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@rkuo2000
rkuo2000 authored Oct 3, 2024
1 parent aec3c40 commit bb6edfe
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95 changes: 95 additions & 0 deletions Arduino/AMB82-mini/RTSP_YOLOv7_Tiny/ObjectClassList.h
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#ifndef __OBJECTCLASSLIST_H__
#define __OBJECTCLASSLIST_H__

struct ObjectDetectionItem {
uint8_t index;
const char* objectName;
uint8_t filter;
};

// List of objects the pre-trained model is capable of recognizing
// Index number is fixed and hard-coded from training
// Set the filter value to 0 to ignore any recognized objects
ObjectDetectionItem itemList[80] = {
{0, "person", 1},
{1, "bicycle", 1},
{2, "car", 1},
{3, "motorbike", 1},
{4, "aeroplane", 1},
{5, "bus", 1},
{6, "train", 1},
{7, "truck", 1},
{8, "boat", 1},
{9, "traffic light", 1},
{10, "fire hydrant", 1},
{11, "stop sign", 1},
{12, "parking meter", 1},
{13, "bench", 1},
{14, "bird", 1},
{15, "cat", 1},
{16, "dog", 1},
{17, "horse", 1},
{18, "sheep", 1},
{19, "cow", 1},
{20, "elephant", 1},
{21, "bear", 1},
{22, "zebra", 1},
{23, "giraffe", 1},
{24, "backpack", 1},
{25, "umbrella", 1},
{26, "handbag", 1},
{27, "tie", 1},
{28, "suitcase", 1},
{29, "frisbee", 1},
{30, "skis", 1},
{31, "snowboard", 1},
{32, "sports ball", 1},
{33, "kite", 1},
{34, "baseball bat", 1},
{35, "baseball glove", 1},
{36, "skateboard", 1},
{37, "surfboard", 1},
{38, "tennis racket", 1},
{39, "bottle", 1},
{40, "wine glass", 1},
{41, "cup", 1},
{42, "fork", 1},
{43, "knife", 1},
{44, "spoon", 1},
{45, "bowl", 1},
{46, "banana", 1},
{47, "apple", 1},
{48, "sandwich", 1},
{49, "orange", 1},
{50, "broccoli", 1},
{51, "carrot", 1},
{52, "hot dog", 1},
{53, "pizza", 1},
{54, "donut", 1},
{55, "cake", 1},
{56, "chair", 1},
{57, "sofa", 1},
{58, "pottedplant", 1},
{59, "bed", 1},
{60, "diningtable", 1},
{61, "toilet", 1},
{62, "tvmonitor", 1},
{63, "laptop", 1},
{64, "mouse", 1},
{65, "remote", 1},
{66, "keyboard", 1},
{67, "cell phone", 1},
{68, "microwave", 1},
{69, "oven", 1},
{70, "toaster", 1},
{71, "sink", 1},
{72, "refrigerator", 1},
{73, "book", 1},
{74, "clock", 1},
{75, "vase", 1},
{76, "scissors", 1},
{77, "teddy bear", 1},
{78, "hair dryer", 1},
{79, "toothbrush", 1}};

#endif
156 changes: 156 additions & 0 deletions Arduino/AMB82-mini/RTSP_YOLOv7_Tiny/RTSP_YOLOv7_TIiny.ino
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/*
Example guide:
https://www.amebaiot.com/en/amebapro2-arduino-neuralnework-object-detection/
NN Model Selection
Select Neural Network(NN) task and models using modelSelect(nntask, objdetmodel, facedetmodel, facerecogmodel).
Replace with NA_MODEL if they are not necessary for your selected NN Task.
NN task
=======
OBJECT_DETECTION/ FACE_DETECTION/ FACE_RECOGNITION
Models
=======
YOLOv3 model DEFAULT_YOLOV3TINY / CUSTOMIZED_YOLOV3TINY
YOLOv4 model DEFAULT_YOLOV4TINY / CUSTOMIZED_YOLOV4TINY
YOLOv7 model DEFAULT_YOLOV7TINY / CUSTOMIZED_YOLOV7TINY
SCRFD model DEFAULT_SCRFD / CUSTOMIZED_SCRFD
MobileFaceNet model DEFAULT_MOBILEFACENET/ CUSTOMIZED_MOBILEFACENET
No model NA_MODEL
*/

#include "WiFi.h"
#include "StreamIO.h"
#include "VideoStream.h"
#include "RTSP.h"
#include "NNObjectDetection.h"
#include "VideoStreamOverlay.h"
#include "ObjectClassList.h"

#define CHANNEL 0
#define CHANNELNN 3

// Lower resolution for NN processing
#define NNWIDTH 576
#define NNHEIGHT 320

VideoSetting config(VIDEO_FHD, 30, VIDEO_H264, 0);
VideoSetting configNN(NNWIDTH, NNHEIGHT, 10, VIDEO_RGB, 0);
NNObjectDetection ObjDet;
RTSP rtsp;
StreamIO videoStreamer(1, 1);
StreamIO videoStreamerNN(1, 1);

char ssid[] = "your_wifi_ssid"; // your network SSID (name)
char pass[] = "your_wifi_password"; // your network password
int status = WL_IDLE_STATUS;

IPAddress ip;
int rtsp_portnum;

void setup() {
Serial.begin(115200);

// attempt to connect to Wifi network:
while (status != WL_CONNECTED) {
Serial.print("Attempting to connect to WPA SSID: ");
Serial.println(ssid);
status = WiFi.begin(ssid, pass);

// wait 2 seconds for connection:
delay(2000);
}
ip = WiFi.localIP();

// Configure camera video channels with video format information
// Adjust the bitrate based on your WiFi network quality
config.setBitrate(2 * 1024 * 1024); // Recommend to use 2Mbps for RTSP streaming to prevent network congestion
Camera.configVideoChannel(CHANNEL, config);
Camera.configVideoChannel(CHANNELNN, configNN);
Camera.videoInit();

// Configure RTSP with corresponding video format information
rtsp.configVideo(config);
rtsp.begin();
rtsp_portnum = rtsp.getPort();

// Configure object detection with corresponding video format information
// Select Neural Network(NN) task and models
ObjDet.configVideo(configNN);
ObjDet.modelSelect(OBJECT_DETECTION, DEFAULT_YOLOV7TINY, NA_MODEL, NA_MODEL);
ObjDet.begin();

// Configure StreamIO object to stream data from video channel to RTSP
videoStreamer.registerInput(Camera.getStream(CHANNEL));
videoStreamer.registerOutput(rtsp);
if (videoStreamer.begin() != 0) {
Serial.println("StreamIO link start failed");
}

// Start data stream from video channel
Camera.channelBegin(CHANNEL);

// Configure StreamIO object to stream data from RGB video channel to object detection
videoStreamerNN.registerInput(Camera.getStream(CHANNELNN));
videoStreamerNN.setStackSize();
videoStreamerNN.setTaskPriority();
videoStreamerNN.registerOutput(ObjDet);
if (videoStreamerNN.begin() != 0) {
Serial.println("StreamIO link start failed");
}

// Start video channel for NN
Camera.channelBegin(CHANNELNN);

// Start OSD drawing on RTSP video channel
OSD.configVideo(CHANNEL, config);
OSD.begin();
}

void loop() {
std::vector<ObjectDetectionResult> results = ObjDet.getResult();

uint16_t im_h = config.height();
uint16_t im_w = config.width();

Serial.print("Network URL for RTSP Streaming: ");
Serial.print("rtsp://");
Serial.print(ip);
Serial.print(":");
Serial.println(rtsp_portnum);
Serial.println(" ");

printf("Total number of objects detected = %d\r\n", ObjDet.getResultCount());
OSD.createBitmap(CHANNEL);

if (ObjDet.getResultCount() > 0) {
for (uint32_t i = 0; i < ObjDet.getResultCount(); i++) {
int obj_type = results[i].type();
if (itemList[obj_type].filter) { // check if item should be ignored

ObjectDetectionResult item = results[i];
// Result coordinates are floats ranging from 0.00 to 1.00
// Multiply with RTSP resolution to get coordinates in pixels
int xmin = (int)(item.xMin() * im_w);
int xmax = (int)(item.xMax() * im_w);
int ymin = (int)(item.yMin() * im_h);
int ymax = (int)(item.yMax() * im_h);

// Draw boundary box
printf("Item %d %s:\t%d %d %d %d\n\r", i, itemList[obj_type].objectName, xmin, xmax, ymin, ymax);
OSD.drawRect(CHANNEL, xmin, ymin, xmax, ymax, 3, OSD_COLOR_WHITE);

// Print identification text
char text_str[20];
snprintf(text_str, sizeof(text_str), "%s %d", itemList[obj_type].objectName, item.score());
OSD.drawText(CHANNEL, xmin, ymin - OSD.getTextHeight(CHANNEL), text_str, OSD_COLOR_CYAN);
}
}
}
OSD.update(CHANNEL);

// delay to wait for new results
delay(100);
}

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