendName:\"webgpu\",kernelFunc:npe};var ym=class{constructor(e,t){this.variableNames=[\"real\",\"imag\"],this.outputShape=[],this.uniforms=\"exponentMultiplier : f32, denominator: f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.component=e,this.shaderKey=`fft_${e}`}getUserCode(){return`\n fn unaryOpComplex(real: f32, expR: f32, imag: f32, expI: f32) -> f32 {\n ${this.component===\"real\"?\"return real * expR - imag * expI;\":\"return real * expI + imag * expR;\"}\n }\n\n fn mulMatDFT(batch: i32, index: i32) -> f32 {\n let indexRatio = f32(index) / f32(uniforms.realShape[1]);\n let exponentMultiplierTimesIndexRatio =\n uniforms.exponentMultiplier * indexRatio;\n\n var result = 0.0;\n\n for (var i = 0; i < uniforms.realShape[1]; i = i + 1) {\n // x = (-2|2 * PI / N) * index * i;\n let x = exponentMultiplierTimesIndexRatio * f32(i);\n let expR = cos(x);\n let expI = sin(x);\n let real = getReal(batch, i);\n let imag = getImag(batch, i);\n\n result = result +\n unaryOpComplex(real, expR, imag, expI) / uniforms.denominator;\n }\n\n return result;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n setOutputAtIndex(index, mulMatDFT(coords[0], coords[1]));\n }\n }\n `}};function Lx(r,e,t){let o=t.tensorMap.get(r.dataId),n=y.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=[],p=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}});i.push(p);let u=p.shape,c=new ym(\"real\",u),l=new ym(\"imag\",u),m=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:u},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:u}],d=e?2*Math.PI:-2*Math.PI,f=e?u[1]:1,h=[{type:\"float32\",data:[d]},{type:\"float32\",data:[f]}],g=t.runWebGPUProgram(c,m,\"float32\",h);i.push(g);let x=t.runWebGPUProgram(l,m,\"float32\",h);i.push(x);let b=xo({inputs:{real:g,imag:x},backend:t});i.push(b);let C=pe({inputs:{x:b},backend:t,attrs:{shape:r.shape}});return i.forEach(S=>t.disposeData(S.dataId)),C}function spe(r){let{inputs:e,backend:t}=r,{input:o}=e;return Lx(o,!1,t)}var iW={kernelName:zi,backendName:\"webgpu\",kernelFunc:spe};var Bx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"flipLeftRight\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordX = uniforms.xShape[2] - coords[2] - 1;\n let outputValue = getX(coords[0], coords[1], coordX, coords[3]);\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var uW={kernelName:Cn,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new Bx(t.shape);return o.runWebGPUProgram(n,[t],t.dtype)}};var ape=ye({opType:Z.FLOOR,cpuKernelImpl:bz}),pW={kernelName:wn,backendName:\"webgpu\",kernelFunc:ape};var ipe=et({opType:fe.FLOOR_DIV,cpuKernelImpl:Cz,dtype:\"int32\"}),cW={kernelName:Sn,backendName:\"webgpu\",kernelFunc:ipe};var zx=class{constructor(e,t,o=!1){this.pixelsOpType=wi.FROM_PIXELS,this.outputShape=[0],this.variableNames=[],this.workgroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[t,1,1]),this.importVideo=o,this.shaderKey=`fromPixels_${this.importVideo}`}getUserCode(){let e=this.importVideo?\"textureLoad(src, vec2(coords.yx));\":\"textureLoad(src, vec2(coords.yx), 0)\";return`\n @binding(1) @group(0) var src: ${this.importVideo?\"texture_external\":\"texture_2d\"};\n ${G(\"index\")} {\n let flatIndex = index * uniforms.numChannels;\n if (flatIndex < uniforms.size) {\n let coords = getCoordsFromIndex(flatIndex);\n let values = ${e};\n for (var i = 0; i < uniforms.numChannels; i = i + 1) {\n result[flatIndex + i] = i32(floor(255.0 * values[i]));\n }\n }\n }\n `}};var lW={kernelName:Du,backendName:\"webgpu\",kernelFunc:upe},tl,p0=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");function upe(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o;if(n==null)throw new Error(\"pixels passed to tf.browser.fromPixels() can not be null\");let a=typeof HTMLVideoElement!=\"undefined\"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!=\"undefined\"&&n instanceof HTMLImageElement,p=typeof HTMLCanvasElement!=\"undefined\"&&n instanceof HTMLCanvasElement||typeof OffscreenCanvas!=\"undefined\"&&n instanceof OffscreenCanvas,u=typeof ImageBitmap!=\"undefined\"&&n instanceof ImageBitmap,[c,l]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],m=[l,c,s],d=A().getBool(\"WEBGPU_IMPORT_EXTERNAL_TEXTURE\")&&a,f=a||i;if(u||p||f){let b;if(d)b=t.device.importExternalTexture({source:n});else{if(f){let L=A().getBool(\"CANVAS2D_WILL_READ_FREQUENTLY_FOR_GPU\");(tl==null||L!==p0)&&(p0=L,tl=document.createElement(\"canvas\").getContext(\"2d\",{willReadFrequently:p0})),tl.canvas.width=c,tl.canvas.height=l,tl.drawImage(n,0,0,c,l),n=tl.canvas}let P=GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING,M=t.textureManager.acquireTexture(m[1],m[0],\"rgba8unorm\",P);t.queue.copyExternalImageToTexture({source:n},{texture:M},[m[1],m[0]]),b=M}let C=y.sizeFromShape(m),S=y.computeStrides(m),k=new zx(m,s,d),_=[{type:\"uint32\",data:[C]},{type:\"uint32\",data:[s]},{type:\"uint32\",data:[...S]}],$=t.makeTensorInfo([l,c],\"int32\"),R=t.tensorMap.get($.dataId);R.resource=b;let D=t.runWebGPUProgram(k,[$],\"int32\",_);return t.disposeData($.dataId),D}let h=n.data,g=h;if(s!=null&&s!==4){g=new Uint8Array(n.width*n.height*s);let b=h.length,C=0;for(let S=0;S(xValue, -meanValue, offsetValue), vec3(inv, inv, 1.0)));\n }\n }\n `}};var mW={kernelName:In,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o,scale:n,offset:s,mean:a,variance:i}=r,{varianceEpsilon:p}=e,u=t,c=[o,a,i],l=null;s!=null&&(l=s.shape,c.push(s));let m=null;n!=null&&(m=n.shape,c.push(n));let d=new Vx(o.shape,a.shape,i.shape,l,m),f=[{type:\"float32\",data:[p]}];return u.runWebGPUProgram(d,c,o.dtype,f)}};function ppe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dataFormat:c,dilations:l,dimRoundingMode:m,activation:d,leakyreluAlpha:f}=o,h=w.convertConv2DDataFormat(c),g=w.computeConv2DInfo(n.shape,s.shape,p,l,u,m,!1,h);return bx({x:n,filter:s,convInfo:g,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:f,activation:d})}var dW={kernelName:Io,backendName:\"webgpu\",kernelFunc:ppe};function cpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:p,pad:u,dilations:c,dimRoundingMode:l,activation:m,leakyreluAlpha:d}=o,f=c;f==null&&(f=[1,1]),y.assert(w.eitherStridesOrDilationsAreOne(p,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${p} and dilations '${f}'`);let h=w.computeConv2DInfo(n.shape,s.shape,p,f,u,l,!0),g=[n,s],x=a!=null,b=i!=null;x&&g.push(a),b&&g.push(i);let C=[{type:\"int32\",data:[h.padInfo.top,h.padInfo.left]},{type:\"int32\",data:[h.inHeight,h.inWidth]}],S;return h.outHeight>4&&h.outWidth>4&&h.strideWidth<=2&&h.inChannels===h.outChannels&&h.dilationHeight===1&&h.dilationWidth===1&&h.inChannels%4===0?(S=new Jc(h,x,m,b),C.push({type:\"int32\",data:[S.virtualWidth]})):(S=new el(h,x,m,b),C.push({type:\"int32\",data:[h.filterHeight]},{type:\"int32\",data:[h.filterWidth]},{type:\"int32\",data:[h.strideHeight,h.strideWidth]},{type:\"int32\",data:[h.dilationHeight,h.dilationWidth]})),m===\"leakyrelu\"&&(C.push({type:\"float32\",data:[d]}),S.uniforms+=\" alpha : f32,\"),t.runWebGPUProgram(S,g,\"float32\",C)}var fW={kernelName:vo,backendName:\"webgpu\",kernelFunc:cpe};var Wx=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32, strides : ${ft(e)},`}getUserCode(){let e;return this.sliceDim>1?e=\"uniforms.strides[j]\":e=\"uniforms.strides\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var flattenIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexTemp = i32(round(getIndices(coords[0], j)));\n let strideNum = ${e};\n flattenIndex = flattenIndex + indexTemp * strideNum;\n }\n\n setOutputAtIndex(index, getA(flattenIndex, coords[1]));\n }\n }\n `}};function lpe(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],i=y.sizeFromShape(o.shape),[p,u,c,l]=w.prepareAndValidate(o,n),m=pe({inputs:{x:n},backend:t,attrs:{shape:[u,a]}}),d=pe({inputs:{x:o},backend:t,attrs:{shape:[y.sizeFromShape(o.shape)/c,c]}});if(t.shouldExecuteOnCPU([o,n])||o.dtype===\"string\"){let b=t.readSync(n.dataId),C=t.bufferSync(o),S=wz(b,C,o.dtype,u,a,c,l,o.shape,i);return t.makeTensorInfo(p,o.dtype,S.values)}let f=new Wx(a,[u,c]),h=[{type:\"int32\",data:[a]},{type:\"int32\",data:l}],g=t.runWebGPUProgram(f,[d,m],d.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:p}});return t.disposeData(m.dataId),t.disposeData(d.dataId),t.disposeData(g.dataId),x}var hW={kernelName:vn,backendName:\"webgpu\",kernelFunc:lpe};var Ux=class{constructor(e,t){this.variableNames=[\"A\",\"indices\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"gather\"}getUserCode(){let e=mpe(this.aShape);return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let resRC = getCoordsFromIndex(index);\n let indexZ = i32(getIndices(resRC.x, resRC.z));\n let inBounds = select(0.0, 1.0, indexZ >= 0 && indexZ < uniforms.aShape[2]);\n setOutputAtIndex(index, inBounds * getA(${e}));\n }\n }\n `}};function mpe(r){let e=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],t=[];for(let o=0;ot.disposeData(D.dataId)),t.makeTensorInfo(u.outputShape,R.dtype,R.values)}let h=new Ux(m.shape,f),g=t.runWebGPUProgram(h,[m,d],m.dtype);l.push(g);let x=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return l.forEach(b=>t.disposeData(b.dataId)),x}var gW={kernelName:aa,backendName:\"webgpu\",kernelFunc:c0};var dpe=et({opType:fe.GREATER,cpuKernelImpl:vz,dtype:\"bool\"}),xW={kernelName:kn,backendName:\"webgpu\",kernelFunc:dpe};var fpe=et({opType:fe.GREATER_EQUAL,dtype:\"bool\",cpuKernelImpl:Iz}),yW={kernelName:Nn,backendName:\"webgpu\",kernelFunc:fpe};function hpe(r){let{inputs:e,backend:t}=r,{input:o}=e;return Lx(o,!0,t)}var bW={kernelName:Vi,backendName:\"webgpu\",kernelFunc:hpe};var gpe=ye({opType:Z.IS_FINITE,dtype:\"bool\"}),CW={kernelName:Tn,backendName:\"webgpu\",kernelFunc:gpe};var xpe=ye({opType:Z.IS_INF,dtype:\"bool\"}),wW={kernelName:_n,backendName:\"webgpu\",kernelFunc:xpe};var ype=ye({opType:Z.IS_NAN,dtype:\"bool\"}),SW={kernelName:En,backendName:\"webgpu\",kernelFunc:ype};function bpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=[{type:\"float32\",data:[s]}],i=new Jr(n.shape,Z.LEAKYRELU,\"alpha : f32,\");return t.runWebGPUProgram(i,[n],\"float32\",a)}var IW={kernelName:$n,backendName:\"webgpu\",kernelFunc:bpe};var Cpe=et({opType:fe.LESS,dtype:\"bool\",cpuKernelImpl:Nz}),vW={kernelName:Rn,backendName:\"webgpu\",kernelFunc:Cpe};var wpe=et({opType:fe.LESS_EQUAL,dtype:\"bool\",cpuKernelImpl:kz}),kW={kernelName:Dn,backendName:\"webgpu\",kernelFunc:wpe};var Gx=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms=\"start : f32, step : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"linSpace\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n setOutputAtIndex(index, uniforms.start + f32(index) * uniforms.step);\n }\n }\n `}};function Spe(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=(n-o)/(s-1),i=new Gx(s),p=[{type:\"float32\",data:[o]},{type:\"float32\",data:[a]}];return e.runWebGPUProgram(i,[],\"float32\",p)}var NW={kernelName:An,backendName:\"webgpu\",kernelFunc:Spe};var Ipe=ye({opType:Z.LOG,cpuKernelImpl:Tz}),TW={kernelName:Fn,backendName:\"webgpu\",kernelFunc:Ipe};var vpe=ye({opType:Z.LOG1P}),_W={kernelName:Pn,backendName:\"webgpu\",kernelFunc:vpe};var kpe=et({opType:fe.LOGICAL_AND,dtype:\"bool\"}),EW={kernelName:On,backendName:\"webgpu\",kernelFunc:kpe};var Npe=ye({opType:Z.LOGICAL_NOT}),$W={kernelName:Mn,backendName:\"webgpu\",kernelFunc:Npe};var Tpe=et({opType:fe.LOGICAL_OR}),RW={kernelName:Ln,backendName:\"webgpu\",kernelFunc:Tpe};var DW=`\n var powValue = 0.0;\n let basis = uniforms.bias + uniforms.alpha * sum;\n if (uniforms.beta == 0.5) {\n powValue = inverseSqrt(basis);\n } else if (uniforms.beta == 1.0) {\n powValue = 1.0 / basis;\n } else {\n powValue = exp(log(basis) * (-uniforms.beta));\n }\n`,Hx=class{constructor(e){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n let d = coords[3];\n\n let x = getX(b, r, c, d);\n var sum = 0.0;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let idx = d + i;\n if (idx >= 0 && idx < uniforms.xShape[3]) {\n let z = getX(b, r, c, idx);\n sum = sum + z * z;\n }\n }\n ${DW}\n\n setOutputAtIndex(index, x * powValue);\n }\n }\n `}},Kx=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.uniforms=\"radius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[256,1,1],this.maxAllowRadius=16,y.assert(t<=this.maxAllowRadius,()=>`Radius must be less than or equal to ${this.maxAllowRadius}, current radius is ${t}`),this.outputShape=e,this.elementsPerWorkgroup=this.workgroupSize[0]-2*this.maxAllowRadius,this.dispatchLayout={x:[3],y:[2],z:[0,1]},this.dispatch=H(this.dispatchLayout,this.outputShape,[this.elementsPerWorkgroup,this.workgroupSize[1],this.workgroupSize[2]]),this.shaderKey=\"lrn_shared\"}getUserCode(){return`\n var lrnSub: array;\n const elementsPerWorkgroup = ${this.elementsPerWorkgroup};\n const maxAllowRadius = ${this.maxAllowRadius};\n\n ${G()} {\n let localDepth = i32(localId.x);\n let workgroupDepth = i32(workgroupId.x) * elementsPerWorkgroup;\n let xDepth = workgroupDepth + localDepth - maxAllowRadius;\n let b = i32(globalId.z) / uniforms.xShape[1];\n let r = i32(globalId.z) - b * uniforms.xShape[1];\n let c = i32(globalId.y);\n let d = workgroupDepth + localDepth;\n\n var x = 0.0;\n if (xDepth >= 0 && xDepth < uniforms.xShape[3]) {\n x = getX(b, r, c, xDepth);\n }\n lrnSub[localDepth] = x;\n workgroupBarrier();\n\n if (localDepth < elementsPerWorkgroup && d < uniforms.outShape[3]) {\n var sum = 0.0;\n let index = localDepth + maxAllowRadius;\n for (var i = -uniforms.radius; i <= uniforms.radius; i = i + 1) {\n let z = lrnSub[index + i];\n sum = sum + z * z;\n }\n ${DW}\n\n setOutputAtCoords(b, r, c, d, lrnSub[index] * powValue);\n }\n } `}};function _pe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:p}=o,u;s>16?u=new Hx(n.shape):u=new Kx(n.shape,s);let c=[{type:\"int32\",data:[s]},{type:\"float32\",data:[a]},{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(u,[n],n.dtype,c)}var AW={kernelName:Bn,backendName:\"webgpu\",kernelFunc:_pe};var qx=class{constructor(e){this.outputShape=[],this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.uniforms=\"depthRadius : i32, bias : f32, alpha : f32, beta : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"lrn_grad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let r = coords[1];\n let c = coords[2];\n\n let MIN_DEPTH_BEGIN = 0;\n let MAX_DEPTH_END = uniforms.outShape[3];\n var result = 0.0;\n for (var d = MIN_DEPTH_BEGIN; d < MAX_DEPTH_END; d++) {\n let depthBegin = max(MIN_DEPTH_BEGIN, d - uniforms.depthRadius);\n let depthEnd = min(MAX_DEPTH_END, d + uniforms.depthRadius + 1);\n\n var norm = 0.0;\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n } else {\n break;\n }\n }\n\n norm = uniforms.alpha * norm + uniforms.bias;\n\n for (var k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; k++) {\n if (k < depthBegin) {\n continue;\n } else if (k >= depthBegin && k < depthEnd) {\n var dyi = -2.0 * uniforms.alpha * uniforms.beta\n * getInputImage(b, r, c, k) * getOutputImage(b, r, c, d) / norm;\n if (k == d) {\n dyi += pow(norm, -1.0 * uniforms.beta);\n }\n if (k == coords[3]) {\n dyi *= getDy(b, r, c, d);\n result += dyi;\n }\n } else {\n break;\n }\n }\n }\n\n setOutputAtIndex(index, result);\n }\n }\n `}};function Epe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:p,alpha:u,beta:c}=o,l=new qx(n.shape),m=[{type:\"int32\",data:[i]},{type:\"float32\",data:[p]},{type:\"float32\",data:[u]},{type:\"float32\",data:[c]}];return t.runWebGPUProgram(l,[n,s,a],n.dtype,m)}var FW={kernelName:Ya,backendName:\"webgpu\",kernelFunc:Epe};var $pe=et({opType:fe.MAX,cpuKernelImpl:Ez}),PW={kernelName:Vn,backendName:\"webgpu\",kernelFunc:$pe};function Rpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:p}=o,c=w.computePool2DInfo(n.shape,s,a,1,i,p);return ax(n,c,\"max\",t)}var OW={kernelName:Wn,backendName:\"webgpu\",kernelFunc:Rpe};function Dpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:p,dimRoundingMode:u}=o,c=[1,1,1],l=w.computePool3DInfo(n.shape,s,a,c,i,u,p),m=new Iu(l,\"max\"),d=[{type:\"int32\",data:[l.strideDepth,l.strideHeight,l.strideWidth]},{type:\"int32\",data:[l.padInfo.front,l.padInfo.top,l.padInfo.left]},{type:\"int32\",data:[l.inDepth,l.inHeight,l.inWidth]},{type:\"int32\",data:[l.effectiveFilterDepth,l.effectiveFilterHeight,l.effectiveFilterWidth]}];return t.runWebGPUProgram(m,[n],n.dtype,d)}var MW={kernelName:ia,backendName:\"webgpu\",kernelFunc:Dpe};var jx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec2, pads : vec2, dilations : vec2, filterDims : vec2,\n outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool2DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords[0];\n let d = coords[3];\n\n let dyRCCorner = vec2(coords.yz) - uniforms.pads;\n let dyRCorner = dyRCCorner.x;\n let dyCCorner = dyRCCorner.y;\n\n // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] - 1;\n for (var wR = 0; wR < uniforms.filterDims[0]; wR += uniforms.dilations[0]) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[0]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[1]; wC += uniforms.dilations[1]) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[1]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyR, idyC, d);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyR, idyC, d));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wR * uniforms.filterDims[1] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n setOutputAtIndex(index, dotProd);\n }\n }\n `}},Xx=class{constructor(e){this.variableNames=[\"dy\",\"maxPos\"],this.uniforms=`strides : vec3, pads : vec3, filterDims : vec3,\n outDepth : i32, outHeight : i32, outWidth : i32`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"maxPool3DBackprop\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let batch = coords.x;\n let ch = coords.u;\n\n let dyCorner = vec3(coords.y, coords.z, coords.w) - uniforms.pads;\n let dyDCorner = dyCorner.x;\n let dyRCorner = dyCorner.y;\n let dyCCorner = dyCorner.z;\n\n // Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get\n // dx(xD, xR, xC, ch).\n // ? = to be determined. : = across all values in that axis.\n var dotProd = 0.0;\n let lastIndex = uniforms.filterDims[0] * uniforms.filterDims[1] * uniforms.filterDims[2] - 1;\n\n for (var wD = 0; wD < uniforms.filterDims[0]; wD++) {\n let dyD = f32(dyDCorner + wD) / f32(uniforms.strides[0]);\n\n if (dyD < 0.0 || dyD >= f32(uniforms.outDepth) || fract(dyD) > 0.0) {\n continue;\n }\n let idyD = i32(dyD);\n\n for (var wR = 0; wR < uniforms.filterDims[1]; wR++) {\n let dyR = f32(dyRCorner + wR) / f32(uniforms.strides[1]);\n\n if (dyR < 0.0 || dyR >= f32(uniforms.outHeight) || fract(dyR) > 0.0) {\n continue;\n }\n let idyR = i32(dyR);\n\n for (var wC = 0; wC < uniforms.filterDims[2]; wC++) {\n let dyC = f32(dyCCorner + wC) / f32(uniforms.strides[2]);\n\n if (dyC < 0.0 || dyC >= f32(uniforms.outWidth) || fract(dyC) > 0.0) {\n continue;\n }\n let idyC = i32(dyC);\n\n let dyValue = getDy(batch, idyD, idyR, idyC, ch);\n let maxPosValue = lastIndex - i32(getMaxPos(batch, idyD, idyR, idyC, ch));\n\n // Get the current value, check it against the value from the\n // position matrix.\n let curPosValue = wD * uniforms.filterDims[1] * uniforms.filterDims[2] + wR * uniforms.filterDims[2] + wC;\n let mask = select(0.0, 1.0, maxPosValue == curPosValue);\n dotProd += dyValue * mask;\n }\n }\n }\n\n setOutputAtIndex(index, dotProd);\n }\n }\n `}};function Ape(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:p,pad:u,dimRoundingMode:c}=o,l=[1,1,1],m=w.computePool3DInfo(a.shape,i,p,l,u,c),d=new Iu(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.front,m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.inDepth,m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[a],\"int32\",f),g=new Xx(m);f=[{type:\"int32\",data:[m.strideDepth,m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterDepth-1-m.padInfo.front,m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.effectiveFilterDepth,m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outDepth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],a.dtype,f);return t.disposeData(h.dataId),x}var LW={kernelName:Gi,backendName:\"webgpu\",kernelFunc:Ape};function Fpe(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;fm([s,a],\"maxPoolGrad\");let{filterSize:p,strides:u,pad:c,dimRoundingMode:l}=o,m=w.computePool2DInfo(i.shape,p,u,1,c,l),d=new Ba(m,\"max\",!0),f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.padInfo.top,m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.inHeight,m.inWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]}],h=t.runWebGPUProgram(d,[i],\"int32\",f),g=new jx(m);f=[{type:\"int32\",data:[m.strideHeight,m.strideWidth]},{type:\"int32\",data:[m.effectiveFilterHeight-1-m.padInfo.top,m.effectiveFilterWidth-1-m.padInfo.left]},{type:\"int32\",data:[m.dilationHeight,m.dilationWidth]},{type:\"int32\",data:[m.effectiveFilterHeight,m.effectiveFilterWidth]},{type:\"int32\",data:[m.outHeight]},{type:\"int32\",data:[m.outWidth]}];let x=t.runWebGPUProgram(g,[n,h],i.dtype,f);return t.disposeData(h.dataId),x}var BW={kernelName:Ui,backendName:\"webgpu\",kernelFunc:Fpe};function Ppe(r){let{inputs:e,backend:t,attrs:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=o,{x:p}=e;y.assert(p.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${p.shape.length}.`);let u=[1,1];y.assert(w.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=w.computePool2DInfo(p.shape,n,s,u,a),l=[{type:\"int32\",data:[c.strideHeight,c.strideWidth]},{type:\"int32\",data:[c.padInfo.top,c.padInfo.left]},{type:\"int32\",data:[c.dilationHeight,c.dilationWidth]},{type:\"int32\",data:[c.inHeight,c.inWidth]},{type:\"int32\",data:[c.effectiveFilterHeight,c.effectiveFilterWidth]}],m=new Ba(c,\"max\",!1),d=t.runWebGPUProgram(m,[p],p.dtype,l);m=new Ba(c,\"max\",!0,!0,i);let f=t.runWebGPUProgram(m,[p],\"int32\",l);return[d,f]}var zW={kernelName:ua,backendName:\"webgpu\",kernelFunc:Ppe};function Ope(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"min\",t)}var VW={kernelName:Gn,backendName:\"webgpu\",kernelFunc:Ope};var Mpe=et({opType:fe.MIN,cpuKernelImpl:$z}),WW={kernelName:Hn,backendName:\"webgpu\",kernelFunc:Mpe};var Yx=class{constructor(e,t,o){this.uniforms=\"\",this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.xShape=e,t.map((n,s)=>{this.uniforms+=` pad${s} : vec2,`}),this.offset=o===\"reflect\"?0:1,this.shaderKey=`mirrorPad_${o}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((u,c)=>`uniforms.pad${c}[0]`).join(\",\"),o=this.xShape.map((u,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:\"\"}`).join(\",\"),n=e===1?\"start\":\"start[i]\",s=e===1?\"end\":\"end[i]\",a=e===1?\"outC\":\"outC[i]\",i=ft(e),p=e>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,e):\"coords\";return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let start = ${i}(${t});\n let end = ${i}(${o});\n var outC = getCoordsFromIndex(index);\n for (var i = 0; i < ${e}; i = i + 1) {\n if (${a} < ${n}) {\n ${a} = ${n} * 2 - ${a} - ${this.offset};\n } else if(${a} >= ${s}) {\n ${a} = (${s} - 1) * 2 - ${a} + ${this.offset};\n }\n }\n let coords = outC - start;\n setOutputAtIndex(index, getX(${p}));\n }\n }\n `}};var UW={kernelName:Kn,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{paddings:n,mode:s}=e,a=t,i=n.map(c=>({type:\"int32\",data:[c[0],c[1]]})),p=new Yx(o.shape,n,s);return a.runWebGPUProgram(p,[o],o.dtype,i)}};var Lpe=et({opType:fe.MOD}),GW={kernelName:qn,backendName:\"webgpu\",kernelFunc:Lpe};var Qx=class{constructor(e,t){this.variableNames=[\"probs\"],this.outputShape=[],this.uniforms=\"seed : f32, numOutcomes: i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"multinomial\"}getUserCode(){return`\n //Based on the work of Dave Hoskins\n //https://www.shadertoy.com/view/4djSRW\n fn random (seed : f32, resultUV : vec2) -> f32 {\n let HASHSCALE1 = 443.8975;\n let p = resultUV * seed;\n var p3 = fract(vec3(p.xyx) * HASHSCALE1);\n p3 = p3 + dot(p3, p3.yzx + 19.19);\n return fract((p3.x + p3.y) * p3.z);\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let batch = coords[0];\n\n let resUV = vec2(f32(coords[1]) / f32(uniforms.outShape[1]),\n f32(coords[0]) / f32(uniforms.outShape[0]));\n let r = random(uniforms.seed, resUV);\n var cdf = 0.0;\n for (var i = 0; i < uniforms.numOutcomes - 1; i = i + 1) {\n cdf = cdf + getProbs(batch, i);\n\n if (r < cdf) {\n setOutputAtIndexI32(index, i);\n return;\n }\n }\n\n // If no other event happened, last event happened.\n setOutputAtIndexI32(index, uniforms.numOutcomes - 1);\n }\n }\n `}};var Zx=class{constructor(e){this.variableNames=[\"logits\"],this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=[this.outputShape[0],1,1],this.outputShape[1]>=4096?this.workgroupSize=[256,1,1]:this.workgroupSize=[64,1,1],this.shaderKey=\"softmax\"}getUserCode(){return`\n var buf : array;\n var rowMaxShared : f32;\n var rowSumShared : f32;\n const blockSize = ${this.workgroupSize[0]};\n ${G(\"index\")} {\n let row = index / blockSize;\n let tid = i32(localId.x);\n let cols = uniforms.outShape[1];\n\n var threadMax = -3.402823e+38f;\n for (var col = tid; col < cols; col += blockSize) {\n let value = getLogits(row, col);\n threadMax = max(threadMax, value);\n }\n if (tid < cols) {\n buf[tid] = threadMax;\n }\n workgroupBarrier();\n\n var reduceSize = min(cols, blockSize);\n for (var currSize = reduceSize >> 1; currSize > 0; currSize = reduceSize >> 1) {\n reduceSize = currSize + (reduceSize & 1);\n if (tid < currSize) {\n buf[tid] = max(buf[tid], buf[tid + reduceSize]);\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowMaxShared = buf[0];\n }\n workgroupBarrier();\n\n var threadSum = 0.0;\n for (var col = tid; col < cols; col += blockSize) {\n let subExp = exp(getLogits(row, col) - rowMaxShared);\n threadSum += subExp;\n }\n buf[tid] = threadSum;\n workgroupBarrier();\n\n for (var currSize = blockSize >> 1; currSize > 0; currSize = currSize >> 1) {\n if (tid < currSize) {\n buf[tid] = buf[tid] + buf[tid + currSize];\n }\n workgroupBarrier();\n }\n\n if (tid == 0) {\n rowSumShared = buf[0];\n }\n workgroupBarrier();\n\n for (var col = tid; col < cols; col += blockSize) {\n let value = exp(getLogits(row, col) - rowMaxShared) / rowSumShared;\n setOutputAtCoords(row, col, value);\n }\n }\n `}};function l0(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=pe({inputs:{x:n},backend:t,attrs:{shape:[y.sizeFromShape(n.shape)/n.shape[s],n.shape[s]]}}),i=new Zx(a.shape),p=t.runWebGPUProgram(i,[a],n.dtype),u=pe({inputs:{x:p},backend:t,attrs:{shape:n.shape}});return t.disposeData(a.dataId),t.disposeData(p.dataId),u}var HW={kernelName:Is,backendName:\"webgpu\",kernelFunc:l0};function Bpe(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,p=i?n:l0({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=p.shape[0],c=p.shape[1],l=new Qx(u,s),m=[{type:\"float32\",data:[a]},{type:\"int32\",data:[c]}],d=t.runWebGPUProgram(l,[p],\"int32\",m);return i||t.disposeData(p.dataId),d}var KW={kernelName:jn,backendName:\"webgpu\",kernelFunc:Bpe};function zpe(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.tensorMap.get(o.dataId),[a,i]=Dz(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n=new Jr(o.shape,Z.NEG);return t.runWebGPUProgram(n,[o],o.dtype)}var qW={kernelName:pa,backendName:\"webgpu\",kernelFunc:zpe};function Vpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:l}=Vt.nonMaxSuppressionV3Impl(u,c,a,i,p);return t.makeTensorInfo([l.length],\"int32\",new Int32Array(l))}var jW={kernelName:Qn,backendName:\"webgpu\",kernelFunc:Vpe};function Wpe(r){console.warn(\"tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:p,softNmsSigma:u}=o,c=t.readSync(n.dataId),l=t.readSync(s.dataId),m=a,d=i,f=p,h=u,{selectedIndices:g,selectedScores:x}=Vt.nonMaxSuppressionV5Impl(c,l,m,d,f,h);return[t.makeTensorInfo([g.length],\"int32\",new Int32Array(g)),t.makeTensorInfo([x.length],\"float32\",new Float32Array(x))]}var XW={kernelName:Zn,backendName:\"webgpu\",kernelFunc:Wpe};var Jx=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"onValue : f32, offValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e,t],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"onehot\"}getUserCode(){return`\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, mix(uniforms.offValue, uniforms.onValue,\n f32(i32(round(getX(coords.x))) == coords.y)));\n }\n }\n `}};function Upe(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{dtype:s,depth:a,onValue:i,offValue:p}=o,u=y.sizeFromShape(n.shape),c=new Jx(u,a),l=pe({inputs:{x:n},backend:t,attrs:{shape:[u]}}),m=[{type:\"float32\",data:[i]},{type:\"float32\",data:[p]}],d=t.runWebGPUProgram(c,[l],s,m);t.disposeData(l.dataId);let f=[...n.shape,a],h=pe({inputs:{x:d},backend:t,attrs:{shape:f}});return t.disposeData(d.dataId),h}var YW={kernelName:Jn,backendName:\"webgpu\",kernelFunc:Upe};function bm(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"complex64\"){let n=vi({inputs:{input:o},backend:t}),s=bm({inputs:{x:n},backend:t}),a=Rp({inputs:{input:o},backend:t}),i=bm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype===\"string\"?\"\":0},backend:t})}var QW={kernelName:Sa,backendName:\"webgpu\",kernelFunc:bm};function ZW(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype===\"string\")throw new Error(\"onesLike is not supported under string dtype\");if(o.dtype===\"complex64\"){let n=vi({inputs:{input:o},backend:t}),s=ZW({inputs:{x:n},backend:t}),a=Rp({inputs:{input:o},backend:t}),i=bm({inputs:{x:a},backend:t}),p=xo({inputs:{real:s,imag:i},backend:t});return t.disposeData(n.dataId),t.disposeData(s.dataId),t.disposeData(a.dataId),t.disposeData(i.dataId),p}else return vt({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var JW={kernelName:ca,backendName:\"webgpu\",kernelFunc:ZW};function Gpe(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Mx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{y.assertShapesMatch(s,c.shape,\"All tensors passed to stack must have matching shapes\"),y.assert(a===c.dtype,()=>\"All tensors passed to stack must have matching dtypes\")});let i=[],p=e.map(c=>{let l=Mx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(l),l}),u=a0({inputs:p,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeData(c.dataId)),u}var eU={kernelName:la,backendName:\"webgpu\",kernelFunc:Gpe};function m0(r,e=!1){let t=r.length,o=ft(t),n=r.map((l,m)=>`uniforms.pad${m}[0]`).join(\",\"),s=r.map((l,m)=>`uniforms.pad${m}[0] + uniforms.xShape${t>1?`[${m}]`:\"\"}`).join(\",\"),a=t>1?`${o}(${n})`:`${n}`,i=t>1?`${o}(${s})`:`${s}`,p=t>1?\"any(paddedCoords < start)\":\"paddedCoords < start\",u=t>1?\"any(paddedCoords >= end)\":\"paddedCoords >= end\",c=t>1?[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,t):\"coords\";return`\n let start = ${a};\n let end = ${i};\n if (${p} || ${u}) {\n setOutputAtIndex(index, ${e?0:\"uniforms.constantValue\"});\n } else {\n let coords = paddedCoords - start;\n setOutputAtIndex(index, getX(${c}));\n }\n `}var ey=class{constructor(e,t){this.variableNames=[\"x\"],this.uniforms=\"constantValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((o,n)=>o[0]+e[n]+o[1]),this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),t.map((o,n)=>{this.uniforms+=` pad${n} : vec2,`}),this.xShape=e,this.shaderKey=\"pad\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let paddedCoords = getCoordsFromIndex(index);\n ${m0(this.xShape)}\n }\n }\n `}};var Hpe=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o;if(s.every(u=>y.arraysEqual(u,[0,0])))return At({inputs:{x:n},backend:t});if(y.sizeFromShape(n.shape)===0){let u=s.map((c,l)=>c[0]+n.shape[l]+c[1]);return vt({backend:t,attrs:{shape:u,value:a,dtype:n.dtype}})}let i=[{type:\"float32\",data:[a]}];s.map(u=>i.push({type:\"int32\",data:[u[0],u[1]]}));let p=new ey(n.shape,s);return t.runWebGPUProgram(p,[n],n.dtype,i)},tU={kernelName:es,backendName:\"webgpu\",kernelFunc:Hpe};var Kpe=et({opType:fe.POW}),rU={kernelName:ts,backendName:\"webgpu\",kernelFunc:Kpe};function qpe(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=new Ii(fe.PRELU,o.shape,n.shape);return t.runWebGPUProgram(s,[o,n],\"float32\")}var oU={kernelName:rs,backendName:\"webgpu\",kernelFunc:qpe};function jpe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return eo(n,s,a,\"prod\",t)}var nU={kernelName:os,backendName:\"webgpu\",kernelFunc:jpe};var Xpe=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=Pz(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},sU={kernelName:ma,backendName:\"webgpu\",kernelFunc:Xpe};var Ype=et({opType:fe.DIV}),aU={kernelName:fn,backendName:\"webgpu\",kernelFunc:Ype};var Qpe=ye({opType:Z.RECIPROCAL}),iU={kernelName:ns,backendName:\"webgpu\",kernelFunc:Qpe};var Zpe=ye({opType:Z.RELU}),uU={kernelName:ss,backendName:\"webgpu\",kernelFunc:Zpe};var Jpe=ye({opType:Z.RELU6}),pU={kernelName:us,backendName:\"webgpu\",kernelFunc:Jpe};var ty=class{constructor(e,t,o){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, halfPixelCenters : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"resizeBilinear\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC =\n (vec2(rc) + vec2(uniforms.halfPixelCenters)) *\n effectiveInputOverOutputRatioRC - vec2(uniforms.halfPixelCenters);\n\n // Compute the four integer indices.\n let sourceFloorRC = vec2(sourceFracIndexRC);\n let sourceCeilRC = vec2(\n min(vec2(uniforms.xShape.yz) - vec2(1.0), ceil(sourceFracIndexRC)));\n\n let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);\n let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);\n let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);\n let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n let fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n let top = topLeft + (topRight - topLeft) * fracRC.y;\n let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n let newValue = top + (bottom - top) * fracRC.x;\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function ece(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,size:a,halfPixelCenters:i}=o,[p,u]=a,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[i?.5:0]}],f=new ty(n.shape,p,u);return t.runWebGPUProgram(f,[n],\"float32\",d)}var cU={kernelName:is,backendName:\"webgpu\",kernelFunc:ece};var ry=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, heightScale : f32, widthScale : f32,\n invHeightScale : f32, invWidthScale : f32, winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeBilinearBackprop_${t}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(startRLerp - f32(uniforms.winHeight / 2));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(startCLerp - f32(uniforms.winWidth / 2));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let dxR = f32(dyR) * uniforms.heightScale;\n let topDxRIndex = i32(floor(dxR));\n let bottomDxRIndex = i32(min(ceil(dxR), f32(uniforms.outShape[1] - 1)));\n let dxRLerp = dxR - f32(topDxRIndex);\n let inverseDxRLerp = 1.0 - dxRLerp;\n\n let dxC = f32(dyC) * uniforms.widthScale;\n let leftDxCIndex = i32(floor(dxC));\n let rightDxCIndex = i32(min(ceil(dxC), f32(uniforms.outShape[2] - 1)));\n let dxCLerp = dxC - f32(leftDxCIndex);\n let inverseDxCLerp = 1.0 - dxCLerp;\n\n if (r == topDxRIndex && c == leftDxCIndex) {\n // topLeft\n accumulator +=\n getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n }\n\n if (r == topDxRIndex && c == rightDxCIndex) {\n // topRight\n accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n }\n\n if (r == bottomDxRIndex && c == leftDxCIndex) {\n // bottomLeft\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n }\n\n if (r == bottomDxRIndex && c == rightDxCIndex) {\n // bottomRight\n accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function tce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ry(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[d]},{type:\"float32\",data:[f]},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var lU={kernelName:Ja,backendName:\"webgpu\",kernelFunc:tce};var oy=class{constructor(e,t,o,n){this.variableNames=[\"x\"],this.uniforms=\"adjustHeightWidth : vec2, roundBase : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,o,e[3]],this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.halfPixelCenters=n,this.shaderKey=`resizeNearest_${n}`}getUserCode(){let e;return this.halfPixelCenters?e=\"max((vec2(rc) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))\":e=\"vec2(rc) * effectiveInputOverOutputRatioRC\",`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let b = coords[0];\n let d = coords[3];\n let rc = coords.yz;\n\n let effectiveInSize = vec2(\n f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveOutSize = vec2(\n f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],\n f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);\n\n let effectiveInputOverOutputRatioRC =\n effectiveInSize / effectiveOutSize;\n\n // Fractional source index\n let sourceFracIndexRC = ${e};\n\n // Compute the coordinators of nearest neighbor point.\n let inputShapeRC = vec2(f32(uniforms.xShape.y), f32(uniforms.xShape.z));\n let sourceNearestRC = vec2(\n min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));\n let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n setOutputAtIndex(index, newValue);\n }\n }\n `}};function rce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[p,u]=i,c=s&&p>1?1:0,l=s&&u>1?1:0,d=[{type:\"float32\",data:[c,l]},{type:\"float32\",data:[s?.5:0]}],f=new oy(n.shape,p,u,a);return t.runWebGPUProgram(f,[n],n.dtype,d)}var mU={kernelName:as,backendName:\"webgpu\",kernelFunc:rce};var ny=class{constructor(e,t){this.variableNames=[\"dy\"],this.uniforms=`effectiveXSize : vec2, effectiveYSize : vec2, invHeightScale : f32, invWidthScale : f32,\n winHeight : i32, winWidth : i32,`,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.alignCorners=t,this.shaderKey=`resizeNearestNeigborBackprop_${t}`}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getOutputCoords();\n let b = coords[0];\n let d = coords[3];\n let r = coords[1];\n let c = coords[2];\n\n var accumulator = 0.0;\n\n // Compute bounds for where in dy we will look\n let startRLerp = floor(f32(r) * uniforms.invHeightScale);\n let startDyR = i32(floor(startRLerp - f32(uniforms.winHeight / 2)));\n\n let startCLerp = floor(f32(c) * uniforms.invWidthScale);\n let startDyC = i32(floor(startCLerp - f32(uniforms.winWidth / 2)));\n\n // Loop over dy\n for (var dyROffset = 0; dyROffset < uniforms.winHeight; dyROffset++) {\n let dyR = startDyR + dyROffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyR < 0 || dyR >= uniforms.dyShape[1]) {\n continue;\n }\n\n for (var dyCOffset = 0; dyCOffset < uniforms.winWidth; dyCOffset++) {\n let dyC = startDyC + dyCOffset;\n\n // Guard against the window exceeding the bounds of dy\n if (dyC < 0 || dyC >= uniforms.dyShape[2]) {\n continue;\n }\n\n let sourceFracRow = f32(uniforms.effectiveXSize[0]) *\n (f32(dyR) / f32(uniforms.effectiveYSize[0]));\n\n let sourceFracCol = f32(uniforms.effectiveXSize[1]) *\n (f32(dyC) / f32(uniforms.effectiveYSize[1]));\n\n let sourceNearestRow =\n i32(min(f32(uniforms.outShape[1] - 1),\n ${this.alignCorners?\"floor(sourceFracRow + 0.5)\":\"floor(sourceFracRow)\"}));\n\n let sourceNearestCol =\n i32(min(f32(uniforms.outShape[2] - 1),\n ${this.alignCorners?\"floor(sourceFracCol + 0.5)\":\"floor(sourceFracCol)\"}));\n\n if (r == sourceNearestRow && c == sourceNearestCol) {\n accumulator += getDy(b, dyR, dyC, d);\n }\n }\n }\n // End loop over dy\n\n setOutputAtIndex(index, accumulator);\n }\n }\n `}};function oce(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,[,i,p]=n.shape,[,u,c]=s.shape,l=[a&&u>1?i-1:i,a&&c>1?p-1:p],m=[a&&u>1?u-1:u,a&&c>1?c-1:c],d=l[0]/m[0],f=l[1]/m[1],h=1/d,g=1/f,x=Math.ceil(h)*2+2,b=Math.ceil(g)*2+2,C=new ny(n.shape,a),S=[{type:\"int32\",data:l},{type:\"int32\",data:m},{type:\"float32\",data:[h]},{type:\"float32\",data:[g]},{type:\"int32\",data:[x]},{type:\"int32\",data:[b]}];return t.runWebGPUProgram(C,[s],s.dtype,S)}var dU={kernelName:Za,backendName:\"webgpu\",kernelFunc:oce};var sy=class{constructor(e){this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\" axis : vec4,\",this.shaderKey=\"reverse\"}getUserCode(){return`\n \n // Using uniform variables as judging conditions, so the function has\n // coherent execution within all threads.\n fn getReverseCoords(coords : vec4) -> vec4 {\n var reverseCoords = coords;\n if (uniforms.axis[0] == 1) {\n reverseCoords[0] = uniforms.xShape[0] - coords[0] - 1;\n }\n if (uniforms.axis[1] == 1) {\n reverseCoords[1] = uniforms.xShape[1] - coords[1] - 1;\n }\n if (uniforms.axis[2] == 1) {\n reverseCoords[2] = uniforms.xShape[2] - coords[2] - 1;\n }\n if (uniforms.axis[3] == 1) {\n reverseCoords[3] = uniforms.xShape[3] - coords[3] - 1;\n }\n\n return reverseCoords;\n }\n \n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let reverseCoords = getReverseCoords(coords);\n setOutputAtIndex(index, getX(reverseCoords[0],\n reverseCoords[1], reverseCoords[2], reverseCoords[3]));\n }\n }\n `}};function nce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length;if(a===0)return At({inputs:{x:n},backend:t});let i=n.shape,p=[1,1,1,1];i.forEach((g,x)=>{let b=x+4-a;p[b]=g});let u=y.parseAxisParam(s,n.shape),c=[0,0,0,0];u.forEach(g=>{let x=g+4-a;c[x]=1});let l=[{type:\"int32\",data:c}],m=pe({inputs:{x:n},backend:t,attrs:{shape:p}}),d=new sy(p),f=t.runWebGPUProgram(d,[m],m.dtype,l);t.disposeData(m.dataId);let h=pe({inputs:{x:f},backend:t,attrs:{shape:i}});return t.disposeData(f.dataId),h}var fU={kernelName:ps,backendName:\"webgpu\",kernelFunc:nce};var ay=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"x\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`centerX : f32, centerY : f32, sinRadians : f32,\n cosRadians : f32,`,this.shaderKey=\"rotate\",this.outputShape=e,typeof t==\"number\"?(this.uniforms+=\" fillValue : f32,\",this.fillSnippet=\"var outputValue = uniforms.fillValue;\",this.shaderKey+=\"_float\"):(this.uniforms+=\" fillValue : vec3,\",this.fillSnippet=\"var outputValue = uniforms.fillValue[coords[3]];\",this.shaderKey+=\"_vec3\")}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let coordXFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *\n uniforms.sinRadians;\n let coordYFloat = (f32(coords[2]) - uniforms.centerX) *\n uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *\n uniforms.cosRadians;\n let coordX = i32(round(coordXFloat + uniforms.centerX));\n let coordY = i32(round(coordYFloat + uniforms.centerY));\n ${this.fillSnippet}\n if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&\n coordY < uniforms.xShape[1]) {\n outputValue = getX(coords[0], coordY, coordX, coords[3]);\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};var hU={kernelName:Ds,backendName:\"webgpu\",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,p=new ay(o.shape,s),[u,c]=w.getImageCenter(a,o.shape[1],o.shape[2]),l=[{type:\"float32\",data:[u]},{type:\"float32\",data:[c]},{type:\"float32\",data:[Math.sin(n)]},{type:\"float32\",data:[Math.cos(n)]}];return typeof s==\"number\"?l.push({type:\"float32\",data:[Number.parseFloat(s.toFixed(2))]}):l.push({type:\"float32\",data:s}),i.runWebGPUProgram(p,[o],o.dtype,l)}};var sce=ye({opType:Z.ROUND}),gU={kernelName:cs,backendName:\"webgpu\",kernelFunc:sce};var ace=ye({opType:Z.RSQRT,cpuKernelImpl:Oz}),xU={kernelName:ls,backendName:\"webgpu\",kernelFunc:ace};var za=class{constructor(e,t,o,n,s,a,i,p=!0){this.variableNames=[\"updates\",\"indices\"],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=i,this.sumDupeIndices=p,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${o}_${n}_${this.sliceDimGreaterThanOne}_${i}_${p}_${s.length}`;let u=ft(s.length);this.uniforms=`sliceDim : i32, strides: ${u}, updatesSize: i32,`,this.updatesRank=n,this.indicesRank=o}getUserCode(){let e=\"\";this.indicesRank===1?e=\"coords[0]\":this.indicesRank===2&&(e=\"coords[0], j\");let t=`getIndices(${e})`,o=this.sliceDimGreaterThanOne?\"uniforms.strides[j]\":\"uniforms.strides\",n=\"\",s=\"\";this.dispatchLayout.x.length===1?(n=\"flattenedIndex\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {\n return index;\n }\n `):this.dispatchLayout.x.length===2&&(n=\"vec2(flattenedIndex, coords[1])\",s=`\n fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2 {\n // N.B. |updates| could be a scalar tensor, conceptually representing a\n // 2D tensor with all values equal to that. By design, its size must be\n // the same as |outShape[1]| in one dimension, and |indicesShape[0]|\n // gives the other.\n let sliceSize = uniforms.outShape[1];\n let d0 = index / sliceSize;\n let d1 = index - d0 * sliceSize;\n return vec2(d0, d1);\n }\n `);let i=`getUpdates(${Array.from({length:this.updatesRank},(u,c)=>`coords[${c}]`).join(\", \")})`;return`\n ${s}\n ${G(\"index\")} {\n if (index < uniforms.updatesSize) {\n let coords = getUpdatesCoordsFromFlatIndex(index);\n var flattenedIndex = 0;\n for (var j = 0; j < uniforms.sliceDim; j = j + 1) {\n let indexInside = i32(round(${t}));\n flattenedIndex = flattenedIndex + indexInside * ${o};\n }\n let updateValue =\n ${Su(this.type)}(${i});\n let flatIndex = getOutputIndexFromCoords(${n});\n\n ${this.sumDupeIndices?Qr(\"&result[flatIndex]\",\"updateValue\",this.type):\"atomicStore(&result[flatIndex], bitcast(updateValue));\"}\n }\n }`}};function ice(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(s,n,a),m=[l/u,u];if(l===0)return t.makeTensorInfo(a,n.dtype);let d=pe({inputs:{x:n},backend:t,attrs:{shape:[p,i]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,u]}}),h=f.dtype,g=vt({backend:t,attrs:{shape:m,value:0,dtype:h}}),x=y.sizeFromShape(f.shape),b=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[x]}],C=new za(f.shape,i,d.shape.length,f.shape.length,c,m,h),S=t.runWebGPUProgram(C,[f,d],h,b,g),k=pe({inputs:{x:S},backend:t,attrs:{shape:a}});return t.disposeData(d.dataId),t.disposeData(f.dataId),t.disposeData(S.dataId),k}var yU={kernelName:ms,backendName:\"webgpu\",kernelFunc:ice};var iy=class{constructor(e,t){this.outputShape=[],this.variableNames=[\"sortedSequence\",\"values\"],this.uniforms=\"numInputs : i32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.side=t,this.shaderKey=`search_sorted_${t}`}getUserCode(){return`\n fn findBound(batch: i32, value: f32) -> i32 {\n var left = i32(0);\n var right = uniforms.numInputs;\n while (left < right) {\n var mid = (left + right) / 2;\n if (getSortedSequence(batch, mid) ${this.side===\"left\"?\"<\":\"<=\"} value) {\n left = mid + 1;\n } else {\n right = mid;\n }\n }\n return right;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let value = getValuesByOutputIndex(index);\n setOutputAtIndexI32(index, findBound(coords[0], value));\n }\n }\n `}};function uce(r){let{inputs:e,backend:t,attrs:o}=r,{sortedSequence:n,values:s}=e,{side:a}=o,i=new iy([s.shape[0],s.shape[1]],a),p=[{type:\"int32\",data:[n.shape[1]]}];return t.runWebGPUProgram(i,[n,s],\"int32\",p)}var bU={kernelName:fs,backendName:\"webgpu\",kernelFunc:uce};var uy=class{constructor(e,t,o){this.variableNames=[\"c\",\"a\",\"b\"],this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.cRank=e,this.rank=o,this.shaderKey=\"select\"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t=\"resRC\",e=\"resRC\";else{let n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],s=[],a=[];for(let i=0;i= 1.0) {\n setOutputAtIndex(index, getA(${t}));\n } else {\n setOutputAtIndex(index, getB(${t}));\n }\n }\n }\n `}};function pce(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new uy(o.shape.length,n.shape,n.shape.length);return t.runWebGPUProgram(a,[o,n,s],dt(n.dtype,s.dtype))}var CU={kernelName:fa,backendName:\"webgpu\",kernelFunc:pce};var cce=ye({opType:Z.SELU}),wU={kernelName:hs,backendName:\"webgpu\",kernelFunc:cce};var lce=ye({opType:Z.SIGMOID}),SU={kernelName:bs,backendName:\"webgpu\",kernelFunc:lce};var mce=ye({opType:Z.SIGN}),IU={kernelName:ys,backendName:\"webgpu\",kernelFunc:mce};var dce=ye({opType:Z.SIN}),vU={kernelName:gs,backendName:\"webgpu\",kernelFunc:dce};var fce=ye({opType:Z.SINH}),kU={kernelName:xs,backendName:\"webgpu\",kernelFunc:fce};var hce=ye({opType:Z.SOFTPLUS}),NU={kernelName:Cs,backendName:\"webgpu\",kernelFunc:hce};var py=class{constructor(e,t,o,n,s,a){this.variableNames=[\"x\"],this.outputShape=[],this.uniforms=\"\",this.workgroupSize=[64,1,1],this.size=!0;let i=new Array(n.length);for(let p=0;p{this.uniforms+=` pad${u} : vec2,`}),this.shaderKey=`spaceToBatchND_${s}`}getUserCode(){let e=ft(this.outputShape.length),t=e0(this.newDim);return`\n ${cm(this.paddedXShape,\"PaddedX\")}\n ${G(\"index\")} {\n if(index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n let switchedIndex = getIndexFromCoords${this.outputShape.length}D(${e}(${t}), uniforms.reshapedPaddedXShape);\n let paddedCoords = getPaddedXCoordsFromIndex(switchedIndex);\n ${m0(this.xShape,!0)}\n }\n }\n `}};var gce=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;y.assert(n.shape.length<=4,()=>\"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet\");let i=s.reduce((b,C)=>b*C),p=[[0,0]];p.push(...a);for(let b=1+s.length;bb[0]+n.shape[C]+b[1]),c=w.getReshaped(u,s,i,!1),l=w.getPermuted(c.length,s.length,!1),m=w.getReshapedPermuted(u,s,i,!1),d=y.computeStrides(u),f=new py(n.shape,u,p,c,l,d.length),h=[{type:\"int32\",data:c},{type:\"int32\",data:d}];p.map(b=>h.push({type:\"int32\",data:[b[0],b[1]]}));let g=t.runWebGPUProgram(f,[n],n.dtype,h),x=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeData(g.dataId),x},TU={kernelName:ga,backendName:\"webgpu\",kernelFunc:gce};var cy=class{constructor(e,t,o){this.variableNames=[\"input\",\"indices\",\"segmentIds\"],this.outputShape=[],this.uniforms=\"segmentSize : i32, sparseSize : i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=e,this.type=o,this.dispatchLayout=X([t]),this.dispatch=H(this.dispatchLayout,[t],this.workgroupSize),this.shaderKey=\"sparseSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.sparseSize) {\n let indexInSegmentIds = index / uniforms.segmentSize;\n let indexInSegment = index % uniforms.segmentSize;\n let indexInInput = indices[indexInSegmentIds];\n let segmentId = segmentIds[indexInSegmentIds];\n\n let value = input[indexInInput * uniforms.segmentSize + indexInSegment];\n let outIndex = segmentId * uniforms.segmentSize + indexInSegment;\n ${Qr(\"&result[outIndex]\",\"value\",this.type)}\n }\n }\n `}},ly=class{constructor(e,t){this.variableNames=[\"segmentIds\"],this.outputShape=[],this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=[e],this.dispatchLayout=X(t),this.dispatch=H(this.dispatchLayout,t,this.workgroupSize),this.shaderKey=\"sparseSegmentIdCountProgram\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.segmentIdsShape) {\n let segmentId = segmentIds[index];\n ${Qr(\"&result[segmentId]\",\"1\",\"int32\")}\n }\n }\n `}},my=class{constructor(e,t){this.variableNames=[\"segmentSum\",\"sameSegmentIdCount\"],this.outputShape=[],this.uniforms=\"segmentSize : i32\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.type=t,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),this.shaderKey=\"sparseSegmentMean\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let segmentId = index / uniforms.segmentSize;\n let count = sameSegmentIdCount[segmentId];\n if (count != 0) {\n ${this.type===\"float32\"?\"setOutputAtIndex(index, segmentSum[index] / f32(count));\":\"setOutputAtIndexI32(index, segmentSum[index] / count);\"}\n }\n }\n }\n `}};function dy(r,e,t,o=!1,n){let a=y.sizeFromShape(r.shape)/r.shape[0],i=r.dtype,p=y.sizeFromShape(e.shape),u=n.readSync(t.dataId),l=p>0?u[p-1]+1:0,m,d=r.shape.slice();d[0]=l;let f=p*a,h=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new cy(d,f,i);let g=[{type:\"int32\",data:[a]},{type:\"int32\",data:[f]}],x=n.runWebGPUProgram(m,[r,e,t],i,g,h);if(o)return x;let b=vt({backend:n,attrs:{shape:[l],value:0,dtype:\"int32\"}});m=new ly(l,t.shape);let C=n.runWebGPUProgram(m,[t],\"int32\",null,b),S=vt({backend:n,attrs:{shape:d,value:0,dtype:i}});m=new my(d,i),g=[{type:\"int32\",data:[a]}];let k=n.runWebGPUProgram(m,[x,C],i,g,S);return n.disposeData(x.dataId),n.disposeData(C.dataId),k}function xce(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;return dy(o,n,s,!1,t)}var _U={kernelName:ya,backendName:\"webgpu\",kernelFunc:xce};function yce(r){let{inputs:e,backend:t}=r,{data:o,indices:n,segmentIds:s}=e;return dy(o,n,s,!0,t)}var EU={kernelName:ba,backendName:\"webgpu\",kernelFunc:yce};var fy=class{constructor(e,t){this.variableNames=[\"A\"],this.workgroupSize=[64,1,1],this.size=!0;let o=new Array(e.length);for(let n=0;n=5)throw Error(`Tile for rank ${r} is not yet supported`);if(r===1)return`(resRC % ${e}aShape)`;let t=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[];for(let n=0;n=5){let p=t.readSync(n.dataId),u=n.dtype===\"string\"?p.map(m=>y.decodeString(m)):p,c=me(n.shape,n.dtype,u),l=Uz(c,s);return t.makeTensorInfo(l.shape,l.dtype,l.values)}let a=new fy(n.shape,s);return t.runWebGPUProgram(a,[n],n.dtype)}var $U={kernelName:po,backendName:\"webgpu\",kernelFunc:Cm};function Cce(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:p,numUpdates:u,sliceSize:c,strides:l,outputSize:m}=w.calculateShapes(s,n,i),d=!1;if(s.dtype===\"string\"){let R=t.bufferSync(n),D=t.bufferSync(s),P=y.decodeString(t.readSync(a.dataId)[0]),O=Mz(R,D,i,m,c,u,p,l,P,d);return t.makeTensorInfo(i,O.dtype,O.values)}let f=[m/c,c],h=pe({inputs:{x:n},backend:t,attrs:{shape:[u,p]}}),g=s.shape.length?pe({inputs:{x:s},backend:t,attrs:{shape:[u,c]}}):At({inputs:{x:s},backend:t}),x=g.dtype,b=t.makeTensorInfo([],x,y.makeZerosTypedArray(1,x)),C=pe({inputs:{x:a},backend:t,attrs:{shape:Array(f.length).fill(1)}}),S=Cm({inputs:{x:C},backend:t,attrs:{reps:f}}),k=y.sizeFromShape([u,c]),_=[{type:\"int32\",data:[p]},{type:\"int32\",data:l},{type:\"int32\",data:[k]}];switch(u){case 0:break;case 1:{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x,d);t.runWebGPUProgram(R,[g,h],x,_,S)}break;default:{let R=new za([u,c],p,h.shape.length,b.shape.length,l,f,x,d);t.runWebGPUProgram(R,[b,h],x,_,S)}{let R=new za([u,c],p,h.shape.length,g.shape.length,l,f,x);t.runWebGPUProgram(R,[g,h],x,_,S)}}let $=pe({inputs:{x:S},backend:t,attrs:{shape:i}});return t.disposeData(h.dataId),t.disposeData(g.dataId),t.disposeData(C.dataId),t.disposeData(b.dataId),t.disposeData(S.dataId),$}var RU={kernelName:vs,backendName:\"webgpu\",kernelFunc:Cce};function wce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=y.parseAxisParam(a,n.shape)[0],p=w.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),l=n.shape.slice();return p.map(m=>{let d=[...l];d[i]=m;let f=Hs({inputs:{x:n},backend:t,attrs:{begin:c,size:d}});return c[i]+=m,f})}var DU={kernelName:xa,backendName:\"webgpu\",kernelFunc:wce};var Sce=ye({opType:Z.SQRT}),AU={kernelName:ws,backendName:\"webgpu\",kernelFunc:Sce};var FU={kernelName:qi,backendName:\"webgpu\",kernelFunc:({inputs:r,backend:e})=>{let{x:t}=r,o=e,n=new Jr(t.shape,Z.SQUARE);return o.runWebGPUProgram(n,[t],t.dtype)}};var Ice=et({opType:fe.SQUARED_DIFFERENCE}),PU={kernelName:ks,backendName:\"webgpu\",kernelFunc:Ice};function vce({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=new Jr(o.shape,Z.STEP,\"stepAlpha : f32,\"),s=[{type:\"float32\",data:[e.alpha]}];return t.runWebGPUProgram(n,[o],o.dtype,s)}var OU={kernelName:wo,backendName:\"webgpu\",kernelFunc:vce};var hy=class{constructor(e){this.variableNames=[\"x\"],this.workPerThread=1,this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize,[this.workPerThread,1,1]);let t=ft(this.outputShape.length);this.uniforms=`begin : ${t}, strides : ${t}, `,this.shaderKey=\"stridedSlice\"}getUserCode(){let e=this.outputShape.length,t=\"\";if(e===1)t=\"coords * uniforms.strides + uniforms.begin\";else{let n=0;t=this.outputShape.map((s,a)=>(n++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${n-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(\",\")}return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n setOutputAtIndex(index, getX(${t}));\n }\n }\n `}};function kce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:p,endMask:u,ellipsisMask:c,newAxisMask:l,shrinkAxisMask:m}=o,{finalShapeSparse:d,finalShape:f,isIdentity:h,sliceDim0:g,isSimpleSlice:x,begin:b,end:C,strides:S}=pt.sliceInfo(n.shape,s,a,i,p,u,c,l,m),k;if(h)k=pe({inputs:{x:n},backend:t,attrs:{shape:f}});else if(g||x){y.assert(n.shape.length>=1,()=>`Input must have rank at least 1, got: ${n.shape.length}`);let _=pt.computeOutShape(b,C,S),$=Hs({inputs:{x:n},backend:t,attrs:{begin:b,size:_}});k=pe({inputs:{x:$},backend:t,attrs:{shape:f}}),t.disposeData($.dataId)}else if(t.shouldExecuteOnCPU([n])){let $=t.readSync(n.dataId),R=me(n.shape,n.dtype,$),D=zz(d,R,S,b);k=t.makeTensorInfo(f,n.dtype,D.values)}else{let $=new hy(d),R=[{type:\"int32\",data:b},{type:\"int32\",data:S}],D=t.runWebGPUProgram($,[n],n.dtype,R);k=pe({inputs:{x:D},backend:t,attrs:{shape:f}}),t.disposeData(D.dataId)}return k}var MU={kernelName:Ns,backendName:\"webgpu\",kernelFunc:kce};function Nce(r){let{inputs:e,backend:t,attrs:o}=r,{separator:n,nGramWidths:s,leftPad:a,rightPad:i,padWidth:p,preserveShortSequences:u}=o,{data:c,dataSplits:l}=e,m=t.readSync(c.dataId),d=t.readSync(l.dataId),[f,h]=Vz(m,d,n,s,a,i,p,u);return[t.makeTensorInfo([f.length],\"string\",f),t.makeTensorInfo(l.shape,\"int32\",h)]}var LU={kernelName:Ca,backendName:\"webgpu\",kernelFunc:Nce};var Tce=et({opType:fe.SUB,cpuKernelImpl:Wz,supportsComplex:!0}),BU={kernelName:Ts,backendName:\"webgpu\",kernelFunc:Tce};var _ce=ye({opType:Z.TAN}),zU={kernelName:_s,backendName:\"webgpu\",kernelFunc:_ce};var Ece=ye({opType:Z.TANH}),VU={kernelName:Es,backendName:\"webgpu\",kernelFunc:Ece};function $ce(r){let{inputs:e,backend:t,attrs:o}=r,{tensor:n,indices:s,updates:a}=e,{}=o,{sliceRank:i,numUpdates:p,sliceSize:u,strides:c,outputSize:l}=w.calculateShapes(a,s,n.shape),m=[l/u,u];if(l===0)return t.makeTensorInfo(n.shape,s.dtype);let d=[],f=pe({inputs:{x:s},backend:t,attrs:{shape:[p,i]}});d.push(f);let h=pe({inputs:{x:a},backend:t,attrs:{shape:[p,u]}});d.push(h);let g=pe({inputs:{x:n},backend:t,attrs:{shape:m}});d.push(g);let x=Cm({inputs:{x:g},backend:t,attrs:{reps:Array(m.length).fill(1)}}),b=new za([p,u],i,f.shape.length,h.shape.length,c,m,n.dtype,!1),C=y.sizeFromShape([p,u]),S=[{type:\"int32\",data:[i]},{type:\"int32\",data:c},{type:\"int32\",data:[C]}],k=t.runWebGPUProgram(b,[h,f],g.dtype,S,x);d.push(k);let _=pe({inputs:{x:k},backend:t,attrs:{shape:n.shape}});return d.forEach($=>t.disposeData($.dataId)),_}var WU={kernelName:ds,backendName:\"webgpu\",kernelFunc:$ce};var gy=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=`inputSize : i32, firstPass : i32, negativeInf : f32,\n dir : i32, inc : i32,`,this.shaderKey=\"swap\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // We compare elements pair-wise within a group of size 2 * inc.\n // The comparing rule for each group alternates between ascending\n // and descending. Within each group, we compare each pair at\n // positions i and i+inc. To decide whether an element at position i\n // is x0 or x1, we mod it by 2 * inc, if the result is smaller than\n // inc, it is in the first half of the group, we denote it as x0,\n // otherwise we denote it as x1.\n // For example, as shown in the Bitonic top K paper referenced\n // above, Figure5(a) shows that element[1] is in the second half of\n // the group when group size is 2, but it is in the first half of\n // the group when group size is 4.\n let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;\n var i = 0;\n if (isFirstInPair) {\n i = elemIdx;\n } else {\n i = elemIdx - uniforms.inc;\n }\n\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.inc;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.inc));\n }\n\n var x0 = f32(0.0);\n var x1 = f32(0.0);\n if (i0 < uniforms.inputSize) {\n x0 = getX(batch, i0);\n } else {\n x0 = uniforms.negativeInf;\n }\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = uniforms.negativeInf;\n }\n\n let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;\n let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);\n if (reverse == isGreater) {\n // Elements in opposite order of direction\n let iTemp = i0;\n i0 = i1;\n i1 = iTemp;\n }\n if (isFirstInPair) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}},xy=class{constructor(e){this.variableNames=[\"x\",\"indices\"],this.workgroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.uniforms=\"inputSize : i32, firstPass : i32, k : i32,\",this.shaderKey=\"merge\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let outC = getCoordsFromIndex(index);\n let batch = outC[0];\n let elemIdx = outC[1];\n // The output size is half of the previous size.\n // If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _\n // (k=4), we only need to output the indices at positions |, the\n // indices at positions _ can be thrown away, see Figure5(b) After\n // Phase 2 (Merge phase) in the Bitonic Top K paper referenced\n // above.\n // For example, the paper shows we only need to output the orange\n // bars. The output sequence should look like this | | | | | | | |.\n // Because the sequence is halved, to map the output index back to\n // the previous sequence to find the corresponding value, we need\n // to double the index. When we double the index, we basically\n // interpolate a position, so 2i looks like\n // | _ | _ | _ | _ | _ | _ | _. We move the | to the first k\n // position of each 2k positions by - elemIdx % k. E.g. for output\n // at index 4,5,6,7, we want to get the corresponding element at\n // original index 8,9,10,11, for output at index 8,9,10,11,\n // we want to get the corresponding element at original index\n // 16,17,18,19, so on and so forth.\n\n var i = 0;\n if (elemIdx < uniforms.k) {\n i = elemIdx;\n } else {\n i = elemIdx * 2 - elemIdx % uniforms.k;\n }\n var i0 = 0;\n if (uniforms.firstPass == 1) {\n i0 = i;\n } else {\n i0 = i32(getIndices(batch, i));\n }\n var i1 = 0;\n if (uniforms.firstPass == 1) {\n i1 = i + uniforms.k;\n } else {\n i1 = i32(getIndices(batch, i + uniforms.k));\n }\n\n let x0 = getX(batch, i0);\n var x1 = f32(0.0);\n if (i1 < uniforms.inputSize) {\n x1 = getX(batch, i1);\n } else {\n x1 = x0;\n }\n\n if (x0 >= x1) {\n setOutputAtIndex(index, f32(i0));\n } else {\n setOutputAtIndex(index, f32(i1));\n }\n }\n }\n `}};function rl(r,e){e!==null&&r.disposeData(e.dataId)}function UU(r){let e=1;for(;ef===null?[l,l]:[l,f],g=(k,_,$)=>{let R=h(),D=new gy($),O=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"float32\",data:[Number.NEGATIVE_INFINITY]},{type:\"int32\",data:[k]},{type:\"int32\",data:[_]}],M=f;f=t.runWebGPUProgram(D,R,\"int32\",O),rl(t,M)};for(let k=1;k=1;$/=2)g(_,$,[c,d])}for(let k=d;k>m;k/=2){let _=h(),$=new xy([c,k/2]),D=[{type:\"int32\",data:[p]},{type:\"int32\",data:[f===null?1:0]},{type:\"int32\",data:[m]}],P=f;f=t.runWebGPUProgram($,_,\"int32\",D),rl(t,P);let O=m/2,M=O*2;for(let L=O;L>=1;L/=2)g(M,L,f.shape)}let x=f;f=Hs({inputs:{x:f},backend:t,attrs:{begin:0,size:[c,s]}}),rl(t,x);let b=c0({inputs:{x:l,indices:f},backend:t,attrs:{axis:1,batchDims:1}});rl(t,l);let C=i.slice(0,-1);C.push(s),x=f,f=pe({inputs:{x:f},attrs:{shape:C},backend:t}),rl(t,x);let S=b;return b=pe({inputs:{x:b},attrs:{shape:C},backend:t}),rl(t,S),[b,f]}var GU={kernelName:$s,backendName:\"webgpu\",kernelFunc:Rce};var yy=class{constructor(e){this.variableNames=[\"Image\",\"Transforms\"],this.uniforms=\"interpolationModeId : i32, fillModeId : i32, fillValue : f32,\",this.workgroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=X(this.outputShape),this.dispatch=H(this.dispatchLayout,this.outputShape,this.workgroupSize),this.shaderKey=\"transform\"}getUserCode(){return`\n fn mapCoord(outCoord : f32, len : f32) -> f32{\n var inCoord = outCoord;\n if(uniforms.fillModeId == 2) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n if (inCoord < sz2) {\n inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +\n inCoord;\n }\n if (inCoord < -len) {\n inCoord = inCoord + sz2;\n } else {\n inCoord = -inCoord - 1.0;\n }\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz2 = 2.0 * len;\n inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));\n if (inCoord >= len) {\n inCoord = sz2 - inCoord - 1.0;\n }\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 3) {\n if (inCoord < 0.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);\n }\n } else if (inCoord > len - 1.0) {\n if (len <= 1.0) {\n inCoord = 0.0;\n } else {\n let sz = len - 1.0;\n inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));\n }\n }\n return clamp(inCoord, 0.0, len - 1.0);\n } else if (uniforms.fillModeId == 4) {\n return clamp(outCoord, 0.0, len - 1.0);\n }\n return outCoord;\n }\n fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,\n channel : i32) -> f32 {\n var outputValue : f32;\n if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {\n outputValue = getImage(batch, coordY, coordX, channel);\n } else {\n outputValue = uniforms.fillValue;\n }\n return outputValue;\n }\n\n ${G(\"index\")} {\n if (index < uniforms.size) {\n let coords = getCoordsFromIndex(index);\n var outputValue : f32;\n let batch = coords[0];\n let x = coords[2];\n let y = coords[1];\n let channel = coords[3];\n let xf = f32(x);\n let yf = f32(y);\n let a1 = getTransforms(batch, 0);\n let a2 = getTransforms(batch, 1);\n let a3 = getTransforms(batch, 2);\n let b1 = getTransforms(batch, 3);\n let b2 = getTransforms(batch, 4);\n let b3 = getTransforms(batch, 5);\n let c1 = getTransforms(batch, 6);\n let c2 = getTransforms(batch, 7);\n let projection = c1 * xf + c2 * yf + 1.0;\n if (projection == 0.0) {\n outputValue = uniforms.fillValue;\n } else {\n let inX = (a1 * xf + a2 * yf + a3) / projection;\n let inY = (b1 * xf + b2 * yf + b3) / projection;\n let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));\n let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));\n\n if (uniforms.interpolationModeId == 1) {\n let coordY = i32(round(mapY));\n let coordX = i32(round(mapX));\n outputValue = readWithFillValue(batch, coordY, coordX,\n channel);\n } else {\n let yFloor = floor(mapY);\n let xFloor = floor(mapX);\n let yCeil = yFloor + 1.0;\n let xCeil = xFloor + 1.0;\n let valueYFloor = (xCeil - mapX) *\n readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);\n let valueYCeil = (xCeil - mapX) *\n readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +\n (mapX - xFloor) *\n readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);\n outputValue = (yCeil - mapY) * valueYFloor +\n (mapY - yFloor) * valueYCeil;\n }\n }\n setOutputAtIndex(index, outputValue);\n }\n }\n `}};function Dce(r){let{inputs:e,backend:t,attrs:o}=r,{image:n,transforms:s}=e,{interpolation:a,fillMode:i,fillValue:p,outputShape:u}=o,[c,l,m,d]=n.shape,[f,h]=u!=null?u:[l,m],g=[c,f,h,d],x=new yy(g),b=a===\"nearest\"?1:2,C;switch(i){case\"constant\":C=1;break;case\"reflect\":C=2;break;case\"wrap\":C=3;break;case\"nearest\":C=4;break;default:C=1;break}let S=[{type:\"int32\",data:[b]},{type:\"int32\",data:[C]},{type:\"float32\",data:[p]}];return t.runWebGPUProgram(x,[n,s],\"float32\",S)}var HU={kernelName:Rs,backendName:\"webgpu\",kernelFunc:Dce};function Ace(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,p=n.shape[s],u=new Array(i-1),c=0;for(let h=0;ht.disposeData(h.dataId)),f}var KU={kernelName:wa,backendName:\"webgpu\",kernelFunc:Ace};var by=class{constructor(e,t,o){if(this.outputShape=[],this.variableNames=[\"x\",\"segmentIds\"],this.uniforms=\"numSegments : i32, xSize: i32,\",this.workgroupSize=[64,1,1],this.atomic=!0,this.outputShape=t,this.dispatchLayout=X(e),this.dispatch=H(this.dispatchLayout,e,this.workgroupSize),o!==\"float32\"&&o!==\"int32\")throw new Error(`UnsortedSegmentSum only supports float32 and int32\n types, does not support ${o} type.`);this.type=o,this.shaderKey=\"unsortedSegmentSum\"}getUserCode(){return`\n ${G(\"index\")} {\n if (index < uniforms.xSize) {\n let coords = getXCoordsFromIndex(index);\n let b = coords[0];\n let inCol = coords[1];\n\n let segmentId = i32(getSegmentIds(inCol));\n if (segmentId >= 0) {\n let flatIndex = b * uniforms.numSegments + segmentId % uniforms.numSegments;\n let value = getX(b, inCol);\n\n ${Qr(\"&result[flatIndex]\",\"value\",this.type)}\n }\n }\n }\n `}};function Fce(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,p=[],u=0,c=w.getAxesPermutation([u],i),l=n;c!=null&&(l=xr({inputs:{x:n},backend:t,attrs:{perm:c}}),p.push(l),u=w.getInnerMostAxes(1,i)[0]);let m=w.segment_util.computeOutShape(l.shape,u,a),d=y.sizeFromShape([l.shape[u]]),f=pe({inputs:{x:l},backend:t,attrs:{shape:[-1,d]}});p.push(f);let h=n.dtype,g=[f.shape[0],a],x=vt({backend:t,attrs:{shape:g,value:0,dtype:h}}),b=new by(f.shape,g,h),C=[{type:\"int32\",data:[a]},{type:\"int32\",data:[y.sizeFromShape(f.shape)]}],S=t.runWebGPUProgram(b,[f,s],h,C,x),k=pe({inputs:{x:S},backend:t,attrs:{shape:m}});p.push(S);let _=k;if(c!=null){p.push(k);let $=w.getUndoAxesPermutation(c);_=xr({inputs:{x:_},backend:t,attrs:{perm:$}})}return p.forEach($=>t.disposeData($.dataId)),_}var qU={kernelName:Qi,backendName:\"webgpu\",kernelFunc:Fce};var Pce=[pz,Kz,qz,jz,Xz,Yz,Zz,Jz,eV,tV,rV,oV,nV,sV,aV,pV,cV,lV,mV,dV,hV,gV,xV,wV,SV,IV,lz,kV,TV,_V,EV,$V,RV,DV,AV,FV,PV,OV,BV,zV,VV,WV,GV,HV,UV,KV,qV,jV,XV,YV,JV,eW,tW,rW,oW,nW,sW,aW,iW,iz,uW,lW,pW,cW,mW,dW,fW,hW,gW,xW,yW,cz,bW,NV,CW,wW,SW,IW,vW,kW,NW,_W,TW,EW,$W,RW,AW,FW,iV,PW,OW,BW,MW,LW,zW,uV,VW,WW,UW,GW,KW,QV,qW,jW,XW,yV,YW,JW,eU,tU,rU,oU,nU,sU,bV,aU,iU,uU,pU,uz,cU,lU,mU,dU,fU,hU,gU,xU,yU,bU,CU,wU,SU,IU,vU,kU,fV,OU,MU,LU,HW,NU,TU,_U,EU,RU,DU,AU,FU,PU,BU,ZV,zU,VU,WU,$U,GU,HU,Qz,KU,qU,QW];for(let r of Pce)ti(r);var jU=\"4.21.0\",Oce=\"4.21.0\",Mce=\"4.21.0\",Lce=\"4.21.0\",Bce=\"4.21.0\",zce=\"4.21.0\",Vce={tfjs:jU,\"tfjs-core\":jU,\"tfjs-converter\":Oce,\"tfjs-backend-cpu\":Mce,\"tfjs-backend-webgl\":Lce,\"tfjs-backend-wasm\":Bce,\"tfjs-backend-webgpu\":zce};var EQt=void 0;export{Xs as Abs,Vo as Acos,Wo as Acosh,Ju as AdadeltaOptimizer,ep as AdagradOptimizer,tp as AdamOptimizer,rp as AdamaxOptimizer,uo as Add,Uo as AddN,Go as All,Ho as Any,Ys as ArgMax,Qs as ArgMin,Ko as Asin,qo as Asinh,jo as Atan,Yo as Atan2,Xo as Atanh,Qo as AvgPool,Zs as AvgPool3D,Ri as AvgPool3DGrad,$i as AvgPoolGrad,pm as BackendWasm,Zo as BatchMatMul,Js as BatchToSpaceND,Jo as Bincount,qa as BitwiseAnd,ea as BroadcastArgs,qce as BroadcastTo,yo as Cast,en as Ceil,bo as ClipByValue,Di as Complex,Ai as ComplexAbs,ta as Concat,tn as Conv2D,Fi as Conv2DBackpropFilter,rn as Conv2DBackpropInput,on as Conv3D,ja as Conv3DBackpropFilterV2,nn as Conv3DBackpropInputV2,sn as Cos,an as Cosh,cn as CropAndResize,un as Cumprod,pn as Cumsum,Bo as DataStorage,ra as DenseBincount,ln as DepthToSpace,mn as DepthwiseConv2dNative,Pi as DepthwiseConv2dNativeBackpropFilter,Oi as DepthwiseConv2dNativeBackpropInput,oa as Diag,dn as Dilation2D,Li as Dilation2DBackpropFilter,Mi as Dilation2DBackpropInput,$u as Draw,nw as ENV,Bi as Einsum,hn as Elu,Xa as EluGrad,dl as Environment,xn as Equal,gn as Erf,yn as Exp,na as ExpandDims,bn as Expm1,zi as FFT,sa as Fill,Cn as FlipLeftRight,wn as Floor,Sn as FloorDiv,Du as FromPixels,In as FusedBatchNorm,Io as FusedConv2D,vo as FusedDepthwiseConv2D,bp as GPGPUContext,vn as GatherNd,aa as GatherV2,Bl as GraphModel,kn as Greater,Nn as GreaterEqual,Vi as IFFT,Co as Identity,Wi as Imag,Tn as IsFinite,_n as IsInf,En as IsNan,ao as KernelBackend,Bn as LRN,Ya as LRNGrad,$n as LeakyRelu,Rn as Less,Dn as LessEqual,An as LinSpace,Fn as Log,Pn as Log1p,jce as LogSoftmax,On as LogicalAnd,Mn as LogicalNot,Ln as LogicalOr,R0 as LogicalXor,Xce as LowerBound,xc as MathBackendCPU,Lc as MathBackendWebGL,Yce as MatrixBandPart,zn as Max,Wn as MaxPool,ia as MaxPool3D,Gi as MaxPool3DGrad,Ui as MaxPoolGrad,ua as MaxPoolWithArgmax,Vn as Maximum,Un as Mean,Gn as Min,Hn as Minimum,Kn as MirrorPad,qn as Mod,op as MomentumOptimizer,jn as Multinomial,Xn as Multiply,pa as Neg,Qn as NonMaxSuppressionV3,Qa as NonMaxSuppressionV4,Zn as NonMaxSuppressionV5,Yn as NotEqual,Nw as OP_SCOPE_SUFFIX,Jn as OneHot,ca as OnesLike,kr as Optimizer,Fl as OptimizerConstructors,la as Pack,es as PadV2,Qce as Pool,ts as Pow,rs as Prelu,os as Prod,np as RMSPropOptimizer,Hp as RaggedGather,Kp as RaggedRange,qp as RaggedTensorToTensor,ma as Range,gw as Rank,Hi as Real,fn as RealDiv,ns as Reciprocal,$t as Reduction,ss as Relu,us as Relu6,da as Reshape,is as ResizeBilinear,Ja as ResizeBilinearGrad,as as ResizeNearestNeighbor,Za as ResizeNearestNeighborGrad,ps as Reverse,Ds as RotateWithOffset,cs as Round,ls as Rsqrt,mi as SGDOptimizer,ms as ScatterNd,fs as SearchSorted,fa as Select,hs as Selu,bs as Sigmoid,ys as Sign,gs as Sin,xs as Sinh,ha as Slice,Is as Softmax,Cs as Softplus,ga as SpaceToBatchND,Ki as SparseFillEmptyRows,ei as SparseReshape,ya as SparseSegmentMean,ba as SparseSegmentSum,vs as SparseToDense,xa as SplitV,ws as Sqrt,qi as Square,ks as SquaredDifference,Ru as StaticRegexReplace,wo as Step,Ns as StridedSlice,Ca as StringNGrams,ji as StringSplit,Xi as StringToHashBucketFast,Ts as Sub,Ss as Sum,_s as Tan,Es as Tanh,mt as Tensor,tt as TensorBuffer,ds as TensorScatterUpdate,po as Tile,$s as TopK,Rs as Transform,co as Transpose,Yi as Unique,wa as Unpack,Qi as UnsortedSegmentSum,Zce as UpperBound,ri as Variable,jc as WebGPUBackend,Sa as ZerosLike,So as _FusedMatMul,Qt as abs,Rk as acos,Dk as acosh,Ce as add,Ak as addN,Fk as all,Pk as any,Ok as argMax,Mk as argMin,Lk as asin,Bk as asinh,zk as atan,Vk as atan2,Wk as atanh,dd as avgPool,Hk as avgPool3d,ak as backend,w as backend_util,Kk as basicLSTMCell,nu as batchNorm,jk as batchNorm2d,Xk as batchNorm3d,Yk as batchNorm4d,fd as batchToSpaceND,hd as bincount,Qk as bitwiseAnd,L6 as booleanMaskAsync,Zk as broadcastArgs,su as broadcastTo,Sr as broadcast_util,cT as browser,me as buffer,Ue as cast,Jk as ceil,e2 as clipByValue,Ur as clone,Er as complex,yt as concat,t2 as concat1d,r2 as concat2d,o2 as concat3d,n2 as concat4d,s2 as conv1d,au as conv2d,a2 as conv2dTranspose,i2 as conv3d,p2 as conv3dTranspose,ale as copyRegisteredKernels,c2 as cos,l2 as cosh,$l as cosineWindow,m2 as cumprod,d2 as cumsum,Ir as customGrad,f2 as denseBincount,Tw as deprecationWarn,h2 as depthToSpace,sc as depthwiseConv2d,V5 as deregisterOp,eu as device_util,g2 as diag,x2 as dilation2d,xme as disableDeprecationWarnings,Ot as dispose,yme as disposeVariables,je as div,b2 as divNoNan,C2 as dot,Y6 as dropout,iu as einsum,bd as elu,gme as enableDebugMode,hme as enableProdMode,Zw as enclosingPowerOfTwo,ur as engine,w2 as ensureShape,A as env,yd as equal,S2 as erf,k2 as euclideanNorm,_o as exp,Ms as expandDims,N2 as expm1,Cd as eye,uc as fft,$a as fill,kme as findBackend,Nme as findBackendFactory,wd as floor,md as floorDiv,GD as forceHalfFloat,Jw as fused,Sd as gather,j6 as gatherND,af as gather_util,sk as getBackend,iw as getGradient,Xp as getKernel,Ym as getKernelsForBackend,aae as getThreadsCount,mv as gpgpu_util,VK as grad,WK as grads,Wu as greater,Id as greaterEqual,ju as ifft,pu as imag,eX as image,Z6 as inTopKAsync,di as io,Hd as irfft,T2 as isFinite,_2 as isInf,E2 as isNaN,$r as keep,Vt as kernel_impls,vd as leakyRelu,Tl as less,ac as lessEqual,tX as linalg,$2 as linspace,M8 as loadGraphModel,L8 as loadGraphModelSync,R2 as localResponseNormalization,pi as log,kd as log1p,D2 as logSigmoid,A2 as logSoftmax,_d as logSumExp,Uu as logicalAnd,Ed as logicalNot,$d as logicalOr,F2 as logicalXor,rX as losses,P2 as lowerBound,Ze as matMul,aT as math,Ra as max,Dd as maxPool,O2 as maxPool3d,M2 as maxPoolWithArgmax,Ad as maximum,Gu as mean,bme as memory,L2 as meshgrid,Nl as min,Hu as minimum,B2 as mirrorPad,z2 as mod,V2 as moments,V6 as movingAverage,se as mul,W2 as multiRNNCell,U2 as multinomial,pr as neg,cS as nextFrame,EQt as node,Vu as norm,Fd as notEqual,El as oneHot,Da as ones,G2 as onesLike,N as op,H2 as outerProduct,Aa as pad,K2 as pad1d,q2 as pad2d,j2 as pad3d,X2 as pad4d,Y2 as pool,ui as pow,Od as prelu,ld as print,Q2 as prod,Cme as profile,Z2 as raggedGather,J2 as raggedRange,e1 as raggedTensorToTensor,t1 as rand,S1 as randomGamma,Wd as randomNormal,I1 as randomStandardNormal,ic as randomUniform,v1 as randomUniformInt,cu as range,Ime as ready,ci as real,k1 as reciprocal,tu as registerBackend,ole as registerGradient,ti as registerKernel,z5 as registerOp,lu as relu,Ud as relu6,vme as removeBackend,W as reshape,mo as reverse,N1 as reverse1d,T1 as reverse2d,_1 as reverse3d,E1 as reverse4d,pc as rfft,Gd as round,$1 as rsqrt,ke as scalar,U6 as scatterND,du as scatter_util,_l as searchSorted,R1 as selu,D1 as separableConv2d,jN as serialization,Sme as setBackend,Tme as setPlatform,sae as setThreadsCount,oae as setWasmPath,nae as setWasmPaths,NI as setWebGLContext,A1 as setdiff1dAsync,Ic as shared,Ea as sigmoid,F1 as sign,Jj as signal,P1 as sin,O1 as sinh,Xe as slice,M1 as slice1d,L1 as slice2d,B1 as slice3d,z1 as slice4d,pt as slice_util,V1 as softmax,Td as softplus,Pd as spaceToBatchND,oX as sparse,K6 as sparseToDense,Zj as spectral,li as split,Rr as sqrt,Zt as square,Kd as squaredDifference,cc as squeeze,vr as stack,qd as step,W1 as stridedSlice,nX as string,Te as sub,ot as sum,oi as sumOutType,U1 as tan,kl as tanh,ar as tensor,Jt as tensor1d,mu as tensor2d,jd as tensor3d,G1 as tensor4d,H1 as tensor5d,K1 as tensor6d,j1 as tensorScatterUpdate,rk as tensor_util,w1 as test_util,De as tidy,uu as tile,wme as time,X1 as topk,OGe as train,mc as transpose,Y1 as truncatedNormal,Q1 as unique,sle as unregisterGradient,nle as unregisterKernel,Z1 as unsortedSegmentSum,fo as unstack,dt as upcastType,J1 as upperBound,y as util,UK as valueAndGrad,GK as valueAndGrads,eN as variable,Vw as variableGrads,Vce as version,z8 as version_converter,OX as version_core,yY as version_cpu,iae as version_wasm,d9 as version_webgl,$at as webgl,Ec as webgl_util,Zv as webgpu_util,lo as where,Yd as whereAsync,Gr as zeros,Gt as zerosLike};\n", "import type { Config } from '../exports';\n\n/**\n * Simple helper functions used accross codebase\n */\n\n// helper function: wrapper around console output\nexport function log(...msg): void {\n const dt = new Date();\n const ts = `${dt.getHours().toString().padStart(2, '0')}:${dt.getMinutes().toString().padStart(2, '0')}:${dt.getSeconds().toString().padStart(2, '0')}.${dt.getMilliseconds().toString().padStart(3, '0')}`;\n if (msg) console.log(ts, 'Human:', ...msg); // eslint-disable-line no-console\n}\n\n// helper function: join two paths\nexport function join(folder: string, file: string): string {\n const separator = folder.endsWith('/') ? '' : '/';\n const skipJoin = file.startsWith('.') || file.startsWith('/') || file.startsWith('http:') || file.startsWith('https:') || file.startsWith('file:');\n const path = skipJoin ? `${file}` : `${folder}${separator}${file}`;\n if (!path.toLocaleLowerCase().includes('.json')) throw new Error(`modelpath error: expecting json file: ${path}`);\n return path;\n}\n\n// helper function: gets elapsed time on both browser and nodejs\nexport const now = () => {\n if (typeof performance !== 'undefined') return performance.now();\n return parseInt((Number(process.hrtime.bigint()) / 1000 / 1000).toString());\n};\n\n// helper function: checks current config validity\nexport function validate(defaults: Partial, config: Partial, parent = 'config', msgs: { reason: string, where: string, expected?: string }[] = []) {\n for (const key of Object.keys(config)) {\n if (typeof config[key] === 'object') {\n validate(defaults[key], config[key], key, msgs);\n } else {\n const defined = defaults && (typeof defaults[key] !== 'undefined');\n if (!defined) msgs.push({ reason: 'unknown property', where: `${parent}.${key} = ${config[key]}` });\n const same = defaults && typeof defaults[key] === typeof config[key];\n if (defined && !same) msgs.push({ reason: 'property type mismatch', where: `${parent}.${key} = ${config[key]}`, expected: typeof defaults[key] });\n }\n // ok = ok && defined && same;\n }\n if (config.debug && parent === 'config' && msgs.length > 0) log('invalid configuration', msgs);\n return msgs;\n}\n\n// helper function: perform deep merge of multiple objects so it allows full inheritance with overrides\nexport function mergeDeep(...objects) {\n const isObject = (obj) => obj && typeof obj === 'object';\n return objects.reduce((prev, obj) => {\n Object.keys(obj || {}).forEach((key) => {\n const pVal = prev[key];\n const oVal = obj[key];\n if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal);\n else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal);\n else prev[key] = oVal;\n });\n return prev;\n }, {});\n}\n\n// helper function: return min and max from input array\nexport const minmax = (data: number[]) => data.reduce((acc: number[], val) => {\n acc[0] = (acc[0] === undefined || val < acc[0]) ? val : acc[0];\n acc[1] = (acc[1] === undefined || val > acc[1]) ? val : acc[1];\n return acc;\n}, []);\n\n// helper function: async wait\nexport async function wait(time: number) {\n const waiting = new Promise((resolve) => { setTimeout(() => resolve(true), time); });\n await waiting;\n}\n", "/* eslint-disable no-multi-spaces */\n\n/** Possible TensorFlow backends */\nexport type BackendEnum = '' | 'cpu' | 'wasm' | 'webgl' | 'humangl' | 'tensorflow' | 'webgpu' | 'none';\n\n/** Possible values for `human.warmup` */\nexport type WarmupEnum = '' | 'none' | 'face' | 'full' | 'body';\n\n/** Possible segmentation model behavior */\nexport type SegmentationEnum = 'default' | 'alpha' | 'foreground' | 'state'\n\n/** Generic config type inherited by all module types */\nexport interface GenericConfig {\n /** is module enabled? */\n enabled: boolean,\n /** path to model json file (relative to `modelBasePath` */\n modelPath: string,\n /** how many max frames to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipFrames: number,\n /** how many max milliseconds to go without re-running model if cached results are acceptable\n * for two-phase models such as face and hand caching applies to bounding boxes detection only */\n skipTime: number,\n}\n\n/** Detector part of face configuration */\nexport interface FaceDetectorConfig extends GenericConfig {\n /** is face rotation correction performed after detecting face?\n * used to correctly analyze faces under high angles\n */\n rotation: boolean,\n /** maximum number of detected faces */\n maxDetected: number,\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n /** minimum size in pixels of a detected face box before resutls are discared */\n minSize: number,\n /** minimum overlap between two detected faces before one is discarded */\n iouThreshold: number,\n /** how much should face box be enlarged over the min/max facial coordinates */\n scale: number,\n /** automatically pad image to square */\n square: boolean,\n /** should child models perform on masked image of a face */\n mask: boolean,\n /** should face detection return processed and cropped face tensor that can with an external model for addtional processing?\n * if enabled it must be manually deallocated to avoid memory leak */\n return: boolean,\n}\n\n/** Mesh part of face configuration */\nexport interface FaceMeshConfig extends GenericConfig {\n /** Keep detected faces that cannot be verified using facemesh */\n keepInvalid: boolean\n}\n\n/** Iris part of face configuration */\nexport interface FaceIrisConfig extends GenericConfig {\n /** how much should iris box be enlarged over the min/max iris coordinates */\n scale: number,\n}\n\n/** Attention part of face configuration */\nexport interface FaceAttentionConfig extends GenericConfig {}\n\n/** Description or face embedding part of face configuration\n * - also used by age and gender detection\n */\nexport interface FaceDescriptionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Emotion part of face configuration */\nexport interface FaceEmotionConfig extends GenericConfig {\n /** minimum confidence for a detected face before results are discarded */\n minConfidence: number,\n}\n\n/** Anti-spoofing part of face configuration */\nexport interface FaceAntiSpoofConfig extends GenericConfig {}\n\n/** Liveness part of face configuration */\nexport interface FaceLivenessConfig extends GenericConfig {}\n\n/** Gear part of face configuration */\nexport interface FaceGearConfig extends GenericConfig {\n /** minimum confidence for a detected race before results are discarded */\n minConfidence: number,\n}\n\n/** Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description */\nexport interface FaceConfig extends GenericConfig {\n detector: Partial,\n mesh: Partial,\n attention: Partial,\n iris: Partial,\n description: Partial,\n emotion: Partial,\n antispoof: Partial,\n liveness: Partial,\n gear: Partial,\n}\n\n/** Configures all body detection specific options */\nexport interface BodyConfig extends GenericConfig {\n /** maximum number of detected bodies */\n maxDetected: number,\n /** minimum confidence for a detected body before results are discarded */\n minConfidence: number,\n /* experimental\n /** experimental: detector used for body model before actual analysis\n detector?: {\n /** experimental: enable body detector before body landmarks\n enabled: boolean,\n /** experimental: path to optional body detector model json file\n modelPath: string,\n /** experimental: minimum confidence for a detected body before results are discarded\n minConfidence: number,\n /** experimental: minimum overlap between two detected bodies before one is discarded\n iouThreshold: number\n },\n */\n}\n\n/** Configures all hand detection specific options */\nexport interface HandConfig extends GenericConfig {\n /** should hand rotation correction be performed after hand detection? */\n rotation: boolean,\n /** minimum confidence for a detected hand before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected hands before one is discarded */\n iouThreshold: number,\n /** maximum number of detected hands */\n maxDetected: number,\n /** should hand landmarks be detected or just return detected hand box */\n landmarks: boolean,\n detector: {\n /** path to hand detector model json */\n modelPath?: string,\n },\n skeleton: {\n /** path to hand skeleton model json */\n modelPath?: string,\n },\n}\n\n/** Configures all object detection specific options */\nexport interface ObjectConfig extends GenericConfig {\n /** minimum confidence for a detected objects before results are discarded */\n minConfidence: number,\n /** minimum overlap between two detected objects before one is discarded */\n iouThreshold: number,\n /** maximum number of detected objects */\n maxDetected: number,\n}\n\n/** Configures all body segmentation module\n * removes background from input containing person\n * if segmentation is enabled it will run as preprocessing task before any other model\n * alternatively leave it disabled and use it on-demand using human.segmentation method which can\n * remove background or replace it with user-provided background\n*/\nexport interface SegmentationConfig extends GenericConfig {\n /** downsample ratio, adjust to reflect approximately how much of input is taken by body */\n ratio: number,\n /** possible rvm segmentation mode */\n mode: SegmentationEnum,\n}\n\n/** Run input through image filters before inference\n * - available only in Browser environments\n * - image filters run with near-zero latency as they are executed on the GPU using WebGL\n*/\nexport interface FilterConfig {\n /** are image filters enabled? */\n enabled: boolean,\n /** perform image histogram equalization\n * - equalization is performed on input as a whole and detected face before its passed for further analysis\n */\n equalization: boolean,\n /** resize input width\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n width: number,\n /** resize input height\n * - if both width and height are set to 0, there is no resizing\n * - if just one is set, second one is scaled automatically\n * - if both are set, values are used as-is\n */\n height: number,\n /** return processed canvas imagedata in result */\n return: boolean,\n /** flip input as mirror image */\n flip: boolean,\n /** apply auto-brighness */\n autoBrightness: boolean,\n /** range: -1 (darken) to 1 (lighten) */\n brightness: number,\n /** range: -1 (reduce contrast) to 1 (increase contrast) */\n contrast: number,\n /** range: 0 (no sharpening) to 1 (maximum sharpening) */\n sharpness: number,\n /** range: 0 (no blur) to N (blur radius in pixels) */\n blur: number\n /** range: -1 (reduce saturation) to 1 (increase saturation) */\n saturation: number,\n /** range: 0 (no change) to 360 (hue rotation in degrees) */\n hue: number,\n /** image negative */\n negative: boolean,\n /** image sepia colors */\n sepia: boolean,\n /** image vintage colors */\n vintage: boolean,\n /** image kodachrome colors */\n kodachrome: boolean,\n /** image technicolor colors */\n technicolor: boolean,\n /** image polaroid camera effect */\n polaroid: boolean,\n /** range: 0 (no pixelate) to N (number of pixels to pixelate) */\n pixelate: number,\n}\n\n/** Controlls gesture detection */\nexport interface GestureConfig {\n /** is gesture detection enabled? */\n enabled: boolean,\n}\n/**\n * Configuration interface definition for **Human** library\n * Contains all configurable parameters\n * Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\nexport interface Config {\n /** Backend used for TFJS operations\n * valid build-in backends are:\n * - Browser: `cpu`, `wasm`, `webgl`, `humangl`, `webgpu`\n * - NodeJS: `cpu`, `wasm`, `tensorflow`\n * default: `webgl` for browser and `tensorflow` for nodejs\n */\n backend: BackendEnum,\n\n /** Path to *.wasm files if backend is set to `wasm`\n *\n * default: auto-detects to link to CDN `jsdelivr` when running in browser\n */\n wasmPath: string,\n\n /** Force WASM loader to use platform fetch\n *\n * default: false\n */\n wasmPlatformFetch: boolean,\n\n /** Print debug statements to console\n *\n * default: `true`\n */\n debug: boolean,\n\n /** Perform model loading and inference concurrently or sequentially\n *\n * default: `true`\n */\n async: boolean,\n\n /** What to use for `human.warmup()`\n * - warmup pre-initializes all models for faster inference but can take significant time on startup\n * - used by `webgl`, `humangl` and `webgpu` backends\n *\n * default: `full`\n */\n warmup: WarmupEnum,\n\n /** Base model path (typically starting with file://, http:// or https://) for all models\n * - individual modelPath values are relative to this path\n *\n * default: `../models/` for browsers and `file://models/` for nodejs\n */\n modelBasePath: string,\n\n /** Cache models in IndexDB on first sucessfull load\n * default: true if indexdb is available (browsers), false if its not (nodejs)\n */\n cacheModels: boolean,\n\n /** Validate kernel ops used in model during model load\n * default: true\n * any errors will be printed on console but will be treated as non-fatal\n */\n validateModels: boolean,\n\n /** Cache sensitivity\n * - values 0..1 where 0.01 means reset cache if input changed more than 1%\n * - set to 0 to disable caching\n *\n * default: 0.7\n */\n cacheSensitivity: number;\n\n /** Explicit flags passed to initialize TFJS */\n flags: Record,\n\n /** Software Kernels\n * Registers software kernel ops running on CPU when accelerated version of kernel is not found in the current backend\n */\n softwareKernels: boolean,\n\n /** Perform immediate garbage collection on deallocated tensors instead of caching them */\n deallocate: boolean;\n\n /** Internal Variable */\n skipAllowed: boolean;\n\n /** Filter config {@link FilterConfig} */\n filter: Partial,\n\n /** Gesture config {@link GestureConfig} */\n gesture: Partial;\n\n /** Face config {@link FaceConfig} */\n face: Partial,\n\n /** Body config {@link BodyConfig} */\n body: Partial,\n\n /** Hand config {@link HandConfig} */\n hand: Partial,\n\n /** Object config {@link ObjectConfig} */\n object: Partial,\n\n /** Segmentation config {@link SegmentationConfig} */\n segmentation: Partial,\n}\n\n/** - [See all default Config values...](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) */\nconst config: Config = {\n backend: '',\n modelBasePath: '',\n cacheModels: true,\n validateModels: true,\n wasmPath: '',\n wasmPlatformFetch: false,\n debug: false,\n async: true,\n warmup: 'full',\n cacheSensitivity: 0.70,\n skipAllowed: false,\n deallocate: false,\n flags: {},\n softwareKernels: false,\n filter: {\n enabled: true,\n equalization: false,\n width: 0,\n height: 0,\n flip: false,\n return: true,\n autoBrightness: true,\n brightness: 0,\n contrast: 0,\n sharpness: 0,\n blur: 0,\n saturation: 0,\n hue: 0,\n negative: false,\n sepia: false,\n vintage: false,\n kodachrome: false,\n technicolor: false,\n polaroid: false,\n pixelate: 0,\n },\n gesture: {\n enabled: true,\n },\n face: {\n enabled: true,\n detector: {\n modelPath: 'blazeface.json',\n rotation: false,\n maxDetected: 1,\n skipFrames: 99,\n skipTime: 2500,\n minConfidence: 0.2,\n minSize: 0,\n iouThreshold: 0.1,\n scale: 1.4,\n mask: false,\n return: false,\n },\n mesh: {\n enabled: true,\n modelPath: 'facemesh.json',\n keepInvalid: false,\n },\n attention: {\n enabled: false,\n modelPath: 'facemesh-attention.json',\n },\n iris: {\n enabled: true,\n scale: 2.3,\n modelPath: 'iris.json',\n },\n emotion: {\n enabled: true,\n minConfidence: 0.1,\n skipFrames: 99,\n skipTime: 1500,\n modelPath: 'emotion.json',\n },\n description: {\n enabled: true,\n modelPath: 'faceres.json',\n skipFrames: 99,\n skipTime: 3000,\n minConfidence: 0.1,\n },\n antispoof: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'antispoof.json',\n },\n liveness: {\n enabled: false,\n skipFrames: 99,\n skipTime: 4000,\n modelPath: 'liveness.json',\n },\n },\n body: {\n enabled: true,\n modelPath: 'movenet-lightning.json',\n maxDetected: -1,\n minConfidence: 0.3,\n skipFrames: 1,\n skipTime: 200,\n },\n hand: {\n enabled: true,\n rotation: true,\n skipFrames: 99,\n skipTime: 1000,\n minConfidence: 0.50,\n iouThreshold: 0.2,\n maxDetected: -1,\n landmarks: true,\n detector: {\n modelPath: 'handtrack.json',\n },\n skeleton: {\n modelPath: 'handlandmark-lite.json',\n },\n },\n object: {\n enabled: false,\n modelPath: 'centernet.json',\n minConfidence: 0.2,\n iouThreshold: 0.4,\n maxDetected: 10,\n skipFrames: 99,\n skipTime: 2000,\n },\n segmentation: {\n enabled: false,\n modelPath: 'rvm.json',\n ratio: 0.5,\n mode: 'default',\n },\n};\n\nexport { config as defaults };\n", "export const vertexIdentity = `\n precision highp float;\n attribute vec2 pos;\n attribute vec2 uv;\n varying vec2 vUv;\n uniform float flipY;\n void main(void) {\n vUv = uv;\n gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);\n }\n`;\n\nexport const fragmentIdentity = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n void main(void) {\n gl_FragColor = texture2D(texture, vUv);\n }\n`;\n\nexport const colorMatrixWithAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];\n gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];\n }\n`;\n\nexport const colorMatrixWithoutAlpha = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform float m[20];\n void main(void) {\n vec4 c = texture2D(texture, vUv);\n gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];\n gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];\n gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];\n gl_FragColor.a = c.a;\n }\n`;\n\nexport const pixelate = `\n precision highp float;\n varying vec2 vUv;\n uniform vec2 size;\n uniform sampler2D texture;\n vec2 pixelate(vec2 coord, vec2 size) {\n return floor( coord / size ) * size;\n }\n void main(void) {\n gl_FragColor = vec4(0.0);\n vec2 coord = pixelate(vUv, size);\n gl_FragColor += texture2D(texture, coord);\n }\n`;\n\nexport const blur = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n void main(void) {\n gl_FragColor = vec4(0.0);\n gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;\n gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv )*0.159576912161;\n gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;\n gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;\n gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;\n gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;\n gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;\n gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;\n gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;\n }\n`;\n\nexport const convolution = `\n precision highp float;\n varying vec2 vUv;\n uniform sampler2D texture;\n uniform vec2 px;\n uniform float m[9];\n void main(void) {\n vec4 c11 = texture2D(texture, vUv - px); // top left\n vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center\n vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right\n vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left\n vec4 c22 = texture2D(texture, vUv); // mid center\n vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right\n vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left\n vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center\n vec4 c33 = texture2D(texture, vUv + px ); // bottom right\n gl_FragColor = \n c11 * m[0] + c12 * m[1] + c22 * m[2] +\n c21 * m[3] + c22 * m[4] + c23 * m[5] +\n c31 * m[6] + c32 * m[7] + c33 * m[8];\n gl_FragColor.a = c22.a;\n }\n`;\n", "/**\n * Image Filters in WebGL algoritm implementation\n * Based on: [WebGLImageFilter](https://github.com/phoboslab/WebGLImageFilter)\n */\n\n/* eslint-disable func-names */\n\nimport * as shaders from './imagefxshaders';\nimport { canvas } from './image';\nimport { log } from '../util/util';\n\nconst collect = (source, prefix: string, collection) => {\n const r = new RegExp('\\\\b' + prefix + ' \\\\w+ (\\\\w+)', 'ig');\n source.replace(r, (match, name) => {\n collection[name] = 0;\n return match;\n });\n};\n\nclass GLProgram {\n uniform = {};\n attribute = {};\n gl: WebGLRenderingContext;\n id: WebGLProgram;\n\n constructor(gl, vertexSource, fragmentSource) {\n this.gl = gl;\n const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER);\n const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER);\n this.id = this.gl.createProgram() as WebGLProgram;\n if (!vertexShader || !fragmentShader) return;\n if (!this.id) {\n log('filter: could not create webgl program');\n return;\n }\n this.gl.attachShader(this.id, vertexShader);\n this.gl.attachShader(this.id, fragmentShader);\n this.gl.linkProgram(this.id);\n if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) {\n log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || 'unknown'}`);\n return;\n }\n this.gl.useProgram(this.id);\n collect(vertexSource, 'attribute', this.attribute); // Collect attributes\n for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a);\n collect(vertexSource, 'uniform', this.uniform); // Collect uniforms\n collect(fragmentSource, 'uniform', this.uniform);\n for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u);\n }\n\n compile = (source, type): WebGLShader | null => {\n const shader = this.gl.createShader(type);\n if (!shader) {\n log('filter: could not create shader');\n return null;\n }\n this.gl.shaderSource(shader, source);\n this.gl.compileShader(shader);\n if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) {\n log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || 'unknown'}`);\n return null;\n }\n return shader;\n };\n}\n\n// function that is instantiated as class so it has private this members\n/**\n * @class GLImageFilter\n * @property {function} reset reset current filter chain\n * @property {function} add add specified filter to filter chain\n * @property {function} apply execute filter chain and draw result\n * @property {function} draw just draw input to result\n */\n\nexport function GLImageFilter() {\n let drawCount = 0;\n let sourceTexture: WebGLTexture | null = null;\n let lastInChain = false;\n let currentFramebufferIndex = -1;\n let tempFramebuffers: [null, null] | [{ fbo: WebGLFramebuffer | null, texture: WebGLTexture | null }] = [null, null];\n let filterChain: Record[] = [];\n let vertexBuffer: WebGLBuffer | null = null;\n let currentProgram: GLProgram | null = null;\n const fxcanvas = canvas(100, 100) as HTMLCanvasElement;\n const shaderProgramCache = { }; // key is the shader program source, value is the compiled program\n const DRAW = { INTERMEDIATE: 1 };\n const gl = fxcanvas.getContext('webgl') as WebGLRenderingContext;\n if (!gl) {\n log('filter: cannot get webgl context');\n return;\n }\n // @ts-ignore used for sanity checks outside of imagefx\n this.gl = gl;\n\n function resize(width, height) {\n if (width === fxcanvas.width && height === fxcanvas.height) return; // Same width/height? Nothing to do here\n fxcanvas.width = width;\n fxcanvas.height = height;\n if (!vertexBuffer) { // Create the context if we don't have it yet\n const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); // Create the vertex buffer for the two triangles [x, y, u, v] * 6\n vertexBuffer = gl.createBuffer();\n gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer);\n gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW);\n gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);\n }\n gl.viewport(0, 0, fxcanvas.width, fxcanvas.height);\n tempFramebuffers = [null, null]; // Delete old temp framebuffers\n }\n\n function createFramebufferTexture(width, height) {\n const fbo = gl.createFramebuffer();\n gl.bindFramebuffer(gl.FRAMEBUFFER, fbo);\n const renderbuffer = gl.createRenderbuffer();\n gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer);\n const texture = gl.createTexture();\n gl.bindTexture(gl.TEXTURE_2D, texture);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n gl.bindTexture(gl.TEXTURE_2D, null);\n gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n return { fbo, texture };\n }\n\n function getTempFramebuffer(index): { fbo: WebGLFramebuffer | null, texture: WebGLTexture | null } {\n tempFramebuffers[index] = tempFramebuffers[index] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);\n return tempFramebuffers[index] as { fbo: WebGLFramebuffer, texture: WebGLTexture };\n }\n\n function draw(flags = 0) {\n if (!currentProgram) return;\n let source: WebGLTexture | null = null;\n let target: WebGLFramebuffer | null = null;\n let flipY = false;\n if (drawCount === 0) source = sourceTexture; // First draw call - use the source texture\n else source = getTempFramebuffer(currentFramebufferIndex).texture || null; // All following draw calls use the temp buffer last drawn to\n drawCount++;\n if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { // Last filter in our chain - draw directly to the WebGL Canvas. We may also have to flip the image vertically now\n target = null;\n flipY = drawCount % 2 === 0;\n } else {\n currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;\n target = getTempFramebuffer(currentFramebufferIndex).fbo || null; // Intermediate draw call - get a temp buffer to draw to\n }\n gl.bindTexture(gl.TEXTURE_2D, source); // Bind the source and target and draw the two triangles\n gl.bindFramebuffer(gl.FRAMEBUFFER, target);\n gl.uniform1f(currentProgram.uniform['flipY'], (flipY ? -1 : 1));\n gl.drawArrays(gl.TRIANGLES, 0, 6);\n }\n\n function compileShader(fragmentSource): GLProgram | null {\n if (shaderProgramCache[fragmentSource]) {\n currentProgram = shaderProgramCache[fragmentSource];\n gl.useProgram((currentProgram ? currentProgram.id : null) || null);\n return currentProgram;\n }\n currentProgram = new GLProgram(gl, shaders.vertexIdentity, fragmentSource);\n if (!currentProgram) {\n log('filter: could not get webgl program');\n return null;\n }\n const floatSize = Float32Array.BYTES_PER_ELEMENT;\n const vertSize = 4 * floatSize;\n gl.enableVertexAttribArray(currentProgram.attribute['pos']);\n gl.vertexAttribPointer(currentProgram.attribute['pos'], 2, gl.FLOAT, false, vertSize, 0 * floatSize);\n gl.enableVertexAttribArray(currentProgram.attribute['uv']);\n gl.vertexAttribPointer(currentProgram.attribute['uv'], 2, gl.FLOAT, false, vertSize, 2 * floatSize);\n shaderProgramCache[fragmentSource] = currentProgram;\n return currentProgram;\n }\n\n const filter = {\n colorMatrix: (matrix: number[]) => { // general color matrix filter\n const m = new Float32Array(matrix);\n m[4] /= 255;\n m[9] /= 255;\n m[14] /= 255;\n m[19] /= 255;\n const shader = (m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0) // Can we ignore the alpha value? Makes things a bit faster.\n ? shaders.colorMatrixWithoutAlpha\n : shaders.colorMatrixWithAlpha;\n const program = compileShader(shader);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n draw();\n },\n\n brightness: (brightness: number) => {\n const b = (brightness || 0) + 1;\n filter.colorMatrix([\n b, 0, 0, 0, 0,\n 0, b, 0, 0, 0,\n 0, 0, b, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n saturation: (amount: number) => {\n const x = (amount || 0) * 2 / 3 + 1;\n const y = ((x - 1) * -0.5);\n filter.colorMatrix([\n x, y, y, 0, 0,\n y, x, y, 0, 0,\n y, y, x, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturate: () => {\n filter.saturation(-1);\n },\n\n contrast: (amount: number) => {\n const v = (amount || 0) + 1;\n const o = -128 * (v - 1);\n filter.colorMatrix([\n v, 0, 0, 0, o,\n 0, v, 0, 0, o,\n 0, 0, v, 0, o,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n negative: () => {\n filter.contrast(-2);\n },\n\n hue: (rotation: number) => {\n rotation = (rotation || 0) / 180 * Math.PI;\n const cos = Math.cos(rotation);\n const sin = Math.sin(rotation);\n const lumR = 0.213;\n const lumG = 0.715;\n const lumB = 0.072;\n filter.colorMatrix([\n lumR + cos * (1 - lumR) + sin * (-lumR), lumG + cos * (-lumG) + sin * (-lumG), lumB + cos * (-lumB) + sin * (1 - lumB), 0, 0,\n lumR + cos * (-lumR) + sin * (0.143), lumG + cos * (1 - lumG) + sin * (0.140), lumB + cos * (-lumB) + sin * (-0.283), 0, 0,\n lumR + cos * (-lumR) + sin * (-(1 - lumR)), lumG + cos * (-lumG) + sin * (lumG), lumB + cos * (1 - lumB) + sin * (lumB), 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n desaturateLuminance: () => {\n filter.colorMatrix([\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0.2764723, 0.9297080, 0.0938197, 0, -37.1,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n sepia: () => {\n filter.colorMatrix([\n 0.393, 0.7689999, 0.18899999, 0, 0,\n 0.349, 0.6859999, 0.16799999, 0, 0,\n 0.272, 0.5339999, 0.13099999, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n brownie: () => {\n filter.colorMatrix([\n 0.5997023498159715, 0.34553243048391263, -0.2708298674538042, 0, 47.43192855600873,\n -0.037703249837783157, 0.8609577587992641, 0.15059552388459913, 0, -36.96841498319127,\n 0.24113635128153335, -0.07441037908422492, 0.44972182064877153, 0, -7.562075277591283,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n vintagePinhole: () => {\n filter.colorMatrix([\n 0.6279345635605994, 0.3202183420819367, -0.03965408211312453, 0, 9.651285835294123,\n 0.02578397704808868, 0.6441188644374771, 0.03259127616149294, 0, 7.462829176470591,\n 0.0466055556782719, -0.0851232987247891, 0.5241648018700465, 0, 5.159190588235296,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n kodachrome: () => {\n filter.colorMatrix([\n 1.1285582396593525, -0.3967382283601348, -0.03992559172921793, 0, 63.72958762196502,\n -0.16404339962244616, 1.0835251566291304, -0.05498805115633132, 0, 24.732407896706203,\n -0.16786010706155763, -0.5603416277695248, 1.6014850761964943, 0, 35.62982807460946,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n technicolor: () => {\n filter.colorMatrix([\n 1.9125277891456083, -0.8545344976951645, -0.09155508482755585, 0, 11.793603434377337,\n -0.3087833385928097, 1.7658908555458428, -0.10601743074722245, 0, -70.35205161461398,\n -0.231103377548616, -0.7501899197440212, 1.847597816108189, 0, 30.950940869491138,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n polaroid: () => {\n filter.colorMatrix([\n 1.438, -0.062, -0.062, 0, 0,\n -0.122, 1.378, -0.122, 0, 0,\n -0.016, -0.016, 1.483, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n shiftToBGR: () => {\n filter.colorMatrix([\n 0, 0, 1, 0, 0,\n 0, 1, 0, 0, 0,\n 1, 0, 0, 0, 0,\n 0, 0, 0, 1, 0,\n ]);\n },\n\n convolution: (matrix: number[]) => { // general convolution Filter\n const m = new Float32Array(matrix);\n const pixelSizeX = 1 / fxcanvas.width;\n const pixelSizeY = 1 / fxcanvas.height;\n const program = compileShader(shaders.convolution);\n if (!program) return;\n gl.uniform1fv(program.uniform['m'], m);\n gl.uniform2f(program.uniform['px'], pixelSizeX, pixelSizeY);\n draw();\n },\n\n detectEdges: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, 1, 0,\n 1, -4, 1,\n 0, 1, 0,\n ]);\n },\n\n sobelX: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, 0, 1,\n -2, 0, 2,\n -1, 0, 1,\n ]);\n },\n\n sobelY: () => {\n // @ts-ignore this\n filter.convolution.call(this, [\n -1, -2, -1,\n 0, 0, 0,\n 1, 2, 1,\n ]);\n },\n\n sharpen: (amount) => {\n const a = amount || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n 0, -1 * a, 0,\n -1 * a, 1 + 4 * a, -1 * a,\n 0, -1 * a, 0,\n ]);\n },\n\n emboss: (size: number) => {\n const s = size || 1;\n // @ts-ignore this\n filter.convolution.call(this, [\n -2 * s, -1 * s, 0,\n -1 * s, 1, 1 * s,\n 0, 1 * s, 2 * s,\n ]);\n },\n\n blur: (size: number) => {\n const blurSizeX = (size / 7) / fxcanvas.width;\n const blurSizeY = (size / 7) / fxcanvas.height;\n const program = compileShader(shaders.blur);\n if (!program) return;\n // Vertical\n gl.uniform2f(program.uniform['px'], 0, blurSizeY);\n draw(DRAW.INTERMEDIATE);\n // Horizontal\n gl.uniform2f(program.uniform['px'], blurSizeX, 0);\n draw();\n },\n\n pixelate: (size: number) => {\n const blurSizeX = (size) / fxcanvas.width;\n const blurSizeY = (size) / fxcanvas.height;\n const program = compileShader(shaders.pixelate);\n if (!program) return;\n gl.uniform2f(program.uniform['size'], blurSizeX, blurSizeY);\n draw();\n },\n };\n\n // @ts-ignore this\n this.add = function (name) {\n const args = Array.prototype.slice.call(arguments, 1); // eslint-disable-line prefer-rest-params\n const func = filter[name];\n filterChain.push({ func, args });\n };\n\n // @ts-ignore this\n this.reset = function () {\n filterChain = [];\n };\n\n // @ts-ignore this\n this.get = function () {\n return filterChain;\n };\n\n // @ts-ignore this\n this.apply = function (image) {\n resize(image.width, image.height);\n drawCount = 0;\n if (!sourceTexture) sourceTexture = gl.createTexture(); // Create the texture for the input image if we haven't yet\n gl.bindTexture(gl.TEXTURE_2D, sourceTexture);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST);\n gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST);\n gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image);\n for (let i = 0; i < filterChain.length; i++) {\n lastInChain = (i === filterChain.length - 1);\n const f = filterChain[i];\n // @ts-ignore function assigment\n f.func.apply(this, f.args || []);\n }\n return fxcanvas;\n };\n\n // @ts-ignore this\n this.draw = function (image) {\n this.add('brightness', 0);\n return this.apply(image);\n };\n}\n", "/**\n * Image enhancements\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function histogramEqualization(inputImage: Tensor): Promise {\n const squeeze = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage;\n const rgb = tf.split(squeeze, 3, 2);\n const min: Tensor[] = [tf.min(rgb[0]), tf.min(rgb[1]), tf.min(rgb[2])]; // minimum pixel value per channel T[]\n const max: Tensor[] = [tf.max(rgb[0]), tf.max(rgb[1]), tf.max(rgb[2])]; // maximum pixel value per channel T[]\n // const absMin = await Promise.all(min.map((channel) => channel.data())); // minimum pixel value per channel A[]\n // const minValue = Math.min(absMax[0][0], absMin[1][0], absMin[2][0]);\n const absMax = await Promise.all(max.map((channel) => channel.data())); // maximum pixel value per channel A[]\n const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]);\n const maxRange = maxValue > 1 ? 255 : 1;\n const factor = maxRange / maxValue;\n let final: Tensor;\n if (factor > 1) {\n const sub = [tf.sub(rgb[0], min[0]), tf.sub(rgb[1], min[1]), tf.sub(rgb[2], min[2])]; // channels offset by min values\n const range = [tf.sub(max[0], min[0]), tf.sub(max[1], min[1]), tf.sub(max[2], min[2])]; // channel ranges\n // const fact = [tf.div(maxRange, absMax[0]), tf.div(maxRange, absMax[1]), tf.div(maxRange, absMax[1])]; // factors between\n const enh = [tf.mul(sub[0], factor), tf.mul(sub[1], factor), tf.mul(sub[2], factor)];\n const stack = tf.stack([enh[0], enh[1], enh[2]], 2);\n final = tf.reshape(stack, [1, squeeze.shape[0] || 0, squeeze.shape[1] || 0, 3]);\n tf.dispose([...sub, ...range, ...enh, stack]);\n } else {\n final = tf.expandDims(squeeze, 0);\n }\n tf.dispose([...rgb, ...min, ...max, rgb, squeeze, inputImage]);\n return final;\n}\n", "/**\n * Image Processing algorithm implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as fxImage from './imagefx';\nimport type { Input, AnyCanvas, Config } from '../exports';\nimport type { Tensor, Tensor3D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport * as enhance from './enhance';\n\nconst maxSize = 3840;\n// internal temp canvases\nlet inCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet outCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\nlet tmpCanvas: AnyCanvas | null = null; // use global variable to avoid recreating canvas on each frame\n// @ts-ignore // imagefx is js module that should be converted to a class\nlet fx: fxImage.GLImageFilter | null; // eslint-disable-line @typescript-eslint/no-redundant-type-constituents\n\nconst last: { inputSum: number, cacheDiff: number, sumMethod: number, inputTensor: undefined | Tensor } = {\n inputSum: 0,\n cacheDiff: 1,\n sumMethod: 0,\n inputTensor: undefined,\n};\n\nexport function reset() {\n last.inputSum = 0;\n last.cacheDiff = 1;\n last.sumMethod = 0;\n last.inputTensor = undefined;\n}\n\nexport function canvas(width: number, height: number): AnyCanvas {\n let c: AnyCanvas;\n if (env.browser) { // browser defines canvas object\n if (env.worker) { // if runing in web worker use OffscreenCanvas\n if (typeof OffscreenCanvas === 'undefined') throw new Error('canvas error: attempted to run in web worker but OffscreenCanvas is not supported');\n c = new OffscreenCanvas(width, height);\n } else { // otherwise use DOM canvas\n if (typeof document !== 'undefined') {\n c = document.createElement('canvas');\n c.width = width;\n c.height = height;\n } else if (typeof navigator !== 'undefined' && navigator.product === 'ReactNative') {\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n else throw new Error('canvas error: attempted to use canvas in react-native without canvas support installed');\n } else {\n throw new Error('canvas error: attempted to run in browser but DOM is not defined');\n }\n }\n } else { // if not running in browser, there is no \"default\" canvas object, so we need monkey patch or fail\n // @ts-ignore // env.canvas is an external monkey-patch\n if (typeof env.Canvas !== 'undefined') c = new env.Canvas(width, height);\n else if (typeof globalThis.Canvas !== 'undefined') c = new globalThis.Canvas(width, height);\n // else throw new Error('canvas error: attempted to use canvas in nodejs without canvas support installed');\n }\n // @ts-ignore its either defined or we already threw an error\n return c;\n}\n\n// helper function to copy canvas from input to output\nexport function copy(input: AnyCanvas, output?: AnyCanvas) {\n const outputCanvas = output || canvas(input.width, input.height);\n const ctx = outputCanvas.getContext('2d') as CanvasRenderingContext2D;\n ctx.drawImage(input, 0, 0);\n return outputCanvas;\n}\n\n// process input image and return tensor\n// input can be tensor, imagedata, htmlimageelement, htmlvideoelement\n// input is resized and run through imagefx filter\nexport async function process(input: Input, config: Config, getTensor: boolean = true): Promise<{ tensor: Tensor4D | null, canvas: AnyCanvas | null }> {\n if (!input) {\n // throw new Error('input is missing');\n if (config.debug) log('input error: input is missing');\n return { tensor: null, canvas: null }; // video may become temporarily unavailable due to onresize\n }\n // sanity checks since different browsers do not implement all dom elements\n if (\n !(input instanceof tf.Tensor)\n && !(typeof Image !== 'undefined' && input instanceof Image)\n && !(typeof globalThis.Canvas !== 'undefined' && input instanceof globalThis.Canvas)\n && !(typeof ImageData !== 'undefined' && input instanceof ImageData)\n && !(typeof ImageBitmap !== 'undefined' && input instanceof ImageBitmap)\n && !(typeof HTMLImageElement !== 'undefined' && input instanceof HTMLImageElement)\n && !(typeof HTMLMediaElement !== 'undefined' && input instanceof HTMLMediaElement)\n && !(typeof HTMLVideoElement !== 'undefined' && input instanceof HTMLVideoElement)\n && !(typeof HTMLCanvasElement !== 'undefined' && input instanceof HTMLCanvasElement)\n && !(typeof OffscreenCanvas !== 'undefined' && input instanceof OffscreenCanvas)\n ) {\n throw new Error('input error: type not recognized');\n }\n if (input instanceof tf.Tensor) { // if input is tensor use as-is without filters but correct shape as needed\n let tensor: Tensor | null = null;\n if (input['isDisposedInternal']) throw new Error('input error: attempted to use tensor but it is disposed');\n if (!(input as Tensor).shape) throw new Error('input error: attempted to use tensor without a shape');\n if ((input as Tensor).shape.length === 3) { // [height, width, 3 || 4]\n if ((input as Tensor).shape[2] === 3) { // [height, width, 3] so add batch\n tensor = tf.expandDims(input, 0);\n } else if ((input as Tensor).shape[2] === 4) { // [height, width, 4] so strip alpha and add batch\n const rgb = tf.slice3d(input as Tensor3D, [0, 0, 0], [-1, -1, 3]);\n tensor = tf.expandDims(rgb, 0);\n tf.dispose(rgb);\n }\n } else if ((input as Tensor).shape.length === 4) { // [1, width, height, 3 || 4]\n if ((input as Tensor).shape[3] === 3) { // [1, width, height, 3] just clone\n tensor = tf.clone(input);\n } else if ((input as Tensor).shape[3] === 4) { // [1, width, height, 4] so strip alpha\n tensor = tf.slice4d(input as Tensor4D, [0, 0, 0, 0], [-1, -1, -1, 3]);\n }\n }\n // at the end shape must be [1, height, width, 3]\n if (tensor == null || tensor.shape.length !== 4 || tensor.shape[0] !== 1 || tensor.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${((input as Tensor).shape).toString()}`);\n if ((tensor).dtype === 'int32') {\n const cast = tf.cast(tensor, 'float32');\n tf.dispose(tensor);\n tensor = cast;\n }\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n }\n // check if resizing will be needed\n if (typeof input['readyState'] !== 'undefined' && (input as HTMLMediaElement).readyState <= 2) {\n if (config.debug) log('input stream is not ready');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n const originalWidth: number = input['naturalWidth'] || input['videoWidth'] || input['width'] || (input['shape'] && (input['shape'][1] > 0));\n const originalHeight: number = input['naturalHeight'] || input['videoHeight'] || input['height'] || (input['shape'] && (input['shape'][2] > 0));\n if (!originalWidth || !originalHeight) {\n if (config.debug) log('cannot determine input dimensions');\n return { tensor: null, canvas: inCanvas }; // video may become temporarily unavailable due to onresize\n }\n let targetWidth: number = originalWidth;\n let targetHeight: number = originalHeight;\n if (targetWidth > maxSize) {\n targetWidth = maxSize;\n targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);\n }\n if (targetHeight > maxSize) {\n targetHeight = maxSize;\n targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);\n }\n\n // create our canvas and resize it if needed\n if ((config.filter?.width || 0) > 0) targetWidth = config.filter.width as number;\n else if ((config.filter?.height || 0) > 0) targetWidth = originalWidth * ((config.filter.height || 0) / originalHeight);\n if ((config.filter.height || 0) > 0) targetHeight = config.filter.height as number;\n else if ((config.filter.width || 0) > 0) targetHeight = originalHeight * ((config.filter.width || 0) / originalWidth);\n if (!targetWidth || !targetHeight) throw new Error('input error: cannot determine dimension');\n if (!inCanvas || (inCanvas.width !== targetWidth) || (inCanvas.height !== targetHeight)) inCanvas = canvas(targetWidth, targetHeight);\n\n // draw input to our canvas\n const inCtx = inCanvas.getContext('2d') as CanvasRenderingContext2D;\n if ((typeof ImageData !== 'undefined') && (input instanceof ImageData)) {\n inCtx.putImageData(input, 0, 0);\n } else {\n if (config.filter.flip && typeof inCtx.translate !== 'undefined') {\n inCtx.translate(originalWidth, 0);\n inCtx.scale(-1, 1);\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n inCtx.setTransform(1, 0, 0, 1, 0, 0); // resets transforms to defaults\n } else {\n inCtx.drawImage(input as AnyCanvas, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height);\n }\n }\n\n if (!outCanvas || (inCanvas.width !== outCanvas.width) || (inCanvas.height !== outCanvas.height)) outCanvas = canvas(inCanvas.width, inCanvas.height); // init output canvas\n\n // imagefx transforms using gl from input canvas to output canvas\n if (config.filter.enabled && env.webgl.supported) {\n if (!fx) fx = env.browser ? new fxImage.GLImageFilter() : null; // && (typeof document !== 'undefined')\n env.filter = !!fx;\n if (!fx?.add) {\n if (config.debug) log('input process error: cannot initialize filters');\n env.webgl.supported = false;\n config.filter.enabled = false;\n copy(inCanvas, outCanvas); // filter failed to initialize\n // return { tensor: null, canvas: inCanvas };\n } else {\n fx.reset();\n if (config.filter.brightness !== 0) fx.add('brightness', config.filter.brightness);\n if (config.filter.contrast !== 0) fx.add('contrast', config.filter.contrast);\n if (config.filter.sharpness !== 0) fx.add('sharpen', config.filter.sharpness);\n if (config.filter.blur !== 0) fx.add('blur', config.filter.blur);\n if (config.filter.saturation !== 0) fx.add('saturation', config.filter.saturation);\n if (config.filter.hue !== 0) fx.add('hue', config.filter.hue);\n if (config.filter.negative) fx.add('negative');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.vintage) fx.add('brownie');\n if (config.filter.sepia) fx.add('sepia');\n if (config.filter.kodachrome) fx.add('kodachrome');\n if (config.filter.technicolor) fx.add('technicolor');\n if (config.filter.polaroid) fx.add('polaroid');\n if (config.filter.pixelate !== 0) fx.add('pixelate', config.filter.pixelate);\n if (fx.get()?.length > 1) outCanvas = fx.apply(inCanvas);\n else outCanvas = fx.draw(inCanvas);\n }\n } else {\n copy(inCanvas, outCanvas); // if no filters applied, output canvas is input canvas\n if (fx) fx = null;\n env.filter = !!fx;\n }\n\n if (!getTensor) return { tensor: null, canvas: outCanvas }; // just canvas was requested\n if (!outCanvas) throw new Error('canvas error: cannot create output');\n\n // create tensor from image unless input was a tensor already\n let pixels;\n let depth = 3;\n if ((typeof ImageData !== 'undefined' && input instanceof ImageData) || ((input as ImageData).data && (input as ImageData).width && (input as ImageData).height)) { // if input is imagedata, just use it\n if (env.browser && tf.browser) {\n pixels = tf.browser ? tf.browser.fromPixels(input as ImageData) : null;\n } else {\n depth = (input as ImageData).data.length / (input as ImageData).height / (input as ImageData).width;\n // const arr = Uint8Array.from(input['data']);\n const arr = new Uint8Array((input as ImageData).data.buffer);\n pixels = tf.tensor(arr, [(input as ImageData).height, (input as ImageData).width, depth], 'int32');\n }\n } else {\n if (!tmpCanvas || (outCanvas.width !== tmpCanvas.width) || (outCanvas.height !== tmpCanvas.height)) tmpCanvas = canvas(outCanvas.width, outCanvas.height); // init output canvas\n if (tf.browser && env.browser) {\n if (config.backend === 'webgl' || config.backend === 'humangl' || config.backend === 'webgpu') {\n pixels = tf.browser.fromPixels(outCanvas as HTMLCanvasElement); // safe to reuse since both backend and context are gl based\n } else {\n tmpCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n pixels = tf.browser.fromPixels(tmpCanvas as HTMLCanvasElement);\n }\n } else {\n const tempCanvas = copy(outCanvas); // cannot use output canvas as it already has gl context so we do a silly one more canvas\n const tempCtx = tempCanvas.getContext('2d') as CanvasRenderingContext2D;\n const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight);\n depth = tempData.data.length / targetWidth / targetHeight;\n const arr = new Uint8Array(tempData.data.buffer);\n pixels = tf.tensor(arr, [targetWidth, targetHeight, depth]);\n }\n }\n if (depth === 4) { // rgba to rgb\n const rgb = tf.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); // strip alpha channel\n tf.dispose(pixels);\n pixels = rgb;\n }\n if (!pixels) throw new Error('input error: cannot create tensor');\n const casted: Tensor = tf.cast(pixels, 'float32');\n const tensor: Tensor = config.filter.equalization ? await enhance.histogramEqualization(casted) : tf.expandDims(casted, 0);\n tf.dispose([pixels, casted]);\n\n if (config.filter.autoBrightness) {\n const max = tf.max(tensor);\n const maxVal = await max.data();\n config.filter.brightness = maxVal[0] > 1 ? (1 - maxVal[0] / 255) : (1 - maxVal[0]);\n tf.dispose(max);\n }\n\n return { tensor: tensor as Tensor4D, canvas: (config.filter.return ? outCanvas : null) };\n}\n\n/*\nconst checksum = async (input: Tensor): Promise => { // use tf sum or js based sum loop depending on which is faster\n const resizeFact = 48;\n const reduced: Tensor = tf.image.resizeBilinear(input, [Math.trunc((input.shape[1] || 1) / resizeFact), Math.trunc((input.shape[2] || 1) / resizeFact)]);\n const tfSum = async (): Promise => {\n const sumT = tf.sum(reduced);\n const sum0 = await sumT.data();\n tf.dispose(sumT);\n return sum0[0];\n };\n const jsSum = async (): Promise => {\n const reducedData = await reduced.data(); // raw image rgb array\n let sum0 = 0;\n for (let i = 0; i < reducedData.length / 3; i++) sum0 += reducedData[3 * i + 2]; // look only at green value of each pixel\n return sum0;\n };\n if (last.sumMethod === 0) {\n const t0 = now();\n await jsSum();\n const t1 = now();\n await tfSum();\n const t2 = now();\n last.sumMethod = t1 - t0 < t2 - t1 ? 1 : 2;\n }\n const res = last.sumMethod === 1 ? await jsSum() : await tfSum();\n tf.dispose(reduced);\n return res;\n};\n*/\n\nexport async function skip(config: Partial, input: Tensor) {\n let skipFrame = false;\n if (config.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; // cache disabled or input is invalid or too large for cache analysis\n /*\n const checkSum = await checksum(input);\n const diff = 100 * (Math.max(checkSum, last.inputSum) / Math.min(checkSum, last.inputSum) - 1);\n last.inputSum = checkSum;\n // if previous frame was skipped, skip this frame if changed more than cacheSensitivity\n // if previous frame was not skipped, then look for cacheSensitivity or difference larger than one in previous frame to avoid resetting cache in subsequent frames unnecessarily\n let skipFrame = diff < Math.max(config.cacheSensitivity, last.cacheDiff);\n // if difference is above 10x threshold, don't use last value to force reset cache for significant change of scenes or images\n last.cacheDiff = diff > 10 * config.cacheSensitivity ? 0 : diff;\n skipFrame = skipFrame && (last.cacheDiff > 0); // if no cached diff value then force no skip\n */\n\n if (!last.inputTensor) {\n last.inputTensor = tf.clone(input);\n } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { // input resolution changed\n tf.dispose(last.inputTensor);\n last.inputTensor = tf.clone(input);\n } else {\n const t: Record = {};\n t.diff = tf.sub(input, last.inputTensor);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; // squared difference relative to input resolution and averaged per channel\n tf.dispose([last.inputTensor, t.diff, t.squared, t.sum]);\n last.inputTensor = tf.clone(input);\n skipFrame = diffRelative <= (config.cacheSensitivity || 0);\n }\n return skipFrame;\n}\n\nexport async function compare(config: Partial, input1: Tensor, input2: Tensor): Promise {\n const t: Record = {};\n if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) {\n if (!config.debug) log('invalid input tensor or tensor shapes do not match:', input1.shape, input2.shape);\n return 0;\n }\n if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) {\n if (!config.debug) log('input tensors must be of shape [1, height, width, 3]:', input1.shape, input2.shape);\n return 0;\n }\n t.input1 = tf.clone(input1);\n t.input2 = (input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2]) ? tf.image.resizeBilinear(input2 as Tensor3D, [input1.shape[1], input1.shape[2]]) : tf.clone(input2);\n t.diff = tf.sub(t.input1, t.input2);\n t.squared = tf.mul(t.diff, t.diff);\n t.sum = tf.sum(t.squared);\n const diffSum = await t.sum.data();\n const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;\n tf.dispose([t.input1, t.input2, t.diff, t.squared, t.sum]);\n return diffRelative;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as image from '../image/image';\n\n/** Env class that holds detected capabilities */\nexport class Env {\n /** Running in Browser */\n browser: boolean;\n /** Running in NodeJS */\n node: boolean;\n /** Running in WebWorker thread */\n worker: boolean;\n /** Detected platform */\n platform: string = '';\n /** Detected agent */\n agent: string = '';\n /** List of supported backends */\n backends: string[] = [];\n /** Has any work been performed so far */\n initial: boolean;\n /** Are image filters supported? */\n filter: boolean | undefined;\n /** TFJS instance details */\n tfjs: {\n version: undefined | string,\n };\n /** Is offscreenCanvas supported? */\n offscreen: undefined | boolean;\n /** Are performance counter instant values or additive */\n perfadd: boolean = false;\n /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */\n tensorflow: {\n version: undefined | string,\n gpu: undefined | boolean,\n } = {\n version: undefined,\n gpu: undefined,\n };\n /** WASM detected capabilities */\n wasm: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n simd: undefined | boolean,\n multithread: undefined | boolean,\n } = {\n supported: undefined,\n backend: undefined,\n simd: undefined,\n multithread: undefined,\n };\n /** WebGL detected capabilities */\n webgl: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n version: undefined | string,\n renderer: undefined | string,\n shader: undefined | string,\n vendor: undefined | string,\n } = {\n supported: undefined,\n backend: undefined,\n version: undefined,\n renderer: undefined,\n shader: undefined,\n vendor: undefined,\n };\n /** WebGPU detected capabilities */\n webgpu: {\n supported: undefined | boolean,\n backend: undefined | boolean,\n adapter: undefined | GPUAdapterInfo,\n } = {\n supported: undefined,\n backend: undefined,\n adapter: undefined,\n };\n /** CPU info */\n cpu: {\n model: undefined | string,\n flags: string[],\n } = {\n model: undefined,\n flags: [],\n };\n /** List of supported kernels for current backend */\n kernels: string[] = [];\n\n /** MonkeyPatch for Canvas/Image/ImageData */\n #canvas: undefined;\n #image: undefined;\n #imageData: undefined;\n\n get Canvas() { return this.#canvas; }\n set Canvas(val) { this.#canvas = val; globalThis.Canvas = val; }\n get Image() { return this.#image; }\n // @ts-ignore monkey-patch;\n set Image(val) { this.#image = val; globalThis.Image = val; }\n get ImageData() { return this.#imageData; }\n // @ts-ignore monkey-patch;\n set ImageData(val) { this.#imageData = val; globalThis.ImageData = val; }\n\n constructor() {\n this.browser = (typeof navigator !== 'undefined') && (typeof navigator.appVersion !== 'undefined');\n this.node = (typeof process !== 'undefined') && (typeof process.versions !== 'undefined') && (typeof process.versions.node !== 'undefined');\n this.tfjs = { version: tf.version['tfjs-core'] };\n this.offscreen = typeof OffscreenCanvas !== 'undefined';\n this.initial = true;\n\n // @ts-ignore WorkerGlobalScope evaluated in browser only\n this.worker = this.browser && this.offscreen ? (typeof WorkerGlobalScope !== 'undefined') : undefined;\n if ((typeof navigator !== 'undefined') && (typeof navigator.userAgent !== 'undefined')) { // TBD replace with navigator.userAgentData once in mainline\n const agent = navigator.userAgent || '';\n const raw = agent.match(/\\(([^()]+)\\)/g);\n if (raw?.[0]) {\n const platformMatch = raw[0].match(/\\(([^()]+)\\)/g);\n this.platform = (platformMatch?.[0]) ? platformMatch[0].replace(/\\(|\\)/g, '') : '';\n this.agent = agent.replace(raw[0], '');\n if (this.platform[1]) this.agent = this.agent.replace(raw[1], '');\n this.agent = this.agent.replace(/ /g, ' ');\n }\n } else if (typeof process !== 'undefined') {\n this.platform = `${process.platform} ${process.arch}`;\n this.agent = `NodeJS ${process.version}`;\n }\n }\n\n /** update backend information */\n async updateBackend() {\n // analyze backends\n this.backends = Object.keys(tf.engine().registryFactory);\n try { // backend may not be initialized\n this.tensorflow = {\n version: (tf.backend()['binding'] ? tf.backend()['binding'].TF_Version : undefined),\n gpu: (tf.backend()['binding'] ? tf.backend()['binding'].isUsingGpuDevice() : undefined),\n };\n } catch { /**/ }\n this.wasm.supported = typeof WebAssembly !== 'undefined';\n this.wasm.backend = this.backends.includes('wasm');\n if (this.wasm.supported && this.wasm.backend) {\n this.wasm.simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n this.wasm.multithread = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n }\n const c = image.canvas(100, 100);\n const gl = c ? c.getContext('webgl2') as WebGL2RenderingContext : undefined; // causes too many gl contexts\n this.webgl.supported = typeof gl !== 'undefined';\n this.webgl.backend = this.backends.includes('webgl');\n if (this.webgl.supported && this.webgl.backend && gl) {\n this.webgl.version = gl.getParameter(gl.VERSION);\n this.webgl.vendor = gl.getParameter(gl.VENDOR);\n this.webgl.renderer = gl.getParameter(gl.RENDERER);\n this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION);\n }\n this.webgpu.supported = this.browser && typeof navigator !== 'undefined' && typeof navigator.gpu !== 'undefined';\n this.webgpu.backend = this.backends.includes('webgpu');\n try {\n if (this.webgpu.supported) {\n const adapter = await navigator.gpu.requestAdapter();\n this.webgpu.adapter = await adapter?.requestAdapterInfo();\n }\n } catch {\n this.webgpu.supported = false;\n }\n try {\n this.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase());\n } catch { /**/ }\n }\n\n /** update cpu information */\n updateCPU() {\n const cpu = { model: '', flags: [] };\n if (this.node && this.platform.startsWith('linux')) {\n /*\n const fs = require('fs');\n try {\n const data = fs.readFileSync('/proc/cpuinfo').toString();\n for (const line of data.split('\\n')) {\n if (line.startsWith('model name')) cpu.model = line.match(/:(.*)/g)[0].replace(':', '').trim();\n if (line.startsWith('flags')) cpu.flags = line.match(/:(.*)/g)[0].replace(':', '').trim().split(' ').sort();\n }\n } catch { }\n */\n }\n if (!this.cpu) Object.defineProperty(this, 'cpu', { value: cpu });\n else this.cpu = cpu;\n }\n}\n\nexport const env = new Env();\n", "import { log } from './util';\n\n// const log = (...msg) => console.log('webcam', ...msg); // eslint-disable-line no-console\n\n/** WebCam configuration */\nexport interface WebCamConfig {\n /**\n * element can be:\n * - string which indicates dom element id\n * - actual HTMLVideo dom element\n * - undefined in which case a new HTMLVideoElement will be created\n */\n element: string | HTMLVideoElement | undefined,\n /** print messages on console */\n debug: boolean,\n /** use front or back camera */\n mode: 'front' | 'back',\n /** camera crop mode */\n crop: boolean,\n /** desired webcam width */\n width: number,\n /** desired webcam height */\n height: number,\n /** deviceId of the video device to use */\n id?: string,\n}\n\nexport class WebCam { // eslint-disable-line @typescript-eslint/no-extraneous-class\n /** current webcam configuration */\n config: WebCamConfig;\n /** instance of dom element associated with webcam stream */\n element: HTMLVideoElement | undefined;\n /** active webcam stream */\n stream: MediaStream | undefined;\n /** enumerated video devices */\n devices: MediaDeviceInfo[] = [];\n\n constructor() {\n this.config = {\n element: undefined,\n debug: true,\n mode: 'front',\n crop: false,\n width: 0,\n height: 0,\n };\n }\n\n /** get active webcam stream track */\n public get track(): MediaStreamTrack | undefined {\n if (!this.stream) return undefined;\n return this.stream.getVideoTracks()[0];\n }\n\n /** get webcam capabilities */\n public get capabilities(): MediaTrackCapabilities | undefined {\n if (!this.track) return undefined;\n return this.track.getCapabilities ? this.track.getCapabilities() : undefined;\n }\n\n /** get webcam constraints */\n public get constraints(): MediaTrackConstraints | undefined {\n if (!this.track) return undefined;\n return this.track.getConstraints ? this.track.getConstraints() : undefined;\n }\n\n /** get webcam settings */\n public get settings(): MediaTrackSettings | undefined {\n if (!this.stream) return undefined;\n const track: MediaStreamTrack = this.stream.getVideoTracks()[0];\n return track.getSettings ? track.getSettings() : undefined;\n }\n\n /** get webcam label */\n public get label(): string {\n if (!this.track) return '';\n return this.track.label;\n }\n\n /** is webcam paused */\n public get paused(): boolean {\n return this.element?.paused || false;\n }\n\n /** webcam current width */\n public get width(): number {\n return this.element?.videoWidth || 0;\n }\n\n /** webcam current height */\n public get height(): number {\n return this.element?.videoHeight || 0;\n }\n\n public enumerate = async (): Promise => {\n try {\n const devices = await navigator.mediaDevices.enumerateDevices();\n this.devices = devices.filter((device) => device.kind === 'videoinput');\n } catch {\n this.devices = [];\n }\n return this.devices;\n };\n\n /** start method initializizes webcam stream and associates it with a dom video element */\n public start = async (webcamConfig?: Partial): Promise => {\n // set config\n if (webcamConfig?.debug) this.config.debug = webcamConfig?.debug;\n if (webcamConfig?.crop) this.config.crop = webcamConfig?.crop;\n if (webcamConfig?.mode) this.config.mode = webcamConfig?.mode;\n if (webcamConfig?.width) this.config.width = webcamConfig?.width;\n if (webcamConfig?.height) this.config.height = webcamConfig?.height;\n if (webcamConfig?.id) this.config.id = webcamConfig?.id;\n\n // use or create dom element\n if (webcamConfig?.element) {\n if (typeof webcamConfig.element === 'string') {\n const el = document.getElementById(webcamConfig.element);\n if (el && el instanceof HTMLVideoElement) {\n this.element = el;\n } else {\n if (this.config.debug) log('webcam', 'cannot get dom element', webcamConfig.element);\n return `webcam error: cannot get dom element: ${webcamConfig.element}`;\n }\n } else if (webcamConfig.element instanceof HTMLVideoElement) {\n this.element = webcamConfig.element;\n } else {\n if (this.config.debug) log('webcam', 'unknown dom element', webcamConfig.element);\n return `webcam error: unknown dom element: ${webcamConfig.element}`;\n }\n } else {\n this.element = document.createElement('video');\n }\n\n // set constraints to use\n const requestedConstraints: MediaStreamConstraints = {\n audio: false,\n video: {\n facingMode: this.config.mode === 'front' ? 'user' : 'environment',\n // @ts-ignore // resizeMode is still not defined in tslib\n resizeMode: this.config.crop ? 'crop-and-scale' : 'none',\n },\n };\n if (this.config?.width > 0) (requestedConstraints.video as MediaTrackConstraints).width = { ideal: this.config.width };\n if (this.config?.height > 0) (requestedConstraints.video as MediaTrackConstraints).height = { ideal: this.config.height };\n if (this.config.id) (requestedConstraints.video as MediaTrackConstraintSet).deviceId = this.config.id;\n\n // set default event listeners\n this.element.addEventListener('play', () => { if (this.config.debug) log('webcam', 'play'); });\n this.element.addEventListener('pause', () => { if (this.config.debug) log('webcam', 'pause'); });\n this.element.addEventListener('click', async () => { // pause when clicked on screen and resume on next click\n if (!this.element || !this.stream) return;\n if (this.element.paused) await this.element.play();\n else this.element.pause();\n });\n\n // get webcam and set it to run in dom element\n if (!navigator?.mediaDevices) {\n if (this.config.debug) log('webcam error', 'no devices');\n return 'webcam error: no devices';\n }\n try {\n this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); // get stream that satisfies constraints\n } catch (err) {\n log('webcam', err);\n return `webcam error: ${err}`;\n }\n if (!this.stream) {\n if (this.config.debug) log('webcam error', 'no stream');\n return 'webcam error no stream';\n }\n this.element.srcObject = this.stream; // assign it to dom element\n const ready = new Promise((resolve) => { // wait until stream is ready\n if (!this.element) resolve(false);\n else this.element.onloadeddata = () => resolve(true);\n });\n await ready;\n await this.element.play(); // start playing\n\n if (this.config.debug) {\n log('webcam', {\n width: this.width,\n height: this.height,\n label: this.label,\n stream: this.stream,\n track: this.track,\n settings: this.settings,\n constraints: this.constraints,\n capabilities: this.capabilities,\n });\n }\n return `webcam: ${this.label}`;\n };\n\n /** pause webcam video method */\n public pause = (): void => {\n if (this.element) this.element.pause();\n };\n\n /** play webcam video method */\n public play = async (): Promise => {\n if (this.element) await this.element.play();\n };\n\n /** stop method stops active webcam stream track and disconnects webcam */\n public stop = (): void => {\n if (this.config.debug) log('webcam', 'stop');\n if (this.track) this.track.stop();\n };\n}\n", "{\n \"antispoof\": 853098,\n \"blazeface\": 538928,\n \"centernet\": 4030290,\n \"emotion\": 820516,\n \"facemesh\": 1477958,\n \"faceres\": 6978814,\n \"handlandmark-lite\": 2023432,\n \"handtrack\": 2964837,\n \"iris\": 2599092,\n \"liveness\": 592976,\n \"models\": 0,\n \"movenet-lightning\": 4650216,\n \"affectnet-mobilenet\": 6920630,\n \"age\": 161240,\n \"blazeface-back\": 538928,\n \"blazeface-front\": 402048,\n \"blazepose-detector\": 5928856,\n \"blazepose-full\": 6339202,\n \"blazepose-heavy\": 27502466,\n \"blazepose-lite\": 2726402,\n \"efficientpose\": 5651240,\n \"faceboxes\": 2013002,\n \"facemesh-attention-pinto\": 2387598,\n \"facemesh-attention\": 2382414,\n \"facemesh-detection-full\": 1026192,\n \"facemesh-detection-short\": 201268,\n \"faceres-deep\": 13957620,\n \"gear-e1\": 112438,\n \"gear-e2\": 112438,\n \"gear\": 1498916,\n \"gender-ssrnet-imdb\": 161236,\n \"gender\": 201808,\n \"handdetect\": 3515612,\n \"handlandmark-full\": 5431368,\n \"handlandmark-sparse\": 5286322,\n \"handskeleton\": 5502280,\n \"meet\": 372228,\n \"mobileface\": 2183192,\n \"mobilefacenet\": 5171976,\n \"movenet-multipose\": 9448838,\n \"movenet-thunder\": 12477112,\n \"nanodet\": 7574558,\n \"posenet\": 5032780,\n \"rvm\": 3739355,\n \"selfie\": 212886,\n \"anti-spoofing\": 853098,\n \"efficientpose-i-lite\": 2269064,\n \"efficientpose-ii-lite\": 5651240,\n \"efficientpose-iv\": 25643252,\n \"insightface-efficientnet-b0\": 13013224,\n \"insightface-ghostnet-strides1\": 8093408,\n \"insightface-ghostnet-strides2\": 8049584,\n \"insightface-mobilenet-emore\": 6938536,\n \"insightface-mobilenet-swish\": 12168584,\n \"nanodet-e\": 12319156,\n \"nanodet-g\": 7574558,\n \"nanodet-m\": 1887474,\n \"nanodet-t\": 5294216\n}", "import * as tf from 'dist/tfjs.esm.js';\nimport { log, join } from '../util/util';\nimport type { GraphModel } from './types';\nimport type { Config } from '../config';\nimport * as modelsDefs from '../../models/models.json';\n\nconst options = {\n cacheModels: true,\n cacheSupported: true,\n verbose: true,\n debug: false,\n modelBasePath: '',\n};\n\nexport interface ModelInfo {\n name: string,\n loaded: boolean,\n inCache: boolean,\n sizeDesired: number,\n sizeFromManifest: number,\n sizeLoadedWeights: number,\n url: string,\n}\n\nexport const modelStats: Record = {};\n\nasync function httpHandler(url: string, init?: RequestInit): Promise {\n if (options.debug) log('load model fetch:', url, init);\n return fetch(url, init);\n}\n\nexport function setModelLoadOptions(config: Config) {\n options.cacheModels = config.cacheModels;\n options.verbose = config.debug;\n options.modelBasePath = config.modelBasePath;\n}\n\nexport async function loadModel(modelPath: string | undefined): Promise {\n let modelUrl = join(options.modelBasePath, modelPath || '');\n if (!modelUrl.toLowerCase().endsWith('.json')) modelUrl += '.json';\n const modelPathSegments = modelUrl.includes('/') ? modelUrl.split('/') : modelUrl.split('\\\\');\n const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace('.json', '');\n const cachedModelName = 'indexeddb://' + shortModelName; // generate short model name for cache\n modelStats[shortModelName] = {\n name: shortModelName,\n loaded: false,\n sizeFromManifest: 0,\n sizeLoadedWeights: 0,\n sizeDesired: modelsDefs[shortModelName],\n inCache: false,\n url: '',\n };\n options.cacheSupported = (typeof indexedDB !== 'undefined'); // check if localStorage and indexedb are available\n let cachedModels = {};\n try {\n cachedModels = (options.cacheSupported && options.cacheModels) ? await tf.io.listModels() : {}; // list all models already in cache // this fails for webview although localStorage is defined\n } catch {\n options.cacheSupported = false;\n }\n modelStats[shortModelName].inCache = (options.cacheSupported && options.cacheModels) && Object.keys(cachedModels).includes(cachedModelName); // is model found in cache\n modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl;\n const tfLoadOptions = typeof fetch === 'undefined' ? {} : { fetchFunc: (url: string, init?: RequestInit) => httpHandler(url, init) };\n let model: GraphModel = new tf.GraphModel(modelStats[shortModelName].url, tfLoadOptions) as unknown as GraphModel; // create model prototype and decide if load from cache or from original modelurl\n modelStats[shortModelName].loaded = false;\n try {\n // @ts-ignore private function\n model.findIOHandler(); // decide how to actually load a model\n if (options.debug) log('model load handler:', model['handler']);\n } catch (err) {\n log('error finding model i/o handler:', modelUrl, err);\n }\n try {\n // @ts-ignore private property\n const artifacts = await model.handler?.load() || null; // load manifest\n modelStats[shortModelName].sizeFromManifest = artifacts?.weightData?.byteLength || 0;\n if (artifacts) model.loadSync(artifacts); // load weights\n else model = await tf.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions) as unknown as GraphModel;\n // @ts-ignore private property\n modelStats[shortModelName].sizeLoadedWeights = model.artifacts?.weightData?.byteLength || model.artifacts?.weightData?.[0].byteLength || 0;\n if (options.verbose) log('load:', { model: shortModelName, url: model['modelUrl'], bytes: modelStats[shortModelName].sizeLoadedWeights });\n modelStats[shortModelName].loaded = true;\n } catch (err) {\n log('error loading model:', modelUrl, err);\n }\n if (modelStats[shortModelName].loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { // save model to cache\n try {\n const saveResult = await model.save(cachedModelName);\n if (options.debug) log('model saved:', cachedModelName, saveResult);\n } catch (err) {\n log('error saving model:', modelUrl, err);\n }\n }\n return model;\n}\n", "{\n \"name\": \"@vladmandic/human\",\n \"version\": \"3.3.3\",\n \"description\": \"Human: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gesture Recognition\",\n \"sideEffects\": false,\n \"main\": \"dist/human.node.js\",\n \"module\": \"dist/human.esm.js\",\n \"browser\": \"dist/human.esm.js\",\n \"types\": \"types/human.d.ts\",\n \"exports\": {\n \"node\": \"./dist/human.node.js\",\n \"script\": \"./dist/human.js\",\n \"module\": \"./dist/human.esm.js\",\n \"types\": \"./types/human.d.ts\",\n \"dist/human\": \"./dist/human.js\",\n \"dist/human.js\": \"./dist/human.js\",\n \"dist/human.esm\": \"./dist/human.esm.js\",\n \"dist/human.esm.js\": \"./dist/human.esm.js\",\n \"dist/human.esm-nobundle\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.esm-nobundle.js\": \"./dist/human.esm-nobundle.js\",\n \"dist/human.node\": \"./dist/human.node.js\",\n \"dist/human.node.js\": \"./dist/human.node.js\",\n \"dist/human.node-wasm\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-wasm.js\": \"./dist/human.node-wasm.js\",\n \"dist/human.node-gpu\": \"./dist/human.node-gpu.js\",\n \"dist/human.node-gpu.js\": \"./dist/human.node-gpu.js\",\n \"require\": \"./dist/human.node.js\",\n \"import\": \"./dist/human.esm.js\"\n },\n \"author\": \"Vladimir Mandic \",\n \"bugs\": {\n \"url\": \"https://github.com/vladmandic/human/issues\"\n },\n \"homepage\": \"https://vladmandic.github.io/human/demo/index.html\",\n \"license\": \"MIT\",\n \"engines\": {\n \"node\": \">=14.0.0\"\n },\n \"repository\": {\n \"type\": \"git\",\n \"url\": \"git+https://github.com/vladmandic/human.git\"\n },\n \"scripts\": {\n \"start\": \"node --no-warnings demo/nodejs/node.js\",\n \"dev\": \"build --profile development\",\n \"clean\": \"build --profile clean\",\n \"build\": \"rimraf test/build.log && node build.js\",\n \"test\": \"node --no-warnings --unhandled-rejections=strict --trace-uncaught test/node.js\",\n \"lint\": \"eslint *.json *.js src demo test models wiki\",\n \"scan\": \"npx auditjs@latest ossi --dev --quiet\"\n },\n \"keywords\": [\n \"human\",\n \"human-library\",\n \"face-detection\",\n \"faceid\",\n \"face-geometry\",\n \"face-embedding\",\n \"face-recognition\",\n \"face-description\",\n \"face-matching\",\n \"body-tracking\",\n \"body-segmentation\",\n \"hand-tracking\",\n \"iris-tracking\",\n \"age-estimation\",\n \"emotion-detection\",\n \"gender-prediction\",\n \"gesture-recognition\",\n \"gaze-tracking\",\n \"age-gender\",\n \"tensorflowjs\",\n \"tfjs\",\n \"tensorflow\"\n ],\n \"devDependencies\": {\n \"@html-eslint/eslint-plugin\": \"^0.26.0\",\n \"@html-eslint/parser\": \"^0.26.0\",\n \"@microsoft/api-extractor\": \"^7.47.9\",\n \"@tensorflow/tfjs-backend-cpu\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-wasm\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-webgl\": \"^4.21.0\",\n \"@tensorflow/tfjs-backend-webgpu\": \"4.21.0\",\n \"@tensorflow/tfjs-converter\": \"^4.21.0\",\n \"@tensorflow/tfjs-core\": \"^4.21.0\",\n \"@tensorflow/tfjs-data\": \"^4.21.0\",\n \"@tensorflow/tfjs-layers\": \"^4.21.0\",\n \"@tensorflow/tfjs-node\": \"^4.21.0\",\n \"@tensorflow/tfjs-node-gpu\": \"^4.21.0\",\n \"@types/emscripten\": \"^1.39.13\",\n \"@types/node\": \"^22.7.5\",\n \"@types/offscreencanvas\": \"^2019.7.3\",\n \"@typescript-eslint/eslint-plugin\": \"^8.8.1\",\n \"@typescript-eslint/parser\": \"^8.8.1\",\n \"@vladmandic/build\": \"^0.10.1\",\n \"@vladmandic/pilogger\": \"^0.5.1\",\n \"@vladmandic/tfjs\": \"github:vladmandic/tfjs\",\n \"canvas\": \"^2.11.2\",\n \"esbuild\": \"^0.23.1\",\n \"eslint\": \"8.57.0\",\n \"eslint-config-airbnb-base\": \"^15.0.0\",\n \"eslint-plugin-html\": \"^8.1.2\",\n \"eslint-plugin-import\": \"^2.31.0\",\n \"eslint-plugin-json\": \"^4.0.1\",\n \"eslint-plugin-markdown\": \"^5.1.0\",\n \"eslint-plugin-node\": \"^11.1.0\",\n \"eslint-plugin-promise\": \"^7.1.0\",\n \"rimraf\": \"^6.0.1\",\n \"tslib\": \"^2.7.0\",\n \"typedoc\": \"0.26.7\",\n \"typescript\": \"5.5.4\"\n }\n}\n", "/** TFJS custom backend registration */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human } from '../human';\nimport { log } from '../util/util';\nimport * as image from '../image/image';\nimport type { AnyCanvas } from '../exports';\n\nexport const config = {\n name: 'humangl',\n priority: 999,\n canvas: null as null | AnyCanvas,\n gl: null as null | WebGL2RenderingContext,\n extensions: [] as string[] | null,\n webGLattr: { // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2\n alpha: false,\n antialias: false,\n premultipliedAlpha: false,\n preserveDrawingBuffer: false,\n depth: false,\n stencil: false,\n failIfMajorPerformanceCaveat: false, // default=true\n desynchronized: true, // default=undefined\n },\n};\n\nfunction extensions(): void {\n /*\n https://www.khronos.org/registry/webgl/extensions/\n https://webglreport.com/?v=2\n */\n const gl = config.gl;\n if (!gl) return;\n config.extensions = gl.getSupportedExtensions();\n // gl.getExtension('KHR_parallel_shader_compile');\n}\n\n/**\n * Registers custom WebGL2 backend to be used by Human library\n *\n * @returns void\n */\nexport function register(instance: Human): void {\n // force backend reload if gl context is not valid\n if (instance.config.backend !== 'humangl') return;\n if ((config.name in tf.engine().registry) && !config?.gl?.getParameter(config.gl.VERSION)) {\n log('humangl error: backend invalid context');\n instance.models.reset();\n /*\n log('resetting humangl backend');\n await tf.removeBackend(config.name);\n await register(instance); // re-register\n */\n }\n if (!tf.findBackend(config.name)) {\n try {\n config.canvas = image.canvas(100, 100);\n } catch (err) {\n log('humangl error: cannot create canvas:', err);\n return;\n }\n try {\n config.gl = config.canvas.getContext('webgl2', config.webGLattr) as WebGL2RenderingContext;\n if (!config.gl) {\n log('humangl error: cannot get webgl context');\n return;\n }\n const glv2 = config.gl.getParameter(config.gl.VERSION).includes('2.0');\n if (!glv2) {\n log('backend override: using fallback webgl backend as webgl 2.0 is not detected');\n instance.config.backend = 'webgl';\n return;\n }\n if (config.canvas) {\n config.canvas.addEventListener('webglcontextlost', (e) => {\n log('humangl error:', e.type);\n log('possible browser memory leak using webgl or conflict with multiple backend registrations');\n instance.emit('error');\n throw new Error('backend error: webgl context lost');\n });\n config.canvas.addEventListener('webglcontextrestored', (e) => {\n log('humangl error: context restored:', e);\n });\n config.canvas.addEventListener('webglcontextcreationerror', (e) => {\n log('humangl error: context create:', e);\n });\n }\n } catch (err) {\n log('humangl error: cannot get webgl context:', err);\n return;\n }\n try {\n tf.setWebGLContext(2, config.gl);\n } catch (err) {\n log('humangl error: cannot set webgl context:', err);\n return;\n }\n try {\n const ctx = new tf.GPGPUContext(config.gl);\n // @ts-ignore uncompatible kernelMs timing info\n tf.registerBackend(config.name, () => new tf.MathBackendWebGL(ctx), config.priority);\n } catch (err) {\n log('humangl error: cannot register webgl backend:', err);\n return;\n }\n try {\n const kernels = tf.getKernelsForBackend('webgl');\n kernels.forEach((kernelConfig) => {\n const newKernelConfig = { ...kernelConfig, backendName: config.name };\n tf.registerKernel(newKernelConfig);\n });\n } catch (err) {\n log('humangl error: cannot update webgl backend registration:', err);\n return;\n }\n try {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_VERSION) tf.env().set('WEBGL_VERSION', 2);\n } catch (err) {\n log('humangl error: cannot set WebGL backend flags:', err);\n return;\n }\n extensions();\n const backend = tf.backend();\n const current = typeof backend['gpgpu'] !== 'undefined' ? backend['getGPGPUContext']().gl : null;\n if (current) {\n if (instance.config.debug) log('humangl backend registered:', { webgl: current.getParameter(current.VERSION) as string, renderer: current.getParameter(current.RENDERER) as string });\n } else {\n log('humangl error: no current gl context:', current, config.gl);\n }\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Tensor } from './types';\n\nexport const constants: Record = {\n tf255: 255.0,\n tf1: 1.0,\n tf2: 2.0,\n tf05: 0.5,\n tf127: 127.5,\n rgb: [0.2989, 0.5870, 0.1140],\n};\n\nexport function init() {\n constants.tf255 = tf.scalar(255.0, 'float32');\n constants.tf1 = tf.scalar(1.0, 'float32');\n constants.tf2 = tf.scalar(2.0, 'float32');\n constants.tf05 = tf.scalar(0.5, 'float32');\n constants.tf127 = tf.scalar(127.5, 'float32');\n constants.rgb = tf.tensor1d([0.2989, 0.5870, 0.1140], 'float32'); // factors for red/green/blue colors when converting to grayscale\n}\n", "/** TFJS backend initialization and customization */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Human, Config, BackendEnum } from '../human';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as humangl from './humangl';\nimport * as constants from './constants';\nimport type { TensorInfo } from './types';\n\nexport async function getBestBackend(): Promise {\n await env.updateBackend(); // update env on backend init\n if (env.tensorflow?.version) return 'tensorflow';\n if (env.webgpu.supported && env.webgpu.backend) return 'webgpu';\n if (env.webgl.supported && env.webgl.backend) return 'webgl';\n if (env.wasm.supported && env.wasm.backend) return 'wasm';\n return 'cpu';\n}\n\nfunction registerCustomOps(config: Config) {\n const newKernels: string[] = [];\n if (!env.kernels.includes('mod')) {\n const kernelMod = {\n kernelName: 'Mod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.sub(op.inputs.a, tf.mul(tf.div(op.inputs.a, op.inputs.b), op.inputs.b))),\n };\n tf.registerKernel(kernelMod);\n env.kernels.push('mod');\n newKernels.push('mod');\n }\n if (!env.kernels.includes('floormod')) {\n const kernelFloorMod = {\n kernelName: 'FloorMod',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => tf.add(tf.mul(tf.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf.mod(op.inputs.a, op.inputs.b))),\n };\n tf.registerKernel(kernelFloorMod);\n env.kernels.push('floormod');\n newKernels.push('floormod');\n }\n /*\n if (!env.kernels.includes('atan2') && config.softwareKernels) {\n const kernelAtan2 = {\n kernelName: 'Atan2',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu');\n const t = tf.atan2(op.inputs.a, op.inputs.b);\n tf.setBackend(backend);\n return t;\n }),\n };\n if (config.debug) log('registered kernel:', 'atan2');\n log('registered kernel:', 'atan2');\n tf.registerKernel(kernelAtan2);\n env.kernels.push('atan2');\n newKernels.push('atan2');\n }\n */\n if (!env.kernels.includes('rotatewithoffset') && config.softwareKernels) {\n const kernelRotateWithOffset = {\n kernelName: 'RotateWithOffset',\n backendName: tf.getBackend(),\n kernelFunc: (op) => tf.tidy(() => {\n const backend = tf.getBackend();\n tf.setBackend('cpu'); // eslint-disable-line @typescript-eslint/no-floating-promises\n const t = tf.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center);\n tf.setBackend(backend); // eslint-disable-line @typescript-eslint/no-floating-promises\n return t;\n }),\n };\n tf.registerKernel(kernelRotateWithOffset);\n env.kernels.push('rotatewithoffset');\n newKernels.push('rotatewithoffset');\n }\n if ((newKernels.length > 0) && config.debug) log('registered kernels:', newKernels);\n}\n\nlet defaultFlags: Record = {};\n\nexport async function check(instance: Human, force = false) {\n instance.state = 'backend';\n if (instance.config.backend?.length === 0) instance.config.backend = await getBestBackend();\n if (force || env.initial || (instance.config.backend && (instance.config.backend.length > 0) && (tf.getBackend() !== instance.config.backend))) {\n const timeStamp = now();\n\n if (instance.config.backend && instance.config.backend.length > 0) {\n // detect web worker\n // @ts-ignore ignore missing type for WorkerGlobalScope as that is the point\n if (typeof window === 'undefined' && typeof WorkerGlobalScope !== 'undefined' && instance.config.debug) {\n if (instance.config.debug) log('running inside web worker');\n }\n\n if (typeof navigator !== 'undefined' && navigator?.userAgent?.toLowerCase().includes('electron')) {\n if (instance.config.debug) log('running inside electron');\n }\n\n // check available backends\n let available = Object.keys(tf.engine().registryFactory as Record);\n if (instance.config.backend === 'humangl' && !available.includes('humangl')) {\n humangl.register(instance);\n available = Object.keys(tf.engine().registryFactory as Record);\n }\n if (instance.config.debug) log('available backends:', available);\n\n // force browser vs node backend\n if (env.browser && !env.node && (instance.config.backend === 'tensorflow') && available.includes('webgl')) {\n if (instance.config.debug) log('override: backend set to tensorflow while running in browser');\n instance.config.backend = 'webgl';\n }\n if (env.node && !env.browser && (instance.config.backend === 'webgl' || instance.config.backend === 'humangl') && available.includes('tensorflow')) {\n if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`);\n instance.config.backend = 'tensorflow';\n }\n\n // handle webgpu\n if (env.browser && instance.config.backend === 'webgpu') {\n if (typeof navigator === 'undefined' || typeof navigator.gpu === 'undefined') {\n log('override: backend set to webgpu but browser does not support webgpu');\n instance.config.backend = 'webgl';\n } else {\n const adapter = await navigator.gpu.requestAdapter();\n if (instance.config.debug) log('enumerated webgpu adapter:', adapter);\n if (!adapter) {\n log('override: backend set to webgpu but browser reports no available gpu');\n instance.config.backend = 'webgl';\n } else {\n // @ts-ignore requestAdapterInfo is not in tslib\n const adapterInfo = 'requestAdapterInfo' in adapter ? await adapter.requestAdapterInfo() : undefined;\n // if (adapter.features) adapter.features.forEach((feature) => log('webgpu features:', feature));\n log('webgpu adapter info:', adapterInfo);\n }\n }\n }\n\n if (!available.includes(instance.config.backend)) {\n log(`error: backend ${instance.config.backend} not found in registry`);\n instance.config.backend = env.node ? 'tensorflow' : 'webgl';\n if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`);\n }\n\n if (instance.config.debug) log('setting backend:', [instance.config.backend]);\n\n // customize wasm\n if (instance.config.backend === 'wasm') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf.env().set('CANVAS2D_WILL_READ_FREQUENTLY', true);\n if (instance.config.debug) log('wasm path:', instance.config.wasmPath);\n if (typeof tf.setWasmPaths !== 'undefined') tf.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch);\n else throw new Error('backend error: attempting to use wasm backend but wasm path is not set');\n let mt = false;\n let simd = false;\n try {\n mt = await tf.env().getAsync('WASM_HAS_MULTITHREAD_SUPPORT') as boolean;\n simd = await tf.env().getAsync('WASM_HAS_SIMD_SUPPORT') as boolean;\n if (instance.config.debug) log(`wasm execution: ${simd ? 'simd' : 'no simd'} ${mt ? 'multithreaded' : 'singlethreaded'}`);\n if (instance.config.debug && !simd) log('warning: wasm simd support is not enabled');\n } catch {\n log('wasm detection failed');\n }\n }\n\n try {\n await tf.setBackend(instance.config.backend);\n await tf.ready();\n } catch (err) {\n log('error: cannot set backend:', instance.config.backend, err);\n return false;\n }\n // @ts-ignore private property\n if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf.env().flags));\n }\n\n // customize humangl\n if (tf.getBackend() === 'humangl' || tf.getBackend() === 'webgl') {\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', true); // default=false \n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_EXP_CONV) tf.env().set('WEBGL_EXP_CONV', true); // default=false \n // if (tf.env().flagRegistry['WEBGL_PACK_DEPTHWISECONV']) tf.env().set('WEBGL_PACK_DEPTHWISECONV', false); // default=true \n // if (tf.env().flagRegistry.USE_SETTIMEOUTCUSTOM) tf.env().set('USE_SETTIMEOUTCUSTOM', true); // default=false \n // if (tf.env().flagRegistry.CPU_HANDOFF_SIZE_THRESHOLD) tf.env().set('CPU_HANDOFF_SIZE_THRESHOLD', 1024); // default=1000\n // if (tf.env().flagRegistry['WEBGL_FORCE_F16_TEXTURES'] && !instance.config.object.enabled) tf.env().set('WEBGL_FORCE_F16_TEXTURES', true); // safe to use 16bit precision\n if (instance.config.debug && typeof instance.config.deallocate !== 'undefined' && instance.config.deallocate) { // hidden param\n log('changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:', true);\n tf.env().set('WEBGL_DELETE_TEXTURE_THRESHOLD', 0);\n }\n }\n\n // customize webgpu\n if (tf.getBackend() === 'webgpu') {\n // if (tf.env().flagRegistry['WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD']) tf.env().set('WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD', 512);\n // if (tf.env().flagRegistry['WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE']) tf.env().set('WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE', 0);\n // if (tf.env().flagRegistry['WEBGPU_CPU_FORWARD']) tf.env().set('WEBGPU_CPU_FORWARD', true);\n }\n\n if (instance.config.debug) {\n // @ts-ignore private property\n const newFlags = tf.env().flags;\n const updatedFlags = {};\n for (const key of Object.keys(newFlags)) {\n if (defaultFlags[key] === newFlags[key]) continue;\n updatedFlags[key] = newFlags[key];\n }\n if (instance.config.debug && Object.keys(updatedFlags).length > 0) log('backend:', tf.getBackend(), 'flags:', updatedFlags);\n }\n\n if (instance.config.flags && Object.keys(instance.config.flags).length > 0) {\n if (instance.config.debug) log('flags:', instance.config['flags']);\n for (const [key, val] of Object.entries(instance.config.flags)) {\n tf.env().set(key, val as number | boolean);\n }\n }\n\n tf.enableProdMode();\n constants.init();\n instance.performance.initBackend = Math.trunc(now() - timeStamp);\n instance.config.backend = tf.getBackend() as BackendEnum;\n await env.updateBackend(); // update env on backend init\n registerCustomOps(instance.config);\n // await env.updateBackend(); // update env on backend init\n // env.initial = false;\n }\n return true;\n}\n\n// register fake missing tfjs ops\nexport function fakeOps(kernelNames: string[], config) {\n // if (config.debug) log('registerKernel:', kernelNames);\n for (const kernelName of kernelNames) {\n const kernelConfig = {\n kernelName,\n backendName: config.backend,\n kernelFunc: (param): TensorInfo => {\n if (config.debug) log('kernelFunc', kernelName, config.backend, param);\n return param?.inputs?.info as TensorInfo;\n },\n // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); },\n };\n tf.registerKernel(kernelConfig);\n }\n env.kernels = tf.getKernelsForBackend(tf.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); // re-scan registered ops\n}\n", "/**\n * Module that implements helper draw functions, exposed as human.draw\n */\n\nimport { mergeDeep, now } from '../util/util';\nimport { env } from '../util/env';\nimport { getCanvasContext, rect } from './primitives';\nimport { options } from './options';\nimport { face } from './face';\nimport { body } from './body';\nimport { hand } from './hand';\nimport { object } from './object';\nimport { gesture } from './gesture';\nimport { defaultLabels } from './labels';\nimport type { Result, PersonResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet drawTime = 0;\n\nexport { options } from './options';\nexport { face } from './face';\nexport { body } from './body';\nexport { hand } from './hand';\nexport { object } from './object';\nexport { gesture } from './gesture';\n\n/** draw combined person results instead of individual detection result objects */\nexport function person(inCanvas: AnyCanvas, result: PersonResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n\n for (let i = 0; i < result.length; i++) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels) {\n const label = `person #${i}`;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);\n }\n ctx.stroke();\n }\n }\n}\n\n/** draw processed canvas */\nexport function canvas(input: AnyCanvas | HTMLImageElement | HTMLVideoElement, output: AnyCanvas) {\n if (!input || !output) return;\n const ctx = getCanvasContext(output) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.drawImage(input, 0, 0);\n}\n\n/** meta-function that performs draw for: canvas, face, body, hand */\nexport async function all(inCanvas: AnyCanvas, result: Result, drawOptions?: Partial) {\n if (!result?.performance || !inCanvas) return null;\n const timeStamp = now();\n const localOptions = mergeDeep(options, drawOptions);\n const promise = Promise.all([\n face(inCanvas, result.face, localOptions),\n body(inCanvas, result.body, localOptions),\n hand(inCanvas, result.hand, localOptions),\n object(inCanvas, result.object, localOptions),\n gesture(inCanvas, result.gesture, localOptions), // gestures do not have buffering\n // person(inCanvas, result.persons, localOptions); // already included above\n ]);\n drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp);\n result.performance.draw = drawTime;\n return promise;\n}\n\n/** sets default label templates for face/body/hand/object/gestures */\nexport function init() {\n options.faceLabels = defaultLabels.face;\n options.bodyLabels = defaultLabels.body;\n options.bodyPartLabels = defaultLabels.bodyPart;\n options.handLabels = defaultLabels.hand;\n options.fingerLabels = defaultLabels.finger;\n options.objectLabels = defaultLabels.object;\n options.gestureLabels = defaultLabels.gesture;\n}\n", "import { log } from '../util/util';\nimport type { AnyCanvas } from '../exports';\nimport type { Point } from '../result';\nimport type { DrawOptions } from './options';\n\nexport const getCanvasContext = (input: AnyCanvas) => {\n if (!input) log('draw error: invalid canvas');\n else if (!input.getContext) log('draw error: canvas context not defined');\n else {\n const ctx = input.getContext('2d', { willReadFrequently: true });\n if (!ctx) log('draw error: cannot get canvas context');\n else return ctx;\n }\n return null;\n};\n\nexport const rad2deg = (theta: number) => Math.round((theta * 180) / Math.PI);\n\nexport const replace = (str: string, source: string, target: string | number) => str.replace(source, typeof target === 'number' ? target.toFixed(1) : target);\n\nexport const colorDepth = (z: number | undefined, opt: DrawOptions): string => { // performance optimization needed\n if (!opt.useDepth || typeof z === 'undefined') return opt.color;\n const rgb = Uint8ClampedArray.from([127 + (2 * z), 127 - (2 * z), 255]);\n return `rgba(${rgb[0]}, ${rgb[1]}, ${rgb[2]}, ${opt.alpha})`;\n};\n\nexport function labels(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, str: string, startX: number, startY: number, localOptions: DrawOptions) {\n const line: string[] = str.replace(/\\[.*\\]/g, '').split('\\n').map((l) => l.trim()); // remove unmatched templates and split into array\n const x = Math.max(0, startX);\n for (let i = line.length - 1; i >= 0; i--) {\n const y = i * localOptions.lineHeight + startY;\n if (localOptions.shadowColor && localOptions.shadowColor !== '') {\n ctx.fillStyle = localOptions.shadowColor;\n ctx.fillText(line[i], x + 5, y + 16);\n }\n ctx.fillStyle = localOptions.labelColor;\n ctx.fillText(line[i], x + 4, y + 15);\n }\n}\n\nexport function point(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, z: number | undefined, localOptions: DrawOptions) {\n ctx.fillStyle = colorDepth(z, localOptions);\n ctx.beginPath();\n ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);\n ctx.fill();\n}\n\nexport function rect(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, x: number, y: number, width: number, height: number, localOptions: DrawOptions) {\n ctx.beginPath();\n ctx.lineWidth = localOptions.lineWidth;\n if (localOptions.useCurves) {\n const cx = (x + x + width) / 2;\n const cy = (y + y + height) / 2;\n ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI);\n } else {\n ctx.moveTo(x + localOptions.roundRect, y);\n ctx.lineTo(x + width - localOptions.roundRect, y);\n ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);\n ctx.lineTo(x + width, y + height - localOptions.roundRect);\n ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);\n ctx.lineTo(x + localOptions.roundRect, y + height);\n ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);\n ctx.lineTo(x, y + localOptions.roundRect);\n ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);\n ctx.closePath();\n }\n ctx.stroke();\n}\n\nexport function lines(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.beginPath();\n ctx.moveTo(points[0][0], points[0][1]);\n for (const pt of points) {\n ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions);\n ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1]));\n }\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function curves(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, points: Point[], localOptions: DrawOptions) {\n if (points.length < 2) return;\n ctx.lineWidth = localOptions.lineWidth;\n if (!localOptions.useCurves || points.length <= 2) {\n lines(ctx, points, localOptions);\n return;\n }\n ctx.moveTo(points[0][0], points[0][1]);\n for (let i = 0; i < points.length - 2; i++) {\n const xc = (points[i][0] + points[i + 1][0]) / 2;\n const yc = (points[i][1] + points[i + 1][1]) / 2;\n ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc);\n }\n ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.closePath();\n ctx.fill();\n }\n}\n\nexport function arrow(ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D, from: Point, to: Point, radius = 5) {\n let angle;\n let x;\n let y;\n ctx.beginPath();\n ctx.moveTo(from[0], from[1]);\n ctx.lineTo(to[0], to[1]);\n angle = Math.atan2(to[1] - from[1], to[0] - from[0]);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.moveTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n angle += (1.0 / 3.0) * (2 * Math.PI);\n x = radius * Math.cos(angle) + to[0];\n y = radius * Math.sin(angle) + to[1];\n ctx.lineTo(x, y);\n ctx.closePath();\n ctx.stroke();\n ctx.fill();\n}\n", "/** Draw Options\n * - Accessed via `human.draw.options` or provided per each draw method as the drawOptions optional parameter\n */\n\nexport interface DrawOptions {\n /** draw line color */\n color: string,\n /** alpha value used for lines */\n alpha: number,\n /** label color */\n labelColor: string,\n /** label shadow color */\n shadowColor: string,\n /** label font */\n font: string,\n /** line spacing between labels */\n lineHeight: number,\n /** line width for drawn lines */\n lineWidth: number,\n /** size of drawn points */\n pointSize: number,\n /** draw rounded boxes by n pixels */\n roundRect: number,\n /** should points be drawn? */\n drawPoints: boolean,\n /** should labels be drawn? */\n drawLabels: boolean,\n /** should face attention keypoints be highlighted */\n drawAttention: boolean;\n /** should detected gestures be drawn? */\n drawGestures: boolean,\n /** should draw boxes around detection results? */\n drawBoxes: boolean,\n /** should draw polygons from detection points? */\n drawPolygons: boolean,\n /** should draw gaze arrows? */\n drawGaze: boolean,\n /** should fill polygons? */\n fillPolygons: boolean,\n /** use z-coordinate when available */\n useDepth: boolean,\n /** should lines be curved? */\n useCurves: boolean,\n /** string template for face labels */\n faceLabels: string,\n /** string template for body labels */\n bodyLabels: string,\n /** string template for body part labels */\n bodyPartLabels: string,\n /** string template for hand labels */\n handLabels: string,\n /** string template for hand labels */\n fingerLabels: string,\n /** string template for object labels */\n objectLabels: string,\n /** string template for gesture labels */\n gestureLabels: string,\n}\n\n/** currently set draw options {@link DrawOptions} */\nexport const options: DrawOptions = {\n color: 'rgba(173, 216, 230, 0.6)' as string, // 'lightblue' with light alpha channel\n labelColor: 'rgba(173, 216, 230, 1)' as string, // 'lightblue' with dark alpha channel\n shadowColor: 'black' as string,\n alpha: 0.5 as number,\n font: 'small-caps 16px \"Segoe UI\"' as string,\n lineHeight: 18 as number,\n lineWidth: 4 as number,\n pointSize: 2 as number,\n roundRect: 8 as number,\n drawPoints: false as boolean,\n drawLabels: true as boolean,\n drawBoxes: true as boolean,\n drawAttention: true as boolean,\n drawGestures: true as boolean,\n drawPolygons: true as boolean,\n drawGaze: true as boolean,\n fillPolygons: false as boolean,\n useDepth: true as boolean,\n useCurves: false as boolean,\n faceLabels: '' as string,\n bodyLabels: '' as string,\n bodyPartLabels: '' as string,\n objectLabels: '' as string,\n handLabels: '' as string,\n fingerLabels: '' as string,\n gestureLabels: '' as string,\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nexport const meshAnnotations: Record = {\n silhouette: [\n 10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,\n 397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,\n 172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109,\n ],\n // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11\n // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10\n // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11\n // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11\n lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409],\n lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291],\n lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415],\n lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],\n lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306],\n lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408],\n lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292],\n lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407],\n rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], // 7\n rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], // 9\n rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], // 7\n rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], // 9\n rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], // 7\n rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], // 9\n rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], // 9\n rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], // 8\n rightEyebrowLower: [35, 124, 46, 53, 52, 65], // 6\n rightEyeIris: [473, 474, 475, 476, 477], // 5\n leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],\n leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],\n leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],\n leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],\n leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],\n leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],\n leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],\n leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],\n leftEyebrowLower: [265, 353, 276, 283, 282, 295],\n leftEyeIris: [468, 469, 470, 471, 472],\n midwayBetweenEyes: [168],\n noseTip: [1],\n noseBottom: [2],\n noseRightCorner: [98],\n noseLeftCorner: [327],\n rightCheek: [205],\n leftCheek: [425],\n};\n\nexport const meshLandmarks: Record = {\n count: 468,\n mouth: 13,\n symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]],\n};\n\nexport const blazeFaceLandmarks: Record = {\n leftEye: 0,\n rightEye: 1,\n nose: 2,\n mouth: 3,\n leftEar: 4,\n rightEar: 5,\n symmetryLine: [3, 2],\n};\n\nexport const irisIndices: { key: string, indices: number[] }[] = [ // A mapping from facemesh model keypoints to iris model keypoints.\n { key: 'EyeUpper0', indices: [9, 10, 11, 12, 13, 14, 15] }, // 7 x 3d\n { key: 'EyeUpper1', indices: [25, 26, 27, 28, 29, 30, 31] }, // 7 x 3d\n { key: 'EyeUpper2', indices: [41, 42, 43, 44, 45, 46, 47] }, // 7 x 3d\n { key: 'EyeLower0', indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, // 7 x 3d\n { key: 'EyeLower1', indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, // 9 x 3d\n { key: 'EyeLower2', indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, // 9 x 3d\n { key: 'EyeLower3', indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, // 9 x 3d\n { key: 'EyebrowUpper', indices: [63, 64, 65, 66, 67, 68, 69, 70] }, // 8 x 3d\n { key: 'EyebrowLower', indices: [48, 49, 50, 51, 52, 53] }, // 6 x 3d\n];\n\nexport const UV468: [number, number][] = [\n [0.499976992607117, 0.652534008026123],\n [0.500025987625122, 0.547487020492554],\n [0.499974012374878, 0.602371990680695],\n [0.482113003730774, 0.471979022026062],\n [0.500150978565216, 0.527155995368958],\n [0.499909996986389, 0.498252987861633],\n [0.499523013830185, 0.40106201171875],\n [0.289712011814117, 0.380764007568359],\n [0.499954998493195, 0.312398016452789],\n [0.499987006187439, 0.269918978214264],\n [0.500023007392883, 0.107050001621246],\n [0.500023007392883, 0.666234016418457],\n [0.5000159740448, 0.679224014282227],\n [0.500023007392883, 0.692348003387451],\n [0.499976992607117, 0.695277988910675],\n [0.499976992607117, 0.70593398809433],\n [0.499976992607117, 0.719385027885437],\n [0.499976992607117, 0.737019002437592],\n [0.499967992305756, 0.781370997428894],\n [0.499816000461578, 0.562981009483337],\n [0.473773002624512, 0.573909997940063],\n [0.104906998574734, 0.254140973091125],\n [0.365929991006851, 0.409575998783112],\n [0.338757991790771, 0.41302502155304],\n [0.311120003461838, 0.409460008144379],\n [0.274657994508743, 0.389131009578705],\n [0.393361985683441, 0.403706014156342],\n [0.345234006643295, 0.344011008739471],\n [0.370094001293182, 0.346076011657715],\n [0.319321990013123, 0.347265005111694],\n [0.297903001308441, 0.353591024875641],\n [0.24779200553894, 0.410809993743896],\n [0.396889001131058, 0.842755019664764],\n [0.280097991228104, 0.375599980354309],\n [0.106310002505779, 0.399955987930298],\n [0.2099249958992, 0.391353011131287],\n [0.355807989835739, 0.534406006336212],\n [0.471751004457474, 0.65040397644043],\n [0.474155008792877, 0.680191993713379],\n [0.439785003662109, 0.657229006290436],\n [0.414617002010345, 0.66654098033905],\n [0.450374007225037, 0.680860996246338],\n [0.428770989179611, 0.682690978050232],\n [0.374971002340317, 0.727805018424988],\n [0.486716985702515, 0.547628998756409],\n [0.485300987958908, 0.527395009994507],\n [0.257764995098114, 0.314490020275116],\n [0.401223003864288, 0.455172002315521],\n [0.429818987846375, 0.548614978790283],\n [0.421351999044418, 0.533740997314453],\n [0.276895999908447, 0.532056987285614],\n [0.483370006084442, 0.499586999416351],\n [0.33721199631691, 0.282882988452911],\n [0.296391993761063, 0.293242990970612],\n [0.169294998049736, 0.193813979625702],\n [0.447580009698868, 0.302609980106354],\n [0.392390012741089, 0.353887975215912],\n [0.354490011930466, 0.696784019470215],\n [0.067304998636246, 0.730105042457581],\n [0.442739009857178, 0.572826027870178],\n [0.457098007202148, 0.584792017936707],\n [0.381974011659622, 0.694710969924927],\n [0.392388999462128, 0.694203019142151],\n [0.277076005935669, 0.271932005882263],\n [0.422551989555359, 0.563233017921448],\n [0.385919004678726, 0.281364023685455],\n [0.383103013038635, 0.255840003490448],\n [0.331431001424789, 0.119714021682739],\n [0.229923993349075, 0.232002973556519],\n [0.364500999450684, 0.189113974571228],\n [0.229622006416321, 0.299540996551514],\n [0.173287004232407, 0.278747975826263],\n [0.472878992557526, 0.666198015213013],\n [0.446828007698059, 0.668527007102966],\n [0.422762006521225, 0.673889994621277],\n [0.445307999849319, 0.580065965652466],\n [0.388103008270264, 0.693961024284363],\n [0.403039008378983, 0.706539988517761],\n [0.403629004955292, 0.693953037261963],\n [0.460041999816895, 0.557139039039612],\n [0.431158006191254, 0.692366003990173],\n [0.452181994915009, 0.692366003990173],\n [0.475387006998062, 0.692366003990173],\n [0.465828001499176, 0.779190003871918],\n [0.472328990697861, 0.736225962638855],\n [0.473087012767792, 0.717857003211975],\n [0.473122000694275, 0.704625964164734],\n [0.473033010959625, 0.695277988910675],\n [0.427942007780075, 0.695277988910675],\n [0.426479011774063, 0.703539967536926],\n [0.423162013292313, 0.711845993995667],\n [0.4183090031147, 0.720062971115112],\n [0.390094995498657, 0.639572978019714],\n [0.013953999616206, 0.560034036636353],\n [0.499913990497589, 0.58014702796936],\n [0.413199990987778, 0.69539999961853],\n [0.409626007080078, 0.701822996139526],\n [0.468080013990402, 0.601534962654114],\n [0.422728985548019, 0.585985004901886],\n [0.463079988956451, 0.593783974647522],\n [0.37211999297142, 0.47341400384903],\n [0.334562003612518, 0.496073007583618],\n [0.411671012639999, 0.546965003013611],\n [0.242175996303558, 0.14767599105835],\n [0.290776997804642, 0.201445996761322],\n [0.327338010072708, 0.256527006626129],\n [0.399509996175766, 0.748921036720276],\n [0.441727995872498, 0.261676013469696],\n [0.429764986038208, 0.187834024429321],\n [0.412198007106781, 0.108901023864746],\n [0.288955003023148, 0.398952007293701],\n [0.218936994671822, 0.435410976409912],\n [0.41278201341629, 0.398970007896423],\n [0.257135003805161, 0.355440020561218],\n [0.427684992551804, 0.437960982322693],\n [0.448339998722076, 0.536936044692993],\n [0.178560003638268, 0.45755398273468],\n [0.247308000922203, 0.457193970680237],\n [0.286267012357712, 0.467674970626831],\n [0.332827985286713, 0.460712015628815],\n [0.368755996227264, 0.447206974029541],\n [0.398963987827301, 0.432654976844788],\n [0.476410001516342, 0.405806005001068],\n [0.189241006970406, 0.523923993110657],\n [0.228962004184723, 0.348950982093811],\n [0.490725994110107, 0.562400996685028],\n [0.404670000076294, 0.485132992267609],\n [0.019469000399113, 0.401564002037048],\n [0.426243007183075, 0.420431017875671],\n [0.396993011236191, 0.548797011375427],\n [0.266469985246658, 0.376977026462555],\n [0.439121007919312, 0.51895797252655],\n [0.032313998788595, 0.644356966018677],\n [0.419054001569748, 0.387154996395111],\n [0.462783008813858, 0.505746960639954],\n [0.238978996872902, 0.779744982719421],\n [0.198220998048782, 0.831938028335571],\n [0.107550002634525, 0.540755033493042],\n [0.183610007166862, 0.740257024765015],\n [0.134409993886948, 0.333683013916016],\n [0.385764002799988, 0.883153975009918],\n [0.490967005491257, 0.579378008842468],\n [0.382384985685349, 0.508572995662689],\n [0.174399003386497, 0.397670984268188],\n [0.318785011768341, 0.39623498916626],\n [0.343364000320435, 0.400596976280212],\n [0.396100014448166, 0.710216999053955],\n [0.187885001301765, 0.588537991046906],\n [0.430987000465393, 0.944064974784851],\n [0.318993002176285, 0.898285031318665],\n [0.266247987747192, 0.869701027870178],\n [0.500023007392883, 0.190576016902924],\n [0.499976992607117, 0.954452991485596],\n [0.366169989109039, 0.398822009563446],\n [0.393207013607025, 0.39553701877594],\n [0.410373002290726, 0.391080021858215],\n [0.194993004202843, 0.342101991176605],\n [0.388664990663528, 0.362284004688263],\n [0.365961998701096, 0.355970978736877],\n [0.343364000320435, 0.355356991291046],\n [0.318785011768341, 0.35834002494812],\n [0.301414996385574, 0.363156020641327],\n [0.058132998645306, 0.319076001644135],\n [0.301414996385574, 0.387449026107788],\n [0.499987989664078, 0.618434011936188],\n [0.415838003158569, 0.624195992946625],\n [0.445681989192963, 0.566076993942261],\n [0.465844005346298, 0.620640993118286],\n [0.49992299079895, 0.351523995399475],\n [0.288718998432159, 0.819945991039276],\n [0.335278987884521, 0.852819979190826],\n [0.440512001514435, 0.902418971061707],\n [0.128294005990028, 0.791940987110138],\n [0.408771991729736, 0.373893976211548],\n [0.455606997013092, 0.451801002025604],\n [0.499877005815506, 0.908990025520325],\n [0.375436991453171, 0.924192011356354],\n [0.11421000212431, 0.615022003650665],\n [0.448662012815475, 0.695277988910675],\n [0.4480200111866, 0.704632043838501],\n [0.447111994028091, 0.715808033943176],\n [0.444831997156143, 0.730794012546539],\n [0.430011987686157, 0.766808986663818],\n [0.406787008047104, 0.685672998428345],\n [0.400738000869751, 0.681069016456604],\n [0.392399996519089, 0.677703022956848],\n [0.367855995893478, 0.663918972015381],\n [0.247923001646996, 0.601333022117615],\n [0.452769994735718, 0.420849978923798],\n [0.43639200925827, 0.359887003898621],\n [0.416164010763168, 0.368713974952698],\n [0.413385987281799, 0.692366003990173],\n [0.228018000721931, 0.683571994304657],\n [0.468268007040024, 0.352671027183533],\n [0.411361992359161, 0.804327011108398],\n [0.499989002943039, 0.469825029373169],\n [0.479153990745544, 0.442654013633728],\n [0.499974012374878, 0.439637005329132],\n [0.432112008333206, 0.493588984012604],\n [0.499886006116867, 0.866917014122009],\n [0.49991300702095, 0.821729004383087],\n [0.456548988819122, 0.819200992584229],\n [0.344549000263214, 0.745438992977142],\n [0.37890899181366, 0.574010014533997],\n [0.374292999505997, 0.780184984207153],\n [0.319687992334366, 0.570737957954407],\n [0.357154995203018, 0.604269981384277],\n [0.295284003019333, 0.621580958366394],\n [0.447750002145767, 0.862477004528046],\n [0.410986006259918, 0.508723020553589],\n [0.31395098567009, 0.775308012962341],\n [0.354128003120422, 0.812552988529205],\n [0.324548006057739, 0.703992962837219],\n [0.189096003770828, 0.646299958229065],\n [0.279776990413666, 0.71465802192688],\n [0.1338230073452, 0.682700991630554],\n [0.336768001317978, 0.644733011722565],\n [0.429883986711502, 0.466521978378296],\n [0.455527991056442, 0.548622965812683],\n [0.437114000320435, 0.558896005153656],\n [0.467287987470627, 0.529924988746643],\n [0.414712011814117, 0.335219979286194],\n [0.37704598903656, 0.322777986526489],\n [0.344107985496521, 0.320150971412659],\n [0.312875986099243, 0.32233202457428],\n [0.283526003360748, 0.333190023899078],\n [0.241245999932289, 0.382785975933075],\n [0.102986000478268, 0.468762993812561],\n [0.267612010240555, 0.424560010433197],\n [0.297879010438919, 0.433175981044769],\n [0.333433985710144, 0.433878004550934],\n [0.366427004337311, 0.426115989685059],\n [0.396012008190155, 0.416696012020111],\n [0.420121014118195, 0.41022801399231],\n [0.007561000064015, 0.480777025222778],\n [0.432949006557465, 0.569517970085144],\n [0.458638995885849, 0.479089021682739],\n [0.473466008901596, 0.545744001865387],\n [0.476087987422943, 0.563830018043518],\n [0.468472003936768, 0.555056989192963],\n [0.433990985155106, 0.582361996173859],\n [0.483518004417419, 0.562983989715576],\n [0.482482999563217, 0.57784903049469],\n [0.42645001411438, 0.389798998832703],\n [0.438998997211456, 0.39649498462677],\n [0.450067013502121, 0.400434017181396],\n [0.289712011814117, 0.368252992630005],\n [0.276670008897781, 0.363372981548309],\n [0.517862021923065, 0.471948027610779],\n [0.710287988185883, 0.380764007568359],\n [0.526226997375488, 0.573909997940063],\n [0.895093023777008, 0.254140973091125],\n [0.634069979190826, 0.409575998783112],\n [0.661242008209229, 0.41302502155304],\n [0.688880026340485, 0.409460008144379],\n [0.725341975688934, 0.389131009578705],\n [0.606630027294159, 0.40370500087738],\n [0.654766023159027, 0.344011008739471],\n [0.629905998706818, 0.346076011657715],\n [0.680678009986877, 0.347265005111694],\n [0.702096998691559, 0.353591024875641],\n [0.75221198797226, 0.410804986953735],\n [0.602918028831482, 0.842862963676453],\n [0.719901978969574, 0.375599980354309],\n [0.893692970275879, 0.399959981441498],\n [0.790081977844238, 0.391354024410248],\n [0.643998026847839, 0.534487962722778],\n [0.528249025344849, 0.65040397644043],\n [0.525849997997284, 0.680191040039062],\n [0.560214996337891, 0.657229006290436],\n [0.585384011268616, 0.66654098033905],\n [0.549625992774963, 0.680860996246338],\n [0.57122802734375, 0.682691991329193],\n [0.624852001667023, 0.72809898853302],\n [0.513050019741058, 0.547281980514526],\n [0.51509702205658, 0.527251958847046],\n [0.742246985435486, 0.314507007598877],\n [0.598631024360657, 0.454979002475739],\n [0.570338010787964, 0.548575043678284],\n [0.578631997108459, 0.533622980117798],\n [0.723087012767792, 0.532054007053375],\n [0.516445994377136, 0.499638974666595],\n [0.662801027297974, 0.282917976379395],\n [0.70362401008606, 0.293271005153656],\n [0.830704987049103, 0.193813979625702],\n [0.552385985851288, 0.302568018436432],\n [0.607609987258911, 0.353887975215912],\n [0.645429015159607, 0.696707010269165],\n [0.932694971561432, 0.730105042457581],\n [0.557260990142822, 0.572826027870178],\n [0.542901992797852, 0.584792017936707],\n [0.6180260181427, 0.694710969924927],\n [0.607590973377228, 0.694203019142151],\n [0.722943007946014, 0.271963000297546],\n [0.577413976192474, 0.563166975975037],\n [0.614082992076874, 0.281386971473694],\n [0.616907000541687, 0.255886018276215],\n [0.668509006500244, 0.119913995265961],\n [0.770092010498047, 0.232020974159241],\n [0.635536015033722, 0.189248979091644],\n [0.77039098739624, 0.299556016921997],\n [0.826722025871277, 0.278755009174347],\n [0.527121007442474, 0.666198015213013],\n [0.553171992301941, 0.668527007102966],\n [0.577238023281097, 0.673889994621277],\n [0.554691970348358, 0.580065965652466],\n [0.611896991729736, 0.693961024284363],\n [0.59696102142334, 0.706539988517761],\n [0.596370995044708, 0.693953037261963],\n [0.539958000183105, 0.557139039039612],\n [0.568841993808746, 0.692366003990173],\n [0.547818005084991, 0.692366003990173],\n [0.52461302280426, 0.692366003990173],\n [0.534089982509613, 0.779141008853912],\n [0.527670979499817, 0.736225962638855],\n [0.526912987232208, 0.717857003211975],\n [0.526877999305725, 0.704625964164734],\n [0.526966989040375, 0.695277988910675],\n [0.572058022022247, 0.695277988910675],\n [0.573521018028259, 0.703539967536926],\n [0.57683801651001, 0.711845993995667],\n [0.581691026687622, 0.720062971115112],\n [0.609944999217987, 0.639909982681274],\n [0.986046016216278, 0.560034036636353],\n [0.5867999792099, 0.69539999961853],\n [0.590372025966644, 0.701822996139526],\n [0.531915009021759, 0.601536989212036],\n [0.577268004417419, 0.585934996604919],\n [0.536915004253387, 0.593786001205444],\n [0.627542972564697, 0.473352015018463],\n [0.665585994720459, 0.495950996875763],\n [0.588353991508484, 0.546862006187439],\n [0.757824003696442, 0.14767599105835],\n [0.709249973297119, 0.201507985591888],\n [0.672684013843536, 0.256581008434296],\n [0.600408971309662, 0.74900496006012],\n [0.55826598405838, 0.261672019958496],\n [0.570303976535797, 0.187870979309082],\n [0.588165998458862, 0.109044015407562],\n [0.711045026779175, 0.398952007293701],\n [0.781069993972778, 0.435405015945435],\n [0.587247014045715, 0.398931980133057],\n [0.742869973182678, 0.355445981025696],\n [0.572156012058258, 0.437651991844177],\n [0.55186802148819, 0.536570012569427],\n [0.821442008018494, 0.457556009292603],\n [0.752701997756958, 0.457181990146637],\n [0.71375697851181, 0.467626988887787],\n [0.66711300611496, 0.460672974586487],\n [0.631101012229919, 0.447153985500336],\n [0.6008620262146, 0.432473003864288],\n [0.523481011390686, 0.405627012252808],\n [0.810747981071472, 0.523926019668579],\n [0.771045982837677, 0.348959028720856],\n [0.509127020835876, 0.562718033790588],\n [0.595292985439301, 0.485023975372314],\n [0.980530977249146, 0.401564002037048],\n [0.573499977588654, 0.420000016689301],\n [0.602994978427887, 0.548687994480133],\n [0.733529984951019, 0.376977026462555],\n [0.560611009597778, 0.519016981124878],\n [0.967685997486115, 0.644356966018677],\n [0.580985009670258, 0.387160003185272],\n [0.537728011608124, 0.505385041236877],\n [0.760966002941132, 0.779752969741821],\n [0.801778972148895, 0.831938028335571],\n [0.892440974712372, 0.54076099395752],\n [0.816350996494293, 0.740260004997253],\n [0.865594983100891, 0.333687007427216],\n [0.614073991775513, 0.883246004581451],\n [0.508952975273132, 0.579437971115112],\n [0.617941975593567, 0.508316040039062],\n [0.825608015060425, 0.397674977779388],\n [0.681214988231659, 0.39623498916626],\n [0.656635999679565, 0.400596976280212],\n [0.603900015354156, 0.710216999053955],\n [0.81208598613739, 0.588539004325867],\n [0.56801301240921, 0.944564998149872],\n [0.681007981300354, 0.898285031318665],\n [0.733752012252808, 0.869701027870178],\n [0.633830010890961, 0.398822009563446],\n [0.606792986392975, 0.39553701877594],\n [0.589659988880157, 0.391062021255493],\n [0.805015981197357, 0.342108011245728],\n [0.611334979534149, 0.362284004688263],\n [0.634037971496582, 0.355970978736877],\n [0.656635999679565, 0.355356991291046],\n [0.681214988231659, 0.35834002494812],\n [0.698584973812103, 0.363156020641327],\n [0.941866993904114, 0.319076001644135],\n [0.698584973812103, 0.387449026107788],\n [0.584177017211914, 0.624107003211975],\n [0.554318010807037, 0.566076993942261],\n [0.534153997898102, 0.62064003944397],\n [0.711217999458313, 0.819975018501282],\n [0.664629995822906, 0.852871000766754],\n [0.559099972248077, 0.902631998062134],\n [0.871706008911133, 0.791940987110138],\n [0.591234028339386, 0.373893976211548],\n [0.544341027736664, 0.451583981513977],\n [0.624562978744507, 0.924192011356354],\n [0.88577002286911, 0.615028977394104],\n [0.551338016986847, 0.695277988910675],\n [0.551980018615723, 0.704632043838501],\n [0.552887976169586, 0.715808033943176],\n [0.555167973041534, 0.730794012546539],\n [0.569944024085999, 0.767035007476807],\n [0.593203008174896, 0.685675978660583],\n [0.599261999130249, 0.681069016456604],\n [0.607599973678589, 0.677703022956848],\n [0.631937980651855, 0.663500010967255],\n [0.752032995223999, 0.601315021514893],\n [0.547226011753082, 0.420395016670227],\n [0.563543975353241, 0.359827995300293],\n [0.583841025829315, 0.368713974952698],\n [0.586614012718201, 0.692366003990173],\n [0.771915018558502, 0.683578014373779],\n [0.531597018241882, 0.352482974529266],\n [0.588370978832245, 0.804440975189209],\n [0.52079701423645, 0.442565023899078],\n [0.567984998226166, 0.493479013442993],\n [0.543282985687256, 0.819254994392395],\n [0.655317008495331, 0.745514988899231],\n [0.621008992195129, 0.574018001556396],\n [0.625559985637665, 0.78031200170517],\n [0.680198013782501, 0.570719003677368],\n [0.64276397228241, 0.604337990283966],\n [0.704662978649139, 0.621529996395111],\n [0.552012026309967, 0.862591981887817],\n [0.589071989059448, 0.508637011051178],\n [0.685944974422455, 0.775357007980347],\n [0.645735025405884, 0.812640011310577],\n [0.675342977046967, 0.703978002071381],\n [0.810858011245728, 0.646304965019226],\n [0.72012197971344, 0.714666962623596],\n [0.866151988506317, 0.682704985141754],\n [0.663187026977539, 0.644596993923187],\n [0.570082008838654, 0.466325998306274],\n [0.544561982154846, 0.548375964164734],\n [0.562758982181549, 0.558784961700439],\n [0.531987011432648, 0.530140042304993],\n [0.585271000862122, 0.335177004337311],\n [0.622952997684479, 0.32277899980545],\n [0.655896008014679, 0.320163011550903],\n [0.687132000923157, 0.322345972061157],\n [0.716481983661652, 0.333200991153717],\n [0.758756995201111, 0.382786989212036],\n [0.897013008594513, 0.468769013881683],\n [0.732392013072968, 0.424547016620636],\n [0.70211398601532, 0.433162987232208],\n [0.66652500629425, 0.433866024017334],\n [0.633504986763, 0.426087975502014],\n [0.603875994682312, 0.416586995124817],\n [0.579657971858978, 0.409945011138916],\n [0.992439985275269, 0.480777025222778],\n [0.567192018032074, 0.569419980049133],\n [0.54136598110199, 0.478899002075195],\n [0.526564002037048, 0.546118021011353],\n [0.523913025856018, 0.563830018043518],\n [0.531529009342194, 0.555056989192963],\n [0.566035985946655, 0.582329034805298],\n [0.51631098985672, 0.563053965568542],\n [0.5174720287323, 0.577877044677734],\n [0.573594987392426, 0.389806985855103],\n [0.560697972774506, 0.395331978797913],\n [0.549755990505219, 0.399751007556915],\n [0.710287988185883, 0.368252992630005],\n [0.723330020904541, 0.363372981548309],\n];\n\nexport const TRI468: number[] = [\n 127, 34, 139, 11, 0, 37, 232, 231, 120, 72, 37, 39, 128, 121, 47, 232, 121, 128, 104, 69, 67, 175, 171, 148, 157, 154, 155, 118, 50, 101, 73, 39, 40, 9,\n 151, 108, 48, 115, 131, 194, 204, 211, 74, 40, 185, 80, 42, 183, 40, 92, 186, 230, 229, 118, 202, 212, 214, 83, 18, 17, 76, 61, 146, 160, 29, 30, 56,\n 157, 173, 106, 204, 194, 135, 214, 192, 203, 165, 98, 21, 71, 68, 51, 45, 4, 144, 24, 23, 77, 146, 91, 205, 50, 187, 201, 200, 18, 91, 106, 182, 90, 91,\n 181, 85, 84, 17, 206, 203, 36, 148, 171, 140, 92, 40, 39, 193, 189, 244, 159, 158, 28, 247, 246, 161, 236, 3, 196, 54, 68, 104, 193, 168, 8, 117,\n 228, 31, 189, 193, 55, 98, 97, 99, 126, 47, 100, 166, 79, 218, 155, 154, 26, 209, 49, 131, 135, 136, 150, 47, 126, 217, 223, 52, 53, 45, 51, 134, 211,\n 170, 140, 67, 69, 108, 43, 106, 91, 230, 119, 120, 226, 130, 247, 63, 53, 52, 238, 20, 242, 46, 70, 156, 78, 62, 96, 46, 53, 63, 143, 34, 227, 173,\n 155, 133, 123, 117, 111, 44, 125, 19, 236, 134, 51, 216, 206, 205, 154, 153, 22, 39, 37, 167, 200, 201, 208, 36, 142, 100, 57, 212, 202, 20, 60, 99, 28,\n 158, 157, 35, 226, 113, 160, 159, 27, 204, 202, 210, 113, 225, 46, 43, 202, 204, 62, 76, 77, 137, 123, 116, 41, 38, 72, 203, 129, 142, 64, 98, 240, 49,\n 102, 64, 41, 73, 74, 212, 216, 207, 42, 74, 184, 169, 170, 211, 170, 149, 176, 105, 66, 69, 122, 6, 168, 123, 147, 187, 96, 77, 90, 65, 55, 107, 89,\n 90, 180, 101, 100, 120, 63, 105, 104, 93, 137, 227, 15, 86, 85, 129, 102, 49, 14, 87, 86, 55, 8, 9, 100, 47, 121, 145, 23, 22, 88, 89, 179, 6, 122,\n 196, 88, 95, 96, 138, 172, 136, 215, 58, 172, 115, 48, 219, 42, 80, 81, 195, 3, 51, 43, 146, 61, 171, 175, 199, 81, 82, 38, 53, 46, 225, 144, 163, 110,\n 246, 33, 7, 52, 65, 66, 229, 228, 117, 34, 127, 234, 107, 108, 69, 109, 108, 151, 48, 64, 235, 62, 78, 191, 129, 209, 126, 111, 35, 143, 163, 161, 246,\n 117, 123, 50, 222, 65, 52, 19, 125, 141, 221, 55, 65, 3, 195, 197, 25, 7, 33, 220, 237, 44, 70, 71, 139, 122, 193, 245, 247, 130, 33, 71, 21, 162,\n 153, 158, 159, 170, 169, 150, 188, 174, 196, 216, 186, 92, 144, 160, 161, 2, 97, 167, 141, 125, 241, 164, 167, 37, 72, 38, 12, 145, 159, 160, 38, 82, 13,\n 63, 68, 71, 226, 35, 111, 158, 153, 154, 101, 50, 205, 206, 92, 165, 209, 198, 217, 165, 167, 97, 220, 115, 218, 133, 112, 243, 239, 238, 241, 214,\n 135, 169, 190, 173, 133, 171, 208, 32, 125, 44, 237, 86, 87, 178, 85, 86, 179, 84, 85, 180, 83, 84, 181, 201, 83, 182, 137, 93, 132, 76, 62, 183, 61,\n 76, 184, 57, 61, 185, 212, 57, 186, 214, 207, 187, 34, 143, 156, 79, 239, 237, 123, 137, 177, 44, 1, 4, 201, 194, 32, 64, 102, 129, 213, 215, 138, 59,\n 166, 219, 242, 99, 97, 2, 94, 141, 75, 59, 235, 24, 110, 228, 25, 130, 226, 23, 24, 229, 22, 23, 230, 26, 22, 231, 112, 26, 232, 189, 190, 243, 221, 56,\n 190, 28, 56, 221, 27, 28, 222, 29, 27, 223, 30, 29, 224, 247, 30, 225, 238, 79, 20, 166, 59, 75, 60, 75, 240, 147, 177, 215, 20, 79, 166, 187, 147, 213,\n 112, 233, 244, 233, 128, 245, 128, 114, 188, 114, 217, 174, 131, 115, 220, 217, 198, 236, 198, 131, 134, 177, 132, 58, 143, 35, 124, 110, 163, 7, 228,\n 110, 25, 356, 389, 368, 11, 302, 267, 452, 350, 349, 302, 303, 269, 357, 343, 277, 452, 453, 357, 333, 332, 297, 175, 152, 377, 384, 398, 382, 347,\n 348, 330, 303, 304, 270, 9, 336, 337, 278, 279, 360, 418, 262, 431, 304, 408, 409, 310, 415, 407, 270, 409, 410, 450, 348, 347, 422, 430, 434, 313,\n 314, 17, 306, 307, 375, 387, 388, 260, 286, 414, 398, 335, 406, 418, 364, 367, 416, 423, 358, 327, 251, 284, 298, 281, 5, 4, 373, 374, 253, 307, 320,\n 321, 425, 427, 411, 421, 313, 18, 321, 405, 406, 320, 404, 405, 315, 16, 17, 426, 425, 266, 377, 400, 369, 322, 391, 269, 417, 465, 464, 386, 257, 258,\n 466, 260, 388, 456, 399, 419, 284, 332, 333, 417, 285, 8, 346, 340, 261, 413, 441, 285, 327, 460, 328, 355, 371, 329, 392, 439, 438, 382, 341, 256,\n 429, 420, 360, 364, 394, 379, 277, 343, 437, 443, 444, 283, 275, 440, 363, 431, 262, 369, 297, 338, 337, 273, 375, 321, 450, 451, 349, 446, 342, 467,\n 293, 334, 282, 458, 461, 462, 276, 353, 383, 308, 324, 325, 276, 300, 293, 372, 345, 447, 382, 398, 362, 352, 345, 340, 274, 1, 19, 456, 248, 281, 436,\n 427, 425, 381, 256, 252, 269, 391, 393, 200, 199, 428, 266, 330, 329, 287, 273, 422, 250, 462, 328, 258, 286, 384, 265, 353, 342, 387, 259, 257, 424,\n 431, 430, 342, 353, 276, 273, 335, 424, 292, 325, 307, 366, 447, 345, 271, 303, 302, 423, 266, 371, 294, 455, 460, 279, 278, 294, 271, 272, 304, 432,\n 434, 427, 272, 407, 408, 394, 430, 431, 395, 369, 400, 334, 333, 299, 351, 417, 168, 352, 280, 411, 325, 319, 320, 295, 296, 336, 319, 403, 404, 330,\n 348, 349, 293, 298, 333, 323, 454, 447, 15, 16, 315, 358, 429, 279, 14, 15, 316, 285, 336, 9, 329, 349, 350, 374, 380, 252, 318, 402, 403, 6, 197, 419,\n 318, 319, 325, 367, 364, 365, 435, 367, 397, 344, 438, 439, 272, 271, 311, 195, 5, 281, 273, 287, 291, 396, 428, 199, 311, 271, 268, 283, 444, 445,\n 373, 254, 339, 263, 466, 249, 282, 334, 296, 449, 347, 346, 264, 447, 454, 336, 296, 299, 338, 10, 151, 278, 439, 455, 292, 407, 415, 358, 371, 355,\n 340, 345, 372, 390, 249, 466, 346, 347, 280, 442, 443, 282, 19, 94, 370, 441, 442, 295, 248, 419, 197, 263, 255, 359, 440, 275, 274, 300, 383, 368,\n 351, 412, 465, 263, 467, 466, 301, 368, 389, 380, 374, 386, 395, 378, 379, 412, 351, 419, 436, 426, 322, 373, 390, 388, 2, 164, 393, 370, 462, 461,\n 164, 0, 267, 302, 11, 12, 374, 373, 387, 268, 12, 13, 293, 300, 301, 446, 261, 340, 385, 384, 381, 330, 266, 425, 426, 423, 391, 429, 355, 437, 391,\n 327, 326, 440, 457, 438, 341, 382, 362, 459, 457, 461, 434, 430, 394, 414, 463, 362, 396, 369, 262, 354, 461, 457, 316, 403, 402, 315, 404, 403, 314,\n 405, 404, 313, 406, 405, 421, 418, 406, 366, 401, 361, 306, 408, 407, 291, 409, 408, 287, 410, 409, 432, 436, 410, 434, 416, 411, 264, 368, 383, 309,\n 438, 457, 352, 376, 401, 274, 275, 4, 421, 428, 262, 294, 327, 358, 433, 416, 367, 289, 455, 439, 462, 370, 326, 2, 326, 370, 305, 460, 455, 254,\n 449, 448, 255, 261, 446, 253, 450, 449, 252, 451, 450, 256, 452, 451, 341, 453, 452, 413, 464, 463, 441, 413, 414, 258, 442, 441, 257, 443, 442, 259,\n 444, 443, 260, 445, 444, 467, 342, 445, 459, 458, 250, 289, 392, 290, 290, 328, 460, 376, 433, 435, 250, 290, 392, 411, 416, 433, 341, 463, 464, 453,\n 464, 465, 357, 465, 412, 343, 412, 399, 360, 363, 440, 437, 399, 456, 420, 456, 363, 401, 435, 288, 372, 383, 353, 339, 255, 249, 448, 261, 255, 133,\n 243, 190, 133, 155, 112, 33, 246, 247, 33, 130, 25, 398, 384, 286, 362, 398, 414, 362, 463, 341, 263, 359, 467, 263, 249, 255, 466, 467, 260, 75, 60,\n 166, 238, 239, 79, 162, 127, 139, 72, 11, 37, 121, 232, 120, 73, 72, 39, 114, 128, 47, 233, 232, 128, 103, 104, 67, 152, 175, 148, 173, 157, 155,\n 119, 118, 101, 74, 73, 40, 107, 9, 108, 49, 48, 131, 32, 194, 211, 184, 74, 185, 191, 80, 183, 185, 40, 186, 119, 230, 118, 210, 202, 214, 84, 83, 17,\n 77, 76, 146, 161, 160, 30, 190, 56, 173, 182, 106, 194, 138, 135, 192, 129, 203, 98, 54, 21, 68, 5, 51, 4, 145, 144, 23, 90, 77, 91, 207, 205, 187, 83,\n 201, 18, 181, 91, 182, 180, 90, 181, 16, 85, 17, 205, 206, 36, 176, 148, 140, 165, 92, 39, 245, 193, 244, 27, 159, 28, 30, 247, 161, 174, 236, 196,\n 103, 54, 104, 55, 193, 8, 111, 117, 31, 221, 189, 55, 240, 98, 99, 142, 126, 100, 219, 166, 218, 112, 155, 26, 198, 209, 131, 169, 135, 150, 114, 47,\n 217, 224, 223, 53, 220, 45, 134, 32, 211, 140, 109, 67, 108, 146, 43, 91, 231, 230, 120, 113, 226, 247, 105, 63, 52, 241, 238, 242, 124, 46, 156, 95,\n 78, 96, 70, 46, 63, 116, 143, 227, 116, 123, 111, 1, 44, 19, 3, 236, 51, 207, 216, 205, 26, 154, 22, 165, 39, 167, 199, 200, 208, 101, 36, 100, 43,\n 57, 202, 242, 20, 99, 56, 28, 157, 124, 35, 113, 29, 160, 27, 211, 204, 210, 124, 113, 46, 106, 43, 204, 96, 62, 77, 227, 137, 116, 73, 41, 72, 36, 203,\n 142, 235, 64, 240, 48, 49, 64, 42, 41, 74, 214, 212, 207, 183, 42, 184, 210, 169, 211, 140, 170, 176, 104, 105, 69, 193, 122, 168, 50, 123, 187, 89, 96,\n 90, 66, 65, 107, 179, 89, 180, 119, 101, 120, 68, 63, 104, 234, 93, 227, 16, 15, 85, 209, 129, 49, 15, 14, 86, 107, 55, 9, 120, 100, 121, 153, 145, 22,\n 178, 88, 179, 197, 6, 196, 89, 88, 96, 135, 138, 136, 138, 215, 172, 218, 115, 219, 41, 42, 81, 5, 195, 51, 57, 43, 61, 208, 171, 199, 41, 81, 38,\n 224, 53, 225, 24, 144, 110, 105, 52, 66, 118, 229, 117, 227, 34, 234, 66, 107, 69, 10, 109, 151, 219, 48, 235, 183, 62, 191, 142, 129, 126, 116, 111,\n 143, 7, 163, 246, 118, 117, 50, 223, 222, 52, 94, 19, 141, 222, 221, 65, 196, 3, 197, 45, 220, 44, 156, 70, 139, 188, 122, 245, 139, 71, 162, 145,\n 153, 159, 149, 170, 150, 122, 188, 196, 206, 216, 92, 163, 144, 161, 164, 2, 167, 242, 141, 241, 0, 164, 37, 11, 72, 12, 144, 145, 160, 12, 38, 13, 70,\n 63, 71, 31, 226, 111, 157, 158, 154, 36, 101, 205, 203, 206, 165, 126, 209, 217, 98, 165, 97, 237, 220, 218, 237, 239, 241, 210, 214, 169, 140, 171, 32,\n 241, 125, 237, 179, 86, 178, 180, 85, 179, 181, 84, 180, 182, 83, 181, 194, 201, 182, 177, 137, 132, 184, 76, 183, 185, 61, 184, 186, 57, 185, 216, 212,\n 186, 192, 214, 187, 139, 34, 156, 218, 79, 237, 147, 123, 177, 45, 44, 4, 208, 201, 32, 98, 64, 129, 192, 213, 138, 235, 59, 219, 141, 242, 97, 97, 2,\n 141, 240, 75, 235, 229, 24, 228, 31, 25, 226, 230, 23, 229, 231, 22, 230, 232, 26, 231, 233, 112, 232, 244, 189, 243, 189, 221, 190, 222, 28, 221,\n 223, 27, 222, 224, 29, 223, 225, 30, 224, 113, 247, 225, 99, 60, 240, 213, 147, 215, 60, 20, 166, 192, 187, 213, 243, 112, 244, 244, 233, 245, 245,\n 128, 188, 188, 114, 174, 134, 131, 220, 174, 217, 236, 236, 198, 134, 215, 177, 58, 156, 143, 124, 25, 110, 7, 31, 228, 25, 264, 356, 368, 0, 11, 267,\n 451, 452, 349, 267, 302, 269, 350, 357, 277, 350, 452, 357, 299, 333, 297, 396, 175, 377, 381, 384, 382, 280, 347, 330, 269, 303, 270, 151, 9, 337,\n 344, 278, 360, 424, 418, 431, 270, 304, 409, 272, 310, 407, 322, 270, 410, 449, 450, 347, 432, 422, 434, 18, 313, 17, 291, 306, 375, 259, 387, 260,\n 424, 335, 418, 434, 364, 416, 391, 423, 327, 301, 251, 298, 275, 281, 4, 254, 373, 253, 375, 307, 321, 280, 425, 411, 200, 421, 18, 335, 321, 406,\n 321, 320, 405, 314, 315, 17, 423, 426, 266, 396, 377, 369, 270, 322, 269, 413, 417, 464, 385, 386, 258, 248, 456, 419, 298, 284, 333, 168, 417, 8,\n 448, 346, 261, 417, 413, 285, 326, 327, 328, 277, 355, 329, 309, 392, 438, 381, 382, 256, 279, 429, 360, 365, 364, 379, 355, 277, 437, 282, 443, 283,\n 281, 275, 363, 395, 431, 369, 299, 297, 337, 335, 273, 321, 348, 450, 349, 359, 446, 467, 283, 293, 282, 250, 458, 462, 300, 276, 383, 292, 308, 325,\n 283, 276, 293, 264, 372, 447, 346, 352, 340, 354, 274, 19, 363, 456, 281, 426, 436, 425, 380, 381, 252, 267, 269, 393, 421, 200, 428, 371, 266, 329,\n 432, 287, 422, 290, 250, 328, 385, 258, 384, 446, 265, 342, 386, 387, 257, 422, 424, 430, 445, 342, 276, 422, 273, 424, 306, 292, 307, 352, 366, 345,\n 268, 271, 302, 358, 423, 371, 327, 294, 460, 331, 279, 294, 303, 271, 304, 436, 432, 427, 304, 272, 408, 395, 394, 431, 378, 395, 400, 296, 334, 299,\n 6, 351, 168, 376, 352, 411, 307, 325, 320, 285, 295, 336, 320, 319, 404, 329, 330, 349, 334, 293, 333, 366, 323, 447, 316, 15, 315, 331, 358, 279,\n 317, 14, 316, 8, 285, 9, 277, 329, 350, 253, 374, 252, 319, 318, 403, 351, 6, 419, 324, 318, 325, 397, 367, 365, 288, 435, 397, 278, 344, 439, 310,\n 272, 311, 248, 195, 281, 375, 273, 291, 175, 396, 199, 312, 311, 268, 276, 283, 445, 390, 373, 339, 295, 282, 296, 448, 449, 346, 356, 264, 454, 337,\n 336, 299, 337, 338, 151, 294, 278, 455, 308, 292, 415, 429, 358, 355, 265, 340, 372, 388, 390, 466, 352, 346, 280, 295, 442, 282, 354, 19, 370, 285,\n 441, 295, 195, 248, 197, 457, 440, 274, 301, 300, 368, 417, 351, 465, 251, 301, 389, 385, 380, 386, 394, 395, 379, 399, 412, 419, 410, 436, 322, 387,\n 373, 388, 326, 2, 393, 354, 370, 461, 393, 164, 267, 268, 302, 12, 386, 374, 387, 312, 268, 13, 298, 293, 301, 265, 446, 340, 380, 385, 381, 280, 330,\n 425, 322, 426, 391, 420, 429, 437, 393, 391, 326, 344, 440, 438, 458, 459, 461, 364, 434, 394, 428, 396, 262, 274, 354, 457, 317, 316, 402, 316, 315,\n 403, 315, 314, 404, 314, 313, 405, 313, 421, 406, 323, 366, 361, 292, 306, 407, 306, 291, 408, 291, 287, 409, 287, 432, 410, 427, 434, 411, 372, 264,\n 383, 459, 309, 457, 366, 352, 401, 1, 274, 4, 418, 421, 262, 331, 294, 358, 435, 433, 367, 392, 289, 439, 328, 462, 326, 94, 2, 370, 289, 305, 455, 339,\n 254, 448, 359, 255, 446, 254, 253, 449, 253, 252, 450, 252, 256, 451, 256, 341, 452, 414, 413, 463, 286, 441, 414, 286, 258, 441, 258, 257, 442, 257,\n 259, 443, 259, 260, 444, 260, 467, 445, 309, 459, 250, 305, 289, 290, 305, 290, 460, 401, 376, 435, 309, 250, 392, 376, 411, 433, 453, 341, 464, 357,\n 453, 465, 343, 357, 412, 437, 343, 399, 344, 360, 440, 420, 437, 456, 360, 420, 363, 361, 401, 288, 265, 372, 353, 390, 339, 249, 339, 448, 255];\n\nexport const TRI68: number[] = [0, 1, 36, 0, 36, 17, 1, 2, 41, 1, 41, 36, 2, 3, 31, 2, 31, 41, 3, 4, 48, 3, 48, 31, 4, 5, 48, 5, 6, 48, 6, 7, 59, 6, 59, 48, 7, 8, 58, 7, 58, 59,\n 8, 9, 56, 8, 56, 57, 8, 57, 58, 9, 10, 55, 9, 55, 56, 10, 11, 54, 10, 54, 55, 11, 12, 54, 12, 13, 54, 13, 14, 35, 13, 35, 54, 14, 15, 46, 14, 46, 35, 15, 16,\n 45, 15, 45, 46, 16, 26, 45, 17, 36, 18, 18, 37, 19, 18, 36, 37, 19, 38, 20, 19, 37, 38, 20, 39, 21, 20, 38, 39, 21, 39, 27, 22, 42, 23, 22, 27, 42, 23, 43, 24,\n 23, 42, 43, 24, 44, 25, 24, 43, 44, 25, 45, 26, 25, 44, 45, 27, 39, 28, 27, 28, 42, 28, 39, 29, 28, 29, 42, 29, 31, 30, 29, 30, 35, 29, 40, 31, 29, 35, 47, 29,\n 39, 40, 29, 47, 42, 30, 31, 32, 30, 32, 33, 30, 33, 34, 30, 34, 35, 31, 50, 32, 31, 40, 41, 31, 48, 49, 31, 49, 50, 32, 51, 33, 32, 50, 51, 33, 51, 34, 34, 52,\n 35, 34, 51, 52, 35, 46, 47, 35, 52, 53, 35, 53, 54, 36, 41, 37, 37, 40, 38, 37, 41, 40, 38, 40, 39, 42, 47, 43, 43, 47, 44, 44, 46, 45, 44, 47, 46, 48, 60, 49,\n 48, 59, 60, 49, 61, 50, 49, 60, 61, 50, 62, 51, 50, 61, 62, 51, 62, 52, 52, 63, 53, 52, 62, 63, 53, 64, 54, 53, 63, 64, 54, 64, 55, 55, 65, 56, 55, 64, 65, 56,\n 66, 57, 56, 65, 66, 57, 66, 58, 58, 67, 59, 58, 66, 67, 59, 67, 60, 60, 67, 61, 61, 66, 62, 61, 67, 66, 62, 66, 63, 63, 65, 64, 63, 66, 65, 21, 27, 22];\n\nexport const TRI33: number[] = [\n /* eyes */ 0, 8, 7, 7, 8, 1, 2, 10, 9, 9, 10, 3,\n /* brows */ 17, 0, 18, 18, 0, 7, 18, 7, 19, 19, 7, 1, 19, 1, 11, 19, 11, 20, 21, 3, 22, 21, 9, 3, 20, 9, 21, 20, 2, 9, 20, 11, 2,\n /* 4head */ 23, 17, 18, 25, 21, 22, 24, 19, 20, 24, 18, 19, 24, 20, 21, 24, 23, 18, 24, 21, 25,\n /* nose */ 11, 12, 4, 11, 4, 13, 1, 12, 11, 11, 13, 2, 12, 14, 4, 4, 14, 13,\n /* up-lip */ 14, 5, 15, 14, 15, 6, 12, 5, 14, 14, 6, 13,\n /* cheeks */ 8, 12, 1, 2, 13, 10, 8, 26, 12, 10, 13, 27, 26, 5, 12, 13, 6, 27, 0, 26, 8, 10, 27, 3,\n /* chin */ 5, 32, 16, 16, 32, 6, 5, 30, 32, 6, 32, 31,\n /* cont */ 26, 30, 5, 27, 6, 31, 0, 28, 26, 3, 27, 29, 17, 28, 0, 3, 29, 22, 23, 28, 17, 22, 29, 25, 28, 30, 26, 27, 31, 29,\n];\n\nexport const TRI7: number[] = [0, 4, 1, 2, 4, 3, 4, 5, 6];\n\nexport const VTX68: number[] = [\n /* cont */ 127, 234, 132, 58, 172, 150, 149, 148, 152, 377, 378, 379, 397, 288, 361, 454, 356,\n /* brows */ 70, 63, 105, 66, 107, 336, 296, 334, 293, 300,\n /* nose */ 168, 6, 195, 4, 98, 97, 2, 326, 327,\n /* eyes */ 33, 160, 158, 133, 153, 144, 362, 385, 387, 263, 373, 380,\n /* lip */ 57, 40, 37, 0, 267, 270, 287, 321, 314, 17, 84, 91,\n /* mouth */ 78, 81, 13, 311, 308, 402, 14, 178,\n];\n\nexport const VTX33: number[] = [33, 133, 362, 263, 1, 62, 308, 159, 145, 386, 374, 6, 102, 331, 2, 13, 14, 70, 105, 107, 336, 334, 300, 54, 10, 284, 50, 280, 234, 454, 58, 288, 152];\n\nexport const VTX7: number[] = [33, 133, 362, 263, 1, 78, 308];\n\nexport const UV68 = VTX68.map((x) => UV468[x]);\n\nexport const UV33 = VTX33.map((x) => UV468[x]);\n\nexport const UV7 = VTX7.map((x) => UV468[x]);\n\n// https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const pairsLips: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nexport const pairsLeftEye: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nexport const pairsLeftEyebrow: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nexport const pairsLeftIris: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nexport const pairsRightEye: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nexport const pairsRightEyebrow: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nexport const pairsRightIris: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nexport const pairsFaceContour: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389],\n [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397],\n [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172],\n [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162],\n [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const contourKeypoints = {\n lips: connectionsToIndices(pairsLips),\n leftEye: connectionsToIndices(pairsLeftEye),\n leftEyebrow: connectionsToIndices(pairsLeftEyebrow),\n leftIris: connectionsToIndices(pairsLeftIris),\n rightEye: connectionsToIndices(pairsRightEye),\n rightEyebrow: connectionsToIndices(pairsRightEyebrow),\n rightIris: connectionsToIndices(pairsRightIris),\n faceOval: connectionsToIndices(pairsFaceContour),\n};\n\nexport const pairsFaceMesh: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11],\n [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72],\n [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175],\n [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73],\n [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74],\n [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40],\n [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76],\n [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56],\n [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21],\n [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144],\n [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91],\n [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85],\n [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193],\n [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247],\n [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117],\n [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98],\n [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209],\n [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47],\n [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67],\n [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230],\n [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46],\n [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46],\n [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236],\n [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154],\n [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57],\n [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28],\n [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113],\n [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62],\n [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64],\n [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41],\n [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170],\n [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122],\n [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89],\n [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63],\n [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14],\n [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100],\n [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88],\n [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215],\n [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43],\n [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81],\n [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229],\n [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107],\n [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129],\n [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117],\n [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3],\n [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220],\n [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71],\n [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188],\n [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164],\n [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38],\n [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206],\n [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165],\n [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214],\n [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171],\n [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84],\n [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201],\n [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57],\n [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214],\n [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44],\n [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64],\n [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2],\n [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24],\n [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26],\n [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189],\n [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29],\n [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247],\n [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147],\n [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187],\n [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114],\n [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217],\n [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110],\n [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356],\n [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357],\n [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333],\n [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9],\n [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418],\n [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450],\n [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313],\n [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335],\n [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423],\n [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307],\n [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421],\n [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426],\n [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322],\n [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456],\n [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417],\n [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355],\n [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382],\n [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443],\n [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431],\n [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446],\n [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458],\n [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372],\n [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274],\n [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269],\n [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266],\n [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265],\n [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424],\n [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366],\n [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423],\n [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432],\n [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394],\n [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352],\n [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295],\n [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323],\n [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358],\n [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374],\n [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6],\n [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344],\n [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195],\n [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283],\n [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282],\n [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338],\n [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292],\n [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442],\n [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441],\n [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300],\n [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263],\n [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436],\n [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370],\n [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293],\n [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330],\n [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440],\n [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459],\n [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354],\n [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315],\n [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366],\n [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291],\n [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264],\n [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352],\n [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433],\n [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462],\n [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255],\n [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252],\n [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441],\n [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257],\n [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459],\n [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290],\n [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341],\n [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357],\n [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420],\n [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372],\n [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133],\n [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33],\n [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263],\n [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466],\n [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72],\n [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73],\n [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152],\n [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74],\n [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184],\n [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185],\n [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77],\n [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190],\n [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54],\n [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145],\n [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181],\n [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16],\n [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245],\n [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30],\n [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111],\n [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240],\n [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198],\n [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114],\n [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109],\n [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231],\n [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124],\n [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70],\n [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3],\n [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26],\n [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43],\n [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56],\n [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124],\n [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96],\n [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235],\n [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42],\n [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140],\n [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193],\n [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179],\n [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68],\n [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15],\n [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120],\n [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89],\n [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138],\n [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57],\n [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41],\n [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118],\n [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66],\n [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142],\n [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118],\n [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196],\n [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156],\n [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122],\n [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164],\n [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12],\n [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31],\n [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98],\n [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237],\n [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179],\n [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181],\n [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184],\n [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186],\n [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218],\n [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45],\n [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235],\n [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97],\n [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230],\n [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232],\n [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222],\n [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224],\n [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213],\n [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192],\n [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188],\n [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174],\n [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25],\n [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264],\n [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350],\n [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299],\n [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151],\n [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424],\n [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449],\n [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18],\n [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434],\n [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301],\n [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280],\n [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335],\n [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396],\n [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413],\n [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168],\n [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417],\n [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381],\n [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365],\n [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395],\n [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335],\n [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250],\n [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292],\n [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354],\n [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426],\n [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371],\n [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290],\n [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422],\n [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422],\n [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358],\n [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331],\n [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395],\n [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296],\n [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285],\n [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329],\n [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331],\n [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8],\n [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351],\n [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397],\n [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248],\n [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175],\n [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295],\n [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356],\n [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308],\n [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265],\n [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285],\n [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457],\n [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394],\n [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410],\n [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268],\n [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298],\n [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420],\n [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344],\n [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274],\n [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316],\n [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323],\n [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306],\n [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372],\n [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366],\n [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435],\n [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328],\n [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359],\n [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253],\n [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286],\n [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258],\n [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309],\n [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305],\n [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453],\n [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343],\n [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360],\n [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265],\n [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n", "// @tensorflow/tfjs-models/face-landmark-detection/src/constants.ts\n// https://github.com/google/mediapipe/mediapipe/python/solutions/face_mesh_connections.py\n\ntype PairArray = [number, number][];\n\nconst LIPS_CONNECTIONS: PairArray = [\n [61, 146], [146, 91], [91, 181], [181, 84], [84, 17], [17, 314], [314, 405], [405, 321], [321, 375], [375, 291], [61, 185], [185, 40], [40, 39], [39, 37], [37, 0], [0, 267], [267, 269], [269, 270], [270, 409], [409, 291],\n [78, 95], [95, 88], [88, 178], [178, 87], [87, 14], [14, 317], [317, 402], [402, 318], [318, 324], [324, 308], [78, 191], [191, 80], [80, 81], [81, 82], [82, 13], [13, 312], [312, 311], [311, 310], [310, 415], [415, 308],\n];\n\nconst LEFT_EYE_CONNECTIONS: PairArray = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]];\n\nconst LEFT_EYEBROW_CONNECTIONS: PairArray = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]];\n\nconst LEFT_IRIS_CONNECTIONS: PairArray = [[474, 475], [475, 476], [476, 477], [477, 474]];\n\nconst RIGHT_EYE_CONNECTIONS: PairArray = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]];\n\nconst RIGHT_EYEBROW_CONNECTIONS: PairArray = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]];\n\nconst RIGHT_IRIS_CONNECTIONS: PairArray = [[469, 470], [470, 471], [471, 472], [472, 469]];\n\nconst FACE_OVAL_CONNECTIONS: PairArray = [\n [10, 338], [338, 297], [297, 332], [332, 284], [284, 251], [251, 389], [389, 356], [356, 454], [454, 323], [323, 361], [361, 288], [288, 397], [397, 365], [365, 379], [379, 378], [378, 400], [400, 377], [377, 152],\n [152, 148], [148, 176], [176, 149], [149, 150], [150, 136], [136, 172], [172, 58], [58, 132], [132, 93], [93, 234], [234, 127], [127, 162], [162, 21], [21, 54], [54, 103], [103, 67], [67, 109], [109, 10],\n];\n\nexport const MEDIAPIPE_FACE_MESH_CONNECTED_KEYPOINTS_PAIRS: PairArray = [\n [127, 34], [34, 139], [139, 127], [11, 0], [0, 37], [37, 11], [232, 231], [231, 120], [120, 232], [72, 37], [37, 39], [39, 72], [128, 121], [121, 47], [47, 128], [232, 121], [121, 128], [128, 232],\n [104, 69], [69, 67], [67, 104], [175, 171], [171, 148], [148, 175], [118, 50], [50, 101], [101, 118], [73, 39], [39, 40], [40, 73], [9, 151], [151, 108], [108, 9], [48, 115], [115, 131], [131, 48],\n [194, 204], [204, 211], [211, 194], [74, 40], [40, 185], [185, 74], [80, 42], [42, 183], [183, 80], [40, 92], [92, 186], [186, 40], [230, 229], [229, 118], [118, 230], [202, 212], [212, 214], [214, 202],\n [83, 18], [18, 17], [17, 83], [76, 61], [61, 146], [146, 76], [160, 29], [29, 30], [30, 160], [56, 157], [157, 173], [173, 56], [106, 204], [204, 194], [194, 106], [135, 214], [214, 192], [192, 135],\n [203, 165], [165, 98], [98, 203], [21, 71], [71, 68], [68, 21], [51, 45], [45, 4], [4, 51], [144, 24], [24, 23], [23, 144], [77, 146], [146, 91], [91, 77], [205, 50], [50, 187], [187, 205],\n [201, 200], [200, 18], [18, 201], [91, 106], [106, 182], [182, 91], [90, 91], [91, 181], [181, 90], [85, 84], [84, 17], [17, 85], [206, 203], [203, 36], [36, 206], [148, 171], [171, 140], [140, 148],\n [92, 40], [40, 39], [39, 92], [193, 189], [189, 244], [244, 193], [159, 158], [158, 28], [28, 159], [247, 246], [246, 161], [161, 247], [236, 3], [3, 196], [196, 236], [54, 68], [68, 104], [104, 54],\n [193, 168], [168, 8], [8, 193], [117, 228], [228, 31], [31, 117], [189, 193], [193, 55], [55, 189], [98, 97], [97, 99], [99, 98], [126, 47], [47, 100], [100, 126], [166, 79], [79, 218], [218, 166],\n [155, 154], [154, 26], [26, 155], [209, 49], [49, 131], [131, 209], [135, 136], [136, 150], [150, 135], [47, 126], [126, 217], [217, 47], [223, 52], [52, 53], [53, 223], [45, 51], [51, 134], [134, 45],\n [211, 170], [170, 140], [140, 211], [67, 69], [69, 108], [108, 67], [43, 106], [106, 91], [91, 43], [230, 119], [119, 120], [120, 230], [226, 130], [130, 247], [247, 226], [63, 53], [53, 52], [52, 63],\n [238, 20], [20, 242], [242, 238], [46, 70], [70, 156], [156, 46], [78, 62], [62, 96], [96, 78], [46, 53], [53, 63], [63, 46], [143, 34], [34, 227], [227, 143], [123, 117], [117, 111], [111, 123],\n [44, 125], [125, 19], [19, 44], [236, 134], [134, 51], [51, 236], [216, 206], [206, 205], [205, 216], [154, 153], [153, 22], [22, 154], [39, 37], [37, 167], [167, 39], [200, 201], [201, 208], [208, 200],\n [36, 142], [142, 100], [100, 36], [57, 212], [212, 202], [202, 57], [20, 60], [60, 99], [99, 20], [28, 158], [158, 157], [157, 28], [35, 226], [226, 113], [113, 35], [160, 159], [159, 27], [27, 160],\n [204, 202], [202, 210], [210, 204], [113, 225], [225, 46], [46, 113], [43, 202], [202, 204], [204, 43], [62, 76], [76, 77], [77, 62], [137, 123], [123, 116], [116, 137], [41, 38], [38, 72], [72, 41],\n [203, 129], [129, 142], [142, 203], [64, 98], [98, 240], [240, 64], [49, 102], [102, 64], [64, 49], [41, 73], [73, 74], [74, 41], [212, 216], [216, 207], [207, 212], [42, 74], [74, 184], [184, 42],\n [169, 170], [170, 211], [211, 169], [170, 149], [149, 176], [176, 170], [105, 66], [66, 69], [69, 105], [122, 6], [6, 168], [168, 122], [123, 147], [147, 187], [187, 123], [96, 77], [77, 90], [90, 96],\n [65, 55], [55, 107], [107, 65], [89, 90], [90, 180], [180, 89], [101, 100], [100, 120], [120, 101], [63, 105], [105, 104], [104, 63], [93, 137], [137, 227], [227, 93], [15, 86], [86, 85], [85, 15],\n [129, 102], [102, 49], [49, 129], [14, 87], [87, 86], [86, 14], [55, 8], [8, 9], [9, 55], [100, 47], [47, 121], [121, 100], [145, 23], [23, 22], [22, 145], [88, 89], [89, 179], [179, 88],\n [6, 122], [122, 196], [196, 6], [88, 95], [95, 96], [96, 88], [138, 172], [172, 136], [136, 138], [215, 58], [58, 172], [172, 215], [115, 48], [48, 219], [219, 115], [42, 80], [80, 81], [81, 42],\n [195, 3], [3, 51], [51, 195], [43, 146], [146, 61], [61, 43], [171, 175], [175, 199], [199, 171], [81, 82], [82, 38], [38, 81], [53, 46], [46, 225], [225, 53], [144, 163], [163, 110], [110, 144],\n [52, 65], [65, 66], [66, 52], [229, 228], [228, 117], [117, 229], [34, 127], [127, 234], [234, 34], [107, 108], [108, 69], [69, 107], [109, 108], [108, 151], [151, 109], [48, 64], [64, 235], [235, 48],\n [62, 78], [78, 191], [191, 62], [129, 209], [209, 126], [126, 129], [111, 35], [35, 143], [143, 111], [117, 123], [123, 50], [50, 117], [222, 65], [65, 52], [52, 222], [19, 125], [125, 141], [141, 19],\n [221, 55], [55, 65], [65, 221], [3, 195], [195, 197], [197, 3], [25, 7], [7, 33], [33, 25], [220, 237], [237, 44], [44, 220], [70, 71], [71, 139], [139, 70], [122, 193], [193, 245], [245, 122],\n [247, 130], [130, 33], [33, 247], [71, 21], [21, 162], [162, 71], [170, 169], [169, 150], [150, 170], [188, 174], [174, 196], [196, 188], [216, 186], [186, 92], [92, 216], [2, 97], [97, 167], [167, 2],\n [141, 125], [125, 241], [241, 141], [164, 167], [167, 37], [37, 164], [72, 38], [38, 12], [12, 72], [38, 82], [82, 13], [13, 38], [63, 68], [68, 71], [71, 63], [226, 35], [35, 111], [111, 226],\n [101, 50], [50, 205], [205, 101], [206, 92], [92, 165], [165, 206], [209, 198], [198, 217], [217, 209], [165, 167], [167, 97], [97, 165], [220, 115], [115, 218], [218, 220], [133, 112], [112, 243], [243, 133],\n [239, 238], [238, 241], [241, 239], [214, 135], [135, 169], [169, 214], [190, 173], [173, 133], [133, 190], [171, 208], [208, 32], [32, 171], [125, 44], [44, 237], [237, 125], [86, 87], [87, 178], [178, 86],\n [85, 86], [86, 179], [179, 85], [84, 85], [85, 180], [180, 84], [83, 84], [84, 181], [181, 83], [201, 83], [83, 182], [182, 201], [137, 93], [93, 132], [132, 137], [76, 62], [62, 183], [183, 76],\n [61, 76], [76, 184], [184, 61], [57, 61], [61, 185], [185, 57], [212, 57], [57, 186], [186, 212], [214, 207], [207, 187], [187, 214], [34, 143], [143, 156], [156, 34], [79, 239], [239, 237], [237, 79],\n [123, 137], [137, 177], [177, 123], [44, 1], [1, 4], [4, 44], [201, 194], [194, 32], [32, 201], [64, 102], [102, 129], [129, 64], [213, 215], [215, 138], [138, 213], [59, 166], [166, 219], [219, 59],\n [242, 99], [99, 97], [97, 242], [2, 94], [94, 141], [141, 2], [75, 59], [59, 235], [235, 75], [24, 110], [110, 228], [228, 24], [25, 130], [130, 226], [226, 25], [23, 24], [24, 229], [229, 23],\n [22, 23], [23, 230], [230, 22], [26, 22], [22, 231], [231, 26], [112, 26], [26, 232], [232, 112], [189, 190], [190, 243], [243, 189], [221, 56], [56, 190], [190, 221], [28, 56], [56, 221], [221, 28],\n [27, 28], [28, 222], [222, 27], [29, 27], [27, 223], [223, 29], [30, 29], [29, 224], [224, 30], [247, 30], [30, 225], [225, 247], [238, 79], [79, 20], [20, 238], [166, 59], [59, 75], [75, 166],\n [60, 75], [75, 240], [240, 60], [147, 177], [177, 215], [215, 147], [20, 79], [79, 166], [166, 20], [187, 147], [147, 213], [213, 187], [112, 233], [233, 244], [244, 112], [233, 128], [128, 245], [245, 233],\n [128, 114], [114, 188], [188, 128], [114, 217], [217, 174], [174, 114], [131, 115], [115, 220], [220, 131], [217, 198], [198, 236], [236, 217], [198, 131], [131, 134], [134, 198], [177, 132], [132, 58], [58, 177],\n [143, 35], [35, 124], [124, 143], [110, 163], [163, 7], [7, 110], [228, 110], [110, 25], [25, 228], [356, 389], [389, 368], [368, 356], [11, 302], [302, 267], [267, 11], [452, 350], [350, 349], [349, 452],\n [302, 303], [303, 269], [269, 302], [357, 343], [343, 277], [277, 357], [452, 453], [453, 357], [357, 452], [333, 332], [332, 297], [297, 333], [175, 152], [152, 377], [377, 175], [347, 348], [348, 330], [330, 347],\n [303, 304], [304, 270], [270, 303], [9, 336], [336, 337], [337, 9], [278, 279], [279, 360], [360, 278], [418, 262], [262, 431], [431, 418], [304, 408], [408, 409], [409, 304], [310, 415], [415, 407], [407, 310],\n [270, 409], [409, 410], [410, 270], [450, 348], [348, 347], [347, 450], [422, 430], [430, 434], [434, 422], [313, 314], [314, 17], [17, 313], [306, 307], [307, 375], [375, 306], [387, 388], [388, 260], [260, 387],\n [286, 414], [414, 398], [398, 286], [335, 406], [406, 418], [418, 335], [364, 367], [367, 416], [416, 364], [423, 358], [358, 327], [327, 423], [251, 284], [284, 298], [298, 251], [281, 5], [5, 4], [4, 281],\n [373, 374], [374, 253], [253, 373], [307, 320], [320, 321], [321, 307], [425, 427], [427, 411], [411, 425], [421, 313], [313, 18], [18, 421], [321, 405], [405, 406], [406, 321], [320, 404], [404, 405], [405, 320],\n [315, 16], [16, 17], [17, 315], [426, 425], [425, 266], [266, 426], [377, 400], [400, 369], [369, 377], [322, 391], [391, 269], [269, 322], [417, 465], [465, 464], [464, 417], [386, 257], [257, 258], [258, 386],\n [466, 260], [260, 388], [388, 466], [456, 399], [399, 419], [419, 456], [284, 332], [332, 333], [333, 284], [417, 285], [285, 8], [8, 417], [346, 340], [340, 261], [261, 346], [413, 441], [441, 285], [285, 413],\n [327, 460], [460, 328], [328, 327], [355, 371], [371, 329], [329, 355], [392, 439], [439, 438], [438, 392], [382, 341], [341, 256], [256, 382], [429, 420], [420, 360], [360, 429], [364, 394], [394, 379], [379, 364],\n [277, 343], [343, 437], [437, 277], [443, 444], [444, 283], [283, 443], [275, 440], [440, 363], [363, 275], [431, 262], [262, 369], [369, 431], [297, 338], [338, 337], [337, 297], [273, 375], [375, 321], [321, 273],\n [450, 451], [451, 349], [349, 450], [446, 342], [342, 467], [467, 446], [293, 334], [334, 282], [282, 293], [458, 461], [461, 462], [462, 458], [276, 353], [353, 383], [383, 276], [308, 324], [324, 325], [325, 308],\n [276, 300], [300, 293], [293, 276], [372, 345], [345, 447], [447, 372], [352, 345], [345, 340], [340, 352], [274, 1], [1, 19], [19, 274], [456, 248], [248, 281], [281, 456], [436, 427], [427, 425], [425, 436],\n [381, 256], [256, 252], [252, 381], [269, 391], [391, 393], [393, 269], [200, 199], [199, 428], [428, 200], [266, 330], [330, 329], [329, 266], [287, 273], [273, 422], [422, 287], [250, 462], [462, 328], [328, 250],\n [258, 286], [286, 384], [384, 258], [265, 353], [353, 342], [342, 265], [387, 259], [259, 257], [257, 387], [424, 431], [431, 430], [430, 424], [342, 353], [353, 276], [276, 342], [273, 335], [335, 424], [424, 273],\n [292, 325], [325, 307], [307, 292], [366, 447], [447, 345], [345, 366], [271, 303], [303, 302], [302, 271], [423, 266], [266, 371], [371, 423], [294, 455], [455, 460], [460, 294], [279, 278], [278, 294], [294, 279],\n [271, 272], [272, 304], [304, 271], [432, 434], [434, 427], [427, 432], [272, 407], [407, 408], [408, 272], [394, 430], [430, 431], [431, 394], [395, 369], [369, 400], [400, 395], [334, 333], [333, 299], [299, 334],\n [351, 417], [417, 168], [168, 351], [352, 280], [280, 411], [411, 352], [325, 319], [319, 320], [320, 325], [295, 296], [296, 336], [336, 295], [319, 403], [403, 404], [404, 319], [330, 348], [348, 349], [349, 330],\n [293, 298], [298, 333], [333, 293], [323, 454], [454, 447], [447, 323], [15, 16], [16, 315], [315, 15], [358, 429], [429, 279], [279, 358], [14, 15], [15, 316], [316, 14], [285, 336], [336, 9], [9, 285],\n [329, 349], [349, 350], [350, 329], [374, 380], [380, 252], [252, 374], [318, 402], [402, 403], [403, 318], [6, 197], [197, 419], [419, 6], [318, 319], [319, 325], [325, 318], [367, 364], [364, 365], [365, 367],\n [435, 367], [367, 397], [397, 435], [344, 438], [438, 439], [439, 344], [272, 271], [271, 311], [311, 272], [195, 5], [5, 281], [281, 195], [273, 287], [287, 291], [291, 273], [396, 428], [428, 199], [199, 396],\n [311, 271], [271, 268], [268, 311], [283, 444], [444, 445], [445, 283], [373, 254], [254, 339], [339, 373], [282, 334], [334, 296], [296, 282], [449, 347], [347, 346], [346, 449], [264, 447], [447, 454], [454, 264],\n [336, 296], [296, 299], [299, 336], [338, 10], [10, 151], [151, 338], [278, 439], [439, 455], [455, 278], [292, 407], [407, 415], [415, 292], [358, 371], [371, 355], [355, 358], [340, 345], [345, 372], [372, 340],\n [346, 347], [347, 280], [280, 346], [442, 443], [443, 282], [282, 442], [19, 94], [94, 370], [370, 19], [441, 442], [442, 295], [295, 441], [248, 419], [419, 197], [197, 248], [263, 255], [255, 359], [359, 263],\n [440, 275], [275, 274], [274, 440], [300, 383], [383, 368], [368, 300], [351, 412], [412, 465], [465, 351], [263, 467], [467, 466], [466, 263], [301, 368], [368, 389], [389, 301], [395, 378], [378, 379], [379, 395],\n [412, 351], [351, 419], [419, 412], [436, 426], [426, 322], [322, 436], [2, 164], [164, 393], [393, 2], [370, 462], [462, 461], [461, 370], [164, 0], [0, 267], [267, 164], [302, 11], [11, 12], [12, 302],\n [268, 12], [12, 13], [13, 268], [293, 300], [300, 301], [301, 293], [446, 261], [261, 340], [340, 446], [330, 266], [266, 425], [425, 330], [426, 423], [423, 391], [391, 426], [429, 355], [355, 437], [437, 429],\n [391, 327], [327, 326], [326, 391], [440, 457], [457, 438], [438, 440], [341, 382], [382, 362], [362, 341], [459, 457], [457, 461], [461, 459], [434, 430], [430, 394], [394, 434], [414, 463], [463, 362], [362, 414],\n [396, 369], [369, 262], [262, 396], [354, 461], [461, 457], [457, 354], [316, 403], [403, 402], [402, 316], [315, 404], [404, 403], [403, 315], [314, 405], [405, 404], [404, 314], [313, 406], [406, 405], [405, 313],\n [421, 418], [418, 406], [406, 421], [366, 401], [401, 361], [361, 366], [306, 408], [408, 407], [407, 306], [291, 409], [409, 408], [408, 291], [287, 410], [410, 409], [409, 287], [432, 436], [436, 410], [410, 432],\n [434, 416], [416, 411], [411, 434], [264, 368], [368, 383], [383, 264], [309, 438], [438, 457], [457, 309], [352, 376], [376, 401], [401, 352], [274, 275], [275, 4], [4, 274], [421, 428], [428, 262], [262, 421],\n [294, 327], [327, 358], [358, 294], [433, 416], [416, 367], [367, 433], [289, 455], [455, 439], [439, 289], [462, 370], [370, 326], [326, 462], [2, 326], [326, 370], [370, 2], [305, 460], [460, 455], [455, 305],\n [254, 449], [449, 448], [448, 254], [255, 261], [261, 446], [446, 255], [253, 450], [450, 449], [449, 253], [252, 451], [451, 450], [450, 252], [256, 452], [452, 451], [451, 256], [341, 453], [453, 452], [452, 341],\n [413, 464], [464, 463], [463, 413], [441, 413], [413, 414], [414, 441], [258, 442], [442, 441], [441, 258], [257, 443], [443, 442], [442, 257], [259, 444], [444, 443], [443, 259], [260, 445], [445, 444], [444, 260],\n [467, 342], [342, 445], [445, 467], [459, 458], [458, 250], [250, 459], [289, 392], [392, 290], [290, 289], [290, 328], [328, 460], [460, 290], [376, 433], [433, 435], [435, 376], [250, 290], [290, 392], [392, 250],\n [411, 416], [416, 433], [433, 411], [341, 463], [463, 464], [464, 341], [453, 464], [464, 465], [465, 453], [357, 465], [465, 412], [412, 357], [343, 412], [412, 399], [399, 343], [360, 363], [363, 440], [440, 360],\n [437, 399], [399, 456], [456, 437], [420, 456], [456, 363], [363, 420], [401, 435], [435, 288], [288, 401], [372, 383], [383, 353], [353, 372], [339, 255], [255, 249], [249, 339], [448, 261], [261, 255], [255, 448],\n [133, 243], [243, 190], [190, 133], [133, 155], [155, 112], [112, 133], [33, 246], [246, 247], [247, 33], [33, 130], [130, 25], [25, 33], [398, 384], [384, 286], [286, 398], [362, 398], [398, 414], [414, 362],\n [362, 463], [463, 341], [341, 362], [263, 359], [359, 467], [467, 263], [263, 249], [249, 255], [255, 263], [466, 467], [467, 260], [260, 466], [75, 60], [60, 166], [166, 75], [238, 239], [239, 79], [79, 238],\n [162, 127], [127, 139], [139, 162], [72, 11], [11, 37], [37, 72], [121, 232], [232, 120], [120, 121], [73, 72], [72, 39], [39, 73], [114, 128], [128, 47], [47, 114], [233, 232], [232, 128], [128, 233],\n [103, 104], [104, 67], [67, 103], [152, 175], [175, 148], [148, 152], [119, 118], [118, 101], [101, 119], [74, 73], [73, 40], [40, 74], [107, 9], [9, 108], [108, 107], [49, 48], [48, 131], [131, 49],\n [32, 194], [194, 211], [211, 32], [184, 74], [74, 185], [185, 184], [191, 80], [80, 183], [183, 191], [185, 40], [40, 186], [186, 185], [119, 230], [230, 118], [118, 119], [210, 202], [202, 214], [214, 210],\n [84, 83], [83, 17], [17, 84], [77, 76], [76, 146], [146, 77], [161, 160], [160, 30], [30, 161], [190, 56], [56, 173], [173, 190], [182, 106], [106, 194], [194, 182], [138, 135], [135, 192], [192, 138],\n [129, 203], [203, 98], [98, 129], [54, 21], [21, 68], [68, 54], [5, 51], [51, 4], [4, 5], [145, 144], [144, 23], [23, 145], [90, 77], [77, 91], [91, 90], [207, 205], [205, 187], [187, 207],\n [83, 201], [201, 18], [18, 83], [181, 91], [91, 182], [182, 181], [180, 90], [90, 181], [181, 180], [16, 85], [85, 17], [17, 16], [205, 206], [206, 36], [36, 205], [176, 148], [148, 140], [140, 176],\n [165, 92], [92, 39], [39, 165], [245, 193], [193, 244], [244, 245], [27, 159], [159, 28], [28, 27], [30, 247], [247, 161], [161, 30], [174, 236], [236, 196], [196, 174], [103, 54], [54, 104], [104, 103],\n [55, 193], [193, 8], [8, 55], [111, 117], [117, 31], [31, 111], [221, 189], [189, 55], [55, 221], [240, 98], [98, 99], [99, 240], [142, 126], [126, 100], [100, 142], [219, 166], [166, 218], [218, 219],\n [112, 155], [155, 26], [26, 112], [198, 209], [209, 131], [131, 198], [169, 135], [135, 150], [150, 169], [114, 47], [47, 217], [217, 114], [224, 223], [223, 53], [53, 224], [220, 45], [45, 134], [134, 220],\n [32, 211], [211, 140], [140, 32], [109, 67], [67, 108], [108, 109], [146, 43], [43, 91], [91, 146], [231, 230], [230, 120], [120, 231], [113, 226], [226, 247], [247, 113], [105, 63], [63, 52], [52, 105],\n [241, 238], [238, 242], [242, 241], [124, 46], [46, 156], [156, 124], [95, 78], [78, 96], [96, 95], [70, 46], [46, 63], [63, 70], [116, 143], [143, 227], [227, 116], [116, 123], [123, 111], [111, 116],\n [1, 44], [44, 19], [19, 1], [3, 236], [236, 51], [51, 3], [207, 216], [216, 205], [205, 207], [26, 154], [154, 22], [22, 26], [165, 39], [39, 167], [167, 165], [199, 200], [200, 208], [208, 199],\n [101, 36], [36, 100], [100, 101], [43, 57], [57, 202], [202, 43], [242, 20], [20, 99], [99, 242], [56, 28], [28, 157], [157, 56], [124, 35], [35, 113], [113, 124], [29, 160], [160, 27], [27, 29],\n [211, 204], [204, 210], [210, 211], [124, 113], [113, 46], [46, 124], [106, 43], [43, 204], [204, 106], [96, 62], [62, 77], [77, 96], [227, 137], [137, 116], [116, 227], [73, 41], [41, 72], [72, 73],\n [36, 203], [203, 142], [142, 36], [235, 64], [64, 240], [240, 235], [48, 49], [49, 64], [64, 48], [42, 41], [41, 74], [74, 42], [214, 212], [212, 207], [207, 214], [183, 42], [42, 184], [184, 183],\n [210, 169], [169, 211], [211, 210], [140, 170], [170, 176], [176, 140], [104, 105], [105, 69], [69, 104], [193, 122], [122, 168], [168, 193], [50, 123], [123, 187], [187, 50], [89, 96], [96, 90], [90, 89],\n [66, 65], [65, 107], [107, 66], [179, 89], [89, 180], [180, 179], [119, 101], [101, 120], [120, 119], [68, 63], [63, 104], [104, 68], [234, 93], [93, 227], [227, 234], [16, 15], [15, 85], [85, 16],\n [209, 129], [129, 49], [49, 209], [15, 14], [14, 86], [86, 15], [107, 55], [55, 9], [9, 107], [120, 100], [100, 121], [121, 120], [153, 145], [145, 22], [22, 153], [178, 88], [88, 179], [179, 178],\n [197, 6], [6, 196], [196, 197], [89, 88], [88, 96], [96, 89], [135, 138], [138, 136], [136, 135], [138, 215], [215, 172], [172, 138], [218, 115], [115, 219], [219, 218], [41, 42], [42, 81], [81, 41],\n [5, 195], [195, 51], [51, 5], [57, 43], [43, 61], [61, 57], [208, 171], [171, 199], [199, 208], [41, 81], [81, 38], [38, 41], [224, 53], [53, 225], [225, 224], [24, 144], [144, 110], [110, 24],\n [105, 52], [52, 66], [66, 105], [118, 229], [229, 117], [117, 118], [227, 34], [34, 234], [234, 227], [66, 107], [107, 69], [69, 66], [10, 109], [109, 151], [151, 10], [219, 48], [48, 235], [235, 219],\n [183, 62], [62, 191], [191, 183], [142, 129], [129, 126], [126, 142], [116, 111], [111, 143], [143, 116], [118, 117], [117, 50], [50, 118], [223, 222], [222, 52], [52, 223], [94, 19], [19, 141], [141, 94],\n [222, 221], [221, 65], [65, 222], [196, 3], [3, 197], [197, 196], [45, 220], [220, 44], [44, 45], [156, 70], [70, 139], [139, 156], [188, 122], [122, 245], [245, 188], [139, 71], [71, 162], [162, 139],\n [149, 170], [170, 150], [150, 149], [122, 188], [188, 196], [196, 122], [206, 216], [216, 92], [92, 206], [164, 2], [2, 167], [167, 164], [242, 141], [141, 241], [241, 242], [0, 164], [164, 37], [37, 0],\n [11, 72], [72, 12], [12, 11], [12, 38], [38, 13], [13, 12], [70, 63], [63, 71], [71, 70], [31, 226], [226, 111], [111, 31], [36, 101], [101, 205], [205, 36], [203, 206], [206, 165], [165, 203],\n [126, 209], [209, 217], [217, 126], [98, 165], [165, 97], [97, 98], [237, 220], [220, 218], [218, 237], [237, 239], [239, 241], [241, 237], [210, 214], [214, 169], [169, 210], [140, 171], [171, 32], [32, 140],\n [241, 125], [125, 237], [237, 241], [179, 86], [86, 178], [178, 179], [180, 85], [85, 179], [179, 180], [181, 84], [84, 180], [180, 181], [182, 83], [83, 181], [181, 182], [194, 201], [201, 182], [182, 194],\n [177, 137], [137, 132], [132, 177], [184, 76], [76, 183], [183, 184], [185, 61], [61, 184], [184, 185], [186, 57], [57, 185], [185, 186], [216, 212], [212, 186], [186, 216], [192, 214], [214, 187], [187, 192],\n [139, 34], [34, 156], [156, 139], [218, 79], [79, 237], [237, 218], [147, 123], [123, 177], [177, 147], [45, 44], [44, 4], [4, 45], [208, 201], [201, 32], [32, 208], [98, 64], [64, 129], [129, 98],\n [192, 213], [213, 138], [138, 192], [235, 59], [59, 219], [219, 235], [141, 242], [242, 97], [97, 141], [97, 2], [2, 141], [141, 97], [240, 75], [75, 235], [235, 240], [229, 24], [24, 228], [228, 229],\n [31, 25], [25, 226], [226, 31], [230, 23], [23, 229], [229, 230], [231, 22], [22, 230], [230, 231], [232, 26], [26, 231], [231, 232], [233, 112], [112, 232], [232, 233], [244, 189], [189, 243], [243, 244],\n [189, 221], [221, 190], [190, 189], [222, 28], [28, 221], [221, 222], [223, 27], [27, 222], [222, 223], [224, 29], [29, 223], [223, 224], [225, 30], [30, 224], [224, 225], [113, 247], [247, 225], [225, 113],\n [99, 60], [60, 240], [240, 99], [213, 147], [147, 215], [215, 213], [60, 20], [20, 166], [166, 60], [192, 187], [187, 213], [213, 192], [243, 112], [112, 244], [244, 243], [244, 233], [233, 245], [245, 244],\n [245, 128], [128, 188], [188, 245], [188, 114], [114, 174], [174, 188], [134, 131], [131, 220], [220, 134], [174, 217], [217, 236], [236, 174], [236, 198], [198, 134], [134, 236], [215, 177], [177, 58], [58, 215],\n [156, 143], [143, 124], [124, 156], [25, 110], [110, 7], [7, 25], [31, 228], [228, 25], [25, 31], [264, 356], [356, 368], [368, 264], [0, 11], [11, 267], [267, 0], [451, 452], [452, 349], [349, 451],\n [267, 302], [302, 269], [269, 267], [350, 357], [357, 277], [277, 350], [350, 452], [452, 357], [357, 350], [299, 333], [333, 297], [297, 299], [396, 175], [175, 377], [377, 396], [280, 347], [347, 330], [330, 280],\n [269, 303], [303, 270], [270, 269], [151, 9], [9, 337], [337, 151], [344, 278], [278, 360], [360, 344], [424, 418], [418, 431], [431, 424], [270, 304], [304, 409], [409, 270], [272, 310], [310, 407], [407, 272],\n [322, 270], [270, 410], [410, 322], [449, 450], [450, 347], [347, 449], [432, 422], [422, 434], [434, 432], [18, 313], [313, 17], [17, 18], [291, 306], [306, 375], [375, 291], [259, 387], [387, 260], [260, 259],\n [424, 335], [335, 418], [418, 424], [434, 364], [364, 416], [416, 434], [391, 423], [423, 327], [327, 391], [301, 251], [251, 298], [298, 301], [275, 281], [281, 4], [4, 275], [254, 373], [373, 253], [253, 254],\n [375, 307], [307, 321], [321, 375], [280, 425], [425, 411], [411, 280], [200, 421], [421, 18], [18, 200], [335, 321], [321, 406], [406, 335], [321, 320], [320, 405], [405, 321], [314, 315], [315, 17], [17, 314],\n [423, 426], [426, 266], [266, 423], [396, 377], [377, 369], [369, 396], [270, 322], [322, 269], [269, 270], [413, 417], [417, 464], [464, 413], [385, 386], [386, 258], [258, 385], [248, 456], [456, 419], [419, 248],\n [298, 284], [284, 333], [333, 298], [168, 417], [417, 8], [8, 168], [448, 346], [346, 261], [261, 448], [417, 413], [413, 285], [285, 417], [326, 327], [327, 328], [328, 326], [277, 355], [355, 329], [329, 277],\n [309, 392], [392, 438], [438, 309], [381, 382], [382, 256], [256, 381], [279, 429], [429, 360], [360, 279], [365, 364], [364, 379], [379, 365], [355, 277], [277, 437], [437, 355], [282, 443], [443, 283], [283, 282],\n [281, 275], [275, 363], [363, 281], [395, 431], [431, 369], [369, 395], [299, 297], [297, 337], [337, 299], [335, 273], [273, 321], [321, 335], [348, 450], [450, 349], [349, 348], [359, 446], [446, 467], [467, 359],\n [283, 293], [293, 282], [282, 283], [250, 458], [458, 462], [462, 250], [300, 276], [276, 383], [383, 300], [292, 308], [308, 325], [325, 292], [283, 276], [276, 293], [293, 283], [264, 372], [372, 447], [447, 264],\n [346, 352], [352, 340], [340, 346], [354, 274], [274, 19], [19, 354], [363, 456], [456, 281], [281, 363], [426, 436], [436, 425], [425, 426], [380, 381], [381, 252], [252, 380], [267, 269], [269, 393], [393, 267],\n [421, 200], [200, 428], [428, 421], [371, 266], [266, 329], [329, 371], [432, 287], [287, 422], [422, 432], [290, 250], [250, 328], [328, 290], [385, 258], [258, 384], [384, 385], [446, 265], [265, 342], [342, 446],\n [386, 387], [387, 257], [257, 386], [422, 424], [424, 430], [430, 422], [445, 342], [342, 276], [276, 445], [422, 273], [273, 424], [424, 422], [306, 292], [292, 307], [307, 306], [352, 366], [366, 345], [345, 352],\n [268, 271], [271, 302], [302, 268], [358, 423], [423, 371], [371, 358], [327, 294], [294, 460], [460, 327], [331, 279], [279, 294], [294, 331], [303, 271], [271, 304], [304, 303], [436, 432], [432, 427], [427, 436],\n [304, 272], [272, 408], [408, 304], [395, 394], [394, 431], [431, 395], [378, 395], [395, 400], [400, 378], [296, 334], [334, 299], [299, 296], [6, 351], [351, 168], [168, 6], [376, 352], [352, 411], [411, 376],\n [307, 325], [325, 320], [320, 307], [285, 295], [295, 336], [336, 285], [320, 319], [319, 404], [404, 320], [329, 330], [330, 349], [349, 329], [334, 293], [293, 333], [333, 334], [366, 323], [323, 447], [447, 366],\n [316, 15], [15, 315], [315, 316], [331, 358], [358, 279], [279, 331], [317, 14], [14, 316], [316, 317], [8, 285], [285, 9], [9, 8], [277, 329], [329, 350], [350, 277], [253, 374], [374, 252], [252, 253],\n [319, 318], [318, 403], [403, 319], [351, 6], [6, 419], [419, 351], [324, 318], [318, 325], [325, 324], [397, 367], [367, 365], [365, 397], [288, 435], [435, 397], [397, 288], [278, 344], [344, 439], [439, 278],\n [310, 272], [272, 311], [311, 310], [248, 195], [195, 281], [281, 248], [375, 273], [273, 291], [291, 375], [175, 396], [396, 199], [199, 175], [312, 311], [311, 268], [268, 312], [276, 283], [283, 445], [445, 276],\n [390, 373], [373, 339], [339, 390], [295, 282], [282, 296], [296, 295], [448, 449], [449, 346], [346, 448], [356, 264], [264, 454], [454, 356], [337, 336], [336, 299], [299, 337], [337, 338], [338, 151], [151, 337],\n [294, 278], [278, 455], [455, 294], [308, 292], [292, 415], [415, 308], [429, 358], [358, 355], [355, 429], [265, 340], [340, 372], [372, 265], [352, 346], [346, 280], [280, 352], [295, 442], [442, 282], [282, 295],\n [354, 19], [19, 370], [370, 354], [285, 441], [441, 295], [295, 285], [195, 248], [248, 197], [197, 195], [457, 440], [440, 274], [274, 457], [301, 300], [300, 368], [368, 301], [417, 351], [351, 465], [465, 417],\n [251, 301], [301, 389], [389, 251], [394, 395], [395, 379], [379, 394], [399, 412], [412, 419], [419, 399], [410, 436], [436, 322], [322, 410], [326, 2], [2, 393], [393, 326], [354, 370], [370, 461], [461, 354],\n [393, 164], [164, 267], [267, 393], [268, 302], [302, 12], [12, 268], [312, 268], [268, 13], [13, 312], [298, 293], [293, 301], [301, 298], [265, 446], [446, 340], [340, 265], [280, 330], [330, 425], [425, 280],\n [322, 426], [426, 391], [391, 322], [420, 429], [429, 437], [437, 420], [393, 391], [391, 326], [326, 393], [344, 440], [440, 438], [438, 344], [458, 459], [459, 461], [461, 458], [364, 434], [434, 394], [394, 364],\n [428, 396], [396, 262], [262, 428], [274, 354], [354, 457], [457, 274], [317, 316], [316, 402], [402, 317], [316, 315], [315, 403], [403, 316], [315, 314], [314, 404], [404, 315], [314, 313], [313, 405], [405, 314],\n [313, 421], [421, 406], [406, 313], [323, 366], [366, 361], [361, 323], [292, 306], [306, 407], [407, 292], [306, 291], [291, 408], [408, 306], [291, 287], [287, 409], [409, 291], [287, 432], [432, 410], [410, 287],\n [427, 434], [434, 411], [411, 427], [372, 264], [264, 383], [383, 372], [459, 309], [309, 457], [457, 459], [366, 352], [352, 401], [401, 366], [1, 274], [274, 4], [4, 1], [418, 421], [421, 262], [262, 418],\n [331, 294], [294, 358], [358, 331], [435, 433], [433, 367], [367, 435], [392, 289], [289, 439], [439, 392], [328, 462], [462, 326], [326, 328], [94, 2], [2, 370], [370, 94], [289, 305], [305, 455], [455, 289],\n [339, 254], [254, 448], [448, 339], [359, 255], [255, 446], [446, 359], [254, 253], [253, 449], [449, 254], [253, 252], [252, 450], [450, 253], [252, 256], [256, 451], [451, 252], [256, 341], [341, 452], [452, 256],\n [414, 413], [413, 463], [463, 414], [286, 441], [441, 414], [414, 286], [286, 258], [258, 441], [441, 286], [258, 257], [257, 442], [442, 258], [257, 259], [259, 443], [443, 257], [259, 260], [260, 444], [444, 259],\n [260, 467], [467, 445], [445, 260], [309, 459], [459, 250], [250, 309], [305, 289], [289, 290], [290, 305], [305, 290], [290, 460], [460, 305], [401, 376], [376, 435], [435, 401], [309, 250], [250, 392], [392, 309],\n [376, 411], [411, 433], [433, 376], [453, 341], [341, 464], [464, 453], [357, 453], [453, 465], [465, 357], [343, 357], [357, 412], [412, 343], [437, 343], [343, 399], [399, 437], [344, 360], [360, 440], [440, 344],\n [420, 437], [437, 456], [456, 420], [360, 420], [420, 363], [363, 360], [361, 401], [401, 288], [288, 361], [265, 372], [372, 353], [353, 265], [390, 339], [339, 249], [249, 390], [339, 448], [448, 255], [255, 339],\n];\n\nfunction connectionsToIndices(connections: PairArray) {\n const indices = connections.map((connection) => connection[0]);\n indices.push(connections[connections.length - 1][1]);\n return indices;\n}\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = {\n lips: connectionsToIndices(LIPS_CONNECTIONS),\n leftEye: connectionsToIndices(LEFT_EYE_CONNECTIONS),\n leftEyebrow: connectionsToIndices(LEFT_EYEBROW_CONNECTIONS),\n leftIris: connectionsToIndices(LEFT_IRIS_CONNECTIONS),\n rightEye: connectionsToIndices(RIGHT_EYE_CONNECTIONS),\n rightEyebrow: connectionsToIndices(RIGHT_EYEBROW_CONNECTIONS),\n rightIris: connectionsToIndices(RIGHT_IRIS_CONNECTIONS),\n faceOval: connectionsToIndices(FACE_OVAL_CONNECTIONS),\n};\n\nconst indexLabelPairs: [number, string][] = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR)\n .map(([label, indices]) => indices.map((index) => [index, label] as [number, string]))\n .flat();\n\nexport const MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs);\n\ntype AssignAverage = number[];\nexport interface LandmarksRefinementConfig {\n indexesMapping: number[]; // Maps indexes of the given set of landmarks to indexes of the resulting set of landmarks. Should be non empty and contain the same amount of indexes as landmarks in the corresponding input\n zRefinement: 'none'|'copy'|AssignAverage; // Z refinement instructions.\n}\n\nexport const LANDMARKS_REFINEMENT_LIPS_CONFIG = [\n 61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, // Lower outer.\n 185, 40, 39, 37, 0, 267, 269, 270, 409, // Upper outer(excluding corners).\n 78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308, // Lower inner.\n 191, 80, 81, 82, 13, 312, 311, 310, 415, // Upper inner(excluding corners).\n 76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306, // Lower semi - outer.\n 184, 74, 73, 72, 11, 302, 303, 304, 408, // Upper semi - outer(excluding corners).\n 62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292, // Lower semi - inner.\n 183, 42, 41, 38, 12, 268, 271, 272, 407, // Upper semi - inner(excluding corners).\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // upper contour (excluding corners).\n 130, 25, 110, 24, 23, 22, 26, 112, 243, // Halo x2 lower contour.\n 247, 30, 29, 27, 28, 56, 190, // Halo x2 upper contour (excluding corners).\n 226, 31, 228, 229, 230, 231, 232, 233, 244, // Halo x3 lower contour.\n 113, 225, 224, 223, 222, 221, 189, // Halo x3 upper contour (excluding corners).\n 35, 124, 46, 53, 52, 65, // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour.\n 143, 111, 117, 118, 119, 120, 121, 128, 245, // Halo x5 lower contour.\n 156, 70, 63, 105, 66, 107, 55, 193, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n 359, 255, 339, 254, 253, 252, 256, 341, 463, // Halo x2 lower contour.\n 467, 260, 259, 257, 258, 286, 414, // Halo x2 upper contour (excluding corners).\n 446, 261, 448, 449, 450, 451, 452, 453, 464, // Halo x3 lower contour.\n 342, 445, 444, 443, 442, 441, 413, // Halo x3 upper contour (excluding corners).\n 265, 353, 276, 283, 282, 295, // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour.\n 372, 340, 346, 347, 348, 349, 350, 357, 465, // Halo x5 lower contour.\n 383, 300, 293, 334, 296, 336, 285, 417, // Halo x5 upper contour (excluding corners) or eyebrow outer contour.\n];\n\nexport const LANDMARKS_REFINEMENT_LEFT_IRIS_CONFIG = [\n 468, // Center.\n 469, // Iris right edge.\n 470, // Iris top edge.\n 471, // Iris left edge.\n 472, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 33, 7, 163, 144, 145, 153, 154, 155, 133, // Lower contour.\n 246, 161, 160, 159, 158, 157, 173, // Upper contour (excluding corners).\n];\n*/\n\nexport const LANDMARKS_REFINEMENT_RIGHT_IRIS_CONFIG = [\n 473, // Center.\n 474, // Iris right edge.\n 475, // Iris top edge.\n 476, // Iris left edge.\n 477, // Iris bottom edge.\n];\n/*\nzRefinement: [\n 263, 249, 390, 373, 374, 380, 381, 382, 362, // Lower contour.\n 466, 388, 387, 386, 385, 384, 398, // Upper contour (excluding corners).\n];\n*/\n", "import { TRI468 as triangulation } from '../face/facemeshcoords';\nimport { mergeDeep } from '../util/util';\nimport { getCanvasContext, rad2deg, rect, point, lines, arrow, labels, replace } from './primitives';\nimport { options } from './options';\nimport * as facemeshConstants from '../face/constants';\nimport type { FaceResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\nlet localOptions: DrawOptions;\n\nfunction drawLabels(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (!localOptions.drawLabels || (localOptions.faceLabels?.length === 0)) return;\n let l = localOptions.faceLabels.slice();\n l = replace(l, '[id]', f.id.toFixed(0));\n if (f.score) l = replace(l, '[score]', 100 * f.score);\n if (f.gender) l = replace(l, '[gender]', f.gender);\n if (f.genderScore) l = replace(l, '[genderScore]', 100 * f.genderScore);\n if (f.age) l = replace(l, '[age]', f.age);\n if (f.distance) l = replace(l, '[distance]', 100 * f.distance);\n if (f.real) l = replace(l, '[real]', 100 * f.real);\n if (f.live) l = replace(l, '[live]', 100 * f.live);\n if (f.emotion && f.emotion.length > 0) {\n const emotion = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`);\n if (emotion.length > 3) emotion.length = 3;\n l = replace(l, '[emotions]', emotion.join(' '));\n }\n if (f.rotation?.angle?.roll) l = replace(l, '[roll]', rad2deg(f.rotation.angle.roll));\n if (f.rotation?.angle?.yaw) l = replace(l, '[yaw]', rad2deg(f.rotation.angle.yaw));\n if (f.rotation?.angle?.pitch) l = replace(l, '[pitch]', rad2deg(f.rotation.angle.pitch));\n if (f.rotation?.gaze?.bearing) l = replace(l, '[gaze]', rad2deg(f.rotation.gaze.bearing));\n labels(ctx, l, f.box[0], f.box[1], localOptions);\n}\n\nfunction drawIrisElipse(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n // iris: array[center, left, top, right, bottom]\n if (f.annotations?.leftEyeIris && f.annotations?.leftEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.leftEyeIris[3][0] - f.annotations.leftEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.leftEyeIris[4][1] - f.annotations.leftEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n if (f.annotations?.rightEyeIris && f.annotations?.rightEyeIris[0]) {\n ctx.strokeStyle = localOptions.useDepth ? 'rgba(255, 200, 255, 0.3)' : localOptions.color;\n ctx.beginPath();\n const sizeX = Math.abs(f.annotations.rightEyeIris[3][0] - f.annotations.rightEyeIris[1][0]) / 2;\n const sizeY = Math.abs(f.annotations.rightEyeIris[4][1] - f.annotations.rightEyeIris[2][1]) / 2;\n ctx.ellipse(f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);\n ctx.stroke();\n if (localOptions.fillPolygons) {\n ctx.fillStyle = localOptions.useDepth ? 'rgba(255, 255, 200, 0.3)' : localOptions.color;\n ctx.fill();\n }\n }\n}\n\nfunction drawGazeSpheres(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.angle && typeof Path2D !== 'undefined') {\n ctx.strokeStyle = 'pink';\n const valX = (f.box[0] + f.box[2] / 2) - (f.box[3] * rad2deg(f.rotation.angle.yaw) / 90);\n const valY = (f.box[1] + f.box[3] / 2) + (f.box[2] * rad2deg(f.rotation.angle.pitch) / 90);\n const pathV = new Path2D(`\n M ${f.box[0] + f.box[2] / 2} ${f.box[1]}\n C\n ${valX} ${f.box[1]},\n ${valX} ${f.box[1] + f.box[3]},\n ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}\n `);\n const pathH = new Path2D(`\n M ${f.box[0]} ${f.box[1] + f.box[3] / 2}\n C \n ${f.box[0]} ${valY},\n ${f.box[0] + f.box[2]} ${valY},\n ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}\n `);\n ctx.stroke(pathH);\n ctx.stroke(pathV);\n }\n}\n\nfunction drawGazeArrows(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawGaze && f.rotation?.gaze.strength && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) {\n ctx.strokeStyle = 'pink';\n ctx.fillStyle = 'pink';\n const leftGaze = [\n f.annotations.leftEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.leftEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4);\n const rightGaze = [\n f.annotations.rightEyeIris[0][0] + (Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3]),\n f.annotations.rightEyeIris[0][1] + (Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2]),\n ];\n arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4);\n }\n}\n\nfunction drawFacePolygons(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPolygons && f.mesh.length >= 468) {\n ctx.lineWidth = 1;\n for (let i = 0; i < triangulation.length / 3; i++) {\n const points = [triangulation[i * 3 + 0], triangulation[i * 3 + 1], triangulation[i * 3 + 2]].map((index) => f.mesh[index]);\n lines(ctx, points, localOptions);\n }\n drawIrisElipse(f, ctx);\n }\n /*\n if (localOptions.drawPolygons && f.contours.length > 1) {\n ctx.lineWidth = 5;\n lines(ctx, f.contours, opt);\n }\n ctx.lineWidth = 1;\n */\n}\n\nfunction drawFacePoints(f: FaceResult, ctx: CanvasRenderingContext2D | OffscreenCanvasRenderingContext2D) {\n if (localOptions.drawPoints) {\n if (f?.mesh.length >= 468) {\n for (let i = 0; i < f.mesh.length; i++) {\n point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions);\n if (localOptions.drawAttention) {\n if (facemeshConstants.LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) + 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n if (facemeshConstants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], (f.mesh[i][2] as number) - 127, localOptions);\n }\n }\n } else {\n for (const [k, v] of Object.entries(f?.annotations || {})) {\n if (!v?.[0]) continue;\n const pt = v[0];\n point(ctx, pt[0], pt[1], 0, localOptions);\n if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions);\n }\n }\n }\n}\n\nfunction drawFaceBoxes(f: FaceResult, ctx) {\n if (localOptions.drawBoxes) {\n rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);\n }\n}\n\n/** draw detected faces */\nexport function face(inCanvas: AnyCanvas, result: FaceResult[], drawOptions?: Partial) {\n localOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n for (const f of result) {\n drawFaceBoxes(f, ctx);\n drawLabels(f, ctx);\n if (f.mesh && f.mesh.length > 0) {\n drawFacePoints(f, ctx);\n drawFacePolygons(f, ctx);\n drawGazeSpheres(f, ctx);\n drawGazeArrows(f, ctx);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, curves, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { BodyResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected bodies */\nexport function body(inCanvas: AnyCanvas, result: BodyResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n for (let i = 0; i < result.length; i++) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n ctx.lineWidth = localOptions.lineWidth;\n ctx.font = localOptions.font;\n if (localOptions.drawBoxes && result[i].box && result[i].box.length === 4) {\n rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.bodyLabels?.length > 0)) {\n let l = localOptions.bodyLabels.slice();\n l = replace(l, '[id]', result[i].id.toFixed(0));\n l = replace(l, '[score]', 100 * result[i].score);\n labels(ctx, l, result[i].box[0], result[i].box[1], localOptions);\n }\n }\n if (localOptions.drawPoints && result[i].keypoints) {\n for (let pt = 0; pt < result[i].keypoints.length; pt++) {\n if (!result[i].keypoints[pt].score || (result[i].keypoints[pt].score === 0)) continue;\n ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions);\n point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions);\n }\n }\n if (localOptions.drawLabels && (localOptions.bodyPartLabels?.length > 0) && result[i].keypoints) {\n ctx.font = localOptions.font;\n for (const pt of result[i].keypoints) {\n if (!pt.score || (pt.score === 0)) continue;\n let l = localOptions.bodyPartLabels.slice();\n l = replace(l, '[label]', pt.part);\n l = replace(l, '[score]', 100 * pt.score);\n labels(ctx, l, pt.position[0], pt.position[1], localOptions);\n }\n }\n if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {\n for (const part of Object.values(result[i].annotations)) {\n for (const connected of part) curves(ctx, connected, localOptions);\n }\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, point, colorDepth, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { HandResult } from '../result';\nimport type { AnyCanvas, DrawOptions, Point } from '../exports';\n\n/** draw detected hands */\nexport function hand(inCanvas: AnyCanvas, result: HandResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.handLabels?.length > 0)) {\n let l = localOptions.handLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n if (localOptions.drawPoints) {\n if (h.keypoints && h.keypoints.length > 0) {\n for (const pt of h.keypoints) {\n ctx.fillStyle = colorDepth(pt[2], localOptions);\n point(ctx, pt[0], pt[1], 0, localOptions);\n }\n }\n }\n if (localOptions.drawLabels && h.annotations && (localOptions.fingerLabels?.length > 0)) {\n for (const [part, pt] of Object.entries(h.annotations)) {\n let l = localOptions.fingerLabels.slice();\n l = replace(l, '[label]', part);\n labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions);\n }\n }\n if (localOptions.drawPolygons && h.annotations) {\n const addHandLine = (part: Point[]) => {\n if (!part || part.length === 0 || !part[0]) return;\n for (let i = 0; i < part.length; i++) {\n ctx.beginPath();\n const z = part[i][2] || 0;\n ctx.strokeStyle = colorDepth(i * z, localOptions);\n ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);\n ctx.lineTo(part[i][0], part[i][1]);\n ctx.stroke();\n }\n };\n ctx.lineWidth = localOptions.lineWidth;\n addHandLine(h.annotations.index);\n addHandLine(h.annotations.middle);\n addHandLine(h.annotations.ring);\n addHandLine(h.annotations.pinky);\n addHandLine(h.annotations.thumb);\n // addPart(h.annotations.palm);\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, rect, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { ObjectResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected objects */\nexport function object(inCanvas: AnyCanvas, result: ObjectResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.lineJoin = 'round';\n ctx.font = localOptions.font;\n for (const h of result) {\n if (localOptions.drawBoxes) {\n ctx.strokeStyle = localOptions.color;\n ctx.fillStyle = localOptions.color;\n rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);\n if (localOptions.drawLabels && (localOptions.objectLabels?.length > 0)) {\n let l = localOptions.objectLabels.slice();\n l = replace(l, '[id]', h.id.toFixed(0));\n l = replace(l, '[label]', h.label);\n l = replace(l, '[score]', 100 * h.score);\n labels(ctx, l, h.box[0], h.box[1], localOptions);\n }\n ctx.stroke();\n }\n }\n}\n", "import { mergeDeep } from '../util/util';\nimport { getCanvasContext, replace, labels } from './primitives';\nimport { options } from './options';\nimport type { GestureResult } from '../result';\nimport type { AnyCanvas, DrawOptions } from '../exports';\n\n/** draw detected gestures */\nexport function gesture(inCanvas: AnyCanvas, result: GestureResult[], drawOptions?: Partial) {\n const localOptions: DrawOptions = mergeDeep(options, drawOptions);\n if (!result || !inCanvas) return;\n if (localOptions.drawGestures && (localOptions.gestureLabels?.length > 0)) {\n const ctx = getCanvasContext(inCanvas) as CanvasRenderingContext2D;\n if (!ctx) return;\n ctx.font = localOptions.font;\n ctx.fillStyle = localOptions.color;\n let i = 1;\n for (let j = 0; j < result.length; j++) {\n const [where, what] = Object.entries(result[j]);\n if ((what.length > 1) && ((what[1] as string).length > 0)) {\n const who = where[1] as number > 0 ? `#${where[1]}` : '';\n let l = localOptions.gestureLabels.slice();\n l = replace(l, '[where]', where[0]);\n l = replace(l, '[who]', who);\n l = replace(l, '[what]', what[1]);\n labels(ctx, l, 8, 2 + (i * localOptions.lineHeight), localOptions);\n i += 1;\n }\n }\n }\n}\n", "export const defaultLabels = {\n face: `face\n confidence: [score]%\n [gender] [genderScore]%\n age: [age] years\n distance: [distance]cm\n real: [real]%\n live: [live]%\n [emotions]\n roll: [roll]\u00B0 yaw:[yaw]\u00B0 pitch:[pitch]\u00B0\n gaze: [gaze]\u00B0`,\n body: 'body [score]%',\n bodyPart: '[label] [score]%',\n object: '[label] [score]%',\n hand: '[label] [score]%',\n finger: '[label]',\n gesture: '[where] [who]: [what]',\n};\n", "/* eslint-disable no-multi-spaces */\n\nexport const kpt: string[] = [\n 'nose', // 0\n 'leftEyeInside', // 1\n 'leftEye', // 2\n 'leftEyeOutside', // 3\n 'rightEyeInside', // 4\n 'rightEye', // 5\n 'rightEyeOutside', // 6\n 'leftEar', // 7\n 'rightEar', // 8\n 'leftMouth', // 9\n 'rightMouth', // 10\n 'leftShoulder', // 11\n 'rightShoulder', // 12\n 'leftElbow', // 13\n 'rightElbow', // 14\n 'leftWrist', // 15\n 'rightWrist', // 16\n 'leftPinky', // 17\n 'rightPinky', // 18\n 'leftIndex', // 19\n 'rightIndex', // 20\n 'leftThumb', // 21\n 'rightThumb', // 22\n 'leftHip', // 23\n 'rightHip', // 24\n 'leftKnee', // 25\n 'rightKnee', // 26\n 'leftAnkle', // 27\n 'rightAnkle', // 28\n 'leftHeel', // 29\n 'rightHeel', // 30\n 'leftFoot', // 31\n 'rightFoot', // 32\n 'bodyCenter', // 33\n 'bodyTop', // 34\n 'leftPalm', // 35 // z-coord not ok\n 'leftHand', // 36 // similar to wrist but z-coord not ok\n 'rightPalm', // 37 // z-coord not ok\n 'rightHand', // 38 // similar to wrist but z-coord not ok\n];\n\nexport const connected: Record = {\n shoulders: ['leftShoulder', 'rightShoulder'],\n hips: ['rightHip', 'leftHip'],\n mouth: ['leftMouth', 'rightMouth'],\n leftLegUpper: ['leftHip', 'leftKnee'],\n leftLegLower: ['leftKnee', 'leftAnkle'],\n leftFoot: ['leftAnkle', 'leftHeel', 'leftFoot'],\n leftTorso: ['leftShoulder', 'leftHip'],\n leftArmUpper: ['leftShoulder', 'leftElbow'],\n leftArmLower: ['leftElbow', 'leftWrist'],\n leftHand: ['leftWrist', 'leftPalm'],\n leftHandPinky: ['leftPalm', 'leftPinky'],\n leftHandIndex: ['leftPalm', 'leftIndex'],\n leftHandThumb: ['leftPalm', 'leftThumb'],\n leftEyeOutline: ['leftEyeInside', 'leftEyeOutside'],\n rightLegUpper: ['rightHip', 'rightKnee'],\n rightLegLower: ['rightKnee', 'rightAnkle'],\n rightFoot: ['rightAnkle', 'rightHeel', 'rightFoot'],\n rightTorso: ['rightShoulder', 'rightHip'],\n rightArmUpper: ['rightShoulder', 'rightElbow'],\n rightArmLower: ['rightElbow', 'rightWrist'],\n rightHand: ['rightWrist', 'rightPalm'],\n rightHandPinky: ['rightPalm', 'rightPinky'],\n rightHandIndex: ['rightPalm', 'rightIndex'],\n rightHandThumb: ['rightPalm', 'rightThumb'],\n rightEyeOutline: ['rightEyeInside', 'rightEyeOutside'],\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport type { Box } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D } from '../tfjs/types';\n\nexport interface DetectedBox { box: Box, boxRaw: Box, score: number }\n\nlet model: GraphModel | null;\nlet inputSize = 224;\nlet anchorTensor: { x, y };\nconst numLayers = 5;\nconst strides = [8, 16, 32, 32, 32];\n\nexport function createAnchors() {\n const anchors: { x: number, y: number }[] = [];\n let layerId = 0;\n while (layerId < numLayers) {\n let anchorCount = 0;\n let lastSameStrideLayer = layerId;\n while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {\n anchorCount += 2;\n lastSameStrideLayer++;\n }\n const stride = strides[layerId];\n const featureMapHeight = Math.ceil(inputSize / stride);\n const featureMapWidth = Math.ceil(inputSize / stride);\n for (let y = 0; y < featureMapHeight; ++y) {\n for (let x = 0; x < featureMapWidth; ++x) {\n for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {\n anchors.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });\n }\n }\n }\n layerId = lastSameStrideLayer;\n }\n anchorTensor = { x: tf.tensor1d(anchors.map((a) => a.x)), y: tf.tensor1d(anchors.map((a) => a.y)) };\n}\n\nexport async function loadDetector(config: Config): Promise {\n if (env.initial) model = null;\n if (!model && config.body['detector'] && config.body['detector'].modelPath || '') {\n model = await loadModel(config.body['detector'].modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug && model) log('cached model:', model['modelUrl']);\n createAnchors();\n return model as GraphModel;\n}\n\nconst cropFactor = [5.0, 5.0];\nexport function decodeBoxes(boxesTensor, anchor) {\n return tf.tidy(() => {\n const split = tf.split(boxesTensor, 12, 1); // first 4 are box data [x,y,w,h] and 4 are keypoints data [x,y] for total of 12\n let xCenter = tf.squeeze(split[0]);\n let yCenter = tf.squeeze(split[1]);\n let width = tf.squeeze(split[2]);\n let height = tf.squeeze(split[3]);\n xCenter = tf.add(tf.div(xCenter, inputSize), anchor.x);\n yCenter = tf.add(tf.div(yCenter, inputSize), anchor.y);\n width = tf.mul(tf.div(width, inputSize), cropFactor[0]);\n height = tf.mul(tf.div(height, inputSize), cropFactor[1]);\n const xMin = tf.sub(xCenter, tf.div(width, 2));\n const yMin = tf.sub(yCenter, tf.div(height, 2));\n const xMax = tf.add(xMin, width);\n const yMax = tf.add(yMin, height);\n const boxes = tf.stack([xMin, yMin, xMax, yMax], 1);\n return boxes;\n });\n}\n\nasync function decodeResults(boxesTensor: Tensor, logitsTensor: Tensor, config: Config, outputSize: [number, number]): Promise {\n const detectedBoxes: DetectedBox[] = [];\n const t: Record = {};\n t.boxes = decodeBoxes(boxesTensor, anchorTensor);\n t.scores = tf.sigmoid(logitsTensor);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, 1, config.body['detector']?.minConfidence || 0.1, config.body['detector']?.iouThreshold || 0.1);\n const nms = await t.nms.data();\n const scores = await t.scores.data();\n const boxes = await t.boxes.array();\n for (const i of Array.from(nms)) {\n const score = scores[i];\n const boxRaw: Box = boxes[i];\n const box: Box = [Math.round(boxRaw[0] * outputSize[0]), Math.round(boxRaw[1] * outputSize[1]), Math.round(boxRaw[2] * outputSize[0]), Math.round(boxRaw[3] * outputSize[1])];\n const detectedBox: DetectedBox = { score, boxRaw, box };\n detectedBoxes.push(detectedBox);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return detectedBoxes;\n}\n\nexport async function detectBoxes(input: Tensor, config: Config, outputSize: [number, number]) {\n const t: Record = {};\n t.res = model?.execute(input, ['Identity']) as Tensor; //\n t.logitsRaw = tf.slice(t.res, [0, 0, 0], [1, -1, 1]);\n t.boxesRaw = tf.slice(t.res, [0, 0, 1], [1, -1, -1]);\n t.logits = tf.squeeze(t.logitsRaw);\n t.boxes = tf.squeeze(t.boxesRaw);\n const boxes = await decodeResults(t.boxes, t.logits, config, outputSize);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import type { Point, Box } from '../result';\n\nexport function calc(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const box: Box = [min[0], min[1], max[0] - min[0], max[1] - min[1]];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function square(keypoints: Point[], outputSize: [number, number] = [1, 1]) {\n const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; // all x/y coords\n const min = [Math.min(...coords[0]), Math.min(...coords[1])];\n const max = [Math.max(...coords[0]), Math.max(...coords[1])];\n const center = [(min[0] + max[0]) / 2, (min[1] + max[1]) / 2]; // find center x and y coord of all fingers\n const dist = Math.max(center[0] - min[0], center[1] - min[1], -center[0] + max[0], -center[1] + max[1]); // largest distance from center in any direction\n const box: Box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)];\n const boxRaw: Box = [box[0] / outputSize[0], box[1] / outputSize[1], box[2] / outputSize[0], box[3] / outputSize[1]];\n return { box, boxRaw };\n}\n\nexport function scale(box: Box, scaleFact: number) {\n const dist = [box[2] * scaleFact, box[3] * scaleFact];\n const newBox: Box = [\n box[0] - (dist[0] - box[2]) / 2,\n box[1] - (dist[1] - box[3]) / 2,\n dist[0],\n dist[1],\n ];\n return newBox;\n}\n\nexport function crop(box: Box) { // [y1, x1, y2, x2] clamped to 0..1\n const yxBox: Box = [Math.max(0, box[1]), Math.max(0, box[0]), Math.min(1, box[3] + box[1]), Math.min(1, box[2] + box[0])];\n return yxBox;\n}\n", "/**\n * BlazePose model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { log, now } from '../util/util';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, Box, Point, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport * as coords from './blazeposecoords';\nimport { loadDetector, detectBoxes, DetectedBox } from './blazeposedetector';\nimport * as box from '../util/box';\nimport { env } from '../util/env';\n\n// const models: [GraphModel | null, GraphModel | null] = [null, null];\nlet model: GraphModel | null;\nlet inputSize = 256;\nlet skipped = Number.MAX_SAFE_INTEGER;\nconst outputNodes: { detector: string[], landmarks: string[] } = {\n landmarks: ['ld_3d', 'activation_segmentation', 'activation_heatmap', 'world_3d', 'output_poseflag'],\n detector: [],\n};\n\nconst cache: BodyResult[] = [];\nlet padding: [number, number][] = [[0, 0], [0, 0], [0, 0], [0, 0]];\nlet lastTime = 0;\n\nconst sigmoid = (x) => (1 - (1 / (1 + Math.exp(x))));\n\nexport const loadDetect = (config: Config): Promise => loadDetector(config);\n\nexport async function loadPose(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.body.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nfunction prepareImage(input: Tensor4D, size: number, cropBox?: Box): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n let final: Tensor;\n if (cropBox) {\n t.cropped = tf.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); // if we have cached box use it to crop input\n }\n if (input.shape[1] !== input.shape[2]) { // only pad if width different than height\n const height: [number, number] = [\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0,\n ];\n const width: [number, number] = [\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0,\n ];\n padding = [\n [0, 0], // dont touch batch\n height, // height before&after\n width, // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(t.cropped || input, padding); // use cropped box if it exists\n t.resize = tf.image.resizeBilinear(t.pad as Tensor4D, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else if (input.shape[1] !== size) { // if input needs resizing\n t.resize = tf.image.resizeBilinear(t.cropped as Tensor4D || input, [size, size]);\n final = tf.div(t.resize, constants.tf255);\n } else { // if input is already in a correct resolution just normalize it\n final = tf.div(t.cropped || input, constants.tf255);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nfunction rescaleKeypoints(keypoints: BodyKeypoint[], outputSize: [number, number], cropBox?: Box): BodyKeypoint[] {\n for (const kpt of keypoints) { // first rescale due to padding\n kpt.position = [\n Math.trunc(kpt.position[0] * (outputSize[0] + padding[2][0] + padding[2][1]) / outputSize[0] - padding[2][0]),\n Math.trunc(kpt.position[1] * (outputSize[1] + padding[1][0] + padding[1][1]) / outputSize[1] - padding[1][0]),\n kpt.position[2] as number,\n ];\n kpt.positionRaw = [kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1], 2 * (kpt.position[2] as number) / (outputSize[0] + outputSize[1])];\n }\n if (cropBox) { // second rescale due to cropping\n const width = cropBox[2] - cropBox[0];\n const height = cropBox[3] - cropBox[1];\n for (const kpt of keypoints) {\n kpt.positionRaw = [\n kpt.positionRaw[0] / height + cropBox[1], // correct offset due to crop\n kpt.positionRaw[1] / width + cropBox[0], // correct offset due to crop\n kpt.positionRaw[2] as number,\n ];\n kpt.position = [\n Math.trunc(kpt.positionRaw[0] * outputSize[0]),\n Math.trunc(kpt.positionRaw[1] * outputSize[1]),\n kpt.positionRaw[2] as number,\n ];\n }\n }\n return keypoints;\n}\n\nfunction fixKeypoints(keypoints: BodyKeypoint[]) {\n // palm z-coord is incorrect around near-zero so we approximate it\n const leftPalm = keypoints.find((k) => k.part === 'leftPalm') as BodyKeypoint;\n const leftWrist = keypoints.find((k) => k.part === 'leftWrist') as BodyKeypoint;\n const leftIndex = keypoints.find((k) => k.part === 'leftIndex') as BodyKeypoint;\n leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;\n const rightPalm = keypoints.find((k) => k.part === 'rightPalm') as BodyKeypoint;\n const rightWrist = keypoints.find((k) => k.part === 'rightWrist') as BodyKeypoint;\n const rightIndex = keypoints.find((k) => k.part === 'rightIndex') as BodyKeypoint;\n rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;\n}\n\nasync function detectLandmarks(input: Tensor, config: Config, outputSize: [number, number]): Promise {\n /**\n * t.ld: 39 keypoints [x,y,z,score,presence] normalized to input size\n * t.segmentation:\n * t.heatmap:\n * t.world: 39 keypoints [x,y,z] normalized to -1..1\n * t.poseflag: body score\n */\n if (!model?.['executor']) return null;\n const t: Record = {};\n [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = model?.execute(input, outputNodes.landmarks) as Tensor[]; // run model\n const poseScore = (await t.poseflag.data())[0];\n const points = await t.ld.data();\n const distances = await t.world.data();\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor])); // dont need tensors after this\n const keypointsRelative: BodyKeypoint[] = [];\n const depth = 5; // each points has x,y,z,visibility,presence\n for (let i = 0; i < points.length / depth; i++) {\n const score = sigmoid(points[depth * i + 3]);\n const presence = sigmoid(points[depth * i + 4]);\n const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;\n const positionRaw: Point = [points[depth * i + 0] / inputSize, points[depth * i + 1] / inputSize, points[depth * i + 2] + 0];\n const position: Point = [Math.trunc(outputSize[0] * positionRaw[0]), Math.trunc(outputSize[1] * positionRaw[1]), positionRaw[2] as number];\n const distance: Point = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];\n keypointsRelative.push({ part: coords.kpt[i] as BodyLandmark, positionRaw, position, distance, score: adjScore });\n }\n if (poseScore < (config.body.minConfidence || 0)) return null;\n fixKeypoints(keypointsRelative);\n const keypoints: BodyKeypoint[] = rescaleKeypoints(keypointsRelative, outputSize); // keypoints were relative to input image which is padded\n const kpts = keypoints.map((k) => k.position);\n const boxes = box.calc(kpts, [outputSize[0], outputSize[1]]); // now find boxes based on rescaled keypoints\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations };\n return body;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n const outputSize: [number, number] = [input.shape[2] || 0, input.shape[1] || 0];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && cache !== null) {\n skipped++;\n } else {\n let boxes: DetectedBox[] = [];\n if (config.body?.['detector']?.['enabled']) {\n const preparedImage = prepareImage(input, 224);\n boxes = await detectBoxes(preparedImage, config, outputSize);\n tf.dispose(preparedImage);\n } else {\n boxes = [{ box: [0, 0, 0, 0] as Box, boxRaw: [0, 0, 1, 1], score: 0 }]; // running without detector\n }\n for (let i = 0; i < boxes.length; i++) {\n const preparedBox = prepareImage(input, 256, boxes[i]?.boxRaw); // padded and resized\n cache.length = 0;\n const bodyResult = await detectLandmarks(preparedBox, config, outputSize);\n tf.dispose(preparedBox);\n if (!bodyResult) continue;\n bodyResult.id = i;\n // bodyResult.score = 0; // TBD\n cache.push(bodyResult);\n }\n /*\n cropBox = [0, 0, 1, 1]; // reset crop coordinates\n if (cache?.boxRaw && config.skipAllowed) {\n const cx = (2.0 * cache.boxRaw[0] + cache.boxRaw[2]) / 2;\n const cy = (2.0 * cache.boxRaw[1] + cache.boxRaw[3]) / 2;\n let size = cache.boxRaw[2] > cache.boxRaw[3] ? cache.boxRaw[2] : cache.boxRaw[3];\n size = (size * 1.0) / 2; // enlarge and half it\n if (cx > 0.1 && cx < 0.9 && cy > 0.1 && cy < 0.9 && size > 0.1) { // only update if box is sane\n const y = 0; // cy - size;\n const x = cx - size;\n cropBox = [y, x, y + 1, x + 1]; // [y0,x0,y1,x1] used for cropping but width/height are not yet implemented so we only reposition image to center of body\n }\n }\n */\n lastTime = now();\n skipped = 0;\n }\n return cache;\n}\n", "/**\n * CoCo Labels used by object detection implementations\n */\nexport const labels = [\n { class: 1, label: 'person' },\n { class: 2, label: 'bicycle' },\n { class: 3, label: 'car' },\n { class: 4, label: 'motorcycle' },\n { class: 5, label: 'airplane' },\n { class: 6, label: 'bus' },\n { class: 7, label: 'train' },\n { class: 8, label: 'truck' },\n { class: 9, label: 'boat' },\n { class: 10, label: 'traffic light' },\n { class: 11, label: 'fire hydrant' },\n { class: 12, label: 'stop sign' },\n { class: 13, label: 'parking meter' },\n { class: 14, label: 'bench' },\n { class: 15, label: 'bird' },\n { class: 16, label: 'cat' },\n { class: 17, label: 'dog' },\n { class: 18, label: 'horse' },\n { class: 19, label: 'sheep' },\n { class: 20, label: 'cow' },\n { class: 21, label: 'elephant' },\n { class: 22, label: 'bear' },\n { class: 23, label: 'zebra' },\n { class: 24, label: 'giraffe' },\n { class: 25, label: 'backpack' },\n { class: 26, label: 'umbrella' },\n { class: 27, label: 'handbag' },\n { class: 28, label: 'tie' },\n { class: 29, label: 'suitcase' },\n { class: 30, label: 'frisbee' },\n { class: 31, label: 'skis' },\n { class: 32, label: 'snowboard' },\n { class: 33, label: 'sports ball' },\n { class: 34, label: 'kite' },\n { class: 35, label: 'baseball bat' },\n { class: 36, label: 'baseball glove' },\n { class: 37, label: 'skateboard' },\n { class: 38, label: 'surfboard' },\n { class: 39, label: 'tennis racket' },\n { class: 40, label: 'bottle' },\n { class: 41, label: 'wine glass' },\n { class: 42, label: 'cup' },\n { class: 43, label: 'fork' },\n { class: 44, label: 'knife' },\n { class: 45, label: 'spoon' },\n { class: 46, label: 'bowl' },\n { class: 47, label: 'banana' },\n { class: 48, label: 'apple' },\n { class: 49, label: 'sandwich' },\n { class: 50, label: 'orange' },\n { class: 51, label: 'broccoli' },\n { class: 52, label: 'carrot' },\n { class: 53, label: 'hot dog' },\n { class: 54, label: 'pizza' },\n { class: 55, label: 'donut' },\n { class: 56, label: 'cake' },\n { class: 57, label: 'chair' },\n { class: 58, label: 'couch' },\n { class: 59, label: 'potted plant' },\n { class: 60, label: 'bed' },\n { class: 61, label: 'dining table' },\n { class: 62, label: 'toilet' },\n { class: 63, label: 'tv' },\n { class: 64, label: 'laptop' },\n { class: 65, label: 'mouse' },\n { class: 66, label: 'remote' },\n { class: 67, label: 'keyboard' },\n { class: 68, label: 'cell phone' },\n { class: 69, label: 'microwave' },\n { class: 70, label: 'oven' },\n { class: 71, label: 'toaster' },\n { class: 72, label: 'sink' },\n { class: 73, label: 'refrigerator' },\n { class: 74, label: 'book' },\n { class: 75, label: 'clock' },\n { class: 76, label: 'vase' },\n { class: 77, label: 'scissors' },\n { class: 78, label: 'teddy bear' },\n { class: 79, label: 'hair drier' },\n { class: 80, label: 'toothbrush' },\n];\n", "/**\n * CenterNet object detection model implementation\n *\n * Based on: [**MB3-CenterNet**](https://github.com/610265158/mobilenetv3_centernet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n // fakeOps(['floormod'], config);\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor | null, outputShape: [number, number], config: Config) {\n if (!res) return [];\n const t: Record = {};\n const results: ObjectResult[] = [];\n const detections = await res.array() as number[][][];\n t.squeeze = tf.squeeze(res);\n const arr = tf.split(t.squeeze, 6, 1); // x1, y1, x2, y2, score, class\n t.stack = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x\n t.boxes = tf.squeeze(t.stack);\n t.scores = tf.squeeze(arr[4]);\n t.classes = tf.squeeze(arr[5]);\n tf.dispose([res, ...arr]);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, config.object.maxDetected || 0, config.object.iouThreshold, (config.object.minConfidence || 0));\n const nms = await t.nms.data();\n let i = 0;\n for (const id of Array.from(nms)) {\n const score = Math.trunc(100 * detections[0][id][4]) / 100;\n const classVal = detections[0][id][5];\n if (Number.isNaN(classVal)) continue;\n const label = labels[classVal].label as ObjectType;\n const [x, y] = [\n detections[0][id][0] / inputSize,\n detections[0][id][1] / inputSize,\n ];\n const boxRaw: Box = [\n x,\n y,\n detections[0][id][2] / inputSize - x,\n detections[0][id][3] / inputSize - y,\n ];\n const box: Box = [\n Math.trunc(boxRaw[0] * outputShape[0]),\n Math.trunc(boxRaw[1] * outputShape[1]),\n Math.trunc(boxRaw[2] * outputShape[0]),\n Math.trunc(boxRaw[3] * outputShape[1]),\n ];\n results.push({ id: i++, score, class: classVal, label, box, boxRaw });\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return results;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const outputSize = [input.shape[2] || 0, input.shape[1] || 0] as [number, number];\n const resize = tf.image.resizeBilinear(input, [inputSize, inputSize]);\n const objectT = config.object.enabled ? model?.execute(resize, ['tower_0/detections']) as Tensor : null;\n lastTime = now();\n tf.dispose(resize);\n\n const obj = await process(objectT, outputSize, config);\n last = obj;\n\n resolve(obj);\n });\n}\n", "export const kpt: string[] = [\n 'head',\n 'neck',\n 'rightShoulder',\n 'rightElbow',\n 'rightWrist',\n 'chest',\n 'leftShoulder',\n 'leftElbow',\n 'leftWrist',\n 'bodyCenter',\n 'rightHip',\n 'rightKnee',\n 'rightAnkle',\n 'leftHip',\n 'leftKnee',\n 'leftAnkle',\n];\n\nexport const connected: Record = {\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * EfficientPose model implementation\n *\n * Based on: [**EfficientPose**](https://github.com/daniegr/EfficientPose)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as coords from './efficientposecoords';\nimport { constants } from '../tfjs/constants';\nimport type { BodyResult, Point, BodyLandmark, BodyAnnotation } from '../result';\nimport type { GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet lastTime = 0;\nconst cache: BodyResult = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} as Record };\n\n// const keypoints: Array = [];\n// let box: Box = [0, 0, 0, 0];\n// let boxRaw: Box = [0, 0, 0, 0];\n// let score = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n// performs argmax and max functions on a 2d tensor\nasync function max2d(inputs, minScore): Promise<[number, number, number]> {\n const [width, height] = inputs.shape;\n const reshaped = tf.reshape(inputs, [height * width]); // combine all data\n const max = tf.max(reshaped, 0);\n const newScore: number = (await max.data())[0]; // get highest score\n if (newScore > minScore) { // skip coordinate calculation is score is too low\n const coordinates = tf.argMax(reshaped, 0);\n const mod = tf.mod(coordinates, width);\n const x = (await mod.data())[0];\n const div = tf.div(coordinates, width);\n const y: number = (await div.data())[0];\n tf.dispose([reshaped, max, coordinates, mod, div]);\n return [x, y, newScore];\n }\n tf.dispose([reshaped, max]);\n return [0, 0, newScore];\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs[0].shape) return [];\n const skipTime = (config.body.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && Object.keys(cache.keypoints).length > 0) {\n skipped++;\n return [cache];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const tensor = tf.tidy(() => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape?.[2] || 0, model?.inputs[0].shape?.[1] || 0], false);\n const enhance = tf.mul(resize, constants.tf2);\n const norm = tf.sub(enhance, constants.tf1);\n return norm;\n });\n let resT;\n if (config.body.enabled) resT = model?.execute(tensor);\n lastTime = now();\n tf.dispose(tensor);\n\n if (resT) {\n cache.keypoints.length = 0;\n const squeeze = tf.squeeze(resT);\n tf.dispose(resT);\n // body parts are basically just a stack of 2d tensors\n const stack = tf.unstack(squeeze, 2);\n tf.dispose(squeeze);\n\n // process each unstacked tensor as a separate body part\n for (let id = 0; id < stack.length; id++) {\n // actual processing to get coordinates and score\n const [x, y, partScore] = await max2d(stack[id], config.body.minConfidence);\n if (partScore > (config.body.minConfidence || 0)) {\n cache.keypoints.push({\n score: Math.round(100 * partScore) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw: [ // normalized to 0..1\n // @ts-ignore model is not undefined here\n x / model.inputs[0].shape[2], y / model.inputs[0].shape[1],\n ],\n position: [ // normalized to input image size\n // @ts-ignore model is not undefined here\n Math.round(image.shape[2] * x / model.inputs[0].shape[2]), Math.round(image.shape[1] * y / model.inputs[0].shape[1]),\n ],\n });\n }\n }\n stack.forEach((s) => tf.dispose(s));\n }\n cache.score = cache.keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const x = cache.keypoints.map((a) => a.position[0]);\n const y = cache.keypoints.map((a) => a.position[1]);\n cache.box = [\n Math.min(...x),\n Math.min(...y),\n Math.max(...x) - Math.min(...x),\n Math.max(...y) - Math.min(...y),\n ];\n const xRaw = cache.keypoints.map((a) => a.positionRaw[0]);\n const yRaw = cache.keypoints.map((a) => a.positionRaw[1]);\n cache.boxRaw = [\n Math.min(...xRaw),\n Math.min(...yRaw),\n Math.max(...xRaw) - Math.min(...xRaw),\n Math.max(...yRaw) - Math.min(...yRaw),\n ];\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = cache.keypoints.find((kpt) => kpt.part === indexes[i]);\n const pt1 = cache.keypoints.find((kpt) => kpt.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n cache.annotations[name] = pt;\n }\n resolve([cache]);\n });\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport { constants } from '../tfjs/constants';\nimport type { Box, Point } from '../result';\nimport { env } from '../util/env';\n\nexport const createBox = (startEndTensor) => ({ startPoint: tf.slice(startEndTensor, [0, 0], [-1, 2]), endPoint: tf.slice(startEndTensor, [0, 2], [-1, 2]) });\n\nexport const disposeBox = (t) => tf.dispose([t.startPoint, t.endPoint]);\n\nexport const getBoxSize = (box): [number, number] => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])];\n\nexport const getBoxCenter = (box): [number, number, number] => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1];\n\nexport const clampBox = (box, input): Box => (box ? [\n Math.trunc(Math.max(0, box.startPoint[0])),\n Math.trunc(Math.max(0, box.startPoint[1])),\n Math.trunc(Math.min((input.shape[2] || 0), box.endPoint[0]) - Math.max(0, box.startPoint[0])),\n Math.trunc(Math.min((input.shape[1] || 0), box.endPoint[1]) - Math.max(0, box.startPoint[1])),\n] : [0, 0, 0, 0]);\n\nexport const getRawBox = (box, input): Box => (box ? [\n box.startPoint[0] / (input.shape[2] || 0),\n box.startPoint[1] / (input.shape[1] || 0),\n (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0),\n (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0),\n] : [0, 0, 0, 0]);\n\nexport const scaleBoxCoordinates = (box, factor, anchor) => {\n const startPoint: Point = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]];\n const endPoint: Point = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]];\n // const centerPoint = [(startPoint[0] + endPoint[0]) / 2, (startPoint[1] + endPoint[1]) / 2];\n const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]);\n /**\n face.mesh = box.landmarks.map((pt) => [\n ((box.startPoint[0] + box.endPoint[0]) / 2) + (pt[0] * input.shape[2] / blazeface.size()),\n ((box.startPoint[1] + box.endPoint[1]) / 2) + (pt[1] * input.shape[1] / blazeface.size()),\n ]);\n */\n\n return { startPoint, endPoint, landmarks, confidence: box.confidence };\n};\n\nexport const cutAndResize = (box, image, cropSize) => {\n const h = image.shape[1];\n const w = image.shape[2];\n const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w];\n const crop = tf.image.cropAndResize(image, [cutBox], [0], cropSize);\n const norm = tf.div(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n};\n\nexport const enlargeBox = (box, factor) => {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize: [number, number] = [factor * size[0] / 2, factor * size[1] / 2];\n return {\n startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]] as Point,\n endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size,\n };\n};\n\nexport const squarifyBox = (box) => {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const halfSize = Math.max(...size) / 2;\n return {\n startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)] as Point,\n endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)] as Point,\n landmarks: box.landmarks,\n confidence: box.confidence,\n size: [Math.round(size[0]), Math.round(size[1])] as [number, number],\n };\n};\n\nexport const calculateLandmarksBoundingBox = (landmarks) => {\n const x = landmarks.map((d) => d[0]);\n const y = landmarks.map((d) => d[1]);\n return {\n startPoint: [Math.min(...x), Math.min(...y)] as Point,\n endPoint: [Math.max(...x), Math.max(...y)] as Point,\n landmarks,\n };\n};\n\nexport const fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]];\n\nexport const normalizeRadians = (angle: number) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n\nexport const computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]));\n\nexport const radToDegrees = (rad) => rad * 180 / Math.PI;\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport const dot = (v1: number[], v2: number[]) => {\n let product = 0;\n for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i];\n return product;\n};\n\nexport const getColumnFrom2DArr = (arr, columnIndex) => {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]);\n return column;\n};\n\nexport const multiplyTransformMatrices = (mat1, mat2) => {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n return product;\n};\n\nexport const buildRotationMatrix = (rotation, center) => {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n};\n\nexport const invertTransformMatrix = (matrix) => {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)];\n return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]];\n};\n\nexport const rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])];\n\nexport const xyDistanceBetweenPoints = (a, b) => Math.sqrt(((a[0] - b[0]) ** 2) + ((a[1] - b[1]) ** 2));\n\nexport function generateAnchors(inputSize: number) {\n const spec = inputSize === 192\n ? { strides: [4], anchors: [1] } // facemesh-detector\n : { strides: [inputSize / 16, inputSize / 8], anchors: [2, 6] }; // blazeface\n const anchors: [number, number][] = [];\n for (let i = 0; i < spec.strides.length; i++) {\n const stride = spec.strides[i];\n const gridRows = Math.floor((inputSize + stride - 1) / stride);\n const gridCols = Math.floor((inputSize + stride - 1) / stride);\n const anchorsNum = spec.anchors[i];\n for (let gridY = 0; gridY < gridRows; gridY++) {\n const anchorY = stride * (gridY + 0.5);\n for (let gridX = 0; gridX < gridCols; gridX++) {\n const anchorX = stride * (gridX + 0.5);\n for (let n = 0; n < anchorsNum; n++) anchors.push([anchorX, anchorY]);\n }\n }\n }\n return anchors;\n}\n\nexport function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize) {\n const boxSize = getBoxSize(box);\n const coordsScaled = coordsRaw.map((coord) => ([ // scaled around zero-point\n (boxSize[0] / inputSize) * (coord[0] - (inputSize / 2)),\n (boxSize[1] / inputSize) * (coord[1] - (inputSize / 2)),\n (coord[2] || 0),\n ]));\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix;\n const coordsRotated = largeAngle ? coordsScaled.map((coord) => ([...rotatePoint(coord, coordsRotationMatrix), coord[2]])) : coordsScaled;\n const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix;\n const boxCenter = getBoxCenter(box);\n const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])];\n return coordsRotated.map((coord) => ([\n Math.trunc(coord[0] + offsets[0]),\n Math.trunc(coord[1] + offsets[1]),\n Math.trunc(coord[2] || 0),\n ]));\n}\n\nexport function correctFaceRotation(rotate, box, input, inputSize) {\n const symmetryLine = (box.landmarks.length >= coords.meshLandmarks.count)\n ? coords.meshLandmarks.symmetryLine\n : coords.blazeFaceLandmarks.symmetryLine;\n let angle = 0; // default\n let rotationMatrix = fixedRotationMatrix; // default\n let face; // default\n\n if (rotate && env.kernels.includes('rotatewithoffset')) {\n angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]);\n const largeAngle = angle && (angle !== 0) && (Math.abs(angle) > 0.2);\n if (largeAngle) { // perform rotation only if angle is sufficiently high\n const center: Point = getBoxCenter(box);\n const centerRaw: Point = [center[0] / input.shape[2], center[1] / input.shape[1]];\n const rotated = tf.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]);\n rotationMatrix = buildRotationMatrix(-angle, center);\n face = cutAndResize(box, rotated, [inputSize, inputSize]);\n tf.dispose(rotated);\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n } else {\n face = cutAndResize(box, input, [inputSize, inputSize]);\n }\n return [angle, rotationMatrix, face];\n}\n\nexport const findFaceCenter = (mesh) => {\n const x = mesh.map((m) => m[0]);\n const y = mesh.map((m) => m[1]);\n // weighted center\n /*\n const sum = (arr: number[]) => arr.reduce((prev, curr) => prev + curr, 0);\n return [sum(x) / mesh.length, sum(y) / mesh.length];\n */\n // absolute center\n return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2];\n};\n\nexport const calculateFaceBox = (mesh, previousBox) => {\n const center = findFaceCenter(mesh);\n const boxSize = getBoxSize(previousBox);\n const calculatedBox = {\n startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2] as Point,\n endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] as Point,\n };\n return calculatedBox;\n};\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n * See `facemesh.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport * as util from './facemeshutil';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { Tensor, GraphModel, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\nimport type { Point } from '../result';\n\nconst keypointsCount = 6;\nlet model: GraphModel | null;\nlet anchors: Tensor | null = null;\nlet inputSize = 0;\nlet inputSizeT: Tensor | null = null;\n\nexport interface DetectBox { startPoint: Point, endPoint: Point, landmarks: Point[], confidence: number, size: [number, number] }\n\nexport const size = () => inputSize;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.detector?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model['executor'] && model.inputs[0].shape) ? model.inputs[0].shape[2] : 256;\n inputSizeT = tf.scalar(inputSize, 'int32') as Tensor;\n anchors = tf.tensor2d(util.generateAnchors(inputSize)) as Tensor;\n return model;\n}\n\nfunction decodeBoxes(boxOutputs: Tensor) {\n if (!anchors || !inputSizeT) return tf.zeros([0, 0]);\n const t: Record = {};\n t.boxStarts = tf.slice(boxOutputs, [0, 1], [-1, 2]);\n t.centers = tf.add(t.boxStarts, anchors);\n t.boxSizes = tf.slice(boxOutputs, [0, 3], [-1, 2]);\n t.boxSizesNormalized = tf.div(t.boxSizes, inputSizeT);\n t.centersNormalized = tf.div(t.centers, inputSizeT);\n t.halfBoxSize = tf.div(t.boxSizesNormalized, constants.tf2);\n t.starts = tf.sub(t.centersNormalized, t.halfBoxSize);\n t.ends = tf.add(t.centersNormalized, t.halfBoxSize);\n t.startNormalized = tf.mul(t.starts, inputSizeT);\n t.endNormalized = tf.mul(t.ends, inputSizeT);\n const boxes = tf.concat2d([t.startNormalized as Tensor2D, t.endNormalized as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n\nexport async function getBoxes(inputImage: Tensor4D, config: Config): Promise {\n // sanity check on input\n if ((!inputImage) || (inputImage['isDisposedInternal']) || (inputImage.shape.length !== 4) || (inputImage.shape[1] < 1) || (inputImage.shape[2] < 1)) return [];\n const t: Record = {};\n let pad = [0, 0];\n let scale = [1, 1];\n if (config?.face?.detector?.square) {\n const xy = Math.max(inputImage.shape[2], inputImage.shape[1]);\n pad = [Math.floor((xy - inputImage.shape[2]) / 2), Math.floor((xy - inputImage.shape[1]) / 2)];\n t.padded = tf.pad(inputImage, [[0, 0], [pad[1], pad[1]], [pad[0], pad[0]], [0, 0]]);\n scale = [inputImage.shape[2] / xy, inputImage.shape[1] / xy];\n pad = [pad[0] / inputSize, pad[1] / inputSize];\n } else {\n t.padded = inputImage.clone();\n }\n t.resized = tf.image.resizeBilinear(t.padded as Tensor4D, [inputSize, inputSize]);\n t.div = tf.div(t.resized, constants.tf127);\n t.normalized = tf.sub(t.div, constants.tf1);\n const res = model?.execute(t.normalized) as Tensor[];\n if (Array.isArray(res) && res.length > 2) { // pinto converted model?\n const sorted = res.sort((a, b) => a.size - b.size);\n t.concat384 = tf.concat([sorted[0], sorted[2]], 2); // dim: 384, 1 + 16\n t.concat512 = tf.concat([sorted[1], sorted[3]], 2); // dim: 512, 1 + 16\n t.concat = tf.concat([t.concat512, t.concat384], 1);\n t.batch = tf.squeeze(t.concat, [0]);\n } else if (Array.isArray(res)) { // new facemesh-detection tfhub model\n t.batch = tf.squeeze(res[0]);\n } else { // original blazeface tfhub model\n t.batch = tf.squeeze(res);\n }\n tf.dispose(res);\n t.boxes = decodeBoxes(t.batch);\n t.logits = tf.slice(t.batch, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.logits);\n t.scores = tf.squeeze(t.sigmoid);\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.scores as Tensor1D, (config.face.detector?.maxDetected || 0), (config.face.detector?.iouThreshold || 0), (config.face.detector?.minConfidence || 0));\n const nms = await t.nms.array() as number[];\n const boxes: DetectBox[] = [];\n const scores = await t.scores.data();\n for (let i = 0; i < nms.length; i++) {\n const confidence = scores[nms[i]];\n if (confidence > (config.face.detector?.minConfidence || 0)) {\n const b: Record = {};\n b.bbox = tf.slice(t.boxes, [nms[i], 0], [1, -1]);\n b.slice = tf.slice(t.batch, [nms[i], keypointsCount - 1], [1, -1]);\n b.squeeze = tf.squeeze(b.slice);\n b.landmarks = tf.reshape(b.squeeze, [keypointsCount, -1]);\n const points = await b.bbox.data();\n const unpadded = [ // TODO fix this math\n points[0] * scale[0] - pad[0],\n points[1] * scale[1] - pad[1],\n points[2] * scale[0] - pad[0],\n points[3] * scale[1] - pad[1],\n ];\n const rawBox = {\n startPoint: [unpadded[0], unpadded[1]] as Point,\n endPoint: [unpadded[2], unpadded[3]] as Point,\n landmarks: (await b.landmarks.array()) as Point[],\n confidence,\n };\n b.anchor = tf.slice(anchors as Tensor, [nms[i], 0], [1, 2]);\n const anchor = await b.anchor.data();\n const scaledBox = util.scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize], anchor);\n const enlargedBox = util.enlargeBox(scaledBox, config.face.detector?.scale || 1.4);\n const squaredBox = util.squarifyBox(enlargedBox);\n if (squaredBox.size[0] > (config.face.detector?.['minSize'] || 0) && squaredBox.size[1] > (config.face.detector?.['minSize'] || 0)) boxes.push(squaredBox);\n Object.keys(b).forEach((tensor) => tf.dispose(b[tensor]));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return boxes;\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport * as coords from './facemeshcoords';\nimport * as util from './facemeshutil';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { Point } from '../result';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\n\nconst leftOutline = coords.meshAnnotations.leftEyeLower0;\nconst rightOutline = coords.meshAnnotations.rightEyeLower0;\n\nconst eyeLandmarks = {\n leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]],\n rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]],\n};\n\nconst irisLandmarks = {\n upperCenter: 3,\n lowerCenter: 4,\n index: 71,\n numCoordinates: 76,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.iris?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize === -1) inputSize = 64;\n return model;\n}\n\n// Replace the raw coordinates returned by facemesh with refined iris model coordinates and update the z coordinate to be an average of the original and the new.\nexport function replaceIrisCoords(rawCoords, newCoords, prefix, keys) {\n for (let i = 0; i < coords.irisIndices.length; i++) {\n const { key, indices } = coords.irisIndices[i];\n const originalIndices = coords.meshAnnotations[`${prefix}${key}`];\n if (!keys || keys.includes(key)) {\n for (let j = 0; j < indices.length; j++) {\n const index = indices[j];\n rawCoords[originalIndices[j]] = [\n newCoords[index][0],\n newCoords[index][1],\n (newCoords[index][2] + rawCoords[originalIndices[j]][2]) / 2,\n ];\n }\n }\n }\n}\n\nexport const getLeftToRightEyeDepthDifference = (rawCoords) => {\n const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];\n const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];\n return leftEyeZ - rightEyeZ;\n};\n\n// Returns a box describing a cropped region around the eye fit for passing to the iris model.\nexport const getEyeBox = (rawCoords, face, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false, scale = 2.3) => {\n const box = util.squarifyBox(util.enlargeBox(util.calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), scale));\n const boxSize = util.getBoxSize(box);\n let crop = tf.image.cropAndResize(face, [[\n box.startPoint[1] / meshSize,\n box.startPoint[0] / meshSize, box.endPoint[1] / meshSize,\n box.endPoint[0] / meshSize,\n ]], [0], [inputSize, inputSize]);\n if (flip && env.kernels.includes('flipleftright')) {\n const flipped = tf.image.flipLeftRight(crop); // flipLeftRight is not defined for tfjs-node\n tf.dispose(crop);\n crop = flipped;\n }\n return { box, boxSize, crop };\n};\n\n// Given a cropped image of an eye, returns the coordinates of the contours surrounding the eye and the iris.\nexport const getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {\n const eyeRawCoords: Point[] = [];\n for (let i = 0; i < irisLandmarks.numCoordinates; i++) {\n const x = eyeData[i * 3];\n const y = eyeData[i * 3 + 1];\n const z = eyeData[i * 3 + 2];\n eyeRawCoords.push([\n (flip ? (1 - (x / inputSize)) : (x / inputSize)) * eyeBoxSize[0] + eyeBox.startPoint[0],\n (y / inputSize) * eyeBoxSize[1] + eyeBox.startPoint[1], z,\n ]);\n }\n return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };\n};\n\n// The z-coordinates returned for the iris are unreliable, so we take the z values from the surrounding keypoints.\nexport const getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {\n const upperCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];\n const lowerCenterZ = rawCoords[coords.meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];\n const averageZ = (upperCenterZ + lowerCenterZ) / 2;\n // Iris indices: 0: center | 1: right | 2: above | 3: left | 4: below\n return irisCoords.map((coord, i) => {\n let z = averageZ;\n if (i === 2) {\n z = upperCenterZ;\n } else if (i === 4) {\n z = lowerCenterZ;\n }\n return [coord[0], coord[1], z];\n });\n};\n\nexport async function augmentIris(rawCoords, face, meshSize, config: Config) {\n if (!model?.['executor']) return rawCoords;\n const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true, config.face.iris?.scale || 2.3);\n const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true, config.face.iris?.scale || 2.3);\n const combined = tf.concat([leftEyeCrop, rightEyeCrop]);\n tf.dispose(leftEyeCrop);\n tf.dispose(rightEyeCrop);\n const eyePredictions = model.execute(combined) as Tensor;\n tf.dispose(combined);\n const eyePredictionsData = await eyePredictions.data();\n tf.dispose(eyePredictions);\n const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);\n const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);\n const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);\n const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false);\n const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);\n if (Math.abs(leftToRightEyeDepthDifference) < 30) { // User is looking straight ahead.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', null);\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', null);\n // If the user is looking to the left or to the right, the iris coordinates tend to diverge too much from the mesh coordinates for them to be merged so we only update a single contour line above and below the eye.\n } else if (leftToRightEyeDepthDifference < 1) { // User is looking towards the right.\n replaceIrisCoords(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']);\n } else { // User is looking towards the left.\n replaceIrisCoords(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']);\n }\n const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left');\n const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right');\n const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords);\n return newCoords;\n}\n", "import * as constants from './constants';\nimport type { Tensor } from '../tfjs/types';\n\nexport async function augment(rawCoords, results: Tensor[]) {\n const t: Record = { // all attention models produce 2d results so it needs to be later augmented with correct z-coords\n // mesh: results[0], // already have it in rawCoords // output_mesh_identity\n // flag: results[1], // already processed in parent // conv_faceflag\n lips: await results.filter((r) => r.size === 160)?.[0]?.data() as Float32Array, // 80 x 2d = 160 // output_lips\n irisL: await results.filter((r) => r.size === 10)?.[0]?.data() as Float32Array, // 5 x 2d = 10 // output_right_iris\n eyeL: await results.filter((r) => r.size === 142)?.[0]?.data() as Float32Array, // 71 x 2d = 142 // output_right_eye\n irisR: await results.filter((r) => r.size === 10)?.[1]?.data() as Float32Array, // 5 x 2d = 10 // output_left_iris\n eyeR: await results.filter((r) => r.size === 142)?.[1]?.data() as Float32Array, // 71 x 2d = 142// output_left_eye\n };\n for (const val of Object.values(t)) {\n if (!val) return rawCoords; // could not find tensor\n }\n\n // augment iris: adds additional 5 keypoints per eye\n const irisLDepth = constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisL.length / 2; i++) rawCoords.push([t.irisL[2 * i + 0], t.irisL[2 * i + 1], irisLDepth]);\n const irisRDepth = constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; // get average z-coord for iris\n for (let i = 0; i < t.irisR.length / 2; i++) rawCoords.push([t.irisR[2 * i + 0], t.irisR[2 * i + 1], irisRDepth]);\n\n // augment eyes: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.eyeL.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t.eyeL[2 * i + 0], t.eyeL[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]];\n for (let i = 0; i < t.eyeR.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t.eyeR[2 * i + 0], t.eyeR[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]];\n\n // augment lips: replaces eye keypoints based on heuristic mapping\n for (let i = 0; i < t.lips.length / 2; i++) rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t.lips[2 * i + 0], t.lips[2 * i + 1], rawCoords[constants.LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]];\n\n return rawCoords;\n}\n", "/**\n * BlazeFace, FaceMesh & Iris model implementation\n *\n * Based on:\n * - [**MediaPipe BlazeFace**](https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view)\n * - Facial Spacial Geometry: [**MediaPipe FaceMesh**](https://drive.google.com/file/d/1VFC_wIpw4O7xBOiTgUldl79d9LA-LsnA/view)\n * - Eye Iris Details: [**MediaPipe Iris**](https://drive.google.com/file/d/1bsWbokp9AklH2ANjCfmjqEzzxO1CNbMu/view)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport * as blazeface from './blazeface';\nimport * as util from './facemeshutil';\nimport * as coords from './facemeshcoords';\nimport * as iris from './iris';\nimport * as attention from './attention';\nimport { histogramEqualization } from '../image/enhance';\nimport { env } from '../util/env';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { FaceResult, FaceLandmark, Point } from '../result';\nimport type { Config } from '../config';\nimport type { DetectBox } from './blazeface';\n\nconst cache = {\n boxes: [] as DetectBox[],\n skipped: Number.MAX_SAFE_INTEGER,\n timestamp: 0,\n};\n\nlet model: GraphModel | null = null;\nlet inputSize = 0;\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n // reset cached boxes\n const skipTime = (config.face.detector?.skipTime || 0) > (now() - cache.timestamp);\n const skipFrame = cache.skipped < (config.face.detector?.skipFrames || 0);\n if (!config.skipAllowed || !skipTime || !skipFrame || cache.boxes.length === 0) {\n cache.boxes = await blazeface.getBoxes(input, config); // get results from blazeface detector\n cache.timestamp = now();\n cache.skipped = 0;\n } else {\n cache.skipped++;\n }\n const faces: FaceResult[] = [];\n const newCache: DetectBox[] = [];\n let id = 0;\n const size = inputSize;\n for (let i = 0; i < cache.boxes.length; i++) {\n const box = cache.boxes[i];\n let angle = 0;\n let rotationMatrix;\n const face: FaceResult = { // init face result\n id: id++,\n mesh: [],\n meshRaw: [],\n box: [0, 0, 0, 0],\n boxRaw: [0, 0, 0, 0],\n score: 0,\n boxScore: 0,\n faceScore: 0,\n size: [0, 0],\n // contoursRaw: [],\n // contours: [],\n annotations: {} as Record,\n };\n\n // optional rotation correction based on detector data only if mesh is disabled otherwise perform it later when we have more accurate mesh data. if no rotation correction this function performs crop\n [angle, rotationMatrix, face.tensor] = util.correctFaceRotation(config.face.detector?.rotation, box, input, config.face.mesh?.enabled ? inputSize : blazeface.size());\n if (config.filter.equalization) {\n const equilized = face.tensor ? await histogramEqualization(face.tensor) : undefined;\n tf.dispose(face.tensor);\n if (equilized) face.tensor = equilized;\n }\n face.boxScore = Math.round(100 * box.confidence) / 100;\n if (!config.face.mesh?.enabled || !model?.['executor']) { // mesh not enabled or not loaded, return resuts from detector only\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.score = face.boxScore;\n face.size = box.size;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n } else if (!model) { // mesh enabled, but not loaded\n if (config.debug) log('face mesh detection requested, but model is not loaded');\n } else { // mesh enabled\n if (config.face.attention?.enabled && !env.kernels.includes('atan2')) {\n config.face.attention.enabled = false;\n tf.dispose(face.tensor);\n return faces;\n }\n const results = model.execute(face.tensor as Tensor) as Tensor[];\n const confidenceT = results.find((t) => t.shape[t.shape.length - 1] === 1) as Tensor;\n const faceConfidence = await confidenceT.data();\n face.faceScore = Math.round(100 * faceConfidence[0]) / 100;\n if (face.faceScore < (config.face.detector?.minConfidence || 1)) { // low confidence in detected mesh\n box.confidence = face.faceScore; // reset confidence of cached box\n if (config.face.mesh['keepInvalid']) {\n face.box = util.clampBox(box, input);\n face.boxRaw = util.getRawBox(box, input);\n face.size = box.size;\n face.score = face.boxScore;\n face.mesh = box.landmarks;\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.blazeFaceLandmarks)) {\n face.annotations[key] = [face.mesh[coords.blazeFaceLandmarks[key] as number]]; // add annotations\n }\n }\n } else {\n const meshT = results.find((t) => t.shape[t.shape.length - 1] === 1404) as Tensor;\n const coordsReshaped = tf.reshape(meshT, [-1, 3]);\n let rawCoords = await coordsReshaped.array();\n tf.dispose(coordsReshaped);\n if (config.face.attention?.enabled) {\n rawCoords = await attention.augment(rawCoords, results); // augment iris results using attention model results\n } else if (config.face.iris?.enabled) {\n rawCoords = await iris.augmentIris(rawCoords, face.tensor, inputSize, config); // run iris model and augment results\n }\n face.mesh = util.transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize); // get processed mesh\n face.meshRaw = face.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size]);\n for (const key of Object.keys(coords.meshAnnotations)) face.annotations[key] = coords.meshAnnotations[key].map((index) => face.mesh[index]); // add annotations\n face.score = face.faceScore;\n const calculatedBox = {\n ...util.calculateFaceBox(face.mesh, box),\n confidence: box.confidence,\n landmarks: box.landmarks,\n size: box.size,\n };\n face.box = util.clampBox(calculatedBox, input);\n face.boxRaw = util.getRawBox(calculatedBox, input);\n face.size = calculatedBox.size;\n /*\n const contoursT = results.find((t) => t.shape[t.shape.length - 1] === 266) as Tensor;\n const contoursData = contoursT && await contoursT.data(); // 133 x 2d points\n face.contoursRaw = [];\n for (let j = 0; j < contoursData.length / 2; j++) face.contoursRaw.push([contoursData[2 * j + 0] / inputSize, contoursData[2 * j + 1] / inputSize]);\n face.contours = face.contoursRaw.map((c) => [Math.trunc((input.shape[2] || 1) * c[0]), Math.trunc((input.shape[1] || 1) * c[1])]);\n */\n newCache.push(calculatedBox);\n }\n tf.dispose(results);\n }\n if (face.score > (config.face.detector?.minConfidence || 1)) faces.push(face);\n else tf.dispose(face.tensor);\n }\n cache.boxes = newCache; // reset cache\n return faces;\n}\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (config.face.attention?.enabled && model?.['signature']) {\n if (Object.keys(model?.['signature']?.outputs || {}).length < 6) model = null;\n }\n if (!model) {\n if (config.face.attention?.enabled) model = await loadModel(config.face.attention.modelPath);\n else model = await loadModel(config.face.mesh?.modelPath);\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n inputSize = (model['executor'] && model?.inputs?.[0].shape) ? model?.inputs?.[0].shape[2] : 256;\n return model;\n}\n\nexport const triangulation = coords.TRI468;\nexport const uvmap = coords.UV468;\n", "/**\n * Emotion model implementation\n *\n * [**Oarriaga**](https://github.com/oarriaga/face_classification)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport type { Emotion } from '../result';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\n\nlet annotations: string[] = [];\nlet model: GraphModel | null;\nconst last: { score: number, emotion: Emotion }[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet rgb = false;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n model = await loadModel(config.face.emotion?.modelPath);\n rgb = model?.inputs?.[0].shape?.[3] === 3;\n if (!rgb) annotations = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']; // oarriaga and gear\n else annotations = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']; // affectnet\n } else if (config.debug) {\n log('cached model:', model['modelUrl']);\n }\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ score: number, emotion: Emotion }[]> {\n if (!model) return [];\n const skipFrame = skipped < (config.face.emotion?.skipFrames || 0);\n const skipTime = (config.face.emotion?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx] && (last[idx].length > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const obj: { score: number, emotion: Emotion }[] = [];\n if (config.face.emotion?.enabled) {\n const t: Record = {};\n const inputSize = model?.inputs[0].shape ? model.inputs[0].shape[2] : 0;\n if (config.face.emotion?.['crop'] > 0) { // optional crop\n const crop = config.face.emotion?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [inputSize, inputSize]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n }\n if (rgb) {\n t.mul = tf.mul(t.resize, 255);\n t.normalize = tf.sub(t.mul, [103.939, 116.779, 123.68]); // affectnet uses specific norm values\n t.emotion = model?.execute(t.normalize) as Tensor; // result is already in range 0..1, no need for additional activation\n } else {\n // [t.red, t.green, t.blue] = tf.split(t.resize, 3, 3);\n // weighted rgb to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n // t.redNorm = tf.mul(t.red, rgb[0]);\n // t.greenNorm = tf.mul(t.green, rgb[1]);\n // t.blueNorm = tf.mul(t.blue, rgb[2]);\n // t.grayscale = tf.addN([t.redNorm, t.greenNorm, t.blueNorm]);\n t.channels = tf.mul(t.resize, constants.rgb);\n t.grayscale = tf.sum(t.channels, 3, true);\n t.grayscaleSub = tf.sub(t.grayscale, constants.tf05);\n t.grayscaleMul = tf.mul(t.grayscaleSub, constants.tf2);\n t.emotion = model?.execute(t.grayscaleMul) as Tensor; // result is already in range 0..1, no need for additional activation\n }\n lastTime = now();\n const data = await t.emotion.data();\n for (let i = 0; i < data.length; i++) {\n if (data[i] > (config.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] as Emotion });\n }\n obj.sort((a, b) => b.score - a.score);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "/**\n * FaceRes model implementation\n *\n * Returns Age, Gender, Descriptor\n * Implements Face similarity function\n *\n * Based on: [**HSE-FaceRes**](https://github.com/HSE-asavchenko/HSE_FaceRec_tf)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel, Tensor4D, Tensor1D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport type { Gender, Race } from '../result';\n\nexport interface FaceRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nlet model: GraphModel | null;\nconst last: FaceRes[] = [];\n\nlet lastTime = 0;\nlet lastCount = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.description?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport function enhance(input, config: Config): Tensor {\n const tensor = (input.image || input.tensor || input) as Tensor4D; // input received from detector is already normalized to 0..1, input is also assumed to be straightened\n if (!model?.inputs[0].shape) return tensor; // model has no shape so no point continuing\n let crop: Tensor;\n if (config.face.description?.['crop'] > 0) { // optional crop\n const cropval = config.face.description?.['crop'];\n const box = [[cropval, cropval, 1 - cropval, 1 - cropval]];\n crop = tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n crop = tf.image.resizeBilinear(tensor, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n const norm: Tensor = tf.mul(crop, constants.tf255);\n tf.dispose(crop);\n return norm;\n /*\n // do a tight crop of image and resize it to fit the model\n const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n const crop = (tensor.shape.length === 3)\n ? tf.image.cropAndResize(tf.expandDims(tensor, 0), box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]) // add batch dimension if missing\n : tf.image.cropAndResize(tensor, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n */\n /*\n // convert to black&white to avoid colorization impact\n const rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\n const [red, green, blue] = tf.split(crop, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n const merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n */\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n const obj: FaceRes = {\n age: 0 as number,\n gender: 'unknown' as Gender,\n genderScore: 0 as number,\n descriptor: [] as number[],\n };\n if (!model?.['executor']) return obj;\n const skipFrame = skipped < (config.face.description?.skipFrames || 0);\n const skipTime = (config.face.description?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && (last?.[idx]?.age > 0) && (last?.[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (config.face.description?.enabled) {\n const enhanced = enhance(image, config);\n const resT = model?.execute(enhanced) as Tensor[];\n lastTime = now();\n tf.dispose(enhanced);\n const genderT = resT.find((t) => t.shape[1] === 1) as Tensor;\n const gender = await genderT.data();\n const confidence = Math.trunc(200 * Math.abs((gender[0] - 0.5))) / 100;\n if (confidence > (config.face.description.minConfidence || 0)) {\n obj.gender = gender[0] <= 0.5 ? 'female' : 'male';\n obj.genderScore = Math.min(0.99, confidence);\n }\n const argmax = tf.argMax(resT.find((t) => t.shape[1] === 100) as Tensor1D, 1);\n const ageIdx: number = (await argmax.data())[0];\n tf.dispose(argmax);\n const ageT = resT.find((t) => t.shape[1] === 100) as Tensor;\n const all = await ageT.data();\n obj.age = Math.round(all[ageIdx - 1] > all[ageIdx + 1] ? 10 * ageIdx - 100 * all[ageIdx - 1] : 10 * ageIdx + 100 * all[ageIdx + 1]) / 10;\n\n if (Number.isNaN(gender[0]) || Number.isNaN(all[0])) log('faceres error:', { model, result: resT });\n\n const desc = resT.find((t) => t.shape[1] === 1024);\n // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8\n // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor\n const descriptor = desc ? await desc.data() : [] as number[];\n obj.descriptor = Array.from(descriptor);\n resT.forEach((t) => tf.dispose(t));\n }\n last[idx] = obj;\n lastCount = count;\n resolve(obj);\n });\n}\n", "import type { Tensor } from '../tfjs/types';\nimport type { FaceResult } from '../result';\n// import * as tf from 'dist/tfjs.esm.js';\nimport { meshAnnotations } from './facemeshcoords';\n\nconst expandFact = 0.1;\nconst alpha = 0.5;\n\n// point inclusion in polygon based on https://wrf.ecse.rpi.edu/Research/Short_Notes/pnpoly.html\nfunction insidePoly(x: number, y: number, polygon: { x: number, y: number }[]): boolean {\n let inside = false;\n let j = polygon.length - 1;\n for (let i = 0; i < polygon.length; j = i++) {\n if (((polygon[i].y > y) !== (polygon[j].y > y)) && (x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x)) inside = !inside;\n }\n return inside;\n}\n\nexport async function mask(face: FaceResult): Promise {\n if (!face.tensor) return face.tensor;\n if (!face.mesh || face.mesh.length < 100) return face.tensor;\n const width = face.tensor.shape[2] || 0;\n const height = face.tensor.shape[1] || 0;\n const buffer = await face.tensor.buffer();\n let silhouette: { x: number, y: number }[] = [];\n for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face.mesh[pt][0] - face.box[0]) / face.box[2], y: (face.mesh[pt][1] - face.box[1]) / face.box[3] }); // add all silhouette points scaled to local box\n if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); // expand silhouette\n for (let x = 0; x < width; x++) {\n for (let y = 0; y < height; y++) {\n const inside = insidePoly(x / width, y / width, silhouette);\n if (!inside) {\n buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);\n buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);\n buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);\n }\n }\n }\n const output = buffer.toTensor();\n // tf.dispose(buffer);\n return output;\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.antispoof?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.antispoof?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.antispoof?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * Anti-spoofing model implementation\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst cached: number[] = [];\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastCount = 0;\nlet lastTime = 0;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.liveness?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model?.['executor']) return 0;\n const skipTime = (config.face.liveness?.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.face.liveness?.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && cached[idx]) {\n skipped++;\n return cached[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);\n const res = model?.execute(resize) as Tensor;\n const num = (await res.data())[0];\n cached[idx] = Math.round(100 * num) / 100;\n lastCount = count;\n lastTime = now();\n tf.dispose([resize, res]);\n resolve(cached[idx]);\n });\n}\n", "/**\n * GEAR [gender/emotion/age/race] model implementation\n *\n * Based on: [**GEAR Predictor**](https://github.com/Udolf15/GEAR-Predictor)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Gender, Race } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nexport interface GearType { age: number, gender: Gender, genderScore: number, race: { score: number, race: Race }[] }\nlet model: GraphModel | null;\nconst last: GearType[] = [];\nconst raceNames = ['white', 'black', 'asian', 'indian', 'other'];\nconst ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face.gear?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise {\n if (!model) return { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n const skipFrame = skipped < (config.face.gear?.skipFrames || 0);\n const skipTime = (config.face.gear?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n // t.resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape[2], model?.inputs[0].shape[1]], false);\n let box = [[0.0, 0.10, 0.90, 0.90]]; // empyrical values for top, left, bottom, right\n if (config.face.gear?.['crop'] > 0) { // optional crop config value\n const crop = config.face.gear?.['crop'];\n box = [[crop, crop, 1 - crop, 1 - crop]];\n }\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const obj: GearType = { age: 0, gender: 'unknown', genderScore: 0, race: [] };\n if (config.face.gear?.enabled) [t.age, t.gender, t.race] = model.execute(t.resize, ['age_output', 'gender_output', 'race_output']) as Tensor[];\n const gender = await t.gender.data();\n obj.gender = gender[0] > gender[1] ? 'male' : 'female';\n obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;\n const race = await t.race.data();\n for (let i = 0; i < race.length; i++) {\n if (race[i] > (config.face.gear?.minConfidence || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] as Race });\n }\n obj.race.sort((a, b) => b.score - a.score);\n // {0: 'Below20', 1: '21-25', 2: '26-30', 3: '31-40',4: '41-50', 5: '51-60', 6: 'Above60'}\n const ageDistribution = Array.from(await t.age.data());\n const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);\n let age = ageSorted[0][0]; // pick best starting point\n for (let i = 1; i < ageSorted.length; i++) age += ageSorted[i][1] * (ageSorted[i][0] - age); // adjust with each other choice by weight\n obj.age = Math.round(10 * age) / 10;\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Age model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { env } from '../util/env';\nimport { constants } from '../tfjs/constants';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\n\nlet model: GraphModel | null;\nconst last: { age: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet'].modelPathAge);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx: number, count: number): Promise<{ age: number }> {\n if (!model) return { age: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.age && (last[idx]?.age > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs || !model.inputs[0] || !model.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.mul(t.resize, constants.tf255);\n const obj = { age: 0 };\n if (config.face['ssrnet']?.enabled) t.age = model.execute(t.enhance) as Tensor;\n if (t.age) {\n const data = await t.age.data();\n obj.age = Math.trunc(10 * data[0]) / 10;\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * Gender model implementation\n *\n * Based on: [**SSR-Net**](https://github.com/shamangary/SSR-Net)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { Gender } from '../result';\nimport type { Config } from '../config';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: { gender: Gender, genderScore: number }[] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\n// tuning values\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale\n\nexport async function load(config: Config) {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['ssrnet']?.modelPathGender);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(image: Tensor4D, config: Config, idx, count): Promise<{ gender: Gender, genderScore: number }> {\n if (!model) return { gender: 'unknown', genderScore: 0 };\n const skipFrame = skipped < (config.face['ssrnet']?.skipFrames || 0);\n const skipTime = (config.face['ssrnet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipFrame && skipTime && (lastCount === count) && last[idx]?.gender && (last[idx]?.genderScore > 0)) {\n skipped++;\n return last[idx];\n }\n skipped = 0;\n return new Promise(async (resolve) => {\n if (!model?.inputs[0].shape) return;\n const t: Record = {};\n if (config.face['ssrnet']?.['crop'] > 0) { // optional crop\n const crop = config.face['ssrnet']?.['crop'];\n const box = [[crop, crop, 1 - crop, 1 - crop]];\n t.resize = tf.image.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n } else {\n t.resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false);\n }\n t.enhance = tf.tidy(() => {\n let normalize: Tensor;\n if (model?.inputs?.[0].shape?.[3] === 1) {\n const [red, green, blue] = tf.split(t.resize, 3, 3);\n const redNorm = tf.mul(red, rgb[0]);\n const greenNorm = tf.mul(green, rgb[1]);\n const blueNorm = tf.mul(blue, rgb[2]);\n const grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\n normalize = tf.mul(tf.sub(grayscale, constants.tf05), 2); // range grayscale:-1..1\n } else {\n normalize = tf.mul(tf.sub(t.resize, constants.tf05), 2); // range rgb:-1..1\n }\n return normalize;\n });\n const obj: { gender: Gender, genderScore: number } = { gender: 'unknown', genderScore: 0 };\n if (config.face['ssrnet']?.enabled) t.gender = model.execute(t.enhance) as Tensor;\n const data = await t.gender.data();\n obj.gender = data[0] > data[1] ? 'female' : 'male'; // returns two values 0..1, bigger one is prediction\n obj.genderScore = data[0] > data[1] ? (Math.trunc(100 * data[0]) / 100) : (Math.trunc(100 * data[1]) / 100);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n last[idx] = obj;\n lastCount = count;\n lastTime = now();\n resolve(obj);\n });\n}\n", "/**\n * MobileFaceNet model implementation\n *\n * Based on: [**BecauseofAI MobileFace**](https://github.com/becauseofAI/MobileFace)\n *\n * Obsolete and replaced by `faceres` that performs age/gender/descriptor analysis\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['mobilefacenet']?.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\n/*\n// convert to black&white to avoid colorization impact\nconst rgb = [0.2989, 0.5870, 0.1140]; // factors for red/green/blue colors when converting to grayscale: https://www.mathworks.com/help/matlab/ref/rgb2gray.html\nconst [red, green, blue] = tf.split(crop, 3, 3);\nconst redNorm = tf.mul(red, rgb[0]);\nconst greenNorm = tf.mul(green, rgb[1]);\nconst blueNorm = tf.mul(blue, rgb[2]);\nconst grayscale = tf.addN([redNorm, greenNorm, blueNorm]);\nconst merge = tf.stack([grayscale, grayscale, grayscale], 3).squeeze(4);\n\n// optional increase image contrast\n// or do it per-channel so mean is done on each channel\n// or do it based on histogram\nconst mean = merge.mean();\nconst factor = 5;\nconst contrast = merge.sub(mean).mul(factor).add(mean);\n*/\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['mobilefacenet']?.skipFrames || 0);\n const skipTime = (config.face['mobilefacenet']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['mobilefacenet']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n /*\n // optional normalize outputs with l2 normalization\n const scaled = tf.tidy(() => {\n const l2 = res.norm('euclidean');\n const scale = res.div(l2);\n return scale;\n });\n\n // optional reduce feature vector complexity\n const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2\n const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it\n */\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "/**\n * InsightFace model implementation\n *\n * Based on: [**DeepInsight InsightFace**](https://github.com/deepinsight/insightface)\n *\n * Alternative face embedding detection\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { Tensor, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nconst last: number[][] = [];\nlet lastCount = 0;\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) model = await loadModel(config.face['insightface'].modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config, idx, count): Promise {\n if (!model?.['executor']) return [];\n const skipFrame = skipped < (config.face['insightface']?.skipFrames || 0);\n const skipTime = (config.face['insightface']?.skipTime || 0) > (now() - lastTime);\n if (config.skipAllowed && skipTime && skipFrame && (lastCount === count) && last[idx]) {\n skipped++;\n return last[idx];\n }\n return new Promise(async (resolve) => {\n let data: number[] = [];\n if (config.face['insightface']?.enabled && model?.inputs[0].shape) {\n const t: Record = {};\n t.crop = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); // just resize to fit the embedding model\n // do a tight crop of image and resize it to fit the model\n // const box = [[0.05, 0.15, 0.85, 0.85]]; // empyrical values for top, left, bottom, right\n // t.crop = tf.image.cropAndResize(input, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n t.data = model.execute(t.crop) as Tensor;\n const output = await t.data.data();\n data = Array.from(output); // convert typed array to simple array\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n last[idx] = data;\n lastCount = count;\n lastTime = now();\n resolve(data);\n });\n}\n", "import type { Point, FaceResult } from '../result';\n\ntype Vector = [number, number, number];\n\nconst calculateGaze = (face: FaceResult): { bearing: number, strength: number } => {\n const radians = (pt1: Point, pt2: Point) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); // function to calculate angle between any two points\n if (!face.annotations.rightEyeIris || !face.annotations.leftEyeIris) return { bearing: 0, strength: 0 };\n\n const offsetIris = [0, -0.1]; // iris center may not align with average of eye extremes\n const eyeRatio = 1; // factor to normalize changes x vs y\n\n const left = (face.mesh[33][2] || 0) > (face.mesh[263][2] || 0); // pick left or right eye depending which one is closer bazed on outsize point z axis\n const irisCenter = left ? face.mesh[473] : face.mesh[468];\n const eyeCenter = left // eye center is average of extreme points on x axis for both x and y, ignoring y extreme points as eyelids naturally open/close more when gazing up/down so relative point is less precise\n ? [(face.mesh[133][0] + face.mesh[33][0]) / 2, (face.mesh[133][1] + face.mesh[33][1]) / 2]\n : [(face.mesh[263][0] + face.mesh[362][0]) / 2, (face.mesh[263][1] + face.mesh[362][1]) / 2];\n const eyeSize = left // eye size is difference between extreme points for both x and y, used to normalize & squarify eye dimensions\n ? [face.mesh[133][0] - face.mesh[33][0], face.mesh[23][1] - face.mesh[27][1]]\n : [face.mesh[263][0] - face.mesh[362][0], face.mesh[253][1] - face.mesh[257][1]];\n const eyeDiff: Point = [ // x distance between extreme point and center point normalized with eye size\n (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],\n eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1],\n ];\n let strength = Math.sqrt((eyeDiff[0] * eyeDiff[0]) + (eyeDiff[1] * eyeDiff[1])); // vector length is a diagonal between two differences\n strength = Math.min(strength, face.boxRaw[2] / 2, face.boxRaw[3] / 2); // limit strength to half of box size to avoid clipping due to low precision\n const bearing = (radians([0, 0], eyeDiff) + (Math.PI / 2)) % Math.PI; // using eyeDiff instead eyeCenter/irisCenter combo due to manual adjustments and rotate clockwise 90degrees\n return { bearing, strength };\n};\n\nexport const calculateFaceAngle = (face: FaceResult, imageSize: [number, number]): {\n angle: { pitch: number, yaw: number, roll: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n} => {\n // const degrees = (theta) => Math.abs(((theta * 180) / Math.PI) % 360);\n const normalize = (v: Vector): Vector => { // normalize vector\n const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);\n v[0] /= length;\n v[1] /= length;\n v[2] /= length;\n return v;\n };\n const subVectors = (a: Vector, b: Vector): Vector => { // vector subtraction (a - b)\n const x = a[0] - b[0];\n const y = a[1] - b[1];\n const z = a[2] - b[2];\n return [x, y, z];\n };\n const crossVectors = (a: Vector, b: Vector): Vector => { // vector cross product (a x b)\n const x = a[1] * b[2] - a[2] * b[1];\n const y = a[2] * b[0] - a[0] * b[2];\n const z = a[0] * b[1] - a[1] * b[0];\n return [x, y, z];\n };\n // 3x3 rotation matrix to Euler angles based on https://www.geometrictools.com/Documentation/EulerAngles.pdf\n const rotationMatrixToEulerAngle = (r: number[]): { pitch: number, yaw: number, roll: number } => {\n const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; // eslint-disable-line @typescript-eslint/no-unused-vars\n let thetaX: number;\n let thetaY: number;\n let thetaZ: number;\n if (r10 < 1) { // YZX calculation\n if (r10 > -1) {\n // thetaZ = Math.asin(r10);\n const cosThetaZ = Math.sqrt(r00 * r00 + r20 * r20); // \n thetaZ = Math.atan2(r10, cosThetaZ);\n thetaY = Math.atan2(-r20, r00);\n thetaX = Math.atan2(-r12, r11);\n } else {\n thetaZ = -Math.PI / 2;\n thetaY = -Math.atan2(r21, r22);\n thetaX = 0;\n }\n } else {\n thetaZ = Math.PI / 2;\n thetaY = Math.atan2(r21, r22);\n thetaX = 0;\n }\n if (Number.isNaN(thetaX)) thetaX = 0;\n if (Number.isNaN(thetaY)) thetaY = 0;\n if (Number.isNaN(thetaZ)) thetaZ = 0;\n // return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };\n return { pitch: -thetaX, yaw: -thetaY, roll: -thetaZ };\n };\n /*\n const meshToEulerAngle = (mesh) => { // simple Euler angle calculation based existing 3D mesh\n const radians = (a1, a2, b1, b2) => Math.atan2(b2 - a2, b1 - a1);\n return { // values are in radians in range of -pi/2 to pi/2 which is -90 to +90 degrees, value of 0 means center\n pitch: radians(mesh[10][1], mesh[10][2], mesh[152][1], mesh[152][2]), // looking at y,z of top and bottom points of the face // pitch is face move up/down\n yaw: radians(mesh[33][0], mesh[33][2], mesh[263][0], mesh[263][2]), // looking at x,z of outside corners of leftEye and rightEye // yaw is face turn left/right\n roll: radians(mesh[33][0], mesh[33][1], mesh[263][0], mesh[263][1]), // looking at x,y of outside corners of leftEye and rightEye // roll is face lean left/right\n };\n };\n */\n\n // initialize gaze and mesh\n const mesh = face.meshRaw;\n if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };\n\n const size = Math.max(face.boxRaw[2] * imageSize[0], face.boxRaw[3] * imageSize[1]) / 1.5;\n // top, bottom, left, right\n const pts: Point[] = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size, pt[1] * imageSize[1] / size, pt[2]] as Point); // make the xyz coordinates proportional, independent of the image/box size\n\n const yAxis = normalize(subVectors(pts[1] as Vector, pts[0] as Vector));\n let xAxis = normalize(subVectors(pts[3] as Vector, pts[2] as Vector));\n const zAxis = normalize(crossVectors(xAxis, yAxis));\n // adjust xAxis to make sure that all axes are perpendicular to each other\n xAxis = crossVectors(yAxis, zAxis);\n\n // Rotation Matrix from Axis Vectors - http://renderdan.blogspot.com/2006/05/rotation-matrix-from-axis-vectors.html\n // 3x3 rotation matrix is flatten to array in row-major order. Note that the rotation represented by this matrix is inverted.\n const matrix: [number, number, number, number, number, number, number, number, number] = [\n xAxis[0], xAxis[1], xAxis[2],\n yAxis[0], yAxis[1], yAxis[2],\n zAxis[0], zAxis[1], zAxis[2],\n ];\n const angle = rotationMatrixToEulerAngle(matrix);\n // const angle = meshToEulerAngle(mesh);\n\n // we have iris keypoints so we can calculate gaze direction\n const gaze = mesh.length === 478 ? calculateGaze(face) : { bearing: 0, strength: 0 };\n\n return { angle, matrix, gaze };\n};\n", "import type { FaceResult } from '../result';\n\nexport function calculateCameraDistance(face: FaceResult, width: number): number {\n // iris points are [center, left, top, right, bottom]\n // average size of human iris is 11.7mm - fairly constant for all ages/genders/races\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // distance of eye from camera in meters\n const cameraDistance = Math.round(1.17 / irisSize) / 100;\n return cameraDistance;\n}\n\nexport function calculateEyesDistance(face: FaceResult, width: number): number {\n // average distance between eyes is 65mm - fairly constant for typical adult male, but varies otherwise\n const f = face?.annotations;\n if (!f?.leftEyeIris || !f?.rightEyeIris) return 0;\n // get size of left and right iris in pixels, pick larger one as its likely to be more accurate and normalize to 0..1 range instead of pixels\n const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width;\n // pixel x and y distance of centers of left and right iris, you can use edges instead\n const irisDistanceXY = [f.leftEyeIris[0][0] - f.rightEyeIris[0][0], f.leftEyeIris[0][1] - f.rightEyeIris[0][1]];\n // absolute distance bewtween eyes in 0..1 range to account for head pitch (we can ignore yaw)\n const irisDistance = Math.sqrt((irisDistanceXY[0] * irisDistanceXY[0]) + (irisDistanceXY[1] * irisDistanceXY[1])) / width;\n // distance between eyes in meters\n const eyesDistance = Math.round(1.17 * irisDistance / irisSize) / 100;\n return eyesDistance;\n}\n", "/**\n * Face algorithm implementation\n * Uses FaceMesh, Emotion and FaceRes models to create a unified pipeline\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { env } from '../util/env';\nimport * as facemesh from './facemesh';\nimport * as emotion from '../gear/emotion';\nimport * as faceres from './faceres';\nimport * as mask from './mask';\nimport * as antispoof from './antispoof';\nimport * as liveness from './liveness';\nimport * as gear from '../gear/gear';\nimport * as ssrnetAge from '../gear/ssrnet-age';\nimport * as ssrnetGender from '../gear/ssrnet-gender';\nimport * as mobilefacenet from './mobilefacenet';\nimport * as insightface from './insightface';\nimport type { FaceResult, Emotion, Gender, Race } from '../result';\nimport type { Tensor4D } from '../tfjs/types';\nimport type { Human } from '../human';\nimport { calculateFaceAngle } from './angles';\nimport { calculateCameraDistance } from './anthropometry';\n\ninterface DescRes { age: number, gender: Gender, genderScore: number, descriptor: number[], race?: { score: number, race: Race }[] }\n\nexport const detectFace = async (instance: Human /* instance of human */, input: Tensor4D): Promise => {\n // run facemesh, includes blazeface and iris\n let timeStamp: number = now();\n let ageRes: { age: number } | Promise<{ age: number }> | null;\n let gearRes: gear.GearType | Promise | null;\n let genderRes: { gender: string, genderScore: number } | Promise<{ gender: string, genderScore: number }> | null;\n let emotionRes: { score: number, emotion: Emotion }[] | Promise<{ score: number, emotion: Emotion }[]>;\n let mobilefacenetRes: number[] | Promise | null;\n let insightfaceRes: number[] | Promise | null;\n let antispoofRes: number | Promise | null;\n let livenessRes: number | Promise | null;\n let descRes: DescRes | Promise | null;\n\n const faceRes: FaceResult[] = [];\n instance.state = 'run:face';\n const faces: FaceResult[] = await facemesh.predict(input, instance.config);\n instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n if (!input.shape || input.shape.length !== 4) return [];\n if (!faces) return [];\n // for (const face of faces) {\n for (let i = 0; i < faces.length; i++) {\n instance.analyze('Get Face');\n\n // is something went wrong, skip the face\n // @ts-ignore possibly undefied\n if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) {\n log('Face object is disposed:', faces[i].tensor);\n continue;\n }\n\n // optional face mask\n if (instance.config.face.detector?.mask) {\n const masked = await mask.mask(faces[i]);\n tf.dispose(faces[i].tensor);\n if (masked) faces[i].tensor = masked;\n }\n\n // calculate face angles\n const rotation = faces[i].mesh && (faces[i].mesh.length > 200) ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;\n\n // run emotion, inherits face from blazeface\n instance.analyze('Start Emotion:');\n if (instance.config.async) {\n emotionRes = instance.config.face.emotion?.enabled ? emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n } else {\n instance.state = 'run:emotion';\n timeStamp = now();\n emotionRes = instance.config.face.emotion?.enabled ? await emotion.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : [];\n instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Emotion:');\n\n // run antispoof, inherits face from blazeface\n instance.analyze('Start AntiSpoof:');\n if (instance.config.async) {\n antispoofRes = instance.config.face.antispoof?.enabled ? antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:antispoof';\n timeStamp = now();\n antispoofRes = instance.config.face.antispoof?.enabled ? await antispoof.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End AntiSpoof:');\n\n // run liveness, inherits face from blazeface\n instance.analyze('Start Liveness:');\n if (instance.config.async) {\n livenessRes = instance.config.face.liveness?.enabled ? liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n } else {\n instance.state = 'run:liveness';\n timeStamp = now();\n livenessRes = instance.config.face.liveness?.enabled ? await liveness.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : 0;\n instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Liveness:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start GEAR:');\n if (instance.config.async) {\n gearRes = instance.config.face.gear?.enabled ? gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:gear';\n timeStamp = now();\n gearRes = instance.config.face.gear?.enabled ? await gear.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.gear = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End GEAR:');\n\n // run gear, inherits face from blazeface\n instance.analyze('Start SSRNet:');\n if (instance.config.async) {\n ageRes = instance.config.face['ssrnet']?.enabled ? ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:ssrnet';\n timeStamp = now();\n ageRes = instance.config.face['ssrnet']?.enabled ? await ssrnetAge.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n genderRes = instance.config.face['ssrnet']?.enabled ? await ssrnetGender.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.ssrnet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End SSRNet:');\n\n // run mobilefacenet alternative, inherits face from blazeface\n instance.analyze('Start MobileFaceNet:');\n if (instance.config.async) {\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n mobilefacenetRes = instance.config.face['mobilefacenet']?.enabled ? await mobilefacenet.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End MobileFaceNet:');\n\n // run insightface alternative, inherits face from blazeface\n instance.analyze('Start InsightFace:');\n if (instance.config.async) {\n insightfaceRes = instance.config.face['insightface']?.enabled ? insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n } else {\n instance.state = 'run:mobilefacenet';\n timeStamp = now();\n insightfaceRes = instance.config.face['insightface']?.enabled ? await insightface.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length) : null;\n instance.performance.mobilefacenet = Math.trunc(now() - timeStamp);\n }\n instance.analyze('End InsightFace:');\n\n // run faceres, inherits face from blazeface\n instance.analyze('Start Description:');\n if (instance.config.async) {\n descRes = faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n } else {\n instance.state = 'run:description';\n timeStamp = now();\n descRes = await faceres.predict(faces[i].tensor as Tensor4D || tf.tensor([]), instance.config, i, faces.length);\n instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n instance.analyze('End Description:');\n\n // if async wait for results\n if (instance.config.async) {\n [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]);\n }\n instance.analyze('Finish Face:');\n\n if (instance.config.face['ssrnet']?.enabled && ageRes && genderRes) { // override age/gender if ssrnet model is used\n descRes = {\n ...(descRes as DescRes),\n age: (ageRes as { age: number}).age,\n gender: (genderRes as { gender: Gender, genderScore: number }).gender,\n genderScore: (genderRes as { gender: Gender, genderScore: number }).genderScore,\n };\n }\n if (instance.config.face.gear?.enabled && gearRes) { // override age/gender/race if gear model is used\n descRes = {\n ...(descRes as DescRes),\n age: (gearRes as gear.GearType).age,\n gender: (gearRes as gear.GearType).gender,\n genderScore: (gearRes as gear.GearType).genderScore,\n race: (gearRes as gear.GearType).race,\n };\n }\n if (instance.config.face['mobilefacenet']?.enabled && mobilefacenetRes) { // override descriptor if mobilefacenet model is used\n (descRes as DescRes).descriptor = mobilefacenetRes as number[];\n }\n\n if (instance.config.face['insightface']?.enabled && insightfaceRes) { // override descriptor if insightface model is used\n (descRes as DescRes).descriptor = insightfaceRes as number[];\n }\n\n const irisSize = instance.config.face.iris?.enabled ? calculateCameraDistance(faces[i], input.shape[2]) : 0;\n\n // optionally return tensor\n const tensor = instance.config.face.detector?.return ? tf.squeeze(faces[i].tensor as Tensor4D) : null;\n // dispose original face tensor\n tf.dispose(faces[i].tensor);\n // delete temp face image\n if (faces[i].tensor) delete faces[i].tensor;\n // combine results\n const res: FaceResult = {\n ...faces[i],\n id: i,\n };\n if ((descRes as DescRes).age) res.age = (descRes as DescRes).age;\n if ((descRes as DescRes).gender) res.gender = (descRes as DescRes).gender;\n if ((descRes as DescRes).genderScore) res.genderScore = (descRes as DescRes).genderScore;\n if ((descRes as DescRes).descriptor) res.embedding = (descRes as DescRes).descriptor;\n if ((descRes as DescRes).race) res.race = (descRes as DescRes).race as { score: number, race: Race }[];\n if (emotionRes) res.emotion = emotionRes as { score: number, emotion: Emotion }[];\n if (antispoofRes) res.real = antispoofRes as number;\n if (livenessRes) res.live = livenessRes as number;\n if (irisSize > 0) res.distance = irisSize;\n if (rotation) res.rotation = rotation;\n if (tensor) res.tensor = tensor;\n faceRes.push(res);\n instance.analyze('End Face');\n }\n instance.analyze('End FaceMesh:');\n if (instance.config.async) {\n if (instance.performance.face) delete instance.performance.face;\n if (instance.performance.age) delete instance.performance.age;\n if (instance.performance.gender) delete instance.performance.gender;\n if (instance.performance.emotion) delete instance.performance.emotion;\n }\n return faceRes;\n};\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nexport const Finger = {\n thumb: 0,\n index: 1,\n middle: 2,\n ring: 3,\n pinky: 4,\n all: [0, 1, 2, 3, 4], // just for convenience\n nameMapping: { 0: 'thumb', 1: 'index', 2: 'middle', 3: 'ring', 4: 'pinky' },\n // Describes mapping of joints based on the 21 points returned by handpose.\n // [0] Palm\n // [1-4] Thumb\n // [5-8] Index\n // [9-12] Middle\n // [13-16] Ring\n // [17-20] Pinky\n pointsMapping: {\n 0: [[0, 1], [1, 2], [2, 3], [3, 4]],\n 1: [[0, 5], [5, 6], [6, 7], [7, 8]],\n 2: [[0, 9], [9, 10], [10, 11], [11, 12]],\n 3: [[0, 13], [13, 14], [14, 15], [15, 16]],\n 4: [[0, 17], [17, 18], [18, 19], [19, 20]],\n },\n getName: (value) => Finger.nameMapping[value],\n getPoints: (value) => Finger.pointsMapping[value],\n};\n\nexport const FingerCurl = {\n none: 0,\n half: 1,\n full: 2,\n nameMapping: { 0: 'none', 1: 'half', 2: 'full' },\n getName: (value) => FingerCurl.nameMapping[value],\n};\n\nexport const FingerDirection = {\n verticalUp: 0,\n verticalDown: 1,\n horizontalLeft: 2,\n horizontalRight: 3,\n diagonalUpRight: 4,\n diagonalUpLeft: 5,\n diagonalDownRight: 6,\n diagonalDownLeft: 7,\n nameMapping: { 0: 'verticalUp', 1: 'verticalDown', 2: 'horizontalLeft', 3: 'horizontalRight', 4: 'diagonalUpRight', 5: 'diagonalUpLeft', 6: 'diagonalDownRight', 7: 'diagonalDownLeft' },\n getName: (value) => FingerDirection.nameMapping[value],\n};\n\nexport class FingerGesture {\n name;\n curls;\n directions;\n weights;\n weightsRelative;\n\n constructor(name) {\n // name (should be unique)\n this.name = name;\n this.curls = {};\n this.directions = {};\n this.weights = [1.0, 1.0, 1.0, 1.0, 1.0];\n this.weightsRelative = [1.0, 1.0, 1.0, 1.0, 1.0];\n }\n\n curl(finger, curl, confidence) {\n if (typeof this.curls[finger] === 'undefined') this.curls[finger] = [];\n this.curls[finger].push([curl, confidence]);\n }\n\n direction(finger, position, confidence) {\n if (!this.directions[finger]) this.directions[finger] = [];\n this.directions[finger].push([position, confidence]);\n }\n\n weight(finger, weight) {\n this.weights[finger] = weight;\n // recalculate relative weights\n const total = this.weights.reduce((a, b) => a + b, 0);\n this.weightsRelative = this.weights.map((el) => el * 5 / total);\n }\n\n matchAgainst(detectedCurls, detectedDirections) {\n let confidence = 0.0;\n // look at the detected curl of each finger and compare with\n // the expected curl of this finger inside current gesture\n for (const fingerIdx in detectedCurls) {\n const detectedCurl = detectedCurls[fingerIdx];\n const expectedCurls = this.curls[fingerIdx];\n if (typeof expectedCurls === 'undefined') {\n // no curl description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible curl of this specific finger\n for (const [expectedCurl, score] of expectedCurls) {\n if (detectedCurl === expectedCurl) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n // same for detected direction of each finger\n for (const fingerIdx in detectedDirections) {\n const detectedDirection = detectedDirections[fingerIdx];\n const expectedDirections = this.directions[fingerIdx];\n if (typeof expectedDirections === 'undefined') {\n // no direction description available for this finger\n // add default confidence of \"1\"\n confidence += this.weightsRelative[fingerIdx];\n continue;\n }\n // compare to each possible direction of this specific finger\n for (const [expectedDirection, score] of expectedDirections) {\n if (detectedDirection === expectedDirection) {\n confidence += score * this.weightsRelative[fingerIdx];\n break;\n }\n }\n }\n return confidence / 10;\n }\n}\n", "/**\n * FingerPose algorithm implementation\n * See `fingerpose.ts` for entry point\n */\n\nimport { Finger, FingerCurl, FingerDirection, FingerGesture } from './fingerdef';\n\nexport const { thumb, index, middle, ring, pinky } = Finger;\nexport const { none, half, full } = FingerCurl;\nexport const { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection;\n\n// describe thumbs up gesture \uD83D\uDC4D\nconst ThumbsUp = new FingerGesture('thumbs up');\nThumbsUp.curl(thumb, none, 1.0);\nThumbsUp.direction(thumb, verticalUp, 1.0);\nThumbsUp.direction(thumb, diagonalUpLeft, 0.25);\nThumbsUp.direction(thumb, diagonalUpRight, 0.25);\nfor (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) {\n ThumbsUp.curl(finger, full, 1.0);\n ThumbsUp.direction(finger, horizontalLeft, 1.0);\n ThumbsUp.direction(finger, horizontalRight, 1.0);\n}\n\n// describe Victory gesture \u270C\uFE0F\nconst Victory = new FingerGesture('victory');\nVictory.curl(thumb, half, 0.5);\nVictory.curl(thumb, none, 0.5);\nVictory.direction(thumb, verticalUp, 1.0);\nVictory.direction(thumb, diagonalUpLeft, 1.0);\nVictory.curl(index, none, 1.0);\nVictory.direction(index, verticalUp, 0.75);\nVictory.direction(index, diagonalUpLeft, 1.0);\nVictory.curl(middle, none, 1.0);\nVictory.direction(middle, verticalUp, 1.0);\nVictory.direction(middle, diagonalUpLeft, 0.75);\nVictory.curl(ring, full, 1.0);\nVictory.direction(ring, verticalUp, 0.2);\nVictory.direction(ring, diagonalUpLeft, 1.0);\nVictory.direction(ring, horizontalLeft, 0.2);\nVictory.curl(pinky, full, 1.0);\nVictory.direction(pinky, verticalUp, 0.2);\nVictory.direction(pinky, diagonalUpLeft, 1.0);\nVictory.direction(pinky, horizontalLeft, 0.2);\nVictory.weight(index, 2);\nVictory.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst Point = new FingerGesture('point');\nPoint.curl(thumb, full, 1.0);\nPoint.curl(index, none, 0.5);\nPoint.curl(middle, full, 0.5);\nPoint.curl(ring, full, 0.5);\nPoint.curl(pinky, full, 0.5);\nPoint.weight(index, 2);\nPoint.weight(middle, 2);\n\n// describe Point gesture \u270C\uFE0F\nconst MiddleFinger = new FingerGesture('middle finger');\nMiddleFinger.curl(thumb, none, 1.0);\nMiddleFinger.curl(index, full, 0.5);\nMiddleFinger.curl(middle, full, 0.5);\nMiddleFinger.curl(ring, full, 0.5);\nMiddleFinger.curl(pinky, full, 0.5);\nMiddleFinger.weight(index, 2);\nMiddleFinger.weight(middle, 2);\n\n// describe Open Palm gesture \u270C\uFE0F\nconst OpenPalm = new FingerGesture('open palm');\nOpenPalm.curl(thumb, none, 0.75);\nOpenPalm.curl(index, none, 0.75);\nOpenPalm.curl(middle, none, 0.75);\nOpenPalm.curl(ring, none, 0.75);\nOpenPalm.curl(pinky, none, 0.75);\n\nexport default [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm];\n", "/**\n * FingerPose algorithm implementation constants\n *\n * Based on: [**FingerPose***](https://github.com/andypotato/fingerpose)\n */\n\n/* eslint-disable camelcase */\n\nimport { Finger, FingerCurl, FingerDirection } from './fingerdef';\nimport Gestures from '../hand/fingergesture';\n\nconst minConfidence = 0.7;\nconst options = {\n // curl estimation\n HALF_CURL_START_LIMIT: 60.0,\n NO_CURL_START_LIMIT: 130.0,\n // direction estimation\n DISTANCE_VOTE_POWER: 1.1,\n SINGLE_ANGLE_VOTE_POWER: 0.9,\n TOTAL_ANGLE_VOTE_POWER: 1.6,\n};\n\nfunction calculateSlope(point1x, point1y, point2x, point2y) {\n const value = (point1y - point2y) / (point1x - point2x);\n let slope = Math.atan(value) * 180 / Math.PI;\n if (slope <= 0) slope = -slope;\n else if (slope > 0) slope = 180 - slope;\n return slope;\n}\n\n// point1, point2 are 2d or 3d point arrays (xy[z])\n// returns either a single scalar (2d) or array of two slopes (3d)\nfunction getSlopes(point1, point2) {\n if (!point1 || !point2) return [0, 0];\n const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);\n if (point1.length === 2) return slopeXY;\n const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);\n return [slopeXY, slopeYZ];\n}\n\nfunction angleOrientationAt(angle, weightageAt = 1.0) {\n let isVertical = 0;\n let isDiagonal = 0;\n let isHorizontal = 0;\n if (angle >= 75.0 && angle <= 105.0) isVertical = 1 * weightageAt;\n else if (angle >= 25.0 && angle <= 155.0) isDiagonal = 1 * weightageAt;\n else isHorizontal = 1 * weightageAt;\n return [isVertical, isDiagonal, isHorizontal];\n}\n\nfunction estimateFingerCurl(startPoint, midPoint, endPoint) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const start_mid_z_dist = startPoint[2] - midPoint[2];\n const start_end_z_dist = startPoint[2] - endPoint[2];\n const mid_end_z_dist = midPoint[2] - endPoint[2];\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);\n let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);\n if (cos_in > 1.0) cos_in = 1.0;\n else if (cos_in < -1.0) cos_in = -1.0;\n let angleOfCurve = Math.acos(cos_in);\n angleOfCurve = (57.2958 * angleOfCurve) % 180;\n let fingerCurl;\n if (angleOfCurve > options.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none;\n else if (angleOfCurve > options.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half;\n else fingerCurl = FingerCurl.full;\n return fingerCurl;\n}\n\nfunction estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n if (max_dist_x === Math.abs(start_end_x_dist)) {\n if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else if (max_dist_x === Math.abs(start_mid_x_dist)) {\n if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n } else {\n if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft;\n else estimatedDirection = FingerDirection.horizontalRight;\n }\n return estimatedDirection;\n}\n\nfunction estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {\n let estimatedDirection;\n if (max_dist_y === Math.abs(start_end_y_dist)) {\n if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else if (max_dist_y === Math.abs(start_mid_y_dist)) {\n if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n } else {\n if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown;\n else estimatedDirection = FingerDirection.verticalUp;\n }\n return estimatedDirection;\n}\n\nfunction estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {\n let estimatedDirection;\n const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n if (reqd_vertical_direction === FingerDirection.verticalUp) {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft;\n else estimatedDirection = FingerDirection.diagonalUpRight;\n } else {\n if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft;\n else estimatedDirection = FingerDirection.diagonalDownRight;\n }\n return estimatedDirection;\n}\n\nfunction calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {\n const start_mid_x_dist = startPoint[0] - midPoint[0];\n const start_end_x_dist = startPoint[0] - endPoint[0];\n const mid_end_x_dist = midPoint[0] - endPoint[0];\n const start_mid_y_dist = startPoint[1] - midPoint[1];\n const start_end_y_dist = startPoint[1] - endPoint[1];\n const mid_end_y_dist = midPoint[1] - endPoint[1];\n const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));\n const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));\n let voteVertical = 0.0;\n let voteDiagonal = 0.0;\n let voteHorizontal = 0.0;\n const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001);\n if (start_end_x_y_dist_ratio > 1.5) voteVertical += options.DISTANCE_VOTE_POWER;\n else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options.DISTANCE_VOTE_POWER;\n else voteHorizontal += options.DISTANCE_VOTE_POWER;\n const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);\n const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);\n const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);\n const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);\n let calc_start_point_x = startPoint[0];\n let calc_start_point_y = startPoint[1];\n let calc_end_point_x = endPoint[0];\n let calc_end_point_y = endPoint[1];\n if (max_dist === start_mid_dist) {\n calc_end_point_x = endPoint[0];\n calc_end_point_y = endPoint[1];\n } else if (max_dist === mid_end_dist) {\n calc_start_point_x = midPoint[0];\n calc_start_point_y = midPoint[1];\n }\n const calcStartPoint = [calc_start_point_x, calc_start_point_y];\n const calcEndPoint = [calc_end_point_x, calc_end_point_y];\n const totalAngle = getSlopes(calcStartPoint, calcEndPoint);\n const votes = angleOrientationAt(totalAngle, options.TOTAL_ANGLE_VOTE_POWER);\n voteVertical += votes[0];\n voteDiagonal += votes[1];\n voteHorizontal += votes[2];\n for (const fingerSlope of fingerSlopes) {\n const fingerVotes = angleOrientationAt(fingerSlope, options.SINGLE_ANGLE_VOTE_POWER);\n voteVertical += fingerVotes[0];\n voteDiagonal += fingerVotes[1];\n voteHorizontal += fingerVotes[2];\n }\n // in case of tie, highest preference goes to Vertical,\n // followed by horizontal and then diagonal\n let estimatedDirection;\n if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);\n } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {\n estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n } else {\n estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);\n }\n return estimatedDirection;\n}\n\nfunction estimate(landmarks) {\n // step 1: calculate slopes\n const slopesXY: number[][] = [];\n const slopesYZ: number[][] = [];\n const fingerCurls: number[] = [];\n const fingerDirections: number[] = [];\n if (!landmarks) return { curls: fingerCurls, directions: fingerDirections };\n\n // step 1: calculate slopes\n for (const finger of Finger.all) {\n const points = Finger.getPoints(finger);\n const slopeAtXY: number[] = [];\n const slopeAtYZ: number[] = [];\n for (const point of points) {\n const point1 = landmarks[point[0]];\n const point2 = landmarks[point[1]];\n // calculate single slope\n const slopes = getSlopes(point1, point2);\n const slopeXY = slopes[0];\n const slopeYZ = slopes[1];\n slopeAtXY.push(slopeXY);\n slopeAtYZ.push(slopeYZ);\n }\n slopesXY.push(slopeAtXY);\n slopesYZ.push(slopeAtYZ);\n }\n\n // step 2: calculate orientations\n for (const finger of Finger.all) {\n // start finger predictions from palm - except for thumb\n const pointIndexAt = (finger === Finger.thumb) ? 1 : 0;\n const fingerPointsAt = Finger.getPoints(finger);\n const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];\n const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];\n const endPoint = landmarks[fingerPointsAt[3][1]];\n // check if finger is curled\n const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);\n const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));\n fingerCurls[finger] = fingerCurled;\n fingerDirections[finger] = fingerPosition;\n }\n return { curls: fingerCurls, directions: fingerDirections };\n}\n\nexport function analyze(keypoints) { // get estimations of curl / direction for each finger\n if (!keypoints || keypoints.length === 0) return null;\n const estimatorRes = estimate(keypoints);\n const landmarks = {};\n for (const fingerIdx of Finger.all) {\n landmarks[Finger.getName(fingerIdx)] = {\n curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),\n direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]),\n };\n }\n return landmarks;\n}\n\nexport function match(keypoints) { // compare gesture description to each known gesture\n const poses: { name: string, confidence: number }[] = [];\n if (!keypoints || keypoints.length === 0) return poses;\n const estimatorRes = estimate(keypoints);\n for (const gesture of Gestures) {\n const confidence = gesture.matchAgainst(estimatorRes.curls, estimatorRes.directions);\n if (confidence >= minConfidence) poses.push({ name: gesture.name, confidence });\n }\n return poses;\n}\n", "/**\n * Gesture detection algorithm\n */\n\nimport type { GestureResult, BodyResult, FaceResult, HandResult, Point } from '../result';\nimport * as fingerPose from '../hand/fingerpose';\n\n/** face gesture type */\nexport type FaceGesture =\n `facing ${'left' | 'center' | 'right'}`\n | `blink ${'left' | 'right'} eye`\n | `mouth ${number}% open`\n | `head ${'up' | 'down'}`;\n\n/** iris gesture type */\nexport type IrisGesture =\n 'facing center'\n | `looking ${'left' | 'right' | 'up' | 'down'}`\n | 'looking center';\n\n/** body gesture type */\nexport type BodyGesture =\n `leaning ${'left' | 'right'}`\n | `raise ${'left' | 'right'} hand`\n | 'i give up';\n\n/** hand gesture type */\nexport type HandGesture =\n `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} forward`\n | `${'thumb' | 'index' | 'middle' | 'ring' | 'pinky'} up`\n | 'victory'\n | 'thumbs up';\n\nexport const body = (res: BodyResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { body: number, gesture: BodyGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n // raising hands\n const leftWrist = res[i].keypoints.find((a) => (a.part === 'leftWrist'));\n const rightWrist = res[i].keypoints.find((a) => (a.part === 'rightWrist'));\n const nose = res[i].keypoints.find((a) => (a.part === 'nose'));\n if (nose && leftWrist && rightWrist && (leftWrist.position[1] < nose.position[1]) && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'i give up' });\n else if (nose && leftWrist && (leftWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise left hand' });\n else if (nose && rightWrist && (rightWrist.position[1] < nose.position[1])) gestures.push({ body: i, gesture: 'raise right hand' });\n\n // leaning\n const leftShoulder = res[i].keypoints.find((a) => (a.part === 'leftShoulder'));\n const rightShoulder = res[i].keypoints.find((a) => (a.part === 'rightShoulder'));\n if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) {\n gestures.push({ body: i, gesture: `leaning ${(leftShoulder.position[1] > rightShoulder.position[1]) ? 'left' : 'right'}` });\n }\n }\n return gestures;\n};\n\nexport const face = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { face: number, gesture: FaceGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (res[i].mesh && res[i].mesh.length > 450) {\n const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0);\n const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0];\n if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: 'facing center' });\n else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? 'left' : 'right'}` });\n const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openLeft < 0.2) gestures.push({ face: i, gesture: 'blink left eye' });\n const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); // center of eye inner lid y coord div center of wider eye border y coord\n if (openRight < 0.2) gestures.push({ face: i, gesture: 'blink right eye' });\n const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1]));\n if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` });\n const chinDepth = res[i].mesh[152][2] || 0;\n if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? 'up' : 'down'}` });\n }\n }\n return gestures;\n};\n\nexport const iris = (res: FaceResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { iris: number, gesture: IrisGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n if (!res[i].annotations?.leftEyeIris?.[0] || !res[i].annotations?.rightEyeIris?.[0]) continue;\n const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0];\n const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1];\n const areaLeft = Math.abs(sizeXLeft * sizeYLeft);\n\n const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0];\n const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1];\n const areaRight = Math.abs(sizeXRight * sizeYRight);\n\n let center = false;\n const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight);\n if (difference < 0.25) {\n center = true;\n gestures.push({ iris: i, gesture: 'facing center' });\n }\n\n const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2];\n const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2];\n if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false;\n if (leftIrisCenterX > rightIrisCenterX) { // check eye with bigger offset\n if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking right' });\n } else {\n if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: 'looking left' });\n }\n\n const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3];\n const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3];\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false;\n if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: 'looking down' });\n if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: 'looking up' });\n\n // still center;\n if (center) gestures.push({ iris: i, gesture: 'looking center' });\n }\n return gestures;\n};\n\nexport const hand = (res: HandResult[]): GestureResult[] => {\n if (!res) return [];\n const gestures: { hand: number, gesture: HandGesture }[] = [];\n for (let i = 0; i < res.length; i++) {\n const fingers: { name: string, position: Point }[] = [];\n if (res[i].annotations) {\n for (const [finger, pos] of Object.entries(res[i].annotations)) {\n if (finger !== 'palmBase' && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); // get tip of each finger\n }\n }\n if (fingers && fingers.length > 0) {\n const closest = fingers.reduce((best, a) => ((best.position[2] || 0) < (a.position[2] || 0) ? best : a));\n gestures.push({ hand: i, gesture: `${closest.name} forward` as HandGesture });\n const highest = fingers.reduce((best, a) => (best.position[1] < a.position[1] ? best : a));\n gestures.push({ hand: i, gesture: `${highest.name} up` as HandGesture });\n }\n if (res[i].keypoints) {\n const poses = fingerPose.match(res[i].keypoints);\n for (const pose of poses) gestures.push({ hand: i, gesture: pose.name as HandGesture });\n }\n }\n return gestures;\n};\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { Point } from '../result';\n\nexport function getBoxSize(box) {\n return [\n Math.abs(box.endPoint[0] - box.startPoint[0]),\n Math.abs(box.endPoint[1] - box.startPoint[1]),\n ];\n}\n\nexport function getBoxCenter(box) {\n return [\n box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,\n box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2,\n ];\n}\n\nexport function cutBoxFromImageAndResize(box, image, cropSize) {\n const h = image.shape[1];\n const w = image.shape[2];\n const boxes = [[\n box.startPoint[1] / h,\n box.startPoint[0] / w,\n box.endPoint[1] / h,\n box.endPoint[0] / w,\n ]];\n return tf.image.cropAndResize(image, boxes, [0], cropSize);\n}\n\nexport function scaleBoxCoordinates(box, factor) {\n const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]] as Point;\n const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]] as Point;\n const palmLandmarks = box.palmLandmarks.map((coord) => {\n const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]];\n return scaledCoord;\n });\n return { startPoint, endPoint, palmLandmarks, confidence: box.confidence };\n}\n\nexport function enlargeBox(box, factor = 1.5) {\n const center = getBoxCenter(box);\n const size = getBoxSize(box);\n const newHalfSize = [factor * size[0] / 2, factor * size[1] / 2];\n const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]] as Point;\n const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function squarifyBox(box) {\n const centers = getBoxCenter(box);\n const size = getBoxSize(box);\n const maxEdge = Math.max(...size);\n const halfSize = maxEdge / 2;\n const startPoint = [centers[0] - halfSize, centers[1] - halfSize] as Point;\n const endPoint = [centers[0] + halfSize, centers[1] + halfSize] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function shiftBox(box, shiftFactor) {\n const boxSize = [\n box.endPoint[0] - box.startPoint[0],\n box.endPoint[1] - box.startPoint[1],\n ];\n const shiftVector = [boxSize[0] * shiftFactor[0], boxSize[1] * shiftFactor[1]];\n const startPoint = [box.startPoint[0] + shiftVector[0], box.startPoint[1] + shiftVector[1]] as Point;\n const endPoint = [box.endPoint[0] + shiftVector[0], box.endPoint[1] + shiftVector[1]] as Point;\n return { startPoint, endPoint, palmLandmarks: box.palmLandmarks };\n}\n\nexport function normalizeRadians(angle) {\n return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI));\n}\n\nexport function computeRotation(point1, point2) {\n const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]);\n return normalizeRadians(radians);\n}\n\nexport const buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]];\n\nexport function dot(v1, v2) {\n let product = 0;\n for (let i = 0; i < v1.length; i++) {\n product += v1[i] * v2[i];\n }\n return product;\n}\n\nexport function getColumnFrom2DArr(arr, columnIndex) {\n const column: number[] = [];\n for (let i = 0; i < arr.length; i++) {\n column.push(arr[i][columnIndex]);\n }\n return column;\n}\n\nexport function multiplyTransformMatrices(mat1, mat2) {\n const product: number[][] = [];\n const size = mat1.length;\n for (let row = 0; row < size; row++) {\n product.push([]);\n for (let col = 0; col < size; col++) {\n product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col)));\n }\n }\n return product;\n}\n\nexport function buildRotationMatrix(rotation, center) {\n const cosA = Math.cos(rotation);\n const sinA = Math.sin(rotation);\n const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]];\n const translationMatrix = buildTranslationMatrix(center[0], center[1]);\n const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix);\n const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]);\n return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix);\n}\n\nexport function invertTransformMatrix(matrix) {\n const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]];\n const translationComponent = [matrix[0][2], matrix[1][2]];\n const invertedTranslation = [\n -dot(rotationComponent[0], translationComponent),\n -dot(rotationComponent[1], translationComponent),\n ];\n return [\n rotationComponent[0].concat(invertedTranslation[0]),\n rotationComponent[1].concat(invertedTranslation[1]),\n [0, 0, 1],\n ];\n}\n\nexport function rotatePoint(homogeneousCoordinate, rotationMatrix) {\n return [\n dot(homogeneousCoordinate, rotationMatrix[0]),\n dot(homogeneousCoordinate, rotationMatrix[1]),\n ];\n}\n", "/**\n * HandPose model implementation constants\n * See `handpose.ts` for entry point\n */\n\nexport const anchors = [\n { x: 0.015625, y: 0.015625 },\n { x: 0.015625, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.046875, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.078125, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.109375, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.140625, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.171875, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.203125, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.234375, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.265625, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.296875, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.328125, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.359375, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.390625, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.421875, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.453125, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.484375, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.515625, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.546875, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.578125, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.609375, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.640625, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.671875, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.703125, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.734375, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.765625, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.796875, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.828125, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.859375, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.890625, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.921875, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.953125, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.984375, y: 0.015625 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.015625, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.046875, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.078125, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.109375, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.140625, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.171875, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.203125, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.234375, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.265625, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.296875, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.328125, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.359375, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.390625, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.421875, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.453125, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.484375, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.515625, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.546875, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.578125, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.609375, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.640625, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.671875, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.703125, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.734375, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.765625, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.796875, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.828125, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.859375, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.890625, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.921875, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.953125, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.984375, y: 0.046875 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.015625, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.046875, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.078125, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.109375, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.140625, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.171875, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.203125, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.234375, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.265625, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.296875, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.328125, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.359375, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.390625, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.421875, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.453125, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.484375, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.515625, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.546875, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.578125, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.609375, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.640625, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.671875, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.703125, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.734375, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.765625, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.796875, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.828125, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.859375, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.890625, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.921875, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.953125, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.984375, y: 0.078125 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.015625, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.046875, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.078125, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.109375, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.140625, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.171875, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.203125, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.234375, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.265625, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.296875, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.328125, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.359375, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.390625, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.421875, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.453125, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.484375, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.515625, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.546875, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.578125, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.609375, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.640625, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.671875, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.703125, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.734375, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.765625, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.796875, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.828125, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.859375, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.890625, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.921875, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.953125, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.984375, y: 0.109375 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.015625, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.046875, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.078125, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.109375, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.140625, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.171875, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.203125, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.234375, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.265625, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.296875, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.328125, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.359375, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.390625, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.421875, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.453125, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.484375, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.515625, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.546875, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.578125, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.609375, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.640625, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.671875, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.703125, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.734375, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.765625, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.796875, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.828125, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.859375, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.890625, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.921875, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.953125, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.984375, y: 0.140625 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.015625, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.046875, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.078125, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.109375, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.140625, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.171875, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.203125, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.234375, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.265625, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.296875, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.328125, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.359375, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.390625, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.421875, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.453125, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.484375, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.515625, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.546875, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.578125, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.609375, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.640625, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.671875, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.703125, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.734375, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.765625, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.796875, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.828125, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.859375, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.890625, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.921875, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.953125, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.984375, y: 0.171875 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.015625, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.046875, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.078125, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.109375, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.140625, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.171875, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.203125, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.234375, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.265625, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.296875, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.328125, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.359375, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.390625, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.421875, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.453125, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.484375, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.515625, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.546875, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.578125, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.609375, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.640625, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.671875, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.703125, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.734375, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.765625, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.796875, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.828125, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.859375, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.890625, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.921875, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.953125, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.984375, y: 0.203125 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.015625, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.046875, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.078125, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.109375, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.140625, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.171875, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.203125, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.234375, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.265625, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.296875, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.328125, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.359375, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.390625, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.421875, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.453125, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.484375, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.515625, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.546875, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.578125, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.609375, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.640625, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.671875, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.703125, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.734375, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.765625, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.796875, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.828125, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.859375, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.890625, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.921875, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.953125, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.984375, y: 0.234375 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.015625, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.046875, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.078125, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.109375, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.140625, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.171875, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.203125, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.234375, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.265625, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.296875, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.328125, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.359375, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.390625, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.421875, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.453125, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.484375, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.515625, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.546875, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.578125, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.609375, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.640625, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.671875, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.703125, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.734375, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.765625, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.796875, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.828125, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.859375, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.890625, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.921875, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.953125, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.984375, y: 0.265625 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.015625, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.046875, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.078125, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.109375, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.140625, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.171875, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.203125, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.234375, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.265625, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.296875, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.328125, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.359375, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.390625, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.421875, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.453125, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.484375, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.515625, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.546875, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.578125, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.609375, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.640625, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.671875, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.703125, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.734375, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.765625, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.796875, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.828125, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.859375, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.890625, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.921875, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.953125, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.984375, y: 0.296875 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.015625, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.046875, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.078125, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.109375, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.140625, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.171875, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.203125, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.234375, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.265625, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.296875, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.328125, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.359375, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.390625, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.421875, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.453125, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.484375, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.515625, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.546875, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.578125, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.609375, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.640625, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.671875, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.703125, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.734375, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.765625, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.796875, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.828125, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.859375, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.890625, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.921875, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.953125, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.984375, y: 0.328125 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.015625, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.046875, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.078125, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.109375, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.140625, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.171875, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.203125, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.234375, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.265625, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.296875, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.328125, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.359375, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.390625, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.421875, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.453125, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.484375, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.515625, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.546875, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.578125, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.609375, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.640625, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.671875, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.703125, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.734375, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.765625, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.796875, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.828125, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.859375, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.890625, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.921875, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.953125, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.984375, y: 0.359375 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.015625, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.046875, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.078125, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.109375, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.140625, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.171875, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.203125, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.234375, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.265625, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.296875, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.328125, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.359375, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.390625, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.421875, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.453125, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.484375, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.515625, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.546875, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.578125, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.609375, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.640625, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.671875, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.703125, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.734375, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.765625, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.796875, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.828125, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.859375, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.890625, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.921875, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.953125, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.984375, y: 0.390625 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.015625, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.046875, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.078125, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.109375, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.140625, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.171875, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.203125, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.234375, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.265625, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.296875, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.328125, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.359375, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.390625, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.421875, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.453125, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.484375, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.515625, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.546875, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.578125, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.609375, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.640625, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.671875, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.703125, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.734375, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.765625, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.796875, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.828125, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.859375, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.890625, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.921875, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.953125, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.984375, y: 0.421875 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.015625, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.046875, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.078125, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.109375, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.140625, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.171875, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.203125, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.234375, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.265625, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.296875, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.328125, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.359375, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.390625, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.421875, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.453125, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.484375, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.515625, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.546875, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.578125, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.609375, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.640625, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.671875, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.703125, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.734375, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.765625, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.796875, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.828125, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.859375, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.890625, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.921875, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.953125, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.984375, y: 0.453125 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.015625, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.046875, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.078125, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.109375, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.140625, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.171875, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.203125, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.234375, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.265625, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.296875, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.328125, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.359375, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.390625, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.421875, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.453125, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.484375, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.515625, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.546875, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.578125, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.609375, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.640625, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.671875, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.703125, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.734375, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.765625, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.796875, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.828125, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.859375, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.890625, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.921875, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.953125, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.984375, y: 0.484375 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.015625, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.046875, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.078125, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.109375, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.140625, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.171875, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.203125, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.234375, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.265625, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.296875, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.328125, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.359375, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.390625, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.421875, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.453125, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.484375, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.515625, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.546875, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.578125, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.609375, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.640625, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.671875, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.703125, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.734375, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.765625, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.796875, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.828125, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.859375, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.890625, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.921875, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.953125, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.984375, y: 0.515625 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.015625, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.046875, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.078125, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.109375, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.140625, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.171875, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.203125, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.234375, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.265625, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.296875, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.328125, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.359375, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.390625, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.421875, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.453125, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.484375, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.515625, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.546875, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.578125, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.609375, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.640625, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.671875, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.703125, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.734375, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.765625, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.796875, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.828125, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.859375, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.890625, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.921875, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.953125, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.984375, y: 0.546875 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.015625, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.046875, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.078125, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.109375, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.140625, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.171875, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.203125, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.234375, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.265625, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.296875, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.328125, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.359375, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.390625, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.421875, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.453125, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.484375, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.515625, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.546875, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.578125, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.609375, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.640625, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.671875, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.703125, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.734375, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.765625, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.796875, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.828125, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.859375, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.890625, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.921875, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.953125, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.984375, y: 0.578125 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.015625, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.046875, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.078125, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.109375, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.140625, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.171875, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.203125, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.234375, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.265625, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.296875, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.328125, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.359375, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.390625, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.421875, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.453125, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.484375, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.515625, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.546875, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.578125, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.609375, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.640625, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.671875, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.703125, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.734375, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.765625, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.796875, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.828125, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.859375, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.890625, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.921875, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.953125, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.984375, y: 0.609375 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.015625, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.046875, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.078125, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.109375, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.140625, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.171875, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.203125, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.234375, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.265625, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.296875, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.328125, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.359375, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.390625, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.421875, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.453125, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.484375, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.515625, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.546875, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.578125, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.609375, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.640625, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.671875, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.703125, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.734375, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.765625, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.796875, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.828125, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.859375, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.890625, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.921875, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.953125, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.984375, y: 0.640625 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.015625, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.046875, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.078125, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.109375, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.140625, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.171875, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.203125, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.234375, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.265625, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.296875, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.328125, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.359375, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.390625, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.421875, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.453125, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.484375, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.515625, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.546875, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.578125, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.609375, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.640625, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.671875, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.703125, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.734375, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.765625, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.796875, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.828125, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.859375, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.890625, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.921875, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.953125, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.984375, y: 0.671875 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.015625, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.046875, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.078125, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.109375, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.140625, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.171875, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.203125, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.234375, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.265625, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.296875, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.328125, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.359375, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.390625, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.421875, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.453125, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.484375, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.515625, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.546875, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.578125, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.609375, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.640625, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.671875, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.703125, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.734375, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.765625, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.796875, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.828125, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.859375, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.890625, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.921875, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.953125, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.984375, y: 0.703125 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.015625, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.046875, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.078125, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.109375, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.140625, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.171875, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.203125, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.234375, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.265625, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.296875, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.328125, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.359375, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.390625, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.421875, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.453125, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.484375, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.515625, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.546875, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.578125, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.609375, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.640625, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.671875, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.703125, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.734375, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.765625, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.796875, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.828125, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.859375, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.890625, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.921875, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.953125, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.984375, y: 0.734375 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.015625, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.046875, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.078125, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.109375, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.140625, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.171875, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.203125, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.234375, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.265625, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.296875, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.328125, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.359375, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.390625, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.421875, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.453125, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.484375, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.515625, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.546875, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.578125, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.609375, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.640625, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.671875, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.703125, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.734375, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.765625, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.796875, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.828125, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.859375, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.890625, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.921875, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.953125, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.984375, y: 0.765625 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.015625, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.046875, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.078125, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.109375, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.140625, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.171875, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.203125, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.234375, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.265625, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.296875, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.328125, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.359375, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.390625, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.421875, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.453125, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.484375, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.515625, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.546875, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.578125, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.609375, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.640625, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.671875, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.703125, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.734375, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.765625, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.796875, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.828125, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.859375, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.890625, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.921875, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.953125, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.984375, y: 0.796875 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.015625, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.046875, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.078125, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.109375, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.140625, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.171875, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.203125, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.234375, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.265625, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.296875, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.328125, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.359375, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.390625, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.421875, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.453125, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.484375, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.515625, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.546875, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.578125, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.609375, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.640625, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.671875, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.703125, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.734375, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.765625, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.796875, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.828125, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.859375, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.890625, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.921875, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.953125, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.984375, y: 0.828125 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.015625, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.046875, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.078125, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.109375, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.140625, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.171875, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.203125, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.234375, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.265625, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.296875, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.328125, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.359375, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.390625, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.421875, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.453125, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.484375, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.515625, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.546875, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.578125, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.609375, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.640625, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.671875, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.703125, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.734375, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.765625, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.796875, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.828125, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.859375, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.890625, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.921875, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.953125, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.984375, y: 0.859375 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.015625, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.046875, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.078125, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.109375, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.140625, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.171875, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.203125, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.234375, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.265625, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.296875, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.328125, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.359375, y: 0.890625 },\n { x: 0.390625, y: 0.890625 },\n { x: 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y: 0.40625 },\n { x: 0.84375, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.90625, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.96875, y: 0.40625 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.03125, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.09375, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.15625, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.21875, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.28125, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.34375, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.40625, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.46875, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.53125, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.59375, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.65625, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.71875, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.78125, y: 0.46875 },\n { x: 0.84375, y: 0.46875 },\n { x: 0.84375, y: 0.46875 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y: 0.59375 },\n { x: 0.96875, y: 0.59375 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.03125, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.09375, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.15625, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.21875, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.28125, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.34375, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.40625, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.46875, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.53125, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.59375, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.65625, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.71875, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.78125, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.84375, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.90625, y: 0.65625 },\n { x: 0.96875, y: 0.65625 },\n { x: 0.96875, y: 0.65625 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0.15625, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.21875, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.28125, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.34375, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.40625, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.46875, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.53125, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.59375, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.65625, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.71875, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.78125, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.84375, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.90625, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.96875, y: 0.96875 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.0625, y: 0.0625 },\n { x: 0.1875, y: 0.0625 },\n 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0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.9375, y: 0.0625 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.0625, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.1875, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.3125, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.4375, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.5625, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.6875, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.8125, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.9375, y: 0.1875 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.0625, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.1875, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 },\n { x: 0.3125, y: 0.3125 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0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.1875, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.3125, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.4375, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.5625, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.6875, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 0.8125, y: 0.4375 },\n { x: 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},\n { x: 0.8125, y: 0.8125 },\n { x: 0.8125, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.9375, y: 0.8125 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.0625, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.1875, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.3125, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.4375, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.5625, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.6875, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.8125, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n { x: 0.9375, y: 0.9375 },\n];\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport * as anchors from './handposeanchors';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, Tensor1D, Tensor2D, Tensor4D, GraphModel } from '../tfjs/types';\nimport type { Point } from '../result';\nimport type { Config } from '../config';\n\nexport class HandDetector {\n model: GraphModel;\n anchors: number[][];\n anchorsTensor: Tensor;\n inputSize: number;\n inputSizeTensor: Tensor;\n doubleInputSizeTensor: Tensor;\n\n constructor(model: GraphModel) {\n this.model = model;\n this.anchors = anchors.anchors.map((anchor) => [anchor.x, anchor.y]);\n this.anchorsTensor = tf.tensor2d(this.anchors);\n this.inputSize = this?.model?.inputs?.[0]?.shape?.[2] || 0;\n this.inputSizeTensor = tf.tensor1d([this.inputSize, this.inputSize]);\n this.doubleInputSizeTensor = tf.tensor1d([this.inputSize * 2, this.inputSize * 2]);\n }\n\n normalizeBoxes(boxes) {\n const t: Record = {};\n t.boxOffsets = tf.slice(boxes, [0, 0], [-1, 2]);\n t.boxSizes = tf.slice(boxes, [0, 2], [-1, 2]);\n t.div = tf.div(t.boxOffsets, this.inputSizeTensor);\n t.boxCenterPoints = tf.add(t.div, this.anchorsTensor);\n t.halfBoxSizes = tf.div(t.boxSizes, this.doubleInputSizeTensor);\n t.sub = tf.sub(t.boxCenterPoints, t.halfBoxSizes);\n t.startPoints = tf.mul(t.sub, this.inputSizeTensor);\n t.add = tf.add(t.boxCenterPoints, t.halfBoxSizes);\n t.endPoints = tf.mul(t.add, this.inputSizeTensor);\n const res = tf.concat2d([t.startPoints as Tensor2D, t.endPoints as Tensor2D], 1);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res as Tensor;\n }\n\n normalizeLandmarks(rawPalmLandmarks, index: number): Tensor {\n const t: Record = {};\n t.reshape = tf.reshape(rawPalmLandmarks, [-1, 7, 2]);\n t.div = tf.div(t.reshape, this.inputSizeTensor);\n t.landmarks = tf.add(t.div, this.anchors[index] ? this.anchors[index] : 0);\n const res = tf.mul(t.landmarks, this.inputSizeTensor);\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return res;\n }\n\n async predict(input: Tensor4D, config: Config): Promise<{ startPoint: Point; endPoint: Point, palmLandmarks: Point[]; confidence: number }[]> {\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [this.inputSize, this.inputSize]);\n t.div = tf.div(t.resize, constants.tf127);\n t.image = tf.sub(t.div, constants.tf1);\n t.batched = this.model.execute(t.image) as Tensor;\n t.predictions = tf.squeeze(t.batched);\n t.slice = tf.slice(t.predictions, [0, 0], [-1, 1]);\n t.sigmoid = tf.sigmoid(t.slice);\n t.scores = tf.squeeze(t.sigmoid);\n const scores = await t.scores.data();\n t.boxes = tf.slice(t.predictions, [0, 1], [-1, 4]);\n t.norm = this.normalizeBoxes(t.boxes);\n // box detection is flaky so we look for 3x boxes than we need results\n t.nms = await tf.image.nonMaxSuppressionAsync(t.norm as Tensor2D, t.scores as Tensor1D, 3 * (config.hand?.maxDetected || 1), config.hand.iouThreshold, config.hand.minConfidence);\n const nms = await t.nms.array() as number[];\n const hands: { startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number }[] = [];\n for (const index of nms) {\n const p: Record = {};\n p.box = tf.slice(t.norm, [index, 0], [1, -1]);\n p.slice = tf.slice(t.predictions, [index, 5], [1, 14]);\n p.norm = this.normalizeLandmarks(p.slice, index);\n p.palmLandmarks = tf.reshape(p.norm, [-1, 2]);\n const box = await p.box.data();\n const startPoint = box.slice(0, 2) as unknown as Point;\n const endPoint = box.slice(2, 4) as unknown as Point;\n const palmLandmarks = await p.palmLandmarks.array();\n const hand = { startPoint, endPoint, palmLandmarks, confidence: scores[index] };\n const scaled = util.scaleBoxCoordinates(hand, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]);\n hands.push(scaled);\n Object.keys(p).forEach((tensor) => tf.dispose(p[tensor]));\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n * See `handpose.ts` for entry point\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport * as util from './handposeutil';\nimport type * as detector from './handposedetector';\nimport { constants } from '../tfjs/constants';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport { env } from '../util/env';\nimport { now } from '../util/util';\nimport type { Point } from '../result';\n\nconst palmBoxEnlargeFactor = 5; // default 3\nconst handBoxEnlargeFactor = 1.65; // default 1.65\nconst palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];\nconst palmLandmarksPalmBase = 0;\nconst palmLandmarksMiddleFingerBase = 2;\nlet lastTime = 0;\n\nexport class HandPipeline {\n handDetector: detector.HandDetector;\n handPoseModel: GraphModel;\n inputSize: number;\n storedBoxes: ({ startPoint: Point; endPoint: Point; palmLandmarks: Point[]; confidence: number } | null)[];\n skipped: number;\n detectedHands: number;\n\n constructor(handDetector, handPoseModel) {\n this.handDetector = handDetector;\n this.handPoseModel = handPoseModel;\n this.inputSize = this.handPoseModel?.inputs?.[0].shape?.[2] || 0;\n this.storedBoxes = [];\n this.skipped = Number.MAX_SAFE_INTEGER;\n this.detectedHands = 0;\n }\n\n calculateLandmarksBoundingBox(landmarks) { // eslint-disable-line class-methods-use-this\n const xs = landmarks.map((d) => d[0]);\n const ys = landmarks.map((d) => d[1]);\n const startPoint = [Math.min(...xs), Math.min(...ys)];\n const endPoint = [Math.max(...xs), Math.max(...ys)];\n return { startPoint, endPoint };\n }\n\n getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {\n const rotatedPalmLandmarks = palmLandmarks.map((coord) => util.rotatePoint([...coord, 1], rotationMatrix));\n const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);\n return util.enlargeBox(util.squarifyBox(boxAroundPalm), palmBoxEnlargeFactor);\n }\n\n getBoxForHandLandmarks(landmarks) {\n const boundingBox = this.calculateLandmarksBoundingBox(landmarks);\n const boxAroundHand = util.enlargeBox(util.squarifyBox(boundingBox), handBoxEnlargeFactor);\n boxAroundHand.palmLandmarks = [];\n for (let i = 0; i < palmLandmarkIds.length; i++) {\n boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));\n }\n return boxAroundHand;\n }\n\n transformRawCoords(rawCoords, box2, angle, rotationMatrix) {\n const boxSize = util.getBoxSize(box2);\n const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];\n const coordsScaled = rawCoords.map((coord) => [\n scaleFactor[0] * (coord[0] - this.inputSize / 2),\n scaleFactor[1] * (coord[1] - this.inputSize / 2),\n scaleFactor[2] * coord[2],\n ]);\n const coordsRotationMatrix = util.buildRotationMatrix(angle, [0, 0]);\n const coordsRotated = coordsScaled.map((coord) => {\n const rotated = util.rotatePoint(coord, coordsRotationMatrix);\n return [...rotated, coord[2]];\n });\n const inverseRotationMatrix = util.invertTransformMatrix(rotationMatrix);\n const boxCenter = [...util.getBoxCenter(box2), 1];\n const originalBoxCenter = [\n util.dot(boxCenter, inverseRotationMatrix[0]),\n util.dot(boxCenter, inverseRotationMatrix[1]),\n ];\n return coordsRotated.map((coord) => [\n Math.trunc(coord[0] + originalBoxCenter[0]),\n Math.trunc(coord[1] + originalBoxCenter[1]),\n Math.trunc(coord[2]),\n ]);\n }\n\n async estimateHands(image, config) {\n let useFreshBox = false;\n\n // run new detector every skipFrames\n let boxes;\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = this.skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n this.skipped++;\n } else {\n boxes = await this.handDetector.predict(image, config);\n this.skipped = 0;\n }\n\n // if detector result count doesn't match current working set, use it to reset current working set\n if (boxes && (boxes.length > 0) && ((boxes.length !== this.detectedHands) && (this.detectedHands !== config.hand.maxDetected) || !config.hand.landmarks)) {\n this.detectedHands = 0;\n this.storedBoxes = [...boxes];\n // for (const possible of boxes) this.storedBoxes.push(possible);\n if (this.storedBoxes.length > 0) useFreshBox = true;\n }\n const hands: { landmarks: Point[], confidence: number, boxConfidence: number, fingerConfidence: number, box: { topLeft: Point, bottomRight: Point } }[] = [];\n\n // go through working set of boxes\n for (let i = 0; i < this.storedBoxes.length; i++) {\n const currentBox = this.storedBoxes[i];\n if (!currentBox) continue;\n if (config.hand.landmarks) {\n const angle = config.hand.rotation ? util.computeRotation(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;\n const palmCenter = util.getBoxCenter(currentBox);\n const palmCenterNormalized: [number, number] = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];\n const rotatedImage = config.hand.rotation && env.kernels.includes('rotatewithoffset') ? tf.image.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();\n const rotationMatrix = util.buildRotationMatrix(-angle, palmCenter);\n const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;\n const croppedInput = util.cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);\n const handImage = tf.div(croppedInput, constants.tf255);\n tf.dispose(croppedInput);\n tf.dispose(rotatedImage);\n const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Tensor[];\n lastTime = now();\n tf.dispose(handImage);\n const confidence = (await confidenceT.data())[0];\n tf.dispose(confidenceT);\n if (confidence >= config.hand.minConfidence / 4) {\n const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);\n const rawCoords = await keypointsReshaped.array();\n tf.dispose(keypoints);\n tf.dispose(keypointsReshaped);\n const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);\n const nextBoundingBox = this.getBoxForHandLandmarks(coords);\n this.storedBoxes[i] = { ...nextBoundingBox, confidence };\n const result = {\n landmarks: coords,\n confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: confidence,\n box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint },\n };\n hands.push(result);\n } else {\n this.storedBoxes[i] = null;\n }\n tf.dispose(keypoints);\n } else {\n // const enlarged = box.enlargeBox(box.squarifyBox(box.shiftBox(currentBox, HAND_BOX_SHIFT_VECTOR)), handBoxEnlargeFactor);\n const enlarged = util.enlargeBox(util.squarifyBox(currentBox), handBoxEnlargeFactor);\n const result = {\n confidence: currentBox.confidence,\n boxConfidence: currentBox.confidence,\n fingerConfidence: 0,\n box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },\n landmarks: [],\n };\n hands.push(result);\n }\n }\n this.storedBoxes = this.storedBoxes.filter((a) => a !== null);\n this.detectedHands = hands.length;\n if (hands.length > config.hand.maxDetected) hands.length = config.hand.maxDetected;\n return hands;\n }\n}\n", "/**\n * HandPose model implementation\n *\n * Based on: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n */\n\nimport { log } from '../util/util';\nimport * as handdetector from './handposedetector';\nimport * as handpipeline from './handposepipeline';\nimport * as fingerPose from './fingerpose';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, Box, Point } from '../result';\nimport type { Tensor, GraphModel } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nconst meshAnnotations = {\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n palm: [0],\n};\n\nlet handDetectorModel: GraphModel | null;\nlet handPoseModel: GraphModel | null;\nlet handPipeline: handpipeline.HandPipeline;\n\nexport function initPipeline() {\n const handDetector = handDetectorModel ? new handdetector.HandDetector(handDetectorModel) : undefined;\n if (handDetector && handPoseModel) handPipeline = new handpipeline.HandPipeline(handDetector, handPoseModel);\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!handPipeline) initPipeline();\n const predictions = await handPipeline.estimateHands(input, config);\n if (!predictions) return [];\n const hands: HandResult[] = [];\n for (let i = 0; i < predictions.length; i++) {\n const annotations = {};\n if (predictions[i].landmarks) {\n for (const key of Object.keys(meshAnnotations)) {\n annotations[key] = meshAnnotations[key].map((index) => predictions[i].landmarks[index]);\n }\n }\n const keypoints = predictions[i].landmarks as unknown as Point[];\n let box: Box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; // maximums so conditionals work\n let boxRaw: Box = [0, 0, 0, 0];\n if (keypoints && keypoints.length > 0) { // if we have landmarks, calculate box based on landmarks\n for (const pt of keypoints) {\n if (pt[0] < box[0]) box[0] = pt[0];\n if (pt[1] < box[1]) box[1] = pt[1];\n if (pt[0] > box[2]) box[2] = pt[0];\n if (pt[1] > box[3]) box[3] = pt[1];\n }\n box[2] -= box[0];\n box[3] -= box[1];\n boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];\n } else { // otherwise use box from prediction\n box = predictions[i].box ? [\n Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),\n Math.trunc(Math.min((input.shape[2] || 0), predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),\n Math.trunc(Math.min((input.shape[1] || 0), predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])),\n ] : [0, 0, 0, 0];\n boxRaw = [\n (predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),\n (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0),\n ];\n }\n const landmarks = fingerPose.analyze(keypoints);\n hands.push({\n id: i,\n score: Math.round(100 * predictions[i].confidence) / 100,\n boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,\n fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,\n label: 'hand',\n box,\n boxRaw,\n keypoints,\n annotations: annotations as HandResult['annotations'],\n landmarks: landmarks as HandResult['landmarks'],\n });\n }\n return hands;\n}\n\nexport async function loadDetect(config: Config): Promise {\n if (env.initial) handDetectorModel = null;\n if (!handDetectorModel) handDetectorModel = await loadModel(config.hand.detector?.modelPath);\n else if (config.debug) log('cached model:', handDetectorModel['modelUrl']);\n return handDetectorModel;\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) handPoseModel = null;\n if (!handPoseModel) handPoseModel = await loadModel(config.hand.skeleton?.modelPath);\n else if (config.debug) log('cached model:', handPoseModel['modelUrl']);\n return handPoseModel;\n}\n", "/**\n * HandTrack model implementation\n *\n * Based on:\n * - Hand Detection & Skeleton: [**MediaPipe HandPose**](https://drive.google.com/file/d/1sv4sSb9BSNVZhLzxXJ0jBv9DqD-4jnAz/view)\n * - Hand Tracking: [**HandTracking**](https://github.com/victordibia/handtracking)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport { loadModel } from '../tfjs/load';\nimport type { HandResult, HandType, Box, Point } from '../result';\nimport type { GraphModel, Tensor, Tensor1D, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as fingerPose from './fingerpose';\nimport { fakeOps } from '../tfjs/backend';\nimport { constants } from '../tfjs/constants';\n\nconst models: [GraphModel | null, GraphModel | null] = [null, null];\nconst modelOutputNodes = ['StatefulPartitionedCall/Postprocessor/Slice', 'StatefulPartitionedCall/Postprocessor/ExpandDims_1'];\n\nconst inputSize = [[0, 0], [0, 0]];\n\nconst classes = ['hand', 'fist', 'pinch', 'point', 'face', 'tip', 'pinchtip'];\nconst faceIndex = 4;\n\nconst boxExpandFact = 1.6;\nconst maxDetectorResolution = 512;\nconst detectorExpandFact = 1.4;\n\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet lastTime = 0;\nlet outputSize: [number, number] = [0, 0];\n\ninterface HandDetectResult {\n id: number,\n score: number,\n box: Box,\n boxRaw: Box,\n label: HandType,\n}\n\nconst cache: {\n boxes: HandDetectResult[],\n hands: HandResult[];\n} = {\n boxes: [],\n hands: [],\n};\n\nconst fingerMap = {\n /*\n thumb: [0, 1, 2, 3, 4],\n index: [0, 5, 6, 7, 8],\n middle: [0, 9, 10, 11, 12],\n ring: [0, 13, 14, 15, 16],\n pinky: [0, 17, 18, 19, 20],\n palm: [0],\n */\n thumb: [1, 2, 3, 4],\n index: [5, 6, 7, 8],\n middle: [9, 10, 11, 12],\n ring: [13, 14, 15, 16],\n pinky: [17, 18, 19, 20],\n base: [0],\n palm: [0, 17, 13, 9, 5, 1, 0],\n};\n\nexport async function loadDetect(config: Config): Promise {\n // HandTrack Model: Original: TFJS Port: \n if (env.initial) models[0] = null;\n if (!models[0]) {\n // handtrack model has some kernel ops defined in model but those are never referenced and non-existent in tfjs\n // ideally need to prune the model itself\n fakeOps(['tensorlistreserve', 'enter', 'tensorlistfromtensor', 'merge', 'loopcond', 'switch', 'exit', 'tensorliststack', 'nextiteration', 'tensorlistsetitem', 'tensorlistgetitem', 'reciprocal', 'shape', 'split', 'where'], config);\n models[0] = await loadModel(config.hand.detector?.modelPath);\n const inputs = models[0]['executor'] ? Object.values(models[0].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[0]['modelUrl']);\n return models[0];\n}\n\nexport async function loadSkeleton(config: Config): Promise {\n if (env.initial) models[1] = null;\n if (!models[1]) {\n models[1] = await loadModel(config.hand.skeleton?.modelPath);\n const inputs = models[1]['executor'] ? Object.values(models[1].modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;\n } else if (config.debug) log('cached model:', models[1]['modelUrl']);\n return models[1];\n}\n\nexport async function load(config: Config): Promise<[GraphModel | null, GraphModel | null]> {\n if (!models[0]) await loadDetect(config);\n if (!models[1]) await loadSkeleton(config);\n return models;\n}\n\nasync function detectHands(input: Tensor4D, config: Config): Promise {\n const hands: HandDetectResult[] = [];\n if (!input || !models[0]) return hands;\n const t: Record = {};\n const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);\n const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); // use dynamic input size but cap at 512\n const width = Math.round(height * ratio / 8) * 8;\n t.resize = tf.image.resizeBilinear(input, [height, width]); // todo: resize with padding\n t.cast = tf.cast(t.resize, 'int32');\n [t.rawScores, t.rawBoxes] = await models[0].executeAsync(t.cast, modelOutputNodes) as Tensor[];\n t.boxes = tf.squeeze(t.rawBoxes, [0, 2]);\n t.scores = tf.squeeze(t.rawScores, [0]);\n const classScores: Tensor[] = tf.unstack(t.scores, 1); // unstack scores based on classes\n tf.dispose(classScores[faceIndex]);\n classScores.splice(faceIndex, 1); // remove faces\n t.filtered = tf.stack(classScores, 1); // restack\n tf.dispose(classScores);\n // t.filtered = t.scores;\n t.max = tf.max(t.filtered, 1); // max overall score\n t.argmax = tf.argMax(t.filtered, 1); // class index of max overall score\n let id = 0;\n t.nms = await tf.image.nonMaxSuppressionAsync(t.boxes as Tensor2D, t.max as Tensor1D, (config.hand.maxDetected || 0) + 1, config.hand.iouThreshold || 0, config.hand.minConfidence || 1);\n const nms = await t.nms.data();\n const scores = await t.max.data();\n const classNum = await t.argmax.data();\n for (const nmsIndex of Array.from(nms)) { // generates results for each class\n const boxSlice = tf.slice(t.boxes, nmsIndex, 1);\n const boxYX = await boxSlice.data();\n tf.dispose(boxSlice);\n const boxData: Box = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; // yx box reshaped to standard box\n const boxRaw: Box = box.scale(boxData, detectorExpandFact);\n const boxFull: Box = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];\n const score = scores[nmsIndex];\n const label = classes[classNum[nmsIndex]] as HandType;\n const hand: HandDetectResult = { id: id++, score, box: boxFull, boxRaw, label };\n hands.push(hand);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n hands.sort((a, b) => b.score - a.score);\n if (hands.length > (config.hand.maxDetected || 1)) hands.length = (config.hand.maxDetected || 1);\n return hands;\n}\n\nasync function detectFingers(input: Tensor4D, h: HandDetectResult, config: Config): Promise {\n const hand: HandResult = { // initial values inherited from hand detect\n id: h.id,\n score: Math.round(100 * h.score) / 100,\n boxScore: Math.round(100 * h.score) / 100,\n fingerScore: 0,\n box: h.box,\n boxRaw: h.boxRaw,\n label: h.label,\n keypoints: [],\n landmarks: {} as HandResult['landmarks'],\n annotations: {} as HandResult['annotations'],\n };\n if (input && models[1] && config.hand.landmarks && h.score > (config.hand.minConfidence || 0)) {\n const t: Record = {};\n const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]] as Box;\n t.crop = tf.image.cropAndResize(input, [boxCrop], [0], [inputSize[1][0], inputSize[1][1]], 'bilinear');\n t.div = tf.div(t.crop, constants.tf255);\n [t.score, t.keypoints] = models[1].execute(t.div, ['Identity_1', 'Identity']) as Tensor[];\n const rawScore = (await t.score.data())[0];\n const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; // reverse sigmoid value\n if (score >= (config.hand.minConfidence || 0)) {\n hand.fingerScore = score;\n t.reshaped = tf.reshape(t.keypoints, [-1, 3]);\n const coordsData: Point[] = await t.reshaped.array() as Point[];\n const coordsRaw: Point[] = coordsData.map((kpt) => [kpt[0] / inputSize[1][1], kpt[1] / inputSize[1][0], (kpt[2] || 0)]);\n const coordsNorm: Point[] = coordsRaw.map((kpt) => [kpt[0] * h.boxRaw[2], kpt[1] * h.boxRaw[3], (kpt[2] || 0)]);\n hand.keypoints = (coordsNorm).map((kpt) => [outputSize[0] * (kpt[0] + h.boxRaw[0]), outputSize[1] * (kpt[1] + h.boxRaw[1]), (kpt[2] || 0)]);\n hand.landmarks = fingerPose.analyze(hand.keypoints) as HandResult['landmarks']; // calculate finger gestures\n for (const key of Object.keys(fingerMap)) { // map keypoints to per-finger annotations\n hand.annotations[key] = fingerMap[key].map((index: number) => (hand.landmarks && hand.keypoints[index] ? hand.keypoints[index] : null));\n }\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n }\n return hand;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!models[0]?.['executor'] || !models[1]?.['executor'] || !models[0].inputs[0].shape || !models[1].inputs[0].shape) return []; // something is wrong with the model\n outputSize = [input.shape[2] || 0, input.shape[1] || 0];\n skipped++; // increment skip frames\n const skipTime = (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.hand.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.hands; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const skipTimeExtended = 3 * (config.hand.skipTime || 0) > (now() - lastTime);\n const skipFrameExtended = skipped < 3 * (config.hand.skipFrames || 0);\n if (config.skipAllowed && cache.hands.length === config.hand.maxDetected) { // we have all detected hands so we're definitely skipping\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else if (config.skipAllowed && skipTimeExtended && skipFrameExtended && cache.hands.length > 0) { // we have some cached results: maybe not enough but anyhow continue for bit longer\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n } else { // finally rerun detector\n cache.boxes = await detectHands(input, config);\n lastTime = now();\n cache.hands = await Promise.all(cache.boxes.map((handBox) => detectFingers(input, handBox, config)));\n skipped = 0;\n }\n\n const oldCache = [...cache.boxes];\n cache.boxes.length = 0; // reset cache\n if (config.cacheSensitivity > 0) {\n for (let i = 0; i < cache.hands.length; i++) {\n const boxKpt = box.square(cache.hands[i].keypoints, outputSize);\n if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache.hands[i].fingerScore && cache.hands[i].fingerScore > (config.hand.minConfidence || 0)) {\n const boxScale = box.scale(boxKpt.box, boxExpandFact);\n const boxScaleRaw = box.scale(boxKpt.boxRaw, boxExpandFact);\n // const boxCrop = box.crop(boxScaleRaw);\n cache.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });\n }\n }\n }\n for (let i = 0; i < cache.hands.length; i++) { // replace detected boxes with calculated boxes in final output\n const bbox = box.calc(cache.hands[i].keypoints, outputSize);\n cache.hands[i].box = bbox.box;\n cache.hands[i].boxRaw = bbox.boxRaw;\n }\n resolve(cache.hands);\n });\n}\n", "/**\n * Type definitions for Human result object\n */\n\nimport type { Tensor } from './tfjs/types';\nimport type { FaceGesture, BodyGesture, HandGesture, IrisGesture } from './gesture/gesture';\nimport type { AnyCanvas } from './exports';\n\n/** generic box as [x, y, width, height] */\nexport type Box = [number, number, number, number];\n/** generic point as [x, y, z?] */\nexport type Point = [number, number, number?];\n\nexport type Emotion = 'angry' | 'disgust' | 'fear' | 'happy' | 'sad' | 'surprise' | 'neutral';\nexport type Gender = 'male' | 'female' | 'unknown';\nexport type Race = 'white' | 'black' | 'asian' | 'indian' | 'other';\nexport type FaceLandmark = 'leftEye' | 'rightEye' | 'nose' | 'mouth' | 'leftEar' | 'rightEar' | 'symmetryLine' | 'silhouette'\n | 'lipsUpperOuter' | 'lipsLowerOuter' | 'lipsUpperInner' | 'lipsLowerInner'\n | 'rightEyeUpper0' | 'rightEyeLower0' | 'rightEyeUpper1' | 'rightEyeLower1' | 'rightEyeUpper2' | 'rightEyeLower2' | 'rightEyeLower3' | 'rightEyebrowUpper' | 'rightEyebrowLower' | 'rightEyeIris'\n | 'leftEyeUpper0' | 'leftEyeLower0' | 'leftEyeUpper1' | 'leftEyeLower1' | 'leftEyeUpper2' | 'leftEyeLower2' | 'leftEyeLower3' | 'leftEyebrowUpper' | 'leftEyebrowLower' | 'leftEyeIris'\n | 'midwayBetweenEyes' | 'noseTip' | 'noseBottom' | 'noseRightCorner' | 'noseLeftCorner' | 'rightCheek' | 'leftCheek';\n\n/** Face results\n * - Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models\n * - Some values may be null if specific model is not enabled\n */\nexport interface FaceResult {\n /** face id */\n id: number\n /** overall face score */\n score: number,\n /** detection score */\n boxScore: number,\n /** mesh score */\n faceScore: number,\n /** detected face box */\n box: Box,\n /** detected face box normalized to 0..1 */\n boxRaw: Box,\n /** detected face box size */\n size: [number, number],\n /** detected face mesh */\n mesh: Point[]\n /** detected face mesh normalized to 0..1 */\n meshRaw: Point[],\n /** face contours as array of 2d points normalized to 0..1 */\n // contoursRaw: Array<[number, number]>,\n /** face contours as array of 2d points */\n // contours: Array<[number, number]>,\n /** mesh keypoints combined into annotated results */\n annotations: Record,\n /** detected age */\n age?: number,\n /** detected gender */\n gender?: Gender,\n /** gender detection score */\n genderScore?: number,\n /** detected emotions */\n emotion?: { score: number, emotion: Emotion }[],\n /** detected race */\n race?: { score: number, race: Race }[],\n /** face descriptor */\n embedding?: number[],\n /** face distance from camera */\n distance?: number,\n /** face anti-spoofing result confidence */\n real?: number,\n /** face liveness result confidence */\n live?: number,\n /** face rotation details */\n rotation?: {\n angle: { roll: number, yaw: number, pitch: number },\n matrix: [number, number, number, number, number, number, number, number, number],\n gaze: { bearing: number, strength: number },\n } | null,\n /** detected face as tensor that can be used in further pipelines */\n tensor?: Tensor,\n}\n\nexport type BodyLandmarkPoseNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkMoveNet = 'nose' | 'leftEye' | 'rightEye' | 'leftEar' | 'rightEar' | 'leftShoulder' | 'rightShoulder' | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle';\nexport type BodyLandmarkEfficientNet = 'head' | 'neck' | 'rightShoulder' | 'rightElbow' | 'rightWrist' | 'chest' | 'leftShoulder' | 'leftElbow' | 'leftWrist' | 'bodyCenter' | 'rightHip' | 'rightKnee' | 'rightAnkle' | 'leftHip' | 'leftKnee' | 'leftAnkle';\nexport type BodyLandmarkBlazePose = 'nose' | 'leftEyeInside' | 'leftEye' | 'leftEyeOutside' | 'rightEyeInside' | 'rightEye' | 'rightEyeOutside' | 'leftEar' | 'rightEar' | 'leftMouth' | 'rightMouth' | 'leftShoulder' | 'rightShoulder'\n | 'leftElbow' | 'rightElbow' | 'leftWrist' | 'rightWrist' | 'leftPinky' | 'rightPinky' | 'leftIndex' | 'rightIndex' | 'leftThumb' | 'rightThumb' | 'leftHip' | 'rightHip' | 'leftKnee' | 'rightKnee' | 'leftAnkle' | 'rightAnkle'\n | 'leftHeel' | 'rightHeel' | 'leftFoot' | 'rightFoot' | 'bodyCenter' | 'bodyTop' | 'leftPalm' | 'leftHand' | 'rightPalm' | 'rightHand';\nexport type BodyLandmark = BodyLandmarkPoseNet | BodyLandmarkMoveNet | BodyLandmarkEfficientNet | BodyLandmarkBlazePose;\nexport type BodyAnnotationBlazePose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'leftEye' | 'rightEye' | 'mouth';\nexport type BodyAnnotationEfficientPose = 'leftLeg' | 'rightLeg' | 'torso' | 'leftArm' | 'rightArm' | 'head';\nexport type BodyAnnotation = BodyAnnotationBlazePose | BodyAnnotationEfficientPose;\n\n/** Body Result keypoints */\nexport interface BodyKeypoint {\n /** body part name */\n part: BodyLandmark,\n /** body part position */\n position: Point,\n /** body part position normalized to 0..1 */\n positionRaw: Point,\n /** body part position relative to body center in meters */\n distance?: Point,\n /** body part detection score */\n score: number,\n}\n\n/** Body results */\nexport interface BodyResult {\n /** body id */\n id: number,\n /** body detection score */\n score: number,\n /** detected body box */\n box: Box,\n /** detected body box normalized to 0..1 */\n boxRaw: Box,\n /** detected body keypoints */\n keypoints: BodyKeypoint[]\n /** detected body keypoints combined into annotated parts */\n annotations: Record,\n}\n\nexport type HandType = 'hand' | 'fist' | 'pinch' | 'point' | 'face' | 'tip' | 'pinchtip';\nexport type Finger = 'index' | 'middle' | 'pinky' | 'ring' | 'thumb' | 'palm';\nexport type FingerCurl = 'none' | 'half' | 'full';\nexport type FingerDirection = 'verticalUp' | 'verticalDown' | 'horizontalLeft' | 'horizontalRight' | 'diagonalUpRight' | 'diagonalUpLeft' | 'diagonalDownRight' | 'diagonalDownLeft';\n\n/** Hand results */\nexport interface HandResult {\n /** hand id */\n id: number,\n /** hand overal score */\n score: number,\n /** hand detection score */\n boxScore: number,\n /** hand skelton score */\n fingerScore: number,\n /** detected hand box */\n box: Box,\n /** detected hand box normalized to 0..1 */\n boxRaw: Box,\n /** detected hand keypoints */\n keypoints: Point[],\n /** detected hand class */\n label: HandType,\n /** detected hand keypoints combined into annotated parts */\n annotations: Record,\n /** detected hand parts annotated with part gestures */\n landmarks: Record,\n}\n\nexport type ObjectType = 'person' | 'bicycle' | 'car' | 'motorcycle' | 'airplane' | 'bus' | 'train' | 'truck' | 'boat' | 'traffic light' | 'fire hydrant' | 'stop sign' | 'parking meter'\n | 'bench' | 'bird' | 'cat' | 'dog' | 'horse' | 'sheep' | 'cow' | 'elephant' | 'bear' | 'zebra' | 'giraffe' | 'backpack' | 'umbrella' | 'handbag' | 'tie' | 'suitcase' | 'frisbee'\n | 'skis' | 'snowboard' | 'sports ball' | 'kite' | 'baseball bat' | 'baseball glove' | 'skateboard' | 'surfboard' | 'tennis racket' | 'bottle' | 'wine glass' | 'cup' | 'fork'\n | 'knife' | 'spoon' | 'bowl' | 'banana' | 'apple' | 'sandwich' | 'orange' | 'broccoli' | 'carrot' | 'hot dog' | 'pizza' | 'donut' | 'cake' | 'chair' | 'couch' | 'potted plant'\n | 'bed' | 'dining table' | 'toilet' | 'tv' | 'laptop' | 'mouse' | 'remote' | 'keyboard' | 'cell phone' | 'microwave' | 'oven' | 'toaster' | 'sink' | 'refrigerator' | 'book'\n | 'clock' | 'vase' | 'scissors' | 'teddy bear' | 'hair drier' | 'toothbrush';\n\n/** Object results */\nexport interface ObjectResult {\n /** object id */\n id: number,\n /** object detection score */\n score: number,\n /** detected object class id */\n class: number,\n /** detected object class name */\n label: ObjectType,\n /** detected object box */\n box: Box,\n /** detected object box normalized to 0..1 */\n boxRaw: Box,\n}\n\n/** Gesture combined results\n * Each result has:\n * - part: part name and number where gesture was detected: `face`, `iris`, `body`, `hand`\n * - gesture: gesture detected\n */\nexport type GestureResult =\n { 'face': number, gesture: FaceGesture }\n | { 'iris': number, gesture: IrisGesture }\n | { 'body': number, gesture: BodyGesture }\n | { 'hand': number, gesture: HandGesture }\n\n/** Person getter\n* - Triggers combining all individual results into a virtual person object\n*/\nexport interface PersonResult {\n /** person id */\n id: number,\n /** face result that belongs to this person */\n face: FaceResult,\n /** body result that belongs to this person */\n body: BodyResult | null,\n /** left and right hand results that belong to this person */\n hands: { left: HandResult | null, right: HandResult | null },\n /** detected gestures specific to this person */\n gestures: GestureResult[],\n /** box that defines the person */\n box: Box,\n /** box that defines the person normalized to 0..1 */\n boxRaw?: Box,\n}\n\n/**\n * Result interface definition for **Human** library\n *\n * Contains all possible detection results\n */\nexport interface Result {\n /** {@link FaceResult}: detection & analysis results */\n face: FaceResult[],\n /** {@link BodyResult}: detection & analysis results */\n body: BodyResult[],\n /** {@link HandResult}: detection & analysis results */\n hand: HandResult[],\n /** {@link GestureResult}: detection & analysis results */\n gesture: GestureResult[],\n /** {@link ObjectResult}: detection & analysis results */\n object: ObjectResult[]\n /** global performance object with timing values for each operation */\n performance: Record,\n /** optional processed canvas that can be used to draw input on screen */\n canvas?: AnyCanvas | null,\n /** timestamp of detection representing the milliseconds elapsed since the UNIX epoch */\n readonly timestamp: number,\n /** getter property that returns unified persons object */\n persons: PersonResult[],\n /** Last known error message */\n error: string | null;\n /** Resolution width */\n width: number,\n /** Resolution height */\n height: number,\n}\n\nexport const empty = (error: string | null = null): Result => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error });\n", "export const kpt: string[] = [ // used to create part labels\n 'nose',\n 'leftEye',\n 'rightEye',\n 'leftEar',\n 'rightEar',\n 'leftShoulder',\n 'rightShoulder',\n 'leftElbow',\n 'rightElbow',\n 'leftWrist',\n 'rightWrist',\n 'leftHip',\n 'rightHip',\n 'leftKnee',\n 'rightKnee',\n 'leftAnkle',\n 'rightAnkle',\n];\n\nexport const horizontal: string[][] = [ // used to fix left vs right\n ['leftEye', 'rightEye'],\n ['leftEar', 'rightEar'],\n ['leftShoulder', 'rightShoulder'],\n ['leftElbow', 'rightElbow'],\n ['leftWrist', 'rightWrist'],\n ['leftHip', 'rightHip'],\n ['leftKnee', 'rightKnee'],\n ['leftAnkle', 'rightAnkle'],\n];\n\nexport const vertical: string[][] = [ // used to remove unlikely keypoint positions\n ['leftKnee', 'leftShoulder'],\n ['rightKnee', 'rightShoulder'],\n ['leftAnkle', 'leftKnee'],\n ['rightAnkle', 'rightKnee'],\n];\n\nexport const relative: string[][][] = [ // used to match relative body parts\n [['leftHip', 'rightHip'], ['leftShoulder', 'rightShoulder']],\n [['leftElbow', 'rightElbow'], ['leftShoulder', 'rightShoulder']],\n];\n\nexport const connected: Record = { // used to create body outline in annotations\n leftLeg: ['leftHip', 'leftKnee', 'leftAnkle'],\n rightLeg: ['rightHip', 'rightKnee', 'rightAnkle'],\n torso: ['leftShoulder', 'rightShoulder', 'rightHip', 'leftHip', 'leftShoulder'],\n leftArm: ['leftShoulder', 'leftElbow', 'leftWrist'],\n rightArm: ['rightShoulder', 'rightElbow', 'rightWrist'],\n head: [],\n};\n", "/**\n * Results interpolation for smoothening of video detection results inbetween detected frames\n */\n\nimport { Result, FaceResult, BodyResult, HandResult, ObjectResult, PersonResult, Box, Point, BodyLandmark, BodyAnnotation, empty, FaceLandmark } from '../result';\nimport type { Config } from '../config';\n\nimport * as moveNetCoords from '../body/movenetcoords';\nimport * as blazePoseCoords from '../body/blazeposecoords';\nimport * as efficientPoseCoords from '../body/efficientposecoords';\nimport { now } from './util';\nimport { env } from './env';\n\nconst bufferedResult: Result = empty();\nlet interpolateTime = 0;\n\nexport function calc(newResult: Result, config: Config): Result {\n const t0 = now();\n if (!newResult) return empty();\n // each record is only updated using deep clone when number of detected record changes, otherwise it will converge by itself\n // otherwise bufferedResult is a shallow clone of result plus updated local calculated values\n // thus mixing by-reference and by-value assignments to minimize memory operations\n\n const elapsed = Date.now() - newResult.timestamp;\n\n /* curve fitted: buffer = 8 - ln(delay)\n interpolation formula: current = ((buffer - 1) * previous + live) / buffer\n - at 50ms delay buffer = ~4.1 => 28% towards live data\n - at 250ms delay buffer = ~2.5 => 40% towards live data\n - at 500ms delay buffer = ~1.8 => 55% towards live data\n - at 750ms delay buffer = ~1.4 => 71% towards live data\n - at 1sec delay buffer = 1 which means live data is used\n */\n const bufferedFactor = elapsed < 1000 ? 8 - Math.log(elapsed + 1) : 1;\n\n if (newResult.canvas) bufferedResult.canvas = newResult.canvas;\n if (newResult.error) bufferedResult.error = newResult.error;\n\n // interpolate body results\n if (!bufferedResult.body || (newResult.body.length !== bufferedResult.body.length)) {\n bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)) as BodyResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.body.length; i++) {\n const box = newResult.body[i].box // update box\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor) as Box;\n const boxRaw = newResult.body[i].boxRaw // update boxRaw\n .map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor) as Box;\n const keypoints = (newResult.body[i].keypoints // update keypoints\n .map((newKpt, j) => ({\n score: newKpt.score,\n part: newKpt.part,\n position: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2],\n ],\n positionRaw: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2],\n ],\n distance: [\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[0] || 0) + (newKpt.distance?.[0] || 0)) / bufferedFactor : newKpt.distance?.[0],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[1] || 0) + (newKpt.distance?.[1] || 0)) / bufferedFactor : newKpt.distance?.[1],\n bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].distance?.[2] || 0) + (newKpt.distance?.[2] || 0)) / bufferedFactor : newKpt.distance?.[2],\n ],\n }))) as { score: number, part: BodyLandmark, position: [number, number, number?], positionRaw: [number, number, number?] }[];\n\n const annotations: Record = {} as Record; // recreate annotations\n let coords = { connected: {} };\n if (config.body.modelPath?.includes('efficientpose')) coords = efficientPoseCoords;\n else if (config.body.modelPath?.includes('blazepose')) coords = blazePoseCoords;\n else if (config.body.modelPath?.includes('movenet')) coords = moveNetCoords;\n for (const [name, indexes] of Object.entries(coords.connected as Record)) {\n const pt: Point[][] = [];\n for (let j = 0; j < indexes.length - 1; j++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[j]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]);\n // if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n if (pt0 && pt1) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations }; // shallow clone plus updated values\n }\n }\n\n // interpolate hand results\n if (!bufferedResult.hand || (newResult.hand.length !== bufferedResult.hand.length)) {\n bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); // deep clone once\n } else {\n for (let i = 0; i < newResult.hand.length; i++) {\n const box = (newResult.hand[i].box// update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.hand[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; // reset keypoints as previous frame did not have them\n const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints // update landmarks\n .map((landmark, j) => landmark\n .map((coord, k) => (((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) as Point)\n : [];\n let annotations = {};\n if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {\n bufferedResult.hand[i].annotations = newResult.hand[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.hand[i].annotations;\n } else if (newResult.hand[i].annotations) {\n for (const key of Object.keys(newResult.hand[i].annotations)) { // update annotations\n annotations[key] = newResult.hand[i]?.annotations?.[key]?.[0]\n ? newResult.hand[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations as HandResult['annotations'] }; // shallow clone plus updated values\n }\n }\n\n // interpolate face results\n if (!bufferedResult.face || (newResult.face.length !== bufferedResult.face.length)) {\n bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)) as FaceResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.face.length; i++) {\n const box = (newResult.face[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.face[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n let annotations: Record = newResult.face[i].annotations;\n if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) {\n bufferedResult.face[i].annotations = newResult.face[i].annotations; // reset annotations as previous frame did not have them\n annotations = bufferedResult.face[i].annotations;\n } else if (newResult.face[i].annotations) {\n for (const key of Object.keys(newResult.face[i].annotations)) { // update annotations\n annotations[key] = newResult.face[i]?.annotations?.[key]?.[0]\n ? newResult.face[i].annotations[key]\n .map((val, j: number) => val\n .map((coord: number, k: number) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor))\n : null;\n }\n }\n if (newResult.face[i].rotation) {\n const rotation: {\n matrix: [number, number, number, number, number, number, number, number, number],\n angle: { roll: number, yaw: number, pitch: number },\n gaze: { bearing: number, strength: number }\n } = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };\n rotation.matrix = newResult.face[i].rotation?.matrix as [number, number, number, number, number, number, number, number, number];\n rotation.angle = {\n roll: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.roll || 0) + (newResult.face[i].rotation?.angle?.roll || 0)) / bufferedFactor,\n yaw: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.yaw || 0) + (newResult.face[i].rotation?.angle?.yaw || 0)) / bufferedFactor,\n pitch: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.angle?.pitch || 0) + (newResult.face[i].rotation?.angle?.pitch || 0)) / bufferedFactor,\n };\n rotation.gaze = {\n // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive\n bearing: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.bearing || 0) + (newResult.face[i].rotation?.gaze.bearing || 0)) / bufferedFactor,\n strength: ((bufferedFactor - 1) * (bufferedResult.face[i].rotation?.gaze.strength || 0) + (newResult.face[i].rotation?.gaze.strength || 0)) / bufferedFactor,\n };\n bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations }; // shallow clone plus updated values\n } else {\n bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations }; // shallow clone plus updated values\n }\n }\n }\n\n // interpolate object detection results\n if (!bufferedResult.object || (newResult.object.length !== bufferedResult.object.length)) {\n bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)) as ObjectResult[]; // deep clone once\n } else {\n for (let i = 0; i < newResult.object.length; i++) {\n const box = (newResult.object[i].box // update box\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor)) as Box;\n const boxRaw = (newResult.object[i].boxRaw // update boxRaw\n .map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor)) as Box;\n bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; // shallow clone plus updated values\n }\n }\n\n // interpolate person results\n if (newResult.persons) {\n const newPersons = newResult.persons; // trigger getter function\n if (!bufferedResult.persons || (newPersons.length !== bufferedResult.persons.length)) {\n bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)) as PersonResult[];\n } else {\n for (let i = 0; i < newPersons.length; i++) { // update person box, we don't update the rest as it's updated as reference anyhow\n bufferedResult.persons[i].box = (newPersons[i].box\n .map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor)) as Box;\n }\n }\n }\n\n // copy latest gestures without interpolation\n if (newResult.gesture) bufferedResult.gesture = newResult.gesture;\n\n // copy resolution info\n bufferedResult.width = newResult.width;\n bufferedResult.height = newResult.height;\n\n // append interpolation performance data\n const t1 = now();\n interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0);\n if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime };\n\n return bufferedResult;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Meet**](https://drive.google.com/file/d/1lnP1bRi9CSqQQXUHa13159vLELYDgDu0/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]);\n // t.softmax = tf.softmax(t.squeeze); // model meet has two channels for fg and bg\n [t.bgRaw, t.fgRaw] = tf.unstack(t.squeeze, 2);\n // t.bg = tf.softmax(t.bgRaw); // we can ignore bg channel\n t.fg = tf.softmax(t.fgRaw);\n t.mul = tf.mul(t.fg, constants.tf255);\n t.expand = tf.expandDims(t.mul, 2);\n t.output = tf.image.resizeBilinear(t.expand as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.output], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.output, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/** Face descriptor type as number array */\nexport type Descriptor = number[]\nexport type MatchOptions = { order?: number, threshold?: number, multiplier?: number, min?: number, max?: number } | undefined;\n\n/** Calculates distance between two descriptors\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n */\nexport function distance(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25 }) {\n // general minkowski distance, euclidean distance is limited case where order is 2\n if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER;\n let sum = 0;\n for (let i = 0; i < descriptor1.length; i++) {\n const diff = (!options.order || options.order === 2) ? (descriptor1[i] - descriptor2[i]) : (Math.abs(descriptor1[i] - descriptor2[i]));\n sum += (!options.order || options.order === 2) ? (diff * diff) : (diff ** options.order);\n }\n const dist = Math.round(100 * (options.multiplier || 20) * sum) / 100;\n return dist;\n}\n\n// invert distance to similarity, normalize to given range and clamp\nconst normalizeDistance = (dist, order, min, max) => {\n if (dist === 0) return 1; // short circuit for identical inputs\n const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); // take root of distance\n const norm = (1 - (root / 100) - min) / (max - min); // normalize to range\n const clamp = Math.round(100 * Math.max(Math.min(norm, 1), 0)) / 100; // clamp to 0..1\n return clamp;\n};\n\n/** Calculates normalized similarity between two face descriptors based on their `distance`\n * @param options - calculation options\n * - order - algorithm to use\n * Euclidean distance if `order` is 2 (default), Minkowski distance algorithm of nth order if `order` is higher than 2\n * - multiplier - by how much to enhance difference analysis in range of 1..100\n * default is 20 which normalizes results to similarity above 0.5 can be considered a match\n * - min - normalize similarity result to a given range\n * - max - normalzie similarity resutl to a given range\n * default is 0.2...0.8\n * Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity\n */\nexport function similarity(descriptor1: Descriptor, descriptor2: Descriptor, options: MatchOptions = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) {\n const dist = distance(descriptor1, descriptor2, options);\n return normalizeDistance(dist, options.order || 2, options.min || 0, options.max || 1);\n}\n\n/** Matches given descriptor to a closest entry in array of descriptors\n * @param descriptor - face descriptor\n * @param descriptors - array of face descriptors to commpare given descriptor to\n * @param options - see `similarity` method for options description\n * Returns\n * - `index` index array index where best match was found or -1 if no matches\n * - `distance` calculated `distance` of given descriptor to the best match\n * - `similarity` calculated normalized `similarity` of given descriptor to the best match\n*/\nexport function find(descriptor: Descriptor, descriptors: Descriptor[], options: MatchOptions = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) {\n if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { // validate input\n return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 };\n }\n let lowestDistance = Number.MAX_SAFE_INTEGER;\n let index = -1;\n for (let i = 0; i < descriptors.length; i++) {\n const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options) : Number.MAX_SAFE_INTEGER;\n if (res < lowestDistance) {\n lowestDistance = res;\n index = i;\n }\n if (lowestDistance < (options.threshold || 0)) break;\n }\n const normalizedSimilarity = normalizeDistance(lowestDistance, options.order || 2, options.min || 0, options.max || 1);\n return { index, distance: lowestDistance, similarity: normalizedSimilarity };\n}\n", "/**\n * Loader and Validator for all models used by Human\n */\n\nimport { env } from './util/env';\nimport { log } from './util/util';\nimport * as antispoof from './face/antispoof';\nimport * as blazeface from './face/blazeface';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as emotion from './gear/emotion';\nimport * as facemesh from './face/facemesh';\nimport * as faceres from './face/faceres';\nimport * as gear from './gear/gear';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as insightface from './face/insightface';\nimport * as iris from './face/iris';\nimport * as liveness from './face/liveness';\nimport * as meet from './segmentation/meet';\nimport * as mobilefacenet from './face/mobilefacenet';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as ssrnetAge from './gear/ssrnet-age';\nimport * as ssrnetGender from './gear/ssrnet-gender';\nimport { modelStats, ModelInfo } from './tfjs/load';\nimport type { GraphModel } from './tfjs/types';\nimport type { Human } from './human';\n\nexport interface KernelOps { name: string, url: string, missing: string[], ops: string[] }\n\nexport function validateModel(instance: Human | null, model: GraphModel | null, name: string): KernelOps | null {\n if (!model) return null;\n if (!instance?.config?.validateModels) return null;\n const simpleOps = ['const', 'placeholder', 'noop', 'pad', 'squeeze', 'add', 'sub', 'mul', 'div'];\n const ignoreOps = ['biasadd', 'fusedbatchnormv3', 'matmul', 'switch', 'shape', 'merge', 'split', 'broadcastto'];\n const ops: string[] = [];\n const missing: string[] = [];\n interface Op { name: string, category: string, op: string }\n const url = model['modelUrl'] as string;\n const executor = model['executor'];\n if (executor?.graph?.nodes) {\n for (const kernel of Object.values(executor.graph.nodes)) {\n const op = (kernel as Op).op.toLowerCase();\n if (!ops.includes(op)) ops.push(op);\n }\n } else {\n if (!executor && instance.config.debug) {\n log('model not loaded', name);\n }\n }\n for (const op of ops) {\n if (!simpleOps.includes(op) // exclude simple ops\n && !ignoreOps.includes(op) // exclude specific ops\n && !instance.env.kernels.includes(op) // check actual kernel ops\n && !instance.env.kernels.includes(op.replace('_', '')) // check variation without _\n && !instance.env.kernels.includes(op.replace('native', '')) // check standard variation\n && !instance.env.kernels.includes(op.replace('v2', ''))) { // check non-versioned variation\n missing.push(op);\n }\n }\n if (instance.config.debug && missing.length > 0) log('model validation failed:', name, missing);\n return missing.length > 0 ? { name, missing, ops, url } : null;\n}\n\n/** structure that holds global stats for currently loaded models */\nexport interface ModelStats {\n numLoadedModels: number,\n numDefinedModels: number,\n percentageLoaded: number,\n totalSizeFromManifest: number,\n totalSizeWeights: number,\n totalSizeLoading: number,\n modelStats: ModelInfo[],\n}\n\n/** Models class used by Human\n * - models: record of all GraphModels\n * - list: returns list of configured models with their stats\n * - loaded: returns array of loaded models\n * - reset: unloads all models\n * - validate: checks loaded models for valid kernel ops vs current backend\n * - stats: live detailed model stats that can be checked during model load phase\n */\nexport class Models {\n private instance: Human;\n models: Record = {};\n\n constructor(currentInstance: Human) {\n this.models = {};\n this.instance = currentInstance;\n }\n\n stats(): ModelStats {\n let totalSizeFromManifest = 0;\n let totalSizeWeights = 0;\n let totalSizeLoading = 0;\n for (const m of Object.values(modelStats)) {\n totalSizeFromManifest += Number.isNaN(+m.sizeFromManifest) ? 0 : m.sizeFromManifest;\n totalSizeWeights += Number.isNaN(+m.sizeLoadedWeights) ? 0 : m.sizeLoadedWeights;\n totalSizeLoading += Number.isNaN(+m.sizeDesired) ? 0 : m.sizeDesired;\n }\n const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0;\n return {\n numLoadedModels: Object.values(modelStats).filter((m) => m?.loaded).length,\n numDefinedModels: Object.keys(this.models).length,\n percentageLoaded,\n totalSizeFromManifest,\n totalSizeWeights,\n totalSizeLoading,\n modelStats: Object.values(modelStats),\n };\n }\n\n reset(): void {\n for (const model of Object.keys(this.models)) this.models[model] = null;\n }\n\n async load(instance?: Human): Promise {\n if (env.initial) this.reset();\n if (instance) this.instance = instance;\n const m: Record> = {};\n // face main models\n m.blazeface = (this.instance.config.face.enabled && !this.models.blazeface) ? blazeface.load(this.instance.config) : null;\n m.antispoof = (this.instance.config.face.enabled && this.instance.config.face.antispoof?.enabled && !this.models.antispoof) ? antispoof.load(this.instance.config) : null;\n m.liveness = (this.instance.config.face.enabled && this.instance.config.face.liveness?.enabled && !this.models.liveness) ? liveness.load(this.instance.config) : null;\n m.faceres = (this.instance.config.face.enabled && this.instance.config.face.description?.enabled && !this.models.faceres) ? faceres.load(this.instance.config) : null;\n m.emotion = (this.instance.config.face.enabled && this.instance.config.face.emotion?.enabled && !this.models.emotion) ? emotion.load(this.instance.config) : null;\n m.iris = (this.instance.config.face.enabled && this.instance.config.face.iris?.enabled && !this.instance.config.face.attention?.enabled && !this.models.iris) ? iris.load(this.instance.config) : null;\n m.facemesh = (this.instance.config.face.enabled && this.instance.config.face.mesh?.enabled && (!this.models.facemesh)) ? facemesh.load(this.instance.config) : null;\n // face alternatives\n m.gear = (this.instance.config.face.enabled && this.instance.config.face['gear']?.enabled && !this.models.gear) ? gear.load(this.instance.config) : null;\n m.ssrnetage = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetage) ? ssrnetAge.load(this.instance.config) : null;\n m.ssrnetgender = (this.instance.config.face.enabled && this.instance.config.face['ssrnet']?.enabled && !this.models.ssrnetgender) ? ssrnetGender.load(this.instance.config) : null;\n m.mobilefacenet = (this.instance.config.face.enabled && this.instance.config.face['mobilefacenet']?.enabled && !this.models.mobilefacenet) ? mobilefacenet.load(this.instance.config) : null;\n m.insightface = (this.instance.config.face.enabled && this.instance.config.face['insightface']?.enabled && !this.models.insightface) ? insightface.load(this.instance.config) : null;\n // body alterinatives\n m.blazepose = (this.instance.config.body.enabled && !this.models.blazepose && this.instance.config.body.modelPath?.includes('blazepose')) ? blazepose.loadPose(this.instance.config) : null;\n m.blazeposedetect = (this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body['detector'] && this.instance.config.body['detector'].modelPath) ? blazepose.loadDetect(this.instance.config) : null;\n m.efficientpose = (this.instance.config.body.enabled && !this.models.efficientpose && this.instance.config.body.modelPath?.includes('efficientpose')) ? efficientpose.load(this.instance.config) : null;\n m.movenet = (this.instance.config.body.enabled && !this.models.movenet && this.instance.config.body.modelPath?.includes('movenet')) ? movenet.load(this.instance.config) : null;\n m.posenet = (this.instance.config.body.enabled && !this.models.posenet && this.instance.config.body.modelPath?.includes('posenet')) ? posenet.load(this.instance.config) : null;\n // hand alternatives\n m.handtrack = (this.instance.config.hand.enabled && !this.models.handtrack && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadDetect(this.instance.config) : null;\n m.handskeleton = (this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && this.instance.config.hand.detector?.modelPath?.includes('handtrack')) ? handtrack.loadSkeleton(this.instance.config) : null;\n // if (this.instance.config.hand.detector?.modelPath?.includes('handdetect')) [m.handpose, m.handskeleton] = (!this.models.handpose) ? await handpose.load(this.instance.config) : [null, null];\n if (this.instance.config.hand.enabled && !this.models.handdetect && this.instance.config.hand.detector?.modelPath?.includes('handdetect')) {\n m.handdetect = handpose.loadDetect(this.instance.config);\n m.handskeleton = handpose.loadSkeleton(this.instance.config);\n }\n // object detection alternatives\n m.centernet = (this.instance.config.object.enabled && !this.models.centernet && this.instance.config.object.modelPath?.includes('centernet')) ? centernet.load(this.instance.config) : null;\n m.nanodet = (this.instance.config.object.enabled && !this.models.nanodet && this.instance.config.object.modelPath?.includes('nanodet')) ? nanodet.load(this.instance.config) : null;\n // segmentation alternatives\n m.selfie = (this.instance.config.segmentation.enabled && !this.models.selfie && this.instance.config.segmentation.modelPath?.includes('selfie')) ? selfie.load(this.instance.config) : null;\n m.meet = (this.instance.config.segmentation.enabled && !this.models.meet && this.instance.config.segmentation.modelPath?.includes('meet')) ? meet.load(this.instance.config) : null;\n m.rvm = (this.instance.config.segmentation.enabled && !this.models.rvm && this.instance.config.segmentation.modelPath?.includes('rvm')) ? rvm.load(this.instance.config) : null;\n\n // models are loaded in parallel asynchronously so lets wait until they are actually loaded\n for (const [model, promise] of Object.entries(m)) {\n if (promise?.['then']) promise['then']((val) => this.models[model] = val);\n }\n await Promise.all(Object.values(m)); // wait so this function does not resolve prematurely\n }\n\n list() {\n const models = Object.keys(this.models).map((model) => ({ name: model, loaded: (this.models[model] !== null), size: 0, url: this.models[model] ? this.models[model]?.['modelUrl'] : null }));\n for (const m of models) {\n const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name));\n if (!stats) continue;\n m.size = modelStats[stats].sizeLoadedWeights;\n m.url = modelStats[stats].url;\n }\n return models;\n }\n\n loaded(): string[] {\n const list = this.list();\n const loaded = list.filter((model) => model.loaded).map((model) => model.name);\n return loaded;\n }\n\n validate(): { name: string, missing: string[] }[] {\n const missing: KernelOps[] = [];\n for (const defined of Object.keys(this.models)) {\n const model: GraphModel | null = this.models[defined as keyof Models];\n if (!model) continue;\n const res = validateModel(this.instance, model, defined);\n if (res) missing.push(res);\n }\n return missing;\n }\n}\n", "import * as tf from 'dist/tfjs.esm.js';\nimport type { BodyKeypoint, BodyResult } from '../result';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport type { Tensor, Tensor3D } from '../tfjs/types';\n\nconst maxJitter = 0.005; // default allowed jitter is within 0.5%\n\nconst cache: {\n keypoints: BodyKeypoint[],\n padding: [number, number][];\n} = {\n keypoints: [],\n padding: [[0, 0], [0, 0], [0, 0], [0, 0]],\n};\n\nexport function bodyParts(body: BodyResult) { // model sometimes mixes up left vs right keypoints so we fix them\n for (const pair of coords.horizontal) { // fix body parts left vs right\n const left = body.keypoints.findIndex((kp) => kp.part === pair[0]);\n const right = body.keypoints.findIndex((kp) => kp.part === pair[1]);\n if (body.keypoints[left] && body.keypoints[right]) {\n if (body.keypoints[left].position[0] < body.keypoints[right].position[0]) {\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n }\n for (const pair of coords.vertical) { // remove body parts with improbable vertical position\n const lower = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const higher = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n if (body.keypoints[lower] && body.keypoints[higher]) {\n if (body.keypoints[lower].position[1] < body.keypoints[higher].position[1]) {\n body.keypoints.splice(lower, 1);\n }\n }\n }\n for (const [pair, compare] of coords.relative) { // rearrange body parts according to their relative position\n const left = body.keypoints.findIndex((kp) => (kp && kp.part === pair[0]));\n const right = body.keypoints.findIndex((kp) => (kp && kp.part === pair[1]));\n const leftTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[0]));\n const rightTo = body.keypoints.findIndex((kp) => (kp && kp.part === compare[1]));\n if (!body.keypoints[leftTo] || !body.keypoints[rightTo]) continue; // only if we have both compare points\n const distanceLeft = body.keypoints[left] ? [\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[left].position[0]),\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[left].position[0]),\n ] : [0, 0];\n const distanceRight = body.keypoints[right] ? [\n Math.abs(body.keypoints[rightTo].position[0] - body.keypoints[right].position[0]),\n Math.abs(body.keypoints[leftTo].position[0] - body.keypoints[right].position[0]),\n ] : [0, 0];\n if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { // should flip keypoints\n const tmp = body.keypoints[left];\n body.keypoints[left] = body.keypoints[right];\n body.keypoints[right] = tmp;\n }\n }\n}\n\nexport function jitter(keypoints: BodyKeypoint[]): BodyKeypoint[] {\n for (let i = 0; i < keypoints.length; i++) {\n if (keypoints[i] && cache.keypoints[i]) {\n const diff = [Math.abs(keypoints[i].positionRaw[0] - cache.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache.keypoints[i].positionRaw[1])];\n if (diff[0] < maxJitter && diff[1] < maxJitter) {\n keypoints[i] = cache.keypoints[i]; // below jitter so replace keypoint\n } else {\n cache.keypoints[i] = keypoints[i]; // above jitter so update cache\n }\n } else {\n cache.keypoints[i] = keypoints[i]; // cache for keypoint doesnt exist so create it here\n }\n }\n return keypoints;\n}\n\nexport function padInput(input: Tensor, inputSize: number): Tensor {\n const t: Record = {};\n if (!input?.shape?.[1] || !input?.shape?.[2]) return input;\n cache.padding = [\n [0, 0], // dont touch batch\n [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], // height before&after\n [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], // width before&after\n [0, 0], // dont touch rbg\n ];\n t.pad = tf.pad(input, cache.padding);\n t.resize = tf.image.resizeBilinear(t.pad as Tensor3D, [inputSize, inputSize]);\n const final = tf.cast(t.resize, 'int32');\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return final;\n}\n\nexport function rescaleBody(body: BodyResult, outputSize: [number, number]): BodyResult {\n body.keypoints = body.keypoints.filter((kpt) => kpt?.position); // filter invalid keypoints\n for (const kpt of body.keypoints) {\n kpt.position = [\n kpt.position[0] * (outputSize[0] + cache.padding[2][0] + cache.padding[2][1]) / outputSize[0] - cache.padding[2][0],\n kpt.position[1] * (outputSize[1] + cache.padding[1][0] + cache.padding[1][1]) / outputSize[1] - cache.padding[1][0],\n ];\n kpt.positionRaw = [\n kpt.position[0] / outputSize[0], kpt.position[1] / outputSize[1],\n ];\n }\n const rescaledBoxes = box.calc(body.keypoints.map((pt) => pt.position), outputSize);\n body.box = rescaledBoxes.box;\n body.boxRaw = rescaledBoxes.boxRaw;\n return body;\n}\n", "/**\n * MoveNet model implementation\n *\n * Based on: [**MoveNet**](https://blog.tensorflow.org/2021/05/next-generation-pose-detection-with-movenet-and-tensorflowjs.html)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport * as box from '../util/box';\nimport * as coords from './movenetcoords';\nimport * as fix from './movenetfix';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyKeypoint, BodyResult, BodyLandmark, BodyAnnotation, Box, Point } from '../result';\nimport type { GraphModel, Tensor } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { fakeOps } from '../tfjs/backend';\nimport { env } from '../util/env';\n\nlet model: GraphModel | null;\nlet inputSize = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\n// const boxExpandFact = 1.5; // increase to 150%\n\nconst cache: {\n boxes: Box[], // unused\n bodies: BodyResult[];\n last: number,\n} = {\n boxes: [],\n bodies: [],\n last: 0,\n};\n\nexport async function load(config: Config): Promise {\n if (env.initial) model = null;\n if (!model) {\n fakeOps(['size'], config);\n model = await loadModel(config.body.modelPath);\n } else if (config.debug) log('cached model:', model['modelUrl']);\n inputSize = (model?.['executor'] && model?.inputs?.[0].shape) ? model.inputs[0].shape[2] : 0;\n if (inputSize < 64) inputSize = 256;\n // @ts-ignore private property\n if (tf.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf.env().set('WEBGL_USE_SHAPES_UNIFORMS', false); // default=false \n return model;\n}\n\nfunction parseSinglePose(res, config, image) {\n const kpt = res[0][0];\n const keypoints: BodyKeypoint[] = [];\n let score = 0;\n for (let id = 0; id < kpt.length; id++) {\n score = kpt[id][2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[id][1], kpt[id][0]];\n keypoints.push({\n score: Math.round(100 * score) / 100,\n part: coords.kpt[id] as BodyLandmark,\n positionRaw,\n position: [ // normalized to input image size\n Math.round((image.shape[2] || 0) * positionRaw[0]),\n Math.round((image.shape[1] || 0) * positionRaw[1]),\n ],\n });\n }\n }\n score = keypoints.reduce((prev, curr) => (curr.score > prev ? curr.score : prev), 0);\n const bodies: BodyResult[] = [];\n const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n const annotations: Record = {};\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n const body: BodyResult = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations };\n fix.bodyParts(body);\n bodies.push(body);\n return bodies;\n}\n\nfunction parseMultiPose(res, config, image) {\n const bodies: BodyResult[] = [];\n for (let id = 0; id < res[0].length; id++) {\n const kpt = res[0][id];\n const boxScore = Math.round(100 * kpt[51 + 4]) / 100;\n if (boxScore > config.body.minConfidence) {\n const keypoints: BodyKeypoint[] = [];\n for (let i = 0; i < 17; i++) {\n const score = kpt[3 * i + 2];\n if (score > config.body.minConfidence) {\n const positionRaw: Point = [kpt[3 * i + 1], kpt[3 * i + 0]];\n keypoints.push({\n part: coords.kpt[i] as BodyLandmark,\n score: Math.round(100 * score) / 100,\n positionRaw,\n position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])],\n });\n }\n }\n // const newBox = box.calc(keypoints.map((pt) => pt.position), [image.shape[2], image.shape[1]]);\n // movenet-multipose has built-in box details\n const boxRaw: Box = [kpt[51 + 1], kpt[51 + 0], kpt[51 + 3] - kpt[51 + 1], kpt[51 + 2] - kpt[51 + 0]];\n const boxNorm: Box = [Math.trunc(boxRaw[0] * (image.shape[2] || 0)), Math.trunc(boxRaw[1] * (image.shape[1] || 0)), Math.trunc(boxRaw[2] * (image.shape[2] || 0)), Math.trunc(boxRaw[3] * (image.shape[1] || 0))];\n const annotations: Record = {} as Record;\n for (const [name, indexes] of Object.entries(coords.connected)) {\n const pt: Point[][] = [];\n for (let i = 0; i < indexes.length - 1; i++) {\n const pt0 = keypoints.find((kp) => kp.part === indexes[i]);\n const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]);\n if (pt0 && pt1 && pt0.score > (config.body.minConfidence || 0) && pt1.score > (config.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]);\n }\n annotations[name] = pt;\n }\n // const body: BodyResult = { id, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations };\n const body: BodyResult = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations };\n fix.bodyParts(body);\n bodies.push(body);\n }\n }\n bodies.sort((a, b) => b.score - a.score);\n if (bodies.length > config.body.maxDetected) bodies.length = config.body.maxDetected;\n return bodies;\n}\n\nexport async function predict(input: Tensor, config: Config): Promise {\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return []; // something is wrong with the model\n if (!config.skipAllowed) cache.boxes.length = 0; // allowed to use cache or not\n skipped++; // increment skip frames\n const skipTime = (config.body.skipTime || 0) > (now() - cache.last);\n const skipFrame = skipped < (config.body.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame) {\n return cache.bodies; // return cached results without running anything\n }\n return new Promise(async (resolve) => {\n const t: Record = {};\n skipped = 0;\n // run detection on squared input and no cached boxes\n t.input = fix.padInput(input, inputSize);\n t.res = model?.execute(t.input) as Tensor;\n cache.last = now();\n const res = await t.res.array();\n cache.bodies = (t.res.shape[2] === 17)\n ? parseSinglePose(res, config, input)\n : parseMultiPose(res, config, input);\n for (const body of cache.bodies) {\n fix.rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);\n fix.jitter(body.keypoints);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n\n resolve(cache.bodies);\n });\n}\n", "/**\n * NanoDet object detection model implementation\n *\n * Based on: [**NanoDet**](https://github.com/RangiLyu/nanodet)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport { labels } from './labels';\nimport type { ObjectResult, ObjectType, Box } from '../result';\nimport type { GraphModel, Tensor, Tensor2D, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\nlet last: ObjectResult[] = [];\nlet lastTime = 0;\nlet skipped = Number.MAX_SAFE_INTEGER;\nlet inputSize = 0;\n\nconst scaleBox = 2.5; // increase box size\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) {\n model = await loadModel(config.object.modelPath);\n const inputs = model?.['executor'] ? Object.values(model.modelSignature['inputs']) : undefined;\n // @ts-ignore model signature properties are not typed and inputs are unreliable for this model\n inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416;\n } else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nasync function process(res: Tensor[], outputShape: [number, number], config: Config) {\n let id = 0;\n let results: ObjectResult[] = [];\n const size = inputSize;\n for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects\n // find scores, boxes, classes\n const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704\n // find boxes and scores output depending on stride\n const scoresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) === labels.length)) as Tensor2D);\n const scores = await scoresT.array(); // optionally use exponential scores or just as-is\n const featuresT = tf.squeeze(res.find((a) => (a.shape[1] === (baseSize ** 2) && (a.shape[2] || 0) < labels.length)) as Tensor2D);\n const boxesMaxT = tf.reshape(featuresT, [-1, 4, (featuresT.shape?.[1] || 0) / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride\n const boxIdxT = tf.argMax(boxesMaxT, 2); // what we need is indexes of features with highest scores, not values itself\n const boxIdx = await boxIdxT.array(); // what we need is indexes of features with highest scores, not values itself\n for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)\n for (let j = 0; j < (scoresT.shape?.[1] || 0); j++) { // one score for each class\n const score = scores[i][j]; // get score for current position\n if (score > (config.object.minConfidence || 0) && j !== 61) {\n const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; // center.x normalized to range 0..1\n const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; // center.y normalized to range 0..1\n const boxOffset = boxIdx[i].map((a: number) => a * (baseSize / strideSize / (size))); // just grab indexes of features with highest scores\n const [x, y] = [\n cx - (scaleBox / strideSize * boxOffset[0]),\n cy - (scaleBox / strideSize * boxOffset[1]),\n ];\n const [w, h] = [\n cx + (scaleBox / strideSize * boxOffset[2]) - x,\n cy + (scaleBox / strideSize * boxOffset[3]) - y,\n ];\n let boxRaw: Box = [x, y, w, h]; // results normalized to range 0..1\n boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))) as Box; // fix out-of-bounds coords\n const box = [ // results normalized to input image pixels\n boxRaw[0] * outputShape[0],\n boxRaw[1] * outputShape[1],\n boxRaw[2] * outputShape[0],\n boxRaw[3] * outputShape[1],\n ];\n const result = {\n id: id++,\n // strideSize,\n score: Math.round(100 * score) / 100,\n class: j + 1,\n label: labels[j].label as ObjectType,\n // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)],\n // centerRaw: [cx, cy],\n box: box.map((a) => Math.trunc(a)) as Box,\n boxRaw,\n };\n results.push(result);\n }\n }\n }\n tf.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]);\n }\n\n // normally nms is run on raw results, but since boxes need to be calculated this way we skip calulcation of\n // unnecessary boxes and run nms only on good candidates (basically it just does IOU analysis as scores are already filtered)\n const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); // switches coordinates from x,y to y,x as expected by tf.nms\n const nmsScores = results.map((a) => a.score);\n let nmsIdx: number[] = [];\n if (nmsBoxes && nmsBoxes.length > 0) {\n const nms = await tf.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config.object.maxDetected || 0, config.object.iouThreshold, config.object.minConfidence);\n nmsIdx = Array.from(await nms.data());\n tf.dispose(nms);\n }\n\n // filter & sort results\n results = results\n .filter((_val, idx) => nmsIdx.includes(idx))\n .sort((a, b) => (b.score - a.score));\n\n return results;\n}\n\nexport async function predict(image: Tensor4D, config: Config): Promise {\n if (!model?.['executor']) return [];\n const skipTime = (config.object.skipTime || 0) > (now() - lastTime);\n const skipFrame = skipped < (config.object.skipFrames || 0);\n if (config.skipAllowed && skipTime && skipFrame && (last.length > 0)) {\n skipped++;\n return last;\n }\n skipped = 0;\n if (!env.kernels.includes('mod') || !env.kernels.includes('sparsetodense')) return last;\n return new Promise(async (resolve) => {\n const outputSize = [image.shape[2] || 0, image.shape[1] || 0];\n const resizeT = tf.image.resizeBilinear(image, [inputSize, inputSize], false);\n const normT = tf.div(resizeT, constants.tf255);\n const transposeT = tf.transpose(normT, [0, 3, 1, 2]);\n\n let objectT;\n if (config.object.enabled) objectT = model.execute(transposeT);\n lastTime = now();\n\n const obj = await process(objectT as Tensor[], outputSize as [number, number], config);\n last = obj;\n tf.dispose([resizeT, normT, transposeT, ...objectT]);\n resolve(obj);\n });\n}\n", "/**\n * PoseNet body detection model implementation constants\n * See `posenet.ts` for entry point\n */\n\nimport type { Point, BodyResult, BodyAnnotation, BodyLandmark } from '../result';\n\nexport const partNames = [\n 'nose', 'leftEye', 'rightEye', 'leftEar', 'rightEar', 'leftShoulder',\n 'rightShoulder', 'leftElbow', 'rightElbow', 'leftWrist', 'rightWrist',\n 'leftHip', 'rightHip', 'leftKnee', 'rightKnee', 'leftAnkle', 'rightAnkle',\n];\n\nexport const count = partNames.length; // 17 keypoints\n\nexport const partIds = partNames.reduce((result, jointName, i) => {\n result[jointName] = i;\n return result;\n}, {});\n\nconst connectedPartNames = [\n ['leftHip', 'leftShoulder'], ['leftElbow', 'leftShoulder'],\n ['leftElbow', 'leftWrist'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['rightHip', 'rightShoulder'],\n ['rightElbow', 'rightShoulder'], ['rightElbow', 'rightWrist'],\n ['rightHip', 'rightKnee'], ['rightKnee', 'rightAnkle'],\n ['leftShoulder', 'rightShoulder'], ['leftHip', 'rightHip'],\n];\nexport const connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => ([partIds[jointNameA], partIds[jointNameB]]));\n\nexport const poseChain = [\n ['nose', 'leftEye'], ['leftEye', 'leftEar'], ['nose', 'rightEye'],\n ['rightEye', 'rightEar'], ['nose', 'leftShoulder'],\n ['leftShoulder', 'leftElbow'], ['leftElbow', 'leftWrist'],\n ['leftShoulder', 'leftHip'], ['leftHip', 'leftKnee'],\n ['leftKnee', 'leftAnkle'], ['nose', 'rightShoulder'],\n ['rightShoulder', 'rightElbow'], ['rightElbow', 'rightWrist'],\n ['rightShoulder', 'rightHip'], ['rightHip', 'rightKnee'],\n ['rightKnee', 'rightAnkle'],\n];\n\nexport function eitherPointDoesntMeetConfidence(a: number, b: number, minConfidence: number) {\n return (a < minConfidence || b < minConfidence);\n}\n\nexport function getAdjacentKeyPoints(keypoints, minConfidence: number) {\n return connectedPartIndices.reduce((result, [leftJoint, rightJoint]) => {\n if (eitherPointDoesntMeetConfidence(keypoints[leftJoint].score, keypoints[rightJoint].score, minConfidence)) {\n return result;\n }\n result.push([keypoints[leftJoint], keypoints[rightJoint]]);\n return result;\n }, []);\n}\n\nexport function getBoundingBox(keypoints): [number, number, number, number] {\n const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({\n maxX: Math.max(maxX, x),\n maxY: Math.max(maxY, y),\n minX: Math.min(minX, x),\n minY: Math.min(minY, y),\n }), {\n maxX: Number.NEGATIVE_INFINITY,\n maxY: Number.NEGATIVE_INFINITY,\n minX: Number.POSITIVE_INFINITY,\n minY: Number.POSITIVE_INFINITY,\n });\n return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];\n}\n\nexport function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]): BodyResult[] {\n const scaleY = height / inputResolutionHeight;\n const scaleX = width / inputResolutionWidth;\n const scalePose = (pose, i): BodyResult => ({\n id: i,\n score: pose.score,\n boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],\n box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],\n keypoints: pose.keypoints.map(({ score, part, position }) => ({\n score: score as number,\n part: part as BodyLandmark,\n position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)] as Point,\n positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] as Point,\n })),\n annotations: {} as Record,\n });\n const scaledPoses = poses.map((pose, i) => scalePose(pose, i));\n return scaledPoses;\n}\n\n// algorithm based on Coursera Lecture from Algorithms, Part 1: https://www.coursera.org/learn/algorithms-part1/lecture/ZjoSM/heapsort\nexport class MaxHeap {\n priorityQueue: unknown[]; // don't touch\n numberOfElements: number;\n getElementValue: unknown; // function call\n\n constructor(maxSize, getElementValue) {\n this.priorityQueue = new Array(maxSize);\n this.numberOfElements = -1;\n this.getElementValue = getElementValue;\n }\n\n enqueue(x) {\n this.priorityQueue[++this.numberOfElements] = x;\n this.swim(this.numberOfElements);\n }\n\n dequeue() {\n const max = this.priorityQueue[0];\n this.exchange(0, this.numberOfElements--);\n this.sink(0);\n this.priorityQueue[this.numberOfElements + 1] = null;\n return max;\n }\n\n empty() { return this.numberOfElements === -1; }\n\n size() { return this.numberOfElements + 1; }\n\n all() { return this.priorityQueue.slice(0, this.numberOfElements + 1); }\n\n max() { return this.priorityQueue[0]; }\n\n swim(k) {\n while (k > 0 && this.less(Math.floor(k / 2), k)) {\n this.exchange(k, Math.floor(k / 2));\n k = Math.floor(k / 2);\n }\n }\n\n sink(k) {\n while (2 * k <= this.numberOfElements) {\n let j = 2 * k;\n if (j < this.numberOfElements && this.less(j, j + 1)) j++;\n if (!this.less(k, j)) break;\n this.exchange(k, j);\n k = j;\n }\n }\n\n getValueAt(i) {\n // @ts-ignore getter is of unknown type\n return this.getElementValue(this.priorityQueue[i]);\n }\n\n less(i, j) {\n return this.getValueAt(i) < this.getValueAt(j);\n }\n\n exchange(i, j) {\n const t = this.priorityQueue[i];\n this.priorityQueue[i] = this.priorityQueue[j];\n this.priorityQueue[j] = t;\n }\n}\n\nexport function getOffsetPoint(y, x, keypoint: number, offsets) {\n return {\n y: offsets.get(y, x, keypoint),\n x: offsets.get(y, x, keypoint + count),\n };\n}\n\nexport function getImageCoords(part, outputStride: number, offsets) {\n const { heatmapY, heatmapX, id: keypoint } = part;\n const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets);\n return {\n x: part.heatmapX * outputStride + x,\n y: part.heatmapY * outputStride + y,\n };\n}\n\nexport function fillArray(element, size) {\n const result = new Array(size);\n for (let i = 0; i < size; i++) {\n result[i] = element;\n }\n return result;\n}\n\nexport function clamp(a, min, max) {\n if (a < min) return min;\n if (a > max) return max;\n return a;\n}\n\nexport function squaredDistance(y1, x1, y2, x2) {\n const dy = y2 - y1;\n const dx = x2 - x1;\n return dy * dy + dx * dx;\n}\n\nexport function addVectors(a: { x: number, y: number }, b: { x: number, y: number }) {\n return { x: a.x + b.x, y: a.y + b.y };\n}\n\nexport function clampVector(a, min, max) {\n return { y: clamp(a.y, min, max), x: clamp(a.x, min, max) };\n}\n", "/**\n * PoseNet body detection model implementation\n *\n * Based on: [**PoseNet**](https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport type { BodyResult, BodyLandmark, Box } from '../result';\nimport type { Tensor, GraphModel, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\nimport * as utils from './posenetutils';\n\nlet model: GraphModel;\nconst poseNetOutputs = ['MobilenetV1/offset_2/BiasAdd'/* offsets */, 'MobilenetV1/heatmap_2/BiasAdd'/* heatmapScores */, 'MobilenetV1/displacement_fwd_2/BiasAdd'/* displacementFwd */, 'MobilenetV1/displacement_bwd_2/BiasAdd'/* displacementBwd */];\nconst localMaximumRadius = 1;\nconst outputStride = 16;\nconst squaredNmsRadius = 50 ** 2;\n\nfunction traverse(edgeId: number, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) {\n const getDisplacement = (point) => ({\n y: displacements.get(point.y, point.x, edgeId),\n x: displacements.get(point.y, point.x, (displacements.shape[2] / 2) + edgeId),\n });\n const getStridedIndexNearPoint = (point, height, width) => ({\n y: utils.clamp(Math.round(point.y / outputStride), 0, height - 1),\n x: utils.clamp(Math.round(point.x / outputStride), 0, width - 1),\n });\n\n const [height, width] = scores.shape;\n // Nearest neighbor interpolation for the source->target displacements.\n const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width);\n const displacement = getDisplacement(sourceKeypointIndices);\n const displacedPoint = utils.addVectors(sourceKeypoint.position, displacement);\n let targetKeypoint = displacedPoint;\n for (let i = 0; i < offsetRefineStep; i++) {\n const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const offsetPoint = utils.getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets);\n targetKeypoint = utils.addVectors(\n { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride },\n { x: offsetPoint.x, y: offsetPoint.y },\n );\n }\n const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width);\n const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId);\n return { position: targetKeypoint, part: utils.partNames[targetId], score };\n}\n\nexport function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) {\n const tuples = utils.poseChain.map(([parentJoinName, childJoinName]) => ([utils.partIds[parentJoinName], utils.partIds[childJoinName]]));\n const edgesFwd = tuples.map(([, childJointId]) => childJointId);\n const edgesBwd = tuples.map(([parentJointId]) => parentJointId);\n const numParts = scores.shape[2]; // [21,21,17]\n const numEdges = edgesFwd.length;\n const keypoints = new Array(numParts);\n // Start a new detection instance at the position of the root.\n const rootPoint = utils.getImageCoords(root.part, outputStride, offsets);\n keypoints[root.part.id] = {\n score: root.score,\n part: utils.partNames[root.part.id] as BodyLandmark,\n position: rootPoint,\n };\n // Decode the part positions upwards in the tree, following the backward displacements.\n for (let edge = numEdges - 1; edge >= 0; --edge) {\n const sourceId = edgesFwd[edge];\n const targetId = edgesBwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd);\n }\n }\n // Decode the part positions downwards in the tree, following the forward displacements.\n for (let edge = 0; edge < numEdges; ++edge) {\n const sourceId = edgesBwd[edge];\n const targetId = edgesFwd[edge];\n if (keypoints[sourceId] && !keypoints[targetId]) {\n keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd);\n }\n }\n return keypoints;\n}\n\nfunction scoreIsMaximumInLocalWindow(keypointId, score: number, heatmapY: number, heatmapX: number, scores) {\n const [height, width]: [number, number] = scores.shape;\n let localMaximum = true;\n const yStart = Math.max(heatmapY - localMaximumRadius, 0);\n const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height);\n for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) {\n const xStart = Math.max(heatmapX - localMaximumRadius, 0);\n const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width);\n for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) {\n if (scores.get(yCurrent, xCurrent, keypointId) > score) {\n localMaximum = false;\n break;\n }\n }\n if (!localMaximum) break;\n }\n return localMaximum;\n}\n\nexport function buildPartWithScoreQueue(minConfidence, scores) {\n const [height, width, numKeypoints] = scores.shape;\n const queue = new utils.MaxHeap(height * width * numKeypoints, ({ score }) => score);\n for (let heatmapY = 0; heatmapY < height; ++heatmapY) {\n for (let heatmapX = 0; heatmapX < width; ++heatmapX) {\n for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) {\n const score = scores.get(heatmapY, heatmapX, keypointId);\n // Only consider parts with score greater or equal to threshold as root candidates.\n if (score < minConfidence) continue;\n // Only consider keypoints whose score is maximum in a local window.\n if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } });\n }\n }\n }\n return queue;\n}\n\nfunction withinRadius(poses, { x, y }, keypointId) {\n return poses.some(({ keypoints }) => {\n const correspondingKeypoint = keypoints[keypointId]?.position;\n if (!correspondingKeypoint) return false;\n return utils.squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius;\n });\n}\n\nfunction getInstanceScore(existingPoses, keypoints) {\n const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => {\n if (!withinRadius(existingPoses, position, keypointId)) result += score;\n return result;\n }, 0.0);\n return notOverlappedKeypointScores / keypoints.length;\n}\n\nexport function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence) {\n const poses: { keypoints, box: Box, score: number }[] = [];\n const queue = buildPartWithScoreQueue(minConfidence, scores);\n // Generate at most maxDetected object instances per image in decreasing root part score order.\n while (poses.length < maxDetected && !queue.empty()) {\n // The top element in the queue is the next root candidate.\n const root = queue.dequeue();\n // Part-based non-maximum suppression: We reject a root candidate if it is within a disk of `nmsRadius` pixels from the corresponding part of a previously detected instance.\n // @ts-ignore this one is tree walk\n const rootImageCoords = utils.getImageCoords(root.part, outputStride, offsets);\n // @ts-ignore this one is tree walk\n if (withinRadius(poses, rootImageCoords, root.part.id)) continue;\n // Else start a new detection instance at the position of the root.\n let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd);\n keypoints = keypoints.filter((a) => a.score > minConfidence);\n const score = getInstanceScore(poses, keypoints);\n const box = utils.getBoundingBox(keypoints);\n if (score > minConfidence) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 });\n }\n return poses;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n /** posenet is mostly obsolete\n * caching is not implemented\n */\n if (!model?.['executor']) return [];\n const res = tf.tidy(() => {\n if (!model.inputs[0].shape) return [];\n const resized = tf.image.resizeBilinear(input, [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n const normalized = tf.sub(tf.div(tf.cast(resized, 'float32'), 127.5), 1.0);\n const results: Tensor[] = model.execute(normalized, poseNetOutputs) as Tensor[];\n const results3d = results.map((y) => tf.squeeze(y, [0]));\n results3d[1] = tf.sigmoid(results3d[1]); // apply sigmoid on scores\n return results3d;\n });\n\n const buffers = await Promise.all(res.map((tensor: Tensor) => tensor.buffer()));\n for (const t of res) tf.dispose(t);\n\n const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config.body.maxDetected, config.body.minConfidence);\n if (!model.inputs[0].shape) return [];\n const scaled = utils.scalePoses(decoded, [input.shape[1], input.shape[2]], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);\n return scaled;\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.body.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**Robust Video Matting**](https://github.com/PeterL1n/RobustVideoMatting)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\n// internal state varaibles\nconst outputNodes = ['fgr', 'pha', 'r1o', 'r2o', 'r3o', 'r4o'];\nconst t: Record = {}; // contains input tensor and recurrent states\nlet ratio = 0;\n\nfunction init(config: Config) {\n tf.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]);\n t.r1i = tf.tensor(0.0);\n t.r2i = tf.tensor(0.0);\n t.r3i = tf.tensor(0.0);\n t.r4i = tf.tensor(0.0);\n ratio = config.segmentation.ratio || 0.5;\n t.downsample_ratio = tf.tensor(ratio); // initialize downsample ratio\n}\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n init(config);\n return model;\n}\n\nconst normalize = (r: Tensor): Tensor => tf.tidy(() => {\n const squeeze = tf.squeeze(r, ([0]));\n const mul = tf.mul(squeeze, constants.tf255);\n const cast = tf.cast(mul, 'int32');\n return cast;\n});\n\nfunction getRGBA(fgr: Tensor | null, pha: Tensor | null): Tensor { // gets rgba // either fgr or pha must be present\n const rgb = fgr\n ? normalize(fgr) // normalize and use value\n : tf.fill([pha!.shape[1] || 0, pha!.shape[2] || 0, 3], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const a = pha\n ? normalize(pha) // normalize and use value\n : tf.fill([fgr!.shape[1] || 0, fgr!.shape[2] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-non-null-assertion\n const rgba = tf.concat([rgb, a], -1);\n tf.dispose([rgb, a]);\n return rgba;\n}\n\nfunction getState(state: Tensor): Tensor { // gets internal recurrent states\n return tf.tidy(() => {\n const r: Record = {};\n r.unstack = tf.unstack(state, -1);\n r.concat = tf.concat(r.unstack, 1);\n r.split = tf.split(r.concat, 4, 1);\n r.stack = tf.concat(r.split, 2);\n r.squeeze = tf.squeeze(r.stack, [0]);\n r.expand = tf.expandDims(r.squeeze, -1);\n r.add = tf.add(r.expand, 1);\n r.mul = tf.mul(r.add, 127.5);\n r.cast = tf.cast(r.mul, 'int32');\n r.tile = tf.tile(r.cast, [1, 1, 3]);\n r.alpha = tf.fill([(r.tile as Tensor).shape[0] || 0, (r.tile as Tensor).shape[1] || 0, 1], 255, 'int32'); // eslint-disable-line @typescript-eslint/no-unnecessary-type-assertion\n return tf.concat([r.tile, r.alpha], -1);\n });\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor']) return null;\n // const expand = tf.expandDims(input, 0);\n t.src = tf.div(input, 255);\n if (ratio !== config.segmentation.ratio) init(config); // reinitialize recurrent states if requested downsample ratio changed\n const [fgr, pha, r1o, r2o, r3o, r4o] = await model.executeAsync(t, outputNodes) as Tensor[]; // execute model\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n rgba = getRGBA(fgr, pha);\n break;\n case 'alpha':\n rgba = getRGBA(null, pha);\n break;\n case 'foreground':\n rgba = getRGBA(fgr, null);\n break;\n case 'state':\n rgba = getState(r1o); // can view any internal recurrent state r10, r20, r3o, r4o\n break;\n default:\n rgba = tf.tensor(0);\n }\n tf.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]);\n [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; // update recurrent states\n return rgba;\n}\n", "/**\n * Image segmentation for body detection model\n *\n * Based on:\n * - [**MediaPipe Selfie**](https://drive.google.com/file/d/1dCfozqknMa068vVsO2j_1FgZkW_e3VWv/preview)\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log } from '../util/util';\nimport { loadModel } from '../tfjs/load';\nimport { constants } from '../tfjs/constants';\nimport type { GraphModel, Tensor, Tensor4D } from '../tfjs/types';\nimport type { Config } from '../config';\nimport { env } from '../util/env';\n\nlet model: GraphModel;\n\nexport async function load(config: Config): Promise {\n if (!model || env.initial) model = await loadModel(config.segmentation.modelPath);\n else if (config.debug) log('cached model:', model['modelUrl']);\n return model;\n}\n\nexport async function predict(input: Tensor4D, config: Config): Promise {\n if (!model) model = await load(config);\n if (!model?.['executor'] || !model?.inputs?.[0].shape) return null; // something is wrong with the model\n const t: Record = {};\n t.resize = tf.image.resizeBilinear(input, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);\n t.norm = tf.div(t.resize, constants.tf255);\n t.res = model.execute(t.norm) as Tensor;\n t.squeeze = tf.squeeze(t.res, [0]); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]\n t.alpha = tf.image.resizeBilinear(t.squeeze as Tensor4D, [input.shape[1] || 0, input.shape[2] || 0]); // model selfie has a single channel that we can use directly\n t.mul = tf.mul(t.alpha, constants.tf255);\n let rgba: Tensor;\n switch (config.segmentation.mode || 'default') {\n case 'default':\n t.input = tf.squeeze(input);\n t.concat = tf.concat([t.input, t.mul], -1);\n rgba = tf.cast(t.concat, 'int32'); // combined original with alpha\n break;\n case 'alpha':\n rgba = tf.cast(t.mul, 'int32'); // just get alpha value from model\n break;\n default:\n rgba = tf.tensor(0);\n }\n Object.keys(t).forEach((tensor) => tf.dispose(t[tensor]));\n return rgba;\n}\n", "/**\n * Analyze detection Results and sort&combine them into per-person view\n */\n\nimport type { FaceResult, BodyResult, HandResult, GestureResult, PersonResult, Box } from '../result';\n\nexport function join(faces: FaceResult[], bodies: BodyResult[], hands: HandResult[], gestures: GestureResult[], shape: number[] | undefined): PersonResult[] {\n let id = 0;\n const persons: PersonResult[] = [];\n for (const face of faces) { // person is defined primarily by face and then we append other objects as found\n const person: PersonResult = { id: id++, face, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] };\n for (const body of bodies) {\n if (face.box[0] > body.box[0] // x within body\n && face.box[0] < body.box[0] + body.box[2]\n && face.box[1] + face.box[3] > body.box[1] // y within body\n && face.box[1] + face.box[3] < body.box[1] + body.box[3]) {\n person.body = body;\n }\n }\n if (person.body) { // only try to join hands if body is found\n for (const hand of hands) {\n if (hand.box[0] + hand.box[2] > person.body.box[0] // x within body for left hand\n && hand.box[0] + hand.box[2] < person.body.box[0] + person.body.box[2]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for left hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.left = hand;\n }\n if (hand.box[0] < person.body.box[0] + person.body.box[2] // x within body for right hand\n && hand.box[0] > person.body.box[0]\n && hand.box[1] + hand.box[3] > person.body.box[1] // x within body for right hand\n && hand.box[1] + hand.box[3] < person.body.box[1] + person.body.box[3]) {\n if (person.hands) person.hands.right = hand;\n }\n }\n }\n for (const gesture of gestures) { // append all gestures according to ids\n if (gesture['face'] !== undefined && gesture['face'] === face.id) person.gestures.push(gesture);\n else if (gesture['iris'] !== undefined && gesture['iris'] === face.id) person.gestures.push(gesture);\n else if (gesture['body'] !== undefined && gesture['body'] === person.body?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.left?.id) person.gestures.push(gesture);\n else if (gesture['hand'] !== undefined && gesture['hand'] === person.hands.right?.id) person.gestures.push(gesture);\n }\n\n // create new overarching box from all boxes belonging to person\n const x: number[] = [];\n const y: number[] = [];\n const extractXY = (box: Box | undefined) => { // extract all [x, y] coordinates from boxes [x, y, width, height]\n if (box && box.length === 4) {\n x.push(box[0], box[0] + box[2]);\n y.push(box[1], box[1] + box[3]);\n }\n };\n extractXY(person.face.box);\n extractXY(person.body?.box);\n extractXY(person.hands.left?.box);\n extractXY(person.hands.right?.box);\n const minX = Math.min(...x);\n const minY = Math.min(...y);\n person.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; // create new overarching box\n\n // shape is known so we calculate boxRaw as well\n if (shape?.[1] && shape?.[2]) person.boxRaw = [person.box[0] / shape[2], person.box[1] / shape[1], person.box[2] / shape[2], person.box[3] / shape[1]];\n\n persons.push(person);\n }\n return persons;\n}\n", "/**\n * Embedded sample images used during warmup in dataURL format\n */\n\n// data:image/jpeg;base64,\nexport const face: string = 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"/**\n * Warmup algorithm that uses embedded images to exercise loaded models for faster future inference\n */\n\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep } from './util/util';\nimport * as sample from './sample';\nimport * as image from './image/image';\nimport * as backend from './tfjs/backend';\nimport { env } from './util/env';\nimport { empty, Result } from './result';\nimport type { Config } from './config';\nimport type { Human } from './human';\nimport type { Tensor, DataType } from './tfjs/types';\n\nasync function warmupBitmap(instance: Human): Promise {\n const b64toBlob = (base64: string, type = 'application/octet-stream') => fetch(`data:${type};base64,${base64}`).then((res) => res.blob());\n let blob: Blob | null;\n let res: Result | undefined;\n switch (instance.config.warmup) {\n case 'face': blob = await b64toBlob(sample.face); break;\n case 'body':\n case 'full': blob = await b64toBlob(sample.body); break;\n default: blob = null;\n }\n if (blob) {\n const bitmap = await createImageBitmap(blob);\n res = await instance.detect(bitmap, instance.config);\n bitmap.close();\n }\n return res;\n}\n\nasync function warmupCanvas(instance: Human): Promise {\n return new Promise((resolve) => {\n let src: string;\n // let size = 0;\n switch (instance.config.warmup) {\n case 'face':\n // size = 256;\n src = 'data:image/jpeg;base64,' + sample.face;\n break;\n case 'full':\n case 'body':\n // size = 1200;\n src = 'data:image/jpeg;base64,' + sample.body;\n break;\n default:\n src = '';\n }\n // src = encodeURI('../assets/human-sample-upper.jpg');\n let img: HTMLImageElement;\n if (typeof Image !== 'undefined') img = new Image();\n // @ts-ignore env.image is an external monkey-patch\n else if (env.Image) img = new env.Image();\n else {\n resolve(undefined);\n return;\n }\n img.onload = async () => {\n const canvas = image.canvas(img.naturalWidth, img.naturalHeight);\n if (!canvas) {\n log('Warmup: Canvas not found');\n resolve(undefined);\n } else {\n const ctx = canvas.getContext('2d') as CanvasRenderingContext2D;\n if (ctx) ctx.drawImage(img, 0, 0);\n // const data = ctx?.getImageData(0, 0, canvas.height, canvas.width);\n const tensor = await instance.image(canvas, true);\n const res = tensor.tensor ? await instance.detect(tensor.tensor, instance.config) : undefined;\n resolve(res);\n }\n };\n if (src) img.src = src;\n else resolve(undefined);\n });\n}\n\nasync function warmupNode(instance: Human): Promise {\n const atob = (str: string) => Buffer.from(str, 'base64');\n let img;\n if (instance.config.warmup === 'face') img = atob(sample.face);\n else img = atob(sample.body);\n let res: Result;\n if (('node' in tf) && (tf.getBackend() === 'tensorflow')) {\n // @ts-ignore\n const data: Tensor = tf['node'].decodeJpeg(img); // eslint-disable-line import/namespace\n const expanded: Tensor = tf.expandDims(data, 0);\n instance.tf.dispose(data);\n // log('Input:', expanded);\n res = await instance.detect(expanded, instance.config);\n instance.tf.dispose(expanded);\n } else {\n if (instance.config.debug) log('Warmup tfjs-node not loaded');\n /*\n const input = await canvasJS.loadImage(img);\n const canvas = canvasJS.createCanvas(input.width, input.height);\n const ctx = canvas.getContext('2d');\n ctx.drawImage(img, 0, 0, input.width, input.height);\n res = await instance.detect(input, instance.config);\n */\n }\n // @ts-ignore\n return res;\n}\n\nasync function runInference(instance: Human) {\n let res: Result | undefined;\n if (typeof createImageBitmap === 'function') res = await warmupBitmap(instance);\n else if ((typeof Image !== 'undefined') || (env.Canvas !== undefined)) res = await warmupCanvas(instance);\n else res = await warmupNode(instance);\n return res;\n}\n\n/** Runs pre-compile on all loaded models */\nexport async function runCompile(instance: Human) {\n // @ts-ignore private property\n if (!tf.env().flagRegistry.ENGINE_COMPILE_ONLY) return; // tfjs does not support compile-only inference\n const backendType = tf.getBackend();\n const webGLBackend = tf.backend();\n if ((backendType !== 'webgl' && backendType !== 'humangl') || !webGLBackend?.['checkCompileCompletion']) {\n // log('compile pass: skip');\n return;\n }\n tf.env().set('ENGINE_COMPILE_ONLY', true);\n const numTensorsStart = tf.engine().state.numTensors;\n const compiledModels: string[] = [];\n for (const [modelName, model] of Object.entries(instance.models.models)) {\n if (!model) continue;\n const shape = (model?.modelSignature && model?.inputs?.[0]?.shape) ? [...model.inputs[0].shape] : [1, 64, 64, 3];\n const dtype: DataType = (model?.modelSignature && model?.inputs?.[0]?.dtype) ? model.inputs[0].dtype : 'float32';\n for (let dim = 0; dim < shape.length; dim++) {\n if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; // override batch number and any dynamic dimensions\n }\n const tensor = tf.zeros(shape, dtype);\n try {\n const res = model.execute(tensor);\n compiledModels.push(modelName);\n if (Array.isArray(res)) res.forEach((t) => tf.dispose(t));\n else tf.dispose(res);\n } catch {\n if (instance.config.debug) log('compile fail model:', modelName);\n }\n tf.dispose(tensor);\n }\n const kernels = await webGLBackend['checkCompileCompletionAsync']();\n webGLBackend['getUniformLocations']();\n if (instance.config.debug) log('compile pass:', { models: compiledModels, kernels: kernels.length });\n tf.env().set('ENGINE_COMPILE_ONLY', false);\n const numTensorsEnd = tf.engine().state.numTensors;\n if ((numTensorsEnd - numTensorsStart) > 0) log('tensor leak:', numTensorsEnd - numTensorsStart);\n}\n\n/** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used in browser environments for `webgl` and `humangl` backends\n * @param userConfig?: Config\n*/\nexport async function warmup(instance: Human, userConfig?: Partial): Promise {\n await backend.check(instance, false);\n const t0 = now();\n instance.state = 'warmup';\n if (userConfig) instance.config = mergeDeep(instance.config, userConfig) as Config;\n if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === 'none') {\n return empty();\n }\n return new Promise(async (resolve) => {\n await instance.models.load();\n await tf.ready();\n await runCompile(instance);\n const res = await runInference(instance);\n const t1 = now();\n if (instance.config.debug) log('warmup', instance.config.warmup, Math.round(t1 - t0), 'ms');\n instance.emit('warmup');\n resolve(res);\n });\n}\n", "/**\n * Human main module\n * @default Human Library\n * @summary \n * @author \n * @copyright \n * @license MIT\n */\n\n// module imports\nimport * as tf from 'dist/tfjs.esm.js';\nimport { log, now, mergeDeep, validate } from './util/util';\nimport { defaults } from './config';\nimport { env, Env } from './util/env';\nimport { WebCam } from './util/webcam';\nimport { setModelLoadOptions } from './tfjs/load';\nimport * as app from '../package.json';\nimport * as backend from './tfjs/backend';\nimport * as draw from './draw/draw';\nimport * as blazepose from './body/blazepose';\nimport * as centernet from './object/centernet';\nimport * as efficientpose from './body/efficientpose';\nimport * as face from './face/face';\nimport * as facemesh from './face/facemesh';\nimport * as gesture from './gesture/gesture';\nimport * as handpose from './hand/handpose';\nimport * as handtrack from './hand/handtrack';\nimport * as image from './image/image';\nimport * as interpolate from './util/interpolate';\nimport * as meet from './segmentation/meet';\nimport * as match from './face/match';\nimport * as models from './models';\nimport * as movenet from './body/movenet';\nimport * as nanodet from './object/nanodet';\nimport * as persons from './util/persons';\nimport * as posenet from './body/posenet';\nimport * as rvm from './segmentation/rvm';\nimport * as selfie from './segmentation/selfie';\nimport * as warmups from './warmup';\n\n// type definitions\nimport { Input, Config, Result, FaceResult, HandResult, BodyResult, ObjectResult, GestureResult, AnyCanvas, empty } from './exports';\nimport type { Tensor, Tensor4D } from './tfjs/types';\n// type exports\nexport * from './exports';\n\n/** **Human** library main class\n *\n * All methods and properties are available only as members of Human class\n *\n * - Configuration object definition: {@link Config}\n * - Results object definition: {@link Result}\n * - Possible inputs: {@link Input}\n *\n * @param userConfig - {@link Config}\n * @returns instance of {@link Human}\n */\nexport class Human {\n /** Current version of Human library in *semver* format */\n version: string;\n\n /** Current configuration\n * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262)\n */\n config: Config;\n\n /** Last known result of detect run\n * - Can be accessed anytime after initial detection\n */\n result: Result;\n\n /** Current state of Human library\n * - Can be polled to determine operations that are currently executed\n * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle'\n */\n state: string;\n\n /** currenty processed image tensor and canvas */\n process: { tensor: Tensor | null, canvas: AnyCanvas | null };\n\n /** Instance of TensorFlow/JS used by Human\n * - Can be embedded or externally provided\n * [TFJS API](https://js.tensorflow.org/api/latest/)\n */\n tf;\n\n /** Object containing environment information used for diagnostics */\n env: Env = env;\n\n /** Draw helper classes that can draw detected objects on canvas using specified draw\n * - canvas: draws input to canvas\n * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions}\n * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas\n */\n // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions };\n draw: typeof draw = draw;\n\n /** Face Matching\n * - similarity: compare two face descriptors and return similarity index\n * - distance: compare two face descriptors and return raw calculated differences\n * - find: compare face descriptor to array of face descriptors and return best match\n */\n match: typeof match = match;\n\n /** Currently loaded models\n * @internal\n * {@link models#Models}\n */\n models: models.Models;\n\n /** Container for events dispatched by Human\n * Possible events:\n * - `create`: triggered when Human object is instantiated\n * - `load`: triggered when models are loaded (explicitly or on-demand)\n * - `image`: triggered when input image is processed\n * - `result`: triggered when detection is complete\n * - `warmup`: triggered when warmup is complete\n * - `error`: triggered on some errors\n */\n events: EventTarget | undefined;\n /** Reference face triangualtion array of 468 points, used for triangle references between points */\n faceTriangulation: number[];\n /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */\n faceUVMap: [number, number][];\n /** Performance object that contains values for all recently performed operations */\n performance: Record; // perf members are dynamically defined as needed\n #numTensors: number;\n #analyzeMemoryLeaks: boolean;\n #checkSanity: boolean;\n // definition end\n\n /** Constructor for **Human** library that is futher used for all operations\n * @param userConfig - user configuration object {@link Config}\n */\n constructor(userConfig?: Partial) {\n /*\n defaults.wasmPath = tf.version['tfjs-core'].includes('-') // custom build or official build\n ? 'https://vladmandic.github.io/tfjs/dist/'\n : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tf.version_core}/dist/`;\n */\n const tfVersion = (tf.version.tfjs || tf.version_core).replace(/-(.*)/, '');\n defaults.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`;\n defaults.modelBasePath = env.browser ? '../models/' : 'file://models/';\n this.version = app.version; // expose version property on instance of class\n Object.defineProperty(this, 'version', { value: app.version }); // expose version property directly on class itself\n this.config = JSON.parse(JSON.stringify(defaults));\n Object.seal(this.config);\n this.config.cacheModels = typeof indexedDB !== 'undefined';\n if (userConfig) this.config = mergeDeep(this.config, userConfig);\n setModelLoadOptions(this.config);\n this.tf = tf;\n this.state = 'idle';\n this.#numTensors = 0;\n this.#analyzeMemoryLeaks = false;\n this.#checkSanity = false;\n this.performance = {};\n this.events = (typeof EventTarget !== 'undefined') ? new EventTarget() : undefined;\n // object that contains all initialized models\n this.models = new models.Models(this);\n // reexport draw methods\n draw.init();\n this.result = empty();\n // export access to image processing\n this.process = { tensor: null, canvas: null };\n // export raw access to underlying models\n this.faceTriangulation = facemesh.triangulation;\n this.faceUVMap = facemesh.uvmap;\n // init model validation\n models.validateModel(this, null, '');\n // include platform info\n this.emit('create');\n if (this.config.debug || this.env.browser) log(`version: ${this.version}`);\n if (this.config.debug) log(`tfjs version: ${this.tf.version['tfjs-core']}`);\n const envTemp = JSON.parse(JSON.stringify(this.env));\n delete envTemp.kernels;\n delete envTemp.initial;\n delete envTemp.perfadd;\n if (this.config.debug) log('environment:', envTemp);\n }\n\n /** internal function to measure tensor leaks */\n analyze = (...msg: string[]) => {\n if (!this.#analyzeMemoryLeaks) return;\n const currentTensors = this.tf.engine().state.numTensors;\n const previousTensors = this.#numTensors;\n this.#numTensors = currentTensors;\n const leaked = currentTensors - previousTensors;\n if (leaked !== 0) log(...msg, leaked);\n };\n\n /** internal function for quick sanity check on inputs @hidden */\n #sanity = (input: Input): null | string => {\n if (!this.#checkSanity) return null;\n if (!input) return 'input is not defined';\n if (this.env.node && !(input instanceof tf.Tensor)) return 'input must be a tensor';\n try {\n this.tf.getBackend();\n } catch {\n return 'backend not loaded';\n }\n return null;\n };\n\n /** Reset configuration to default values */\n reset(): void {\n const currentBackend = this.config.backend; // save backend;\n this.config = JSON.parse(JSON.stringify(defaults));\n this.config.backend = currentBackend;\n image.reset();\n env.initial = true;\n }\n\n /** Validate current configuration schema */\n validate(userConfig?: Partial) {\n const msgs = validate(defaults, userConfig || this.config);\n if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig) as Config;\n return msgs;\n }\n\n /** Utility wrapper for performance.now() */\n now(): number { // eslint-disable-line class-methods-use-this\n return now();\n }\n\n /** Process input as return canvas and tensor\n *\n * @param input - any input {@link Input}\n * @param getTensor - should image processing also return tensor or just canvas\n * Returns object with `tensor` and `canvas`\n */\n image(input: Input, getTensor: boolean = false) {\n return image.process(input, this.config, getTensor);\n }\n\n /** Segmentation method takes any input and returns RGBA tensor\n * Note: Segmentation is not triggered as part of detect process\n *\n * @param input - {@link Input}\n * Returns tensor which contains image data in RGBA format\n */\n async segmentation(input: Input, userConfig?: Partial): Promise {\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (!this.config.segmentation.enabled) return null;\n const processed = await image.process(input, this.config);\n if (!processed.tensor) return null;\n let tensor: Tensor | null = null;\n if (this.config.segmentation.modelPath?.includes('rvm')) tensor = await rvm.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('meet')) tensor = await meet.predict(processed.tensor, this.config);\n if (this.config.segmentation.modelPath?.includes('selfie')) tensor = await selfie.predict(processed.tensor, this.config);\n tf.dispose(processed.tensor);\n return tensor;\n }\n\n /** Compare two input tensors for pixel similarity\n * - use `human.image` to process any valid input and get a tensor that can be used for compare\n * - when passing manually generated tensors:\n * - both input tensors must be in format [1, height, width, 3]\n * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor\n * - return value is pixel similarity score normalized by input resolution and rgb channels\n */\n compare(firstImageTensor: Tensor, secondImageTensor: Tensor): Promise {\n return image.compare(this.config, firstImageTensor, secondImageTensor);\n }\n\n /** Explicit backend initialization\n * - Normally done implicitly during initial load phase\n * - Call to explictly register and initialize TFJS backend without any other operations\n * - Use when changing backend during runtime\n */\n async init(): Promise {\n await backend.check(this, true);\n await this.tf.ready();\n image.reset();\n }\n\n /** WebCam helper methods\n *\n */\n public webcam = new WebCam();\n\n /** Load method preloads all configured models on-demand\n * - Not explicitly required as any required model is load implicitly on it's first run\n *\n * @param userConfig - {@link Config}\n */\n async load(userConfig?: Partial): Promise {\n this.state = 'load';\n const timeStamp = now();\n const count = Object.values(this.models.models).filter((model) => model).length;\n if (userConfig) this.config = mergeDeep(this.config, userConfig) as Config;\n if (this.env.initial) { // print version info on first run and check for correct backend setup\n if (!await backend.check(this, false)) log('error: backend check failed');\n await tf.ready();\n if (this.env.browser) {\n if (this.config.debug) log('configuration:', this.config);\n if (this.config.debug) log('tf flags:', this.tf.ENV.flags);\n }\n }\n\n await this.models.load(this); // actually loads models\n if (this.env.initial && this.config.debug) log('tf engine state:', this.tf.engine().state.numBytes, 'bytes', this.tf.engine().state.numTensors, 'tensors'); // print memory stats on first run\n this.env.initial = false;\n\n const loaded = Object.values(this.models.models).filter((model) => model).length;\n if (loaded !== count) { // number of loaded models changed\n this.models.validate(); // validate kernel ops used by model against current backend\n this.emit('load');\n }\n\n const current = Math.trunc(now() - timeStamp);\n if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;\n }\n\n /** emit event */\n emit = (event: string) => {\n if (this.events?.dispatchEvent) this.events.dispatchEvent(new Event(event));\n };\n\n /** Runs interpolation using last known result and returns smoothened result\n * Interpolation is based on time since last known result so can be called independently\n *\n * @param result - {@link Result} optional use specific result set to run interpolation on\n * @returns result - {@link Result}\n */\n next(result: Result = this.result): Result {\n return interpolate.calc(result, this.config);\n }\n\n /** Warmup method pre-initializes all configured models for faster inference\n * - can take significant time on startup\n * - only used for `webgl` and `humangl` backends\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async warmup(userConfig?: Partial) {\n const t0 = now();\n const res = await warmups.warmup(this, userConfig);\n const t1 = now();\n this.performance.warmup = Math.trunc(t1 - t0);\n return res;\n }\n\n /** Run detect with tensorflow profiling\n * - result object will contain total exeuction time information for top-20 kernels\n * - actual detection object can be accessed via `human.result`\n */\n async profile(input: Input, userConfig?: Partial): Promise<{ kernel: string, time: number, perc: number }[]> {\n // @ts-ignore profile wraps method return values\n const profile = await this.tf.profile(() => this.detect(input, userConfig));\n const kernels: Record = {};\n let total = 0;\n for (const kernel of profile.kernels) { // sum kernel time values per kernel\n const ms = Number(kernel.kernelTimeMs) || 0;\n if (kernels[kernel.name]) kernels[kernel.name] += ms;\n else kernels[kernel.name] = ms;\n total += ms;\n }\n const kernelArr: { kernel: string, time: number, perc: number }[] = [];\n Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1] as unknown as number, perc: 0 })); // convert to array\n for (const kernel of kernelArr) {\n kernel.perc = Math.round(1000 * kernel.time / total) / 1000;\n kernel.time = Math.round(1000 * kernel.time) / 1000;\n }\n kernelArr.sort((a, b) => b.time - a.time); // sort\n kernelArr.length = 20; // crop\n return kernelArr;\n }\n\n /** Main detection method\n * - Analyze configuration: {@link Config}\n * - Pre-process input: {@link Input}\n * - Run inference for all configured models\n * - Process and return result: {@link Result}\n *\n * @param input - {@link Input}\n * @param userConfig - {@link Config}\n * @returns result - {@link Result}\n */\n async detect(input: Input, userConfig?: Partial): Promise {\n // detection happens inside a promise\n this.state = 'detect';\n return new Promise(async (resolve) => {\n this.state = 'config';\n let timeStamp;\n\n // update configuration\n this.config = mergeDeep(this.config, userConfig) as Config;\n\n // sanity checks\n this.state = 'check';\n const error = this.#sanity(input);\n if (error) {\n log(error, input);\n this.emit('error');\n resolve(empty(error));\n }\n\n const timeStart = now();\n\n // load models if enabled\n await this.load();\n\n timeStamp = now();\n this.state = 'image';\n const img = await image.process(input, this.config) as { canvas: AnyCanvas, tensor: Tensor4D };\n this.process = img;\n this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Get Image:');\n\n if (!img.tensor) {\n if (this.config.debug) log('could not convert input to tensor');\n this.emit('error');\n resolve(empty('could not convert input to tensor'));\n return;\n }\n this.emit('image');\n\n timeStamp = now();\n this.config.skipAllowed = await image.skip(this.config, img.tensor);\n this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; // disable autoBrightness on scene change\n if (!this.performance.totalFrames) this.performance.totalFrames = 0;\n if (!this.performance.cachedFrames) this.performance.cachedFrames = 0;\n (this.performance.totalFrames)++;\n if (this.config.skipAllowed) this.performance.cachedFrames++;\n this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n this.analyze('Check Changed:');\n\n // prepare where to store model results\n // keep them with weak typing as it can be promise or not\n let faceRes: FaceResult[] | Promise | never[] = [];\n let bodyRes: BodyResult[] | Promise | never[] = [];\n let handRes: HandResult[] | Promise | never[] = [];\n let objectRes: ObjectResult[] | Promise | never[] = [];\n\n // run face detection followed by all models that rely on face bounding box: face mesh, age, gender, emotion\n this.state = 'detect:face';\n if (this.config.async) {\n faceRes = this.config.face.enabled ? face.detectFace(this, img.tensor) : [];\n if (this.performance.face) delete this.performance.face;\n } else {\n timeStamp = now();\n faceRes = this.config.face.enabled ? await face.detectFace(this, img.tensor) : [];\n this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n\n if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; // need face result for auto-detect number of hands or bodies\n\n // run body: can be posenet, blazepose, efficientpose, movenet\n this.analyze('Start Body:');\n this.state = 'detect:body';\n const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of bodies\n if (this.config.async) {\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? movenet.predict(img.tensor, bodyConfig) : [];\n if (this.performance.body) delete this.performance.body;\n } else {\n timeStamp = now();\n if (this.config.body.modelPath?.includes('posenet')) bodyRes = this.config.body.enabled ? await posenet.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('blazepose')) bodyRes = this.config.body.enabled ? await blazepose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('efficientpose')) bodyRes = this.config.body.enabled ? await efficientpose.predict(img.tensor, bodyConfig) : [];\n else if (this.config.body.modelPath?.includes('movenet')) bodyRes = this.config.body.enabled ? await movenet.predict(img.tensor, bodyConfig) : [];\n this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Body:');\n\n // run handpose\n this.analyze('Start Hand:');\n this.state = 'detect:hand';\n const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * (faceRes as FaceResult[]).length : 1 } }) : this.config; // autodetect number of hands\n if (this.config.async) {\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? handtrack.predict(img.tensor, handConfig) : [];\n if (this.performance.hand) delete this.performance.hand;\n } else {\n timeStamp = now();\n if (this.config.hand.detector?.modelPath?.includes('handdetect')) handRes = this.config.hand.enabled ? await handpose.predict(img.tensor, handConfig) : [];\n else if (this.config.hand.detector?.modelPath?.includes('handtrack')) handRes = this.config.hand.enabled ? await handtrack.predict(img.tensor, handConfig) : [];\n this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Hand:');\n\n // run object detection\n this.analyze('Start Object:');\n this.state = 'detect:object';\n if (this.config.async) {\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? centernet.predict(img.tensor, this.config) : [];\n if (this.performance.object) delete this.performance.object;\n } else {\n timeStamp = now();\n if (this.config.object.modelPath?.includes('nanodet')) objectRes = this.config.object.enabled ? await nanodet.predict(img.tensor, this.config) : [];\n else if (this.config.object.modelPath?.includes('centernet')) objectRes = this.config.object.enabled ? await centernet.predict(img.tensor, this.config) : [];\n this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n }\n this.analyze('End Object:');\n\n // if async wait for results\n this.state = 'detect:await';\n if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);\n\n // run gesture analysis last\n this.state = 'detect:gesture';\n let gestureRes: GestureResult[] = [];\n if (this.config.gesture.enabled) {\n timeStamp = now();\n gestureRes = [...gesture.face(faceRes as FaceResult[]), ...gesture.body(bodyRes as BodyResult[]), ...gesture.hand(handRes as HandResult[]), ...gesture.iris(faceRes as FaceResult[])];\n if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);\n else if (this.performance.gesture) delete this.performance.gesture;\n }\n\n this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);\n const shape = this.process.tensor?.shape || [0, 0, 0, 0];\n this.result = {\n face: faceRes as FaceResult[],\n body: bodyRes as BodyResult[],\n hand: handRes as HandResult[],\n gesture: gestureRes,\n object: objectRes as ObjectResult[],\n performance: this.performance,\n canvas: this.process.canvas,\n timestamp: Date.now(),\n error: null,\n width: shape[2],\n height: shape[1],\n get persons() { return persons.join(faceRes as FaceResult[], bodyRes as BodyResult[], handRes as HandResult[], gestureRes, shape); },\n };\n\n // finally dispose input tensor\n tf.dispose(img.tensor);\n\n // log('Result:', result);\n this.emit('detect');\n this.state = 'idle';\n resolve(this.result);\n });\n }\n\n /** Helper function\n * @param ms - sleep time in miliseconds\n */\n async sleep(ms: number): Promise { // eslint-disable-line class-methods-use-this\n return new Promise((resolve) => { setTimeout(resolve, ms); });\n }\n\n /** internal structure that keeps track of processed videos @hidden */\n #loops: Record = {};\n /** Continously detect video frames\n * @param element - HTMLVideoElement input\n * @param run - boolean run continously or stop if already running, default true\n * @param delay - number delay detection between frames for number of miliseconds, default 0\n */\n async video(element: HTMLVideoElement, run: boolean = true, delay: number = 0) {\n if (run) {\n if (!this.#loops[element.id]) {\n if (this.config.debug) log('video start', element.id);\n this.#loops[element.id] = true;\n }\n if (!element.paused && this.#loops[element.id] && (element.readyState >= 2)) await this.detect(element);\n if (delay > 0) await this.sleep(delay);\n if (this.#loops[element.id]) requestAnimationFrame(() => this.video(element, run, delay));\n } else {\n if (this.config.debug) log('video stop', element.id);\n this.#loops[element.id] = false;\n }\n }\n}\n\n/** Class Human as default export */\n/* eslint no-restricted-exports: [\"off\", { \"restrictedNamedExports\": [\"default\"] }] */\nexport { Human as default, match, draw, models };\n"], + "mappings": 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return to; -}; -var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps( - // If the importer is in node compatibility mode or this is not an ESM - // file that has been converted to a CommonJS file using a Babel- - // compatible transform (i.e. "__esModule" has not been set), then set - // "default" to the CommonJS "module.exports" for node compatibility. - isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target, - mod3 -)); -var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3); -var __publicField = (obj, key, value) => __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); -var __accessCheck = (obj, member, msg) => member.has(obj) || __typeError("Cannot " + msg); -var __privateGet = (obj, member, getter) => (__accessCheck(obj, member, "read from private field"), getter ? getter.call(obj) : member.get(obj)); -var 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if (!__hasOwnProp2.call(to, key) && key !== except) - __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); - } - return to; - }; - var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default")); - var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3); - var tf_node_gpu_exports = {}; - __export2(tf_node_gpu_exports, { - version: () => version7 - }); - module2.exports = __toCommonJS2(tf_node_gpu_exports); - __reExport(tf_node_gpu_exports, require("@tensorflow/tfjs-node-gpu"), module2.exports); - var version4 = "4.21.0"; - var version22 = "4.21.0"; - var version32 = "4.21.0"; - var version42 = "4.21.0"; - var version5 = "4.21.0"; - var version6 = "4.21.0"; - var version7 = { - tfjs: version4, - "tfjs-core": version4, - // 'tfjs-data': tfjsDataVersion, - // 'tfjs-layers': tfjsLayersVersion, - "tfjs-converter": version22, - "tfjs-backend-cpu": version32, - "tfjs-backend-webgl": version42, - "tfjs-backend-wasm": version5, - "tfjs-backend-webgpu": version6 - }; - } -}); - -// src/human.ts -var human_exports = {}; -__export(human_exports, { - Env: () => Env, - Human: () => Human, - default: () => Human, - defaults: () => config, - draw: () => draw_exports, - empty: () => empty, - env: () => env, - match: () => match_exports, - models: () => models_exports2 -}); -module.exports = __toCommonJS(human_exports); -var tf38 = __toESM(require_tfjs_esm()); - -// src/util/util.ts -function log(...msg) { - const dt = /* @__PURE__ */ new Date(); - const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; - if (msg) console.log(ts, "Human:", ...msg); -} -function join(folder, file) { - const separator = folder.endsWith("/") ? "" : "/"; - const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); - const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; - if (!path.toLocaleLowerCase().includes(".json")) throw new Error(`modelpath error: expecting json file: ${path}`); - return path; -} -var now = () => { - if (typeof performance !== "undefined") return performance.now(); - return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); -}; -function validate(defaults, config3, parent = "config", msgs = []) { - for (const key of Object.keys(config3)) { - if (typeof config3[key] === "object") { - validate(defaults[key], config3[key], key, msgs); - } else { - const defined = defaults && typeof defaults[key] !== "undefined"; - if (!defined) msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); - const same = defaults && typeof defaults[key] === typeof config3[key]; - 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n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var v=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Jt(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Jt(e[r],t[r],r,o);else{let s=e&&typeof e[r]!="undefined";s||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let A=e&&typeof e[r]==typeof t[r];s&&!A&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&b("invalid configuration",o),o}function i0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let 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Ke={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,scale:1.4,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,scale:2.3,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var W0=Z(V());var I=Z(V());var W1=` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -309,8 +14,7 @@ var vertexIdentity = ` vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`; -var colorMatrixWithAlpha = ` +`;var D1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -322,8 +26,7 @@ var colorMatrixWithAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`; -var colorMatrixWithoutAlpha = ` +`,F1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -335,8 +38,7 @@ var colorMatrixWithoutAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`; -var pixelate = ` +`,B1=` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -349,8 +51,7 @@ var pixelate = ` vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`; -var blur = ` +`,H1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -373,8 +74,7 @@ var blur = ` gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`; -var convolution = ` +`,G1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -396,6031 +96,20 @@ var convolution = ` c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`; - -// src/image/imagefx.ts -var collect = (source, prefix, collection) => { - const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); - source.replace(r, (match2, name) => { - collection[name] = 0; - return match2; - }); -}; -var GLProgram = class { - constructor(gl, vertexSource, fragmentSource) { - __publicField(this, "uniform", {}); - __publicField(this, "attribute", {}); - __publicField(this, "gl"); - __publicField(this, "id"); - __publicField(this, "compile", (source, type) => { - const shader = this.gl.createShader(type); - if (!shader) { - log("filter: could not create shader"); - return null; - } - this.gl.shaderSource(shader, source); - this.gl.compileShader(shader); - if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { - log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); - return null; - } - return shader; - }); - this.gl = gl; - const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); - const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); - this.id = this.gl.createProgram(); - if (!vertexShader || !fragmentShader) return; - if (!this.id) { - log("filter: could not create webgl program"); - return; - } - this.gl.attachShader(this.id, vertexShader); - this.gl.attachShader(this.id, fragmentShader); - this.gl.linkProgram(this.id); - if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { - log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); - return; - } - this.gl.useProgram(this.id); - collect(vertexSource, "attribute", this.attribute); - for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a); - collect(vertexSource, "uniform", this.uniform); - collect(fragmentSource, "uniform", this.uniform); - for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u); - } -}; -function GLImageFilter() { - let drawCount = 0; - let sourceTexture = null; - let lastInChain = false; - let currentFramebufferIndex = -1; - let tempFramebuffers = [null, null]; - let filterChain = []; - let vertexBuffer = null; - let currentProgram = null; - const fxcanvas = canvas(100, 100); - const shaderProgramCache = {}; - const DRAW = { INTERMEDIATE: 1 }; - const gl = fxcanvas.getContext("webgl"); - if (!gl) { - log("filter: cannot get webgl context"); - return; - } - this.gl = gl; - function resize(width, height) { - if (width === fxcanvas.width && height === fxcanvas.height) return; - fxcanvas.width = width; - fxcanvas.height = height; - if (!vertexBuffer) { - const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); - vertexBuffer = gl.createBuffer(); - gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); - gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); - gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); - } - gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); - tempFramebuffers = [null, null]; - } - function createFramebufferTexture(width, height) { - const fbo = gl.createFramebuffer(); - gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); - const renderbuffer = gl.createRenderbuffer(); - gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); - const texture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, texture); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); - gl.bindTexture(gl.TEXTURE_2D, null); - gl.bindFramebuffer(gl.FRAMEBUFFER, null); - return { fbo, texture }; - } - function getTempFramebuffer(index2) { - tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); - return tempFramebuffers[index2]; - } - function draw(flags = 0) { - if (!currentProgram) return; - let source = null; - let target = null; - let flipY = false; - if (drawCount === 0) source = sourceTexture; - else source = getTempFramebuffer(currentFramebufferIndex).texture || null; - drawCount++; - if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { - target = null; - flipY = drawCount % 2 === 0; - } else { - currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; - target = getTempFramebuffer(currentFramebufferIndex).fbo || null; - } - gl.bindTexture(gl.TEXTURE_2D, source); - gl.bindFramebuffer(gl.FRAMEBUFFER, target); - gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); - gl.drawArrays(gl.TRIANGLES, 0, 6); - } - function compileShader(fragmentSource) { - if (shaderProgramCache[fragmentSource]) { - currentProgram = shaderProgramCache[fragmentSource]; - gl.useProgram((currentProgram ? currentProgram.id : null) || null); - return currentProgram; - } - currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); - if (!currentProgram) { - log("filter: could not get webgl program"); - return null; - } - const floatSize = Float32Array.BYTES_PER_ELEMENT; - const vertSize = 4 * floatSize; - gl.enableVertexAttribArray(currentProgram.attribute["pos"]); - gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); - gl.enableVertexAttribArray(currentProgram.attribute["uv"]); - gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); - shaderProgramCache[fragmentSource] = currentProgram; - return currentProgram; - } - const filter = { - colorMatrix: (matrix) => { - const m = new Float32Array(matrix); - m[4] /= 255; - m[9] /= 255; - m[14] /= 255; - m[19] /= 255; - const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; - const program = compileShader(shader); - if (!program) return; - gl.uniform1fv(program.uniform["m"], m); - draw(); - }, - brightness: (brightness) => { - const b = (brightness || 0) + 1; - filter.colorMatrix([ - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - saturation: (amount) => { - const x = (amount || 0) * 2 / 3 + 1; - const y = (x - 1) * -0.5; - filter.colorMatrix([ - x, - y, - y, - 0, - 0, - y, - x, - y, - 0, - 0, - y, - y, - x, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturate: () => { - filter.saturation(-1); - }, - contrast: (amount) => { - const v = (amount || 0) + 1; - const o = -128 * (v - 1); - filter.colorMatrix([ - v, - 0, - 0, - 0, - o, - 0, - v, - 0, - 0, - o, - 0, - 0, - v, - 0, - o, - 0, - 0, - 0, - 1, - 0 - ]); - }, - negative: () => { - filter.contrast(-2); - }, - hue: (rotation) => { - rotation = (rotation || 0) / 180 * Math.PI; - const cos = Math.cos(rotation); - const sin = Math.sin(rotation); - const lumR = 0.213; - const lumG = 0.715; - const lumB = 0.072; - filter.colorMatrix([ - lumR + cos * (1 - lumR) + sin * -lumR, - lumG + cos * -lumG + sin * -lumG, - lumB + cos * -lumB + sin * (1 - lumB), - 0, - 0, - lumR + cos * -lumR + sin * 0.143, - lumG + cos * (1 - lumG) + sin * 0.14, - lumB + cos * -lumB + sin * -0.283, - 0, - 0, - lumR + cos * -lumR + sin * -(1 - lumR), - lumG + cos * -lumG + sin * lumG, - lumB + cos * (1 - lumB) + sin * lumB, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturateLuminance: () => { - filter.colorMatrix([ - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0, - 0, - 0, - 1, - 0 - ]); - }, - sepia: () => { - filter.colorMatrix([ - 0.393, - 0.7689999, - 0.18899999, - 0, - 0, - 0.349, - 0.6859999, - 0.16799999, - 0, - 0, - 0.272, - 0.5339999, - 0.13099999, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - brownie: () => { - filter.colorMatrix([ - 0.5997023498159715, - 0.34553243048391263, - -0.2708298674538042, - 0, - 47.43192855600873, - -0.037703249837783157, - 0.8609577587992641, - 0.15059552388459913, - 0, - -36.96841498319127, - 0.24113635128153335, - -0.07441037908422492, - 0.44972182064877153, - 0, - -7.562075277591283, - 0, - 0, - 0, - 1, - 0 - ]); - }, - vintagePinhole: () => { - filter.colorMatrix([ - 0.6279345635605994, - 0.3202183420819367, - -0.03965408211312453, - 0, - 9.651285835294123, - 0.02578397704808868, - 0.6441188644374771, - 0.03259127616149294, - 0, - 7.462829176470591, - 0.0466055556782719, - -0.0851232987247891, - 0.5241648018700465, - 0, - 5.159190588235296, - 0, - 0, - 0, - 1, - 0 - ]); - }, - kodachrome: () => { - filter.colorMatrix([ - 1.1285582396593525, - -0.3967382283601348, - -0.03992559172921793, - 0, - 63.72958762196502, - -0.16404339962244616, - 1.0835251566291304, - -0.05498805115633132, - 0, - 24.732407896706203, - -0.16786010706155763, - -0.5603416277695248, - 1.6014850761964943, - 0, - 35.62982807460946, - 0, - 0, - 0, - 1, - 0 - ]); - }, - technicolor: () => { - filter.colorMatrix([ - 1.9125277891456083, - -0.8545344976951645, - -0.09155508482755585, - 0, - 11.793603434377337, - -0.3087833385928097, - 1.7658908555458428, - -0.10601743074722245, - 0, - -70.35205161461398, - -0.231103377548616, - -0.7501899197440212, - 1.847597816108189, - 0, - 30.950940869491138, - 0, - 0, - 0, - 1, - 0 - ]); - }, - polaroid: () => { - filter.colorMatrix([ - 1.438, - -0.062, - -0.062, - 0, - 0, - -0.122, - 1.378, - -0.122, - 0, - 0, - -0.016, - -0.016, - 1.483, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - shiftToBGR: () => { - filter.colorMatrix([ - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - convolution: (matrix) => { - const m = new Float32Array(matrix); - const pixelSizeX = 1 / fxcanvas.width; - const pixelSizeY = 1 / fxcanvas.height; - const program = compileShader(convolution); - if (!program) return; - gl.uniform1fv(program.uniform["m"], m); - gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); - draw(); - }, - detectEdges: () => { - filter.convolution.call(this, [ - 0, - 1, - 0, - 1, - -4, - 1, - 0, - 1, - 0 - ]); - }, - sobelX: () => { - filter.convolution.call(this, [ - -1, - 0, - 1, - -2, - 0, - 2, - -1, - 0, - 1 - ]); - }, - sobelY: () => { - filter.convolution.call(this, [ - -1, - -2, - -1, - 0, - 0, - 0, - 1, - 2, - 1 - ]); - }, - sharpen: (amount) => { - const a = amount || 1; - filter.convolution.call(this, [ - 0, - -1 * a, - 0, - -1 * a, - 1 + 4 * a, - -1 * a, - 0, - -1 * a, - 0 - ]); - }, - emboss: (size2) => { - const s = size2 || 1; - filter.convolution.call(this, [ - -2 * s, - -1 * s, - 0, - -1 * s, - 1, - 1 * s, - 0, - 1 * s, - 2 * s - ]); - }, - blur: (size2) => { - const blurSizeX = size2 / 7 / fxcanvas.width; - const blurSizeY = size2 / 7 / fxcanvas.height; - const program = compileShader(blur); - if (!program) return; - gl.uniform2f(program.uniform["px"], 0, blurSizeY); - draw(DRAW.INTERMEDIATE); - gl.uniform2f(program.uniform["px"], blurSizeX, 0); - draw(); - }, - pixelate: (size2) => { - const blurSizeX = size2 / fxcanvas.width; - const blurSizeY = size2 / fxcanvas.height; - const program = compileShader(pixelate); - if (!program) return; - gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); - draw(); - } - }; - this.add = function(name) { - const args = Array.prototype.slice.call(arguments, 1); - const func = filter[name]; - filterChain.push({ func, args }); - }; - this.reset = function() { - filterChain = []; - }; - this.get = function() { - return filterChain; - }; - this.apply = function(image28) { - resize(image28.width, image28.height); - drawCount = 0; - if (!sourceTexture) sourceTexture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, sourceTexture); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); - for (let i = 0; i < filterChain.length; i++) { - lastInChain = i === filterChain.length - 1; - const f = filterChain[i]; - f.func.apply(this, f.args || []); - } - return fxcanvas; - }; - this.draw = function(image28) { - this.add("brightness", 0); - return this.apply(image28); - }; -} - -// src/image/enhance.ts -var tf = __toESM(require_tfjs_esm()); -async function histogramEqualization(inputImage) { - const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage; - const rgb3 = tf.split(squeeze14, 3, 2); - const min2 = [tf.min(rgb3[0]), tf.min(rgb3[1]), tf.min(rgb3[2])]; - const max5 = [tf.max(rgb3[0]), tf.max(rgb3[1]), tf.max(rgb3[2])]; - const absMax = await Promise.all(max5.map((channel) => channel.data())); - const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); - const maxRange = maxValue > 1 ? 255 : 1; - const factor = maxRange / maxValue; - let final; - if (factor > 1) { - const sub11 = [tf.sub(rgb3[0], min2[0]), tf.sub(rgb3[1], min2[1]), tf.sub(rgb3[2], min2[2])]; - const range = [tf.sub(max5[0], min2[0]), tf.sub(max5[1], min2[1]), tf.sub(max5[2], min2[2])]; - const enh = [tf.mul(sub11[0], factor), tf.mul(sub11[1], factor), tf.mul(sub11[2], factor)]; - const stack5 = tf.stack([enh[0], enh[1], enh[2]], 2); - final = tf.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); - tf.dispose([...sub11, ...range, ...enh, stack5]); - } else { - final = tf.expandDims(squeeze14, 0); - } - tf.dispose([...rgb3, ...min2, ...max5, rgb3, squeeze14, inputImage]); - return final; -} - -// src/image/image.ts -var maxSize = 3840; -var inCanvas = null; -var outCanvas = null; -var tmpCanvas = null; -var fx; -var last = { - inputSum: 0, - cacheDiff: 1, - sumMethod: 0, - inputTensor: void 0 -}; -function reset() { - last.inputSum = 0; - last.cacheDiff = 1; - last.sumMethod = 0; - last.inputTensor = void 0; -} -function canvas(width, height) { - let c; - if (env.browser) { - if (env.worker) { - if (typeof OffscreenCanvas === "undefined") throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); - c = new OffscreenCanvas(width, height); - } else { - if (typeof document !== "undefined") { - c = document.createElement("canvas"); - c.width = width; - c.height = height; - } else if (typeof navigator !== "undefined" && navigator.product === "ReactNative") { - if (typeof env.Canvas !== "undefined") c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") c = new globalThis.Canvas(width, height); - else throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed"); - } else { - throw new Error("canvas error: attempted to run in browser but DOM is not defined"); - } - } - } else { - if (typeof env.Canvas !== "undefined") c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") c = new globalThis.Canvas(width, height); - } - return c; -} -function copy(input, output) { - const outputCanvas = output || canvas(input.width, input.height); - const ctx = outputCanvas.getContext("2d"); - ctx.drawImage(input, 0, 0); - return outputCanvas; -} -async function process2(input, config3, getTensor = true) { - var _a, _b, _c; - if (!input) { - if (config3.debug) log("input error: input is missing"); - return { tensor: null, canvas: null }; - } - if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { - throw new Error("input error: type not recognized"); - } - if (input instanceof tf2.Tensor) { - let tensor7 = null; - if (input["isDisposedInternal"]) throw new Error("input error: attempted to use tensor but it is disposed"); - if (!input.shape) throw new Error("input error: attempted to use tensor without a shape"); - if (input.shape.length === 3) { - if (input.shape[2] === 3) { - tensor7 = tf2.expandDims(input, 0); - } else if (input.shape[2] === 4) { - const rgb3 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]); - tensor7 = tf2.expandDims(rgb3, 0); - tf2.dispose(rgb3); - } - } else if (input.shape.length === 4) { - if (input.shape[3] === 3) { - tensor7 = tf2.clone(input); - } else if (input.shape[3] === 4) { - tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); - } - } - if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); - if (tensor7.dtype === "int32") { - const cast8 = tf2.cast(tensor7, "float32"); - tf2.dispose(tensor7); - tensor7 = cast8; - } - return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; - } - if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { - if (config3.debug) log("input stream is not ready"); - return { tensor: null, canvas: inCanvas }; - } - const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; - const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; - if (!originalWidth || !originalHeight) { - if (config3.debug) log("cannot determine input dimensions"); - return { tensor: null, canvas: inCanvas }; - } - let targetWidth = originalWidth; - let targetHeight = originalHeight; - if (targetWidth > maxSize) { - targetWidth = maxSize; - targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); - } - if (targetHeight > maxSize) { - targetHeight = maxSize; - targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); - } - if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) targetWidth = config3.filter.width; - else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); - if ((config3.filter.height || 0) > 0) targetHeight = config3.filter.height; - else if ((config3.filter.width || 0) > 0) targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); - if (!targetWidth || !targetHeight) throw new Error("input error: cannot determine dimension"); - if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) inCanvas = canvas(targetWidth, targetHeight); - const inCtx = inCanvas.getContext("2d"); - if (typeof ImageData !== "undefined" && input instanceof ImageData) { - inCtx.putImageData(input, 0, 0); - } else { - if (config3.filter.flip && typeof inCtx.translate !== "undefined") { - inCtx.translate(originalWidth, 0); - inCtx.scale(-1, 1); - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - inCtx.setTransform(1, 0, 0, 1, 0, 0); - } else { - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - } - } - if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) outCanvas = canvas(inCanvas.width, inCanvas.height); - if (config3.filter.enabled && env.webgl.supported) { - if (!fx) fx = env.browser ? new GLImageFilter() : null; - env.filter = !!fx; - if (!(fx == null ? void 0 : fx.add)) { - if (config3.debug) log("input process error: cannot initialize filters"); - env.webgl.supported = false; - config3.filter.enabled = false; - copy(inCanvas, outCanvas); - } else { - fx.reset(); - if (config3.filter.brightness !== 0) fx.add("brightness", config3.filter.brightness); - if (config3.filter.contrast !== 0) fx.add("contrast", config3.filter.contrast); - if (config3.filter.sharpness !== 0) fx.add("sharpen", config3.filter.sharpness); - if (config3.filter.blur !== 0) fx.add("blur", config3.filter.blur); - if (config3.filter.saturation !== 0) fx.add("saturation", config3.filter.saturation); - if (config3.filter.hue !== 0) fx.add("hue", config3.filter.hue); - if (config3.filter.negative) fx.add("negative"); - if (config3.filter.sepia) fx.add("sepia"); - if (config3.filter.vintage) fx.add("brownie"); - if (config3.filter.sepia) fx.add("sepia"); - if (config3.filter.kodachrome) fx.add("kodachrome"); - if (config3.filter.technicolor) fx.add("technicolor"); - if (config3.filter.polaroid) fx.add("polaroid"); - if (config3.filter.pixelate !== 0) fx.add("pixelate", config3.filter.pixelate); - if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) outCanvas = fx.apply(inCanvas); - else outCanvas = fx.draw(inCanvas); - } - } else { - copy(inCanvas, outCanvas); - if (fx) fx = null; - env.filter = !!fx; - } - if (!getTensor) return { tensor: null, canvas: outCanvas }; - if (!outCanvas) throw new Error("canvas error: cannot create output"); - let pixels; - let depth = 3; - if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && tf2.browser) { - pixels = tf2.browser ? tf2.browser.fromPixels(input) : null; - } else { - depth = input.data.length / input.height / input.width; - const arr = new Uint8Array(input.data.buffer); - pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32"); - } - } else { - if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (tf2.browser && env.browser) { - if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = tf2.browser.fromPixels(outCanvas); - } else { - tmpCanvas = copy(outCanvas); - pixels = tf2.browser.fromPixels(tmpCanvas); - } - } else { - const tempCanvas = copy(outCanvas); - const tempCtx = tempCanvas.getContext("2d"); - const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); - depth = tempData.data.length / targetWidth / targetHeight; - const arr = new Uint8Array(tempData.data.buffer); - pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]); - } - } - if (depth === 4) { - const rgb3 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); - tf2.dispose(pixels); - pixels = rgb3; - } - if (!pixels) throw new Error("input error: cannot create tensor"); - const casted = tf2.cast(pixels, "float32"); - const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0); - tf2.dispose([pixels, casted]); - if (config3.filter.autoBrightness) { - const max5 = tf2.max(tensor6); - const maxVal = await max5.data(); - config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; - tf2.dispose(max5); - } - return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; -} -async function skip(config3, input) { - let skipFrame = false; - if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; - if (!last.inputTensor) { - last.inputTensor = tf2.clone(input); - } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { - tf2.dispose(last.inputTensor); - last.inputTensor = tf2.clone(input); - } else { - const t2 = {}; - t2.diff = tf2.sub(input, last.inputTensor); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; - tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); - last.inputTensor = tf2.clone(input); - skipFrame = diffRelative <= (config3.cacheSensitivity || 0); - } - return skipFrame; -} -async function compare(config3, input1, input2) { - const t2 = {}; - if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { - if (!config3.debug) log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); - return 0; - } - if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { - if (!config3.debug) log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); - return 0; - } - t2.input1 = tf2.clone(input1); - t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2); - t2.diff = tf2.sub(t2.input1, t2.input2); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; - tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); - return diffRelative; -} - -// src/util/env.ts -var _canvas, _image, _imageData; -var Env = class { - constructor() { - /** Running in Browser */ - __publicField(this, "browser"); - /** Running in NodeJS */ - __publicField(this, "node"); - /** Running in WebWorker thread */ - __publicField(this, "worker"); - /** Detected platform */ - __publicField(this, "platform", ""); - /** Detected agent */ - __publicField(this, "agent", ""); - /** List of supported backends */ - __publicField(this, "backends", []); - /** Has any work been performed so far */ - __publicField(this, "initial"); - /** Are image filters supported? */ - __publicField(this, "filter"); - /** TFJS instance details */ - __publicField(this, "tfjs"); - /** Is offscreenCanvas supported? */ - __publicField(this, "offscreen"); - /** Are performance counter instant values or additive */ - __publicField(this, "perfadd", false); - /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */ - __publicField(this, "tensorflow", { - version: void 0, - gpu: void 0 - }); - /** WASM detected capabilities */ - __publicField(this, "wasm", { - supported: void 0, - backend: void 0, - simd: void 0, - multithread: void 0 - }); - /** WebGL detected capabilities */ - __publicField(this, "webgl", { - supported: void 0, - backend: void 0, - version: void 0, - renderer: void 0, - shader: void 0, - vendor: void 0 - }); - /** WebGPU detected capabilities */ - __publicField(this, "webgpu", { - supported: void 0, - backend: void 0, - adapter: void 0 - }); - /** CPU info */ - __publicField(this, "cpu", { - model: void 0, - flags: [] - }); - /** List of supported kernels for current backend */ - __publicField(this, "kernels", []); - /** MonkeyPatch for Canvas/Image/ImageData */ - __privateAdd(this, _canvas); - __privateAdd(this, _image); - __privateAdd(this, _imageData); - this.browser = typeof navigator !== "undefined" && typeof navigator.appVersion !== "undefined"; - this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; - this.tfjs = { version: tf3.version["tfjs-core"] }; - this.offscreen = typeof OffscreenCanvas !== "undefined"; - this.initial = true; - this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; - if (typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined") { - const agent = navigator.userAgent || ""; - const raw = agent.match(/\(([^()]+)\)/g); - if (raw == null ? void 0 : raw[0]) { - const platformMatch = raw[0].match(/\(([^()]+)\)/g); - this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; - this.agent = agent.replace(raw[0], ""); - if (this.platform[1]) this.agent = this.agent.replace(raw[1], ""); - this.agent = this.agent.replace(/ /g, " "); - } - } else if (typeof process !== "undefined") { - this.platform = `${process.platform} ${process.arch}`; - this.agent = `NodeJS ${process.version}`; - } - } - get Canvas() { - return __privateGet(this, _canvas); - } - set Canvas(val) { - __privateSet(this, _canvas, val); - globalThis.Canvas = val; - } - get Image() { - return __privateGet(this, _image); - } - // @ts-ignore monkey-patch; - set Image(val) { - __privateSet(this, _image, val); - globalThis.Image = val; - } - get ImageData() { - return __privateGet(this, _imageData); - } - // @ts-ignore monkey-patch; - set ImageData(val) { - __privateSet(this, _imageData, val); - globalThis.ImageData = val; - } - /** update backend information */ - async updateBackend() { - this.backends = Object.keys(tf3.engine().registryFactory); - try { - this.tensorflow = { - version: tf3.backend()["binding"] ? tf3.backend()["binding"].TF_Version : void 0, - gpu: tf3.backend()["binding"] ? tf3.backend()["binding"].isUsingGpuDevice() : void 0 - }; - } catch (e) { - } - this.wasm.supported = typeof WebAssembly !== "undefined"; - this.wasm.backend = this.backends.includes("wasm"); - if (this.wasm.supported && this.wasm.backend) { - this.wasm.simd = await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - this.wasm.multithread = await tf3.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - } - const c = canvas(100, 100); - const gl = c ? c.getContext("webgl2") : void 0; - this.webgl.supported = typeof gl !== "undefined"; - this.webgl.backend = this.backends.includes("webgl"); - if (this.webgl.supported && this.webgl.backend && gl) { - this.webgl.version = gl.getParameter(gl.VERSION); - this.webgl.vendor = gl.getParameter(gl.VENDOR); - this.webgl.renderer = gl.getParameter(gl.RENDERER); - this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); - } - this.webgpu.supported = this.browser && typeof navigator !== "undefined" && typeof navigator.gpu !== "undefined"; - this.webgpu.backend = this.backends.includes("webgpu"); - try { - if (this.webgpu.supported) { - const adapter = await navigator.gpu.requestAdapter(); - this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); - } - } catch (e) { - this.webgpu.supported = false; - } - try { - this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); - } catch (e) { - } - } - /** update cpu information */ - updateCPU() { - const cpu = { model: "", flags: [] }; - if (this.node && this.platform.startsWith("linux")) { - } - if (!this.cpu) Object.defineProperty(this, "cpu", { value: cpu }); - else this.cpu = cpu; - } -}; -_canvas = new WeakMap(); -_image = new WeakMap(); -_imageData = new WeakMap(); -var env = new Env(); - -// src/util/webcam.ts -var WebCam = class { - constructor() { - // eslint-disable-line @typescript-eslint/no-extraneous-class - /** current webcam configuration */ - __publicField(this, "config"); - /** instance of dom element associated with webcam stream */ - __publicField(this, "element"); - /** active webcam stream */ - __publicField(this, "stream"); - /** enumerated video devices */ - __publicField(this, "devices", []); - __publicField(this, "enumerate", async () => { - try { - const devices = await navigator.mediaDevices.enumerateDevices(); - this.devices = devices.filter((device) => device.kind === "videoinput"); - } catch (e) { - this.devices = []; - } - return this.devices; - }); - /** start method initializizes webcam stream and associates it with a dom video element */ - __publicField(this, "start", async (webcamConfig) => { - var _a, _b; - if (webcamConfig == null ? void 0 : webcamConfig.debug) this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; - if (webcamConfig == null ? void 0 : webcamConfig.crop) this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; - if (webcamConfig == null ? void 0 : webcamConfig.mode) this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; - if (webcamConfig == null ? void 0 : webcamConfig.width) this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; - if (webcamConfig == null ? void 0 : webcamConfig.height) this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; - if (webcamConfig == null ? void 0 : webcamConfig.id) this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; - if (webcamConfig == null ? void 0 : webcamConfig.element) { - if (typeof webcamConfig.element === "string") { - const el = document.getElementById(webcamConfig.element); - if (el && el instanceof HTMLVideoElement) { - this.element = el; - } else { - if (this.config.debug) log("webcam", "cannot get dom element", webcamConfig.element); - return `webcam error: cannot get dom element: ${webcamConfig.element}`; - } - } else if (webcamConfig.element instanceof HTMLVideoElement) { - this.element = webcamConfig.element; - } else { - if (this.config.debug) log("webcam", "unknown dom element", webcamConfig.element); - return `webcam error: unknown dom element: ${webcamConfig.element}`; - } - } else { - this.element = document.createElement("video"); - } - const requestedConstraints = { - audio: false, - video: { - facingMode: this.config.mode === "front" ? "user" : "environment", - // @ts-ignore // resizeMode is still not defined in tslib - resizeMode: this.config.crop ? "crop-and-scale" : "none" - } - }; - if (((_a = this.config) == null ? void 0 : _a.width) > 0) requestedConstraints.video.width = { ideal: this.config.width }; - if (((_b = this.config) == null ? void 0 : _b.height) > 0) requestedConstraints.video.height = { ideal: this.config.height }; - if (this.config.id) requestedConstraints.video.deviceId = this.config.id; - this.element.addEventListener("play", () => { - if (this.config.debug) log("webcam", "play"); - }); - this.element.addEventListener("pause", () => { - if (this.config.debug) log("webcam", "pause"); - }); - this.element.addEventListener("click", async () => { - if (!this.element || !this.stream) return; - if (this.element.paused) await this.element.play(); - else this.element.pause(); - }); - if (!(navigator == null ? void 0 : navigator.mediaDevices)) { - if (this.config.debug) log("webcam error", "no devices"); - return "webcam error: no devices"; - } - try { - this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); - } catch (err) { - log("webcam", err); - return `webcam error: ${err}`; - } - if (!this.stream) { - if (this.config.debug) log("webcam error", "no stream"); - return "webcam error no stream"; - } - this.element.srcObject = this.stream; - const ready4 = new Promise((resolve) => { - if (!this.element) resolve(false); - else this.element.onloadeddata = () => resolve(true); - }); - await ready4; - await this.element.play(); - if (this.config.debug) { - log("webcam", { - width: this.width, - height: this.height, - label: this.label, - stream: this.stream, - track: this.track, - settings: this.settings, - constraints: this.constraints, - capabilities: this.capabilities - }); - } - return `webcam: ${this.label}`; - }); - /** pause webcam video method */ - __publicField(this, "pause", () => { - if (this.element) this.element.pause(); - }); - /** play webcam video method */ - __publicField(this, "play", async () => { - if (this.element) await this.element.play(); - }); - /** stop method stops active webcam stream track and disconnects webcam */ - __publicField(this, "stop", () => { - if (this.config.debug) log("webcam", "stop"); - if (this.track) this.track.stop(); - }); - this.config = { - element: void 0, - debug: true, - mode: "front", - crop: false, - width: 0, - height: 0 - }; - } - /** get active webcam stream track */ - get track() { - if (!this.stream) return void 0; - return this.stream.getVideoTracks()[0]; - } - /** get webcam capabilities */ - get capabilities() { - if (!this.track) return void 0; - return this.track.getCapabilities ? this.track.getCapabilities() : void 0; - } - /** get webcam constraints */ - get constraints() { - if (!this.track) return void 0; - return this.track.getConstraints ? this.track.getConstraints() : void 0; - } - /** get webcam settings */ - get settings() { - if (!this.stream) return void 0; - const track = this.stream.getVideoTracks()[0]; - return track.getSettings ? track.getSettings() : void 0; - } - /** get webcam label */ - get label() { - if (!this.track) return ""; - return this.track.label; - } - /** is webcam paused */ - get paused() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.paused) || false; - } - /** webcam current width */ - get width() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; - } - /** webcam current height */ - get height() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; - } -}; - -// src/tfjs/load.ts -var tf4 = __toESM(require_tfjs_esm()); - -// models/models.json -var models_exports = {}; -__export(models_exports, { - "affectnet-mobilenet": () => affectnet_mobilenet, - age: () => age, - "anti-spoofing": () => anti_spoofing, - antispoof: () => antispoof, - blazeface: () => blazeface, - "blazeface-back": () => blazeface_back, - "blazeface-front": () => blazeface_front, - "blazepose-detector": () => blazepose_detector, - "blazepose-full": () => blazepose_full, - "blazepose-heavy": () => blazepose_heavy, - "blazepose-lite": () => blazepose_lite, - centernet: () => centernet, - default: () => models_default, - efficientpose: () => efficientpose, - "efficientpose-i-lite": () => efficientpose_i_lite, - "efficientpose-ii-lite": () => efficientpose_ii_lite, - "efficientpose-iv": () => efficientpose_iv, - emotion: () => emotion, - faceboxes: () => faceboxes, - facemesh: () => facemesh, - "facemesh-attention": () => facemesh_attention, - "facemesh-attention-pinto": () => facemesh_attention_pinto, - "facemesh-detection-full": () => facemesh_detection_full, - "facemesh-detection-short": () => facemesh_detection_short, - faceres: () => faceres, - "faceres-deep": () => faceres_deep, - gear: () => gear, - "gear-e1": () => gear_e1, - "gear-e2": () => gear_e2, - gender: () => gender, - "gender-ssrnet-imdb": () => gender_ssrnet_imdb, - handdetect: () => handdetect, - "handlandmark-full": () => handlandmark_full, - "handlandmark-lite": () => handlandmark_lite, - "handlandmark-sparse": () => handlandmark_sparse, - handskeleton: () => handskeleton, - handtrack: () => handtrack, - "insightface-efficientnet-b0": () => insightface_efficientnet_b0, - "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, - "insightface-mobilenet-emore": () => insightface_mobilenet_emore, - "insightface-mobilenet-swish": () => insightface_mobilenet_swish, - iris: () => iris, - liveness: () => liveness, - meet: () => meet, - mobileface: () => mobileface, - mobilefacenet: () => mobilefacenet, - models: () => models, - "movenet-lightning": () => movenet_lightning, - "movenet-multipose": () => movenet_multipose, - "movenet-thunder": () => movenet_thunder, - nanodet: () => nanodet, - "nanodet-e": () => nanodet_e, - "nanodet-g": () => nanodet_g, - "nanodet-m": () => nanodet_m, - "nanodet-t": () => nanodet_t, - posenet: () => posenet, - rvm: () => rvm, - selfie: () => selfie -}); -var antispoof = 853098; -var blazeface = 538928; -var centernet = 4030290; -var emotion = 820516; -var facemesh = 1477958; -var faceres = 6978814; -var handlandmark_lite = 2023432; -var handtrack = 2964837; -var iris = 2599092; -var liveness = 592976; -var models = 0; -var movenet_lightning = 4650216; -var affectnet_mobilenet = 6920630; -var age = 161240; -var blazeface_back = 538928; -var blazeface_front = 402048; -var blazepose_detector = 5928856; -var blazepose_full = 6339202; -var blazepose_heavy = 27502466; -var blazepose_lite = 2726402; -var efficientpose = 5651240; -var faceboxes = 2013002; -var facemesh_attention_pinto = 2387598; -var facemesh_attention = 2382414; -var facemesh_detection_full = 1026192; -var facemesh_detection_short = 201268; -var faceres_deep = 13957620; -var gear_e1 = 112438; -var gear_e2 = 112438; -var gear = 1498916; -var gender_ssrnet_imdb = 161236; -var gender = 201808; -var handdetect = 3515612; -var handlandmark_full = 5431368; -var handlandmark_sparse = 5286322; -var handskeleton = 5502280; -var meet = 372228; -var mobileface = 2183192; -var mobilefacenet = 5171976; -var movenet_multipose = 9448838; -var movenet_thunder = 12477112; -var nanodet = 7574558; -var posenet = 5032780; -var rvm = 3739355; -var selfie = 212886; -var anti_spoofing = 853098; -var efficientpose_i_lite = 2269064; -var efficientpose_ii_lite = 5651240; -var efficientpose_iv = 25643252; -var insightface_efficientnet_b0 = 13013224; -var insightface_ghostnet_strides1 = 8093408; -var insightface_ghostnet_strides2 = 8049584; -var insightface_mobilenet_emore = 6938536; -var insightface_mobilenet_swish = 12168584; -var nanodet_e = 12319156; -var nanodet_g = 7574558; -var nanodet_m = 1887474; -var nanodet_t = 5294216; -var models_default = { - antispoof, - blazeface, - centernet, - emotion, - facemesh, - faceres, - "handlandmark-lite": handlandmark_lite, - handtrack, - iris, - liveness, - models, - "movenet-lightning": movenet_lightning, - "affectnet-mobilenet": affectnet_mobilenet, - age, - "blazeface-back": blazeface_back, - "blazeface-front": blazeface_front, - "blazepose-detector": blazepose_detector, - "blazepose-full": blazepose_full, - "blazepose-heavy": blazepose_heavy, - "blazepose-lite": blazepose_lite, - efficientpose, - faceboxes, - "facemesh-attention-pinto": facemesh_attention_pinto, - "facemesh-attention": facemesh_attention, - "facemesh-detection-full": facemesh_detection_full, - "facemesh-detection-short": facemesh_detection_short, - "faceres-deep": faceres_deep, - "gear-e1": gear_e1, - "gear-e2": gear_e2, - gear, - "gender-ssrnet-imdb": gender_ssrnet_imdb, - gender, - handdetect, - "handlandmark-full": handlandmark_full, - "handlandmark-sparse": handlandmark_sparse, - handskeleton, - meet, - mobileface, - mobilefacenet, - "movenet-multipose": movenet_multipose, - "movenet-thunder": movenet_thunder, - nanodet, - posenet, - rvm, - selfie, - "anti-spoofing": anti_spoofing, - "efficientpose-i-lite": efficientpose_i_lite, - "efficientpose-ii-lite": efficientpose_ii_lite, - "efficientpose-iv": efficientpose_iv, - "insightface-efficientnet-b0": insightface_efficientnet_b0, - "insightface-ghostnet-strides1": insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": insightface_ghostnet_strides2, - "insightface-mobilenet-emore": insightface_mobilenet_emore, - "insightface-mobilenet-swish": insightface_mobilenet_swish, - "nanodet-e": nanodet_e, - "nanodet-g": nanodet_g, - "nanodet-m": nanodet_m, - "nanodet-t": nanodet_t -}; - -// src/tfjs/load.ts -var options = { - cacheModels: true, - cacheSupported: true, - verbose: true, - debug: false, - modelBasePath: "" -}; -var modelStats = {}; -async function httpHandler(url, init4) { - if (options.debug) log("load model fetch:", url, init4); - return fetch(url, init4); -} -function setModelLoadOptions(config3) { - options.cacheModels = config3.cacheModels; - options.verbose = config3.debug; - options.modelBasePath = config3.modelBasePath; -} -async function loadModel(modelPath) { - var _a, _b, _c, _d, _e, _f; - let modelUrl = join(options.modelBasePath, modelPath || ""); - if (!modelUrl.toLowerCase().endsWith(".json")) modelUrl += ".json"; - const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); - const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); - const cachedModelName = "indexeddb://" + shortModelName; - modelStats[shortModelName] = { - name: shortModelName, - loaded: false, - sizeFromManifest: 0, - sizeLoadedWeights: 0, - sizeDesired: models_exports[shortModelName], - inCache: false, - url: "" - }; - options.cacheSupported = typeof indexedDB !== "undefined"; - let cachedModels = {}; - try { - cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {}; - } catch (e) { - options.cacheSupported = false; - } - modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); - modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; - const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; - let model23 = new tf4.GraphModel(modelStats[shortModelName].url, tfLoadOptions); - modelStats[shortModelName].loaded = false; - try { - model23.findIOHandler(); - if (options.debug) log("model load handler:", model23["handler"]); - } catch (err) { - log("error finding model i/o handler:", modelUrl, err); - } - try { - const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; - modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; - if (artifacts) model23.loadSync(artifacts); - else model23 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); - modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || ((_f = (_e = model23.artifacts) == null ? void 0 : _e.weightData) == null ? void 0 : _f[0].byteLength) || 0; - if (options.verbose) log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); - modelStats[shortModelName].loaded = true; - } catch (err) { - log("error loading model:", modelUrl, err); - } - if (modelStats[shortModelName].loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { - try { - const saveResult = await model23.save(cachedModelName); - if (options.debug) log("model saved:", cachedModelName, saveResult); - } catch (err) { - log("error saving model:", modelUrl, err); - } - } - return model23; -} - -// package.json -var version2 = "3.3.2"; - -// src/tfjs/backend.ts -var tf7 = __toESM(require_tfjs_esm()); - -// src/tfjs/humangl.ts -var tf5 = __toESM(require_tfjs_esm()); -var config2 = { - name: "humangl", - priority: 999, - canvas: null, - gl: null, - extensions: [], - webGLattr: { - // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2 - alpha: false, - antialias: false, - premultipliedAlpha: false, - preserveDrawingBuffer: false, - depth: false, - stencil: false, - failIfMajorPerformanceCaveat: false, - // default=true - desynchronized: true - // default=undefined - } -}; -function extensions() { - const gl = config2.gl; - if (!gl) return; - config2.extensions = gl.getSupportedExtensions(); -} -function register(instance) { - var _a; - if (instance.config.backend !== "humangl") return; - if (config2.name in tf5.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { - log("humangl error: backend invalid context"); - instance.models.reset(); - } - if (!tf5.findBackend(config2.name)) { - try { - config2.canvas = canvas(100, 100); - } catch (err) { - log("humangl error: cannot create canvas:", err); - return; - } - try { - config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); - if (!config2.gl) { - log("humangl error: cannot get webgl context"); - return; - } - const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); - if (!glv2) { - log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); - instance.config.backend = "webgl"; - return; - } - if (config2.canvas) { - config2.canvas.addEventListener("webglcontextlost", (e) => { - log("humangl error:", e.type); - log("possible browser memory leak using webgl or conflict with multiple backend registrations"); - instance.emit("error"); - throw new Error("backend error: webgl context lost"); - }); - config2.canvas.addEventListener("webglcontextrestored", (e) => { - log("humangl error: context restored:", e); - }); - config2.canvas.addEventListener("webglcontextcreationerror", (e) => { - log("humangl error: context create:", e); - }); - } - } catch (err) { - log("humangl error: cannot get webgl context:", err); - return; - } - try { - tf5.setWebGLContext(2, config2.gl); - } catch (err) { - log("humangl error: cannot set webgl context:", err); - return; - } - try { - const ctx = new tf5.GPGPUContext(config2.gl); - tf5.registerBackend(config2.name, () => new tf5.MathBackendWebGL(ctx), config2.priority); - } catch (err) { - log("humangl error: cannot register webgl backend:", err); - return; - } - try { - const kernels = tf5.getKernelsForBackend("webgl"); - kernels.forEach((kernelConfig) => { - const newKernelConfig = { ...kernelConfig, backendName: config2.name }; - tf5.registerKernel(newKernelConfig); - }); - } catch (err) { - log("humangl error: cannot update webgl backend registration:", err); - return; - } - try { - if (tf5.env().flagRegistry.WEBGL_VERSION) tf5.env().set("WEBGL_VERSION", 2); - } catch (err) { - log("humangl error: cannot set WebGL backend flags:", err); - return; - } - extensions(); - const backend4 = tf5.backend(); - const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; - if (current) { - if (instance.config.debug) log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); - } else { - log("humangl error: no current gl context:", current, config2.gl); - } - } -} - -// src/tfjs/constants.ts -var tf6 = __toESM(require_tfjs_esm()); -var constants = { - tf255: 255, - tf1: 1, - tf2: 2, - tf05: 0.5, - tf127: 127.5, - rgb: [0.2989, 0.587, 0.114] -}; -function init() { - constants.tf255 = tf6.scalar(255, "float32"); - constants.tf1 = tf6.scalar(1, "float32"); - constants.tf2 = tf6.scalar(2, "float32"); - constants.tf05 = tf6.scalar(0.5, "float32"); - constants.tf127 = tf6.scalar(127.5, "float32"); - constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32"); -} - -// src/tfjs/backend.ts -async function getBestBackend() { - var _a; - await env.updateBackend(); - if ((_a = env.tensorflow) == null ? void 0 : _a.version) return "tensorflow"; - if (env.webgpu.supported && env.webgpu.backend) return "webgpu"; - if (env.webgl.supported && env.webgl.backend) return "webgl"; - if (env.wasm.supported && env.wasm.backend) return "wasm"; - return "cpu"; -} -function registerCustomOps(config3) { - const newKernels = []; - if (!env.kernels.includes("mod")) { - const kernelMod = { - kernelName: "Mod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.sub(op.inputs.a, tf7.mul(tf7.div(op.inputs.a, op.inputs.b), op.inputs.b))) - }; - tf7.registerKernel(kernelMod); - env.kernels.push("mod"); - newKernels.push("mod"); - } - if (!env.kernels.includes("floormod")) { - const kernelFloorMod = { - kernelName: "FloorMod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.add(tf7.mul(tf7.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf7.mod(op.inputs.a, op.inputs.b))) - }; - tf7.registerKernel(kernelFloorMod); - env.kernels.push("floormod"); - newKernels.push("floormod"); - } - if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { - const kernelRotateWithOffset = { - kernelName: "RotateWithOffset", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => { - const backend4 = tf7.getBackend(); - tf7.setBackend("cpu"); - const t2 = tf7.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); - tf7.setBackend(backend4); - return t2; - }) - }; - tf7.registerKernel(kernelRotateWithOffset); - env.kernels.push("rotatewithoffset"); - newKernels.push("rotatewithoffset"); - } - if (newKernels.length > 0 && config3.debug) log("registered kernels:", newKernels); -} -var defaultFlags = {}; -async function check(instance, force = false) { - var _a, _b; - instance.state = "backend"; - if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) instance.config.backend = await getBestBackend(); - if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tf7.getBackend() !== instance.config.backend) { - const timeStamp = now(); - if (instance.config.backend && instance.config.backend.length > 0) { - if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { - if (instance.config.debug) log("running inside web worker"); - } - if (typeof navigator !== "undefined" && ((_b = navigator == null ? void 0 : navigator.userAgent) == null ? void 0 : _b.toLowerCase().includes("electron"))) { - if (instance.config.debug) log("running inside electron"); - } - let available = Object.keys(tf7.engine().registryFactory); - if (instance.config.backend === "humangl" && !available.includes("humangl")) { - register(instance); - available = Object.keys(tf7.engine().registryFactory); - } - if (instance.config.debug) log("available backends:", available); - if (env.browser && !env.node && instance.config.backend === "tensorflow" && available.includes("webgl")) { - if (instance.config.debug) log("override: backend set to tensorflow while running in browser"); - instance.config.backend = "webgl"; - } - if (env.node && !env.browser && (instance.config.backend === "webgl" || instance.config.backend === "humangl") && available.includes("tensorflow")) { - if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`); - instance.config.backend = "tensorflow"; - } - if (env.browser && instance.config.backend === "webgpu") { - if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { - log("override: backend set to webgpu but browser does not support webgpu"); - instance.config.backend = "webgl"; - } else { - const adapter = await navigator.gpu.requestAdapter(); - if (instance.config.debug) log("enumerated webgpu adapter:", adapter); - if (!adapter) { - log("override: backend set to webgpu but browser reports no available gpu"); - instance.config.backend = "webgl"; - } else { - const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; - log("webgpu adapter info:", adapterInfo); - } - } - } - if (!available.includes(instance.config.backend)) { - log(`error: backend ${instance.config.backend} not found in registry`); - instance.config.backend = env.node ? "tensorflow" : "webgl"; - if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`); - } - if (instance.config.debug) log("setting backend:", [instance.config.backend]); - if (instance.config.backend === "wasm") { - if (tf7.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf7.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); - if (instance.config.debug) log("wasm path:", instance.config.wasmPath); - if (typeof tf7.setWasmPaths !== "undefined") tf7.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); - else throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); - let mt = false; - let simd = false; - try { - mt = await tf7.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - simd = await tf7.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - if (instance.config.debug) log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); - if (instance.config.debug && !simd) log("warning: wasm simd support is not enabled"); - } catch (e) { - log("wasm detection failed"); - } - } - try { - await tf7.setBackend(instance.config.backend); - await tf7.ready(); - } catch (err) { - log("error: cannot set backend:", instance.config.backend, err); - return false; - } - if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf7.env().flags)); - } - if (tf7.getBackend() === "humangl" || tf7.getBackend() === "webgl") { - if (tf7.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf7.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); - if (tf7.env().flagRegistry.WEBGL_EXP_CONV) tf7.env().set("WEBGL_EXP_CONV", true); - if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { - log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); - tf7.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); - } - } - if (tf7.getBackend() === "webgpu") { - } - if (instance.config.debug) { - const newFlags = tf7.env().flags; - const updatedFlags = {}; - for (const key of Object.keys(newFlags)) { - if (defaultFlags[key] === newFlags[key]) continue; - updatedFlags[key] = newFlags[key]; - } - if (instance.config.debug && Object.keys(updatedFlags).length > 0) log("backend:", tf7.getBackend(), "flags:", updatedFlags); - } - if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { - if (instance.config.debug) log("flags:", instance.config["flags"]); - for (const [key, val] of Object.entries(instance.config.flags)) { - tf7.env().set(key, val); - } - } - tf7.enableProdMode(); - init(); - instance.performance.initBackend = Math.trunc(now() - timeStamp); - instance.config.backend = tf7.getBackend(); - await env.updateBackend(); - registerCustomOps(instance.config); - } - return true; -} -function fakeOps(kernelNames, config3) { - for (const kernelName of kernelNames) { - const kernelConfig = { - kernelName, - backendName: config3.backend, - kernelFunc: (param) => { - var _a; - if (config3.debug) log("kernelFunc", kernelName, config3.backend, param); - return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; - } - // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, - // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, - }; - tf7.registerKernel(kernelConfig); - } - env.kernels = tf7.getKernelsForBackend(tf7.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); -} - -// src/draw/draw.ts -var draw_exports = {}; -__export(draw_exports, { - all: () => all, - body: () => body, - canvas: () => canvas2, - face: () => face, - gesture: () => gesture, - hand: () => hand, - init: () => init2, - object: () => object, - options: () => options2, - person: () => person -}); - -// src/draw/primitives.ts -var getCanvasContext = (input) => { - if (!input) log("draw error: invalid canvas"); - else if (!input.getContext) log("draw error: canvas context not defined"); - else { - const ctx = input.getContext("2d", { willReadFrequently: true }); - if (!ctx) log("draw error: cannot get canvas context"); - else return ctx; - } - return null; -}; -var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); -var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); -var colorDepth = (z, opt) => { - if (!opt.useDepth || typeof z === "undefined") return opt.color; - const rgb3 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); - return `rgba(${rgb3[0]}, ${rgb3[1]}, ${rgb3[2]}, ${opt.alpha})`; -}; -function labels(ctx, str, startX, startY, localOptions2) { - const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); - const x = Math.max(0, startX); - for (let i = line.length - 1; i >= 0; i--) { - const y = i * localOptions2.lineHeight + startY; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(line[i], x + 5, y + 16); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(line[i], x + 4, y + 15); - } -} -function point(ctx, x, y, z, localOptions2) { - ctx.fillStyle = colorDepth(z, localOptions2); - ctx.beginPath(); - ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); - ctx.fill(); -} -function rect(ctx, x, y, width, height, localOptions2) { - ctx.beginPath(); - ctx.lineWidth = localOptions2.lineWidth; - if (localOptions2.useCurves) { - const cx = (x + x + width) / 2; - const cy = (y + y + height) / 2; - ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); - } else { - ctx.moveTo(x + localOptions2.roundRect, y); - ctx.lineTo(x + width - localOptions2.roundRect, y); - ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); - ctx.lineTo(x + width, y + height - localOptions2.roundRect); - ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); - ctx.lineTo(x + localOptions2.roundRect, y + height); - ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); - ctx.lineTo(x, y + localOptions2.roundRect); - ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); - ctx.closePath(); - } - ctx.stroke(); -} -function lines(ctx, points, localOptions2) { - if (points.length < 2) return; - ctx.beginPath(); - ctx.moveTo(points[0][0], points[0][1]); - for (const pt of points) { - ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); - ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); - } - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function curves(ctx, points, localOptions2) { - if (points.length < 2) return; - ctx.lineWidth = localOptions2.lineWidth; - if (!localOptions2.useCurves || points.length <= 2) { - lines(ctx, points, localOptions2); - return; - } - ctx.moveTo(points[0][0], points[0][1]); - for (let i = 0; i < points.length - 2; i++) { - const xc = (points[i][0] + points[i + 1][0]) / 2; - const yc = (points[i][1] + points[i + 1][1]) / 2; - ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); - } - ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function arrow(ctx, from, to, radius = 5) { - let angle; - let x; - let y; - ctx.beginPath(); - ctx.moveTo(from[0], from[1]); - ctx.lineTo(to[0], to[1]); - angle = Math.atan2(to[1] - from[1], to[0] - from[0]); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.moveTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - ctx.closePath(); - ctx.stroke(); - ctx.fill(); -} - -// src/draw/options.ts -var options2 = { - color: "rgba(173, 216, 230, 0.6)", - // 'lightblue' with light alpha channel - labelColor: "rgba(173, 216, 230, 1)", - // 'lightblue' with dark alpha channel - shadowColor: "black", - alpha: 0.5, - font: 'small-caps 16px "Segoe UI"', - lineHeight: 18, - lineWidth: 4, - pointSize: 2, - roundRect: 8, - drawPoints: false, - drawLabels: true, - drawBoxes: true, - drawAttention: true, - drawGestures: true, - drawPolygons: true, - drawGaze: true, - fillPolygons: false, - useDepth: true, - useCurves: false, - faceLabels: "", - bodyLabels: "", - bodyPartLabels: "", - objectLabels: "", - handLabels: "", - fingerLabels: "", - gestureLabels: "" -}; - -// src/face/facemeshcoords.ts -var meshAnnotations = { - silhouette: [ - 10, - 338, - 297, - 332, - 284, - 251, - 389, - 356, - 454, - 323, - 361, - 288, - 397, - 365, - 379, - 378, - 400, - 377, - 152, - 148, - 176, - 149, - 150, - 136, - 172, - 58, - 132, - 93, - 234, - 127, - 162, - 21, - 54, - 103, - 67, - 109 - ], - // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11 - // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10 - // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11 - // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11 - lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], - lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], - lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], - lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], - lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], - lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], - lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], - lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], - rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], - // 7 - rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], - // 9 - rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], - // 7 - rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], - // 9 - rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], - // 7 - rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], - // 9 - rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], - // 9 - rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], - // 8 - rightEyebrowLower: [35, 124, 46, 53, 52, 65], - // 6 - rightEyeIris: [473, 474, 475, 476, 477], - // 5 - leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], - leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], - leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], - leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], - leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], - leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], - leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], - leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], - leftEyebrowLower: [265, 353, 276, 283, 282, 295], - leftEyeIris: [468, 469, 470, 471, 472], - midwayBetweenEyes: [168], - noseTip: [1], - noseBottom: [2], - noseRightCorner: [98], - noseLeftCorner: [327], - rightCheek: [205], - leftCheek: [425] -}; -var meshLandmarks = { - count: 468, - mouth: 13, - symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] -}; -var blazeFaceLandmarks = { - leftEye: 0, - rightEye: 1, - nose: 2, - mouth: 3, - leftEar: 4, - rightEar: 5, - symmetryLine: [3, 2] -}; -var irisIndices = [ - // A mapping from facemesh model keypoints to iris model keypoints. - { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, - // 7 x 3d - { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, - // 7 x 3d - { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, - // 7 x 3d - { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, - // 7 x 3d - { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, - // 9 x 3d - { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, - // 9 x 3d - { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, - // 9 x 3d - { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, - 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const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var pairsLips = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var pairsFaceContour = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -var contourKeypoints = { - lips: connectionsToIndices(pairsLips), - leftEye: connectionsToIndices(pairsLeftEye), - leftEyebrow: connectionsToIndices(pairsLeftEyebrow), - leftIris: connectionsToIndices(pairsLeftIris), - rightEye: connectionsToIndices(pairsRightEye), - rightEyebrow: connectionsToIndices(pairsRightEyebrow), - rightIris: connectionsToIndices(pairsRightIris), - faceOval: connectionsToIndices(pairsFaceContour) -}; - -// src/face/constants.ts -var LIPS_CONNECTIONS = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var FACE_OVAL_CONNECTIONS = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -function connectionsToIndices2(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { - lips: connectionsToIndices2(LIPS_CONNECTIONS), - leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), - leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), - leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), - rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), - rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), - rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), - faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) -}; -var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); -var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); -var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ - 61, - 146, - 91, - 181, - 84, - 17, - 314, - 405, - 321, - 375, - 291, - // Lower outer. - 185, - 40, - 39, - 37, - 0, - 267, - 269, - 270, - 409, - // Upper outer(excluding corners). - 78, - 95, - 88, - 178, - 87, - 14, - 317, - 402, - 318, - 324, - 308, - // Lower inner. - 191, - 80, - 81, - 82, - 13, - 312, - 311, - 310, - 415, - // Upper inner(excluding corners). - 76, - 77, - 90, - 180, - 85, - 16, - 315, - 404, - 320, - 307, - 306, - // Lower semi - outer. - 184, - 74, - 73, - 72, - 11, - 302, - 303, - 304, - 408, - // Upper semi - outer(excluding corners). - 62, - 96, - 89, - 179, - 86, - 15, - 316, - 403, - 319, - 325, - 292, - // Lower semi - inner. - 183, - 42, - 41, - 38, - 12, - 268, - 271, - 272, - 407 - // Upper semi - inner(excluding corners). -]; -var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ - 33, - 7, - 163, - 144, - 145, - 153, - 154, - 155, - 133, - // Lower contour. - 246, - 161, - 160, - 159, - 158, - 157, - 173, - // upper contour (excluding corners). - 130, - 25, - 110, - 24, - 23, - 22, - 26, - 112, - 243, - // Halo x2 lower contour. - 247, - 30, - 29, - 27, - 28, - 56, - 190, - // Halo x2 upper contour (excluding corners). - 226, - 31, - 228, - 229, - 230, - 231, - 232, - 233, - 244, - // Halo x3 lower contour. - 113, - 225, - 224, - 223, - 222, - 221, - 189, - // Halo x3 upper contour (excluding corners). - 35, - 124, - 46, - 53, - 52, - 65, - // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour. - 143, - 111, - 117, - 118, - 119, - 120, - 121, - 128, - 245, - // Halo x5 lower contour. - 156, - 70, - 63, - 105, - 66, - 107, - 55, - 193 - // Halo x5 upper contour (excluding corners) or eyebrow outer contour. -]; -var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ - 263, - 249, - 390, - 373, - 374, - 380, - 381, - 382, - 362, - // Lower contour. - 466, - 388, - 387, - 386, - 385, - 384, - 398, - // Upper contour (excluding corners). - 359, - 255, - 339, - 254, - 253, - 252, - 256, - 341, - 463, - // Halo x2 lower contour. - 467, - 260, - 259, - 257, - 258, - 286, - 414, - // Halo x2 upper contour (excluding corners). - 446, - 261, - 448, - 449, - 450, - 451, - 452, - 453, - 464, - // Halo x3 lower contour. - 342, - 445, - 444, - 443, - 442, - 441, - 413, - // Halo x3 upper contour (excluding corners). - 265, - 353, - 276, - 283, - 282, - 295, - // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour. - 372, - 340, - 346, - 347, - 348, - 349, - 350, - 357, - 465, - // Halo x5 lower contour. - 383, - 300, - 293, - 334, - 296, - 336, - 285, - 417 - // Halo x5 upper contour (excluding corners) or eyebrow outer contour. -]; - -// src/draw/face.ts -var localOptions; -function drawLabels(f, ctx) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) return; - let l = localOptions.faceLabels.slice(); - l = replace(l, "[id]", f.id.toFixed(0)); - if (f.score) l = replace(l, "[score]", 100 * f.score); - if (f.gender) l = replace(l, "[gender]", f.gender); - if (f.genderScore) l = replace(l, "[genderScore]", 100 * f.genderScore); - if (f.age) l = replace(l, "[age]", f.age); - if (f.distance) l = replace(l, "[distance]", 100 * f.distance); - if (f.real) l = replace(l, "[real]", 100 * f.real); - if (f.live) l = replace(l, "[live]", 100 * f.live); - if (f.emotion && f.emotion.length > 0) { - const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); - if (emotion2.length > 3) emotion2.length = 3; - l = replace(l, "[emotions]", emotion2.join(" ")); - 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p=Math.cos(g),u=Math.sin(g),k=.213,P=.715,N=.072;T.colorMatrix([k+p*(1-k)+u*-k,P+p*-P+u*-P,N+p*-N+u*(1-N),0,0,k+p*-k+u*.143,P+p*(1-P)+u*.14,N+p*-N+u*-.283,0,0,k+p*-k+u*-(1-k),P+p*-P+u*P,N+p*(1-N)+u*N,0,0,0,0,0,1,0])},desaturateLuminance:()=>{T.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{T.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{T.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{T.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{T.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{T.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{T.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{T.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:g=>{let p=new Float32Array(g),u=1/l.width,k=1/l.height,P=h(G1);P&&(i.uniform1fv(P.uniform.m,p),i.uniform2f(P.uniform.px,u,k),f())},detectEdges:()=>{T.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{T.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{T.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:g=>{let p=g||1;T.convolution.call(this,[0,-1*p,0,-1*p,1+4*p,-1*p,0,-1*p,0])},emboss:g=>{let p=g||1;T.convolution.call(this,[-2*p,-1*p,0,-1*p,1,1*p,0,1*p,2*p])},blur:g=>{let p=g/7/l.width,u=g/7/l.height,k=h(H1);k&&(i.uniform2f(k.uniform.px,0,u),f(d.INTERMEDIATE),i.uniform2f(k.uniform.px,p,0),f())},pixelate:g=>{let p=g/l.width,u=g/l.height,k=h(B1);k&&(i.uniform2f(k.uniform.size,p,u),f())}};this.add=function(g){let p=Array.prototype.slice.call(arguments,1),u=T[g];s.push({func:u,args:p})},this.reset=function(){s=[]},this.get=function(){return s},this.apply=function(g){y(g.width,g.height),e=0,t||(t=i.createTexture()),i.bindTexture(i.TEXTURE_2D,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,g);for(let p=0;pd.data())),A=Math.max(s[0][0],s[1][0],s[2][0]),l=(A>1?255:1)/A,c;if(l>1){let d=[U.sub(n[0],o[0]),U.sub(n[1],o[1]),U.sub(n[2],o[2])],i=[U.sub(r[0],o[0]),U.sub(r[1],o[1]),U.sub(r[2],o[2])],y=[U.mul(d[0],l),U.mul(d[1],l),U.mul(d[2],l)],x=U.stack([y[0],y[1],y[2]],2);c=U.reshape(x,[1,t.shape[0]||0,t.shape[1]||0,3]),U.dispose([...d,...i,...y,x])}else c=U.expandDims(t,0);return U.dispose([...n,...o,...r,n,t,e]),c}var q2=3840,M0=null,P0=null,y2=null,t0,ne={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function $t(){ne.inputSum=0,ne.cacheDiff=1,ne.sumMethod=0,ne.inputTensor=void 0}function Ae(e,t){let n;if(R.browser)if(R.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else if(typeof document!="undefined")n=document.createElement("canvas"),n.width=e,n.height=t;else if(typeof navigator!="undefined"&&navigator.product==="ReactNative")if(typeof R.Canvas!="undefined")n=new R.Canvas(e,t);else if(typeof globalThis.Canvas!="undefined")n=new globalThis.Canvas(e,t);else throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed");else throw new Error("canvas error: attempted to run in browser but DOM is not defined");else typeof R.Canvas!="undefined"?n=new R.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function X2(e,t){let n=t||Ae(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function U2(e,t,n=!0){var y,x,m;if(!e)return t.debug&&b("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof I.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type not recognized");if(e instanceof I.Tensor){let f=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new 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s=o,A=r;if(s>q2&&(s=q2,A=Math.trunc(s*r/o)),A>q2&&(A=q2,s=Math.trunc(A*o/r)),(((y=t.filter)==null?void 0:y.width)||0)>0?s=t.filter.width:(((x=t.filter)==null?void 0:x.height)||0)>0&&(s=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?A=t.filter.height:(t.filter.width||0)>0&&(A=r*((t.filter.width||0)/o)),!s||!A)throw new Error("input error: cannot determine dimension");(!M0||M0.width!==s||M0.height!==A)&&(M0=Ae(s,A));let a=M0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),(!P0||M0.width!==P0.width||M0.height!==P0.height)&&(P0=Ae(M0.width,M0.height)),t.filter.enabled&&R.webgl.supported?(t0||(t0=R.browser?new V1:null),R.filter=!!t0,t0!=null&&t0.add?(t0.reset(),t.filter.brightness!==0&&t0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&t0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&t0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&t0.add("blur",t.filter.blur),t.filter.saturation!==0&&t0.add("saturation",t.filter.saturation),t.filter.hue!==0&&t0.add("hue",t.filter.hue),t.filter.negative&&t0.add("negative"),t.filter.sepia&&t0.add("sepia"),t.filter.vintage&&t0.add("brownie"),t.filter.sepia&&t0.add("sepia"),t.filter.kodachrome&&t0.add("kodachrome"),t.filter.technicolor&&t0.add("technicolor"),t.filter.polaroid&&t0.add("polaroid"),t.filter.pixelate!==0&&t0.add("pixelate",t.filter.pixelate),((m=t0.get())==null?void 0:m.length)>1?P0=t0.apply(M0):P0=t0.draw(M0)):(t.debug&&b("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,X2(M0,P0))):(X2(M0,P0),t0&&(t0=null),R.filter=!!t0),!n)return{tensor:null,canvas:P0};if(!P0)throw new 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f.data();t.filter.brightness=h[0]>1?1-h[0]/255:1-h[0],I.dispose(f)}return{tensor:i,canvas:t.filter.return?P0:null}}async function Z1(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>3840||t.shape[2]>2160)return n;if(!ne.inputTensor)ne.inputTensor=I.clone(t);else if(ne.inputTensor.shape[1]!==t.shape[1]||ne.inputTensor.shape[2]!==t.shape[2])I.dispose(ne.inputTensor),ne.inputTensor=I.clone(t);else{let o={};o.diff=I.sub(t,ne.inputTensor),o.squared=I.mul(o.diff,o.diff),o.sum=I.sum(o.squared);let s=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;I.dispose([ne.inputTensor,o.diff,o.squared,o.sum]),ne.inputTensor=I.clone(t),n=s<=(e.cacheSensitivity||0)}return n}async function q1(e,t,n){let o={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||b("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||b("input tensors must 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Y0(this,z2)}set Canvas(t){Me(this,z2,t),globalThis.Canvas=t}get Image(){return Y0(this,S2)}set Image(t){Me(this,S2,t),globalThis.Image=t}get ImageData(){return Y0(this,j2)}set ImageData(t){Me(this,j2,t),globalThis.ImageData=t}async updateBackend(){this.backends=Object.keys(W0.engine().registryFactory);try{this.tensorflow={version:W0.backend().binding?W0.backend().binding.TF_Version:void 0,gpu:W0.backend().binding?W0.backend().binding.isUsingGpuDevice():void 0}}catch(o){}this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&(this.wasm.simd=await W0.env().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await W0.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Ae(100,100),n=t?t.getContext("webgl2"):void 0;this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.vendor=n.getParameter(n.VENDOR),this.webgl.renderer=n.getParameter(n.RENDERER),this.webgl.shader=n.getParameter(n.SHADING_LANGUAGE_VERSION)),this.webgpu.supported=this.browser&&typeof navigator!="undefined"&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();this.webgpu.adapter=await(o==null?void 0:o.requestAdapterInfo())}}catch(o){this.webgpu.supported=!1}try{this.kernels=W0.getKernelsForBackend(W0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}};z2=new WeakMap,S2=new WeakMap,j2=new WeakMap;var R=new E2;var 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n,o,r,s;if((n=e.annotations)!=null&&n.leftEyeIris&&((o=e.annotations)!=null&&o.leftEyeIris[0])){t.strokeStyle=X.useDepth?"rgba(255, 200, 255, 0.3)":X.color,t.beginPath();let A=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,a=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),X.fillPolygons&&(t.fillStyle=X.useDepth?"rgba(255, 255, 200, 0.3)":X.color,t.fill())}if((r=e.annotations)!=null&&r.rightEyeIris&&((s=e.annotations)!=null&&s.rightEyeIris[0])){t.strokeStyle=X.useDepth?"rgba(255, 200, 255, 0.3)":X.color,t.beginPath();let A=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,a=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),X.fillPolygons&&(t.fillStyle=X.useDepth?"rgba(255, 255, 200, 0.3)":X.color,t.fill())}}function Zs(e,t){var n;if(X.drawGaze&&((n=e.rotation)!=null&&n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*Je(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Je(e.rotation.angle.pitch)/90,s=new Path2D(` + M ${e.box[0]+e.box[2]/2} ${e.box[1]} C - ${valX} ${f.box[1]}, - ${valX} ${f.box[1] + f.box[3]}, - ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} - `); - const pathH = new Path2D(` - M ${f.box[0]} ${f.box[1] + f.box[3] / 2} + ${o} ${e.box[1]}, + ${o} ${e.box[1]+e.box[3]}, + ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} + `),A=new Path2D(` + M ${e.box[0]} ${e.box[1]+e.box[3]/2} C - ${f.box[0]} ${valY}, - ${f.box[0] + f.box[2]} ${valY}, - ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} - `); - ctx.stroke(pathH); - ctx.stroke(pathV); - } -} -function drawGazeArrows(f, ctx) { - var _a; - if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { - ctx.strokeStyle = "pink"; - ctx.fillStyle = "pink"; - const leftGaze = [ - f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); - const rightGaze = [ - f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); - } -} -function drawFacePolygons(f, ctx) { - if (localOptions.drawPolygons && f.mesh.length >= 468) { - ctx.lineWidth = 1; - for (let i = 0; i < TRI468.length / 3; i++) { - const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); - lines(ctx, points, localOptions); - } - drawIrisElipse(f, ctx); - } -} -function drawFacePoints(f, ctx) { - if (localOptions.drawPoints) { - if ((f == null ? void 0 : f.mesh.length) >= 468) { - for (let i = 0; i < f.mesh.length; i++) { - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); - if (localOptions.drawAttention) { - if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); - if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - } - } - } else { - for (const [k, v] of Object.entries((f == null ? void 0 : f.annotations) || {})) { - if (!(v == null ? void 0 : v[0])) continue; - const pt = v[0]; - point(ctx, pt[0], pt[1], 0, localOptions); - if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions); - } - } - } -} -function drawFaceBoxes(f, ctx) { - if (localOptions.drawBoxes) { - rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); - } -} -function face(inCanvas2, result, drawOptions) { - localOptions = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.font = localOptions.font; - ctx.strokeStyle = localOptions.color; - ctx.fillStyle = localOptions.color; - for (const f of result) { - drawFaceBoxes(f, ctx); - drawLabels(f, ctx); - if (f.mesh && f.mesh.length > 0) { - drawFacePoints(f, ctx); - drawFacePolygons(f, ctx); - drawGazeSpheres(f, ctx); - drawGazeArrows(f, ctx); - } - } -} - -// src/draw/body.ts -function body(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - for (let i = 0; i < result.length; i++) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - ctx.lineWidth = localOptions2.lineWidth; - ctx.font = localOptions2.font; - if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.bodyLabels.slice(); - l = replace(l, "[id]", result[i].id.toFixed(0)); - l = replace(l, "[score]", 100 * result[i].score); - labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); - } - } - if (localOptions2.drawPoints && result[i].keypoints) { - for (let pt = 0; pt < result[i].keypoints.length; pt++) { - if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) continue; - ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); - point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); - } - } - if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { - ctx.font = localOptions2.font; - for (const pt of result[i].keypoints) { - if (!pt.score || pt.score === 0) continue; - let l = localOptions2.bodyPartLabels.slice(); - l = replace(l, "[label]", pt.part); - l = replace(l, "[score]", 100 * pt.score); - labels(ctx, l, pt.position[0], pt.position[1], localOptions2); - } - } - if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { - for (const part of Object.values(result[i].annotations)) { - for (const connected4 of part) curves(ctx, connected4, localOptions2); - } - } - } -} - -// src/draw/hand.ts -function hand(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.handLabels.slice(); - l = replace(l, "[id]", h.id.toFixed(0)); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - if (localOptions2.drawPoints) { - if (h.keypoints && h.keypoints.length > 0) { - for (const pt of h.keypoints) { - ctx.fillStyle = colorDepth(pt[2], localOptions2); - point(ctx, pt[0], pt[1], 0, localOptions2); - } - } - } - if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { - for (const [part, pt] of Object.entries(h.annotations)) { - let l = localOptions2.fingerLabels.slice(); - l = replace(l, "[label]", part); - labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); - } - } - if (localOptions2.drawPolygons && h.annotations) { - const addHandLine = (part) => { - if (!part || part.length === 0 || !part[0]) return; - for (let i = 0; i < part.length; i++) { - ctx.beginPath(); - const z = part[i][2] || 0; - ctx.strokeStyle = colorDepth(i * z, localOptions2); - ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); - ctx.lineTo(part[i][0], part[i][1]); - ctx.stroke(); - } - }; - ctx.lineWidth = localOptions2.lineWidth; - addHandLine(h.annotations.index); - addHandLine(h.annotations.middle); - addHandLine(h.annotations.ring); - addHandLine(h.annotations.pinky); - addHandLine(h.annotations.thumb); - } - } -} - -// src/draw/object.ts -function object(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.objectLabels.slice(); - l = replace(l, "[id]", h.id.toFixed(0)); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - } -} - -// src/draw/gesture.ts -function gesture(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.font = localOptions2.font; - ctx.fillStyle = localOptions2.color; - let i = 1; - for (let j = 0; j < result.length; j++) { - const [where, what] = Object.entries(result[j]); - if (what.length > 1 && what[1].length > 0) { - const who = where[1] > 0 ? `#${where[1]}` : ""; - let l = localOptions2.gestureLabels.slice(); - l = replace(l, "[where]", where[0]); - l = replace(l, "[who]", who); - l = replace(l, "[what]", what[1]); - labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); - i += 1; - } - } - } -} - -// src/draw/labels.ts -var defaultLabels = { - face: `face + ${e.box[0]} ${r}, + ${e.box[0]+e.box[2]} ${r}, + ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} + `);t.stroke(A),t.stroke(s)}}function qs(e,t){var n;if(X.drawGaze&&((n=e.rotation)!=null&&n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];o5(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];o5(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Xs(e,t){if(X.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<$e.length/3;n++){let o=[$e[n*3+0],$e[n*3+1],$e[n*3+2]].map(r=>e.mesh[r]);n5(t,o,X)}Vs(e,t)}}function Us(e,t){if(X.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let n=0;n0&&(Us(r,o),Xs(r,o),Zs(r,o),qs(r,o))}}function _2(e,t,n){var s,A;let o=i0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round";for(let a=0;a0)){let l=o.bodyLabels.slice();l=Y(l,"[id]",t[a].id.toFixed(0)),l=Y(l,"[score]",100*t[a].score),re(r,l,t[a].box[0],t[a].box[1],o)}if(o.drawPoints&&t[a].keypoints)for(let l=0;l0&&t[a].keypoints){r.font=o.font;for(let l of t[a].keypoints){if(!l.score||l.score===0)continue;let 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d=o.fingerLabels.slice();d=Y(d,"[label]",l),re(r,d,c[c.length-1][0],c[c.length-1][1],o)}if(o.drawPolygons&&a.annotations){let l=c=>{if(!(!c||c.length===0||!c[0]))for(let d=0;d0?d-1:0][0],c[d>0?d-1:0][1]),r.lineTo(c[d][0],c[d][1]),r.stroke()}};r.lineWidth=o.lineWidth,l(a.annotations.index),l(a.annotations.middle),l(a.annotations.ring),l(a.annotations.pinky),l(a.annotations.thumb)}}}}function et(e,t,n){var s;let o=i0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let A of t)if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,be(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&((s=o.objectLabels)==null?void 0:s.length)>0){let a=o.objectLabels.slice();a=Y(a,"[id]",A.id.toFixed(0)),a=Y(a,"[label]",A.label),a=Y(a,"[score]",100*A.score),re(r,a,A.box[0],A.box[1],o)}r.stroke()}}}function tt(e,t,n){var r;let o=i0(f0,n);if(!(!t||!e)&&o.drawGestures&&((r=o.gestureLabels)==null?void 0:r.length)>0){let s=oe(e);if(!s)return;s.font=o.font,s.fillStyle=o.color;let A=1;for(let a=0;a1&&c[1].length>0){let d=l[1]>0?`#${l[1]}`:"",i=o.gestureLabels.slice();i=Y(i,"[where]",l[0]),i=Y(i,"[who]",d),i=Y(i,"[what]",c[1]),re(s,i,8,2+A*o.lineHeight,o),A+=1}}}}var Le={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -6429,7450 +118,7 @@ var defaultLabels = { live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`, - body: "body [score]%", - bodyPart: "[label] [score]%", - object: "[label] [score]%", - hand: "[label] [score]%", - finger: "[label]", - gesture: "[where] [who]: [what]" -}; - -// src/draw/draw.ts -var drawTime = 0; -function person(inCanvas2, result, drawOptions) { - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (let i = 0; i < result.length; i++) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels) { - const label = `person #${i}`; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.stroke(); - } - } -} -function canvas2(input, output) { - if (!input || !output) return; - const ctx = getCanvasContext(output); - if (!ctx) return; - ctx.drawImage(input, 0, 0); -} -async function all(inCanvas2, result, drawOptions) { - if (!(result == null ? void 0 : result.performance) || !inCanvas2) return null; - const timeStamp = now(); - const localOptions2 = mergeDeep(options2, drawOptions); - const promise = Promise.all([ - face(inCanvas2, result.face, localOptions2), - body(inCanvas2, result.body, localOptions2), - hand(inCanvas2, result.hand, localOptions2), - object(inCanvas2, result.object, localOptions2), - gesture(inCanvas2, result.gesture, localOptions2) - // gestures do not have buffering - // person(inCanvas, result.persons, localOptions); // already included above - ]); - drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); - result.performance.draw = drawTime; - return promise; -} -function init2() { - options2.faceLabels = defaultLabels.face; - options2.bodyLabels = defaultLabels.body; - options2.bodyPartLabels = defaultLabels.bodyPart; - options2.handLabels = defaultLabels.hand; - options2.fingerLabels = defaultLabels.finger; - options2.objectLabels = defaultLabels.object; - options2.gestureLabels = defaultLabels.gesture; -} - -// src/body/blazepose.ts -var tf9 = __toESM(require_tfjs_esm()); - -// src/body/blazeposecoords.ts -var blazeposecoords_exports = {}; -__export(blazeposecoords_exports, { - connected: () => connected, - kpt: () => kpt -}); -var kpt = [ - "nose", - // 0 - "leftEyeInside", - // 1 - "leftEye", - // 2 - "leftEyeOutside", - // 3 - "rightEyeInside", - // 4 - "rightEye", - // 5 - "rightEyeOutside", - // 6 - "leftEar", - // 7 - "rightEar", - // 8 - "leftMouth", - // 9 - "rightMouth", - // 10 - "leftShoulder", - // 11 - "rightShoulder", - // 12 - "leftElbow", - // 13 - "rightElbow", - // 14 - "leftWrist", - // 15 - "rightWrist", - // 16 - "leftPinky", - // 17 - "rightPinky", - // 18 - "leftIndex", - // 19 - "rightIndex", - // 20 - "leftThumb", - // 21 - "rightThumb", - // 22 - "leftHip", - // 23 - "rightHip", - // 24 - "leftKnee", - // 25 - "rightKnee", - // 26 - "leftAnkle", - // 27 - "rightAnkle", - // 28 - "leftHeel", - // 29 - "rightHeel", - // 30 - "leftFoot", - // 31 - "rightFoot", - // 32 - "bodyCenter", - // 33 - "bodyTop", - // 34 - "leftPalm", - // 35 // z-coord not ok - "leftHand", - // 36 // similar to wrist but z-coord not ok - "rightPalm", - // 37 // z-coord not ok - "rightHand" - // 38 // similar to wrist but z-coord not ok -]; -var connected = { - shoulders: ["leftShoulder", "rightShoulder"], - hips: ["rightHip", "leftHip"], - mouth: ["leftMouth", "rightMouth"], - leftLegUpper: ["leftHip", "leftKnee"], - leftLegLower: ["leftKnee", "leftAnkle"], - leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], - leftTorso: ["leftShoulder", "leftHip"], - leftArmUpper: ["leftShoulder", "leftElbow"], - leftArmLower: ["leftElbow", "leftWrist"], - leftHand: ["leftWrist", "leftPalm"], - leftHandPinky: ["leftPalm", "leftPinky"], - leftHandIndex: ["leftPalm", "leftIndex"], - leftHandThumb: ["leftPalm", "leftThumb"], - leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], - rightLegUpper: ["rightHip", "rightKnee"], - rightLegLower: ["rightKnee", "rightAnkle"], - rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], - rightTorso: ["rightShoulder", "rightHip"], - rightArmUpper: ["rightShoulder", "rightElbow"], - rightArmLower: ["rightElbow", "rightWrist"], - rightHand: ["rightWrist", "rightPalm"], - rightHandPinky: ["rightPalm", "rightPinky"], - rightHandIndex: ["rightPalm", "rightIndex"], - rightHandThumb: ["rightPalm", "rightThumb"], - rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] -}; - -// src/body/blazeposedetector.ts -var tf8 = __toESM(require_tfjs_esm()); -var model; -var inputSize = 224; -var anchorTensor; -var numLayers = 5; -var strides = [8, 16, 32, 32, 32]; -function createAnchors() { - const anchors3 = []; - let layerId = 0; - while (layerId < numLayers) { - let anchorCount = 0; - let lastSameStrideLayer = layerId; - while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { - anchorCount += 2; - lastSameStrideLayer++; - } - const stride = strides[layerId]; - const featureMapHeight = Math.ceil(inputSize / stride); - const featureMapWidth = Math.ceil(inputSize / stride); - for (let y = 0; y < featureMapHeight; ++y) { - for (let x = 0; x < featureMapWidth; ++x) { - for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { - anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); - } - } - } - layerId = lastSameStrideLayer; - } - anchorTensor = { x: tf8.tensor1d(anchors3.map((a) => a.x)), y: tf8.tensor1d(anchors3.map((a) => a.y)) }; -} -async function loadDetector(config3) { - if (env.initial) model = null; - if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { - model = await loadModel(config3.body["detector"].modelPath); - const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; - inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug && model) log("cached model:", model["modelUrl"]); - createAnchors(); - return model; -} -var cropFactor = [5, 5]; -function decodeBoxes(boxesTensor, anchor) { - return tf8.tidy(() => { - const split6 = tf8.split(boxesTensor, 12, 1); - let xCenter = tf8.squeeze(split6[0]); - let yCenter = tf8.squeeze(split6[1]); - let width = tf8.squeeze(split6[2]); - let height = tf8.squeeze(split6[3]); - xCenter = tf8.add(tf8.div(xCenter, inputSize), anchor.x); - yCenter = tf8.add(tf8.div(yCenter, inputSize), anchor.y); - width = tf8.mul(tf8.div(width, inputSize), cropFactor[0]); - height = tf8.mul(tf8.div(height, inputSize), cropFactor[1]); - const xMin = tf8.sub(xCenter, tf8.div(width, 2)); - const yMin = tf8.sub(yCenter, tf8.div(height, 2)); - const xMax = tf8.add(xMin, width); - const yMax = tf8.add(yMin, height); - const boxes = tf8.stack([xMin, yMin, xMax, yMax], 1); - return boxes; - }); -} -async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { - var _a, _b; - const detectedBoxes = []; - const t2 = {}; - t2.boxes = decodeBoxes(boxesTensor, anchorTensor); - t2.scores = tf8.sigmoid(logitsTensor); - t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); - const nms = await t2.nms.data(); - const scores = await t2.scores.data(); - const boxes = await t2.boxes.array(); - for (const i of Array.from(nms)) { - const score = scores[i]; - const boxRaw = boxes[i]; - const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; - const detectedBox = { score, boxRaw, box }; - detectedBoxes.push(detectedBox); - } - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return detectedBoxes; -} -async function detectBoxes(input, config3, outputSize2) { - const t2 = {}; - t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); - t2.logitsRaw = tf8.slice(t2.res, [0, 0, 0], [1, -1, 1]); - t2.boxesRaw = tf8.slice(t2.res, [0, 0, 1], [1, -1, -1]); - t2.logits = tf8.squeeze(t2.logitsRaw); - t2.boxes = tf8.squeeze(t2.boxesRaw); - const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return boxes; -} - -// src/util/box.ts -function calc(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function square(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; - const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); - const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function scale(box, scaleFact) { - const dist = [box[2] * scaleFact, box[3] * scaleFact]; - const newBox = [ - box[0] - (dist[0] - box[2]) / 2, - box[1] - (dist[1] - box[3]) / 2, - dist[0], - dist[1] - ]; - return newBox; -} - -// src/body/blazepose.ts -var model2; -var inputSize2 = 256; -var skipped = Number.MAX_SAFE_INTEGER; -var outputNodes = { - landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], - detector: [] -}; -var cache = []; -var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; -var lastTime = 0; -var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); -var loadDetect = (config3) => loadDetector(config3); -async function loadPose(config3) { - if (env.initial) model2 = null; - if (!model2) { - model2 = await loadModel(config3.body.modelPath); - const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; - inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug) log("cached model:", model2["modelUrl"]); - return model2; -} -function prepareImage(input, size2, cropBox) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) return input; - let final; - if (cropBox) { - t2.cropped = tf9.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); - } - if (input.shape[1] !== input.shape[2]) { - const height = [ - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 - ]; - const width = [ - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 - ]; - padding = [ - [0, 0], - // dont touch batch - height, - // height before&after - width, - // width before&after - [0, 0] - // dont touch rbg - ]; - t2.pad = tf9.pad(t2.cropped || input, padding); - t2.resize = tf9.image.resizeBilinear(t2.pad, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else if (input.shape[1] !== size2) { - t2.resize = tf9.image.resizeBilinear(t2.cropped || input, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else { - final = tf9.div(t2.cropped || input, constants.tf255); - } - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - return final; -} -function rescaleKeypoints(keypoints, outputSize2, cropBox) { - for (const kpt4 of keypoints) { - kpt4.position = [ - Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), - Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), - kpt4.position[2] - ]; - kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; - } - if (cropBox) { - const width = cropBox[2] - cropBox[0]; - const height = cropBox[3] - cropBox[1]; - for (const kpt4 of keypoints) { - kpt4.positionRaw = [ - kpt4.positionRaw[0] / height + cropBox[1], - // correct offset due to crop - kpt4.positionRaw[1] / width + cropBox[0], - // correct offset due to crop - kpt4.positionRaw[2] - ]; - kpt4.position = [ - Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), - Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), - kpt4.positionRaw[2] - ]; - } - } - return keypoints; -} -function fixKeypoints(keypoints) { - const leftPalm = keypoints.find((k) => k.part === "leftPalm"); - const leftWrist = keypoints.find((k) => k.part === "leftWrist"); - const leftIndex = keypoints.find((k) => k.part === "leftIndex"); - leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; - const rightPalm = keypoints.find((k) => k.part === "rightPalm"); - const rightWrist = keypoints.find((k) => k.part === "rightWrist"); - const rightIndex = keypoints.find((k) => k.part === "rightIndex"); - rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; -} -async function detectLandmarks(input, config3, outputSize2) { - if (!(model2 == null ? void 0 : model2["executor"])) return null; - const t2 = {}; - [ - t2.ld, - t2.segmentation, - t2.heatmap, - t2.world, - t2.poseflag - /* 1,1 */ - ] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); - const poseScore = (await t2.poseflag.data())[0]; - const points = await t2.ld.data(); - const distances = await t2.world.data(); - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - const keypointsRelative = []; - const depth = 5; - for (let i = 0; i < points.length / depth; i++) { - const score = sigmoid2(points[depth * i + 3]); - const presence = sigmoid2(points[depth * i + 4]); - const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; - const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; - const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; - const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; - keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); - } - if (poseScore < (config3.body.minConfidence || 0)) return null; - fixKeypoints(keypointsRelative); - const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); - const kpts = keypoints.map((k) => k.position); - const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; - return body4; -} -async function predict(input, config3) { - var _a, _b, _c; - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime; - const skipFrame = skipped < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { - skipped++; - } else { - let boxes = []; - if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { - const preparedImage = prepareImage(input, 224); - boxes = await detectBoxes(preparedImage, config3, outputSize2); - tf9.dispose(preparedImage); - } else { - boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; - } - for (let i = 0; i < boxes.length; i++) { - const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); - cache.length = 0; - const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); - tf9.dispose(preparedBox); - if (!bodyResult) continue; - bodyResult.id = i; - cache.push(bodyResult); - } - lastTime = now(); - skipped = 0; - } - return cache; -} - -// src/object/centernet.ts -var tf10 = __toESM(require_tfjs_esm()); - -// src/object/labels.ts -var labels2 = [ - { class: 1, label: "person" }, - { class: 2, label: "bicycle" }, - { class: 3, label: "car" }, - { class: 4, label: "motorcycle" }, - { class: 5, label: "airplane" }, - { class: 6, label: "bus" }, - { class: 7, label: "train" }, - { class: 8, label: "truck" }, - { class: 9, label: "boat" }, - { class: 10, label: "traffic light" }, - { class: 11, label: "fire hydrant" }, - { class: 12, label: "stop sign" }, - { class: 13, label: "parking meter" }, - { class: 14, label: "bench" }, - { class: 15, label: "bird" }, - { class: 16, label: "cat" }, - { class: 17, label: "dog" }, - { class: 18, label: "horse" }, - { class: 19, label: "sheep" }, - { class: 20, label: "cow" }, - { class: 21, label: "elephant" }, - { class: 22, label: "bear" }, - { class: 23, label: "zebra" }, - { class: 24, label: "giraffe" }, - { class: 25, label: "backpack" }, - { class: 26, label: "umbrella" }, - { class: 27, label: "handbag" }, - { class: 28, label: "tie" }, - { class: 29, label: "suitcase" }, - { class: 30, label: "frisbee" }, - { class: 31, label: "skis" }, - { class: 32, label: "snowboard" }, - { class: 33, label: "sports ball" }, - { class: 34, label: "kite" }, - { class: 35, label: "baseball bat" }, - { class: 36, label: "baseball glove" }, - { class: 37, label: "skateboard" }, - { class: 38, label: "surfboard" }, - { class: 39, label: "tennis racket" }, - { class: 40, label: "bottle" }, - { class: 41, label: "wine glass" }, - { class: 42, label: "cup" }, - { class: 43, label: "fork" }, - { class: 44, label: "knife" }, - { class: 45, label: "spoon" }, - { class: 46, label: "bowl" }, - { class: 47, label: "banana" }, - { class: 48, label: "apple" }, - { class: 49, label: "sandwich" }, - { class: 50, label: "orange" }, - { class: 51, label: "broccoli" }, - { class: 52, label: "carrot" }, - { class: 53, label: "hot dog" }, - { class: 54, label: "pizza" }, - { class: 55, label: "donut" }, - { class: 56, label: "cake" }, - { class: 57, label: "chair" }, - { class: 58, label: "couch" }, - { class: 59, label: "potted plant" }, - { class: 60, label: "bed" }, - { class: 61, label: "dining table" }, - { class: 62, label: "toilet" }, - { class: 63, label: "tv" }, - { class: 64, label: "laptop" }, - { class: 65, label: "mouse" }, - { class: 66, label: "remote" }, - { class: 67, label: "keyboard" }, - { class: 68, label: "cell phone" }, - { class: 69, label: "microwave" }, - { class: 70, label: "oven" }, - { class: 71, label: "toaster" }, - { class: 72, label: "sink" }, - { class: 73, label: "refrigerator" }, - { class: 74, label: "book" }, - { class: 75, label: "clock" }, - { class: 76, label: "vase" }, - { class: 77, label: "scissors" }, - { class: 78, label: "teddy bear" }, - { class: 79, label: "hair drier" }, - { class: 80, label: "toothbrush" } -]; - -// src/object/centernet.ts -var model3; -var inputSize3 = 0; -var last2 = []; -var lastTime2 = 0; -var skipped2 = Number.MAX_SAFE_INTEGER; -async function load(config3) { - if (env.initial) model3 = null; - if (!model3) { - model3 = await loadModel(config3.object.modelPath); - const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; - inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", model3["modelUrl"]); - return model3; -} -async function process3(res, outputShape, config3) { - if (!res) return []; - const t2 = {}; - const results = []; - const detections = await res.array(); - t2.squeeze = tf10.squeeze(res); - const arr = tf10.split(t2.squeeze, 6, 1); - t2.stack = tf10.stack([arr[1], arr[0], arr[3], arr[2]], 1); - t2.boxes = tf10.squeeze(t2.stack); - t2.scores = tf10.squeeze(arr[4]); - t2.classes = tf10.squeeze(arr[5]); - tf10.dispose([res, ...arr]); - t2.nms = await tf10.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); - const nms = await t2.nms.data(); - let i = 0; - for (const id of Array.from(nms)) { - const score = Math.trunc(100 * detections[0][id][4]) / 100; - const classVal = detections[0][id][5]; - if (Number.isNaN(classVal)) continue; - const label = labels2[classVal].label; - const [x, y] = [ - detections[0][id][0] / inputSize3, - detections[0][id][1] / inputSize3 - ]; - const boxRaw = [ - x, - y, - detections[0][id][2] / inputSize3 - x, - detections[0][id][3] / inputSize3 - y - ]; - const box = [ - Math.trunc(boxRaw[0] * outputShape[0]), - Math.trunc(boxRaw[1] * outputShape[1]), - Math.trunc(boxRaw[2] * outputShape[0]), - Math.trunc(boxRaw[3] * outputShape[1]) - ]; - results.push({ id: i++, score, class: classVal, label, box, boxRaw }); - } - Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6])); - return results; -} -async function predict2(input, config3) { - if (!(model3 == null ? void 0 : model3["executor"])) return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; - const skipFrame = skipped2 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { - skipped2++; - return last2; - } - skipped2 = 0; - return new Promise(async (resolve) => { - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const resize = tf10.image.resizeBilinear(input, [inputSize3, inputSize3]); - const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; - lastTime2 = now(); - tf10.dispose(resize); - const obj = await process3(objectT, outputSize2, config3); - last2 = obj; - resolve(obj); - }); -} - -// src/body/efficientpose.ts -var tf11 = __toESM(require_tfjs_esm()); - -// src/body/efficientposecoords.ts -var efficientposecoords_exports = {}; -__export(efficientposecoords_exports, { - connected: () => connected2, - kpt: () => kpt2 -}); -var kpt2 = [ - "head", - "neck", - "rightShoulder", - "rightElbow", - "rightWrist", - "chest", - "leftShoulder", - "leftElbow", - "leftWrist", - "bodyCenter", - "rightHip", - "rightKnee", - "rightAnkle", - "leftHip", - "leftKnee", - "leftAnkle" -]; -var connected2 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/body/efficientpose.ts -var model4; -var lastTime3 = 0; -var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; -var skipped3 = Number.MAX_SAFE_INTEGER; -async function load2(config3) { - if (env.initial) model4 = null; - if (!model4) model4 = await loadModel(config3.body.modelPath); - else if (config3.debug) log("cached model:", model4["modelUrl"]); - return model4; -} -async function max2d(inputs, minScore) { - const [width, height] = inputs.shape; - const reshaped = tf11.reshape(inputs, [height * width]); - const max5 = tf11.max(reshaped, 0); - const newScore = (await max5.data())[0]; - if (newScore > minScore) { - const coordinates = tf11.argMax(reshaped, 0); - const mod3 = tf11.mod(coordinates, width); - const x = (await mod3.data())[0]; - const div15 = tf11.div(coordinates, width); - const y = (await div15.data())[0]; - tf11.dispose([reshaped, max5, coordinates, mod3, div15]); - return [x, y, newScore]; - } - tf11.dispose([reshaped, max5]); - return [0, 0, newScore]; -} -async function predict3(image28, config3) { - if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) return []; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; - const skipFrame = skipped3 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { - skipped3++; - return [cache2]; - } - skipped3 = 0; - return new Promise(async (resolve) => { - const tensor6 = tf11.tidy(() => { - var _a, _b; - const resize = tf11.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); - const enhance2 = tf11.mul(resize, constants.tf2); - const norm = tf11.sub(enhance2, constants.tf1); - return norm; - }); - let resT; - if (config3.body.enabled) resT = model4 == null ? void 0 : model4.execute(tensor6); - lastTime3 = now(); - tf11.dispose(tensor6); - if (resT) { - cache2.keypoints.length = 0; - const squeeze14 = tf11.squeeze(resT); - tf11.dispose(resT); - const stack5 = tf11.unstack(squeeze14, 2); - tf11.dispose(squeeze14); - for (let id = 0; id < stack5.length; id++) { - const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); - if (partScore > (config3.body.minConfidence || 0)) { - cache2.keypoints.push({ - score: Math.round(100 * partScore) / 100, - part: kpt2[id], - positionRaw: [ - // normalized to 0..1 - // @ts-ignore model is not undefined here - x2 / model4.inputs[0].shape[2], - y2 / model4.inputs[0].shape[1] - ], - position: [ - // normalized to input image size - // @ts-ignore model is not undefined here - Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), - Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) - ] - }); - } - } - stack5.forEach((s) => tf11.dispose(s)); - } - cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const x = cache2.keypoints.map((a) => a.position[0]); - const y = cache2.keypoints.map((a) => a.position[1]); - cache2.box = [ - Math.min(...x), - Math.min(...y), - Math.max(...x) - Math.min(...x), - Math.max(...y) - Math.min(...y) - ]; - const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); - const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); - cache2.boxRaw = [ - Math.min(...xRaw), - Math.min(...yRaw), - Math.max(...xRaw) - Math.min(...xRaw), - Math.max(...yRaw) - Math.min(...yRaw) - ]; - for (const [name, indexes] of Object.entries(connected2)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - cache2.annotations[name] = pt; - } - resolve([cache2]); - }); -} - -// src/face/face.ts -var tf25 = __toESM(require_tfjs_esm()); - -// src/face/facemesh.ts -var tf15 = __toESM(require_tfjs_esm()); - -// src/face/blazeface.ts -var tf13 = __toESM(require_tfjs_esm()); - -// src/face/facemeshutil.ts -var tf12 = __toESM(require_tfjs_esm()); -var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; -var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; -var clampBox = (box, input) => box ? [ - Math.trunc(Math.max(0, box.startPoint[0])), - Math.trunc(Math.max(0, box.startPoint[1])), - Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), - Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) -] : [0, 0, 0, 0]; -var getRawBox = (box, input) => box ? [ - box.startPoint[0] / (input.shape[2] || 0), - box.startPoint[1] / (input.shape[1] || 0), - (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), - (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) -] : [0, 0, 0, 0]; -var scaleBoxCoordinates = (box, factor, anchor) => { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]); - return { startPoint, endPoint, landmarks, confidence: box.confidence }; -}; -var cutAndResize = (box, image28, cropSize) => { - const h = image28.shape[1]; - const w = image28.shape[2]; - const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; - const crop = tf12.image.cropAndResize(image28, [cutBox], [0], cropSize); - const norm = tf12.div(crop, constants.tf255); - tf12.dispose(crop); - return norm; -}; -var enlargeBox = (box, factor) => { - const center = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - return { - startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], - endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], - landmarks: box.landmarks, - confidence: box.confidence, - size: size2 - }; -}; -var squarifyBox = (box) => { - const centers = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = Math.max(...size2) / 2; - return { - startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], - endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], - landmarks: box.landmarks, - confidence: box.confidence, - size: [Math.round(size2[0]), Math.round(size2[1])] - }; -}; -var calculateLandmarksBoundingBox = (landmarks) => { - const x = landmarks.map((d) => d[0]); - const y = landmarks.map((d) => d[1]); - return { - startPoint: [Math.min(...x), Math.min(...y)], - endPoint: [Math.max(...x), Math.max(...y)], - landmarks - }; -}; -var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; -var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); -var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -var dot = (v1, v2) => { - let product = 0; - for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i]; - return product; -}; -var getColumnFrom2DArr = (arr, columnIndex) => { - const column = []; - for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]); - return column; -}; -var multiplyTransformMatrices = (mat1, mat2) => { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); - } - return product; -}; -var buildRotationMatrix = (rotation, center) => { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); - return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); -}; -var invertTransformMatrix = (matrix) => { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; - return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; -}; -var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; -function generateAnchors(inputSize10) { - const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; - const anchors3 = []; - for (let i = 0; i < spec.strides.length; i++) { - const stride = spec.strides[i]; - const gridRows = Math.floor((inputSize10 + stride - 1) / stride); - const gridCols = Math.floor((inputSize10 + stride - 1) / stride); - const anchorsNum = spec.anchors[i]; - for (let gridY = 0; gridY < gridRows; gridY++) { - const anchorY = stride * (gridY + 0.5); - for (let gridX = 0; gridX < gridCols; gridX++) { - const anchorX = stride * (gridX + 0.5); - for (let n = 0; n < anchorsNum; n++) anchors3.push([anchorX, anchorY]); - } - } - } - return anchors3; -} -function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { - const boxSize = getBoxSize(box); - const coordsScaled = coordsRaw.map((coord) => [ - // scaled around zero-point - boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), - boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), - coord[2] || 0 - ]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; - const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; - const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; - const boxCenter = getBoxCenter(box); - const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + offsets[0]), - Math.trunc(coord[1] + offsets[1]), - Math.trunc(coord[2] || 0) - ]); -} -function correctFaceRotation(rotate, box, input, inputSize10) { - const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; - let angle = 0; - let rotationMatrix = fixedRotationMatrix; - let face4; - if (rotate && env.kernels.includes("rotatewithoffset")) { - angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - if (largeAngle) { - const center = getBoxCenter(box); - const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; - const rotated = tf12.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); - rotationMatrix = buildRotationMatrix(-angle, center); - face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); - tf12.dispose(rotated); - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - return [angle, rotationMatrix, face4]; -} -var findFaceCenter = (mesh) => { - const x = mesh.map((m) => m[0]); - const y = mesh.map((m) => m[1]); - return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; -}; -var calculateFaceBox = (mesh, previousBox) => { - const center = findFaceCenter(mesh); - const boxSize = getBoxSize(previousBox); - const calculatedBox = { - startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], - endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] - }; - return calculatedBox; -}; - -// src/face/blazeface.ts -var keypointsCount = 6; -var model5; -var anchors = null; -var inputSize4 = 0; -var inputSizeT = null; -var size = () => inputSize4; -async function load3(config3) { - var _a; - if (env.initial) model5 = null; - if (!model5) model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model5["modelUrl"]); - inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; - inputSizeT = tf13.scalar(inputSize4, "int32"); - anchors = tf13.tensor2d(generateAnchors(inputSize4)); - return model5; -} -function decodeBoxes2(boxOutputs) { - if (!anchors || !inputSizeT) return tf13.zeros([0, 0]); - const t2 = {}; - t2.boxStarts = tf13.slice(boxOutputs, [0, 1], [-1, 2]); - t2.centers = tf13.add(t2.boxStarts, anchors); - t2.boxSizes = tf13.slice(boxOutputs, [0, 3], [-1, 2]); - t2.boxSizesNormalized = tf13.div(t2.boxSizes, inputSizeT); - t2.centersNormalized = tf13.div(t2.centers, inputSizeT); - t2.halfBoxSize = tf13.div(t2.boxSizesNormalized, constants.tf2); - t2.starts = tf13.sub(t2.centersNormalized, t2.halfBoxSize); - t2.ends = tf13.add(t2.centersNormalized, t2.halfBoxSize); - t2.startNormalized = tf13.mul(t2.starts, inputSizeT); - t2.endNormalized = tf13.mul(t2.ends, inputSizeT); - const boxes = tf13.concat2d([t2.startNormalized, t2.endNormalized], 1); - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} -async function getBoxes(inputImage, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) return []; - const t2 = {}; - let pad4 = [0, 0]; - let scale2 = [1, 1]; - if ((_b = (_a = config3 == null ? void 0 : config3.face) == null ? void 0 : _a.detector) == null ? void 0 : _b.square) { - const xy = Math.max(inputImage.shape[2], inputImage.shape[1]); - pad4 = [Math.floor((xy - inputImage.shape[2]) / 2), Math.floor((xy - inputImage.shape[1]) / 2)]; - t2.padded = tf13.pad(inputImage, [[0, 0], [pad4[1], pad4[1]], [pad4[0], pad4[0]], [0, 0]]); - scale2 = [inputImage.shape[2] / xy, inputImage.shape[1] / xy]; - pad4 = [pad4[0] / inputSize4, pad4[1] / inputSize4]; - } else { - t2.padded = inputImage.clone(); - } - t2.resized = tf13.image.resizeBilinear(t2.padded, [inputSize4, inputSize4]); - t2.div = tf13.div(t2.resized, constants.tf127); - t2.normalized = tf13.sub(t2.div, constants.tf1); - const res = model5 == null ? void 0 : model5.execute(t2.normalized); - if (Array.isArray(res) && res.length > 2) { - const sorted = res.sort((a, b) => a.size - b.size); - t2.concat384 = tf13.concat([sorted[0], sorted[2]], 2); - t2.concat512 = tf13.concat([sorted[1], sorted[3]], 2); - t2.concat = tf13.concat([t2.concat512, t2.concat384], 1); - t2.batch = tf13.squeeze(t2.concat, [0]); - } else if (Array.isArray(res)) { - t2.batch = tf13.squeeze(res[0]); - } else { - t2.batch = tf13.squeeze(res); - } - tf13.dispose(res); - t2.boxes = decodeBoxes2(t2.batch); - t2.logits = tf13.slice(t2.batch, [0, 0], [-1, 1]); - t2.sigmoid = tf13.sigmoid(t2.logits); - t2.scores = tf13.squeeze(t2.sigmoid); - t2.nms = await tf13.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_c = config3.face.detector) == null ? void 0 : _c.maxDetected) || 0, ((_d = config3.face.detector) == null ? void 0 : _d.iouThreshold) || 0, ((_e = config3.face.detector) == null ? void 0 : _e.minConfidence) || 0); - const nms = await t2.nms.array(); - const boxes = []; - const scores = await t2.scores.data(); - for (let i = 0; i < nms.length; i++) { - const confidence = scores[nms[i]]; - if (confidence > (((_f = config3.face.detector) == null ? void 0 : _f.minConfidence) || 0)) { - const b = {}; - b.bbox = tf13.slice(t2.boxes, [nms[i], 0], [1, -1]); - b.slice = tf13.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); - b.squeeze = tf13.squeeze(b.slice); - b.landmarks = tf13.reshape(b.squeeze, [keypointsCount, -1]); - const points = await b.bbox.data(); - const unpadded = [ - // TODO fix this math - points[0] * scale2[0] - pad4[0], - points[1] * scale2[1] - pad4[1], - points[2] * scale2[0] - pad4[0], - points[3] * scale2[1] - pad4[1] - ]; - const rawBox = { - startPoint: [unpadded[0], unpadded[1]], - endPoint: [unpadded[2], unpadded[3]], - landmarks: await b.landmarks.array(), - confidence - }; - b.anchor = tf13.slice(anchors, [nms[i], 0], [1, 2]); - const anchor = await b.anchor.data(); - const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4], anchor); - const enlargedBox = enlargeBox(scaledBox, ((_g = config3.face.detector) == null ? void 0 : _g.scale) || 1.4); - const squaredBox = squarifyBox(enlargedBox); - if (squaredBox.size[0] > (((_h = config3.face.detector) == null ? void 0 : _h["minSize"]) || 0) && squaredBox.size[1] > (((_i = config3.face.detector) == null ? void 0 : _i["minSize"]) || 0)) boxes.push(squaredBox); - Object.keys(b).forEach((tensor6) => tf13.dispose(b[tensor6])); - } - } - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} - -// src/face/iris.ts -var tf14 = __toESM(require_tfjs_esm()); -var model6; -var inputSize5 = 0; -var leftOutline = meshAnnotations.leftEyeLower0; -var rightOutline = meshAnnotations.rightEyeLower0; -var eyeLandmarks = { - leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], - rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] -}; -var irisLandmarks = { - upperCenter: 3, - lowerCenter: 4, - index: 71, - numCoordinates: 76 -}; -async function load4(config3) { - var _a, _b; - if (env.initial) model6 = null; - if (!model6) model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model6["modelUrl"]); - inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; - if (inputSize5 === -1) inputSize5 = 64; - return model6; -} -function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { - for (let i = 0; i < irisIndices.length; i++) { - const { key, indices } = irisIndices[i]; - const originalIndices = meshAnnotations[`${prefix}${key}`]; - if (!keys || keys.includes(key)) { - for (let j = 0; j < indices.length; j++) { - const index2 = indices[j]; - rawCoords[originalIndices[j]] = [ - newCoords[index2][0], - newCoords[index2][1], - (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 - ]; - } - } - } -} -var getLeftToRightEyeDepthDifference = (rawCoords) => { - const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; - const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; - return leftEyeZ - rightEyeZ; -}; -var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false, scale2 = 2.3) => { - const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), scale2)); - const boxSize = getBoxSize(box); - let crop = tf14.image.cropAndResize(face4, [[ - box.startPoint[1] / meshSize, - box.startPoint[0] / meshSize, - box.endPoint[1] / meshSize, - box.endPoint[0] / meshSize - ]], [0], [inputSize5, inputSize5]); - if (flip && env.kernels.includes("flipleftright")) { - const flipped = tf14.image.flipLeftRight(crop); - tf14.dispose(crop); - crop = flipped; - } - return { box, boxSize, crop }; -}; -var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { - const eyeRawCoords = []; - for (let i = 0; i < irisLandmarks.numCoordinates; i++) { - const x = eyeData[i * 3]; - const y = eyeData[i * 3 + 1]; - const z = eyeData[i * 3 + 2]; - eyeRawCoords.push([ - (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], - y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], - z - ]); - } - return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; -}; -var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { - const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; - const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; - const averageZ = (upperCenterZ + lowerCenterZ) / 2; - return irisCoords.map((coord, i) => { - let z = averageZ; - if (i === 2) { - z = upperCenterZ; - } else if (i === 4) { - z = lowerCenterZ; - } - return [coord[0], coord[1], z]; - }); -}; -async function augmentIris(rawCoords, face4, meshSize, config3) { - var _a, _b; - if (!(model6 == null ? void 0 : model6["executor"])) return rawCoords; - const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true, ((_a = config3.face.iris) == null ? void 0 : _a.scale) || 2.3); - const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true, ((_b = config3.face.iris) == null ? void 0 : _b.scale) || 2.3); - const combined = tf14.concat([leftEyeCrop, rightEyeCrop]); - tf14.dispose(leftEyeCrop); - tf14.dispose(rightEyeCrop); - const eyePredictions = model6.execute(combined); - tf14.dispose(combined); - const eyePredictionsData = await eyePredictions.data(); - tf14.dispose(eyePredictions); - const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); - const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); - const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); - const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); - const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); - if (Math.abs(leftToRightEyeDepthDifference) < 30) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); - } else if (leftToRightEyeDepthDifference < 1) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); - } else { - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); - } - const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); - const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); - const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); - return newCoords; -} - -// src/face/attention.ts -async function augment(rawCoords, results) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const t2 = { - // all attention models produce 2d results so it needs to be later augmented with correct z-coords - // mesh: results[0], // already have it in rawCoords // output_mesh_identity - // flag: results[1], // already processed in parent // conv_faceflag - lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), - // 80 x 2d = 160 // output_lips - irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), - // 5 x 2d = 10 // output_right_iris - eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), - // 71 x 2d = 142 // output_right_eye - irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), - // 5 x 2d = 10 // output_left_iris - eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) - // 71 x 2d = 142// output_left_eye - }; - for (const val of Object.values(t2)) { - if (!val) return rawCoords; - } - const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisL.length / 2; i++) rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); - const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisR.length / 2; i++) rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); - for (let i = 0; i < t2.eyeL.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.eyeR.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.lips.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; - return rawCoords; -} - -// src/face/facemesh.ts -var cache3 = { - boxes: [], - skipped: Number.MAX_SAFE_INTEGER, - timestamp: 0 -}; -var model7 = null; -var inputSize6 = 0; -async function predict4(input, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; - const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); - if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { - cache3.boxes = await getBoxes(input, config3); - cache3.timestamp = now(); - cache3.skipped = 0; - } else { - cache3.skipped++; - } - const faces = []; - const newCache = []; - let id = 0; - const size2 = inputSize6; - for (let i = 0; i < cache3.boxes.length; i++) { - const box = cache3.boxes[i]; - let angle = 0; - let rotationMatrix; - const face4 = { - // init face result - id: id++, - mesh: [], - meshRaw: [], - box: [0, 0, 0, 0], - boxRaw: [0, 0, 0, 0], - score: 0, - boxScore: 0, - faceScore: 0, - size: [0, 0], - // contoursRaw: [], - // contours: [], - annotations: {} - }; - [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); - if (config3.filter.equalization) { - const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; - tf15.dispose(face4.tensor); - if (equilized) face4.tensor = equilized; - } - face4.boxScore = Math.round(100 * box.confidence) / 100; - if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled) || !(model7 == null ? void 0 : model7["executor"])) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.size = box.size; - face4.mesh = box.landmarks; - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } else if (!model7) { - if (config3.debug) log("face mesh detection requested, but model is not loaded"); - } else { - if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { - config3.face.attention.enabled = false; - tf15.dispose(face4.tensor); - return faces; - } - const results = model7.execute(face4.tensor); - const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); - const faceConfidence = await confidenceT.data(); - face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; - if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { - box.confidence = face4.faceScore; - if (config3.face.mesh["keepInvalid"]) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.size = box.size; - face4.score = face4.boxScore; - face4.mesh = box.landmarks; - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } - } else { - const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); - const coordsReshaped = tf15.reshape(meshT, [-1, 3]); - let rawCoords = await coordsReshaped.array(); - tf15.dispose(coordsReshaped); - if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { - rawCoords = await augment(rawCoords, results); - } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { - rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6, config3); - } - face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(meshAnnotations)) face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); - face4.score = face4.faceScore; - const calculatedBox = { - ...calculateFaceBox(face4.mesh, box), - confidence: box.confidence, - landmarks: box.landmarks, - size: box.size - }; - face4.box = clampBox(calculatedBox, input); - face4.boxRaw = getRawBox(calculatedBox, input); - face4.size = calculatedBox.size; - newCache.push(calculatedBox); - } - tf15.dispose(results); - } - if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) faces.push(face4); - else tf15.dispose(face4.tensor); - } - cache3.boxes = newCache; - return faces; -} -async function load5(config3) { - var _a, _b, _c, _d, _e, _f; - if (env.initial) model7 = null; - if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { - if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) model7 = null; - } - if (!model7) { - if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) model7 = await loadModel(config3.face.attention.modelPath); - else model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); - } else if (config3.debug) { - log("cached model:", model7["modelUrl"]); - } - inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; - return model7; -} -var triangulation = TRI468; -var uvmap = UV468; - -// src/gear/emotion.ts -var tf16 = __toESM(require_tfjs_esm()); -var annotations = []; -var model8; -var last3 = []; -var lastCount = 0; -var lastTime4 = 0; -var skipped4 = Number.MAX_SAFE_INTEGER; -var rgb = false; -async function load6(config3) { - var _a, _b, _c; - if (env.initial) model8 = null; - if (!model8) { - model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); - rgb = ((_c = (_b = model8 == null ? void 0 : model8.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[3]) === 3; - if (!rgb) annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; - else annotations = ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]; - } else if (config3.debug) { - log("cached model:", model8["modelUrl"]); - } - return model8; -} -async function predict5(image28, config3, idx, count2) { - var _a, _b; - if (!model8) return []; - const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; - if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { - skipped4++; - return last3[idx]; - } - skipped4 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c; - const obj = []; - if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { - const t2 = {}; - const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; - if (((_b2 = config3.face.emotion) == null ? void 0 : _b2["crop"]) > 0) { - const crop = (_c = config3.face.emotion) == null ? void 0 : _c["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tf16.image.cropAndResize(image28, box, [0], [inputSize10, inputSize10]); - } else { - t2.resize = tf16.image.resizeBilinear(image28, [inputSize10, inputSize10], false); - } - if (rgb) { - t2.mul = tf16.mul(t2.resize, 255); - t2.normalize = tf16.sub(t2.mul, [103.939, 116.779, 123.68]); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.normalize); - } else { - t2.channels = tf16.mul(t2.resize, constants.rgb); - t2.grayscale = tf16.sum(t2.channels, 3, true); - t2.grayscaleSub = tf16.sub(t2.grayscale, constants.tf05); - t2.grayscaleMul = tf16.mul(t2.grayscaleSub, constants.tf2); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); - } - lastTime4 = now(); - const data = await t2.emotion.data(); - for (let i = 0; i < data.length; i++) { - if (data[i] > (config3.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); - } - obj.sort((a, b) => b.score - a.score); - Object.keys(t2).forEach((tensor6) => tf16.dispose(t2[tensor6])); - } - last3[idx] = obj; - lastCount = count2; - resolve(obj); - }); -} - -// src/face/faceres.ts -var tf17 = __toESM(require_tfjs_esm()); -var model9; -var last4 = []; -var lastTime5 = 0; -var lastCount2 = 0; -var skipped5 = Number.MAX_SAFE_INTEGER; -async function load7(config3) { - var _a; - if (env.initial) model9 = null; - if (!model9) model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model9["modelUrl"]); - return model9; -} -function enhance(input, config3) { - var _a, _b; - const tensor6 = input.image || input.tensor || input; - if (!(model9 == null ? void 0 : model9.inputs[0].shape)) return tensor6; - let crop; - if (((_a = config3.face.description) == null ? void 0 : _a["crop"]) > 0) { - const cropval = (_b = config3.face.description) == null ? void 0 : _b["crop"]; - const box = [[cropval, cropval, 1 - cropval, 1 - cropval]]; - crop = tf17.image.cropAndResize(tensor6, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]); - } else { - crop = tf17.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); - } - const norm = tf17.mul(crop, constants.tf255); - tf17.dispose(crop); - return norm; -} -async function predict6(image28, config3, idx, count2) { - var _a, _b, _c, _d; - const obj = { - age: 0, - gender: "unknown", - genderScore: 0, - descriptor: [] - }; - if (!(model9 == null ? void 0 : model9["executor"])) return obj; - const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; - if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped5++; - return last4[idx]; - } - skipped5 = 0; - return new Promise(async (resolve) => { - var _a2; - if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { - const enhanced = enhance(image28, config3); - const resT = model9 == null ? void 0 : model9.execute(enhanced); - lastTime5 = now(); - tf17.dispose(enhanced); - const genderT = resT.find((t2) => t2.shape[1] === 1); - const gender2 = await genderT.data(); - const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; - if (confidence > (config3.face.description.minConfidence || 0)) { - obj.gender = gender2[0] <= 0.5 ? "female" : "male"; - obj.genderScore = Math.min(0.99, confidence); - } - const argmax = tf17.argMax(resT.find((t2) => t2.shape[1] === 100), 1); - const ageIdx = (await argmax.data())[0]; - tf17.dispose(argmax); - const ageT = resT.find((t2) => t2.shape[1] === 100); - const all2 = await ageT.data(); - obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; - if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) log("faceres error:", { model: model9, result: resT }); - const desc = resT.find((t2) => t2.shape[1] === 1024); - const descriptor = desc ? await desc.data() : []; - obj.descriptor = Array.from(descriptor); - resT.forEach((t2) => tf17.dispose(t2)); - } - last4[idx] = obj; - lastCount2 = count2; - resolve(obj); - }); -} - -// src/face/mask.ts -var expandFact = 0.1; -var alpha = 0.5; -function insidePoly(x, y, polygon) { - let inside = false; - let j = polygon.length - 1; - for (let i = 0; i < polygon.length; j = i++) { - if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) inside = !inside; - } - return inside; -} -async function mask(face4) { - if (!face4.tensor) return face4.tensor; - if (!face4.mesh || face4.mesh.length < 100) return face4.tensor; - const width = face4.tensor.shape[2] || 0; - const height = face4.tensor.shape[1] || 0; - const buffer = await face4.tensor.buffer(); - let silhouette = []; - for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); - if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); - for (let x = 0; x < width; x++) { - for (let y = 0; y < height; y++) { - const inside = insidePoly(x / width, y / width, silhouette); - if (!inside) { - buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); - buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); - buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); - } - } - } - const output = buffer.toTensor(); - return output; -} - -// src/face/antispoof.ts -var tf18 = __toESM(require_tfjs_esm()); -var model10; -var cached = []; -var skipped6 = Number.MAX_SAFE_INTEGER; -var lastCount3 = 0; -var lastTime6 = 0; -async function load8(config3) { - var _a; - if (env.initial) model10 = null; - if (!model10) model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model10["modelUrl"]); - return model10; -} -async function predict7(image28, config3, idx, count2) { - var _a, _b; - if (!(model10 == null ? void 0 : model10["executor"])) return 0; - const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; - const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { - skipped6++; - return cached[idx]; - } - skipped6 = 0; - return new Promise(async (resolve) => { - const resize = tf18.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); - const res = model10 == null ? void 0 : model10.execute(resize); - const num = (await res.data())[0]; - cached[idx] = Math.round(100 * num) / 100; - lastCount3 = count2; - lastTime6 = now(); - tf18.dispose([resize, res]); - resolve(cached[idx]); - }); -} - -// src/face/liveness.ts -var tf19 = __toESM(require_tfjs_esm()); -var model11; -var cached2 = []; -var skipped7 = Number.MAX_SAFE_INTEGER; -var lastCount4 = 0; -var lastTime7 = 0; -async function load9(config3) { - var _a; - if (env.initial) model11 = null; - if (!model11) model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model11["modelUrl"]); - return model11; -} -async function predict8(image28, config3, idx, count2) { - var _a, _b; - if (!(model11 == null ? void 0 : model11["executor"])) return 0; - const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; - const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { - skipped7++; - return cached2[idx]; - } - skipped7 = 0; - return new Promise(async (resolve) => { - const resize = tf19.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); - const res = model11 == null ? void 0 : model11.execute(resize); - const num = (await res.data())[0]; - cached2[idx] = Math.round(100 * num) / 100; - lastCount4 = count2; - lastTime7 = now(); - tf19.dispose([resize, res]); - resolve(cached2[idx]); - }); -} - -// src/gear/gear.ts -var tf20 = __toESM(require_tfjs_esm()); -var model12; -var last5 = []; -var raceNames = ["white", "black", "asian", "indian", "other"]; -var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; -var lastCount5 = 0; -var lastTime8 = 0; -var skipped8 = Number.MAX_SAFE_INTEGER; -async function load10(config3) { - var _a; - if (env.initial) model12 = null; - if (!model12) model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model12["modelUrl"]); - return model12; -} -async function predict9(image28, config3, idx, count2) { - var _a, _b; - if (!model12) return { age: 0, gender: "unknown", genderScore: 0, race: [] }; - const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; - if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { - skipped8++; - return last5[idx]; - } - skipped8 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c, _d; - if (!(model12 == null ? void 0 : model12.inputs[0].shape)) return; - const t2 = {}; - let box = [[0, 0.1, 0.9, 0.9]]; - if (((_a2 = config3.face.gear) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face.gear) == null ? void 0 : _b2["crop"]; - box = [[crop, crop, 1 - crop, 1 - crop]]; - } - t2.resize = tf20.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); - const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; - if ((_c = config3.face.gear) == null ? void 0 : _c.enabled) [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); - const gender2 = await t2.gender.data(); - obj.gender = gender2[0] > gender2[1] ? "male" : "female"; - obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; - const race = await t2.race.data(); - for (let i = 0; i < race.length; i++) { - if (race[i] > (((_d = config3.face.gear) == null ? void 0 : _d.minConfidence) || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); - } - obj.race.sort((a, b) => b.score - a.score); - const ageDistribution = Array.from(await t2.age.data()); - const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); - let age2 = ageSorted[0][0]; - for (let i = 1; i < ageSorted.length; i++) age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); - obj.age = Math.round(10 * age2) / 10; - Object.keys(t2).forEach((tensor6) => tf20.dispose(t2[tensor6])); - last5[idx] = obj; - lastCount5 = count2; - lastTime8 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-age.ts -var tf21 = __toESM(require_tfjs_esm()); -var model13; -var last6 = []; -var lastCount6 = 0; -var lastTime9 = 0; -var skipped9 = Number.MAX_SAFE_INTEGER; -async function load11(config3) { - if (env.initial) model13 = null; - if (!model13) model13 = await loadModel(config3.face["ssrnet"].modelPathAge); - else if (config3.debug) log("cached model:", model13["modelUrl"]); - return model13; -} -async function predict10(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model13) return { age: 0 }; - const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; - if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { - skipped9++; - return last6[idx]; - } - skipped9 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c2; - if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) return; - const t2 = {}; - if (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tf21.image.cropAndResize(image28, box, [0], [model13.inputs[0].shape[2], model13.inputs[0].shape[1]]); - } else { - t2.resize = tf21.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); - } - t2.enhance = tf21.mul(t2.resize, constants.tf255); - const obj = { age: 0 }; - if ((_c2 = config3.face["ssrnet"]) == null ? void 0 : _c2.enabled) t2.age = model13.execute(t2.enhance); - if (t2.age) { - const data = await t2.age.data(); - obj.age = Math.trunc(10 * data[0]) / 10; - } - Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6])); - last6[idx] = obj; - lastCount6 = count2; - lastTime9 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-gender.ts -var tf22 = __toESM(require_tfjs_esm()); -var model14; -var last7 = []; -var lastCount7 = 0; -var lastTime10 = 0; -var skipped10 = Number.MAX_SAFE_INTEGER; -var rgb2 = [0.2989, 0.587, 0.114]; -async function load12(config3) { - var _a; - if (env.initial) model14 = null; - if (!model14) model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); - else if (config3.debug) log("cached model:", model14["modelUrl"]); - return model14; -} -async function predict11(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model14) return { gender: "unknown", genderScore: 0 }; - const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; - if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped10++; - return last7[idx]; - } - skipped10 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c2; - if (!(model14 == null ? void 0 : model14.inputs[0].shape)) return; - const t2 = {}; - if (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tf22.image.cropAndResize(image28, box, [0], [model14.inputs[0].shape[2], model14.inputs[0].shape[1]]); - } else { - t2.resize = tf22.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); - } - t2.enhance = tf22.tidy(() => { - var _a3, _b3; - let normalize2; - if (((_b3 = (_a3 = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a3[0].shape) == null ? void 0 : _b3[3]) === 1) { - const [red, green, blue] = tf22.split(t2.resize, 3, 3); - const redNorm = tf22.mul(red, rgb2[0]); - const greenNorm = tf22.mul(green, rgb2[1]); - const blueNorm = tf22.mul(blue, rgb2[2]); - const grayscale = tf22.addN([redNorm, greenNorm, blueNorm]); - normalize2 = tf22.mul(tf22.sub(grayscale, constants.tf05), 2); - } else { - normalize2 = tf22.mul(tf22.sub(t2.resize, constants.tf05), 2); - } - return normalize2; - }); - const obj = { gender: "unknown", genderScore: 0 }; - if ((_c2 = config3.face["ssrnet"]) == null ? void 0 : _c2.enabled) t2.gender = model14.execute(t2.enhance); - const data = await t2.gender.data(); - obj.gender = data[0] > data[1] ? "female" : "male"; - obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; - Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6])); - last7[idx] = obj; - lastCount7 = count2; - lastTime10 = now(); - resolve(obj); - }); -} - -// src/face/mobilefacenet.ts -var tf23 = __toESM(require_tfjs_esm()); -var model15; -var last8 = []; -var lastCount8 = 0; -var lastTime11 = 0; -var skipped11 = Number.MAX_SAFE_INTEGER; -async function load13(config3) { - var _a; - if (env.initial) model15 = null; - if (!model15) model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model15["modelUrl"]); - return model15; -} -async function predict12(input, config3, idx, count2) { - var _a, _b; - if (!(model15 == null ? void 0 : model15["executor"])) return []; - const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; - if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { - skipped11++; - return last8[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf23.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); - t2.data = model15.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf23.dispose(t2[tensor6])); - } - last8[idx] = data; - lastCount8 = count2; - lastTime11 = now(); - resolve(data); - }); -} - -// src/face/insightface.ts -var tf24 = __toESM(require_tfjs_esm()); -var model16; -var last9 = []; -var lastCount9 = 0; -var lastTime12 = 0; -var skipped12 = Number.MAX_SAFE_INTEGER; -async function load14(config3) { - if (env.initial) model16 = null; - if (!model16) model16 = await loadModel(config3.face["insightface"].modelPath); - else if (config3.debug) log("cached model:", model16["modelUrl"]); - return model16; -} -async function predict13(input, config3, idx, count2) { - var _a, _b; - if (!(model16 == null ? void 0 : model16["executor"])) return []; - const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; - if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { - skipped12++; - return last9[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf24.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); - t2.data = model16.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6])); - } - last9[idx] = data; - lastCount9 = count2; - lastTime12 = now(); - resolve(data); - }); -} - -// src/face/angles.ts -var calculateGaze = (face4) => { - const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); - if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) return { bearing: 0, strength: 0 }; - const offsetIris = [0, -0.1]; - const eyeRatio = 1; - const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); - const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; - const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; - const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; - const eyeDiff = [ - // x distance between extreme point and center point normalized with eye size - (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], - eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] - ]; - let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); - strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); - const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; - return { bearing, strength }; -}; -var calculateFaceAngle = (face4, imageSize) => { - const normalize2 = (v) => { - const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); - v[0] /= length; - v[1] /= length; - v[2] /= length; - return v; - }; - const subVectors = (a, b) => { - const x = a[0] - b[0]; - const y = a[1] - b[1]; - const z = a[2] - b[2]; - return [x, y, z]; - }; - const crossVectors = (a, b) => { - const x = a[1] * b[2] - a[2] * b[1]; - const y = a[2] * b[0] - a[0] * b[2]; - const z = a[0] * b[1] - a[1] * b[0]; - return [x, y, z]; - }; - const rotationMatrixToEulerAngle = (r) => { - const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; - let thetaX; - let thetaY; - let thetaZ; - if (r10 < 1) { - if (r10 > -1) { - const cosThetaZ = Math.sqrt(r00 * r00 + r20 * r20); - thetaZ = Math.atan2(r10, cosThetaZ); - thetaY = Math.atan2(-r20, r00); - thetaX = Math.atan2(-r12, r11); - } else { - thetaZ = -Math.PI / 2; - thetaY = -Math.atan2(r21, r22); - thetaX = 0; - } - } else { - thetaZ = Math.PI / 2; - thetaY = Math.atan2(r21, r22); - thetaX = 0; - } - if (Number.isNaN(thetaX)) thetaX = 0; - if (Number.isNaN(thetaY)) thetaY = 0; - if (Number.isNaN(thetaZ)) thetaZ = 0; - return { pitch: -thetaX, yaw: -thetaY, roll: -thetaZ }; - }; - const mesh = face4.meshRaw; - if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; - const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; - const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); - const yAxis = normalize2(subVectors(pts[1], pts[0])); - let xAxis = normalize2(subVectors(pts[3], pts[2])); - const zAxis = normalize2(crossVectors(xAxis, yAxis)); - xAxis = crossVectors(yAxis, zAxis); - const matrix = [ - xAxis[0], - xAxis[1], - xAxis[2], - yAxis[0], - yAxis[1], - yAxis[2], - zAxis[0], - zAxis[1], - zAxis[2] - ]; - const angle = rotationMatrixToEulerAngle(matrix); - const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; - return { angle, matrix, gaze }; -}; - -// src/face/anthropometry.ts -function calculateCameraDistance(face4, width) { - const f = face4 == null ? void 0 : face4.annotations; - if (!(f == null ? void 0 : f.leftEyeIris) || !(f == null ? void 0 : f.rightEyeIris)) return 0; - const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; - const cameraDistance = Math.round(1.17 / irisSize) / 100; - return cameraDistance; -} - -// src/face/face.ts -var detectFace = async (instance, input) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - let timeStamp = now(); - let ageRes; - let gearRes; - let genderRes; - let emotionRes; - let mobilefacenetRes; - let insightfaceRes; - let antispoofRes; - let livenessRes; - let descRes; - const faceRes = []; - instance.state = "run:face"; - const faces = await predict4(input, instance.config); - instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - if (!input.shape || input.shape.length !== 4) return []; - if (!faces) return []; - for (let i = 0; i < faces.length; i++) { - instance.analyze("Get Face"); - if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { - log("Face object is disposed:", faces[i].tensor); - continue; - } - if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { - const masked = await mask(faces[i]); - tf25.dispose(faces[i].tensor); - if (masked) faces[i].tensor = masked; - } - const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; - instance.analyze("Start Emotion:"); - if (instance.config.async) { - emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - } else { - instance.state = "run:emotion"; - timeStamp = now(); - emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Emotion:"); - instance.analyze("Start AntiSpoof:"); - if (instance.config.async) { - antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:antispoof"; - timeStamp = now(); - antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End AntiSpoof:"); - instance.analyze("Start Liveness:"); - if (instance.config.async) { - livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:liveness"; - timeStamp = now(); - livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Liveness:"); - instance.analyze("Start GEAR:"); - if (instance.config.async) { - gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:gear"; - timeStamp = now(); - gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.gear = Math.trunc(now() - timeStamp); - } - instance.analyze("End GEAR:"); - instance.analyze("Start SSRNet:"); - if (instance.config.async) { - ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:ssrnet"; - timeStamp = now(); - ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.ssrnet = Math.trunc(now() - timeStamp); - } - instance.analyze("End SSRNet:"); - instance.analyze("Start MobileFaceNet:"); - if (instance.config.async) { - mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End MobileFaceNet:"); - instance.analyze("Start InsightFace:"); - if (instance.config.async) { - insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End InsightFace:"); - instance.analyze("Start Description:"); - if (instance.config.async) { - descRes = predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - } else { - instance.state = "run:description"; - timeStamp = now(); - descRes = await predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Description:"); - if (instance.config.async) { - [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); - } - instance.analyze("Finish Face:"); - if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { - descRes = { - ...descRes, - age: ageRes.age, - gender: genderRes.gender, - genderScore: genderRes.genderScore - }; - } - if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { - descRes = { - ...descRes, - age: gearRes.age, - gender: gearRes.gender, - genderScore: gearRes.genderScore, - race: gearRes.race - }; - } - if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { - descRes.descriptor = mobilefacenetRes; - } - if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { - descRes.descriptor = insightfaceRes; - } - const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; - const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tf25.squeeze(faces[i].tensor) : null; - tf25.dispose(faces[i].tensor); - if (faces[i].tensor) delete faces[i].tensor; - const res = { - ...faces[i], - id: i - }; - if (descRes.age) res.age = descRes.age; - if (descRes.gender) res.gender = descRes.gender; - if (descRes.genderScore) res.genderScore = descRes.genderScore; - if (descRes.descriptor) res.embedding = descRes.descriptor; - if (descRes.race) res.race = descRes.race; - if (emotionRes) res.emotion = emotionRes; - if (antispoofRes) res.real = antispoofRes; - if (livenessRes) res.live = livenessRes; - if (irisSize > 0) res.distance = irisSize; - if (rotation) res.rotation = rotation; - if (tensor6) res.tensor = tensor6; - faceRes.push(res); - instance.analyze("End Face"); - } - instance.analyze("End FaceMesh:"); - if (instance.config.async) { - if (instance.performance.face) delete instance.performance.face; - if (instance.performance.age) delete instance.performance.age; - if (instance.performance.gender) delete instance.performance.gender; - if (instance.performance.emotion) delete instance.performance.emotion; - } - return faceRes; -}; - -// src/hand/fingerdef.ts -var Finger = { - thumb: 0, - index: 1, - middle: 2, - ring: 3, - pinky: 4, - all: [0, 1, 2, 3, 4], - // just for convenience - nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, - // Describes mapping of joints based on the 21 points returned by handpose. - // [0] Palm - // [1-4] Thumb - // [5-8] Index - // [9-12] Middle - // [13-16] Ring - // [17-20] Pinky - pointsMapping: { - 0: [[0, 1], [1, 2], [2, 3], [3, 4]], - 1: [[0, 5], [5, 6], [6, 7], [7, 8]], - 2: [[0, 9], [9, 10], [10, 11], [11, 12]], - 3: [[0, 13], [13, 14], [14, 15], [15, 16]], - 4: [[0, 17], [17, 18], [18, 19], [19, 20]] - }, - getName: (value) => Finger.nameMapping[value], - getPoints: (value) => Finger.pointsMapping[value] -}; -var FingerCurl = { - none: 0, - half: 1, - full: 2, - nameMapping: { 0: "none", 1: "half", 2: "full" }, - getName: (value) => FingerCurl.nameMapping[value] -}; -var FingerDirection = { - verticalUp: 0, - verticalDown: 1, - horizontalLeft: 2, - horizontalRight: 3, - diagonalUpRight: 4, - diagonalUpLeft: 5, - diagonalDownRight: 6, - diagonalDownLeft: 7, - nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, - getName: (value) => FingerDirection.nameMapping[value] -}; -var FingerGesture = class { - constructor(name) { - __publicField(this, "name"); - __publicField(this, "curls"); - __publicField(this, "directions"); - __publicField(this, "weights"); - __publicField(this, "weightsRelative"); - this.name = name; - this.curls = {}; - this.directions = {}; - this.weights = [1, 1, 1, 1, 1]; - this.weightsRelative = [1, 1, 1, 1, 1]; - } - curl(finger, curl, confidence) { - if (typeof this.curls[finger] === "undefined") this.curls[finger] = []; - this.curls[finger].push([curl, confidence]); - } - direction(finger, position, confidence) { - if (!this.directions[finger]) this.directions[finger] = []; - this.directions[finger].push([position, confidence]); - } - weight(finger, weight) { - this.weights[finger] = weight; - const total = this.weights.reduce((a, b) => a + b, 0); - this.weightsRelative = this.weights.map((el) => el * 5 / total); - } - matchAgainst(detectedCurls, detectedDirections) { - let confidence = 0; - for (const fingerIdx in detectedCurls) { - const detectedCurl = detectedCurls[fingerIdx]; - const expectedCurls = this.curls[fingerIdx]; - if (typeof expectedCurls === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedCurl, score] of expectedCurls) { - if (detectedCurl === expectedCurl) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - for (const fingerIdx in detectedDirections) { - const detectedDirection = detectedDirections[fingerIdx]; - const expectedDirections = this.directions[fingerIdx]; - if (typeof expectedDirections === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedDirection, score] of expectedDirections) { - if (detectedDirection === expectedDirection) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - return confidence / 10; - } -}; - -// src/hand/fingergesture.ts -var { thumb, index, middle, ring, pinky } = Finger; -var { none, half, full } = FingerCurl; -var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; -var ThumbsUp = new FingerGesture("thumbs up"); -ThumbsUp.curl(thumb, none, 1); -ThumbsUp.direction(thumb, verticalUp, 1); -ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); -ThumbsUp.direction(thumb, diagonalUpRight, 0.25); -for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { - ThumbsUp.curl(finger, full, 1); - ThumbsUp.direction(finger, horizontalLeft, 1); - ThumbsUp.direction(finger, horizontalRight, 1); -} -var Victory = new FingerGesture("victory"); -Victory.curl(thumb, half, 0.5); -Victory.curl(thumb, none, 0.5); -Victory.direction(thumb, verticalUp, 1); -Victory.direction(thumb, diagonalUpLeft, 1); -Victory.curl(index, none, 1); -Victory.direction(index, verticalUp, 0.75); -Victory.direction(index, diagonalUpLeft, 1); -Victory.curl(middle, none, 1); -Victory.direction(middle, verticalUp, 1); -Victory.direction(middle, diagonalUpLeft, 0.75); -Victory.curl(ring, full, 1); -Victory.direction(ring, verticalUp, 0.2); -Victory.direction(ring, diagonalUpLeft, 1); -Victory.direction(ring, horizontalLeft, 0.2); -Victory.curl(pinky, full, 1); -Victory.direction(pinky, verticalUp, 0.2); -Victory.direction(pinky, diagonalUpLeft, 1); -Victory.direction(pinky, horizontalLeft, 0.2); -Victory.weight(index, 2); -Victory.weight(middle, 2); -var Point = new FingerGesture("point"); -Point.curl(thumb, full, 1); -Point.curl(index, none, 0.5); -Point.curl(middle, full, 0.5); -Point.curl(ring, full, 0.5); -Point.curl(pinky, full, 0.5); -Point.weight(index, 2); -Point.weight(middle, 2); -var MiddleFinger = new FingerGesture("middle finger"); -MiddleFinger.curl(thumb, none, 1); -MiddleFinger.curl(index, full, 0.5); -MiddleFinger.curl(middle, full, 0.5); -MiddleFinger.curl(ring, full, 0.5); -MiddleFinger.curl(pinky, full, 0.5); -MiddleFinger.weight(index, 2); -MiddleFinger.weight(middle, 2); -var OpenPalm = new FingerGesture("open palm"); -OpenPalm.curl(thumb, none, 0.75); -OpenPalm.curl(index, none, 0.75); -OpenPalm.curl(middle, none, 0.75); -OpenPalm.curl(ring, none, 0.75); -OpenPalm.curl(pinky, none, 0.75); -var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; - -// src/hand/fingerpose.ts -var minConfidence = 0.7; -var options3 = { - // curl estimation - HALF_CURL_START_LIMIT: 60, - NO_CURL_START_LIMIT: 130, - // direction estimation - DISTANCE_VOTE_POWER: 1.1, - SINGLE_ANGLE_VOTE_POWER: 0.9, - TOTAL_ANGLE_VOTE_POWER: 1.6 -}; -function calculateSlope(point1x, point1y, point2x, point2y) { - const value = (point1y - point2y) / (point1x - point2x); - let slope = Math.atan(value) * 180 / Math.PI; - if (slope <= 0) slope = -slope; - else if (slope > 0) slope = 180 - slope; - return slope; -} -function getSlopes(point1, point2) { - if (!point1 || !point2) return [0, 0]; - const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); - if (point1.length === 2) return slopeXY; - const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); - return [slopeXY, slopeYZ]; -} -function angleOrientationAt(angle, weightageAt = 1) { - let isVertical = 0; - let isDiagonal = 0; - let isHorizontal = 0; - if (angle >= 75 && angle <= 105) isVertical = 1 * weightageAt; - else if (angle >= 25 && angle <= 155) isDiagonal = 1 * weightageAt; - else isHorizontal = 1 * weightageAt; - return [isVertical, isDiagonal, isHorizontal]; -} -function estimateFingerCurl(startPoint, midPoint, endPoint) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const start_mid_z_dist = startPoint[2] - midPoint[2]; - const start_end_z_dist = startPoint[2] - endPoint[2]; - const mid_end_z_dist = midPoint[2] - endPoint[2]; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); - let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); - if (cos_in > 1) cos_in = 1; - else if (cos_in < -1) cos_in = -1; - let angleOfCurve = Math.acos(cos_in); - angleOfCurve = 57.2958 * angleOfCurve % 180; - let fingerCurl; - if (angleOfCurve > options3.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none; - else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half; - else fingerCurl = FingerCurl.full; - return fingerCurl; -} -function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - if (max_dist_x === Math.abs(start_end_x_dist)) { - if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } else if (max_dist_x === Math.abs(start_mid_x_dist)) { - if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } else { - if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } - return estimatedDirection; -} -function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { - let estimatedDirection; - if (max_dist_y === Math.abs(start_end_y_dist)) { - if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } else if (max_dist_y === Math.abs(start_mid_y_dist)) { - if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } else { - if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } - return estimatedDirection; -} -function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - if (reqd_vertical_direction === FingerDirection.verticalUp) { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft; - else estimatedDirection = FingerDirection.diagonalUpRight; - } else { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft; - else estimatedDirection = FingerDirection.diagonalDownRight; - } - return estimatedDirection; -} -function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); - const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); - let voteVertical = 0; - let voteDiagonal = 0; - let voteHorizontal = 0; - const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); - if (start_end_x_y_dist_ratio > 1.5) voteVertical += options3.DISTANCE_VOTE_POWER; - else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options3.DISTANCE_VOTE_POWER; - else voteHorizontal += options3.DISTANCE_VOTE_POWER; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); - const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); - let calc_start_point_x = startPoint[0]; - let calc_start_point_y = startPoint[1]; - let calc_end_point_x = endPoint[0]; - let calc_end_point_y = endPoint[1]; - if (max_dist === start_mid_dist) { - calc_end_point_x = endPoint[0]; - calc_end_point_y = endPoint[1]; - } else if (max_dist === mid_end_dist) { - calc_start_point_x = midPoint[0]; - calc_start_point_y = midPoint[1]; - } - const calcStartPoint = [calc_start_point_x, calc_start_point_y]; - const calcEndPoint = [calc_end_point_x, calc_end_point_y]; - const totalAngle = getSlopes(calcStartPoint, calcEndPoint); - const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); - voteVertical += votes[0]; - voteDiagonal += votes[1]; - voteHorizontal += votes[2]; - for (const fingerSlope of fingerSlopes) { - const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); - voteVertical += fingerVotes[0]; - voteDiagonal += fingerVotes[1]; - voteHorizontal += fingerVotes[2]; - } - let estimatedDirection; - if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } else { - estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } - return estimatedDirection; -} -function estimate(landmarks) { - const slopesXY = []; - const slopesYZ = []; - const fingerCurls = []; - const fingerDirections = []; - if (!landmarks) return { curls: fingerCurls, directions: fingerDirections }; - for (const finger of Finger.all) { - const points = Finger.getPoints(finger); - const slopeAtXY = []; - const slopeAtYZ = []; - for (const point2 of points) { - const point1 = landmarks[point2[0]]; - const point22 = landmarks[point2[1]]; - const slopes = getSlopes(point1, point22); - const slopeXY = slopes[0]; - const slopeYZ = slopes[1]; - slopeAtXY.push(slopeXY); - slopeAtYZ.push(slopeYZ); - } - slopesXY.push(slopeAtXY); - slopesYZ.push(slopeAtYZ); - } - for (const finger of Finger.all) { - const pointIndexAt = finger === Finger.thumb ? 1 : 0; - const fingerPointsAt = Finger.getPoints(finger); - const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; - const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; - const endPoint = landmarks[fingerPointsAt[3][1]]; - const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); - const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); - fingerCurls[finger] = fingerCurled; - fingerDirections[finger] = fingerPosition; - } - return { curls: fingerCurls, directions: fingerDirections }; -} -function analyze(keypoints) { - if (!keypoints || keypoints.length === 0) return null; - const estimatorRes = estimate(keypoints); - const landmarks = {}; - for (const fingerIdx of Finger.all) { - landmarks[Finger.getName(fingerIdx)] = { - curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), - direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) - }; - } - return landmarks; -} -function match(keypoints) { - const poses = []; - if (!keypoints || keypoints.length === 0) return poses; - const estimatorRes = estimate(keypoints); - for (const gesture2 of fingergesture_default) { - const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); - if (confidence >= minConfidence) poses.push({ name: gesture2.name, confidence }); - } - return poses; -} - -// src/gesture/gesture.ts -var body2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); - const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); - const nose = res[i].keypoints.find((a) => a.part === "nose"); - if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "i give up" }); - else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "raise left hand" }); - else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "raise right hand" }); - const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); - const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); - if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { - gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); - } - } - return gestures; -}; -var face2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (res[i].mesh && res[i].mesh.length > 450) { - const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); - const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; - if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: "facing center" }); - else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); - const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); - if (openLeft < 0.2) gestures.push({ face: i, gesture: "blink left eye" }); - const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); - if (openRight < 0.2) gestures.push({ face: i, gesture: "blink right eye" }); - const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); - if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); - const chinDepth = res[i].mesh[152][2] || 0; - if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); - } - } - return gestures; -}; -var iris2 = (res) => { - var _a, _b, _c, _d; - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) continue; - const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; - const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; - const areaLeft = Math.abs(sizeXLeft * sizeYLeft); - const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; - const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; - const areaRight = Math.abs(sizeXRight * sizeYRight); - let center = false; - const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); - if (difference < 0.25) { - center = true; - gestures.push({ iris: i, gesture: "facing center" }); - } - const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; - const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; - if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false; - if (leftIrisCenterX > rightIrisCenterX) { - if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: "looking right" }); - } else { - if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: "looking left" }); - } - const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; - const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: "looking down" }); - if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: "looking up" }); - if (center) gestures.push({ iris: i, gesture: "looking center" }); - } - return gestures; -}; -var hand2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const fingers = []; - if (res[i].annotations) { - for (const [finger, pos] of Object.entries(res[i].annotations)) { - if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); - } - } - if (fingers && fingers.length > 0) { - const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); - gestures.push({ hand: i, gesture: `${closest.name} forward` }); - const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); - gestures.push({ hand: i, gesture: `${highest.name} up` }); - } - if (res[i].keypoints) { - const poses = match(res[i].keypoints); - for (const pose of poses) gestures.push({ hand: i, gesture: pose.name }); - } - } - return gestures; -}; - -// src/hand/handposedetector.ts -var tf27 = __toESM(require_tfjs_esm()); - -// src/hand/handposeutil.ts -var tf26 = __toESM(require_tfjs_esm()); -function getBoxSize2(box) { - return [ - Math.abs(box.endPoint[0] - box.startPoint[0]), - Math.abs(box.endPoint[1] - box.startPoint[1]) - ]; -} -function getBoxCenter2(box) { - return [ - box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, - box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 - ]; -} -function cutBoxFromImageAndResize(box, image28, cropSize) { - const h = image28.shape[1]; - const w = image28.shape[2]; - const boxes = [[ - box.startPoint[1] / h, - box.startPoint[0] / w, - box.endPoint[1] / h, - box.endPoint[0] / w - ]]; - return tf26.image.cropAndResize(image28, boxes, [0], cropSize); -} -function scaleBoxCoordinates2(box, factor) { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const palmLandmarks = box.palmLandmarks.map((coord) => { - const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; - return scaledCoord; - }); - return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; -} -function enlargeBox2(box, factor = 1.5) { - const center = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; - const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function squarifyBox2(box) { - const centers = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const maxEdge = Math.max(...size2); - const halfSize = maxEdge / 2; - const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; - const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function normalizeRadians2(angle) { - return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -} -function computeRotation2(point1, point2) { - const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); - return normalizeRadians2(radians); -} -var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -function dot2(v1, v2) { - let product = 0; - for (let i = 0; i < v1.length; i++) { - product += v1[i] * v2[i]; - } - return product; -} -function getColumnFrom2DArr2(arr, columnIndex) { - const column = []; - for (let i = 0; i < arr.length; i++) { - column.push(arr[i][columnIndex]); - } - return column; -} -function multiplyTransformMatrices2(mat1, mat2) { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) { - product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); - } - } - return product; -} -function buildRotationMatrix2(rotation, center) { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix2(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); - return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); -} -function invertTransformMatrix2(matrix) { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [ - -dot2(rotationComponent[0], translationComponent), - -dot2(rotationComponent[1], translationComponent) - ]; - return [ - rotationComponent[0].concat(invertedTranslation[0]), - rotationComponent[1].concat(invertedTranslation[1]), - [0, 0, 1] - ]; -} -function rotatePoint2(homogeneousCoordinate, rotationMatrix) { - return [ - dot2(homogeneousCoordinate, rotationMatrix[0]), - dot2(homogeneousCoordinate, rotationMatrix[1]) - ]; -} - -// src/hand/handposeanchors.ts -var anchors2 = [ - { x: 0.015625, y: 0.015625 }, - { x: 0.015625, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - { x: 0.890625, y: 0.078125 }, - { x: 0.890625, y: 0.078125 }, - { x: 0.921875, y: 0.078125 }, - { x: 0.921875, y: 0.078125 }, - { x: 0.953125, y: 0.078125 }, - { x: 0.953125, y: 0.078125 }, - { x: 0.984375, y: 0.078125 }, - { x: 0.984375, y: 0.078125 }, - { x: 0.015625, y: 0.109375 }, - { x: 0.015625, y: 0.109375 }, - { x: 0.046875, y: 0.109375 }, - { x: 0.046875, y: 0.109375 }, - { x: 0.078125, y: 0.109375 }, - { x: 0.078125, y: 0.109375 }, - { x: 0.109375, y: 0.109375 }, - { x: 0.109375, y: 0.109375 }, - { x: 0.140625, y: 0.109375 }, - { x: 0.140625, y: 0.109375 }, - { x: 0.171875, y: 0.109375 }, - { x: 0.171875, y: 0.109375 }, - { x: 0.203125, y: 0.109375 }, - { x: 0.203125, y: 0.109375 }, - { x: 0.234375, y: 0.109375 }, - { x: 0.234375, y: 0.109375 }, - { x: 0.265625, y: 0.109375 }, - { x: 0.265625, y: 0.109375 }, - { x: 0.296875, y: 0.109375 }, - { x: 0.296875, y: 0.109375 }, - { x: 0.328125, y: 0.109375 }, - { x: 0.328125, y: 0.109375 }, - { x: 0.359375, y: 0.109375 }, - { x: 0.359375, y: 0.109375 }, - { x: 0.390625, y: 0.109375 }, - { x: 0.390625, y: 0.109375 }, - { x: 0.421875, y: 0.109375 }, - { x: 0.421875, y: 0.109375 }, - { x: 0.453125, y: 0.109375 }, - { x: 0.453125, y: 0.109375 }, - { x: 0.484375, y: 0.109375 }, - { x: 0.484375, y: 0.109375 }, - { x: 0.515625, y: 0.109375 }, - { x: 0.515625, y: 0.109375 }, - { x: 0.546875, y: 0.109375 }, - { x: 0.546875, y: 0.109375 }, - { x: 0.578125, y: 0.109375 }, - { x: 0.578125, y: 0.109375 }, - { x: 0.609375, y: 0.109375 }, - { x: 0.609375, y: 0.109375 }, - { x: 0.640625, y: 0.109375 }, - { x: 0.640625, y: 0.109375 }, - { x: 0.671875, y: 0.109375 }, - { x: 0.671875, y: 0.109375 }, - { x: 0.703125, y: 0.109375 }, - { x: 0.703125, y: 0.109375 }, - { x: 0.734375, y: 0.109375 }, - { x: 0.734375, y: 0.109375 }, - { x: 0.765625, y: 0.109375 }, - { x: 0.765625, y: 0.109375 }, - { x: 0.796875, y: 0.109375 }, - { x: 0.796875, y: 0.109375 }, - { x: 0.828125, y: 0.109375 }, - { x: 0.828125, y: 0.109375 }, - { x: 0.859375, y: 0.109375 }, - { x: 0.859375, y: 0.109375 }, - 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-// src/hand/handposedetector.ts -var HandDetector = class { - constructor(model23) { - __publicField(this, "model"); - __publicField(this, "anchors"); - __publicField(this, "anchorsTensor"); - __publicField(this, "inputSize"); - __publicField(this, "inputSizeTensor"); - __publicField(this, "doubleInputSizeTensor"); - var _a, _b, _c, _d; - this.model = model23; - this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); - this.anchorsTensor = tf27.tensor2d(this.anchors); - this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; - this.inputSizeTensor = tf27.tensor1d([this.inputSize, this.inputSize]); - this.doubleInputSizeTensor = tf27.tensor1d([this.inputSize * 2, this.inputSize * 2]); - } - normalizeBoxes(boxes) { - const t2 = {}; - t2.boxOffsets = tf27.slice(boxes, [0, 0], [-1, 2]); - t2.boxSizes = tf27.slice(boxes, [0, 2], [-1, 2]); - t2.div = tf27.div(t2.boxOffsets, this.inputSizeTensor); - t2.boxCenterPoints = tf27.add(t2.div, this.anchorsTensor); - t2.halfBoxSizes = tf27.div(t2.boxSizes, this.doubleInputSizeTensor); - t2.sub = tf27.sub(t2.boxCenterPoints, t2.halfBoxSizes); - t2.startPoints = tf27.mul(t2.sub, this.inputSizeTensor); - t2.add = tf27.add(t2.boxCenterPoints, t2.halfBoxSizes); - t2.endPoints = tf27.mul(t2.add, this.inputSizeTensor); - const res = tf27.concat2d([t2.startPoints, t2.endPoints], 1); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - normalizeLandmarks(rawPalmLandmarks, index2) { - const t2 = {}; - t2.reshape = tf27.reshape(rawPalmLandmarks, [-1, 7, 2]); - t2.div = tf27.div(t2.reshape, this.inputSizeTensor); - t2.landmarks = tf27.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); - const res = tf27.mul(t2.landmarks, this.inputSizeTensor); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - async predict(input, config3) { - var _a; - const t2 = {}; - t2.resize = tf27.image.resizeBilinear(input, [this.inputSize, this.inputSize]); - t2.div = tf27.div(t2.resize, constants.tf127); - t2.image = tf27.sub(t2.div, constants.tf1); - t2.batched = this.model.execute(t2.image); - t2.predictions = tf27.squeeze(t2.batched); - t2.slice = tf27.slice(t2.predictions, [0, 0], [-1, 1]); - t2.sigmoid = tf27.sigmoid(t2.slice); - t2.scores = tf27.squeeze(t2.sigmoid); - const scores = await t2.scores.data(); - t2.boxes = tf27.slice(t2.predictions, [0, 1], [-1, 4]); - t2.norm = this.normalizeBoxes(t2.boxes); - t2.nms = await tf27.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); - const nms = await t2.nms.array(); - const hands = []; - for (const index2 of nms) { - const p = {}; - p.box = tf27.slice(t2.norm, [index2, 0], [1, -1]); - p.slice = tf27.slice(t2.predictions, [index2, 5], [1, 14]); - p.norm = this.normalizeLandmarks(p.slice, index2); - p.palmLandmarks = tf27.reshape(p.norm, [-1, 2]); - const box = await p.box.data(); - const startPoint = box.slice(0, 2); - const endPoint = box.slice(2, 4); - const palmLandmarks = await p.palmLandmarks.array(); - const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; - const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); - hands.push(scaled); - Object.keys(p).forEach((tensor6) => tf27.dispose(p[tensor6])); - } - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return hands; - } -}; - -// src/hand/handposepipeline.ts -var tf28 = __toESM(require_tfjs_esm()); -var palmBoxEnlargeFactor = 5; -var handBoxEnlargeFactor = 1.65; -var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; -var palmLandmarksPalmBase = 0; -var palmLandmarksMiddleFingerBase = 2; -var lastTime13 = 0; -var HandPipeline = class { - constructor(handDetector, handPoseModel2) { - __publicField(this, "handDetector"); - __publicField(this, "handPoseModel"); - __publicField(this, "inputSize"); - __publicField(this, "storedBoxes"); - __publicField(this, "skipped"); - __publicField(this, "detectedHands"); - var _a, _b, _c; - this.handDetector = handDetector; - this.handPoseModel = handPoseModel2; - this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; - this.storedBoxes = []; - this.skipped = Number.MAX_SAFE_INTEGER; - this.detectedHands = 0; - } - calculateLandmarksBoundingBox(landmarks) { - const xs = landmarks.map((d) => d[0]); - const ys = landmarks.map((d) => d[1]); - const startPoint = [Math.min(...xs), Math.min(...ys)]; - const endPoint = [Math.max(...xs), Math.max(...ys)]; - return { startPoint, endPoint }; - } - getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { - const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); - const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); - return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); - } - getBoxForHandLandmarks(landmarks) { - const boundingBox = this.calculateLandmarksBoundingBox(landmarks); - const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); - boxAroundHand.palmLandmarks = []; - for (let i = 0; i < palmLandmarkIds.length; i++) { - boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); - } - return boxAroundHand; - } - transformRawCoords(rawCoords, box2, angle, rotationMatrix) { - const boxSize = getBoxSize2(box2); - const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; - const coordsScaled = rawCoords.map((coord) => [ - scaleFactor[0] * (coord[0] - this.inputSize / 2), - scaleFactor[1] * (coord[1] - this.inputSize / 2), - scaleFactor[2] * coord[2] - ]); - const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); - const coordsRotated = coordsScaled.map((coord) => { - const rotated = rotatePoint2(coord, coordsRotationMatrix); - return [...rotated, coord[2]]; - }); - const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); - const boxCenter = [...getBoxCenter2(box2), 1]; - const originalBoxCenter = [ - dot2(boxCenter, inverseRotationMatrix[0]), - dot2(boxCenter, inverseRotationMatrix[1]) - ]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + originalBoxCenter[0]), - Math.trunc(coord[1] + originalBoxCenter[1]), - Math.trunc(coord[2]) - ]); - } - async estimateHands(image28, config3) { - let useFreshBox = false; - let boxes; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; - const skipFrame = this.skipped < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - this.skipped++; - } else { - boxes = await this.handDetector.predict(image28, config3); - this.skipped = 0; - } - if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { - this.detectedHands = 0; - this.storedBoxes = [...boxes]; - if (this.storedBoxes.length > 0) useFreshBox = true; - } - const hands = []; - for (let i = 0; i < this.storedBoxes.length; i++) { - const currentBox = this.storedBoxes[i]; - if (!currentBox) continue; - if (config3.hand.landmarks) { - const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; - const palmCenter = getBoxCenter2(currentBox); - const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; - const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf28.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); - const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); - const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; - const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); - const handImage = tf28.div(croppedInput, constants.tf255); - tf28.dispose(croppedInput); - tf28.dispose(rotatedImage); - const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); - lastTime13 = now(); - tf28.dispose(handImage); - const confidence = (await confidenceT.data())[0]; - tf28.dispose(confidenceT); - if (confidence >= config3.hand.minConfidence / 4) { - const keypointsReshaped = tf28.reshape(keypoints, [-1, 3]); - const rawCoords = await keypointsReshaped.array(); - tf28.dispose(keypoints); - tf28.dispose(keypointsReshaped); - const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); - const nextBoundingBox = this.getBoxForHandLandmarks(coords); - this.storedBoxes[i] = { ...nextBoundingBox, confidence }; - const result = { - landmarks: coords, - confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: confidence, - box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } - }; - hands.push(result); - } else { - this.storedBoxes[i] = null; - } - tf28.dispose(keypoints); - } else { - const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); - const result = { - confidence: currentBox.confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: 0, - box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, - landmarks: [] - }; - hands.push(result); - } - } - this.storedBoxes = this.storedBoxes.filter((a) => a !== null); - this.detectedHands = hands.length; - if (hands.length > config3.hand.maxDetected) hands.length = config3.hand.maxDetected; - return hands; - } -}; - -// src/hand/handpose.ts -var meshAnnotations2 = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - palm: [0] -}; -var handDetectorModel; -var handPoseModel; -var handPipeline; -function initPipeline() { - const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; - if (handDetector && handPoseModel) handPipeline = new HandPipeline(handDetector, handPoseModel); -} -async function predict14(input, config3) { - if (!handPipeline) initPipeline(); - const predictions = await handPipeline.estimateHands(input, config3); - if (!predictions) return []; - const hands = []; - for (let i = 0; i < predictions.length; i++) { - const annotations2 = {}; - if (predictions[i].landmarks) { - for (const key of Object.keys(meshAnnotations2)) { - annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); - } - } - const keypoints = predictions[i].landmarks; - let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; - let boxRaw = [0, 0, 0, 0]; - if (keypoints && keypoints.length > 0) { - for (const pt of keypoints) { - if (pt[0] < box[0]) box[0] = pt[0]; - if (pt[1] < box[1]) box[1] = pt[1]; - if (pt[0] > box[2]) box[2] = pt[0]; - if (pt[1] > box[3]) box[3] = pt[1]; - } - box[2] -= box[0]; - box[3] -= box[1]; - boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; - } else { - box = predictions[i].box ? [ - Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), - Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) - ] : [0, 0, 0, 0]; - boxRaw = [ - predictions[i].box.topLeft[0] / (input.shape[2] || 0), - predictions[i].box.topLeft[1] / (input.shape[1] || 0), - (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), - (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) - ]; - } - const landmarks = analyze(keypoints); - hands.push({ - id: i, - score: Math.round(100 * predictions[i].confidence) / 100, - boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, - fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, - label: "hand", - box, - boxRaw, - keypoints, - annotations: annotations2, - landmarks - }); - } - return hands; -} -async function loadDetect2(config3) { - var _a; - if (env.initial) handDetectorModel = null; - if (!handDetectorModel) handDetectorModel = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", handDetectorModel["modelUrl"]); - return handDetectorModel; -} -async function loadSkeleton(config3) { - var _a; - if (env.initial) handPoseModel = null; - if (!handPoseModel) handPoseModel = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", handPoseModel["modelUrl"]); - return handPoseModel; -} - -// src/hand/handtrack.ts -var tf29 = __toESM(require_tfjs_esm()); -var models2 = [null, null]; -var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; -var inputSize7 = [[0, 0], [0, 0]]; -var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; -var faceIndex = 4; -var boxExpandFact = 1.6; -var maxDetectorResolution = 512; -var detectorExpandFact = 1.4; -var skipped13 = Number.MAX_SAFE_INTEGER; -var lastTime14 = 0; -var outputSize = [0, 0]; -var cache4 = { - boxes: [], - hands: [] -}; -var fingerMap = { - /* - thumb: [0, 1, 2, 3, 4], - index: [0, 5, 6, 7, 8], - middle: [0, 9, 10, 11, 12], - ring: [0, 13, 14, 15, 16], - pinky: [0, 17, 18, 19, 20], - palm: [0], - */ - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - base: [0], - palm: [0, 17, 13, 9, 5, 1, 0] -}; -async function loadDetect3(config3) { - var _a; - if (env.initial) models2[0] = null; - if (!models2[0]) { - fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); - models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; - inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", models2[0]["modelUrl"]); - return models2[0]; -} -async function loadSkeleton2(config3) { - var _a; - if (env.initial) models2[1] = null; - if (!models2[1]) { - models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; - inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", models2[1]["modelUrl"]); - return models2[1]; -} -async function detectHands(input, config3) { - const hands = []; - if (!input || !models2[0]) return hands; - const t2 = {}; - const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); - const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); - const width = Math.round(height * ratio2 / 8) * 8; - t2.resize = tf29.image.resizeBilinear(input, [height, width]); - t2.cast = tf29.cast(t2.resize, "int32"); - [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); - t2.boxes = tf29.squeeze(t2.rawBoxes, [0, 2]); - t2.scores = tf29.squeeze(t2.rawScores, [0]); - const classScores = tf29.unstack(t2.scores, 1); - tf29.dispose(classScores[faceIndex]); - classScores.splice(faceIndex, 1); - t2.filtered = tf29.stack(classScores, 1); - tf29.dispose(classScores); - t2.max = tf29.max(t2.filtered, 1); - t2.argmax = tf29.argMax(t2.filtered, 1); - let id = 0; - t2.nms = await tf29.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); - const nms = await t2.nms.data(); - const scores = await t2.max.data(); - const classNum = await t2.argmax.data(); - for (const nmsIndex of Array.from(nms)) { - const boxSlice = tf29.slice(t2.boxes, nmsIndex, 1); - const boxYX = await boxSlice.data(); - tf29.dispose(boxSlice); - const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; - const boxRaw = scale(boxData, detectorExpandFact); - const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; - const score = scores[nmsIndex]; - const label = classes[classNum[nmsIndex]]; - const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; - hands.push(hand3); - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - hands.sort((a, b) => b.score - a.score); - if (hands.length > (config3.hand.maxDetected || 1)) hands.length = config3.hand.maxDetected || 1; - return hands; -} -async function detectFingers(input, h, config3) { - const hand3 = { - // initial values inherited from hand detect - id: h.id, - score: Math.round(100 * h.score) / 100, - boxScore: Math.round(100 * h.score) / 100, - fingerScore: 0, - box: h.box, - boxRaw: h.boxRaw, - label: h.label, - keypoints: [], - landmarks: {}, - annotations: {} - }; - if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { - const t2 = {}; - const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; - t2.crop = tf29.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); - t2.div = tf29.div(t2.crop, constants.tf255); - [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); - const rawScore = (await t2.score.data())[0]; - const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; - if (score >= (config3.hand.minConfidence || 0)) { - hand3.fingerScore = score; - t2.reshaped = tf29.reshape(t2.keypoints, [-1, 3]); - const coordsData = await t2.reshaped.array(); - const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); - const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); - hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); - hand3.landmarks = analyze(hand3.keypoints); - for (const key of Object.keys(fingerMap)) { - hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); - } - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - } - return hand3; -} -async function predict15(input, config3) { - var _a, _b; - if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) return []; - outputSize = [input.shape[2] || 0, input.shape[1] || 0]; - skipped13++; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrame = skipped13 < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache4.hands; - } - return new Promise(async (resolve) => { - const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); - if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else { - cache4.boxes = await detectHands(input, config3); - lastTime14 = now(); - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - skipped13 = 0; - } - const oldCache = [...cache4.boxes]; - cache4.boxes.length = 0; - if (config3.cacheSensitivity > 0) { - for (let i = 0; i < cache4.hands.length; i++) { - const boxKpt = square(cache4.hands[i].keypoints, outputSize); - if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { - const boxScale = scale(boxKpt.box, boxExpandFact); - const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); - cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); - } - } - } - for (let i = 0; i < cache4.hands.length; i++) { - const bbox = calc(cache4.hands[i].keypoints, outputSize); - cache4.hands[i].box = bbox.box; - cache4.hands[i].boxRaw = bbox.boxRaw; - } - resolve(cache4.hands); - }); -} - -// src/result.ts -var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); - -// src/body/movenetcoords.ts -var movenetcoords_exports = {}; -__export(movenetcoords_exports, { - connected: () => connected3, - horizontal: () => horizontal, - kpt: () => kpt3, - relative: () => relative, - vertical: () => vertical -}); -var kpt3 = [ - // used to create part labels - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var horizontal = [ - // used to fix left vs right - ["leftEye", "rightEye"], - ["leftEar", "rightEar"], - ["leftShoulder", "rightShoulder"], - ["leftElbow", "rightElbow"], - ["leftWrist", "rightWrist"], - ["leftHip", "rightHip"], - ["leftKnee", "rightKnee"], - ["leftAnkle", "rightAnkle"] -]; -var vertical = [ - // used to remove unlikely keypoint positions - ["leftKnee", "leftShoulder"], - ["rightKnee", "rightShoulder"], - ["leftAnkle", "leftKnee"], - ["rightAnkle", "rightKnee"] -]; -var relative = [ - // used to match relative body parts - [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], - [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] -]; -var connected3 = { - // used to create body outline in annotations - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/util/interpolate.ts -var bufferedResult = empty(); -var interpolateTime = 0; -function calc2(newResult, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z; - const t0 = now(); - if (!newResult) return empty(); - const elapsed = Date.now() - newResult.timestamp; - const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; - if (newResult.canvas) bufferedResult.canvas = newResult.canvas; - if (newResult.error) bufferedResult.error = newResult.error; - if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { - bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); - } else { - for (let i = 0; i < newResult.body.length; i++) { - const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); - const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); - const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; - return { - score: newKpt.score, - part: newKpt.part, - position: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] - ], - positionRaw: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] - ], - distance: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] - ] - }; - }); - const annotations2 = {}; - let coords = { connected: {} }; - if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) coords = efficientposecoords_exports; - else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) coords = blazeposecoords_exports; - else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) coords = movenetcoords_exports; - for (const [name, indexes] of Object.entries(coords.connected)) { - const pt = []; - for (let j = 0; j < indexes.length - 1; j++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[j]); - const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); - if (pt0 && pt1) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { - bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); - } else { - for (let i = 0; i < newResult.hand.length; i++) { - const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); - if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; - const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; - let annotations2 = {}; - if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { - bufferedResult.hand[i].annotations = newResult.hand[i].annotations; - annotations2 = bufferedResult.hand[i].annotations; - } else if (newResult.hand[i].annotations) { - for (const key of Object.keys(newResult.hand[i].annotations)) { - annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { - bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); - } else { - for (let i = 0; i < newResult.face.length; i++) { - const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); - let annotations2 = newResult.face[i].annotations; - if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) { - bufferedResult.face[i].annotations = newResult.face[i].annotations; - annotations2 = bufferedResult.face[i].annotations; - } else if (newResult.face[i].annotations) { - for (const key of Object.keys(newResult.face[i].annotations)) { - annotations2[key] = ((_i = (_h = (_g = newResult.face[i]) == null ? void 0 : _g.annotations) == null ? void 0 : _h[key]) == null ? void 0 : _i[0]) ? newResult.face[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - if (newResult.face[i].rotation) { - const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; - rotation.matrix = (_j = newResult.face[i].rotation) == null ? void 0 : _j.matrix; - rotation.angle = { - roll: ((bufferedFactor - 1) * (((_l = (_k = bufferedResult.face[i].rotation) == null ? void 0 : _k.angle) == null ? void 0 : _l.roll) || 0) + (((_n = (_m = newResult.face[i].rotation) == null ? void 0 : _m.angle) == null ? void 0 : _n.roll) || 0)) / bufferedFactor, - yaw: ((bufferedFactor - 1) * (((_p = (_o = bufferedResult.face[i].rotation) == null ? void 0 : _o.angle) == null ? void 0 : _p.yaw) || 0) + (((_r = (_q = newResult.face[i].rotation) == null ? void 0 : _q.angle) == null ? void 0 : _r.yaw) || 0)) / bufferedFactor, - pitch: ((bufferedFactor - 1) * (((_t = (_s = bufferedResult.face[i].rotation) == null ? void 0 : _s.angle) == null ? void 0 : _t.pitch) || 0) + (((_v = (_u = newResult.face[i].rotation) == null ? void 0 : _u.angle) == null ? void 0 : _v.pitch) || 0)) / bufferedFactor - }; - rotation.gaze = { - // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive - bearing: ((bufferedFactor - 1) * (((_w = bufferedResult.face[i].rotation) == null ? void 0 : _w.gaze.bearing) || 0) + (((_x = newResult.face[i].rotation) == null ? void 0 : _x.gaze.bearing) || 0)) / bufferedFactor, - strength: ((bufferedFactor - 1) * (((_y = bufferedResult.face[i].rotation) == null ? void 0 : _y.gaze.strength) || 0) + (((_z = newResult.face[i].rotation) == null ? void 0 : _z.gaze.strength) || 0)) / bufferedFactor - }; - bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations: annotations2 }; - } else { - bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations: annotations2 }; - } - } - } - if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { - bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); - } else { - for (let i = 0; i < newResult.object.length; i++) { - const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); - bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; - } - } - if (newResult.persons) { - const newPersons = newResult.persons; - if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { - bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); - } else { - for (let i = 0; i < newPersons.length; i++) { - bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); - } - } - } - if (newResult.gesture) bufferedResult.gesture = newResult.gesture; - bufferedResult.width = newResult.width; - bufferedResult.height = newResult.height; - const t1 = now(); - interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); - if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; - return bufferedResult; -} - -// src/segmentation/meet.ts -var tf30 = __toESM(require_tfjs_esm()); -var model17; -async function load15(config3) { - if (!model17 || env.initial) model17 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model17["modelUrl"]); - return model17; -} -async function predict16(input, config3) { - var _a; - if (!model17) model17 = await load15(config3); - if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) return null; - const t2 = {}; - t2.resize = tf30.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); - t2.norm = tf30.div(t2.resize, constants.tf255); - t2.res = model17.execute(t2.norm); - t2.squeeze = tf30.squeeze(t2.res, [0]); - [t2.bgRaw, t2.fgRaw] = tf30.unstack(t2.squeeze, 2); - t2.fg = tf30.softmax(t2.fgRaw); - t2.mul = tf30.mul(t2.fg, constants.tf255); - t2.expand = tf30.expandDims(t2.mul, 2); - t2.output = tf30.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf30.squeeze(input); - t2.concat = tf30.concat([t2.input, t2.output], -1); - rgba = tf30.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf30.cast(t2.output, "int32"); - break; - default: - rgba = tf30.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf30.dispose(t2[tensor6])); - return rgba; -} - -// src/face/match.ts -var match_exports = {}; -__export(match_exports, { - distance: () => distance, - find: () => find, - similarity: () => similarity -}); -function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { - if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER; - let sum3 = 0; - for (let i = 0; i < descriptor1.length; i++) { - const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); - sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; - } - const dist = Math.round(100 * (options4.multiplier || 20) * sum3) / 100; - return dist; -} -var normalizeDistance = (dist, order, min2, max5) => { - if (dist === 0) return 1; - const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); - const norm = (1 - root / 100 - min2) / (max5 - min2); - const clamp2 = Math.round(100 * Math.max(Math.min(norm, 1), 0)) / 100; - return clamp2; -}; -function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { - const dist = distance(descriptor1, descriptor2, options4); - return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); -} -function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { - if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { - return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; - } - let lowestDistance = Number.MAX_SAFE_INTEGER; - let index2 = -1; - for (let i = 0; i < descriptors.length; i++) { - const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; - if (res < lowestDistance) { - lowestDistance = res; - index2 = i; - } - if (lowestDistance < (options4.threshold || 0)) break; - } - const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); - return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; -} - -// src/models.ts -var models_exports2 = {}; -__export(models_exports2, { - Models: () => Models, - validateModel: () => validateModel -}); - -// src/body/movenet.ts -var tf32 = __toESM(require_tfjs_esm()); - -// src/body/movenetfix.ts -var tf31 = __toESM(require_tfjs_esm()); -var maxJitter = 5e-3; -var cache5 = { - keypoints: [], - padding: [[0, 0], [0, 0], [0, 0], [0, 0]] -}; -function bodyParts(body4) { - for (const pair of horizontal) { - const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); - if (body4.keypoints[left] && body4.keypoints[right]) { - if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } - } - for (const pair of vertical) { - const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - if (body4.keypoints[lower] && body4.keypoints[higher]) { - if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { - body4.keypoints.splice(lower, 1); - } - } - } - for (const [pair, compare2] of relative) { - const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); - const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); - if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) continue; - const distanceLeft = body4.keypoints[left] ? [ - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) - ] : [0, 0]; - const distanceRight = body4.keypoints[right] ? [ - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) - ] : [0, 0]; - if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } -} -function jitter(keypoints) { - for (let i = 0; i < keypoints.length; i++) { - if (keypoints[i] && cache5.keypoints[i]) { - const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; - if (diff[0] < maxJitter && diff[1] < maxJitter) { - keypoints[i] = cache5.keypoints[i]; - } else { - cache5.keypoints[i] = keypoints[i]; - } - } else { - cache5.keypoints[i] = keypoints[i]; - } - } - return keypoints; -} -function padInput(input, inputSize10) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) return input; - cache5.padding = [ - [0, 0], - // dont touch batch - [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], - // height before&after - [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], - // width before&after - [0, 0] - // dont touch rbg - ]; - t2.pad = tf31.pad(input, cache5.padding); - t2.resize = tf31.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); - const final = tf31.cast(t2.resize, "int32"); - Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6])); - return final; -} -function rescaleBody(body4, outputSize2) { - body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); - for (const kpt4 of body4.keypoints) { - kpt4.position = [ - kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], - kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] - ]; - kpt4.positionRaw = [ - kpt4.position[0] / outputSize2[0], - kpt4.position[1] / outputSize2[1] - ]; - } - const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); - body4.box = rescaledBoxes.box; - body4.boxRaw = rescaledBoxes.boxRaw; - return body4; -} - -// src/body/movenet.ts -var model18; -var inputSize8 = 0; -var skipped14 = Number.MAX_SAFE_INTEGER; -var cache6 = { - boxes: [], - bodies: [], - last: 0 -}; -async function load16(config3) { - var _a; - if (env.initial) model18 = null; - if (!model18) { - fakeOps(["size"], config3); - model18 = await loadModel(config3.body.modelPath); - } else if (config3.debug) log("cached model:", model18["modelUrl"]); - inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; - if (inputSize8 < 64) inputSize8 = 256; - if (tf32.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf32.env().set("WEBGL_USE_SHAPES_UNIFORMS", false); - return model18; -} -function parseSinglePose(res, config3, image28) { - const kpt4 = res[0][0]; - const keypoints = []; - let score = 0; - for (let id = 0; id < kpt4.length; id++) { - score = kpt4[id][2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[id][1], kpt4[id][0]]; - keypoints.push({ - score: Math.round(100 * score) / 100, - part: kpt3[id], - positionRaw, - position: [ - // normalized to input image size - Math.round((image28.shape[2] || 0) * positionRaw[0]), - Math.round((image28.shape[1] || 0) * positionRaw[1]) - ] - }); - } - } - score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const bodies = []; - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - return bodies; -} -function parseMultiPose(res, config3, image28) { - const bodies = []; - for (let id = 0; id < res[0].length; id++) { - const kpt4 = res[0][id]; - const boxScore = Math.round(100 * kpt4[51 + 4]) / 100; - if (boxScore > config3.body.minConfidence) { - const keypoints = []; - for (let i = 0; i < 17; i++) { - const score = kpt4[3 * i + 2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; - keypoints.push({ - part: kpt3[i], - score: Math.round(100 * score) / 100, - positionRaw, - position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] - }); - } - } - const boxRaw = [kpt4[51 + 1], kpt4[51 + 0], kpt4[51 + 3] - kpt4[51 + 1], kpt4[51 + 2] - kpt4[51 + 0]]; - const boxNorm = [Math.trunc(boxRaw[0] * (image28.shape[2] || 0)), Math.trunc(boxRaw[1] * (image28.shape[1] || 0)), Math.trunc(boxRaw[2] * (image28.shape[2] || 0)), Math.trunc(boxRaw[3] * (image28.shape[1] || 0))]; - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - } - } - bodies.sort((a, b) => b.score - a.score); - if (bodies.length > config3.body.maxDetected) bodies.length = config3.body.maxDetected; - return bodies; -} -async function predict17(input, config3) { - var _a; - if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) return []; - if (!config3.skipAllowed) cache6.boxes.length = 0; - skipped14++; - const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; - const skipFrame = skipped14 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache6.bodies; - } - return new Promise(async (resolve) => { - const t2 = {}; - skipped14 = 0; - t2.input = padInput(input, inputSize8); - t2.res = model18 == null ? void 0 : model18.execute(t2.input); - cache6.last = now(); - const res = await t2.res.array(); - cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); - for (const body4 of cache6.bodies) { - rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); - jitter(body4.keypoints); - } - Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6])); - resolve(cache6.bodies); - }); -} - -// src/object/nanodet.ts -var tf33 = __toESM(require_tfjs_esm()); -var model19; -var last10 = []; -var lastTime15 = 0; -var skipped15 = Number.MAX_SAFE_INTEGER; -var inputSize9 = 0; -var scaleBox = 2.5; -async function load17(config3) { - if (!model19 || env.initial) { - model19 = await loadModel(config3.object.modelPath); - const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; - inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; - } else if (config3.debug) log("cached model:", model19["modelUrl"]); - return model19; -} -async function process4(res, outputShape, config3) { - var _a, _b; - let id = 0; - let results = []; - const size2 = inputSize9; - for (const strideSize of [1, 2, 4]) { - const baseSize = strideSize * 13; - const scoresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); - const scores = await scoresT.array(); - const featuresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); - const boxesMaxT = tf33.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); - const boxIdxT = tf33.argMax(boxesMaxT, 2); - const boxIdx = await boxIdxT.array(); - for (let i = 0; i < scoresT.shape[0]; i++) { - for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { - const score = scores[i][j]; - if (score > (config3.object.minConfidence || 0) && j !== 61) { - const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; - const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; - const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); - const [x, y] = [ - cx - scaleBox / strideSize * boxOffset[0], - cy - scaleBox / strideSize * boxOffset[1] - ]; - const [w, h] = [ - cx + scaleBox / strideSize * boxOffset[2] - x, - cy + scaleBox / strideSize * boxOffset[3] - y - ]; - let boxRaw = [x, y, w, h]; - boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); - const box = [ - // results normalized to input image pixels - boxRaw[0] * outputShape[0], - boxRaw[1] * outputShape[1], - boxRaw[2] * outputShape[0], - boxRaw[3] * outputShape[1] - ]; - const result = { - id: id++, - // strideSize, - score: Math.round(100 * score) / 100, - class: j + 1, - label: labels2[j].label, - // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)], - // centerRaw: [cx, cy], - box: box.map((a) => Math.trunc(a)), - boxRaw - }; - results.push(result); - } - } - } - tf33.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); - } - const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); - const nmsScores = results.map((a) => a.score); - let nmsIdx = []; - if (nmsBoxes && nmsBoxes.length > 0) { - const nms = await tf33.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); - nmsIdx = Array.from(await nms.data()); - tf33.dispose(nms); - } - results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); - return results; -} -async function predict18(image28, config3) { - if (!(model19 == null ? void 0 : model19["executor"])) return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; - const skipFrame = skipped15 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { - skipped15++; - return last10; - } - skipped15 = 0; - if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) return last10; - return new Promise(async (resolve) => { - const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; - const resizeT = tf33.image.resizeBilinear(image28, [inputSize9, inputSize9], false); - const normT = tf33.div(resizeT, constants.tf255); - const transposeT = tf33.transpose(normT, [0, 3, 1, 2]); - let objectT; - if (config3.object.enabled) objectT = model19.execute(transposeT); - lastTime15 = now(); - const obj = await process4(objectT, outputSize2, config3); - last10 = obj; - tf33.dispose([resizeT, normT, transposeT, ...objectT]); - resolve(obj); - }); -} - -// src/body/posenet.ts -var tf34 = __toESM(require_tfjs_esm()); - -// src/body/posenetutils.ts -var partNames = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var count = partNames.length; -var partIds = partNames.reduce((result, jointName, i) => { - result[jointName] = i; - return result; -}, {}); -var connectedPartNames = [ - ["leftHip", "leftShoulder"], - ["leftElbow", "leftShoulder"], - ["leftElbow", "leftWrist"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["rightHip", "rightShoulder"], - ["rightElbow", "rightShoulder"], - ["rightElbow", "rightWrist"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"], - ["leftShoulder", "rightShoulder"], - ["leftHip", "rightHip"] -]; -var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); -var poseChain = [ - ["nose", "leftEye"], - ["leftEye", "leftEar"], - ["nose", "rightEye"], - ["rightEye", "rightEar"], - ["nose", "leftShoulder"], - ["leftShoulder", "leftElbow"], - ["leftElbow", "leftWrist"], - ["leftShoulder", "leftHip"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["nose", "rightShoulder"], - ["rightShoulder", "rightElbow"], - ["rightElbow", "rightWrist"], - ["rightShoulder", "rightHip"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"] -]; -function getBoundingBox(keypoints) { - const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ - maxX: Math.max(maxX, x), - maxY: Math.max(maxY, y), - minX: Math.min(minX, x), - minY: Math.min(minY, y) - }), { - maxX: Number.NEGATIVE_INFINITY, - maxY: Number.NEGATIVE_INFINITY, - minX: Number.POSITIVE_INFINITY, - minY: Number.POSITIVE_INFINITY - }); - return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; -} -function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { - const scaleY = height / inputResolutionHeight; - const scaleX = width / inputResolutionWidth; - const scalePose = (pose, i) => ({ - id: i, - score: pose.score, - boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], - box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], - keypoints: pose.keypoints.map(({ score, part, position }) => ({ - score, - part, - position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], - positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] - })), - annotations: {} - }); - const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); - return scaledPoses; -} -var MaxHeap = class { - // function call - constructor(maxSize2, getElementValue) { - __publicField(this, "priorityQueue"); - // don't touch - __publicField(this, "numberOfElements"); - __publicField(this, "getElementValue"); - this.priorityQueue = new Array(maxSize2); - this.numberOfElements = -1; - this.getElementValue = getElementValue; - } - enqueue(x) { - this.priorityQueue[++this.numberOfElements] = x; - this.swim(this.numberOfElements); - } - dequeue() { - const max5 = this.priorityQueue[0]; - this.exchange(0, this.numberOfElements--); - this.sink(0); - this.priorityQueue[this.numberOfElements + 1] = null; - return max5; - } - empty() { - return this.numberOfElements === -1; - } - size() { - return this.numberOfElements + 1; - } - all() { - return this.priorityQueue.slice(0, this.numberOfElements + 1); - } - max() { - return this.priorityQueue[0]; - } - swim(k) { - while (k > 0 && this.less(Math.floor(k / 2), k)) { - this.exchange(k, Math.floor(k / 2)); - k = Math.floor(k / 2); - } - } - sink(k) { - while (2 * k <= this.numberOfElements) { - let j = 2 * k; - if (j < this.numberOfElements && this.less(j, j + 1)) j++; - if (!this.less(k, j)) break; - this.exchange(k, j); - k = j; - } - } - getValueAt(i) { - return this.getElementValue(this.priorityQueue[i]); - } - less(i, j) { - return this.getValueAt(i) < this.getValueAt(j); - } - exchange(i, j) { - const t2 = this.priorityQueue[i]; - this.priorityQueue[i] = this.priorityQueue[j]; - this.priorityQueue[j] = t2; - } -}; -function getOffsetPoint(y, x, keypoint, offsets) { - return { - y: offsets.get(y, x, keypoint), - x: offsets.get(y, x, keypoint + count) - }; -} -function getImageCoords(part, outputStride2, offsets) { - const { heatmapY, heatmapX, id: keypoint } = part; - const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); - return { - x: part.heatmapX * outputStride2 + x, - y: part.heatmapY * outputStride2 + y - }; -} -function clamp(a, min2, max5) { - if (a < min2) return min2; - if (a > max5) return max5; - return a; -} -function squaredDistance(y1, x1, y2, x2) { - const dy = y2 - y1; - const dx = x2 - x1; - return dy * dy + dx * dx; -} -function addVectors(a, b) { - return { x: a.x + b.x, y: a.y + b.y }; -} - -// src/body/posenet.ts -var model20; -var poseNetOutputs = [ - "MobilenetV1/offset_2/BiasAdd", - "MobilenetV1/heatmap_2/BiasAdd", - "MobilenetV1/displacement_fwd_2/BiasAdd", - "MobilenetV1/displacement_bwd_2/BiasAdd" - /* displacementBwd */ -]; -var localMaximumRadius = 1; -var outputStride = 16; -var squaredNmsRadius = 50 ** 2; -function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { - const getDisplacement = (point2) => ({ - y: displacements.get(point2.y, point2.x, edgeId), - x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) - }); - const getStridedIndexNearPoint = (point2, height2, width2) => ({ - y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), - x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) - }); - const [height, width] = scores.shape; - const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); - const displacement = getDisplacement(sourceKeypointIndices); - const displacedPoint = addVectors(sourceKeypoint.position, displacement); - let targetKeypoint = displacedPoint; - for (let i = 0; i < offsetRefineStep; i++) { - const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); - targetKeypoint = addVectors( - { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, - { x: offsetPoint.x, y: offsetPoint.y } - ); - } - const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); - return { position: targetKeypoint, part: partNames[targetId], score }; -} -function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { - const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); - const edgesFwd = tuples.map(([, childJointId]) => childJointId); - const edgesBwd = tuples.map(([parentJointId]) => parentJointId); - const numParts = scores.shape[2]; - const numEdges = edgesFwd.length; - const keypoints = new Array(numParts); - const rootPoint = getImageCoords(root.part, outputStride, offsets); - keypoints[root.part.id] = { - score: root.score, - part: partNames[root.part.id], - position: rootPoint - }; - for (let edge = numEdges - 1; edge >= 0; --edge) { - const sourceId = edgesFwd[edge]; - const targetId = edgesBwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); - } - } - for (let edge = 0; edge < numEdges; ++edge) { - const sourceId = edgesBwd[edge]; - const targetId = edgesFwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); - } - } - return keypoints; -} -function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { - const [height, width] = scores.shape; - let localMaximum = true; - const yStart = Math.max(heatmapY - localMaximumRadius, 0); - const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); - for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { - const xStart = Math.max(heatmapX - localMaximumRadius, 0); - const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); - for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { - if (scores.get(yCurrent, xCurrent, keypointId) > score) { - localMaximum = false; - break; - } - } - if (!localMaximum) break; - } - return localMaximum; -} -function buildPartWithScoreQueue(minConfidence2, scores) { - const [height, width, numKeypoints] = scores.shape; - const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); - for (let heatmapY = 0; heatmapY < height; ++heatmapY) { - for (let heatmapX = 0; heatmapX < width; ++heatmapX) { - for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { - const score = scores.get(heatmapY, heatmapX, keypointId); - if (score < minConfidence2) continue; - if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); - } - } - } - return queue; -} -function withinRadius(poses, { x, y }, keypointId) { - return poses.some(({ keypoints }) => { - var _a; - const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; - if (!correspondingKeypoint) return false; - return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; - }); -} -function getInstanceScore(existingPoses, keypoints) { - const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { - if (!withinRadius(existingPoses, position, keypointId)) result += score; - return result; - }, 0); - return notOverlappedKeypointScores / keypoints.length; -} -function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { - const poses = []; - const queue = buildPartWithScoreQueue(minConfidence2, scores); - while (poses.length < maxDetected && !queue.empty()) { - const root = queue.dequeue(); - const rootImageCoords = getImageCoords(root.part, outputStride, offsets); - if (withinRadius(poses, rootImageCoords, root.part.id)) continue; - let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); - keypoints = keypoints.filter((a) => a.score > minConfidence2); - const score = getInstanceScore(poses, keypoints); - const box = getBoundingBox(keypoints); - if (score > minConfidence2) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); - } - return poses; -} -async function predict19(input, config3) { - if (!(model20 == null ? void 0 : model20["executor"])) return []; - const res = tf34.tidy(() => { - if (!model20.inputs[0].shape) return []; - const resized = tf34.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - const normalized = tf34.sub(tf34.div(tf34.cast(resized, "float32"), 127.5), 1); - const results = model20.execute(normalized, poseNetOutputs); - const results3d = results.map((y) => tf34.squeeze(y, [0])); - results3d[1] = tf34.sigmoid(results3d[1]); - return results3d; - }); - const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); - for (const t2 of res) tf34.dispose(t2); - const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); - if (!model20.inputs[0].shape) return []; - const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - return scaled; -} -async function load18(config3) { - if (!model20 || env.initial) model20 = await loadModel(config3.body.modelPath); - else if (config3.debug) log("cached model:", model20["modelUrl"]); - return model20; -} - -// src/segmentation/rvm.ts -var tf35 = __toESM(require_tfjs_esm()); -var model21; -var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; -var t = {}; -var ratio = 0; -function init3(config3) { - tf35.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); - t.r1i = tf35.tensor(0); - t.r2i = tf35.tensor(0); - t.r3i = tf35.tensor(0); - t.r4i = tf35.tensor(0); - ratio = config3.segmentation.ratio || 0.5; - t.downsample_ratio = tf35.tensor(ratio); -} -async function load19(config3) { - if (!model21 || env.initial) model21 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model21["modelUrl"]); - init3(config3); - return model21; -} -var normalize = (r) => tf35.tidy(() => { - const squeeze14 = tf35.squeeze(r, [0]); - const mul15 = tf35.mul(squeeze14, constants.tf255); - const cast8 = tf35.cast(mul15, "int32"); - return cast8; -}); -function getRGBA(fgr, pha) { - const rgb3 = fgr ? normalize(fgr) : tf35.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); - const a = pha ? normalize(pha) : tf35.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); - const rgba = tf35.concat([rgb3, a], -1); - tf35.dispose([rgb3, a]); - return rgba; -} -function getState(state) { - return tf35.tidy(() => { - const r = {}; - r.unstack = tf35.unstack(state, -1); - r.concat = tf35.concat(r.unstack, 1); - r.split = tf35.split(r.concat, 4, 1); - r.stack = tf35.concat(r.split, 2); - r.squeeze = tf35.squeeze(r.stack, [0]); - r.expand = tf35.expandDims(r.squeeze, -1); - r.add = tf35.add(r.expand, 1); - r.mul = tf35.mul(r.add, 127.5); - r.cast = tf35.cast(r.mul, "int32"); - r.tile = tf35.tile(r.cast, [1, 1, 3]); - r.alpha = tf35.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); - return tf35.concat([r.tile, r.alpha], -1); - }); -} -async function predict20(input, config3) { - if (!model21) model21 = await load19(config3); - if (!(model21 == null ? void 0 : model21["executor"])) return null; - t.src = tf35.div(input, 255); - if (ratio !== config3.segmentation.ratio) init3(config3); - const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - rgba = getRGBA(fgr, pha); - break; - case "alpha": - rgba = getRGBA(null, pha); - break; - case "foreground": - rgba = getRGBA(fgr, null); - break; - case "state": - rgba = getState(r1o); - break; - default: - rgba = tf35.tensor(0); - } - tf35.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); - [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; - return rgba; -} - -// src/segmentation/selfie.ts -var tf36 = __toESM(require_tfjs_esm()); -var model22; -async function load20(config3) { - if (!model22 || env.initial) model22 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model22["modelUrl"]); - return model22; -} -async function predict21(input, config3) { - var _a; - if (!model22) model22 = await load20(config3); - if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) return null; - const t2 = {}; - t2.resize = tf36.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); - t2.norm = tf36.div(t2.resize, constants.tf255); - t2.res = model22.execute(t2.norm); - t2.squeeze = tf36.squeeze(t2.res, [0]); - t2.alpha = tf36.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); - t2.mul = tf36.mul(t2.alpha, constants.tf255); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf36.squeeze(input); - t2.concat = tf36.concat([t2.input, t2.mul], -1); - rgba = tf36.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf36.cast(t2.mul, "int32"); - break; - default: - rgba = tf36.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf36.dispose(t2[tensor6])); - return rgba; -} - -// src/models.ts -function validateModel(instance, model23, name) { - var _a, _b; - if (!model23) return null; - if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) return null; - const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; - const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; - const ops = []; - const missing = []; - const url = model23["modelUrl"]; - const executor = model23["executor"]; - if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { - for (const kernel of Object.values(executor.graph.nodes)) { - const op = kernel.op.toLowerCase(); - if (!ops.includes(op)) ops.push(op); - } - } else { - if (!executor && instance.config.debug) { - log("model not loaded", name); - } - } - for (const op of ops) { - if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { - missing.push(op); - } - } - if (instance.config.debug && missing.length > 0) log("model validation failed:", name, missing); - return missing.length > 0 ? { name, missing, ops, url } : null; -} -var Models = class { - constructor(currentInstance) { - __publicField(this, "instance"); - __publicField(this, "models", {}); - this.models = {}; - this.instance = currentInstance; - } - stats() { - let totalSizeFromManifest = 0; - let totalSizeWeights = 0; - let totalSizeLoading = 0; - for (const m of Object.values(modelStats)) { - totalSizeFromManifest += Number.isNaN(+m.sizeFromManifest) ? 0 : m.sizeFromManifest; - totalSizeWeights += Number.isNaN(+m.sizeLoadedWeights) ? 0 : m.sizeLoadedWeights; - totalSizeLoading += Number.isNaN(+m.sizeDesired) ? 0 : m.sizeDesired; - } - const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; - return { - numLoadedModels: Object.values(modelStats).filter((m) => m == null ? void 0 : m.loaded).length, - numDefinedModels: Object.keys(this.models).length, - percentageLoaded, - totalSizeFromManifest, - totalSizeWeights, - totalSizeLoading, - modelStats: Object.values(modelStats) - }; - } - reset() { - for (const model23 of Object.keys(this.models)) this.models[model23] = null; - } - async load(instance) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; - if (env.initial) this.reset(); - if (instance) this.instance = instance; - const m = {}; - m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; - m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; - m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; - m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; - m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; - m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; - m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; - m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; - m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; - m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; - m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; - m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; - m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; - m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; - m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; - m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load16(this.instance.config) : null; - m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load18(this.instance.config) : null; - m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect3(this.instance.config) : null; - m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton2(this.instance.config) : null; - if (this.instance.config.hand.enabled && !this.models.handdetect && ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect"))) { - m.handdetect = loadDetect2(this.instance.config); - m.handskeleton = loadSkeleton(this.instance.config); - } - m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; - m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load17(this.instance.config) : null; - m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load20(this.instance.config) : null; - m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load15(this.instance.config) : null; - m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load19(this.instance.config) : null; - for (const [model23, promise] of Object.entries(m)) { - if (promise == null ? void 0 : promise["then"]) promise["then"]((val) => this.models[model23] = val); - } - await Promise.all(Object.values(m)); - } - list() { - const models3 = Object.keys(this.models).map((model23) => { - var _a; - return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; - }); - for (const m of models3) { - const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); - if (!stats) continue; - m.size = modelStats[stats].sizeLoadedWeights; - m.url = modelStats[stats].url; - } - return models3; - } - loaded() { - const list = this.list(); - const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); - return loaded; - } - validate() { - const missing = []; - for (const defined of Object.keys(this.models)) { - const model23 = this.models[defined]; - if (!model23) continue; - const res = validateModel(this.instance, model23, defined); - if (res) missing.push(res); - } - return missing; - } -}; - -// src/util/persons.ts -function join2(faces, bodies, hands, gestures, shape) { - var _a, _b, _c, _d, _e, _f; - let id = 0; - const persons = []; - for (const face4 of faces) { - const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; - for (const body4 of bodies) { - if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { - person2.body = body4; - } - } - if (person2.body) { - for (const hand3 of hands) { - if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) person2.hands.left = hand3; - } - if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) person2.hands.right = hand3; - } - } - } - for (const gesture2 of gestures) { - if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) person2.gestures.push(gesture2); - else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) person2.gestures.push(gesture2); - else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) person2.gestures.push(gesture2); - } - const x = []; - const y = []; - const extractXY = (box) => { - if (box && box.length === 4) { - x.push(box[0], box[0] + box[2]); - y.push(box[1], box[1] + box[3]); - } - }; - extractXY(person2.face.box); - extractXY((_d = person2.body) == null ? void 0 : _d.box); - extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); - extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); - const minX = Math.min(...x); - const minY = Math.min(...y); - person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; - if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; - persons.push(person2); - } - return persons; -} - -// src/warmup.ts -var tf37 = __toESM(require_tfjs_esm()); - -// src/sample.ts -var face3 = ` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var a5=0;function Ks(e,t,n){let o=i0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let s=0;sc5,kpt:()=>l5});var l5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],c5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=Z(V());var se,n2=224,e3,_s=5,rt=[8,16,32,32,32];function $s(){let e=[],t=0;for(;t<_s;){let n=0,o=t;for(;on.x)),y:D.tensor1d(e.map(n=>n.y))}}async function t3(e){if(R.initial&&(se=null),!se&&e.body.detector&&e.body.detector.modelPath){se=await O(e.body.detector.modelPath);let t=se!=null&&se.executor?Object.values(se.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&se&&b("cached model:",se.modelUrl);return $s(),se}var $1=[5,5];function eA(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),$1[0]),A=D.mul(D.div(A,n2),$1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),d=D.add(l,A);return D.stack([a,l,c,d],1)})}async function tA(e,t,n,o){var c,d;let r=[],s={};s.boxes=eA(e,e3),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((d=n.body.detector)==null?void 0:d.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let y=a[i],x=l[i],m=[Math.round(x[0]*o[0]),Math.round(x[1]*o[1]),Math.round(x[2]*o[0]),Math.round(x[3]*o[1])],f={score:y,boxRaw:x,box:m};r.push(f)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function n3(e,t,n){let o={};o.res=se==null?void 0:se.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await tA(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function Oe(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function o3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function st(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var Z0,x5=256,d5=Number.MAX_SAFE_INTEGER,nA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},at=[],Ce=[[0,0],[0,0],[0,0],[0,0]],r3=0,s3=e=>1-1/(1+Math.exp(e)),a3=e=>t3(e);async function i3(e){if(R.initial&&(Z0=null),Z0)e.debug&&b("cached model:",Z0.modelUrl);else{Z0=await O(e.body.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;x5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Z0}function A3(e,t,n){var s,A;let o={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let r;if(n&&(o.cropped=q0.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Ce=[[0,0],a,l,[0,0]],o.pad=q0.pad(o.cropped||e,Ce),o.resize=q0.image.resizeBilinear(o.pad,[t,t]),r=q0.div(o.resize,C.tf255)}else e.shape[1]!==t?(o.resize=q0.image.resizeBilinear(o.cropped||e,[t,t]),r=q0.div(o.resize,C.tf255)):r=q0.div(o.cropped||e,C.tf255);return Object.keys(o).forEach(a=>q0.dispose(o[a])),r}function oA(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+Ce[2][0]+Ce[2][1])/t[0]-Ce[2][0]),Math.trunc(o.position[1]*(t[1]+Ce[1][0]+Ce[1][1])/t[1]-Ce[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(n){let o=n[2]-n[0],r=n[3]-n[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+n[1],s.positionRaw[1]/o+n[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function rA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),s=e.find(a=>a.part==="rightWrist"),A=e.find(a=>a.part==="rightIndex");r.position[2]=((s.position[2]||0)+(A.position[2]||0))/2}async function sA(e,t,n){if(!(Z0!=null&&Z0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=Z0==null?void 0:Z0.execute(e,nA.landmarks);let r=(await o.poseflag.data())[0],s=await o.ld.data(),A=await o.world.data();Object.keys(o).forEach(m=>q0.dispose(o[m]));let a=[],l=5;for(let m=0;mm.position),i=Oe(d,[n[0],n[1]]),y={};for(let[m,f]of Object.entries(c5)){let h=[];for(let T=0;Tu.part===f[T]),p=c.find(u=>u.part===f[T+1]);g&&p&&h.push([g.position,p.position])}y[m]=h}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:y}}async function y5(e,t){var s,A,a;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>v()-r3,r=d5<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&&at!==null)d5++;else{let l=[];if((A=(s=t.body)==null?void 0:s.detector)!=null&&A.enabled){let c=A3(e,224);l=await n3(c,t,n),q0.dispose(c)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let c=0;cF0.dispose(o[c])),r}async function p5(e,t){if(!(X0!=null&&X0.executor))return[];let 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w3(e){var t;return R.initial&&(xe=null),xe?e.debug&&b("cached model:",xe.modelUrl):xe=await O((t=e.face.detector)==null?void 0:t.modelPath),ge=xe.executor&&xe.inputs[0].shape?xe.inputs[0].shape[2]:256,u2=L.scalar(ge,"int32"),ft=L.tensor2d(T3(ge)),xe}function fA(e){if(!ft||!u2)return L.zeros([0,0]);let t={};t.boxStarts=L.slice(e,[0,1],[-1,2]),t.centers=L.add(t.boxStarts,ft),t.boxSizes=L.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=L.div(t.boxSizes,u2),t.centersNormalized=L.div(t.centers,u2),t.halfBoxSize=L.div(t.boxSizesNormalized,C.tf2),t.starts=L.sub(t.centersNormalized,t.halfBoxSize),t.ends=L.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=L.mul(t.starts,u2),t.endNormalized=L.mul(t.ends,u2);let n=L.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>L.dispose(t[o])),n}async function E3(e,t){var c,d,i,y,x,m,f,h,T;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={},o=[0,0],r=[1,1];if((d=(c=t==null?void 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0:i.maxDetected)||0,((y=t.face.detector)==null?void 0:y.iouThreshold)||0,((x=t.face.detector)==null?void 0:x.minConfidence)||0);let A=await n.nms.array(),a=[],l=await n.scores.data();for(let g=0;g(((m=t.face.detector)==null?void 0:m.minConfidence)||0)){let u={};u.bbox=L.slice(n.boxes,[A[g],0],[1,-1]),u.slice=L.slice(n.batch,[A[g],P3-1],[1,-1]),u.squeeze=L.squeeze(u.slice),u.landmarks=L.reshape(u.squeeze,[P3,-1]);let k=await u.bbox.data(),P=[k[0]*r[0]-o[0],k[1]*r[1]-o[1],k[2]*r[0]-o[0],k[3]*r[1]-o[1]],N={startPoint:[P[0],P[1]],endPoint:[P[2],P[3]],landmarks:await u.landmarks.array(),confidence:p};u.anchor=L.slice(ft,[A[g],0],[1,2]);let B=await u.anchor.data(),J=h3(N,[(e.shape[2]||0)/ge,(e.shape[1]||0)/ge],B),H=xt(J,((f=t.face.detector)==null?void 0:f.scale)||1.4),K=yt(H);K.size[0]>(((h=t.face.detector)==null?void 0:h.minSize)||0)&&K.size[1]>(((T=t.face.detector)==null?void 0:T.minSize)||0)&&a.push(K),Object.keys(u).forEach(e0=>L.dispose(u[e0]))}}return Object.keys(n).forEach(g=>L.dispose(n[g])),a}var ye=Z(V());var K0,We=0,M5=ae.leftEyeLower0,P5=ae.rightEyeLower0,h2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function I3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&b("cached model:",K0.modelUrl):K0=await O((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function mt(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},S3=(e,t,n,o,r,s=!1,A=2.3)=>{let a=yt(xt(b3([e[n],e[o]]),A)),l=p2(a),c=ye.image.cropAndResize(t,[[a.startPoint[1]/r,a.startPoint[0]/r,a.endPoint[1]/r,a.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let d=ye.image.flipLeftRight(c);ye.dispose(c),c=d}return{box:a,boxSize:l,crop:c}},j3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function L3(e,t,n,o){var N,B;if(!(K0!=null&&K0.executor))return e;let{box:r,boxSize:s,crop:A}=S3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0,((N=o.face.iris)==null?void 0:N.scale)||2.3),{box:a,boxSize:l,crop:c}=S3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0,((B=o.face.iris)==null?void 0:B.scale)||2.3),d=ye.concat([A,c]);ye.dispose(A),ye.dispose(c);let i=K0.execute(d);ye.dispose(d);let y=await i.data();ye.dispose(i);let x=y.slice(0,b2.numCoordinates*3),{rawCoords:m,iris:f}=j3(x,r,s,!0),h=y.slice(b2.numCoordinates*3),{rawCoords:T,iris:g}=j3(h,a,l,!1),p=mA(e);Math.abs(p)<30?(mt(e,m,"left",null),mt(e,T,"right",null)):p<1?mt(e,m,"left",["EyeUpper0","EyeLower0"]):mt(e,T,"right",["EyeUpper0","EyeLower0"]);let u=N3(e,f,"left"),k=N3(e,g,"right");return e.concat(u).concat(k)}async function C3(e,t){var s,A,a,l,c,d,i,y,x,m;let n={lips:await((A=(s=t.filter(f=>f.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(f=>f.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((d=(c=t.filter(f=>f.size===142))==null?void 0:c[0])==null?void 0:d.data()),irisR:await((y=(i=t.filter(f=>f.size===10))==null?void 0:i[1])==null?void 0:y.data()),eyeR:await((m=(x=t.filter(f=>f.size===142))==null?void 0:x[1])==null?void 0:m.data())};for(let f of Object.values(n))if(!f)return e;let o=e2.reduce((f,h)=>f+=e[h][2],0)/e2.length;for(let f=0;ff+=e[h][2],0)/t2.length;for(let f=0;fv()-Te.timestamp,o=Te.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||Te.boxes.length===0?(Te.boxes=await E3(e,t),Te.timestamp=v(),Te.skipped=0):Te.skipped++;let r=[],s=[],A=0,a=O2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!s0)t.debug&&b("face mesh detection 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${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},Fn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A.06||h>.06)&&(x=!1),f>h?h>.04&&t.push({iris:A,gesture:"looking right"}):f>.04&&t.push({iris:A,gesture:"looking left"});let T=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],g=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(g<.01||T<.01||g>.022||T>.022)&&(x=!1),(g<.01||T<.01)&&t.push({iris:A,gesture:"looking down"}),(g>.022||T>.022)&&t.push({iris:A,gesture:"looking up"}),x&&t.push({iris:A,gesture:"looking center"})}return t},Bn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function Et(e,t=1.5){let n=C2(e),o=wt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function zt(e){let t=C2(e),n=wt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function NA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Xn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return NA(n)}var Hn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function qe(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let d=await c.box.data(),i=d.slice(0,2),y=d.slice(2,4),x=await c.palmLandmarks.array(),m={startPoint:i,endPoint:y,palmLandmarks:x,confidence:r[l]},f=qn(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(f),Object.keys(c).forEach(h=>W.dispose(c[h]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var $0=Z(V());var CA=5,Jn=1.65,Qn=[0,5,9,13,17,1,2],WA=0,DA=2,_n=0,jt=class{constructor(t,n){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>e1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Et(zt(r),CA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Et(zt(n),Jn);o.palmLandmarks=[];for(let r=0;r[A[0]*(x[0]-this.inputSize/2),A[1]*(x[1]-this.inputSize/2),A[2]*x[2]]),l=$5(o,[0,0]),c=a.map(x=>[...e1(x,l),x[2]]),d=Un(r),i=[...C2(n),1],y=[qe(i,d[0]),qe(i,d[1])];return c.map(x=>[Math.trunc(x[0]+y[0]),Math.trunc(x[1]+y[1]),Math.trunc(x[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-_n,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A?this.skipped++:(r=await this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,f,d,m),B=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...B,confidence:u};let J={landmarks:N,confidence:u,boxConfidence:c.confidence,fingerConfidence:u,box:{topLeft:B.startPoint,bottomRight:B.endPoint}};a.push(J)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let d=Et(zt(c),Jn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var $n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,t1;function BA(){let e=l2?new St(l2):void 0;e&&c2&&(t1=new jt(e,c2))}async function n1(e,t){t1||BA();let n=await t1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let d of A)d[0]a[2]&&(a[2]=d[0]),d[1]>a[3]&&(a[3]=d[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else 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model:",c2.modelUrl):c2=await O((t=e.hand.skeleton)==null?void 0:t.modelPath),c2}var $=Z(V());var p0=[null,null],HA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],GA=["hand","fist","pinch","point","face","tip","pinchtip"],oo=4,ro=1.6,VA=512,ZA=1.4,Nt=Number.MAX_SAFE_INTEGER,o1=0,Ee=[0,0],m0={boxes:[],hands:[]},so={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function Ao(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&b("cached model:",p0[0].modelUrl);else{J2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&b("cached model:",p0[1].modelUrl);else{p0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function qA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,VA),A=Math.round(s*r/8)*8;o.resize=$.image.resizeBilinear(e,[s,A]),o.cast=$.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,HA),o.boxes=$.squeeze(o.rawBoxes,[0,2]),o.scores=$.squeeze(o.rawScores,[0]);let a=$.unstack(o.scores,1);$.dispose(a[oo]),a.splice(oo,1),o.filtered=$.stack(a,1),$.dispose(a),o.max=$.max(o.filtered,1),o.argmax=$.argMax(o.filtered,1);let l=0;o.nms=await $.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),d=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let x=$.slice(o.boxes,y,1),m=await x.data();$.dispose(x);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],h=st(f,ZA),T=[Math.trunc(f[0]*Ee[0]),Math.trunc(f[1]*Ee[1]),Math.trunc(f[2]*Ee[0]),Math.trunc(f[3]*Ee[1])],g=d[y],p=GA[i[y]],u={id:l++,score:g,box:T,boxRaw:h,label:p};n.push(u)}return Object.keys(o).forEach(y=>$.dispose(o[y])),n.sort((y,x)=>x.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function r1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=$.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=$.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=$.reshape(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=d.map(i=>[Ee[0]*(i[0]+t.boxRaw[0]),Ee[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=kt(o.keypoints);for(let i of Object.keys(so))o.annotations[i]=so[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>$.dispose(r[l]))}return o}async function s1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];Ee=[e.shape[2]||0,e.shape[1]||0],Nt++;let n=(t.hand.skipTime||0)>v()-o1,o=Nt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-o1,l=Nt<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(d=>r1(e,d,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(d=>r1(e,d,t))):(m0.boxes=await qA(e,t),o1=v(),m0.hands=await Promise.all(m0.boxes.map(d=>r1(e,d,t))),Nt=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[d].fingerScore&&m0.hands[d].fingerScore>(t.hand.minConfidence||0)){let y=st(i.box,ro),x=st(i.boxRaw,ro);m0.boxes.push({...c[d],box:y,boxRaw:x})}}for(let d=0;d({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var W2={};Se(W2,{connected:()=>Lt,horizontal:()=>A1,kpt:()=>It,relative:()=>i1,vertical:()=>a1});var It=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],A1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],a1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],i1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Lt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=ve(),l1=0;function lo(e,t){var A,a,l,c,d,i,y,x,m,f,h,T,g,p,u,k,P,N,B,J,H,K,e0,n0,o0,N0;let n=v();if(!e)return ve();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let M=0;M((r-1)*z.body[M].box[q]+G)/r),C0=e.body[M].boxRaw.map((G,q)=>((r-1)*z.body[M].boxRaw[q]+G)/r),x0=e.body[M].keypoints.map((G,q)=>{var 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Mo(e){let t=e.reduce(({maxX:n,maxY:o,minX:r,minY:s},{position:{x:A,y:a}})=>({maxX:Math.max(n,A),maxY:Math.max(o,a),minX:Math.min(r,A),minY:Math.min(s,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function Po(e,[t,n],[o,r]){let s=t/o,A=n/r,a=(c,d)=>({id:d,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/o,c.box[2]/r,c.box[3]/o],box:[Math.trunc(c.box[0]*A),Math.trunc(c.box[1]*s),Math.trunc(c.box[2]*A),Math.trunc(c.box[3]*s)],keypoints:c.keypoints.map(({score:i,part:y,position:x})=>({score:i,part:y,position:[Math.trunc(x.x*A),Math.trunc(x.y*s)],positionRaw:[x.x/o,x.y/o]})),annotations:{}});return e.map((c,d)=>a(c,d))}var Bt=class{constructor(t,n){w(this,"priorityQueue");w(this,"numberOfElements");w(this,"getElementValue");this.priorityQueue=new 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a=T=>({y:s.get(T.y,T.x,e),x:s.get(T.y,T.x,s.shape[2]/2+e)}),l=(T,g,p)=>({y:b1(Math.round(T.y/M2),0,g-1),x:b1(Math.round(T.x/M2),0,p-1)}),[c,d]=o.shape,i=l(t.position,c,d),y=a(i),m=g1(t.position,y);for(let T=0;T[F2[y],F2[x]]),A=s.map(([,y])=>y),a=s.map(([y])=>y),l=t.shape[2],c=A.length,d=new Array(l),i=h1(e.part,M2,n);d[e.part.id]={score:e.score,part:B2[e.part.id],position:i};for(let y=c-1;y>=0;--y){let x=A[y],m=a[y];d[x]&&!d[m]&&(d[m]=wo(y,d[x],m,t,n,r))}for(let y=0;yt){a=!1;break}if(!a)break}return a}function Aa(e,t){let[n,o,r]=t.shape,s=new Bt(n*o*r,({score:A})=>A);for(let A=0;A{var A;let s=(A=r[o])==null?void 0:A.position;return s?ko(n,t,s.y,s.x)<=oa:!1})}function aa(e,t){return t.reduce((o,{position:r,score:s},A)=>(Eo(e,r,A)||(o+=s),o),0)/t.length}function ia(e,t,n,o,r,s){let A=[],a=Aa(s,t);for(;A.lengthx.score>s);let i=aa(A,d),y=Mo(d);i>s&&A.push({keypoints:d,box:y,score:Math.round(100*i)/100})}return A}async function T1(e,t){if(!(te!=null&&te.executor))return[];let n=V0.tidy(()=>{if(!te.inputs[0].shape)return[];let A=V0.image.resizeBilinear(e,[te.inputs[0].shape[2],te.inputs[0].shape[1]]),a=V0.sub(V0.div(V0.cast(A,"float32"),127.5),1),c=te.execute(a,na).map(d=>V0.squeeze(d,[0]));return c[1]=V0.sigmoid(c[1]),c}),o=await Promise.all(n.map(A=>A.buffer()));for(let A of n)V0.dispose(A);let r=ia(o[0],o[1],o[2],o[3],t.body.maxDetected,t.body.minConfidence);return te.inputs[0].shape?Po(r,[e.shape[1],e.shape[2]],[te.inputs[0].shape[2],te.inputs[0].shape[1]]):[]}async function zo(e){return!te||R.initial?te=await O(e.body.modelPath):e.debug&&b("cached model:",te.modelUrl),te}var F=Z(V());var Re,la=["fgr","pha","r1o","r2o","r3o","r4o"],g0={},R1=0;function No(e){F.dispose([g0.r1i,g0.r2i,g0.r3i,g0.r4i,g0.downsample_ratio]),g0.r1i=F.tensor(0),g0.r2i=F.tensor(0),g0.r3i=F.tensor(0),g0.r4i=F.tensor(0),R1=e.segmentation.ratio||.5,g0.downsample_ratio=F.tensor(R1)}async function M1(e){return!Re||R.initial?Re=await O(e.segmentation.modelPath):e.debug&&b("cached 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Re.executeAsync(g0,la),l;switch(t.segmentation.mode||"default"){case"default":l=v1(n,o);break;case"alpha":l=v1(null,o);break;case"foreground":l=v1(n,null);break;case"state":l=ca(r);break;default:l=F.tensor(0)}return F.dispose([g0.src,n,o,g0.r1i,g0.r2i,g0.r3i,g0.r4i]),[g0.r1i,g0.r2i,g0.r3i,g0.r4i]=[r,s,A,a],l}var S0=Z(V());var O0;async function P1(e){return!O0||R.initial?O0=await O(e.segmentation.modelPath):e.debug&&b("cached model:",O0.modelUrl),O0}async function Oo(e,t){var r;if(O0||(O0=await P1(t)),!(O0!=null&&O0.executor)||!((r=O0==null?void 0:O0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=S0.image.resizeBilinear(e,[O0.inputs[0].shape?O0.inputs[0].shape[1]:0,O0.inputs[0].shape?O0.inputs[0].shape[2]:0],!1),n.norm=S0.div(n.resize,C.tf255),n.res=O0.execute(n.norm),n.squeeze=S0.squeeze(n.res,[0]),n.alpha=S0.image.resizeBilinear(n.squeeze,[e.shape[1]||0,e.shape[2]||0]),n.mul=S0.mul(n.alpha,C.tf255);let o;switch(t.segmentation.mode||"default"){case"default":n.input=S0.squeeze(e),n.concat=S0.concat([n.input,n.mul],-1),o=S0.cast(n.concat,"int32");break;case"alpha":o=S0.cast(n.mul,"int32");break;default:o=S0.tensor(0)}return Object.keys(n).forEach(s=>S0.dispose(n[s])),o}function Gt(e,t,n){var c,d;if(!t||!((c=e==null?void 0:e.config)!=null&&c.validateModels))return null;let o=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"],r=["biasadd","fusedbatchnormv3","matmul","switch","shape","merge","split","broadcastto"],s=[],A=[],a=t.modelUrl,l=t.executor;if((d=l==null?void 0:l.graph)!=null&&d.nodes)for(let i of Object.values(l.graph.nodes)){let y=i.op.toLowerCase();s.includes(y)||s.push(y)}else!l&&e.config.debug&&b("model not loaded",n);for(let i of s)!o.includes(i)&&!r.includes(i)&&!e.env.kernels.includes(i)&&!e.env.kernels.includes(i.replace("_",""))&&!e.env.kernels.includes(i.replace("native",""))&&!e.env.kernels.includes(i.replace("v2",""))&&A.push(i);return 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n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?w3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)!=null&&o.enabled)&&!this.models.antispoof?_3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)!=null&&r.enabled)&&!this.models.liveness?nn(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)!=null&&s.enabled)&&!this.models.faceres?U3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((A=this.instance.config.face.emotion)!=null&&A.enabled)&&!this.models.emotion?V3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((a=this.instance.config.face.iris)!=null&&a.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?I3(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((c=this.instance.config.face.mesh)!=null&&c.enabled)&&!this.models.facemesh?D3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((d=this.instance.config.face.gear)!=null&&d.enabled)&&!this.models.gear?An(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)!=null&&i.enabled)&&!this.models.ssrnetage?dn(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((y=this.instance.config.face.ssrnet)!=null&&y.enabled)&&!this.models.ssrnetgender?mn(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((x=this.instance.config.face.mobilefacenet)!=null&&x.enabled)&&!this.models.mobilefacenet?gn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((m=this.instance.config.face.insightface)!=null&&m.enabled)&&!this.models.insightface?Pn(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((f=this.instance.config.body.modelPath)!=null&&f.includes("blazepose"))?i3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?a3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((h=this.instance.config.body.modelPath)!=null&&h.includes("efficientpose"))?f3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((T=this.instance.config.body.modelPath)!=null&&T.includes("movenet"))?ho(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((g=this.instance.config.body.modelPath)!=null&&g.includes("posenet"))?zo(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((u=(p=this.instance.config.hand.detector)==null?void 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t)x.box[0]>u.box[0]&&x.box[0]u.box[1]&&x.box[1]+x.box[3]m.body.box[0]&&u.box[0]+u.box[2]m.body.box[1]&&u.box[1]+u.box[3]m.body.box[0]&&u.box[1]+u.box[3]>m.body.box[1]&&u.box[1]+u.box[3]{u&&u.length===4&&(f.push(u[0],u[0]+u[2]),h.push(u[1],u[1]+u[3]))};T(m.face.box),T((d=m.body)==null?void 0:d.box),T((i=m.hands.left)==null?void 0:i.box),T((y=m.hands.right)==null?void 0:y.box);let g=Math.min(...f),p=Math.min(...h);m.box=[g,p,Math.max(...f)-g,Math.max(...h)-p],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),A.push(m)}return A}var a0=Z(V());var Vt=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -14023,8 +269,7 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l 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warmupBitmap(instance) { - const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); - let blob; - let res; - switch (instance.config.warmup) { - case "face": - blob = await b64toBlob(face3); - break; - case "body": - case "full": - blob = await b64toBlob(body3); - break; - default: - blob = null; - } - if (blob) { - const bitmap = await createImageBitmap(blob); - res = await instance.detect(bitmap, instance.config); - bitmap.close(); - } - return res; -} -async function warmupCanvas(instance) { - return new Promise((resolve) => { - let src; - switch (instance.config.warmup) { - case "face": - src = "data:image/jpeg;base64," + face3; - break; - case "full": - case "body": - src = "data:image/jpeg;base64," + body3; - break; - default: - src = ""; - } - let img; - if (typeof Image !== "undefined") img = new Image(); - else if (env.Image) img = new env.Image(); - else { - resolve(void 0); - return; - } - img.onload = async () => { - const canvas3 = canvas(img.naturalWidth, img.naturalHeight); - if (!canvas3) { - log("Warmup: Canvas not found"); - resolve(void 0); - } else { - const ctx = canvas3.getContext("2d"); - if (ctx) ctx.drawImage(img, 0, 0); - const tensor6 = await instance.image(canvas3, true); - const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; - resolve(res); - } - }; - if (src) img.src = src; - else resolve(void 0); - }); -} -async function warmupNode(instance) { - const atob = (str) => Buffer.from(str, "base64"); - let img; - if (instance.config.warmup === "face") img = atob(face3); - else img = atob(body3); - let res; - if ("node" in tf37 && tf37.getBackend() === "tensorflow") { - const data = tf37["node"].decodeJpeg(img); - const expanded = tf37.expandDims(data, 0); - instance.tf.dispose(data); - res = await instance.detect(expanded, instance.config); - instance.tf.dispose(expanded); - } else { - if (instance.config.debug) log("Warmup tfjs-node not loaded"); - } - return res; -} -async function runInference(instance) { - let res; - if (typeof createImageBitmap === "function") res = await warmupBitmap(instance); - else if (typeof Image !== "undefined" || env.Canvas !== void 0) res = await warmupCanvas(instance); - else res = await warmupNode(instance); - return res; -} -async function runCompile(instance) { - var _a, _b, _c, _d; - if (!tf37.env().flagRegistry.ENGINE_COMPILE_ONLY) return; - const backendType = tf37.getBackend(); - const webGLBackend = tf37.backend(); - if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { - return; - } - tf37.env().set("ENGINE_COMPILE_ONLY", true); - const numTensorsStart = tf37.engine().state.numTensors; - const compiledModels = []; - for (const [modelName, model23] of Object.entries(instance.models.models)) { - if (!model23) continue; - const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; - const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; - for (let dim = 0; dim < shape.length; dim++) { - if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; - } - const tensor6 = tf37.zeros(shape, dtype); - try { - const res = model23.execute(tensor6); - compiledModels.push(modelName); - if (Array.isArray(res)) res.forEach((t2) => tf37.dispose(t2)); - else tf37.dispose(res); - } catch (e) { - if (instance.config.debug) log("compile fail model:", modelName); - } - tf37.dispose(tensor6); - } - const kernels = await webGLBackend["checkCompileCompletionAsync"](); - webGLBackend["getUniformLocations"](); - if (instance.config.debug) log("compile pass:", { models: compiledModels, kernels: kernels.length }); - tf37.env().set("ENGINE_COMPILE_ONLY", false); - const numTensorsEnd = tf37.engine().state.numTensors; - if (numTensorsEnd - numTensorsStart > 0) log("tensor leak:", numTensorsEnd - numTensorsStart); -} -async function warmup(instance, userConfig) { - await check(instance, false); - const t0 = now(); - instance.state = "warmup"; - if (userConfig) instance.config = mergeDeep(instance.config, userConfig); - if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { - return empty(); - } - return new Promise(async (resolve) => { - await instance.models.load(); - await tf37.ready(); - await runCompile(instance); - const res = await runInference(instance); - const t1 = now(); - if (instance.config.debug) log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); - instance.emit("warmup"); - resolve(res); - }); -} - -// src/human.ts -var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; -var Human = class { - // definition end - /** Constructor for **Human** library that is futher used for all operations - * @param userConfig - user configuration object {@link Config} - */ - constructor(userConfig) { - /** Current version of Human library in *semver* format */ - __publicField(this, "version"); - /** Current configuration - * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) - */ - __publicField(this, "config"); - /** Last known result of detect run - * - Can be accessed anytime after initial detection - */ - __publicField(this, "result"); - /** Current state of Human library - * - Can be polled to determine operations that are currently executed - * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle' - */ - __publicField(this, "state"); - /** currenty processed image tensor and canvas */ - __publicField(this, "process"); - /** Instance of TensorFlow/JS used by Human - * - Can be embedded or externally provided - * [TFJS API](https://js.tensorflow.org/api/latest/) - */ - __publicField(this, "tf"); - /** Object containing environment information used for diagnostics */ - __publicField(this, "env", env); - /** Draw helper classes that can draw detected objects on canvas using specified draw - * - canvas: draws input to canvas - * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions} - * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas - */ - // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions }; - __publicField(this, "draw", draw_exports); - /** Face Matching - * - similarity: compare two face descriptors and return similarity index - * - distance: compare two face descriptors and return raw calculated differences - * - find: compare face descriptor to array of face descriptors and return best match - */ - __publicField(this, "match", match_exports); - /** Currently loaded models - * @internal - * {@link models#Models} - */ - __publicField(this, "models"); - /** Container for events dispatched by Human - * Possible events: - * - `create`: triggered when Human object is instantiated - * - `load`: triggered when models are loaded (explicitly or on-demand) - * - `image`: triggered when input image is processed - * - `result`: triggered when detection is complete - * - `warmup`: triggered when warmup is complete - * - `error`: triggered on some errors - */ - __publicField(this, "events"); - /** Reference face triangualtion array of 468 points, used for triangle references between points */ - __publicField(this, "faceTriangulation"); - /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */ - __publicField(this, "faceUVMap"); - /** Performance object that contains values for all recently performed operations */ - __publicField(this, "performance"); - // perf members are dynamically defined as needed - __privateAdd(this, _numTensors); - __privateAdd(this, _analyzeMemoryLeaks); - __privateAdd(this, _checkSanity); - /** internal function to measure tensor leaks */ - __publicField(this, "analyze", (...msg) => { - if (!__privateGet(this, _analyzeMemoryLeaks)) return; - const currentTensors = this.tf.engine().state.numTensors; - const previousTensors = __privateGet(this, _numTensors); - __privateSet(this, _numTensors, currentTensors); - const leaked = currentTensors - previousTensors; - if (leaked !== 0) log(...msg, leaked); - }); - /** internal function for quick sanity check on inputs @hidden */ - __privateAdd(this, _sanity, (input) => { - if (!__privateGet(this, _checkSanity)) return null; - if (!input) return "input is not defined"; - if (this.env.node && !(input instanceof tf38.Tensor)) return "input must be a tensor"; - try { - this.tf.getBackend(); - } catch (e) { - return "backend not loaded"; - } - return null; - }); - /** WebCam helper methods - * - */ - __publicField(this, "webcam", new WebCam()); - /** emit event */ - __publicField(this, "emit", (event) => { - var _a; - if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) this.events.dispatchEvent(new Event(event)); - }); - /** internal structure that keeps track of processed videos @hidden */ - __privateAdd(this, _loops, {}); - const tfVersion = (tf38.version.tfjs || tf38.version_core).replace(/-(.*)/, ""); - config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; - config.modelBasePath = env.browser ? "../models/" : "file://models/"; - this.version = version2; - Object.defineProperty(this, "version", { value: version2 }); - this.config = JSON.parse(JSON.stringify(config)); - Object.seal(this.config); - this.config.cacheModels = typeof indexedDB !== "undefined"; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - setModelLoadOptions(this.config); - this.tf = tf38; - this.state = "idle"; - __privateSet(this, _numTensors, 0); - __privateSet(this, _analyzeMemoryLeaks, false); - __privateSet(this, _checkSanity, false); - this.performance = {}; - this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; - this.models = new Models(this); - init2(); - this.result = empty(); - this.process = { tensor: null, canvas: null }; - this.faceTriangulation = triangulation; - this.faceUVMap = uvmap; - validateModel(this, null, ""); - this.emit("create"); - if (this.config.debug || this.env.browser) log(`version: ${this.version}`); - if (this.config.debug) log(`tfjs version: ${this.tf.version["tfjs-core"]}`); - const envTemp = JSON.parse(JSON.stringify(this.env)); - delete envTemp.kernels; - delete envTemp.initial; - delete envTemp.perfadd; - if (this.config.debug) log("environment:", envTemp); - } - /** Reset configuration to default values */ - reset() { - const currentBackend = this.config.backend; - this.config = JSON.parse(JSON.stringify(config)); - this.config.backend = currentBackend; - reset(); - env.initial = true; - } - /** Validate current configuration schema */ - validate(userConfig) { - const msgs = validate(config, userConfig || this.config); - if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig); - return msgs; - } - /** Utility wrapper for performance.now() */ - now() { - return now(); - } - /** Process input as return canvas and tensor - * - * @param input - any input {@link Input} - * @param getTensor - should image processing also return tensor or just canvas - * Returns object with `tensor` and `canvas` - */ - image(input, getTensor = false) { - return process2(input, this.config, getTensor); - } - /** Segmentation method takes any input and returns RGBA tensor - * Note: Segmentation is not triggered as part of detect process - * - * @param input - {@link Input} - * Returns tensor which contains image data in RGBA format - */ - async segmentation(input, userConfig) { - var _a, _b, _c; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - if (!this.config.segmentation.enabled) return null; - const processed = await process2(input, this.config); - if (!processed.tensor) return null; - let tensor6 = null; - if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) tensor6 = await predict20(processed.tensor, this.config); - if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) tensor6 = await predict16(processed.tensor, this.config); - if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) tensor6 = await predict21(processed.tensor, this.config); - tf38.dispose(processed.tensor); - return tensor6; - } - /** Compare two input tensors for pixel similarity - * - use `human.image` to process any valid input and get a tensor that can be used for compare - * - when passing manually generated tensors: - * - both input tensors must be in format [1, height, width, 3] - * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor - * - return value is pixel similarity score normalized by input resolution and rgb channels - */ - compare(firstImageTensor, secondImageTensor) { - return compare(this.config, firstImageTensor, secondImageTensor); - } - /** Explicit backend initialization - * - Normally done implicitly during initial load phase - * - Call to explictly register and initialize TFJS backend without any other operations - * - Use when changing backend during runtime - */ - async init() { - await check(this, true); - await this.tf.ready(); - reset(); - } - /** Load method preloads all configured models on-demand - * - Not explicitly required as any required model is load implicitly on it's first run - * - * @param userConfig - {@link Config} - */ - async load(userConfig) { - this.state = "load"; - const timeStamp = now(); - const count2 = Object.values(this.models.models).filter((model23) => model23).length; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - if (this.env.initial) { - if (!await check(this, false)) log("error: backend check failed"); - await tf38.ready(); - if (this.env.browser) { - if (this.config.debug) log("configuration:", this.config); - if (this.config.debug) log("tf flags:", this.tf.ENV.flags); - } - } - await this.models.load(this); - if (this.env.initial && this.config.debug) log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); - this.env.initial = false; - const loaded = Object.values(this.models.models).filter((model23) => model23).length; - if (loaded !== count2) { - this.models.validate(); - this.emit("load"); - } - const current = Math.trunc(now() - timeStamp); - if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; - } - /** Runs interpolation using last known result and returns smoothened result - * Interpolation is based on time since last known result so can be called independently - * - * @param result - {@link Result} optional use specific result set to run interpolation on - * @returns result - {@link Result} - */ - next(result = this.result) { - return calc2(result, this.config); - } - /** Warmup method pre-initializes all configured models for faster inference - * - can take significant time on startup - * - only used for `webgl` and `humangl` backends - * @param userConfig - {@link Config} - * @returns result - {@link Result} - */ - async warmup(userConfig) { - const t0 = now(); - const res = await warmup(this, userConfig); - const t1 = now(); - this.performance.warmup = Math.trunc(t1 - t0); - return res; - } - /** Run detect with tensorflow profiling - * - result object will contain total exeuction time information for top-20 kernels - * - actual detection object can be accessed via `human.result` - */ - async profile(input, userConfig) { - const profile = await this.tf.profile(() => this.detect(input, userConfig)); - const kernels = {}; - let total = 0; - for (const kernel of profile.kernels) { - const ms = Number(kernel.kernelTimeMs) || 0; - if (kernels[kernel.name]) kernels[kernel.name] += ms; - else kernels[kernel.name] = ms; - total += ms; - } - const kernelArr = []; - Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); - for (const kernel of kernelArr) { - kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; - kernel.time = Math.round(1e3 * kernel.time) / 1e3; - } - kernelArr.sort((a, b) => b.time - a.time); - kernelArr.length = 20; - return kernelArr; - } - /** Main detection method - * - Analyze configuration: {@link Config} - * - Pre-process input: {@link Input} - * - Run inference for all configured models - * - Process and return result: {@link Result} - * - * @param input - {@link Input} - * @param userConfig - {@link Config} - * @returns result - {@link Result} - */ - async detect(input, userConfig) { - this.state = "detect"; - return new Promise(async (resolve) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; - this.state = "config"; - let timeStamp; - this.config = mergeDeep(this.config, userConfig); - this.state = "check"; - const error = __privateGet(this, _sanity).call(this, input); - if (error) { - log(error, input); - this.emit("error"); - resolve(empty(error)); - } - const timeStart = now(); - await this.load(); - timeStamp = now(); - this.state = "image"; - const img = await process2(input, this.config); - this.process = img; - this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Get Image:"); - if (!img.tensor) { - if (this.config.debug) log("could not convert input to tensor"); - this.emit("error"); - resolve(empty("could not convert input to tensor")); - return; - } - this.emit("image"); - timeStamp = now(); - this.config.skipAllowed = await skip(this.config, img.tensor); - this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; - if (!this.performance.totalFrames) this.performance.totalFrames = 0; - if (!this.performance.cachedFrames) this.performance.cachedFrames = 0; - this.performance.totalFrames++; - if (this.config.skipAllowed) this.performance.cachedFrames++; - this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Check Changed:"); - let faceRes = []; - let bodyRes = []; - let handRes = []; - let objectRes = []; - this.state = "detect:face"; - if (this.config.async) { - faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; - if (this.performance.face) delete this.performance.face; - } else { - timeStamp = now(); - faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; - this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; - this.analyze("Start Body:"); - this.state = "detect:body"; - const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; - else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; - else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; - else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; - if (this.performance.body) delete this.performance.body; - } else { - timeStamp = now(); - if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; - else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; - else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; - else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; - this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Body:"); - this.analyze("Start Hand:"); - this.state = "detect:hand"; - const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; - else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; - if (this.performance.hand) delete this.performance.hand; - } else { - timeStamp = now(); - if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; - else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; - this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Hand:"); - this.analyze("Start Object:"); - this.state = "detect:object"; - if (this.config.async) { - if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; - else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; - if (this.performance.object) delete this.performance.object; - } else { - timeStamp = now(); - if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; - else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; - this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Object:"); - this.state = "detect:await"; - if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); - this.state = "detect:gesture"; - let gestureRes = []; - if (this.config.gesture.enabled) { - timeStamp = now(); - gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; - if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - else if (this.performance.gesture) delete this.performance.gesture; - } - this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); - const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; - this.result = { - face: faceRes, - body: bodyRes, - hand: handRes, - gesture: gestureRes, - object: objectRes, - performance: this.performance, - canvas: this.process.canvas, - timestamp: Date.now(), - error: null, - width: shape[2], - height: shape[1], - get persons() { - return join2(faceRes, bodyRes, handRes, gestureRes, shape); - } - }; - tf38.dispose(img.tensor); - this.emit("detect"); - this.state = "idle"; - resolve(this.result); - }); - } - /** Helper function - * @param ms - sleep time in miliseconds - */ - async sleep(ms) { - return new Promise((resolve) => { - setTimeout(resolve, ms); - }); - } - /** Continously detect video frames - * @param element - HTMLVideoElement input - * @param run - boolean run continously or stop if already running, default true - * @param delay - number delay detection between frames for number of miliseconds, default 0 - */ - async video(element, run = true, delay = 0) { - if (run) { - if (!__privateGet(this, _loops)[element.id]) { - if (this.config.debug) log("video start", element.id); - __privateGet(this, _loops)[element.id] = true; - } - if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) await this.detect(element); - if (delay > 0) await this.sleep(delay); - if (__privateGet(this, _loops)[element.id]) requestAnimationFrame(() => this.video(element, run, delay)); - } else { - if (this.config.debug) log("video stop", element.id); - __privateGet(this, _loops)[element.id] = false; - } - } -}; -_numTensors = new WeakMap(); -_analyzeMemoryLeaks = new WeakMap(); -_checkSanity = new WeakMap(); -_sanity = new WeakMap(); -_loops = new WeakMap(); -// Annotate the CommonJS export names for ESM import in node: -0 && (module.exports = { - Env, - Human, - defaults, - draw, - empty, - env, - match, - models -}); 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a0&&a0.getBackend()==="tensorflow"){let r=a0.node.decodeJpeg(n),s=a0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&b("Warmup tfjs-node not loaded");return o}async function pa(e){let t;return typeof createImageBitmap=="function"?t=await ya(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await fa(e):t=await ma(e),t}async function ua(e){var a,l,c,d;if(!a0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=a0.getBackend(),n=a0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;a0.env().set("ENGINE_COMPILE_ONLY",!0);let o=a0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models.models)){if(!y)continue;let x=y!=null&&y.modelSignature&&((l=(a=y==null?void 0:y.inputs)==null?void 0:a[0])!=null&&l.shape)?[...y.inputs[0].shape]:[1,64,64,3],m=y!=null&&y.modelSignature&&((d=(c=y==null?void 0:y.inputs)==null?void 0:c[0])!=null&&d.dtype)?y.inputs[0].dtype:"float32";for(let h=0;ha0.dispose(T)):a0.dispose(h)}catch(h){e.config.debug&&b("compile fail model:",i)}a0.dispose(f)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&b("compile pass:",{models:r,kernels:s.length}),a0.env().set("ENGINE_COMPILE_ONLY",!1);let A=a0.engine().state.numTensors;A-o>0&&b("tensor leak:",A-o)}async function Do(e,t){await N2(e,!1);let n=v();return e.state="warmup",t&&(e.config=i0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?ve():new Promise(async o=>{await e.models.load(),await a0.ready(),await ua(e);let r=await pa(e),s=v();e.config.debug&&b("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,G2,V2,qt,Ye,w1=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env",R);w(this,"draw",nt);w(this,"match",Ot);w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");ue(this,P2);ue(this,G2);ue(this,V2);w(this,"analyze",(...t)=>{if(!Y0(this,G2))return;let n=this.tf.engine().state.numTensors,o=Y0(this,P2);Me(this,P2,n);let r=n-o;r!==0&&b(...t,r)});ue(this,qt,t=>{if(!Y0(this,V2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof de.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});w(this,"webcam",new K2);w(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(de.version.tfjs||de.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=t5,Object.defineProperty(this,"version",{value:t5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=i0(this.config,t)),X1(this.config),this.tf=de,this.state="idle",Me(this,P2,0),Me(this,G2,!1),Me(this,V2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new H2(this),i5(),this.result=ve(),this.process={tensor:null,canvas:null},this.faceTriangulation=F3,this.faceUVMap=B3,Gt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&b("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,$t(),R.initial=!0}validate(t){let n=Jt(Ke,t||this.config);return n.length===0&&(this.config=i0(this.config,t)),n}now(){return v()}image(t,n=!1){return U2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=i0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await U2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Io(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await co(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Oo(o.tensor,this.config)),de.dispose(o.tensor),r}compare(t,n){return q1(this.config,t,n)}async init(){await N2(this,!0),await this.tf.ready(),$t()}async load(t){this.state="load";let n=v(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=i0(this.config,t)),this.env.initial&&(await N2(this,!1)||b("error: backend check failed"),await de.ready(),this.env.browser&&(this.config.debug&&b("configuration:",this.config),this.config.debug&&b("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&b("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(v()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return lo(t,this.config)}async warmup(t){let n=v(),o=await Do(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var h,T,g,p,u,k,P,N,B,J,H,K,e0,n0,o0,N0,M,T0,C0,x0,Q;this.state="config";let r;this.config=i0(this.config,n),this.state="check";let s=Y0(this,qt).call(this,t);s&&(b(s,t),this.emit("error"),o(ve(s)));let A=v();await this.load(),r=v(),this.state="image";let a=await U2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),o(ve("could not convert input to tensor"));return}this.emit("image"),r=v(),this.config.skipAllowed=await Z1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],d=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?J5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await J5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?i0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?T1(a.tensor,y):[]:(T=this.config.body.modelPath)!=null&&T.includes("blazepose")?c=this.config.body.enabled?y5(a.tensor,y):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?c=this.config.body.enabled?g5(a.tensor,y):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?f1(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=v(),(u=this.config.body.modelPath)!=null&&u.includes("posenet")?c=this.config.body.enabled?await T1(a.tensor,y):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?c=this.config.body.enabled?await y5(a.tensor,y):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await g5(a.tensor,y):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(c=this.config.body.enabled?await f1(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let x=this.config.hand.maxDetected===-1?i0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((J=(B=this.config.hand.detector)==null?void 0:B.modelPath)!=null&&J.includes("handdetect")?d=this.config.hand.enabled?n1(a.tensor,x):[]:(K=(H=this.config.hand.detector)==null?void 0:H.modelPath)!=null&&K.includes("handtrack")&&(d=this.config.hand.enabled?s1(a.tensor,x):[]),this.performance.hand&&delete this.performance.hand):(r=v(),(n0=(e0=this.config.hand.detector)==null?void 0:e0.modelPath)!=null&&n0.includes("handdetect")?d=this.config.hand.enabled?await n1(a.tensor,x):[]:(N0=(o0=this.config.hand.detector)==null?void 0:o0.modelPath)!=null&&N0.includes("handtrack")&&(d=this.config.hand.enabled?await s1(a.tensor,x):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((M=this.config.object.modelPath)!=null&&M.includes("nanodet")?i=this.config.object.enabled?p1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?p5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=v(),(C0=this.config.object.modelPath)!=null&&C0.includes("nanodet")?i=this.config.object.enabled?await p1(a.tensor,this.config):[]:(x0=this.config.object.modelPath)!=null&&x0.includes("centernet")&&(i=this.config.object.enabled?await p5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,d,i]=await Promise.all([l,c,d,i])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=v(),m=[...Dn(l),...Wn(c),...Bn(d),...Fn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(v()-A):Math.trunc(v()-A);let f=((Q=this.process.tensor)==null?void 0:Q.shape)||[0,0,0,0];this.result={face:l,body:c,hand:d,gesture:m,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return Wo(l,c,d,m,f)}},de.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(Y0(this,Ye)[t.id]||(this.config.debug&&b("video start",t.id),Y0(this,Ye)[t.id]=!0),!t.paused&&Y0(this,Ye)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),Y0(this,Ye)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&b("video stop",t.id),Y0(this,Ye)[t.id]=!1)}};P2=new WeakMap,G2=new WeakMap,V2=new WeakMap,qt=new WeakMap,Ye=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models}); diff --git a/dist/human.node-wasm.js b/dist/human.node-wasm.js index fdb9752d..b609c6ff 100644 --- a/dist/human.node-wasm.js +++ b/dist/human.node-wasm.js @@ -4,304 +4,7 @@ author: ' */ -"use strict"; -var __create = Object.create; -var __defProp = Object.defineProperty; -var __getOwnPropDesc = Object.getOwnPropertyDescriptor; -var __getOwnPropNames = Object.getOwnPropertyNames; -var __getProtoOf = Object.getPrototypeOf; -var __hasOwnProp = Object.prototype.hasOwnProperty; -var __typeError = (msg) => { - throw TypeError(msg); -}; -var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; -var __commonJS = (cb, mod3) => function __require() { - return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports; -}; -var __export = (target, all2) => { - for (var name in all2) - __defProp(target, name, { get: all2[name], enumerable: true }); -}; -var __copyProps = (to, from, except, desc) => { - if (from && typeof from === "object" || typeof from === "function") { - for (let key of __getOwnPropNames(from)) - if (!__hasOwnProp.call(to, key) && key !== except) - __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); - } - return to; -}; -var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps( - // If the importer is in node compatibility mode or this is not an ESM - // file that has been converted to a CommonJS file using a Babel- - // compatible transform (i.e. "__esModule" has not been set), then set - // "default" to the CommonJS "module.exports" for node compatibility. - isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target, - mod3 -)); -var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3); -var __publicField = (obj, key, value) => __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); -var __accessCheck = (obj, member, msg) => member.has(obj) || __typeError("Cannot " + msg); -var __privateGet = (obj, member, getter) => (__accessCheck(obj, member, "read from private field"), getter ? getter.call(obj) : member.get(obj)); -var __privateAdd = (obj, member, value) => member.has(obj) ? __typeError("Cannot add the same private member more than once") : member instanceof WeakSet ? member.add(obj) : member.set(obj, value); -var __privateSet = (obj, member, value, setter) => (__accessCheck(obj, member, "write to private field"), setter ? setter.call(obj, value) : member.set(obj, value), value); - -// dist/tfjs.esm.js -var require_tfjs_esm = __commonJS({ - "dist/tfjs.esm.js"(exports2, module2) { - "use strict"; - var __defProp2 = Object.defineProperty; - var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor; - var __getOwnPropNames2 = Object.getOwnPropertyNames; - var __hasOwnProp2 = Object.prototype.hasOwnProperty; - var __export2 = (target, all2) => { - for (var name in all2) - __defProp2(target, name, { get: all2[name], enumerable: true }); - }; - var __copyProps2 = (to, from, except, desc) => { - if (from && typeof from === "object" || typeof from === "function") { - for (let key of __getOwnPropNames2(from)) - if (!__hasOwnProp2.call(to, key) && key !== except) - __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); - } - return to; - }; - var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default")); - var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3); - var tf_node_wasm_exports = {}; - __export2(tf_node_wasm_exports, { - version: () => version7 - }); - module2.exports = __toCommonJS2(tf_node_wasm_exports); - __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-core"), module2.exports); - __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-converter"), module2.exports); - __reExport(tf_node_wasm_exports, require("@tensorflow/tfjs-backend-wasm"), module2.exports); - var version4 = "4.21.0"; - var version22 = "4.21.0"; - var version32 = "4.21.0"; - var version42 = "4.21.0"; - var version5 = "4.21.0"; - var version6 = "4.21.0"; - var version7 = { - tfjs: version4, - "tfjs-core": version4, - // 'tfjs-data': tfjsDataVersion, - // 'tfjs-layers': tfjsLayersVersion, - "tfjs-converter": version22, - "tfjs-backend-cpu": version32, - "tfjs-backend-webgl": version42, - "tfjs-backend-wasm": version5, - "tfjs-backend-webgpu": version6 - }; - } -}); - -// src/human.ts -var human_exports = {}; -__export(human_exports, { - Env: () => Env, - Human: () => Human, - default: () => Human, - defaults: () => config, - draw: () => draw_exports, - empty: () => empty, - env: () => env, - match: () => match_exports, - models: () => models_exports2 -}); -module.exports = __toCommonJS(human_exports); -var tf38 = __toESM(require_tfjs_esm()); - -// src/util/util.ts -function log(...msg) { - const dt = /* @__PURE__ */ new Date(); - const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; - if (msg) console.log(ts, "Human:", ...msg); -} -function join(folder, file) { - const separator = folder.endsWith("/") ? "" : "/"; - const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); - const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; - if (!path.toLocaleLowerCase().includes(".json")) throw new Error(`modelpath error: expecting json file: ${path}`); - return path; -} -var now = () => { - if (typeof performance !== "undefined") return performance.now(); - return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); -}; -function validate(defaults, config3, parent = "config", msgs = []) { - for (const key of Object.keys(config3)) { - if (typeof config3[key] === "object") { - validate(defaults[key], config3[key], key, msgs); - } else { - const defined = defaults && typeof defaults[key] !== "undefined"; - if (!defined) msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); - const same = defaults && typeof defaults[key] === typeof config3[key]; - if (defined && !same) msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); - } - } - if (config3.debug && parent === "config" && msgs.length > 0) log("invalid configuration", msgs); - return msgs; -} -function mergeDeep(...objects) { - const isObject = (obj) => obj && typeof obj === "object"; - return objects.reduce((prev, obj) => { - Object.keys(obj || {}).forEach((key) => { - const pVal = prev[key]; - const oVal = obj[key]; - if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal); - else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal); - else prev[key] = oVal; - }); - return prev; - }, {}); -} - -// src/config.ts -var config = { - backend: "", - modelBasePath: "", - cacheModels: true, - validateModels: true, - wasmPath: "", - wasmPlatformFetch: false, - debug: false, - async: true, - warmup: "full", - cacheSensitivity: 0.7, - skipAllowed: false, - deallocate: false, - flags: {}, - softwareKernels: false, - filter: { - enabled: true, - equalization: false, - width: 0, - height: 0, - flip: false, - return: true, - autoBrightness: true, - brightness: 0, - contrast: 0, - sharpness: 0, - blur: 0, - saturation: 0, - hue: 0, - negative: false, - sepia: false, - vintage: false, - kodachrome: false, - technicolor: false, - polaroid: false, - pixelate: 0 - }, - gesture: { - enabled: true - }, - face: { - enabled: true, - detector: { - modelPath: "blazeface.json", - rotation: false, - maxDetected: 1, - skipFrames: 99, - skipTime: 2500, - minConfidence: 0.2, - minSize: 0, - iouThreshold: 0.1, - scale: 1.4, - mask: false, - return: false - }, - mesh: { - enabled: true, - modelPath: "facemesh.json", - keepInvalid: false - }, - attention: { - enabled: false, - modelPath: "facemesh-attention.json" - }, - iris: { - enabled: true, - scale: 2.3, - modelPath: "iris.json" - }, - emotion: { - enabled: true, - minConfidence: 0.1, - skipFrames: 99, - skipTime: 1500, - modelPath: "emotion.json" - }, - description: { - enabled: true, - modelPath: "faceres.json", - skipFrames: 99, - skipTime: 3e3, - minConfidence: 0.1 - }, - antispoof: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "antispoof.json" - }, - liveness: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "liveness.json" - } - }, - body: { - enabled: true, - modelPath: "movenet-lightning.json", - maxDetected: -1, - minConfidence: 0.3, - skipFrames: 1, - skipTime: 200 - }, - hand: { - enabled: true, - rotation: true, - skipFrames: 99, - skipTime: 1e3, - minConfidence: 0.5, - iouThreshold: 0.2, - maxDetected: -1, - landmarks: true, - detector: { - modelPath: "handtrack.json" - }, - skeleton: { - modelPath: "handlandmark-lite.json" - } - }, - object: { - enabled: false, - modelPath: "centernet.json", - minConfidence: 0.2, - iouThreshold: 0.4, - maxDetected: 10, - skipFrames: 99, - skipTime: 2e3 - }, - segmentation: { - enabled: false, - modelPath: "rvm.json", - ratio: 0.5, - mode: "default" - } -}; - -// src/util/env.ts -var tf3 = __toESM(require_tfjs_esm()); - -// src/image/image.ts -var tf2 = __toESM(require_tfjs_esm()); - -// src/image/imagefxshaders.ts -var vertexIdentity = ` +"use strict";var Bo=Object.create;var w2=Object.defineProperty;var Ho=Object.getOwnPropertyDescriptor;var Go=Object.getOwnPropertyNames;var Vo=Object.getPrototypeOf,Zo=Object.prototype.hasOwnProperty;var I1=e=>{throw TypeError(e)};var qo=(e,t,n)=>t in e?w2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Xo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var n in t)w2(e,n,{get:t[n],enumerable:!0})},O1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Go(t))!Zo.call(e,r)&&r!==n&&w2(e,r,{get:()=>t[r],enumerable:!(o=Ho(t,r))||o.enumerable});return e};var Z=(e,t,n)=>(n=e!=null?Bo(Vo(e)):{},O1(t||!e||!e.__esModule?w2(n,"default",{value:e,enumerable:!0}):n,e)),Uo=e=>O1(w2({},"__esModule",{value:!0}),e);var w=(e,t,n)=>qo(e,typeof t!="symbol"?t+"":t,n),L1=(e,t,n)=>t.has(e)||I1("Cannot "+n);var Y0=(e,t,n)=>(L1(e,t,"read from private field"),n?n.call(e):t.get(e)),ue=(e,t,n)=>t.has(e)?I1("Cannot add the same private member more than once"):t instanceof WeakSet?t.add(e):t.set(e,n),Me=(e,t,n,o)=>(L1(e,t,"write to private field"),o?o.call(e,n):t.set(e,n),n);var V=Xo((Ta,z2)=>{"use strict";var Kt=Object.defineProperty,Yo=Object.getOwnPropertyDescriptor,Ko=Object.getOwnPropertyNames,Jo=Object.prototype.hasOwnProperty,Qo=(e,t)=>{for(var n in t)Kt(e,n,{get:t[n],enumerable:!0})},Yt=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Ko(t))!Jo.call(e,r)&&r!==n&&Kt(e,r,{get:()=>t[r],enumerable:!(o=Yo(t,r))||o.enumerable});return e},Jt=(e,t,n)=>(Yt(e,t,"default"),n&&Yt(n,t,"default")),_o=e=>Yt(Kt({},"__esModule",{value:!0}),e),E2={};Qo(E2,{version:()=>rr});z2.exports=_o(E2);Jt(E2,require("@tensorflow/tfjs-core"),z2.exports);Jt(E2,require("@tensorflow/tfjs-converter"),z2.exports);Jt(E2,require("@tensorflow/tfjs-backend-wasm"),z2.exports);var C1="4.21.0",$o="4.21.0",er="4.21.0",tr="4.21.0",nr="4.21.0",or="4.21.0",rr={tfjs:C1,"tfjs-core":C1,"tfjs-converter":$o,"tfjs-backend-cpu":er,"tfjs-backend-webgl":tr,"tfjs-backend-wasm":nr,"tfjs-backend-webgpu":or}});var ba={};Se(ba,{Env:()=>S2,Human:()=>E1,default:()=>E1,defaults:()=>Ke,draw:()=>rt,empty:()=>ve,env:()=>R,match:()=>Wt,models:()=>w1});module.exports=Uo(ba);var de=Z(V());function b(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function W1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var v=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Qt(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Qt(e[r],t[r],r,o);else{let s=e&&typeof e[r]!="undefined";s||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let A=e&&typeof e[r]==typeof t[r];s&&!A&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&b("invalid configuration",o),o}function i0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let s=n[r],A=o[r];Array.isArray(s)&&Array.isArray(A)?n[r]=s.concat(...A):t(s)&&t(A)?n[r]=i0(s,A):n[r]=A}),n),{})}var Ke={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,scale:1.4,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,scale:2.3,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var W0=Z(V());var I=Z(V());var D1=` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -311,8 +14,7 @@ var vertexIdentity = ` vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`; -var colorMatrixWithAlpha = ` +`;var F1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -324,8 +26,7 @@ var colorMatrixWithAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`; -var colorMatrixWithoutAlpha = ` +`,B1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -337,8 +38,7 @@ var colorMatrixWithoutAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`; -var pixelate = ` +`,H1=` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -351,8 +51,7 @@ var pixelate = ` vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`; -var blur = ` +`,G1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -375,8 +74,7 @@ var blur = ` gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`; -var convolution = ` +`,V1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -398,6031 +96,20 @@ var convolution = ` c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`; - -// src/image/imagefx.ts -var collect = (source, prefix, collection) => { - const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); - source.replace(r, (match2, name) => { - collection[name] = 0; - return match2; - }); -}; -var GLProgram = class { - constructor(gl, vertexSource, fragmentSource) { - __publicField(this, "uniform", {}); - __publicField(this, "attribute", {}); - __publicField(this, "gl"); - __publicField(this, "id"); - __publicField(this, "compile", (source, type) => { - const shader = this.gl.createShader(type); - if (!shader) { - log("filter: could not create shader"); - return null; - } - this.gl.shaderSource(shader, source); - this.gl.compileShader(shader); - if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { - log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); - return null; - } - return shader; - }); - this.gl = gl; - const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); - const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); - this.id = this.gl.createProgram(); - if (!vertexShader || !fragmentShader) return; - if (!this.id) { - log("filter: could not create webgl program"); - return; - } - this.gl.attachShader(this.id, vertexShader); - this.gl.attachShader(this.id, fragmentShader); - this.gl.linkProgram(this.id); - if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { - log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); - return; - } - this.gl.useProgram(this.id); - collect(vertexSource, "attribute", this.attribute); - for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a); - collect(vertexSource, "uniform", this.uniform); - collect(fragmentSource, "uniform", this.uniform); - for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u); - } -}; -function GLImageFilter() { - let drawCount = 0; - let sourceTexture = null; - let lastInChain = false; - let currentFramebufferIndex = -1; - let tempFramebuffers = [null, null]; - let filterChain = []; - let vertexBuffer = null; - let currentProgram = null; - const fxcanvas = canvas(100, 100); - const shaderProgramCache = {}; - const DRAW = { INTERMEDIATE: 1 }; - const gl = fxcanvas.getContext("webgl"); - if (!gl) { - log("filter: cannot get webgl context"); - return; - } - this.gl = gl; - function resize(width, height) { - if (width === fxcanvas.width && height === fxcanvas.height) return; - fxcanvas.width = width; - fxcanvas.height = height; - if (!vertexBuffer) { - const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); - vertexBuffer = gl.createBuffer(); - gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); - gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); - gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); - } - gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); - tempFramebuffers = [null, null]; - } - function createFramebufferTexture(width, height) { - const fbo = gl.createFramebuffer(); - gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); - const renderbuffer = gl.createRenderbuffer(); - gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); - const texture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, texture); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); - gl.bindTexture(gl.TEXTURE_2D, null); - gl.bindFramebuffer(gl.FRAMEBUFFER, null); - return { fbo, texture }; - } - function getTempFramebuffer(index2) { - tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); - return tempFramebuffers[index2]; - } - function draw(flags = 0) { - if (!currentProgram) return; - let source = null; - let target = null; - let flipY = false; - if (drawCount === 0) source = sourceTexture; - else source = getTempFramebuffer(currentFramebufferIndex).texture || null; - drawCount++; - if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { - target = null; - flipY = drawCount % 2 === 0; - } else { - currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; - target = getTempFramebuffer(currentFramebufferIndex).fbo || null; - } - gl.bindTexture(gl.TEXTURE_2D, source); - gl.bindFramebuffer(gl.FRAMEBUFFER, target); - gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); - gl.drawArrays(gl.TRIANGLES, 0, 6); - } - function compileShader(fragmentSource) { - if (shaderProgramCache[fragmentSource]) { - currentProgram = shaderProgramCache[fragmentSource]; - gl.useProgram((currentProgram ? currentProgram.id : null) || null); - return currentProgram; - } - currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); - if (!currentProgram) { - log("filter: could not get webgl program"); - return null; - } - const floatSize = Float32Array.BYTES_PER_ELEMENT; - const vertSize = 4 * floatSize; - gl.enableVertexAttribArray(currentProgram.attribute["pos"]); - gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); - gl.enableVertexAttribArray(currentProgram.attribute["uv"]); - gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); - shaderProgramCache[fragmentSource] = currentProgram; - return currentProgram; - } - const filter = { - colorMatrix: (matrix) => { - const m = new Float32Array(matrix); - m[4] /= 255; - m[9] /= 255; - m[14] /= 255; - m[19] /= 255; - const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; - const program = compileShader(shader); - if (!program) return; - gl.uniform1fv(program.uniform["m"], m); - draw(); - }, - brightness: (brightness) => { - const b = (brightness || 0) + 1; - filter.colorMatrix([ - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - saturation: (amount) => { - const x = (amount || 0) * 2 / 3 + 1; - const y = (x - 1) * -0.5; - filter.colorMatrix([ - x, - y, - y, - 0, - 0, - y, - x, - y, - 0, - 0, - y, - y, - x, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturate: () => { - filter.saturation(-1); - }, - contrast: (amount) => { - const v = (amount || 0) + 1; - const o = -128 * (v - 1); - filter.colorMatrix([ - v, - 0, - 0, - 0, - o, - 0, - v, - 0, - 0, - o, - 0, - 0, - v, - 0, - o, - 0, - 0, - 0, - 1, - 0 - ]); - }, - negative: () => { - filter.contrast(-2); - }, - hue: (rotation) => { - rotation = (rotation || 0) / 180 * Math.PI; - const cos = Math.cos(rotation); - const sin = Math.sin(rotation); - const lumR = 0.213; - const lumG = 0.715; - const lumB = 0.072; - filter.colorMatrix([ - lumR + cos * (1 - lumR) + sin * -lumR, - lumG + cos * -lumG + sin * -lumG, - lumB + cos * -lumB + sin * (1 - lumB), - 0, - 0, - lumR + cos * -lumR + sin * 0.143, - lumG + cos * (1 - lumG) + sin * 0.14, - lumB + cos * -lumB + sin * -0.283, - 0, - 0, - lumR + cos * -lumR + sin * -(1 - lumR), - lumG + cos * -lumG + sin * lumG, - lumB + cos * (1 - lumB) + sin * lumB, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturateLuminance: () => { - filter.colorMatrix([ - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0, - 0, - 0, - 1, - 0 - ]); - }, - sepia: () => { - filter.colorMatrix([ - 0.393, - 0.7689999, - 0.18899999, - 0, - 0, - 0.349, - 0.6859999, - 0.16799999, - 0, - 0, - 0.272, - 0.5339999, - 0.13099999, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - brownie: () => { - filter.colorMatrix([ - 0.5997023498159715, - 0.34553243048391263, - -0.2708298674538042, - 0, - 47.43192855600873, - -0.037703249837783157, - 0.8609577587992641, - 0.15059552388459913, - 0, - -36.96841498319127, - 0.24113635128153335, - -0.07441037908422492, - 0.44972182064877153, - 0, - -7.562075277591283, - 0, - 0, - 0, - 1, - 0 - ]); - }, - vintagePinhole: () => { - filter.colorMatrix([ - 0.6279345635605994, - 0.3202183420819367, - -0.03965408211312453, - 0, - 9.651285835294123, - 0.02578397704808868, - 0.6441188644374771, - 0.03259127616149294, - 0, - 7.462829176470591, - 0.0466055556782719, - -0.0851232987247891, - 0.5241648018700465, - 0, - 5.159190588235296, - 0, - 0, - 0, - 1, - 0 - ]); - }, - kodachrome: () => { - filter.colorMatrix([ - 1.1285582396593525, - -0.3967382283601348, - -0.03992559172921793, - 0, - 63.72958762196502, - -0.16404339962244616, - 1.0835251566291304, - -0.05498805115633132, - 0, - 24.732407896706203, - -0.16786010706155763, - -0.5603416277695248, - 1.6014850761964943, - 0, - 35.62982807460946, - 0, - 0, - 0, - 1, - 0 - ]); - }, - technicolor: () => { - filter.colorMatrix([ - 1.9125277891456083, - -0.8545344976951645, - -0.09155508482755585, - 0, - 11.793603434377337, - -0.3087833385928097, - 1.7658908555458428, - -0.10601743074722245, - 0, - -70.35205161461398, - -0.231103377548616, - -0.7501899197440212, - 1.847597816108189, - 0, - 30.950940869491138, - 0, - 0, - 0, - 1, - 0 - ]); - }, - polaroid: () => { - filter.colorMatrix([ - 1.438, - -0.062, - -0.062, - 0, - 0, - -0.122, - 1.378, - -0.122, - 0, - 0, - -0.016, - -0.016, - 1.483, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - shiftToBGR: () => { - filter.colorMatrix([ - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - convolution: (matrix) => { - const m = new Float32Array(matrix); - const pixelSizeX = 1 / fxcanvas.width; - const pixelSizeY = 1 / fxcanvas.height; - const program = compileShader(convolution); - if (!program) return; - gl.uniform1fv(program.uniform["m"], m); - gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); - draw(); - }, - detectEdges: () => { - filter.convolution.call(this, [ - 0, - 1, - 0, - 1, - -4, - 1, - 0, - 1, - 0 - ]); - }, - sobelX: () => { - filter.convolution.call(this, [ - -1, - 0, - 1, - -2, - 0, - 2, - -1, - 0, - 1 - ]); - }, - sobelY: () => { - filter.convolution.call(this, [ - -1, - -2, - -1, - 0, - 0, - 0, - 1, - 2, - 1 - ]); - }, - sharpen: (amount) => { - const a = amount || 1; - filter.convolution.call(this, [ - 0, - -1 * a, - 0, - -1 * a, - 1 + 4 * a, - -1 * a, - 0, - -1 * a, - 0 - ]); - }, - emboss: (size2) => { - const s = size2 || 1; - filter.convolution.call(this, [ - -2 * s, - -1 * s, - 0, - -1 * s, - 1, - 1 * s, - 0, - 1 * s, - 2 * s - ]); - }, - blur: (size2) => { - const blurSizeX = size2 / 7 / fxcanvas.width; - const blurSizeY = size2 / 7 / fxcanvas.height; - const program = compileShader(blur); - if (!program) return; - gl.uniform2f(program.uniform["px"], 0, blurSizeY); - draw(DRAW.INTERMEDIATE); - gl.uniform2f(program.uniform["px"], blurSizeX, 0); - draw(); - }, - pixelate: (size2) => { - const blurSizeX = size2 / fxcanvas.width; - const blurSizeY = size2 / fxcanvas.height; - const program = compileShader(pixelate); - if (!program) return; - gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); - draw(); - } - }; - this.add = function(name) { - const args = Array.prototype.slice.call(arguments, 1); - const func = filter[name]; - filterChain.push({ func, args }); - }; - this.reset = function() { - filterChain = []; - }; - this.get = function() { - return filterChain; - }; - this.apply = function(image28) { - resize(image28.width, image28.height); - drawCount = 0; - if (!sourceTexture) sourceTexture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, sourceTexture); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); - for (let i = 0; i < filterChain.length; i++) { - lastInChain = i === filterChain.length - 1; - const f = filterChain[i]; - f.func.apply(this, f.args || []); - } - return fxcanvas; - }; - this.draw = function(image28) { - this.add("brightness", 0); - return this.apply(image28); - }; -} - -// src/image/enhance.ts -var tf = __toESM(require_tfjs_esm()); -async function histogramEqualization(inputImage) { - const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage; - const rgb3 = tf.split(squeeze14, 3, 2); - const min2 = [tf.min(rgb3[0]), tf.min(rgb3[1]), tf.min(rgb3[2])]; - const max5 = [tf.max(rgb3[0]), tf.max(rgb3[1]), tf.max(rgb3[2])]; - const absMax = await Promise.all(max5.map((channel) => channel.data())); - const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); - const maxRange = maxValue > 1 ? 255 : 1; - const factor = maxRange / maxValue; - let final; - if (factor > 1) { - const sub11 = [tf.sub(rgb3[0], min2[0]), tf.sub(rgb3[1], min2[1]), tf.sub(rgb3[2], min2[2])]; - const range = [tf.sub(max5[0], min2[0]), tf.sub(max5[1], min2[1]), tf.sub(max5[2], min2[2])]; - const enh = [tf.mul(sub11[0], factor), tf.mul(sub11[1], factor), tf.mul(sub11[2], factor)]; - const stack5 = tf.stack([enh[0], enh[1], enh[2]], 2); - final = tf.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); - tf.dispose([...sub11, ...range, ...enh, stack5]); - } else { - final = tf.expandDims(squeeze14, 0); - } - tf.dispose([...rgb3, ...min2, ...max5, rgb3, squeeze14, inputImage]); - return final; -} - -// src/image/image.ts -var maxSize = 3840; -var inCanvas = null; -var outCanvas = null; -var tmpCanvas = null; -var fx; -var last = { - inputSum: 0, - cacheDiff: 1, - sumMethod: 0, - inputTensor: void 0 -}; -function reset() { - last.inputSum = 0; - last.cacheDiff = 1; - last.sumMethod = 0; - last.inputTensor = void 0; -} -function canvas(width, height) { - let c; - if (env.browser) { - if (env.worker) { - if (typeof OffscreenCanvas === "undefined") throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); - c = new OffscreenCanvas(width, height); - } else { - if (typeof document !== "undefined") { - c = document.createElement("canvas"); - c.width = width; - c.height = height; - } else if (typeof navigator !== "undefined" && navigator.product === "ReactNative") { - if (typeof env.Canvas !== "undefined") c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") c = new globalThis.Canvas(width, height); - else throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed"); - } else { - throw new Error("canvas error: attempted to run in browser but DOM is not defined"); - } - } - } else { - if (typeof env.Canvas !== "undefined") c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") c = new globalThis.Canvas(width, height); - } - return c; -} -function copy(input, output) { - const outputCanvas = output || canvas(input.width, input.height); - const ctx = outputCanvas.getContext("2d"); - ctx.drawImage(input, 0, 0); - return outputCanvas; -} -async function process2(input, config3, getTensor = true) { - var _a, _b, _c; - if (!input) { - if (config3.debug) log("input error: input is missing"); - return { tensor: null, canvas: null }; - } - if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { - throw new Error("input error: type not recognized"); - } - if (input instanceof tf2.Tensor) { - let tensor7 = null; - if (input["isDisposedInternal"]) throw new Error("input error: attempted to use tensor but it is disposed"); - if (!input.shape) throw new Error("input error: attempted to use tensor without a shape"); - if (input.shape.length === 3) { - if (input.shape[2] === 3) { - tensor7 = tf2.expandDims(input, 0); - } else if (input.shape[2] === 4) { - const rgb3 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]); - tensor7 = tf2.expandDims(rgb3, 0); - tf2.dispose(rgb3); - } - } else if (input.shape.length === 4) { - if (input.shape[3] === 3) { - tensor7 = tf2.clone(input); - } else if (input.shape[3] === 4) { - tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); - } - } - if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); - if (tensor7.dtype === "int32") { - const cast8 = tf2.cast(tensor7, "float32"); - tf2.dispose(tensor7); - tensor7 = cast8; - } - return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; - } - if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { - if (config3.debug) log("input stream is not ready"); - return { tensor: null, canvas: inCanvas }; - } - const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; - const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; - if (!originalWidth || !originalHeight) { - if (config3.debug) log("cannot determine input dimensions"); - return { tensor: null, canvas: inCanvas }; - } - let targetWidth = originalWidth; - let targetHeight = originalHeight; - if (targetWidth > maxSize) { - targetWidth = maxSize; - targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); - } - if (targetHeight > maxSize) { - targetHeight = maxSize; - targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); - } - if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) targetWidth = config3.filter.width; - else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); - if ((config3.filter.height || 0) > 0) targetHeight = config3.filter.height; - else if ((config3.filter.width || 0) > 0) targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); - if (!targetWidth || !targetHeight) throw new Error("input error: cannot determine dimension"); - if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) inCanvas = canvas(targetWidth, targetHeight); - const inCtx = inCanvas.getContext("2d"); - if (typeof ImageData !== "undefined" && input instanceof ImageData) { - inCtx.putImageData(input, 0, 0); - } else { - if (config3.filter.flip && typeof inCtx.translate !== "undefined") { - inCtx.translate(originalWidth, 0); - inCtx.scale(-1, 1); - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - inCtx.setTransform(1, 0, 0, 1, 0, 0); - } else { - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - } - } - if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) outCanvas = canvas(inCanvas.width, inCanvas.height); - if (config3.filter.enabled && env.webgl.supported) { - if (!fx) fx = env.browser ? new GLImageFilter() : null; - env.filter = !!fx; - if (!(fx == null ? void 0 : fx.add)) { - if (config3.debug) log("input process error: cannot initialize filters"); - env.webgl.supported = false; - config3.filter.enabled = false; - copy(inCanvas, outCanvas); - } else { - fx.reset(); - if (config3.filter.brightness !== 0) fx.add("brightness", config3.filter.brightness); - if (config3.filter.contrast !== 0) fx.add("contrast", config3.filter.contrast); - if (config3.filter.sharpness !== 0) fx.add("sharpen", config3.filter.sharpness); - if (config3.filter.blur !== 0) fx.add("blur", config3.filter.blur); - if (config3.filter.saturation !== 0) fx.add("saturation", config3.filter.saturation); - if (config3.filter.hue !== 0) fx.add("hue", config3.filter.hue); - if (config3.filter.negative) fx.add("negative"); - if (config3.filter.sepia) fx.add("sepia"); - if (config3.filter.vintage) fx.add("brownie"); - if (config3.filter.sepia) fx.add("sepia"); - if (config3.filter.kodachrome) fx.add("kodachrome"); - if (config3.filter.technicolor) fx.add("technicolor"); - if (config3.filter.polaroid) fx.add("polaroid"); - if (config3.filter.pixelate !== 0) fx.add("pixelate", config3.filter.pixelate); - if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) outCanvas = fx.apply(inCanvas); - else outCanvas = fx.draw(inCanvas); - } - } else { - copy(inCanvas, outCanvas); - if (fx) fx = null; - env.filter = !!fx; - } - if (!getTensor) return { tensor: null, canvas: outCanvas }; - if (!outCanvas) throw new Error("canvas error: cannot create output"); - let pixels; - let depth = 3; - if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && tf2.browser) { - pixels = tf2.browser ? tf2.browser.fromPixels(input) : null; - } else { - depth = input.data.length / input.height / input.width; - const arr = new Uint8Array(input.data.buffer); - pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32"); - } - } else { - if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (tf2.browser && env.browser) { - if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = tf2.browser.fromPixels(outCanvas); - } else { - tmpCanvas = copy(outCanvas); - pixels = tf2.browser.fromPixels(tmpCanvas); - } - } else { - const tempCanvas = copy(outCanvas); - const tempCtx = tempCanvas.getContext("2d"); - const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); - depth = tempData.data.length / targetWidth / targetHeight; - const arr = new Uint8Array(tempData.data.buffer); - pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]); - } - } - if (depth === 4) { - const rgb3 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); - tf2.dispose(pixels); - pixels = rgb3; - } - if (!pixels) throw new Error("input error: cannot create tensor"); - const casted = tf2.cast(pixels, "float32"); - const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0); - tf2.dispose([pixels, casted]); - if (config3.filter.autoBrightness) { - const max5 = tf2.max(tensor6); - const maxVal = await max5.data(); - config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; - tf2.dispose(max5); - } - return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; -} -async function skip(config3, input) { - let skipFrame = false; - if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; - if (!last.inputTensor) { - last.inputTensor = tf2.clone(input); - } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { - tf2.dispose(last.inputTensor); - last.inputTensor = tf2.clone(input); - } else { - const t2 = {}; - t2.diff = tf2.sub(input, last.inputTensor); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; - tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); - last.inputTensor = tf2.clone(input); - skipFrame = diffRelative <= (config3.cacheSensitivity || 0); - } - return skipFrame; -} -async function compare(config3, input1, input2) { - const t2 = {}; - if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { - if (!config3.debug) log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); - return 0; - } - if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { - if (!config3.debug) log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); - return 0; - } - t2.input1 = tf2.clone(input1); - t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2); - t2.diff = tf2.sub(t2.input1, t2.input2); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; - tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); - return diffRelative; -} - -// src/util/env.ts -var _canvas, _image, _imageData; -var Env = class { - constructor() { - /** Running in Browser */ - __publicField(this, "browser"); - /** Running in NodeJS */ - __publicField(this, "node"); - /** Running in WebWorker thread */ - __publicField(this, "worker"); - /** Detected platform */ - __publicField(this, "platform", ""); - /** Detected agent */ - __publicField(this, "agent", ""); - /** List of supported backends */ - __publicField(this, "backends", []); - /** Has any work been performed so far */ - __publicField(this, "initial"); - /** Are image filters supported? */ - __publicField(this, "filter"); - /** TFJS instance details */ - __publicField(this, "tfjs"); - /** Is offscreenCanvas supported? */ - __publicField(this, "offscreen"); - /** Are performance counter instant values or additive */ - __publicField(this, "perfadd", false); - /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */ - __publicField(this, "tensorflow", { - version: void 0, - gpu: void 0 - }); - /** WASM detected capabilities */ - __publicField(this, "wasm", { - supported: void 0, - backend: void 0, - simd: void 0, - multithread: void 0 - }); - /** WebGL detected capabilities */ - __publicField(this, "webgl", { - supported: void 0, - backend: void 0, - version: void 0, - renderer: void 0, - shader: void 0, - vendor: void 0 - }); - /** WebGPU detected capabilities */ - __publicField(this, "webgpu", { - supported: void 0, - backend: void 0, - adapter: void 0 - }); - /** CPU info */ - __publicField(this, "cpu", { - model: void 0, - flags: [] - }); - /** List of supported kernels for current backend */ - __publicField(this, "kernels", []); - /** MonkeyPatch for Canvas/Image/ImageData */ - __privateAdd(this, _canvas); - __privateAdd(this, _image); - __privateAdd(this, _imageData); - this.browser = typeof navigator !== "undefined" && typeof navigator.appVersion !== "undefined"; - this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; - this.tfjs = { version: tf3.version["tfjs-core"] }; - this.offscreen = typeof OffscreenCanvas !== "undefined"; - this.initial = true; - this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; - if (typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined") { - const agent = navigator.userAgent || ""; - const raw = agent.match(/\(([^()]+)\)/g); - if (raw == null ? void 0 : raw[0]) { - const platformMatch = raw[0].match(/\(([^()]+)\)/g); - this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; - this.agent = agent.replace(raw[0], ""); - if (this.platform[1]) this.agent = this.agent.replace(raw[1], ""); - this.agent = this.agent.replace(/ /g, " "); - } - } else if (typeof process !== "undefined") { - this.platform = `${process.platform} ${process.arch}`; - this.agent = `NodeJS ${process.version}`; - } - } - get Canvas() { - return __privateGet(this, _canvas); - } - set Canvas(val) { - __privateSet(this, _canvas, val); - globalThis.Canvas = val; - } - get Image() { - return __privateGet(this, _image); - } - // @ts-ignore monkey-patch; - set Image(val) { - __privateSet(this, _image, val); - globalThis.Image = val; - } - get ImageData() { - return __privateGet(this, _imageData); - } - // @ts-ignore monkey-patch; - set ImageData(val) { - __privateSet(this, _imageData, val); - globalThis.ImageData = val; - } - /** update backend information */ - async updateBackend() { - this.backends = Object.keys(tf3.engine().registryFactory); - try { - this.tensorflow = { - version: tf3.backend()["binding"] ? tf3.backend()["binding"].TF_Version : void 0, - gpu: tf3.backend()["binding"] ? tf3.backend()["binding"].isUsingGpuDevice() : void 0 - }; - } catch (e) { - } - this.wasm.supported = typeof WebAssembly !== "undefined"; - this.wasm.backend = this.backends.includes("wasm"); - if (this.wasm.supported && this.wasm.backend) { - this.wasm.simd = await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - this.wasm.multithread = await tf3.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - } - const c = canvas(100, 100); - const gl = c ? c.getContext("webgl2") : void 0; - this.webgl.supported = typeof gl !== "undefined"; - this.webgl.backend = this.backends.includes("webgl"); - if (this.webgl.supported && this.webgl.backend && gl) { - this.webgl.version = gl.getParameter(gl.VERSION); - this.webgl.vendor = gl.getParameter(gl.VENDOR); - this.webgl.renderer = gl.getParameter(gl.RENDERER); - this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); - } - this.webgpu.supported = this.browser && typeof navigator !== "undefined" && typeof navigator.gpu !== "undefined"; - this.webgpu.backend = this.backends.includes("webgpu"); - try { - if (this.webgpu.supported) { - const adapter = await navigator.gpu.requestAdapter(); - this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); - } - } catch (e) { - this.webgpu.supported = false; - } - try { - this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); - } catch (e) { - } - } - /** update cpu information */ - updateCPU() { - const cpu = { model: "", flags: [] }; - if (this.node && this.platform.startsWith("linux")) { - } - if (!this.cpu) Object.defineProperty(this, "cpu", { value: cpu }); - else this.cpu = cpu; - } -}; -_canvas = new WeakMap(); -_image = new WeakMap(); -_imageData = new WeakMap(); -var env = new Env(); - -// src/util/webcam.ts -var WebCam = class { - constructor() { - // eslint-disable-line @typescript-eslint/no-extraneous-class - /** current webcam configuration */ - __publicField(this, "config"); - /** instance of dom element associated with webcam stream */ - __publicField(this, "element"); - /** active webcam stream */ - __publicField(this, "stream"); - /** enumerated video devices */ - __publicField(this, "devices", []); - __publicField(this, "enumerate", async () => { - try { - const devices = await navigator.mediaDevices.enumerateDevices(); - this.devices = devices.filter((device) => device.kind === "videoinput"); - } catch (e) { - this.devices = []; - } - return this.devices; - }); - /** start method initializizes webcam stream and associates it with a dom video element */ - __publicField(this, "start", async (webcamConfig) => { - var _a, _b; - if (webcamConfig == null ? void 0 : webcamConfig.debug) this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; - if (webcamConfig == null ? void 0 : webcamConfig.crop) this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; - if (webcamConfig == null ? void 0 : webcamConfig.mode) this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; - if (webcamConfig == null ? void 0 : webcamConfig.width) this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; - if (webcamConfig == null ? void 0 : webcamConfig.height) this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; - if (webcamConfig == null ? void 0 : webcamConfig.id) this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; - if (webcamConfig == null ? void 0 : webcamConfig.element) { - if (typeof webcamConfig.element === "string") { - const el = document.getElementById(webcamConfig.element); - if (el && el instanceof HTMLVideoElement) { - this.element = el; - } else { - if (this.config.debug) log("webcam", "cannot get dom element", webcamConfig.element); - return `webcam error: cannot get dom element: ${webcamConfig.element}`; - } - } else if (webcamConfig.element instanceof HTMLVideoElement) { - this.element = webcamConfig.element; - } else { - if (this.config.debug) log("webcam", "unknown dom element", webcamConfig.element); - return `webcam error: unknown dom element: ${webcamConfig.element}`; - } - } else { - this.element = document.createElement("video"); - } - const requestedConstraints = { - audio: false, - video: { - facingMode: this.config.mode === "front" ? "user" : "environment", - // @ts-ignore // resizeMode is still not defined in tslib - resizeMode: this.config.crop ? "crop-and-scale" : "none" - } - }; - if (((_a = this.config) == null ? void 0 : _a.width) > 0) requestedConstraints.video.width = { ideal: this.config.width }; - if (((_b = this.config) == null ? void 0 : _b.height) > 0) requestedConstraints.video.height = { ideal: this.config.height }; - if (this.config.id) requestedConstraints.video.deviceId = this.config.id; - this.element.addEventListener("play", () => { - if (this.config.debug) log("webcam", "play"); - }); - this.element.addEventListener("pause", () => { - if (this.config.debug) log("webcam", "pause"); - }); - this.element.addEventListener("click", async () => { - if (!this.element || !this.stream) return; - if (this.element.paused) await this.element.play(); - else this.element.pause(); - }); - if (!(navigator == null ? void 0 : navigator.mediaDevices)) { - if (this.config.debug) log("webcam error", "no devices"); - return "webcam error: no devices"; - } - try { - this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); - } catch (err) { - log("webcam", err); - return `webcam error: ${err}`; - } - if (!this.stream) { - if (this.config.debug) log("webcam error", "no stream"); - return "webcam error no stream"; - } - this.element.srcObject = this.stream; - const ready4 = new Promise((resolve) => { - if (!this.element) resolve(false); - else this.element.onloadeddata = () => resolve(true); - }); - await ready4; - await this.element.play(); - if (this.config.debug) { - log("webcam", { - width: this.width, - height: this.height, - label: this.label, - stream: this.stream, - track: this.track, - settings: this.settings, - constraints: this.constraints, - capabilities: this.capabilities - }); - } - return `webcam: ${this.label}`; - }); - /** pause webcam video method */ - __publicField(this, "pause", () => { - if (this.element) this.element.pause(); - }); - /** play webcam video method */ - __publicField(this, "play", async () => { - if (this.element) await this.element.play(); - }); - /** stop method stops active webcam stream track and disconnects webcam */ - __publicField(this, "stop", () => { - if (this.config.debug) log("webcam", "stop"); - if (this.track) this.track.stop(); - }); - this.config = { - element: void 0, - debug: true, - mode: "front", - crop: false, - width: 0, - height: 0 - }; - } - /** get active webcam stream track */ - get track() { - if (!this.stream) return void 0; - return this.stream.getVideoTracks()[0]; - } - /** get webcam capabilities */ - get capabilities() { - if (!this.track) return void 0; - return this.track.getCapabilities ? this.track.getCapabilities() : void 0; - } - /** get webcam constraints */ - get constraints() { - if (!this.track) return void 0; - return this.track.getConstraints ? this.track.getConstraints() : void 0; - } - /** get webcam settings */ - get settings() { - if (!this.stream) return void 0; - const track = this.stream.getVideoTracks()[0]; - return track.getSettings ? track.getSettings() : void 0; - } - /** get webcam label */ - get label() { - if (!this.track) return ""; - return this.track.label; - } - /** is webcam paused */ - get paused() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.paused) || false; - } - /** webcam current width */ - get width() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; - } - /** webcam current height */ - get height() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; - } -}; - -// src/tfjs/load.ts -var tf4 = __toESM(require_tfjs_esm()); - -// models/models.json -var models_exports = {}; -__export(models_exports, { - "affectnet-mobilenet": () => affectnet_mobilenet, - age: () => age, - "anti-spoofing": () => anti_spoofing, - antispoof: () => antispoof, - blazeface: () => blazeface, - "blazeface-back": () => blazeface_back, - "blazeface-front": () => blazeface_front, - "blazepose-detector": () => blazepose_detector, - "blazepose-full": () => blazepose_full, - "blazepose-heavy": () => blazepose_heavy, - "blazepose-lite": () => blazepose_lite, - centernet: () => centernet, - default: () => models_default, - efficientpose: () => efficientpose, - "efficientpose-i-lite": () => efficientpose_i_lite, - "efficientpose-ii-lite": () => efficientpose_ii_lite, - "efficientpose-iv": () => efficientpose_iv, - emotion: () => emotion, - faceboxes: () => faceboxes, - facemesh: () => facemesh, - "facemesh-attention": () => facemesh_attention, - "facemesh-attention-pinto": () => facemesh_attention_pinto, - "facemesh-detection-full": () => facemesh_detection_full, - "facemesh-detection-short": () => facemesh_detection_short, - faceres: () => faceres, - "faceres-deep": () => faceres_deep, - gear: () => gear, - "gear-e1": () => gear_e1, - "gear-e2": () => gear_e2, - gender: () => gender, - "gender-ssrnet-imdb": () => gender_ssrnet_imdb, - handdetect: () => handdetect, - "handlandmark-full": () => handlandmark_full, - "handlandmark-lite": () => handlandmark_lite, - "handlandmark-sparse": () => handlandmark_sparse, - handskeleton: () => handskeleton, - handtrack: () => handtrack, - "insightface-efficientnet-b0": () => insightface_efficientnet_b0, - "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, - "insightface-mobilenet-emore": () => insightface_mobilenet_emore, - "insightface-mobilenet-swish": () => insightface_mobilenet_swish, - iris: () => iris, - liveness: () => liveness, - meet: () => meet, - mobileface: () => mobileface, - mobilefacenet: () => mobilefacenet, - models: () => models, - "movenet-lightning": () => movenet_lightning, - "movenet-multipose": () => movenet_multipose, - "movenet-thunder": () => movenet_thunder, - nanodet: () => nanodet, - "nanodet-e": () => nanodet_e, - "nanodet-g": () => nanodet_g, - "nanodet-m": () => nanodet_m, - "nanodet-t": () => nanodet_t, - posenet: () => posenet, - rvm: () => rvm, - selfie: () => selfie -}); -var antispoof = 853098; -var blazeface = 538928; -var centernet = 4030290; -var emotion = 820516; -var facemesh = 1477958; -var faceres = 6978814; -var handlandmark_lite = 2023432; -var handtrack = 2964837; -var iris = 2599092; -var liveness = 592976; -var models = 0; -var movenet_lightning = 4650216; -var affectnet_mobilenet = 6920630; -var age = 161240; -var blazeface_back = 538928; -var blazeface_front = 402048; -var blazepose_detector = 5928856; -var blazepose_full = 6339202; -var blazepose_heavy = 27502466; -var blazepose_lite = 2726402; -var efficientpose = 5651240; -var faceboxes = 2013002; -var facemesh_attention_pinto = 2387598; -var facemesh_attention = 2382414; -var facemesh_detection_full = 1026192; -var facemesh_detection_short = 201268; -var faceres_deep = 13957620; -var gear_e1 = 112438; -var gear_e2 = 112438; -var gear = 1498916; -var gender_ssrnet_imdb = 161236; -var gender = 201808; -var handdetect = 3515612; -var handlandmark_full = 5431368; -var handlandmark_sparse = 5286322; -var handskeleton = 5502280; -var meet = 372228; -var mobileface = 2183192; -var mobilefacenet = 5171976; -var movenet_multipose = 9448838; -var movenet_thunder = 12477112; -var nanodet = 7574558; -var posenet = 5032780; -var rvm = 3739355; -var selfie = 212886; -var anti_spoofing = 853098; -var efficientpose_i_lite = 2269064; -var efficientpose_ii_lite = 5651240; -var efficientpose_iv = 25643252; -var insightface_efficientnet_b0 = 13013224; -var insightface_ghostnet_strides1 = 8093408; -var insightface_ghostnet_strides2 = 8049584; -var insightface_mobilenet_emore = 6938536; -var insightface_mobilenet_swish = 12168584; -var nanodet_e = 12319156; -var nanodet_g = 7574558; -var nanodet_m = 1887474; -var nanodet_t = 5294216; -var models_default = { - antispoof, - blazeface, - centernet, - emotion, - facemesh, - faceres, - "handlandmark-lite": handlandmark_lite, - handtrack, - iris, - liveness, - models, - "movenet-lightning": movenet_lightning, - "affectnet-mobilenet": affectnet_mobilenet, - age, - "blazeface-back": blazeface_back, - "blazeface-front": blazeface_front, - "blazepose-detector": blazepose_detector, - "blazepose-full": blazepose_full, - "blazepose-heavy": blazepose_heavy, - "blazepose-lite": blazepose_lite, - efficientpose, - faceboxes, - "facemesh-attention-pinto": facemesh_attention_pinto, - "facemesh-attention": facemesh_attention, - "facemesh-detection-full": facemesh_detection_full, - "facemesh-detection-short": facemesh_detection_short, - "faceres-deep": faceres_deep, - "gear-e1": gear_e1, - "gear-e2": gear_e2, - gear, - "gender-ssrnet-imdb": gender_ssrnet_imdb, - gender, - handdetect, - "handlandmark-full": handlandmark_full, - "handlandmark-sparse": handlandmark_sparse, - handskeleton, - meet, - mobileface, - mobilefacenet, - "movenet-multipose": movenet_multipose, - "movenet-thunder": movenet_thunder, - nanodet, - posenet, - rvm, - selfie, - "anti-spoofing": anti_spoofing, - "efficientpose-i-lite": efficientpose_i_lite, - "efficientpose-ii-lite": efficientpose_ii_lite, - "efficientpose-iv": efficientpose_iv, - "insightface-efficientnet-b0": insightface_efficientnet_b0, - "insightface-ghostnet-strides1": insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": insightface_ghostnet_strides2, - "insightface-mobilenet-emore": insightface_mobilenet_emore, - "insightface-mobilenet-swish": insightface_mobilenet_swish, - "nanodet-e": nanodet_e, - "nanodet-g": nanodet_g, - "nanodet-m": nanodet_m, - "nanodet-t": nanodet_t -}; - -// src/tfjs/load.ts -var options = { - cacheModels: true, - cacheSupported: true, - verbose: true, - debug: false, - modelBasePath: "" -}; -var modelStats = {}; -async function httpHandler(url, init4) { - if (options.debug) log("load model fetch:", url, init4); - return fetch(url, init4); -} -function setModelLoadOptions(config3) { - options.cacheModels = config3.cacheModels; - options.verbose = config3.debug; - options.modelBasePath = config3.modelBasePath; -} -async function loadModel(modelPath) { - var _a, _b, _c, _d, _e, _f; - let modelUrl = join(options.modelBasePath, modelPath || ""); - if (!modelUrl.toLowerCase().endsWith(".json")) modelUrl += ".json"; - const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); - const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); - const cachedModelName = "indexeddb://" + shortModelName; - modelStats[shortModelName] = { - name: shortModelName, - loaded: false, - sizeFromManifest: 0, - sizeLoadedWeights: 0, - sizeDesired: models_exports[shortModelName], - inCache: false, - url: "" - }; - options.cacheSupported = typeof indexedDB !== "undefined"; - let cachedModels = {}; - try { - cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {}; - } catch (e) { - options.cacheSupported = false; - } - modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); - modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; - const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; - let model23 = new tf4.GraphModel(modelStats[shortModelName].url, tfLoadOptions); - modelStats[shortModelName].loaded = false; - try { - model23.findIOHandler(); - if (options.debug) log("model load handler:", model23["handler"]); - } catch (err) { - log("error finding model i/o handler:", modelUrl, err); - } - try { - const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; - modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; - if (artifacts) model23.loadSync(artifacts); - else model23 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); - modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || ((_f = (_e = model23.artifacts) == null ? void 0 : _e.weightData) == null ? void 0 : _f[0].byteLength) || 0; - if (options.verbose) log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); - modelStats[shortModelName].loaded = true; - } catch (err) { - log("error loading model:", modelUrl, err); - } - if (modelStats[shortModelName].loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { - try { - const saveResult = await model23.save(cachedModelName); - if (options.debug) log("model saved:", cachedModelName, saveResult); - } catch (err) { - log("error saving model:", modelUrl, err); - } - } - return model23; -} - -// package.json -var version2 = "3.3.2"; - -// src/tfjs/backend.ts -var tf7 = __toESM(require_tfjs_esm()); - -// src/tfjs/humangl.ts -var tf5 = __toESM(require_tfjs_esm()); -var config2 = { - name: "humangl", - priority: 999, - canvas: null, - gl: null, - extensions: [], - webGLattr: { - // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2 - alpha: false, - antialias: false, - premultipliedAlpha: false, - preserveDrawingBuffer: false, - depth: false, - stencil: false, - failIfMajorPerformanceCaveat: false, - // default=true - desynchronized: true - // default=undefined - } -}; -function extensions() { - const gl = config2.gl; - if (!gl) return; - config2.extensions = gl.getSupportedExtensions(); -} -function register(instance) { - var _a; - if (instance.config.backend !== "humangl") return; - if (config2.name in tf5.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { - log("humangl error: backend invalid context"); - instance.models.reset(); - } - if (!tf5.findBackend(config2.name)) { - try { - config2.canvas = canvas(100, 100); - } catch (err) { - log("humangl error: cannot create canvas:", err); - return; - } - try { - config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); - if (!config2.gl) { - log("humangl error: cannot get webgl context"); - return; - } - const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); - if (!glv2) { - log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); - instance.config.backend = "webgl"; - return; - } - if (config2.canvas) { - config2.canvas.addEventListener("webglcontextlost", (e) => { - log("humangl error:", e.type); - log("possible browser memory leak using webgl or conflict with multiple backend registrations"); - instance.emit("error"); - throw new Error("backend error: webgl context lost"); - }); - config2.canvas.addEventListener("webglcontextrestored", (e) => { - log("humangl error: context restored:", e); - }); - config2.canvas.addEventListener("webglcontextcreationerror", (e) => { - log("humangl error: context create:", e); - }); - } - } catch (err) { - log("humangl error: cannot get webgl context:", err); - return; - } - try { - tf5.setWebGLContext(2, config2.gl); - } catch (err) { - log("humangl error: cannot set webgl context:", err); - return; - } - try { - const ctx = new tf5.GPGPUContext(config2.gl); - tf5.registerBackend(config2.name, () => new tf5.MathBackendWebGL(ctx), config2.priority); - } catch (err) { - log("humangl error: cannot register webgl backend:", err); - return; - } - try { - const kernels = tf5.getKernelsForBackend("webgl"); - kernels.forEach((kernelConfig) => { - const newKernelConfig = { ...kernelConfig, backendName: config2.name }; - tf5.registerKernel(newKernelConfig); - }); - } catch (err) { - log("humangl error: cannot update webgl backend registration:", err); - return; - } - try { - if (tf5.env().flagRegistry.WEBGL_VERSION) tf5.env().set("WEBGL_VERSION", 2); - } catch (err) { - log("humangl error: cannot set WebGL backend flags:", err); - return; - } - extensions(); - const backend4 = tf5.backend(); - const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; - if (current) { - if (instance.config.debug) log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); - } else { - log("humangl error: no current gl context:", current, config2.gl); - } - } -} - -// src/tfjs/constants.ts -var tf6 = __toESM(require_tfjs_esm()); -var constants = { - tf255: 255, - tf1: 1, - tf2: 2, - tf05: 0.5, - tf127: 127.5, - rgb: [0.2989, 0.587, 0.114] -}; -function init() { - constants.tf255 = tf6.scalar(255, "float32"); - constants.tf1 = tf6.scalar(1, "float32"); - constants.tf2 = tf6.scalar(2, "float32"); - constants.tf05 = tf6.scalar(0.5, "float32"); - constants.tf127 = tf6.scalar(127.5, "float32"); - constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32"); -} - -// src/tfjs/backend.ts -async function getBestBackend() { - var _a; - await env.updateBackend(); - if ((_a = env.tensorflow) == null ? void 0 : _a.version) return "tensorflow"; - if (env.webgpu.supported && env.webgpu.backend) return "webgpu"; - if (env.webgl.supported && env.webgl.backend) return "webgl"; - if (env.wasm.supported && env.wasm.backend) return "wasm"; - return "cpu"; -} -function registerCustomOps(config3) { - const newKernels = []; - if (!env.kernels.includes("mod")) { - const kernelMod = { - kernelName: "Mod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.sub(op.inputs.a, tf7.mul(tf7.div(op.inputs.a, op.inputs.b), op.inputs.b))) - }; - tf7.registerKernel(kernelMod); - env.kernels.push("mod"); - newKernels.push("mod"); - } - if (!env.kernels.includes("floormod")) { - const kernelFloorMod = { - kernelName: "FloorMod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.add(tf7.mul(tf7.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf7.mod(op.inputs.a, op.inputs.b))) - }; - tf7.registerKernel(kernelFloorMod); - env.kernels.push("floormod"); - newKernels.push("floormod"); - } - if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { - const kernelRotateWithOffset = { - kernelName: "RotateWithOffset", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => { - const backend4 = tf7.getBackend(); - tf7.setBackend("cpu"); - const t2 = tf7.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); - tf7.setBackend(backend4); - return t2; - }) - }; - tf7.registerKernel(kernelRotateWithOffset); - env.kernels.push("rotatewithoffset"); - newKernels.push("rotatewithoffset"); - } - if (newKernels.length > 0 && config3.debug) log("registered kernels:", newKernels); -} -var defaultFlags = {}; -async function check(instance, force = false) { - var _a, _b; - instance.state = "backend"; - if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) instance.config.backend = await getBestBackend(); - if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tf7.getBackend() !== instance.config.backend) { - const timeStamp = now(); - if (instance.config.backend && instance.config.backend.length > 0) { - if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { - if (instance.config.debug) log("running inside web worker"); - } - if (typeof navigator !== "undefined" && ((_b = navigator == null ? void 0 : navigator.userAgent) == null ? void 0 : _b.toLowerCase().includes("electron"))) { - if (instance.config.debug) log("running inside electron"); - } - let available = Object.keys(tf7.engine().registryFactory); - if (instance.config.backend === "humangl" && !available.includes("humangl")) { - register(instance); - available = Object.keys(tf7.engine().registryFactory); - } - if (instance.config.debug) log("available backends:", available); - if (env.browser && !env.node && instance.config.backend === "tensorflow" && available.includes("webgl")) { - if (instance.config.debug) log("override: backend set to tensorflow while running in browser"); - instance.config.backend = "webgl"; - } - if (env.node && !env.browser && (instance.config.backend === "webgl" || instance.config.backend === "humangl") && available.includes("tensorflow")) { - if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`); - instance.config.backend = "tensorflow"; - } - if (env.browser && instance.config.backend === "webgpu") { - if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { - log("override: backend set to webgpu but browser does not support webgpu"); - instance.config.backend = "webgl"; - } else { - const adapter = await navigator.gpu.requestAdapter(); - if (instance.config.debug) log("enumerated webgpu adapter:", adapter); - if (!adapter) { - log("override: backend set to webgpu but browser reports no available gpu"); - instance.config.backend = "webgl"; - } else { - const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; - log("webgpu adapter info:", adapterInfo); - } - } - } - if (!available.includes(instance.config.backend)) { - log(`error: backend ${instance.config.backend} not found in registry`); - instance.config.backend = env.node ? "tensorflow" : "webgl"; - if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`); - } - if (instance.config.debug) log("setting backend:", [instance.config.backend]); - if (instance.config.backend === "wasm") { - if (tf7.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf7.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); - if (instance.config.debug) log("wasm path:", instance.config.wasmPath); - if (typeof tf7.setWasmPaths !== "undefined") tf7.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); - else throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); - let mt = false; - let simd = false; - try { - mt = await tf7.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - simd = await tf7.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - if (instance.config.debug) log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); - if (instance.config.debug && !simd) log("warning: wasm simd support is not enabled"); - } catch (e) { - log("wasm detection failed"); - } - } - try { - await tf7.setBackend(instance.config.backend); - await tf7.ready(); - } catch (err) { - log("error: cannot set backend:", instance.config.backend, err); - return false; - } - if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf7.env().flags)); - } - if (tf7.getBackend() === "humangl" || tf7.getBackend() === "webgl") { - if (tf7.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf7.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); - if (tf7.env().flagRegistry.WEBGL_EXP_CONV) tf7.env().set("WEBGL_EXP_CONV", true); - if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { - log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); - tf7.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); - } - } - if (tf7.getBackend() === "webgpu") { - } - if (instance.config.debug) { - const newFlags = tf7.env().flags; - const updatedFlags = {}; - for (const key of Object.keys(newFlags)) { - if (defaultFlags[key] === newFlags[key]) continue; - updatedFlags[key] = newFlags[key]; - } - if (instance.config.debug && Object.keys(updatedFlags).length > 0) log("backend:", tf7.getBackend(), "flags:", updatedFlags); - } - if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { - if (instance.config.debug) log("flags:", instance.config["flags"]); - for (const [key, val] of Object.entries(instance.config.flags)) { - tf7.env().set(key, val); - } - } - tf7.enableProdMode(); - init(); - instance.performance.initBackend = Math.trunc(now() - timeStamp); - instance.config.backend = tf7.getBackend(); - await env.updateBackend(); - registerCustomOps(instance.config); - } - return true; -} -function fakeOps(kernelNames, config3) { - for (const kernelName of kernelNames) { - const kernelConfig = { - kernelName, - backendName: config3.backend, - kernelFunc: (param) => { - var _a; - if (config3.debug) log("kernelFunc", kernelName, config3.backend, param); - return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; - } - // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, - // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, - }; - tf7.registerKernel(kernelConfig); - } - env.kernels = tf7.getKernelsForBackend(tf7.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); -} - -// src/draw/draw.ts -var draw_exports = {}; -__export(draw_exports, { - all: () => all, - body: () => body, - canvas: () => canvas2, - face: () => face, - gesture: () => gesture, - hand: () => hand, - init: () => init2, - object: () => object, - options: () => options2, - person: () => person -}); - -// src/draw/primitives.ts -var getCanvasContext = (input) => { - if (!input) log("draw error: invalid canvas"); - else if (!input.getContext) log("draw error: canvas context not defined"); - else { - const ctx = input.getContext("2d", { willReadFrequently: true }); - if (!ctx) log("draw error: cannot get canvas context"); - else return ctx; - } - return null; -}; -var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); -var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); -var colorDepth = (z, opt) => { - if (!opt.useDepth || typeof z === "undefined") return opt.color; - const rgb3 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); - return `rgba(${rgb3[0]}, ${rgb3[1]}, ${rgb3[2]}, ${opt.alpha})`; -}; -function labels(ctx, str, startX, startY, localOptions2) { - const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); - const x = Math.max(0, startX); - for (let i = line.length - 1; i >= 0; i--) { - const y = i * localOptions2.lineHeight + startY; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(line[i], x + 5, y + 16); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(line[i], x + 4, y + 15); - } -} -function point(ctx, x, y, z, localOptions2) { - ctx.fillStyle = colorDepth(z, localOptions2); - ctx.beginPath(); - ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); - ctx.fill(); -} -function rect(ctx, x, y, width, height, localOptions2) { - ctx.beginPath(); - ctx.lineWidth = localOptions2.lineWidth; - if (localOptions2.useCurves) { - const cx = (x + x + width) / 2; - const cy = (y + y + height) / 2; - ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); - } else { - ctx.moveTo(x + localOptions2.roundRect, y); - ctx.lineTo(x + width - localOptions2.roundRect, y); - ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); - ctx.lineTo(x + width, y + height - localOptions2.roundRect); - ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); - ctx.lineTo(x + localOptions2.roundRect, y + height); - ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); - ctx.lineTo(x, y + localOptions2.roundRect); - ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); - ctx.closePath(); - } - ctx.stroke(); -} -function lines(ctx, points, localOptions2) { - if (points.length < 2) return; - ctx.beginPath(); - ctx.moveTo(points[0][0], points[0][1]); - for (const pt of points) { - ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); - ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); - } - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function curves(ctx, points, localOptions2) { - if (points.length < 2) return; - ctx.lineWidth = localOptions2.lineWidth; - if (!localOptions2.useCurves || points.length <= 2) { - lines(ctx, points, localOptions2); - return; - } - ctx.moveTo(points[0][0], points[0][1]); - for (let i = 0; i < points.length - 2; i++) { - const xc = (points[i][0] + points[i + 1][0]) / 2; - const yc = (points[i][1] + points[i + 1][1]) / 2; - ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); - } - ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function arrow(ctx, from, to, radius = 5) { - let angle; - let x; - let y; - ctx.beginPath(); - ctx.moveTo(from[0], from[1]); - ctx.lineTo(to[0], to[1]); - angle = Math.atan2(to[1] - from[1], to[0] - from[0]); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.moveTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - ctx.closePath(); - ctx.stroke(); - ctx.fill(); -} - -// src/draw/options.ts -var options2 = { - color: "rgba(173, 216, 230, 0.6)", - // 'lightblue' with light alpha channel - labelColor: "rgba(173, 216, 230, 1)", - // 'lightblue' with dark alpha channel - shadowColor: "black", - alpha: 0.5, - font: 'small-caps 16px "Segoe UI"', - lineHeight: 18, - lineWidth: 4, - pointSize: 2, - roundRect: 8, - drawPoints: false, - drawLabels: true, - drawBoxes: true, - drawAttention: true, - drawGestures: true, - drawPolygons: true, - drawGaze: true, - fillPolygons: false, - useDepth: true, - useCurves: false, - faceLabels: "", - bodyLabels: "", - bodyPartLabels: "", - objectLabels: "", - handLabels: "", - fingerLabels: "", - gestureLabels: "" -}; - -// src/face/facemeshcoords.ts -var meshAnnotations = { - silhouette: [ - 10, - 338, - 297, - 332, - 284, - 251, - 389, - 356, - 454, - 323, - 361, - 288, - 397, - 365, - 379, - 378, - 400, - 377, - 152, - 148, - 176, - 149, - 150, - 136, - 172, - 58, - 132, - 93, - 234, - 127, - 162, - 21, - 54, - 103, - 67, - 109 - ], - // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11 - // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10 - // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11 - // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11 - lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], - lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], - lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], - lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], - lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], - lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], - lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], - lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], - rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], - // 7 - rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], - // 9 - rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], - // 7 - rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], - // 9 - rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], - // 7 - rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], - // 9 - rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], - // 9 - rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], - // 8 - rightEyebrowLower: [35, 124, 46, 53, 52, 65], - // 6 - rightEyeIris: [473, 474, 475, 476, 477], - // 5 - leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], - leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], - leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], - leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], - leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], - leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], - leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], - leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], - leftEyebrowLower: [265, 353, 276, 283, 282, 295], - leftEyeIris: [468, 469, 470, 471, 472], - midwayBetweenEyes: [168], - noseTip: [1], - noseBottom: [2], - noseRightCorner: [98], - noseLeftCorner: [327], - rightCheek: [205], - leftCheek: [425] -}; -var meshLandmarks = { - count: 468, - mouth: 13, - symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] -}; -var blazeFaceLandmarks = { - leftEye: 0, - rightEye: 1, - nose: 2, - mouth: 3, - leftEar: 4, - rightEar: 5, - symmetryLine: [3, 2] -}; -var irisIndices = [ - // A mapping from facemesh model keypoints to iris model keypoints. - { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, - // 7 x 3d - { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, - // 7 x 3d - { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, - // 7 x 3d - { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, - // 7 x 3d - { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, - // 9 x 3d - { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, - // 9 x 3d - { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, - // 9 x 3d - { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, - 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const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var pairsLips = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var pairsFaceContour = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -var contourKeypoints = { - lips: connectionsToIndices(pairsLips), - leftEye: connectionsToIndices(pairsLeftEye), - leftEyebrow: connectionsToIndices(pairsLeftEyebrow), - leftIris: connectionsToIndices(pairsLeftIris), - rightEye: connectionsToIndices(pairsRightEye), - rightEyebrow: connectionsToIndices(pairsRightEyebrow), - rightIris: connectionsToIndices(pairsRightIris), - faceOval: connectionsToIndices(pairsFaceContour) -}; - -// src/face/constants.ts -var LIPS_CONNECTIONS = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var FACE_OVAL_CONNECTIONS = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -function connectionsToIndices2(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { - lips: connectionsToIndices2(LIPS_CONNECTIONS), - leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), - leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), - leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), - rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), - rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), - rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), - faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) -}; -var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); -var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); -var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ - 61, - 146, - 91, - 181, - 84, - 17, - 314, - 405, - 321, - 375, - 291, - // Lower outer. - 185, - 40, - 39, - 37, - 0, - 267, - 269, - 270, - 409, - // Upper outer(excluding corners). - 78, - 95, - 88, - 178, - 87, - 14, - 317, - 402, - 318, - 324, - 308, - // Lower inner. - 191, - 80, - 81, - 82, - 13, - 312, - 311, - 310, - 415, - // Upper inner(excluding corners). - 76, - 77, - 90, - 180, - 85, - 16, - 315, - 404, - 320, - 307, - 306, - // Lower semi - outer. - 184, - 74, - 73, - 72, - 11, - 302, - 303, - 304, - 408, - // Upper semi - outer(excluding corners). - 62, - 96, - 89, - 179, - 86, - 15, - 316, - 403, - 319, - 325, - 292, - // Lower semi - inner. - 183, - 42, - 41, - 38, - 12, - 268, - 271, - 272, - 407 - // Upper semi - inner(excluding corners). -]; -var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ - 33, - 7, - 163, - 144, - 145, - 153, - 154, - 155, - 133, - // Lower contour. - 246, - 161, - 160, - 159, - 158, - 157, - 173, - // upper contour (excluding corners). - 130, - 25, - 110, - 24, - 23, - 22, - 26, - 112, - 243, - // Halo x2 lower contour. - 247, - 30, - 29, - 27, - 28, - 56, - 190, - // Halo x2 upper contour (excluding corners). - 226, - 31, - 228, - 229, - 230, - 231, - 232, - 233, - 244, - // Halo x3 lower contour. - 113, - 225, - 224, - 223, - 222, - 221, - 189, - // Halo x3 upper contour (excluding corners). - 35, - 124, - 46, - 53, - 52, - 65, - // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour. - 143, - 111, - 117, - 118, - 119, - 120, - 121, - 128, - 245, - // Halo x5 lower contour. - 156, - 70, - 63, - 105, - 66, - 107, - 55, - 193 - // Halo x5 upper contour (excluding corners) or eyebrow outer contour. -]; -var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ - 263, - 249, - 390, - 373, - 374, - 380, - 381, - 382, - 362, - // Lower contour. - 466, - 388, - 387, - 386, - 385, - 384, - 398, - // Upper contour (excluding corners). - 359, - 255, - 339, - 254, - 253, - 252, - 256, - 341, - 463, - // Halo x2 lower contour. - 467, - 260, - 259, - 257, - 258, - 286, - 414, - // Halo x2 upper contour (excluding corners). - 446, - 261, - 448, - 449, - 450, - 451, - 452, - 453, - 464, - // Halo x3 lower contour. - 342, - 445, - 444, - 443, - 442, - 441, - 413, - // Halo x3 upper contour (excluding corners). - 265, - 353, - 276, - 283, - 282, - 295, - // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour. - 372, - 340, - 346, - 347, - 348, - 349, - 350, - 357, - 465, - // Halo x5 lower contour. - 383, - 300, - 293, - 334, - 296, - 336, - 285, - 417 - // Halo x5 upper contour (excluding corners) or eyebrow outer contour. -]; - -// src/draw/face.ts -var localOptions; -function drawLabels(f, ctx) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) return; - let l = localOptions.faceLabels.slice(); - l = replace(l, "[id]", f.id.toFixed(0)); - if (f.score) l = replace(l, "[score]", 100 * f.score); - if (f.gender) l = replace(l, "[gender]", f.gender); - if (f.genderScore) l = replace(l, "[genderScore]", 100 * f.genderScore); - if (f.age) l = replace(l, "[age]", f.age); - if (f.distance) l = replace(l, "[distance]", 100 * f.distance); - if (f.real) l = replace(l, "[real]", 100 * f.real); - if (f.live) l = replace(l, "[live]", 100 * f.live); - if (f.emotion && f.emotion.length > 0) { - const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); - if (emotion2.length > 3) emotion2.length = 3; - l = replace(l, "[emotions]", emotion2.join(" ")); - 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p=Math.cos(g),u=Math.sin(g),k=.213,P=.715,N=.072;T.colorMatrix([k+p*(1-k)+u*-k,P+p*-P+u*-P,N+p*-N+u*(1-N),0,0,k+p*-k+u*.143,P+p*(1-P)+u*.14,N+p*-N+u*-.283,0,0,k+p*-k+u*-(1-k),P+p*-P+u*P,N+p*(1-N)+u*N,0,0,0,0,0,1,0])},desaturateLuminance:()=>{T.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{T.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{T.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{T.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{T.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{T.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{T.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{T.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:g=>{let p=new Float32Array(g),u=1/l.width,k=1/l.height,P=h(V1);P&&(i.uniform1fv(P.uniform.m,p),i.uniform2f(P.uniform.px,u,k),f())},detectEdges:()=>{T.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{T.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{T.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:g=>{let p=g||1;T.convolution.call(this,[0,-1*p,0,-1*p,1+4*p,-1*p,0,-1*p,0])},emboss:g=>{let p=g||1;T.convolution.call(this,[-2*p,-1*p,0,-1*p,1,1*p,0,1*p,2*p])},blur:g=>{let p=g/7/l.width,u=g/7/l.height,k=h(G1);k&&(i.uniform2f(k.uniform.px,0,u),f(d.INTERMEDIATE),i.uniform2f(k.uniform.px,p,0),f())},pixelate:g=>{let p=g/l.width,u=g/l.height,k=h(H1);k&&(i.uniform2f(k.uniform.size,p,u),f())}};this.add=function(g){let p=Array.prototype.slice.call(arguments,1),u=T[g];s.push({func:u,args:p})},this.reset=function(){s=[]},this.get=function(){return s},this.apply=function(g){y(g.width,g.height),e=0,t||(t=i.createTexture()),i.bindTexture(i.TEXTURE_2D,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,g);for(let p=0;pd.data())),A=Math.max(s[0][0],s[1][0],s[2][0]),l=(A>1?255:1)/A,c;if(l>1){let d=[U.sub(n[0],o[0]),U.sub(n[1],o[1]),U.sub(n[2],o[2])],i=[U.sub(r[0],o[0]),U.sub(r[1],o[1]),U.sub(r[2],o[2])],y=[U.mul(d[0],l),U.mul(d[1],l),U.mul(d[2],l)],x=U.stack([y[0],y[1],y[2]],2);c=U.reshape(x,[1,t.shape[0]||0,t.shape[1]||0,3]),U.dispose([...d,...i,...y,x])}else c=U.expandDims(t,0);return U.dispose([...n,...o,...r,n,t,e]),c}var U2=3840,M0=null,P0=null,y2=null,t0,ne={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function e5(){ne.inputSum=0,ne.cacheDiff=1,ne.sumMethod=0,ne.inputTensor=void 0}function Ae(e,t){let n;if(R.browser)if(R.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else if(typeof document!="undefined")n=document.createElement("canvas"),n.width=e,n.height=t;else if(typeof navigator!="undefined"&&navigator.product==="ReactNative")if(typeof R.Canvas!="undefined")n=new R.Canvas(e,t);else if(typeof globalThis.Canvas!="undefined")n=new globalThis.Canvas(e,t);else throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed");else throw new Error("canvas error: attempted to run in browser but DOM is not defined");else typeof R.Canvas!="undefined"?n=new R.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function Y2(e,t){let n=t||Ae(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function K2(e,t,n=!0){var y,x,m;if(!e)return t.debug&&b("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof I.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type not recognized");if(e instanceof I.Tensor){let f=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new 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s=o,A=r;if(s>U2&&(s=U2,A=Math.trunc(s*r/o)),A>U2&&(A=U2,s=Math.trunc(A*o/r)),(((y=t.filter)==null?void 0:y.width)||0)>0?s=t.filter.width:(((x=t.filter)==null?void 0:x.height)||0)>0&&(s=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?A=t.filter.height:(t.filter.width||0)>0&&(A=r*((t.filter.width||0)/o)),!s||!A)throw new Error("input error: cannot determine dimension");(!M0||M0.width!==s||M0.height!==A)&&(M0=Ae(s,A));let a=M0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),(!P0||M0.width!==P0.width||M0.height!==P0.height)&&(P0=Ae(M0.width,M0.height)),t.filter.enabled&&R.webgl.supported?(t0||(t0=R.browser?new Z1:null),R.filter=!!t0,t0!=null&&t0.add?(t0.reset(),t.filter.brightness!==0&&t0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&t0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&t0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&t0.add("blur",t.filter.blur),t.filter.saturation!==0&&t0.add("saturation",t.filter.saturation),t.filter.hue!==0&&t0.add("hue",t.filter.hue),t.filter.negative&&t0.add("negative"),t.filter.sepia&&t0.add("sepia"),t.filter.vintage&&t0.add("brownie"),t.filter.sepia&&t0.add("sepia"),t.filter.kodachrome&&t0.add("kodachrome"),t.filter.technicolor&&t0.add("technicolor"),t.filter.polaroid&&t0.add("polaroid"),t.filter.pixelate!==0&&t0.add("pixelate",t.filter.pixelate),((m=t0.get())==null?void 0:m.length)>1?P0=t0.apply(M0):P0=t0.draw(M0)):(t.debug&&b("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,Y2(M0,P0))):(Y2(M0,P0),t0&&(t0=null),R.filter=!!t0),!n)return{tensor:null,canvas:P0};if(!P0)throw new 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f.data();t.filter.brightness=h[0]>1?1-h[0]/255:1-h[0],I.dispose(f)}return{tensor:i,canvas:t.filter.return?P0:null}}async function q1(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>3840||t.shape[2]>2160)return n;if(!ne.inputTensor)ne.inputTensor=I.clone(t);else if(ne.inputTensor.shape[1]!==t.shape[1]||ne.inputTensor.shape[2]!==t.shape[2])I.dispose(ne.inputTensor),ne.inputTensor=I.clone(t);else{let o={};o.diff=I.sub(t,ne.inputTensor),o.squared=I.mul(o.diff,o.diff),o.sum=I.sum(o.squared);let s=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;I.dispose([ne.inputTensor,o.diff,o.squared,o.sum]),ne.inputTensor=I.clone(t),n=s<=(e.cacheSensitivity||0)}return n}async function X1(e,t,n){let o={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||b("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||b("input tensors must 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Y0(this,j2)}set Canvas(t){Me(this,j2,t),globalThis.Canvas=t}get Image(){return Y0(this,N2)}set Image(t){Me(this,N2,t),globalThis.Image=t}get ImageData(){return Y0(this,I2)}set ImageData(t){Me(this,I2,t),globalThis.ImageData=t}async updateBackend(){this.backends=Object.keys(W0.engine().registryFactory);try{this.tensorflow={version:W0.backend().binding?W0.backend().binding.TF_Version:void 0,gpu:W0.backend().binding?W0.backend().binding.isUsingGpuDevice():void 0}}catch(o){}this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&(this.wasm.simd=await W0.env().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await W0.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Ae(100,100),n=t?t.getContext("webgl2"):void 0;this.webgl.supported=typeof 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n,o,r,s;if((n=e.annotations)!=null&&n.leftEyeIris&&((o=e.annotations)!=null&&o.leftEyeIris[0])){t.strokeStyle=X.useDepth?"rgba(255, 200, 255, 0.3)":X.color,t.beginPath();let A=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,a=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),X.fillPolygons&&(t.fillStyle=X.useDepth?"rgba(255, 255, 200, 0.3)":X.color,t.fill())}if((r=e.annotations)!=null&&r.rightEyeIris&&((s=e.annotations)!=null&&s.rightEyeIris[0])){t.strokeStyle=X.useDepth?"rgba(255, 200, 255, 0.3)":X.color,t.beginPath();let A=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,a=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),X.fillPolygons&&(t.fillStyle=X.useDepth?"rgba(255, 255, 200, 0.3)":X.color,t.fill())}}function Zs(e,t){var n;if(X.drawGaze&&((n=e.rotation)!=null&&n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*Je(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Je(e.rotation.angle.pitch)/90,s=new Path2D(` + M ${e.box[0]+e.box[2]/2} ${e.box[1]} C - ${valX} ${f.box[1]}, - ${valX} ${f.box[1] + f.box[3]}, - ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} - `); - const pathH = new Path2D(` - M ${f.box[0]} ${f.box[1] + f.box[3] / 2} + ${o} ${e.box[1]}, + ${o} ${e.box[1]+e.box[3]}, + ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} + `),A=new Path2D(` + M ${e.box[0]} ${e.box[1]+e.box[3]/2} C - ${f.box[0]} ${valY}, - ${f.box[0] + f.box[2]} ${valY}, - ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} - `); - ctx.stroke(pathH); - ctx.stroke(pathV); - } -} -function drawGazeArrows(f, ctx) { - var _a; - if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { - ctx.strokeStyle = "pink"; - ctx.fillStyle = "pink"; - const leftGaze = [ - f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); - const rightGaze = [ - f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); - } -} -function drawFacePolygons(f, ctx) { - if (localOptions.drawPolygons && f.mesh.length >= 468) { - ctx.lineWidth = 1; - for (let i = 0; i < TRI468.length / 3; i++) { - const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); - lines(ctx, points, localOptions); - } - drawIrisElipse(f, ctx); - } -} -function drawFacePoints(f, ctx) { - if (localOptions.drawPoints) { - if ((f == null ? void 0 : f.mesh.length) >= 468) { - for (let i = 0; i < f.mesh.length; i++) { - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); - if (localOptions.drawAttention) { - if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); - if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - } - } - } else { - for (const [k, v] of Object.entries((f == null ? void 0 : f.annotations) || {})) { - if (!(v == null ? void 0 : v[0])) continue; - const pt = v[0]; - point(ctx, pt[0], pt[1], 0, localOptions); - if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions); - } - } - } -} -function drawFaceBoxes(f, ctx) { - if (localOptions.drawBoxes) { - rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); - } -} -function face(inCanvas2, result, drawOptions) { - localOptions = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.font = localOptions.font; - ctx.strokeStyle = localOptions.color; - ctx.fillStyle = localOptions.color; - for (const f of result) { - drawFaceBoxes(f, ctx); - drawLabels(f, ctx); - if (f.mesh && f.mesh.length > 0) { - drawFacePoints(f, ctx); - drawFacePolygons(f, ctx); - drawGazeSpheres(f, ctx); - drawGazeArrows(f, ctx); - } - } -} - -// src/draw/body.ts -function body(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - for (let i = 0; i < result.length; i++) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - ctx.lineWidth = localOptions2.lineWidth; - ctx.font = localOptions2.font; - if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.bodyLabels.slice(); - l = replace(l, "[id]", result[i].id.toFixed(0)); - l = replace(l, "[score]", 100 * result[i].score); - labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); - } - } - if (localOptions2.drawPoints && result[i].keypoints) { - for (let pt = 0; pt < result[i].keypoints.length; pt++) { - if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) continue; - ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); - point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); - } - } - if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { - ctx.font = localOptions2.font; - for (const pt of result[i].keypoints) { - if (!pt.score || pt.score === 0) continue; - let l = localOptions2.bodyPartLabels.slice(); - l = replace(l, "[label]", pt.part); - l = replace(l, "[score]", 100 * pt.score); - labels(ctx, l, pt.position[0], pt.position[1], localOptions2); - } - } - if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { - for (const part of Object.values(result[i].annotations)) { - for (const connected4 of part) curves(ctx, connected4, localOptions2); - } - } - } -} - -// src/draw/hand.ts -function hand(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.handLabels.slice(); - l = replace(l, "[id]", h.id.toFixed(0)); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - if (localOptions2.drawPoints) { - if (h.keypoints && h.keypoints.length > 0) { - for (const pt of h.keypoints) { - ctx.fillStyle = colorDepth(pt[2], localOptions2); - point(ctx, pt[0], pt[1], 0, localOptions2); - } - } - } - if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { - for (const [part, pt] of Object.entries(h.annotations)) { - let l = localOptions2.fingerLabels.slice(); - l = replace(l, "[label]", part); - labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); - } - } - if (localOptions2.drawPolygons && h.annotations) { - const addHandLine = (part) => { - if (!part || part.length === 0 || !part[0]) return; - for (let i = 0; i < part.length; i++) { - ctx.beginPath(); - const z = part[i][2] || 0; - ctx.strokeStyle = colorDepth(i * z, localOptions2); - ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); - ctx.lineTo(part[i][0], part[i][1]); - ctx.stroke(); - } - }; - ctx.lineWidth = localOptions2.lineWidth; - addHandLine(h.annotations.index); - addHandLine(h.annotations.middle); - addHandLine(h.annotations.ring); - addHandLine(h.annotations.pinky); - addHandLine(h.annotations.thumb); - } - } -} - -// src/draw/object.ts -function object(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.objectLabels.slice(); - l = replace(l, "[id]", h.id.toFixed(0)); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - } -} - -// src/draw/gesture.ts -function gesture(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.font = localOptions2.font; - ctx.fillStyle = localOptions2.color; - let i = 1; - for (let j = 0; j < result.length; j++) { - const [where, what] = Object.entries(result[j]); - if (what.length > 1 && what[1].length > 0) { - const who = where[1] > 0 ? `#${where[1]}` : ""; - let l = localOptions2.gestureLabels.slice(); - l = replace(l, "[where]", where[0]); - l = replace(l, "[who]", who); - l = replace(l, "[what]", what[1]); - labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); - i += 1; - } - } - } -} - -// src/draw/labels.ts -var defaultLabels = { - face: `face + ${e.box[0]} ${r}, + ${e.box[0]+e.box[2]} ${r}, + ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} + `);t.stroke(A),t.stroke(s)}}function qs(e,t){var n;if(X.drawGaze&&((n=e.rotation)!=null&&n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];r5(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];r5(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Xs(e,t){if(X.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<$e.length/3;n++){let o=[$e[n*3+0],$e[n*3+1],$e[n*3+2]].map(r=>e.mesh[r]);o5(t,o,X)}Vs(e,t)}}function Us(e,t){if(X.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let n=0;n0&&(Us(r,o),Xs(r,o),Zs(r,o),qs(r,o))}}function et(e,t,n){var s,A;let o=i0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round";for(let a=0;a0)){let l=o.bodyLabels.slice();l=Y(l,"[id]",t[a].id.toFixed(0)),l=Y(l,"[score]",100*t[a].score),re(r,l,t[a].box[0],t[a].box[1],o)}if(o.drawPoints&&t[a].keypoints)for(let l=0;l0&&t[a].keypoints){r.font=o.font;for(let l of t[a].keypoints){if(!l.score||l.score===0)continue;let c=o.bodyPartLabels.slice();c=Y(c,"[label]",l.part),c=Y(c,"[score]",100*l.score),re(r,c,l.position[0],l.position[1],o)}}if(o.drawPolygons&&t[a].keypoints&&t[a].annotations)for(let l of Object.values(t[a].annotations))for(let c of l)_1(r,c,o)}}}function tt(e,t,n){var s,A;let o=i0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let a of t){if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,be(r,a.box[0],a.box[1],a.box[2],a.box[3],o),o.drawLabels&&((s=o.handLabels)==null?void 0:s.length)>0){let l=o.handLabels.slice();l=Y(l,"[id]",a.id.toFixed(0)),l=Y(l,"[label]",a.label),l=Y(l,"[score]",100*a.score),re(r,l,a.box[0],a.box[1],o)}r.stroke()}if(o.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let l of a.keypoints)r.fillStyle=Qe(l[2],o),he(r,l[0],l[1],0,o);if(o.drawLabels&&a.annotations&&((A=o.fingerLabels)==null?void 0:A.length)>0)for(let[l,c]of Object.entries(a.annotations)){let d=o.fingerLabels.slice();d=Y(d,"[label]",l),re(r,d,c[c.length-1][0],c[c.length-1][1],o)}if(o.drawPolygons&&a.annotations){let l=c=>{if(!(!c||c.length===0||!c[0]))for(let d=0;d0?d-1:0][0],c[d>0?d-1:0][1]),r.lineTo(c[d][0],c[d][1]),r.stroke()}};r.lineWidth=o.lineWidth,l(a.annotations.index),l(a.annotations.middle),l(a.annotations.ring),l(a.annotations.pinky),l(a.annotations.thumb)}}}}function nt(e,t,n){var s;let o=i0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let A of t)if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,be(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&((s=o.objectLabels)==null?void 0:s.length)>0){let a=o.objectLabels.slice();a=Y(a,"[id]",A.id.toFixed(0)),a=Y(a,"[label]",A.label),a=Y(a,"[score]",100*A.score),re(r,a,A.box[0],A.box[1],o)}r.stroke()}}}function ot(e,t,n){var r;let o=i0(f0,n);if(!(!t||!e)&&o.drawGestures&&((r=o.gestureLabels)==null?void 0:r.length)>0){let s=oe(e);if(!s)return;s.font=o.font,s.fillStyle=o.color;let A=1;for(let a=0;a1&&c[1].length>0){let d=l[1]>0?`#${l[1]}`:"",i=o.gestureLabels.slice();i=Y(i,"[where]",l[0]),i=Y(i,"[who]",d),i=Y(i,"[what]",c[1]),re(s,i,8,2+A*o.lineHeight,o),A+=1}}}}var Oe={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -6431,7450 +118,7 @@ var defaultLabels = { live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`, - body: "body [score]%", - bodyPart: "[label] [score]%", - object: "[label] [score]%", - hand: "[label] [score]%", - finger: "[label]", - gesture: "[where] [who]: [what]" -}; - -// src/draw/draw.ts -var drawTime = 0; -function person(inCanvas2, result, drawOptions) { - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (let i = 0; i < result.length; i++) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels) { - const label = `person #${i}`; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.stroke(); - } - } -} -function canvas2(input, output) { - if (!input || !output) return; - const ctx = getCanvasContext(output); - if (!ctx) return; - ctx.drawImage(input, 0, 0); -} -async function all(inCanvas2, result, drawOptions) { - if (!(result == null ? void 0 : result.performance) || !inCanvas2) return null; - const timeStamp = now(); - const localOptions2 = mergeDeep(options2, drawOptions); - const promise = Promise.all([ - face(inCanvas2, result.face, localOptions2), - body(inCanvas2, result.body, localOptions2), - hand(inCanvas2, result.hand, localOptions2), - object(inCanvas2, result.object, localOptions2), - gesture(inCanvas2, result.gesture, localOptions2) - // gestures do not have buffering - // person(inCanvas, result.persons, localOptions); // already included above - ]); - drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); - result.performance.draw = drawTime; - return promise; -} -function init2() { - options2.faceLabels = defaultLabels.face; - options2.bodyLabels = defaultLabels.body; - options2.bodyPartLabels = defaultLabels.bodyPart; - options2.handLabels = defaultLabels.hand; - options2.fingerLabels = defaultLabels.finger; - options2.objectLabels = defaultLabels.object; - options2.gestureLabels = defaultLabels.gesture; -} - -// src/body/blazepose.ts -var tf9 = __toESM(require_tfjs_esm()); - -// src/body/blazeposecoords.ts -var blazeposecoords_exports = {}; -__export(blazeposecoords_exports, { - connected: () => connected, - kpt: () => kpt -}); -var kpt = [ - "nose", - // 0 - "leftEyeInside", - // 1 - "leftEye", - // 2 - "leftEyeOutside", - // 3 - "rightEyeInside", - // 4 - "rightEye", - // 5 - "rightEyeOutside", - // 6 - "leftEar", - // 7 - "rightEar", - // 8 - "leftMouth", - // 9 - "rightMouth", - // 10 - "leftShoulder", - // 11 - "rightShoulder", - // 12 - "leftElbow", - // 13 - "rightElbow", - // 14 - "leftWrist", - // 15 - "rightWrist", - // 16 - "leftPinky", - // 17 - "rightPinky", - // 18 - "leftIndex", - // 19 - "rightIndex", - // 20 - "leftThumb", - // 21 - "rightThumb", - // 22 - "leftHip", - // 23 - "rightHip", - // 24 - "leftKnee", - // 25 - "rightKnee", - // 26 - "leftAnkle", - // 27 - "rightAnkle", - // 28 - "leftHeel", - // 29 - "rightHeel", - // 30 - "leftFoot", - // 31 - "rightFoot", - // 32 - "bodyCenter", - // 33 - "bodyTop", - // 34 - "leftPalm", - // 35 // z-coord not ok - "leftHand", - // 36 // similar to wrist but z-coord not ok - "rightPalm", - // 37 // z-coord not ok - "rightHand" - // 38 // similar to wrist but z-coord not ok -]; -var connected = { - shoulders: ["leftShoulder", "rightShoulder"], - hips: ["rightHip", "leftHip"], - mouth: ["leftMouth", "rightMouth"], - leftLegUpper: ["leftHip", "leftKnee"], - leftLegLower: ["leftKnee", "leftAnkle"], - leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], - leftTorso: ["leftShoulder", "leftHip"], - leftArmUpper: ["leftShoulder", "leftElbow"], - leftArmLower: ["leftElbow", "leftWrist"], - leftHand: ["leftWrist", "leftPalm"], - leftHandPinky: ["leftPalm", "leftPinky"], - leftHandIndex: ["leftPalm", "leftIndex"], - leftHandThumb: ["leftPalm", "leftThumb"], - leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], - rightLegUpper: ["rightHip", "rightKnee"], - rightLegLower: ["rightKnee", "rightAnkle"], - rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], - rightTorso: ["rightShoulder", "rightHip"], - rightArmUpper: ["rightShoulder", "rightElbow"], - rightArmLower: ["rightElbow", "rightWrist"], - rightHand: ["rightWrist", "rightPalm"], - rightHandPinky: ["rightPalm", "rightPinky"], - rightHandIndex: ["rightPalm", "rightIndex"], - rightHandThumb: ["rightPalm", "rightThumb"], - rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] -}; - -// src/body/blazeposedetector.ts -var tf8 = __toESM(require_tfjs_esm()); -var model; -var inputSize = 224; -var anchorTensor; -var numLayers = 5; -var strides = [8, 16, 32, 32, 32]; -function createAnchors() { - const anchors3 = []; - let layerId = 0; - while (layerId < numLayers) { - let anchorCount = 0; - let lastSameStrideLayer = layerId; - while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { - anchorCount += 2; - lastSameStrideLayer++; - } - const stride = strides[layerId]; - const featureMapHeight = Math.ceil(inputSize / stride); - const featureMapWidth = Math.ceil(inputSize / stride); - for (let y = 0; y < featureMapHeight; ++y) { - for (let x = 0; x < featureMapWidth; ++x) { - for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { - anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); - } - } - } - layerId = lastSameStrideLayer; - } - anchorTensor = { x: tf8.tensor1d(anchors3.map((a) => a.x)), y: tf8.tensor1d(anchors3.map((a) => a.y)) }; -} -async function loadDetector(config3) { - if (env.initial) model = null; - if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { - model = await loadModel(config3.body["detector"].modelPath); - const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; - inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug && model) log("cached model:", model["modelUrl"]); - createAnchors(); - return model; -} -var cropFactor = [5, 5]; -function decodeBoxes(boxesTensor, anchor) { - return tf8.tidy(() => { - const split6 = tf8.split(boxesTensor, 12, 1); - let xCenter = tf8.squeeze(split6[0]); - let yCenter = tf8.squeeze(split6[1]); - let width = tf8.squeeze(split6[2]); - let height = tf8.squeeze(split6[3]); - xCenter = tf8.add(tf8.div(xCenter, inputSize), anchor.x); - yCenter = tf8.add(tf8.div(yCenter, inputSize), anchor.y); - width = tf8.mul(tf8.div(width, inputSize), cropFactor[0]); - height = tf8.mul(tf8.div(height, inputSize), cropFactor[1]); - const xMin = tf8.sub(xCenter, tf8.div(width, 2)); - const yMin = tf8.sub(yCenter, tf8.div(height, 2)); - const xMax = tf8.add(xMin, width); - const yMax = tf8.add(yMin, height); - const boxes = tf8.stack([xMin, yMin, xMax, yMax], 1); - return boxes; - }); -} -async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { - var _a, _b; - const detectedBoxes = []; - const t2 = {}; - t2.boxes = decodeBoxes(boxesTensor, anchorTensor); - t2.scores = tf8.sigmoid(logitsTensor); - t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); - const nms = await t2.nms.data(); - const scores = await t2.scores.data(); - const boxes = await t2.boxes.array(); - for (const i of Array.from(nms)) { - const score = scores[i]; - const boxRaw = boxes[i]; - const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; - const detectedBox = { score, boxRaw, box }; - detectedBoxes.push(detectedBox); - } - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return detectedBoxes; -} -async function detectBoxes(input, config3, outputSize2) { - const t2 = {}; - t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); - t2.logitsRaw = tf8.slice(t2.res, [0, 0, 0], [1, -1, 1]); - t2.boxesRaw = tf8.slice(t2.res, [0, 0, 1], [1, -1, -1]); - t2.logits = tf8.squeeze(t2.logitsRaw); - t2.boxes = tf8.squeeze(t2.boxesRaw); - const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return boxes; -} - -// src/util/box.ts -function calc(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function square(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; - const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); - const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function scale(box, scaleFact) { - const dist = [box[2] * scaleFact, box[3] * scaleFact]; - const newBox = [ - box[0] - (dist[0] - box[2]) / 2, - box[1] - (dist[1] - box[3]) / 2, - dist[0], - dist[1] - ]; - return newBox; -} - -// src/body/blazepose.ts -var model2; -var inputSize2 = 256; -var skipped = Number.MAX_SAFE_INTEGER; -var outputNodes = { - landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], - detector: [] -}; -var cache = []; -var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; -var lastTime = 0; -var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); -var loadDetect = (config3) => loadDetector(config3); -async function loadPose(config3) { - if (env.initial) model2 = null; - if (!model2) { - model2 = await loadModel(config3.body.modelPath); - const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; - inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug) log("cached model:", model2["modelUrl"]); - return model2; -} -function prepareImage(input, size2, cropBox) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) return input; - let final; - if (cropBox) { - t2.cropped = tf9.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); - } - if (input.shape[1] !== input.shape[2]) { - const height = [ - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 - ]; - const width = [ - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 - ]; - padding = [ - [0, 0], - // dont touch batch - height, - // height before&after - width, - // width before&after - [0, 0] - // dont touch rbg - ]; - t2.pad = tf9.pad(t2.cropped || input, padding); - t2.resize = tf9.image.resizeBilinear(t2.pad, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else if (input.shape[1] !== size2) { - t2.resize = tf9.image.resizeBilinear(t2.cropped || input, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else { - final = tf9.div(t2.cropped || input, constants.tf255); - } - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - return final; -} -function rescaleKeypoints(keypoints, outputSize2, cropBox) { - for (const kpt4 of keypoints) { - kpt4.position = [ - Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), - Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), - kpt4.position[2] - ]; - kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; - } - if (cropBox) { - const width = cropBox[2] - cropBox[0]; - const height = cropBox[3] - cropBox[1]; - for (const kpt4 of keypoints) { - kpt4.positionRaw = [ - kpt4.positionRaw[0] / height + cropBox[1], - // correct offset due to crop - kpt4.positionRaw[1] / width + cropBox[0], - // correct offset due to crop - kpt4.positionRaw[2] - ]; - kpt4.position = [ - Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), - Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), - kpt4.positionRaw[2] - ]; - } - } - return keypoints; -} -function fixKeypoints(keypoints) { - const leftPalm = keypoints.find((k) => k.part === "leftPalm"); - const leftWrist = keypoints.find((k) => k.part === "leftWrist"); - const leftIndex = keypoints.find((k) => k.part === "leftIndex"); - leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; - const rightPalm = keypoints.find((k) => k.part === "rightPalm"); - const rightWrist = keypoints.find((k) => k.part === "rightWrist"); - const rightIndex = keypoints.find((k) => k.part === "rightIndex"); - rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; -} -async function detectLandmarks(input, config3, outputSize2) { - if (!(model2 == null ? void 0 : model2["executor"])) return null; - const t2 = {}; - [ - t2.ld, - t2.segmentation, - t2.heatmap, - t2.world, - t2.poseflag - /* 1,1 */ - ] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); - const poseScore = (await t2.poseflag.data())[0]; - const points = await t2.ld.data(); - const distances = await t2.world.data(); - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - const keypointsRelative = []; - const depth = 5; - for (let i = 0; i < points.length / depth; i++) { - const score = sigmoid2(points[depth * i + 3]); - const presence = sigmoid2(points[depth * i + 4]); - const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; - const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; - const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; - const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; - keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); - } - if (poseScore < (config3.body.minConfidence || 0)) return null; - fixKeypoints(keypointsRelative); - const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); - const kpts = keypoints.map((k) => k.position); - const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; - return body4; -} -async function predict(input, config3) { - var _a, _b, _c; - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime; - const skipFrame = skipped < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { - skipped++; - } else { - let boxes = []; - if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { - const preparedImage = prepareImage(input, 224); - boxes = await detectBoxes(preparedImage, config3, outputSize2); - tf9.dispose(preparedImage); - } else { - boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; - } - for (let i = 0; i < boxes.length; i++) { - const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); - cache.length = 0; - const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); - tf9.dispose(preparedBox); - if (!bodyResult) continue; - bodyResult.id = i; - cache.push(bodyResult); - } - lastTime = now(); - skipped = 0; - } - return cache; -} - -// src/object/centernet.ts -var tf10 = __toESM(require_tfjs_esm()); - -// src/object/labels.ts -var labels2 = [ - { class: 1, label: "person" }, - { class: 2, label: "bicycle" }, - { class: 3, label: "car" }, - { class: 4, label: "motorcycle" }, - { class: 5, label: "airplane" }, - { class: 6, label: "bus" }, - { class: 7, label: "train" }, - { class: 8, label: "truck" }, - { class: 9, label: "boat" }, - { class: 10, label: "traffic light" }, - { class: 11, label: "fire hydrant" }, - { class: 12, label: "stop sign" }, - { class: 13, label: "parking meter" }, - { class: 14, label: "bench" }, - { class: 15, label: "bird" }, - { class: 16, label: "cat" }, - { class: 17, label: "dog" }, - { class: 18, label: "horse" }, - { class: 19, label: "sheep" }, - { class: 20, label: "cow" }, - { class: 21, label: "elephant" }, - { class: 22, label: "bear" }, - { class: 23, label: "zebra" }, - { class: 24, label: "giraffe" }, - { class: 25, label: "backpack" }, - { class: 26, label: "umbrella" }, - { class: 27, label: "handbag" }, - { class: 28, label: "tie" }, - { class: 29, label: "suitcase" }, - { class: 30, label: "frisbee" }, - { class: 31, label: "skis" }, - { class: 32, label: "snowboard" }, - { class: 33, label: "sports ball" }, - { class: 34, label: "kite" }, - { class: 35, label: "baseball bat" }, - { class: 36, label: "baseball glove" }, - { class: 37, label: "skateboard" }, - { class: 38, label: "surfboard" }, - { class: 39, label: "tennis racket" }, - { class: 40, label: "bottle" }, - { class: 41, label: "wine glass" }, - { class: 42, label: "cup" }, - { class: 43, label: "fork" }, - { class: 44, label: "knife" }, - { class: 45, label: "spoon" }, - { class: 46, label: "bowl" }, - { class: 47, label: "banana" }, - { class: 48, label: "apple" }, - { class: 49, label: "sandwich" }, - { class: 50, label: "orange" }, - { class: 51, label: "broccoli" }, - { class: 52, label: "carrot" }, - { class: 53, label: "hot dog" }, - { class: 54, label: "pizza" }, - { class: 55, label: "donut" }, - { class: 56, label: "cake" }, - { class: 57, label: "chair" }, - { class: 58, label: "couch" }, - { class: 59, label: "potted plant" }, - { class: 60, label: "bed" }, - { class: 61, label: "dining table" }, - { class: 62, label: "toilet" }, - { class: 63, label: "tv" }, - { class: 64, label: "laptop" }, - { class: 65, label: "mouse" }, - { class: 66, label: "remote" }, - { class: 67, label: "keyboard" }, - { class: 68, label: "cell phone" }, - { class: 69, label: "microwave" }, - { class: 70, label: "oven" }, - { class: 71, label: "toaster" }, - { class: 72, label: "sink" }, - { class: 73, label: "refrigerator" }, - { class: 74, label: "book" }, - { class: 75, label: "clock" }, - { class: 76, label: "vase" }, - { class: 77, label: "scissors" }, - { class: 78, label: "teddy bear" }, - { class: 79, label: "hair drier" }, - { class: 80, label: "toothbrush" } -]; - -// src/object/centernet.ts -var model3; -var inputSize3 = 0; -var last2 = []; -var lastTime2 = 0; -var skipped2 = Number.MAX_SAFE_INTEGER; -async function load(config3) { - if (env.initial) model3 = null; - if (!model3) { - model3 = await loadModel(config3.object.modelPath); - const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; - inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", model3["modelUrl"]); - return model3; -} -async function process3(res, outputShape, config3) { - if (!res) return []; - const t2 = {}; - const results = []; - const detections = await res.array(); - t2.squeeze = tf10.squeeze(res); - const arr = tf10.split(t2.squeeze, 6, 1); - t2.stack = tf10.stack([arr[1], arr[0], arr[3], arr[2]], 1); - t2.boxes = tf10.squeeze(t2.stack); - t2.scores = tf10.squeeze(arr[4]); - t2.classes = tf10.squeeze(arr[5]); - tf10.dispose([res, ...arr]); - t2.nms = await tf10.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); - const nms = await t2.nms.data(); - let i = 0; - for (const id of Array.from(nms)) { - const score = Math.trunc(100 * detections[0][id][4]) / 100; - const classVal = detections[0][id][5]; - if (Number.isNaN(classVal)) continue; - const label = labels2[classVal].label; - const [x, y] = [ - detections[0][id][0] / inputSize3, - detections[0][id][1] / inputSize3 - ]; - const boxRaw = [ - x, - y, - detections[0][id][2] / inputSize3 - x, - detections[0][id][3] / inputSize3 - y - ]; - const box = [ - Math.trunc(boxRaw[0] * outputShape[0]), - Math.trunc(boxRaw[1] * outputShape[1]), - Math.trunc(boxRaw[2] * outputShape[0]), - Math.trunc(boxRaw[3] * outputShape[1]) - ]; - results.push({ id: i++, score, class: classVal, label, box, boxRaw }); - } - Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6])); - return results; -} -async function predict2(input, config3) { - if (!(model3 == null ? void 0 : model3["executor"])) return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; - const skipFrame = skipped2 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { - skipped2++; - return last2; - } - skipped2 = 0; - return new Promise(async (resolve) => { - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const resize = tf10.image.resizeBilinear(input, [inputSize3, inputSize3]); - const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; - lastTime2 = now(); - tf10.dispose(resize); - const obj = await process3(objectT, outputSize2, config3); - last2 = obj; - resolve(obj); - }); -} - -// src/body/efficientpose.ts -var tf11 = __toESM(require_tfjs_esm()); - -// src/body/efficientposecoords.ts -var efficientposecoords_exports = {}; -__export(efficientposecoords_exports, { - connected: () => connected2, - kpt: () => kpt2 -}); -var kpt2 = [ - "head", - "neck", - "rightShoulder", - "rightElbow", - "rightWrist", - "chest", - "leftShoulder", - "leftElbow", - "leftWrist", - "bodyCenter", - "rightHip", - "rightKnee", - "rightAnkle", - "leftHip", - "leftKnee", - "leftAnkle" -]; -var connected2 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/body/efficientpose.ts -var model4; -var lastTime3 = 0; -var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; -var skipped3 = Number.MAX_SAFE_INTEGER; -async function load2(config3) { - if (env.initial) model4 = null; - if (!model4) model4 = await loadModel(config3.body.modelPath); - else if (config3.debug) log("cached model:", model4["modelUrl"]); - return model4; -} -async function max2d(inputs, minScore) { - const [width, height] = inputs.shape; - const reshaped = tf11.reshape(inputs, [height * width]); - const max5 = tf11.max(reshaped, 0); - const newScore = (await max5.data())[0]; - if (newScore > minScore) { - const coordinates = tf11.argMax(reshaped, 0); - const mod3 = tf11.mod(coordinates, width); - const x = (await mod3.data())[0]; - const div15 = tf11.div(coordinates, width); - const y = (await div15.data())[0]; - tf11.dispose([reshaped, max5, coordinates, mod3, div15]); - return [x, y, newScore]; - } - tf11.dispose([reshaped, max5]); - return [0, 0, newScore]; -} -async function predict3(image28, config3) { - if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) return []; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; - const skipFrame = skipped3 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { - skipped3++; - return [cache2]; - } - skipped3 = 0; - return new Promise(async (resolve) => { - const tensor6 = tf11.tidy(() => { - var _a, _b; - const resize = tf11.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); - const enhance2 = tf11.mul(resize, constants.tf2); - const norm = tf11.sub(enhance2, constants.tf1); - return norm; - }); - let resT; - if (config3.body.enabled) resT = model4 == null ? void 0 : model4.execute(tensor6); - lastTime3 = now(); - tf11.dispose(tensor6); - if (resT) { - cache2.keypoints.length = 0; - const squeeze14 = tf11.squeeze(resT); - tf11.dispose(resT); - const stack5 = tf11.unstack(squeeze14, 2); - tf11.dispose(squeeze14); - for (let id = 0; id < stack5.length; id++) { - const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); - if (partScore > (config3.body.minConfidence || 0)) { - cache2.keypoints.push({ - score: Math.round(100 * partScore) / 100, - part: kpt2[id], - positionRaw: [ - // normalized to 0..1 - // @ts-ignore model is not undefined here - x2 / model4.inputs[0].shape[2], - y2 / model4.inputs[0].shape[1] - ], - position: [ - // normalized to input image size - // @ts-ignore model is not undefined here - Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), - Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) - ] - }); - } - } - stack5.forEach((s) => tf11.dispose(s)); - } - cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const x = cache2.keypoints.map((a) => a.position[0]); - const y = cache2.keypoints.map((a) => a.position[1]); - cache2.box = [ - Math.min(...x), - Math.min(...y), - Math.max(...x) - Math.min(...x), - Math.max(...y) - Math.min(...y) - ]; - const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); - const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); - cache2.boxRaw = [ - Math.min(...xRaw), - Math.min(...yRaw), - Math.max(...xRaw) - Math.min(...xRaw), - Math.max(...yRaw) - Math.min(...yRaw) - ]; - for (const [name, indexes] of Object.entries(connected2)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - cache2.annotations[name] = pt; - } - resolve([cache2]); - }); -} - -// src/face/face.ts -var tf25 = __toESM(require_tfjs_esm()); - -// src/face/facemesh.ts -var tf15 = __toESM(require_tfjs_esm()); - -// src/face/blazeface.ts -var tf13 = __toESM(require_tfjs_esm()); - -// src/face/facemeshutil.ts -var tf12 = __toESM(require_tfjs_esm()); -var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; -var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; -var clampBox = (box, input) => box ? [ - Math.trunc(Math.max(0, box.startPoint[0])), - Math.trunc(Math.max(0, box.startPoint[1])), - Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), - Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) -] : [0, 0, 0, 0]; -var getRawBox = (box, input) => box ? [ - box.startPoint[0] / (input.shape[2] || 0), - box.startPoint[1] / (input.shape[1] || 0), - (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), - (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) -] : [0, 0, 0, 0]; -var scaleBoxCoordinates = (box, factor, anchor) => { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]); - return { startPoint, endPoint, landmarks, confidence: box.confidence }; -}; -var cutAndResize = (box, image28, cropSize) => { - const h = image28.shape[1]; - const w = image28.shape[2]; - const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; - const crop = tf12.image.cropAndResize(image28, [cutBox], [0], cropSize); - const norm = tf12.div(crop, constants.tf255); - tf12.dispose(crop); - return norm; -}; -var enlargeBox = (box, factor) => { - const center = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - return { - startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], - endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], - landmarks: box.landmarks, - confidence: box.confidence, - size: size2 - }; -}; -var squarifyBox = (box) => { - const centers = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = Math.max(...size2) / 2; - return { - startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], - endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], - landmarks: box.landmarks, - confidence: box.confidence, - size: [Math.round(size2[0]), Math.round(size2[1])] - }; -}; -var calculateLandmarksBoundingBox = (landmarks) => { - const x = landmarks.map((d) => d[0]); - const y = landmarks.map((d) => d[1]); - return { - startPoint: [Math.min(...x), Math.min(...y)], - endPoint: [Math.max(...x), Math.max(...y)], - landmarks - }; -}; -var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; -var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); -var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -var dot = (v1, v2) => { - let product = 0; - for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i]; - return product; -}; -var getColumnFrom2DArr = (arr, columnIndex) => { - const column = []; - for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]); - return column; -}; -var multiplyTransformMatrices = (mat1, mat2) => { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); - } - return product; -}; -var buildRotationMatrix = (rotation, center) => { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); - return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); -}; -var invertTransformMatrix = (matrix) => { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; - return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; -}; -var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; -function generateAnchors(inputSize10) { - const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; - const anchors3 = []; - for (let i = 0; i < spec.strides.length; i++) { - const stride = spec.strides[i]; - const gridRows = Math.floor((inputSize10 + stride - 1) / stride); - const gridCols = Math.floor((inputSize10 + stride - 1) / stride); - const anchorsNum = spec.anchors[i]; - for (let gridY = 0; gridY < gridRows; gridY++) { - const anchorY = stride * (gridY + 0.5); - for (let gridX = 0; gridX < gridCols; gridX++) { - const anchorX = stride * (gridX + 0.5); - for (let n = 0; n < anchorsNum; n++) anchors3.push([anchorX, anchorY]); - } - } - } - return anchors3; -} -function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { - const boxSize = getBoxSize(box); - const coordsScaled = coordsRaw.map((coord) => [ - // scaled around zero-point - boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), - boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), - coord[2] || 0 - ]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; - const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; - const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; - const boxCenter = getBoxCenter(box); - const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + offsets[0]), - Math.trunc(coord[1] + offsets[1]), - Math.trunc(coord[2] || 0) - ]); -} -function correctFaceRotation(rotate, box, input, inputSize10) { - const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; - let angle = 0; - let rotationMatrix = fixedRotationMatrix; - let face4; - if (rotate && env.kernels.includes("rotatewithoffset")) { - angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - if (largeAngle) { - const center = getBoxCenter(box); - const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; - const rotated = tf12.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); - rotationMatrix = buildRotationMatrix(-angle, center); - face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); - tf12.dispose(rotated); - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - return [angle, rotationMatrix, face4]; -} -var findFaceCenter = (mesh) => { - const x = mesh.map((m) => m[0]); - const y = mesh.map((m) => m[1]); - return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; -}; -var calculateFaceBox = (mesh, previousBox) => { - const center = findFaceCenter(mesh); - const boxSize = getBoxSize(previousBox); - const calculatedBox = { - startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], - endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] - }; - return calculatedBox; -}; - -// src/face/blazeface.ts -var keypointsCount = 6; -var model5; -var anchors = null; -var inputSize4 = 0; -var inputSizeT = null; -var size = () => inputSize4; -async function load3(config3) { - var _a; - if (env.initial) model5 = null; - if (!model5) model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model5["modelUrl"]); - inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; - inputSizeT = tf13.scalar(inputSize4, "int32"); - anchors = tf13.tensor2d(generateAnchors(inputSize4)); - return model5; -} -function decodeBoxes2(boxOutputs) { - if (!anchors || !inputSizeT) return tf13.zeros([0, 0]); - const t2 = {}; - t2.boxStarts = tf13.slice(boxOutputs, [0, 1], [-1, 2]); - t2.centers = tf13.add(t2.boxStarts, anchors); - t2.boxSizes = tf13.slice(boxOutputs, [0, 3], [-1, 2]); - t2.boxSizesNormalized = tf13.div(t2.boxSizes, inputSizeT); - t2.centersNormalized = tf13.div(t2.centers, inputSizeT); - t2.halfBoxSize = tf13.div(t2.boxSizesNormalized, constants.tf2); - t2.starts = tf13.sub(t2.centersNormalized, t2.halfBoxSize); - t2.ends = tf13.add(t2.centersNormalized, t2.halfBoxSize); - t2.startNormalized = tf13.mul(t2.starts, inputSizeT); - t2.endNormalized = tf13.mul(t2.ends, inputSizeT); - const boxes = tf13.concat2d([t2.startNormalized, t2.endNormalized], 1); - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} -async function getBoxes(inputImage, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) return []; - const t2 = {}; - let pad4 = [0, 0]; - let scale2 = [1, 1]; - if ((_b = (_a = config3 == null ? void 0 : config3.face) == null ? void 0 : _a.detector) == null ? void 0 : _b.square) { - const xy = Math.max(inputImage.shape[2], inputImage.shape[1]); - pad4 = [Math.floor((xy - inputImage.shape[2]) / 2), Math.floor((xy - inputImage.shape[1]) / 2)]; - t2.padded = tf13.pad(inputImage, [[0, 0], [pad4[1], pad4[1]], [pad4[0], pad4[0]], [0, 0]]); - scale2 = [inputImage.shape[2] / xy, inputImage.shape[1] / xy]; - pad4 = [pad4[0] / inputSize4, pad4[1] / inputSize4]; - } else { - t2.padded = inputImage.clone(); - } - t2.resized = tf13.image.resizeBilinear(t2.padded, [inputSize4, inputSize4]); - t2.div = tf13.div(t2.resized, constants.tf127); - t2.normalized = tf13.sub(t2.div, constants.tf1); - const res = model5 == null ? void 0 : model5.execute(t2.normalized); - if (Array.isArray(res) && res.length > 2) { - const sorted = res.sort((a, b) => a.size - b.size); - t2.concat384 = tf13.concat([sorted[0], sorted[2]], 2); - t2.concat512 = tf13.concat([sorted[1], sorted[3]], 2); - t2.concat = tf13.concat([t2.concat512, t2.concat384], 1); - t2.batch = tf13.squeeze(t2.concat, [0]); - } else if (Array.isArray(res)) { - t2.batch = tf13.squeeze(res[0]); - } else { - t2.batch = tf13.squeeze(res); - } - tf13.dispose(res); - t2.boxes = decodeBoxes2(t2.batch); - t2.logits = tf13.slice(t2.batch, [0, 0], [-1, 1]); - t2.sigmoid = tf13.sigmoid(t2.logits); - t2.scores = tf13.squeeze(t2.sigmoid); - t2.nms = await tf13.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_c = config3.face.detector) == null ? void 0 : _c.maxDetected) || 0, ((_d = config3.face.detector) == null ? void 0 : _d.iouThreshold) || 0, ((_e = config3.face.detector) == null ? void 0 : _e.minConfidence) || 0); - const nms = await t2.nms.array(); - const boxes = []; - const scores = await t2.scores.data(); - for (let i = 0; i < nms.length; i++) { - const confidence = scores[nms[i]]; - if (confidence > (((_f = config3.face.detector) == null ? void 0 : _f.minConfidence) || 0)) { - const b = {}; - b.bbox = tf13.slice(t2.boxes, [nms[i], 0], [1, -1]); - b.slice = tf13.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); - b.squeeze = tf13.squeeze(b.slice); - b.landmarks = tf13.reshape(b.squeeze, [keypointsCount, -1]); - const points = await b.bbox.data(); - const unpadded = [ - // TODO fix this math - points[0] * scale2[0] - pad4[0], - points[1] * scale2[1] - pad4[1], - points[2] * scale2[0] - pad4[0], - points[3] * scale2[1] - pad4[1] - ]; - const rawBox = { - startPoint: [unpadded[0], unpadded[1]], - endPoint: [unpadded[2], unpadded[3]], - landmarks: await b.landmarks.array(), - confidence - }; - b.anchor = tf13.slice(anchors, [nms[i], 0], [1, 2]); - const anchor = await b.anchor.data(); - const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4], anchor); - const enlargedBox = enlargeBox(scaledBox, ((_g = config3.face.detector) == null ? void 0 : _g.scale) || 1.4); - const squaredBox = squarifyBox(enlargedBox); - if (squaredBox.size[0] > (((_h = config3.face.detector) == null ? void 0 : _h["minSize"]) || 0) && squaredBox.size[1] > (((_i = config3.face.detector) == null ? void 0 : _i["minSize"]) || 0)) boxes.push(squaredBox); - Object.keys(b).forEach((tensor6) => tf13.dispose(b[tensor6])); - } - } - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} - -// src/face/iris.ts -var tf14 = __toESM(require_tfjs_esm()); -var model6; -var inputSize5 = 0; -var leftOutline = meshAnnotations.leftEyeLower0; -var rightOutline = meshAnnotations.rightEyeLower0; -var eyeLandmarks = { - leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], - rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] -}; -var irisLandmarks = { - upperCenter: 3, - lowerCenter: 4, - index: 71, - numCoordinates: 76 -}; -async function load4(config3) { - var _a, _b; - if (env.initial) model6 = null; - if (!model6) model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model6["modelUrl"]); - inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; - if (inputSize5 === -1) inputSize5 = 64; - return model6; -} -function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { - for (let i = 0; i < irisIndices.length; i++) { - const { key, indices } = irisIndices[i]; - const originalIndices = meshAnnotations[`${prefix}${key}`]; - if (!keys || keys.includes(key)) { - for (let j = 0; j < indices.length; j++) { - const index2 = indices[j]; - rawCoords[originalIndices[j]] = [ - newCoords[index2][0], - newCoords[index2][1], - (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 - ]; - } - } - } -} -var getLeftToRightEyeDepthDifference = (rawCoords) => { - const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; - const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; - return leftEyeZ - rightEyeZ; -}; -var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false, scale2 = 2.3) => { - const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), scale2)); - const boxSize = getBoxSize(box); - let crop = tf14.image.cropAndResize(face4, [[ - box.startPoint[1] / meshSize, - box.startPoint[0] / meshSize, - box.endPoint[1] / meshSize, - box.endPoint[0] / meshSize - ]], [0], [inputSize5, inputSize5]); - if (flip && env.kernels.includes("flipleftright")) { - const flipped = tf14.image.flipLeftRight(crop); - tf14.dispose(crop); - crop = flipped; - } - return { box, boxSize, crop }; -}; -var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { - const eyeRawCoords = []; - for (let i = 0; i < irisLandmarks.numCoordinates; i++) { - const x = eyeData[i * 3]; - const y = eyeData[i * 3 + 1]; - const z = eyeData[i * 3 + 2]; - eyeRawCoords.push([ - (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], - y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], - z - ]); - } - return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; -}; -var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { - const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; - const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; - const averageZ = (upperCenterZ + lowerCenterZ) / 2; - return irisCoords.map((coord, i) => { - let z = averageZ; - if (i === 2) { - z = upperCenterZ; - } else if (i === 4) { - z = lowerCenterZ; - } - return [coord[0], coord[1], z]; - }); -}; -async function augmentIris(rawCoords, face4, meshSize, config3) { - var _a, _b; - if (!(model6 == null ? void 0 : model6["executor"])) return rawCoords; - const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true, ((_a = config3.face.iris) == null ? void 0 : _a.scale) || 2.3); - const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true, ((_b = config3.face.iris) == null ? void 0 : _b.scale) || 2.3); - const combined = tf14.concat([leftEyeCrop, rightEyeCrop]); - tf14.dispose(leftEyeCrop); - tf14.dispose(rightEyeCrop); - const eyePredictions = model6.execute(combined); - tf14.dispose(combined); - const eyePredictionsData = await eyePredictions.data(); - tf14.dispose(eyePredictions); - const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); - const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); - const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); - const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); - const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); - if (Math.abs(leftToRightEyeDepthDifference) < 30) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); - } else if (leftToRightEyeDepthDifference < 1) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); - } else { - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); - } - const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); - const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); - const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); - return newCoords; -} - -// src/face/attention.ts -async function augment(rawCoords, results) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const t2 = { - // all attention models produce 2d results so it needs to be later augmented with correct z-coords - // mesh: results[0], // already have it in rawCoords // output_mesh_identity - // flag: results[1], // already processed in parent // conv_faceflag - lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), - // 80 x 2d = 160 // output_lips - irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), - // 5 x 2d = 10 // output_right_iris - eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), - // 71 x 2d = 142 // output_right_eye - irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), - // 5 x 2d = 10 // output_left_iris - eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) - // 71 x 2d = 142// output_left_eye - }; - for (const val of Object.values(t2)) { - if (!val) return rawCoords; - } - const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisL.length / 2; i++) rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); - const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisR.length / 2; i++) rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); - for (let i = 0; i < t2.eyeL.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.eyeR.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.lips.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; - return rawCoords; -} - -// src/face/facemesh.ts -var cache3 = { - boxes: [], - skipped: Number.MAX_SAFE_INTEGER, - timestamp: 0 -}; -var model7 = null; -var inputSize6 = 0; -async function predict4(input, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; - const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); - if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { - cache3.boxes = await getBoxes(input, config3); - cache3.timestamp = now(); - cache3.skipped = 0; - } else { - cache3.skipped++; - } - const faces = []; - const newCache = []; - let id = 0; - const size2 = inputSize6; - for (let i = 0; i < cache3.boxes.length; i++) { - const box = cache3.boxes[i]; - let angle = 0; - let rotationMatrix; - const face4 = { - // init face result - id: id++, - mesh: [], - meshRaw: [], - box: [0, 0, 0, 0], - boxRaw: [0, 0, 0, 0], - score: 0, - boxScore: 0, - faceScore: 0, - size: [0, 0], - // contoursRaw: [], - // contours: [], - annotations: {} - }; - [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); - if (config3.filter.equalization) { - const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; - tf15.dispose(face4.tensor); - if (equilized) face4.tensor = equilized; - } - face4.boxScore = Math.round(100 * box.confidence) / 100; - if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled) || !(model7 == null ? void 0 : model7["executor"])) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.size = box.size; - face4.mesh = box.landmarks; - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } else if (!model7) { - if (config3.debug) log("face mesh detection requested, but model is not loaded"); - } else { - if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { - config3.face.attention.enabled = false; - tf15.dispose(face4.tensor); - return faces; - } - const results = model7.execute(face4.tensor); - const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); - const faceConfidence = await confidenceT.data(); - face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; - if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { - box.confidence = face4.faceScore; - if (config3.face.mesh["keepInvalid"]) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.size = box.size; - face4.score = face4.boxScore; - face4.mesh = box.landmarks; - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } - } else { - const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); - const coordsReshaped = tf15.reshape(meshT, [-1, 3]); - let rawCoords = await coordsReshaped.array(); - tf15.dispose(coordsReshaped); - if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { - rawCoords = await augment(rawCoords, results); - } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { - rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6, config3); - } - face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(meshAnnotations)) face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); - face4.score = face4.faceScore; - const calculatedBox = { - ...calculateFaceBox(face4.mesh, box), - confidence: box.confidence, - landmarks: box.landmarks, - size: box.size - }; - face4.box = clampBox(calculatedBox, input); - face4.boxRaw = getRawBox(calculatedBox, input); - face4.size = calculatedBox.size; - newCache.push(calculatedBox); - } - tf15.dispose(results); - } - if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) faces.push(face4); - else tf15.dispose(face4.tensor); - } - cache3.boxes = newCache; - return faces; -} -async function load5(config3) { - var _a, _b, _c, _d, _e, _f; - if (env.initial) model7 = null; - if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { - if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) model7 = null; - } - if (!model7) { - if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) model7 = await loadModel(config3.face.attention.modelPath); - else model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); - } else if (config3.debug) { - log("cached model:", model7["modelUrl"]); - } - inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; - return model7; -} -var triangulation = TRI468; -var uvmap = UV468; - -// src/gear/emotion.ts -var tf16 = __toESM(require_tfjs_esm()); -var annotations = []; -var model8; -var last3 = []; -var lastCount = 0; -var lastTime4 = 0; -var skipped4 = Number.MAX_SAFE_INTEGER; -var rgb = false; -async function load6(config3) { - var _a, _b, _c; - if (env.initial) model8 = null; - if (!model8) { - model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); - rgb = ((_c = (_b = model8 == null ? void 0 : model8.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[3]) === 3; - if (!rgb) annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; - else annotations = ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]; - } else if (config3.debug) { - log("cached model:", model8["modelUrl"]); - } - return model8; -} -async function predict5(image28, config3, idx, count2) { - var _a, _b; - if (!model8) return []; - const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; - if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { - skipped4++; - return last3[idx]; - } - skipped4 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c; - const obj = []; - if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { - const t2 = {}; - const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; - if (((_b2 = config3.face.emotion) == null ? void 0 : _b2["crop"]) > 0) { - const crop = (_c = config3.face.emotion) == null ? void 0 : _c["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tf16.image.cropAndResize(image28, box, [0], [inputSize10, inputSize10]); - } else { - t2.resize = tf16.image.resizeBilinear(image28, [inputSize10, inputSize10], false); - } - if (rgb) { - t2.mul = tf16.mul(t2.resize, 255); - t2.normalize = tf16.sub(t2.mul, [103.939, 116.779, 123.68]); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.normalize); - } else { - t2.channels = tf16.mul(t2.resize, constants.rgb); - t2.grayscale = tf16.sum(t2.channels, 3, true); - t2.grayscaleSub = tf16.sub(t2.grayscale, constants.tf05); - t2.grayscaleMul = tf16.mul(t2.grayscaleSub, constants.tf2); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); - } - lastTime4 = now(); - const data = await t2.emotion.data(); - for (let i = 0; i < data.length; i++) { - if (data[i] > (config3.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); - } - obj.sort((a, b) => b.score - a.score); - Object.keys(t2).forEach((tensor6) => tf16.dispose(t2[tensor6])); - } - last3[idx] = obj; - lastCount = count2; - resolve(obj); - }); -} - -// src/face/faceres.ts -var tf17 = __toESM(require_tfjs_esm()); -var model9; -var last4 = []; -var lastTime5 = 0; -var lastCount2 = 0; -var skipped5 = Number.MAX_SAFE_INTEGER; -async function load7(config3) { - var _a; - if (env.initial) model9 = null; - if (!model9) model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model9["modelUrl"]); - return model9; -} -function enhance(input, config3) { - var _a, _b; - const tensor6 = input.image || input.tensor || input; - if (!(model9 == null ? void 0 : model9.inputs[0].shape)) return tensor6; - let crop; - if (((_a = config3.face.description) == null ? void 0 : _a["crop"]) > 0) { - const cropval = (_b = config3.face.description) == null ? void 0 : _b["crop"]; - const box = [[cropval, cropval, 1 - cropval, 1 - cropval]]; - crop = tf17.image.cropAndResize(tensor6, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]); - } else { - crop = tf17.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); - } - const norm = tf17.mul(crop, constants.tf255); - tf17.dispose(crop); - return norm; -} -async function predict6(image28, config3, idx, count2) { - var _a, _b, _c, _d; - const obj = { - age: 0, - gender: "unknown", - genderScore: 0, - descriptor: [] - }; - if (!(model9 == null ? void 0 : model9["executor"])) return obj; - const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; - if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped5++; - return last4[idx]; - } - skipped5 = 0; - return new Promise(async (resolve) => { - var _a2; - if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { - const enhanced = enhance(image28, config3); - const resT = model9 == null ? void 0 : model9.execute(enhanced); - lastTime5 = now(); - tf17.dispose(enhanced); - const genderT = resT.find((t2) => t2.shape[1] === 1); - const gender2 = await genderT.data(); - const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; - if (confidence > (config3.face.description.minConfidence || 0)) { - obj.gender = gender2[0] <= 0.5 ? "female" : "male"; - obj.genderScore = Math.min(0.99, confidence); - } - const argmax = tf17.argMax(resT.find((t2) => t2.shape[1] === 100), 1); - const ageIdx = (await argmax.data())[0]; - tf17.dispose(argmax); - const ageT = resT.find((t2) => t2.shape[1] === 100); - const all2 = await ageT.data(); - obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; - if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) log("faceres error:", { model: model9, result: resT }); - const desc = resT.find((t2) => t2.shape[1] === 1024); - const descriptor = desc ? await desc.data() : []; - obj.descriptor = Array.from(descriptor); - resT.forEach((t2) => tf17.dispose(t2)); - } - last4[idx] = obj; - lastCount2 = count2; - resolve(obj); - }); -} - -// src/face/mask.ts -var expandFact = 0.1; -var alpha = 0.5; -function insidePoly(x, y, polygon) { - let inside = false; - let j = polygon.length - 1; - for (let i = 0; i < polygon.length; j = i++) { - if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) inside = !inside; - } - return inside; -} -async function mask(face4) { - if (!face4.tensor) return face4.tensor; - if (!face4.mesh || face4.mesh.length < 100) return face4.tensor; - const width = face4.tensor.shape[2] || 0; - const height = face4.tensor.shape[1] || 0; - const buffer = await face4.tensor.buffer(); - let silhouette = []; - for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); - if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); - for (let x = 0; x < width; x++) { - for (let y = 0; y < height; y++) { - const inside = insidePoly(x / width, y / width, silhouette); - if (!inside) { - buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); - buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); - buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); - } - } - } - const output = buffer.toTensor(); - return output; -} - -// src/face/antispoof.ts -var tf18 = __toESM(require_tfjs_esm()); -var model10; -var cached = []; -var skipped6 = Number.MAX_SAFE_INTEGER; -var lastCount3 = 0; -var lastTime6 = 0; -async function load8(config3) { - var _a; - if (env.initial) model10 = null; - if (!model10) model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model10["modelUrl"]); - return model10; -} -async function predict7(image28, config3, idx, count2) { - var _a, _b; - if (!(model10 == null ? void 0 : model10["executor"])) return 0; - const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; - const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { - skipped6++; - return cached[idx]; - } - skipped6 = 0; - return new Promise(async (resolve) => { - const resize = tf18.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); - const res = model10 == null ? void 0 : model10.execute(resize); - const num = (await res.data())[0]; - cached[idx] = Math.round(100 * num) / 100; - lastCount3 = count2; - lastTime6 = now(); - tf18.dispose([resize, res]); - resolve(cached[idx]); - }); -} - -// src/face/liveness.ts -var tf19 = __toESM(require_tfjs_esm()); -var model11; -var cached2 = []; -var skipped7 = Number.MAX_SAFE_INTEGER; -var lastCount4 = 0; -var lastTime7 = 0; -async function load9(config3) { - var _a; - if (env.initial) model11 = null; - if (!model11) model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model11["modelUrl"]); - return model11; -} -async function predict8(image28, config3, idx, count2) { - var _a, _b; - if (!(model11 == null ? void 0 : model11["executor"])) return 0; - const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; - const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { - skipped7++; - return cached2[idx]; - } - skipped7 = 0; - return new Promise(async (resolve) => { - const resize = tf19.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); - const res = model11 == null ? void 0 : model11.execute(resize); - const num = (await res.data())[0]; - cached2[idx] = Math.round(100 * num) / 100; - lastCount4 = count2; - lastTime7 = now(); - tf19.dispose([resize, res]); - resolve(cached2[idx]); - }); -} - -// src/gear/gear.ts -var tf20 = __toESM(require_tfjs_esm()); -var model12; -var last5 = []; -var raceNames = ["white", "black", "asian", "indian", "other"]; -var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; -var lastCount5 = 0; -var lastTime8 = 0; -var skipped8 = Number.MAX_SAFE_INTEGER; -async function load10(config3) { - var _a; - if (env.initial) model12 = null; - if (!model12) model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model12["modelUrl"]); - return model12; -} -async function predict9(image28, config3, idx, count2) { - var _a, _b; - if (!model12) return { age: 0, gender: "unknown", genderScore: 0, race: [] }; - const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; - if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { - skipped8++; - return last5[idx]; - } - skipped8 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c, _d; - if (!(model12 == null ? void 0 : model12.inputs[0].shape)) return; - const t2 = {}; - let box = [[0, 0.1, 0.9, 0.9]]; - if (((_a2 = config3.face.gear) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face.gear) == null ? void 0 : _b2["crop"]; - box = [[crop, crop, 1 - crop, 1 - crop]]; - } - t2.resize = tf20.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); - const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; - if ((_c = config3.face.gear) == null ? void 0 : _c.enabled) [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); - const gender2 = await t2.gender.data(); - obj.gender = gender2[0] > gender2[1] ? "male" : "female"; - obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; - const race = await t2.race.data(); - for (let i = 0; i < race.length; i++) { - if (race[i] > (((_d = config3.face.gear) == null ? void 0 : _d.minConfidence) || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); - } - obj.race.sort((a, b) => b.score - a.score); - const ageDistribution = Array.from(await t2.age.data()); - const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); - let age2 = ageSorted[0][0]; - for (let i = 1; i < ageSorted.length; i++) age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); - obj.age = Math.round(10 * age2) / 10; - Object.keys(t2).forEach((tensor6) => tf20.dispose(t2[tensor6])); - last5[idx] = obj; - lastCount5 = count2; - lastTime8 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-age.ts -var tf21 = __toESM(require_tfjs_esm()); -var model13; -var last6 = []; -var lastCount6 = 0; -var lastTime9 = 0; -var skipped9 = Number.MAX_SAFE_INTEGER; -async function load11(config3) { - if (env.initial) model13 = null; - if (!model13) model13 = await loadModel(config3.face["ssrnet"].modelPathAge); - else if (config3.debug) log("cached model:", model13["modelUrl"]); - return model13; -} -async function predict10(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model13) return { age: 0 }; - const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; - if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { - skipped9++; - return last6[idx]; - } - skipped9 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c2; - if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) return; - const t2 = {}; - if (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tf21.image.cropAndResize(image28, box, [0], [model13.inputs[0].shape[2], model13.inputs[0].shape[1]]); - } else { - t2.resize = tf21.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); - } - t2.enhance = tf21.mul(t2.resize, constants.tf255); - const obj = { age: 0 }; - if ((_c2 = config3.face["ssrnet"]) == null ? void 0 : _c2.enabled) t2.age = model13.execute(t2.enhance); - if (t2.age) { - const data = await t2.age.data(); - obj.age = Math.trunc(10 * data[0]) / 10; - } - Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6])); - last6[idx] = obj; - lastCount6 = count2; - lastTime9 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-gender.ts -var tf22 = __toESM(require_tfjs_esm()); -var model14; -var last7 = []; -var lastCount7 = 0; -var lastTime10 = 0; -var skipped10 = Number.MAX_SAFE_INTEGER; -var rgb2 = [0.2989, 0.587, 0.114]; -async function load12(config3) { - var _a; - if (env.initial) model14 = null; - if (!model14) model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); - else if (config3.debug) log("cached model:", model14["modelUrl"]); - return model14; -} -async function predict11(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model14) return { gender: "unknown", genderScore: 0 }; - const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; - if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped10++; - return last7[idx]; - } - skipped10 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c2; - if (!(model14 == null ? void 0 : model14.inputs[0].shape)) return; - const t2 = {}; - if (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tf22.image.cropAndResize(image28, box, [0], [model14.inputs[0].shape[2], model14.inputs[0].shape[1]]); - } else { - t2.resize = tf22.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); - } - t2.enhance = tf22.tidy(() => { - var _a3, _b3; - let normalize2; - if (((_b3 = (_a3 = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a3[0].shape) == null ? void 0 : _b3[3]) === 1) { - const [red, green, blue] = tf22.split(t2.resize, 3, 3); - const redNorm = tf22.mul(red, rgb2[0]); - const greenNorm = tf22.mul(green, rgb2[1]); - const blueNorm = tf22.mul(blue, rgb2[2]); - const grayscale = tf22.addN([redNorm, greenNorm, blueNorm]); - normalize2 = tf22.mul(tf22.sub(grayscale, constants.tf05), 2); - } else { - normalize2 = tf22.mul(tf22.sub(t2.resize, constants.tf05), 2); - } - return normalize2; - }); - const obj = { gender: "unknown", genderScore: 0 }; - if ((_c2 = config3.face["ssrnet"]) == null ? void 0 : _c2.enabled) t2.gender = model14.execute(t2.enhance); - const data = await t2.gender.data(); - obj.gender = data[0] > data[1] ? "female" : "male"; - obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; - Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6])); - last7[idx] = obj; - lastCount7 = count2; - lastTime10 = now(); - resolve(obj); - }); -} - -// src/face/mobilefacenet.ts -var tf23 = __toESM(require_tfjs_esm()); -var model15; -var last8 = []; -var lastCount8 = 0; -var lastTime11 = 0; -var skipped11 = Number.MAX_SAFE_INTEGER; -async function load13(config3) { - var _a; - if (env.initial) model15 = null; - if (!model15) model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model15["modelUrl"]); - return model15; -} -async function predict12(input, config3, idx, count2) { - var _a, _b; - if (!(model15 == null ? void 0 : model15["executor"])) return []; - const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; - if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { - skipped11++; - return last8[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf23.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); - t2.data = model15.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf23.dispose(t2[tensor6])); - } - last8[idx] = data; - lastCount8 = count2; - lastTime11 = now(); - resolve(data); - }); -} - -// src/face/insightface.ts -var tf24 = __toESM(require_tfjs_esm()); -var model16; -var last9 = []; -var lastCount9 = 0; -var lastTime12 = 0; -var skipped12 = Number.MAX_SAFE_INTEGER; -async function load14(config3) { - if (env.initial) model16 = null; - if (!model16) model16 = await loadModel(config3.face["insightface"].modelPath); - else if (config3.debug) log("cached model:", model16["modelUrl"]); - return model16; -} -async function predict13(input, config3, idx, count2) { - var _a, _b; - if (!(model16 == null ? void 0 : model16["executor"])) return []; - const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; - if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { - skipped12++; - return last9[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf24.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); - t2.data = model16.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6])); - } - last9[idx] = data; - lastCount9 = count2; - lastTime12 = now(); - resolve(data); - }); -} - -// src/face/angles.ts -var calculateGaze = (face4) => { - const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); - if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) return { bearing: 0, strength: 0 }; - const offsetIris = [0, -0.1]; - const eyeRatio = 1; - const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); - const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; - const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; - const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; - const eyeDiff = [ - // x distance between extreme point and center point normalized with eye size - (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], - eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] - ]; - let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); - strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); - const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; - return { bearing, strength }; -}; -var calculateFaceAngle = (face4, imageSize) => { - const normalize2 = (v) => { - const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); - v[0] /= length; - v[1] /= length; - v[2] /= length; - return v; - }; - const subVectors = (a, b) => { - const x = a[0] - b[0]; - const y = a[1] - b[1]; - const z = a[2] - b[2]; - return [x, y, z]; - }; - const crossVectors = (a, b) => { - const x = a[1] * b[2] - a[2] * b[1]; - const y = a[2] * b[0] - a[0] * b[2]; - const z = a[0] * b[1] - a[1] * b[0]; - return [x, y, z]; - }; - const rotationMatrixToEulerAngle = (r) => { - const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; - let thetaX; - let thetaY; - let thetaZ; - if (r10 < 1) { - if (r10 > -1) { - const cosThetaZ = Math.sqrt(r00 * r00 + r20 * r20); - thetaZ = Math.atan2(r10, cosThetaZ); - thetaY = Math.atan2(-r20, r00); - thetaX = Math.atan2(-r12, r11); - } else { - thetaZ = -Math.PI / 2; - thetaY = -Math.atan2(r21, r22); - thetaX = 0; - } - } else { - thetaZ = Math.PI / 2; - thetaY = Math.atan2(r21, r22); - thetaX = 0; - } - if (Number.isNaN(thetaX)) thetaX = 0; - if (Number.isNaN(thetaY)) thetaY = 0; - if (Number.isNaN(thetaZ)) thetaZ = 0; - return { pitch: -thetaX, yaw: -thetaY, roll: -thetaZ }; - }; - const mesh = face4.meshRaw; - if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; - const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; - const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); - const yAxis = normalize2(subVectors(pts[1], pts[0])); - let xAxis = normalize2(subVectors(pts[3], pts[2])); - const zAxis = normalize2(crossVectors(xAxis, yAxis)); - xAxis = crossVectors(yAxis, zAxis); - const matrix = [ - xAxis[0], - xAxis[1], - xAxis[2], - yAxis[0], - yAxis[1], - yAxis[2], - zAxis[0], - zAxis[1], - zAxis[2] - ]; - const angle = rotationMatrixToEulerAngle(matrix); - const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; - return { angle, matrix, gaze }; -}; - -// src/face/anthropometry.ts -function calculateCameraDistance(face4, width) { - const f = face4 == null ? void 0 : face4.annotations; - if (!(f == null ? void 0 : f.leftEyeIris) || !(f == null ? void 0 : f.rightEyeIris)) return 0; - const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; - const cameraDistance = Math.round(1.17 / irisSize) / 100; - return cameraDistance; -} - -// src/face/face.ts -var detectFace = async (instance, input) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - let timeStamp = now(); - let ageRes; - let gearRes; - let genderRes; - let emotionRes; - let mobilefacenetRes; - let insightfaceRes; - let antispoofRes; - let livenessRes; - let descRes; - const faceRes = []; - instance.state = "run:face"; - const faces = await predict4(input, instance.config); - instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - if (!input.shape || input.shape.length !== 4) return []; - if (!faces) return []; - for (let i = 0; i < faces.length; i++) { - instance.analyze("Get Face"); - if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { - log("Face object is disposed:", faces[i].tensor); - continue; - } - if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { - const masked = await mask(faces[i]); - tf25.dispose(faces[i].tensor); - if (masked) faces[i].tensor = masked; - } - const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; - instance.analyze("Start Emotion:"); - if (instance.config.async) { - emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - } else { - instance.state = "run:emotion"; - timeStamp = now(); - emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Emotion:"); - instance.analyze("Start AntiSpoof:"); - if (instance.config.async) { - antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:antispoof"; - timeStamp = now(); - antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End AntiSpoof:"); - instance.analyze("Start Liveness:"); - if (instance.config.async) { - livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:liveness"; - timeStamp = now(); - livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Liveness:"); - instance.analyze("Start GEAR:"); - if (instance.config.async) { - gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:gear"; - timeStamp = now(); - gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.gear = Math.trunc(now() - timeStamp); - } - instance.analyze("End GEAR:"); - instance.analyze("Start SSRNet:"); - if (instance.config.async) { - ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:ssrnet"; - timeStamp = now(); - ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.ssrnet = Math.trunc(now() - timeStamp); - } - instance.analyze("End SSRNet:"); - instance.analyze("Start MobileFaceNet:"); - if (instance.config.async) { - mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End MobileFaceNet:"); - instance.analyze("Start InsightFace:"); - if (instance.config.async) { - insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End InsightFace:"); - instance.analyze("Start Description:"); - if (instance.config.async) { - descRes = predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - } else { - instance.state = "run:description"; - timeStamp = now(); - descRes = await predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Description:"); - if (instance.config.async) { - [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); - } - instance.analyze("Finish Face:"); - if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { - descRes = { - ...descRes, - age: ageRes.age, - gender: genderRes.gender, - genderScore: genderRes.genderScore - }; - } - if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { - descRes = { - ...descRes, - age: gearRes.age, - gender: gearRes.gender, - genderScore: gearRes.genderScore, - race: gearRes.race - }; - } - if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { - descRes.descriptor = mobilefacenetRes; - } - if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { - descRes.descriptor = insightfaceRes; - } - const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; - const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tf25.squeeze(faces[i].tensor) : null; - tf25.dispose(faces[i].tensor); - if (faces[i].tensor) delete faces[i].tensor; - const res = { - ...faces[i], - id: i - }; - if (descRes.age) res.age = descRes.age; - if (descRes.gender) res.gender = descRes.gender; - if (descRes.genderScore) res.genderScore = descRes.genderScore; - if (descRes.descriptor) res.embedding = descRes.descriptor; - if (descRes.race) res.race = descRes.race; - if (emotionRes) res.emotion = emotionRes; - if (antispoofRes) res.real = antispoofRes; - if (livenessRes) res.live = livenessRes; - if (irisSize > 0) res.distance = irisSize; - if (rotation) res.rotation = rotation; - if (tensor6) res.tensor = tensor6; - faceRes.push(res); - instance.analyze("End Face"); - } - instance.analyze("End FaceMesh:"); - if (instance.config.async) { - if (instance.performance.face) delete instance.performance.face; - if (instance.performance.age) delete instance.performance.age; - if (instance.performance.gender) delete instance.performance.gender; - if (instance.performance.emotion) delete instance.performance.emotion; - } - return faceRes; -}; - -// src/hand/fingerdef.ts -var Finger = { - thumb: 0, - index: 1, - middle: 2, - ring: 3, - pinky: 4, - all: [0, 1, 2, 3, 4], - // just for convenience - nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, - // Describes mapping of joints based on the 21 points returned by handpose. - // [0] Palm - // [1-4] Thumb - // [5-8] Index - // [9-12] Middle - // [13-16] Ring - // [17-20] Pinky - pointsMapping: { - 0: [[0, 1], [1, 2], [2, 3], [3, 4]], - 1: [[0, 5], [5, 6], [6, 7], [7, 8]], - 2: [[0, 9], [9, 10], [10, 11], [11, 12]], - 3: [[0, 13], [13, 14], [14, 15], [15, 16]], - 4: [[0, 17], [17, 18], [18, 19], [19, 20]] - }, - getName: (value) => Finger.nameMapping[value], - getPoints: (value) => Finger.pointsMapping[value] -}; -var FingerCurl = { - none: 0, - half: 1, - full: 2, - nameMapping: { 0: "none", 1: "half", 2: "full" }, - getName: (value) => FingerCurl.nameMapping[value] -}; -var FingerDirection = { - verticalUp: 0, - verticalDown: 1, - horizontalLeft: 2, - horizontalRight: 3, - diagonalUpRight: 4, - diagonalUpLeft: 5, - diagonalDownRight: 6, - diagonalDownLeft: 7, - nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, - getName: (value) => FingerDirection.nameMapping[value] -}; -var FingerGesture = class { - constructor(name) { - __publicField(this, "name"); - __publicField(this, "curls"); - __publicField(this, "directions"); - __publicField(this, "weights"); - __publicField(this, "weightsRelative"); - this.name = name; - this.curls = {}; - this.directions = {}; - this.weights = [1, 1, 1, 1, 1]; - this.weightsRelative = [1, 1, 1, 1, 1]; - } - curl(finger, curl, confidence) { - if (typeof this.curls[finger] === "undefined") this.curls[finger] = []; - this.curls[finger].push([curl, confidence]); - } - direction(finger, position, confidence) { - if (!this.directions[finger]) this.directions[finger] = []; - this.directions[finger].push([position, confidence]); - } - weight(finger, weight) { - this.weights[finger] = weight; - const total = this.weights.reduce((a, b) => a + b, 0); - this.weightsRelative = this.weights.map((el) => el * 5 / total); - } - matchAgainst(detectedCurls, detectedDirections) { - let confidence = 0; - for (const fingerIdx in detectedCurls) { - const detectedCurl = detectedCurls[fingerIdx]; - const expectedCurls = this.curls[fingerIdx]; - if (typeof expectedCurls === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedCurl, score] of expectedCurls) { - if (detectedCurl === expectedCurl) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - for (const fingerIdx in detectedDirections) { - const detectedDirection = detectedDirections[fingerIdx]; - const expectedDirections = this.directions[fingerIdx]; - if (typeof expectedDirections === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedDirection, score] of expectedDirections) { - if (detectedDirection === expectedDirection) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - return confidence / 10; - } -}; - -// src/hand/fingergesture.ts -var { thumb, index, middle, ring, pinky } = Finger; -var { none, half, full } = FingerCurl; -var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; -var ThumbsUp = new FingerGesture("thumbs up"); -ThumbsUp.curl(thumb, none, 1); -ThumbsUp.direction(thumb, verticalUp, 1); -ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); -ThumbsUp.direction(thumb, diagonalUpRight, 0.25); -for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { - ThumbsUp.curl(finger, full, 1); - ThumbsUp.direction(finger, horizontalLeft, 1); - ThumbsUp.direction(finger, horizontalRight, 1); -} -var Victory = new FingerGesture("victory"); -Victory.curl(thumb, half, 0.5); -Victory.curl(thumb, none, 0.5); -Victory.direction(thumb, verticalUp, 1); -Victory.direction(thumb, diagonalUpLeft, 1); -Victory.curl(index, none, 1); -Victory.direction(index, verticalUp, 0.75); -Victory.direction(index, diagonalUpLeft, 1); -Victory.curl(middle, none, 1); -Victory.direction(middle, verticalUp, 1); -Victory.direction(middle, diagonalUpLeft, 0.75); -Victory.curl(ring, full, 1); -Victory.direction(ring, verticalUp, 0.2); -Victory.direction(ring, diagonalUpLeft, 1); -Victory.direction(ring, horizontalLeft, 0.2); -Victory.curl(pinky, full, 1); -Victory.direction(pinky, verticalUp, 0.2); -Victory.direction(pinky, diagonalUpLeft, 1); -Victory.direction(pinky, horizontalLeft, 0.2); -Victory.weight(index, 2); -Victory.weight(middle, 2); -var Point = new FingerGesture("point"); -Point.curl(thumb, full, 1); -Point.curl(index, none, 0.5); -Point.curl(middle, full, 0.5); -Point.curl(ring, full, 0.5); -Point.curl(pinky, full, 0.5); -Point.weight(index, 2); -Point.weight(middle, 2); -var MiddleFinger = new FingerGesture("middle finger"); -MiddleFinger.curl(thumb, none, 1); -MiddleFinger.curl(index, full, 0.5); -MiddleFinger.curl(middle, full, 0.5); -MiddleFinger.curl(ring, full, 0.5); -MiddleFinger.curl(pinky, full, 0.5); -MiddleFinger.weight(index, 2); -MiddleFinger.weight(middle, 2); -var OpenPalm = new FingerGesture("open palm"); -OpenPalm.curl(thumb, none, 0.75); -OpenPalm.curl(index, none, 0.75); -OpenPalm.curl(middle, none, 0.75); -OpenPalm.curl(ring, none, 0.75); -OpenPalm.curl(pinky, none, 0.75); -var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; - -// src/hand/fingerpose.ts -var minConfidence = 0.7; -var options3 = { - // curl estimation - HALF_CURL_START_LIMIT: 60, - NO_CURL_START_LIMIT: 130, - // direction estimation - DISTANCE_VOTE_POWER: 1.1, - SINGLE_ANGLE_VOTE_POWER: 0.9, - TOTAL_ANGLE_VOTE_POWER: 1.6 -}; -function calculateSlope(point1x, point1y, point2x, point2y) { - const value = (point1y - point2y) / (point1x - point2x); - let slope = Math.atan(value) * 180 / Math.PI; - if (slope <= 0) slope = -slope; - else if (slope > 0) slope = 180 - slope; - return slope; -} -function getSlopes(point1, point2) { - if (!point1 || !point2) return [0, 0]; - const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); - if (point1.length === 2) return slopeXY; - const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); - return [slopeXY, slopeYZ]; -} -function angleOrientationAt(angle, weightageAt = 1) { - let isVertical = 0; - let isDiagonal = 0; - let isHorizontal = 0; - if (angle >= 75 && angle <= 105) isVertical = 1 * weightageAt; - else if (angle >= 25 && angle <= 155) isDiagonal = 1 * weightageAt; - else isHorizontal = 1 * weightageAt; - return [isVertical, isDiagonal, isHorizontal]; -} -function estimateFingerCurl(startPoint, midPoint, endPoint) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const start_mid_z_dist = startPoint[2] - midPoint[2]; - const start_end_z_dist = startPoint[2] - endPoint[2]; - const mid_end_z_dist = midPoint[2] - endPoint[2]; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); - let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); - if (cos_in > 1) cos_in = 1; - else if (cos_in < -1) cos_in = -1; - let angleOfCurve = Math.acos(cos_in); - angleOfCurve = 57.2958 * angleOfCurve % 180; - let fingerCurl; - if (angleOfCurve > options3.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none; - else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half; - else fingerCurl = FingerCurl.full; - return fingerCurl; -} -function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - if (max_dist_x === Math.abs(start_end_x_dist)) { - if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } else if (max_dist_x === Math.abs(start_mid_x_dist)) { - if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } else { - if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } - return estimatedDirection; -} -function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { - let estimatedDirection; - if (max_dist_y === Math.abs(start_end_y_dist)) { - if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } else if (max_dist_y === Math.abs(start_mid_y_dist)) { - if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } else { - if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } - return estimatedDirection; -} -function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - if (reqd_vertical_direction === FingerDirection.verticalUp) { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft; - else estimatedDirection = FingerDirection.diagonalUpRight; - } else { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft; - else estimatedDirection = FingerDirection.diagonalDownRight; - } - return estimatedDirection; -} -function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); - const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); - let voteVertical = 0; - let voteDiagonal = 0; - let voteHorizontal = 0; - const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); - if (start_end_x_y_dist_ratio > 1.5) voteVertical += options3.DISTANCE_VOTE_POWER; - else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options3.DISTANCE_VOTE_POWER; - else voteHorizontal += options3.DISTANCE_VOTE_POWER; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); - const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); - let calc_start_point_x = startPoint[0]; - let calc_start_point_y = startPoint[1]; - let calc_end_point_x = endPoint[0]; - let calc_end_point_y = endPoint[1]; - if (max_dist === start_mid_dist) { - calc_end_point_x = endPoint[0]; - calc_end_point_y = endPoint[1]; - } else if (max_dist === mid_end_dist) { - calc_start_point_x = midPoint[0]; - calc_start_point_y = midPoint[1]; - } - const calcStartPoint = [calc_start_point_x, calc_start_point_y]; - const calcEndPoint = [calc_end_point_x, calc_end_point_y]; - const totalAngle = getSlopes(calcStartPoint, calcEndPoint); - const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); - voteVertical += votes[0]; - voteDiagonal += votes[1]; - voteHorizontal += votes[2]; - for (const fingerSlope of fingerSlopes) { - const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); - voteVertical += fingerVotes[0]; - voteDiagonal += fingerVotes[1]; - voteHorizontal += fingerVotes[2]; - } - let estimatedDirection; - if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } else { - estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } - return estimatedDirection; -} -function estimate(landmarks) { - const slopesXY = []; - const slopesYZ = []; - const fingerCurls = []; - const fingerDirections = []; - if (!landmarks) return { curls: fingerCurls, directions: fingerDirections }; - for (const finger of Finger.all) { - const points = Finger.getPoints(finger); - const slopeAtXY = []; - const slopeAtYZ = []; - for (const point2 of points) { - const point1 = landmarks[point2[0]]; - const point22 = landmarks[point2[1]]; - const slopes = getSlopes(point1, point22); - const slopeXY = slopes[0]; - const slopeYZ = slopes[1]; - slopeAtXY.push(slopeXY); - slopeAtYZ.push(slopeYZ); - } - slopesXY.push(slopeAtXY); - slopesYZ.push(slopeAtYZ); - } - for (const finger of Finger.all) { - const pointIndexAt = finger === Finger.thumb ? 1 : 0; - const fingerPointsAt = Finger.getPoints(finger); - const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; - const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; - const endPoint = landmarks[fingerPointsAt[3][1]]; - const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); - const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); - fingerCurls[finger] = fingerCurled; - fingerDirections[finger] = fingerPosition; - } - return { curls: fingerCurls, directions: fingerDirections }; -} -function analyze(keypoints) { - if (!keypoints || keypoints.length === 0) return null; - const estimatorRes = estimate(keypoints); - const landmarks = {}; - for (const fingerIdx of Finger.all) { - landmarks[Finger.getName(fingerIdx)] = { - curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), - direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) - }; - } - return landmarks; -} -function match(keypoints) { - const poses = []; - if (!keypoints || keypoints.length === 0) return poses; - const estimatorRes = estimate(keypoints); - for (const gesture2 of fingergesture_default) { - const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); - if (confidence >= minConfidence) poses.push({ name: gesture2.name, confidence }); - } - return poses; -} - -// src/gesture/gesture.ts -var body2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); - const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); - const nose = res[i].keypoints.find((a) => a.part === "nose"); - if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "i give up" }); - else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "raise left hand" }); - else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "raise right hand" }); - const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); - const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); - if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { - gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); - } - } - return gestures; -}; -var face2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (res[i].mesh && res[i].mesh.length > 450) { - const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); - const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; - if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: "facing center" }); - else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); - const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); - if (openLeft < 0.2) gestures.push({ face: i, gesture: "blink left eye" }); - const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); - if (openRight < 0.2) gestures.push({ face: i, gesture: "blink right eye" }); - const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); - if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); - const chinDepth = res[i].mesh[152][2] || 0; - if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); - } - } - return gestures; -}; -var iris2 = (res) => { - var _a, _b, _c, _d; - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) continue; - const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; - const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; - const areaLeft = Math.abs(sizeXLeft * sizeYLeft); - const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; - const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; - const areaRight = Math.abs(sizeXRight * sizeYRight); - let center = false; - const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); - if (difference < 0.25) { - center = true; - gestures.push({ iris: i, gesture: "facing center" }); - } - const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; - const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; - if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false; - if (leftIrisCenterX > rightIrisCenterX) { - if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: "looking right" }); - } else { - if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: "looking left" }); - } - const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; - const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: "looking down" }); - if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: "looking up" }); - if (center) gestures.push({ iris: i, gesture: "looking center" }); - } - return gestures; -}; -var hand2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const fingers = []; - if (res[i].annotations) { - for (const [finger, pos] of Object.entries(res[i].annotations)) { - if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); - } - } - if (fingers && fingers.length > 0) { - const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); - gestures.push({ hand: i, gesture: `${closest.name} forward` }); - const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); - gestures.push({ hand: i, gesture: `${highest.name} up` }); - } - if (res[i].keypoints) { - const poses = match(res[i].keypoints); - for (const pose of poses) gestures.push({ hand: i, gesture: pose.name }); - } - } - return gestures; -}; - -// src/hand/handposedetector.ts -var tf27 = __toESM(require_tfjs_esm()); - -// src/hand/handposeutil.ts -var tf26 = __toESM(require_tfjs_esm()); -function getBoxSize2(box) { - return [ - Math.abs(box.endPoint[0] - box.startPoint[0]), - Math.abs(box.endPoint[1] - box.startPoint[1]) - ]; -} -function getBoxCenter2(box) { - return [ - box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, - box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 - ]; -} -function cutBoxFromImageAndResize(box, image28, cropSize) { - const h = image28.shape[1]; - const w = image28.shape[2]; - const boxes = [[ - box.startPoint[1] / h, - box.startPoint[0] / w, - box.endPoint[1] / h, - box.endPoint[0] / w - ]]; - return tf26.image.cropAndResize(image28, boxes, [0], cropSize); -} -function scaleBoxCoordinates2(box, factor) { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const palmLandmarks = box.palmLandmarks.map((coord) => { - const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; - return scaledCoord; - }); - return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; -} -function enlargeBox2(box, factor = 1.5) { - const center = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; - const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function squarifyBox2(box) { - const centers = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const maxEdge = Math.max(...size2); - const halfSize = maxEdge / 2; - const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; - const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function normalizeRadians2(angle) { - return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -} -function computeRotation2(point1, point2) { - const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); - return normalizeRadians2(radians); -} -var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -function dot2(v1, v2) { - let product = 0; - for (let i = 0; i < v1.length; i++) { - product += v1[i] * v2[i]; - } - return product; -} -function getColumnFrom2DArr2(arr, columnIndex) { - const column = []; - for (let i = 0; i < arr.length; i++) { - column.push(arr[i][columnIndex]); - } - return column; -} -function multiplyTransformMatrices2(mat1, mat2) { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) { - product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); - } - } - return product; -} -function buildRotationMatrix2(rotation, center) { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix2(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); - return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); -} -function invertTransformMatrix2(matrix) { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [ - -dot2(rotationComponent[0], translationComponent), - -dot2(rotationComponent[1], translationComponent) - ]; - return [ - rotationComponent[0].concat(invertedTranslation[0]), - rotationComponent[1].concat(invertedTranslation[1]), - [0, 0, 1] - ]; -} -function rotatePoint2(homogeneousCoordinate, rotationMatrix) { - return [ - dot2(homogeneousCoordinate, rotationMatrix[0]), - dot2(homogeneousCoordinate, rotationMatrix[1]) - ]; -} - -// src/hand/handposeanchors.ts -var anchors2 = [ - { x: 0.015625, y: 0.015625 }, - { x: 0.015625, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - { x: 0.890625, y: 0.078125 }, - { x: 0.890625, y: 0.078125 }, - { x: 0.921875, y: 0.078125 }, - { x: 0.921875, y: 0.078125 }, - { x: 0.953125, y: 0.078125 }, - { x: 0.953125, y: 0.078125 }, - { x: 0.984375, y: 0.078125 }, - { x: 0.984375, y: 0.078125 }, - { x: 0.015625, y: 0.109375 }, - { x: 0.015625, y: 0.109375 }, - { x: 0.046875, y: 0.109375 }, - { x: 0.046875, y: 0.109375 }, - { x: 0.078125, y: 0.109375 }, - { x: 0.078125, y: 0.109375 }, - { x: 0.109375, y: 0.109375 }, - { x: 0.109375, y: 0.109375 }, - { x: 0.140625, y: 0.109375 }, - { x: 0.140625, y: 0.109375 }, - { x: 0.171875, y: 0.109375 }, - { x: 0.171875, y: 0.109375 }, - { x: 0.203125, y: 0.109375 }, - { x: 0.203125, y: 0.109375 }, - { x: 0.234375, y: 0.109375 }, - { x: 0.234375, y: 0.109375 }, - { x: 0.265625, y: 0.109375 }, - { x: 0.265625, y: 0.109375 }, - { x: 0.296875, y: 0.109375 }, - { x: 0.296875, y: 0.109375 }, - { x: 0.328125, y: 0.109375 }, - { x: 0.328125, y: 0.109375 }, - { x: 0.359375, y: 0.109375 }, - { x: 0.359375, y: 0.109375 }, - { x: 0.390625, y: 0.109375 }, - { x: 0.390625, y: 0.109375 }, - { x: 0.421875, y: 0.109375 }, - { x: 0.421875, y: 0.109375 }, - { x: 0.453125, y: 0.109375 }, - { x: 0.453125, y: 0.109375 }, - { x: 0.484375, y: 0.109375 }, - { x: 0.484375, y: 0.109375 }, - { x: 0.515625, y: 0.109375 }, - { x: 0.515625, y: 0.109375 }, - { x: 0.546875, y: 0.109375 }, - { x: 0.546875, y: 0.109375 }, - { x: 0.578125, y: 0.109375 }, - { x: 0.578125, y: 0.109375 }, - { x: 0.609375, y: 0.109375 }, - { x: 0.609375, y: 0.109375 }, - { x: 0.640625, y: 0.109375 }, - { x: 0.640625, y: 0.109375 }, - { x: 0.671875, y: 0.109375 }, - { x: 0.671875, y: 0.109375 }, - { x: 0.703125, y: 0.109375 }, - { x: 0.703125, y: 0.109375 }, - { x: 0.734375, y: 0.109375 }, - { x: 0.734375, y: 0.109375 }, - { x: 0.765625, y: 0.109375 }, - { x: 0.765625, y: 0.109375 }, - { x: 0.796875, y: 0.109375 }, - { x: 0.796875, y: 0.109375 }, - { x: 0.828125, y: 0.109375 }, - { x: 0.828125, y: 0.109375 }, - { x: 0.859375, y: 0.109375 }, - { x: 0.859375, y: 0.109375 }, - 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-// src/hand/handposedetector.ts -var HandDetector = class { - constructor(model23) { - __publicField(this, "model"); - __publicField(this, "anchors"); - __publicField(this, "anchorsTensor"); - __publicField(this, "inputSize"); - __publicField(this, "inputSizeTensor"); - __publicField(this, "doubleInputSizeTensor"); - var _a, _b, _c, _d; - this.model = model23; - this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); - this.anchorsTensor = tf27.tensor2d(this.anchors); - this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; - this.inputSizeTensor = tf27.tensor1d([this.inputSize, this.inputSize]); - this.doubleInputSizeTensor = tf27.tensor1d([this.inputSize * 2, this.inputSize * 2]); - } - normalizeBoxes(boxes) { - const t2 = {}; - t2.boxOffsets = tf27.slice(boxes, [0, 0], [-1, 2]); - t2.boxSizes = tf27.slice(boxes, [0, 2], [-1, 2]); - t2.div = tf27.div(t2.boxOffsets, this.inputSizeTensor); - t2.boxCenterPoints = tf27.add(t2.div, this.anchorsTensor); - t2.halfBoxSizes = tf27.div(t2.boxSizes, this.doubleInputSizeTensor); - t2.sub = tf27.sub(t2.boxCenterPoints, t2.halfBoxSizes); - t2.startPoints = tf27.mul(t2.sub, this.inputSizeTensor); - t2.add = tf27.add(t2.boxCenterPoints, t2.halfBoxSizes); - t2.endPoints = tf27.mul(t2.add, this.inputSizeTensor); - const res = tf27.concat2d([t2.startPoints, t2.endPoints], 1); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - normalizeLandmarks(rawPalmLandmarks, index2) { - const t2 = {}; - t2.reshape = tf27.reshape(rawPalmLandmarks, [-1, 7, 2]); - t2.div = tf27.div(t2.reshape, this.inputSizeTensor); - t2.landmarks = tf27.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); - const res = tf27.mul(t2.landmarks, this.inputSizeTensor); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - async predict(input, config3) { - var _a; - const t2 = {}; - t2.resize = tf27.image.resizeBilinear(input, [this.inputSize, this.inputSize]); - t2.div = tf27.div(t2.resize, constants.tf127); - t2.image = tf27.sub(t2.div, constants.tf1); - t2.batched = this.model.execute(t2.image); - t2.predictions = tf27.squeeze(t2.batched); - t2.slice = tf27.slice(t2.predictions, [0, 0], [-1, 1]); - t2.sigmoid = tf27.sigmoid(t2.slice); - t2.scores = tf27.squeeze(t2.sigmoid); - const scores = await t2.scores.data(); - t2.boxes = tf27.slice(t2.predictions, [0, 1], [-1, 4]); - t2.norm = this.normalizeBoxes(t2.boxes); - t2.nms = await tf27.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); - const nms = await t2.nms.array(); - const hands = []; - for (const index2 of nms) { - const p = {}; - p.box = tf27.slice(t2.norm, [index2, 0], [1, -1]); - p.slice = tf27.slice(t2.predictions, [index2, 5], [1, 14]); - p.norm = this.normalizeLandmarks(p.slice, index2); - p.palmLandmarks = tf27.reshape(p.norm, [-1, 2]); - const box = await p.box.data(); - const startPoint = box.slice(0, 2); - const endPoint = box.slice(2, 4); - const palmLandmarks = await p.palmLandmarks.array(); - const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; - const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); - hands.push(scaled); - Object.keys(p).forEach((tensor6) => tf27.dispose(p[tensor6])); - } - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return hands; - } -}; - -// src/hand/handposepipeline.ts -var tf28 = __toESM(require_tfjs_esm()); -var palmBoxEnlargeFactor = 5; -var handBoxEnlargeFactor = 1.65; -var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; -var palmLandmarksPalmBase = 0; -var palmLandmarksMiddleFingerBase = 2; -var lastTime13 = 0; -var HandPipeline = class { - constructor(handDetector, handPoseModel2) { - __publicField(this, "handDetector"); - __publicField(this, "handPoseModel"); - __publicField(this, "inputSize"); - __publicField(this, "storedBoxes"); - __publicField(this, "skipped"); - __publicField(this, "detectedHands"); - var _a, _b, _c; - this.handDetector = handDetector; - this.handPoseModel = handPoseModel2; - this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; - this.storedBoxes = []; - this.skipped = Number.MAX_SAFE_INTEGER; - this.detectedHands = 0; - } - calculateLandmarksBoundingBox(landmarks) { - const xs = landmarks.map((d) => d[0]); - const ys = landmarks.map((d) => d[1]); - const startPoint = [Math.min(...xs), Math.min(...ys)]; - const endPoint = [Math.max(...xs), Math.max(...ys)]; - return { startPoint, endPoint }; - } - getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { - const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); - const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); - return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); - } - getBoxForHandLandmarks(landmarks) { - const boundingBox = this.calculateLandmarksBoundingBox(landmarks); - const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); - boxAroundHand.palmLandmarks = []; - for (let i = 0; i < palmLandmarkIds.length; i++) { - boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); - } - return boxAroundHand; - } - transformRawCoords(rawCoords, box2, angle, rotationMatrix) { - const boxSize = getBoxSize2(box2); - const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; - const coordsScaled = rawCoords.map((coord) => [ - scaleFactor[0] * (coord[0] - this.inputSize / 2), - scaleFactor[1] * (coord[1] - this.inputSize / 2), - scaleFactor[2] * coord[2] - ]); - const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); - const coordsRotated = coordsScaled.map((coord) => { - const rotated = rotatePoint2(coord, coordsRotationMatrix); - return [...rotated, coord[2]]; - }); - const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); - const boxCenter = [...getBoxCenter2(box2), 1]; - const originalBoxCenter = [ - dot2(boxCenter, inverseRotationMatrix[0]), - dot2(boxCenter, inverseRotationMatrix[1]) - ]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + originalBoxCenter[0]), - Math.trunc(coord[1] + originalBoxCenter[1]), - Math.trunc(coord[2]) - ]); - } - async estimateHands(image28, config3) { - let useFreshBox = false; - let boxes; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; - const skipFrame = this.skipped < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - this.skipped++; - } else { - boxes = await this.handDetector.predict(image28, config3); - this.skipped = 0; - } - if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { - this.detectedHands = 0; - this.storedBoxes = [...boxes]; - if (this.storedBoxes.length > 0) useFreshBox = true; - } - const hands = []; - for (let i = 0; i < this.storedBoxes.length; i++) { - const currentBox = this.storedBoxes[i]; - if (!currentBox) continue; - if (config3.hand.landmarks) { - const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; - const palmCenter = getBoxCenter2(currentBox); - const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; - const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf28.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); - const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); - const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; - const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); - const handImage = tf28.div(croppedInput, constants.tf255); - tf28.dispose(croppedInput); - tf28.dispose(rotatedImage); - const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); - lastTime13 = now(); - tf28.dispose(handImage); - const confidence = (await confidenceT.data())[0]; - tf28.dispose(confidenceT); - if (confidence >= config3.hand.minConfidence / 4) { - const keypointsReshaped = tf28.reshape(keypoints, [-1, 3]); - const rawCoords = await keypointsReshaped.array(); - tf28.dispose(keypoints); - tf28.dispose(keypointsReshaped); - const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); - const nextBoundingBox = this.getBoxForHandLandmarks(coords); - this.storedBoxes[i] = { ...nextBoundingBox, confidence }; - const result = { - landmarks: coords, - confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: confidence, - box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } - }; - hands.push(result); - } else { - this.storedBoxes[i] = null; - } - tf28.dispose(keypoints); - } else { - const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); - const result = { - confidence: currentBox.confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: 0, - box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, - landmarks: [] - }; - hands.push(result); - } - } - this.storedBoxes = this.storedBoxes.filter((a) => a !== null); - this.detectedHands = hands.length; - if (hands.length > config3.hand.maxDetected) hands.length = config3.hand.maxDetected; - return hands; - } -}; - -// src/hand/handpose.ts -var meshAnnotations2 = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - palm: [0] -}; -var handDetectorModel; -var handPoseModel; -var handPipeline; -function initPipeline() { - const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; - if (handDetector && handPoseModel) handPipeline = new HandPipeline(handDetector, handPoseModel); -} -async function predict14(input, config3) { - if (!handPipeline) initPipeline(); - const predictions = await handPipeline.estimateHands(input, config3); - if (!predictions) return []; - const hands = []; - for (let i = 0; i < predictions.length; i++) { - const annotations2 = {}; - if (predictions[i].landmarks) { - for (const key of Object.keys(meshAnnotations2)) { - annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); - } - } - const keypoints = predictions[i].landmarks; - let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; - let boxRaw = [0, 0, 0, 0]; - if (keypoints && keypoints.length > 0) { - for (const pt of keypoints) { - if (pt[0] < box[0]) box[0] = pt[0]; - if (pt[1] < box[1]) box[1] = pt[1]; - if (pt[0] > box[2]) box[2] = pt[0]; - if (pt[1] > box[3]) box[3] = pt[1]; - } - box[2] -= box[0]; - box[3] -= box[1]; - boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; - } else { - box = predictions[i].box ? [ - Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), - Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) - ] : [0, 0, 0, 0]; - boxRaw = [ - predictions[i].box.topLeft[0] / (input.shape[2] || 0), - predictions[i].box.topLeft[1] / (input.shape[1] || 0), - (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), - (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) - ]; - } - const landmarks = analyze(keypoints); - hands.push({ - id: i, - score: Math.round(100 * predictions[i].confidence) / 100, - boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, - fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, - label: "hand", - box, - boxRaw, - keypoints, - annotations: annotations2, - landmarks - }); - } - return hands; -} -async function loadDetect2(config3) { - var _a; - if (env.initial) handDetectorModel = null; - if (!handDetectorModel) handDetectorModel = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", handDetectorModel["modelUrl"]); - return handDetectorModel; -} -async function loadSkeleton(config3) { - var _a; - if (env.initial) handPoseModel = null; - if (!handPoseModel) handPoseModel = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", handPoseModel["modelUrl"]); - return handPoseModel; -} - -// src/hand/handtrack.ts -var tf29 = __toESM(require_tfjs_esm()); -var models2 = [null, null]; -var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; -var inputSize7 = [[0, 0], [0, 0]]; -var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; -var faceIndex = 4; -var boxExpandFact = 1.6; -var maxDetectorResolution = 512; -var detectorExpandFact = 1.4; -var skipped13 = Number.MAX_SAFE_INTEGER; -var lastTime14 = 0; -var outputSize = [0, 0]; -var cache4 = { - boxes: [], - hands: [] -}; -var fingerMap = { - /* - thumb: [0, 1, 2, 3, 4], - index: [0, 5, 6, 7, 8], - middle: [0, 9, 10, 11, 12], - ring: [0, 13, 14, 15, 16], - pinky: [0, 17, 18, 19, 20], - palm: [0], - */ - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - base: [0], - palm: [0, 17, 13, 9, 5, 1, 0] -}; -async function loadDetect3(config3) { - var _a; - if (env.initial) models2[0] = null; - if (!models2[0]) { - fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); - models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; - inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", models2[0]["modelUrl"]); - return models2[0]; -} -async function loadSkeleton2(config3) { - var _a; - if (env.initial) models2[1] = null; - if (!models2[1]) { - models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; - inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", models2[1]["modelUrl"]); - return models2[1]; -} -async function detectHands(input, config3) { - const hands = []; - if (!input || !models2[0]) return hands; - const t2 = {}; - const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); - const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); - const width = Math.round(height * ratio2 / 8) * 8; - t2.resize = tf29.image.resizeBilinear(input, [height, width]); - t2.cast = tf29.cast(t2.resize, "int32"); - [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); - t2.boxes = tf29.squeeze(t2.rawBoxes, [0, 2]); - t2.scores = tf29.squeeze(t2.rawScores, [0]); - const classScores = tf29.unstack(t2.scores, 1); - tf29.dispose(classScores[faceIndex]); - classScores.splice(faceIndex, 1); - t2.filtered = tf29.stack(classScores, 1); - tf29.dispose(classScores); - t2.max = tf29.max(t2.filtered, 1); - t2.argmax = tf29.argMax(t2.filtered, 1); - let id = 0; - t2.nms = await tf29.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); - const nms = await t2.nms.data(); - const scores = await t2.max.data(); - const classNum = await t2.argmax.data(); - for (const nmsIndex of Array.from(nms)) { - const boxSlice = tf29.slice(t2.boxes, nmsIndex, 1); - const boxYX = await boxSlice.data(); - tf29.dispose(boxSlice); - const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; - const boxRaw = scale(boxData, detectorExpandFact); - const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; - const score = scores[nmsIndex]; - const label = classes[classNum[nmsIndex]]; - const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; - hands.push(hand3); - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - hands.sort((a, b) => b.score - a.score); - if (hands.length > (config3.hand.maxDetected || 1)) hands.length = config3.hand.maxDetected || 1; - return hands; -} -async function detectFingers(input, h, config3) { - const hand3 = { - // initial values inherited from hand detect - id: h.id, - score: Math.round(100 * h.score) / 100, - boxScore: Math.round(100 * h.score) / 100, - fingerScore: 0, - box: h.box, - boxRaw: h.boxRaw, - label: h.label, - keypoints: [], - landmarks: {}, - annotations: {} - }; - if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { - const t2 = {}; - const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; - t2.crop = tf29.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); - t2.div = tf29.div(t2.crop, constants.tf255); - [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); - const rawScore = (await t2.score.data())[0]; - const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; - if (score >= (config3.hand.minConfidence || 0)) { - hand3.fingerScore = score; - t2.reshaped = tf29.reshape(t2.keypoints, [-1, 3]); - const coordsData = await t2.reshaped.array(); - const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); - const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); - hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); - hand3.landmarks = analyze(hand3.keypoints); - for (const key of Object.keys(fingerMap)) { - hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); - } - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - } - return hand3; -} -async function predict15(input, config3) { - var _a, _b; - if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) return []; - outputSize = [input.shape[2] || 0, input.shape[1] || 0]; - skipped13++; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrame = skipped13 < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache4.hands; - } - return new Promise(async (resolve) => { - const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); - if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else { - cache4.boxes = await detectHands(input, config3); - lastTime14 = now(); - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - skipped13 = 0; - } - const oldCache = [...cache4.boxes]; - cache4.boxes.length = 0; - if (config3.cacheSensitivity > 0) { - for (let i = 0; i < cache4.hands.length; i++) { - const boxKpt = square(cache4.hands[i].keypoints, outputSize); - if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { - const boxScale = scale(boxKpt.box, boxExpandFact); - const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); - cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); - } - } - } - for (let i = 0; i < cache4.hands.length; i++) { - const bbox = calc(cache4.hands[i].keypoints, outputSize); - cache4.hands[i].box = bbox.box; - cache4.hands[i].boxRaw = bbox.boxRaw; - } - resolve(cache4.hands); - }); -} - -// src/result.ts -var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); - -// src/body/movenetcoords.ts -var movenetcoords_exports = {}; -__export(movenetcoords_exports, { - connected: () => connected3, - horizontal: () => horizontal, - kpt: () => kpt3, - relative: () => relative, - vertical: () => vertical -}); -var kpt3 = [ - // used to create part labels - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var horizontal = [ - // used to fix left vs right - ["leftEye", "rightEye"], - ["leftEar", "rightEar"], - ["leftShoulder", "rightShoulder"], - ["leftElbow", "rightElbow"], - ["leftWrist", "rightWrist"], - ["leftHip", "rightHip"], - ["leftKnee", "rightKnee"], - ["leftAnkle", "rightAnkle"] -]; -var vertical = [ - // used to remove unlikely keypoint positions - ["leftKnee", "leftShoulder"], - ["rightKnee", "rightShoulder"], - ["leftAnkle", "leftKnee"], - ["rightAnkle", "rightKnee"] -]; -var relative = [ - // used to match relative body parts - [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], - [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] -]; -var connected3 = { - // used to create body outline in annotations - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/util/interpolate.ts -var bufferedResult = empty(); -var interpolateTime = 0; -function calc2(newResult, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z; - const t0 = now(); - if (!newResult) return empty(); - const elapsed = Date.now() - newResult.timestamp; - const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; - if (newResult.canvas) bufferedResult.canvas = newResult.canvas; - if (newResult.error) bufferedResult.error = newResult.error; - if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { - bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); - } else { - for (let i = 0; i < newResult.body.length; i++) { - const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); - const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); - const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; - return { - score: newKpt.score, - part: newKpt.part, - position: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] - ], - positionRaw: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] - ], - distance: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] - ] - }; - }); - const annotations2 = {}; - let coords = { connected: {} }; - if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) coords = efficientposecoords_exports; - else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) coords = blazeposecoords_exports; - else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) coords = movenetcoords_exports; - for (const [name, indexes] of Object.entries(coords.connected)) { - const pt = []; - for (let j = 0; j < indexes.length - 1; j++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[j]); - const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); - if (pt0 && pt1) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { - bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); - } else { - for (let i = 0; i < newResult.hand.length; i++) { - const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); - if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; - const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; - let annotations2 = {}; - if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { - bufferedResult.hand[i].annotations = newResult.hand[i].annotations; - annotations2 = bufferedResult.hand[i].annotations; - } else if (newResult.hand[i].annotations) { - for (const key of Object.keys(newResult.hand[i].annotations)) { - annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { - bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); - } else { - for (let i = 0; i < newResult.face.length; i++) { - const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); - let annotations2 = newResult.face[i].annotations; - if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) { - bufferedResult.face[i].annotations = newResult.face[i].annotations; - annotations2 = bufferedResult.face[i].annotations; - } else if (newResult.face[i].annotations) { - for (const key of Object.keys(newResult.face[i].annotations)) { - annotations2[key] = ((_i = (_h = (_g = newResult.face[i]) == null ? void 0 : _g.annotations) == null ? void 0 : _h[key]) == null ? void 0 : _i[0]) ? newResult.face[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - if (newResult.face[i].rotation) { - const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; - rotation.matrix = (_j = newResult.face[i].rotation) == null ? void 0 : _j.matrix; - rotation.angle = { - roll: ((bufferedFactor - 1) * (((_l = (_k = bufferedResult.face[i].rotation) == null ? void 0 : _k.angle) == null ? void 0 : _l.roll) || 0) + (((_n = (_m = newResult.face[i].rotation) == null ? void 0 : _m.angle) == null ? void 0 : _n.roll) || 0)) / bufferedFactor, - yaw: ((bufferedFactor - 1) * (((_p = (_o = bufferedResult.face[i].rotation) == null ? void 0 : _o.angle) == null ? void 0 : _p.yaw) || 0) + (((_r = (_q = newResult.face[i].rotation) == null ? void 0 : _q.angle) == null ? void 0 : _r.yaw) || 0)) / bufferedFactor, - pitch: ((bufferedFactor - 1) * (((_t = (_s = bufferedResult.face[i].rotation) == null ? void 0 : _s.angle) == null ? void 0 : _t.pitch) || 0) + (((_v = (_u = newResult.face[i].rotation) == null ? void 0 : _u.angle) == null ? void 0 : _v.pitch) || 0)) / bufferedFactor - }; - rotation.gaze = { - // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive - bearing: ((bufferedFactor - 1) * (((_w = bufferedResult.face[i].rotation) == null ? void 0 : _w.gaze.bearing) || 0) + (((_x = newResult.face[i].rotation) == null ? void 0 : _x.gaze.bearing) || 0)) / bufferedFactor, - strength: ((bufferedFactor - 1) * (((_y = bufferedResult.face[i].rotation) == null ? void 0 : _y.gaze.strength) || 0) + (((_z = newResult.face[i].rotation) == null ? void 0 : _z.gaze.strength) || 0)) / bufferedFactor - }; - bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations: annotations2 }; - } else { - bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations: annotations2 }; - } - } - } - if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { - bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); - } else { - for (let i = 0; i < newResult.object.length; i++) { - const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); - bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; - } - } - if (newResult.persons) { - const newPersons = newResult.persons; - if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { - bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); - } else { - for (let i = 0; i < newPersons.length; i++) { - bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); - } - } - } - if (newResult.gesture) bufferedResult.gesture = newResult.gesture; - bufferedResult.width = newResult.width; - bufferedResult.height = newResult.height; - const t1 = now(); - interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); - if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; - return bufferedResult; -} - -// src/segmentation/meet.ts -var tf30 = __toESM(require_tfjs_esm()); -var model17; -async function load15(config3) { - if (!model17 || env.initial) model17 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model17["modelUrl"]); - return model17; -} -async function predict16(input, config3) { - var _a; - if (!model17) model17 = await load15(config3); - if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) return null; - const t2 = {}; - t2.resize = tf30.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); - t2.norm = tf30.div(t2.resize, constants.tf255); - t2.res = model17.execute(t2.norm); - t2.squeeze = tf30.squeeze(t2.res, [0]); - [t2.bgRaw, t2.fgRaw] = tf30.unstack(t2.squeeze, 2); - t2.fg = tf30.softmax(t2.fgRaw); - t2.mul = tf30.mul(t2.fg, constants.tf255); - t2.expand = tf30.expandDims(t2.mul, 2); - t2.output = tf30.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf30.squeeze(input); - t2.concat = tf30.concat([t2.input, t2.output], -1); - rgba = tf30.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf30.cast(t2.output, "int32"); - break; - default: - rgba = tf30.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf30.dispose(t2[tensor6])); - return rgba; -} - -// src/face/match.ts -var match_exports = {}; -__export(match_exports, { - distance: () => distance, - find: () => find, - similarity: () => similarity -}); -function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { - if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER; - let sum3 = 0; - for (let i = 0; i < descriptor1.length; i++) { - const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); - sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; - } - const dist = Math.round(100 * (options4.multiplier || 20) * sum3) / 100; - return dist; -} -var normalizeDistance = (dist, order, min2, max5) => { - if (dist === 0) return 1; - const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); - const norm = (1 - root / 100 - min2) / (max5 - min2); - const clamp2 = Math.round(100 * Math.max(Math.min(norm, 1), 0)) / 100; - return clamp2; -}; -function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { - const dist = distance(descriptor1, descriptor2, options4); - return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); -} -function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { - if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { - return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; - } - let lowestDistance = Number.MAX_SAFE_INTEGER; - let index2 = -1; - for (let i = 0; i < descriptors.length; i++) { - const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; - if (res < lowestDistance) { - lowestDistance = res; - index2 = i; - } - if (lowestDistance < (options4.threshold || 0)) break; - } - const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); - return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; -} - -// src/models.ts -var models_exports2 = {}; -__export(models_exports2, { - Models: () => Models, - validateModel: () => validateModel -}); - -// src/body/movenet.ts -var tf32 = __toESM(require_tfjs_esm()); - -// src/body/movenetfix.ts -var tf31 = __toESM(require_tfjs_esm()); -var maxJitter = 5e-3; -var cache5 = { - keypoints: [], - padding: [[0, 0], [0, 0], [0, 0], [0, 0]] -}; -function bodyParts(body4) { - for (const pair of horizontal) { - const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); - if (body4.keypoints[left] && body4.keypoints[right]) { - if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } - } - for (const pair of vertical) { - const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - if (body4.keypoints[lower] && body4.keypoints[higher]) { - if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { - body4.keypoints.splice(lower, 1); - } - } - } - for (const [pair, compare2] of relative) { - const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); - const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); - if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) continue; - const distanceLeft = body4.keypoints[left] ? [ - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) - ] : [0, 0]; - const distanceRight = body4.keypoints[right] ? [ - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) - ] : [0, 0]; - if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } -} -function jitter(keypoints) { - for (let i = 0; i < keypoints.length; i++) { - if (keypoints[i] && cache5.keypoints[i]) { - const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; - if (diff[0] < maxJitter && diff[1] < maxJitter) { - keypoints[i] = cache5.keypoints[i]; - } else { - cache5.keypoints[i] = keypoints[i]; - } - } else { - cache5.keypoints[i] = keypoints[i]; - } - } - return keypoints; -} -function padInput(input, inputSize10) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) return input; - cache5.padding = [ - [0, 0], - // dont touch batch - [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], - // height before&after - [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], - // width before&after - [0, 0] - // dont touch rbg - ]; - t2.pad = tf31.pad(input, cache5.padding); - t2.resize = tf31.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); - const final = tf31.cast(t2.resize, "int32"); - Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6])); - return final; -} -function rescaleBody(body4, outputSize2) { - body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); - for (const kpt4 of body4.keypoints) { - kpt4.position = [ - kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], - kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] - ]; - kpt4.positionRaw = [ - kpt4.position[0] / outputSize2[0], - kpt4.position[1] / outputSize2[1] - ]; - } - const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); - body4.box = rescaledBoxes.box; - body4.boxRaw = rescaledBoxes.boxRaw; - return body4; -} - -// src/body/movenet.ts -var model18; -var inputSize8 = 0; -var skipped14 = Number.MAX_SAFE_INTEGER; -var cache6 = { - boxes: [], - bodies: [], - last: 0 -}; -async function load16(config3) { - var _a; - if (env.initial) model18 = null; - if (!model18) { - fakeOps(["size"], config3); - model18 = await loadModel(config3.body.modelPath); - } else if (config3.debug) log("cached model:", model18["modelUrl"]); - inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; - if (inputSize8 < 64) inputSize8 = 256; - if (tf32.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf32.env().set("WEBGL_USE_SHAPES_UNIFORMS", false); - return model18; -} -function parseSinglePose(res, config3, image28) { - const kpt4 = res[0][0]; - const keypoints = []; - let score = 0; - for (let id = 0; id < kpt4.length; id++) { - score = kpt4[id][2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[id][1], kpt4[id][0]]; - keypoints.push({ - score: Math.round(100 * score) / 100, - part: kpt3[id], - positionRaw, - position: [ - // normalized to input image size - Math.round((image28.shape[2] || 0) * positionRaw[0]), - Math.round((image28.shape[1] || 0) * positionRaw[1]) - ] - }); - } - } - score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const bodies = []; - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - return bodies; -} -function parseMultiPose(res, config3, image28) { - const bodies = []; - for (let id = 0; id < res[0].length; id++) { - const kpt4 = res[0][id]; - const boxScore = Math.round(100 * kpt4[51 + 4]) / 100; - if (boxScore > config3.body.minConfidence) { - const keypoints = []; - for (let i = 0; i < 17; i++) { - const score = kpt4[3 * i + 2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; - keypoints.push({ - part: kpt3[i], - score: Math.round(100 * score) / 100, - positionRaw, - position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] - }); - } - } - const boxRaw = [kpt4[51 + 1], kpt4[51 + 0], kpt4[51 + 3] - kpt4[51 + 1], kpt4[51 + 2] - kpt4[51 + 0]]; - const boxNorm = [Math.trunc(boxRaw[0] * (image28.shape[2] || 0)), Math.trunc(boxRaw[1] * (image28.shape[1] || 0)), Math.trunc(boxRaw[2] * (image28.shape[2] || 0)), Math.trunc(boxRaw[3] * (image28.shape[1] || 0))]; - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - } - } - bodies.sort((a, b) => b.score - a.score); - if (bodies.length > config3.body.maxDetected) bodies.length = config3.body.maxDetected; - return bodies; -} -async function predict17(input, config3) { - var _a; - if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) return []; - if (!config3.skipAllowed) cache6.boxes.length = 0; - skipped14++; - const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; - const skipFrame = skipped14 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache6.bodies; - } - return new Promise(async (resolve) => { - const t2 = {}; - skipped14 = 0; - t2.input = padInput(input, inputSize8); - t2.res = model18 == null ? void 0 : model18.execute(t2.input); - cache6.last = now(); - const res = await t2.res.array(); - cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); - for (const body4 of cache6.bodies) { - rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); - jitter(body4.keypoints); - } - Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6])); - resolve(cache6.bodies); - }); -} - -// src/object/nanodet.ts -var tf33 = __toESM(require_tfjs_esm()); -var model19; -var last10 = []; -var lastTime15 = 0; -var skipped15 = Number.MAX_SAFE_INTEGER; -var inputSize9 = 0; -var scaleBox = 2.5; -async function load17(config3) { - if (!model19 || env.initial) { - model19 = await loadModel(config3.object.modelPath); - const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; - inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; - } else if (config3.debug) log("cached model:", model19["modelUrl"]); - return model19; -} -async function process4(res, outputShape, config3) { - var _a, _b; - let id = 0; - let results = []; - const size2 = inputSize9; - for (const strideSize of [1, 2, 4]) { - const baseSize = strideSize * 13; - const scoresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); - const scores = await scoresT.array(); - const featuresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); - const boxesMaxT = tf33.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); - const boxIdxT = tf33.argMax(boxesMaxT, 2); - const boxIdx = await boxIdxT.array(); - for (let i = 0; i < scoresT.shape[0]; i++) { - for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { - const score = scores[i][j]; - if (score > (config3.object.minConfidence || 0) && j !== 61) { - const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; - const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; - const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); - const [x, y] = [ - cx - scaleBox / strideSize * boxOffset[0], - cy - scaleBox / strideSize * boxOffset[1] - ]; - const [w, h] = [ - cx + scaleBox / strideSize * boxOffset[2] - x, - cy + scaleBox / strideSize * boxOffset[3] - y - ]; - let boxRaw = [x, y, w, h]; - boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); - const box = [ - // results normalized to input image pixels - boxRaw[0] * outputShape[0], - boxRaw[1] * outputShape[1], - boxRaw[2] * outputShape[0], - boxRaw[3] * outputShape[1] - ]; - const result = { - id: id++, - // strideSize, - score: Math.round(100 * score) / 100, - class: j + 1, - label: labels2[j].label, - // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)], - // centerRaw: [cx, cy], - box: box.map((a) => Math.trunc(a)), - boxRaw - }; - results.push(result); - } - } - } - tf33.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); - } - const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); - const nmsScores = results.map((a) => a.score); - let nmsIdx = []; - if (nmsBoxes && nmsBoxes.length > 0) { - const nms = await tf33.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); - nmsIdx = Array.from(await nms.data()); - tf33.dispose(nms); - } - results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); - return results; -} -async function predict18(image28, config3) { - if (!(model19 == null ? void 0 : model19["executor"])) return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; - const skipFrame = skipped15 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { - skipped15++; - return last10; - } - skipped15 = 0; - if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) return last10; - return new Promise(async (resolve) => { - const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; - const resizeT = tf33.image.resizeBilinear(image28, [inputSize9, inputSize9], false); - const normT = tf33.div(resizeT, constants.tf255); - const transposeT = tf33.transpose(normT, [0, 3, 1, 2]); - let objectT; - if (config3.object.enabled) objectT = model19.execute(transposeT); - lastTime15 = now(); - const obj = await process4(objectT, outputSize2, config3); - last10 = obj; - tf33.dispose([resizeT, normT, transposeT, ...objectT]); - resolve(obj); - }); -} - -// src/body/posenet.ts -var tf34 = __toESM(require_tfjs_esm()); - -// src/body/posenetutils.ts -var partNames = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var count = partNames.length; -var partIds = partNames.reduce((result, jointName, i) => { - result[jointName] = i; - return result; -}, {}); -var connectedPartNames = [ - ["leftHip", "leftShoulder"], - ["leftElbow", "leftShoulder"], - ["leftElbow", "leftWrist"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["rightHip", "rightShoulder"], - ["rightElbow", "rightShoulder"], - ["rightElbow", "rightWrist"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"], - ["leftShoulder", "rightShoulder"], - ["leftHip", "rightHip"] -]; -var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); -var poseChain = [ - ["nose", "leftEye"], - ["leftEye", "leftEar"], - ["nose", "rightEye"], - ["rightEye", "rightEar"], - ["nose", "leftShoulder"], - ["leftShoulder", "leftElbow"], - ["leftElbow", "leftWrist"], - ["leftShoulder", "leftHip"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["nose", "rightShoulder"], - ["rightShoulder", "rightElbow"], - ["rightElbow", "rightWrist"], - ["rightShoulder", "rightHip"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"] -]; -function getBoundingBox(keypoints) { - const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ - maxX: Math.max(maxX, x), - maxY: Math.max(maxY, y), - minX: Math.min(minX, x), - minY: Math.min(minY, y) - }), { - maxX: Number.NEGATIVE_INFINITY, - maxY: Number.NEGATIVE_INFINITY, - minX: Number.POSITIVE_INFINITY, - minY: Number.POSITIVE_INFINITY - }); - return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; -} -function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { - const scaleY = height / inputResolutionHeight; - const scaleX = width / inputResolutionWidth; - const scalePose = (pose, i) => ({ - id: i, - score: pose.score, - boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], - box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], - keypoints: pose.keypoints.map(({ score, part, position }) => ({ - score, - part, - position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], - positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] - })), - annotations: {} - }); - const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); - return scaledPoses; -} -var MaxHeap = class { - // function call - constructor(maxSize2, getElementValue) { - __publicField(this, "priorityQueue"); - // don't touch - __publicField(this, "numberOfElements"); - __publicField(this, "getElementValue"); - this.priorityQueue = new Array(maxSize2); - this.numberOfElements = -1; - this.getElementValue = getElementValue; - } - enqueue(x) { - this.priorityQueue[++this.numberOfElements] = x; - this.swim(this.numberOfElements); - } - dequeue() { - const max5 = this.priorityQueue[0]; - this.exchange(0, this.numberOfElements--); - this.sink(0); - this.priorityQueue[this.numberOfElements + 1] = null; - return max5; - } - empty() { - return this.numberOfElements === -1; - } - size() { - return this.numberOfElements + 1; - } - all() { - return this.priorityQueue.slice(0, this.numberOfElements + 1); - } - max() { - return this.priorityQueue[0]; - } - swim(k) { - while (k > 0 && this.less(Math.floor(k / 2), k)) { - this.exchange(k, Math.floor(k / 2)); - k = Math.floor(k / 2); - } - } - sink(k) { - while (2 * k <= this.numberOfElements) { - let j = 2 * k; - if (j < this.numberOfElements && this.less(j, j + 1)) j++; - if (!this.less(k, j)) break; - this.exchange(k, j); - k = j; - } - } - getValueAt(i) { - return this.getElementValue(this.priorityQueue[i]); - } - less(i, j) { - return this.getValueAt(i) < this.getValueAt(j); - } - exchange(i, j) { - const t2 = this.priorityQueue[i]; - this.priorityQueue[i] = this.priorityQueue[j]; - this.priorityQueue[j] = t2; - } -}; -function getOffsetPoint(y, x, keypoint, offsets) { - return { - y: offsets.get(y, x, keypoint), - x: offsets.get(y, x, keypoint + count) - }; -} -function getImageCoords(part, outputStride2, offsets) { - const { heatmapY, heatmapX, id: keypoint } = part; - const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); - return { - x: part.heatmapX * outputStride2 + x, - y: part.heatmapY * outputStride2 + y - }; -} -function clamp(a, min2, max5) { - if (a < min2) return min2; - if (a > max5) return max5; - return a; -} -function squaredDistance(y1, x1, y2, x2) { - const dy = y2 - y1; - const dx = x2 - x1; - return dy * dy + dx * dx; -} -function addVectors(a, b) { - return { x: a.x + b.x, y: a.y + b.y }; -} - -// src/body/posenet.ts -var model20; -var poseNetOutputs = [ - "MobilenetV1/offset_2/BiasAdd", - "MobilenetV1/heatmap_2/BiasAdd", - "MobilenetV1/displacement_fwd_2/BiasAdd", - "MobilenetV1/displacement_bwd_2/BiasAdd" - /* displacementBwd */ -]; -var localMaximumRadius = 1; -var outputStride = 16; -var squaredNmsRadius = 50 ** 2; -function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { - const getDisplacement = (point2) => ({ - y: displacements.get(point2.y, point2.x, edgeId), - x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) - }); - const getStridedIndexNearPoint = (point2, height2, width2) => ({ - y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), - x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) - }); - const [height, width] = scores.shape; - const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); - const displacement = getDisplacement(sourceKeypointIndices); - const displacedPoint = addVectors(sourceKeypoint.position, displacement); - let targetKeypoint = displacedPoint; - for (let i = 0; i < offsetRefineStep; i++) { - const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); - targetKeypoint = addVectors( - { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, - { x: offsetPoint.x, y: offsetPoint.y } - ); - } - const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); - return { position: targetKeypoint, part: partNames[targetId], score }; -} -function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { - const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); - const edgesFwd = tuples.map(([, childJointId]) => childJointId); - const edgesBwd = tuples.map(([parentJointId]) => parentJointId); - const numParts = scores.shape[2]; - const numEdges = edgesFwd.length; - const keypoints = new Array(numParts); - const rootPoint = getImageCoords(root.part, outputStride, offsets); - keypoints[root.part.id] = { - score: root.score, - part: partNames[root.part.id], - position: rootPoint - }; - for (let edge = numEdges - 1; edge >= 0; --edge) { - const sourceId = edgesFwd[edge]; - const targetId = edgesBwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); - } - } - for (let edge = 0; edge < numEdges; ++edge) { - const sourceId = edgesBwd[edge]; - const targetId = edgesFwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); - } - } - return keypoints; -} -function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { - const [height, width] = scores.shape; - let localMaximum = true; - const yStart = Math.max(heatmapY - localMaximumRadius, 0); - const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); - for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { - const xStart = Math.max(heatmapX - localMaximumRadius, 0); - const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); - for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { - if (scores.get(yCurrent, xCurrent, keypointId) > score) { - localMaximum = false; - break; - } - } - if (!localMaximum) break; - } - return localMaximum; -} -function buildPartWithScoreQueue(minConfidence2, scores) { - const [height, width, numKeypoints] = scores.shape; - const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); - for (let heatmapY = 0; heatmapY < height; ++heatmapY) { - for (let heatmapX = 0; heatmapX < width; ++heatmapX) { - for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { - const score = scores.get(heatmapY, heatmapX, keypointId); - if (score < minConfidence2) continue; - if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); - } - } - } - return queue; -} -function withinRadius(poses, { x, y }, keypointId) { - return poses.some(({ keypoints }) => { - var _a; - const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; - if (!correspondingKeypoint) return false; - return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; - }); -} -function getInstanceScore(existingPoses, keypoints) { - const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { - if (!withinRadius(existingPoses, position, keypointId)) result += score; - return result; - }, 0); - return notOverlappedKeypointScores / keypoints.length; -} -function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { - const poses = []; - const queue = buildPartWithScoreQueue(minConfidence2, scores); - while (poses.length < maxDetected && !queue.empty()) { - const root = queue.dequeue(); - const rootImageCoords = getImageCoords(root.part, outputStride, offsets); - if (withinRadius(poses, rootImageCoords, root.part.id)) continue; - let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); - keypoints = keypoints.filter((a) => a.score > minConfidence2); - const score = getInstanceScore(poses, keypoints); - const box = getBoundingBox(keypoints); - if (score > minConfidence2) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); - } - return poses; -} -async function predict19(input, config3) { - if (!(model20 == null ? void 0 : model20["executor"])) return []; - const res = tf34.tidy(() => { - if (!model20.inputs[0].shape) return []; - const resized = tf34.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - const normalized = tf34.sub(tf34.div(tf34.cast(resized, "float32"), 127.5), 1); - const results = model20.execute(normalized, poseNetOutputs); - const results3d = results.map((y) => tf34.squeeze(y, [0])); - results3d[1] = tf34.sigmoid(results3d[1]); - return results3d; - }); - const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); - for (const t2 of res) tf34.dispose(t2); - const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); - if (!model20.inputs[0].shape) return []; - const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - return scaled; -} -async function load18(config3) { - if (!model20 || env.initial) model20 = await loadModel(config3.body.modelPath); - else if (config3.debug) log("cached model:", model20["modelUrl"]); - return model20; -} - -// src/segmentation/rvm.ts -var tf35 = __toESM(require_tfjs_esm()); -var model21; -var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; -var t = {}; -var ratio = 0; -function init3(config3) { - tf35.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); - t.r1i = tf35.tensor(0); - t.r2i = tf35.tensor(0); - t.r3i = tf35.tensor(0); - t.r4i = tf35.tensor(0); - ratio = config3.segmentation.ratio || 0.5; - t.downsample_ratio = tf35.tensor(ratio); -} -async function load19(config3) { - if (!model21 || env.initial) model21 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model21["modelUrl"]); - init3(config3); - return model21; -} -var normalize = (r) => tf35.tidy(() => { - const squeeze14 = tf35.squeeze(r, [0]); - const mul15 = tf35.mul(squeeze14, constants.tf255); - const cast8 = tf35.cast(mul15, "int32"); - return cast8; -}); -function getRGBA(fgr, pha) { - const rgb3 = fgr ? normalize(fgr) : tf35.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); - const a = pha ? normalize(pha) : tf35.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); - const rgba = tf35.concat([rgb3, a], -1); - tf35.dispose([rgb3, a]); - return rgba; -} -function getState(state) { - return tf35.tidy(() => { - const r = {}; - r.unstack = tf35.unstack(state, -1); - r.concat = tf35.concat(r.unstack, 1); - r.split = tf35.split(r.concat, 4, 1); - r.stack = tf35.concat(r.split, 2); - r.squeeze = tf35.squeeze(r.stack, [0]); - r.expand = tf35.expandDims(r.squeeze, -1); - r.add = tf35.add(r.expand, 1); - r.mul = tf35.mul(r.add, 127.5); - r.cast = tf35.cast(r.mul, "int32"); - r.tile = tf35.tile(r.cast, [1, 1, 3]); - r.alpha = tf35.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); - return tf35.concat([r.tile, r.alpha], -1); - }); -} -async function predict20(input, config3) { - if (!model21) model21 = await load19(config3); - if (!(model21 == null ? void 0 : model21["executor"])) return null; - t.src = tf35.div(input, 255); - if (ratio !== config3.segmentation.ratio) init3(config3); - const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - rgba = getRGBA(fgr, pha); - break; - case "alpha": - rgba = getRGBA(null, pha); - break; - case "foreground": - rgba = getRGBA(fgr, null); - break; - case "state": - rgba = getState(r1o); - break; - default: - rgba = tf35.tensor(0); - } - tf35.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); - [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; - return rgba; -} - -// src/segmentation/selfie.ts -var tf36 = __toESM(require_tfjs_esm()); -var model22; -async function load20(config3) { - if (!model22 || env.initial) model22 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model22["modelUrl"]); - return model22; -} -async function predict21(input, config3) { - var _a; - if (!model22) model22 = await load20(config3); - if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) return null; - const t2 = {}; - t2.resize = tf36.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); - t2.norm = tf36.div(t2.resize, constants.tf255); - t2.res = model22.execute(t2.norm); - t2.squeeze = tf36.squeeze(t2.res, [0]); - t2.alpha = tf36.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); - t2.mul = tf36.mul(t2.alpha, constants.tf255); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf36.squeeze(input); - t2.concat = tf36.concat([t2.input, t2.mul], -1); - rgba = tf36.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf36.cast(t2.mul, "int32"); - break; - default: - rgba = tf36.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf36.dispose(t2[tensor6])); - return rgba; -} - -// src/models.ts -function validateModel(instance, model23, name) { - var _a, _b; - if (!model23) return null; - if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) return null; - const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; - const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; - const ops = []; - const missing = []; - const url = model23["modelUrl"]; - const executor = model23["executor"]; - if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { - for (const kernel of Object.values(executor.graph.nodes)) { - const op = kernel.op.toLowerCase(); - if (!ops.includes(op)) ops.push(op); - } - } else { - if (!executor && instance.config.debug) { - log("model not loaded", name); - } - } - for (const op of ops) { - if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { - missing.push(op); - } - } - if (instance.config.debug && missing.length > 0) log("model validation failed:", name, missing); - return missing.length > 0 ? { name, missing, ops, url } : null; -} -var Models = class { - constructor(currentInstance) { - __publicField(this, "instance"); - __publicField(this, "models", {}); - this.models = {}; - this.instance = currentInstance; - } - stats() { - let totalSizeFromManifest = 0; - let totalSizeWeights = 0; - let totalSizeLoading = 0; - for (const m of Object.values(modelStats)) { - totalSizeFromManifest += Number.isNaN(+m.sizeFromManifest) ? 0 : m.sizeFromManifest; - totalSizeWeights += Number.isNaN(+m.sizeLoadedWeights) ? 0 : m.sizeLoadedWeights; - totalSizeLoading += Number.isNaN(+m.sizeDesired) ? 0 : m.sizeDesired; - } - const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; - return { - numLoadedModels: Object.values(modelStats).filter((m) => m == null ? void 0 : m.loaded).length, - numDefinedModels: Object.keys(this.models).length, - percentageLoaded, - totalSizeFromManifest, - totalSizeWeights, - totalSizeLoading, - modelStats: Object.values(modelStats) - }; - } - reset() { - for (const model23 of Object.keys(this.models)) this.models[model23] = null; - } - async load(instance) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; - if (env.initial) this.reset(); - if (instance) this.instance = instance; - const m = {}; - m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; - m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; - m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; - m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; - m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; - m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; - m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; - m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; - m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; - m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; - m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; - m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; - m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; - m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; - m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; - m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load16(this.instance.config) : null; - m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load18(this.instance.config) : null; - m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect3(this.instance.config) : null; - m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton2(this.instance.config) : null; - if (this.instance.config.hand.enabled && !this.models.handdetect && ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect"))) { - m.handdetect = loadDetect2(this.instance.config); - m.handskeleton = loadSkeleton(this.instance.config); - } - m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; - m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load17(this.instance.config) : null; - m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load20(this.instance.config) : null; - m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load15(this.instance.config) : null; - m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load19(this.instance.config) : null; - for (const [model23, promise] of Object.entries(m)) { - if (promise == null ? void 0 : promise["then"]) promise["then"]((val) => this.models[model23] = val); - } - await Promise.all(Object.values(m)); - } - list() { - const models3 = Object.keys(this.models).map((model23) => { - var _a; - return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; - }); - for (const m of models3) { - const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); - if (!stats) continue; - m.size = modelStats[stats].sizeLoadedWeights; - m.url = modelStats[stats].url; - } - return models3; - } - loaded() { - const list = this.list(); - const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); - return loaded; - } - validate() { - const missing = []; - for (const defined of Object.keys(this.models)) { - const model23 = this.models[defined]; - if (!model23) continue; - const res = validateModel(this.instance, model23, defined); - if (res) missing.push(res); - } - return missing; - } -}; - -// src/util/persons.ts -function join2(faces, bodies, hands, gestures, shape) { - var _a, _b, _c, _d, _e, _f; - let id = 0; - const persons = []; - for (const face4 of faces) { - const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; - for (const body4 of bodies) { - if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { - person2.body = body4; - } - } - if (person2.body) { - for (const hand3 of hands) { - if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) person2.hands.left = hand3; - } - if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) person2.hands.right = hand3; - } - } - } - for (const gesture2 of gestures) { - if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) person2.gestures.push(gesture2); - else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) person2.gestures.push(gesture2); - else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) person2.gestures.push(gesture2); - } - const x = []; - const y = []; - const extractXY = (box) => { - if (box && box.length === 4) { - x.push(box[0], box[0] + box[2]); - y.push(box[1], box[1] + box[3]); - } - }; - extractXY(person2.face.box); - extractXY((_d = person2.body) == null ? void 0 : _d.box); - extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); - extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); - const minX = Math.min(...x); - const minY = Math.min(...y); - person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; - if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; - persons.push(person2); - } - return persons; -} - -// src/warmup.ts -var tf37 = __toESM(require_tfjs_esm()); - -// src/sample.ts -var face3 = ` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var i5=0;function Ks(e,t,n){let o=i0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let s=0;sd5,kpt:()=>c5});var c5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],d5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=Z(V());var se,n2=224,t3,_s=5,At=[8,16,32,32,32];function $s(){let e=[],t=0;for(;t<_s;){let n=0,o=t;for(;on.x)),y:D.tensor1d(e.map(n=>n.y))}}async function n3(e){if(R.initial&&(se=null),!se&&e.body.detector&&e.body.detector.modelPath){se=await L(e.body.detector.modelPath);let t=se!=null&&se.executor?Object.values(se.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&se&&b("cached model:",se.modelUrl);return $s(),se}var e3=[5,5];function eA(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),e3[0]),A=D.mul(D.div(A,n2),e3[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),d=D.add(l,A);return D.stack([a,l,c,d],1)})}async function tA(e,t,n,o){var c,d;let r=[],s={};s.boxes=eA(e,t3),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((d=n.body.detector)==null?void 0:d.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let y=a[i],x=l[i],m=[Math.round(x[0]*o[0]),Math.round(x[1]*o[1]),Math.round(x[2]*o[0]),Math.round(x[3]*o[1])],f={score:y,boxRaw:x,box:m};r.push(f)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function o3(e,t,n){let o={};o.res=se==null?void 0:se.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await tA(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function Le(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function r3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function at(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var Z0,y5=256,x5=Number.MAX_SAFE_INTEGER,nA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},lt=[],Ce=[[0,0],[0,0],[0,0],[0,0]],s3=0,A3=e=>1-1/(1+Math.exp(e)),i3=e=>n3(e);async function l3(e){if(R.initial&&(Z0=null),Z0)e.debug&&b("cached model:",Z0.modelUrl);else{Z0=await L(e.body.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;y5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Z0}function a3(e,t,n){var s,A;let o={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let r;if(n&&(o.cropped=q0.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Ce=[[0,0],a,l,[0,0]],o.pad=q0.pad(o.cropped||e,Ce),o.resize=q0.image.resizeBilinear(o.pad,[t,t]),r=q0.div(o.resize,C.tf255)}else e.shape[1]!==t?(o.resize=q0.image.resizeBilinear(o.cropped||e,[t,t]),r=q0.div(o.resize,C.tf255)):r=q0.div(o.cropped||e,C.tf255);return Object.keys(o).forEach(a=>q0.dispose(o[a])),r}function oA(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+Ce[2][0]+Ce[2][1])/t[0]-Ce[2][0]),Math.trunc(o.position[1]*(t[1]+Ce[1][0]+Ce[1][1])/t[1]-Ce[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(n){let o=n[2]-n[0],r=n[3]-n[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+n[1],s.positionRaw[1]/o+n[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function rA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),s=e.find(a=>a.part==="rightWrist"),A=e.find(a=>a.part==="rightIndex");r.position[2]=((s.position[2]||0)+(A.position[2]||0))/2}async function sA(e,t,n){if(!(Z0!=null&&Z0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=Z0==null?void 0:Z0.execute(e,nA.landmarks);let r=(await o.poseflag.data())[0],s=await o.ld.data(),A=await o.world.data();Object.keys(o).forEach(m=>q0.dispose(o[m]));let a=[],l=5;for(let m=0;mm.position),i=Le(d,[n[0],n[1]]),y={};for(let[m,f]of Object.entries(d5)){let h=[];for(let T=0;Tu.part===f[T]),p=c.find(u=>u.part===f[T+1]);g&&p&&h.push([g.position,p.position])}y[m]=h}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:y}}async function f5(e,t){var s,A,a;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>v()-s3,r=x5<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&<!==null)x5++;else{let l=[];if((A=(s=t.body)==null?void 0:s.detector)!=null&&A.enabled){let c=a3(e,224);l=await o3(c,t,n),q0.dispose(c)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let c=0;cF0.dispose(o[c])),r}async function u5(e,t){if(!(X0!=null&&X0.executor))return[];let 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E3(e){var t;return R.initial&&(xe=null),xe?e.debug&&b("cached model:",xe.modelUrl):xe=await L((t=e.face.detector)==null?void 0:t.modelPath),ge=xe.executor&&xe.inputs[0].shape?xe.inputs[0].shape[2]:256,u2=O.scalar(ge,"int32"),pt=O.tensor2d(v3(ge)),xe}function fA(e){if(!pt||!u2)return O.zeros([0,0]);let t={};t.boxStarts=O.slice(e,[0,1],[-1,2]),t.centers=O.add(t.boxStarts,pt),t.boxSizes=O.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=O.div(t.boxSizes,u2),t.centersNormalized=O.div(t.centers,u2),t.halfBoxSize=O.div(t.boxSizesNormalized,C.tf2),t.starts=O.sub(t.centersNormalized,t.halfBoxSize),t.ends=O.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=O.mul(t.starts,u2),t.endNormalized=O.mul(t.ends,u2);let n=O.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>O.dispose(t[o])),n}async function z3(e,t){var c,d,i,y,x,m,f,h,T;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={},o=[0,0],r=[1,1];if((d=(c=t==null?void 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0:i.maxDetected)||0,((y=t.face.detector)==null?void 0:y.iouThreshold)||0,((x=t.face.detector)==null?void 0:x.minConfidence)||0);let A=await n.nms.array(),a=[],l=await n.scores.data();for(let g=0;g(((m=t.face.detector)==null?void 0:m.minConfidence)||0)){let u={};u.bbox=O.slice(n.boxes,[A[g],0],[1,-1]),u.slice=O.slice(n.batch,[A[g],k3-1],[1,-1]),u.squeeze=O.squeeze(u.slice),u.landmarks=O.reshape(u.squeeze,[k3,-1]);let k=await u.bbox.data(),P=[k[0]*r[0]-o[0],k[1]*r[1]-o[1],k[2]*r[0]-o[0],k[3]*r[1]-o[1]],N={startPoint:[P[0],P[1]],endPoint:[P[2],P[3]],landmarks:await u.landmarks.array(),confidence:p};u.anchor=O.slice(pt,[A[g],0],[1,2]);let B=await u.anchor.data(),J=b3(N,[(e.shape[2]||0)/ge,(e.shape[1]||0)/ge],B),H=ft(J,((f=t.face.detector)==null?void 0:f.scale)||1.4),K=mt(H);K.size[0]>(((h=t.face.detector)==null?void 0:h.minSize)||0)&&K.size[1]>(((T=t.face.detector)==null?void 0:T.minSize)||0)&&a.push(K),Object.keys(u).forEach(e0=>O.dispose(u[e0]))}}return Object.keys(n).forEach(g=>O.dispose(n[g])),a}var ye=Z(V());var K0,We=0,P5=ae.leftEyeLower0,k5=ae.rightEyeLower0,h2={leftBounds:[P5[0],P5[P5.length-1]],rightBounds:[k5[0],k5[k5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function O3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&b("cached model:",K0.modelUrl):K0=await L((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function ut(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},j3=(e,t,n,o,r,s=!1,A=2.3)=>{let a=mt(ft(g3([e[n],e[o]]),A)),l=p2(a),c=ye.image.cropAndResize(t,[[a.startPoint[1]/r,a.startPoint[0]/r,a.endPoint[1]/r,a.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let d=ye.image.flipLeftRight(c);ye.dispose(c),c=d}return{box:a,boxSize:l,crop:c}},N3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function L3(e,t,n,o){var N,B;if(!(K0!=null&&K0.executor))return e;let{box:r,boxSize:s,crop:A}=j3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0,((N=o.face.iris)==null?void 0:N.scale)||2.3),{box:a,boxSize:l,crop:c}=j3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0,((B=o.face.iris)==null?void 0:B.scale)||2.3),d=ye.concat([A,c]);ye.dispose(A),ye.dispose(c);let i=K0.execute(d);ye.dispose(d);let y=await i.data();ye.dispose(i);let x=y.slice(0,b2.numCoordinates*3),{rawCoords:m,iris:f}=N3(x,r,s,!0),h=y.slice(b2.numCoordinates*3),{rawCoords:T,iris:g}=N3(h,a,l,!1),p=mA(e);Math.abs(p)<30?(ut(e,m,"left",null),ut(e,T,"right",null)):p<1?ut(e,m,"left",["EyeUpper0","EyeLower0"]):ut(e,T,"right",["EyeUpper0","EyeLower0"]);let u=I3(e,f,"left"),k=I3(e,g,"right");return e.concat(u).concat(k)}async function W3(e,t){var s,A,a,l,c,d,i,y,x,m;let n={lips:await((A=(s=t.filter(f=>f.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(f=>f.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((d=(c=t.filter(f=>f.size===142))==null?void 0:c[0])==null?void 0:d.data()),irisR:await((y=(i=t.filter(f=>f.size===10))==null?void 0:i[1])==null?void 0:y.data()),eyeR:await((m=(x=t.filter(f=>f.size===142))==null?void 0:x[1])==null?void 0:m.data())};for(let f of Object.values(n))if(!f)return e;let o=e2.reduce((f,h)=>f+=e[h][2],0)/e2.length;for(let f=0;ff+=e[h][2],0)/t2.length;for(let f=0;fv()-Te.timestamp,o=Te.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||Te.boxes.length===0?(Te.boxes=await z3(e,t),Te.timestamp=v(),Te.skipped=0):Te.skipped++;let r=[],s=[],A=0,a=W2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!s0)t.debug&&b("face mesh detection 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o;if(((s=t.face.description)==null?void 0:s.crop)>0){let a=(A=t.face.description)==null?void 0:A.crop,l=[[a,a,1-a,1-a]];o=ie.image.cropAndResize(n,l,[0],[w0.inputs[0].shape[2],w0.inputs[0].shape[1]])}else o=ie.image.resizeBilinear(n,[w0.inputs[0].shape[2],w0.inputs[0].shape[1]],!1);let r=ie.mul(o,C.tf255);return ie.dispose(o),r}async function I5(e,t,n,o){var a,l,c,d;let r={age:0,gender:"unknown",genderScore:0,descriptor:[]};if(!(w0!=null&&w0.executor))return r;let s=N5<(((a=t.face.description)==null?void 0:a.skipFrames)||0),A=(((l=t.face.description)==null?void 0:l.skipTime)||0)>v()-X3;return t.skipAllowed&&s&&A&&U3===o&&((c=Fe==null?void 0:Fe[n])==null?void 0:c.age)>0&&((d=Fe==null?void 0:Fe[n])==null?void 0:d.genderScore)>0?(N5++,Fe[n]):(N5=0,new Promise(async i=>{var y;if((y=t.face.description)!=null&&y.enabled){let x=uA(e,t),m=w0==null?void 0:w0.execute(x);X3=v(),ie.dispose(x);let h=await 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i.resize=R0.image.resizeBilinear(e,[I0.inputs[0].shape[2],I0.inputs[0].shape[1]],!1);i.enhance=R0.tidy(()=>{var g,p;let T;if(((p=(g=I0==null?void 0:I0.inputs)==null?void 0:g[0].shape)==null?void 0:p[3])===1){let[u,k,P]=R0.split(i.resize,3,3),N=R0.mul(u,q5[0]),B=R0.mul(k,q5[1]),J=R0.mul(P,q5[2]),H=R0.addN([N,B,J]);T=R0.mul(R0.sub(H,C.tf05),2)}else T=R0.mul(R0.sub(i.resize,C.tf05),2);return T});let y={gender:"unknown",genderScore:0};(h=t.face.ssrnet)!=null&&h.enabled&&(i.gender=I0.execute(i.enhance));let x=await i.gender.data();y.gender=x[0]>x[1]?"female":"male",y.genderScore=x[0]>x[1]?Math.trunc(100*x[0])/100:Math.trunc(100*x[1])/100,Object.keys(i).forEach(T=>R0.dispose(i[T])),Pt[n]=y,fn=o,mn=v(),d(y)}))}var kt=Z(V());var Q0,U5=[],hn=0,bn=0,gn=Number.MAX_SAFE_INTEGER;async function Tn(e){var t;return R.initial&&(Q0=null),Q0?e.debug&&b("cached model:",Q0.modelUrl):Q0=await L((t=e.face.mobilefacenet)==null?void 0:t.modelPath),Q0}async function Y5(e,t,n,o){var A,a;if(!(Q0!=null&&Q0.executor))return[];let r=gn<(((A=t.face.mobilefacenet)==null?void 0:A.skipFrames)||0),s=(((a=t.face.mobilefacenet)==null?void 0:a.skipTime)||0)>v()-bn;return t.skipAllowed&&s&&r&&hn===o&&U5[n]?(gn++,U5[n]):new Promise(async l=>{var d;let c=[];if((d=t.face.mobilefacenet)!=null&&d.enabled&&(Q0!=null&&Q0.inputs[0].shape)){let i={};i.crop=kt.image.resizeBilinear(e,[Q0.inputs[0].shape[2],Q0.inputs[0].shape[1]],!1),i.data=Q0.execute(i.crop);let y=await i.data.data();c=Array.from(y),Object.keys(i).forEach(x=>kt.dispose(i[x]))}U5[n]=c,hn=o,bn=v(),l(c)})}var wt=Z(V());var _0,K5=[],Rn=0,Mn=0,Pn=Number.MAX_SAFE_INTEGER;async function kn(e){return R.initial&&(_0=null),_0?e.debug&&b("cached model:",_0.modelUrl):_0=await L(e.face.insightface.modelPath),_0}async function J5(e,t,n,o){var A,a;if(!(_0!=null&&_0.executor))return[];let r=Pn<(((A=t.face.insightface)==null?void 0:A.skipFrames)||0),s=(((a=t.face.insightface)==null?void 0:a.skipTime)||0)>v()-Mn;return 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${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},Bn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A.06||h>.06)&&(x=!1),f>h?h>.04&&t.push({iris:A,gesture:"looking right"}):f>.04&&t.push({iris:A,gesture:"looking left"});let T=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],g=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(g<.01||T<.01||g>.022||T>.022)&&(x=!1),(g<.01||T<.01)&&t.push({iris:A,gesture:"looking down"}),(g>.022||T>.022)&&t.push({iris:A,gesture:"looking up"}),x&&t.push({iris:A,gesture:"looking center"})}return t},Hn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function St(e,t=1.5){let n=D2(e),o=zt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function jt(e){let t=D2(e),n=zt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function NA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Un(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return NA(n)}var Gn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function qe(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let d=await c.box.data(),i=d.slice(0,2),y=d.slice(2,4),x=await c.palmLandmarks.array(),m={startPoint:i,endPoint:y,palmLandmarks:x,confidence:r[l]},f=Xn(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(f),Object.keys(c).forEach(h=>W.dispose(c[h]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var $0=Z(V());var CA=5,Qn=1.65,_n=[0,5,9,13,17,1,2],WA=0,DA=2,$n=0,It=class{constructor(t,n){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>t1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return St(jt(r),CA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=St(jt(n),Qn);o.palmLandmarks=[];for(let r=0;r<_n.length;r++)o.palmLandmarks.push(t[_n[r]].slice(0,2));return o}transformRawCoords(t,n,o,r){let s=zt(n),A=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],a=t.map(x=>[A[0]*(x[0]-this.inputSize/2),A[1]*(x[1]-this.inputSize/2),A[2]*x[2]]),l=e1(o,[0,0]),c=a.map(x=>[...t1(x,l),x[2]]),d=Yn(r),i=[...D2(n),1],y=[qe(i,d[0]),qe(i,d[1])];return c.map(x=>[Math.trunc(x[0]+y[0]),Math.trunc(x[1]+y[1]),Math.trunc(x[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-$n,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A?this.skipped++:(r=await this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,f,d,m),B=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...B,confidence:u};let J={landmarks:N,confidence:u,boxConfidence:c.confidence,fingerConfidence:u,box:{topLeft:B.startPoint,bottomRight:B.endPoint}};a.push(J)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let d=St(jt(c),Qn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var eo={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,n1;function BA(){let e=l2?new Nt(l2):void 0;e&&c2&&(n1=new It(e,c2))}async function o1(e,t){n1||BA();let n=await n1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let d of A)d[0]a[2]&&(a[2]=d[0]),d[1]>a[3]&&(a[3]=d[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else a=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=Et(A);o.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:a,boxRaw:l,keypoints:A,annotations:s,landmarks:c})}return o}async function to(e){var t;return R.initial&&(l2=null),l2?e.debug&&b("cached model:",l2.modelUrl):l2=await L((t=e.hand.detector)==null?void 0:t.modelPath),l2}async function no(e){var t;return R.initial&&(c2=null),c2?e.debug&&b("cached model:",c2.modelUrl):c2=await L((t=e.hand.skeleton)==null?void 0:t.modelPath),c2}var $=Z(V());var p0=[null,null],HA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],GA=["hand","fist","pinch","point","face","tip","pinchtip"],ro=4,so=1.6,VA=512,ZA=1.4,Ot=Number.MAX_SAFE_INTEGER,r1=0,Ee=[0,0],m0={boxes:[],hands:[]},Ao={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function ao(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&b("cached model:",p0[0].modelUrl);else{_2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await L((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function io(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&b("cached model:",p0[1].modelUrl);else{p0[1]=await L((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function qA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,VA),A=Math.round(s*r/8)*8;o.resize=$.image.resizeBilinear(e,[s,A]),o.cast=$.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,HA),o.boxes=$.squeeze(o.rawBoxes,[0,2]),o.scores=$.squeeze(o.rawScores,[0]);let a=$.unstack(o.scores,1);$.dispose(a[ro]),a.splice(ro,1),o.filtered=$.stack(a,1),$.dispose(a),o.max=$.max(o.filtered,1),o.argmax=$.argMax(o.filtered,1);let l=0;o.nms=await $.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),d=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let x=$.slice(o.boxes,y,1),m=await x.data();$.dispose(x);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],h=at(f,ZA),T=[Math.trunc(f[0]*Ee[0]),Math.trunc(f[1]*Ee[1]),Math.trunc(f[2]*Ee[0]),Math.trunc(f[3]*Ee[1])],g=d[y],p=GA[i[y]],u={id:l++,score:g,box:T,boxRaw:h,label:p};n.push(u)}return Object.keys(o).forEach(y=>$.dispose(o[y])),n.sort((y,x)=>x.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function s1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=$.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=$.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=$.reshape(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=d.map(i=>[Ee[0]*(i[0]+t.boxRaw[0]),Ee[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=Et(o.keypoints);for(let i of Object.keys(Ao))o.annotations[i]=Ao[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>$.dispose(r[l]))}return o}async function A1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];Ee=[e.shape[2]||0,e.shape[1]||0],Ot++;let n=(t.hand.skipTime||0)>v()-r1,o=Ot<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-r1,l=Ot<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(d=>s1(e,d,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(d=>s1(e,d,t))):(m0.boxes=await qA(e,t),r1=v(),m0.hands=await Promise.all(m0.boxes.map(d=>s1(e,d,t))),Ot=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[d].fingerScore&&m0.hands[d].fingerScore>(t.hand.minConfidence||0)){let y=at(i.box,so),x=at(i.boxRaw,so);m0.boxes.push({...c[d],box:y,boxRaw:x})}}for(let d=0;d({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var F2={};Se(F2,{connected:()=>Ct,horizontal:()=>a1,kpt:()=>Lt,relative:()=>l1,vertical:()=>i1});var Lt=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],a1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],i1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],l1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Ct={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=ve(),c1=0;function co(e,t){var A,a,l,c,d,i,y,x,m,f,h,T,g,p,u,k,P,N,B,J,H,K,e0,n0,o0,N0;let n=v();if(!e)return ve();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let M=0;M((r-1)*z.body[M].box[q]+G)/r),C0=e.body[M].boxRaw.map((G,q)=>((r-1)*z.body[M].boxRaw[q]+G)/r),x0=e.body[M].keypoints.map((G,q)=>{var 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0:ze[0],z.body[M].keypoints[q]?((r-1)*(((k2=z.body[M].keypoints[q].distance)==null?void 0:k2[1])||0)+(((x2=G.distance)==null?void 0:x2[1])||0))/r:(z1=G.distance)==null?void 0:z1[1],z.body[M].keypoints[q]?((r-1)*(((S1=z.body[M].keypoints[q].distance)==null?void 0:S1[2])||0)+(((j1=G.distance)==null?void 0:j1[2])||0))/r:(N1=G.distance)==null?void 0:N1[2]]}}),Q={},E={connected:{}};(A=t.body.modelPath)!=null&&A.includes("efficientpose")?E=ct:(a=t.body.modelPath)!=null&&a.includes("blazepose")?E=st:(l=t.body.modelPath)!=null&&l.includes("movenet")&&(E=F2);for(let[G,q]of Object.entries(E.connected)){let U0=[];for(let y0=0;y0x2.part===q[y0]),k2=x0.find(x2=>x2.part===q[y0+1]);ze&&k2&&U0.push([ze.position,k2.position])}Q[G]=U0}z.body[M]={...e.body[M],box:T0,boxRaw:C0,keypoints:x0,annotations:Q}}if(!z.hand||e.hand.length!==z.hand.length)z.hand=JSON.parse(JSON.stringify(e.hand));else for(let 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o;switch(t.segmentation.mode||"default"){case"default":n.input=S0.squeeze(e),n.concat=S0.concat([n.input,n.mul],-1),o=S0.cast(n.concat,"int32");break;case"alpha":o=S0.cast(n.mul,"int32");break;default:o=S0.tensor(0)}return Object.keys(n).forEach(s=>S0.dispose(n[s])),o}function Zt(e,t,n){var c,d;if(!t||!((c=e==null?void 0:e.config)!=null&&c.validateModels))return null;let o=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"],r=["biasadd","fusedbatchnormv3","matmul","switch","shape","merge","split","broadcastto"],s=[],A=[],a=t.modelUrl,l=t.executor;if((d=l==null?void 0:l.graph)!=null&&d.nodes)for(let i of Object.values(l.graph.nodes)){let y=i.op.toLowerCase();s.includes(y)||s.push(y)}else!l&&e.config.debug&&b("model not loaded",n);for(let i of s)!o.includes(i)&&!r.includes(i)&&!e.env.kernels.includes(i)&&!e.env.kernels.includes(i.replace("_",""))&&!e.env.kernels.includes(i.replace("native",""))&&!e.env.kernels.includes(i.replace("v2",""))&&A.push(i);return 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t)x.box[0]>u.box[0]&&x.box[0]u.box[1]&&x.box[1]+x.box[3]m.body.box[0]&&u.box[0]+u.box[2]m.body.box[1]&&u.box[1]+u.box[3]m.body.box[0]&&u.box[1]+u.box[3]>m.body.box[1]&&u.box[1]+u.box[3]{u&&u.length===4&&(f.push(u[0],u[0]+u[2]),h.push(u[1],u[1]+u[3]))};T(m.face.box),T((d=m.body)==null?void 0:d.box),T((i=m.hands.left)==null?void 0:i.box),T((y=m.hands.right)==null?void 0:y.box);let g=Math.min(...f),p=Math.min(...h);m.box=[g,p,Math.max(...f)-g,Math.max(...h)-p],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),A.push(m)}return A}var a0=Z(V());var qt=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -14025,8 +269,7 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l 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warmupBitmap(instance) { - const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); - let blob; - let res; - switch (instance.config.warmup) { - case "face": - blob = await b64toBlob(face3); - break; - case "body": - case "full": - blob = await b64toBlob(body3); - break; - default: - blob = null; - } - if (blob) { - const bitmap = await createImageBitmap(blob); - res = await instance.detect(bitmap, instance.config); - bitmap.close(); - } - return res; -} -async function warmupCanvas(instance) { - return new Promise((resolve) => { - let src; - switch (instance.config.warmup) { - case "face": - src = "data:image/jpeg;base64," + face3; - break; - case "full": - case "body": - src = "data:image/jpeg;base64," + body3; - break; - default: - src = ""; - } - let img; - if (typeof Image !== "undefined") img = new Image(); - else if (env.Image) img = new env.Image(); - else { - resolve(void 0); - return; - } - img.onload = async () => { - const canvas3 = canvas(img.naturalWidth, img.naturalHeight); - if (!canvas3) { - log("Warmup: Canvas not found"); - resolve(void 0); - } else { - const ctx = canvas3.getContext("2d"); - if (ctx) ctx.drawImage(img, 0, 0); - const tensor6 = await instance.image(canvas3, true); - const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; - resolve(res); - } - }; - if (src) img.src = src; - else resolve(void 0); - }); -} -async function warmupNode(instance) { - const atob = (str) => Buffer.from(str, "base64"); - let img; - if (instance.config.warmup === "face") img = atob(face3); - else img = atob(body3); - let res; - if ("node" in tf37 && tf37.getBackend() === "tensorflow") { - const data = tf37["node"].decodeJpeg(img); - const expanded = tf37.expandDims(data, 0); - instance.tf.dispose(data); - res = await instance.detect(expanded, instance.config); - instance.tf.dispose(expanded); - } else { - if (instance.config.debug) log("Warmup tfjs-node not loaded"); - } - return res; -} -async function runInference(instance) { - let res; - if (typeof createImageBitmap === "function") res = await warmupBitmap(instance); - else if (typeof Image !== "undefined" || env.Canvas !== void 0) res = await warmupCanvas(instance); - else res = await warmupNode(instance); - return res; -} -async function runCompile(instance) { - var _a, _b, _c, _d; - if (!tf37.env().flagRegistry.ENGINE_COMPILE_ONLY) return; - const backendType = tf37.getBackend(); - const webGLBackend = tf37.backend(); - if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { - return; - } - tf37.env().set("ENGINE_COMPILE_ONLY", true); - const numTensorsStart = tf37.engine().state.numTensors; - const compiledModels = []; - for (const [modelName, model23] of Object.entries(instance.models.models)) { - if (!model23) continue; - const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; - const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; - for (let dim = 0; dim < shape.length; dim++) { - if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; - } - const tensor6 = tf37.zeros(shape, dtype); - try { - const res = model23.execute(tensor6); - compiledModels.push(modelName); - if (Array.isArray(res)) res.forEach((t2) => tf37.dispose(t2)); - else tf37.dispose(res); - } catch (e) { - if (instance.config.debug) log("compile fail model:", modelName); - } - tf37.dispose(tensor6); - } - const kernels = await webGLBackend["checkCompileCompletionAsync"](); - webGLBackend["getUniformLocations"](); - if (instance.config.debug) log("compile pass:", { models: compiledModels, kernels: kernels.length }); - tf37.env().set("ENGINE_COMPILE_ONLY", false); - const numTensorsEnd = tf37.engine().state.numTensors; - if (numTensorsEnd - numTensorsStart > 0) log("tensor leak:", numTensorsEnd - numTensorsStart); -} -async function warmup(instance, userConfig) { - await check(instance, false); - const t0 = now(); - instance.state = "warmup"; - if (userConfig) instance.config = mergeDeep(instance.config, userConfig); - if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { - return empty(); - } - return new Promise(async (resolve) => { - await instance.models.load(); - await tf37.ready(); - await runCompile(instance); - const res = await runInference(instance); - const t1 = now(); - if (instance.config.debug) log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); - instance.emit("warmup"); - resolve(res); - }); -} - -// src/human.ts -var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; -var Human = class { - // definition end - /** Constructor for **Human** library that is futher used for all operations - * @param userConfig - user configuration object {@link Config} - */ - constructor(userConfig) { - /** Current version of Human library in *semver* format */ - __publicField(this, "version"); - /** Current configuration - * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) - */ - __publicField(this, "config"); - /** Last known result of detect run - * - Can be accessed anytime after initial detection - */ - __publicField(this, "result"); - /** Current state of Human library - * - Can be polled to determine operations that are currently executed - * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle' - */ - __publicField(this, "state"); - /** currenty processed image tensor and canvas */ - __publicField(this, "process"); - /** Instance of TensorFlow/JS used by Human - * - Can be embedded or externally provided - * [TFJS API](https://js.tensorflow.org/api/latest/) - */ - __publicField(this, "tf"); - /** Object containing environment information used for diagnostics */ - __publicField(this, "env", env); - /** Draw helper classes that can draw detected objects on canvas using specified draw - * - canvas: draws input to canvas - * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions} - * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas - */ - // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions }; - __publicField(this, "draw", draw_exports); - /** Face Matching - * - similarity: compare two face descriptors and return similarity index - * - distance: compare two face descriptors and return raw calculated differences - * - find: compare face descriptor to array of face descriptors and return best match - */ - __publicField(this, "match", match_exports); - /** Currently loaded models - * @internal - * {@link models#Models} - */ - __publicField(this, "models"); - /** Container for events dispatched by Human - * Possible events: - * - `create`: triggered when Human object is instantiated - * - `load`: triggered when models are loaded (explicitly or on-demand) - * - `image`: triggered when input image is processed - * - `result`: triggered when detection is complete - * - `warmup`: triggered when warmup is complete - * - `error`: triggered on some errors - */ - __publicField(this, "events"); - /** Reference face triangualtion array of 468 points, used for triangle references between points */ - __publicField(this, "faceTriangulation"); - /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */ - __publicField(this, "faceUVMap"); - /** Performance object that contains values for all recently performed operations */ - __publicField(this, "performance"); - // perf members are dynamically defined as needed - __privateAdd(this, _numTensors); - __privateAdd(this, _analyzeMemoryLeaks); - __privateAdd(this, _checkSanity); - /** internal function to measure tensor leaks */ - __publicField(this, "analyze", (...msg) => { - if (!__privateGet(this, _analyzeMemoryLeaks)) return; - const currentTensors = this.tf.engine().state.numTensors; - const previousTensors = __privateGet(this, _numTensors); - __privateSet(this, _numTensors, currentTensors); - const leaked = currentTensors - previousTensors; - if (leaked !== 0) log(...msg, leaked); - }); - /** internal function for quick sanity check on inputs @hidden */ - __privateAdd(this, _sanity, (input) => { - if (!__privateGet(this, _checkSanity)) return null; - if (!input) return "input is not defined"; - if (this.env.node && !(input instanceof tf38.Tensor)) return "input must be a tensor"; - try { - this.tf.getBackend(); - } catch (e) { - return "backend not loaded"; - } - return null; - }); - /** WebCam helper methods - * - */ - __publicField(this, "webcam", new WebCam()); - /** emit event */ - __publicField(this, "emit", (event) => { - var _a; - if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) this.events.dispatchEvent(new Event(event)); - }); - /** internal structure that keeps track of processed videos @hidden */ - __privateAdd(this, _loops, {}); - const tfVersion = (tf38.version.tfjs || tf38.version_core).replace(/-(.*)/, ""); - config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; - config.modelBasePath = env.browser ? "../models/" : "file://models/"; - this.version = version2; - Object.defineProperty(this, "version", { value: version2 }); - this.config = JSON.parse(JSON.stringify(config)); - Object.seal(this.config); - this.config.cacheModels = typeof indexedDB !== "undefined"; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - setModelLoadOptions(this.config); - this.tf = tf38; - this.state = "idle"; - __privateSet(this, _numTensors, 0); - __privateSet(this, _analyzeMemoryLeaks, false); - __privateSet(this, _checkSanity, false); - this.performance = {}; - this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; - this.models = new Models(this); - init2(); - this.result = empty(); - this.process = { tensor: null, canvas: null }; - this.faceTriangulation = triangulation; - this.faceUVMap = uvmap; - validateModel(this, null, ""); - this.emit("create"); - if (this.config.debug || this.env.browser) log(`version: ${this.version}`); - if (this.config.debug) log(`tfjs version: ${this.tf.version["tfjs-core"]}`); - const envTemp = JSON.parse(JSON.stringify(this.env)); - delete envTemp.kernels; - delete envTemp.initial; - delete envTemp.perfadd; - if (this.config.debug) log("environment:", envTemp); - } - /** Reset configuration to default values */ - reset() { - const currentBackend = this.config.backend; - this.config = JSON.parse(JSON.stringify(config)); - this.config.backend = currentBackend; - reset(); - env.initial = true; - } - /** Validate current configuration schema */ - validate(userConfig) { - const msgs = validate(config, userConfig || this.config); - if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig); - return msgs; - } - /** Utility wrapper for performance.now() */ - now() { - return now(); - } - /** Process input as return canvas and tensor - * - * @param input - any input {@link Input} - * @param getTensor - should image processing also return tensor or just canvas - * Returns object with `tensor` and `canvas` - */ - image(input, getTensor = false) { - return process2(input, this.config, getTensor); - } - /** Segmentation method takes any input and returns RGBA tensor - * Note: Segmentation is not triggered as part of detect process - * - * @param input - {@link Input} - * Returns tensor which contains image data in RGBA format - */ - async segmentation(input, userConfig) { - var _a, _b, _c; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - if (!this.config.segmentation.enabled) return null; - const processed = await process2(input, this.config); - if (!processed.tensor) return null; - let tensor6 = null; - if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) tensor6 = await predict20(processed.tensor, this.config); - if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) tensor6 = await predict16(processed.tensor, this.config); - if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) tensor6 = await predict21(processed.tensor, this.config); - tf38.dispose(processed.tensor); - return tensor6; - } - /** Compare two input tensors for pixel similarity - * - use `human.image` to process any valid input and get a tensor that can be used for compare - * - when passing manually generated tensors: - * - both input tensors must be in format [1, height, width, 3] - * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor - * - return value is pixel similarity score normalized by input resolution and rgb channels - */ - compare(firstImageTensor, secondImageTensor) { - return compare(this.config, firstImageTensor, secondImageTensor); - } - /** Explicit backend initialization - * - Normally done implicitly during initial load phase - * - Call to explictly register and initialize TFJS backend without any other operations - * - Use when changing backend during runtime - */ - async init() { - await check(this, true); - await this.tf.ready(); - reset(); - } - /** Load method preloads all configured models on-demand - * - Not explicitly required as any required model is load implicitly on it's first run - * - * @param userConfig - {@link Config} - */ - async load(userConfig) { - this.state = "load"; - const timeStamp = now(); - const count2 = Object.values(this.models.models).filter((model23) => model23).length; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - if (this.env.initial) { - if (!await check(this, false)) log("error: backend check failed"); - await tf38.ready(); - if (this.env.browser) { - if (this.config.debug) log("configuration:", this.config); - if (this.config.debug) log("tf flags:", this.tf.ENV.flags); - } - } - await this.models.load(this); - if (this.env.initial && this.config.debug) log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); - this.env.initial = false; - const loaded = Object.values(this.models.models).filter((model23) => model23).length; - if (loaded !== count2) { - this.models.validate(); - this.emit("load"); - } - const current = Math.trunc(now() - timeStamp); - if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; - } - /** Runs interpolation using last known result and returns smoothened result - * Interpolation is based on time since last known result so can be called independently - * - * @param result - {@link Result} optional use specific result set to run interpolation on - * @returns result - {@link Result} - */ - next(result = this.result) { - return calc2(result, this.config); - } - /** Warmup method pre-initializes all configured models for faster inference - * - can take significant time on startup - * - only used for `webgl` and `humangl` backends - * @param userConfig - {@link Config} - * @returns result - {@link Result} - */ - async warmup(userConfig) { - const t0 = now(); - const res = await warmup(this, userConfig); - const t1 = now(); - this.performance.warmup = Math.trunc(t1 - t0); - return res; - } - /** Run detect with tensorflow profiling - * - result object will contain total exeuction time information for top-20 kernels - * - actual detection object can be accessed via `human.result` - */ - async profile(input, userConfig) { - const profile = await this.tf.profile(() => this.detect(input, userConfig)); - const kernels = {}; - let total = 0; - for (const kernel of profile.kernels) { - const ms = Number(kernel.kernelTimeMs) || 0; - if (kernels[kernel.name]) kernels[kernel.name] += ms; - else kernels[kernel.name] = ms; - total += ms; - } - const kernelArr = []; - Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); - for (const kernel of kernelArr) { - kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; - kernel.time = Math.round(1e3 * kernel.time) / 1e3; - } - kernelArr.sort((a, b) => b.time - a.time); - kernelArr.length = 20; - return kernelArr; - } - /** Main detection method - * - Analyze configuration: {@link Config} - * - Pre-process input: {@link Input} - * - Run inference for all configured models - * - Process and return result: {@link Result} - * - * @param input - {@link Input} - * @param userConfig - {@link Config} - * @returns result - {@link Result} - */ - async detect(input, userConfig) { - this.state = "detect"; - return new Promise(async (resolve) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; - this.state = "config"; - let timeStamp; - this.config = mergeDeep(this.config, userConfig); - this.state = "check"; - const error = __privateGet(this, _sanity).call(this, input); - if (error) { - log(error, input); - this.emit("error"); - resolve(empty(error)); - } - const timeStart = now(); - await this.load(); - timeStamp = now(); - this.state = "image"; - const img = await process2(input, this.config); - this.process = img; - this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Get Image:"); - if (!img.tensor) { - if (this.config.debug) log("could not convert input to tensor"); - this.emit("error"); - resolve(empty("could not convert input to tensor")); - return; - } - this.emit("image"); - timeStamp = now(); - this.config.skipAllowed = await skip(this.config, img.tensor); - this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; - if (!this.performance.totalFrames) this.performance.totalFrames = 0; - if (!this.performance.cachedFrames) this.performance.cachedFrames = 0; - this.performance.totalFrames++; - if (this.config.skipAllowed) this.performance.cachedFrames++; - this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Check Changed:"); - let faceRes = []; - let bodyRes = []; - let handRes = []; - let objectRes = []; - this.state = "detect:face"; - if (this.config.async) { - faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; - if (this.performance.face) delete this.performance.face; - } else { - timeStamp = now(); - faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; - this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; - this.analyze("Start Body:"); - this.state = "detect:body"; - const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; - else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; - else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; - else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; - if (this.performance.body) delete this.performance.body; - } else { - timeStamp = now(); - if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; - else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; - else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; - else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; - this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Body:"); - this.analyze("Start Hand:"); - this.state = "detect:hand"; - const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; - else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; - if (this.performance.hand) delete this.performance.hand; - } else { - timeStamp = now(); - if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; - else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; - this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Hand:"); - this.analyze("Start Object:"); - this.state = "detect:object"; - if (this.config.async) { - if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; - else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; - if (this.performance.object) delete this.performance.object; - } else { - timeStamp = now(); - if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; - else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; - this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Object:"); - this.state = "detect:await"; - if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); - this.state = "detect:gesture"; - let gestureRes = []; - if (this.config.gesture.enabled) { - timeStamp = now(); - gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; - if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - else if (this.performance.gesture) delete this.performance.gesture; - } - this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); - const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; - this.result = { - face: faceRes, - body: bodyRes, - hand: handRes, - gesture: gestureRes, - object: objectRes, - performance: this.performance, - canvas: this.process.canvas, - timestamp: Date.now(), - error: null, - width: shape[2], - height: shape[1], - get persons() { - return join2(faceRes, bodyRes, handRes, gestureRes, shape); - } - }; - tf38.dispose(img.tensor); - this.emit("detect"); - this.state = "idle"; - resolve(this.result); - }); - } - /** Helper function - * @param ms - sleep time in miliseconds - */ - async sleep(ms) { - return new Promise((resolve) => { - setTimeout(resolve, ms); - }); - } - /** Continously detect video frames - * @param element - HTMLVideoElement input - * @param run - boolean run continously or stop if already running, default true - * @param delay - number delay detection between frames for number of miliseconds, default 0 - */ - async video(element, run = true, delay = 0) { - if (run) { - if (!__privateGet(this, _loops)[element.id]) { - if (this.config.debug) log("video start", element.id); - __privateGet(this, _loops)[element.id] = true; - } - if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) await this.detect(element); - if (delay > 0) await this.sleep(delay); - if (__privateGet(this, _loops)[element.id]) requestAnimationFrame(() => this.video(element, run, delay)); - } else { - if (this.config.debug) log("video stop", element.id); - __privateGet(this, _loops)[element.id] = false; - } - } -}; -_numTensors = new WeakMap(); -_analyzeMemoryLeaks = new WeakMap(); -_checkSanity = new WeakMap(); -_sanity = new WeakMap(); -_loops = new WeakMap(); -// Annotate the CommonJS export names for ESM import in node: -0 && (module.exports = { - Env, - Human, - defaults, - draw, - empty, - env, - match, - models -}); 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a0&&a0.getBackend()==="tensorflow"){let r=a0.node.decodeJpeg(n),s=a0.expandDims(r,0);e.tf.dispose(r),o=await e.detect(s,e.config),e.tf.dispose(s)}else e.config.debug&&b("Warmup tfjs-node not loaded");return o}async function pa(e){let t;return typeof createImageBitmap=="function"?t=await ya(e):typeof Image!="undefined"||R.Canvas!==void 0?t=await fa(e):t=await ma(e),t}async function ua(e){var a,l,c,d;if(!a0.env().flagRegistry.ENGINE_COMPILE_ONLY)return;let t=a0.getBackend(),n=a0.backend();if(t!=="webgl"&&t!=="humangl"||!(n!=null&&n.checkCompileCompletion))return;a0.env().set("ENGINE_COMPILE_ONLY",!0);let o=a0.engine().state.numTensors,r=[];for(let[i,y]of Object.entries(e.models.models)){if(!y)continue;let x=y!=null&&y.modelSignature&&((l=(a=y==null?void 0:y.inputs)==null?void 0:a[0])!=null&&l.shape)?[...y.inputs[0].shape]:[1,64,64,3],m=y!=null&&y.modelSignature&&((d=(c=y==null?void 0:y.inputs)==null?void 0:c[0])!=null&&d.dtype)?y.inputs[0].dtype:"float32";for(let h=0;ha0.dispose(T)):a0.dispose(h)}catch(h){e.config.debug&&b("compile fail model:",i)}a0.dispose(f)}let s=await n.checkCompileCompletionAsync();n.getUniformLocations(),e.config.debug&&b("compile pass:",{models:r,kernels:s.length}),a0.env().set("ENGINE_COMPILE_ONLY",!1);let A=a0.engine().state.numTensors;A-o>0&&b("tensor leak:",A-o)}async function Fo(e,t){await O2(e,!1);let n=v();return e.state="warmup",t&&(e.config=i0(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none"?ve():new Promise(async o=>{await e.models.load(),await a0.ready(),await ua(e);let r=await pa(e),s=v();e.config.debug&&b("warmup",e.config.warmup,Math.round(s-n),"ms"),e.emit("warmup"),o(r)})}var P2,Z2,q2,Ut,Ye,E1=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env",R);w(this,"draw",rt);w(this,"match",Wt);w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");ue(this,P2);ue(this,Z2);ue(this,q2);w(this,"analyze",(...t)=>{if(!Y0(this,Z2))return;let n=this.tf.engine().state.numTensors,o=Y0(this,P2);Me(this,P2,n);let r=n-o;r!==0&&b(...t,r)});ue(this,Ut,t=>{if(!Y0(this,q2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof de.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});w(this,"webcam",new Q2);w(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(de.version.tfjs||de.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=n5,Object.defineProperty(this,"version",{value:n5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=i0(this.config,t)),U1(this.config),this.tf=de,this.state="idle",Me(this,P2,0),Me(this,Z2,!1),Me(this,q2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new V2(this),l5(),this.result=ve(),this.process={tensor:null,canvas:null},this.faceTriangulation=B3,this.faceUVMap=H3,Zt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&b("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,e5(),R.initial=!0}validate(t){let n=Qt(Ke,t||this.config);return n.length===0&&(this.config=i0(this.config,t)),n}now(){return v()}image(t,n=!1){return K2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=i0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await K2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Oo(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await xo(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Co(o.tensor,this.config)),de.dispose(o.tensor),r}compare(t,n){return X1(this.config,t,n)}async init(){await O2(this,!0),await this.tf.ready(),e5()}async load(t){this.state="load";let n=v(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=i0(this.config,t)),this.env.initial&&(await O2(this,!1)||b("error: backend check failed"),await de.ready(),this.env.browser&&(this.config.debug&&b("configuration:",this.config),this.config.debug&&b("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&b("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(v()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return co(t,this.config)}async warmup(t){let n=v(),o=await Fo(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let l=Number(a.kernelTimeMs)||0;r[a.name]?r[a.name]+=l:r[a.name]=l,s+=l}let A=[];Object.entries(r).forEach(a=>A.push({kernel:a[0],time:a[1],perc:0}));for(let a of A)a.perc=Math.round(1e3*a.time/s)/1e3,a.time=Math.round(1e3*a.time)/1e3;return A.sort((a,l)=>l.time-a.time),A.length=20,A}async detect(t,n){return this.state="detect",new Promise(async o=>{var h,T,g,p,u,k,P,N,B,J,H,K,e0,n0,o0,N0,M,T0,C0,x0,Q;this.state="config";let r;this.config=i0(this.config,n),this.state="check";let s=Y0(this,Ut).call(this,t);s&&(b(s,t),this.emit("error"),o(ve(s)));let A=v();await this.load(),r=v(),this.state="image";let a=await K2(t,this.config);if(this.process=a,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Get Image:"),!a.tensor){this.config.debug&&b("could not convert input to tensor"),this.emit("error"),o(ve("could not convert input to tensor"));return}this.emit("image"),r=v(),this.config.skipAllowed=await q1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],d=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?Q5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await Q5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?i0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?v1(a.tensor,y):[]:(T=this.config.body.modelPath)!=null&&T.includes("blazepose")?c=this.config.body.enabled?f5(a.tensor,y):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?c=this.config.body.enabled?T5(a.tensor,y):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?m1(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=v(),(u=this.config.body.modelPath)!=null&&u.includes("posenet")?c=this.config.body.enabled?await v1(a.tensor,y):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?c=this.config.body.enabled?await f5(a.tensor,y):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await T5(a.tensor,y):[]:(N=this.config.body.modelPath)!=null&&N.includes("movenet")&&(c=this.config.body.enabled?await m1(a.tensor,y):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let x=this.config.hand.maxDetected===-1?i0(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?((J=(B=this.config.hand.detector)==null?void 0:B.modelPath)!=null&&J.includes("handdetect")?d=this.config.hand.enabled?o1(a.tensor,x):[]:(K=(H=this.config.hand.detector)==null?void 0:H.modelPath)!=null&&K.includes("handtrack")&&(d=this.config.hand.enabled?A1(a.tensor,x):[]),this.performance.hand&&delete this.performance.hand):(r=v(),(n0=(e0=this.config.hand.detector)==null?void 0:e0.modelPath)!=null&&n0.includes("handdetect")?d=this.config.hand.enabled?await o1(a.tensor,x):[]:(N0=(o0=this.config.hand.detector)==null?void 0:o0.modelPath)!=null&&N0.includes("handtrack")&&(d=this.config.hand.enabled?await A1(a.tensor,x):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?((M=this.config.object.modelPath)!=null&&M.includes("nanodet")?i=this.config.object.enabled?u1(a.tensor,this.config):[]:(T0=this.config.object.modelPath)!=null&&T0.includes("centernet")&&(i=this.config.object.enabled?u5(a.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=v(),(C0=this.config.object.modelPath)!=null&&C0.includes("nanodet")?i=this.config.object.enabled?await u1(a.tensor,this.config):[]:(x0=this.config.object.modelPath)!=null&&x0.includes("centernet")&&(i=this.config.object.enabled?await u5(a.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,d,i]=await Promise.all([l,c,d,i])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=v(),m=[...Fn(l),...Dn(c),...Hn(d),...Bn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(v()-A):Math.trunc(v()-A);let f=((Q=this.process.tensor)==null?void 0:Q.shape)||[0,0,0,0];this.result={face:l,body:c,hand:d,gesture:m,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return Do(l,c,d,m,f)}},de.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(Y0(this,Ye)[t.id]||(this.config.debug&&b("video start",t.id),Y0(this,Ye)[t.id]=!0),!t.paused&&Y0(this,Ye)[t.id]&&t.readyState>=2&&await this.detect(t),o>0&&await this.sleep(o),Y0(this,Ye)[t.id]&&requestAnimationFrame(()=>this.video(t,n,o))):(this.config.debug&&b("video stop",t.id),Y0(this,Ye)[t.id]=!1)}};P2=new WeakMap,Z2=new WeakMap,q2=new WeakMap,Ut=new WeakMap,Ye=new WeakMap;0&&(module.exports={Env,Human,defaults,draw,empty,env,match,models}); diff --git a/dist/human.node.js b/dist/human.node.js index bd954b1b..7ce46e24 100644 --- a/dist/human.node.js +++ b/dist/human.node.js @@ -4,302 +4,7 @@ author: ' */ -"use strict"; -var __create = Object.create; -var __defProp = Object.defineProperty; -var __getOwnPropDesc = Object.getOwnPropertyDescriptor; -var __getOwnPropNames = Object.getOwnPropertyNames; -var __getProtoOf = Object.getPrototypeOf; -var __hasOwnProp = Object.prototype.hasOwnProperty; -var __typeError = (msg) => { - throw TypeError(msg); -}; -var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value; -var __commonJS = (cb, mod3) => function __require() { - return mod3 || (0, cb[__getOwnPropNames(cb)[0]])((mod3 = { exports: {} }).exports, mod3), mod3.exports; -}; -var __export = (target, all2) => { - for (var name in all2) - __defProp(target, name, { get: all2[name], enumerable: true }); -}; -var __copyProps = (to, from, except, desc) => { - if (from && typeof from === "object" || typeof from === "function") { - for (let key of __getOwnPropNames(from)) - if (!__hasOwnProp.call(to, key) && key !== except) - __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable }); - } - return to; -}; -var __toESM = (mod3, isNodeMode, target) => (target = mod3 != null ? __create(__getProtoOf(mod3)) : {}, __copyProps( - // If the importer is in node compatibility mode or this is not an ESM - // file that has been converted to a CommonJS file using a Babel- - // compatible transform (i.e. "__esModule" has not been set), then set - // "default" to the CommonJS "module.exports" for node compatibility. - isNodeMode || !mod3 || !mod3.__esModule ? __defProp(target, "default", { value: mod3, enumerable: true }) : target, - mod3 -)); -var __toCommonJS = (mod3) => __copyProps(__defProp({}, "__esModule", { value: true }), mod3); -var __publicField = (obj, key, value) => __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value); -var __accessCheck = (obj, member, msg) => member.has(obj) || __typeError("Cannot " + msg); -var __privateGet = (obj, member, getter) => (__accessCheck(obj, member, "read from private field"), getter ? getter.call(obj) : member.get(obj)); -var __privateAdd = (obj, member, value) => member.has(obj) ? __typeError("Cannot add the same private member more than once") : member instanceof WeakSet ? member.add(obj) : member.set(obj, value); -var __privateSet = (obj, member, value, setter) => (__accessCheck(obj, member, "write to private field"), setter ? setter.call(obj, value) : member.set(obj, value), value); - -// dist/tfjs.esm.js -var require_tfjs_esm = __commonJS({ - "dist/tfjs.esm.js"(exports2, module2) { - "use strict"; - var __defProp2 = Object.defineProperty; - var __getOwnPropDesc2 = Object.getOwnPropertyDescriptor; - var __getOwnPropNames2 = Object.getOwnPropertyNames; - var __hasOwnProp2 = Object.prototype.hasOwnProperty; - var __export2 = (target, all2) => { - for (var name in all2) - __defProp2(target, name, { get: all2[name], enumerable: true }); - }; - var __copyProps2 = (to, from, except, desc) => { - if (from && typeof from === "object" || typeof from === "function") { - for (let key of __getOwnPropNames2(from)) - if (!__hasOwnProp2.call(to, key) && key !== except) - __defProp2(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc2(from, key)) || desc.enumerable }); - } - return to; - }; - var __reExport = (target, mod3, secondTarget) => (__copyProps2(target, mod3, "default"), secondTarget && __copyProps2(secondTarget, mod3, "default")); - var __toCommonJS2 = (mod3) => __copyProps2(__defProp2({}, "__esModule", { value: true }), mod3); - var tf_node_exports = {}; - __export2(tf_node_exports, { - version: () => version7 - }); - module2.exports = __toCommonJS2(tf_node_exports); - __reExport(tf_node_exports, require("@tensorflow/tfjs-node"), module2.exports); - var version4 = "4.21.0"; - var version22 = "4.21.0"; - var version32 = "4.21.0"; - var version42 = "4.21.0"; - var version5 = "4.21.0"; - var version6 = "4.21.0"; - var version7 = { - tfjs: version4, - "tfjs-core": version4, - // 'tfjs-data': tfjsDataVersion, - // 'tfjs-layers': tfjsLayersVersion, - "tfjs-converter": version22, - "tfjs-backend-cpu": version32, - "tfjs-backend-webgl": version42, - "tfjs-backend-wasm": version5, - "tfjs-backend-webgpu": version6 - }; - } -}); - -// src/human.ts -var human_exports = {}; -__export(human_exports, { - Env: () => Env, - Human: () => Human, - default: () => Human, - defaults: () => config, - draw: () => draw_exports, - empty: () => empty, - env: () => env, - match: () => match_exports, - models: () => models_exports2 -}); -module.exports = __toCommonJS(human_exports); -var tf38 = __toESM(require_tfjs_esm()); - -// src/util/util.ts -function log(...msg) { - const dt = /* @__PURE__ */ new Date(); - const ts = `${dt.getHours().toString().padStart(2, "0")}:${dt.getMinutes().toString().padStart(2, "0")}:${dt.getSeconds().toString().padStart(2, "0")}.${dt.getMilliseconds().toString().padStart(3, "0")}`; - if (msg) console.log(ts, "Human:", ...msg); -} -function join(folder, file) { - const separator = folder.endsWith("/") ? "" : "/"; - const skipJoin = file.startsWith(".") || file.startsWith("/") || file.startsWith("http:") || file.startsWith("https:") || file.startsWith("file:"); - const path = skipJoin ? `${file}` : `${folder}${separator}${file}`; - if (!path.toLocaleLowerCase().includes(".json")) throw new Error(`modelpath error: expecting json file: ${path}`); - return path; -} -var now = () => { - if (typeof performance !== "undefined") return performance.now(); - return parseInt((Number(process.hrtime.bigint()) / 1e3 / 1e3).toString()); -}; -function validate(defaults, config3, parent = "config", msgs = []) { - for (const key of Object.keys(config3)) { - if (typeof config3[key] === "object") { - validate(defaults[key], config3[key], key, msgs); - } else { - const defined = defaults && typeof defaults[key] !== "undefined"; - if (!defined) msgs.push({ reason: "unknown property", where: `${parent}.${key} = ${config3[key]}` }); - const same = defaults && typeof defaults[key] === typeof config3[key]; - if (defined && !same) msgs.push({ reason: "property type mismatch", where: `${parent}.${key} = ${config3[key]}`, expected: typeof defaults[key] }); - } - } - if (config3.debug && parent === "config" && msgs.length > 0) log("invalid configuration", msgs); - return msgs; -} -function mergeDeep(...objects) { - const isObject = (obj) => obj && typeof obj === "object"; - return objects.reduce((prev, obj) => { - Object.keys(obj || {}).forEach((key) => { - const pVal = prev[key]; - const oVal = obj[key]; - if (Array.isArray(pVal) && Array.isArray(oVal)) prev[key] = pVal.concat(...oVal); - else if (isObject(pVal) && isObject(oVal)) prev[key] = mergeDeep(pVal, oVal); - else prev[key] = oVal; - }); - return prev; - }, {}); -} - -// src/config.ts -var config = { - backend: "", - modelBasePath: "", - cacheModels: true, - validateModels: true, - wasmPath: "", - wasmPlatformFetch: false, - debug: false, - async: true, - warmup: "full", - cacheSensitivity: 0.7, - skipAllowed: false, - deallocate: false, - flags: {}, - softwareKernels: false, - filter: { - enabled: true, - equalization: false, - width: 0, - height: 0, - flip: false, - return: true, - autoBrightness: true, - brightness: 0, - contrast: 0, - sharpness: 0, - blur: 0, - saturation: 0, - hue: 0, - negative: false, - sepia: false, - vintage: false, - kodachrome: false, - technicolor: false, - polaroid: false, - pixelate: 0 - }, - gesture: { - enabled: true - }, - face: { - enabled: true, - detector: { - modelPath: "blazeface.json", - rotation: false, - maxDetected: 1, - skipFrames: 99, - skipTime: 2500, - minConfidence: 0.2, - minSize: 0, - iouThreshold: 0.1, - scale: 1.4, - mask: false, - return: false - }, - mesh: { - enabled: true, - modelPath: "facemesh.json", - keepInvalid: false - }, - attention: { - enabled: false, - modelPath: "facemesh-attention.json" - }, - iris: { - enabled: true, - scale: 2.3, - modelPath: "iris.json" - }, - emotion: { - enabled: true, - minConfidence: 0.1, - skipFrames: 99, - skipTime: 1500, - modelPath: "emotion.json" - }, - description: { - enabled: true, - modelPath: "faceres.json", - skipFrames: 99, - skipTime: 3e3, - minConfidence: 0.1 - }, - antispoof: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "antispoof.json" - }, - liveness: { - enabled: false, - skipFrames: 99, - skipTime: 4e3, - modelPath: "liveness.json" - } - }, - body: { - enabled: true, - modelPath: "movenet-lightning.json", - maxDetected: -1, - minConfidence: 0.3, - skipFrames: 1, - skipTime: 200 - }, - hand: { - enabled: true, - rotation: true, - skipFrames: 99, - skipTime: 1e3, - minConfidence: 0.5, - iouThreshold: 0.2, - maxDetected: -1, - landmarks: true, - detector: { - modelPath: "handtrack.json" - }, - skeleton: { - modelPath: "handlandmark-lite.json" - } - }, - object: { - enabled: false, - modelPath: "centernet.json", - minConfidence: 0.2, - iouThreshold: 0.4, - maxDetected: 10, - skipFrames: 99, - skipTime: 2e3 - }, - segmentation: { - enabled: false, - modelPath: "rvm.json", - ratio: 0.5, - mode: "default" - } -}; - -// src/util/env.ts -var tf3 = __toESM(require_tfjs_esm()); - -// src/image/image.ts -var tf2 = __toESM(require_tfjs_esm()); - -// src/image/imagefxshaders.ts -var vertexIdentity = ` +"use strict";var Fo=Object.create;var w2=Object.defineProperty;var Bo=Object.getOwnPropertyDescriptor;var Ho=Object.getOwnPropertyNames;var Go=Object.getPrototypeOf,Vo=Object.prototype.hasOwnProperty;var N1=e=>{throw TypeError(e)};var Zo=(e,t,n)=>t in e?w2(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var qo=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),Se=(e,t)=>{for(var n in t)w2(e,n,{get:t[n],enumerable:!0})},I1=(e,t,n,o)=>{if(t&&typeof t=="object"||typeof t=="function")for(let r of Ho(t))!Vo.call(e,r)&&r!==n&&w2(e,r,{get:()=>t[r],enumerable:!(o=Bo(t,r))||o.enumerable});return e};var 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e},Qo=(e,t,n)=>(Xt(e,t,"default"),n&&Xt(n,t,"default")),_o=e=>Xt(Ut({},"__esModule",{value:!0}),e),Yt={};Jo(Yt,{version:()=>rr});Kt.exports=_o(Yt);Qo(Yt,require("@tensorflow/tfjs-node"),Kt.exports);var O1="4.21.0",$o="4.21.0",er="4.21.0",tr="4.21.0",nr="4.21.0",or="4.21.0",rr={tfjs:O1,"tfjs-core":O1,"tfjs-converter":$o,"tfjs-backend-cpu":er,"tfjs-backend-webgl":tr,"tfjs-backend-wasm":nr,"tfjs-backend-webgpu":or}});var ba={};Se(ba,{Env:()=>E2,Human:()=>w1,default:()=>w1,defaults:()=>Ke,draw:()=>nt,empty:()=>ve,env:()=>R,match:()=>Ot,models:()=>k1});module.exports=Xo(ba);var de=Z(V());function b(...e){let t=new Date,n=`${t.getHours().toString().padStart(2,"0")}:${t.getMinutes().toString().padStart(2,"0")}:${t.getSeconds().toString().padStart(2,"0")}.${t.getMilliseconds().toString().padStart(3,"0")}`;e&&console.log(n,"Human:",...e)}function C1(e,t){let n=e.endsWith("/")?"":"/",r=t.startsWith(".")||t.startsWith("/")||t.startsWith("http:")||t.startsWith("https:")||t.startsWith("file:")?`${t}`:`${e}${n}${t}`;if(!r.toLocaleLowerCase().includes(".json"))throw new Error(`modelpath error: expecting json file: ${r}`);return r}var v=()=>typeof performance!="undefined"?performance.now():parseInt((Number(process.hrtime.bigint())/1e3/1e3).toString());function Jt(e,t,n="config",o=[]){for(let r of Object.keys(t))if(typeof t[r]=="object")Jt(e[r],t[r],r,o);else{let s=e&&typeof e[r]!="undefined";s||o.push({reason:"unknown property",where:`${n}.${r} = ${t[r]}`});let A=e&&typeof e[r]==typeof t[r];s&&!A&&o.push({reason:"property type mismatch",where:`${n}.${r} = ${t[r]}`,expected:typeof e[r]})}return t.debug&&n==="config"&&o.length>0&&b("invalid configuration",o),o}function i0(...e){let t=n=>n&&typeof n=="object";return e.reduce((n,o)=>(Object.keys(o||{}).forEach(r=>{let s=n[r],A=o[r];Array.isArray(s)&&Array.isArray(A)?n[r]=s.concat(...A):t(s)&&t(A)?n[r]=i0(s,A):n[r]=A}),n),{})}var Ke={backend:"",modelBasePath:"",cacheModels:!0,validateModels:!0,wasmPath:"",wasmPlatformFetch:!1,debug:!1,async:!0,warmup:"full",cacheSensitivity:.7,skipAllowed:!1,deallocate:!1,flags:{},softwareKernels:!1,filter:{enabled:!0,equalization:!1,width:0,height:0,flip:!1,return:!0,autoBrightness:!0,brightness:0,contrast:0,sharpness:0,blur:0,saturation:0,hue:0,negative:!1,sepia:!1,vintage:!1,kodachrome:!1,technicolor:!1,polaroid:!1,pixelate:0},gesture:{enabled:!0},face:{enabled:!0,detector:{modelPath:"blazeface.json",rotation:!1,maxDetected:1,skipFrames:99,skipTime:2500,minConfidence:.2,minSize:0,iouThreshold:.1,scale:1.4,mask:!1,return:!1},mesh:{enabled:!0,modelPath:"facemesh.json",keepInvalid:!1},attention:{enabled:!1,modelPath:"facemesh-attention.json"},iris:{enabled:!0,scale:2.3,modelPath:"iris.json"},emotion:{enabled:!0,minConfidence:.1,skipFrames:99,skipTime:1500,modelPath:"emotion.json"},description:{enabled:!0,modelPath:"faceres.json",skipFrames:99,skipTime:3e3,minConfidence:.1},antispoof:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"antispoof.json"},liveness:{enabled:!1,skipFrames:99,skipTime:4e3,modelPath:"liveness.json"}},body:{enabled:!0,modelPath:"movenet-lightning.json",maxDetected:-1,minConfidence:.3,skipFrames:1,skipTime:200},hand:{enabled:!0,rotation:!0,skipFrames:99,skipTime:1e3,minConfidence:.5,iouThreshold:.2,maxDetected:-1,landmarks:!0,detector:{modelPath:"handtrack.json"},skeleton:{modelPath:"handlandmark-lite.json"}},object:{enabled:!1,modelPath:"centernet.json",minConfidence:.2,iouThreshold:.4,maxDetected:10,skipFrames:99,skipTime:2e3},segmentation:{enabled:!1,modelPath:"rvm.json",ratio:.5,mode:"default"}};var W0=Z(V());var I=Z(V());var W1=` precision highp float; attribute vec2 pos; attribute vec2 uv; @@ -309,8 +14,7 @@ var vertexIdentity = ` vUv = uv; gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.); } -`; -var colorMatrixWithAlpha = ` +`;var D1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -322,8 +26,7 @@ var colorMatrixWithAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14]; gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19]; } -`; -var colorMatrixWithoutAlpha = ` +`,F1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -335,8 +38,7 @@ var colorMatrixWithoutAlpha = ` gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14]; gl_FragColor.a = c.a; } -`; -var pixelate = ` +`,B1=` precision highp float; varying vec2 vUv; uniform vec2 size; @@ -349,8 +51,7 @@ var pixelate = ` vec2 coord = pixelate(vUv, size); gl_FragColor += texture2D(texture, coord); } -`; -var blur = ` +`,H1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -373,8 +74,7 @@ var blur = ` gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794; gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265; } -`; -var convolution = ` +`,G1=` precision highp float; varying vec2 vUv; uniform sampler2D texture; @@ -396,6031 +96,20 @@ var convolution = ` c31 * m[6] + c32 * m[7] + c33 * m[8]; gl_FragColor.a = c22.a; } -`; - -// src/image/imagefx.ts -var collect = (source, prefix, collection) => { - const r = new RegExp("\\b" + prefix + " \\w+ (\\w+)", "ig"); - source.replace(r, (match2, name) => { - collection[name] = 0; - return match2; - }); -}; -var GLProgram = class { - constructor(gl, vertexSource, fragmentSource) { - __publicField(this, "uniform", {}); - __publicField(this, "attribute", {}); - __publicField(this, "gl"); - __publicField(this, "id"); - __publicField(this, "compile", (source, type) => { - const shader = this.gl.createShader(type); - if (!shader) { - log("filter: could not create shader"); - return null; - } - this.gl.shaderSource(shader, source); - this.gl.compileShader(shader); - if (!this.gl.getShaderParameter(shader, this.gl.COMPILE_STATUS)) { - log(`filter: gl compile failed: ${this.gl.getShaderInfoLog(shader) || "unknown"}`); - return null; - } - return shader; - }); - this.gl = gl; - const vertexShader = this.compile(vertexSource, this.gl.VERTEX_SHADER); - const fragmentShader = this.compile(fragmentSource, this.gl.FRAGMENT_SHADER); - this.id = this.gl.createProgram(); - if (!vertexShader || !fragmentShader) return; - if (!this.id) { - log("filter: could not create webgl program"); - return; - } - this.gl.attachShader(this.id, vertexShader); - this.gl.attachShader(this.id, fragmentShader); - this.gl.linkProgram(this.id); - if (!this.gl.getProgramParameter(this.id, this.gl.LINK_STATUS)) { - log(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id) || "unknown"}`); - return; - } - this.gl.useProgram(this.id); - collect(vertexSource, "attribute", this.attribute); - for (const a in this.attribute) this.attribute[a] = this.gl.getAttribLocation(this.id, a); - collect(vertexSource, "uniform", this.uniform); - collect(fragmentSource, "uniform", this.uniform); - for (const u in this.uniform) this.uniform[u] = this.gl.getUniformLocation(this.id, u); - } -}; -function GLImageFilter() { - let drawCount = 0; - let sourceTexture = null; - let lastInChain = false; - let currentFramebufferIndex = -1; - let tempFramebuffers = [null, null]; - let filterChain = []; - let vertexBuffer = null; - let currentProgram = null; - const fxcanvas = canvas(100, 100); - const shaderProgramCache = {}; - const DRAW = { INTERMEDIATE: 1 }; - const gl = fxcanvas.getContext("webgl"); - if (!gl) { - log("filter: cannot get webgl context"); - return; - } - this.gl = gl; - function resize(width, height) { - if (width === fxcanvas.width && height === fxcanvas.height) return; - fxcanvas.width = width; - fxcanvas.height = height; - if (!vertexBuffer) { - const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]); - vertexBuffer = gl.createBuffer(); - gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); - gl.bufferData(gl.ARRAY_BUFFER, vertices, gl.STATIC_DRAW); - gl.pixelStorei(gl.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true); - } - gl.viewport(0, 0, fxcanvas.width, fxcanvas.height); - tempFramebuffers = [null, null]; - } - function createFramebufferTexture(width, height) { - const fbo = gl.createFramebuffer(); - gl.bindFramebuffer(gl.FRAMEBUFFER, fbo); - const renderbuffer = gl.createRenderbuffer(); - gl.bindRenderbuffer(gl.RENDERBUFFER, renderbuffer); - const texture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, texture); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, width, height, 0, gl.RGBA, gl.UNSIGNED_BYTE, null); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); - gl.bindTexture(gl.TEXTURE_2D, null); - gl.bindFramebuffer(gl.FRAMEBUFFER, null); - return { fbo, texture }; - } - function getTempFramebuffer(index2) { - tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height); - return tempFramebuffers[index2]; - } - function draw(flags = 0) { - if (!currentProgram) return; - let source = null; - let target = null; - let flipY = false; - if (drawCount === 0) source = sourceTexture; - else source = getTempFramebuffer(currentFramebufferIndex).texture || null; - drawCount++; - if (lastInChain && !(flags & DRAW.INTERMEDIATE)) { - target = null; - flipY = drawCount % 2 === 0; - } else { - currentFramebufferIndex = (currentFramebufferIndex + 1) % 2; - target = getTempFramebuffer(currentFramebufferIndex).fbo || null; - } - gl.bindTexture(gl.TEXTURE_2D, source); - gl.bindFramebuffer(gl.FRAMEBUFFER, target); - gl.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1); - gl.drawArrays(gl.TRIANGLES, 0, 6); - } - function compileShader(fragmentSource) { - if (shaderProgramCache[fragmentSource]) { - currentProgram = shaderProgramCache[fragmentSource]; - gl.useProgram((currentProgram ? currentProgram.id : null) || null); - return currentProgram; - } - currentProgram = new GLProgram(gl, vertexIdentity, fragmentSource); - if (!currentProgram) { - log("filter: could not get webgl program"); - return null; - } - const floatSize = Float32Array.BYTES_PER_ELEMENT; - const vertSize = 4 * floatSize; - gl.enableVertexAttribArray(currentProgram.attribute["pos"]); - gl.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl.FLOAT, false, vertSize, 0 * floatSize); - gl.enableVertexAttribArray(currentProgram.attribute["uv"]); - gl.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl.FLOAT, false, vertSize, 2 * floatSize); - shaderProgramCache[fragmentSource] = currentProgram; - return currentProgram; - } - const filter = { - colorMatrix: (matrix) => { - const m = new Float32Array(matrix); - m[4] /= 255; - m[9] /= 255; - m[14] /= 255; - m[19] /= 255; - const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha; - const program = compileShader(shader); - if (!program) return; - gl.uniform1fv(program.uniform["m"], m); - draw(); - }, - brightness: (brightness) => { - const b = (brightness || 0) + 1; - filter.colorMatrix([ - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - b, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - saturation: (amount) => { - const x = (amount || 0) * 2 / 3 + 1; - const y = (x - 1) * -0.5; - filter.colorMatrix([ - x, - y, - y, - 0, - 0, - y, - x, - y, - 0, - 0, - y, - y, - x, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturate: () => { - filter.saturation(-1); - }, - contrast: (amount) => { - const v = (amount || 0) + 1; - const o = -128 * (v - 1); - filter.colorMatrix([ - v, - 0, - 0, - 0, - o, - 0, - v, - 0, - 0, - o, - 0, - 0, - v, - 0, - o, - 0, - 0, - 0, - 1, - 0 - ]); - }, - negative: () => { - filter.contrast(-2); - }, - hue: (rotation) => { - rotation = (rotation || 0) / 180 * Math.PI; - const cos = Math.cos(rotation); - const sin = Math.sin(rotation); - const lumR = 0.213; - const lumG = 0.715; - const lumB = 0.072; - filter.colorMatrix([ - lumR + cos * (1 - lumR) + sin * -lumR, - lumG + cos * -lumG + sin * -lumG, - lumB + cos * -lumB + sin * (1 - lumB), - 0, - 0, - lumR + cos * -lumR + sin * 0.143, - lumG + cos * (1 - lumG) + sin * 0.14, - lumB + cos * -lumB + sin * -0.283, - 0, - 0, - lumR + cos * -lumR + sin * -(1 - lumR), - lumG + cos * -lumG + sin * lumG, - lumB + cos * (1 - lumB) + sin * lumB, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - desaturateLuminance: () => { - filter.colorMatrix([ - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0.2764723, - 0.929708, - 0.0938197, - 0, - -37.1, - 0, - 0, - 0, - 1, - 0 - ]); - }, - sepia: () => { - filter.colorMatrix([ - 0.393, - 0.7689999, - 0.18899999, - 0, - 0, - 0.349, - 0.6859999, - 0.16799999, - 0, - 0, - 0.272, - 0.5339999, - 0.13099999, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - brownie: () => { - filter.colorMatrix([ - 0.5997023498159715, - 0.34553243048391263, - -0.2708298674538042, - 0, - 47.43192855600873, - -0.037703249837783157, - 0.8609577587992641, - 0.15059552388459913, - 0, - -36.96841498319127, - 0.24113635128153335, - -0.07441037908422492, - 0.44972182064877153, - 0, - -7.562075277591283, - 0, - 0, - 0, - 1, - 0 - ]); - }, - vintagePinhole: () => { - filter.colorMatrix([ - 0.6279345635605994, - 0.3202183420819367, - -0.03965408211312453, - 0, - 9.651285835294123, - 0.02578397704808868, - 0.6441188644374771, - 0.03259127616149294, - 0, - 7.462829176470591, - 0.0466055556782719, - -0.0851232987247891, - 0.5241648018700465, - 0, - 5.159190588235296, - 0, - 0, - 0, - 1, - 0 - ]); - }, - kodachrome: () => { - filter.colorMatrix([ - 1.1285582396593525, - -0.3967382283601348, - -0.03992559172921793, - 0, - 63.72958762196502, - -0.16404339962244616, - 1.0835251566291304, - -0.05498805115633132, - 0, - 24.732407896706203, - -0.16786010706155763, - -0.5603416277695248, - 1.6014850761964943, - 0, - 35.62982807460946, - 0, - 0, - 0, - 1, - 0 - ]); - }, - technicolor: () => { - filter.colorMatrix([ - 1.9125277891456083, - -0.8545344976951645, - -0.09155508482755585, - 0, - 11.793603434377337, - -0.3087833385928097, - 1.7658908555458428, - -0.10601743074722245, - 0, - -70.35205161461398, - -0.231103377548616, - -0.7501899197440212, - 1.847597816108189, - 0, - 30.950940869491138, - 0, - 0, - 0, - 1, - 0 - ]); - }, - polaroid: () => { - filter.colorMatrix([ - 1.438, - -0.062, - -0.062, - 0, - 0, - -0.122, - 1.378, - -0.122, - 0, - 0, - -0.016, - -0.016, - 1.483, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - shiftToBGR: () => { - filter.colorMatrix([ - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 1, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 1, - 0 - ]); - }, - convolution: (matrix) => { - const m = new Float32Array(matrix); - const pixelSizeX = 1 / fxcanvas.width; - const pixelSizeY = 1 / fxcanvas.height; - const program = compileShader(convolution); - if (!program) return; - gl.uniform1fv(program.uniform["m"], m); - gl.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY); - draw(); - }, - detectEdges: () => { - filter.convolution.call(this, [ - 0, - 1, - 0, - 1, - -4, - 1, - 0, - 1, - 0 - ]); - }, - sobelX: () => { - filter.convolution.call(this, [ - -1, - 0, - 1, - -2, - 0, - 2, - -1, - 0, - 1 - ]); - }, - sobelY: () => { - filter.convolution.call(this, [ - -1, - -2, - -1, - 0, - 0, - 0, - 1, - 2, - 1 - ]); - }, - sharpen: (amount) => { - const a = amount || 1; - filter.convolution.call(this, [ - 0, - -1 * a, - 0, - -1 * a, - 1 + 4 * a, - -1 * a, - 0, - -1 * a, - 0 - ]); - }, - emboss: (size2) => { - const s = size2 || 1; - filter.convolution.call(this, [ - -2 * s, - -1 * s, - 0, - -1 * s, - 1, - 1 * s, - 0, - 1 * s, - 2 * s - ]); - }, - blur: (size2) => { - const blurSizeX = size2 / 7 / fxcanvas.width; - const blurSizeY = size2 / 7 / fxcanvas.height; - const program = compileShader(blur); - if (!program) return; - gl.uniform2f(program.uniform["px"], 0, blurSizeY); - draw(DRAW.INTERMEDIATE); - gl.uniform2f(program.uniform["px"], blurSizeX, 0); - draw(); - }, - pixelate: (size2) => { - const blurSizeX = size2 / fxcanvas.width; - const blurSizeY = size2 / fxcanvas.height; - const program = compileShader(pixelate); - if (!program) return; - gl.uniform2f(program.uniform["size"], blurSizeX, blurSizeY); - draw(); - } - }; - this.add = function(name) { - const args = Array.prototype.slice.call(arguments, 1); - const func = filter[name]; - filterChain.push({ func, args }); - }; - this.reset = function() { - filterChain = []; - }; - this.get = function() { - return filterChain; - }; - this.apply = function(image28) { - resize(image28.width, image28.height); - drawCount = 0; - if (!sourceTexture) sourceTexture = gl.createTexture(); - gl.bindTexture(gl.TEXTURE_2D, sourceTexture); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.NEAREST); - gl.texParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.NEAREST); - gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, image28); - for (let i = 0; i < filterChain.length; i++) { - lastInChain = i === filterChain.length - 1; - const f = filterChain[i]; - f.func.apply(this, f.args || []); - } - return fxcanvas; - }; - this.draw = function(image28) { - this.add("brightness", 0); - return this.apply(image28); - }; -} - -// src/image/enhance.ts -var tf = __toESM(require_tfjs_esm()); -async function histogramEqualization(inputImage) { - const squeeze14 = inputImage.shape.length === 4 ? tf.squeeze(inputImage) : inputImage; - const rgb3 = tf.split(squeeze14, 3, 2); - const min2 = [tf.min(rgb3[0]), tf.min(rgb3[1]), tf.min(rgb3[2])]; - const max5 = [tf.max(rgb3[0]), tf.max(rgb3[1]), tf.max(rgb3[2])]; - const absMax = await Promise.all(max5.map((channel) => channel.data())); - const maxValue = Math.max(absMax[0][0], absMax[1][0], absMax[2][0]); - const maxRange = maxValue > 1 ? 255 : 1; - const factor = maxRange / maxValue; - let final; - if (factor > 1) { - const sub11 = [tf.sub(rgb3[0], min2[0]), tf.sub(rgb3[1], min2[1]), tf.sub(rgb3[2], min2[2])]; - const range = [tf.sub(max5[0], min2[0]), tf.sub(max5[1], min2[1]), tf.sub(max5[2], min2[2])]; - const enh = [tf.mul(sub11[0], factor), tf.mul(sub11[1], factor), tf.mul(sub11[2], factor)]; - const stack5 = tf.stack([enh[0], enh[1], enh[2]], 2); - final = tf.reshape(stack5, [1, squeeze14.shape[0] || 0, squeeze14.shape[1] || 0, 3]); - tf.dispose([...sub11, ...range, ...enh, stack5]); - } else { - final = tf.expandDims(squeeze14, 0); - } - tf.dispose([...rgb3, ...min2, ...max5, rgb3, squeeze14, inputImage]); - return final; -} - -// src/image/image.ts -var maxSize = 3840; -var inCanvas = null; -var outCanvas = null; -var tmpCanvas = null; -var fx; -var last = { - inputSum: 0, - cacheDiff: 1, - sumMethod: 0, - inputTensor: void 0 -}; -function reset() { - last.inputSum = 0; - last.cacheDiff = 1; - last.sumMethod = 0; - last.inputTensor = void 0; -} -function canvas(width, height) { - let c; - if (env.browser) { - if (env.worker) { - if (typeof OffscreenCanvas === "undefined") throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported"); - c = new OffscreenCanvas(width, height); - } else { - if (typeof document !== "undefined") { - c = document.createElement("canvas"); - c.width = width; - c.height = height; - } else if (typeof navigator !== "undefined" && navigator.product === "ReactNative") { - if (typeof env.Canvas !== "undefined") c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") c = new globalThis.Canvas(width, height); - else throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed"); - } else { - throw new Error("canvas error: attempted to run in browser but DOM is not defined"); - } - } - } else { - if (typeof env.Canvas !== "undefined") c = new env.Canvas(width, height); - else if (typeof globalThis.Canvas !== "undefined") c = new globalThis.Canvas(width, height); - } - return c; -} -function copy(input, output) { - const outputCanvas = output || canvas(input.width, input.height); - const ctx = outputCanvas.getContext("2d"); - ctx.drawImage(input, 0, 0); - return outputCanvas; -} -async function process2(input, config3, getTensor = true) { - var _a, _b, _c; - if (!input) { - if (config3.debug) log("input error: input is missing"); - return { tensor: null, canvas: null }; - } - if (!(input instanceof tf2.Tensor) && !(typeof Image !== "undefined" && input instanceof Image) && !(typeof globalThis.Canvas !== "undefined" && input instanceof globalThis.Canvas) && !(typeof ImageData !== "undefined" && input instanceof ImageData) && !(typeof ImageBitmap !== "undefined" && input instanceof ImageBitmap) && !(typeof HTMLImageElement !== "undefined" && input instanceof HTMLImageElement) && !(typeof HTMLMediaElement !== "undefined" && input instanceof HTMLMediaElement) && !(typeof HTMLVideoElement !== "undefined" && input instanceof HTMLVideoElement) && !(typeof HTMLCanvasElement !== "undefined" && input instanceof HTMLCanvasElement) && !(typeof OffscreenCanvas !== "undefined" && input instanceof OffscreenCanvas)) { - throw new Error("input error: type not recognized"); - } - if (input instanceof tf2.Tensor) { - let tensor7 = null; - if (input["isDisposedInternal"]) throw new Error("input error: attempted to use tensor but it is disposed"); - if (!input.shape) throw new Error("input error: attempted to use tensor without a shape"); - if (input.shape.length === 3) { - if (input.shape[2] === 3) { - tensor7 = tf2.expandDims(input, 0); - } else if (input.shape[2] === 4) { - const rgb3 = tf2.slice3d(input, [0, 0, 0], [-1, -1, 3]); - tensor7 = tf2.expandDims(rgb3, 0); - tf2.dispose(rgb3); - } - } else if (input.shape.length === 4) { - if (input.shape[3] === 3) { - tensor7 = tf2.clone(input); - } else if (input.shape[3] === 4) { - tensor7 = tf2.slice4d(input, [0, 0, 0, 0], [-1, -1, -1, 3]); - } - } - if (tensor7 == null || tensor7.shape.length !== 4 || tensor7.shape[0] !== 1 || tensor7.shape[3] !== 3) throw new Error(`input error: attempted to use tensor with unrecognized shape: ${input.shape.toString()}`); - if (tensor7.dtype === "int32") { - const cast8 = tf2.cast(tensor7, "float32"); - tf2.dispose(tensor7); - tensor7 = cast8; - } - return { tensor: tensor7, canvas: config3.filter.return ? outCanvas : null }; - } - if (typeof input["readyState"] !== "undefined" && input.readyState <= 2) { - if (config3.debug) log("input stream is not ready"); - return { tensor: null, canvas: inCanvas }; - } - const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0; - const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0; - if (!originalWidth || !originalHeight) { - if (config3.debug) log("cannot determine input dimensions"); - return { tensor: null, canvas: inCanvas }; - } - let targetWidth = originalWidth; - let targetHeight = originalHeight; - if (targetWidth > maxSize) { - targetWidth = maxSize; - targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth); - } - if (targetHeight > maxSize) { - targetHeight = maxSize; - targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight); - } - if ((((_a = config3.filter) == null ? void 0 : _a.width) || 0) > 0) targetWidth = config3.filter.width; - else if ((((_b = config3.filter) == null ? void 0 : _b.height) || 0) > 0) targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight); - if ((config3.filter.height || 0) > 0) targetHeight = config3.filter.height; - else if ((config3.filter.width || 0) > 0) targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth); - if (!targetWidth || !targetHeight) throw new Error("input error: cannot determine dimension"); - if (!inCanvas || inCanvas.width !== targetWidth || inCanvas.height !== targetHeight) inCanvas = canvas(targetWidth, targetHeight); - const inCtx = inCanvas.getContext("2d"); - if (typeof ImageData !== "undefined" && input instanceof ImageData) { - inCtx.putImageData(input, 0, 0); - } else { - if (config3.filter.flip && typeof inCtx.translate !== "undefined") { - inCtx.translate(originalWidth, 0); - inCtx.scale(-1, 1); - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - inCtx.setTransform(1, 0, 0, 1, 0, 0); - } else { - inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas.width, inCanvas.height); - } - } - if (!outCanvas || inCanvas.width !== outCanvas.width || inCanvas.height !== outCanvas.height) outCanvas = canvas(inCanvas.width, inCanvas.height); - if (config3.filter.enabled && env.webgl.supported) { - if (!fx) fx = env.browser ? new GLImageFilter() : null; - env.filter = !!fx; - if (!(fx == null ? void 0 : fx.add)) { - if (config3.debug) log("input process error: cannot initialize filters"); - env.webgl.supported = false; - config3.filter.enabled = false; - copy(inCanvas, outCanvas); - } else { - fx.reset(); - if (config3.filter.brightness !== 0) fx.add("brightness", config3.filter.brightness); - if (config3.filter.contrast !== 0) fx.add("contrast", config3.filter.contrast); - if (config3.filter.sharpness !== 0) fx.add("sharpen", config3.filter.sharpness); - if (config3.filter.blur !== 0) fx.add("blur", config3.filter.blur); - if (config3.filter.saturation !== 0) fx.add("saturation", config3.filter.saturation); - if (config3.filter.hue !== 0) fx.add("hue", config3.filter.hue); - if (config3.filter.negative) fx.add("negative"); - if (config3.filter.sepia) fx.add("sepia"); - if (config3.filter.vintage) fx.add("brownie"); - if (config3.filter.sepia) fx.add("sepia"); - if (config3.filter.kodachrome) fx.add("kodachrome"); - if (config3.filter.technicolor) fx.add("technicolor"); - if (config3.filter.polaroid) fx.add("polaroid"); - if (config3.filter.pixelate !== 0) fx.add("pixelate", config3.filter.pixelate); - if (((_c = fx.get()) == null ? void 0 : _c.length) > 1) outCanvas = fx.apply(inCanvas); - else outCanvas = fx.draw(inCanvas); - } - } else { - copy(inCanvas, outCanvas); - if (fx) fx = null; - env.filter = !!fx; - } - if (!getTensor) return { tensor: null, canvas: outCanvas }; - if (!outCanvas) throw new Error("canvas error: cannot create output"); - let pixels; - let depth = 3; - if (typeof ImageData !== "undefined" && input instanceof ImageData || input.data && input.width && input.height) { - if (env.browser && tf2.browser) { - pixels = tf2.browser ? tf2.browser.fromPixels(input) : null; - } else { - depth = input.data.length / input.height / input.width; - const arr = new Uint8Array(input.data.buffer); - pixels = tf2.tensor(arr, [input.height, input.width, depth], "int32"); - } - } else { - if (!tmpCanvas || outCanvas.width !== tmpCanvas.width || outCanvas.height !== tmpCanvas.height) tmpCanvas = canvas(outCanvas.width, outCanvas.height); - if (tf2.browser && env.browser) { - if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") { - pixels = tf2.browser.fromPixels(outCanvas); - } else { - tmpCanvas = copy(outCanvas); - pixels = tf2.browser.fromPixels(tmpCanvas); - } - } else { - const tempCanvas = copy(outCanvas); - const tempCtx = tempCanvas.getContext("2d"); - const tempData = tempCtx.getImageData(0, 0, targetWidth, targetHeight); - depth = tempData.data.length / targetWidth / targetHeight; - const arr = new Uint8Array(tempData.data.buffer); - pixels = tf2.tensor(arr, [targetWidth, targetHeight, depth]); - } - } - if (depth === 4) { - const rgb3 = tf2.slice3d(pixels, [0, 0, 0], [-1, -1, 3]); - tf2.dispose(pixels); - pixels = rgb3; - } - if (!pixels) throw new Error("input error: cannot create tensor"); - const casted = tf2.cast(pixels, "float32"); - const tensor6 = config3.filter.equalization ? await histogramEqualization(casted) : tf2.expandDims(casted, 0); - tf2.dispose([pixels, casted]); - if (config3.filter.autoBrightness) { - const max5 = tf2.max(tensor6); - const maxVal = await max5.data(); - config3.filter.brightness = maxVal[0] > 1 ? 1 - maxVal[0] / 255 : 1 - maxVal[0]; - tf2.dispose(max5); - } - return { tensor: tensor6, canvas: config3.filter.return ? outCanvas : null }; -} -async function skip(config3, input) { - let skipFrame = false; - if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 3840 || input.shape[2] > 2160) return skipFrame; - if (!last.inputTensor) { - last.inputTensor = tf2.clone(input); - } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) { - tf2.dispose(last.inputTensor); - last.inputTensor = tf2.clone(input); - } else { - const t2 = {}; - t2.diff = tf2.sub(input, last.inputTensor); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3; - tf2.dispose([last.inputTensor, t2.diff, t2.squared, t2.sum]); - last.inputTensor = tf2.clone(input); - skipFrame = diffRelative <= (config3.cacheSensitivity || 0); - } - return skipFrame; -} -async function compare(config3, input1, input2) { - const t2 = {}; - if (!input1 || !input2 || input1.shape.length !== 4 || input1.shape.length !== input2.shape.length) { - if (!config3.debug) log("invalid input tensor or tensor shapes do not match:", input1.shape, input2.shape); - return 0; - } - if (input1.shape[0] !== 1 || input2.shape[0] !== 1 || input1.shape[3] !== 3 || input2.shape[3] !== 3) { - if (!config3.debug) log("input tensors must be of shape [1, height, width, 3]:", input1.shape, input2.shape); - return 0; - } - t2.input1 = tf2.clone(input1); - t2.input2 = input1.shape[1] !== input2.shape[1] || input1.shape[2] !== input2.shape[2] ? tf2.image.resizeBilinear(input2, [input1.shape[1], input1.shape[2]]) : tf2.clone(input2); - t2.diff = tf2.sub(t2.input1, t2.input2); - t2.squared = tf2.mul(t2.diff, t2.diff); - t2.sum = tf2.sum(t2.squared); - const diffSum = await t2.sum.data(); - const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3; - tf2.dispose([t2.input1, t2.input2, t2.diff, t2.squared, t2.sum]); - return diffRelative; -} - -// src/util/env.ts -var _canvas, _image, _imageData; -var Env = class { - constructor() { - /** Running in Browser */ - __publicField(this, "browser"); - /** Running in NodeJS */ - __publicField(this, "node"); - /** Running in WebWorker thread */ - __publicField(this, "worker"); - /** Detected platform */ - __publicField(this, "platform", ""); - /** Detected agent */ - __publicField(this, "agent", ""); - /** List of supported backends */ - __publicField(this, "backends", []); - /** Has any work been performed so far */ - __publicField(this, "initial"); - /** Are image filters supported? */ - __publicField(this, "filter"); - /** TFJS instance details */ - __publicField(this, "tfjs"); - /** Is offscreenCanvas supported? */ - __publicField(this, "offscreen"); - /** Are performance counter instant values or additive */ - __publicField(this, "perfadd", false); - /** If using tfjs-node get version of underlying tensorflow shared library and if gpu acceleration is enabled */ - __publicField(this, "tensorflow", { - version: void 0, - gpu: void 0 - }); - /** WASM detected capabilities */ - __publicField(this, "wasm", { - supported: void 0, - backend: void 0, - simd: void 0, - multithread: void 0 - }); - /** WebGL detected capabilities */ - __publicField(this, "webgl", { - supported: void 0, - backend: void 0, - version: void 0, - renderer: void 0, - shader: void 0, - vendor: void 0 - }); - /** WebGPU detected capabilities */ - __publicField(this, "webgpu", { - supported: void 0, - backend: void 0, - adapter: void 0 - }); - /** CPU info */ - __publicField(this, "cpu", { - model: void 0, - flags: [] - }); - /** List of supported kernels for current backend */ - __publicField(this, "kernels", []); - /** MonkeyPatch for Canvas/Image/ImageData */ - __privateAdd(this, _canvas); - __privateAdd(this, _image); - __privateAdd(this, _imageData); - this.browser = typeof navigator !== "undefined" && typeof navigator.appVersion !== "undefined"; - this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined"; - this.tfjs = { version: tf3.version["tfjs-core"] }; - this.offscreen = typeof OffscreenCanvas !== "undefined"; - this.initial = true; - this.worker = this.browser && this.offscreen ? typeof WorkerGlobalScope !== "undefined" : void 0; - if (typeof navigator !== "undefined" && typeof navigator.userAgent !== "undefined") { - const agent = navigator.userAgent || ""; - const raw = agent.match(/\(([^()]+)\)/g); - if (raw == null ? void 0 : raw[0]) { - const platformMatch = raw[0].match(/\(([^()]+)\)/g); - this.platform = (platformMatch == null ? void 0 : platformMatch[0]) ? platformMatch[0].replace(/\(|\)/g, "") : ""; - this.agent = agent.replace(raw[0], ""); - if (this.platform[1]) this.agent = this.agent.replace(raw[1], ""); - this.agent = this.agent.replace(/ /g, " "); - } - } else if (typeof process !== "undefined") { - this.platform = `${process.platform} ${process.arch}`; - this.agent = `NodeJS ${process.version}`; - } - } - get Canvas() { - return __privateGet(this, _canvas); - } - set Canvas(val) { - __privateSet(this, _canvas, val); - globalThis.Canvas = val; - } - get Image() { - return __privateGet(this, _image); - } - // @ts-ignore monkey-patch; - set Image(val) { - __privateSet(this, _image, val); - globalThis.Image = val; - } - get ImageData() { - return __privateGet(this, _imageData); - } - // @ts-ignore monkey-patch; - set ImageData(val) { - __privateSet(this, _imageData, val); - globalThis.ImageData = val; - } - /** update backend information */ - async updateBackend() { - this.backends = Object.keys(tf3.engine().registryFactory); - try { - this.tensorflow = { - version: tf3.backend()["binding"] ? tf3.backend()["binding"].TF_Version : void 0, - gpu: tf3.backend()["binding"] ? tf3.backend()["binding"].isUsingGpuDevice() : void 0 - }; - } catch (e) { - } - this.wasm.supported = typeof WebAssembly !== "undefined"; - this.wasm.backend = this.backends.includes("wasm"); - if (this.wasm.supported && this.wasm.backend) { - this.wasm.simd = await tf3.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - this.wasm.multithread = await tf3.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - } - const c = canvas(100, 100); - const gl = c ? c.getContext("webgl2") : void 0; - this.webgl.supported = typeof gl !== "undefined"; - this.webgl.backend = this.backends.includes("webgl"); - if (this.webgl.supported && this.webgl.backend && gl) { - this.webgl.version = gl.getParameter(gl.VERSION); - this.webgl.vendor = gl.getParameter(gl.VENDOR); - this.webgl.renderer = gl.getParameter(gl.RENDERER); - this.webgl.shader = gl.getParameter(gl.SHADING_LANGUAGE_VERSION); - } - this.webgpu.supported = this.browser && typeof navigator !== "undefined" && typeof navigator.gpu !== "undefined"; - this.webgpu.backend = this.backends.includes("webgpu"); - try { - if (this.webgpu.supported) { - const adapter = await navigator.gpu.requestAdapter(); - this.webgpu.adapter = await (adapter == null ? void 0 : adapter.requestAdapterInfo()); - } - } catch (e) { - this.webgpu.supported = false; - } - try { - this.kernels = tf3.getKernelsForBackend(tf3.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); - } catch (e) { - } - } - /** update cpu information */ - updateCPU() { - const cpu = { model: "", flags: [] }; - if (this.node && this.platform.startsWith("linux")) { - } - if (!this.cpu) Object.defineProperty(this, "cpu", { value: cpu }); - else this.cpu = cpu; - } -}; -_canvas = new WeakMap(); -_image = new WeakMap(); -_imageData = new WeakMap(); -var env = new Env(); - -// src/util/webcam.ts -var WebCam = class { - constructor() { - // eslint-disable-line @typescript-eslint/no-extraneous-class - /** current webcam configuration */ - __publicField(this, "config"); - /** instance of dom element associated with webcam stream */ - __publicField(this, "element"); - /** active webcam stream */ - __publicField(this, "stream"); - /** enumerated video devices */ - __publicField(this, "devices", []); - __publicField(this, "enumerate", async () => { - try { - const devices = await navigator.mediaDevices.enumerateDevices(); - this.devices = devices.filter((device) => device.kind === "videoinput"); - } catch (e) { - this.devices = []; - } - return this.devices; - }); - /** start method initializizes webcam stream and associates it with a dom video element */ - __publicField(this, "start", async (webcamConfig) => { - var _a, _b; - if (webcamConfig == null ? void 0 : webcamConfig.debug) this.config.debug = webcamConfig == null ? void 0 : webcamConfig.debug; - if (webcamConfig == null ? void 0 : webcamConfig.crop) this.config.crop = webcamConfig == null ? void 0 : webcamConfig.crop; - if (webcamConfig == null ? void 0 : webcamConfig.mode) this.config.mode = webcamConfig == null ? void 0 : webcamConfig.mode; - if (webcamConfig == null ? void 0 : webcamConfig.width) this.config.width = webcamConfig == null ? void 0 : webcamConfig.width; - if (webcamConfig == null ? void 0 : webcamConfig.height) this.config.height = webcamConfig == null ? void 0 : webcamConfig.height; - if (webcamConfig == null ? void 0 : webcamConfig.id) this.config.id = webcamConfig == null ? void 0 : webcamConfig.id; - if (webcamConfig == null ? void 0 : webcamConfig.element) { - if (typeof webcamConfig.element === "string") { - const el = document.getElementById(webcamConfig.element); - if (el && el instanceof HTMLVideoElement) { - this.element = el; - } else { - if (this.config.debug) log("webcam", "cannot get dom element", webcamConfig.element); - return `webcam error: cannot get dom element: ${webcamConfig.element}`; - } - } else if (webcamConfig.element instanceof HTMLVideoElement) { - this.element = webcamConfig.element; - } else { - if (this.config.debug) log("webcam", "unknown dom element", webcamConfig.element); - return `webcam error: unknown dom element: ${webcamConfig.element}`; - } - } else { - this.element = document.createElement("video"); - } - const requestedConstraints = { - audio: false, - video: { - facingMode: this.config.mode === "front" ? "user" : "environment", - // @ts-ignore // resizeMode is still not defined in tslib - resizeMode: this.config.crop ? "crop-and-scale" : "none" - } - }; - if (((_a = this.config) == null ? void 0 : _a.width) > 0) requestedConstraints.video.width = { ideal: this.config.width }; - if (((_b = this.config) == null ? void 0 : _b.height) > 0) requestedConstraints.video.height = { ideal: this.config.height }; - if (this.config.id) requestedConstraints.video.deviceId = this.config.id; - this.element.addEventListener("play", () => { - if (this.config.debug) log("webcam", "play"); - }); - this.element.addEventListener("pause", () => { - if (this.config.debug) log("webcam", "pause"); - }); - this.element.addEventListener("click", async () => { - if (!this.element || !this.stream) return; - if (this.element.paused) await this.element.play(); - else this.element.pause(); - }); - if (!(navigator == null ? void 0 : navigator.mediaDevices)) { - if (this.config.debug) log("webcam error", "no devices"); - return "webcam error: no devices"; - } - try { - this.stream = await navigator.mediaDevices.getUserMedia(requestedConstraints); - } catch (err) { - log("webcam", err); - return `webcam error: ${err}`; - } - if (!this.stream) { - if (this.config.debug) log("webcam error", "no stream"); - return "webcam error no stream"; - } - this.element.srcObject = this.stream; - const ready4 = new Promise((resolve) => { - if (!this.element) resolve(false); - else this.element.onloadeddata = () => resolve(true); - }); - await ready4; - await this.element.play(); - if (this.config.debug) { - log("webcam", { - width: this.width, - height: this.height, - label: this.label, - stream: this.stream, - track: this.track, - settings: this.settings, - constraints: this.constraints, - capabilities: this.capabilities - }); - } - return `webcam: ${this.label}`; - }); - /** pause webcam video method */ - __publicField(this, "pause", () => { - if (this.element) this.element.pause(); - }); - /** play webcam video method */ - __publicField(this, "play", async () => { - if (this.element) await this.element.play(); - }); - /** stop method stops active webcam stream track and disconnects webcam */ - __publicField(this, "stop", () => { - if (this.config.debug) log("webcam", "stop"); - if (this.track) this.track.stop(); - }); - this.config = { - element: void 0, - debug: true, - mode: "front", - crop: false, - width: 0, - height: 0 - }; - } - /** get active webcam stream track */ - get track() { - if (!this.stream) return void 0; - return this.stream.getVideoTracks()[0]; - } - /** get webcam capabilities */ - get capabilities() { - if (!this.track) return void 0; - return this.track.getCapabilities ? this.track.getCapabilities() : void 0; - } - /** get webcam constraints */ - get constraints() { - if (!this.track) return void 0; - return this.track.getConstraints ? this.track.getConstraints() : void 0; - } - /** get webcam settings */ - get settings() { - if (!this.stream) return void 0; - const track = this.stream.getVideoTracks()[0]; - return track.getSettings ? track.getSettings() : void 0; - } - /** get webcam label */ - get label() { - if (!this.track) return ""; - return this.track.label; - } - /** is webcam paused */ - get paused() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.paused) || false; - } - /** webcam current width */ - get width() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoWidth) || 0; - } - /** webcam current height */ - get height() { - var _a; - return ((_a = this.element) == null ? void 0 : _a.videoHeight) || 0; - } -}; - -// src/tfjs/load.ts -var tf4 = __toESM(require_tfjs_esm()); - -// models/models.json -var models_exports = {}; -__export(models_exports, { - "affectnet-mobilenet": () => affectnet_mobilenet, - age: () => age, - "anti-spoofing": () => anti_spoofing, - antispoof: () => antispoof, - blazeface: () => blazeface, - "blazeface-back": () => blazeface_back, - "blazeface-front": () => blazeface_front, - "blazepose-detector": () => blazepose_detector, - "blazepose-full": () => blazepose_full, - "blazepose-heavy": () => blazepose_heavy, - "blazepose-lite": () => blazepose_lite, - centernet: () => centernet, - default: () => models_default, - efficientpose: () => efficientpose, - "efficientpose-i-lite": () => efficientpose_i_lite, - "efficientpose-ii-lite": () => efficientpose_ii_lite, - "efficientpose-iv": () => efficientpose_iv, - emotion: () => emotion, - faceboxes: () => faceboxes, - facemesh: () => facemesh, - "facemesh-attention": () => facemesh_attention, - "facemesh-attention-pinto": () => facemesh_attention_pinto, - "facemesh-detection-full": () => facemesh_detection_full, - "facemesh-detection-short": () => facemesh_detection_short, - faceres: () => faceres, - "faceres-deep": () => faceres_deep, - gear: () => gear, - "gear-e1": () => gear_e1, - "gear-e2": () => gear_e2, - gender: () => gender, - "gender-ssrnet-imdb": () => gender_ssrnet_imdb, - handdetect: () => handdetect, - "handlandmark-full": () => handlandmark_full, - "handlandmark-lite": () => handlandmark_lite, - "handlandmark-sparse": () => handlandmark_sparse, - handskeleton: () => handskeleton, - handtrack: () => handtrack, - "insightface-efficientnet-b0": () => insightface_efficientnet_b0, - "insightface-ghostnet-strides1": () => insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": () => insightface_ghostnet_strides2, - "insightface-mobilenet-emore": () => insightface_mobilenet_emore, - "insightface-mobilenet-swish": () => insightface_mobilenet_swish, - iris: () => iris, - liveness: () => liveness, - meet: () => meet, - mobileface: () => mobileface, - mobilefacenet: () => mobilefacenet, - models: () => models, - "movenet-lightning": () => movenet_lightning, - "movenet-multipose": () => movenet_multipose, - "movenet-thunder": () => movenet_thunder, - nanodet: () => nanodet, - "nanodet-e": () => nanodet_e, - "nanodet-g": () => nanodet_g, - "nanodet-m": () => nanodet_m, - "nanodet-t": () => nanodet_t, - posenet: () => posenet, - rvm: () => rvm, - selfie: () => selfie -}); -var antispoof = 853098; -var blazeface = 538928; -var centernet = 4030290; -var emotion = 820516; -var facemesh = 1477958; -var faceres = 6978814; -var handlandmark_lite = 2023432; -var handtrack = 2964837; -var iris = 2599092; -var liveness = 592976; -var models = 0; -var movenet_lightning = 4650216; -var affectnet_mobilenet = 6920630; -var age = 161240; -var blazeface_back = 538928; -var blazeface_front = 402048; -var blazepose_detector = 5928856; -var blazepose_full = 6339202; -var blazepose_heavy = 27502466; -var blazepose_lite = 2726402; -var efficientpose = 5651240; -var faceboxes = 2013002; -var facemesh_attention_pinto = 2387598; -var facemesh_attention = 2382414; -var facemesh_detection_full = 1026192; -var facemesh_detection_short = 201268; -var faceres_deep = 13957620; -var gear_e1 = 112438; -var gear_e2 = 112438; -var gear = 1498916; -var gender_ssrnet_imdb = 161236; -var gender = 201808; -var handdetect = 3515612; -var handlandmark_full = 5431368; -var handlandmark_sparse = 5286322; -var handskeleton = 5502280; -var meet = 372228; -var mobileface = 2183192; -var mobilefacenet = 5171976; -var movenet_multipose = 9448838; -var movenet_thunder = 12477112; -var nanodet = 7574558; -var posenet = 5032780; -var rvm = 3739355; -var selfie = 212886; -var anti_spoofing = 853098; -var efficientpose_i_lite = 2269064; -var efficientpose_ii_lite = 5651240; -var efficientpose_iv = 25643252; -var insightface_efficientnet_b0 = 13013224; -var insightface_ghostnet_strides1 = 8093408; -var insightface_ghostnet_strides2 = 8049584; -var insightface_mobilenet_emore = 6938536; -var insightface_mobilenet_swish = 12168584; -var nanodet_e = 12319156; -var nanodet_g = 7574558; -var nanodet_m = 1887474; -var nanodet_t = 5294216; -var models_default = { - antispoof, - blazeface, - centernet, - emotion, - facemesh, - faceres, - "handlandmark-lite": handlandmark_lite, - handtrack, - iris, - liveness, - models, - "movenet-lightning": movenet_lightning, - "affectnet-mobilenet": affectnet_mobilenet, - age, - "blazeface-back": blazeface_back, - "blazeface-front": blazeface_front, - "blazepose-detector": blazepose_detector, - "blazepose-full": blazepose_full, - "blazepose-heavy": blazepose_heavy, - "blazepose-lite": blazepose_lite, - efficientpose, - faceboxes, - "facemesh-attention-pinto": facemesh_attention_pinto, - "facemesh-attention": facemesh_attention, - "facemesh-detection-full": facemesh_detection_full, - "facemesh-detection-short": facemesh_detection_short, - "faceres-deep": faceres_deep, - "gear-e1": gear_e1, - "gear-e2": gear_e2, - gear, - "gender-ssrnet-imdb": gender_ssrnet_imdb, - gender, - handdetect, - "handlandmark-full": handlandmark_full, - "handlandmark-sparse": handlandmark_sparse, - handskeleton, - meet, - mobileface, - mobilefacenet, - "movenet-multipose": movenet_multipose, - "movenet-thunder": movenet_thunder, - nanodet, - posenet, - rvm, - selfie, - "anti-spoofing": anti_spoofing, - "efficientpose-i-lite": efficientpose_i_lite, - "efficientpose-ii-lite": efficientpose_ii_lite, - "efficientpose-iv": efficientpose_iv, - "insightface-efficientnet-b0": insightface_efficientnet_b0, - "insightface-ghostnet-strides1": insightface_ghostnet_strides1, - "insightface-ghostnet-strides2": insightface_ghostnet_strides2, - "insightface-mobilenet-emore": insightface_mobilenet_emore, - "insightface-mobilenet-swish": insightface_mobilenet_swish, - "nanodet-e": nanodet_e, - "nanodet-g": nanodet_g, - "nanodet-m": nanodet_m, - "nanodet-t": nanodet_t -}; - -// src/tfjs/load.ts -var options = { - cacheModels: true, - cacheSupported: true, - verbose: true, - debug: false, - modelBasePath: "" -}; -var modelStats = {}; -async function httpHandler(url, init4) { - if (options.debug) log("load model fetch:", url, init4); - return fetch(url, init4); -} -function setModelLoadOptions(config3) { - options.cacheModels = config3.cacheModels; - options.verbose = config3.debug; - options.modelBasePath = config3.modelBasePath; -} -async function loadModel(modelPath) { - var _a, _b, _c, _d, _e, _f; - let modelUrl = join(options.modelBasePath, modelPath || ""); - if (!modelUrl.toLowerCase().endsWith(".json")) modelUrl += ".json"; - const modelPathSegments = modelUrl.includes("/") ? modelUrl.split("/") : modelUrl.split("\\"); - const shortModelName = modelPathSegments[modelPathSegments.length - 1].replace(".json", ""); - const cachedModelName = "indexeddb://" + shortModelName; - modelStats[shortModelName] = { - name: shortModelName, - loaded: false, - sizeFromManifest: 0, - sizeLoadedWeights: 0, - sizeDesired: models_exports[shortModelName], - inCache: false, - url: "" - }; - options.cacheSupported = typeof indexedDB !== "undefined"; - let cachedModels = {}; - try { - cachedModels = options.cacheSupported && options.cacheModels ? await tf4.io.listModels() : {}; - } catch (e) { - options.cacheSupported = false; - } - modelStats[shortModelName].inCache = options.cacheSupported && options.cacheModels && Object.keys(cachedModels).includes(cachedModelName); - modelStats[shortModelName].url = modelStats[shortModelName].inCache ? cachedModelName : modelUrl; - const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init4) => httpHandler(url, init4) }; - let model23 = new tf4.GraphModel(modelStats[shortModelName].url, tfLoadOptions); - modelStats[shortModelName].loaded = false; - try { - model23.findIOHandler(); - if (options.debug) log("model load handler:", model23["handler"]); - } catch (err) { - log("error finding model i/o handler:", modelUrl, err); - } - try { - const artifacts = await ((_a = model23.handler) == null ? void 0 : _a.load()) || null; - modelStats[shortModelName].sizeFromManifest = ((_b = artifacts == null ? void 0 : artifacts.weightData) == null ? void 0 : _b.byteLength) || 0; - if (artifacts) model23.loadSync(artifacts); - else model23 = await tf4.loadGraphModel(modelStats[shortModelName].inCache ? cachedModelName : modelUrl, tfLoadOptions); - modelStats[shortModelName].sizeLoadedWeights = ((_d = (_c = model23.artifacts) == null ? void 0 : _c.weightData) == null ? void 0 : _d.byteLength) || ((_f = (_e = model23.artifacts) == null ? void 0 : _e.weightData) == null ? void 0 : _f[0].byteLength) || 0; - if (options.verbose) log("load:", { model: shortModelName, url: model23["modelUrl"], bytes: modelStats[shortModelName].sizeLoadedWeights }); - modelStats[shortModelName].loaded = true; - } catch (err) { - log("error loading model:", modelUrl, err); - } - if (modelStats[shortModelName].loaded && options.cacheModels && options.cacheSupported && !modelStats[shortModelName].inCache) { - try { - const saveResult = await model23.save(cachedModelName); - if (options.debug) log("model saved:", cachedModelName, saveResult); - } catch (err) { - log("error saving model:", modelUrl, err); - } - } - return model23; -} - -// package.json -var version2 = "3.3.2"; - -// src/tfjs/backend.ts -var tf7 = __toESM(require_tfjs_esm()); - -// src/tfjs/humangl.ts -var tf5 = __toESM(require_tfjs_esm()); -var config2 = { - name: "humangl", - priority: 999, - canvas: null, - gl: null, - extensions: [], - webGLattr: { - // https://www.khronos.org/registry/webgl/specs/latest/1.0/#5.2 - alpha: false, - antialias: false, - premultipliedAlpha: false, - preserveDrawingBuffer: false, - depth: false, - stencil: false, - failIfMajorPerformanceCaveat: false, - // default=true - desynchronized: true - // default=undefined - } -}; -function extensions() { - const gl = config2.gl; - if (!gl) return; - config2.extensions = gl.getSupportedExtensions(); -} -function register(instance) { - var _a; - if (instance.config.backend !== "humangl") return; - if (config2.name in tf5.engine().registry && !((_a = config2 == null ? void 0 : config2.gl) == null ? void 0 : _a.getParameter(config2.gl.VERSION))) { - log("humangl error: backend invalid context"); - instance.models.reset(); - } - if (!tf5.findBackend(config2.name)) { - try { - config2.canvas = canvas(100, 100); - } catch (err) { - log("humangl error: cannot create canvas:", err); - return; - } - try { - config2.gl = config2.canvas.getContext("webgl2", config2.webGLattr); - if (!config2.gl) { - log("humangl error: cannot get webgl context"); - return; - } - const glv2 = config2.gl.getParameter(config2.gl.VERSION).includes("2.0"); - if (!glv2) { - log("backend override: using fallback webgl backend as webgl 2.0 is not detected"); - instance.config.backend = "webgl"; - return; - } - if (config2.canvas) { - config2.canvas.addEventListener("webglcontextlost", (e) => { - log("humangl error:", e.type); - log("possible browser memory leak using webgl or conflict with multiple backend registrations"); - instance.emit("error"); - throw new Error("backend error: webgl context lost"); - }); - config2.canvas.addEventListener("webglcontextrestored", (e) => { - log("humangl error: context restored:", e); - }); - config2.canvas.addEventListener("webglcontextcreationerror", (e) => { - log("humangl error: context create:", e); - }); - } - } catch (err) { - log("humangl error: cannot get webgl context:", err); - return; - } - try { - tf5.setWebGLContext(2, config2.gl); - } catch (err) { - log("humangl error: cannot set webgl context:", err); - return; - } - try { - const ctx = new tf5.GPGPUContext(config2.gl); - tf5.registerBackend(config2.name, () => new tf5.MathBackendWebGL(ctx), config2.priority); - } catch (err) { - log("humangl error: cannot register webgl backend:", err); - return; - } - try { - const kernels = tf5.getKernelsForBackend("webgl"); - kernels.forEach((kernelConfig) => { - const newKernelConfig = { ...kernelConfig, backendName: config2.name }; - tf5.registerKernel(newKernelConfig); - }); - } catch (err) { - log("humangl error: cannot update webgl backend registration:", err); - return; - } - try { - if (tf5.env().flagRegistry.WEBGL_VERSION) tf5.env().set("WEBGL_VERSION", 2); - } catch (err) { - log("humangl error: cannot set WebGL backend flags:", err); - return; - } - extensions(); - const backend4 = tf5.backend(); - const current = typeof backend4["gpgpu"] !== "undefined" ? backend4["getGPGPUContext"]().gl : null; - if (current) { - if (instance.config.debug) log("humangl backend registered:", { webgl: current.getParameter(current.VERSION), renderer: current.getParameter(current.RENDERER) }); - } else { - log("humangl error: no current gl context:", current, config2.gl); - } - } -} - -// src/tfjs/constants.ts -var tf6 = __toESM(require_tfjs_esm()); -var constants = { - tf255: 255, - tf1: 1, - tf2: 2, - tf05: 0.5, - tf127: 127.5, - rgb: [0.2989, 0.587, 0.114] -}; -function init() { - constants.tf255 = tf6.scalar(255, "float32"); - constants.tf1 = tf6.scalar(1, "float32"); - constants.tf2 = tf6.scalar(2, "float32"); - constants.tf05 = tf6.scalar(0.5, "float32"); - constants.tf127 = tf6.scalar(127.5, "float32"); - constants.rgb = tf6.tensor1d([0.2989, 0.587, 0.114], "float32"); -} - -// src/tfjs/backend.ts -async function getBestBackend() { - var _a; - await env.updateBackend(); - if ((_a = env.tensorflow) == null ? void 0 : _a.version) return "tensorflow"; - if (env.webgpu.supported && env.webgpu.backend) return "webgpu"; - if (env.webgl.supported && env.webgl.backend) return "webgl"; - if (env.wasm.supported && env.wasm.backend) return "wasm"; - return "cpu"; -} -function registerCustomOps(config3) { - const newKernels = []; - if (!env.kernels.includes("mod")) { - const kernelMod = { - kernelName: "Mod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.sub(op.inputs.a, tf7.mul(tf7.div(op.inputs.a, op.inputs.b), op.inputs.b))) - }; - tf7.registerKernel(kernelMod); - env.kernels.push("mod"); - newKernels.push("mod"); - } - if (!env.kernels.includes("floormod")) { - const kernelFloorMod = { - kernelName: "FloorMod", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => tf7.add(tf7.mul(tf7.floorDiv(op.inputs.a, op.inputs.b), op.inputs.b), tf7.mod(op.inputs.a, op.inputs.b))) - }; - tf7.registerKernel(kernelFloorMod); - env.kernels.push("floormod"); - newKernels.push("floormod"); - } - if (!env.kernels.includes("rotatewithoffset") && config3.softwareKernels) { - const kernelRotateWithOffset = { - kernelName: "RotateWithOffset", - backendName: tf7.getBackend(), - kernelFunc: (op) => tf7.tidy(() => { - const backend4 = tf7.getBackend(); - tf7.setBackend("cpu"); - const t2 = tf7.image.rotateWithOffset(op.inputs.image, op.attrs.radians, op.attrs.fillValue, op.attrs.center); - tf7.setBackend(backend4); - return t2; - }) - }; - tf7.registerKernel(kernelRotateWithOffset); - env.kernels.push("rotatewithoffset"); - newKernels.push("rotatewithoffset"); - } - if (newKernels.length > 0 && config3.debug) log("registered kernels:", newKernels); -} -var defaultFlags = {}; -async function check(instance, force = false) { - var _a, _b; - instance.state = "backend"; - if (((_a = instance.config.backend) == null ? void 0 : _a.length) === 0) instance.config.backend = await getBestBackend(); - if (force || env.initial || instance.config.backend && instance.config.backend.length > 0 && tf7.getBackend() !== instance.config.backend) { - const timeStamp = now(); - if (instance.config.backend && instance.config.backend.length > 0) { - if (typeof window === "undefined" && typeof WorkerGlobalScope !== "undefined" && instance.config.debug) { - if (instance.config.debug) log("running inside web worker"); - } - if (typeof navigator !== "undefined" && ((_b = navigator == null ? void 0 : navigator.userAgent) == null ? void 0 : _b.toLowerCase().includes("electron"))) { - if (instance.config.debug) log("running inside electron"); - } - let available = Object.keys(tf7.engine().registryFactory); - if (instance.config.backend === "humangl" && !available.includes("humangl")) { - register(instance); - available = Object.keys(tf7.engine().registryFactory); - } - if (instance.config.debug) log("available backends:", available); - if (env.browser && !env.node && instance.config.backend === "tensorflow" && available.includes("webgl")) { - if (instance.config.debug) log("override: backend set to tensorflow while running in browser"); - instance.config.backend = "webgl"; - } - if (env.node && !env.browser && (instance.config.backend === "webgl" || instance.config.backend === "humangl") && available.includes("tensorflow")) { - if (instance.config.debug) log(`override: backend set to ${instance.config.backend} while running in nodejs`); - instance.config.backend = "tensorflow"; - } - if (env.browser && instance.config.backend === "webgpu") { - if (typeof navigator === "undefined" || typeof navigator.gpu === "undefined") { - log("override: backend set to webgpu but browser does not support webgpu"); - instance.config.backend = "webgl"; - } else { - const adapter = await navigator.gpu.requestAdapter(); - if (instance.config.debug) log("enumerated webgpu adapter:", adapter); - if (!adapter) { - log("override: backend set to webgpu but browser reports no available gpu"); - instance.config.backend = "webgl"; - } else { - const adapterInfo = "requestAdapterInfo" in adapter ? await adapter.requestAdapterInfo() : void 0; - log("webgpu adapter info:", adapterInfo); - } - } - } - if (!available.includes(instance.config.backend)) { - log(`error: backend ${instance.config.backend} not found in registry`); - instance.config.backend = env.node ? "tensorflow" : "webgl"; - if (instance.config.debug) log(`override: setting backend ${instance.config.backend}`); - } - if (instance.config.debug) log("setting backend:", [instance.config.backend]); - if (instance.config.backend === "wasm") { - if (tf7.env().flagRegistry.CANVAS2D_WILL_READ_FREQUENTLY) tf7.env().set("CANVAS2D_WILL_READ_FREQUENTLY", true); - if (instance.config.debug) log("wasm path:", instance.config.wasmPath); - if (typeof tf7.setWasmPaths !== "undefined") tf7.setWasmPaths(instance.config.wasmPath, instance.config.wasmPlatformFetch); - else throw new Error("backend error: attempting to use wasm backend but wasm path is not set"); - let mt = false; - let simd = false; - try { - mt = await tf7.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"); - simd = await tf7.env().getAsync("WASM_HAS_SIMD_SUPPORT"); - if (instance.config.debug) log(`wasm execution: ${simd ? "simd" : "no simd"} ${mt ? "multithreaded" : "singlethreaded"}`); - if (instance.config.debug && !simd) log("warning: wasm simd support is not enabled"); - } catch (e) { - log("wasm detection failed"); - } - } - try { - await tf7.setBackend(instance.config.backend); - await tf7.ready(); - } catch (err) { - log("error: cannot set backend:", instance.config.backend, err); - return false; - } - if (instance.config.debug) defaultFlags = JSON.parse(JSON.stringify(tf7.env().flags)); - } - if (tf7.getBackend() === "humangl" || tf7.getBackend() === "webgl") { - if (tf7.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf7.env().set("WEBGL_USE_SHAPES_UNIFORMS", true); - if (tf7.env().flagRegistry.WEBGL_EXP_CONV) tf7.env().set("WEBGL_EXP_CONV", true); - if (instance.config.debug && typeof instance.config.deallocate !== "undefined" && instance.config.deallocate) { - log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true); - tf7.env().set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0); - } - } - if (tf7.getBackend() === "webgpu") { - } - if (instance.config.debug) { - const newFlags = tf7.env().flags; - const updatedFlags = {}; - for (const key of Object.keys(newFlags)) { - if (defaultFlags[key] === newFlags[key]) continue; - updatedFlags[key] = newFlags[key]; - } - if (instance.config.debug && Object.keys(updatedFlags).length > 0) log("backend:", tf7.getBackend(), "flags:", updatedFlags); - } - if (instance.config.flags && Object.keys(instance.config.flags).length > 0) { - if (instance.config.debug) log("flags:", instance.config["flags"]); - for (const [key, val] of Object.entries(instance.config.flags)) { - tf7.env().set(key, val); - } - } - tf7.enableProdMode(); - init(); - instance.performance.initBackend = Math.trunc(now() - timeStamp); - instance.config.backend = tf7.getBackend(); - await env.updateBackend(); - registerCustomOps(instance.config); - } - return true; -} -function fakeOps(kernelNames, config3) { - for (const kernelName of kernelNames) { - const kernelConfig = { - kernelName, - backendName: config3.backend, - kernelFunc: (param) => { - var _a; - if (config3.debug) log("kernelFunc", kernelName, config3.backend, param); - return (_a = param == null ? void 0 : param.inputs) == null ? void 0 : _a.info; - } - // setupFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, - // disposeFunc: () => { if (config.debug) log('kernelFunc', kernelName, config.backend); }, - }; - tf7.registerKernel(kernelConfig); - } - env.kernels = tf7.getKernelsForBackend(tf7.getBackend()).map((kernel) => kernel.kernelName.toLowerCase()); -} - -// src/draw/draw.ts -var draw_exports = {}; -__export(draw_exports, { - all: () => all, - body: () => body, - canvas: () => canvas2, - face: () => face, - gesture: () => gesture, - hand: () => hand, - init: () => init2, - object: () => object, - options: () => options2, - person: () => person -}); - -// src/draw/primitives.ts -var getCanvasContext = (input) => { - if (!input) log("draw error: invalid canvas"); - else if (!input.getContext) log("draw error: canvas context not defined"); - else { - const ctx = input.getContext("2d", { willReadFrequently: true }); - if (!ctx) log("draw error: cannot get canvas context"); - else return ctx; - } - return null; -}; -var rad2deg = (theta) => Math.round(theta * 180 / Math.PI); -var replace = (str, source, target) => str.replace(source, typeof target === "number" ? target.toFixed(1) : target); -var colorDepth = (z, opt) => { - if (!opt.useDepth || typeof z === "undefined") return opt.color; - const rgb3 = Uint8ClampedArray.from([127 + 2 * z, 127 - 2 * z, 255]); - return `rgba(${rgb3[0]}, ${rgb3[1]}, ${rgb3[2]}, ${opt.alpha})`; -}; -function labels(ctx, str, startX, startY, localOptions2) { - const line = str.replace(/\[.*\]/g, "").split("\n").map((l) => l.trim()); - const x = Math.max(0, startX); - for (let i = line.length - 1; i >= 0; i--) { - const y = i * localOptions2.lineHeight + startY; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(line[i], x + 5, y + 16); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(line[i], x + 4, y + 15); - } -} -function point(ctx, x, y, z, localOptions2) { - ctx.fillStyle = colorDepth(z, localOptions2); - ctx.beginPath(); - ctx.arc(x, y, localOptions2.pointSize, 0, 2 * Math.PI); - ctx.fill(); -} -function rect(ctx, x, y, width, height, localOptions2) { - ctx.beginPath(); - ctx.lineWidth = localOptions2.lineWidth; - if (localOptions2.useCurves) { - const cx = (x + x + width) / 2; - const cy = (y + y + height) / 2; - ctx.ellipse(cx, cy, width / 2, height / 2, 0, 0, 2 * Math.PI); - } else { - ctx.moveTo(x + localOptions2.roundRect, y); - ctx.lineTo(x + width - localOptions2.roundRect, y); - ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions2.roundRect); - ctx.lineTo(x + width, y + height - localOptions2.roundRect); - ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions2.roundRect, y + height); - ctx.lineTo(x + localOptions2.roundRect, y + height); - ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions2.roundRect); - ctx.lineTo(x, y + localOptions2.roundRect); - ctx.quadraticCurveTo(x, y, x + localOptions2.roundRect, y); - ctx.closePath(); - } - ctx.stroke(); -} -function lines(ctx, points, localOptions2) { - if (points.length < 2) return; - ctx.beginPath(); - ctx.moveTo(points[0][0], points[0][1]); - for (const pt of points) { - ctx.strokeStyle = colorDepth(pt[2] || 0, localOptions2); - ctx.lineTo(Math.trunc(pt[0]), Math.trunc(pt[1])); - } - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function curves(ctx, points, localOptions2) { - if (points.length < 2) return; - ctx.lineWidth = localOptions2.lineWidth; - if (!localOptions2.useCurves || points.length <= 2) { - lines(ctx, points, localOptions2); - return; - } - ctx.moveTo(points[0][0], points[0][1]); - for (let i = 0; i < points.length - 2; i++) { - const xc = (points[i][0] + points[i + 1][0]) / 2; - const yc = (points[i][1] + points[i + 1][1]) / 2; - ctx.quadraticCurveTo(points[i][0], points[i][1], xc, yc); - } - ctx.quadraticCurveTo(points[points.length - 2][0], points[points.length - 2][1], points[points.length - 1][0], points[points.length - 1][1]); - ctx.stroke(); - if (localOptions2.fillPolygons) { - ctx.closePath(); - ctx.fill(); - } -} -function arrow(ctx, from, to, radius = 5) { - let angle; - let x; - let y; - ctx.beginPath(); - ctx.moveTo(from[0], from[1]); - ctx.lineTo(to[0], to[1]); - angle = Math.atan2(to[1] - from[1], to[0] - from[0]); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.moveTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - angle += 1 / 3 * (2 * Math.PI); - x = radius * Math.cos(angle) + to[0]; - y = radius * Math.sin(angle) + to[1]; - ctx.lineTo(x, y); - ctx.closePath(); - ctx.stroke(); - ctx.fill(); -} - -// src/draw/options.ts -var options2 = { - color: "rgba(173, 216, 230, 0.6)", - // 'lightblue' with light alpha channel - labelColor: "rgba(173, 216, 230, 1)", - // 'lightblue' with dark alpha channel - shadowColor: "black", - alpha: 0.5, - font: 'small-caps 16px "Segoe UI"', - lineHeight: 18, - lineWidth: 4, - pointSize: 2, - roundRect: 8, - drawPoints: false, - drawLabels: true, - drawBoxes: true, - drawAttention: true, - drawGestures: true, - drawPolygons: true, - drawGaze: true, - fillPolygons: false, - useDepth: true, - useCurves: false, - faceLabels: "", - bodyLabels: "", - bodyPartLabels: "", - objectLabels: "", - handLabels: "", - fingerLabels: "", - gestureLabels: "" -}; - -// src/face/facemeshcoords.ts -var meshAnnotations = { - silhouette: [ - 10, - 338, - 297, - 332, - 284, - 251, - 389, - 356, - 454, - 323, - 361, - 288, - 397, - 365, - 379, - 378, - 400, - 377, - 152, - 148, - 176, - 149, - 150, - 136, - 172, - 58, - 132, - 93, - 234, - 127, - 162, - 21, - 54, - 103, - 67, - 109 - ], - // lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291], // 11 - // lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291], // 10 - // lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308], // 11 - // lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], // 11 - lipsUpperOuter: [185, 40, 39, 37, 0, 267, 269, 270, 409], - lipsLowerOuter: [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291], - lipsUpperInner: [191, 80, 81, 82, 13, 312, 311, 310, 415], - lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308], - lipsLowerSemiOuter: [76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306], - lipsUpperSemiOuter: [184, 74, 73, 72, 11, 302, 303, 304, 408], - lipsLowerSemiInner: [62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292], - lipsUpperSemiInner: [183, 42, 41, 38, 12, 268, 271, 272, 407], - rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173], - // 7 - rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133], - // 9 - rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190], - // 7 - rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243], - // 9 - rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189], - // 7 - rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244], - // 9 - rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245], - // 9 - rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193], - // 8 - rightEyebrowLower: [35, 124, 46, 53, 52, 65], - // 6 - rightEyeIris: [473, 474, 475, 476, 477], - // 5 - leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398], - leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362], - leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414], - leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463], - leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413], - leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464], - leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465], - leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417], - leftEyebrowLower: [265, 353, 276, 283, 282, 295], - leftEyeIris: [468, 469, 470, 471, 472], - midwayBetweenEyes: [168], - noseTip: [1], - noseBottom: [2], - noseRightCorner: [98], - noseLeftCorner: [327], - rightCheek: [205], - leftCheek: [425] -}; -var meshLandmarks = { - count: 468, - mouth: 13, - symmetryLine: [13, meshAnnotations.midwayBetweenEyes[0]] -}; -var blazeFaceLandmarks = { - leftEye: 0, - rightEye: 1, - nose: 2, - mouth: 3, - leftEar: 4, - rightEar: 5, - symmetryLine: [3, 2] -}; -var irisIndices = [ - // A mapping from facemesh model keypoints to iris model keypoints. - { key: "EyeUpper0", indices: [9, 10, 11, 12, 13, 14, 15] }, - // 7 x 3d - { key: "EyeUpper1", indices: [25, 26, 27, 28, 29, 30, 31] }, - // 7 x 3d - { key: "EyeUpper2", indices: [41, 42, 43, 44, 45, 46, 47] }, - // 7 x 3d - { key: "EyeLower0", indices: [0, 1, 2, 3, 4, 5, 6, 7, 8] }, - // 7 x 3d - { key: "EyeLower1", indices: [16, 17, 18, 19, 20, 21, 22, 23, 24] }, - // 9 x 3d - { key: "EyeLower2", indices: [32, 33, 34, 35, 36, 37, 38, 39, 40] }, - // 9 x 3d - { key: "EyeLower3", indices: [54, 55, 56, 57, 58, 59, 60, 61, 62] }, - // 9 x 3d - { key: "EyebrowUpper", indices: [63, 64, 65, 66, 67, 68, 69, 70] }, - 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const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var pairsLips = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var pairsLeftEye = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var pairsLeftEyebrow = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var pairsLeftIris = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var pairsRightEye = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var pairsRightEyebrow = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var pairsRightIris = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var pairsFaceContour = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -var contourKeypoints = { - lips: connectionsToIndices(pairsLips), - leftEye: connectionsToIndices(pairsLeftEye), - leftEyebrow: connectionsToIndices(pairsLeftEyebrow), - leftIris: connectionsToIndices(pairsLeftIris), - rightEye: connectionsToIndices(pairsRightEye), - rightEyebrow: connectionsToIndices(pairsRightEyebrow), - rightIris: connectionsToIndices(pairsRightIris), - faceOval: connectionsToIndices(pairsFaceContour) -}; - -// src/face/constants.ts -var LIPS_CONNECTIONS = [ - [61, 146], - [146, 91], - [91, 181], - [181, 84], - [84, 17], - [17, 314], - [314, 405], - [405, 321], - [321, 375], - [375, 291], - [61, 185], - [185, 40], - [40, 39], - [39, 37], - [37, 0], - [0, 267], - [267, 269], - [269, 270], - [270, 409], - [409, 291], - [78, 95], - [95, 88], - [88, 178], - [178, 87], - [87, 14], - [14, 317], - [317, 402], - [402, 318], - [318, 324], - [324, 308], - [78, 191], - [191, 80], - [80, 81], - [81, 82], - [82, 13], - [13, 312], - [312, 311], - [311, 310], - [310, 415], - [415, 308] -]; -var LEFT_EYE_CONNECTIONS = [[263, 249], [249, 390], [390, 373], [373, 374], [374, 380], [380, 381], [381, 382], [382, 362], [263, 466], [466, 388], [388, 387], [387, 386], [386, 385], [385, 384], [384, 398], [398, 362]]; -var LEFT_EYEBROW_CONNECTIONS = [[276, 283], [283, 282], [282, 295], [295, 285], [300, 293], [293, 334], [334, 296], [296, 336]]; -var LEFT_IRIS_CONNECTIONS = [[474, 475], [475, 476], [476, 477], [477, 474]]; -var RIGHT_EYE_CONNECTIONS = [[33, 7], [7, 163], [163, 144], [144, 145], [145, 153], [153, 154], [154, 155], [155, 133], [33, 246], [246, 161], [161, 160], [160, 159], [159, 158], [158, 157], [157, 173], [173, 133]]; -var RIGHT_EYEBROW_CONNECTIONS = [[46, 53], [53, 52], [52, 65], [65, 55], [70, 63], [63, 105], [105, 66], [66, 107]]; -var RIGHT_IRIS_CONNECTIONS = [[469, 470], [470, 471], [471, 472], [472, 469]]; -var FACE_OVAL_CONNECTIONS = [ - [10, 338], - [338, 297], - [297, 332], - [332, 284], - [284, 251], - [251, 389], - [389, 356], - [356, 454], - [454, 323], - [323, 361], - [361, 288], - [288, 397], - [397, 365], - [365, 379], - [379, 378], - [378, 400], - [400, 377], - [377, 152], - [152, 148], - [148, 176], - [176, 149], - [149, 150], - [150, 136], - [136, 172], - [172, 58], - [58, 132], - [132, 93], - [93, 234], - [234, 127], - [127, 162], - [162, 21], - [21, 54], - [54, 103], - [103, 67], - [67, 109], - [109, 10] -]; -function connectionsToIndices2(connections) { - const indices = connections.map((connection) => connection[0]); - indices.push(connections[connections.length - 1][1]); - return indices; -} -var MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR = { - lips: connectionsToIndices2(LIPS_CONNECTIONS), - leftEye: connectionsToIndices2(LEFT_EYE_CONNECTIONS), - leftEyebrow: connectionsToIndices2(LEFT_EYEBROW_CONNECTIONS), - leftIris: connectionsToIndices2(LEFT_IRIS_CONNECTIONS), - rightEye: connectionsToIndices2(RIGHT_EYE_CONNECTIONS), - rightEyebrow: connectionsToIndices2(RIGHT_EYEBROW_CONNECTIONS), - rightIris: connectionsToIndices2(RIGHT_IRIS_CONNECTIONS), - faceOval: connectionsToIndices2(FACE_OVAL_CONNECTIONS) -}; -var indexLabelPairs = Object.entries(MEDIAPIPE_FACE_MESH_KEYPOINTS_BY_CONTOUR).map(([label, indices]) => indices.map((index2) => [index2, label])).flat(); -var MEDIAPIPE_FACE_MESH_KEYPOINTS = new Map(indexLabelPairs); -var LANDMARKS_REFINEMENT_LIPS_CONFIG = [ - 61, - 146, - 91, - 181, - 84, - 17, - 314, - 405, - 321, - 375, - 291, - // Lower outer. - 185, - 40, - 39, - 37, - 0, - 267, - 269, - 270, - 409, - // Upper outer(excluding corners). - 78, - 95, - 88, - 178, - 87, - 14, - 317, - 402, - 318, - 324, - 308, - // Lower inner. - 191, - 80, - 81, - 82, - 13, - 312, - 311, - 310, - 415, - // Upper inner(excluding corners). - 76, - 77, - 90, - 180, - 85, - 16, - 315, - 404, - 320, - 307, - 306, - // Lower semi - outer. - 184, - 74, - 73, - 72, - 11, - 302, - 303, - 304, - 408, - // Upper semi - outer(excluding corners). - 62, - 96, - 89, - 179, - 86, - 15, - 316, - 403, - 319, - 325, - 292, - // Lower semi - inner. - 183, - 42, - 41, - 38, - 12, - 268, - 271, - 272, - 407 - // Upper semi - inner(excluding corners). -]; -var LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG = [ - 33, - 7, - 163, - 144, - 145, - 153, - 154, - 155, - 133, - // Lower contour. - 246, - 161, - 160, - 159, - 158, - 157, - 173, - // upper contour (excluding corners). - 130, - 25, - 110, - 24, - 23, - 22, - 26, - 112, - 243, - // Halo x2 lower contour. - 247, - 30, - 29, - 27, - 28, - 56, - 190, - // Halo x2 upper contour (excluding corners). - 226, - 31, - 228, - 229, - 230, - 231, - 232, - 233, - 244, - // Halo x3 lower contour. - 113, - 225, - 224, - 223, - 222, - 221, - 189, - // Halo x3 upper contour (excluding corners). - 35, - 124, - 46, - 53, - 52, - 65, - // Halo x4 upper contour (no lower because of mesh structure) or eyebrow inner contour. - 143, - 111, - 117, - 118, - 119, - 120, - 121, - 128, - 245, - // Halo x5 lower contour. - 156, - 70, - 63, - 105, - 66, - 107, - 55, - 193 - // Halo x5 upper contour (excluding corners) or eyebrow outer contour. -]; -var LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG = [ - 263, - 249, - 390, - 373, - 374, - 380, - 381, - 382, - 362, - // Lower contour. - 466, - 388, - 387, - 386, - 385, - 384, - 398, - // Upper contour (excluding corners). - 359, - 255, - 339, - 254, - 253, - 252, - 256, - 341, - 463, - // Halo x2 lower contour. - 467, - 260, - 259, - 257, - 258, - 286, - 414, - // Halo x2 upper contour (excluding corners). - 446, - 261, - 448, - 449, - 450, - 451, - 452, - 453, - 464, - // Halo x3 lower contour. - 342, - 445, - 444, - 443, - 442, - 441, - 413, - // Halo x3 upper contour (excluding corners). - 265, - 353, - 276, - 283, - 282, - 295, - // Halo x4 upper contour (no lower because of mesh structure) or/ eyebrow inner contour. - 372, - 340, - 346, - 347, - 348, - 349, - 350, - 357, - 465, - // Halo x5 lower contour. - 383, - 300, - 293, - 334, - 296, - 336, - 285, - 417 - // Halo x5 upper contour (excluding corners) or eyebrow outer contour. -]; - -// src/draw/face.ts -var localOptions; -function drawLabels(f, ctx) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!localOptions.drawLabels || ((_a = localOptions.faceLabels) == null ? void 0 : _a.length) === 0) return; - let l = localOptions.faceLabels.slice(); - l = replace(l, "[id]", f.id.toFixed(0)); - if (f.score) l = replace(l, "[score]", 100 * f.score); - if (f.gender) l = replace(l, "[gender]", f.gender); - if (f.genderScore) l = replace(l, "[genderScore]", 100 * f.genderScore); - if (f.age) l = replace(l, "[age]", f.age); - if (f.distance) l = replace(l, "[distance]", 100 * f.distance); - if (f.real) l = replace(l, "[real]", 100 * f.real); - if (f.live) l = replace(l, "[live]", 100 * f.live); - if (f.emotion && f.emotion.length > 0) { - const emotion2 = f.emotion.map((a) => `${Math.trunc(100 * a.score)}% ${a.emotion}`); - if (emotion2.length > 3) emotion2.length = 3; - l = replace(l, "[emotions]", emotion2.join(" ")); - 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p=Math.cos(g),u=Math.sin(g),k=.213,P=.715,N=.072;T.colorMatrix([k+p*(1-k)+u*-k,P+p*-P+u*-P,N+p*-N+u*(1-N),0,0,k+p*-k+u*.143,P+p*(1-P)+u*.14,N+p*-N+u*-.283,0,0,k+p*-k+u*-(1-k),P+p*-P+u*P,N+p*(1-N)+u*N,0,0,0,0,0,1,0])},desaturateLuminance:()=>{T.colorMatrix([.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,.2764723,.929708,.0938197,0,-37.1,0,0,0,1,0])},sepia:()=>{T.colorMatrix([.393,.7689999,.18899999,0,0,.349,.6859999,.16799999,0,0,.272,.5339999,.13099999,0,0,0,0,0,1,0])},brownie:()=>{T.colorMatrix([.5997023498159715,.34553243048391263,-.2708298674538042,0,47.43192855600873,-.037703249837783157,.8609577587992641,.15059552388459913,0,-36.96841498319127,.24113635128153335,-.07441037908422492,.44972182064877153,0,-7.562075277591283,0,0,0,1,0])},vintagePinhole:()=>{T.colorMatrix([.6279345635605994,.3202183420819367,-.03965408211312453,0,9.651285835294123,.02578397704808868,.6441188644374771,.03259127616149294,0,7.462829176470591,.0466055556782719,-.0851232987247891,.5241648018700465,0,5.159190588235296,0,0,0,1,0])},kodachrome:()=>{T.colorMatrix([1.1285582396593525,-.3967382283601348,-.03992559172921793,0,63.72958762196502,-.16404339962244616,1.0835251566291304,-.05498805115633132,0,24.732407896706203,-.16786010706155763,-.5603416277695248,1.6014850761964943,0,35.62982807460946,0,0,0,1,0])},technicolor:()=>{T.colorMatrix([1.9125277891456083,-.8545344976951645,-.09155508482755585,0,11.793603434377337,-.3087833385928097,1.7658908555458428,-.10601743074722245,0,-70.35205161461398,-.231103377548616,-.7501899197440212,1.847597816108189,0,30.950940869491138,0,0,0,1,0])},polaroid:()=>{T.colorMatrix([1.438,-.062,-.062,0,0,-.122,1.378,-.122,0,0,-.016,-.016,1.483,0,0,0,0,0,1,0])},shiftToBGR:()=>{T.colorMatrix([0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0])},convolution:g=>{let p=new Float32Array(g),u=1/l.width,k=1/l.height,P=h(G1);P&&(i.uniform1fv(P.uniform.m,p),i.uniform2f(P.uniform.px,u,k),f())},detectEdges:()=>{T.convolution.call(this,[0,1,0,1,-4,1,0,1,0])},sobelX:()=>{T.convolution.call(this,[-1,0,1,-2,0,2,-1,0,1])},sobelY:()=>{T.convolution.call(this,[-1,-2,-1,0,0,0,1,2,1])},sharpen:g=>{let p=g||1;T.convolution.call(this,[0,-1*p,0,-1*p,1+4*p,-1*p,0,-1*p,0])},emboss:g=>{let p=g||1;T.convolution.call(this,[-2*p,-1*p,0,-1*p,1,1*p,0,1*p,2*p])},blur:g=>{let p=g/7/l.width,u=g/7/l.height,k=h(H1);k&&(i.uniform2f(k.uniform.px,0,u),f(d.INTERMEDIATE),i.uniform2f(k.uniform.px,p,0),f())},pixelate:g=>{let p=g/l.width,u=g/l.height,k=h(B1);k&&(i.uniform2f(k.uniform.size,p,u),f())}};this.add=function(g){let p=Array.prototype.slice.call(arguments,1),u=T[g];s.push({func:u,args:p})},this.reset=function(){s=[]},this.get=function(){return s},this.apply=function(g){y(g.width,g.height),e=0,t||(t=i.createTexture()),i.bindTexture(i.TEXTURE_2D,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.NEAREST),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,g);for(let p=0;pd.data())),A=Math.max(s[0][0],s[1][0],s[2][0]),l=(A>1?255:1)/A,c;if(l>1){let d=[U.sub(n[0],o[0]),U.sub(n[1],o[1]),U.sub(n[2],o[2])],i=[U.sub(r[0],o[0]),U.sub(r[1],o[1]),U.sub(r[2],o[2])],y=[U.mul(d[0],l),U.mul(d[1],l),U.mul(d[2],l)],x=U.stack([y[0],y[1],y[2]],2);c=U.reshape(x,[1,t.shape[0]||0,t.shape[1]||0,3]),U.dispose([...d,...i,...y,x])}else c=U.expandDims(t,0);return U.dispose([...n,...o,...r,n,t,e]),c}var q2=3840,M0=null,P0=null,y2=null,t0,ne={inputSum:0,cacheDiff:1,sumMethod:0,inputTensor:void 0};function $t(){ne.inputSum=0,ne.cacheDiff=1,ne.sumMethod=0,ne.inputTensor=void 0}function Ae(e,t){let n;if(R.browser)if(R.worker){if(typeof OffscreenCanvas=="undefined")throw new Error("canvas error: attempted to run in web worker but OffscreenCanvas is not supported");n=new OffscreenCanvas(e,t)}else if(typeof document!="undefined")n=document.createElement("canvas"),n.width=e,n.height=t;else if(typeof navigator!="undefined"&&navigator.product==="ReactNative")if(typeof R.Canvas!="undefined")n=new R.Canvas(e,t);else if(typeof globalThis.Canvas!="undefined")n=new globalThis.Canvas(e,t);else throw new Error("canvas error: attempted to use canvas in react-native without canvas support installed");else throw new Error("canvas error: attempted to run in browser but DOM is not defined");else typeof R.Canvas!="undefined"?n=new R.Canvas(e,t):typeof globalThis.Canvas!="undefined"&&(n=new globalThis.Canvas(e,t));return n}function X2(e,t){let n=t||Ae(e.width,e.height);return n.getContext("2d").drawImage(e,0,0),n}async function U2(e,t,n=!0){var y,x,m;if(!e)return t.debug&&b("input error: input is missing"),{tensor:null,canvas:null};if(!(e instanceof I.Tensor)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof globalThis.Canvas!="undefined"&&e instanceof globalThis.Canvas)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("input error: type not recognized");if(e instanceof I.Tensor){let f=null;if(e.isDisposedInternal)throw new Error("input error: attempted to use tensor but it is disposed");if(!e.shape)throw new 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s=o,A=r;if(s>q2&&(s=q2,A=Math.trunc(s*r/o)),A>q2&&(A=q2,s=Math.trunc(A*o/r)),(((y=t.filter)==null?void 0:y.width)||0)>0?s=t.filter.width:(((x=t.filter)==null?void 0:x.height)||0)>0&&(s=o*((t.filter.height||0)/r)),(t.filter.height||0)>0?A=t.filter.height:(t.filter.width||0)>0&&(A=r*((t.filter.width||0)/o)),!s||!A)throw new Error("input error: cannot determine dimension");(!M0||M0.width!==s||M0.height!==A)&&(M0=Ae(s,A));let a=M0.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?a.putImageData(e,0,0):t.filter.flip&&typeof a.translate!="undefined"?(a.translate(o,0),a.scale(-1,1),a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),a.setTransform(1,0,0,1,0,0)):a.drawImage(e,0,0,o,r,0,0,M0.width,M0.height),(!P0||M0.width!==P0.width||M0.height!==P0.height)&&(P0=Ae(M0.width,M0.height)),t.filter.enabled&&R.webgl.supported?(t0||(t0=R.browser?new V1:null),R.filter=!!t0,t0!=null&&t0.add?(t0.reset(),t.filter.brightness!==0&&t0.add("brightness",t.filter.brightness),t.filter.contrast!==0&&t0.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&t0.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&t0.add("blur",t.filter.blur),t.filter.saturation!==0&&t0.add("saturation",t.filter.saturation),t.filter.hue!==0&&t0.add("hue",t.filter.hue),t.filter.negative&&t0.add("negative"),t.filter.sepia&&t0.add("sepia"),t.filter.vintage&&t0.add("brownie"),t.filter.sepia&&t0.add("sepia"),t.filter.kodachrome&&t0.add("kodachrome"),t.filter.technicolor&&t0.add("technicolor"),t.filter.polaroid&&t0.add("polaroid"),t.filter.pixelate!==0&&t0.add("pixelate",t.filter.pixelate),((m=t0.get())==null?void 0:m.length)>1?P0=t0.apply(M0):P0=t0.draw(M0)):(t.debug&&b("input process error: cannot initialize filters"),R.webgl.supported=!1,t.filter.enabled=!1,X2(M0,P0))):(X2(M0,P0),t0&&(t0=null),R.filter=!!t0),!n)return{tensor:null,canvas:P0};if(!P0)throw new 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f.data();t.filter.brightness=h[0]>1?1-h[0]/255:1-h[0],I.dispose(f)}return{tensor:i,canvas:t.filter.return?P0:null}}async function Z1(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>3840||t.shape[2]>2160)return n;if(!ne.inputTensor)ne.inputTensor=I.clone(t);else if(ne.inputTensor.shape[1]!==t.shape[1]||ne.inputTensor.shape[2]!==t.shape[2])I.dispose(ne.inputTensor),ne.inputTensor=I.clone(t);else{let o={};o.diff=I.sub(t,ne.inputTensor),o.squared=I.mul(o.diff,o.diff),o.sum=I.sum(o.squared);let s=(await o.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;I.dispose([ne.inputTensor,o.diff,o.squared,o.sum]),ne.inputTensor=I.clone(t),n=s<=(e.cacheSensitivity||0)}return n}async function q1(e,t,n){let o={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||b("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||b("input tensors must 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Y0(this,z2)}set Canvas(t){Me(this,z2,t),globalThis.Canvas=t}get Image(){return Y0(this,S2)}set Image(t){Me(this,S2,t),globalThis.Image=t}get ImageData(){return Y0(this,j2)}set ImageData(t){Me(this,j2,t),globalThis.ImageData=t}async updateBackend(){this.backends=Object.keys(W0.engine().registryFactory);try{this.tensorflow={version:W0.backend().binding?W0.backend().binding.TF_Version:void 0,gpu:W0.backend().binding?W0.backend().binding.isUsingGpuDevice():void 0}}catch(o){}this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&(this.wasm.simd=await W0.env().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await W0.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Ae(100,100),n=t?t.getContext("webgl2"):void 0;this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&n&&(this.webgl.version=n.getParameter(n.VERSION),this.webgl.vendor=n.getParameter(n.VENDOR),this.webgl.renderer=n.getParameter(n.RENDERER),this.webgl.shader=n.getParameter(n.SHADING_LANGUAGE_VERSION)),this.webgpu.supported=this.browser&&typeof navigator!="undefined"&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{if(this.webgpu.supported){let o=await navigator.gpu.requestAdapter();this.webgpu.adapter=await(o==null?void 0:o.requestAdapterInfo())}}catch(o){this.webgpu.supported=!1}try{this.kernels=W0.getKernelsForBackend(W0.getBackend()).map(o=>o.kernelName.toLowerCase())}catch(o){}}updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}};z2=new WeakMap,S2=new WeakMap,j2=new WeakMap;var R=new E2;var 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n,o,r,s;if((n=e.annotations)!=null&&n.leftEyeIris&&((o=e.annotations)!=null&&o.leftEyeIris[0])){t.strokeStyle=X.useDepth?"rgba(255, 200, 255, 0.3)":X.color,t.beginPath();let A=Math.abs(e.annotations.leftEyeIris[3][0]-e.annotations.leftEyeIris[1][0])/2,a=Math.abs(e.annotations.leftEyeIris[4][1]-e.annotations.leftEyeIris[2][1])/2;t.ellipse(e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),X.fillPolygons&&(t.fillStyle=X.useDepth?"rgba(255, 255, 200, 0.3)":X.color,t.fill())}if((r=e.annotations)!=null&&r.rightEyeIris&&((s=e.annotations)!=null&&s.rightEyeIris[0])){t.strokeStyle=X.useDepth?"rgba(255, 200, 255, 0.3)":X.color,t.beginPath();let A=Math.abs(e.annotations.rightEyeIris[3][0]-e.annotations.rightEyeIris[1][0])/2,a=Math.abs(e.annotations.rightEyeIris[4][1]-e.annotations.rightEyeIris[2][1])/2;t.ellipse(e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1],A,a,0,0,2*Math.PI),t.stroke(),X.fillPolygons&&(t.fillStyle=X.useDepth?"rgba(255, 255, 200, 0.3)":X.color,t.fill())}}function Zs(e,t){var n;if(X.drawGaze&&((n=e.rotation)!=null&&n.angle)&&typeof Path2D!="undefined"){t.strokeStyle="pink";let o=e.box[0]+e.box[2]/2-e.box[3]*Je(e.rotation.angle.yaw)/90,r=e.box[1]+e.box[3]/2+e.box[2]*Je(e.rotation.angle.pitch)/90,s=new Path2D(` + M ${e.box[0]+e.box[2]/2} ${e.box[1]} C - ${valX} ${f.box[1]}, - ${valX} ${f.box[1] + f.box[3]}, - ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]} - `); - const pathH = new Path2D(` - M ${f.box[0]} ${f.box[1] + f.box[3] / 2} + ${o} ${e.box[1]}, + ${o} ${e.box[1]+e.box[3]}, + ${e.box[0]+e.box[2]/2} ${e.box[1]+e.box[3]} + `),A=new Path2D(` + M ${e.box[0]} ${e.box[1]+e.box[3]/2} C - ${f.box[0]} ${valY}, - ${f.box[0] + f.box[2]} ${valY}, - ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2} - `); - ctx.stroke(pathH); - ctx.stroke(pathV); - } -} -function drawGazeArrows(f, ctx) { - var _a; - if (localOptions.drawGaze && ((_a = f.rotation) == null ? void 0 : _a.gaze.strength) && f.rotation.gaze.bearing && f.annotations.leftEyeIris && f.annotations.rightEyeIris && f.annotations.leftEyeIris[0] && f.annotations.rightEyeIris[0]) { - ctx.strokeStyle = "pink"; - ctx.fillStyle = "pink"; - const leftGaze = [ - f.annotations.leftEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.leftEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.leftEyeIris[0][0], f.annotations.leftEyeIris[0][1]], [leftGaze[0], leftGaze[1]], 4); - const rightGaze = [ - f.annotations.rightEyeIris[0][0] + Math.sin(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[3], - f.annotations.rightEyeIris[0][1] + Math.cos(f.rotation.gaze.bearing) * f.rotation.gaze.strength * f.box[2] - ]; - arrow(ctx, [f.annotations.rightEyeIris[0][0], f.annotations.rightEyeIris[0][1]], [rightGaze[0], rightGaze[1]], 4); - } -} -function drawFacePolygons(f, ctx) { - if (localOptions.drawPolygons && f.mesh.length >= 468) { - ctx.lineWidth = 1; - for (let i = 0; i < TRI468.length / 3; i++) { - const points = [TRI468[i * 3 + 0], TRI468[i * 3 + 1], TRI468[i * 3 + 2]].map((index2) => f.mesh[index2]); - lines(ctx, points, localOptions); - } - drawIrisElipse(f, ctx); - } -} -function drawFacePoints(f, ctx) { - if (localOptions.drawPoints) { - if ((f == null ? void 0 : f.mesh.length) >= 468) { - for (let i = 0; i < f.mesh.length; i++) { - point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2], localOptions); - if (localOptions.drawAttention) { - if (LANDMARKS_REFINEMENT_LIPS_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] + 127, localOptions); - if (LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - if (LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.includes(i)) point(ctx, f.mesh[i][0], f.mesh[i][1], f.mesh[i][2] - 127, localOptions); - } - } - } else { - for (const [k, v] of Object.entries((f == null ? void 0 : f.annotations) || {})) { - if (!(v == null ? void 0 : v[0])) continue; - const pt = v[0]; - point(ctx, pt[0], pt[1], 0, localOptions); - if (localOptions.drawLabels) labels(ctx, k, pt[0], pt[1], localOptions); - } - } - } -} -function drawFaceBoxes(f, ctx) { - if (localOptions.drawBoxes) { - rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions); - } -} -function face(inCanvas2, result, drawOptions) { - localOptions = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.font = localOptions.font; - ctx.strokeStyle = localOptions.color; - ctx.fillStyle = localOptions.color; - for (const f of result) { - drawFaceBoxes(f, ctx); - drawLabels(f, ctx); - if (f.mesh && f.mesh.length > 0) { - drawFacePoints(f, ctx); - drawFacePolygons(f, ctx); - drawGazeSpheres(f, ctx); - drawGazeArrows(f, ctx); - } - } -} - -// src/draw/body.ts -function body(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - for (let i = 0; i < result.length; i++) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - ctx.lineWidth = localOptions2.lineWidth; - ctx.font = localOptions2.font; - if (localOptions2.drawBoxes && result[i].box && result[i].box.length === 4) { - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.bodyLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.bodyLabels.slice(); - l = replace(l, "[id]", result[i].id.toFixed(0)); - l = replace(l, "[score]", 100 * result[i].score); - labels(ctx, l, result[i].box[0], result[i].box[1], localOptions2); - } - } - if (localOptions2.drawPoints && result[i].keypoints) { - for (let pt = 0; pt < result[i].keypoints.length; pt++) { - if (!result[i].keypoints[pt].score || result[i].keypoints[pt].score === 0) continue; - ctx.fillStyle = colorDepth(result[i].keypoints[pt].position[2], localOptions2); - point(ctx, result[i].keypoints[pt].position[0], result[i].keypoints[pt].position[1], 0, localOptions2); - } - } - if (localOptions2.drawLabels && ((_b = localOptions2.bodyPartLabels) == null ? void 0 : _b.length) > 0 && result[i].keypoints) { - ctx.font = localOptions2.font; - for (const pt of result[i].keypoints) { - if (!pt.score || pt.score === 0) continue; - let l = localOptions2.bodyPartLabels.slice(); - l = replace(l, "[label]", pt.part); - l = replace(l, "[score]", 100 * pt.score); - labels(ctx, l, pt.position[0], pt.position[1], localOptions2); - } - } - if (localOptions2.drawPolygons && result[i].keypoints && result[i].annotations) { - for (const part of Object.values(result[i].annotations)) { - for (const connected4 of part) curves(ctx, connected4, localOptions2); - } - } - } -} - -// src/draw/hand.ts -function hand(inCanvas2, result, drawOptions) { - var _a, _b; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.handLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.handLabels.slice(); - l = replace(l, "[id]", h.id.toFixed(0)); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - if (localOptions2.drawPoints) { - if (h.keypoints && h.keypoints.length > 0) { - for (const pt of h.keypoints) { - ctx.fillStyle = colorDepth(pt[2], localOptions2); - point(ctx, pt[0], pt[1], 0, localOptions2); - } - } - } - if (localOptions2.drawLabels && h.annotations && ((_b = localOptions2.fingerLabels) == null ? void 0 : _b.length) > 0) { - for (const [part, pt] of Object.entries(h.annotations)) { - let l = localOptions2.fingerLabels.slice(); - l = replace(l, "[label]", part); - labels(ctx, l, pt[pt.length - 1][0], pt[pt.length - 1][1], localOptions2); - } - } - if (localOptions2.drawPolygons && h.annotations) { - const addHandLine = (part) => { - if (!part || part.length === 0 || !part[0]) return; - for (let i = 0; i < part.length; i++) { - ctx.beginPath(); - const z = part[i][2] || 0; - ctx.strokeStyle = colorDepth(i * z, localOptions2); - ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]); - ctx.lineTo(part[i][0], part[i][1]); - ctx.stroke(); - } - }; - ctx.lineWidth = localOptions2.lineWidth; - addHandLine(h.annotations.index); - addHandLine(h.annotations.middle); - addHandLine(h.annotations.ring); - addHandLine(h.annotations.pinky); - addHandLine(h.annotations.thumb); - } - } -} - -// src/draw/object.ts -function object(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (const h of result) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions2); - if (localOptions2.drawLabels && ((_a = localOptions2.objectLabels) == null ? void 0 : _a.length) > 0) { - let l = localOptions2.objectLabels.slice(); - l = replace(l, "[id]", h.id.toFixed(0)); - l = replace(l, "[label]", h.label); - l = replace(l, "[score]", 100 * h.score); - labels(ctx, l, h.box[0], h.box[1], localOptions2); - } - ctx.stroke(); - } - } -} - -// src/draw/gesture.ts -function gesture(inCanvas2, result, drawOptions) { - var _a; - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - if (localOptions2.drawGestures && ((_a = localOptions2.gestureLabels) == null ? void 0 : _a.length) > 0) { - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.font = localOptions2.font; - ctx.fillStyle = localOptions2.color; - let i = 1; - for (let j = 0; j < result.length; j++) { - const [where, what] = Object.entries(result[j]); - if (what.length > 1 && what[1].length > 0) { - const who = where[1] > 0 ? `#${where[1]}` : ""; - let l = localOptions2.gestureLabels.slice(); - l = replace(l, "[where]", where[0]); - l = replace(l, "[who]", who); - l = replace(l, "[what]", what[1]); - labels(ctx, l, 8, 2 + i * localOptions2.lineHeight, localOptions2); - i += 1; - } - } - } -} - -// src/draw/labels.ts -var defaultLabels = { - face: `face + ${e.box[0]} ${r}, + ${e.box[0]+e.box[2]} ${r}, + ${e.box[0]+e.box[2]} ${e.box[1]+e.box[3]/2} + `);t.stroke(A),t.stroke(s)}}function qs(e,t){var n;if(X.drawGaze&&((n=e.rotation)!=null&&n.gaze.strength)&&e.rotation.gaze.bearing&&e.annotations.leftEyeIris&&e.annotations.rightEyeIris&&e.annotations.leftEyeIris[0]&&e.annotations.rightEyeIris[0]){t.strokeStyle="pink",t.fillStyle="pink";let o=[e.annotations.leftEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.leftEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];o5(t,[e.annotations.leftEyeIris[0][0],e.annotations.leftEyeIris[0][1]],[o[0],o[1]],4);let r=[e.annotations.rightEyeIris[0][0]+Math.sin(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[3],e.annotations.rightEyeIris[0][1]+Math.cos(e.rotation.gaze.bearing)*e.rotation.gaze.strength*e.box[2]];o5(t,[e.annotations.rightEyeIris[0][0],e.annotations.rightEyeIris[0][1]],[r[0],r[1]],4)}}function Xs(e,t){if(X.drawPolygons&&e.mesh.length>=468){t.lineWidth=1;for(let n=0;n<$e.length/3;n++){let o=[$e[n*3+0],$e[n*3+1],$e[n*3+2]].map(r=>e.mesh[r]);n5(t,o,X)}Vs(e,t)}}function Us(e,t){if(X.drawPoints)if((e==null?void 0:e.mesh.length)>=468)for(let n=0;n0&&(Us(r,o),Xs(r,o),Zs(r,o),qs(r,o))}}function _2(e,t,n){var s,A;let o=i0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round";for(let a=0;a0)){let l=o.bodyLabels.slice();l=Y(l,"[id]",t[a].id.toFixed(0)),l=Y(l,"[score]",100*t[a].score),re(r,l,t[a].box[0],t[a].box[1],o)}if(o.drawPoints&&t[a].keypoints)for(let l=0;l0&&t[a].keypoints){r.font=o.font;for(let l of t[a].keypoints){if(!l.score||l.score===0)continue;let 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d=o.fingerLabels.slice();d=Y(d,"[label]",l),re(r,d,c[c.length-1][0],c[c.length-1][1],o)}if(o.drawPolygons&&a.annotations){let l=c=>{if(!(!c||c.length===0||!c[0]))for(let d=0;d0?d-1:0][0],c[d>0?d-1:0][1]),r.lineTo(c[d][0],c[d][1]),r.stroke()}};r.lineWidth=o.lineWidth,l(a.annotations.index),l(a.annotations.middle),l(a.annotations.ring),l(a.annotations.pinky),l(a.annotations.thumb)}}}}function et(e,t,n){var s;let o=i0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let A of t)if(o.drawBoxes){if(r.strokeStyle=o.color,r.fillStyle=o.color,be(r,A.box[0],A.box[1],A.box[2],A.box[3],o),o.drawLabels&&((s=o.objectLabels)==null?void 0:s.length)>0){let a=o.objectLabels.slice();a=Y(a,"[id]",A.id.toFixed(0)),a=Y(a,"[label]",A.label),a=Y(a,"[score]",100*A.score),re(r,a,A.box[0],A.box[1],o)}r.stroke()}}}function tt(e,t,n){var r;let o=i0(f0,n);if(!(!t||!e)&&o.drawGestures&&((r=o.gestureLabels)==null?void 0:r.length)>0){let s=oe(e);if(!s)return;s.font=o.font,s.fillStyle=o.color;let A=1;for(let a=0;a1&&c[1].length>0){let d=l[1]>0?`#${l[1]}`:"",i=o.gestureLabels.slice();i=Y(i,"[where]",l[0]),i=Y(i,"[who]",d),i=Y(i,"[what]",c[1]),re(s,i,8,2+A*o.lineHeight,o),A+=1}}}}var Le={face:`face confidence: [score]% [gender] [genderScore]% age: [age] years @@ -6429,7450 +118,7 @@ var defaultLabels = { live: [live]% [emotions] roll: [roll]\xB0 yaw:[yaw]\xB0 pitch:[pitch]\xB0 - gaze: [gaze]\xB0`, - body: "body [score]%", - bodyPart: "[label] [score]%", - object: "[label] [score]%", - hand: "[label] [score]%", - finger: "[label]", - gesture: "[where] [who]: [what]" -}; - -// src/draw/draw.ts -var drawTime = 0; -function person(inCanvas2, result, drawOptions) { - const localOptions2 = mergeDeep(options2, drawOptions); - if (!result || !inCanvas2) return; - const ctx = getCanvasContext(inCanvas2); - if (!ctx) return; - ctx.lineJoin = "round"; - ctx.font = localOptions2.font; - for (let i = 0; i < result.length; i++) { - if (localOptions2.drawBoxes) { - ctx.strokeStyle = localOptions2.color; - ctx.fillStyle = localOptions2.color; - rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions2); - if (localOptions2.drawLabels) { - const label = `person #${i}`; - if (localOptions2.shadowColor && localOptions2.shadowColor !== "") { - ctx.fillStyle = localOptions2.shadowColor; - ctx.fillText(label, result[i].box[0] + 3, 1 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.fillStyle = localOptions2.labelColor; - ctx.fillText(label, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions2.lineHeight, result[i].box[2]); - } - ctx.stroke(); - } - } -} -function canvas2(input, output) { - if (!input || !output) return; - const ctx = getCanvasContext(output); - if (!ctx) return; - ctx.drawImage(input, 0, 0); -} -async function all(inCanvas2, result, drawOptions) { - if (!(result == null ? void 0 : result.performance) || !inCanvas2) return null; - const timeStamp = now(); - const localOptions2 = mergeDeep(options2, drawOptions); - const promise = Promise.all([ - face(inCanvas2, result.face, localOptions2), - body(inCanvas2, result.body, localOptions2), - hand(inCanvas2, result.hand, localOptions2), - object(inCanvas2, result.object, localOptions2), - gesture(inCanvas2, result.gesture, localOptions2) - // gestures do not have buffering - // person(inCanvas, result.persons, localOptions); // already included above - ]); - drawTime = env.perfadd ? drawTime + Math.round(now() - timeStamp) : Math.round(now() - timeStamp); - result.performance.draw = drawTime; - return promise; -} -function init2() { - options2.faceLabels = defaultLabels.face; - options2.bodyLabels = defaultLabels.body; - options2.bodyPartLabels = defaultLabels.bodyPart; - options2.handLabels = defaultLabels.hand; - options2.fingerLabels = defaultLabels.finger; - options2.objectLabels = defaultLabels.object; - options2.gestureLabels = defaultLabels.gesture; -} - -// src/body/blazepose.ts -var tf9 = __toESM(require_tfjs_esm()); - -// src/body/blazeposecoords.ts -var blazeposecoords_exports = {}; -__export(blazeposecoords_exports, { - connected: () => connected, - kpt: () => kpt -}); -var kpt = [ - "nose", - // 0 - "leftEyeInside", - // 1 - "leftEye", - // 2 - "leftEyeOutside", - // 3 - "rightEyeInside", - // 4 - "rightEye", - // 5 - "rightEyeOutside", - // 6 - "leftEar", - // 7 - "rightEar", - // 8 - "leftMouth", - // 9 - "rightMouth", - // 10 - "leftShoulder", - // 11 - "rightShoulder", - // 12 - "leftElbow", - // 13 - "rightElbow", - // 14 - "leftWrist", - // 15 - "rightWrist", - // 16 - "leftPinky", - // 17 - "rightPinky", - // 18 - "leftIndex", - // 19 - "rightIndex", - // 20 - "leftThumb", - // 21 - "rightThumb", - // 22 - "leftHip", - // 23 - "rightHip", - // 24 - "leftKnee", - // 25 - "rightKnee", - // 26 - "leftAnkle", - // 27 - "rightAnkle", - // 28 - "leftHeel", - // 29 - "rightHeel", - // 30 - "leftFoot", - // 31 - "rightFoot", - // 32 - "bodyCenter", - // 33 - "bodyTop", - // 34 - "leftPalm", - // 35 // z-coord not ok - "leftHand", - // 36 // similar to wrist but z-coord not ok - "rightPalm", - // 37 // z-coord not ok - "rightHand" - // 38 // similar to wrist but z-coord not ok -]; -var connected = { - shoulders: ["leftShoulder", "rightShoulder"], - hips: ["rightHip", "leftHip"], - mouth: ["leftMouth", "rightMouth"], - leftLegUpper: ["leftHip", "leftKnee"], - leftLegLower: ["leftKnee", "leftAnkle"], - leftFoot: ["leftAnkle", "leftHeel", "leftFoot"], - leftTorso: ["leftShoulder", "leftHip"], - leftArmUpper: ["leftShoulder", "leftElbow"], - leftArmLower: ["leftElbow", "leftWrist"], - leftHand: ["leftWrist", "leftPalm"], - leftHandPinky: ["leftPalm", "leftPinky"], - leftHandIndex: ["leftPalm", "leftIndex"], - leftHandThumb: ["leftPalm", "leftThumb"], - leftEyeOutline: ["leftEyeInside", "leftEyeOutside"], - rightLegUpper: ["rightHip", "rightKnee"], - rightLegLower: ["rightKnee", "rightAnkle"], - rightFoot: ["rightAnkle", "rightHeel", "rightFoot"], - rightTorso: ["rightShoulder", "rightHip"], - rightArmUpper: ["rightShoulder", "rightElbow"], - rightArmLower: ["rightElbow", "rightWrist"], - rightHand: ["rightWrist", "rightPalm"], - rightHandPinky: ["rightPalm", "rightPinky"], - rightHandIndex: ["rightPalm", "rightIndex"], - rightHandThumb: ["rightPalm", "rightThumb"], - rightEyeOutline: ["rightEyeInside", "rightEyeOutside"] -}; - -// src/body/blazeposedetector.ts -var tf8 = __toESM(require_tfjs_esm()); -var model; -var inputSize = 224; -var anchorTensor; -var numLayers = 5; -var strides = [8, 16, 32, 32, 32]; -function createAnchors() { - const anchors3 = []; - let layerId = 0; - while (layerId < numLayers) { - let anchorCount = 0; - let lastSameStrideLayer = layerId; - while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) { - anchorCount += 2; - lastSameStrideLayer++; - } - const stride = strides[layerId]; - const featureMapHeight = Math.ceil(inputSize / stride); - const featureMapWidth = Math.ceil(inputSize / stride); - for (let y = 0; y < featureMapHeight; ++y) { - for (let x = 0; x < featureMapWidth; ++x) { - for (let anchorId = 0; anchorId < anchorCount; ++anchorId) { - anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight }); - } - } - } - layerId = lastSameStrideLayer; - } - anchorTensor = { x: tf8.tensor1d(anchors3.map((a) => a.x)), y: tf8.tensor1d(anchors3.map((a) => a.y)) }; -} -async function loadDetector(config3) { - if (env.initial) model = null; - if (!model && config3.body["detector"] && config3.body["detector"].modelPath || "") { - model = await loadModel(config3.body["detector"].modelPath); - const inputs = (model == null ? void 0 : model["executor"]) ? Object.values(model.modelSignature["inputs"]) : void 0; - inputSize = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug && model) log("cached model:", model["modelUrl"]); - createAnchors(); - return model; -} -var cropFactor = [5, 5]; -function decodeBoxes(boxesTensor, anchor) { - return tf8.tidy(() => { - const split6 = tf8.split(boxesTensor, 12, 1); - let xCenter = tf8.squeeze(split6[0]); - let yCenter = tf8.squeeze(split6[1]); - let width = tf8.squeeze(split6[2]); - let height = tf8.squeeze(split6[3]); - xCenter = tf8.add(tf8.div(xCenter, inputSize), anchor.x); - yCenter = tf8.add(tf8.div(yCenter, inputSize), anchor.y); - width = tf8.mul(tf8.div(width, inputSize), cropFactor[0]); - height = tf8.mul(tf8.div(height, inputSize), cropFactor[1]); - const xMin = tf8.sub(xCenter, tf8.div(width, 2)); - const yMin = tf8.sub(yCenter, tf8.div(height, 2)); - const xMax = tf8.add(xMin, width); - const yMax = tf8.add(yMin, height); - const boxes = tf8.stack([xMin, yMin, xMax, yMax], 1); - return boxes; - }); -} -async function decodeResults(boxesTensor, logitsTensor, config3, outputSize2) { - var _a, _b; - const detectedBoxes = []; - const t2 = {}; - t2.boxes = decodeBoxes(boxesTensor, anchorTensor); - t2.scores = tf8.sigmoid(logitsTensor); - t2.nms = await tf8.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, 1, ((_a = config3.body["detector"]) == null ? void 0 : _a.minConfidence) || 0.1, ((_b = config3.body["detector"]) == null ? void 0 : _b.iouThreshold) || 0.1); - const nms = await t2.nms.data(); - const scores = await t2.scores.data(); - const boxes = await t2.boxes.array(); - for (const i of Array.from(nms)) { - const score = scores[i]; - const boxRaw = boxes[i]; - const box = [Math.round(boxRaw[0] * outputSize2[0]), Math.round(boxRaw[1] * outputSize2[1]), Math.round(boxRaw[2] * outputSize2[0]), Math.round(boxRaw[3] * outputSize2[1])]; - const detectedBox = { score, boxRaw, box }; - detectedBoxes.push(detectedBox); - } - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return detectedBoxes; -} -async function detectBoxes(input, config3, outputSize2) { - const t2 = {}; - t2.res = model == null ? void 0 : model.execute(input, ["Identity"]); - t2.logitsRaw = tf8.slice(t2.res, [0, 0, 0], [1, -1, 1]); - t2.boxesRaw = tf8.slice(t2.res, [0, 0, 1], [1, -1, -1]); - t2.logits = tf8.squeeze(t2.logitsRaw); - t2.boxes = tf8.squeeze(t2.boxesRaw); - const boxes = await decodeResults(t2.boxes, t2.logits, config3, outputSize2); - Object.keys(t2).forEach((tensor6) => tf8.dispose(t2[tensor6])); - return boxes; -} - -// src/util/box.ts -function calc(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const box = [min2[0], min2[1], max5[0] - min2[0], max5[1] - min2[1]]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function square(keypoints, outputSize2 = [1, 1]) { - const coords = [keypoints.map((pt) => pt[0]), keypoints.map((pt) => pt[1])]; - const min2 = [Math.min(...coords[0]), Math.min(...coords[1])]; - const max5 = [Math.max(...coords[0]), Math.max(...coords[1])]; - const center = [(min2[0] + max5[0]) / 2, (min2[1] + max5[1]) / 2]; - const dist = Math.max(center[0] - min2[0], center[1] - min2[1], -center[0] + max5[0], -center[1] + max5[1]); - const box = [Math.trunc(center[0] - dist), Math.trunc(center[1] - dist), Math.trunc(2 * dist), Math.trunc(2 * dist)]; - const boxRaw = [box[0] / outputSize2[0], box[1] / outputSize2[1], box[2] / outputSize2[0], box[3] / outputSize2[1]]; - return { box, boxRaw }; -} -function scale(box, scaleFact) { - const dist = [box[2] * scaleFact, box[3] * scaleFact]; - const newBox = [ - box[0] - (dist[0] - box[2]) / 2, - box[1] - (dist[1] - box[3]) / 2, - dist[0], - dist[1] - ]; - return newBox; -} - -// src/body/blazepose.ts -var model2; -var inputSize2 = 256; -var skipped = Number.MAX_SAFE_INTEGER; -var outputNodes = { - landmarks: ["ld_3d", "activation_segmentation", "activation_heatmap", "world_3d", "output_poseflag"], - detector: [] -}; -var cache = []; -var padding = [[0, 0], [0, 0], [0, 0], [0, 0]]; -var lastTime = 0; -var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x)); -var loadDetect = (config3) => loadDetector(config3); -async function loadPose(config3) { - if (env.initial) model2 = null; - if (!model2) { - model2 = await loadModel(config3.body.modelPath); - const inputs = (model2 == null ? void 0 : model2["executor"]) ? Object.values(model2.modelSignature["inputs"]) : void 0; - inputSize2 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - } else if (config3.debug) log("cached model:", model2["modelUrl"]); - return model2; -} -function prepareImage(input, size2, cropBox) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) return input; - let final; - if (cropBox) { - t2.cropped = tf9.image.cropAndResize(input, [cropBox], [0], [input.shape[1], input.shape[2]]); - } - if (input.shape[1] !== input.shape[2]) { - const height = [ - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, - input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0 - ]; - const width = [ - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, - input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0 - ]; - padding = [ - [0, 0], - // dont touch batch - height, - // height before&after - width, - // width before&after - [0, 0] - // dont touch rbg - ]; - t2.pad = tf9.pad(t2.cropped || input, padding); - t2.resize = tf9.image.resizeBilinear(t2.pad, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else if (input.shape[1] !== size2) { - t2.resize = tf9.image.resizeBilinear(t2.cropped || input, [size2, size2]); - final = tf9.div(t2.resize, constants.tf255); - } else { - final = tf9.div(t2.cropped || input, constants.tf255); - } - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - return final; -} -function rescaleKeypoints(keypoints, outputSize2, cropBox) { - for (const kpt4 of keypoints) { - kpt4.position = [ - Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]), - Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]), - kpt4.position[2] - ]; - kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])]; - } - if (cropBox) { - const width = cropBox[2] - cropBox[0]; - const height = cropBox[3] - cropBox[1]; - for (const kpt4 of keypoints) { - kpt4.positionRaw = [ - kpt4.positionRaw[0] / height + cropBox[1], - // correct offset due to crop - kpt4.positionRaw[1] / width + cropBox[0], - // correct offset due to crop - kpt4.positionRaw[2] - ]; - kpt4.position = [ - Math.trunc(kpt4.positionRaw[0] * outputSize2[0]), - Math.trunc(kpt4.positionRaw[1] * outputSize2[1]), - kpt4.positionRaw[2] - ]; - } - } - return keypoints; -} -function fixKeypoints(keypoints) { - const leftPalm = keypoints.find((k) => k.part === "leftPalm"); - const leftWrist = keypoints.find((k) => k.part === "leftWrist"); - const leftIndex = keypoints.find((k) => k.part === "leftIndex"); - leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2; - const rightPalm = keypoints.find((k) => k.part === "rightPalm"); - const rightWrist = keypoints.find((k) => k.part === "rightWrist"); - const rightIndex = keypoints.find((k) => k.part === "rightIndex"); - rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2; -} -async function detectLandmarks(input, config3, outputSize2) { - if (!(model2 == null ? void 0 : model2["executor"])) return null; - const t2 = {}; - [ - t2.ld, - t2.segmentation, - t2.heatmap, - t2.world, - t2.poseflag - /* 1,1 */ - ] = model2 == null ? void 0 : model2.execute(input, outputNodes.landmarks); - const poseScore = (await t2.poseflag.data())[0]; - const points = await t2.ld.data(); - const distances = await t2.world.data(); - Object.keys(t2).forEach((tensor6) => tf9.dispose(t2[tensor6])); - const keypointsRelative = []; - const depth = 5; - for (let i = 0; i < points.length / depth; i++) { - const score = sigmoid2(points[depth * i + 3]); - const presence = sigmoid2(points[depth * i + 4]); - const adjScore = Math.trunc(100 * score * presence * poseScore) / 100; - const positionRaw = [points[depth * i + 0] / inputSize2, points[depth * i + 1] / inputSize2, points[depth * i + 2] + 0]; - const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]]; - const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0]; - keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore }); - } - if (poseScore < (config3.body.minConfidence || 0)) return null; - fixKeypoints(keypointsRelative); - const keypoints = rescaleKeypoints(keypointsRelative, outputSize2); - const kpts = keypoints.map((k) => k.position); - const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 }; - return body4; -} -async function predict(input, config3) { - var _a, _b, _c; - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime; - const skipFrame = skipped < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && cache !== null) { - skipped++; - } else { - let boxes = []; - if ((_b = (_a = config3.body) == null ? void 0 : _a["detector"]) == null ? void 0 : _b["enabled"]) { - const preparedImage = prepareImage(input, 224); - boxes = await detectBoxes(preparedImage, config3, outputSize2); - tf9.dispose(preparedImage); - } else { - boxes = [{ box: [0, 0, 0, 0], boxRaw: [0, 0, 1, 1], score: 0 }]; - } - for (let i = 0; i < boxes.length; i++) { - const preparedBox = prepareImage(input, 256, (_c = boxes[i]) == null ? void 0 : _c.boxRaw); - cache.length = 0; - const bodyResult = await detectLandmarks(preparedBox, config3, outputSize2); - tf9.dispose(preparedBox); - if (!bodyResult) continue; - bodyResult.id = i; - cache.push(bodyResult); - } - lastTime = now(); - skipped = 0; - } - return cache; -} - -// src/object/centernet.ts -var tf10 = __toESM(require_tfjs_esm()); - -// src/object/labels.ts -var labels2 = [ - { class: 1, label: "person" }, - { class: 2, label: "bicycle" }, - { class: 3, label: "car" }, - { class: 4, label: "motorcycle" }, - { class: 5, label: "airplane" }, - { class: 6, label: "bus" }, - { class: 7, label: "train" }, - { class: 8, label: "truck" }, - { class: 9, label: "boat" }, - { class: 10, label: "traffic light" }, - { class: 11, label: "fire hydrant" }, - { class: 12, label: "stop sign" }, - { class: 13, label: "parking meter" }, - { class: 14, label: "bench" }, - { class: 15, label: "bird" }, - { class: 16, label: "cat" }, - { class: 17, label: "dog" }, - { class: 18, label: "horse" }, - { class: 19, label: "sheep" }, - { class: 20, label: "cow" }, - { class: 21, label: "elephant" }, - { class: 22, label: "bear" }, - { class: 23, label: "zebra" }, - { class: 24, label: "giraffe" }, - { class: 25, label: "backpack" }, - { class: 26, label: "umbrella" }, - { class: 27, label: "handbag" }, - { class: 28, label: "tie" }, - { class: 29, label: "suitcase" }, - { class: 30, label: "frisbee" }, - { class: 31, label: "skis" }, - { class: 32, label: "snowboard" }, - { class: 33, label: "sports ball" }, - { class: 34, label: "kite" }, - { class: 35, label: "baseball bat" }, - { class: 36, label: "baseball glove" }, - { class: 37, label: "skateboard" }, - { class: 38, label: "surfboard" }, - { class: 39, label: "tennis racket" }, - { class: 40, label: "bottle" }, - { class: 41, label: "wine glass" }, - { class: 42, label: "cup" }, - { class: 43, label: "fork" }, - { class: 44, label: "knife" }, - { class: 45, label: "spoon" }, - { class: 46, label: "bowl" }, - { class: 47, label: "banana" }, - { class: 48, label: "apple" }, - { class: 49, label: "sandwich" }, - { class: 50, label: "orange" }, - { class: 51, label: "broccoli" }, - { class: 52, label: "carrot" }, - { class: 53, label: "hot dog" }, - { class: 54, label: "pizza" }, - { class: 55, label: "donut" }, - { class: 56, label: "cake" }, - { class: 57, label: "chair" }, - { class: 58, label: "couch" }, - { class: 59, label: "potted plant" }, - { class: 60, label: "bed" }, - { class: 61, label: "dining table" }, - { class: 62, label: "toilet" }, - { class: 63, label: "tv" }, - { class: 64, label: "laptop" }, - { class: 65, label: "mouse" }, - { class: 66, label: "remote" }, - { class: 67, label: "keyboard" }, - { class: 68, label: "cell phone" }, - { class: 69, label: "microwave" }, - { class: 70, label: "oven" }, - { class: 71, label: "toaster" }, - { class: 72, label: "sink" }, - { class: 73, label: "refrigerator" }, - { class: 74, label: "book" }, - { class: 75, label: "clock" }, - { class: 76, label: "vase" }, - { class: 77, label: "scissors" }, - { class: 78, label: "teddy bear" }, - { class: 79, label: "hair drier" }, - { class: 80, label: "toothbrush" } -]; - -// src/object/centernet.ts -var model3; -var inputSize3 = 0; -var last2 = []; -var lastTime2 = 0; -var skipped2 = Number.MAX_SAFE_INTEGER; -async function load(config3) { - if (env.initial) model3 = null; - if (!model3) { - model3 = await loadModel(config3.object.modelPath); - const inputs = (model3 == null ? void 0 : model3["executor"]) ? Object.values(model3.modelSignature["inputs"]) : void 0; - inputSize3 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", model3["modelUrl"]); - return model3; -} -async function process3(res, outputShape, config3) { - if (!res) return []; - const t2 = {}; - const results = []; - const detections = await res.array(); - t2.squeeze = tf10.squeeze(res); - const arr = tf10.split(t2.squeeze, 6, 1); - t2.stack = tf10.stack([arr[1], arr[0], arr[3], arr[2]], 1); - t2.boxes = tf10.squeeze(t2.stack); - t2.scores = tf10.squeeze(arr[4]); - t2.classes = tf10.squeeze(arr[5]); - tf10.dispose([res, ...arr]); - t2.nms = await tf10.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence || 0); - const nms = await t2.nms.data(); - let i = 0; - for (const id of Array.from(nms)) { - const score = Math.trunc(100 * detections[0][id][4]) / 100; - const classVal = detections[0][id][5]; - if (Number.isNaN(classVal)) continue; - const label = labels2[classVal].label; - const [x, y] = [ - detections[0][id][0] / inputSize3, - detections[0][id][1] / inputSize3 - ]; - const boxRaw = [ - x, - y, - detections[0][id][2] / inputSize3 - x, - detections[0][id][3] / inputSize3 - y - ]; - const box = [ - Math.trunc(boxRaw[0] * outputShape[0]), - Math.trunc(boxRaw[1] * outputShape[1]), - Math.trunc(boxRaw[2] * outputShape[0]), - Math.trunc(boxRaw[3] * outputShape[1]) - ]; - results.push({ id: i++, score, class: classVal, label, box, boxRaw }); - } - Object.keys(t2).forEach((tensor6) => tf10.dispose(t2[tensor6])); - return results; -} -async function predict2(input, config3) { - if (!(model3 == null ? void 0 : model3["executor"])) return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime2; - const skipFrame = skipped2 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last2.length > 0) { - skipped2++; - return last2; - } - skipped2 = 0; - return new Promise(async (resolve) => { - const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0]; - const resize = tf10.image.resizeBilinear(input, [inputSize3, inputSize3]); - const objectT = config3.object.enabled ? model3 == null ? void 0 : model3.execute(resize, ["tower_0/detections"]) : null; - lastTime2 = now(); - tf10.dispose(resize); - const obj = await process3(objectT, outputSize2, config3); - last2 = obj; - resolve(obj); - }); -} - -// src/body/efficientpose.ts -var tf11 = __toESM(require_tfjs_esm()); - -// src/body/efficientposecoords.ts -var efficientposecoords_exports = {}; -__export(efficientposecoords_exports, { - connected: () => connected2, - kpt: () => kpt2 -}); -var kpt2 = [ - "head", - "neck", - "rightShoulder", - "rightElbow", - "rightWrist", - "chest", - "leftShoulder", - "leftElbow", - "leftWrist", - "bodyCenter", - "rightHip", - "rightKnee", - "rightAnkle", - "leftHip", - "leftKnee", - "leftAnkle" -]; -var connected2 = { - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/body/efficientpose.ts -var model4; -var lastTime3 = 0; -var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} }; -var skipped3 = Number.MAX_SAFE_INTEGER; -async function load2(config3) { - if (env.initial) model4 = null; - if (!model4) model4 = await loadModel(config3.body.modelPath); - else if (config3.debug) log("cached model:", model4["modelUrl"]); - return model4; -} -async function max2d(inputs, minScore) { - const [width, height] = inputs.shape; - const reshaped = tf11.reshape(inputs, [height * width]); - const max5 = tf11.max(reshaped, 0); - const newScore = (await max5.data())[0]; - if (newScore > minScore) { - const coordinates = tf11.argMax(reshaped, 0); - const mod3 = tf11.mod(coordinates, width); - const x = (await mod3.data())[0]; - const div15 = tf11.div(coordinates, width); - const y = (await div15.data())[0]; - tf11.dispose([reshaped, max5, coordinates, mod3, div15]); - return [x, y, newScore]; - } - tf11.dispose([reshaped, max5]); - return [0, 0, newScore]; -} -async function predict3(image28, config3) { - if (!(model4 == null ? void 0 : model4["executor"]) || !(model4 == null ? void 0 : model4.inputs[0].shape)) return []; - const skipTime = (config3.body.skipTime || 0) > now() - lastTime3; - const skipFrame = skipped3 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) { - skipped3++; - return [cache2]; - } - skipped3 = 0; - return new Promise(async (resolve) => { - const tensor6 = tf11.tidy(() => { - var _a, _b; - const resize = tf11.image.resizeBilinear(image28, [((_a = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _a[2]) || 0, ((_b = model4 == null ? void 0 : model4.inputs[0].shape) == null ? void 0 : _b[1]) || 0], false); - const enhance2 = tf11.mul(resize, constants.tf2); - const norm = tf11.sub(enhance2, constants.tf1); - return norm; - }); - let resT; - if (config3.body.enabled) resT = model4 == null ? void 0 : model4.execute(tensor6); - lastTime3 = now(); - tf11.dispose(tensor6); - if (resT) { - cache2.keypoints.length = 0; - const squeeze14 = tf11.squeeze(resT); - tf11.dispose(resT); - const stack5 = tf11.unstack(squeeze14, 2); - tf11.dispose(squeeze14); - for (let id = 0; id < stack5.length; id++) { - const [x2, y2, partScore] = await max2d(stack5[id], config3.body.minConfidence); - if (partScore > (config3.body.minConfidence || 0)) { - cache2.keypoints.push({ - score: Math.round(100 * partScore) / 100, - part: kpt2[id], - positionRaw: [ - // normalized to 0..1 - // @ts-ignore model is not undefined here - x2 / model4.inputs[0].shape[2], - y2 / model4.inputs[0].shape[1] - ], - position: [ - // normalized to input image size - // @ts-ignore model is not undefined here - Math.round(image28.shape[2] * x2 / model4.inputs[0].shape[2]), - Math.round(image28.shape[1] * y2 / model4.inputs[0].shape[1]) - ] - }); - } - } - stack5.forEach((s) => tf11.dispose(s)); - } - cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const x = cache2.keypoints.map((a) => a.position[0]); - const y = cache2.keypoints.map((a) => a.position[1]); - cache2.box = [ - Math.min(...x), - Math.min(...y), - Math.max(...x) - Math.min(...x), - Math.max(...y) - Math.min(...y) - ]; - const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]); - const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]); - cache2.boxRaw = [ - Math.min(...xRaw), - Math.min(...yRaw), - Math.max(...xRaw) - Math.min(...xRaw), - Math.max(...yRaw) - Math.min(...yRaw) - ]; - for (const [name, indexes] of Object.entries(connected2)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]); - const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - cache2.annotations[name] = pt; - } - resolve([cache2]); - }); -} - -// src/face/face.ts -var tf25 = __toESM(require_tfjs_esm()); - -// src/face/facemesh.ts -var tf15 = __toESM(require_tfjs_esm()); - -// src/face/blazeface.ts -var tf13 = __toESM(require_tfjs_esm()); - -// src/face/facemeshutil.ts -var tf12 = __toESM(require_tfjs_esm()); -var getBoxSize = (box) => [Math.abs(box.endPoint[0] - box.startPoint[0]), Math.abs(box.endPoint[1] - box.startPoint[1])]; -var getBoxCenter = (box) => [box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2, 1]; -var clampBox = (box, input) => box ? [ - Math.trunc(Math.max(0, box.startPoint[0])), - Math.trunc(Math.max(0, box.startPoint[1])), - Math.trunc(Math.min(input.shape[2] || 0, box.endPoint[0]) - Math.max(0, box.startPoint[0])), - Math.trunc(Math.min(input.shape[1] || 0, box.endPoint[1]) - Math.max(0, box.startPoint[1])) -] : [0, 0, 0, 0]; -var getRawBox = (box, input) => box ? [ - box.startPoint[0] / (input.shape[2] || 0), - box.startPoint[1] / (input.shape[1] || 0), - (box.endPoint[0] - box.startPoint[0]) / (input.shape[2] || 0), - (box.endPoint[1] - box.startPoint[1]) / (input.shape[1] || 0) -] : [0, 0, 0, 0]; -var scaleBoxCoordinates = (box, factor, anchor) => { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const landmarks = box.landmarks.map((pt) => [(pt[0] + anchor[0]) * factor[0], (pt[1] + anchor[1]) * factor[1]]); - return { startPoint, endPoint, landmarks, confidence: box.confidence }; -}; -var cutAndResize = (box, image28, cropSize) => { - const h = image28.shape[1]; - const w = image28.shape[2]; - const cutBox = [box.startPoint[1] / h, box.startPoint[0] / w, box.endPoint[1] / h, box.endPoint[0] / w]; - const crop = tf12.image.cropAndResize(image28, [cutBox], [0], cropSize); - const norm = tf12.div(crop, constants.tf255); - tf12.dispose(crop); - return norm; -}; -var enlargeBox = (box, factor) => { - const center = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - return { - startPoint: [center[0] - halfSize[0], center[1] - halfSize[1]], - endPoint: [center[0] + halfSize[0], center[1] + halfSize[1]], - landmarks: box.landmarks, - confidence: box.confidence, - size: size2 - }; -}; -var squarifyBox = (box) => { - const centers = getBoxCenter(box); - const size2 = getBoxSize(box); - const halfSize = Math.max(...size2) / 2; - return { - startPoint: [Math.round(centers[0] - halfSize), Math.round(centers[1] - halfSize)], - endPoint: [Math.round(centers[0] + halfSize), Math.round(centers[1] + halfSize)], - landmarks: box.landmarks, - confidence: box.confidence, - size: [Math.round(size2[0]), Math.round(size2[1])] - }; -}; -var calculateLandmarksBoundingBox = (landmarks) => { - const x = landmarks.map((d) => d[0]); - const y = landmarks.map((d) => d[1]); - return { - startPoint: [Math.min(...x), Math.min(...y)], - endPoint: [Math.max(...x), Math.max(...y)], - landmarks - }; -}; -var fixedRotationMatrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]; -var normalizeRadians = (angle) => angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -var computeRotation = (point1, point2) => normalizeRadians(Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0])); -var buildTranslationMatrix = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -var dot = (v1, v2) => { - let product = 0; - for (let i = 0; i < v1.length; i++) product += v1[i] * v2[i]; - return product; -}; -var getColumnFrom2DArr = (arr, columnIndex) => { - const column = []; - for (let i = 0; i < arr.length; i++) column.push(arr[i][columnIndex]); - return column; -}; -var multiplyTransformMatrices = (mat1, mat2) => { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) product[row].push(dot(mat1[row], getColumnFrom2DArr(mat2, col))); - } - return product; -}; -var buildRotationMatrix = (rotation, center) => { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix(-center[0], -center[1]); - return multiplyTransformMatrices(translationTimesRotation, negativeTranslationMatrix); -}; -var invertTransformMatrix = (matrix) => { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [-dot(rotationComponent[0], translationComponent), -dot(rotationComponent[1], translationComponent)]; - return [rotationComponent[0].concat(invertedTranslation[0]), rotationComponent[1].concat(invertedTranslation[1]), [0, 0, 1]]; -}; -var rotatePoint = (homogeneousCoordinate, rotationMatrix) => [dot(homogeneousCoordinate, rotationMatrix[0]), dot(homogeneousCoordinate, rotationMatrix[1])]; -function generateAnchors(inputSize10) { - const spec = inputSize10 === 192 ? { strides: [4], anchors: [1] } : { strides: [inputSize10 / 16, inputSize10 / 8], anchors: [2, 6] }; - const anchors3 = []; - for (let i = 0; i < spec.strides.length; i++) { - const stride = spec.strides[i]; - const gridRows = Math.floor((inputSize10 + stride - 1) / stride); - const gridCols = Math.floor((inputSize10 + stride - 1) / stride); - const anchorsNum = spec.anchors[i]; - for (let gridY = 0; gridY < gridRows; gridY++) { - const anchorY = stride * (gridY + 0.5); - for (let gridX = 0; gridX < gridCols; gridX++) { - const anchorX = stride * (gridX + 0.5); - for (let n = 0; n < anchorsNum; n++) anchors3.push([anchorX, anchorY]); - } - } - } - return anchors3; -} -function transformRawCoords(coordsRaw, box, angle, rotationMatrix, inputSize10) { - const boxSize = getBoxSize(box); - const coordsScaled = coordsRaw.map((coord) => [ - // scaled around zero-point - boxSize[0] / inputSize10 * (coord[0] - inputSize10 / 2), - boxSize[1] / inputSize10 * (coord[1] - inputSize10 / 2), - coord[2] || 0 - ]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - const coordsRotationMatrix = largeAngle ? buildRotationMatrix(angle, [0, 0]) : fixedRotationMatrix; - const coordsRotated = largeAngle ? coordsScaled.map((coord) => [...rotatePoint(coord, coordsRotationMatrix), coord[2]]) : coordsScaled; - const inverseRotationMatrix = largeAngle ? invertTransformMatrix(rotationMatrix) : fixedRotationMatrix; - const boxCenter = getBoxCenter(box); - const offsets = [dot(boxCenter, inverseRotationMatrix[0]), dot(boxCenter, inverseRotationMatrix[1])]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + offsets[0]), - Math.trunc(coord[1] + offsets[1]), - Math.trunc(coord[2] || 0) - ]); -} -function correctFaceRotation(rotate, box, input, inputSize10) { - const symmetryLine = box.landmarks.length >= meshLandmarks.count ? meshLandmarks.symmetryLine : blazeFaceLandmarks.symmetryLine; - let angle = 0; - let rotationMatrix = fixedRotationMatrix; - let face4; - if (rotate && env.kernels.includes("rotatewithoffset")) { - angle = computeRotation(box.landmarks[symmetryLine[0]], box.landmarks[symmetryLine[1]]); - const largeAngle = angle && angle !== 0 && Math.abs(angle) > 0.2; - if (largeAngle) { - const center = getBoxCenter(box); - const centerRaw = [center[0] / input.shape[2], center[1] / input.shape[1]]; - const rotated = tf12.image.rotateWithOffset(input, angle, 0, [centerRaw[0], centerRaw[1]]); - rotationMatrix = buildRotationMatrix(-angle, center); - face4 = cutAndResize(box, rotated, [inputSize10, inputSize10]); - tf12.dispose(rotated); - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - } else { - face4 = cutAndResize(box, input, [inputSize10, inputSize10]); - } - return [angle, rotationMatrix, face4]; -} -var findFaceCenter = (mesh) => { - const x = mesh.map((m) => m[0]); - const y = mesh.map((m) => m[1]); - return [Math.min(...x) + (Math.max(...x) - Math.min(...x)) / 2, Math.min(...y) + (Math.max(...y) - Math.min(...y)) / 2]; -}; -var calculateFaceBox = (mesh, previousBox) => { - const center = findFaceCenter(mesh); - const boxSize = getBoxSize(previousBox); - const calculatedBox = { - startPoint: [center[0] - boxSize[0] / 2, center[1] - boxSize[1] / 2], - endPoint: [center[0] + boxSize[0] / 2, center[1] + boxSize[1] / 2] - }; - return calculatedBox; -}; - -// src/face/blazeface.ts -var keypointsCount = 6; -var model5; -var anchors = null; -var inputSize4 = 0; -var inputSizeT = null; -var size = () => inputSize4; -async function load3(config3) { - var _a; - if (env.initial) model5 = null; - if (!model5) model5 = await loadModel((_a = config3.face.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model5["modelUrl"]); - inputSize4 = model5["executor"] && model5.inputs[0].shape ? model5.inputs[0].shape[2] : 256; - inputSizeT = tf13.scalar(inputSize4, "int32"); - anchors = tf13.tensor2d(generateAnchors(inputSize4)); - return model5; -} -function decodeBoxes2(boxOutputs) { - if (!anchors || !inputSizeT) return tf13.zeros([0, 0]); - const t2 = {}; - t2.boxStarts = tf13.slice(boxOutputs, [0, 1], [-1, 2]); - t2.centers = tf13.add(t2.boxStarts, anchors); - t2.boxSizes = tf13.slice(boxOutputs, [0, 3], [-1, 2]); - t2.boxSizesNormalized = tf13.div(t2.boxSizes, inputSizeT); - t2.centersNormalized = tf13.div(t2.centers, inputSizeT); - t2.halfBoxSize = tf13.div(t2.boxSizesNormalized, constants.tf2); - t2.starts = tf13.sub(t2.centersNormalized, t2.halfBoxSize); - t2.ends = tf13.add(t2.centersNormalized, t2.halfBoxSize); - t2.startNormalized = tf13.mul(t2.starts, inputSizeT); - t2.endNormalized = tf13.mul(t2.ends, inputSizeT); - const boxes = tf13.concat2d([t2.startNormalized, t2.endNormalized], 1); - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} -async function getBoxes(inputImage, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i; - if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1) return []; - const t2 = {}; - let pad4 = [0, 0]; - let scale2 = [1, 1]; - if ((_b = (_a = config3 == null ? void 0 : config3.face) == null ? void 0 : _a.detector) == null ? void 0 : _b.square) { - const xy = Math.max(inputImage.shape[2], inputImage.shape[1]); - pad4 = [Math.floor((xy - inputImage.shape[2]) / 2), Math.floor((xy - inputImage.shape[1]) / 2)]; - t2.padded = tf13.pad(inputImage, [[0, 0], [pad4[1], pad4[1]], [pad4[0], pad4[0]], [0, 0]]); - scale2 = [inputImage.shape[2] / xy, inputImage.shape[1] / xy]; - pad4 = [pad4[0] / inputSize4, pad4[1] / inputSize4]; - } else { - t2.padded = inputImage.clone(); - } - t2.resized = tf13.image.resizeBilinear(t2.padded, [inputSize4, inputSize4]); - t2.div = tf13.div(t2.resized, constants.tf127); - t2.normalized = tf13.sub(t2.div, constants.tf1); - const res = model5 == null ? void 0 : model5.execute(t2.normalized); - if (Array.isArray(res) && res.length > 2) { - const sorted = res.sort((a, b) => a.size - b.size); - t2.concat384 = tf13.concat([sorted[0], sorted[2]], 2); - t2.concat512 = tf13.concat([sorted[1], sorted[3]], 2); - t2.concat = tf13.concat([t2.concat512, t2.concat384], 1); - t2.batch = tf13.squeeze(t2.concat, [0]); - } else if (Array.isArray(res)) { - t2.batch = tf13.squeeze(res[0]); - } else { - t2.batch = tf13.squeeze(res); - } - tf13.dispose(res); - t2.boxes = decodeBoxes2(t2.batch); - t2.logits = tf13.slice(t2.batch, [0, 0], [-1, 1]); - t2.sigmoid = tf13.sigmoid(t2.logits); - t2.scores = tf13.squeeze(t2.sigmoid); - t2.nms = await tf13.image.nonMaxSuppressionAsync(t2.boxes, t2.scores, ((_c = config3.face.detector) == null ? void 0 : _c.maxDetected) || 0, ((_d = config3.face.detector) == null ? void 0 : _d.iouThreshold) || 0, ((_e = config3.face.detector) == null ? void 0 : _e.minConfidence) || 0); - const nms = await t2.nms.array(); - const boxes = []; - const scores = await t2.scores.data(); - for (let i = 0; i < nms.length; i++) { - const confidence = scores[nms[i]]; - if (confidence > (((_f = config3.face.detector) == null ? void 0 : _f.minConfidence) || 0)) { - const b = {}; - b.bbox = tf13.slice(t2.boxes, [nms[i], 0], [1, -1]); - b.slice = tf13.slice(t2.batch, [nms[i], keypointsCount - 1], [1, -1]); - b.squeeze = tf13.squeeze(b.slice); - b.landmarks = tf13.reshape(b.squeeze, [keypointsCount, -1]); - const points = await b.bbox.data(); - const unpadded = [ - // TODO fix this math - points[0] * scale2[0] - pad4[0], - points[1] * scale2[1] - pad4[1], - points[2] * scale2[0] - pad4[0], - points[3] * scale2[1] - pad4[1] - ]; - const rawBox = { - startPoint: [unpadded[0], unpadded[1]], - endPoint: [unpadded[2], unpadded[3]], - landmarks: await b.landmarks.array(), - confidence - }; - b.anchor = tf13.slice(anchors, [nms[i], 0], [1, 2]); - const anchor = await b.anchor.data(); - const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize4, (inputImage.shape[1] || 0) / inputSize4], anchor); - const enlargedBox = enlargeBox(scaledBox, ((_g = config3.face.detector) == null ? void 0 : _g.scale) || 1.4); - const squaredBox = squarifyBox(enlargedBox); - if (squaredBox.size[0] > (((_h = config3.face.detector) == null ? void 0 : _h["minSize"]) || 0) && squaredBox.size[1] > (((_i = config3.face.detector) == null ? void 0 : _i["minSize"]) || 0)) boxes.push(squaredBox); - Object.keys(b).forEach((tensor6) => tf13.dispose(b[tensor6])); - } - } - Object.keys(t2).forEach((tensor6) => tf13.dispose(t2[tensor6])); - return boxes; -} - -// src/face/iris.ts -var tf14 = __toESM(require_tfjs_esm()); -var model6; -var inputSize5 = 0; -var leftOutline = meshAnnotations.leftEyeLower0; -var rightOutline = meshAnnotations.rightEyeLower0; -var eyeLandmarks = { - leftBounds: [leftOutline[0], leftOutline[leftOutline.length - 1]], - rightBounds: [rightOutline[0], rightOutline[rightOutline.length - 1]] -}; -var irisLandmarks = { - upperCenter: 3, - lowerCenter: 4, - index: 71, - numCoordinates: 76 -}; -async function load4(config3) { - var _a, _b; - if (env.initial) model6 = null; - if (!model6) model6 = await loadModel((_a = config3.face.iris) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model6["modelUrl"]); - inputSize5 = (model6 == null ? void 0 : model6["executor"]) && ((_b = model6.inputs) == null ? void 0 : _b[0].shape) ? model6.inputs[0].shape[2] : 0; - if (inputSize5 === -1) inputSize5 = 64; - return model6; -} -function replaceIrisCoords(rawCoords, newCoords, prefix, keys) { - for (let i = 0; i < irisIndices.length; i++) { - const { key, indices } = irisIndices[i]; - const originalIndices = meshAnnotations[`${prefix}${key}`]; - if (!keys || keys.includes(key)) { - for (let j = 0; j < indices.length; j++) { - const index2 = indices[j]; - rawCoords[originalIndices[j]] = [ - newCoords[index2][0], - newCoords[index2][1], - (newCoords[index2][2] + rawCoords[originalIndices[j]][2]) / 2 - ]; - } - } - } -} -var getLeftToRightEyeDepthDifference = (rawCoords) => { - const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2]; - const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2]; - return leftEyeZ - rightEyeZ; -}; -var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false, scale2 = 2.3) => { - const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), scale2)); - const boxSize = getBoxSize(box); - let crop = tf14.image.cropAndResize(face4, [[ - box.startPoint[1] / meshSize, - box.startPoint[0] / meshSize, - box.endPoint[1] / meshSize, - box.endPoint[0] / meshSize - ]], [0], [inputSize5, inputSize5]); - if (flip && env.kernels.includes("flipleftright")) { - const flipped = tf14.image.flipLeftRight(crop); - tf14.dispose(crop); - crop = flipped; - } - return { box, boxSize, crop }; -}; -var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => { - const eyeRawCoords = []; - for (let i = 0; i < irisLandmarks.numCoordinates; i++) { - const x = eyeData[i * 3]; - const y = eyeData[i * 3 + 1]; - const z = eyeData[i * 3 + 2]; - eyeRawCoords.push([ - (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0], - y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1], - z - ]); - } - return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) }; -}; -var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => { - const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2]; - const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2]; - const averageZ = (upperCenterZ + lowerCenterZ) / 2; - return irisCoords.map((coord, i) => { - let z = averageZ; - if (i === 2) { - z = upperCenterZ; - } else if (i === 4) { - z = lowerCenterZ; - } - return [coord[0], coord[1], z]; - }); -}; -async function augmentIris(rawCoords, face4, meshSize, config3) { - var _a, _b; - if (!(model6 == null ? void 0 : model6["executor"])) return rawCoords; - const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true, ((_a = config3.face.iris) == null ? void 0 : _a.scale) || 2.3); - const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true, ((_b = config3.face.iris) == null ? void 0 : _b.scale) || 2.3); - const combined = tf14.concat([leftEyeCrop, rightEyeCrop]); - tf14.dispose(leftEyeCrop); - tf14.dispose(rightEyeCrop); - const eyePredictions = model6.execute(combined); - tf14.dispose(combined); - const eyePredictionsData = await eyePredictions.data(); - tf14.dispose(eyePredictions); - const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3); - const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true); - const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3); - const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize, false); - const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords); - if (Math.abs(leftToRightEyeDepthDifference) < 30) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", null); - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", null); - } else if (leftToRightEyeDepthDifference < 1) { - replaceIrisCoords(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]); - } else { - replaceIrisCoords(rawCoords, rightEyeRawCoords, "right", ["EyeUpper0", "EyeLower0"]); - } - const adjustedLeftIrisCoords = getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, "left"); - const adjustedRightIrisCoords = getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, "right"); - const newCoords = rawCoords.concat(adjustedLeftIrisCoords).concat(adjustedRightIrisCoords); - return newCoords; -} - -// src/face/attention.ts -async function augment(rawCoords, results) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const t2 = { - // all attention models produce 2d results so it needs to be later augmented with correct z-coords - // mesh: results[0], // already have it in rawCoords // output_mesh_identity - // flag: results[1], // already processed in parent // conv_faceflag - lips: await ((_b = (_a = results.filter((r) => r.size === 160)) == null ? void 0 : _a[0]) == null ? void 0 : _b.data()), - // 80 x 2d = 160 // output_lips - irisL: await ((_d = (_c = results.filter((r) => r.size === 10)) == null ? void 0 : _c[0]) == null ? void 0 : _d.data()), - // 5 x 2d = 10 // output_right_iris - eyeL: await ((_f = (_e = results.filter((r) => r.size === 142)) == null ? void 0 : _e[0]) == null ? void 0 : _f.data()), - // 71 x 2d = 142 // output_right_eye - irisR: await ((_h = (_g = results.filter((r) => r.size === 10)) == null ? void 0 : _g[1]) == null ? void 0 : _h.data()), - // 5 x 2d = 10 // output_left_iris - eyeR: await ((_j = (_i = results.filter((r) => r.size === 142)) == null ? void 0 : _i[1]) == null ? void 0 : _j.data()) - // 71 x 2d = 142// output_left_eye - }; - for (const val of Object.values(t2)) { - if (!val) return rawCoords; - } - const irisLDepth = LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisL.length / 2; i++) rawCoords.push([t2.irisL[2 * i + 0], t2.irisL[2 * i + 1], irisLDepth]); - const irisRDepth = LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.reduce((prev, curr) => prev += rawCoords[curr][2], 0) / LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG.length; - for (let i = 0; i < t2.irisR.length / 2; i++) rawCoords.push([t2.irisR[2 * i + 0], t2.irisR[2 * i + 1], irisRDepth]); - for (let i = 0; i < t2.eyeL.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]] = [t2.eyeL[2 * i + 0], t2.eyeL[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LEFT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.eyeR.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]] = [t2.eyeR[2 * i + 0], t2.eyeR[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_RIGHT_EYE_CONFIG[i]][2]]; - for (let i = 0; i < t2.lips.length / 2; i++) rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]] = [t2.lips[2 * i + 0], t2.lips[2 * i + 1], rawCoords[LANDMARKS_REFINEMENT_LIPS_CONFIG[i]][2]]; - return rawCoords; -} - -// src/face/facemesh.ts -var cache3 = { - boxes: [], - skipped: Number.MAX_SAFE_INTEGER, - timestamp: 0 -}; -var model7 = null; -var inputSize6 = 0; -async function predict4(input, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j; - const skipTime = (((_a = config3.face.detector) == null ? void 0 : _a.skipTime) || 0) > now() - cache3.timestamp; - const skipFrame = cache3.skipped < (((_b = config3.face.detector) == null ? void 0 : _b.skipFrames) || 0); - if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) { - cache3.boxes = await getBoxes(input, config3); - cache3.timestamp = now(); - cache3.skipped = 0; - } else { - cache3.skipped++; - } - const faces = []; - const newCache = []; - let id = 0; - const size2 = inputSize6; - for (let i = 0; i < cache3.boxes.length; i++) { - const box = cache3.boxes[i]; - let angle = 0; - let rotationMatrix; - const face4 = { - // init face result - id: id++, - mesh: [], - meshRaw: [], - box: [0, 0, 0, 0], - boxRaw: [0, 0, 0, 0], - score: 0, - boxScore: 0, - faceScore: 0, - size: [0, 0], - // contoursRaw: [], - // contours: [], - annotations: {} - }; - [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d = config3.face.mesh) == null ? void 0 : _d.enabled) ? inputSize6 : size()); - if (config3.filter.equalization) { - const equilized = face4.tensor ? await histogramEqualization(face4.tensor) : void 0; - tf15.dispose(face4.tensor); - if (equilized) face4.tensor = equilized; - } - face4.boxScore = Math.round(100 * box.confidence) / 100; - if (!((_e = config3.face.mesh) == null ? void 0 : _e.enabled) || !(model7 == null ? void 0 : model7["executor"])) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.score = face4.boxScore; - face4.size = box.size; - face4.mesh = box.landmarks; - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } else if (!model7) { - if (config3.debug) log("face mesh detection requested, but model is not loaded"); - } else { - if (((_f = config3.face.attention) == null ? void 0 : _f.enabled) && !env.kernels.includes("atan2")) { - config3.face.attention.enabled = false; - tf15.dispose(face4.tensor); - return faces; - } - const results = model7.execute(face4.tensor); - const confidenceT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1); - const faceConfidence = await confidenceT.data(); - face4.faceScore = Math.round(100 * faceConfidence[0]) / 100; - if (face4.faceScore < (((_g = config3.face.detector) == null ? void 0 : _g.minConfidence) || 1)) { - box.confidence = face4.faceScore; - if (config3.face.mesh["keepInvalid"]) { - face4.box = clampBox(box, input); - face4.boxRaw = getRawBox(box, input); - face4.size = box.size; - face4.score = face4.boxScore; - face4.mesh = box.landmarks; - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 1), pt[1] / (input.shape[1] || 1), (pt[2] || 0) / size2]); - for (const key of Object.keys(blazeFaceLandmarks)) { - face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]]; - } - } - } else { - const meshT = results.find((t2) => t2.shape[t2.shape.length - 1] === 1404); - const coordsReshaped = tf15.reshape(meshT, [-1, 3]); - let rawCoords = await coordsReshaped.array(); - tf15.dispose(coordsReshaped); - if ((_h = config3.face.attention) == null ? void 0 : _h.enabled) { - rawCoords = await augment(rawCoords, results); - } else if ((_i = config3.face.iris) == null ? void 0 : _i.enabled) { - rawCoords = await augmentIris(rawCoords, face4.tensor, inputSize6, config3); - } - face4.mesh = transformRawCoords(rawCoords, box, angle, rotationMatrix, inputSize6); - face4.meshRaw = face4.mesh.map((pt) => [pt[0] / (input.shape[2] || 0), pt[1] / (input.shape[1] || 0), (pt[2] || 0) / size2]); - for (const key of Object.keys(meshAnnotations)) face4.annotations[key] = meshAnnotations[key].map((index2) => face4.mesh[index2]); - face4.score = face4.faceScore; - const calculatedBox = { - ...calculateFaceBox(face4.mesh, box), - confidence: box.confidence, - landmarks: box.landmarks, - size: box.size - }; - face4.box = clampBox(calculatedBox, input); - face4.boxRaw = getRawBox(calculatedBox, input); - face4.size = calculatedBox.size; - newCache.push(calculatedBox); - } - tf15.dispose(results); - } - if (face4.score > (((_j = config3.face.detector) == null ? void 0 : _j.minConfidence) || 1)) faces.push(face4); - else tf15.dispose(face4.tensor); - } - cache3.boxes = newCache; - return faces; -} -async function load5(config3) { - var _a, _b, _c, _d, _e, _f; - if (env.initial) model7 = null; - if (((_a = config3.face.attention) == null ? void 0 : _a.enabled) && (model7 == null ? void 0 : model7["signature"])) { - if (Object.keys(((_b = model7 == null ? void 0 : model7["signature"]) == null ? void 0 : _b.outputs) || {}).length < 6) model7 = null; - } - if (!model7) { - if ((_c = config3.face.attention) == null ? void 0 : _c.enabled) model7 = await loadModel(config3.face.attention.modelPath); - else model7 = await loadModel((_d = config3.face.mesh) == null ? void 0 : _d.modelPath); - } else if (config3.debug) { - log("cached model:", model7["modelUrl"]); - } - inputSize6 = model7["executor"] && ((_e = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _e[0].shape) ? (_f = model7 == null ? void 0 : model7.inputs) == null ? void 0 : _f[0].shape[2] : 256; - return model7; -} -var triangulation = TRI468; -var uvmap = UV468; - -// src/gear/emotion.ts -var tf16 = __toESM(require_tfjs_esm()); -var annotations = []; -var model8; -var last3 = []; -var lastCount = 0; -var lastTime4 = 0; -var skipped4 = Number.MAX_SAFE_INTEGER; -var rgb = false; -async function load6(config3) { - var _a, _b, _c; - if (env.initial) model8 = null; - if (!model8) { - model8 = await loadModel((_a = config3.face.emotion) == null ? void 0 : _a.modelPath); - rgb = ((_c = (_b = model8 == null ? void 0 : model8.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[3]) === 3; - if (!rgb) annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]; - else annotations = ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprise"]; - } else if (config3.debug) { - log("cached model:", model8["modelUrl"]); - } - return model8; -} -async function predict5(image28, config3, idx, count2) { - var _a, _b; - if (!model8) return []; - const skipFrame = skipped4 < (((_a = config3.face.emotion) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.emotion) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime4; - if (config3.skipAllowed && skipTime && skipFrame && lastCount === count2 && last3[idx] && last3[idx].length > 0) { - skipped4++; - return last3[idx]; - } - skipped4 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c; - const obj = []; - if ((_a2 = config3.face.emotion) == null ? void 0 : _a2.enabled) { - const t2 = {}; - const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0; - if (((_b2 = config3.face.emotion) == null ? void 0 : _b2["crop"]) > 0) { - const crop = (_c = config3.face.emotion) == null ? void 0 : _c["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tf16.image.cropAndResize(image28, box, [0], [inputSize10, inputSize10]); - } else { - t2.resize = tf16.image.resizeBilinear(image28, [inputSize10, inputSize10], false); - } - if (rgb) { - t2.mul = tf16.mul(t2.resize, 255); - t2.normalize = tf16.sub(t2.mul, [103.939, 116.779, 123.68]); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.normalize); - } else { - t2.channels = tf16.mul(t2.resize, constants.rgb); - t2.grayscale = tf16.sum(t2.channels, 3, true); - t2.grayscaleSub = tf16.sub(t2.grayscale, constants.tf05); - t2.grayscaleMul = tf16.mul(t2.grayscaleSub, constants.tf2); - t2.emotion = model8 == null ? void 0 : model8.execute(t2.grayscaleMul); - } - lastTime4 = now(); - const data = await t2.emotion.data(); - for (let i = 0; i < data.length; i++) { - if (data[i] > (config3.face.emotion.minConfidence || 0)) obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] }); - } - obj.sort((a, b) => b.score - a.score); - Object.keys(t2).forEach((tensor6) => tf16.dispose(t2[tensor6])); - } - last3[idx] = obj; - lastCount = count2; - resolve(obj); - }); -} - -// src/face/faceres.ts -var tf17 = __toESM(require_tfjs_esm()); -var model9; -var last4 = []; -var lastTime5 = 0; -var lastCount2 = 0; -var skipped5 = Number.MAX_SAFE_INTEGER; -async function load7(config3) { - var _a; - if (env.initial) model9 = null; - if (!model9) model9 = await loadModel((_a = config3.face.description) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model9["modelUrl"]); - return model9; -} -function enhance(input, config3) { - var _a, _b; - const tensor6 = input.image || input.tensor || input; - if (!(model9 == null ? void 0 : model9.inputs[0].shape)) return tensor6; - let crop; - if (((_a = config3.face.description) == null ? void 0 : _a["crop"]) > 0) { - const cropval = (_b = config3.face.description) == null ? void 0 : _b["crop"]; - const box = [[cropval, cropval, 1 - cropval, 1 - cropval]]; - crop = tf17.image.cropAndResize(tensor6, box, [0], [model9.inputs[0].shape[2], model9.inputs[0].shape[1]]); - } else { - crop = tf17.image.resizeBilinear(tensor6, [model9.inputs[0].shape[2], model9.inputs[0].shape[1]], false); - } - const norm = tf17.mul(crop, constants.tf255); - tf17.dispose(crop); - return norm; -} -async function predict6(image28, config3, idx, count2) { - var _a, _b, _c, _d; - const obj = { - age: 0, - gender: "unknown", - genderScore: 0, - descriptor: [] - }; - if (!(model9 == null ? void 0 : model9["executor"])) return obj; - const skipFrame = skipped5 < (((_a = config3.face.description) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.description) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime5; - if (config3.skipAllowed && skipFrame && skipTime && lastCount2 === count2 && ((_c = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _c.age) > 0 && ((_d = last4 == null ? void 0 : last4[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped5++; - return last4[idx]; - } - skipped5 = 0; - return new Promise(async (resolve) => { - var _a2; - if ((_a2 = config3.face.description) == null ? void 0 : _a2.enabled) { - const enhanced = enhance(image28, config3); - const resT = model9 == null ? void 0 : model9.execute(enhanced); - lastTime5 = now(); - tf17.dispose(enhanced); - const genderT = resT.find((t2) => t2.shape[1] === 1); - const gender2 = await genderT.data(); - const confidence = Math.trunc(200 * Math.abs(gender2[0] - 0.5)) / 100; - if (confidence > (config3.face.description.minConfidence || 0)) { - obj.gender = gender2[0] <= 0.5 ? "female" : "male"; - obj.genderScore = Math.min(0.99, confidence); - } - const argmax = tf17.argMax(resT.find((t2) => t2.shape[1] === 100), 1); - const ageIdx = (await argmax.data())[0]; - tf17.dispose(argmax); - const ageT = resT.find((t2) => t2.shape[1] === 100); - const all2 = await ageT.data(); - obj.age = Math.round(all2[ageIdx - 1] > all2[ageIdx + 1] ? 10 * ageIdx - 100 * all2[ageIdx - 1] : 10 * ageIdx + 100 * all2[ageIdx + 1]) / 10; - if (Number.isNaN(gender2[0]) || Number.isNaN(all2[0])) log("faceres error:", { model: model9, result: resT }); - const desc = resT.find((t2) => t2.shape[1] === 1024); - const descriptor = desc ? await desc.data() : []; - obj.descriptor = Array.from(descriptor); - resT.forEach((t2) => tf17.dispose(t2)); - } - last4[idx] = obj; - lastCount2 = count2; - resolve(obj); - }); -} - -// src/face/mask.ts -var expandFact = 0.1; -var alpha = 0.5; -function insidePoly(x, y, polygon) { - let inside = false; - let j = polygon.length - 1; - for (let i = 0; i < polygon.length; j = i++) { - if (polygon[i].y > y !== polygon[j].y > y && x < (polygon[j].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j].y - polygon[i].y) + polygon[i].x) inside = !inside; - } - return inside; -} -async function mask(face4) { - if (!face4.tensor) return face4.tensor; - if (!face4.mesh || face4.mesh.length < 100) return face4.tensor; - const width = face4.tensor.shape[2] || 0; - const height = face4.tensor.shape[1] || 0; - const buffer = await face4.tensor.buffer(); - let silhouette = []; - for (const pt of meshAnnotations.silhouette) silhouette.push({ x: (face4.mesh[pt][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt][1] - face4.box[1]) / face4.box[3] }); - if (expandFact && expandFact > 0) silhouette = silhouette.map((pt) => ({ x: pt.x > 0.5 ? pt.x + expandFact : pt.x - expandFact, y: pt.y > 0.5 ? pt.y + expandFact : pt.y - expandFact })); - for (let x = 0; x < width; x++) { - for (let y = 0; y < height; y++) { - const inside = insidePoly(x / width, y / width, silhouette); - if (!inside) { - buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0); - buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1); - buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2); - } - } - } - const output = buffer.toTensor(); - return output; -} - -// src/face/antispoof.ts -var tf18 = __toESM(require_tfjs_esm()); -var model10; -var cached = []; -var skipped6 = Number.MAX_SAFE_INTEGER; -var lastCount3 = 0; -var lastTime6 = 0; -async function load8(config3) { - var _a; - if (env.initial) model10 = null; - if (!model10) model10 = await loadModel((_a = config3.face.antispoof) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model10["modelUrl"]); - return model10; -} -async function predict7(image28, config3, idx, count2) { - var _a, _b; - if (!(model10 == null ? void 0 : model10["executor"])) return 0; - const skipTime = (((_a = config3.face.antispoof) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime6; - const skipFrame = skipped6 < (((_b = config3.face.antispoof) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount3 === count2 && cached[idx]) { - skipped6++; - return cached[idx]; - } - skipped6 = 0; - return new Promise(async (resolve) => { - const resize = tf18.image.resizeBilinear(image28, [(model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[2] : 0, (model10 == null ? void 0 : model10.inputs[0].shape) ? model10.inputs[0].shape[1] : 0], false); - const res = model10 == null ? void 0 : model10.execute(resize); - const num = (await res.data())[0]; - cached[idx] = Math.round(100 * num) / 100; - lastCount3 = count2; - lastTime6 = now(); - tf18.dispose([resize, res]); - resolve(cached[idx]); - }); -} - -// src/face/liveness.ts -var tf19 = __toESM(require_tfjs_esm()); -var model11; -var cached2 = []; -var skipped7 = Number.MAX_SAFE_INTEGER; -var lastCount4 = 0; -var lastTime7 = 0; -async function load9(config3) { - var _a; - if (env.initial) model11 = null; - if (!model11) model11 = await loadModel((_a = config3.face.liveness) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model11["modelUrl"]); - return model11; -} -async function predict8(image28, config3, idx, count2) { - var _a, _b; - if (!(model11 == null ? void 0 : model11["executor"])) return 0; - const skipTime = (((_a = config3.face.liveness) == null ? void 0 : _a.skipTime) || 0) > now() - lastTime7; - const skipFrame = skipped7 < (((_b = config3.face.liveness) == null ? void 0 : _b.skipFrames) || 0); - if (config3.skipAllowed && skipTime && skipFrame && lastCount4 === count2 && cached2[idx]) { - skipped7++; - return cached2[idx]; - } - skipped7 = 0; - return new Promise(async (resolve) => { - const resize = tf19.image.resizeBilinear(image28, [(model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[2] : 0, (model11 == null ? void 0 : model11.inputs[0].shape) ? model11.inputs[0].shape[1] : 0], false); - const res = model11 == null ? void 0 : model11.execute(resize); - const num = (await res.data())[0]; - cached2[idx] = Math.round(100 * num) / 100; - lastCount4 = count2; - lastTime7 = now(); - tf19.dispose([resize, res]); - resolve(cached2[idx]); - }); -} - -// src/gear/gear.ts -var tf20 = __toESM(require_tfjs_esm()); -var model12; -var last5 = []; -var raceNames = ["white", "black", "asian", "indian", "other"]; -var ageWeights = [15, 23, 28, 35.5, 45.5, 55.5, 65]; -var lastCount5 = 0; -var lastTime8 = 0; -var skipped8 = Number.MAX_SAFE_INTEGER; -async function load10(config3) { - var _a; - if (env.initial) model12 = null; - if (!model12) model12 = await loadModel((_a = config3.face.gear) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model12["modelUrl"]); - return model12; -} -async function predict9(image28, config3, idx, count2) { - var _a, _b; - if (!model12) return { age: 0, gender: "unknown", genderScore: 0, race: [] }; - const skipFrame = skipped8 < (((_a = config3.face.gear) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face.gear) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime8; - if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last5[idx]) { - skipped8++; - return last5[idx]; - } - skipped8 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c, _d; - if (!(model12 == null ? void 0 : model12.inputs[0].shape)) return; - const t2 = {}; - let box = [[0, 0.1, 0.9, 0.9]]; - if (((_a2 = config3.face.gear) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face.gear) == null ? void 0 : _b2["crop"]; - box = [[crop, crop, 1 - crop, 1 - crop]]; - } - t2.resize = tf20.image.cropAndResize(image28, box, [0], [model12.inputs[0].shape[2], model12.inputs[0].shape[1]]); - const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] }; - if ((_c = config3.face.gear) == null ? void 0 : _c.enabled) [t2.age, t2.gender, t2.race] = model12.execute(t2.resize, ["age_output", "gender_output", "race_output"]); - const gender2 = await t2.gender.data(); - obj.gender = gender2[0] > gender2[1] ? "male" : "female"; - obj.genderScore = Math.round(100 * (gender2[0] > gender2[1] ? gender2[0] : gender2[1])) / 100; - const race = await t2.race.data(); - for (let i = 0; i < race.length; i++) { - if (race[i] > (((_d = config3.face.gear) == null ? void 0 : _d.minConfidence) || 0.2)) obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] }); - } - obj.race.sort((a, b) => b.score - a.score); - const ageDistribution = Array.from(await t2.age.data()); - const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]); - let age2 = ageSorted[0][0]; - for (let i = 1; i < ageSorted.length; i++) age2 += ageSorted[i][1] * (ageSorted[i][0] - age2); - obj.age = Math.round(10 * age2) / 10; - Object.keys(t2).forEach((tensor6) => tf20.dispose(t2[tensor6])); - last5[idx] = obj; - lastCount5 = count2; - lastTime8 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-age.ts -var tf21 = __toESM(require_tfjs_esm()); -var model13; -var last6 = []; -var lastCount6 = 0; -var lastTime9 = 0; -var skipped9 = Number.MAX_SAFE_INTEGER; -async function load11(config3) { - if (env.initial) model13 = null; - if (!model13) model13 = await loadModel(config3.face["ssrnet"].modelPathAge); - else if (config3.debug) log("cached model:", model13["modelUrl"]); - return model13; -} -async function predict10(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model13) return { age: 0 }; - const skipFrame = skipped9 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime9; - if (config3.skipAllowed && skipFrame && skipTime && lastCount6 === count2 && ((_c = last6[idx]) == null ? void 0 : _c.age) && ((_d = last6[idx]) == null ? void 0 : _d.age) > 0) { - skipped9++; - return last6[idx]; - } - skipped9 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c2; - if (!(model13 == null ? void 0 : model13.inputs) || !model13.inputs[0] || !model13.inputs[0].shape) return; - const t2 = {}; - if (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tf21.image.cropAndResize(image28, box, [0], [model13.inputs[0].shape[2], model13.inputs[0].shape[1]]); - } else { - t2.resize = tf21.image.resizeBilinear(image28, [model13.inputs[0].shape[2], model13.inputs[0].shape[1]], false); - } - t2.enhance = tf21.mul(t2.resize, constants.tf255); - const obj = { age: 0 }; - if ((_c2 = config3.face["ssrnet"]) == null ? void 0 : _c2.enabled) t2.age = model13.execute(t2.enhance); - if (t2.age) { - const data = await t2.age.data(); - obj.age = Math.trunc(10 * data[0]) / 10; - } - Object.keys(t2).forEach((tensor6) => tf21.dispose(t2[tensor6])); - last6[idx] = obj; - lastCount6 = count2; - lastTime9 = now(); - resolve(obj); - }); -} - -// src/gear/ssrnet-gender.ts -var tf22 = __toESM(require_tfjs_esm()); -var model14; -var last7 = []; -var lastCount7 = 0; -var lastTime10 = 0; -var skipped10 = Number.MAX_SAFE_INTEGER; -var rgb2 = [0.2989, 0.587, 0.114]; -async function load12(config3) { - var _a; - if (env.initial) model14 = null; - if (!model14) model14 = await loadModel((_a = config3.face["ssrnet"]) == null ? void 0 : _a.modelPathGender); - else if (config3.debug) log("cached model:", model14["modelUrl"]); - return model14; -} -async function predict11(image28, config3, idx, count2) { - var _a, _b, _c, _d; - if (!model14) return { gender: "unknown", genderScore: 0 }; - const skipFrame = skipped10 < (((_a = config3.face["ssrnet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["ssrnet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime10; - if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last7[idx]) == null ? void 0 : _c.gender) && ((_d = last7[idx]) == null ? void 0 : _d.genderScore) > 0) { - skipped10++; - return last7[idx]; - } - skipped10 = 0; - return new Promise(async (resolve) => { - var _a2, _b2, _c2; - if (!(model14 == null ? void 0 : model14.inputs[0].shape)) return; - const t2 = {}; - if (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2["crop"]) > 0) { - const crop = (_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2["crop"]; - const box = [[crop, crop, 1 - crop, 1 - crop]]; - t2.resize = tf22.image.cropAndResize(image28, box, [0], [model14.inputs[0].shape[2], model14.inputs[0].shape[1]]); - } else { - t2.resize = tf22.image.resizeBilinear(image28, [model14.inputs[0].shape[2], model14.inputs[0].shape[1]], false); - } - t2.enhance = tf22.tidy(() => { - var _a3, _b3; - let normalize2; - if (((_b3 = (_a3 = model14 == null ? void 0 : model14.inputs) == null ? void 0 : _a3[0].shape) == null ? void 0 : _b3[3]) === 1) { - const [red, green, blue] = tf22.split(t2.resize, 3, 3); - const redNorm = tf22.mul(red, rgb2[0]); - const greenNorm = tf22.mul(green, rgb2[1]); - const blueNorm = tf22.mul(blue, rgb2[2]); - const grayscale = tf22.addN([redNorm, greenNorm, blueNorm]); - normalize2 = tf22.mul(tf22.sub(grayscale, constants.tf05), 2); - } else { - normalize2 = tf22.mul(tf22.sub(t2.resize, constants.tf05), 2); - } - return normalize2; - }); - const obj = { gender: "unknown", genderScore: 0 }; - if ((_c2 = config3.face["ssrnet"]) == null ? void 0 : _c2.enabled) t2.gender = model14.execute(t2.enhance); - const data = await t2.gender.data(); - obj.gender = data[0] > data[1] ? "female" : "male"; - obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100; - Object.keys(t2).forEach((tensor6) => tf22.dispose(t2[tensor6])); - last7[idx] = obj; - lastCount7 = count2; - lastTime10 = now(); - resolve(obj); - }); -} - -// src/face/mobilefacenet.ts -var tf23 = __toESM(require_tfjs_esm()); -var model15; -var last8 = []; -var lastCount8 = 0; -var lastTime11 = 0; -var skipped11 = Number.MAX_SAFE_INTEGER; -async function load13(config3) { - var _a; - if (env.initial) model15 = null; - if (!model15) model15 = await loadModel((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", model15["modelUrl"]); - return model15; -} -async function predict12(input, config3, idx, count2) { - var _a, _b; - if (!(model15 == null ? void 0 : model15["executor"])) return []; - const skipFrame = skipped11 < (((_a = config3.face["mobilefacenet"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["mobilefacenet"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime11; - if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && last8[idx]) { - skipped11++; - return last8[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["mobilefacenet"]) == null ? void 0 : _a2.enabled) && (model15 == null ? void 0 : model15.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf23.image.resizeBilinear(input, [model15.inputs[0].shape[2], model15.inputs[0].shape[1]], false); - t2.data = model15.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf23.dispose(t2[tensor6])); - } - last8[idx] = data; - lastCount8 = count2; - lastTime11 = now(); - resolve(data); - }); -} - -// src/face/insightface.ts -var tf24 = __toESM(require_tfjs_esm()); -var model16; -var last9 = []; -var lastCount9 = 0; -var lastTime12 = 0; -var skipped12 = Number.MAX_SAFE_INTEGER; -async function load14(config3) { - if (env.initial) model16 = null; - if (!model16) model16 = await loadModel(config3.face["insightface"].modelPath); - else if (config3.debug) log("cached model:", model16["modelUrl"]); - return model16; -} -async function predict13(input, config3, idx, count2) { - var _a, _b; - if (!(model16 == null ? void 0 : model16["executor"])) return []; - const skipFrame = skipped12 < (((_a = config3.face["insightface"]) == null ? void 0 : _a.skipFrames) || 0); - const skipTime = (((_b = config3.face["insightface"]) == null ? void 0 : _b.skipTime) || 0) > now() - lastTime12; - if (config3.skipAllowed && skipTime && skipFrame && lastCount9 === count2 && last9[idx]) { - skipped12++; - return last9[idx]; - } - return new Promise(async (resolve) => { - var _a2; - let data = []; - if (((_a2 = config3.face["insightface"]) == null ? void 0 : _a2.enabled) && (model16 == null ? void 0 : model16.inputs[0].shape)) { - const t2 = {}; - t2.crop = tf24.image.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]], false); - t2.data = model16.execute(t2.crop); - const output = await t2.data.data(); - data = Array.from(output); - Object.keys(t2).forEach((tensor6) => tf24.dispose(t2[tensor6])); - } - last9[idx] = data; - lastCount9 = count2; - lastTime12 = now(); - resolve(data); - }); -} - -// src/face/angles.ts -var calculateGaze = (face4) => { - const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]); - if (!face4.annotations.rightEyeIris || !face4.annotations.leftEyeIris) return { bearing: 0, strength: 0 }; - const offsetIris = [0, -0.1]; - const eyeRatio = 1; - const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0); - const irisCenter = left ? face4.mesh[473] : face4.mesh[468]; - const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2]; - const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]]; - const eyeDiff = [ - // x distance between extreme point and center point normalized with eye size - (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0], - eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1] - ]; - let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]); - strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2); - const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI; - return { bearing, strength }; -}; -var calculateFaceAngle = (face4, imageSize) => { - const normalize2 = (v) => { - const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]); - v[0] /= length; - v[1] /= length; - v[2] /= length; - return v; - }; - const subVectors = (a, b) => { - const x = a[0] - b[0]; - const y = a[1] - b[1]; - const z = a[2] - b[2]; - return [x, y, z]; - }; - const crossVectors = (a, b) => { - const x = a[1] * b[2] - a[2] * b[1]; - const y = a[2] * b[0] - a[0] * b[2]; - const z = a[0] * b[1] - a[1] * b[0]; - return [x, y, z]; - }; - const rotationMatrixToEulerAngle = (r) => { - const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r; - let thetaX; - let thetaY; - let thetaZ; - if (r10 < 1) { - if (r10 > -1) { - const cosThetaZ = Math.sqrt(r00 * r00 + r20 * r20); - thetaZ = Math.atan2(r10, cosThetaZ); - thetaY = Math.atan2(-r20, r00); - thetaX = Math.atan2(-r12, r11); - } else { - thetaZ = -Math.PI / 2; - thetaY = -Math.atan2(r21, r22); - thetaX = 0; - } - } else { - thetaZ = Math.PI / 2; - thetaY = Math.atan2(r21, r22); - thetaX = 0; - } - if (Number.isNaN(thetaX)) thetaX = 0; - if (Number.isNaN(thetaY)) thetaY = 0; - if (Number.isNaN(thetaZ)) thetaZ = 0; - return { pitch: -thetaX, yaw: -thetaY, roll: -thetaZ }; - }; - const mesh = face4.meshRaw; - if (!mesh || mesh.length < 300) return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } }; - const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5; - const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt) => [pt[0] * imageSize[0] / size2, pt[1] * imageSize[1] / size2, pt[2]]); - const yAxis = normalize2(subVectors(pts[1], pts[0])); - let xAxis = normalize2(subVectors(pts[3], pts[2])); - const zAxis = normalize2(crossVectors(xAxis, yAxis)); - xAxis = crossVectors(yAxis, zAxis); - const matrix = [ - xAxis[0], - xAxis[1], - xAxis[2], - yAxis[0], - yAxis[1], - yAxis[2], - zAxis[0], - zAxis[1], - zAxis[2] - ]; - const angle = rotationMatrixToEulerAngle(matrix); - const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 }; - return { angle, matrix, gaze }; -}; - -// src/face/anthropometry.ts -function calculateCameraDistance(face4, width) { - const f = face4 == null ? void 0 : face4.annotations; - if (!(f == null ? void 0 : f.leftEyeIris) || !(f == null ? void 0 : f.rightEyeIris)) return 0; - const irisSize = Math.max(Math.abs(f.leftEyeIris[3][0] - f.leftEyeIris[1][0]), Math.abs(f.rightEyeIris[3][0] - f.rightEyeIris[1][0])) / width; - const cameraDistance = Math.round(1.17 / irisSize) / 100; - return cameraDistance; -} - -// src/face/face.ts -var detectFace = async (instance, input) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w; - let timeStamp = now(); - let ageRes; - let gearRes; - let genderRes; - let emotionRes; - let mobilefacenetRes; - let insightfaceRes; - let antispoofRes; - let livenessRes; - let descRes; - const faceRes = []; - instance.state = "run:face"; - const faces = await predict4(input, instance.config); - instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - if (!input.shape || input.shape.length !== 4) return []; - if (!faces) return []; - for (let i = 0; i < faces.length; i++) { - instance.analyze("Get Face"); - if (!faces[i].tensor || faces[i].tensor.isDisposedInternal) { - log("Face object is disposed:", faces[i].tensor); - continue; - } - if ((_a = instance.config.face.detector) == null ? void 0 : _a.mask) { - const masked = await mask(faces[i]); - tf25.dispose(faces[i].tensor); - if (masked) faces[i].tensor = masked; - } - const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null; - instance.analyze("Start Emotion:"); - if (instance.config.async) { - emotionRes = ((_b = instance.config.face.emotion) == null ? void 0 : _b.enabled) ? predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - } else { - instance.state = "run:emotion"; - timeStamp = now(); - emotionRes = ((_c = instance.config.face.emotion) == null ? void 0 : _c.enabled) ? await predict5(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : []; - instance.performance.emotion = env.perfadd ? (instance.performance.emotion || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Emotion:"); - instance.analyze("Start AntiSpoof:"); - if (instance.config.async) { - antispoofRes = ((_d = instance.config.face.antispoof) == null ? void 0 : _d.enabled) ? predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:antispoof"; - timeStamp = now(); - antispoofRes = ((_e = instance.config.face.antispoof) == null ? void 0 : _e.enabled) ? await predict7(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.antispoof = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End AntiSpoof:"); - instance.analyze("Start Liveness:"); - if (instance.config.async) { - livenessRes = ((_f = instance.config.face.liveness) == null ? void 0 : _f.enabled) ? predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - } else { - instance.state = "run:liveness"; - timeStamp = now(); - livenessRes = ((_g = instance.config.face.liveness) == null ? void 0 : _g.enabled) ? await predict8(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : 0; - instance.performance.liveness = env.perfadd ? (instance.performance.antispoof || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Liveness:"); - instance.analyze("Start GEAR:"); - if (instance.config.async) { - gearRes = ((_h = instance.config.face.gear) == null ? void 0 : _h.enabled) ? predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:gear"; - timeStamp = now(); - gearRes = ((_i = instance.config.face.gear) == null ? void 0 : _i.enabled) ? await predict9(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.gear = Math.trunc(now() - timeStamp); - } - instance.analyze("End GEAR:"); - instance.analyze("Start SSRNet:"); - if (instance.config.async) { - ageRes = ((_j = instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) ? predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_k = instance.config.face["ssrnet"]) == null ? void 0 : _k.enabled) ? predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:ssrnet"; - timeStamp = now(); - ageRes = ((_l = instance.config.face["ssrnet"]) == null ? void 0 : _l.enabled) ? await predict10(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - genderRes = ((_m = instance.config.face["ssrnet"]) == null ? void 0 : _m.enabled) ? await predict11(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.ssrnet = Math.trunc(now() - timeStamp); - } - instance.analyze("End SSRNet:"); - instance.analyze("Start MobileFaceNet:"); - if (instance.config.async) { - mobilefacenetRes = ((_n = instance.config.face["mobilefacenet"]) == null ? void 0 : _n.enabled) ? predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - mobilefacenetRes = ((_o = instance.config.face["mobilefacenet"]) == null ? void 0 : _o.enabled) ? await predict12(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End MobileFaceNet:"); - instance.analyze("Start InsightFace:"); - if (instance.config.async) { - insightfaceRes = ((_p = instance.config.face["insightface"]) == null ? void 0 : _p.enabled) ? predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - } else { - instance.state = "run:mobilefacenet"; - timeStamp = now(); - insightfaceRes = ((_q = instance.config.face["insightface"]) == null ? void 0 : _q.enabled) ? await predict13(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length) : null; - instance.performance.mobilefacenet = Math.trunc(now() - timeStamp); - } - instance.analyze("End InsightFace:"); - instance.analyze("Start Description:"); - if (instance.config.async) { - descRes = predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - } else { - instance.state = "run:description"; - timeStamp = now(); - descRes = await predict6(faces[i].tensor || tf25.tensor([]), instance.config, i, faces.length); - instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - instance.analyze("End Description:"); - if (instance.config.async) { - [ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, insightfaceRes, descRes, gearRes, antispoofRes, livenessRes]); - } - instance.analyze("Finish Face:"); - if (((_r = instance.config.face["ssrnet"]) == null ? void 0 : _r.enabled) && ageRes && genderRes) { - descRes = { - ...descRes, - age: ageRes.age, - gender: genderRes.gender, - genderScore: genderRes.genderScore - }; - } - if (((_s = instance.config.face.gear) == null ? void 0 : _s.enabled) && gearRes) { - descRes = { - ...descRes, - age: gearRes.age, - gender: gearRes.gender, - genderScore: gearRes.genderScore, - race: gearRes.race - }; - } - if (((_t = instance.config.face["mobilefacenet"]) == null ? void 0 : _t.enabled) && mobilefacenetRes) { - descRes.descriptor = mobilefacenetRes; - } - if (((_u = instance.config.face["insightface"]) == null ? void 0 : _u.enabled) && insightfaceRes) { - descRes.descriptor = insightfaceRes; - } - const irisSize = ((_v = instance.config.face.iris) == null ? void 0 : _v.enabled) ? calculateCameraDistance(faces[i], input.shape[2]) : 0; - const tensor6 = ((_w = instance.config.face.detector) == null ? void 0 : _w.return) ? tf25.squeeze(faces[i].tensor) : null; - tf25.dispose(faces[i].tensor); - if (faces[i].tensor) delete faces[i].tensor; - const res = { - ...faces[i], - id: i - }; - if (descRes.age) res.age = descRes.age; - if (descRes.gender) res.gender = descRes.gender; - if (descRes.genderScore) res.genderScore = descRes.genderScore; - if (descRes.descriptor) res.embedding = descRes.descriptor; - if (descRes.race) res.race = descRes.race; - if (emotionRes) res.emotion = emotionRes; - if (antispoofRes) res.real = antispoofRes; - if (livenessRes) res.live = livenessRes; - if (irisSize > 0) res.distance = irisSize; - if (rotation) res.rotation = rotation; - if (tensor6) res.tensor = tensor6; - faceRes.push(res); - instance.analyze("End Face"); - } - instance.analyze("End FaceMesh:"); - if (instance.config.async) { - if (instance.performance.face) delete instance.performance.face; - if (instance.performance.age) delete instance.performance.age; - if (instance.performance.gender) delete instance.performance.gender; - if (instance.performance.emotion) delete instance.performance.emotion; - } - return faceRes; -}; - -// src/hand/fingerdef.ts -var Finger = { - thumb: 0, - index: 1, - middle: 2, - ring: 3, - pinky: 4, - all: [0, 1, 2, 3, 4], - // just for convenience - nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" }, - // Describes mapping of joints based on the 21 points returned by handpose. - // [0] Palm - // [1-4] Thumb - // [5-8] Index - // [9-12] Middle - // [13-16] Ring - // [17-20] Pinky - pointsMapping: { - 0: [[0, 1], [1, 2], [2, 3], [3, 4]], - 1: [[0, 5], [5, 6], [6, 7], [7, 8]], - 2: [[0, 9], [9, 10], [10, 11], [11, 12]], - 3: [[0, 13], [13, 14], [14, 15], [15, 16]], - 4: [[0, 17], [17, 18], [18, 19], [19, 20]] - }, - getName: (value) => Finger.nameMapping[value], - getPoints: (value) => Finger.pointsMapping[value] -}; -var FingerCurl = { - none: 0, - half: 1, - full: 2, - nameMapping: { 0: "none", 1: "half", 2: "full" }, - getName: (value) => FingerCurl.nameMapping[value] -}; -var FingerDirection = { - verticalUp: 0, - verticalDown: 1, - horizontalLeft: 2, - horizontalRight: 3, - diagonalUpRight: 4, - diagonalUpLeft: 5, - diagonalDownRight: 6, - diagonalDownLeft: 7, - nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" }, - getName: (value) => FingerDirection.nameMapping[value] -}; -var FingerGesture = class { - constructor(name) { - __publicField(this, "name"); - __publicField(this, "curls"); - __publicField(this, "directions"); - __publicField(this, "weights"); - __publicField(this, "weightsRelative"); - this.name = name; - this.curls = {}; - this.directions = {}; - this.weights = [1, 1, 1, 1, 1]; - this.weightsRelative = [1, 1, 1, 1, 1]; - } - curl(finger, curl, confidence) { - if (typeof this.curls[finger] === "undefined") this.curls[finger] = []; - this.curls[finger].push([curl, confidence]); - } - direction(finger, position, confidence) { - if (!this.directions[finger]) this.directions[finger] = []; - this.directions[finger].push([position, confidence]); - } - weight(finger, weight) { - this.weights[finger] = weight; - const total = this.weights.reduce((a, b) => a + b, 0); - this.weightsRelative = this.weights.map((el) => el * 5 / total); - } - matchAgainst(detectedCurls, detectedDirections) { - let confidence = 0; - for (const fingerIdx in detectedCurls) { - const detectedCurl = detectedCurls[fingerIdx]; - const expectedCurls = this.curls[fingerIdx]; - if (typeof expectedCurls === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedCurl, score] of expectedCurls) { - if (detectedCurl === expectedCurl) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - for (const fingerIdx in detectedDirections) { - const detectedDirection = detectedDirections[fingerIdx]; - const expectedDirections = this.directions[fingerIdx]; - if (typeof expectedDirections === "undefined") { - confidence += this.weightsRelative[fingerIdx]; - continue; - } - for (const [expectedDirection, score] of expectedDirections) { - if (detectedDirection === expectedDirection) { - confidence += score * this.weightsRelative[fingerIdx]; - break; - } - } - } - return confidence / 10; - } -}; - -// src/hand/fingergesture.ts -var { thumb, index, middle, ring, pinky } = Finger; -var { none, half, full } = FingerCurl; -var { verticalUp, verticalDown, horizontalLeft, horizontalRight, diagonalUpRight, diagonalUpLeft, diagonalDownRight, diagonalDownLeft } = FingerDirection; -var ThumbsUp = new FingerGesture("thumbs up"); -ThumbsUp.curl(thumb, none, 1); -ThumbsUp.direction(thumb, verticalUp, 1); -ThumbsUp.direction(thumb, diagonalUpLeft, 0.25); -ThumbsUp.direction(thumb, diagonalUpRight, 0.25); -for (const finger of [Finger.index, Finger.middle, Finger.ring, Finger.pinky]) { - ThumbsUp.curl(finger, full, 1); - ThumbsUp.direction(finger, horizontalLeft, 1); - ThumbsUp.direction(finger, horizontalRight, 1); -} -var Victory = new FingerGesture("victory"); -Victory.curl(thumb, half, 0.5); -Victory.curl(thumb, none, 0.5); -Victory.direction(thumb, verticalUp, 1); -Victory.direction(thumb, diagonalUpLeft, 1); -Victory.curl(index, none, 1); -Victory.direction(index, verticalUp, 0.75); -Victory.direction(index, diagonalUpLeft, 1); -Victory.curl(middle, none, 1); -Victory.direction(middle, verticalUp, 1); -Victory.direction(middle, diagonalUpLeft, 0.75); -Victory.curl(ring, full, 1); -Victory.direction(ring, verticalUp, 0.2); -Victory.direction(ring, diagonalUpLeft, 1); -Victory.direction(ring, horizontalLeft, 0.2); -Victory.curl(pinky, full, 1); -Victory.direction(pinky, verticalUp, 0.2); -Victory.direction(pinky, diagonalUpLeft, 1); -Victory.direction(pinky, horizontalLeft, 0.2); -Victory.weight(index, 2); -Victory.weight(middle, 2); -var Point = new FingerGesture("point"); -Point.curl(thumb, full, 1); -Point.curl(index, none, 0.5); -Point.curl(middle, full, 0.5); -Point.curl(ring, full, 0.5); -Point.curl(pinky, full, 0.5); -Point.weight(index, 2); -Point.weight(middle, 2); -var MiddleFinger = new FingerGesture("middle finger"); -MiddleFinger.curl(thumb, none, 1); -MiddleFinger.curl(index, full, 0.5); -MiddleFinger.curl(middle, full, 0.5); -MiddleFinger.curl(ring, full, 0.5); -MiddleFinger.curl(pinky, full, 0.5); -MiddleFinger.weight(index, 2); -MiddleFinger.weight(middle, 2); -var OpenPalm = new FingerGesture("open palm"); -OpenPalm.curl(thumb, none, 0.75); -OpenPalm.curl(index, none, 0.75); -OpenPalm.curl(middle, none, 0.75); -OpenPalm.curl(ring, none, 0.75); -OpenPalm.curl(pinky, none, 0.75); -var fingergesture_default = [ThumbsUp, Victory, Point, MiddleFinger, OpenPalm]; - -// src/hand/fingerpose.ts -var minConfidence = 0.7; -var options3 = { - // curl estimation - HALF_CURL_START_LIMIT: 60, - NO_CURL_START_LIMIT: 130, - // direction estimation - DISTANCE_VOTE_POWER: 1.1, - SINGLE_ANGLE_VOTE_POWER: 0.9, - TOTAL_ANGLE_VOTE_POWER: 1.6 -}; -function calculateSlope(point1x, point1y, point2x, point2y) { - const value = (point1y - point2y) / (point1x - point2x); - let slope = Math.atan(value) * 180 / Math.PI; - if (slope <= 0) slope = -slope; - else if (slope > 0) slope = 180 - slope; - return slope; -} -function getSlopes(point1, point2) { - if (!point1 || !point2) return [0, 0]; - const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]); - if (point1.length === 2) return slopeXY; - const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]); - return [slopeXY, slopeYZ]; -} -function angleOrientationAt(angle, weightageAt = 1) { - let isVertical = 0; - let isDiagonal = 0; - let isHorizontal = 0; - if (angle >= 75 && angle <= 105) isVertical = 1 * weightageAt; - else if (angle >= 25 && angle <= 155) isDiagonal = 1 * weightageAt; - else isHorizontal = 1 * weightageAt; - return [isVertical, isDiagonal, isHorizontal]; -} -function estimateFingerCurl(startPoint, midPoint, endPoint) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const start_mid_z_dist = startPoint[2] - midPoint[2]; - const start_end_z_dist = startPoint[2] - endPoint[2]; - const mid_end_z_dist = midPoint[2] - endPoint[2]; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist); - let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist); - if (cos_in > 1) cos_in = 1; - else if (cos_in < -1) cos_in = -1; - let angleOfCurve = Math.acos(cos_in); - angleOfCurve = 57.2958 * angleOfCurve % 180; - let fingerCurl; - if (angleOfCurve > options3.NO_CURL_START_LIMIT) fingerCurl = FingerCurl.none; - else if (angleOfCurve > options3.HALF_CURL_START_LIMIT) fingerCurl = FingerCurl.half; - else fingerCurl = FingerCurl.full; - return fingerCurl; -} -function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - if (max_dist_x === Math.abs(start_end_x_dist)) { - if (start_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } else if (max_dist_x === Math.abs(start_mid_x_dist)) { - if (start_mid_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } else { - if (mid_end_x_dist > 0) estimatedDirection = FingerDirection.horizontalLeft; - else estimatedDirection = FingerDirection.horizontalRight; - } - return estimatedDirection; -} -function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) { - let estimatedDirection; - if (max_dist_y === Math.abs(start_end_y_dist)) { - if (start_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } else if (max_dist_y === Math.abs(start_mid_y_dist)) { - if (start_mid_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } else { - if (mid_end_y_dist < 0) estimatedDirection = FingerDirection.verticalDown; - else estimatedDirection = FingerDirection.verticalUp; - } - return estimatedDirection; -} -function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) { - let estimatedDirection; - const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - if (reqd_vertical_direction === FingerDirection.verticalUp) { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalUpLeft; - else estimatedDirection = FingerDirection.diagonalUpRight; - } else { - if (reqd_horizontal_direction === FingerDirection.horizontalLeft) estimatedDirection = FingerDirection.diagonalDownLeft; - else estimatedDirection = FingerDirection.diagonalDownRight; - } - return estimatedDirection; -} -function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) { - const start_mid_x_dist = startPoint[0] - midPoint[0]; - const start_end_x_dist = startPoint[0] - endPoint[0]; - const mid_end_x_dist = midPoint[0] - endPoint[0]; - const start_mid_y_dist = startPoint[1] - midPoint[1]; - const start_end_y_dist = startPoint[1] - endPoint[1]; - const mid_end_y_dist = midPoint[1] - endPoint[1]; - const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist)); - const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist)); - let voteVertical = 0; - let voteDiagonal = 0; - let voteHorizontal = 0; - const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5); - if (start_end_x_y_dist_ratio > 1.5) voteVertical += options3.DISTANCE_VOTE_POWER; - else if (start_end_x_y_dist_ratio > 0.66) voteDiagonal += options3.DISTANCE_VOTE_POWER; - else voteHorizontal += options3.DISTANCE_VOTE_POWER; - const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist); - const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist); - const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist); - const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist); - let calc_start_point_x = startPoint[0]; - let calc_start_point_y = startPoint[1]; - let calc_end_point_x = endPoint[0]; - let calc_end_point_y = endPoint[1]; - if (max_dist === start_mid_dist) { - calc_end_point_x = endPoint[0]; - calc_end_point_y = endPoint[1]; - } else if (max_dist === mid_end_dist) { - calc_start_point_x = midPoint[0]; - calc_start_point_y = midPoint[1]; - } - const calcStartPoint = [calc_start_point_x, calc_start_point_y]; - const calcEndPoint = [calc_end_point_x, calc_end_point_y]; - const totalAngle = getSlopes(calcStartPoint, calcEndPoint); - const votes = angleOrientationAt(totalAngle, options3.TOTAL_ANGLE_VOTE_POWER); - voteVertical += votes[0]; - voteDiagonal += votes[1]; - voteHorizontal += votes[2]; - for (const fingerSlope of fingerSlopes) { - const fingerVotes = angleOrientationAt(fingerSlope, options3.SINGLE_ANGLE_VOTE_POWER); - voteVertical += fingerVotes[0]; - voteDiagonal += fingerVotes[1]; - voteHorizontal += fingerVotes[2]; - } - let estimatedDirection; - if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y); - } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) { - estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } else { - estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x); - } - return estimatedDirection; -} -function estimate(landmarks) { - const slopesXY = []; - const slopesYZ = []; - const fingerCurls = []; - const fingerDirections = []; - if (!landmarks) return { curls: fingerCurls, directions: fingerDirections }; - for (const finger of Finger.all) { - const points = Finger.getPoints(finger); - const slopeAtXY = []; - const slopeAtYZ = []; - for (const point2 of points) { - const point1 = landmarks[point2[0]]; - const point22 = landmarks[point2[1]]; - const slopes = getSlopes(point1, point22); - const slopeXY = slopes[0]; - const slopeYZ = slopes[1]; - slopeAtXY.push(slopeXY); - slopeAtYZ.push(slopeYZ); - } - slopesXY.push(slopeAtXY); - slopesYZ.push(slopeAtYZ); - } - for (const finger of Finger.all) { - const pointIndexAt = finger === Finger.thumb ? 1 : 0; - const fingerPointsAt = Finger.getPoints(finger); - const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]]; - const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]]; - const endPoint = landmarks[fingerPointsAt[3][1]]; - const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint); - const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt)); - fingerCurls[finger] = fingerCurled; - fingerDirections[finger] = fingerPosition; - } - return { curls: fingerCurls, directions: fingerDirections }; -} -function analyze(keypoints) { - if (!keypoints || keypoints.length === 0) return null; - const estimatorRes = estimate(keypoints); - const landmarks = {}; - for (const fingerIdx of Finger.all) { - landmarks[Finger.getName(fingerIdx)] = { - curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]), - direction: FingerDirection.getName(estimatorRes.directions[fingerIdx]) - }; - } - return landmarks; -} -function match(keypoints) { - const poses = []; - if (!keypoints || keypoints.length === 0) return poses; - const estimatorRes = estimate(keypoints); - for (const gesture2 of fingergesture_default) { - const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions); - if (confidence >= minConfidence) poses.push({ name: gesture2.name, confidence }); - } - return poses; -} - -// src/gesture/gesture.ts -var body2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const leftWrist = res[i].keypoints.find((a) => a.part === "leftWrist"); - const rightWrist = res[i].keypoints.find((a) => a.part === "rightWrist"); - const nose = res[i].keypoints.find((a) => a.part === "nose"); - if (nose && leftWrist && rightWrist && leftWrist.position[1] < nose.position[1] && rightWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "i give up" }); - else if (nose && leftWrist && leftWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "raise left hand" }); - else if (nose && rightWrist && rightWrist.position[1] < nose.position[1]) gestures.push({ body: i, gesture: "raise right hand" }); - const leftShoulder = res[i].keypoints.find((a) => a.part === "leftShoulder"); - const rightShoulder = res[i].keypoints.find((a) => a.part === "rightShoulder"); - if (leftShoulder && rightShoulder && Math.abs(leftShoulder.positionRaw[1] - rightShoulder.positionRaw[1]) > 0.1) { - gestures.push({ body: i, gesture: `leaning ${leftShoulder.position[1] > rightShoulder.position[1] ? "left" : "right"}` }); - } - } - return gestures; -}; -var face2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (res[i].mesh && res[i].mesh.length > 450) { - const zDiff = (res[i].mesh[33][2] || 0) - (res[i].mesh[263][2] || 0); - const xDiff = res[i].mesh[33][0] - res[i].mesh[263][0]; - if (Math.abs(zDiff / xDiff) <= 0.15) gestures.push({ face: i, gesture: "facing center" }); - else gestures.push({ face: i, gesture: `facing ${zDiff < 0 ? "left" : "right"}` }); - const openLeft = Math.abs(res[i].mesh[374][1] - res[i].mesh[386][1]) / Math.abs(res[i].mesh[443][1] - res[i].mesh[450][1]); - if (openLeft < 0.2) gestures.push({ face: i, gesture: "blink left eye" }); - const openRight = Math.abs(res[i].mesh[145][1] - res[i].mesh[159][1]) / Math.abs(res[i].mesh[223][1] - res[i].mesh[230][1]); - if (openRight < 0.2) gestures.push({ face: i, gesture: "blink right eye" }); - const mouthOpen = Math.min(100, 500 * Math.abs(res[i].mesh[13][1] - res[i].mesh[14][1]) / Math.abs(res[i].mesh[10][1] - res[i].mesh[152][1])); - if (mouthOpen > 10) gestures.push({ face: i, gesture: `mouth ${Math.trunc(mouthOpen)}% open` }); - const chinDepth = res[i].mesh[152][2] || 0; - if (Math.abs(chinDepth) > 10) gestures.push({ face: i, gesture: `head ${chinDepth < 0 ? "up" : "down"}` }); - } - } - return gestures; -}; -var iris2 = (res) => { - var _a, _b, _c, _d; - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - if (!((_b = (_a = res[i].annotations) == null ? void 0 : _a.leftEyeIris) == null ? void 0 : _b[0]) || !((_d = (_c = res[i].annotations) == null ? void 0 : _c.rightEyeIris) == null ? void 0 : _d[0])) continue; - const sizeXLeft = res[i].annotations.leftEyeIris[3][0] - res[i].annotations.leftEyeIris[1][0]; - const sizeYLeft = res[i].annotations.leftEyeIris[4][1] - res[i].annotations.leftEyeIris[2][1]; - const areaLeft = Math.abs(sizeXLeft * sizeYLeft); - const sizeXRight = res[i].annotations.rightEyeIris[3][0] - res[i].annotations.rightEyeIris[1][0]; - const sizeYRight = res[i].annotations.rightEyeIris[4][1] - res[i].annotations.rightEyeIris[2][1]; - const areaRight = Math.abs(sizeXRight * sizeYRight); - let center = false; - const difference = Math.abs(areaLeft - areaRight) / Math.max(areaLeft, areaRight); - if (difference < 0.25) { - center = true; - gestures.push({ iris: i, gesture: "facing center" }); - } - const leftIrisCenterX = Math.abs(res[i].mesh[263][0] - res[i].annotations.leftEyeIris[0][0]) / res[i].box[2]; - const rightIrisCenterX = Math.abs(res[i].mesh[33][0] - res[i].annotations.rightEyeIris[0][0]) / res[i].box[2]; - if (leftIrisCenterX > 0.06 || rightIrisCenterX > 0.06) center = false; - if (leftIrisCenterX > rightIrisCenterX) { - if (rightIrisCenterX > 0.04) gestures.push({ iris: i, gesture: "looking right" }); - } else { - if (leftIrisCenterX > 0.04) gestures.push({ iris: i, gesture: "looking left" }); - } - const rightIrisCenterY = Math.abs(res[i].mesh[145][1] - res[i].annotations.rightEyeIris[0][1]) / res[i].box[3]; - const leftIrisCenterY = Math.abs(res[i].mesh[374][1] - res[i].annotations.leftEyeIris[0][1]) / res[i].box[3]; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01 || leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) center = false; - if (leftIrisCenterY < 0.01 || rightIrisCenterY < 0.01) gestures.push({ iris: i, gesture: "looking down" }); - if (leftIrisCenterY > 0.022 || rightIrisCenterY > 0.022) gestures.push({ iris: i, gesture: "looking up" }); - if (center) gestures.push({ iris: i, gesture: "looking center" }); - } - return gestures; -}; -var hand2 = (res) => { - if (!res) return []; - const gestures = []; - for (let i = 0; i < res.length; i++) { - const fingers = []; - if (res[i].annotations) { - for (const [finger, pos] of Object.entries(res[i].annotations)) { - if (finger !== "palmBase" && Array.isArray(pos) && pos[0]) fingers.push({ name: finger.toLowerCase(), position: pos[0] }); - } - } - if (fingers && fingers.length > 0) { - const closest = fingers.reduce((best, a) => (best.position[2] || 0) < (a.position[2] || 0) ? best : a); - gestures.push({ hand: i, gesture: `${closest.name} forward` }); - const highest = fingers.reduce((best, a) => best.position[1] < a.position[1] ? best : a); - gestures.push({ hand: i, gesture: `${highest.name} up` }); - } - if (res[i].keypoints) { - const poses = match(res[i].keypoints); - for (const pose of poses) gestures.push({ hand: i, gesture: pose.name }); - } - } - return gestures; -}; - -// src/hand/handposedetector.ts -var tf27 = __toESM(require_tfjs_esm()); - -// src/hand/handposeutil.ts -var tf26 = __toESM(require_tfjs_esm()); -function getBoxSize2(box) { - return [ - Math.abs(box.endPoint[0] - box.startPoint[0]), - Math.abs(box.endPoint[1] - box.startPoint[1]) - ]; -} -function getBoxCenter2(box) { - return [ - box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2, - box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2 - ]; -} -function cutBoxFromImageAndResize(box, image28, cropSize) { - const h = image28.shape[1]; - const w = image28.shape[2]; - const boxes = [[ - box.startPoint[1] / h, - box.startPoint[0] / w, - box.endPoint[1] / h, - box.endPoint[0] / w - ]]; - return tf26.image.cropAndResize(image28, boxes, [0], cropSize); -} -function scaleBoxCoordinates2(box, factor) { - const startPoint = [box.startPoint[0] * factor[0], box.startPoint[1] * factor[1]]; - const endPoint = [box.endPoint[0] * factor[0], box.endPoint[1] * factor[1]]; - const palmLandmarks = box.palmLandmarks.map((coord) => { - const scaledCoord = [coord[0] * factor[0], coord[1] * factor[1]]; - return scaledCoord; - }); - return { startPoint, endPoint, palmLandmarks, confidence: box.confidence }; -} -function enlargeBox2(box, factor = 1.5) { - const center = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const newHalfSize = [factor * size2[0] / 2, factor * size2[1] / 2]; - const startPoint = [center[0] - newHalfSize[0], center[1] - newHalfSize[1]]; - const endPoint = [center[0] + newHalfSize[0], center[1] + newHalfSize[1]]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function squarifyBox2(box) { - const centers = getBoxCenter2(box); - const size2 = getBoxSize2(box); - const maxEdge = Math.max(...size2); - const halfSize = maxEdge / 2; - const startPoint = [centers[0] - halfSize, centers[1] - halfSize]; - const endPoint = [centers[0] + halfSize, centers[1] + halfSize]; - return { startPoint, endPoint, palmLandmarks: box.palmLandmarks }; -} -function normalizeRadians2(angle) { - return angle - 2 * Math.PI * Math.floor((angle + Math.PI) / (2 * Math.PI)); -} -function computeRotation2(point1, point2) { - const radians = Math.PI / 2 - Math.atan2(-(point2[1] - point1[1]), point2[0] - point1[0]); - return normalizeRadians2(radians); -} -var buildTranslationMatrix2 = (x, y) => [[1, 0, x], [0, 1, y], [0, 0, 1]]; -function dot2(v1, v2) { - let product = 0; - for (let i = 0; i < v1.length; i++) { - product += v1[i] * v2[i]; - } - return product; -} -function getColumnFrom2DArr2(arr, columnIndex) { - const column = []; - for (let i = 0; i < arr.length; i++) { - column.push(arr[i][columnIndex]); - } - return column; -} -function multiplyTransformMatrices2(mat1, mat2) { - const product = []; - const size2 = mat1.length; - for (let row = 0; row < size2; row++) { - product.push([]); - for (let col = 0; col < size2; col++) { - product[row].push(dot2(mat1[row], getColumnFrom2DArr2(mat2, col))); - } - } - return product; -} -function buildRotationMatrix2(rotation, center) { - const cosA = Math.cos(rotation); - const sinA = Math.sin(rotation); - const rotationMatrix = [[cosA, -sinA, 0], [sinA, cosA, 0], [0, 0, 1]]; - const translationMatrix = buildTranslationMatrix2(center[0], center[1]); - const translationTimesRotation = multiplyTransformMatrices2(translationMatrix, rotationMatrix); - const negativeTranslationMatrix = buildTranslationMatrix2(-center[0], -center[1]); - return multiplyTransformMatrices2(translationTimesRotation, negativeTranslationMatrix); -} -function invertTransformMatrix2(matrix) { - const rotationComponent = [[matrix[0][0], matrix[1][0]], [matrix[0][1], matrix[1][1]]]; - const translationComponent = [matrix[0][2], matrix[1][2]]; - const invertedTranslation = [ - -dot2(rotationComponent[0], translationComponent), - -dot2(rotationComponent[1], translationComponent) - ]; - return [ - rotationComponent[0].concat(invertedTranslation[0]), - rotationComponent[1].concat(invertedTranslation[1]), - [0, 0, 1] - ]; -} -function rotatePoint2(homogeneousCoordinate, rotationMatrix) { - return [ - dot2(homogeneousCoordinate, rotationMatrix[0]), - dot2(homogeneousCoordinate, rotationMatrix[1]) - ]; -} - -// src/hand/handposeanchors.ts -var anchors2 = [ - { x: 0.015625, y: 0.015625 }, - { x: 0.015625, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.046875, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.078125, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.109375, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.140625, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.171875, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.203125, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.234375, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.265625, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.296875, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.328125, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.359375, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.390625, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.421875, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.453125, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.484375, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.515625, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.546875, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.578125, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.609375, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.640625, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.671875, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.703125, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.734375, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.765625, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.796875, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.828125, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.859375, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.890625, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.921875, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.953125, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.984375, y: 0.015625 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.015625, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.046875, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.078125, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.109375, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.140625, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.171875, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.203125, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.234375, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.265625, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.296875, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.328125, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.359375, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.390625, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.421875, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.453125, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.484375, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.515625, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.546875, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.578125, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.609375, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.640625, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.671875, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.703125, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.734375, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.765625, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.796875, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.828125, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.859375, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.890625, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.921875, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.953125, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.984375, y: 0.046875 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.015625, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.046875, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.078125, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.109375, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.140625, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.171875, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.203125, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.234375, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.265625, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.296875, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.328125, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.359375, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.390625, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.421875, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.453125, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.484375, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.515625, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.546875, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.578125, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.609375, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.640625, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.671875, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.703125, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.734375, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.765625, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.796875, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.828125, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - { x: 0.859375, y: 0.078125 }, - { x: 0.890625, y: 0.078125 }, - { x: 0.890625, y: 0.078125 }, - { x: 0.921875, y: 0.078125 }, - { x: 0.921875, y: 0.078125 }, - { x: 0.953125, y: 0.078125 }, - { x: 0.953125, y: 0.078125 }, - { x: 0.984375, y: 0.078125 }, - { x: 0.984375, y: 0.078125 }, - { x: 0.015625, y: 0.109375 }, - { x: 0.015625, y: 0.109375 }, - { x: 0.046875, y: 0.109375 }, - { x: 0.046875, y: 0.109375 }, - { x: 0.078125, y: 0.109375 }, - { x: 0.078125, y: 0.109375 }, - { x: 0.109375, y: 0.109375 }, - { x: 0.109375, y: 0.109375 }, - { x: 0.140625, y: 0.109375 }, - { x: 0.140625, y: 0.109375 }, - { x: 0.171875, y: 0.109375 }, - { x: 0.171875, y: 0.109375 }, - { x: 0.203125, y: 0.109375 }, - { x: 0.203125, y: 0.109375 }, - { x: 0.234375, y: 0.109375 }, - { x: 0.234375, y: 0.109375 }, - { x: 0.265625, y: 0.109375 }, - { x: 0.265625, y: 0.109375 }, - { x: 0.296875, y: 0.109375 }, - { x: 0.296875, y: 0.109375 }, - { x: 0.328125, y: 0.109375 }, - { x: 0.328125, y: 0.109375 }, - { x: 0.359375, y: 0.109375 }, - { x: 0.359375, y: 0.109375 }, - { x: 0.390625, y: 0.109375 }, - { x: 0.390625, y: 0.109375 }, - { x: 0.421875, y: 0.109375 }, - { x: 0.421875, y: 0.109375 }, - { x: 0.453125, y: 0.109375 }, - { x: 0.453125, y: 0.109375 }, - { x: 0.484375, y: 0.109375 }, - { x: 0.484375, y: 0.109375 }, - { x: 0.515625, y: 0.109375 }, - { x: 0.515625, y: 0.109375 }, - { x: 0.546875, y: 0.109375 }, - { x: 0.546875, y: 0.109375 }, - { x: 0.578125, y: 0.109375 }, - { x: 0.578125, y: 0.109375 }, - { x: 0.609375, y: 0.109375 }, - { x: 0.609375, y: 0.109375 }, - { x: 0.640625, y: 0.109375 }, - { x: 0.640625, y: 0.109375 }, - { x: 0.671875, y: 0.109375 }, - { x: 0.671875, y: 0.109375 }, - { x: 0.703125, y: 0.109375 }, - { x: 0.703125, y: 0.109375 }, - { x: 0.734375, y: 0.109375 }, - { x: 0.734375, y: 0.109375 }, - { x: 0.765625, y: 0.109375 }, - { x: 0.765625, y: 0.109375 }, - { x: 0.796875, y: 0.109375 }, - { x: 0.796875, y: 0.109375 }, - { x: 0.828125, y: 0.109375 }, - { x: 0.828125, y: 0.109375 }, - { x: 0.859375, y: 0.109375 }, - { x: 0.859375, y: 0.109375 }, - 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-// src/hand/handposedetector.ts -var HandDetector = class { - constructor(model23) { - __publicField(this, "model"); - __publicField(this, "anchors"); - __publicField(this, "anchorsTensor"); - __publicField(this, "inputSize"); - __publicField(this, "inputSizeTensor"); - __publicField(this, "doubleInputSizeTensor"); - var _a, _b, _c, _d; - this.model = model23; - this.anchors = anchors2.map((anchor) => [anchor.x, anchor.y]); - this.anchorsTensor = tf27.tensor2d(this.anchors); - this.inputSize = ((_d = (_c = (_b = (_a = this == null ? void 0 : this.model) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0]) == null ? void 0 : _c.shape) == null ? void 0 : _d[2]) || 0; - this.inputSizeTensor = tf27.tensor1d([this.inputSize, this.inputSize]); - this.doubleInputSizeTensor = tf27.tensor1d([this.inputSize * 2, this.inputSize * 2]); - } - normalizeBoxes(boxes) { - const t2 = {}; - t2.boxOffsets = tf27.slice(boxes, [0, 0], [-1, 2]); - t2.boxSizes = tf27.slice(boxes, [0, 2], [-1, 2]); - t2.div = tf27.div(t2.boxOffsets, this.inputSizeTensor); - t2.boxCenterPoints = tf27.add(t2.div, this.anchorsTensor); - t2.halfBoxSizes = tf27.div(t2.boxSizes, this.doubleInputSizeTensor); - t2.sub = tf27.sub(t2.boxCenterPoints, t2.halfBoxSizes); - t2.startPoints = tf27.mul(t2.sub, this.inputSizeTensor); - t2.add = tf27.add(t2.boxCenterPoints, t2.halfBoxSizes); - t2.endPoints = tf27.mul(t2.add, this.inputSizeTensor); - const res = tf27.concat2d([t2.startPoints, t2.endPoints], 1); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - normalizeLandmarks(rawPalmLandmarks, index2) { - const t2 = {}; - t2.reshape = tf27.reshape(rawPalmLandmarks, [-1, 7, 2]); - t2.div = tf27.div(t2.reshape, this.inputSizeTensor); - t2.landmarks = tf27.add(t2.div, this.anchors[index2] ? this.anchors[index2] : 0); - const res = tf27.mul(t2.landmarks, this.inputSizeTensor); - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return res; - } - async predict(input, config3) { - var _a; - const t2 = {}; - t2.resize = tf27.image.resizeBilinear(input, [this.inputSize, this.inputSize]); - t2.div = tf27.div(t2.resize, constants.tf127); - t2.image = tf27.sub(t2.div, constants.tf1); - t2.batched = this.model.execute(t2.image); - t2.predictions = tf27.squeeze(t2.batched); - t2.slice = tf27.slice(t2.predictions, [0, 0], [-1, 1]); - t2.sigmoid = tf27.sigmoid(t2.slice); - t2.scores = tf27.squeeze(t2.sigmoid); - const scores = await t2.scores.data(); - t2.boxes = tf27.slice(t2.predictions, [0, 1], [-1, 4]); - t2.norm = this.normalizeBoxes(t2.boxes); - t2.nms = await tf27.image.nonMaxSuppressionAsync(t2.norm, t2.scores, 3 * (((_a = config3.hand) == null ? void 0 : _a.maxDetected) || 1), config3.hand.iouThreshold, config3.hand.minConfidence); - const nms = await t2.nms.array(); - const hands = []; - for (const index2 of nms) { - const p = {}; - p.box = tf27.slice(t2.norm, [index2, 0], [1, -1]); - p.slice = tf27.slice(t2.predictions, [index2, 5], [1, 14]); - p.norm = this.normalizeLandmarks(p.slice, index2); - p.palmLandmarks = tf27.reshape(p.norm, [-1, 2]); - const box = await p.box.data(); - const startPoint = box.slice(0, 2); - const endPoint = box.slice(2, 4); - const palmLandmarks = await p.palmLandmarks.array(); - const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] }; - const scaled = scaleBoxCoordinates2(hand3, [(input.shape[2] || 1) / this.inputSize, (input.shape[1] || 0) / this.inputSize]); - hands.push(scaled); - Object.keys(p).forEach((tensor6) => tf27.dispose(p[tensor6])); - } - Object.keys(t2).forEach((tensor6) => tf27.dispose(t2[tensor6])); - return hands; - } -}; - -// src/hand/handposepipeline.ts -var tf28 = __toESM(require_tfjs_esm()); -var palmBoxEnlargeFactor = 5; -var handBoxEnlargeFactor = 1.65; -var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2]; -var palmLandmarksPalmBase = 0; -var palmLandmarksMiddleFingerBase = 2; -var lastTime13 = 0; -var HandPipeline = class { - constructor(handDetector, handPoseModel2) { - __publicField(this, "handDetector"); - __publicField(this, "handPoseModel"); - __publicField(this, "inputSize"); - __publicField(this, "storedBoxes"); - __publicField(this, "skipped"); - __publicField(this, "detectedHands"); - var _a, _b, _c; - this.handDetector = handDetector; - this.handPoseModel = handPoseModel2; - this.inputSize = ((_c = (_b = (_a = this.handPoseModel) == null ? void 0 : _a.inputs) == null ? void 0 : _b[0].shape) == null ? void 0 : _c[2]) || 0; - this.storedBoxes = []; - this.skipped = Number.MAX_SAFE_INTEGER; - this.detectedHands = 0; - } - calculateLandmarksBoundingBox(landmarks) { - const xs = landmarks.map((d) => d[0]); - const ys = landmarks.map((d) => d[1]); - const startPoint = [Math.min(...xs), Math.min(...ys)]; - const endPoint = [Math.max(...xs), Math.max(...ys)]; - return { startPoint, endPoint }; - } - getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) { - const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix)); - const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks); - return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor); - } - getBoxForHandLandmarks(landmarks) { - const boundingBox = this.calculateLandmarksBoundingBox(landmarks); - const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor); - boxAroundHand.palmLandmarks = []; - for (let i = 0; i < palmLandmarkIds.length; i++) { - boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2)); - } - return boxAroundHand; - } - transformRawCoords(rawCoords, box2, angle, rotationMatrix) { - const boxSize = getBoxSize2(box2); - const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2]; - const coordsScaled = rawCoords.map((coord) => [ - scaleFactor[0] * (coord[0] - this.inputSize / 2), - scaleFactor[1] * (coord[1] - this.inputSize / 2), - scaleFactor[2] * coord[2] - ]); - const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]); - const coordsRotated = coordsScaled.map((coord) => { - const rotated = rotatePoint2(coord, coordsRotationMatrix); - return [...rotated, coord[2]]; - }); - const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix); - const boxCenter = [...getBoxCenter2(box2), 1]; - const originalBoxCenter = [ - dot2(boxCenter, inverseRotationMatrix[0]), - dot2(boxCenter, inverseRotationMatrix[1]) - ]; - return coordsRotated.map((coord) => [ - Math.trunc(coord[0] + originalBoxCenter[0]), - Math.trunc(coord[1] + originalBoxCenter[1]), - Math.trunc(coord[2]) - ]); - } - async estimateHands(image28, config3) { - let useFreshBox = false; - let boxes; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime13; - const skipFrame = this.skipped < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - this.skipped++; - } else { - boxes = await this.handDetector.predict(image28, config3); - this.skipped = 0; - } - if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) { - this.detectedHands = 0; - this.storedBoxes = [...boxes]; - if (this.storedBoxes.length > 0) useFreshBox = true; - } - const hands = []; - for (let i = 0; i < this.storedBoxes.length; i++) { - const currentBox = this.storedBoxes[i]; - if (!currentBox) continue; - if (config3.hand.landmarks) { - const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0; - const palmCenter = getBoxCenter2(currentBox); - const palmCenterNormalized = [palmCenter[0] / image28.shape[2], palmCenter[1] / image28.shape[1]]; - const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? tf28.image.rotateWithOffset(image28, angle, 0, palmCenterNormalized) : image28.clone(); - const rotationMatrix = buildRotationMatrix2(-angle, palmCenter); - const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox; - const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]); - const handImage = tf28.div(croppedInput, constants.tf255); - tf28.dispose(croppedInput); - tf28.dispose(rotatedImage); - const [confidenceT, keypoints] = this.handPoseModel.execute(handImage); - lastTime13 = now(); - tf28.dispose(handImage); - const confidence = (await confidenceT.data())[0]; - tf28.dispose(confidenceT); - if (confidence >= config3.hand.minConfidence / 4) { - const keypointsReshaped = tf28.reshape(keypoints, [-1, 3]); - const rawCoords = await keypointsReshaped.array(); - tf28.dispose(keypoints); - tf28.dispose(keypointsReshaped); - const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix); - const nextBoundingBox = this.getBoxForHandLandmarks(coords); - this.storedBoxes[i] = { ...nextBoundingBox, confidence }; - const result = { - landmarks: coords, - confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: confidence, - box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint } - }; - hands.push(result); - } else { - this.storedBoxes[i] = null; - } - tf28.dispose(keypoints); - } else { - const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor); - const result = { - confidence: currentBox.confidence, - boxConfidence: currentBox.confidence, - fingerConfidence: 0, - box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint }, - landmarks: [] - }; - hands.push(result); - } - } - this.storedBoxes = this.storedBoxes.filter((a) => a !== null); - this.detectedHands = hands.length; - if (hands.length > config3.hand.maxDetected) hands.length = config3.hand.maxDetected; - return hands; - } -}; - -// src/hand/handpose.ts -var meshAnnotations2 = { - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - palm: [0] -}; -var handDetectorModel; -var handPoseModel; -var handPipeline; -function initPipeline() { - const handDetector = handDetectorModel ? new HandDetector(handDetectorModel) : void 0; - if (handDetector && handPoseModel) handPipeline = new HandPipeline(handDetector, handPoseModel); -} -async function predict14(input, config3) { - if (!handPipeline) initPipeline(); - const predictions = await handPipeline.estimateHands(input, config3); - if (!predictions) return []; - const hands = []; - for (let i = 0; i < predictions.length; i++) { - const annotations2 = {}; - if (predictions[i].landmarks) { - for (const key of Object.keys(meshAnnotations2)) { - annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]); - } - } - const keypoints = predictions[i].landmarks; - let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0]; - let boxRaw = [0, 0, 0, 0]; - if (keypoints && keypoints.length > 0) { - for (const pt of keypoints) { - if (pt[0] < box[0]) box[0] = pt[0]; - if (pt[1] < box[1]) box[1] = pt[1]; - if (pt[0] > box[2]) box[2] = pt[0]; - if (pt[1] > box[3]) box[3] = pt[1]; - } - box[2] -= box[0]; - box[3] -= box[1]; - boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)]; - } else { - box = predictions[i].box ? [ - Math.trunc(Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.max(0, predictions[i].box.topLeft[1])), - Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])), - Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1])) - ] : [0, 0, 0, 0]; - boxRaw = [ - predictions[i].box.topLeft[0] / (input.shape[2] || 0), - predictions[i].box.topLeft[1] / (input.shape[1] || 0), - (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0), - (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0) - ]; - } - const landmarks = analyze(keypoints); - hands.push({ - id: i, - score: Math.round(100 * predictions[i].confidence) / 100, - boxScore: Math.round(100 * predictions[i].boxConfidence) / 100, - fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100, - label: "hand", - box, - boxRaw, - keypoints, - annotations: annotations2, - landmarks - }); - } - return hands; -} -async function loadDetect2(config3) { - var _a; - if (env.initial) handDetectorModel = null; - if (!handDetectorModel) handDetectorModel = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", handDetectorModel["modelUrl"]); - return handDetectorModel; -} -async function loadSkeleton(config3) { - var _a; - if (env.initial) handPoseModel = null; - if (!handPoseModel) handPoseModel = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - else if (config3.debug) log("cached model:", handPoseModel["modelUrl"]); - return handPoseModel; -} - -// src/hand/handtrack.ts -var tf29 = __toESM(require_tfjs_esm()); -var models2 = [null, null]; -var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"]; -var inputSize7 = [[0, 0], [0, 0]]; -var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"]; -var faceIndex = 4; -var boxExpandFact = 1.6; -var maxDetectorResolution = 512; -var detectorExpandFact = 1.4; -var skipped13 = Number.MAX_SAFE_INTEGER; -var lastTime14 = 0; -var outputSize = [0, 0]; -var cache4 = { - boxes: [], - hands: [] -}; -var fingerMap = { - /* - thumb: [0, 1, 2, 3, 4], - index: [0, 5, 6, 7, 8], - middle: [0, 9, 10, 11, 12], - ring: [0, 13, 14, 15, 16], - pinky: [0, 17, 18, 19, 20], - palm: [0], - */ - thumb: [1, 2, 3, 4], - index: [5, 6, 7, 8], - middle: [9, 10, 11, 12], - ring: [13, 14, 15, 16], - pinky: [17, 18, 19, 20], - base: [0], - palm: [0, 17, 13, 9, 5, 1, 0] -}; -async function loadDetect3(config3) { - var _a; - if (env.initial) models2[0] = null; - if (!models2[0]) { - fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3); - models2[0] = await loadModel((_a = config3.hand.detector) == null ? void 0 : _a.modelPath); - const inputs = models2[0]["executor"] ? Object.values(models2[0].modelSignature["inputs"]) : void 0; - inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", models2[0]["modelUrl"]); - return models2[0]; -} -async function loadSkeleton2(config3) { - var _a; - if (env.initial) models2[1] = null; - if (!models2[1]) { - models2[1] = await loadModel((_a = config3.hand.skeleton) == null ? void 0 : _a.modelPath); - const inputs = models2[1]["executor"] ? Object.values(models2[1].modelSignature["inputs"]) : void 0; - inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0; - inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0; - } else if (config3.debug) log("cached model:", models2[1]["modelUrl"]); - return models2[1]; -} -async function detectHands(input, config3) { - const hands = []; - if (!input || !models2[0]) return hands; - const t2 = {}; - const ratio2 = (input.shape[2] || 1) / (input.shape[1] || 1); - const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution); - const width = Math.round(height * ratio2 / 8) * 8; - t2.resize = tf29.image.resizeBilinear(input, [height, width]); - t2.cast = tf29.cast(t2.resize, "int32"); - [t2.rawScores, t2.rawBoxes] = await models2[0].executeAsync(t2.cast, modelOutputNodes); - t2.boxes = tf29.squeeze(t2.rawBoxes, [0, 2]); - t2.scores = tf29.squeeze(t2.rawScores, [0]); - const classScores = tf29.unstack(t2.scores, 1); - tf29.dispose(classScores[faceIndex]); - classScores.splice(faceIndex, 1); - t2.filtered = tf29.stack(classScores, 1); - tf29.dispose(classScores); - t2.max = tf29.max(t2.filtered, 1); - t2.argmax = tf29.argMax(t2.filtered, 1); - let id = 0; - t2.nms = await tf29.image.nonMaxSuppressionAsync(t2.boxes, t2.max, (config3.hand.maxDetected || 0) + 1, config3.hand.iouThreshold || 0, config3.hand.minConfidence || 1); - const nms = await t2.nms.data(); - const scores = await t2.max.data(); - const classNum = await t2.argmax.data(); - for (const nmsIndex of Array.from(nms)) { - const boxSlice = tf29.slice(t2.boxes, nmsIndex, 1); - const boxYX = await boxSlice.data(); - tf29.dispose(boxSlice); - const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]]; - const boxRaw = scale(boxData, detectorExpandFact); - const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])]; - const score = scores[nmsIndex]; - const label = classes[classNum[nmsIndex]]; - const hand3 = { id: id++, score, box: boxFull, boxRaw, label }; - hands.push(hand3); - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - hands.sort((a, b) => b.score - a.score); - if (hands.length > (config3.hand.maxDetected || 1)) hands.length = config3.hand.maxDetected || 1; - return hands; -} -async function detectFingers(input, h, config3) { - const hand3 = { - // initial values inherited from hand detect - id: h.id, - score: Math.round(100 * h.score) / 100, - boxScore: Math.round(100 * h.score) / 100, - fingerScore: 0, - box: h.box, - boxRaw: h.boxRaw, - label: h.label, - keypoints: [], - landmarks: {}, - annotations: {} - }; - if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) { - const t2 = {}; - const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]]; - t2.crop = tf29.image.cropAndResize(input, [boxCrop], [0], [inputSize7[1][0], inputSize7[1][1]], "bilinear"); - t2.div = tf29.div(t2.crop, constants.tf255); - [t2.score, t2.keypoints] = models2[1].execute(t2.div, ["Identity_1", "Identity"]); - const rawScore = (await t2.score.data())[0]; - const score = (100 - Math.trunc(100 / (1 + Math.exp(rawScore)))) / 100; - if (score >= (config3.hand.minConfidence || 0)) { - hand3.fingerScore = score; - t2.reshaped = tf29.reshape(t2.keypoints, [-1, 3]); - const coordsData = await t2.reshaped.array(); - const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]); - const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]); - hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]); - hand3.landmarks = analyze(hand3.keypoints); - for (const key of Object.keys(fingerMap)) { - hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null); - } - } - Object.keys(t2).forEach((tensor6) => tf29.dispose(t2[tensor6])); - } - return hand3; -} -async function predict15(input, config3) { - var _a, _b; - if (!((_a = models2[0]) == null ? void 0 : _a["executor"]) || !((_b = models2[1]) == null ? void 0 : _b["executor"]) || !models2[0].inputs[0].shape || !models2[1].inputs[0].shape) return []; - outputSize = [input.shape[2] || 0, input.shape[1] || 0]; - skipped13++; - const skipTime = (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrame = skipped13 < (config3.hand.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache4.hands; - } - return new Promise(async (resolve) => { - const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime14; - const skipFrameExtended = skipped13 < 3 * (config3.hand.skipFrames || 0); - if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) { - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - } else { - cache4.boxes = await detectHands(input, config3); - lastTime14 = now(); - cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3))); - skipped13 = 0; - } - const oldCache = [...cache4.boxes]; - cache4.boxes.length = 0; - if (config3.cacheSensitivity > 0) { - for (let i = 0; i < cache4.hands.length; i++) { - const boxKpt = square(cache4.hands[i].keypoints, outputSize); - if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) { - const boxScale = scale(boxKpt.box, boxExpandFact); - const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact); - cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw }); - } - } - } - for (let i = 0; i < cache4.hands.length; i++) { - const bbox = calc(cache4.hands[i].keypoints, outputSize); - cache4.hands[i].box = bbox.box; - cache4.hands[i].boxRaw = bbox.boxRaw; - } - resolve(cache4.hands); - }); -} - -// src/result.ts -var empty = (error = null) => ({ face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, width: 0, height: 0, error }); - -// src/body/movenetcoords.ts -var movenetcoords_exports = {}; -__export(movenetcoords_exports, { - connected: () => connected3, - horizontal: () => horizontal, - kpt: () => kpt3, - relative: () => relative, - vertical: () => vertical -}); -var kpt3 = [ - // used to create part labels - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var horizontal = [ - // used to fix left vs right - ["leftEye", "rightEye"], - ["leftEar", "rightEar"], - ["leftShoulder", "rightShoulder"], - ["leftElbow", "rightElbow"], - ["leftWrist", "rightWrist"], - ["leftHip", "rightHip"], - ["leftKnee", "rightKnee"], - ["leftAnkle", "rightAnkle"] -]; -var vertical = [ - // used to remove unlikely keypoint positions - ["leftKnee", "leftShoulder"], - ["rightKnee", "rightShoulder"], - ["leftAnkle", "leftKnee"], - ["rightAnkle", "rightKnee"] -]; -var relative = [ - // used to match relative body parts - [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]], - [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]] -]; -var connected3 = { - // used to create body outline in annotations - leftLeg: ["leftHip", "leftKnee", "leftAnkle"], - rightLeg: ["rightHip", "rightKnee", "rightAnkle"], - torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"], - leftArm: ["leftShoulder", "leftElbow", "leftWrist"], - rightArm: ["rightShoulder", "rightElbow", "rightWrist"], - head: [] -}; - -// src/util/interpolate.ts -var bufferedResult = empty(); -var interpolateTime = 0; -function calc2(newResult, config3) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z; - const t0 = now(); - if (!newResult) return empty(); - const elapsed = Date.now() - newResult.timestamp; - const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1; - if (newResult.canvas) bufferedResult.canvas = newResult.canvas; - if (newResult.error) bufferedResult.error = newResult.error; - if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) { - bufferedResult.body = JSON.parse(JSON.stringify(newResult.body)); - } else { - for (let i = 0; i < newResult.body.length; i++) { - const box = newResult.body[i].box.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j] + newBoxCoord) / bufferedFactor); - const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j] + newBoxCoord) / bufferedFactor); - const keypoints = newResult.body[i].keypoints.map((newKpt, j) => { - var _a2, _b2, _c2, _d2, _e2, _f2, _g2, _h2, _i2; - return { - score: newKpt.score, - part: newKpt.part, - position: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2] - ], - positionRaw: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2] - ], - distance: [ - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_a2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _a2[0]) || 0) + (((_b2 = newKpt.distance) == null ? void 0 : _b2[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_d2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _d2[1]) || 0) + (((_e2 = newKpt.distance) == null ? void 0 : _e2[1]) || 0)) / bufferedFactor : (_f2 = newKpt.distance) == null ? void 0 : _f2[1], - bufferedResult.body[i].keypoints[j] ? ((bufferedFactor - 1) * (((_g2 = bufferedResult.body[i].keypoints[j].distance) == null ? void 0 : _g2[2]) || 0) + (((_h2 = newKpt.distance) == null ? void 0 : _h2[2]) || 0)) / bufferedFactor : (_i2 = newKpt.distance) == null ? void 0 : _i2[2] - ] - }; - }); - const annotations2 = {}; - let coords = { connected: {} }; - if ((_a = config3.body.modelPath) == null ? void 0 : _a.includes("efficientpose")) coords = efficientposecoords_exports; - else if ((_b = config3.body.modelPath) == null ? void 0 : _b.includes("blazepose")) coords = blazeposecoords_exports; - else if ((_c = config3.body.modelPath) == null ? void 0 : _c.includes("movenet")) coords = movenetcoords_exports; - for (const [name, indexes] of Object.entries(coords.connected)) { - const pt = []; - for (let j = 0; j < indexes.length - 1; j++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[j]); - const pt1 = keypoints.find((kp) => kp.part === indexes[j + 1]); - if (pt0 && pt1) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - bufferedResult.body[i] = { ...newResult.body[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.hand || newResult.hand.length !== bufferedResult.hand.length) { - bufferedResult.hand = JSON.parse(JSON.stringify(newResult.hand)); - } else { - for (let i = 0; i < newResult.hand.length; i++) { - const box = newResult.hand[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.hand[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j] + b) / bufferedFactor); - if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length) bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints; - const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j][k] || 1) + (coord || 0)) / bufferedFactor)) : []; - let annotations2 = {}; - if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) { - bufferedResult.hand[i].annotations = newResult.hand[i].annotations; - annotations2 = bufferedResult.hand[i].annotations; - } else if (newResult.hand[i].annotations) { - for (const key of Object.keys(newResult.hand[i].annotations)) { - annotations2[key] = ((_f = (_e = (_d = newResult.hand[i]) == null ? void 0 : _d.annotations) == null ? void 0 : _e[key]) == null ? void 0 : _f[0]) ? newResult.hand[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 }; - } - } - if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) { - bufferedResult.face = JSON.parse(JSON.stringify(newResult.face)); - } else { - for (let i = 0; i < newResult.face.length; i++) { - const box = newResult.face[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.face[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j] + b) / bufferedFactor); - let annotations2 = newResult.face[i].annotations; - if (Object.keys(bufferedResult.face[i].annotations).length !== Object.keys(newResult.face[i].annotations).length) { - bufferedResult.face[i].annotations = newResult.face[i].annotations; - annotations2 = bufferedResult.face[i].annotations; - } else if (newResult.face[i].annotations) { - for (const key of Object.keys(newResult.face[i].annotations)) { - annotations2[key] = ((_i = (_h = (_g = newResult.face[i]) == null ? void 0 : _g.annotations) == null ? void 0 : _h[key]) == null ? void 0 : _i[0]) ? newResult.face[i].annotations[key].map((val, j) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.face[i].annotations[key][j][k] + coord) / bufferedFactor)) : null; - } - } - if (newResult.face[i].rotation) { - const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } }; - rotation.matrix = (_j = newResult.face[i].rotation) == null ? void 0 : _j.matrix; - rotation.angle = { - roll: ((bufferedFactor - 1) * (((_l = (_k = bufferedResult.face[i].rotation) == null ? void 0 : _k.angle) == null ? void 0 : _l.roll) || 0) + (((_n = (_m = newResult.face[i].rotation) == null ? void 0 : _m.angle) == null ? void 0 : _n.roll) || 0)) / bufferedFactor, - yaw: ((bufferedFactor - 1) * (((_p = (_o = bufferedResult.face[i].rotation) == null ? void 0 : _o.angle) == null ? void 0 : _p.yaw) || 0) + (((_r = (_q = newResult.face[i].rotation) == null ? void 0 : _q.angle) == null ? void 0 : _r.yaw) || 0)) / bufferedFactor, - pitch: ((bufferedFactor - 1) * (((_t = (_s = bufferedResult.face[i].rotation) == null ? void 0 : _s.angle) == null ? void 0 : _t.pitch) || 0) + (((_v = (_u = newResult.face[i].rotation) == null ? void 0 : _u.angle) == null ? void 0 : _v.pitch) || 0)) / bufferedFactor - }; - rotation.gaze = { - // not fully correct due projection on circle, also causes wrap-around draw on jump from negative to positive - bearing: ((bufferedFactor - 1) * (((_w = bufferedResult.face[i].rotation) == null ? void 0 : _w.gaze.bearing) || 0) + (((_x = newResult.face[i].rotation) == null ? void 0 : _x.gaze.bearing) || 0)) / bufferedFactor, - strength: ((bufferedFactor - 1) * (((_y = bufferedResult.face[i].rotation) == null ? void 0 : _y.gaze.strength) || 0) + (((_z = newResult.face[i].rotation) == null ? void 0 : _z.gaze.strength) || 0)) / bufferedFactor - }; - bufferedResult.face[i] = { ...newResult.face[i], rotation, box, boxRaw, annotations: annotations2 }; - } else { - bufferedResult.face[i] = { ...newResult.face[i], box, boxRaw, annotations: annotations2 }; - } - } - } - if (!bufferedResult.object || newResult.object.length !== bufferedResult.object.length) { - bufferedResult.object = JSON.parse(JSON.stringify(newResult.object)); - } else { - for (let i = 0; i < newResult.object.length; i++) { - const box = newResult.object[i].box.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j] + b) / bufferedFactor); - const boxRaw = newResult.object[i].boxRaw.map((b, j) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j] + b) / bufferedFactor); - bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw }; - } - } - if (newResult.persons) { - const newPersons = newResult.persons; - if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) { - bufferedResult.persons = JSON.parse(JSON.stringify(newPersons)); - } else { - for (let i = 0; i < newPersons.length; i++) { - bufferedResult.persons[i].box = newPersons[i].box.map((box, j) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j] + box) / bufferedFactor); - } - } - } - if (newResult.gesture) bufferedResult.gesture = newResult.gesture; - bufferedResult.width = newResult.width; - bufferedResult.height = newResult.height; - const t1 = now(); - interpolateTime = env.perfadd ? interpolateTime + Math.round(t1 - t0) : Math.round(t1 - t0); - if (newResult.performance) bufferedResult.performance = { ...newResult.performance, interpolate: interpolateTime }; - return bufferedResult; -} - -// src/segmentation/meet.ts -var tf30 = __toESM(require_tfjs_esm()); -var model17; -async function load15(config3) { - if (!model17 || env.initial) model17 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model17["modelUrl"]); - return model17; -} -async function predict16(input, config3) { - var _a; - if (!model17) model17 = await load15(config3); - if (!(model17 == null ? void 0 : model17["executor"]) || !((_a = model17 == null ? void 0 : model17.inputs) == null ? void 0 : _a[0].shape)) return null; - const t2 = {}; - t2.resize = tf30.image.resizeBilinear(input, [model17.inputs[0].shape ? model17.inputs[0].shape[1] : 0, model17.inputs[0].shape ? model17.inputs[0].shape[2] : 0], false); - t2.norm = tf30.div(t2.resize, constants.tf255); - t2.res = model17.execute(t2.norm); - t2.squeeze = tf30.squeeze(t2.res, [0]); - [t2.bgRaw, t2.fgRaw] = tf30.unstack(t2.squeeze, 2); - t2.fg = tf30.softmax(t2.fgRaw); - t2.mul = tf30.mul(t2.fg, constants.tf255); - t2.expand = tf30.expandDims(t2.mul, 2); - t2.output = tf30.image.resizeBilinear(t2.expand, [input.shape[1] || 0, input.shape[2] || 0]); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf30.squeeze(input); - t2.concat = tf30.concat([t2.input, t2.output], -1); - rgba = tf30.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf30.cast(t2.output, "int32"); - break; - default: - rgba = tf30.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf30.dispose(t2[tensor6])); - return rgba; -} - -// src/face/match.ts -var match_exports = {}; -__export(match_exports, { - distance: () => distance, - find: () => find, - similarity: () => similarity -}); -function distance(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25 }) { - if (!descriptor1 || !descriptor1) return Number.MAX_SAFE_INTEGER; - let sum3 = 0; - for (let i = 0; i < descriptor1.length; i++) { - const diff = !options4.order || options4.order === 2 ? descriptor1[i] - descriptor2[i] : Math.abs(descriptor1[i] - descriptor2[i]); - sum3 += !options4.order || options4.order === 2 ? diff * diff : diff ** options4.order; - } - const dist = Math.round(100 * (options4.multiplier || 20) * sum3) / 100; - return dist; -} -var normalizeDistance = (dist, order, min2, max5) => { - if (dist === 0) return 1; - const root = order === 2 ? Math.sqrt(dist) : dist ** (1 / order); - const norm = (1 - root / 100 - min2) / (max5 - min2); - const clamp2 = Math.round(100 * Math.max(Math.min(norm, 1), 0)) / 100; - return clamp2; -}; -function similarity(descriptor1, descriptor2, options4 = { order: 2, multiplier: 25, min: 0.2, max: 0.8 }) { - const dist = distance(descriptor1, descriptor2, options4); - return normalizeDistance(dist, options4.order || 2, options4.min || 0, options4.max || 1); -} -function find(descriptor, descriptors, options4 = { order: 2, multiplier: 25, threshold: 0, min: 0.2, max: 0.8 }) { - if (!Array.isArray(descriptor) || !Array.isArray(descriptors) || descriptor.length < 64 || descriptors.length === 0) { - return { index: -1, distance: Number.POSITIVE_INFINITY, similarity: 0 }; - } - let lowestDistance = Number.MAX_SAFE_INTEGER; - let index2 = -1; - for (let i = 0; i < descriptors.length; i++) { - const res = descriptors[i].length === descriptor.length ? distance(descriptor, descriptors[i], options4) : Number.MAX_SAFE_INTEGER; - if (res < lowestDistance) { - lowestDistance = res; - index2 = i; - } - if (lowestDistance < (options4.threshold || 0)) break; - } - const normalizedSimilarity = normalizeDistance(lowestDistance, options4.order || 2, options4.min || 0, options4.max || 1); - return { index: index2, distance: lowestDistance, similarity: normalizedSimilarity }; -} - -// src/models.ts -var models_exports2 = {}; -__export(models_exports2, { - Models: () => Models, - validateModel: () => validateModel -}); - -// src/body/movenet.ts -var tf32 = __toESM(require_tfjs_esm()); - -// src/body/movenetfix.ts -var tf31 = __toESM(require_tfjs_esm()); -var maxJitter = 5e-3; -var cache5 = { - keypoints: [], - padding: [[0, 0], [0, 0], [0, 0], [0, 0]] -}; -function bodyParts(body4) { - for (const pair of horizontal) { - const left = body4.keypoints.findIndex((kp) => kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp.part === pair[1]); - if (body4.keypoints[left] && body4.keypoints[right]) { - if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } - } - for (const pair of vertical) { - const lower = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const higher = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - if (body4.keypoints[lower] && body4.keypoints[higher]) { - if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) { - body4.keypoints.splice(lower, 1); - } - } - } - for (const [pair, compare2] of relative) { - const left = body4.keypoints.findIndex((kp) => kp && kp.part === pair[0]); - const right = body4.keypoints.findIndex((kp) => kp && kp.part === pair[1]); - const leftTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[0]); - const rightTo = body4.keypoints.findIndex((kp) => kp && kp.part === compare2[1]); - if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo]) continue; - const distanceLeft = body4.keypoints[left] ? [ - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]), - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0]) - ] : [0, 0]; - const distanceRight = body4.keypoints[right] ? [ - Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]), - Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0]) - ] : [0, 0]; - if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) { - const tmp = body4.keypoints[left]; - body4.keypoints[left] = body4.keypoints[right]; - body4.keypoints[right] = tmp; - } - } -} -function jitter(keypoints) { - for (let i = 0; i < keypoints.length; i++) { - if (keypoints[i] && cache5.keypoints[i]) { - const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])]; - if (diff[0] < maxJitter && diff[1] < maxJitter) { - keypoints[i] = cache5.keypoints[i]; - } else { - cache5.keypoints[i] = keypoints[i]; - } - } else { - cache5.keypoints[i] = keypoints[i]; - } - } - return keypoints; -} -function padInput(input, inputSize10) { - var _a, _b; - const t2 = {}; - if (!((_a = input == null ? void 0 : input.shape) == null ? void 0 : _a[1]) || !((_b = input == null ? void 0 : input.shape) == null ? void 0 : _b[2])) return input; - cache5.padding = [ - [0, 0], - // dont touch batch - [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0], - // height before&after - [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0], - // width before&after - [0, 0] - // dont touch rbg - ]; - t2.pad = tf31.pad(input, cache5.padding); - t2.resize = tf31.image.resizeBilinear(t2.pad, [inputSize10, inputSize10]); - const final = tf31.cast(t2.resize, "int32"); - Object.keys(t2).forEach((tensor6) => tf31.dispose(t2[tensor6])); - return final; -} -function rescaleBody(body4, outputSize2) { - body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 == null ? void 0 : kpt4.position); - for (const kpt4 of body4.keypoints) { - kpt4.position = [ - kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0], - kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0] - ]; - kpt4.positionRaw = [ - kpt4.position[0] / outputSize2[0], - kpt4.position[1] / outputSize2[1] - ]; - } - const rescaledBoxes = calc(body4.keypoints.map((pt) => pt.position), outputSize2); - body4.box = rescaledBoxes.box; - body4.boxRaw = rescaledBoxes.boxRaw; - return body4; -} - -// src/body/movenet.ts -var model18; -var inputSize8 = 0; -var skipped14 = Number.MAX_SAFE_INTEGER; -var cache6 = { - boxes: [], - bodies: [], - last: 0 -}; -async function load16(config3) { - var _a; - if (env.initial) model18 = null; - if (!model18) { - fakeOps(["size"], config3); - model18 = await loadModel(config3.body.modelPath); - } else if (config3.debug) log("cached model:", model18["modelUrl"]); - inputSize8 = (model18 == null ? void 0 : model18["executor"]) && ((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape) ? model18.inputs[0].shape[2] : 0; - if (inputSize8 < 64) inputSize8 = 256; - if (tf32.env().flagRegistry.WEBGL_USE_SHAPES_UNIFORMS) tf32.env().set("WEBGL_USE_SHAPES_UNIFORMS", false); - return model18; -} -function parseSinglePose(res, config3, image28) { - const kpt4 = res[0][0]; - const keypoints = []; - let score = 0; - for (let id = 0; id < kpt4.length; id++) { - score = kpt4[id][2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[id][1], kpt4[id][0]]; - keypoints.push({ - score: Math.round(100 * score) / 100, - part: kpt3[id], - positionRaw, - position: [ - // normalized to input image size - Math.round((image28.shape[2] || 0) * positionRaw[0]), - Math.round((image28.shape[1] || 0) * positionRaw[1]) - ] - }); - } - } - score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0); - const bodies = []; - const newBox = calc(keypoints.map((pt) => pt.position), [image28.shape[2], image28.shape[1]]); - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - return bodies; -} -function parseMultiPose(res, config3, image28) { - const bodies = []; - for (let id = 0; id < res[0].length; id++) { - const kpt4 = res[0][id]; - const boxScore = Math.round(100 * kpt4[51 + 4]) / 100; - if (boxScore > config3.body.minConfidence) { - const keypoints = []; - for (let i = 0; i < 17; i++) { - const score = kpt4[3 * i + 2]; - if (score > config3.body.minConfidence) { - const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]]; - keypoints.push({ - part: kpt3[i], - score: Math.round(100 * score) / 100, - positionRaw, - position: [Math.round((image28.shape[2] || 0) * positionRaw[0]), Math.round((image28.shape[1] || 0) * positionRaw[1])] - }); - } - } - const boxRaw = [kpt4[51 + 1], kpt4[51 + 0], kpt4[51 + 3] - kpt4[51 + 1], kpt4[51 + 2] - kpt4[51 + 0]]; - const boxNorm = [Math.trunc(boxRaw[0] * (image28.shape[2] || 0)), Math.trunc(boxRaw[1] * (image28.shape[1] || 0)), Math.trunc(boxRaw[2] * (image28.shape[2] || 0)), Math.trunc(boxRaw[3] * (image28.shape[1] || 0))]; - const annotations2 = {}; - for (const [name, indexes] of Object.entries(connected3)) { - const pt = []; - for (let i = 0; i < indexes.length - 1; i++) { - const pt0 = keypoints.find((kp) => kp.part === indexes[i]); - const pt1 = keypoints.find((kp) => kp.part === indexes[i + 1]); - if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0)) pt.push([pt0.position, pt1.position]); - } - annotations2[name] = pt; - } - const body4 = { id, score: boxScore, box: boxNorm, boxRaw, keypoints: [...keypoints], annotations: annotations2 }; - bodyParts(body4); - bodies.push(body4); - } - } - bodies.sort((a, b) => b.score - a.score); - if (bodies.length > config3.body.maxDetected) bodies.length = config3.body.maxDetected; - return bodies; -} -async function predict17(input, config3) { - var _a; - if (!(model18 == null ? void 0 : model18["executor"]) || !((_a = model18 == null ? void 0 : model18.inputs) == null ? void 0 : _a[0].shape)) return []; - if (!config3.skipAllowed) cache6.boxes.length = 0; - skipped14++; - const skipTime = (config3.body.skipTime || 0) > now() - cache6.last; - const skipFrame = skipped14 < (config3.body.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame) { - return cache6.bodies; - } - return new Promise(async (resolve) => { - const t2 = {}; - skipped14 = 0; - t2.input = padInput(input, inputSize8); - t2.res = model18 == null ? void 0 : model18.execute(t2.input); - cache6.last = now(); - const res = await t2.res.array(); - cache6.bodies = t2.res.shape[2] === 17 ? parseSinglePose(res, config3, input) : parseMultiPose(res, config3, input); - for (const body4 of cache6.bodies) { - rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]); - jitter(body4.keypoints); - } - Object.keys(t2).forEach((tensor6) => tf32.dispose(t2[tensor6])); - resolve(cache6.bodies); - }); -} - -// src/object/nanodet.ts -var tf33 = __toESM(require_tfjs_esm()); -var model19; -var last10 = []; -var lastTime15 = 0; -var skipped15 = Number.MAX_SAFE_INTEGER; -var inputSize9 = 0; -var scaleBox = 2.5; -async function load17(config3) { - if (!model19 || env.initial) { - model19 = await loadModel(config3.object.modelPath); - const inputs = (model19 == null ? void 0 : model19["executor"]) ? Object.values(model19.modelSignature["inputs"]) : void 0; - inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 416; - } else if (config3.debug) log("cached model:", model19["modelUrl"]); - return model19; -} -async function process4(res, outputShape, config3) { - var _a, _b; - let id = 0; - let results = []; - const size2 = inputSize9; - for (const strideSize of [1, 2, 4]) { - const baseSize = strideSize * 13; - const scoresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels2.length)); - const scores = await scoresT.array(); - const featuresT = tf33.squeeze(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels2.length)); - const boxesMaxT = tf33.reshape(featuresT, [-1, 4, (((_a = featuresT.shape) == null ? void 0 : _a[1]) || 0) / 4]); - const boxIdxT = tf33.argMax(boxesMaxT, 2); - const boxIdx = await boxIdxT.array(); - for (let i = 0; i < scoresT.shape[0]; i++) { - for (let j = 0; j < (((_b = scoresT.shape) == null ? void 0 : _b[1]) || 0); j++) { - const score = scores[i][j]; - if (score > (config3.object.minConfidence || 0) && j !== 61) { - const cx = (0.5 + Math.trunc(i % baseSize)) / baseSize; - const cy = (0.5 + Math.trunc(i / baseSize)) / baseSize; - const boxOffset = boxIdx[i].map((a) => a * (baseSize / strideSize / size2)); - const [x, y] = [ - cx - scaleBox / strideSize * boxOffset[0], - cy - scaleBox / strideSize * boxOffset[1] - ]; - const [w, h] = [ - cx + scaleBox / strideSize * boxOffset[2] - x, - cy + scaleBox / strideSize * boxOffset[3] - y - ]; - let boxRaw = [x, y, w, h]; - boxRaw = boxRaw.map((a) => Math.max(0, Math.min(a, 1))); - const box = [ - // results normalized to input image pixels - boxRaw[0] * outputShape[0], - boxRaw[1] * outputShape[1], - boxRaw[2] * outputShape[0], - boxRaw[3] * outputShape[1] - ]; - const result = { - id: id++, - // strideSize, - score: Math.round(100 * score) / 100, - class: j + 1, - label: labels2[j].label, - // center: [Math.trunc(outputShape[0] * cx), Math.trunc(outputShape[1] * cy)], - // centerRaw: [cx, cy], - box: box.map((a) => Math.trunc(a)), - boxRaw - }; - results.push(result); - } - } - } - tf33.dispose([scoresT, featuresT, boxesMaxT, boxIdxT]); - } - const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]); - const nmsScores = results.map((a) => a.score); - let nmsIdx = []; - if (nmsBoxes && nmsBoxes.length > 0) { - const nms = await tf33.image.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected || 0, config3.object.iouThreshold, config3.object.minConfidence); - nmsIdx = Array.from(await nms.data()); - tf33.dispose(nms); - } - results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score); - return results; -} -async function predict18(image28, config3) { - if (!(model19 == null ? void 0 : model19["executor"])) return []; - const skipTime = (config3.object.skipTime || 0) > now() - lastTime15; - const skipFrame = skipped15 < (config3.object.skipFrames || 0); - if (config3.skipAllowed && skipTime && skipFrame && last10.length > 0) { - skipped15++; - return last10; - } - skipped15 = 0; - if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense")) return last10; - return new Promise(async (resolve) => { - const outputSize2 = [image28.shape[2] || 0, image28.shape[1] || 0]; - const resizeT = tf33.image.resizeBilinear(image28, [inputSize9, inputSize9], false); - const normT = tf33.div(resizeT, constants.tf255); - const transposeT = tf33.transpose(normT, [0, 3, 1, 2]); - let objectT; - if (config3.object.enabled) objectT = model19.execute(transposeT); - lastTime15 = now(); - const obj = await process4(objectT, outputSize2, config3); - last10 = obj; - tf33.dispose([resizeT, normT, transposeT, ...objectT]); - resolve(obj); - }); -} - -// src/body/posenet.ts -var tf34 = __toESM(require_tfjs_esm()); - -// src/body/posenetutils.ts -var partNames = [ - "nose", - "leftEye", - "rightEye", - "leftEar", - "rightEar", - "leftShoulder", - "rightShoulder", - "leftElbow", - "rightElbow", - "leftWrist", - "rightWrist", - "leftHip", - "rightHip", - "leftKnee", - "rightKnee", - "leftAnkle", - "rightAnkle" -]; -var count = partNames.length; -var partIds = partNames.reduce((result, jointName, i) => { - result[jointName] = i; - return result; -}, {}); -var connectedPartNames = [ - ["leftHip", "leftShoulder"], - ["leftElbow", "leftShoulder"], - ["leftElbow", "leftWrist"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["rightHip", "rightShoulder"], - ["rightElbow", "rightShoulder"], - ["rightElbow", "rightWrist"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"], - ["leftShoulder", "rightShoulder"], - ["leftHip", "rightHip"] -]; -var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]); -var poseChain = [ - ["nose", "leftEye"], - ["leftEye", "leftEar"], - ["nose", "rightEye"], - ["rightEye", "rightEar"], - ["nose", "leftShoulder"], - ["leftShoulder", "leftElbow"], - ["leftElbow", "leftWrist"], - ["leftShoulder", "leftHip"], - ["leftHip", "leftKnee"], - ["leftKnee", "leftAnkle"], - ["nose", "rightShoulder"], - ["rightShoulder", "rightElbow"], - ["rightElbow", "rightWrist"], - ["rightShoulder", "rightHip"], - ["rightHip", "rightKnee"], - ["rightKnee", "rightAnkle"] -]; -function getBoundingBox(keypoints) { - const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({ - maxX: Math.max(maxX, x), - maxY: Math.max(maxY, y), - minX: Math.min(minX, x), - minY: Math.min(minY, y) - }), { - maxX: Number.NEGATIVE_INFINITY, - maxY: Number.NEGATIVE_INFINITY, - minX: Number.POSITIVE_INFINITY, - minY: Number.POSITIVE_INFINITY - }); - return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY]; -} -function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) { - const scaleY = height / inputResolutionHeight; - const scaleX = width / inputResolutionWidth; - const scalePose = (pose, i) => ({ - id: i, - score: pose.score, - boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight], - box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)], - keypoints: pose.keypoints.map(({ score, part, position }) => ({ - score, - part, - position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)], - positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight] - })), - annotations: {} - }); - const scaledPoses = poses.map((pose, i) => scalePose(pose, i)); - return scaledPoses; -} -var MaxHeap = class { - // function call - constructor(maxSize2, getElementValue) { - __publicField(this, "priorityQueue"); - // don't touch - __publicField(this, "numberOfElements"); - __publicField(this, "getElementValue"); - this.priorityQueue = new Array(maxSize2); - this.numberOfElements = -1; - this.getElementValue = getElementValue; - } - enqueue(x) { - this.priorityQueue[++this.numberOfElements] = x; - this.swim(this.numberOfElements); - } - dequeue() { - const max5 = this.priorityQueue[0]; - this.exchange(0, this.numberOfElements--); - this.sink(0); - this.priorityQueue[this.numberOfElements + 1] = null; - return max5; - } - empty() { - return this.numberOfElements === -1; - } - size() { - return this.numberOfElements + 1; - } - all() { - return this.priorityQueue.slice(0, this.numberOfElements + 1); - } - max() { - return this.priorityQueue[0]; - } - swim(k) { - while (k > 0 && this.less(Math.floor(k / 2), k)) { - this.exchange(k, Math.floor(k / 2)); - k = Math.floor(k / 2); - } - } - sink(k) { - while (2 * k <= this.numberOfElements) { - let j = 2 * k; - if (j < this.numberOfElements && this.less(j, j + 1)) j++; - if (!this.less(k, j)) break; - this.exchange(k, j); - k = j; - } - } - getValueAt(i) { - return this.getElementValue(this.priorityQueue[i]); - } - less(i, j) { - return this.getValueAt(i) < this.getValueAt(j); - } - exchange(i, j) { - const t2 = this.priorityQueue[i]; - this.priorityQueue[i] = this.priorityQueue[j]; - this.priorityQueue[j] = t2; - } -}; -function getOffsetPoint(y, x, keypoint, offsets) { - return { - y: offsets.get(y, x, keypoint), - x: offsets.get(y, x, keypoint + count) - }; -} -function getImageCoords(part, outputStride2, offsets) { - const { heatmapY, heatmapX, id: keypoint } = part; - const { y, x } = getOffsetPoint(heatmapY, heatmapX, keypoint, offsets); - return { - x: part.heatmapX * outputStride2 + x, - y: part.heatmapY * outputStride2 + y - }; -} -function clamp(a, min2, max5) { - if (a < min2) return min2; - if (a > max5) return max5; - return a; -} -function squaredDistance(y1, x1, y2, x2) { - const dy = y2 - y1; - const dx = x2 - x1; - return dy * dy + dx * dx; -} -function addVectors(a, b) { - return { x: a.x + b.x, y: a.y + b.y }; -} - -// src/body/posenet.ts -var model20; -var poseNetOutputs = [ - "MobilenetV1/offset_2/BiasAdd", - "MobilenetV1/heatmap_2/BiasAdd", - "MobilenetV1/displacement_fwd_2/BiasAdd", - "MobilenetV1/displacement_bwd_2/BiasAdd" - /* displacementBwd */ -]; -var localMaximumRadius = 1; -var outputStride = 16; -var squaredNmsRadius = 50 ** 2; -function traverse(edgeId, sourceKeypoint, targetId, scores, offsets, displacements, offsetRefineStep = 2) { - const getDisplacement = (point2) => ({ - y: displacements.get(point2.y, point2.x, edgeId), - x: displacements.get(point2.y, point2.x, displacements.shape[2] / 2 + edgeId) - }); - const getStridedIndexNearPoint = (point2, height2, width2) => ({ - y: clamp(Math.round(point2.y / outputStride), 0, height2 - 1), - x: clamp(Math.round(point2.x / outputStride), 0, width2 - 1) - }); - const [height, width] = scores.shape; - const sourceKeypointIndices = getStridedIndexNearPoint(sourceKeypoint.position, height, width); - const displacement = getDisplacement(sourceKeypointIndices); - const displacedPoint = addVectors(sourceKeypoint.position, displacement); - let targetKeypoint = displacedPoint; - for (let i = 0; i < offsetRefineStep; i++) { - const targetKeypointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const offsetPoint = getOffsetPoint(targetKeypointIndices.y, targetKeypointIndices.x, targetId, offsets); - targetKeypoint = addVectors( - { x: targetKeypointIndices.x * outputStride, y: targetKeypointIndices.y * outputStride }, - { x: offsetPoint.x, y: offsetPoint.y } - ); - } - const targetKeyPointIndices = getStridedIndexNearPoint(targetKeypoint, height, width); - const score = scores.get(targetKeyPointIndices.y, targetKeyPointIndices.x, targetId); - return { position: targetKeypoint, part: partNames[targetId], score }; -} -function decodePose(root, scores, offsets, displacementsFwd, displacementsBwd) { - const tuples = poseChain.map(([parentJoinName, childJoinName]) => [partIds[parentJoinName], partIds[childJoinName]]); - const edgesFwd = tuples.map(([, childJointId]) => childJointId); - const edgesBwd = tuples.map(([parentJointId]) => parentJointId); - const numParts = scores.shape[2]; - const numEdges = edgesFwd.length; - const keypoints = new Array(numParts); - const rootPoint = getImageCoords(root.part, outputStride, offsets); - keypoints[root.part.id] = { - score: root.score, - part: partNames[root.part.id], - position: rootPoint - }; - for (let edge = numEdges - 1; edge >= 0; --edge) { - const sourceId = edgesFwd[edge]; - const targetId = edgesBwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsBwd); - } - } - for (let edge = 0; edge < numEdges; ++edge) { - const sourceId = edgesBwd[edge]; - const targetId = edgesFwd[edge]; - if (keypoints[sourceId] && !keypoints[targetId]) { - keypoints[targetId] = traverse(edge, keypoints[sourceId], targetId, scores, offsets, displacementsFwd); - } - } - return keypoints; -} -function scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores) { - const [height, width] = scores.shape; - let localMaximum = true; - const yStart = Math.max(heatmapY - localMaximumRadius, 0); - const yEnd = Math.min(heatmapY + localMaximumRadius + 1, height); - for (let yCurrent = yStart; yCurrent < yEnd; ++yCurrent) { - const xStart = Math.max(heatmapX - localMaximumRadius, 0); - const xEnd = Math.min(heatmapX + localMaximumRadius + 1, width); - for (let xCurrent = xStart; xCurrent < xEnd; ++xCurrent) { - if (scores.get(yCurrent, xCurrent, keypointId) > score) { - localMaximum = false; - break; - } - } - if (!localMaximum) break; - } - return localMaximum; -} -function buildPartWithScoreQueue(minConfidence2, scores) { - const [height, width, numKeypoints] = scores.shape; - const queue = new MaxHeap(height * width * numKeypoints, ({ score }) => score); - for (let heatmapY = 0; heatmapY < height; ++heatmapY) { - for (let heatmapX = 0; heatmapX < width; ++heatmapX) { - for (let keypointId = 0; keypointId < numKeypoints; ++keypointId) { - const score = scores.get(heatmapY, heatmapX, keypointId); - if (score < minConfidence2) continue; - if (scoreIsMaximumInLocalWindow(keypointId, score, heatmapY, heatmapX, scores)) queue.enqueue({ score, part: { heatmapY, heatmapX, id: keypointId } }); - } - } - } - return queue; -} -function withinRadius(poses, { x, y }, keypointId) { - return poses.some(({ keypoints }) => { - var _a; - const correspondingKeypoint = (_a = keypoints[keypointId]) == null ? void 0 : _a.position; - if (!correspondingKeypoint) return false; - return squaredDistance(y, x, correspondingKeypoint.y, correspondingKeypoint.x) <= squaredNmsRadius; - }); -} -function getInstanceScore(existingPoses, keypoints) { - const notOverlappedKeypointScores = keypoints.reduce((result, { position, score }, keypointId) => { - if (!withinRadius(existingPoses, position, keypointId)) result += score; - return result; - }, 0); - return notOverlappedKeypointScores / keypoints.length; -} -function decode(offsets, scores, displacementsFwd, displacementsBwd, maxDetected, minConfidence2) { - const poses = []; - const queue = buildPartWithScoreQueue(minConfidence2, scores); - while (poses.length < maxDetected && !queue.empty()) { - const root = queue.dequeue(); - const rootImageCoords = getImageCoords(root.part, outputStride, offsets); - if (withinRadius(poses, rootImageCoords, root.part.id)) continue; - let keypoints = decodePose(root, scores, offsets, displacementsFwd, displacementsBwd); - keypoints = keypoints.filter((a) => a.score > minConfidence2); - const score = getInstanceScore(poses, keypoints); - const box = getBoundingBox(keypoints); - if (score > minConfidence2) poses.push({ keypoints, box, score: Math.round(100 * score) / 100 }); - } - return poses; -} -async function predict19(input, config3) { - if (!(model20 == null ? void 0 : model20["executor"])) return []; - const res = tf34.tidy(() => { - if (!model20.inputs[0].shape) return []; - const resized = tf34.image.resizeBilinear(input, [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - const normalized = tf34.sub(tf34.div(tf34.cast(resized, "float32"), 127.5), 1); - const results = model20.execute(normalized, poseNetOutputs); - const results3d = results.map((y) => tf34.squeeze(y, [0])); - results3d[1] = tf34.sigmoid(results3d[1]); - return results3d; - }); - const buffers = await Promise.all(res.map((tensor6) => tensor6.buffer())); - for (const t2 of res) tf34.dispose(t2); - const decoded = decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence); - if (!model20.inputs[0].shape) return []; - const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model20.inputs[0].shape[2], model20.inputs[0].shape[1]]); - return scaled; -} -async function load18(config3) { - if (!model20 || env.initial) model20 = await loadModel(config3.body.modelPath); - else if (config3.debug) log("cached model:", model20["modelUrl"]); - return model20; -} - -// src/segmentation/rvm.ts -var tf35 = __toESM(require_tfjs_esm()); -var model21; -var outputNodes2 = ["fgr", "pha", "r1o", "r2o", "r3o", "r4o"]; -var t = {}; -var ratio = 0; -function init3(config3) { - tf35.dispose([t.r1i, t.r2i, t.r3i, t.r4i, t.downsample_ratio]); - t.r1i = tf35.tensor(0); - t.r2i = tf35.tensor(0); - t.r3i = tf35.tensor(0); - t.r4i = tf35.tensor(0); - ratio = config3.segmentation.ratio || 0.5; - t.downsample_ratio = tf35.tensor(ratio); -} -async function load19(config3) { - if (!model21 || env.initial) model21 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model21["modelUrl"]); - init3(config3); - return model21; -} -var normalize = (r) => tf35.tidy(() => { - const squeeze14 = tf35.squeeze(r, [0]); - const mul15 = tf35.mul(squeeze14, constants.tf255); - const cast8 = tf35.cast(mul15, "int32"); - return cast8; -}); -function getRGBA(fgr, pha) { - const rgb3 = fgr ? normalize(fgr) : tf35.fill([pha.shape[1] || 0, pha.shape[2] || 0, 3], 255, "int32"); - const a = pha ? normalize(pha) : tf35.fill([fgr.shape[1] || 0, fgr.shape[2] || 0, 1], 255, "int32"); - const rgba = tf35.concat([rgb3, a], -1); - tf35.dispose([rgb3, a]); - return rgba; -} -function getState(state) { - return tf35.tidy(() => { - const r = {}; - r.unstack = tf35.unstack(state, -1); - r.concat = tf35.concat(r.unstack, 1); - r.split = tf35.split(r.concat, 4, 1); - r.stack = tf35.concat(r.split, 2); - r.squeeze = tf35.squeeze(r.stack, [0]); - r.expand = tf35.expandDims(r.squeeze, -1); - r.add = tf35.add(r.expand, 1); - r.mul = tf35.mul(r.add, 127.5); - r.cast = tf35.cast(r.mul, "int32"); - r.tile = tf35.tile(r.cast, [1, 1, 3]); - r.alpha = tf35.fill([r.tile.shape[0] || 0, r.tile.shape[1] || 0, 1], 255, "int32"); - return tf35.concat([r.tile, r.alpha], -1); - }); -} -async function predict20(input, config3) { - if (!model21) model21 = await load19(config3); - if (!(model21 == null ? void 0 : model21["executor"])) return null; - t.src = tf35.div(input, 255); - if (ratio !== config3.segmentation.ratio) init3(config3); - const [fgr, pha, r1o, r2o, r3o, r4o] = await model21.executeAsync(t, outputNodes2); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - rgba = getRGBA(fgr, pha); - break; - case "alpha": - rgba = getRGBA(null, pha); - break; - case "foreground": - rgba = getRGBA(fgr, null); - break; - case "state": - rgba = getState(r1o); - break; - default: - rgba = tf35.tensor(0); - } - tf35.dispose([t.src, fgr, pha, t.r1i, t.r2i, t.r3i, t.r4i]); - [t.r1i, t.r2i, t.r3i, t.r4i] = [r1o, r2o, r3o, r4o]; - return rgba; -} - -// src/segmentation/selfie.ts -var tf36 = __toESM(require_tfjs_esm()); -var model22; -async function load20(config3) { - if (!model22 || env.initial) model22 = await loadModel(config3.segmentation.modelPath); - else if (config3.debug) log("cached model:", model22["modelUrl"]); - return model22; -} -async function predict21(input, config3) { - var _a; - if (!model22) model22 = await load20(config3); - if (!(model22 == null ? void 0 : model22["executor"]) || !((_a = model22 == null ? void 0 : model22.inputs) == null ? void 0 : _a[0].shape)) return null; - const t2 = {}; - t2.resize = tf36.image.resizeBilinear(input, [model22.inputs[0].shape ? model22.inputs[0].shape[1] : 0, model22.inputs[0].shape ? model22.inputs[0].shape[2] : 0], false); - t2.norm = tf36.div(t2.resize, constants.tf255); - t2.res = model22.execute(t2.norm); - t2.squeeze = tf36.squeeze(t2.res, [0]); - t2.alpha = tf36.image.resizeBilinear(t2.squeeze, [input.shape[1] || 0, input.shape[2] || 0]); - t2.mul = tf36.mul(t2.alpha, constants.tf255); - let rgba; - switch (config3.segmentation.mode || "default") { - case "default": - t2.input = tf36.squeeze(input); - t2.concat = tf36.concat([t2.input, t2.mul], -1); - rgba = tf36.cast(t2.concat, "int32"); - break; - case "alpha": - rgba = tf36.cast(t2.mul, "int32"); - break; - default: - rgba = tf36.tensor(0); - } - Object.keys(t2).forEach((tensor6) => tf36.dispose(t2[tensor6])); - return rgba; -} - -// src/models.ts -function validateModel(instance, model23, name) { - var _a, _b; - if (!model23) return null; - if (!((_a = instance == null ? void 0 : instance.config) == null ? void 0 : _a.validateModels)) return null; - const simpleOps = ["const", "placeholder", "noop", "pad", "squeeze", "add", "sub", "mul", "div"]; - const ignoreOps = ["biasadd", "fusedbatchnormv3", "matmul", "switch", "shape", "merge", "split", "broadcastto"]; - const ops = []; - const missing = []; - const url = model23["modelUrl"]; - const executor = model23["executor"]; - if ((_b = executor == null ? void 0 : executor.graph) == null ? void 0 : _b.nodes) { - for (const kernel of Object.values(executor.graph.nodes)) { - const op = kernel.op.toLowerCase(); - if (!ops.includes(op)) ops.push(op); - } - } else { - if (!executor && instance.config.debug) { - log("model not loaded", name); - } - } - for (const op of ops) { - if (!simpleOps.includes(op) && !ignoreOps.includes(op) && !instance.env.kernels.includes(op) && !instance.env.kernels.includes(op.replace("_", "")) && !instance.env.kernels.includes(op.replace("native", "")) && !instance.env.kernels.includes(op.replace("v2", ""))) { - missing.push(op); - } - } - if (instance.config.debug && missing.length > 0) log("model validation failed:", name, missing); - return missing.length > 0 ? { name, missing, ops, url } : null; -} -var Models = class { - constructor(currentInstance) { - __publicField(this, "instance"); - __publicField(this, "models", {}); - this.models = {}; - this.instance = currentInstance; - } - stats() { - let totalSizeFromManifest = 0; - let totalSizeWeights = 0; - let totalSizeLoading = 0; - for (const m of Object.values(modelStats)) { - totalSizeFromManifest += Number.isNaN(+m.sizeFromManifest) ? 0 : m.sizeFromManifest; - totalSizeWeights += Number.isNaN(+m.sizeLoadedWeights) ? 0 : m.sizeLoadedWeights; - totalSizeLoading += Number.isNaN(+m.sizeDesired) ? 0 : m.sizeDesired; - } - const percentageLoaded = totalSizeLoading > 0 ? totalSizeWeights / totalSizeLoading : 0; - return { - numLoadedModels: Object.values(modelStats).filter((m) => m == null ? void 0 : m.loaded).length, - numDefinedModels: Object.keys(this.models).length, - percentageLoaded, - totalSizeFromManifest, - totalSizeWeights, - totalSizeLoading, - modelStats: Object.values(modelStats) - }; - } - reset() { - for (const model23 of Object.keys(this.models)) this.models[model23] = null; - } - async load(instance) { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A; - if (env.initial) this.reset(); - if (instance) this.instance = instance; - const m = {}; - m.blazeface = this.instance.config.face.enabled && !this.models.blazeface ? load3(this.instance.config) : null; - m.antispoof = this.instance.config.face.enabled && ((_a = this.instance.config.face.antispoof) == null ? void 0 : _a.enabled) && !this.models.antispoof ? load8(this.instance.config) : null; - m.liveness = this.instance.config.face.enabled && ((_b = this.instance.config.face.liveness) == null ? void 0 : _b.enabled) && !this.models.liveness ? load9(this.instance.config) : null; - m.faceres = this.instance.config.face.enabled && ((_c = this.instance.config.face.description) == null ? void 0 : _c.enabled) && !this.models.faceres ? load7(this.instance.config) : null; - m.emotion = this.instance.config.face.enabled && ((_d = this.instance.config.face.emotion) == null ? void 0 : _d.enabled) && !this.models.emotion ? load6(this.instance.config) : null; - m.iris = this.instance.config.face.enabled && ((_e = this.instance.config.face.iris) == null ? void 0 : _e.enabled) && !((_f = this.instance.config.face.attention) == null ? void 0 : _f.enabled) && !this.models.iris ? load4(this.instance.config) : null; - m.facemesh = this.instance.config.face.enabled && ((_g = this.instance.config.face.mesh) == null ? void 0 : _g.enabled) && !this.models.facemesh ? load5(this.instance.config) : null; - m.gear = this.instance.config.face.enabled && ((_h = this.instance.config.face["gear"]) == null ? void 0 : _h.enabled) && !this.models.gear ? load10(this.instance.config) : null; - m.ssrnetage = this.instance.config.face.enabled && ((_i = this.instance.config.face["ssrnet"]) == null ? void 0 : _i.enabled) && !this.models.ssrnetage ? load11(this.instance.config) : null; - m.ssrnetgender = this.instance.config.face.enabled && ((_j = this.instance.config.face["ssrnet"]) == null ? void 0 : _j.enabled) && !this.models.ssrnetgender ? load12(this.instance.config) : null; - m.mobilefacenet = this.instance.config.face.enabled && ((_k = this.instance.config.face["mobilefacenet"]) == null ? void 0 : _k.enabled) && !this.models.mobilefacenet ? load13(this.instance.config) : null; - m.insightface = this.instance.config.face.enabled && ((_l = this.instance.config.face["insightface"]) == null ? void 0 : _l.enabled) && !this.models.insightface ? load14(this.instance.config) : null; - m.blazepose = this.instance.config.body.enabled && !this.models.blazepose && ((_m = this.instance.config.body.modelPath) == null ? void 0 : _m.includes("blazepose")) ? loadPose(this.instance.config) : null; - m.blazeposedetect = this.instance.config.body.enabled && !this.models.blazeposedetect && this.instance.config.body["detector"] && this.instance.config.body["detector"].modelPath ? loadDetect(this.instance.config) : null; - m.efficientpose = this.instance.config.body.enabled && !this.models.efficientpose && ((_n = this.instance.config.body.modelPath) == null ? void 0 : _n.includes("efficientpose")) ? load2(this.instance.config) : null; - m.movenet = this.instance.config.body.enabled && !this.models.movenet && ((_o = this.instance.config.body.modelPath) == null ? void 0 : _o.includes("movenet")) ? load16(this.instance.config) : null; - m.posenet = this.instance.config.body.enabled && !this.models.posenet && ((_p = this.instance.config.body.modelPath) == null ? void 0 : _p.includes("posenet")) ? load18(this.instance.config) : null; - m.handtrack = this.instance.config.hand.enabled && !this.models.handtrack && ((_r = (_q = this.instance.config.hand.detector) == null ? void 0 : _q.modelPath) == null ? void 0 : _r.includes("handtrack")) ? loadDetect3(this.instance.config) : null; - m.handskeleton = this.instance.config.hand.enabled && this.instance.config.hand.landmarks && !this.models.handskeleton && ((_t = (_s = this.instance.config.hand.detector) == null ? void 0 : _s.modelPath) == null ? void 0 : _t.includes("handtrack")) ? loadSkeleton2(this.instance.config) : null; - if (this.instance.config.hand.enabled && !this.models.handdetect && ((_v = (_u = this.instance.config.hand.detector) == null ? void 0 : _u.modelPath) == null ? void 0 : _v.includes("handdetect"))) { - m.handdetect = loadDetect2(this.instance.config); - m.handskeleton = loadSkeleton(this.instance.config); - } - m.centernet = this.instance.config.object.enabled && !this.models.centernet && ((_w = this.instance.config.object.modelPath) == null ? void 0 : _w.includes("centernet")) ? load(this.instance.config) : null; - m.nanodet = this.instance.config.object.enabled && !this.models.nanodet && ((_x = this.instance.config.object.modelPath) == null ? void 0 : _x.includes("nanodet")) ? load17(this.instance.config) : null; - m.selfie = this.instance.config.segmentation.enabled && !this.models.selfie && ((_y = this.instance.config.segmentation.modelPath) == null ? void 0 : _y.includes("selfie")) ? load20(this.instance.config) : null; - m.meet = this.instance.config.segmentation.enabled && !this.models.meet && ((_z = this.instance.config.segmentation.modelPath) == null ? void 0 : _z.includes("meet")) ? load15(this.instance.config) : null; - m.rvm = this.instance.config.segmentation.enabled && !this.models.rvm && ((_A = this.instance.config.segmentation.modelPath) == null ? void 0 : _A.includes("rvm")) ? load19(this.instance.config) : null; - for (const [model23, promise] of Object.entries(m)) { - if (promise == null ? void 0 : promise["then"]) promise["then"]((val) => this.models[model23] = val); - } - await Promise.all(Object.values(m)); - } - list() { - const models3 = Object.keys(this.models).map((model23) => { - var _a; - return { name: model23, loaded: this.models[model23] !== null, size: 0, url: this.models[model23] ? (_a = this.models[model23]) == null ? void 0 : _a["modelUrl"] : null }; - }); - for (const m of models3) { - const stats = Object.keys(modelStats).find((s) => s.startsWith(m.name)); - if (!stats) continue; - m.size = modelStats[stats].sizeLoadedWeights; - m.url = modelStats[stats].url; - } - return models3; - } - loaded() { - const list = this.list(); - const loaded = list.filter((model23) => model23.loaded).map((model23) => model23.name); - return loaded; - } - validate() { - const missing = []; - for (const defined of Object.keys(this.models)) { - const model23 = this.models[defined]; - if (!model23) continue; - const res = validateModel(this.instance, model23, defined); - if (res) missing.push(res); - } - return missing; - } -}; - -// src/util/persons.ts -function join2(faces, bodies, hands, gestures, shape) { - var _a, _b, _c, _d, _e, _f; - let id = 0; - const persons = []; - for (const face4 of faces) { - const person2 = { id: id++, face: face4, body: null, hands: { left: null, right: null }, gestures: [], box: [0, 0, 0, 0] }; - for (const body4 of bodies) { - if (face4.box[0] > body4.box[0] && face4.box[0] < body4.box[0] + body4.box[2] && face4.box[1] + face4.box[3] > body4.box[1] && face4.box[1] + face4.box[3] < body4.box[1] + body4.box[3]) { - person2.body = body4; - } - } - if (person2.body) { - for (const hand3 of hands) { - if (hand3.box[0] + hand3.box[2] > person2.body.box[0] && hand3.box[0] + hand3.box[2] < person2.body.box[0] + person2.body.box[2] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) person2.hands.left = hand3; - } - if (hand3.box[0] < person2.body.box[0] + person2.body.box[2] && hand3.box[0] > person2.body.box[0] && hand3.box[1] + hand3.box[3] > person2.body.box[1] && hand3.box[1] + hand3.box[3] < person2.body.box[1] + person2.body.box[3]) { - if (person2.hands) person2.hands.right = hand3; - } - } - } - for (const gesture2 of gestures) { - if (gesture2["face"] !== void 0 && gesture2["face"] === face4.id) person2.gestures.push(gesture2); - else if (gesture2["iris"] !== void 0 && gesture2["iris"] === face4.id) person2.gestures.push(gesture2); - else if (gesture2["body"] !== void 0 && gesture2["body"] === ((_a = person2.body) == null ? void 0 : _a.id)) person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_b = person2.hands.left) == null ? void 0 : _b.id)) person2.gestures.push(gesture2); - else if (gesture2["hand"] !== void 0 && gesture2["hand"] === ((_c = person2.hands.right) == null ? void 0 : _c.id)) person2.gestures.push(gesture2); - } - const x = []; - const y = []; - const extractXY = (box) => { - if (box && box.length === 4) { - x.push(box[0], box[0] + box[2]); - y.push(box[1], box[1] + box[3]); - } - }; - extractXY(person2.face.box); - extractXY((_d = person2.body) == null ? void 0 : _d.box); - extractXY((_e = person2.hands.left) == null ? void 0 : _e.box); - extractXY((_f = person2.hands.right) == null ? void 0 : _f.box); - const minX = Math.min(...x); - const minY = Math.min(...y); - person2.box = [minX, minY, Math.max(...x) - minX, Math.max(...y) - minY]; - if ((shape == null ? void 0 : shape[1]) && (shape == null ? void 0 : shape[2])) person2.boxRaw = [person2.box[0] / shape[2], person2.box[1] / shape[1], person2.box[2] / shape[2], person2.box[3] / shape[1]]; - persons.push(person2); - } - return persons; -} - -// src/warmup.ts -var tf37 = __toESM(require_tfjs_esm()); - -// src/sample.ts -var face3 = ` + gaze: [gaze]\xB0`,body:"body [score]%",bodyPart:"[label] [score]%",object:"[label] [score]%",hand:"[label] [score]%",finger:"[label]",gesture:"[where] [who]: [what]"};var a5=0;function Ks(e,t,n){let o=i0(f0,n);if(!t||!e)return;let r=oe(e);if(r){r.lineJoin="round",r.font=o.font;for(let s=0;sc5,kpt:()=>l5});var l5=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],c5={shoulders:["leftShoulder","rightShoulder"],hips:["rightHip","leftHip"],mouth:["leftMouth","rightMouth"],leftLegUpper:["leftHip","leftKnee"],leftLegLower:["leftKnee","leftAnkle"],leftFoot:["leftAnkle","leftHeel","leftFoot"],leftTorso:["leftShoulder","leftHip"],leftArmUpper:["leftShoulder","leftElbow"],leftArmLower:["leftElbow","leftWrist"],leftHand:["leftWrist","leftPalm"],leftHandPinky:["leftPalm","leftPinky"],leftHandIndex:["leftPalm","leftIndex"],leftHandThumb:["leftPalm","leftThumb"],leftEyeOutline:["leftEyeInside","leftEyeOutside"],rightLegUpper:["rightHip","rightKnee"],rightLegLower:["rightKnee","rightAnkle"],rightFoot:["rightAnkle","rightHeel","rightFoot"],rightTorso:["rightShoulder","rightHip"],rightArmUpper:["rightShoulder","rightElbow"],rightArmLower:["rightElbow","rightWrist"],rightHand:["rightWrist","rightPalm"],rightHandPinky:["rightPalm","rightPinky"],rightHandIndex:["rightPalm","rightIndex"],rightHandThumb:["rightPalm","rightThumb"],rightEyeOutline:["rightEyeInside","rightEyeOutside"]};var D=Z(V());var se,n2=224,e3,_s=5,rt=[8,16,32,32,32];function $s(){let e=[],t=0;for(;t<_s;){let n=0,o=t;for(;on.x)),y:D.tensor1d(e.map(n=>n.y))}}async function t3(e){if(R.initial&&(se=null),!se&&e.body.detector&&e.body.detector.modelPath){se=await O(e.body.detector.modelPath);let t=se!=null&&se.executor?Object.values(se.modelSignature.inputs):void 0;n2=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}else e.debug&&se&&b("cached model:",se.modelUrl);return $s(),se}var $1=[5,5];function eA(e,t){return D.tidy(()=>{let n=D.split(e,12,1),o=D.squeeze(n[0]),r=D.squeeze(n[1]),s=D.squeeze(n[2]),A=D.squeeze(n[3]);o=D.add(D.div(o,n2),t.x),r=D.add(D.div(r,n2),t.y),s=D.mul(D.div(s,n2),$1[0]),A=D.mul(D.div(A,n2),$1[1]);let a=D.sub(o,D.div(s,2)),l=D.sub(r,D.div(A,2)),c=D.add(a,s),d=D.add(l,A);return D.stack([a,l,c,d],1)})}async function tA(e,t,n,o){var c,d;let r=[],s={};s.boxes=eA(e,e3),s.scores=D.sigmoid(t),s.nms=await D.image.nonMaxSuppressionAsync(s.boxes,s.scores,1,((c=n.body.detector)==null?void 0:c.minConfidence)||.1,((d=n.body.detector)==null?void 0:d.iouThreshold)||.1);let A=await s.nms.data(),a=await s.scores.data(),l=await s.boxes.array();for(let i of Array.from(A)){let y=a[i],x=l[i],m=[Math.round(x[0]*o[0]),Math.round(x[1]*o[1]),Math.round(x[2]*o[0]),Math.round(x[3]*o[1])],f={score:y,boxRaw:x,box:m};r.push(f)}return Object.keys(s).forEach(i=>D.dispose(s[i])),r}async function n3(e,t,n){let o={};o.res=se==null?void 0:se.execute(e,["Identity"]),o.logitsRaw=D.slice(o.res,[0,0,0],[1,-1,1]),o.boxesRaw=D.slice(o.res,[0,0,1],[1,-1,-1]),o.logits=D.squeeze(o.logitsRaw),o.boxes=D.squeeze(o.boxesRaw);let r=await tA(o.boxes,o.logits,t,n);return Object.keys(o).forEach(s=>D.dispose(o[s])),r}function Oe(e,t=[1,1]){let n=[e.map(a=>a[0]),e.map(a=>a[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[o[0],o[1],r[0]-o[0],r[1]-o[1]],A=[s[0]/t[0],s[1]/t[1],s[2]/t[0],s[3]/t[1]];return{box:s,boxRaw:A}}function o3(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],o=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],s=[(o[0]+r[0])/2,(o[1]+r[1])/2],A=Math.max(s[0]-o[0],s[1]-o[1],-s[0]+r[0],-s[1]+r[1]),a=[Math.trunc(s[0]-A),Math.trunc(s[1]-A),Math.trunc(2*A),Math.trunc(2*A)],l=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:l}}function st(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}var Z0,x5=256,d5=Number.MAX_SAFE_INTEGER,nA={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},at=[],Ce=[[0,0],[0,0],[0,0],[0,0]],r3=0,s3=e=>1-1/(1+Math.exp(e)),a3=e=>t3(e);async function i3(e){if(R.initial&&(Z0=null),Z0)e.debug&&b("cached model:",Z0.modelUrl);else{Z0=await O(e.body.modelPath);let t=Z0!=null&&Z0.executor?Object.values(Z0.modelSignature.inputs):void 0;x5=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0}return Z0}function A3(e,t,n){var s,A;let o={};if(!((s=e==null?void 0:e.shape)!=null&&s[1])||!((A=e==null?void 0:e.shape)!=null&&A[2]))return e;let r;if(n&&(o.cropped=q0.image.cropAndResize(e,[n],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let a=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],l=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];Ce=[[0,0],a,l,[0,0]],o.pad=q0.pad(o.cropped||e,Ce),o.resize=q0.image.resizeBilinear(o.pad,[t,t]),r=q0.div(o.resize,C.tf255)}else e.shape[1]!==t?(o.resize=q0.image.resizeBilinear(o.cropped||e,[t,t]),r=q0.div(o.resize,C.tf255)):r=q0.div(o.cropped||e,C.tf255);return Object.keys(o).forEach(a=>q0.dispose(o[a])),r}function oA(e,t,n){for(let o of e)o.position=[Math.trunc(o.position[0]*(t[0]+Ce[2][0]+Ce[2][1])/t[0]-Ce[2][0]),Math.trunc(o.position[1]*(t[1]+Ce[1][0]+Ce[1][1])/t[1]-Ce[1][0]),o.position[2]],o.positionRaw=[o.position[0]/t[0],o.position[1]/t[1],2*o.position[2]/(t[0]+t[1])];if(n){let o=n[2]-n[0],r=n[3]-n[1];for(let s of e)s.positionRaw=[s.positionRaw[0]/r+n[1],s.positionRaw[1]/o+n[0],s.positionRaw[2]],s.position=[Math.trunc(s.positionRaw[0]*t[0]),Math.trunc(s.positionRaw[1]*t[1]),s.positionRaw[2]]}return e}function rA(e){let t=e.find(a=>a.part==="leftPalm"),n=e.find(a=>a.part==="leftWrist"),o=e.find(a=>a.part==="leftIndex");t.position[2]=((n.position[2]||0)+(o.position[2]||0))/2;let r=e.find(a=>a.part==="rightPalm"),s=e.find(a=>a.part==="rightWrist"),A=e.find(a=>a.part==="rightIndex");r.position[2]=((s.position[2]||0)+(A.position[2]||0))/2}async function sA(e,t,n){if(!(Z0!=null&&Z0.executor))return null;let o={};[o.ld,o.segmentation,o.heatmap,o.world,o.poseflag]=Z0==null?void 0:Z0.execute(e,nA.landmarks);let r=(await o.poseflag.data())[0],s=await o.ld.data(),A=await o.world.data();Object.keys(o).forEach(m=>q0.dispose(o[m]));let a=[],l=5;for(let m=0;mm.position),i=Oe(d,[n[0],n[1]]),y={};for(let[m,f]of Object.entries(c5)){let h=[];for(let T=0;Tu.part===f[T]),p=c.find(u=>u.part===f[T+1]);g&&p&&h.push([g.position,p.position])}y[m]=h}return{id:0,score:Math.trunc(100*r)/100,box:i.box,boxRaw:i.boxRaw,keypoints:c,annotations:y}}async function y5(e,t){var s,A,a;let n=[e.shape[2]||0,e.shape[1]||0],o=(t.body.skipTime||0)>v()-r3,r=d5<(t.body.skipFrames||0);if(t.skipAllowed&&o&&r&&at!==null)d5++;else{let l=[];if((A=(s=t.body)==null?void 0:s.detector)!=null&&A.enabled){let c=A3(e,224);l=await n3(c,t,n),q0.dispose(c)}else l=[{box:[0,0,0,0],boxRaw:[0,0,1,1],score:0}];for(let c=0;cF0.dispose(o[c])),r}async function p5(e,t){if(!(X0!=null&&X0.executor))return[];let 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w3(e){var t;return R.initial&&(xe=null),xe?e.debug&&b("cached model:",xe.modelUrl):xe=await O((t=e.face.detector)==null?void 0:t.modelPath),ge=xe.executor&&xe.inputs[0].shape?xe.inputs[0].shape[2]:256,u2=L.scalar(ge,"int32"),ft=L.tensor2d(T3(ge)),xe}function fA(e){if(!ft||!u2)return L.zeros([0,0]);let t={};t.boxStarts=L.slice(e,[0,1],[-1,2]),t.centers=L.add(t.boxStarts,ft),t.boxSizes=L.slice(e,[0,3],[-1,2]),t.boxSizesNormalized=L.div(t.boxSizes,u2),t.centersNormalized=L.div(t.centers,u2),t.halfBoxSize=L.div(t.boxSizesNormalized,C.tf2),t.starts=L.sub(t.centersNormalized,t.halfBoxSize),t.ends=L.add(t.centersNormalized,t.halfBoxSize),t.startNormalized=L.mul(t.starts,u2),t.endNormalized=L.mul(t.ends,u2);let n=L.concat2d([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(o=>L.dispose(t[o])),n}async function E3(e,t){var c,d,i,y,x,m,f,h,T;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={},o=[0,0],r=[1,1];if((d=(c=t==null?void 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0:i.maxDetected)||0,((y=t.face.detector)==null?void 0:y.iouThreshold)||0,((x=t.face.detector)==null?void 0:x.minConfidence)||0);let A=await n.nms.array(),a=[],l=await n.scores.data();for(let g=0;g(((m=t.face.detector)==null?void 0:m.minConfidence)||0)){let u={};u.bbox=L.slice(n.boxes,[A[g],0],[1,-1]),u.slice=L.slice(n.batch,[A[g],P3-1],[1,-1]),u.squeeze=L.squeeze(u.slice),u.landmarks=L.reshape(u.squeeze,[P3,-1]);let k=await u.bbox.data(),P=[k[0]*r[0]-o[0],k[1]*r[1]-o[1],k[2]*r[0]-o[0],k[3]*r[1]-o[1]],N={startPoint:[P[0],P[1]],endPoint:[P[2],P[3]],landmarks:await u.landmarks.array(),confidence:p};u.anchor=L.slice(ft,[A[g],0],[1,2]);let B=await u.anchor.data(),J=h3(N,[(e.shape[2]||0)/ge,(e.shape[1]||0)/ge],B),H=xt(J,((f=t.face.detector)==null?void 0:f.scale)||1.4),K=yt(H);K.size[0]>(((h=t.face.detector)==null?void 0:h.minSize)||0)&&K.size[1]>(((T=t.face.detector)==null?void 0:T.minSize)||0)&&a.push(K),Object.keys(u).forEach(e0=>L.dispose(u[e0]))}}return Object.keys(n).forEach(g=>L.dispose(n[g])),a}var ye=Z(V());var K0,We=0,M5=ae.leftEyeLower0,P5=ae.rightEyeLower0,h2={leftBounds:[M5[0],M5[M5.length-1]],rightBounds:[P5[0],P5[P5.length-1]]},b2={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function I3(e){var t,n;return R.initial&&(K0=null),K0?e.debug&&b("cached model:",K0.modelUrl):K0=await O((t=e.face.iris)==null?void 0:t.modelPath),We=K0!=null&&K0.executor&&((n=K0.inputs)!=null&&n[0].shape)?K0.inputs[0].shape[2]:0,We===-1&&(We=64),K0}function mt(e,t,n,o){for(let r=0;r{let t=e[h2.leftBounds[0]][2],n=e[h2.rightBounds[0]][2];return t-n},S3=(e,t,n,o,r,s=!1,A=2.3)=>{let a=yt(xt(b3([e[n],e[o]]),A)),l=p2(a),c=ye.image.cropAndResize(t,[[a.startPoint[1]/r,a.startPoint[0]/r,a.endPoint[1]/r,a.endPoint[0]/r]],[0],[We,We]);if(s&&R.kernels.includes("flipleftright")){let d=ye.image.flipLeftRight(c);ye.dispose(c),c=d}return{box:a,boxSize:l,crop:c}},j3=(e,t,n,o=!1)=>{let r=[];for(let s=0;s{let o=e[ae[`${n}EyeUpper0`][b2.upperCenter]][2],r=e[ae[`${n}EyeLower0`][b2.lowerCenter]][2],s=(o+r)/2;return t.map((A,a)=>{let l=s;return a===2?l=o:a===4&&(l=r),[A[0],A[1],l]})};async function L3(e,t,n,o){var N,B;if(!(K0!=null&&K0.executor))return e;let{box:r,boxSize:s,crop:A}=S3(e,t,h2.leftBounds[0],h2.leftBounds[1],n,!0,((N=o.face.iris)==null?void 0:N.scale)||2.3),{box:a,boxSize:l,crop:c}=S3(e,t,h2.rightBounds[0],h2.rightBounds[1],n,!0,((B=o.face.iris)==null?void 0:B.scale)||2.3),d=ye.concat([A,c]);ye.dispose(A),ye.dispose(c);let i=K0.execute(d);ye.dispose(d);let y=await i.data();ye.dispose(i);let x=y.slice(0,b2.numCoordinates*3),{rawCoords:m,iris:f}=j3(x,r,s,!0),h=y.slice(b2.numCoordinates*3),{rawCoords:T,iris:g}=j3(h,a,l,!1),p=mA(e);Math.abs(p)<30?(mt(e,m,"left",null),mt(e,T,"right",null)):p<1?mt(e,m,"left",["EyeUpper0","EyeLower0"]):mt(e,T,"right",["EyeUpper0","EyeLower0"]);let u=N3(e,f,"left"),k=N3(e,g,"right");return e.concat(u).concat(k)}async function C3(e,t){var s,A,a,l,c,d,i,y,x,m;let n={lips:await((A=(s=t.filter(f=>f.size===160))==null?void 0:s[0])==null?void 0:A.data()),irisL:await((l=(a=t.filter(f=>f.size===10))==null?void 0:a[0])==null?void 0:l.data()),eyeL:await((d=(c=t.filter(f=>f.size===142))==null?void 0:c[0])==null?void 0:d.data()),irisR:await((y=(i=t.filter(f=>f.size===10))==null?void 0:i[1])==null?void 0:y.data()),eyeR:await((m=(x=t.filter(f=>f.size===142))==null?void 0:x[1])==null?void 0:m.data())};for(let f of Object.values(n))if(!f)return e;let o=e2.reduce((f,h)=>f+=e[h][2],0)/e2.length;for(let f=0;ff+=e[h][2],0)/t2.length;for(let f=0;fv()-Te.timestamp,o=Te.skipped<(((c=t.face.detector)==null?void 0:c.skipFrames)||0);!t.skipAllowed||!n||!o||Te.boxes.length===0?(Te.boxes=await E3(e,t),Te.timestamp=v(),Te.skipped=0):Te.skipped++;let r=[],s=[],A=0,a=O2;for(let g=0;g[N[0]/(e.shape[2]||0),N[1]/(e.shape[1]||0),(N[2]||0)/a]);for(let N of Object.keys(_e))P.annotations[N]=[P.mesh[_e[N]]]}else if(!s0)t.debug&&b("face mesh detection 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${o<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let a=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));a>10&&t.push({face:n,gesture:`mouth ${Math.trunc(a)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},Fn=e=>{var n,o,r,s;if(!e)return[];let t=[];for(let A=0;A.06||h>.06)&&(x=!1),f>h?h>.04&&t.push({iris:A,gesture:"looking right"}):f>.04&&t.push({iris:A,gesture:"looking left"});let T=Math.abs(e[A].mesh[145][1]-e[A].annotations.rightEyeIris[0][1])/e[A].box[3],g=Math.abs(e[A].mesh[374][1]-e[A].annotations.leftEyeIris[0][1])/e[A].box[3];(g<.01||T<.01||g>.022||T>.022)&&(x=!1),(g<.01||T<.01)&&t.push({iris:A,gesture:"looking down"}),(g>.022||T>.022)&&t.push({iris:A,gesture:"looking up"}),x&&t.push({iris:A,gesture:"looking center"})}return t},Bn=e=>{if(!e)return[];let t=[];for(let n=0;n0){let r=o.reduce((A,a)=>(A.position[2]||0)<(a.position[2]||0)?A:a);t.push({hand:n,gesture:`${r.name} forward`});let s=o.reduce((A,a)=>A.position[1][s[0]*t[0],s[1]*t[1]]);return{startPoint:n,endPoint:o,palmLandmarks:r,confidence:e.confidence}}function Et(e,t=1.5){let n=C2(e),o=wt(e),r=[t*o[0]/2,t*o[1]/2],s=[n[0]-r[0],n[1]-r[1]],A=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function zt(e){let t=C2(e),n=wt(e),r=Math.max(...n)/2,s=[t[0]-r,t[1]-r],A=[t[0]+r,t[1]+r];return{startPoint:s,endPoint:A,palmLandmarks:e.palmLandmarks}}function NA(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function Xn(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return NA(n)}var Hn=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function qe(e,t){let n=0;for(let o=0;o[A.x,A.y]),this.anchorsTensor=W.tensor2d(this.anchors),this.inputSize=((s=(r=(o=(n=this==null?void 0:this.model)==null?void 0:n.inputs)==null?void 0:o[0])==null?void 0:r.shape)==null?void 0:s[2])||0,this.inputSizeTensor=W.tensor1d([this.inputSize,this.inputSize]),this.doubleInputSizeTensor=W.tensor1d([this.inputSize*2,this.inputSize*2])}normalizeBoxes(t){let n={};n.boxOffsets=W.slice(t,[0,0],[-1,2]),n.boxSizes=W.slice(t,[0,2],[-1,2]),n.div=W.div(n.boxOffsets,this.inputSizeTensor),n.boxCenterPoints=W.add(n.div,this.anchorsTensor),n.halfBoxSizes=W.div(n.boxSizes,this.doubleInputSizeTensor),n.sub=W.sub(n.boxCenterPoints,n.halfBoxSizes),n.startPoints=W.mul(n.sub,this.inputSizeTensor),n.add=W.add(n.boxCenterPoints,n.halfBoxSizes),n.endPoints=W.mul(n.add,this.inputSizeTensor);let o=W.concat2d([n.startPoints,n.endPoints],1);return Object.keys(n).forEach(r=>W.dispose(n[r])),o}normalizeLandmarks(t,n){let o={};o.reshape=W.reshape(t,[-1,7,2]),o.div=W.div(o.reshape,this.inputSizeTensor),o.landmarks=W.add(o.div,this.anchors[n]?this.anchors[n]:0);let r=W.mul(o.landmarks,this.inputSizeTensor);return Object.keys(o).forEach(s=>W.dispose(o[s])),r}async predict(t,n){var a;let o={};o.resize=W.image.resizeBilinear(t,[this.inputSize,this.inputSize]),o.div=W.div(o.resize,C.tf127),o.image=W.sub(o.div,C.tf1),o.batched=this.model.execute(o.image),o.predictions=W.squeeze(o.batched),o.slice=W.slice(o.predictions,[0,0],[-1,1]),o.sigmoid=W.sigmoid(o.slice),o.scores=W.squeeze(o.sigmoid);let r=await o.scores.data();o.boxes=W.slice(o.predictions,[0,1],[-1,4]),o.norm=this.normalizeBoxes(o.boxes),o.nms=await W.image.nonMaxSuppressionAsync(o.norm,o.scores,3*(((a=n.hand)==null?void 0:a.maxDetected)||1),n.hand.iouThreshold,n.hand.minConfidence);let s=await o.nms.array(),A=[];for(let l of s){let c={};c.box=W.slice(o.norm,[l,0],[1,-1]),c.slice=W.slice(o.predictions,[l,5],[1,14]),c.norm=this.normalizeLandmarks(c.slice,l),c.palmLandmarks=W.reshape(c.norm,[-1,2]);let d=await c.box.data(),i=d.slice(0,2),y=d.slice(2,4),x=await c.palmLandmarks.array(),m={startPoint:i,endPoint:y,palmLandmarks:x,confidence:r[l]},f=qn(m,[(t.shape[2]||1)/this.inputSize,(t.shape[1]||0)/this.inputSize]);A.push(f),Object.keys(c).forEach(h=>W.dispose(c[h]))}return Object.keys(o).forEach(l=>W.dispose(o[l])),A}};var $0=Z(V());var CA=5,Jn=1.65,Qn=[0,5,9,13,17,1,2],WA=0,DA=2,_n=0,jt=class{constructor(t,n){w(this,"handDetector");w(this,"handPoseModel");w(this,"inputSize");w(this,"storedBoxes");w(this,"skipped");w(this,"detectedHands");var o,r,s;this.handDetector=t,this.handPoseModel=n,this.inputSize=((s=(r=(o=this.handPoseModel)==null?void 0:o.inputs)==null?void 0:r[0].shape)==null?void 0:s[2])||0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(A=>A[0]),o=t.map(A=>A[1]),r=[Math.min(...n),Math.min(...o)],s=[Math.max(...n),Math.max(...o)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let o=t.map(s=>e1([...s,1],n)),r=this.calculateLandmarksBoundingBox(o);return Et(zt(r),CA)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),o=Et(zt(n),Jn);o.palmLandmarks=[];for(let r=0;r[A[0]*(x[0]-this.inputSize/2),A[1]*(x[1]-this.inputSize/2),A[2]*x[2]]),l=$5(o,[0,0]),c=a.map(x=>[...e1(x,l),x[2]]),d=Un(r),i=[...C2(n),1],y=[qe(i,d[0]),qe(i,d[1])];return c.map(x=>[Math.trunc(x[0]+y[0]),Math.trunc(x[1]+y[1]),Math.trunc(x[2])])}async estimateHands(t,n){let o=!1,r,s=(n.hand.skipTime||0)>v()-_n,A=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&s&&A?this.skipped++:(r=await this.handDetector.predict(t,n),this.skipped=0),r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(o=!0));let a=[];for(let l=0;l=n.hand.minConfidence/4){let k=$0.reshape(p,[-1,3]),P=await k.array();$0.dispose(p),$0.dispose(k);let N=this.transformRawCoords(P,f,d,m),B=this.getBoxForHandLandmarks(N);this.storedBoxes[l]={...B,confidence:u};let J={landmarks:N,confidence:u,boxConfidence:c.confidence,fingerConfidence:u,box:{topLeft:B.startPoint,bottomRight:B.endPoint}};a.push(J)}else this.storedBoxes[l]=null;$0.dispose(p)}else{let d=Et(zt(c),Jn),i={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:d.startPoint,bottomRight:d.endPoint},landmarks:[]};a.push(i)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=a.length,a.length>n.hand.maxDetected&&(a.length=n.hand.maxDetected),a}};var $n={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},l2,c2,t1;function BA(){let e=l2?new St(l2):void 0;e&&c2&&(t1=new jt(e,c2))}async function n1(e,t){t1||BA();let n=await t1.estimateHands(e,t);if(!n)return[];let o=[];for(let r=0;rn[r].landmarks[i]);let A=n[r].landmarks,a=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(A&&A.length>0){for(let d of A)d[0]a[2]&&(a[2]=d[0]),d[1]>a[3]&&(a[3]=d[1]);a[2]-=a[0],a[3]-=a[1],l=[a[0]/(e.shape[2]||0),a[1]/(e.shape[1]||0),a[2]/(e.shape[2]||0),a[3]/(e.shape[1]||0)]}else 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model:",c2.modelUrl):c2=await O((t=e.hand.skeleton)==null?void 0:t.modelPath),c2}var $=Z(V());var p0=[null,null],HA=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],Xe=[[0,0],[0,0]],GA=["hand","fist","pinch","point","face","tip","pinchtip"],oo=4,ro=1.6,VA=512,ZA=1.4,Nt=Number.MAX_SAFE_INTEGER,o1=0,Ee=[0,0],m0={boxes:[],hands:[]},so={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],base:[0],palm:[0,17,13,9,5,1,0]};async function Ao(e){var t;if(R.initial&&(p0[0]=null),p0[0])e.debug&&b("cached model:",p0[0].modelUrl);else{J2(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),p0[0]=await O((t=e.hand.detector)==null?void 0:t.modelPath);let n=p0[0].executor?Object.values(p0[0].modelSignature.inputs):void 0;Xe[0][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[0][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[0]}async function ao(e){var t;if(R.initial&&(p0[1]=null),p0[1])e.debug&&b("cached model:",p0[1].modelUrl);else{p0[1]=await O((t=e.hand.skeleton)==null?void 0:t.modelPath);let n=p0[1].executor?Object.values(p0[1].modelSignature.inputs):void 0;Xe[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Xe[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0}return p0[1]}async function qA(e,t){let n=[];if(!e||!p0[0])return n;let o={},r=(e.shape[2]||1)/(e.shape[1]||1),s=Math.min(Math.round((e.shape[1]||0)/8)*8,VA),A=Math.round(s*r/8)*8;o.resize=$.image.resizeBilinear(e,[s,A]),o.cast=$.cast(o.resize,"int32"),[o.rawScores,o.rawBoxes]=await p0[0].executeAsync(o.cast,HA),o.boxes=$.squeeze(o.rawBoxes,[0,2]),o.scores=$.squeeze(o.rawScores,[0]);let a=$.unstack(o.scores,1);$.dispose(a[oo]),a.splice(oo,1),o.filtered=$.stack(a,1),$.dispose(a),o.max=$.max(o.filtered,1),o.argmax=$.argMax(o.filtered,1);let l=0;o.nms=await $.image.nonMaxSuppressionAsync(o.boxes,o.max,(t.hand.maxDetected||0)+1,t.hand.iouThreshold||0,t.hand.minConfidence||1);let c=await o.nms.data(),d=await o.max.data(),i=await o.argmax.data();for(let y of Array.from(c)){let x=$.slice(o.boxes,y,1),m=await x.data();$.dispose(x);let f=[m[1],m[0],m[3]-m[1],m[2]-m[0]],h=st(f,ZA),T=[Math.trunc(f[0]*Ee[0]),Math.trunc(f[1]*Ee[1]),Math.trunc(f[2]*Ee[0]),Math.trunc(f[3]*Ee[1])],g=d[y],p=GA[i[y]],u={id:l++,score:g,box:T,boxRaw:h,label:p};n.push(u)}return Object.keys(o).forEach(y=>$.dispose(o[y])),n.sort((y,x)=>x.score-y.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function r1(e,t,n){let o={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&p0[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={},s=[t.boxRaw[1],t.boxRaw[0],t.boxRaw[3]+t.boxRaw[1],t.boxRaw[2]+t.boxRaw[0]];r.crop=$.image.cropAndResize(e,[s],[0],[Xe[1][0],Xe[1][1]],"bilinear"),r.div=$.div(r.crop,C.tf255),[r.score,r.keypoints]=p0[1].execute(r.div,["Identity_1","Identity"]);let A=(await r.score.data())[0],a=(100-Math.trunc(100/(1+Math.exp(A))))/100;if(a>=(n.hand.minConfidence||0)){o.fingerScore=a,r.reshaped=$.reshape(r.keypoints,[-1,3]);let d=(await r.reshaped.array()).map(i=>[i[0]/Xe[1][1],i[1]/Xe[1][0],i[2]||0]).map(i=>[i[0]*t.boxRaw[2],i[1]*t.boxRaw[3],i[2]||0]);o.keypoints=d.map(i=>[Ee[0]*(i[0]+t.boxRaw[0]),Ee[1]*(i[1]+t.boxRaw[1]),i[2]||0]),o.landmarks=kt(o.keypoints);for(let i of Object.keys(so))o.annotations[i]=so[i].map(y=>o.landmarks&&o.keypoints[y]?o.keypoints[y]:null)}Object.keys(r).forEach(l=>$.dispose(r[l]))}return o}async function s1(e,t){var r,s;if(!((r=p0[0])!=null&&r.executor)||!((s=p0[1])!=null&&s.executor)||!p0[0].inputs[0].shape||!p0[1].inputs[0].shape)return[];Ee=[e.shape[2]||0,e.shape[1]||0],Nt++;let n=(t.hand.skipTime||0)>v()-o1,o=Nt<(t.hand.skipFrames||0);return t.skipAllowed&&n&&o?m0.hands:new Promise(async A=>{let a=3*(t.hand.skipTime||0)>v()-o1,l=Nt<3*(t.hand.skipFrames||0);t.skipAllowed&&m0.hands.length===t.hand.maxDetected?m0.hands=await Promise.all(m0.boxes.map(d=>r1(e,d,t))):t.skipAllowed&&a&&l&&m0.hands.length>0?m0.hands=await Promise.all(m0.boxes.map(d=>r1(e,d,t))):(m0.boxes=await qA(e,t),o1=v(),m0.hands=await Promise.all(m0.boxes.map(d=>r1(e,d,t))),Nt=0);let c=[...m0.boxes];if(m0.boxes.length=0,t.cacheSensitivity>0)for(let d=0;d.05&&i.box[3]/(e.shape[1]||1)>.05&&m0.hands[d].fingerScore&&m0.hands[d].fingerScore>(t.hand.minConfidence||0)){let y=st(i.box,ro),x=st(i.boxRaw,ro);m0.boxes.push({...c[d],box:y,boxRaw:x})}}for(let d=0;d({face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,width:0,height:0,error:e});var W2={};Se(W2,{connected:()=>Lt,horizontal:()=>A1,kpt:()=>It,relative:()=>i1,vertical:()=>a1});var It=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],A1=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],a1=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],i1=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],Lt={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var z=ve(),l1=0;function lo(e,t){var A,a,l,c,d,i,y,x,m,f,h,T,g,p,u,k,P,N,B,J,H,K,e0,n0,o0,N0;let n=v();if(!e)return ve();let o=Date.now()-e.timestamp,r=o<1e3?8-Math.log(o+1):1;if(e.canvas&&(z.canvas=e.canvas),e.error&&(z.error=e.error),!z.body||e.body.length!==z.body.length)z.body=JSON.parse(JSON.stringify(e.body));else for(let M=0;M((r-1)*z.body[M].box[q]+G)/r),C0=e.body[M].boxRaw.map((G,q)=>((r-1)*z.body[M].boxRaw[q]+G)/r),x0=e.body[M].keypoints.map((G,q)=>{var 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Mo(e){let t=e.reduce(({maxX:n,maxY:o,minX:r,minY:s},{position:{x:A,y:a}})=>({maxX:Math.max(n,A),maxY:Math.max(o,a),minX:Math.min(r,A),minY:Math.min(s,a)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function Po(e,[t,n],[o,r]){let s=t/o,A=n/r,a=(c,d)=>({id:d,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/o,c.box[2]/r,c.box[3]/o],box:[Math.trunc(c.box[0]*A),Math.trunc(c.box[1]*s),Math.trunc(c.box[2]*A),Math.trunc(c.box[3]*s)],keypoints:c.keypoints.map(({score:i,part:y,position:x})=>({score:i,part:y,position:[Math.trunc(x.x*A),Math.trunc(x.y*s)],positionRaw:[x.x/o,x.y/o]})),annotations:{}});return e.map((c,d)=>a(c,d))}var Bt=class{constructor(t,n){w(this,"priorityQueue");w(this,"numberOfElements");w(this,"getElementValue");this.priorityQueue=new 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a=T=>({y:s.get(T.y,T.x,e),x:s.get(T.y,T.x,s.shape[2]/2+e)}),l=(T,g,p)=>({y:b1(Math.round(T.y/M2),0,g-1),x:b1(Math.round(T.x/M2),0,p-1)}),[c,d]=o.shape,i=l(t.position,c,d),y=a(i),m=g1(t.position,y);for(let T=0;T[F2[y],F2[x]]),A=s.map(([,y])=>y),a=s.map(([y])=>y),l=t.shape[2],c=A.length,d=new Array(l),i=h1(e.part,M2,n);d[e.part.id]={score:e.score,part:B2[e.part.id],position:i};for(let y=c-1;y>=0;--y){let x=A[y],m=a[y];d[x]&&!d[m]&&(d[m]=wo(y,d[x],m,t,n,r))}for(let y=0;yt){a=!1;break}if(!a)break}return a}function Aa(e,t){let[n,o,r]=t.shape,s=new Bt(n*o*r,({score:A})=>A);for(let A=0;A{var A;let s=(A=r[o])==null?void 0:A.position;return s?ko(n,t,s.y,s.x)<=oa:!1})}function aa(e,t){return t.reduce((o,{position:r,score:s},A)=>(Eo(e,r,A)||(o+=s),o),0)/t.length}function ia(e,t,n,o,r,s){let A=[],a=Aa(s,t);for(;A.lengthx.score>s);let i=aa(A,d),y=Mo(d);i>s&&A.push({keypoints:d,box:y,score:Math.round(100*i)/100})}return A}async function T1(e,t){if(!(te!=null&&te.executor))return[];let n=V0.tidy(()=>{if(!te.inputs[0].shape)return[];let A=V0.image.resizeBilinear(e,[te.inputs[0].shape[2],te.inputs[0].shape[1]]),a=V0.sub(V0.div(V0.cast(A,"float32"),127.5),1),c=te.execute(a,na).map(d=>V0.squeeze(d,[0]));return c[1]=V0.sigmoid(c[1]),c}),o=await Promise.all(n.map(A=>A.buffer()));for(let A of n)V0.dispose(A);let r=ia(o[0],o[1],o[2],o[3],t.body.maxDetected,t.body.minConfidence);return te.inputs[0].shape?Po(r,[e.shape[1],e.shape[2]],[te.inputs[0].shape[2],te.inputs[0].shape[1]]):[]}async function zo(e){return!te||R.initial?te=await O(e.body.modelPath):e.debug&&b("cached model:",te.modelUrl),te}var F=Z(V());var Re,la=["fgr","pha","r1o","r2o","r3o","r4o"],g0={},R1=0;function No(e){F.dispose([g0.r1i,g0.r2i,g0.r3i,g0.r4i,g0.downsample_ratio]),g0.r1i=F.tensor(0),g0.r2i=F.tensor(0),g0.r3i=F.tensor(0),g0.r4i=F.tensor(0),R1=e.segmentation.ratio||.5,g0.downsample_ratio=F.tensor(R1)}async function M1(e){return!Re||R.initial?Re=await O(e.segmentation.modelPath):e.debug&&b("cached 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Re.executeAsync(g0,la),l;switch(t.segmentation.mode||"default"){case"default":l=v1(n,o);break;case"alpha":l=v1(null,o);break;case"foreground":l=v1(n,null);break;case"state":l=ca(r);break;default:l=F.tensor(0)}return F.dispose([g0.src,n,o,g0.r1i,g0.r2i,g0.r3i,g0.r4i]),[g0.r1i,g0.r2i,g0.r3i,g0.r4i]=[r,s,A,a],l}var S0=Z(V());var O0;async function P1(e){return!O0||R.initial?O0=await O(e.segmentation.modelPath):e.debug&&b("cached model:",O0.modelUrl),O0}async function Oo(e,t){var r;if(O0||(O0=await P1(t)),!(O0!=null&&O0.executor)||!((r=O0==null?void 0:O0.inputs)!=null&&r[0].shape))return null;let n={};n.resize=S0.image.resizeBilinear(e,[O0.inputs[0].shape?O0.inputs[0].shape[1]:0,O0.inputs[0].shape?O0.inputs[0].shape[2]:0],!1),n.norm=S0.div(n.resize,C.tf255),n.res=O0.execute(n.norm),n.squeeze=S0.squeeze(n.res,[0]),n.alpha=S0.image.resizeBilinear(n.squeeze,[e.shape[1]||0,e.shape[2]||0]),n.mul=S0.mul(n.alpha,C.tf255);let o;switch(t.segmentation.mode||"default"){case"default":n.input=S0.squeeze(e),n.concat=S0.concat([n.input,n.mul],-1),o=S0.cast(n.concat,"int32");break;case"alpha":o=S0.cast(n.mul,"int32");break;default:o=S0.tensor(0)}return Object.keys(n).forEach(s=>S0.dispose(n[s])),o}function Gt(e,t,n){var c,d;if(!t||!((c=e==null?void 0:e.config)!=null&&c.validateModels))return null;let o=["const","placeholder","noop","pad","squeeze","add","sub","mul","div"],r=["biasadd","fusedbatchnormv3","matmul","switch","shape","merge","split","broadcastto"],s=[],A=[],a=t.modelUrl,l=t.executor;if((d=l==null?void 0:l.graph)!=null&&d.nodes)for(let i of Object.values(l.graph.nodes)){let y=i.op.toLowerCase();s.includes(y)||s.push(y)}else!l&&e.config.debug&&b("model not loaded",n);for(let i of s)!o.includes(i)&&!r.includes(i)&&!e.env.kernels.includes(i)&&!e.env.kernels.includes(i.replace("_",""))&&!e.env.kernels.includes(i.replace("native",""))&&!e.env.kernels.includes(i.replace("v2",""))&&A.push(i);return 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n={};n.blazeface=this.instance.config.face.enabled&&!this.models.blazeface?w3(this.instance.config):null,n.antispoof=this.instance.config.face.enabled&&((o=this.instance.config.face.antispoof)!=null&&o.enabled)&&!this.models.antispoof?_3(this.instance.config):null,n.liveness=this.instance.config.face.enabled&&((r=this.instance.config.face.liveness)!=null&&r.enabled)&&!this.models.liveness?nn(this.instance.config):null,n.faceres=this.instance.config.face.enabled&&((s=this.instance.config.face.description)!=null&&s.enabled)&&!this.models.faceres?U3(this.instance.config):null,n.emotion=this.instance.config.face.enabled&&((A=this.instance.config.face.emotion)!=null&&A.enabled)&&!this.models.emotion?V3(this.instance.config):null,n.iris=this.instance.config.face.enabled&&((a=this.instance.config.face.iris)!=null&&a.enabled)&&!((l=this.instance.config.face.attention)!=null&&l.enabled)&&!this.models.iris?I3(this.instance.config):null,n.facemesh=this.instance.config.face.enabled&&((c=this.instance.config.face.mesh)!=null&&c.enabled)&&!this.models.facemesh?D3(this.instance.config):null,n.gear=this.instance.config.face.enabled&&((d=this.instance.config.face.gear)!=null&&d.enabled)&&!this.models.gear?An(this.instance.config):null,n.ssrnetage=this.instance.config.face.enabled&&((i=this.instance.config.face.ssrnet)!=null&&i.enabled)&&!this.models.ssrnetage?dn(this.instance.config):null,n.ssrnetgender=this.instance.config.face.enabled&&((y=this.instance.config.face.ssrnet)!=null&&y.enabled)&&!this.models.ssrnetgender?mn(this.instance.config):null,n.mobilefacenet=this.instance.config.face.enabled&&((x=this.instance.config.face.mobilefacenet)!=null&&x.enabled)&&!this.models.mobilefacenet?gn(this.instance.config):null,n.insightface=this.instance.config.face.enabled&&((m=this.instance.config.face.insightface)!=null&&m.enabled)&&!this.models.insightface?Pn(this.instance.config):null,n.blazepose=this.instance.config.body.enabled&&!this.models.blazepose&&((f=this.instance.config.body.modelPath)!=null&&f.includes("blazepose"))?i3(this.instance.config):null,n.blazeposedetect=this.instance.config.body.enabled&&!this.models.blazeposedetect&&this.instance.config.body.detector&&this.instance.config.body.detector.modelPath?a3(this.instance.config):null,n.efficientpose=this.instance.config.body.enabled&&!this.models.efficientpose&&((h=this.instance.config.body.modelPath)!=null&&h.includes("efficientpose"))?f3(this.instance.config):null,n.movenet=this.instance.config.body.enabled&&!this.models.movenet&&((T=this.instance.config.body.modelPath)!=null&&T.includes("movenet"))?ho(this.instance.config):null,n.posenet=this.instance.config.body.enabled&&!this.models.posenet&&((g=this.instance.config.body.modelPath)!=null&&g.includes("posenet"))?zo(this.instance.config):null,n.handtrack=this.instance.config.hand.enabled&&!this.models.handtrack&&((u=(p=this.instance.config.hand.detector)==null?void 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t)x.box[0]>u.box[0]&&x.box[0]u.box[1]&&x.box[1]+x.box[3]m.body.box[0]&&u.box[0]+u.box[2]m.body.box[1]&&u.box[1]+u.box[3]m.body.box[0]&&u.box[1]+u.box[3]>m.body.box[1]&&u.box[1]+u.box[3]{u&&u.length===4&&(f.push(u[0],u[0]+u[2]),h.push(u[1],u[1]+u[3]))};T(m.face.box),T((d=m.body)==null?void 0:d.box),T((i=m.hands.left)==null?void 0:i.box),T((y=m.hands.right)==null?void 0:y.box);let g=Math.min(...f),p=Math.min(...h);m.box=[g,p,Math.max(...f)-g,Math.max(...h)-p],r!=null&&r[1]&&(r!=null&&r[2])&&(m.boxRaw=[m.box[0]/r[2],m.box[1]/r[1],m.box[2]/r[2],m.box[3]/r[1]]),A.push(m)}return A}var a0=Z(V());var Vt=` /9j/4AAQSkZJRgABAQEAYABgAAD/4QBoRXhpZgAATU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUA AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu bmV0IDQuMi4xMwAA/9sAQwAGBAUGBQQGBgUGBwcGCAoQCgoJCQoUDg8MEBcUGBgXFBYWGh0lHxob @@ -14023,8 +269,7 @@ PQ4GJ+ashuK0MhWaoWcA0AaOmASMK7jRNPWYBmHyiuepO2x10qfcv6vYxCzYqoGK4HVYVTJrmb5l 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warmupBitmap(instance) { - const b64toBlob = (base64, type = "application/octet-stream") => fetch(`data:${type};base64,${base64}`).then((res2) => res2.blob()); - let blob; - let res; - switch (instance.config.warmup) { - case "face": - blob = await b64toBlob(face3); - break; - case "body": - case "full": - blob = await b64toBlob(body3); - break; - default: - blob = null; - } - if (blob) { - const bitmap = await createImageBitmap(blob); - res = await instance.detect(bitmap, instance.config); - bitmap.close(); - } - return res; -} -async function warmupCanvas(instance) { - return new Promise((resolve) => { - let src; - switch (instance.config.warmup) { - case "face": - src = "data:image/jpeg;base64," + face3; - break; - case "full": - case "body": - src = "data:image/jpeg;base64," + body3; - break; - default: - src = ""; - } - let img; - if (typeof Image !== "undefined") img = new Image(); - else if (env.Image) img = new env.Image(); - else { - resolve(void 0); - return; - } - img.onload = async () => { - const canvas3 = canvas(img.naturalWidth, img.naturalHeight); - if (!canvas3) { - log("Warmup: Canvas not found"); - resolve(void 0); - } else { - const ctx = canvas3.getContext("2d"); - if (ctx) ctx.drawImage(img, 0, 0); - const tensor6 = await instance.image(canvas3, true); - const res = tensor6.tensor ? await instance.detect(tensor6.tensor, instance.config) : void 0; - resolve(res); - } - }; - if (src) img.src = src; - else resolve(void 0); - }); -} -async function warmupNode(instance) { - const atob = (str) => Buffer.from(str, "base64"); - let img; - if (instance.config.warmup === "face") img = atob(face3); - else img = atob(body3); - let res; - if ("node" in tf37 && tf37.getBackend() === "tensorflow") { - const data = tf37["node"].decodeJpeg(img); - const expanded = tf37.expandDims(data, 0); - instance.tf.dispose(data); - res = await instance.detect(expanded, instance.config); - instance.tf.dispose(expanded); - } else { - if (instance.config.debug) log("Warmup tfjs-node not loaded"); - } - return res; -} -async function runInference(instance) { - let res; - if (typeof createImageBitmap === "function") res = await warmupBitmap(instance); - else if (typeof Image !== "undefined" || env.Canvas !== void 0) res = await warmupCanvas(instance); - else res = await warmupNode(instance); - return res; -} -async function runCompile(instance) { - var _a, _b, _c, _d; - if (!tf37.env().flagRegistry.ENGINE_COMPILE_ONLY) return; - const backendType = tf37.getBackend(); - const webGLBackend = tf37.backend(); - if (backendType !== "webgl" && backendType !== "humangl" || !(webGLBackend == null ? void 0 : webGLBackend["checkCompileCompletion"])) { - return; - } - tf37.env().set("ENGINE_COMPILE_ONLY", true); - const numTensorsStart = tf37.engine().state.numTensors; - const compiledModels = []; - for (const [modelName, model23] of Object.entries(instance.models.models)) { - if (!model23) continue; - const shape = (model23 == null ? void 0 : model23.modelSignature) && ((_b = (_a = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _a[0]) == null ? void 0 : _b.shape) ? [...model23.inputs[0].shape] : [1, 64, 64, 3]; - const dtype = (model23 == null ? void 0 : model23.modelSignature) && ((_d = (_c = model23 == null ? void 0 : model23.inputs) == null ? void 0 : _c[0]) == null ? void 0 : _d.dtype) ? model23.inputs[0].dtype : "float32"; - for (let dim = 0; dim < shape.length; dim++) { - if (shape[dim] === -1) shape[dim] = dim === 0 ? 1 : 64; - } - const tensor6 = tf37.zeros(shape, dtype); - try { - const res = model23.execute(tensor6); - compiledModels.push(modelName); - if (Array.isArray(res)) res.forEach((t2) => tf37.dispose(t2)); - else tf37.dispose(res); - } catch (e) { - if (instance.config.debug) log("compile fail model:", modelName); - } - tf37.dispose(tensor6); - } - const kernels = await webGLBackend["checkCompileCompletionAsync"](); - webGLBackend["getUniformLocations"](); - if (instance.config.debug) log("compile pass:", { models: compiledModels, kernels: kernels.length }); - tf37.env().set("ENGINE_COMPILE_ONLY", false); - const numTensorsEnd = tf37.engine().state.numTensors; - if (numTensorsEnd - numTensorsStart > 0) log("tensor leak:", numTensorsEnd - numTensorsStart); -} -async function warmup(instance, userConfig) { - await check(instance, false); - const t0 = now(); - instance.state = "warmup"; - if (userConfig) instance.config = mergeDeep(instance.config, userConfig); - if (!instance.config.warmup || instance.config.warmup.length === 0 || instance.config.warmup === "none") { - return empty(); - } - return new Promise(async (resolve) => { - await instance.models.load(); - await tf37.ready(); - await runCompile(instance); - const res = await runInference(instance); - const t1 = now(); - if (instance.config.debug) log("warmup", instance.config.warmup, Math.round(t1 - t0), "ms"); - instance.emit("warmup"); - resolve(res); - }); -} - -// src/human.ts -var _numTensors, _analyzeMemoryLeaks, _checkSanity, _sanity, _loops; -var Human = class { - // definition end - /** Constructor for **Human** library that is futher used for all operations - * @param userConfig - user configuration object {@link Config} - */ - constructor(userConfig) { - /** Current version of Human library in *semver* format */ - __publicField(this, "version"); - /** Current configuration - * - Defaults: [config](https://github.com/vladmandic/human/blob/main/src/config.ts#L262) - */ - __publicField(this, "config"); - /** Last known result of detect run - * - Can be accessed anytime after initial detection - */ - __publicField(this, "result"); - /** Current state of Human library - * - Can be polled to determine operations that are currently executed - * - Progresses through: 'config', 'check', 'backend', 'load', 'run:', 'idle' - */ - __publicField(this, "state"); - /** currenty processed image tensor and canvas */ - __publicField(this, "process"); - /** Instance of TensorFlow/JS used by Human - * - Can be embedded or externally provided - * [TFJS API](https://js.tensorflow.org/api/latest/) - */ - __publicField(this, "tf"); - /** Object containing environment information used for diagnostics */ - __publicField(this, "env", env); - /** Draw helper classes that can draw detected objects on canvas using specified draw - * - canvas: draws input to canvas - * - options: are global settings for all draw operations, can be overriden for each draw method {@link DrawOptions} - * - face, body, hand, gesture, object, person: draws detected results as overlays on canvas - */ - // draw: { canvas: typeof draw.canvas, face: typeof draw.face, body: typeof draw.body, hand: typeof draw.hand, gesture: typeof draw.gesture, object: typeof draw.object, person: typeof draw.person, all: typeof draw.all, options: DrawOptions }; - __publicField(this, "draw", draw_exports); - /** Face Matching - * - similarity: compare two face descriptors and return similarity index - * - distance: compare two face descriptors and return raw calculated differences - * - find: compare face descriptor to array of face descriptors and return best match - */ - __publicField(this, "match", match_exports); - /** Currently loaded models - * @internal - * {@link models#Models} - */ - __publicField(this, "models"); - /** Container for events dispatched by Human - * Possible events: - * - `create`: triggered when Human object is instantiated - * - `load`: triggered when models are loaded (explicitly or on-demand) - * - `image`: triggered when input image is processed - * - `result`: triggered when detection is complete - * - `warmup`: triggered when warmup is complete - * - `error`: triggered on some errors - */ - __publicField(this, "events"); - /** Reference face triangualtion array of 468 points, used for triangle references between points */ - __publicField(this, "faceTriangulation"); - /** Refernce UV map of 468 values, used for 3D mapping of the face mesh */ - __publicField(this, "faceUVMap"); - /** Performance object that contains values for all recently performed operations */ - __publicField(this, "performance"); - // perf members are dynamically defined as needed - __privateAdd(this, _numTensors); - __privateAdd(this, _analyzeMemoryLeaks); - __privateAdd(this, _checkSanity); - /** internal function to measure tensor leaks */ - __publicField(this, "analyze", (...msg) => { - if (!__privateGet(this, _analyzeMemoryLeaks)) return; - const currentTensors = this.tf.engine().state.numTensors; - const previousTensors = __privateGet(this, _numTensors); - __privateSet(this, _numTensors, currentTensors); - const leaked = currentTensors - previousTensors; - if (leaked !== 0) log(...msg, leaked); - }); - /** internal function for quick sanity check on inputs @hidden */ - __privateAdd(this, _sanity, (input) => { - if (!__privateGet(this, _checkSanity)) return null; - if (!input) return "input is not defined"; - if (this.env.node && !(input instanceof tf38.Tensor)) return "input must be a tensor"; - try { - this.tf.getBackend(); - } catch (e) { - return "backend not loaded"; - } - return null; - }); - /** WebCam helper methods - * - */ - __publicField(this, "webcam", new WebCam()); - /** emit event */ - __publicField(this, "emit", (event) => { - var _a; - if ((_a = this.events) == null ? void 0 : _a.dispatchEvent) this.events.dispatchEvent(new Event(event)); - }); - /** internal structure that keeps track of processed videos @hidden */ - __privateAdd(this, _loops, {}); - const tfVersion = (tf38.version.tfjs || tf38.version_core).replace(/-(.*)/, ""); - config.wasmPath = `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tfVersion}/dist/`; - config.modelBasePath = env.browser ? "../models/" : "file://models/"; - this.version = version2; - Object.defineProperty(this, "version", { value: version2 }); - this.config = JSON.parse(JSON.stringify(config)); - Object.seal(this.config); - this.config.cacheModels = typeof indexedDB !== "undefined"; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - setModelLoadOptions(this.config); - this.tf = tf38; - this.state = "idle"; - __privateSet(this, _numTensors, 0); - __privateSet(this, _analyzeMemoryLeaks, false); - __privateSet(this, _checkSanity, false); - this.performance = {}; - this.events = typeof EventTarget !== "undefined" ? new EventTarget() : void 0; - this.models = new Models(this); - init2(); - this.result = empty(); - this.process = { tensor: null, canvas: null }; - this.faceTriangulation = triangulation; - this.faceUVMap = uvmap; - validateModel(this, null, ""); - this.emit("create"); - if (this.config.debug || this.env.browser) log(`version: ${this.version}`); - if (this.config.debug) log(`tfjs version: ${this.tf.version["tfjs-core"]}`); - const envTemp = JSON.parse(JSON.stringify(this.env)); - delete envTemp.kernels; - delete envTemp.initial; - delete envTemp.perfadd; - if (this.config.debug) log("environment:", envTemp); - } - /** Reset configuration to default values */ - reset() { - const currentBackend = this.config.backend; - this.config = JSON.parse(JSON.stringify(config)); - this.config.backend = currentBackend; - reset(); - env.initial = true; - } - /** Validate current configuration schema */ - validate(userConfig) { - const msgs = validate(config, userConfig || this.config); - if (msgs.length === 0) this.config = mergeDeep(this.config, userConfig); - return msgs; - } - /** Utility wrapper for performance.now() */ - now() { - return now(); - } - /** Process input as return canvas and tensor - * - * @param input - any input {@link Input} - * @param getTensor - should image processing also return tensor or just canvas - * Returns object with `tensor` and `canvas` - */ - image(input, getTensor = false) { - return process2(input, this.config, getTensor); - } - /** Segmentation method takes any input and returns RGBA tensor - * Note: Segmentation is not triggered as part of detect process - * - * @param input - {@link Input} - * Returns tensor which contains image data in RGBA format - */ - async segmentation(input, userConfig) { - var _a, _b, _c; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - if (!this.config.segmentation.enabled) return null; - const processed = await process2(input, this.config); - if (!processed.tensor) return null; - let tensor6 = null; - if ((_a = this.config.segmentation.modelPath) == null ? void 0 : _a.includes("rvm")) tensor6 = await predict20(processed.tensor, this.config); - if ((_b = this.config.segmentation.modelPath) == null ? void 0 : _b.includes("meet")) tensor6 = await predict16(processed.tensor, this.config); - if ((_c = this.config.segmentation.modelPath) == null ? void 0 : _c.includes("selfie")) tensor6 = await predict21(processed.tensor, this.config); - tf38.dispose(processed.tensor); - return tensor6; - } - /** Compare two input tensors for pixel similarity - * - use `human.image` to process any valid input and get a tensor that can be used for compare - * - when passing manually generated tensors: - * - both input tensors must be in format [1, height, width, 3] - * - if resolution of tensors does not match, second tensor will be resized to match resolution of the first tensor - * - return value is pixel similarity score normalized by input resolution and rgb channels - */ - compare(firstImageTensor, secondImageTensor) { - return compare(this.config, firstImageTensor, secondImageTensor); - } - /** Explicit backend initialization - * - Normally done implicitly during initial load phase - * - Call to explictly register and initialize TFJS backend without any other operations - * - Use when changing backend during runtime - */ - async init() { - await check(this, true); - await this.tf.ready(); - reset(); - } - /** Load method preloads all configured models on-demand - * - Not explicitly required as any required model is load implicitly on it's first run - * - * @param userConfig - {@link Config} - */ - async load(userConfig) { - this.state = "load"; - const timeStamp = now(); - const count2 = Object.values(this.models.models).filter((model23) => model23).length; - if (userConfig) this.config = mergeDeep(this.config, userConfig); - if (this.env.initial) { - if (!await check(this, false)) log("error: backend check failed"); - await tf38.ready(); - if (this.env.browser) { - if (this.config.debug) log("configuration:", this.config); - if (this.config.debug) log("tf flags:", this.tf.ENV.flags); - } - } - await this.models.load(this); - if (this.env.initial && this.config.debug) log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors"); - this.env.initial = false; - const loaded = Object.values(this.models.models).filter((model23) => model23).length; - if (loaded !== count2) { - this.models.validate(); - this.emit("load"); - } - const current = Math.trunc(now() - timeStamp); - if (current > (this.performance.loadModels || 0)) this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current; - } - /** Runs interpolation using last known result and returns smoothened result - * Interpolation is based on time since last known result so can be called independently - * - * @param result - {@link Result} optional use specific result set to run interpolation on - * @returns result - {@link Result} - */ - next(result = this.result) { - return calc2(result, this.config); - } - /** Warmup method pre-initializes all configured models for faster inference - * - can take significant time on startup - * - only used for `webgl` and `humangl` backends - * @param userConfig - {@link Config} - * @returns result - {@link Result} - */ - async warmup(userConfig) { - const t0 = now(); - const res = await warmup(this, userConfig); - const t1 = now(); - this.performance.warmup = Math.trunc(t1 - t0); - return res; - } - /** Run detect with tensorflow profiling - * - result object will contain total exeuction time information for top-20 kernels - * - actual detection object can be accessed via `human.result` - */ - async profile(input, userConfig) { - const profile = await this.tf.profile(() => this.detect(input, userConfig)); - const kernels = {}; - let total = 0; - for (const kernel of profile.kernels) { - const ms = Number(kernel.kernelTimeMs) || 0; - if (kernels[kernel.name]) kernels[kernel.name] += ms; - else kernels[kernel.name] = ms; - total += ms; - } - const kernelArr = []; - Object.entries(kernels).forEach((key) => kernelArr.push({ kernel: key[0], time: key[1], perc: 0 })); - for (const kernel of kernelArr) { - kernel.perc = Math.round(1e3 * kernel.time / total) / 1e3; - kernel.time = Math.round(1e3 * kernel.time) / 1e3; - } - kernelArr.sort((a, b) => b.time - a.time); - kernelArr.length = 20; - return kernelArr; - } - /** Main detection method - * - Analyze configuration: {@link Config} - * - Pre-process input: {@link Input} - * - Run inference for all configured models - * - Process and return result: {@link Result} - * - * @param input - {@link Input} - * @param userConfig - {@link Config} - * @returns result - {@link Result} - */ - async detect(input, userConfig) { - this.state = "detect"; - return new Promise(async (resolve) => { - var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u; - this.state = "config"; - let timeStamp; - this.config = mergeDeep(this.config, userConfig); - this.state = "check"; - const error = __privateGet(this, _sanity).call(this, input); - if (error) { - log(error, input); - this.emit("error"); - resolve(empty(error)); - } - const timeStart = now(); - await this.load(); - timeStamp = now(); - this.state = "image"; - const img = await process2(input, this.config); - this.process = img; - this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Get Image:"); - if (!img.tensor) { - if (this.config.debug) log("could not convert input to tensor"); - this.emit("error"); - resolve(empty("could not convert input to tensor")); - return; - } - this.emit("image"); - timeStamp = now(); - this.config.skipAllowed = await skip(this.config, img.tensor); - this.config.filter.autoBrightness = (this.config.filter.autoBrightness || false) && this.config.skipAllowed; - if (!this.performance.totalFrames) this.performance.totalFrames = 0; - if (!this.performance.cachedFrames) this.performance.cachedFrames = 0; - this.performance.totalFrames++; - if (this.config.skipAllowed) this.performance.cachedFrames++; - this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - this.analyze("Check Changed:"); - let faceRes = []; - let bodyRes = []; - let handRes = []; - let objectRes = []; - this.state = "detect:face"; - if (this.config.async) { - faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : []; - if (this.performance.face) delete this.performance.face; - } else { - timeStamp = now(); - faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : []; - this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1)) faceRes = await faceRes; - this.analyze("Start Body:"); - this.state = "detect:body"; - const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_a = this.config.body.modelPath) == null ? void 0 : _a.includes("posenet")) bodyRes = this.config.body.enabled ? predict19(img.tensor, bodyConfig) : []; - else if ((_b = this.config.body.modelPath) == null ? void 0 : _b.includes("blazepose")) bodyRes = this.config.body.enabled ? predict(img.tensor, bodyConfig) : []; - else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose")) bodyRes = this.config.body.enabled ? predict3(img.tensor, bodyConfig) : []; - else if ((_d = this.config.body.modelPath) == null ? void 0 : _d.includes("movenet")) bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : []; - if (this.performance.body) delete this.performance.body; - } else { - timeStamp = now(); - if ((_e = this.config.body.modelPath) == null ? void 0 : _e.includes("posenet")) bodyRes = this.config.body.enabled ? await predict19(img.tensor, bodyConfig) : []; - else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose")) bodyRes = this.config.body.enabled ? await predict(img.tensor, bodyConfig) : []; - else if ((_g = this.config.body.modelPath) == null ? void 0 : _g.includes("efficientpose")) bodyRes = this.config.body.enabled ? await predict3(img.tensor, bodyConfig) : []; - else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet")) bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : []; - this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Body:"); - this.analyze("Start Hand:"); - this.state = "detect:hand"; - const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config; - if (this.config.async) { - if ((_j = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j.includes("handdetect")) handRes = this.config.hand.enabled ? predict14(img.tensor, handConfig) : []; - else if ((_l = (_k = this.config.hand.detector) == null ? void 0 : _k.modelPath) == null ? void 0 : _l.includes("handtrack")) handRes = this.config.hand.enabled ? predict15(img.tensor, handConfig) : []; - if (this.performance.hand) delete this.performance.hand; - } else { - timeStamp = now(); - if ((_n = (_m = this.config.hand.detector) == null ? void 0 : _m.modelPath) == null ? void 0 : _n.includes("handdetect")) handRes = this.config.hand.enabled ? await predict14(img.tensor, handConfig) : []; - else if ((_p = (_o = this.config.hand.detector) == null ? void 0 : _o.modelPath) == null ? void 0 : _p.includes("handtrack")) handRes = this.config.hand.enabled ? await predict15(img.tensor, handConfig) : []; - this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Hand:"); - this.analyze("Start Object:"); - this.state = "detect:object"; - if (this.config.async) { - if ((_q = this.config.object.modelPath) == null ? void 0 : _q.includes("nanodet")) objectRes = this.config.object.enabled ? predict18(img.tensor, this.config) : []; - else if ((_r = this.config.object.modelPath) == null ? void 0 : _r.includes("centernet")) objectRes = this.config.object.enabled ? predict2(img.tensor, this.config) : []; - if (this.performance.object) delete this.performance.object; - } else { - timeStamp = now(); - if ((_s = this.config.object.modelPath) == null ? void 0 : _s.includes("nanodet")) objectRes = this.config.object.enabled ? await predict18(img.tensor, this.config) : []; - else if ((_t = this.config.object.modelPath) == null ? void 0 : _t.includes("centernet")) objectRes = this.config.object.enabled ? await predict2(img.tensor, this.config) : []; - this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - } - this.analyze("End Object:"); - this.state = "detect:await"; - if (this.config.async) [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]); - this.state = "detect:gesture"; - let gestureRes = []; - if (this.config.gesture.enabled) { - timeStamp = now(); - gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris2(faceRes)]; - if (!this.config.async) this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp); - else if (this.performance.gesture) delete this.performance.gesture; - } - this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart); - const shape = ((_u = this.process.tensor) == null ? void 0 : _u.shape) || [0, 0, 0, 0]; - this.result = { - face: faceRes, - body: bodyRes, - hand: handRes, - gesture: gestureRes, - object: objectRes, - performance: this.performance, - canvas: this.process.canvas, - timestamp: Date.now(), - error: null, - width: shape[2], - height: shape[1], - get persons() { - return join2(faceRes, bodyRes, handRes, gestureRes, shape); - } - }; - tf38.dispose(img.tensor); - this.emit("detect"); - this.state = "idle"; - resolve(this.result); - }); - } - /** Helper function - * @param ms - sleep time in miliseconds - */ - async sleep(ms) { - return new Promise((resolve) => { - setTimeout(resolve, ms); - }); - } - /** Continously detect video frames - * @param element - HTMLVideoElement input - * @param run - boolean run continously or stop if already running, default true - * @param delay - number delay detection between frames for number of miliseconds, default 0 - */ - async video(element, run = true, delay = 0) { - if (run) { - if (!__privateGet(this, _loops)[element.id]) { - if (this.config.debug) log("video start", element.id); - __privateGet(this, _loops)[element.id] = true; - } - if (!element.paused && __privateGet(this, _loops)[element.id] && element.readyState >= 2) await this.detect(element); - if (delay > 0) await this.sleep(delay); - if (__privateGet(this, _loops)[element.id]) requestAnimationFrame(() => this.video(element, run, delay)); - } else { - if (this.config.debug) log("video stop", element.id); - __privateGet(this, _loops)[element.id] = false; - } - } -}; -_numTensors = new WeakMap(); -_analyzeMemoryLeaks = new WeakMap(); -_checkSanity = new WeakMap(); -_sanity = new WeakMap(); -_loops = new WeakMap(); -// Annotate the CommonJS export names for ESM import in node: -0 && (module.exports = { - Env, - Human, - defaults, - draw, - empty, - env, - match, - models -}); 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P2,G2,V2,qt,Ye,w1=class{constructor(t){w(this,"version");w(this,"config");w(this,"result");w(this,"state");w(this,"process");w(this,"tf");w(this,"env",R);w(this,"draw",nt);w(this,"match",Ot);w(this,"models");w(this,"events");w(this,"faceTriangulation");w(this,"faceUVMap");w(this,"performance");ue(this,P2);ue(this,G2);ue(this,V2);w(this,"analyze",(...t)=>{if(!Y0(this,G2))return;let n=this.tf.engine().state.numTensors,o=Y0(this,P2);Me(this,P2,n);let r=n-o;r!==0&&b(...t,r)});ue(this,qt,t=>{if(!Y0(this,V2))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof de.Tensor))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});w(this,"webcam",new K2);w(this,"emit",t=>{var n;(n=this.events)!=null&&n.dispatchEvent&&this.events.dispatchEvent(new Event(t))});ue(this,Ye,{});let n=(de.version.tfjs||de.version_core).replace(/-(.*)/,"");Ke.wasmPath=`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${n}/dist/`,Ke.modelBasePath=R.browser?"../models/":"file://models/",this.version=t5,Object.defineProperty(this,"version",{value:t5}),this.config=JSON.parse(JSON.stringify(Ke)),Object.seal(this.config),this.config.cacheModels=typeof indexedDB!="undefined",t&&(this.config=i0(this.config,t)),X1(this.config),this.tf=de,this.state="idle",Me(this,P2,0),Me(this,G2,!1),Me(this,V2,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new H2(this),i5(),this.result=ve(),this.process={tensor:null,canvas:null},this.faceTriangulation=F3,this.faceUVMap=B3,Gt(this,null,""),this.emit("create"),(this.config.debug||this.env.browser)&&b(`version: ${this.version}`),this.config.debug&&b(`tfjs version: ${this.tf.version["tfjs-core"]}`);let o=JSON.parse(JSON.stringify(this.env));delete o.kernels,delete o.initial,delete o.perfadd,this.config.debug&&b("environment:",o)}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ke)),this.config.backend=t,$t(),R.initial=!0}validate(t){let n=Jt(Ke,t||this.config);return n.length===0&&(this.config=i0(this.config,t)),n}now(){return v()}image(t,n=!1){return U2(t,this.config,n)}async segmentation(t,n){var s,A,a;if(n&&(this.config=i0(this.config,n)),!this.config.segmentation.enabled)return null;let o=await U2(t,this.config);if(!o.tensor)return null;let r=null;return(s=this.config.segmentation.modelPath)!=null&&s.includes("rvm")&&(r=await Io(o.tensor,this.config)),(A=this.config.segmentation.modelPath)!=null&&A.includes("meet")&&(r=await co(o.tensor,this.config)),(a=this.config.segmentation.modelPath)!=null&&a.includes("selfie")&&(r=await Oo(o.tensor,this.config)),de.dispose(o.tensor),r}compare(t,n){return q1(this.config,t,n)}async init(){await N2(this,!0),await this.tf.ready(),$t()}async load(t){this.state="load";let n=v(),o=Object.values(this.models.models).filter(A=>A).length;t&&(this.config=i0(this.config,t)),this.env.initial&&(await N2(this,!1)||b("error: backend check failed"),await de.ready(),this.env.browser&&(this.config.debug&&b("configuration:",this.config),this.config.debug&&b("tf flags:",this.tf.ENV.flags))),await this.models.load(this),this.env.initial&&this.config.debug&&b("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models.models).filter(A=>A).length!==o&&(this.models.validate(),this.emit("load"));let s=Math.trunc(v()-n);s>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+s:s)}next(t=this.result){return lo(t,this.config)}async warmup(t){let n=v(),o=await Do(this,t),r=v();return this.performance.warmup=Math.trunc(r-n),o}async profile(t,n){let o=await this.tf.profile(()=>this.detect(t,n)),r={},s=0;for(let a of o.kernels){let 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Z1(this.config,a.tensor),this.config.filter.autoBrightness=(this.config.filter.autoBrightness||!1)&&this.config.skipAllowed,this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(v()-r):Math.trunc(v()-r),this.analyze("Check Changed:");let l=[],c=[],d=[],i=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?J5(this,a.tensor):[],this.performance.face&&delete this.performance.face):(r=v(),l=this.config.face.enabled?await J5(this,a.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let y=this.config.body.maxDetected===-1?i0(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?((h=this.config.body.modelPath)!=null&&h.includes("posenet")?c=this.config.body.enabled?T1(a.tensor,y):[]:(T=this.config.body.modelPath)!=null&&T.includes("blazepose")?c=this.config.body.enabled?y5(a.tensor,y):[]:(g=this.config.body.modelPath)!=null&&g.includes("efficientpose")?c=this.config.body.enabled?g5(a.tensor,y):[]:(p=this.config.body.modelPath)!=null&&p.includes("movenet")&&(c=this.config.body.enabled?f1(a.tensor,y):[]),this.performance.body&&delete this.performance.body):(r=v(),(u=this.config.body.modelPath)!=null&&u.includes("posenet")?c=this.config.body.enabled?await T1(a.tensor,y):[]:(k=this.config.body.modelPath)!=null&&k.includes("blazepose")?c=this.config.body.enabled?await y5(a.tensor,y):[]:(P=this.config.body.modelPath)!=null&&P.includes("efficientpose")?c=this.config.body.enabled?await 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Object:"),this.state="detect:await",this.config.async&&([l,c,d,i]=await Promise.all([l,c,d,i])),this.state="detect:gesture";let m=[];this.config.gesture.enabled&&(r=v(),m=[...Dn(l),...Wn(c),...Bn(d),...Fn(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(v()-r):Math.trunc(v()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(v()-A):Math.trunc(v()-A);let f=((Q=this.process.tensor)==null?void 0:Q.shape)||[0,0,0,0];this.result={face:l,body:c,hand:d,gesture:m,object:i,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,width:f[2],height:f[1],get persons(){return Wo(l,c,d,m,f)}},de.dispose(a.tensor),this.emit("detect"),this.state="idle",o(this.result)})}async sleep(t){return new Promise(n=>{setTimeout(n,t)})}async video(t,n=!0,o=0){n?(Y0(this,Ye)[t.id]||(this.config.debug&&b("video 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this.comp(e)<0};de.lt=de.lessThan;de.lessThanOrEqual=function(e){return this.comp(e)<=0};de.lte=de.lessThanOrEqual;de.le=de.lessThanOrEqual;de.greaterThan=function(e){return this.comp(e)>0};de.gt=de.greaterThan;de.greaterThanOrEqual=function(e){return this.comp(e)>=0};de.gte=de.greaterThanOrEqual;de.ge=de.greaterThanOrEqual;de.compare=function(e){if(Wr(e)||(e=As(e)),this.eq(e))return 0;var t=this.isNegative(),o=e.isNegative();return t&&!o?-1:!t&&o?1:this.unsigned?e.high>>>0>this.high>>>0||e.high===this.high&&e.low>>>0>this.low>>>0?-1:1:this.sub(e).isNegative()?-1:1};de.comp=de.compare;de.negate=function(){return!this.unsigned&&this.eq(Vr)?Vr:this.not().add(Yp)};de.neg=de.negate;de.add=function(e){Wr(e)||(e=As(e));var t=this.high>>>16,o=this.high&65535,n=this.low>>>16,s=this.low&65535,a=e.high>>>16,i=e.high&65535,p=e.low>>>16,u=e.low&65535,c=0,l=0,m=0,d=0;return 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node_modules/.pnpm/@tensorflow+tfjs-core@4.21.0/node_modules/@tensorflow/tfjs-core/package.json -var version = "4.21.0"; - -// node_modules/.pnpm/@tensorflow+tfjs-converter@4.21.0_@tensorflow+tfjs-core@4.21.0/node_modules/@tensorflow/tfjs-converter/package.json -var version2 = "4.21.0"; - -// node_modules/.pnpm/@tensorflow+tfjs-backend-cpu@4.21.0_@tensorflow+tfjs-core@4.21.0/node_modules/@tensorflow/tfjs-backend-cpu/package.json -var version3 = "4.21.0"; - -// node_modules/.pnpm/@tensorflow+tfjs-backend-webgl@4.21.0_@tensorflow+tfjs-core@4.21.0/node_modules/@tensorflow/tfjs-backend-webgl/package.json -var version4 = "4.21.0"; - -// node_modules/.pnpm/@tensorflow+tfjs-backend-wasm@4.21.0_@tensorflow+tfjs-core@4.21.0/node_modules/@tensorflow/tfjs-backend-wasm/package.json -var version5 = "4.21.0"; - -// node_modules/.pnpm/@tensorflow+tfjs-backend-webgpu@4.21.0_@tensorflow+tfjs-core@4.21.0/node_modules/@tensorflow/tfjs-backend-webgpu/package.json -var version6 = "4.21.0"; - -// tfjs/tf-version.ts -var version7 = { - tfjs: version, - "tfjs-core": version, - // 'tfjs-data': tfjsDataVersion, - // 'tfjs-layers': tfjsLayersVersion, - "tfjs-converter": version2, - "tfjs-backend-cpu": version3, - "tfjs-backend-webgl": version4, - "tfjs-backend-wasm": version5, - "tfjs-backend-webgpu": version6 -}; -export { - version7 as version -}; +var e="4.21.0";var s="4.21.0";var t="4.21.0";var n="4.21.0";var r="4.21.0";var i="4.21.0";var h={tfjs:e,"tfjs-core":e,"tfjs-converter":s,"tfjs-backend-cpu":t,"tfjs-backend-webgl":n,"tfjs-backend-wasm":r,"tfjs-backend-webgpu":i};export{h as version}; diff --git a/test/build.log b/test/build.log index e61256c6..a797fd9f 100644 --- a/test/build.log +++ b/test/build.log @@ -1,1584 +1,51 @@ -2024-09-11 12:16:40 DATA:  Build {"name":"@vladmandic/human","version":"3.3.2"} -2024-09-11 12:16:40 INFO:  Application: {"name":"@vladmandic/human","version":"3.3.2"} -2024-09-11 12:16:40 INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} -2024-09-11 12:16:40 INFO:  Toolchain: {"build":"0.10.1","esbuild":"0.23.1","typescript":"5.5.4","typedoc":"0.26.7","eslint":"8.57.0"} -2024-09-11 12:16:40 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} -2024-09-11 12:16:40 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} -2024-09-11 12:16:40 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":358} -2024-09-11 12:16:40 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":566,"outputBytes":957} -2024-09-11 12:16:40 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":677328,"outputBytes":321372} -2024-09-11 12:16:40 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":574,"outputBytes":965} -2024-09-11 12:16:40 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":677336,"outputBytes":321376} -2024-09-11 12:16:40 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":662,"outputBytes":2003} -2024-09-11 12:16:40 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":678374,"outputBytes":321487} -2024-09-11 12:16:40 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1403,"outputBytes":690} -2024-09-11 12:16:40 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677061,"outputBytes":319948} -2024-09-11 12:16:40 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1403,"outputBytes":1267270} -2024-09-11 12:16:40 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1943641,"outputBytes":1582950} -2024-09-11 12:16:41 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1943641,"outputBytes":2066661} -2024-09-11 12:16:42 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":78} -2024-09-11 12:16:43 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":81,"generated":true} -2024-09-11 12:16:43 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":2970} -2024-09-11 12:16:43 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":9401} -2024-09-11 12:16:43 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":22791} -2024-09-11 12:16:50 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":171,"errors":0,"warnings":0} -2024-09-11 12:16:50 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} -2024-09-11 12:16:50 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} -2024-09-11 12:16:50 INFO:  Done... -2024-09-11 12:16:50 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} -2024-09-11 12:16:50 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} -2024-09-11 12:16:50 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} -2024-09-11 12:16:50 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} -2024-09-11 12:16:50 STATE: Filter: {"input":"types/tfjs-core.d.ts"} -2024-09-11 12:16:51 ERROR: API-Extractor: {} -2024-09-11 12:16:51 STATE: Filter: {"input":"types/human.d.ts"} -2024-09-11 12:16:51 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} -2024-09-11 12:16:51 STATE: Write: {"output":"dist/human.esm.d.ts"} -2024-09-11 12:16:51 STATE: Write: {"output":"dist/human.d.ts"} -2024-09-11 12:16:51 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} -2024-09-11 12:16:51 STATE: Write: {"output":"dist/human.node.d.ts"} -2024-09-11 12:16:51 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} -2024-09-11 12:16:51 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} -2024-09-11 12:16:51 STATE: Models {"folder":"./models","models":12} -2024-09-11 12:16:51 STATE: Models {"folder":"../human-models/models","models":44} -2024-09-11 12:16:51 STATE: Models {"folder":"../blazepose/model/","models":4} -2024-09-11 12:16:51 STATE: Models {"folder":"../anti-spoofing/model","models":1} -2024-09-11 12:16:51 STATE: Models {"folder":"../efficientpose/models","models":3} -2024-09-11 12:16:51 STATE: Models {"folder":"../insightface/models","models":5} -2024-09-11 12:16:51 STATE: Models {"folder":"../movenet/models","models":3} -2024-09-11 12:16:51 STATE: Models {"folder":"../nanodet/models","models":4} -2024-09-11 12:16:52 STATE: Models: {"count":58,"totalSize":380063249} -2024-09-11 12:16:52 INFO:  Human Build complete... {"logFile":"test/build.log"} -2024-10-14 08:20:11 INFO:  @vladmandic/human version 3.3.2 -2024-10-14 08:20:11 INFO:  User: vlado Platform: linux Arch: x64 Node: v21.7.3 -2024-10-14 08:20:11 INFO:  Application: {"name":"@vladmandic/human","version":"3.3.2"} -2024-10-14 08:20:11 INFO:  Environment: {"profile":"development","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} -2024-10-14 08:20:11 INFO:  Toolchain: {"build":"0.10.1","esbuild":"0.23.1","typescript":"5.5.4","typedoc":"0.26.7","eslint":"8.57.0"} -2024-10-14 08:20:11 INFO:  Build: {"profile":"development","steps":["serve","watch","compile"]} -2024-10-14 08:20:11 STATE: WebServer: {"ssl":false,"port":8000,"root":"."} -2024-10-14 08:20:11 STATE: WebServer: {"ssl":true,"port":8001,"root":".","sslKey":"node_modules/@vladmandic/build/cert/https.key","sslCrt":"node_modules/@vladmandic/build/cert/https.crt"} -2024-10-14 08:20:11 STATE: Watch: {"locations":["src/**/*","tfjs/**/*","demo/**/*.ts"]} -2024-10-14 08:20:11 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:20:11 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:20:11 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678267,"outputBytes":522237} -2024-10-14 08:20:11 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:20:11 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":678295,"outputBytes":522257} -2024-10-14 08:20:11 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1739,"outputBytes":2193} -2024-10-14 08:20:11 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":678564,"outputBytes":522448} -2024-10-14 08:20:11 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2480,"outputBytes":1044} -2024-10-14 08:20:11 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677415,"outputBytes":524809} -2024-10-14 08:20:11 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:20:11 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1943893,"outputBytes":1583202} -2024-10-14 08:20:11 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1943893,"outputBytes":2066981} -2024-10-14 08:20:11 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":4521} -2024-10-14 08:20:11 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":15111} -2024-10-14 08:20:11 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":51040} -2024-10-14 08:20:11 INFO:  Listening... 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INFO:  Watch: {"event":"modify","input":"src/models.ts"} -2024-10-14 08:34:47 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:34:47 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:34:47 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678430,"outputBytes":522400} -2024-10-14 08:34:47 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:34:47 STATE: Compile: 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INFO:  Watch: {"event":"modify","input":"src/models.ts"} -2024-10-14 08:35:50 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:35:50 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:35:50 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678454,"outputBytes":522424} -2024-10-14 08:35:50 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:35:50 STATE: Compile: 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STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678438,"outputBytes":522408} -2024-10-14 08:38:17 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:38:17 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":678466,"outputBytes":522428} -2024-10-14 08:38:17 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1739,"outputBytes":2193} -2024-10-14 08:38:17 STATE: Compile: 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INFO:  Watch: {"event":"modify","input":"src/models.ts"} -2024-10-14 08:39:32 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:39:32 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:39:32 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678433,"outputBytes":522403} -2024-10-14 08:39:32 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:39:32 STATE: Compile: 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INFO:  Watch: {"event":"modify","input":"src/models.ts"} -2024-10-14 08:39:37 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:39:37 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:39:37 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678343,"outputBytes":522313} -2024-10-14 08:39:37 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:39:37 STATE: Compile: 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INFO:  Watch: {"event":"modify","input":"src/models.ts"} -2024-10-14 08:44:20 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:44:20 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:44:20 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678599,"outputBytes":522596} -2024-10-14 08:44:20 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:44:20 STATE: Compile: 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STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":678749,"outputBytes":522633} -2024-10-14 08:49:36 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2480,"outputBytes":1044} -2024-10-14 08:49:36 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677600,"outputBytes":524994} -2024-10-14 08:49:36 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:49:36 STATE: Compile: 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-2024-10-14 08:50:00 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677619,"outputBytes":524994} -2024-10-14 08:50:00 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:50:00 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1944097,"outputBytes":1583342} -2024-10-14 08:50:00 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1944097,"outputBytes":2067166} -2024-10-14 08:50:00 STATE: Compile: 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STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":678787,"outputBytes":522652} -2024-10-14 08:50:35 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2480,"outputBytes":1044} -2024-10-14 08:50:35 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677638,"outputBytes":525013} -2024-10-14 08:50:35 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:50:36 STATE: Compile: 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-2024-10-14 08:51:09 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677692,"outputBytes":525067} -2024-10-14 08:51:09 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:51:10 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1944170,"outputBytes":1583374} -2024-10-14 08:51:10 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1944170,"outputBytes":2067239} -2024-10-14 08:51:10 STATE: Compile: 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INFO:  Watch: {"event":"modify","input":"src/models.ts"} -2024-10-14 08:51:56 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:51:56 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:51:56 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678521,"outputBytes":522472} -2024-10-14 08:51:56 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:51:56 STATE: Compile: 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INFO:  Watch: {"event":"modify","input":"src/models.ts"} -2024-10-14 08:52:28 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:52:28 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:52:28 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678546,"outputBytes":522497} -2024-10-14 08:52:28 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:52:28 STATE: Compile: 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{"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1944173,"outputBytes":1583405} -2024-10-14 08:52:38 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1944173,"outputBytes":2067262} -2024-10-14 08:52:38 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":4521} -2024-10-14 08:52:38 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":15111} -2024-10-14 08:52:38 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":51040} -2024-10-14 08:52:47 INFO:  Watch: {"event":"modify","input":"src/models.ts"} -2024-10-14 08:52:47 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:52:47 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:52:47 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678546,"outputBytes":522517} -2024-10-14 08:52:47 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:52:47 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":678574,"outputBytes":522537} -2024-10-14 08:52:47 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1739,"outputBytes":2193} -2024-10-14 08:52:47 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":678843,"outputBytes":522728} -2024-10-14 08:52:47 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2480,"outputBytes":1044} -2024-10-14 08:52:47 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677694,"outputBytes":525089} -2024-10-14 08:52:48 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:52:48 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1944172,"outputBytes":1583396} -2024-10-14 08:52:48 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1944172,"outputBytes":2067261} -2024-10-14 08:52:48 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":4521} -2024-10-14 08:52:48 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":15111} -2024-10-14 08:52:48 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":51040} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/html","size":536,"url":"/tmp/simple.html","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/javascript","size":2067261,"url":"/dist/human.esm.js","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":3667895,"url":"/dist/human.esm.js.map","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"image/x-icon","size":261950,"url":"/favicon.ico","remote":"::1"} -2024-10-14 08:52:52 WARN:  HTTP: {"method":"GET","ver":"1.1","status":404,"url":"/models/wrong-path.json","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":71432,"url":"/models/faceres.json","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":95845,"url":"/models/facemesh.json","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":602812,"url":"/models/handtrack.json","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":17980,"url":"/models/emotion.json","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":122025,"url":"/models/iris.json","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":161813,"url":"/models/movenet-lightning.json","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":83013,"url":"/models/handlandmark-lite.json","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":6978814,"url":"/models/faceres.bin","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":820516,"url":"/models/emotion.bin","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2599092,"url":"/models/iris.bin","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":1477958,"url":"/models/facemesh.bin","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":4650216,"url":"/models/movenet-lightning.bin","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2964837,"url":"/models/handtrack.bin","remote":"::1"} -2024-10-14 08:52:52 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2023432,"url":"/models/handlandmark-lite.bin","remote":"::1"} -2024-10-14 08:52:54 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/javascript","size":4033,"url":"/index-pwa.js","remote":"::1"} -2024-10-14 08:55:57 INFO:  Watch: {"event":"modify","input":"src/models.ts"} -2024-10-14 08:55:57 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:55:57 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:55:57 ERROR: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":54,"file":"src/models.ts","length":1,"line":105,"lineText":" totalSizeLoading += Number.isNaN(+m.sizeDesired)) ? 0 : m.sizeDesired;","namespace":"","suggestion":";"},"notes":[],"pluginName":"","text":"Expected \";\" but found \")\""}]} -2024-10-14 08:55:57 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:55:57 ERROR: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":54,"file":"src/models.ts","length":1,"line":105,"lineText":" totalSizeLoading += Number.isNaN(+m.sizeDesired)) ? 0 : m.sizeDesired;","namespace":"","suggestion":";"},"notes":[],"pluginName":"","text":"Expected \";\" but found \")\""}]} -2024-10-14 08:55:57 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1739,"outputBytes":2193} -2024-10-14 08:55:57 ERROR: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":54,"file":"src/models.ts","length":1,"line":105,"lineText":" totalSizeLoading += Number.isNaN(+m.sizeDesired)) ? 0 : m.sizeDesired;","namespace":"","suggestion":";"},"notes":[],"pluginName":"","text":"Expected \";\" but found \")\""}]} -2024-10-14 08:55:57 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2480,"outputBytes":1044} -2024-10-14 08:55:57 ERROR: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":54,"file":"src/models.ts","length":1,"line":105,"lineText":" totalSizeLoading += Number.isNaN(+m.sizeDesired)) ? 0 : m.sizeDesired;","namespace":"","suggestion":";"},"notes":[],"pluginName":"","text":"Expected \";\" but found \")\""}]} -2024-10-14 08:55:57 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:55:57 ERROR: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":54,"file":"src/models.ts","length":1,"line":105,"lineText":" totalSizeLoading += Number.isNaN(+m.sizeDesired)) ? 0 : m.sizeDesired;","namespace":"","suggestion":";"},"notes":[],"pluginName":"","text":"Expected \";\" but found \")\""}]} -2024-10-14 08:55:57 ERROR: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":54,"file":"src/models.ts","length":1,"line":105,"lineText":" totalSizeLoading += Number.isNaN(+m.sizeDesired)) ? 0 : m.sizeDesired;","namespace":"","suggestion":";"},"notes":[],"pluginName":"","text":"Expected \";\" but found \")\""}]} -2024-10-14 08:55:57 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":4521} -2024-10-14 08:55:57 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":15111} -2024-10-14 08:55:57 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":51040} -2024-10-14 08:56:01 INFO:  Watch: {"event":"modify","input":"src/models.ts"} -2024-10-14 08:56:01 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:56:01 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:56:01 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678537,"outputBytes":522508} -2024-10-14 08:56:01 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:56:01 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":678565,"outputBytes":522528} -2024-10-14 08:56:01 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1739,"outputBytes":2193} -2024-10-14 08:56:01 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":678834,"outputBytes":522719} -2024-10-14 08:56:01 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2480,"outputBytes":1044} -2024-10-14 08:56:01 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677685,"outputBytes":525080} -2024-10-14 08:56:01 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:56:01 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1944163,"outputBytes":1583397} -2024-10-14 08:56:01 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1944163,"outputBytes":2067252} -2024-10-14 08:56:01 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":4521} -2024-10-14 08:56:01 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":15111} -2024-10-14 08:56:01 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":51040} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/html","size":536,"url":"/tmp/simple.html","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/javascript","size":2067252,"url":"/dist/human.esm.js","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":3667889,"url":"/dist/human.esm.js.map","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"image/x-icon","size":261950,"url":"/favicon.ico","remote":"::1"} -2024-10-14 08:56:03 WARN:  HTTP: {"method":"GET","ver":"1.1","status":404,"url":"/models/wrong-path.json","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":71432,"url":"/models/faceres.json","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":17980,"url":"/models/emotion.json","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":122025,"url":"/models/iris.json","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":95845,"url":"/models/facemesh.json","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":161813,"url":"/models/movenet-lightning.json","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":83013,"url":"/models/handlandmark-lite.json","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":602812,"url":"/models/handtrack.json","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":6978814,"url":"/models/faceres.bin","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":820516,"url":"/models/emotion.bin","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2599092,"url":"/models/iris.bin","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":1477958,"url":"/models/facemesh.bin","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":4650216,"url":"/models/movenet-lightning.bin","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2964837,"url":"/models/handtrack.bin","remote":"::1"} -2024-10-14 08:56:03 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2023432,"url":"/models/handlandmark-lite.bin","remote":"::1"} -2024-10-14 08:56:05 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/javascript","size":4033,"url":"/index-pwa.js","remote":"::1"} -2024-10-14 08:57:03 INFO:  Watch: {"event":"modify","input":"src/tfjs/load.ts"} -2024-10-14 08:57:03 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:57:03 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:57:03 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678560,"outputBytes":522531} -2024-10-14 08:57:03 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:57:03 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":678588,"outputBytes":522551} -2024-10-14 08:57:03 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1739,"outputBytes":2193} -2024-10-14 08:57:03 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":678857,"outputBytes":522742} -2024-10-14 08:57:03 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2480,"outputBytes":1044} -2024-10-14 08:57:03 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677708,"outputBytes":525103} -2024-10-14 08:57:03 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:57:03 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1944186,"outputBytes":1583408} -2024-10-14 08:57:03 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1944186,"outputBytes":2067275} -2024-10-14 08:57:03 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":4521} -2024-10-14 08:57:03 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":15111} -2024-10-14 08:57:03 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":51040} -2024-10-14 08:57:51 INFO:  Watch: {"event":"modify","input":"src/tfjs/load.ts"} -2024-10-14 08:57:51 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:57:51 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:57:51 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678641,"outputBytes":522679} -2024-10-14 08:57:51 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:57:51 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":678669,"outputBytes":522699} -2024-10-14 08:57:51 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1739,"outputBytes":2193} -2024-10-14 08:57:51 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":678938,"outputBytes":522890} -2024-10-14 08:57:51 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2480,"outputBytes":1044} -2024-10-14 08:57:51 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677789,"outputBytes":525251} -2024-10-14 08:57:51 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:57:51 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1944267,"outputBytes":1583506} -2024-10-14 08:57:51 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1944267,"outputBytes":2067426} -2024-10-14 08:57:51 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":4521} -2024-10-14 08:57:51 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":15111} -2024-10-14 08:57:51 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":51040} -2024-10-14 08:57:52 INFO:  Watch: {"event":"modify","input":"src/tfjs/load.ts","skip":true} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/html","size":536,"url":"/tmp/simple.html","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/javascript","size":2067426,"url":"/dist/human.esm.js","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":3668103,"url":"/dist/human.esm.js.map","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"image/x-icon","size":261950,"url":"/favicon.ico","remote":"::1"} -2024-10-14 08:57:58 WARN:  HTTP: {"method":"GET","ver":"1.1","status":404,"url":"/models/wrong-path.json","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":71432,"url":"/models/faceres.json","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":17980,"url":"/models/emotion.json","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":122025,"url":"/models/iris.json","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":95845,"url":"/models/facemesh.json","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":161813,"url":"/models/movenet-lightning.json","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":602812,"url":"/models/handtrack.json","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":83013,"url":"/models/handlandmark-lite.json","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":6978814,"url":"/models/faceres.bin","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":820516,"url":"/models/emotion.bin","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2599092,"url":"/models/iris.bin","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":1477958,"url":"/models/facemesh.bin","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":4650216,"url":"/models/movenet-lightning.bin","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2964837,"url":"/models/handtrack.bin","remote":"::1"} -2024-10-14 08:57:58 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2023432,"url":"/models/handlandmark-lite.bin","remote":"::1"} -2024-10-14 08:58:00 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/javascript","size":4033,"url":"/index-pwa.js","remote":"::1"} -2024-10-14 08:58:13 INFO:  Watch: {"event":"modify","input":"src/tfjs/load.ts"} -2024-10-14 08:58:13 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:58:13 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:58:13 ERROR: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":68,"file":"src/tfjs/load.ts","length":1,"line":79,"lineText":" console.log('HERE', shortModelName, model.artifacts?.weightData?);","namespace":"","suggestion":""},"notes":[],"pluginName":"","text":"Unexpected \")\""}]} -2024-10-14 08:58:13 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:58:13 ERROR: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":68,"file":"src/tfjs/load.ts","length":1,"line":79,"lineText":" console.log('HERE', shortModelName, model.artifacts?.weightData?);","namespace":"","suggestion":""},"notes":[],"pluginName":"","text":"Unexpected \")\""}]} -2024-10-14 08:58:13 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1739,"outputBytes":2193} -2024-10-14 08:58:13 ERROR: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":68,"file":"src/tfjs/load.ts","length":1,"line":79,"lineText":" console.log('HERE', shortModelName, model.artifacts?.weightData?);","namespace":"","suggestion":""},"notes":[],"pluginName":"","text":"Unexpected \")\""}]} -2024-10-14 08:58:13 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2480,"outputBytes":1044} -2024-10-14 08:58:13 ERROR: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":68,"file":"src/tfjs/load.ts","length":1,"line":79,"lineText":" console.log('HERE', shortModelName, model.artifacts?.weightData?);","namespace":"","suggestion":""},"notes":[],"pluginName":"","text":"Unexpected \")\""}]} -2024-10-14 08:58:13 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:58:13 ERROR: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":68,"file":"src/tfjs/load.ts","length":1,"line":79,"lineText":" console.log('HERE', shortModelName, model.artifacts?.weightData?);","namespace":"","suggestion":""},"notes":[],"pluginName":"","text":"Unexpected \")\""}]} -2024-10-14 08:58:13 ERROR: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts"} {"errors":[{"id":"","location":{"column":68,"file":"src/tfjs/load.ts","length":1,"line":79,"lineText":" console.log('HERE', shortModelName, model.artifacts?.weightData?);","namespace":"","suggestion":""},"notes":[],"pluginName":"","text":"Unexpected \")\""}]} -2024-10-14 08:58:13 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":4521} -2024-10-14 08:58:13 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":15111} -2024-10-14 08:58:13 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":51040} -2024-10-14 08:58:15 INFO:  Watch: {"event":"modify","input":"src/tfjs/load.ts"} -2024-10-14 08:58:15 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:58:15 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:58:15 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678630,"outputBytes":522635} -2024-10-14 08:58:15 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:58:15 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":678658,"outputBytes":522655} -2024-10-14 08:58:15 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1739,"outputBytes":2193} -2024-10-14 08:58:15 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":678927,"outputBytes":522846} -2024-10-14 08:58:15 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2480,"outputBytes":1044} -2024-10-14 08:58:15 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677778,"outputBytes":525207} -2024-10-14 08:58:15 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:58:16 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1944256,"outputBytes":1583474} -2024-10-14 08:58:16 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1944256,"outputBytes":2067379} -2024-10-14 08:58:16 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":4521} -2024-10-14 08:58:16 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":15111} -2024-10-14 08:58:16 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":51040} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/html","size":536,"url":"/tmp/simple.html","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/javascript","size":2067379,"url":"/dist/human.esm.js","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":3668055,"url":"/dist/human.esm.js.map","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"image/x-icon","size":261950,"url":"/favicon.ico","remote":"::1"} -2024-10-14 08:58:17 WARN:  HTTP: {"method":"GET","ver":"1.1","status":404,"url":"/models/wrong-path.json","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":71432,"url":"/models/faceres.json","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":17980,"url":"/models/emotion.json","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":122025,"url":"/models/iris.json","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":83013,"url":"/models/handlandmark-lite.json","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":95845,"url":"/models/facemesh.json","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":161813,"url":"/models/movenet-lightning.json","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":602812,"url":"/models/handtrack.json","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":6978814,"url":"/models/faceres.bin","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":820516,"url":"/models/emotion.bin","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2599092,"url":"/models/iris.bin","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":1477958,"url":"/models/facemesh.bin","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":4650216,"url":"/models/movenet-lightning.bin","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2964837,"url":"/models/handtrack.bin","remote":"::1"} -2024-10-14 08:58:17 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2023432,"url":"/models/handlandmark-lite.bin","remote":"::1"} -2024-10-14 08:58:19 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"text/javascript","size":4033,"url":"/index-pwa.js","remote":"::1"} -2024-10-14 08:58:37 INFO:  Watch: {"event":"modify","input":"src/tfjs/load.ts"} -2024-10-14 08:58:37 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:58:37 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:58:37 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678642,"outputBytes":522679} -2024-10-14 08:58:37 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:58:37 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":678670,"outputBytes":522699} -2024-10-14 08:58:37 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1739,"outputBytes":2193} -2024-10-14 08:58:37 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":678939,"outputBytes":522890} -2024-10-14 08:58:37 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2480,"outputBytes":1044} -2024-10-14 08:58:37 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677790,"outputBytes":525251} -2024-10-14 08:58:37 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":2480,"outputBytes":1267522} -2024-10-14 08:58:37 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1944268,"outputBytes":1583506} -2024-10-14 08:58:37 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1944268,"outputBytes":2067426} -2024-10-14 08:58:37 STATE: Compile: 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INFO:  Watch: {"event":"modify","input":"src/tfjs/load.ts"} -2024-10-14 08:59:04 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:59:04 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:59:04 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678693,"outputBytes":522796} -2024-10-14 08:59:04 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:59:04 STATE: Compile: 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INFO:  Watch: {"event":"modify","input":"src/tfjs/load.ts"} -2024-10-14 08:59:30 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:59:30 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:59:30 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678607,"outputBytes":522645} -2024-10-14 08:59:30 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:59:30 STATE: Compile: 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{"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":4521} -2024-10-14 08:59:30 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":15111} -2024-10-14 08:59:30 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":51040} -2024-10-14 08:59:32 INFO:  Watch: {"event":"modify","input":"src/tfjs/load.ts","skip":true} -2024-10-14 08:59:42 INFO:  Watch: {"event":"modify","input":"src/models.ts"} -2024-10-14 08:59:42 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":1435} -2024-10-14 08:59:42 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1643,"outputBytes":1896} -2024-10-14 08:59:42 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":678570,"outputBytes":522608} -2024-10-14 08:59:42 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1651,"outputBytes":1924} -2024-10-14 08:59:42 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":678598,"outputBytes":522628} -2024-10-14 08:59:42 STATE: Compile: 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{"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":161813,"url":"/models/movenet-lightning.json","remote":"::1"} -2024-10-14 08:59:43 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":602812,"url":"/models/handtrack.json","remote":"::1"} -2024-10-14 08:59:43 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/json","size":83013,"url":"/models/handlandmark-lite.json","remote":"::1"} -2024-10-14 08:59:43 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":6978814,"url":"/models/faceres.bin","remote":"::1"} -2024-10-14 08:59:43 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":820516,"url":"/models/emotion.bin","remote":"::1"} -2024-10-14 08:59:43 DATA:  HTTP: {"method":"GET","ver":"1.1","status":200,"mime":"application/octet-stream","size":2599092,"url":"/models/iris.bin","remote":"::1"} -2024-10-14 08:59:43 DATA:  HTTP: 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INFO:  Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} +2024-10-14 09:05:52 INFO:  Toolchain: {"build":"0.10.1","esbuild":"0.23.1","typescript":"5.5.4","typedoc":"0.26.7","eslint":"8.57.0"} +2024-10-14 09:05:52 INFO:  Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} +2024-10-14 09:05:52 STATE: Clean: {"locations":["dist/*","types/*","typedoc/*"]} +2024-10-14 09:05:52 STATE: Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1289,"outputBytes":358} +2024-10-14 09:05:52 STATE: Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":566,"outputBytes":957} +2024-10-14 09:05:52 STATE: Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":80,"inputBytes":677631,"outputBytes":321636} +2024-10-14 09:05:52 STATE: Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":574,"outputBytes":965} +2024-10-14 09:05:52 STATE: Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":80,"inputBytes":677639,"outputBytes":321640} +2024-10-14 09:05:52 STATE: Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":662,"outputBytes":2003} +2024-10-14 09:05:52 STATE: Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":80,"inputBytes":678677,"outputBytes":321751} +2024-10-14 09:05:52 STATE: Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":1403,"outputBytes":690} +2024-10-14 09:05:52 STATE: Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":80,"inputBytes":677364,"outputBytes":320212} +2024-10-14 09:05:52 STATE: Compile: {"name":"tfjs/browser/esm/bundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":10,"inputBytes":1403,"outputBytes":1267270} +2024-10-14 09:05:52 STATE: Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":80,"inputBytes":1943944,"outputBytes":1583214} +2024-10-14 09:05:52 STATE: Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":80,"inputBytes":1943944,"outputBytes":2067035} +2024-10-14 09:05:54 STATE: Typings: {"input":"src/human.ts","output":"types/lib","files":78} +2024-10-14 09:05:55 STATE: TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":81,"generated":true} +2024-10-14 09:05:55 STATE: Compile: {"name":"demo/typescript","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":6318,"outputBytes":2970} +2024-10-14 09:05:55 STATE: Compile: {"name":"demo/faceid","format":"esm","platform":"browser","input":"demo/faceid/index.ts","output":"demo/faceid/index.js","files":2,"inputBytes":17502,"outputBytes":9401} +2024-10-14 09:05:55 STATE: Compile: {"name":"demo/tracker","format":"esm","platform":"browser","input":"demo/tracker/index.ts","output":"demo/tracker/index.js","files":2,"inputBytes":54375,"outputBytes":22791} +2024-10-14 09:06:02 STATE: Lint: {"locations":["**/*.json","src/**/*.ts","test/**/*.js","demo/**/*.js","**/*.md"],"files":171,"errors":0,"warnings":0} +2024-10-14 09:06:02 STATE: ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} +2024-10-14 09:06:02 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs-core.d.ts","output":"types/tfjs-core.d.ts"} +2024-10-14 09:06:02 INFO:  Done... +2024-10-14 09:06:02 STATE: Copy: {"input":"node_modules/@vladmandic/tfjs/types/tfjs.d.ts","output":"types/tfjs.esm.d.ts"} +2024-10-14 09:06:02 STATE: Copy: {"input":"src/types/tsconfig.json","output":"types/tsconfig.json"} +2024-10-14 09:06:02 STATE: Copy: {"input":"src/types/eslint.json","output":"types/.eslintrc.json"} +2024-10-14 09:06:02 STATE: Copy: {"input":"src/types/tfjs.esm.d.ts","output":"dist/tfjs.esm.d.ts"} +2024-10-14 09:06:02 STATE: Filter: {"input":"types/tfjs-core.d.ts"} +2024-10-14 09:06:03 ERROR: API-Extractor: {} +2024-10-14 09:06:03 STATE: Filter: {"input":"types/human.d.ts"} +2024-10-14 09:06:03 STATE: Write: {"output":"dist/human.esm-nobundle.d.ts"} +2024-10-14 09:06:03 STATE: Write: {"output":"dist/human.esm.d.ts"} +2024-10-14 09:06:03 STATE: Write: {"output":"dist/human.d.ts"} +2024-10-14 09:06:03 STATE: Write: {"output":"dist/human.node-gpu.d.ts"} +2024-10-14 09:06:03 STATE: Write: {"output":"dist/human.node.d.ts"} +2024-10-14 09:06:03 STATE: Write: {"output":"dist/human.node-wasm.d.ts"} +2024-10-14 09:06:03 INFO:  Analyze models: {"folders":8,"result":"models/models.json"} +2024-10-14 09:06:03 STATE: Models {"folder":"./models","models":12} +2024-10-14 09:06:03 STATE: Models {"folder":"../human-models/models","models":44} +2024-10-14 09:06:03 STATE: Models {"folder":"../blazepose/model/","models":4} +2024-10-14 09:06:03 STATE: Models {"folder":"../anti-spoofing/model","models":1} +2024-10-14 09:06:03 STATE: Models {"folder":"../efficientpose/models","models":3} +2024-10-14 09:06:03 STATE: Models {"folder":"../insightface/models","models":5} +2024-10-14 09:06:03 STATE: Models {"folder":"../movenet/models","models":3} +2024-10-14 09:06:03 STATE: Models {"folder":"../nanodet/models","models":4} +2024-10-14 09:06:03 STATE: Models: {"count":58,"totalSize":380063249} +2024-10-14 09:06:03 INFO:  Human Build complete... {"logFile":"test/build.log"} diff --git a/typedoc/assets/search.js b/typedoc/assets/search.js index 7e384747..bfd05cf0 100644 --- a/typedoc/assets/search.js +++ b/typedoc/assets/search.js @@ -1 +1 @@ -window.searchData = 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"; \ No newline at end of file diff --git a/typedoc/classes/Env.html b/typedoc/classes/Env.html index 718abc8f..469664f1 100644 --- a/typedoc/classes/Env.html +++ b/typedoc/classes/Env.html @@ -1,4 +1,4 @@ -Env | @vladmandic/human - v3.3.2

Class Env

Env class that holds detected capabilities

+Env | @vladmandic/human - v3.3.3

Class Env

Env class that holds detected capabilities

Constructors

constructor

Properties

agent backends @@ -41,4 +41,4 @@

worker

worker: boolean

Running in WebWorker thread

Accessors

Canvas

Image

ImageData

Methods

updateBackend

  • updateBackend(): Promise<void>

  • update backend information

    Returns Promise<void>

updateCPU

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Returns void

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diff --git a/typedoc/classes/GraphModel.html b/typedoc/classes/GraphModel.html index f498294a..c686dfff 100644 --- a/typedoc/classes/GraphModel.html +++ b/typedoc/classes/GraphModel.html @@ -1,4 +1,4 @@ -GraphModel | @vladmandic/human - v3.3.2

Class GraphModel<ModelURL>

A tf.GraphModel is a directed, acyclic graph built from a +GraphModel | @vladmandic/human - v3.3.3

Class GraphModel<ModelURL>

A tf.GraphModel is a directed, acyclic graph built from a SavedModel GraphDef and allows inference execution.

A tf.GraphModel can only be created by loading from a model converted from a TensorFlow SavedModel using @@ -125,4 +125,4 @@

Doc

Returns Promise<SaveResult>

A Promise of SaveResult, which summarizes the result of the saving, such as byte sizes of the saved artifacts for the model's topology and weight values.

-

Doc

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+

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diff --git a/typedoc/classes/Human.html b/typedoc/classes/Human.html index 956a6c0d..b4f1d568 100644 --- a/typedoc/classes/Human.html +++ b/typedoc/classes/Human.html @@ -1,4 +1,4 @@ -Human | @vladmandic/human - v3.3.2

Class Human

Human* library main class

+Human | @vladmandic/human - v3.3.3

Class Human

Human* library main class

All methods and properties are available only as members of Human class

  • Configuration object definition: Config
    • @@ -151,4 +151,4 @@

    Returns

Parameters

Returns Promise<undefined | Result>

result - Result

-

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diff --git a/typedoc/classes/Tensor-1.html b/typedoc/classes/Tensor-1.html index 75446a01..02bca61a 100644 --- a/typedoc/classes/Tensor-1.html +++ b/typedoc/classes/Tensor-1.html @@ -1,4 +1,4 @@ -Tensor | @vladmandic/human - v3.3.2

Class Tensor<R>

A tf.Tensor object represents an immutable, multidimensional array of +Tensor | @vladmandic/human - v3.3.3

Class Tensor<R>

A tf.Tensor object represents an immutable, multidimensional array of numbers that has a shape and a data type.

For performance reasons, functions that create tensors do not necessarily perform a copy of the data passed to them (e.g. if the data is passed as a @@ -222,4 +222,4 @@

Parameters

  • Optionalverbose: boolean

    Whether to print verbose information about the tensor, including dtype and size.

Returns void

Doc

prod

  • prod<T>(this, axis?, keepDims?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

reciprocal

  • reciprocal<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

relu

  • relu<T>(): T

  • Type Parameters

    Returns T

relu6

  • relu6<T>(): T

  • Type Parameters

    Returns T

reshape

  • reshape<T>(shape): T

  • Type Parameters

    Parameters

    • shape: number[]

    Returns T

reshapeAs

  • reshapeAs<T>(x): T

  • Type Parameters

    Parameters

    Returns T

resizeBilinear

  • resizeBilinear<T>(newShape2D, alignCorners?, halfPixelCenters?): T

  • Type Parameters

    Parameters

    • newShape2D: [number, number]
    • OptionalalignCorners: boolean
    • OptionalhalfPixelCenters: boolean

    Returns T

resizeNearestNeighbor

  • resizeNearestNeighbor<T>(newShape2D, alignCorners?, halfFloatCenters?): T

  • Type Parameters

    Parameters

    • newShape2D: [number, number]
    • OptionalalignCorners: boolean
    • OptionalhalfFloatCenters: boolean

    Returns T

reverse

  • reverse<T>(this, axis?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number | number[]

    Returns T

rfft

  • rfft<T>(this): Tensor<Rank>

  • Type Parameters

    Parameters

    Returns Tensor<Rank>

round

  • round<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

rsqrt

  • rsqrt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

selu

  • selu<T>(): T

  • Type Parameters

    Returns T

separableConv2d

  • separableConv2d<T>(depthwiseFilter, pointwiseFilter, strides, pad, dilation?, dataFormat?): T

  • Type Parameters

    Parameters

    • depthwiseFilter: Tensor4D | TensorLike4D
    • pointwiseFilter: TensorLike | Tensor4D
    • strides: number | [number, number]
    • pad: "valid" | "same"
    • Optionaldilation: number | [number, number]
    • OptionaldataFormat: "NHWC" | "NCHW"

    Returns T

sigmoid

  • sigmoid<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sign

  • sign<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sin

  • sin<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

sinh

  • sinh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

slice

  • slice<T>(this, begin, size?): T

  • Type Parameters

    Parameters

    • this: T
    • begin: number | number[]
    • Optionalsize: number | number[]

    Returns T

softmax

  • softmax<T>(this, dim?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionaldim: number

    Returns T

softplus

  • softplus<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

spaceToBatchND

  • spaceToBatchND<R>(blockShape, paddings): Tensor<R>

  • Type Parameters

    Parameters

    • blockShape: number[]
    • paddings: number[][]

    Returns Tensor<R>

split

  • split<T>(numOrSizeSplits, axis?): T[]

  • Type Parameters

    Parameters

    • numOrSizeSplits: number | number[]
    • Optionalaxis: number

    Returns T[]

sqrt

  • sqrt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

square

  • square<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

squaredDifference

  • squaredDifference<T>(b): T

  • Type Parameters

    Parameters

    Returns T

squeeze

  • squeeze<T>(axis?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number[]

    Returns T

stack

  • stack<T>(x, axis?): T

  • Type Parameters

    Parameters

    Returns T

step

  • step<T>(this, alpha?): T

  • Type Parameters

    Parameters

    • this: T
    • Optionalalpha: number

    Returns T

stridedSlice

  • stridedSlice<T>(this, begin, end, strides, beginMask?, endMask?, ellipsisMask?, newAxisMask?, shrinkAxisMask?): Tensor<Rank>

  • Type Parameters

    Parameters

    • this: Tensor<Rank>
    • begin: number[]
    • end: number[]
    • strides: number[]
    • OptionalbeginMask: number
    • OptionalendMask: number
    • OptionalellipsisMask: number
    • OptionalnewAxisMask: number
    • OptionalshrinkAxisMask: number

    Returns Tensor<Rank>

sub

  • sub<T>(b): T

  • Type Parameters

    Parameters

    Returns T

sum

  • sum<T>(axis?, keepDims?): T

  • Type Parameters

    Parameters

    • Optionalaxis: number | number[]
    • OptionalkeepDims: boolean

    Returns T

tan

  • tan<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

tanh

  • tanh<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

throwIfDisposed

  • throwIfDisposed(): void

  • Returns void

tile

  • tile<T>(b): T

  • Type Parameters

    Parameters

    • b: number[]

    Returns T

toBool

  • toBool<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toFloat

  • toFloat<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

toInt

  • toInt<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

topk

  • topk<T>(this, k?, sorted?): {
        indices: T;
        values: T;
    }

  • Type Parameters

    Parameters

    • this: T
    • Optionalk: number
    • Optionalsorted: boolean

    Returns {
        indices: T;
        values: T;
    }

    • indices: T
    • values: T

toString

  • toString(verbose?): string

  • Returns a human-readable description of the tensor. Useful for logging.

    -

    Parameters

    • Optionalverbose: boolean

    Returns string

    Doc

transpose

  • transpose<T>(perm?): T

  • Type Parameters

    Parameters

    • Optionalperm: number[]

    Returns T

unique

  • unique<T>(this, axis?): {
        indices: T;
        values: T;
    }

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number

    Returns {
        indices: T;
        values: T;
    }

    • indices: T
    • values: T

unsortedSegmentSum

  • unsortedSegmentSum<T>(this, segmentIds, numSegments): T

  • Type Parameters

    Parameters

    • this: T
    • segmentIds: Tensor1D | TensorLike1D
    • numSegments: number

    Returns T

unstack

  • unstack<T>(axis?): T[]

  • Type Parameters

    Parameters

    • Optionalaxis: number

    Returns T[]

variable

  • variable(trainable?, name?, dtype?): Variable<R>

  • Parameters

    • Optionaltrainable: boolean
    • Optionalname: string
    • Optionaldtype: keyof DataTypeMap

    Returns Variable<R>

where

  • where<T>(condition, x): T

  • Type Parameters

    Parameters

    Returns T

zerosLike

  • zerosLike<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

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absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
+

Parameters

  • Optionalverbose: boolean

Returns string

Doc

transpose

  • transpose<T>(perm?): T

  • Type Parameters

    Parameters

    • Optionalperm: number[]

    Returns T

unique

  • unique<T>(this, axis?): {
        indices: T;
        values: T;
    }

  • Type Parameters

    Parameters

    • this: T
    • Optionalaxis: number

    Returns {
        indices: T;
        values: T;
    }

    • indices: T
    • values: T

unsortedSegmentSum

  • unsortedSegmentSum<T>(this, segmentIds, numSegments): T

  • Type Parameters

    Parameters

    • this: T
    • segmentIds: Tensor1D | TensorLike1D
    • numSegments: number

    Returns T

unstack

  • unstack<T>(axis?): T[]

  • Type Parameters

    Parameters

    • Optionalaxis: number

    Returns T[]

variable

  • variable(trainable?, name?, dtype?): Variable<R>

  • Parameters

    • Optionaltrainable: boolean
    • Optionalname: string
    • Optionaldtype: keyof DataTypeMap

    Returns Variable<R>

where

  • where<T>(condition, x): T

  • Type Parameters

    Parameters

    Returns T

zerosLike

  • zerosLike<T>(this): T

  • Type Parameters

    Parameters

    • this: T

    Returns T

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absacosacoshaddallanyargMaxargMinarrayarraySyncas1Das2Das3Das4Das5DasinasinhasScalarasTypeatanatan2atanhavgPoolbatchNormbatchToSpaceNDbroadcastTobufferbufferSyncbytescastceilclipByValuecloneconcatconv1dconv2dconv2dTransposecoscoshcumprodcumsumdatadataSyncdataToGPUdepthToSpacedepthwiseConv2ddilation2ddisposedivdivNoNandoteluequalerfeuclideanNormexpexpandDimsexpm1fftflattenfloorfloorDivgathergreatergreaterEqualifftirfftisFiniteisInfisNaNleakyRelulesslessEquallocalResponseNormalizationloglog1plogicalAndlogicalNotlogicalOrlogicalXorlogSigmoidlogSoftmaxlogSumExpmatMulmaxmaximummaxPoolmeanminminimummirrorPadmodmulnegnormnotEqualoneHotonesLikepadpoolpowpreluprintprodreciprocalrelurelu6reshapereshapeAsresizeBilinearresizeNearestNeighborreverserfftroundrsqrtseluseparableConv2dsigmoidsignsinsinhslicesoftmaxsoftplusspaceToBatchNDsplitsqrtsquaresquaredDifferencesqueezestackstepstridedSlicesubsumtantanhthrowIfDisposedtiletoBooltoFloattoInttopktoStringtransposeuniqueunsortedSegmentSumunstackvariablewherezerosLike
diff --git a/typedoc/classes/WebCam.html b/typedoc/classes/WebCam.html index c9733be6..ae1be056 100644 --- a/typedoc/classes/WebCam.html +++ b/typedoc/classes/WebCam.html @@ -1,4 +1,4 @@ -WebCam | @vladmandic/human - v3.3.2

Class WebCam

Constructors

constructor +WebCam | @vladmandic/human - v3.3.3

Class WebCam

Constructors

constructor

Properties

config devices element @@ -32,4 +32,4 @@

Returns void

play

start

  • start(webcamConfig?): Promise<string>

  • start method initializizes webcam stream and associates it with a dom video element

    Parameters

    Returns Promise<string>

stop

  • stop(): void

  • stop method stops active webcam stream track and disconnects webcam

    -

    Returns void

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+

Returns void

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Accessors
Methods
diff --git a/typedoc/classes/models.Models.html b/typedoc/classes/models.Models.html index 419bb718..ce11d9f3 100644 --- a/typedoc/classes/models.Models.html +++ b/typedoc/classes/models.Models.html @@ -1,4 +1,4 @@ -Models | @vladmandic/human - v3.3.2

Class Models

Models class used by Human

+Models | @vladmandic/human - v3.3.3

Class Models

Models class used by Human

  • models: record of all GraphModels
  • list: returns list of configured models with their stats
    • @@ -15,4 +15,4 @@ reset stats validate -

Constructors

constructor

Properties

models

models: Record<string, null | GraphModel<string | IOHandler>> = {}

Methods

list

  • list(): {
        loaded: boolean;
        name: string;
        size: number;
        url: any;
    }[]

  • Returns {
        loaded: boolean;
        name: string;
        size: number;
        url: any;
    }[]

load

  • load(instance?): Promise<void>

  • Parameters

    Returns Promise<void>

loaded

reset

stats

validate

  • validate(): {
        missing: string[];
        name: string;
    }[]

  • Returns {
        missing: string[];
        name: string;
    }[]

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+

Constructors

constructor

Properties

models

models: Record<string, null | GraphModel<string | IOHandler>> = {}

Methods

list

  • list(): {
        loaded: boolean;
        name: string;
        size: number;
        url: any;
    }[]

  • Returns {
        loaded: boolean;
        name: string;
        size: number;
        url: any;
    }[]

load

  • load(instance?): Promise<void>

  • Parameters

    Returns Promise<void>

loaded

reset

stats

validate

  • validate(): {
        missing: string[];
        name: string;
    }[]

  • Returns {
        missing: string[];
        name: string;
    }[]

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diff --git a/typedoc/enums/Rank.html b/typedoc/enums/Rank.html index 8ce4e585..1195ac39 100644 --- a/typedoc/enums/Rank.html +++ b/typedoc/enums/Rank.html @@ -1,8 +1,8 @@ -Rank | @vladmandic/human - v3.3.2

Enumeration Rank

Enumeration Members

R0 +Rank | @vladmandic/human - v3.3.3

Enumeration Rank

Enumeration Members

R0 R1 R2 R3 R4 R5 R6 -

Enumeration Members

R0

R0: "R0"

R1

R1: "R1"

R2

R2: "R2"

R3

R3: "R3"

R4

R4: "R4"

R5

R5: "R5"

R6

R6: "R6"

Settings

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On This Page

Enumeration Members
+

Enumeration Members

R0

R0: "R0"

R1

R1: "R1"

R2

R2: "R2"

R3

R3: "R3"

R4

R4: "R4"

R5

R5: "R5"

R6

R6: "R6"

Settings

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On This Page

Enumeration Members
diff --git a/typedoc/functions/draw.all.html b/typedoc/functions/draw.all.html index aaad56f6..49fa11e8 100644 --- a/typedoc/functions/draw.all.html +++ b/typedoc/functions/draw.all.html @@ -1,2 +1,2 @@ -all | @vladmandic/human - v3.3.2

Function all

  • all(inCanvas, result, drawOptions?): Promise<null | [void, void, void, void, void]>

  • meta-function that performs draw for: canvas, face, body, hand

    -

    Parameters

    Returns Promise<null | [void, void, void, void, void]>

Settings

Member Visibility
+all | @vladmandic/human - v3.3.3

Function all

  • all(inCanvas, result, drawOptions?): Promise<null | [void, void, void, void, void]>

  • meta-function that performs draw for: canvas, face, body, hand

    +

    Parameters

    Returns Promise<null | [void, void, void, void, void]>

Settings

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diff --git a/typedoc/functions/draw.body.html b/typedoc/functions/draw.body.html index f432acff..d1314799 100644 --- a/typedoc/functions/draw.body.html +++ b/typedoc/functions/draw.body.html @@ -1,2 +1,2 @@ -body | @vladmandic/human - v3.3.2

Function body

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+body | @vladmandic/human - v3.3.3

Function body

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diff --git a/typedoc/functions/draw.canvas.html b/typedoc/functions/draw.canvas.html index 982bae5c..03daae6a 100644 --- a/typedoc/functions/draw.canvas.html +++ b/typedoc/functions/draw.canvas.html @@ -1,2 +1,2 @@ -canvas | @vladmandic/human - v3.3.2

Function canvas

Settings

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+canvas | @vladmandic/human - v3.3.3

Function canvas

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diff --git a/typedoc/functions/draw.face.html b/typedoc/functions/draw.face.html index d0b46be8..4dd3c93f 100644 --- a/typedoc/functions/draw.face.html +++ b/typedoc/functions/draw.face.html @@ -1,2 +1,2 @@ -face | @vladmandic/human - v3.3.2

Function face

Settings

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+face | @vladmandic/human - v3.3.3

Function face

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diff --git a/typedoc/functions/draw.gesture.html b/typedoc/functions/draw.gesture.html index 8061a48f..6a7e7d79 100644 --- a/typedoc/functions/draw.gesture.html +++ b/typedoc/functions/draw.gesture.html @@ -1,2 +1,2 @@ -gesture | @vladmandic/human - v3.3.2

Function gesture

Settings

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+gesture | @vladmandic/human - v3.3.3

Function gesture

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diff --git a/typedoc/functions/draw.hand.html b/typedoc/functions/draw.hand.html index 45675607..6ea39440 100644 --- a/typedoc/functions/draw.hand.html +++ b/typedoc/functions/draw.hand.html @@ -1,2 +1,2 @@ -hand | @vladmandic/human - v3.3.2

Function hand

Settings

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+hand | @vladmandic/human - v3.3.3

Function hand

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diff --git a/typedoc/functions/draw.init.html b/typedoc/functions/draw.init.html index a44e7b26..32b5ceb2 100644 --- a/typedoc/functions/draw.init.html +++ b/typedoc/functions/draw.init.html @@ -1,2 +1,2 @@ -init | @vladmandic/human - v3.3.2

Function init

  • init(): void

  • sets default label templates for face/body/hand/object/gestures

    -

    Returns void

Settings

Member Visibility
+init | @vladmandic/human - v3.3.3

Function init

  • init(): void

  • sets default label templates for face/body/hand/object/gestures

    +

    Returns void

Settings

Member Visibility
diff --git a/typedoc/functions/draw.object.html b/typedoc/functions/draw.object.html index 23df8ef6..895e4c1c 100644 --- a/typedoc/functions/draw.object.html +++ b/typedoc/functions/draw.object.html @@ -1,2 +1,2 @@ -object | @vladmandic/human - v3.3.2

Function object

Settings

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+object | @vladmandic/human - v3.3.3

Function object

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diff --git a/typedoc/functions/draw.person.html b/typedoc/functions/draw.person.html index 2adb2ab1..1b379eb9 100644 --- a/typedoc/functions/draw.person.html +++ b/typedoc/functions/draw.person.html @@ -1,2 +1,2 @@ -person | @vladmandic/human - v3.3.2

Function person

  • person(inCanvas, result, drawOptions?): void

  • draw combined person results instead of individual detection result objects

    -

    Parameters

    Returns void

Settings

Member Visibility
+person | @vladmandic/human - v3.3.3

Function person

  • person(inCanvas, result, drawOptions?): void

  • draw combined person results instead of individual detection result objects

    +

    Parameters

    Returns void

Settings

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diff --git a/typedoc/functions/empty.html b/typedoc/functions/empty.html index bdc5b9d2..218a090f 100644 --- a/typedoc/functions/empty.html +++ b/typedoc/functions/empty.html @@ -1 +1 @@ -empty | @vladmandic/human - v3.3.2

Function empty

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+empty | @vladmandic/human - v3.3.3

Function empty

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diff --git a/typedoc/functions/match.distance.html b/typedoc/functions/match.distance.html index bec4b10d..51b2ee31 100644 --- a/typedoc/functions/match.distance.html +++ b/typedoc/functions/match.distance.html @@ -1,4 +1,4 @@ -distance | @vladmandic/human - v3.3.2

Function distance

  • distance(descriptor1, descriptor2, options?): number

  • Calculates distance between two descriptors

    +distance | @vladmandic/human - v3.3.3

    Function distance

    • distance(descriptor1, descriptor2, options?): number

    • Calculates distance between two descriptors

      Parameters

      • descriptor1: Descriptor
      • descriptor2: Descriptor
      • options: MatchOptions = ...

        calculation options

        • order - algorithm to use @@ -6,4 +6,4 @@
        • multiplier - by how much to enhance difference analysis in range of 1..100 default is 20 which normalizes results to similarity above 0.5 can be considered a match
        -

      Returns number

    Settings

    Member Visibility
    +

Returns number

Settings

Member Visibility
diff --git a/typedoc/functions/match.find.html b/typedoc/functions/match.find.html index c5b88989..8b4579f5 100644 --- a/typedoc/functions/match.find.html +++ b/typedoc/functions/match.find.html @@ -1,4 +1,4 @@ -find | @vladmandic/human - v3.3.2

Function find

  • find(descriptor, descriptors, options?): {
        distance: number;
        index: number;
        similarity: number;
    }

  • Matches given descriptor to a closest entry in array of descriptors

    +find | @vladmandic/human - v3.3.3

    Function find

    • find(descriptor, descriptors, options?): {
          distance: number;
          index: number;
          similarity: number;
      }

    • Matches given descriptor to a closest entry in array of descriptors

      Parameters

      • descriptor: Descriptor

        face descriptor

      • descriptors: Descriptor[]

        array of face descriptors to commpare given descriptor to

      • options: MatchOptions = ...

        see similarity method for options description @@ -8,4 +8,4 @@

      • distance calculated distance of given descriptor to the best match
      • similarity calculated normalized similarity of given descriptor to the best match
      -

    Returns {
        distance: number;
        index: number;
        similarity: number;
    }

    • distance: number
    • index: number
    • similarity: number

Settings

Member Visibility
+

Returns {
    distance: number;
    index: number;
    similarity: number;
}

  • distance: number
  • index: number
  • similarity: number

Settings

Member Visibility
diff --git a/typedoc/functions/match.similarity.html b/typedoc/functions/match.similarity.html index 40496c27..1a37ef1f 100644 --- a/typedoc/functions/match.similarity.html +++ b/typedoc/functions/match.similarity.html @@ -1,4 +1,4 @@ -similarity | @vladmandic/human - v3.3.2

Function similarity

  • similarity(descriptor1, descriptor2, options?): number

  • Calculates normalized similarity between two face descriptors based on their distance

    +similarity | @vladmandic/human - v3.3.3

    Function similarity

    • similarity(descriptor1, descriptor2, options?): number

    • Calculates normalized similarity between two face descriptors based on their distance

      Parameters

      • descriptor1: Descriptor
      • descriptor2: Descriptor
      • options: MatchOptions = ...

        calculation options

        • order - algorithm to use @@ -10,4 +10,4 @@ default is 0.2...0.8 Returns similarity between two face descriptors normalized to 0..1 range where 0 is no similarity and 1 is perfect similarity
        -

      Returns number

    Settings

    Member Visibility
    +

Returns number

Settings

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diff --git a/typedoc/functions/models.validateModel.html b/typedoc/functions/models.validateModel.html index 1968ca5e..ca018dd6 100644 --- a/typedoc/functions/models.validateModel.html +++ b/typedoc/functions/models.validateModel.html @@ -1 +1 @@ -validateModel | @vladmandic/human - v3.3.2

Function validateModel

Settings

Member Visibility
+validateModel | @vladmandic/human - v3.3.3

Function validateModel

Settings

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diff --git a/typedoc/hierarchy.html b/typedoc/hierarchy.html index f89a1d57..9469ea59 100644 --- a/typedoc/hierarchy.html +++ b/typedoc/hierarchy.html @@ -1 +1 @@ -@vladmandic/human - v3.3.2

@vladmandic/human - v3.3.2

Class Hierarchy

Settings

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+@vladmandic/human - v3.3.3

@vladmandic/human - v3.3.3

Class Hierarchy

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diff --git a/typedoc/index.html b/typedoc/index.html index 98813038..9c449585 100644 --- a/typedoc/index.html +++ b/typedoc/index.html @@ -1,4 +1,4 @@ -@vladmandic/human - v3.3.2

@vladmandic/human - v3.3.2

References

default +@vladmandic/human - v3.3.3

@vladmandic/human - v3.3.3

References

default

Namespaces

draw match models @@ -80,4 +80,4 @@ env

Functions

empty

References

default

Renames and re-exports Human

Class Human as default export

-

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+

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References
diff --git a/typedoc/interfaces/BodyConfig.html b/typedoc/interfaces/BodyConfig.html index 71b6b56d..edc2542c 100644 --- a/typedoc/interfaces/BodyConfig.html +++ b/typedoc/interfaces/BodyConfig.html @@ -1,4 +1,4 @@ -BodyConfig | @vladmandic/human - v3.3.2

Interface BodyConfig

Configures all body detection specific options

+BodyConfig | @vladmandic/human - v3.3.3

Interface BodyConfig

Configures all body detection specific options

interface BodyConfig {
    enabled: boolean;
    maxDetected: number;
    minConfidence: number;
    modelPath: string;
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

enabled maxDetected minConfidence @@ -13,4 +13,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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Properties
+

Settings

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On This Page

Properties
diff --git a/typedoc/interfaces/BodyKeypoint.html b/typedoc/interfaces/BodyKeypoint.html index aa76efa1..26f218c4 100644 --- a/typedoc/interfaces/BodyKeypoint.html +++ b/typedoc/interfaces/BodyKeypoint.html @@ -1,4 +1,4 @@ -BodyKeypoint | @vladmandic/human - v3.3.2

Interface BodyKeypoint

Body Result keypoints

+BodyKeypoint | @vladmandic/human - v3.3.3

Interface BodyKeypoint

Body Result keypoints

interface BodyKeypoint {
    distance?: Point;
    part: BodyLandmark;
    position: Point;
    positionRaw: Point;
    score: number;
}

Properties

distance? part position @@ -9,4 +9,4 @@

position

position: Point

body part position

positionRaw

positionRaw: Point

body part position normalized to 0..1

score

score: number

body part detection score

-

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diff --git a/typedoc/interfaces/BodyResult.html b/typedoc/interfaces/BodyResult.html index d44f5be0..9de5b745 100644 --- a/typedoc/interfaces/BodyResult.html +++ b/typedoc/interfaces/BodyResult.html @@ -1,4 +1,4 @@ -BodyResult | @vladmandic/human - v3.3.2

Interface BodyResult

Body results

+BodyResult | @vladmandic/human - v3.3.3

Interface BodyResult

Body results

interface BodyResult {
    annotations: Record<BodyAnnotation, Point[][]>;
    box: Box;
    boxRaw: Box;
    id: number;
    keypoints: BodyKeypoint[];
    score: number;
}

Properties

annotations box boxRaw @@ -11,4 +11,4 @@

id

id: number

body id

keypoints

keypoints: BodyKeypoint[]

detected body keypoints

score

score: number

body detection score

-

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diff --git a/typedoc/interfaces/Config.html b/typedoc/interfaces/Config.html index a2f22368..1cd6f2f6 100644 --- a/typedoc/interfaces/Config.html +++ b/typedoc/interfaces/Config.html @@ -1,4 +1,4 @@ -Config | @vladmandic/human - v3.3.2

Interface Config

Configuration interface definition for Human library +Config | @vladmandic/human - v3.3.3

Interface Config

Configuration interface definition for Human library Contains all configurable parameters Defaults: config

interface Config {
    async: boolean;
    backend: BackendEnum;
    body: Partial<BodyConfig>;
    cacheModels: boolean;
    cacheSensitivity: number;
    deallocate: boolean;
    debug: boolean;
    face: Partial<FaceConfig>;
    filter: Partial<FilterConfig>;
    flags: Record<string, unknown>;
    gesture: Partial<GestureConfig>;
    hand: Partial<HandConfig>;
    modelBasePath: string;
    object: Partial<ObjectConfig>;
    segmentation: Partial<SegmentationConfig>;
    skipAllowed: boolean;
    softwareKernels: boolean;
    validateModels: boolean;
    warmup: WarmupEnum;
    wasmPath: string;
    wasmPlatformFetch: boolean;
}

Properties

async @@ -71,4 +71,4 @@

default: auto-detects to link to CDN jsdelivr when running in browser

wasmPlatformFetch

wasmPlatformFetch: boolean

Force WASM loader to use platform fetch

default: false

-

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diff --git a/typedoc/interfaces/DrawOptions.html b/typedoc/interfaces/DrawOptions.html index 36af9dce..baddc9e6 100644 --- a/typedoc/interfaces/DrawOptions.html +++ b/typedoc/interfaces/DrawOptions.html @@ -1,4 +1,4 @@ -DrawOptions | @vladmandic/human - v3.3.2

Interface DrawOptions

Draw Options

+DrawOptions | @vladmandic/human - v3.3.3

Interface DrawOptions

Draw Options

  • Accessed via human.draw.options or provided per each draw method as the drawOptions optional parameter
@@ -54,4 +54,4 @@

shadowColor

shadowColor: string

label shadow color

useCurves

useCurves: boolean

should lines be curved?

useDepth

useDepth: boolean

use z-coordinate when available

-

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diff --git a/typedoc/interfaces/FaceAntiSpoofConfig.html b/typedoc/interfaces/FaceAntiSpoofConfig.html index db39a58e..484e41ff 100644 --- a/typedoc/interfaces/FaceAntiSpoofConfig.html +++ b/typedoc/interfaces/FaceAntiSpoofConfig.html @@ -1,4 +1,4 @@ -FaceAntiSpoofConfig | @vladmandic/human - v3.3.2

Interface FaceAntiSpoofConfig

Anti-spoofing part of face configuration

+FaceAntiSpoofConfig | @vladmandic/human - v3.3.3

Interface FaceAntiSpoofConfig

Anti-spoofing part of face configuration

interface FaceAntiSpoofConfig {
    enabled: boolean;
    modelPath: string;
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

enabled modelPath skipFrames @@ -9,4 +9,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/FaceAttentionConfig.html b/typedoc/interfaces/FaceAttentionConfig.html index 48c0a832..9c6af132 100644 --- a/typedoc/interfaces/FaceAttentionConfig.html +++ b/typedoc/interfaces/FaceAttentionConfig.html @@ -1,4 +1,4 @@ -FaceAttentionConfig | @vladmandic/human - v3.3.2

Interface FaceAttentionConfig

Attention part of face configuration

+FaceAttentionConfig | @vladmandic/human - v3.3.3

Interface FaceAttentionConfig

Attention part of face configuration

interface FaceAttentionConfig {
    enabled: boolean;
    modelPath: string;
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

enabled modelPath skipFrames @@ -9,4 +9,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/FaceConfig.html b/typedoc/interfaces/FaceConfig.html index 0215b1f6..39c0ee4d 100644 --- a/typedoc/interfaces/FaceConfig.html +++ b/typedoc/interfaces/FaceConfig.html @@ -1,4 +1,4 @@ -FaceConfig | @vladmandic/human - v3.3.2

Interface FaceConfig

Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description

+FaceConfig | @vladmandic/human - v3.3.3

Interface FaceConfig

Configures all face-specific options: face detection, mesh analysis, age, gender, emotion detection and face description

interface FaceConfig {
    antispoof: Partial<FaceAntiSpoofConfig>;
    attention: Partial<FaceAttentionConfig>;
    description: Partial<FaceDescriptionConfig>;
    detector: Partial<FaceDetectorConfig>;
    emotion: Partial<FaceEmotionConfig>;
    enabled: boolean;
    gear: Partial<FaceGearConfig>;
    iris: Partial<FaceIrisConfig>;
    liveness: Partial<FaceLivenessConfig>;
    mesh: Partial<FaceMeshConfig>;
    modelPath: string;
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

antispoof attention description @@ -18,4 +18,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/FaceDescriptionConfig.html b/typedoc/interfaces/FaceDescriptionConfig.html index 39143cb9..2729271c 100644 --- a/typedoc/interfaces/FaceDescriptionConfig.html +++ b/typedoc/interfaces/FaceDescriptionConfig.html @@ -1,4 +1,4 @@ -FaceDescriptionConfig | @vladmandic/human - v3.3.2

Interface FaceDescriptionConfig

Description or face embedding part of face configuration

+FaceDescriptionConfig | @vladmandic/human - v3.3.3

Interface FaceDescriptionConfig

Description or face embedding part of face configuration

  • also used by age and gender detection
@@ -14,4 +14,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/FaceDetectorConfig.html b/typedoc/interfaces/FaceDetectorConfig.html index b5cde091..a18ea5cd 100644 --- a/typedoc/interfaces/FaceDetectorConfig.html +++ b/typedoc/interfaces/FaceDetectorConfig.html @@ -1,4 +1,4 @@ -FaceDetectorConfig | @vladmandic/human - v3.3.2

Interface FaceDetectorConfig

Detector part of face configuration

+FaceDetectorConfig | @vladmandic/human - v3.3.3

Interface FaceDetectorConfig

Detector part of face configuration

interface FaceDetectorConfig {
    enabled: boolean;
    iouThreshold: number;
    mask: boolean;
    maxDetected: number;
    minConfidence: number;
    minSize: number;
    modelPath: string;
    return: boolean;
    rotation: boolean;
    scale: number;
    skipFrames: number;
    skipTime: number;
    square: boolean;
}

Hierarchy (view full)

Properties

enabled iouThreshold mask @@ -29,4 +29,4 @@

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

square

square: boolean

automatically pad image to square

-

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diff --git a/typedoc/interfaces/FaceEmotionConfig.html b/typedoc/interfaces/FaceEmotionConfig.html index ed8dff08..02dec833 100644 --- a/typedoc/interfaces/FaceEmotionConfig.html +++ b/typedoc/interfaces/FaceEmotionConfig.html @@ -1,4 +1,4 @@ -FaceEmotionConfig | @vladmandic/human - v3.3.2

Interface FaceEmotionConfig

Emotion part of face configuration

+FaceEmotionConfig | @vladmandic/human - v3.3.3

Interface FaceEmotionConfig

Emotion part of face configuration

interface FaceEmotionConfig {
    enabled: boolean;
    minConfidence: number;
    modelPath: string;
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

enabled minConfidence modelPath @@ -11,4 +11,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/FaceGearConfig.html b/typedoc/interfaces/FaceGearConfig.html index 1f239c8d..3e1e6127 100644 --- a/typedoc/interfaces/FaceGearConfig.html +++ b/typedoc/interfaces/FaceGearConfig.html @@ -1,4 +1,4 @@ -FaceGearConfig | @vladmandic/human - v3.3.2

Interface FaceGearConfig

Gear part of face configuration

+FaceGearConfig | @vladmandic/human - v3.3.3

Interface FaceGearConfig

Gear part of face configuration

interface FaceGearConfig {
    enabled: boolean;
    minConfidence: number;
    modelPath: string;
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

enabled minConfidence modelPath @@ -11,4 +11,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/FaceIrisConfig.html b/typedoc/interfaces/FaceIrisConfig.html index e745bb11..16b8db87 100644 --- a/typedoc/interfaces/FaceIrisConfig.html +++ b/typedoc/interfaces/FaceIrisConfig.html @@ -1,4 +1,4 @@ -FaceIrisConfig | @vladmandic/human - v3.3.2

Interface FaceIrisConfig

Iris part of face configuration

+FaceIrisConfig | @vladmandic/human - v3.3.3

Interface FaceIrisConfig

Iris part of face configuration

interface FaceIrisConfig {
    enabled: boolean;
    modelPath: string;
    scale: number;
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

enabled modelPath scale @@ -11,4 +11,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/FaceLivenessConfig.html b/typedoc/interfaces/FaceLivenessConfig.html index 5e02b98b..03043bc4 100644 --- a/typedoc/interfaces/FaceLivenessConfig.html +++ b/typedoc/interfaces/FaceLivenessConfig.html @@ -1,4 +1,4 @@ -FaceLivenessConfig | @vladmandic/human - v3.3.2

Interface FaceLivenessConfig

Liveness part of face configuration

+FaceLivenessConfig | @vladmandic/human - v3.3.3

Interface FaceLivenessConfig

Liveness part of face configuration

interface FaceLivenessConfig {
    enabled: boolean;
    modelPath: string;
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

enabled modelPath skipFrames @@ -9,4 +9,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/FaceMeshConfig.html b/typedoc/interfaces/FaceMeshConfig.html index cbb2f6b2..5dc12dc5 100644 --- a/typedoc/interfaces/FaceMeshConfig.html +++ b/typedoc/interfaces/FaceMeshConfig.html @@ -1,4 +1,4 @@ -FaceMeshConfig | @vladmandic/human - v3.3.2

Interface FaceMeshConfig

Mesh part of face configuration

+FaceMeshConfig | @vladmandic/human - v3.3.3

Interface FaceMeshConfig

Mesh part of face configuration

interface FaceMeshConfig {
    enabled: boolean;
    keepInvalid: boolean;
    modelPath: string;
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

enabled keepInvalid modelPath @@ -11,4 +11,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/FaceResult.html b/typedoc/interfaces/FaceResult.html index 9fa54b7d..211c8619 100644 --- a/typedoc/interfaces/FaceResult.html +++ b/typedoc/interfaces/FaceResult.html @@ -1,4 +1,4 @@ -FaceResult | @vladmandic/human - v3.3.2

Interface FaceResult

Face results

+FaceResult | @vladmandic/human - v3.3.3

Interface FaceResult

Face results

  • Combined results of face detector, face mesh, age, gender, emotion, embedding, iris models
  • Some values may be null if specific model is not enabled
    • @@ -45,4 +45,4 @@

score

score: number

overall face score

size

size: [number, number]

detected face box size

Optionaltensor

tensor?: Tensor<Rank>

detected face as tensor that can be used in further pipelines

-

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diff --git a/typedoc/interfaces/FilterConfig.html b/typedoc/interfaces/FilterConfig.html index 103b2b8a..40a8cf52 100644 --- a/typedoc/interfaces/FilterConfig.html +++ b/typedoc/interfaces/FilterConfig.html @@ -1,4 +1,4 @@ -FilterConfig | @vladmandic/human - v3.3.2

Interface FilterConfig

Run input through image filters before inference

+FilterConfig | @vladmandic/human - v3.3.3

Interface FilterConfig

Run input through image filters before inference

  • available only in Browser environments
  • image filters run with near-zero latency as they are executed on the GPU using WebGL
    • @@ -56,4 +56,4 @@
  • if just one is set, second one is scaled automatically
  • if both are set, values are used as-is
-

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diff --git a/typedoc/interfaces/GenericConfig.html b/typedoc/interfaces/GenericConfig.html index bf683d44..959e925a 100644 --- a/typedoc/interfaces/GenericConfig.html +++ b/typedoc/interfaces/GenericConfig.html @@ -1,4 +1,4 @@ -GenericConfig | @vladmandic/human - v3.3.2

Interface GenericConfig

Generic config type inherited by all module types

+GenericConfig | @vladmandic/human - v3.3.3

Interface GenericConfig

Generic config type inherited by all module types

interface GenericConfig {
    enabled: boolean;
    modelPath: string;
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

enabled modelPath skipFrames @@ -9,4 +9,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/GestureConfig.html b/typedoc/interfaces/GestureConfig.html index 8af6030d..42cd0fd3 100644 --- a/typedoc/interfaces/GestureConfig.html +++ b/typedoc/interfaces/GestureConfig.html @@ -1,4 +1,4 @@ -GestureConfig | @vladmandic/human - v3.3.2

Interface GestureConfig

Controlls gesture detection

+GestureConfig | @vladmandic/human - v3.3.3

Interface GestureConfig

Controlls gesture detection

interface GestureConfig {
    enabled: boolean;
}

Properties

enabled

Properties

enabled

enabled: boolean

is gesture detection enabled?

-

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diff --git a/typedoc/interfaces/HandConfig.html b/typedoc/interfaces/HandConfig.html index ad40c45f..3ee71df2 100644 --- a/typedoc/interfaces/HandConfig.html +++ b/typedoc/interfaces/HandConfig.html @@ -1,4 +1,4 @@ -HandConfig | @vladmandic/human - v3.3.2

Interface HandConfig

Configures all hand detection specific options

+HandConfig | @vladmandic/human - v3.3.3

Interface HandConfig

Configures all hand detection specific options

interface HandConfig {
    detector: {
        modelPath?: string;
    };
    enabled: boolean;
    iouThreshold: number;
    landmarks: boolean;
    maxDetected: number;
    minConfidence: number;
    modelPath: string;
    rotation: boolean;
    skeleton: {
        modelPath?: string;
    };
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

detector enabled iouThreshold @@ -23,4 +23,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/HandResult.html b/typedoc/interfaces/HandResult.html index 6c5f7d8f..9615c25d 100644 --- a/typedoc/interfaces/HandResult.html +++ b/typedoc/interfaces/HandResult.html @@ -1,4 +1,4 @@ -HandResult | @vladmandic/human - v3.3.2

Interface HandResult

Hand results

+HandResult | @vladmandic/human - v3.3.3

Interface HandResult

Hand results

interface HandResult {
    annotations: Record<Finger, Point[]>;
    box: Box;
    boxRaw: Box;
    boxScore: number;
    fingerScore: number;
    id: number;
    keypoints: Point[];
    label: HandType;
    landmarks: Record<Finger, {
        curl: FingerCurl;
        direction: FingerDirection;
    }>;
    score: number;
}

Properties

annotations box boxRaw @@ -19,4 +19,4 @@

label

label: HandType

detected hand class

landmarks

landmarks: Record<Finger, {
    curl: FingerCurl;
    direction: FingerDirection;
}>

detected hand parts annotated with part gestures

score

score: number

hand overal score

-

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diff --git a/typedoc/interfaces/ModelInfo.html b/typedoc/interfaces/ModelInfo.html index 2535e3e6..15c51e5b 100644 --- a/typedoc/interfaces/ModelInfo.html +++ b/typedoc/interfaces/ModelInfo.html @@ -1,7 +1,8 @@ -ModelInfo | @vladmandic/human - v3.3.2

Interface ModelInfo

interface ModelInfo {
    inCache: boolean;
    name: string;
    sizeDesired: number;
    sizeFromManifest: number;
    sizeLoadedWeights: number;
    url: string;
}

Properties

inCache +ModelInfo | @vladmandic/human - v3.3.3

Interface ModelInfo

interface ModelInfo {
    inCache: boolean;
    loaded: boolean;
    name: string;
    sizeDesired: number;
    sizeFromManifest: number;
    sizeLoadedWeights: number;
    url: string;
}

Properties

inCache +loaded name sizeDesired sizeFromManifest sizeLoadedWeights url -

Properties

inCache

inCache: boolean

name

name: string

sizeDesired

sizeDesired: number

sizeFromManifest

sizeFromManifest: number

sizeLoadedWeights

sizeLoadedWeights: number

url

url: string

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inCache

inCache: boolean

loaded

loaded: boolean

name

name: string

sizeDesired

sizeDesired: number

sizeFromManifest

sizeFromManifest: number

sizeLoadedWeights

sizeLoadedWeights: number

url

url: string

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diff --git a/typedoc/interfaces/ObjectConfig.html b/typedoc/interfaces/ObjectConfig.html index e1e149c0..12c41150 100644 --- a/typedoc/interfaces/ObjectConfig.html +++ b/typedoc/interfaces/ObjectConfig.html @@ -1,4 +1,4 @@ -ObjectConfig | @vladmandic/human - v3.3.2

Interface ObjectConfig

Configures all object detection specific options

+ObjectConfig | @vladmandic/human - v3.3.3

Interface ObjectConfig

Configures all object detection specific options

interface ObjectConfig {
    enabled: boolean;
    iouThreshold: number;
    maxDetected: number;
    minConfidence: number;
    modelPath: string;
    skipFrames: number;
    skipTime: number;
}

Hierarchy (view full)

Properties

enabled iouThreshold maxDetected @@ -15,4 +15,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/ObjectResult.html b/typedoc/interfaces/ObjectResult.html index 39898f1c..5c32bf4b 100644 --- a/typedoc/interfaces/ObjectResult.html +++ b/typedoc/interfaces/ObjectResult.html @@ -1,4 +1,4 @@ -ObjectResult | @vladmandic/human - v3.3.2

Interface ObjectResult

Object results

+ObjectResult | @vladmandic/human - v3.3.3

Interface ObjectResult

Object results

interface ObjectResult {
    box: Box;
    boxRaw: Box;
    class: number;
    id: number;
    label: ObjectType;
    score: number;
}

Properties

box boxRaw class @@ -11,4 +11,4 @@

id

id: number

object id

label

label: ObjectType

detected object class name

score

score: number

object detection score

-

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diff --git a/typedoc/interfaces/PersonResult.html b/typedoc/interfaces/PersonResult.html index 5b944474..927c71ee 100644 --- a/typedoc/interfaces/PersonResult.html +++ b/typedoc/interfaces/PersonResult.html @@ -1,4 +1,4 @@ -PersonResult | @vladmandic/human - v3.3.2

Interface PersonResult

Person getter

+PersonResult | @vladmandic/human - v3.3.3

Interface PersonResult

Person getter

  • Triggers combining all individual results into a virtual person object
@@ -16,4 +16,4 @@

gestures

gestures: GestureResult[]

detected gestures specific to this person

hands

hands: {
    left: null | HandResult;
    right: null | HandResult;
}

left and right hand results that belong to this person

id

id: number

person id

-

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diff --git a/typedoc/interfaces/Result.html b/typedoc/interfaces/Result.html index 04ff2682..9e119895 100644 --- a/typedoc/interfaces/Result.html +++ b/typedoc/interfaces/Result.html @@ -1,4 +1,4 @@ -Result | @vladmandic/human - v3.3.2

Interface Result

Result interface definition for Human library

+Result | @vladmandic/human - v3.3.3

Interface Result

Result interface definition for Human library

Contains all possible detection results

interface Result {
    body: BodyResult[];
    canvas?: null | AnyCanvas;
    error: null | string;
    face: FaceResult[];
    gesture: GestureResult[];
    hand: HandResult[];
    height: number;
    object: ObjectResult[];
    performance: Record<string, number>;
    persons: PersonResult[];
    timestamp: number;
    width: number;
}

Properties

body canvas? @@ -24,4 +24,4 @@

persons

persons: PersonResult[]

getter property that returns unified persons object

Readonlytimestamp

timestamp: number

timestamp of detection representing the milliseconds elapsed since the UNIX epoch

width

width: number

Resolution width

-

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diff --git a/typedoc/interfaces/SegmentationConfig.html b/typedoc/interfaces/SegmentationConfig.html index 4143f80a..f7548292 100644 --- a/typedoc/interfaces/SegmentationConfig.html +++ b/typedoc/interfaces/SegmentationConfig.html @@ -1,4 +1,4 @@ -SegmentationConfig | @vladmandic/human - v3.3.2

Interface SegmentationConfig

Configures all body segmentation module +SegmentationConfig | @vladmandic/human - v3.3.3

Interface SegmentationConfig

Configures all body segmentation module removes background from input containing person if segmentation is enabled it will run as preprocessing task before any other model alternatively leave it disabled and use it on-demand using human.segmentation method which can @@ -17,4 +17,4 @@ for two-phase models such as face and hand caching applies to bounding boxes detection only

skipTime

skipTime: number

how many max milliseconds to go without re-running model if cached results are acceptable for two-phase models such as face and hand caching applies to bounding boxes detection only

-

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diff --git a/typedoc/interfaces/WebCamConfig.html b/typedoc/interfaces/WebCamConfig.html index 018cd724..508ca4ba 100644 --- a/typedoc/interfaces/WebCamConfig.html +++ b/typedoc/interfaces/WebCamConfig.html @@ -1,4 +1,4 @@ -WebCamConfig | @vladmandic/human - v3.3.2

Interface WebCamConfig

WebCam configuration

+WebCamConfig | @vladmandic/human - v3.3.3

Interface WebCamConfig

WebCam configuration

interface WebCamConfig {
    crop: boolean;
    debug: boolean;
    element: undefined | string | HTMLVideoElement;
    height: number;
    id?: string;
    mode: "front" | "back";
    width: number;
}

Properties

crop debug element @@ -18,4 +18,4 @@

Optionalid

id?: string

deviceId of the video device to use

mode

mode: "front" | "back"

use front or back camera

width

width: number

desired webcam width

-

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diff --git a/typedoc/interfaces/models.KernelOps.html b/typedoc/interfaces/models.KernelOps.html index 2ca5dcf2..1566516b 100644 --- a/typedoc/interfaces/models.KernelOps.html +++ b/typedoc/interfaces/models.KernelOps.html @@ -1,5 +1,5 @@ -KernelOps | @vladmandic/human - v3.3.2

Interface KernelOps

interface KernelOps {
    missing: string[];
    name: string;
    ops: string[];
    url: string;
}

Properties

missing +KernelOps | @vladmandic/human - v3.3.3

Interface KernelOps

interface KernelOps {
    missing: string[];
    name: string;
    ops: string[];
    url: string;
}

Properties

missing name ops url -

Properties

missing

missing: string[]

name

name: string

ops

ops: string[]

url

url: string

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+

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missing

missing: string[]

name

name: string

ops

ops: string[]

url

url: string

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diff --git a/typedoc/interfaces/models.ModelStats.html b/typedoc/interfaces/models.ModelStats.html index 9efbb749..6f3dc8b1 100644 --- a/typedoc/interfaces/models.ModelStats.html +++ b/typedoc/interfaces/models.ModelStats.html @@ -1,4 +1,4 @@ -ModelStats | @vladmandic/human - v3.3.2

Interface ModelStats

structure that holds global stats for currently loaded models

+ModelStats | @vladmandic/human - v3.3.3

Interface ModelStats

structure that holds global stats for currently loaded models

interface ModelStats {
    modelStats: ModelInfo[];
    numDefinedModels: number;
    numLoadedModels: number;
    percentageLoaded: number;
    totalSizeFromManifest: number;
    totalSizeLoading: number;
    totalSizeWeights: number;
}

Properties

modelStats numDefinedModels numLoadedModels @@ -6,4 +6,4 @@ totalSizeFromManifest totalSizeLoading totalSizeWeights -

Properties

modelStats

modelStats: ModelInfo[]

numDefinedModels

numDefinedModels: number

numLoadedModels

numLoadedModels: number

percentageLoaded

percentageLoaded: number

totalSizeFromManifest

totalSizeFromManifest: number

totalSizeLoading

totalSizeLoading: number

totalSizeWeights

totalSizeWeights: number

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+

Properties

modelStats

modelStats: ModelInfo[]

numDefinedModels

numDefinedModels: number

numLoadedModels

numLoadedModels: number

percentageLoaded

percentageLoaded: number

totalSizeFromManifest

totalSizeFromManifest: number

totalSizeLoading

totalSizeLoading: number

totalSizeWeights

totalSizeWeights: number

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diff --git a/typedoc/modules/Tensor.html b/typedoc/modules/Tensor.html index 79431ea8..839c4e65 100644 --- a/typedoc/modules/Tensor.html +++ b/typedoc/modules/Tensor.html @@ -1,2 +1,2 @@ -Tensor | @vladmandic/human - v3.3.2

Namespace Tensor

Explict reexport of main @tensorflow/tfjs types

-

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+Tensor | @vladmandic/human - v3.3.3

Namespace Tensor

Explict reexport of main @tensorflow/tfjs types

+

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diff --git a/typedoc/modules/draw.html b/typedoc/modules/draw.html index c133ceb0..ed918b10 100644 --- a/typedoc/modules/draw.html +++ b/typedoc/modules/draw.html @@ -1,4 +1,4 @@ -draw | @vladmandic/human - v3.3.2

Namespace draw

Class Human as default export

+draw | @vladmandic/human - v3.3.3

Namespace draw

Class Human as default export

Index

Variables

options

Functions

all body @@ -9,4 +9,4 @@ init object person -

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diff --git a/typedoc/modules/match.html b/typedoc/modules/match.html index dac3a2e5..10f9160c 100644 --- a/typedoc/modules/match.html +++ b/typedoc/modules/match.html @@ -1,7 +1,7 @@ -match | @vladmandic/human - v3.3.2

Namespace match

Class Human as default export

+match | @vladmandic/human - v3.3.3

Namespace match

Class Human as default export

Index

Type Aliases

Descriptor MatchOptions

Functions

distance find similarity -

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diff --git a/typedoc/modules/models.html b/typedoc/modules/models.html index 569ee2ad..96cd57f9 100644 --- a/typedoc/modules/models.html +++ b/typedoc/modules/models.html @@ -1,6 +1,6 @@ -models | @vladmandic/human - v3.3.2

Namespace models

Class Human as default export

+models | @vladmandic/human - v3.3.3

Namespace models

Class Human as default export

Index

Classes

Models

Interfaces

KernelOps ModelStats

Functions

validateModel -

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diff --git a/typedoc/types/AnyCanvas.html b/typedoc/types/AnyCanvas.html index 190ced20..c5e1cb0f 100644 --- a/typedoc/types/AnyCanvas.html +++ b/typedoc/types/AnyCanvas.html @@ -1,2 +1,2 @@ -AnyCanvas | @vladmandic/human - v3.3.2

Type Alias AnyCanvas

AnyCanvas: HTMLCanvasElement | OffscreenCanvas

Defines all possible canvas types

-

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+AnyCanvas | @vladmandic/human - v3.3.3

Type Alias AnyCanvas

AnyCanvas: HTMLCanvasElement | OffscreenCanvas

Defines all possible canvas types

+

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diff --git a/typedoc/types/AnyImage.html b/typedoc/types/AnyImage.html index 3d670e6d..033877a4 100644 --- a/typedoc/types/AnyImage.html +++ b/typedoc/types/AnyImage.html @@ -1,2 +1,2 @@ -AnyImage | @vladmandic/human - v3.3.2

Type Alias AnyImage

AnyImage: HTMLImageElement | typeof Image

Defines all possible image types

-

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+AnyImage | @vladmandic/human - v3.3.3

Type Alias AnyImage

AnyImage: HTMLImageElement | typeof Image

Defines all possible image types

+

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diff --git a/typedoc/types/AnyVideo.html b/typedoc/types/AnyVideo.html index a05cf62b..1afc6e25 100644 --- a/typedoc/types/AnyVideo.html +++ b/typedoc/types/AnyVideo.html @@ -1,2 +1,2 @@ -AnyVideo | @vladmandic/human - v3.3.2

Type Alias AnyVideo

AnyVideo: HTMLMediaElement | HTMLVideoElement

Defines all possible video types

-

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+AnyVideo | @vladmandic/human - v3.3.3

Type Alias AnyVideo

AnyVideo: HTMLMediaElement | HTMLVideoElement

Defines all possible video types

+

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diff --git a/typedoc/types/BackendEnum.html b/typedoc/types/BackendEnum.html index 00c5c7a4..b5136f2f 100644 --- a/typedoc/types/BackendEnum.html +++ b/typedoc/types/BackendEnum.html @@ -1,2 +1,2 @@ -BackendEnum | @vladmandic/human - v3.3.2

Type Alias BackendEnum

BackendEnum:
    | ""
    | "cpu"
    | "wasm"
    | "webgl"
    | "humangl"
    | "tensorflow"
    | "webgpu"
    | "none"

Possible TensorFlow backends

-

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+BackendEnum | @vladmandic/human - v3.3.3

Type Alias BackendEnum

BackendEnum:
    | ""
    | "cpu"
    | "wasm"
    | "webgl"
    | "humangl"
    | "tensorflow"
    | "webgpu"
    | "none"

Possible TensorFlow backends

+

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diff --git a/typedoc/types/BodyAnnotation.html b/typedoc/types/BodyAnnotation.html index 6efa9ec5..a32f078b 100644 --- a/typedoc/types/BodyAnnotation.html +++ b/typedoc/types/BodyAnnotation.html @@ -1 +1 @@ -BodyAnnotation | @vladmandic/human - v3.3.2

Type Alias BodyAnnotation

BodyAnnotation: BodyAnnotationBlazePose | BodyAnnotationEfficientPose

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+BodyAnnotation | @vladmandic/human - v3.3.3

Type Alias BodyAnnotation

BodyAnnotation: BodyAnnotationBlazePose | BodyAnnotationEfficientPose

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diff --git a/typedoc/types/BodyAnnotationBlazePose.html b/typedoc/types/BodyAnnotationBlazePose.html index 9354ffe5..7b92a666 100644 --- a/typedoc/types/BodyAnnotationBlazePose.html +++ b/typedoc/types/BodyAnnotationBlazePose.html @@ -1 +1 @@ -BodyAnnotationBlazePose | @vladmandic/human - v3.3.2

Type Alias BodyAnnotationBlazePose

BodyAnnotationBlazePose:
    | "leftLeg"
    | "rightLeg"
    | "torso"
    | "leftArm"
    | "rightArm"
    | "leftEye"
    | "rightEye"
    | "mouth"

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+BodyAnnotationBlazePose | @vladmandic/human - v3.3.3

Type Alias BodyAnnotationBlazePose

BodyAnnotationBlazePose:
    | "leftLeg"
    | "rightLeg"
    | "torso"
    | "leftArm"
    | "rightArm"
    | "leftEye"
    | "rightEye"
    | "mouth"

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diff --git a/typedoc/types/BodyAnnotationEfficientPose.html b/typedoc/types/BodyAnnotationEfficientPose.html index 8dc596a9..72afcad4 100644 --- a/typedoc/types/BodyAnnotationEfficientPose.html +++ b/typedoc/types/BodyAnnotationEfficientPose.html @@ -1 +1 @@ -BodyAnnotationEfficientPose | @vladmandic/human - v3.3.2

Type Alias BodyAnnotationEfficientPose

BodyAnnotationEfficientPose:
    | "leftLeg"
    | "rightLeg"
    | "torso"
    | "leftArm"
    | "rightArm"
    | "head"

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+BodyAnnotationEfficientPose | @vladmandic/human - v3.3.3

Type Alias BodyAnnotationEfficientPose

BodyAnnotationEfficientPose:
    | "leftLeg"
    | "rightLeg"
    | "torso"
    | "leftArm"
    | "rightArm"
    | "head"

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diff --git a/typedoc/types/BodyGesture.html b/typedoc/types/BodyGesture.html index dff7cc6d..81600a30 100644 --- a/typedoc/types/BodyGesture.html +++ b/typedoc/types/BodyGesture.html @@ -1,2 +1,2 @@ -BodyGesture | @vladmandic/human - v3.3.2

Type Alias BodyGesture

BodyGesture: `leaning ${"left" | "right"}` | `raise ${"left" | "right"} hand` | "i give up"

body gesture type

-

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+BodyGesture | @vladmandic/human - v3.3.3

Type Alias BodyGesture

BodyGesture: `leaning ${"left" | "right"}` | `raise ${"left" | "right"} hand` | "i give up"

body gesture type

+

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diff --git a/typedoc/types/BodyLandmark.html b/typedoc/types/BodyLandmark.html index 8bbd4776..2686d993 100644 --- a/typedoc/types/BodyLandmark.html +++ b/typedoc/types/BodyLandmark.html @@ -1 +1 @@ -BodyLandmark | @vladmandic/human - v3.3.2

Type Alias BodyLandmark

BodyLandmark:
    | BodyLandmarkPoseNet
    | BodyLandmarkMoveNet
    | BodyLandmarkEfficientNet
    | BodyLandmarkBlazePose

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+BodyLandmark | @vladmandic/human - v3.3.3

Type Alias BodyLandmark

BodyLandmark:
    | BodyLandmarkPoseNet
    | BodyLandmarkMoveNet
    | BodyLandmarkEfficientNet
    | BodyLandmarkBlazePose

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diff --git a/typedoc/types/BodyLandmarkBlazePose.html b/typedoc/types/BodyLandmarkBlazePose.html index 8696d754..fa855a91 100644 --- a/typedoc/types/BodyLandmarkBlazePose.html +++ b/typedoc/types/BodyLandmarkBlazePose.html @@ -1 +1 @@ -BodyLandmarkBlazePose | @vladmandic/human - v3.3.2

Type Alias BodyLandmarkBlazePose

BodyLandmarkBlazePose:
    | "nose"
    | "leftEyeInside"
    | "leftEye"
    | "leftEyeOutside"
    | "rightEyeInside"
    | "rightEye"
    | "rightEyeOutside"
    | "leftEar"
    | "rightEar"
    | "leftMouth"
    | "rightMouth"
    | "leftShoulder"
    | "rightShoulder"
    | "leftElbow"
    | "rightElbow"
    | "leftWrist"
    | "rightWrist"
    | "leftPinky"
    | "rightPinky"
    | "leftIndex"
    | "rightIndex"
    | "leftThumb"
    | "rightThumb"
    | "leftHip"
    | "rightHip"
    | "leftKnee"
    | "rightKnee"
    | "leftAnkle"
    | "rightAnkle"
    | "leftHeel"
    | "rightHeel"
    | "leftFoot"
    | "rightFoot"
    | "bodyCenter"
    | "bodyTop"
    | "leftPalm"
    | "leftHand"
    | "rightPalm"
    | "rightHand"

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+BodyLandmarkBlazePose | @vladmandic/human - v3.3.3

Type Alias BodyLandmarkBlazePose

BodyLandmarkBlazePose:
    | "nose"
    | "leftEyeInside"
    | "leftEye"
    | "leftEyeOutside"
    | "rightEyeInside"
    | "rightEye"
    | "rightEyeOutside"
    | "leftEar"
    | "rightEar"
    | "leftMouth"
    | "rightMouth"
    | "leftShoulder"
    | "rightShoulder"
    | "leftElbow"
    | "rightElbow"
    | "leftWrist"
    | "rightWrist"
    | "leftPinky"
    | "rightPinky"
    | "leftIndex"
    | "rightIndex"
    | "leftThumb"
    | "rightThumb"
    | "leftHip"
    | "rightHip"
    | "leftKnee"
    | "rightKnee"
    | "leftAnkle"
    | "rightAnkle"
    | "leftHeel"
    | "rightHeel"
    | "leftFoot"
    | "rightFoot"
    | "bodyCenter"
    | "bodyTop"
    | "leftPalm"
    | "leftHand"
    | "rightPalm"
    | "rightHand"

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diff --git a/typedoc/types/BodyLandmarkEfficientNet.html b/typedoc/types/BodyLandmarkEfficientNet.html index e8649b49..210b033e 100644 --- a/typedoc/types/BodyLandmarkEfficientNet.html +++ b/typedoc/types/BodyLandmarkEfficientNet.html @@ -1 +1 @@ -BodyLandmarkEfficientNet | @vladmandic/human - v3.3.2

Type Alias BodyLandmarkEfficientNet

BodyLandmarkEfficientNet:
    | "head"
    | "neck"
    | "rightShoulder"
    | "rightElbow"
    | "rightWrist"
    | "chest"
    | "leftShoulder"
    | "leftElbow"
    | "leftWrist"
    | "bodyCenter"
    | "rightHip"
    | "rightKnee"
    | "rightAnkle"
    | "leftHip"
    | "leftKnee"
    | "leftAnkle"

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+BodyLandmarkEfficientNet | @vladmandic/human - v3.3.3

Type Alias BodyLandmarkEfficientNet

BodyLandmarkEfficientNet:
    | "head"
    | "neck"
    | "rightShoulder"
    | "rightElbow"
    | "rightWrist"
    | "chest"
    | "leftShoulder"
    | "leftElbow"
    | "leftWrist"
    | "bodyCenter"
    | "rightHip"
    | "rightKnee"
    | "rightAnkle"
    | "leftHip"
    | "leftKnee"
    | "leftAnkle"

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diff --git a/typedoc/types/BodyLandmarkMoveNet.html b/typedoc/types/BodyLandmarkMoveNet.html index 74409b42..4d5c4f0e 100644 --- a/typedoc/types/BodyLandmarkMoveNet.html +++ b/typedoc/types/BodyLandmarkMoveNet.html @@ -1 +1 @@ -BodyLandmarkMoveNet | @vladmandic/human - v3.3.2

Type Alias BodyLandmarkMoveNet

BodyLandmarkMoveNet:
    | "nose"
    | "leftEye"
    | "rightEye"
    | "leftEar"
    | "rightEar"
    | "leftShoulder"
    | "rightShoulder"
    | "leftElbow"
    | "rightElbow"
    | "leftWrist"
    | "rightWrist"
    | "leftHip"
    | "rightHip"
    | "leftKnee"
    | "rightKnee"
    | "leftAnkle"
    | "rightAnkle"

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+BodyLandmarkMoveNet | @vladmandic/human - v3.3.3

Type Alias BodyLandmarkMoveNet

BodyLandmarkMoveNet:
    | "nose"
    | "leftEye"
    | "rightEye"
    | "leftEar"
    | "rightEar"
    | "leftShoulder"
    | "rightShoulder"
    | "leftElbow"
    | "rightElbow"
    | "leftWrist"
    | "rightWrist"
    | "leftHip"
    | "rightHip"
    | "leftKnee"
    | "rightKnee"
    | "leftAnkle"
    | "rightAnkle"

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diff --git a/typedoc/types/BodyLandmarkPoseNet.html b/typedoc/types/BodyLandmarkPoseNet.html index 6f41a81b..567d0025 100644 --- a/typedoc/types/BodyLandmarkPoseNet.html +++ b/typedoc/types/BodyLandmarkPoseNet.html @@ -1 +1 @@ -BodyLandmarkPoseNet | @vladmandic/human - v3.3.2

Type Alias BodyLandmarkPoseNet

BodyLandmarkPoseNet:
    | "nose"
    | "leftEye"
    | "rightEye"
    | "leftEar"
    | "rightEar"
    | "leftShoulder"
    | "rightShoulder"
    | "leftElbow"
    | "rightElbow"
    | "leftWrist"
    | "rightWrist"
    | "leftHip"
    | "rightHip"
    | "leftKnee"
    | "rightKnee"
    | "leftAnkle"
    | "rightAnkle"

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+BodyLandmarkPoseNet | @vladmandic/human - v3.3.3

Type Alias BodyLandmarkPoseNet

BodyLandmarkPoseNet:
    | "nose"
    | "leftEye"
    | "rightEye"
    | "leftEar"
    | "rightEar"
    | "leftShoulder"
    | "rightShoulder"
    | "leftElbow"
    | "rightElbow"
    | "leftWrist"
    | "rightWrist"
    | "leftHip"
    | "rightHip"
    | "leftKnee"
    | "rightKnee"
    | "leftAnkle"
    | "rightAnkle"

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diff --git a/typedoc/types/Box.html b/typedoc/types/Box.html index 03b54ca1..8dc79330 100644 --- a/typedoc/types/Box.html +++ b/typedoc/types/Box.html @@ -1,2 +1,2 @@ -Box | @vladmandic/human - v3.3.2

Type Alias Box

Box: [number, number, number, number]

generic box as [x, y, width, height]

-

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+Box | @vladmandic/human - v3.3.3

Type Alias Box

Box: [number, number, number, number]

generic box as [x, y, width, height]

+

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diff --git a/typedoc/types/Emotion.html b/typedoc/types/Emotion.html index b0180f96..e4b603f4 100644 --- a/typedoc/types/Emotion.html +++ b/typedoc/types/Emotion.html @@ -1 +1 @@ -Emotion | @vladmandic/human - v3.3.2

Type Alias Emotion

Emotion:
    | "angry"
    | "disgust"
    | "fear"
    | "happy"
    | "sad"
    | "surprise"
    | "neutral"

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+Emotion | @vladmandic/human - v3.3.3

Type Alias Emotion

Emotion:
    | "angry"
    | "disgust"
    | "fear"
    | "happy"
    | "sad"
    | "surprise"
    | "neutral"

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diff --git a/typedoc/types/Events.html b/typedoc/types/Events.html index d86b66d9..11da45f4 100644 --- a/typedoc/types/Events.html +++ b/typedoc/types/Events.html @@ -1,4 +1,4 @@ -Events | @vladmandic/human - v3.3.2

Type Alias Events

Events:
    | "create"
    | "load"
    | "image"
    | "result"
    | "warmup"
    | "error"

Events dispatched by human.events

+Events | @vladmandic/human - v3.3.3

Type Alias Events

Events:
    | "create"
    | "load"
    | "image"
    | "result"
    | "warmup"
    | "error"

Events dispatched by human.events

  • create: triggered when Human object is instantiated
  • load: triggered when models are loaded (explicitly or on-demand)
    • @@ -6,4 +6,4 @@
  • result: triggered when detection is complete
  • warmup: triggered when warmup is complete
-

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diff --git a/typedoc/types/ExternalCanvas.html b/typedoc/types/ExternalCanvas.html index 0777d079..b099fc05 100644 --- a/typedoc/types/ExternalCanvas.html +++ b/typedoc/types/ExternalCanvas.html @@ -1,2 +1,2 @@ -ExternalCanvas | @vladmandic/human - v3.3.2

Type Alias ExternalCanvas

ExternalCanvas: typeof Canvas

Defines possible externally defined canvas

-

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+ExternalCanvas | @vladmandic/human - v3.3.3

Type Alias ExternalCanvas

ExternalCanvas: typeof Canvas

Defines possible externally defined canvas

+

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diff --git a/typedoc/types/FaceGesture.html b/typedoc/types/FaceGesture.html index c4f20ede..906b9c18 100644 --- a/typedoc/types/FaceGesture.html +++ b/typedoc/types/FaceGesture.html @@ -1,2 +1,2 @@ -FaceGesture | @vladmandic/human - v3.3.2

Type Alias FaceGesture

FaceGesture:
    | `facing ${"left" | "center" | "right"}`
    | `blink ${"left" | "right"} eye`
    | `mouth ${number}% open`
    | `head ${"up" | "down"}`

face gesture type

-

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+FaceGesture | @vladmandic/human - v3.3.3

Type Alias FaceGesture

FaceGesture:
    | `facing ${"left" | "center" | "right"}`
    | `blink ${"left" | "right"} eye`
    | `mouth ${number}% open`
    | `head ${"up" | "down"}`

face gesture type

+

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diff --git a/typedoc/types/FaceLandmark.html b/typedoc/types/FaceLandmark.html index 498de2f2..429f7463 100644 --- a/typedoc/types/FaceLandmark.html +++ b/typedoc/types/FaceLandmark.html @@ -1 +1 @@ -FaceLandmark | @vladmandic/human - v3.3.2

Type Alias FaceLandmark

FaceLandmark:
    | "leftEye"
    | "rightEye"
    | "nose"
    | "mouth"
    | "leftEar"
    | "rightEar"
    | "symmetryLine"
    | "silhouette"
    | "lipsUpperOuter"
    | "lipsLowerOuter"
    | "lipsUpperInner"
    | "lipsLowerInner"
    | "rightEyeUpper0"
    | "rightEyeLower0"
    | "rightEyeUpper1"
    | "rightEyeLower1"
    | "rightEyeUpper2"
    | "rightEyeLower2"
    | "rightEyeLower3"
    | "rightEyebrowUpper"
    | "rightEyebrowLower"
    | "rightEyeIris"
    | "leftEyeUpper0"
    | "leftEyeLower0"
    | "leftEyeUpper1"
    | "leftEyeLower1"
    | "leftEyeUpper2"
    | "leftEyeLower2"
    | "leftEyeLower3"
    | "leftEyebrowUpper"
    | "leftEyebrowLower"
    | "leftEyeIris"
    | "midwayBetweenEyes"
    | "noseTip"
    | "noseBottom"
    | "noseRightCorner"
    | "noseLeftCorner"
    | "rightCheek"
    | "leftCheek"

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+FaceLandmark | @vladmandic/human - v3.3.3

Type Alias FaceLandmark

FaceLandmark:
    | "leftEye"
    | "rightEye"
    | "nose"
    | "mouth"
    | "leftEar"
    | "rightEar"
    | "symmetryLine"
    | "silhouette"
    | "lipsUpperOuter"
    | "lipsLowerOuter"
    | "lipsUpperInner"
    | "lipsLowerInner"
    | "rightEyeUpper0"
    | "rightEyeLower0"
    | "rightEyeUpper1"
    | "rightEyeLower1"
    | "rightEyeUpper2"
    | "rightEyeLower2"
    | "rightEyeLower3"
    | "rightEyebrowUpper"
    | "rightEyebrowLower"
    | "rightEyeIris"
    | "leftEyeUpper0"
    | "leftEyeLower0"
    | "leftEyeUpper1"
    | "leftEyeLower1"
    | "leftEyeUpper2"
    | "leftEyeLower2"
    | "leftEyeLower3"
    | "leftEyebrowUpper"
    | "leftEyebrowLower"
    | "leftEyeIris"
    | "midwayBetweenEyes"
    | "noseTip"
    | "noseBottom"
    | "noseRightCorner"
    | "noseLeftCorner"
    | "rightCheek"
    | "leftCheek"

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diff --git a/typedoc/types/Finger.html b/typedoc/types/Finger.html index 1f33d6c1..17395f56 100644 --- a/typedoc/types/Finger.html +++ b/typedoc/types/Finger.html @@ -1 +1 @@ -Finger | @vladmandic/human - v3.3.2

Type Alias Finger

Finger:
    | "index"
    | "middle"
    | "pinky"
    | "ring"
    | "thumb"
    | "palm"

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+Finger | @vladmandic/human - v3.3.3

Type Alias Finger

Finger:
    | "index"
    | "middle"
    | "pinky"
    | "ring"
    | "thumb"
    | "palm"

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diff --git a/typedoc/types/FingerCurl.html b/typedoc/types/FingerCurl.html index f0e5bf00..0f7d619f 100644 --- a/typedoc/types/FingerCurl.html +++ b/typedoc/types/FingerCurl.html @@ -1 +1 @@ -FingerCurl | @vladmandic/human - v3.3.2

Type Alias FingerCurl

FingerCurl: "none" | "half" | "full"

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+FingerCurl | @vladmandic/human - v3.3.3

Type Alias FingerCurl

FingerCurl: "none" | "half" | "full"

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diff --git a/typedoc/types/FingerDirection.html b/typedoc/types/FingerDirection.html index 16dc6c28..51db9373 100644 --- a/typedoc/types/FingerDirection.html +++ b/typedoc/types/FingerDirection.html @@ -1 +1 @@ -FingerDirection | @vladmandic/human - v3.3.2

Type Alias FingerDirection

FingerDirection:
    | "verticalUp"
    | "verticalDown"
    | "horizontalLeft"
    | "horizontalRight"
    | "diagonalUpRight"
    | "diagonalUpLeft"
    | "diagonalDownRight"
    | "diagonalDownLeft"

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+FingerDirection | @vladmandic/human - v3.3.3

Type Alias FingerDirection

FingerDirection:
    | "verticalUp"
    | "verticalDown"
    | "horizontalLeft"
    | "horizontalRight"
    | "diagonalUpRight"
    | "diagonalUpLeft"
    | "diagonalDownRight"
    | "diagonalDownLeft"

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diff --git a/typedoc/types/Gender.html b/typedoc/types/Gender.html index 52ed42b0..02395654 100644 --- a/typedoc/types/Gender.html +++ b/typedoc/types/Gender.html @@ -1 +1 @@ -Gender | @vladmandic/human - v3.3.2

Type Alias Gender

Gender: "male" | "female" | "unknown"

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+Gender | @vladmandic/human - v3.3.3

Type Alias Gender

Gender: "male" | "female" | "unknown"

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diff --git a/typedoc/types/GestureResult.html b/typedoc/types/GestureResult.html index df1d67f4..05b6cd26 100644 --- a/typedoc/types/GestureResult.html +++ b/typedoc/types/GestureResult.html @@ -1,7 +1,7 @@ -GestureResult | @vladmandic/human - v3.3.2

Type Alias GestureResult

GestureResult:
    | {
        face: number;
        gesture: FaceGesture;
    }
    | {
        gesture: IrisGesture;
        iris: number;
    }
    | {
        body: number;
        gesture: BodyGesture;
    }
    | {
        gesture: HandGesture;
        hand: number;
    }

Gesture combined results +GestureResult | @vladmandic/human - v3.3.3

Type Alias GestureResult

GestureResult:
    | {
        face: number;
        gesture: FaceGesture;
    }
    | {
        gesture: IrisGesture;
        iris: number;
    }
    | {
        body: number;
        gesture: BodyGesture;
    }
    | {
        gesture: HandGesture;
        hand: number;
    }

Gesture combined results Each result has:

  • part: part name and number where gesture was detected: face, iris, body, hand
  • gesture: gesture detected
-

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+

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diff --git a/typedoc/types/HandGesture.html b/typedoc/types/HandGesture.html index 092317fa..d6cdc963 100644 --- a/typedoc/types/HandGesture.html +++ b/typedoc/types/HandGesture.html @@ -1,2 +1,2 @@ -HandGesture | @vladmandic/human - v3.3.2

Type Alias HandGesture

HandGesture:
    | `${
        | "thumb"
        | "index"
        | "middle"
        | "ring"
        | "pinky"} forward`
    | `${
        | "thumb"
        | "index"
        | "middle"
        | "ring"
        | "pinky"} up`
    | "victory"
    | "thumbs up"

hand gesture type

-

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+HandGesture | @vladmandic/human - v3.3.3

Type Alias HandGesture

HandGesture:
    | `${
        | "thumb"
        | "index"
        | "middle"
        | "ring"
        | "pinky"} forward`
    | `${
        | "thumb"
        | "index"
        | "middle"
        | "ring"
        | "pinky"} up`
    | "victory"
    | "thumbs up"

hand gesture type

+

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diff --git a/typedoc/types/HandType.html b/typedoc/types/HandType.html index 1f1d1d0c..44008126 100644 --- a/typedoc/types/HandType.html +++ b/typedoc/types/HandType.html @@ -1 +1 @@ -HandType | @vladmandic/human - v3.3.2

Type Alias HandType

HandType:
    | "hand"
    | "fist"
    | "pinch"
    | "point"
    | "face"
    | "tip"
    | "pinchtip"

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+HandType | @vladmandic/human - v3.3.3

Type Alias HandType

HandType:
    | "hand"
    | "fist"
    | "pinch"
    | "point"
    | "face"
    | "tip"
    | "pinchtip"

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diff --git a/typedoc/types/ImageObjects.html b/typedoc/types/ImageObjects.html index 66d470cd..c6412570 100644 --- a/typedoc/types/ImageObjects.html +++ b/typedoc/types/ImageObjects.html @@ -1,2 +1,2 @@ -ImageObjects | @vladmandic/human - v3.3.2

Type Alias ImageObjects

ImageObjects: ImageData | ImageBitmap

Defines all possible image objects

-

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+ImageObjects | @vladmandic/human - v3.3.3

Type Alias ImageObjects

ImageObjects: ImageData | ImageBitmap

Defines all possible image objects

+

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diff --git a/typedoc/types/Input.html b/typedoc/types/Input.html index dd0544ac..3c91868d 100644 --- a/typedoc/types/Input.html +++ b/typedoc/types/Input.html @@ -1,2 +1,2 @@ -Input | @vladmandic/human - v3.3.2

Type Alias Input

Input:
    | Tensor
    | AnyCanvas
    | AnyImage
    | AnyVideo
    | ImageObjects
    | ExternalCanvas

Defines all possible input types for Human detection

-

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+Input | @vladmandic/human - v3.3.3

Type Alias Input

Input:
    | Tensor
    | AnyCanvas
    | AnyImage
    | AnyVideo
    | ImageObjects
    | ExternalCanvas

Defines all possible input types for Human detection

+

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diff --git a/typedoc/types/IrisGesture.html b/typedoc/types/IrisGesture.html index 7661485d..096adc75 100644 --- a/typedoc/types/IrisGesture.html +++ b/typedoc/types/IrisGesture.html @@ -1,2 +1,2 @@ -IrisGesture | @vladmandic/human - v3.3.2

Type Alias IrisGesture

IrisGesture: "facing center" | `looking ${
    | "left"
    | "right"
    | "up"
    | "down"}` | "looking center"

iris gesture type

-

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+IrisGesture | @vladmandic/human - v3.3.3

Type Alias IrisGesture

IrisGesture: "facing center" | `looking ${
    | "left"
    | "right"
    | "up"
    | "down"}` | "looking center"

iris gesture type

+

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diff --git a/typedoc/types/ObjectType.html b/typedoc/types/ObjectType.html index feb159ba..1e50f995 100644 --- a/typedoc/types/ObjectType.html +++ b/typedoc/types/ObjectType.html @@ -1 +1 @@ -ObjectType | @vladmandic/human - v3.3.2

Type Alias ObjectType

ObjectType:
    | "person"
    | "bicycle"
    | "car"
    | "motorcycle"
    | "airplane"
    | "bus"
    | "train"
    | "truck"
    | "boat"
    | "traffic light"
    | "fire hydrant"
    | "stop sign"
    | "parking meter"
    | "bench"
    | "bird"
    | "cat"
    | "dog"
    | "horse"
    | "sheep"
    | "cow"
    | "elephant"
    | "bear"
    | "zebra"
    | "giraffe"
    | "backpack"
    | "umbrella"
    | "handbag"
    | "tie"
    | "suitcase"
    | "frisbee"
    | "skis"
    | "snowboard"
    | "sports ball"
    | "kite"
    | "baseball bat"
    | "baseball glove"
    | "skateboard"
    | "surfboard"
    | "tennis racket"
    | "bottle"
    | "wine glass"
    | "cup"
    | "fork"
    | "knife"
    | "spoon"
    | "bowl"
    | "banana"
    | "apple"
    | "sandwich"
    | "orange"
    | "broccoli"
    | "carrot"
    | "hot dog"
    | "pizza"
    | "donut"
    | "cake"
    | "chair"
    | "couch"
    | "potted plant"
    | "bed"
    | "dining table"
    | "toilet"
    | "tv"
    | "laptop"
    | "mouse"
    | "remote"
    | "keyboard"
    | "cell phone"
    | "microwave"
    | "oven"
    | "toaster"
    | "sink"
    | "refrigerator"
    | "book"
    | "clock"
    | "vase"
    | "scissors"
    | "teddy bear"
    | "hair drier"
    | "toothbrush"

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+ObjectType | @vladmandic/human - v3.3.3

Type Alias ObjectType

ObjectType:
    | "person"
    | "bicycle"
    | "car"
    | "motorcycle"
    | "airplane"
    | "bus"
    | "train"
    | "truck"
    | "boat"
    | "traffic light"
    | "fire hydrant"
    | "stop sign"
    | "parking meter"
    | "bench"
    | "bird"
    | "cat"
    | "dog"
    | "horse"
    | "sheep"
    | "cow"
    | "elephant"
    | "bear"
    | "zebra"
    | "giraffe"
    | "backpack"
    | "umbrella"
    | "handbag"
    | "tie"
    | "suitcase"
    | "frisbee"
    | "skis"
    | "snowboard"
    | "sports ball"
    | "kite"
    | "baseball bat"
    | "baseball glove"
    | "skateboard"
    | "surfboard"
    | "tennis racket"
    | "bottle"
    | "wine glass"
    | "cup"
    | "fork"
    | "knife"
    | "spoon"
    | "bowl"
    | "banana"
    | "apple"
    | "sandwich"
    | "orange"
    | "broccoli"
    | "carrot"
    | "hot dog"
    | "pizza"
    | "donut"
    | "cake"
    | "chair"
    | "couch"
    | "potted plant"
    | "bed"
    | "dining table"
    | "toilet"
    | "tv"
    | "laptop"
    | "mouse"
    | "remote"
    | "keyboard"
    | "cell phone"
    | "microwave"
    | "oven"
    | "toaster"
    | "sink"
    | "refrigerator"
    | "book"
    | "clock"
    | "vase"
    | "scissors"
    | "teddy bear"
    | "hair drier"
    | "toothbrush"

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diff --git a/typedoc/types/Point.html b/typedoc/types/Point.html index 67491e5d..0a58e22b 100644 --- a/typedoc/types/Point.html +++ b/typedoc/types/Point.html @@ -1,2 +1,2 @@ -Point | @vladmandic/human - v3.3.2

Type Alias Point

Point: [number, number, number?]

generic point as [x, y, z?]

-

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+Point | @vladmandic/human - v3.3.3

Type Alias Point

Point: [number, number, number?]

generic point as [x, y, z?]

+

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diff --git a/typedoc/types/Race.html b/typedoc/types/Race.html index cb829b5b..c7258cbb 100644 --- a/typedoc/types/Race.html +++ b/typedoc/types/Race.html @@ -1 +1 @@ -Race | @vladmandic/human - v3.3.2

Type Alias Race

Race:
    | "white"
    | "black"
    | "asian"
    | "indian"
    | "other"

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+Race | @vladmandic/human - v3.3.3

Type Alias Race

Race:
    | "white"
    | "black"
    | "asian"
    | "indian"
    | "other"

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diff --git a/typedoc/types/SegmentationEnum.html b/typedoc/types/SegmentationEnum.html index 06fe5288..32fd4269 100644 --- a/typedoc/types/SegmentationEnum.html +++ b/typedoc/types/SegmentationEnum.html @@ -1,2 +1,2 @@ -SegmentationEnum | @vladmandic/human - v3.3.2

Type Alias SegmentationEnum

SegmentationEnum:
    | "default"
    | "alpha"
    | "foreground"
    | "state"

Possible segmentation model behavior

-

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+SegmentationEnum | @vladmandic/human - v3.3.3

Type Alias SegmentationEnum

SegmentationEnum:
    | "default"
    | "alpha"
    | "foreground"
    | "state"

Possible segmentation model behavior

+

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diff --git a/typedoc/types/Tensor1D.html b/typedoc/types/Tensor1D.html index 15d47c81..a578d201 100644 --- a/typedoc/types/Tensor1D.html +++ b/typedoc/types/Tensor1D.html @@ -1,2 +1,2 @@ -Tensor1D | @vladmandic/human - v3.3.2

Type Alias Tensor1D

Tensor1D: Tensor<R1>

Doclink

Tensor

-

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+Tensor1D | @vladmandic/human - v3.3.3

Type Alias Tensor1D

Tensor1D: Tensor<R1>

Doclink

Tensor

+

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diff --git a/typedoc/types/Tensor2D.html b/typedoc/types/Tensor2D.html index dfaea60c..0d57859e 100644 --- a/typedoc/types/Tensor2D.html +++ b/typedoc/types/Tensor2D.html @@ -1,2 +1,2 @@ -Tensor2D | @vladmandic/human - v3.3.2

Type Alias Tensor2D

Tensor2D: Tensor<R2>

Doclink

Tensor

-

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+Tensor2D | @vladmandic/human - v3.3.3

Type Alias Tensor2D

Tensor2D: Tensor<R2>

Doclink

Tensor

+

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diff --git a/typedoc/types/Tensor3D.html b/typedoc/types/Tensor3D.html index 38718c6e..ad6a2725 100644 --- a/typedoc/types/Tensor3D.html +++ b/typedoc/types/Tensor3D.html @@ -1,2 +1,2 @@ -Tensor3D | @vladmandic/human - v3.3.2

Type Alias Tensor3D

Tensor3D: Tensor<R3>

Doclink

Tensor

-

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+Tensor3D | @vladmandic/human - v3.3.3

Type Alias Tensor3D

Tensor3D: Tensor<R3>

Doclink

Tensor

+

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diff --git a/typedoc/types/Tensor4D.html b/typedoc/types/Tensor4D.html index 01ce3ff1..0a8a6681 100644 --- a/typedoc/types/Tensor4D.html +++ b/typedoc/types/Tensor4D.html @@ -1,2 +1,2 @@ -Tensor4D | @vladmandic/human - v3.3.2

Type Alias Tensor4D

Tensor4D: Tensor<R4>

Doclink

Tensor

-

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+Tensor4D | @vladmandic/human - v3.3.3

Type Alias Tensor4D

Tensor4D: Tensor<R4>

Doclink

Tensor

+

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diff --git a/typedoc/types/TensorLike.html b/typedoc/types/TensorLike.html index 10dfd3bd..928d440c 100644 --- a/typedoc/types/TensorLike.html +++ b/typedoc/types/TensorLike.html @@ -1,2 +1,2 @@ -TensorLike | @vladmandic/human - v3.3.2

Type Alias TensorLike

TensorLike:
    | TypedArray
    | number
    | boolean
    | string
    | RecursiveArray<number | number[] | TypedArray>
    | RecursiveArray<boolean>
    | RecursiveArray<string>
    | Uint8Array[]

Docalias

TypedArray|Array

-

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+TensorLike | @vladmandic/human - v3.3.3

Type Alias TensorLike

TensorLike:
    | TypedArray
    | number
    | boolean
    | string
    | RecursiveArray<number | number[] | TypedArray>
    | RecursiveArray<boolean>
    | RecursiveArray<string>
    | Uint8Array[]

Docalias

TypedArray|Array

+

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diff --git a/typedoc/types/WarmupEnum.html b/typedoc/types/WarmupEnum.html index 06ae4dbe..7ffc168c 100644 --- a/typedoc/types/WarmupEnum.html +++ b/typedoc/types/WarmupEnum.html @@ -1,2 +1,2 @@ -WarmupEnum | @vladmandic/human - v3.3.2

Type Alias WarmupEnum

WarmupEnum:
    | ""
    | "none"
    | "face"
    | "full"
    | "body"

Possible values for human.warmup

-

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+WarmupEnum | @vladmandic/human - v3.3.3

Type Alias WarmupEnum

WarmupEnum:
    | ""
    | "none"
    | "face"
    | "full"
    | "body"

Possible values for human.warmup

+

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diff --git a/typedoc/types/match.Descriptor.html b/typedoc/types/match.Descriptor.html index 65381d39..f7640f43 100644 --- a/typedoc/types/match.Descriptor.html +++ b/typedoc/types/match.Descriptor.html @@ -1,2 +1,2 @@ -Descriptor | @vladmandic/human - v3.3.2

Type Alias Descriptor

Descriptor: number[]

Face descriptor type as number array

-

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+Descriptor | @vladmandic/human - v3.3.3

Type Alias Descriptor

Descriptor: number[]

Face descriptor type as number array

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diff --git a/typedoc/types/match.MatchOptions.html b/typedoc/types/match.MatchOptions.html index 39c651b5..d32ff290 100644 --- a/typedoc/types/match.MatchOptions.html +++ b/typedoc/types/match.MatchOptions.html @@ -1 +1 @@ -MatchOptions | @vladmandic/human - v3.3.2

Type Alias MatchOptions

MatchOptions: {
    max?: number;
    min?: number;
    multiplier?: number;
    order?: number;
    threshold?: number;
} | undefined

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+MatchOptions | @vladmandic/human - v3.3.3

Type Alias MatchOptions

MatchOptions: {
    max?: number;
    min?: number;
    multiplier?: number;
    order?: number;
    threshold?: number;
} | undefined

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diff --git a/typedoc/variables/defaults.html b/typedoc/variables/defaults.html index 24eb98a7..df90eb59 100644 --- a/typedoc/variables/defaults.html +++ b/typedoc/variables/defaults.html @@ -1,4 +1,4 @@ -defaults | @vladmandic/human - v3.3.2

Variable defaultsConst

defaults: Config = ...

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diff --git a/typedoc/variables/draw.options.html b/typedoc/variables/draw.options.html index 1a937067..11cb88a8 100644 --- a/typedoc/variables/draw.options.html +++ b/typedoc/variables/draw.options.html @@ -1,2 +1,2 @@ -options | @vladmandic/human - v3.3.2

Variable optionsConst

options: DrawOptions = ...

currently set draw options DrawOptions

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+options | @vladmandic/human - v3.3.3

Variable optionsConst

options: DrawOptions = ...

currently set draw options DrawOptions

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diff --git a/typedoc/variables/env-1.html b/typedoc/variables/env-1.html index 82cd9a61..e9e97a1e 100644 --- a/typedoc/variables/env-1.html +++ b/typedoc/variables/env-1.html @@ -1 +1 @@ -env | @vladmandic/human - v3.3.2

Variable envConst

env: Env = ...

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+env | @vladmandic/human - v3.3.3

Variable envConst

env: Env = ...

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