diff --git a/404.html b/404.html index cae08e17..8df84139 100644 --- a/404.html +++ b/404.html @@ -14,8 +14,8 @@ - - + +
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diff --git a/api-reference-docs/_static/documentation_options.js b/api-reference-docs/_static/documentation_options.js index d5dcf857..57aaa2d0 100644 --- a/api-reference-docs/_static/documentation_options.js +++ b/api-reference-docs/_static/documentation_options.js @@ -1,5 +1,5 @@ const DOCUMENTATION_OPTIONS = { - VERSION: '0.16.0', + VERSION: '0.17.0', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/api-reference-docs/genindex.html b/api-reference-docs/genindex.html index 35ffbc4d..cd1a9c8d 100644 --- a/api-reference-docs/genindex.html +++ b/api-reference-docs/genindex.html @@ -3,7 +3,7 @@ - Index — Groundlight Python SDK 0.16.0 documentation + Index — Groundlight Python SDK 0.17.0 documentation @@ -13,7 +13,7 @@ - + @@ -33,7 +33,7 @@ Groundlight Python SDK
- 0.16.0 + 0.17.0
@@ -207,13 +207,23 @@

M

  • (model.ImageQuery attribute)
    • - - +

    N

    diff --git a/api-reference-docs/index.html b/api-reference-docs/index.html index b31fb4bd..8afdc125 100644 --- a/api-reference-docs/index.html +++ b/api-reference-docs/index.html @@ -4,7 +4,7 @@ - Welcome to Groundlight Python SDK’s documentation! — Groundlight Python SDK 0.16.0 documentation + Welcome to Groundlight Python SDK’s documentation! — Groundlight Python SDK 0.17.0 documentation @@ -14,7 +14,7 @@ - + @@ -35,7 +35,7 @@ Groundlight Python SDK
    - 0.16.0 + 0.17.0
    diff --git a/api-reference-docs/models.html b/api-reference-docs/models.html index f358200d..abbedc76 100644 --- a/api-reference-docs/models.html +++ b/api-reference-docs/models.html @@ -4,7 +4,7 @@ - SDK Client — Groundlight Python SDK 0.16.0 documentation + SDK Client — Groundlight Python SDK 0.17.0 documentation @@ -14,7 +14,7 @@ - + @@ -35,7 +35,7 @@ Groundlight Python SDK
    - 0.16.0 + 0.17.0
    @@ -85,6 +85,7 @@
  • Detector.name
  • Detector.query
  • Detector.type
    • +
  • Detector.model_computed_fields
  • ImageQuery
  • PaginatedDetectorList
  • PaginatedImageQueryList
    • @@ -800,7 +804,11 @@

    API Response Objects "$defs": { "DetectorTypeEnum": { "const": "detector", - "title": "DetectorTypeEnum" + "enum": [ + "detector" + ], + "title": "DetectorTypeEnum", + "type": "string" } }, "required": [ @@ -906,6 +914,12 @@

    API Response Objects

    The type of this object.

    +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    +
    @@ -1038,7 +1052,11 @@

    API Response Objects }, "ImageQueryTypeEnum": { "const": "image_query", - "title": "ImageQueryTypeEnum" + "enum": [ + "image_query" + ], + "title": "ImageQueryTypeEnum", + "type": "string" }, "ResultTypeEnum": { "enum": [ @@ -1139,6 +1157,12 @@

    API Response Objects

    The type of this object.

    +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    +
    @@ -1282,7 +1306,11 @@

    API Response Objects }, "DetectorTypeEnum": { "const": "detector", - "title": "DetectorTypeEnum" + "enum": [ + "detector" + ], + "title": "DetectorTypeEnum", + "type": "string" } }, "required": [ @@ -1322,6 +1350,12 @@

    API Response Objectsfield results: List[Detector] [Required]

    +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    +
    @@ -1513,7 +1547,11 @@

    API Response Objects }, "ImageQueryTypeEnum": { "const": "image_query", - "title": "ImageQueryTypeEnum" + "enum": [ + "image_query" + ], + "title": "ImageQueryTypeEnum", + "type": "string" }, "ResultTypeEnum": { "enum": [ @@ -1561,6 +1599,12 @@

    API Response Objectsfield results: List[ImageQuery] [Required]

    +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
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    - 0.16.0 + 0.17.0
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The application creates a detector to identify when the service desk is being utilized by a customer. It checks the detector every minute, and once an hour, it prints out a summary of the percentage of time that the service counter is in use. At the end of the day, it emails the daily log."}),"\n",(0,i.jsx)(n.p,{children:"This retail analytics application can be beneficial in various ways:"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:["\n",(0,i.jsxs)(n.p,{children:[(0,i.jsx)(n.strong,{children:"Staff allocation and scheduling:"})," By analyzing the usage patterns of the service counter, store managers can optimize staff allocation and scheduling, ensuring that enough employees are available during peak hours and reducing wait times for customers."]}),"\n"]}),"\n",(0,i.jsxs)(n.li,{children:["\n",(0,i.jsxs)(n.p,{children:[(0,i.jsx)(n.strong,{children:"Identifying trends:"})," The application can help identify trends in customer behavior, such as busier times of the day or specific days of the week with higher traffic. 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Store managers would receive hourly summaries of the service counter usage and a daily log via email, enabling them to make informed decisions to improve store operations and customer experience."}),"\n",(0,i.jsx)(n.h2,{id:"requirements",children:"Requirements"}),"\n",(0,i.jsxs)(n.ul,{children:["\n",(0,i.jsxs)(n.li,{children:[(0,i.jsx)(n.a,{href:"/docs/installation/",children:"Groundlight SDK"})," with Python 3.7 or higher"]}),"\n",(0,i.jsx)(n.li,{children:"A supported USB or network-connected camera"}),"\n",(0,i.jsx)(n.li,{children:"An email account with SMTP access to send the daily log"}),"\n"]}),"\n",(0,i.jsx)(n.h2,{id:"installation",children:"Installation"}),"\n",(0,i.jsx)(n.p,{children:"Ensure you have Python 3.7 or higher installed, and then install the Groundlight SDK, OpenCV library, and other required libraries:"}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-bash",children:"pip install groundlight opencv-python pillow\n"})}),"\n",(0,i.jsx)(n.h2,{id:"creating-the-application",children:"Creating the Application"}),"\n",(0,i.jsxs)(n.ol,{children:["\n",(0,i.jsxs)(n.li,{children:["\n",(0,i.jsxs)(n.p,{children:["First, log in to the ",(0,i.jsx)(n.a,{href:"https://app.groundlight.ai",children:"Groundlight application"})," and get an ",(0,i.jsx)(n.a,{href:"api-tokens",children:"API Token"}),"."]}),"\n"]}),"\n",(0,i.jsxs)(n.li,{children:["\n",(0,i.jsx)(n.p,{children:"Next, we'll write the Python script for the application. 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Every hour, it prints a summary of the counter's usage percentage, and at the end of the day, it emails the daily log using the ",(0,i.jsx)(n.code,{children:"send_email"})," function."]}),"\n",(0,i.jsxs)(n.p,{children:["Save the script as ",(0,i.jsx)(n.code,{children:"service_counter_monitor.py"})," and run it:"]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-bash",children:"python service_counter_monitor.py\n"})})]})}function u(e={}){const{wrapper:n}={...(0,r.a)(),...e.components};return n?(0,i.jsx)(n,{...e,children:(0,i.jsx)(d,{...e})}):d(e)}},1151:(e,n,t)=>{t.d(n,{Z:()=>o,a:()=>s});var i=t(7294);const r={},a=i.createContext(r);function s(e){const n=i.useContext(a);return i.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function o(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:s(e.components),i.createElement(a.Provider,{value:n},e.children)}}}]); \ No newline at end of file +"use strict";(self.webpackChunkweb=self.webpackChunkweb||[]).push([[1355],{1110:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>l,contentTitle:()=>s,default:()=>u,frontMatter:()=>a,metadata:()=>o,toc:()=>c});var i=t(5893),r=t(1151);const a={},s="A Serious Example: Retail Analytics",o={id:"getting-started/retail-analytics",title:"A Serious Example: Retail Analytics",description:"Tracking utilization of a customer service counter",source:"@site/docs/getting-started/3-retail-analytics.md",sourceDirName:"getting-started",slug:"/getting-started/retail-analytics",permalink:"/python-sdk/docs/getting-started/retail-analytics",draft:!1,unlisted:!1,editUrl:"https://github.com/groundlight/python-sdk/tree/main/docs/docs/getting-started/3-retail-analytics.md",tags:[],version:"current",sidebarPosition:3,frontMatter:{},sidebar:"tutorialSidebar",previous:{title:"Writing Queries",permalink:"/python-sdk/docs/getting-started/writing-queries"},next:{title:"A Fun Example: Dog-on-Couch Detector",permalink:"/python-sdk/docs/getting-started/dog-on-couch"}},l={},c=[{value:"Tracking utilization of a customer service counter",id:"tracking-utilization-of-a-customer-service-counter",level:2},{value:"Requirements",id:"requirements",level:2},{value:"Installation",id:"installation",level:2},{value:"Creating the Application",id:"creating-the-application",level:2}];function d(e){const n={a:"a",code:"code",h1:"h1",h2:"h2",li:"li",ol:"ol",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,r.a)(),...e.components};return(0,i.jsxs)(i.Fragment,{children:[(0,i.jsx)(n.h1,{id:"a-serious-example-retail-analytics",children:"A Serious Example: Retail Analytics"}),"\n",(0,i.jsx)(n.h2,{id:"tracking-utilization-of-a-customer-service-counter",children:"Tracking utilization of a customer service counter"}),"\n",(0,i.jsx)(n.p,{children:"This example demonstrates the application of Groundlight to a retail analytics solution, which monitors the usage of a service counter by customers throughout the day. 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You will need to customize this for your particular network environment."}),"\n"]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",children:"def send_email(sender, receiver, subject, body):\n msg = MIMEMultipart()\n msg['From'] = sender\n msg['To'] = receiver\n msg['Subject'] = subject\n msg.attach(MIMEText(body, 'plain'))\n\n server = smtplib.SMTP('smtp.example.com', 587)\n server.starttls()\n server.login(sender, \"your-password\")\n text = msg.as_string()\n server.sendmail(sender, receiver, text)\n server.quit()\n"})}),"\n",(0,i.jsxs)(n.ol,{start:"5",children:["\n",(0,i.jsx)(n.li,{children:"Define when your business's operating hours are:"}),"\n"]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:"notest",children:"START_OF_BUSINESS = 9 # e.g. 9am\nEND_OF_BUSINESS = 17 # e.g. 5pm\n\ndef is_within_business_hours():\n current_hour = datetime.now().hour\n return START_OF_BUSINESS <= current_hour < END_OF_BUSINESS\n\n"})}),"\n",(0,i.jsxs)(n.ol,{start:"6",children:["\n",(0,i.jsx)(n.li,{children:"Write the main application loop:"}),"\n"]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-python",metastring:"notest",children:'gl = Groundlight()\n\ndetector = gl.get_or_create_detector(\n name="counter-in-use",\n query="Is there a customer at the service counter?",\n # We can get away with relatively low confidence since we\'re aggregating\n confidence_threshold=0.8)\n\nDELAY = 60\n\nlog = []\ndaily_log = []\nnext_hourly_start = datetime.now().replace(minute=0, second=0, microsecond=0) + timedelta(hours=1)\n\nwhile True:\n if not is_within_business_hours():\n time.sleep(DELAY)\n continue\n\n image = capture_image()\n if not image:\n print("Failed to capture image")\n time.sleep(DELAY)\n continue\n\n try:\n iq = gl.submit_image_query(image=image, detector=detector, wait=60)\n except Exception as e:\n print(f"Error submitting image query: {e}")\n time.sleep(DELAY)\n continue\n\n answer = iq.result.label\n log.append(answer)\n\n if datetime.now() >= next_hourly_start:\n next_hourly_start += timedelta(hours=1)\n\n percent_in_use = (log.count("YES") / len(log)) * 100\n current_time = datetime.now().replace(hour=START_OF_BUSINESS, minute=0, second=0)\n formatted_time = current_time.strftime("%I%p") # like 3pm\n msg = f"Hourly summary for {formatted_time}: {percent_in_use:.0f}% counter in use"\n print(msg)\n daily_log.append(msg)\n log = []\n\n current_hour = datetime.now().hour\n if current_hour == END_OF_BUSINESS and not daily_log == []:\n daily_summary = "Daily summary:\\n"\n for msg in daily_log:\n daily_summary += f"{msg}\\n"\n\n print(daily_summary)\n send_email(sender="counterbot@example.com",\n receiver="manager@example.com",\n subject="Daily Service Counter Usage Log",\n body=daily_summary)\n daily_log = []\n\n time.sleep(DELAY)\n'})}),"\n",(0,i.jsxs)(n.p,{children:["This application captures an image using the ",(0,i.jsx)(n.code,{children:"capture_image"})," function, then submits it to the Groundlight API for analysis. If a customer is detected at the counter, it logs the event. Every hour, it prints a summary of the counter's usage percentage, and at the end of the day, it emails the daily log using the ",(0,i.jsx)(n.code,{children:"send_email"})," function."]}),"\n",(0,i.jsxs)(n.p,{children:["Save the script as ",(0,i.jsx)(n.code,{children:"service_counter_monitor.py"})," and run it:"]}),"\n",(0,i.jsx)(n.pre,{children:(0,i.jsx)(n.code,{className:"language-bash",children:"python service_counter_monitor.py\n"})})]})}function u(e={}){const{wrapper:n}={...(0,r.a)(),...e.components};return n?(0,i.jsx)(n,{...e,children:(0,i.jsx)(d,{...e})}):d(e)}},1151:(e,n,t)=>{t.d(n,{Z:()=>o,a:()=>s});var i=t(7294);const r={},a=i.createContext(r);function s(e){const n=i.useContext(a);return i.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function o(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:s(e.components),i.createElement(a.Provider,{value:n},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/325ecf53.c9b476d6.js b/assets/js/325ecf53.d60b1f4e.js similarity index 98% rename from assets/js/325ecf53.c9b476d6.js rename to assets/js/325ecf53.d60b1f4e.js index 36f55bc6..c1c78a99 100644 --- a/assets/js/325ecf53.c9b476d6.js +++ b/assets/js/325ecf53.d60b1f4e.js @@ -1 +1 @@ -"use strict";(self.webpackChunkweb=self.webpackChunkweb||[]).push([[4045],{9138:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>l,contentTitle:()=>s,default:()=>h,frontMatter:()=>r,metadata:()=>o,toc:()=>d});var i=n(5893),a=n(1151);const r={},s="Getting Started",o={id:"getting-started/getting-started",title:"Getting Started",description:"Computer Vision powered by Natural Language",source:"@site/docs/getting-started/getting-started.mdx",sourceDirName:"getting-started",slug:"/getting-started/",permalink:"/python-sdk/docs/getting-started/",draft:!1,unlisted:!1,editUrl:"https://github.com/groundlight/python-sdk/tree/main/docs/docs/getting-started/getting-started.mdx",tags:[],version:"current",frontMatter:{},sidebar:"tutorialSidebar",next:{title:"API Tokens",permalink:"/python-sdk/docs/getting-started/api-tokens"}},l={},d=[{value:"Computer Vision powered by Natural Language",id:"computer-vision-powered-by-natural-language",level:2},{value:"How does it work?",id:"how-does-it-work",level:3},{value:"Escalation Technology",id:"escalation-technology",level:3},{value:"Building a simple visual application",id:"building-a-simple-visual-application",level:2}];function c(e){const t={a:"a",code:"code",em:"em",h1:"h1",h2:"h2",h3:"h3",img:"img",li:"li",ol:"ol",p:"p",pre:"pre",...(0,a.a)(),...e.components};return(0,i.jsxs)(i.Fragment,{children:[(0,i.jsx)(t.h1,{id:"getting-started",children:"Getting Started"}),"\n",(0,i.jsx)(t.h2,{id:"computer-vision-powered-by-natural-language",children:"Computer Vision powered by Natural Language"}),"\n",(0,i.jsx)(t.p,{children:"Build a working computer vision system in just a few lines of python:"}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:'from groundlight import Groundlight\n\ngl = Groundlight()\ndet = gl.get_or_create_detector(name="doorway", query="Is the doorway open?")\nimg = "./docs/static/img/doorway.jpg" # Image can be a file or a Python object\nimage_query = gl.submit_image_query(detector=det, image=img)\nprint(f"The answer is {image_query.result}")\n'})}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsxs)(t.em,{children:['Note: The SDK is currently in "beta" phase. Interfaces are subject to change in future versions. We will follow ',(0,i.jsx)(t.a,{href:"https://semver.org/",children:"semver"})," semantics for breaking changes."]})}),"\n",(0,i.jsx)(t.h3,{id:"how-does-it-work",children:"How does it work?"}),"\n",(0,i.jsxs)(t.p,{children:["Your images are first analyzed by machine learning (ML) models which are automatically trained on your data. If those models have high enough ",(0,i.jsx)(t.a,{href:"/python-sdk/docs/building-applications/managing-confidence",children:"confidence"}),", that's your answer. But if the models are unsure, then the images are progressively escalated to more resource-intensive analysis methods up to real-time human review. So what you get is a computer vision system that starts working right away without even needing to first gather and label a dataset. At first it will operate with high latency, because people need to review the image queries. But over time, the ML systems will learn and improve so queries come back faster with higher confidence."]}),"\n",(0,i.jsx)(t.h3,{id:"escalation-technology",children:"Escalation Technology"}),"\n",(0,i.jsx)(t.p,{children:"Groundlight's Escalation Technology combines the power of generative AI using our Visual LLM, along with the speed of edge computing, and the reliability of real-time human oversight."}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsx)(t.img,{alt:"diagram showing escalation technology",src:n(7354).Z+"",width:"600",height:"338"})}),"\n",(0,i.jsx)(t.h2,{id:"building-a-simple-visual-application",children:"Building a simple visual application"}),"\n",(0,i.jsxs)(t.ol,{children:["\n",(0,i.jsxs)(t.li,{children:["\n",(0,i.jsxs)(t.p,{children:["Install the ",(0,i.jsx)(t.code,{children:"groundlight"})," SDK. Requires python version 3.7 or higher. See ",(0,i.jsx)(t.a,{href:"#Prerequisites",children:"prerequisites"}),"."]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-shell",children:"pip3 install groundlight\n"})}),"\n"]}),"\n",(0,i.jsxs)(t.li,{children:["\n",(0,i.jsxs)(t.p,{children:["Head over to the ",(0,i.jsx)(t.a,{href:"https://app.groundlight.ai/reef/my-account/api-tokens",children:"groundlight web\napp"})," to create an ",(0,i.jsx)(t.a,{href:"/docs/getting-started/api-tokens",children:"API token"}),". You will\nneed to set the ",(0,i.jsx)(t.code,{children:"GROUNDLIGHT_API_TOKEN"})," environment variable to access the API."]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-shell",children:"export GROUNDLIGHT_API_TOKEN=api_2GdXMflhJi6L_example\n"})}),"\n"]}),"\n",(0,i.jsxs)(t.li,{children:["\n",(0,i.jsx)(t.p,{children:"Create a python script."}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",metastring:'title="ask.py"',children:'from groundlight import Groundlight\n\ngl = Groundlight()\ndet = gl.get_or_create_detector(name="doorway", query="Is the doorway open?")\nimg = "./docs/static/img/doorway.jpg" # Image can be a file or a Python object\nimage_query = gl.submit_image_query(detector=det, image=img)\nprint(f"The answer is {image_query.result}")\n'})}),"\n"]}),"\n",(0,i.jsxs)(t.li,{children:["\n",(0,i.jsx)(t.p,{children:"Run it!"}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-shell",children:"python ask.py\n"})}),"\n"]}),"\n"]})]})}function h(e={}){const{wrapper:t}={...(0,a.a)(),...e.components};return t?(0,i.jsx)(t,{...e,children:(0,i.jsx)(c,{...e})}):c(e)}},7354:(e,t,n)=>{n.d(t,{Z:()=>i});const i=n.p+"assets/images/escalation_diagram-05fa7abe4f95261f01a79faf002e0925.jpg"},1151:(e,t,n)=>{n.d(t,{Z:()=>o,a:()=>s});var i=n(7294);const a={},r=i.createContext(a);function s(e){const t=i.useContext(r);return i.useMemo((function(){return"function"==typeof e?e(t):{...t,...e}}),[t,e])}function o(e){let t;return t=e.disableParentContext?"function"==typeof e.components?e.components(a):e.components||a:s(e.components),i.createElement(r.Provider,{value:t},e.children)}}}]); \ No newline at end of file +"use strict";(self.webpackChunkweb=self.webpackChunkweb||[]).push([[4045],{9138:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>l,contentTitle:()=>s,default:()=>h,frontMatter:()=>r,metadata:()=>o,toc:()=>d});var i=n(5893),a=n(1151);const r={},s="Getting Started",o={id:"getting-started/getting-started",title:"Getting Started",description:"Computer Vision powered by Natural Language",source:"@site/docs/getting-started/getting-started.mdx",sourceDirName:"getting-started",slug:"/getting-started/",permalink:"/python-sdk/docs/getting-started/",draft:!1,unlisted:!1,editUrl:"https://github.com/groundlight/python-sdk/tree/main/docs/docs/getting-started/getting-started.mdx",tags:[],version:"current",frontMatter:{},sidebar:"tutorialSidebar",next:{title:"API Tokens",permalink:"/python-sdk/docs/getting-started/api-tokens"}},l={},d=[{value:"Computer Vision powered by Natural Language",id:"computer-vision-powered-by-natural-language",level:2},{value:"How does it work?",id:"how-does-it-work",level:3},{value:"Escalation Technology",id:"escalation-technology",level:3},{value:"Building a simple visual application",id:"building-a-simple-visual-application",level:2}];function c(e){const t={a:"a",code:"code",em:"em",h1:"h1",h2:"h2",h3:"h3",img:"img",li:"li",ol:"ol",p:"p",pre:"pre",...(0,a.a)(),...e.components};return(0,i.jsxs)(i.Fragment,{children:[(0,i.jsx)(t.h1,{id:"getting-started",children:"Getting Started"}),"\n",(0,i.jsx)(t.h2,{id:"computer-vision-powered-by-natural-language",children:"Computer Vision powered by Natural Language"}),"\n",(0,i.jsx)(t.p,{children:"Build a working computer vision system in just a few lines of python:"}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",children:'from groundlight import Groundlight\n\ngl = Groundlight()\ndet = gl.get_or_create_detector(name="doorway", query="Is the doorway open?")\nimg = "./docs/static/img/doorway.jpg" # Image can be a file or a Python object\nimage_query = gl.submit_image_query(detector=det, image=img)\nprint(f"The answer is {image_query.result}")\n'})}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsxs)(t.em,{children:['Note: The SDK is currently in "beta" phase. Interfaces are subject to change in future versions. We will follow ',(0,i.jsx)(t.a,{href:"https://semver.org/",children:"semver"})," semantics for breaking changes."]})}),"\n",(0,i.jsx)(t.h3,{id:"how-does-it-work",children:"How does it work?"}),"\n",(0,i.jsxs)(t.p,{children:["Your images are first analyzed by machine learning (ML) models which are automatically trained on your data. If those models have high enough ",(0,i.jsx)(t.a,{href:"/python-sdk/docs/building-applications/managing-confidence",children:"confidence"}),", that's your answer. But if the models are unsure, then the images are progressively escalated to more resource-intensive analysis methods up to real-time human review. So what you get is a computer vision system that starts working right away without even needing to first gather and label a dataset. At first it will operate with high latency, because people need to review the image queries. But over time, the ML systems will learn and improve so queries come back faster with higher confidence."]}),"\n",(0,i.jsx)(t.h3,{id:"escalation-technology",children:"Escalation Technology"}),"\n",(0,i.jsx)(t.p,{children:"Groundlight's Escalation Technology combines the power of generative AI using our Visual LLM, along with the speed of edge computing, and the reliability of real-time human oversight."}),"\n",(0,i.jsx)(t.p,{children:(0,i.jsx)(t.img,{alt:"diagram showing escalation technology",src:n(7354).Z+"",width:"600",height:"338"})}),"\n",(0,i.jsx)(t.h2,{id:"building-a-simple-visual-application",children:"Building a simple visual application"}),"\n",(0,i.jsxs)(t.ol,{children:["\n",(0,i.jsxs)(t.li,{children:["\n",(0,i.jsxs)(t.p,{children:["Install the ",(0,i.jsx)(t.code,{children:"groundlight"})," SDK. Requires python version 3.8 or higher. See ",(0,i.jsx)(t.a,{href:"#Prerequisites",children:"prerequisites"}),"."]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-shell",children:"pip3 install groundlight\n"})}),"\n"]}),"\n",(0,i.jsxs)(t.li,{children:["\n",(0,i.jsxs)(t.p,{children:["Head over to the ",(0,i.jsx)(t.a,{href:"https://app.groundlight.ai/reef/my-account/api-tokens",children:"groundlight web\napp"})," to create an ",(0,i.jsx)(t.a,{href:"/docs/getting-started/api-tokens",children:"API token"}),". You will\nneed to set the ",(0,i.jsx)(t.code,{children:"GROUNDLIGHT_API_TOKEN"})," environment variable to access the API."]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-shell",children:"export GROUNDLIGHT_API_TOKEN=api_2GdXMflhJi6L_example\n"})}),"\n"]}),"\n",(0,i.jsxs)(t.li,{children:["\n",(0,i.jsx)(t.p,{children:"Create a python script."}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",metastring:'title="ask.py"',children:'from groundlight import Groundlight\n\ngl = Groundlight()\ndet = gl.get_or_create_detector(name="doorway", query="Is the doorway open?")\nimg = "./docs/static/img/doorway.jpg" # Image can be a file or a Python object\nimage_query = gl.submit_image_query(detector=det, image=img)\nprint(f"The answer is {image_query.result}")\n'})}),"\n"]}),"\n",(0,i.jsxs)(t.li,{children:["\n",(0,i.jsx)(t.p,{children:"Run it!"}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-shell",children:"python ask.py\n"})}),"\n"]}),"\n"]})]})}function h(e={}){const{wrapper:t}={...(0,a.a)(),...e.components};return t?(0,i.jsx)(t,{...e,children:(0,i.jsx)(c,{...e})}):c(e)}},7354:(e,t,n)=>{n.d(t,{Z:()=>i});const i=n.p+"assets/images/escalation_diagram-05fa7abe4f95261f01a79faf002e0925.jpg"},1151:(e,t,n)=>{n.d(t,{Z:()=>o,a:()=>s});var i=n(7294);const a={},r=i.createContext(a);function s(e){const t=i.useContext(r);return i.useMemo((function(){return"function"==typeof e?e(t):{...t,...e}}),[t,e])}function o(e){let t;return t=e.disableParentContext?"function"==typeof e.components?e.components(a):e.components||a:s(e.components),i.createElement(r.Provider,{value:t},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/7aefb68d.b086bf7e.js b/assets/js/7aefb68d.27ccedf7.js similarity index 76% rename from assets/js/7aefb68d.b086bf7e.js rename to assets/js/7aefb68d.27ccedf7.js index 2ffe315f..3d2a9ced 100644 --- a/assets/js/7aefb68d.b086bf7e.js +++ b/assets/js/7aefb68d.27ccedf7.js @@ -1 +1 @@ -"use strict";(self.webpackChunkweb=self.webpackChunkweb||[]).push([[3201],{8523:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>l,contentTitle:()=>a,default:()=>h,frontMatter:()=>s,metadata:()=>o,toc:()=>d});var i=n(5893),r=n(1151);const s={},a="A Quick Example: Live Stream Alert",o={id:"getting-started/streaming",title:"A Quick Example: Live Stream Alert",description:"A quick example to get used to setting up detectors and asking good questions: set up a monitor on a live stream.",source:"@site/docs/getting-started/5-streaming.md",sourceDirName:"getting-started",slug:"/getting-started/streaming",permalink:"/python-sdk/docs/getting-started/streaming",draft:!1,unlisted:!1,editUrl:"https://github.com/groundlight/python-sdk/tree/main/docs/docs/getting-started/5-streaming.md",tags:[],version:"current",sidebarPosition:5,frontMatter:{},sidebar:"tutorialSidebar",previous:{title:"A Fun Example: Dog-on-Couch Detector",permalink:"/python-sdk/docs/getting-started/dog-on-couch"},next:{title:"Building Applications",permalink:"/python-sdk/docs/building-applications/"}},l={},d=[{value:"Requirements",id:"requirements",level:2},{value:"Installation",id:"installation",level:2},{value:"Creating the Application",id:"creating-the-application",level:2}];function c(e){const t={a:"a",code:"code",h1:"h1",h2:"h2",li:"li",ol:"ol",p:"p",pre:"pre",ul:"ul",...(0,r.a)(),...e.components};return(0,i.jsxs)(i.Fragment,{children:[(0,i.jsx)(t.h1,{id:"a-quick-example-live-stream-alert",children:"A Quick Example: Live Stream Alert"}),"\n",(0,i.jsx)(t.p,{children:"A quick example to get used to setting up detectors and asking good questions: set up a monitor on a live stream."}),"\n",(0,i.jsx)(t.h2,{id:"requirements",children:"Requirements"}),"\n",(0,i.jsxs)(t.ul,{children:["\n",(0,i.jsxs)(t.li,{children:[(0,i.jsx)(t.a,{href:"/docs/installation/",children:"Groundlight SDK"})," with Python 3.7 or higher"]}),"\n",(0,i.jsx)(t.li,{children:"The video ID of a YouTube live stream you'd like to monitor"}),"\n"]}),"\n",(0,i.jsx)(t.h2,{id:"installation",children:"Installation"}),"\n",(0,i.jsx)(t.p,{children:"Ensure you have Python 3.7 or higher installed, and then install the Groundlight SDK and OpenCV library:"}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-bash",children:"# MacOS\nbrew install ffmpeg\n\n# Ubuntu/Fedora linux\nsudo apt install -y ffmpeg\n\npip install groundlight pillow ffmpeg yt-dlp typer\n"})}),"\n",(0,i.jsx)(t.h2,{id:"creating-the-application",children:"Creating the Application"}),"\n",(0,i.jsxs)(t.ol,{children:["\n",(0,i.jsxs)(t.li,{children:["Save this command as a shell script ",(0,i.jsx)(t.code,{children:"get_latest_frame.sh"}),":"]}),"\n"]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{children:'#!/bin/bash\n\nffmpeg -i "$(yt-dlp -g $1 | head -n 1)" -vframes 1 last.jpg -y\n'})}),"\n",(0,i.jsxs)(t.p,{children:["This will download the most recent frame from a YouTube live stream and save it to a local file ",(0,i.jsx)(t.code,{children:"last.jpg"}),"."]}),"\n",(0,i.jsxs)(t.ol,{start:"2",children:["\n",(0,i.jsx)(t.li,{children:"Ensure that the script has execute permissions. You can add execute permissions using the following command:"}),"\n"]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{children:"chmod +x get_latest_frame.sh\n"})}),"\n",(0,i.jsxs)(t.ol,{start:"3",children:["\n",(0,i.jsxs)(t.li,{children:["\n",(0,i.jsxs)(t.p,{children:["Log in to the ",(0,i.jsx)(t.a,{href:"https://app.groundlight.ai",children:"Groundlight application"})," and get an ",(0,i.jsx)(t.a,{href:"api-tokens",children:"API Token"}),"."]}),"\n"]}),"\n",(0,i.jsxs)(t.li,{children:["\n",(0,i.jsx)(t.p,{children:"Next, we'll write the Python script for the application."}),"\n"]}),"\n"]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-python",metastring:"notest",children:'import os\nimport subprocess\nimport typer\nfrom groundlight import Groundlight\nfrom PIL import Image\n\n\ndef main(*, video_id: str = None, detector_name: str = None, query: str = None, confidence: float = 0.75, wait: int = 60):\n """\n Run the script to get the stream\'s last frame as a subprocess, and submit result as an image query to a Groundlight detector\n :param video_id: Video ID of the YouTube live stream (the URLs have the form https://www.youtube.com/watch?v=)\n :param detector_name: Name for your Groundlight detector\n :param query: Question you want to ask of the stream (we will alert on the answer of NO)\n """\n gl = Groundlight()\n detector = gl.get_or_create_detector(name=detector_name, query=query, confidence_threshold=confidence)\n\n while True:\n p = subprocess.run(["./get_latest_frame.sh", video_id])\n if p.returncode != 0:\n raise RuntimeError(f"Could not get image from video ID: {video_id}. 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Process exited with return code {p.returncode}.")\n\n image = Image.open("last.jpg").convert("RGB")\n response = gl.submit_image_query(detector=detector, image=image, wait=wait)\n\n if response.result.label == "NO":\n os.system("say \'Alert!\'") # this may not work on all operating systems\n\n\nif __name__ == "__main__":\n typer.run(main)\n\n'})}),"\n",(0,i.jsxs)(t.ol,{start:"5",children:["\n",(0,i.jsxs)(t.li,{children:["Save the script as ",(0,i.jsx)(t.code,{children:"streaming_alert.py"})," in the same directory as ",(0,i.jsx)(t.code,{children:"get_latest_frame.sh"})," above and run it:"]}),"\n"]}),"\n",(0,i.jsx)(t.pre,{children:(0,i.jsx)(t.code,{className:"language-bash",children:"python streaming_alert.py --video-id= --detector-name= --query=\n"})})]})}function h(e={}){const{wrapper:t}={...(0,r.a)(),...e.components};return t?(0,i.jsx)(t,{...e,children:(0,i.jsx)(c,{...e})}):c(e)}},1151:(e,t,n)=>{n.d(t,{Z:()=>o,a:()=>a});var i=n(7294);const r={},s=i.createContext(r);function a(e){const t=i.useContext(s);return i.useMemo((function(){return"function"==typeof e?e(t):{...t,...e}}),[t,e])}function o(e){let t;return t=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:a(e.components),i.createElement(s.Provider,{value:t},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/8091dcef.358130fc.js b/assets/js/8091dcef.78915233.js similarity index 94% rename from assets/js/8091dcef.358130fc.js rename to assets/js/8091dcef.78915233.js index ef29da8e..1ea5d919 100644 --- a/assets/js/8091dcef.358130fc.js +++ b/assets/js/8091dcef.78915233.js @@ -1 +1 @@ -"use strict";(self.webpackChunkweb=self.webpackChunkweb||[]).push([[5100],{8636:(n,e,i)=>{i.r(e),i.d(e,{assets:()=>a,contentTitle:()=>l,default:()=>c,frontMatter:()=>s,metadata:()=>r,toc:()=>h});var t=i(5893),o=i(1151);const s={},l="Installing on macOS",r={id:"installation/macos",title:"Installing on macOS",description:"This guide will help you install the Groundlight SDK on macOS. 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If the dog is detected on the couch, it plays the pre-recorded sound using the ",(0,o.jsx)(n.code,{children:"play_sound"})," function."]}),"\n",(0,o.jsxs)(n.p,{children:["Save the script as ",(0,o.jsx)(n.code,{children:"dog_on_couch_detector.py"})," and run it:"]}),"\n",(0,o.jsx)(n.pre,{children:(0,o.jsx)(n.code,{className:"language-bash",children:"python dog_on_couch_detector.py\n"})})]})}function h(e={}){const{wrapper:n}={...(0,r.a)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(d,{...e})}):d(e)}},1151:(e,n,t)=>{t.d(n,{Z:()=>c,a:()=>a});var o=t(7294);const r={},i=o.createContext(r);function a(e){const n=o.useContext(i);return o.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function c(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(r):e.components||r:a(e.components),o.createElement(i.Provider,{value:n},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/f705f88e.7799550b.js b/assets/js/f705f88e.1ba90f45.js similarity index 94% rename from assets/js/f705f88e.7799550b.js rename to assets/js/f705f88e.1ba90f45.js index 6f2c30b7..290956be 100644 --- a/assets/js/f705f88e.7799550b.js +++ b/assets/js/f705f88e.1ba90f45.js @@ -1 +1 @@ -"use strict";(self.webpackChunkweb=self.webpackChunkweb||[]).push([[537],{4440:(n,e,i)=>{i.r(e),i.d(e,{assets:()=>a,contentTitle:()=>l,default:()=>c,frontMatter:()=>o,metadata:()=>r,toc:()=>d});var t=i(5893),s=i(1151);const o={},l="Installing on NVIDIA Jetson",r={id:"installation/nvidia-jetson",title:"Installing on NVIDIA Jetson",description:"This guide will help you install the Groundlight SDK on NVIDIA Jetson devices. 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I.QueryLexer.FIELD:return e.nextClause(),I.QueryParser.parseField;case I.QueryLexer.EDIT_DISTANCE:return I.QueryParser.parseEditDistance;case I.QueryLexer.BOOST:return I.QueryParser.parseBoost;case I.QueryLexer.PRESENCE:return e.nextClause(),I.QueryParser.parsePresence;default:r="Unexpected lexeme type '"+o.type+"'";throw new I.QueryParseError(r,o.start,o.end)}else e.nextClause()}},void 0===(o="function"==typeof(r=function(){return I})?r.call(t,n,t,e):r)||(e.exports=o)}()},813:function(e){e.exports=function(){"use strict";var e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},t=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},n=function(){function e(e,t){for(var n=0;n1&&void 0!==arguments[1])||arguments[1],o=arguments.length>2&&void 0!==arguments[2]?arguments[2]:[],a=arguments.length>3&&void 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c=function(){--s<=0&&a(l)};s||c(),i.forEach((function(t){e.matches(t,o.exclude)?c():o.onIframeReady(t,(function(e){n(t)&&(l++,r(e)),c()}),c)}))}},{key:"createIterator",value:function(e,t,n){return document.createNodeIterator(e,t,n,!1)}},{key:"createInstanceOnIframe",value:function(t){return new e(t.querySelector("html"),this.iframes)}},{key:"compareNodeIframe",value:function(e,t,n){if(e.compareDocumentPosition(n)&Node.DOCUMENT_POSITION_PRECEDING){if(null===t)return!0;if(t.compareDocumentPosition(n)&Node.DOCUMENT_POSITION_FOLLOWING)return!0}return!1}},{key:"getIteratorNode",value:function(e){var t=e.previousNode();return{prevNode:t,node:(null===t||e.nextNode())&&e.nextNode()}}},{key:"checkIframeFilter",value:function(e,t,n,r){var o=!1,a=!1;return r.forEach((function(e,t){e.val===n&&(o=t,a=e.handled)})),this.compareNodeIframe(e,t,n)?(!1!==o||a?!1===o||a||(r[o].handled=!0):r.push({val:n,handled:!0}),!0):(!1===o&&r.push({val:n,handled:!1}),!1)}},{key:"handleOpenIframes",value:function(e,t,n,r){var o=this;e.forEach((function(e){e.handled||o.getIframeContents(e.val,(function(e){o.createInstanceOnIframe(e).forEachNode(t,n,r)}))}))}},{key:"iterateThroughNodes",value:function(e,t,n,r,o){for(var a=this,i=this.createIterator(t,e,r),s=[],l=[],c=void 0,u=void 0,d=function(){var e=a.getIteratorNode(i);return u=e.prevNode,c=e.node};d();)this.iframes&&this.forEachIframe(t,(function(e){return a.checkIframeFilter(c,u,e,s)}),(function(t){a.createInstanceOnIframe(t).forEachNode(e,(function(e){return l.push(e)}),r)})),l.push(c);l.forEach((function(e){n(e)})),this.iframes&&this.handleOpenIframes(s,e,n,r),o()}},{key:"forEachNode",value:function(e,t,n){var r=this,o=arguments.length>3&&void 0!==arguments[3]?arguments[3]:function(){},a=this.getContexts(),i=a.length;i||o(),a.forEach((function(a){var s=function(){r.iterateThroughNodes(e,a,t,n,(function(){--i<=0&&o()}))};r.iframes?r.waitForIframes(a,s):s()}))}}],[{key:"matches",value:function(e,t){var n="string"==typeof t?[t]:t,r=e.matches||e.matchesSelector||e.msMatchesSelector||e.mozMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector;if(r){var o=!1;return n.every((function(t){return!r.call(e,t)||(o=!0,!1)})),o}return!1}}]),e}(),a=function(){function a(e){t(this,a),this.ctx=e,this.ie=!1;var n=window.navigator.userAgent;(n.indexOf("MSIE")>-1||n.indexOf("Trident")>-1)&&(this.ie=!0)}return n(a,[{key:"log",value:function(t){var n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"debug",r=this.opt.log;this.opt.debug&&"object"===(void 0===r?"undefined":e(r))&&"function"==typeof r[n]&&r[n]("mark.js: "+t)}},{key:"escapeStr",value:function(e){return e.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g,"\\$&")}},{key:"createRegExp",value:function(e){return"disabled"!==this.opt.wildcards&&(e=this.setupWildcardsRegExp(e)),e=this.escapeStr(e),Object.keys(this.opt.synonyms).length&&(e=this.createSynonymsRegExp(e)),(this.opt.ignoreJoiners||this.opt.ignorePunctuation.length)&&(e=this.setupIgnoreJoinersRegExp(e)),this.opt.diacritics&&(e=this.createDiacriticsRegExp(e)),e=this.createMergedBlanksRegExp(e),(this.opt.ignoreJoiners||this.opt.ignorePunctuation.length)&&(e=this.createJoinersRegExp(e)),"disabled"!==this.opt.wildcards&&(e=this.createWildcardsRegExp(e)),e=this.createAccuracyRegExp(e)}},{key:"createSynonymsRegExp",value:function(e){var t=this.opt.synonyms,n=this.opt.caseSensitive?"":"i",r=this.opt.ignoreJoiners||this.opt.ignorePunctuation.length?"\0":"";for(var o in t)if(t.hasOwnProperty(o)){var a=t[o],i="disabled"!==this.opt.wildcards?this.setupWildcardsRegExp(o):this.escapeStr(o),s="disabled"!==this.opt.wildcards?this.setupWildcardsRegExp(a):this.escapeStr(a);""!==i&&""!==s&&(e=e.replace(new RegExp("("+this.escapeStr(i)+"|"+this.escapeStr(s)+")","gm"+n),r+"("+this.processSynomyms(i)+"|"+this.processSynomyms(s)+")"+r))}return e}},{key:"processSynomyms",value:function(e){return(this.opt.ignoreJoiners||this.opt.ignorePunctuation.length)&&(e=this.setupIgnoreJoinersRegExp(e)),e}},{key:"setupWildcardsRegExp",value:function(e){return(e=e.replace(/(?:\\)*\?/g,(function(e){return"\\"===e.charAt(0)?"?":"\x01"}))).replace(/(?:\\)*\*/g,(function(e){return"\\"===e.charAt(0)?"*":"\x02"}))}},{key:"createWildcardsRegExp",value:function(e){var t="withSpaces"===this.opt.wildcards;return e.replace(/\u0001/g,t?"[\\S\\s]?":"\\S?").replace(/\u0002/g,t?"[\\S\\s]*?":"\\S*")}},{key:"setupIgnoreJoinersRegExp",value:function(e){return e.replace(/[^(|)\\]/g,(function(e,t,n){var r=n.charAt(t+1);return/[(|)\\]/.test(r)||""===r?e:e+"\0"}))}},{key:"createJoinersRegExp",value:function(e){var t=[],n=this.opt.ignorePunctuation;return Array.isArray(n)&&n.length&&t.push(this.escapeStr(n.join(""))),this.opt.ignoreJoiners&&t.push("\\u00ad\\u200b\\u200c\\u200d"),t.length?e.split(/\u0000+/).join("["+t.join("")+"]*"):e}},{key:"createDiacriticsRegExp",value:function(e){var t=this.opt.caseSensitive?"":"i",n=this.opt.caseSensitive?["a\xe0\xe1\u1ea3\xe3\u1ea1\u0103\u1eb1\u1eaf\u1eb3\u1eb5\u1eb7\xe2\u1ea7\u1ea5\u1ea9\u1eab\u1ead\xe4\xe5\u0101\u0105","A\xc0\xc1\u1ea2\xc3\u1ea0\u0102\u1eb0\u1eae\u1eb2\u1eb4\u1eb6\xc2\u1ea6\u1ea4\u1ea8\u1eaa\u1eac\xc4\xc5\u0100\u0104","c\xe7\u0107\u010d","C\xc7\u0106\u010c","d\u0111\u010f","D\u0110\u010e","e\xe8\xe9\u1ebb\u1ebd\u1eb9\xea\u1ec1\u1ebf\u1ec3\u1ec5\u1ec7\xeb\u011b\u0113\u0119","E\xc8\xc9\u1eba\u1ebc\u1eb8\xca\u1ec0\u1ebe\u1ec2\u1ec4\u1ec6\xcb\u011a\u0112\u0118","i\xec\xed\u1ec9\u0129\u1ecb\xee\xef\u012b","I\xcc\xcd\u1ec8\u0128\u1eca\xce\xcf\u012a","l\u0142","L\u0141","n\xf1\u0148\u0144","N\xd1\u0147\u0143","o\xf2\xf3\u1ecf\xf5\u1ecd\xf4\u1ed3\u1ed1\u1ed5\u1ed7\u1ed9\u01a1\u1edf\u1ee1\u1edb\u1edd\u1ee3\xf6\xf8\u014d","O\xd2\xd3\u1ece\xd5\u1ecc\xd4\u1ed2\u1ed0\u1ed4\u1ed6\u1ed8\u01a0\u1ede\u1ee0\u1eda\u1edc\u1ee2\xd6\xd8\u014c","r\u0159","R\u0158","s\u0161\u015b\u0219\u015f","S\u0160\u015a\u0218\u015e","t\u0165\u021b\u0163","T\u0164\u021a\u0162","u\xf9\xfa\u1ee7\u0169\u1ee5\u01b0\u1eeb\u1ee9\u1eed\u1eef\u1ef1\xfb\xfc\u016f\u016b","U\xd9\xda\u1ee6\u0168\u1ee4\u01af\u1eea\u1ee8\u1eec\u1eee\u1ef0\xdb\xdc\u016e\u016a","y\xfd\u1ef3\u1ef7\u1ef9\u1ef5\xff","Y\xdd\u1ef2\u1ef6\u1ef8\u1ef4\u0178","z\u017e\u017c\u017a","Z\u017d\u017b\u0179"]:["a\xe0\xe1\u1ea3\xe3\u1ea1\u0103\u1eb1\u1eaf\u1eb3\u1eb5\u1eb7\xe2\u1ea7\u1ea5\u1ea9\u1eab\u1ead\xe4\xe5\u0101\u0105A\xc0\xc1\u1ea2\xc3\u1ea0\u0102\u1eb0\u1eae\u1eb2\u1eb4\u1eb6\xc2\u1ea6\u1ea4\u1ea8\u1eaa\u1eac\xc4\xc5\u0100\u0104","c\xe7\u0107\u010dC\xc7\u0106\u010c","d\u0111\u010fD\u0110\u010e","e\xe8\xe9\u1ebb\u1ebd\u1eb9\xea\u1ec1\u1ebf\u1ec3\u1ec5\u1ec7\xeb\u011b\u0113\u0119E\xc8\xc9\u1eba\u1ebc\u1eb8\xca\u1ec0\u1ebe\u1ec2\u1ec4\u1ec6\xcb\u011a\u0112\u0118","i\xec\xed\u1ec9\u0129\u1ecb\xee\xef\u012bI\xcc\xcd\u1ec8\u0128\u1eca\xce\xcf\u012a","l\u0142L\u0141","n\xf1\u0148\u0144N\xd1\u0147\u0143","o\xf2\xf3\u1ecf\xf5\u1ecd\xf4\u1ed3\u1ed1\u1ed5\u1ed7\u1ed9\u01a1\u1edf\u1ee1\u1edb\u1edd\u1ee3\xf6\xf8\u014dO\xd2\xd3\u1ece\xd5\u1ecc\xd4\u1ed2\u1ed0\u1ed4\u1ed6\u1ed8\u01a0\u1ede\u1ee0\u1eda\u1edc\u1ee2\xd6\xd8\u014c","r\u0159R\u0158","s\u0161\u015b\u0219\u015fS\u0160\u015a\u0218\u015e","t\u0165\u021b\u0163T\u0164\u021a\u0162","u\xf9\xfa\u1ee7\u0169\u1ee5\u01b0\u1eeb\u1ee9\u1eed\u1eef\u1ef1\xfb\xfc\u016f\u016bU\xd9\xda\u1ee6\u0168\u1ee4\u01af\u1eea\u1ee8\u1eec\u1eee\u1ef0\xdb\xdc\u016e\u016a","y\xfd\u1ef3\u1ef7\u1ef9\u1ef5\xffY\xdd\u1ef2\u1ef6\u1ef8\u1ef4\u0178","z\u017e\u017c\u017aZ\u017d\u017b\u0179"],r=[];return e.split("").forEach((function(o){n.every((function(n){if(-1!==n.indexOf(o)){if(r.indexOf(n)>-1)return!1;e=e.replace(new RegExp("["+n+"]","gm"+t),"["+n+"]"),r.push(n)}return!0}))})),e}},{key:"createMergedBlanksRegExp",value:function(e){return e.replace(/[\s]+/gim,"[\\s]+")}},{key:"createAccuracyRegExp",value:function(e){var t=this,n="!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\xa1\xbf",r=this.opt.accuracy,o="string"==typeof r?r:r.value,a="string"==typeof r?[]:r.limiters,i="";switch(a.forEach((function(e){i+="|"+t.escapeStr(e)})),o){case"partially":default:return"()("+e+")";case"complementary":return"()([^"+(i="\\s"+(i||this.escapeStr(n)))+"]*"+e+"[^"+i+"]*)";case"exactly":return"(^|\\s"+i+")("+e+")(?=$|\\s"+i+")"}}},{key:"getSeparatedKeywords",value:function(e){var t=this,n=[];return e.forEach((function(e){t.opt.separateWordSearch?e.split(" ").forEach((function(e){e.trim()&&-1===n.indexOf(e)&&n.push(e)})):e.trim()&&-1===n.indexOf(e)&&n.push(e)})),{keywords:n.sort((function(e,t){return t.length-e.length})),length:n.length}}},{key:"isNumeric",value:function(e){return Number(parseFloat(e))==e}},{key:"checkRanges",value:function(e){var t=this;if(!Array.isArray(e)||"[object Object]"!==Object.prototype.toString.call(e[0]))return this.log("markRanges() will only accept an array of objects"),this.opt.noMatch(e),[];var n=[],r=0;return e.sort((function(e,t){return e.start-t.start})).forEach((function(e){var o=t.callNoMatchOnInvalidRanges(e,r),a=o.start,i=o.end;o.valid&&(e.start=a,e.length=i-a,n.push(e),r=i)})),n}},{key:"callNoMatchOnInvalidRanges",value:function(e,t){var n=void 0,r=void 0,o=!1;return e&&void 0!==e.start?(r=(n=parseInt(e.start,10))+parseInt(e.length,10),this.isNumeric(e.start)&&this.isNumeric(e.length)&&r-t>0&&r-n>0?o=!0:(this.log("Ignoring invalid or overlapping range: "+JSON.stringify(e)),this.opt.noMatch(e))):(this.log("Ignoring invalid range: 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