项目总结：yolov5+bytrack 入侵检测

yolov5+bytrack可以实现行人跟踪，每个人都会有一个ID，这就能很好的入侵人数做计算，或者能保存每个入侵者的图像

思路是：

准备一个与检测图像同样大小的attribute_bord，指定的禁入区域值为255，安全区值为0

每一帧都用行人目标的中心点坐标去attribute_bord上索要属性值，检查是否已进入禁入区

如进入禁入区，则将其ID加入入侵列表中。

效果：

关键代码：

*!
    @Description : https://github.com/shaoshengsong/
    @Author      : shaoshengsong
    @Date        : 2022-09-23 02:52:22
*/
#include <fstream>
#include <sstream>
//#include <iostream>
//#include <cstring>
 
 
#include <opencv2/opencv.hpp>
 
#include "YOLOv5Detector.h"
 
//#include "FeatureTensor.h"
#include "BYTETracker.h" //bytetrack
#include "tracker.h"//deepsort
//Deep SORT parameter
 
// https://cloud.tencent.com/developer/article/2099504
 
using namespace std;
using namespace cv;
 
std::vector<cv::Point> pts;
void on_Mouse(int event, int x, int y, int flags, void* param){
    if (event == EVENT_LBUTTONDOWN)// 左键按下
    {
        Point pt = Point(x, y);
        pts.push_back(pt);
    }
}
 
void invade_detect(Mat& img, vector<Point>& pts, vector<STrack>& bytrack_result){
 
    int invaded = 255; //安全区标识板颜色值为0
    vector<int> invade_IDs; // 入侵人员id容器
    Mat attribute_board = Mat::zeros(img.rows, img.cols, CV_8UC1); // 标识板
 
    if(pts.size() == 2){
        line(img,pts[0], pts[1], Scalar(0,0,180), 2, 8);
    }
    else if (pts.size() > 2) {
        fillPoly(attribute_board, pts, Scalar(invaded)); // 设置入侵区域标识板颜色值为255
        polylines(img, pts, true, Scalar(0,0,180), 2, 8); // 画出入侵区域
    }
 
    for (size_t i=0; i<bytrack_result.size(); i++){
 
        STrack temp_person = bytrack_result[i];
 
 
        // perosn bbox center point
        int left = temp_person.tlwh[0];
        int top = temp_person.tlwh[1];
        int width = temp_person.tlwh[2];
        int height = temp_person.tlwh[3];
        Point center = Point(left + width/2, top + height/2);
 
        // whether invade
        int whether_invade = attribute_board.at<uchar>(center.y, center.x);
        if (whether_invade == invaded){ // 闯入划定区域
 
 
            if(!std::count(invade_IDs.begin(), invade_IDs.end(), temp_person.track_id )) { // 新入侵者
                invade_IDs.push_back(temp_person.track_id ); // 加入入侵者列表
 
                rectangle(img, cv::Rect(temp_person.tlwh[0], temp_person.tlwh[1],
                        temp_person.tlwh[2], temp_person.tlwh[3]), Scalar(0,0,255),2, 8); // 画bbox，红框
                string label = "Person ID:" + to_string(temp_person.track_id);
                putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,255), 2, 8);
            }
 
        }
        else{ // 为闯入划定区域
            rectangle(img, cv::Rect(temp_person.tlwh[0], temp_person.tlwh[1],
                    temp_person.tlwh[2], temp_person.tlwh[3]), Scalar(255,255,255),2, 8); // 画bbox，白框
            string label = "Person ID:" + to_string(temp_person.track_id);
            putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255,255,255), 2, 8);
        }
 
    }
 
}
 
 
 
std::vector<STrack>  test_bytetrack(cv::Mat& frame, std::vector<detect_result>& results,
                    BYTETracker& tracker, std::vector<std::string> & classes)
{
 
    std::vector<detect_result> objects;
 
 
    for (detect_result dr : results)
    {
 
        if(dr.classId == 0 ) // person
        {
            objects.push_back(dr);
        }
    }
 
    // bytetrack主函数
    std::vector<STrack> output_stracks = tracker.update(objects);
 
//    // 对于track结果
//    for (unsigned long i = 0; i < output_stracks.size(); i++)
//    {
//        std::vector<float> tlwh = output_stracks[i].tlwh;
 
//        //  目标像素不能小于20个像素、宽高比需 < 1.6
//        // 其实这里加一个判定不是很合理，因为 bytetrack主函数中ID已经增加了，只不过没有显示该目标而已
        bool vertical = tlwh[2] / tlwh[3] > 1.6;
        if (tlwh[2] * tlwh[3] > 20 && !vertical)
//        if(1)
//        {
//            // 框出目标，并标注ID
//            cv::Scalar s = tracker.get_color(output_stracks[i].track_id);
//            cv::putText(frame, cv::format("%s--%d",classes[output_stracks[i].class_index].c_str(),output_stracks[i].track_id),
//                    cv::Point(tlwh[0], tlwh[1] - 5),
//                    0, 0.6, cv::Scalar(0, 0, 255), 2, cv::LINE_AA);
//            cv::rectangle(frame, cv::Rect(tlwh[0], tlwh[1], tlwh[2], tlwh[3]), s, 2);
 
//        }
//    }
 
 
    return output_stracks;
 
}
int main(int argc, char *argv[])
{
    // 加载类别名称
    std::vector<std::string> classes;
    std::string file="/home/jason/work/my-deploy/01-bytetrack-deepsort/coco_80_labels_list.txt";
    std::ifstream ifs(file);
    if (!ifs.is_open())
        CV_Error(cv::Error::StsError, "File " + file + " not found");
    std::string line;
    while (std::getline(ifs, line))
    {
        classes.push_back(line);
    }
 
 
    // 检测器
    std::cout<<"classes:"<<classes.size();
    std::shared_ptr<YOLOv5Detector> detector(new YOLOv5Detector());
    detector->init(k_detect_model_path);
 
    //bytetrack设置
    int fps=20;
    BYTETracker bytetracker(fps, 30); // 后面的30是30帧没有发现，
 
    // 读取视频
    std::cout<<"begin read video"<<std::endl;
    cv::VideoCapture capture("/home/jason/work/my-deploy/01-bytetrack-deepsort/1.mp4");
    if (!capture.isOpened()) {
        printf("could not read this video file...\n");
        return -1;
    }
    std::cout<<"end read video"<<std::endl;
 
    // yolo检测结果
    std::vector<detect_result> results;
 
    // 输出另存
    cv::VideoWriter video("/home/jason/work/my-deploy/01-bytetrack-deepsort/out.avi",cv::VideoWriter::fourcc('M','J','P','G'),10, cv::Size(1920,1080));
 
    //
    namedWindow("bytrack && invade");
    setMouseCallback("bytrack && invade", on_Mouse, 0);
 
 
    int num_frames = 0;
    cv::Mat frame;
    while (true)
    {
 
 
 
        if (!capture.read(frame)) // if not success, break loop
        {
            std::cout<<"\n Cannot read the video file. please check your video.\n";
            break;
        }
        num_frames ++;
 
        //Second/Millisecond/Microsecond  秒s/毫秒ms/微秒us
        auto start = std::chrono::system_clock::now();
 
        // 获得检测结果
        detector->detect(frame, results);
 
        // 计算检测耗时
        auto end = std::chrono::system_clock::now();
        auto detect_time =std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count();//ms
//        std::cout<<classes.size()<<":"<<results.size()<<":"<<num_frames<<std::endl;
        printf("视频尺寸：%d宽  * %d高\n", frame.cols, frame.rows );
        printf("帧数:%d  检测器耗时：%dms ",num_frames, (int)detect_time);
 
 
        // 进行跟踪
        std::vector<STrack> temp_tracks;
        auto start2 = std::chrono::system_clock::now();
        temp_tracks = test_bytetrack(frame, results,bytetracker,classes);
        auto end2 = std::chrono::system_clock::now();
 
        // 计算跟踪器耗时
        auto detect_time2 =std::chrono::duration_cast<std::chrono::milliseconds>(end2 - start2).count();//ms
        printf("跟踪器耗时：%dms \n", (int)detect_time2);
 
 
        // 入侵检测
        invade_detect(frame, pts, temp_tracks);
        cv::imshow("bytrack && invade", frame);
 
        // 保存结果至指定路径
        video.write(frame);
 
        if(cv::waitKey(1) == 27) // Wait for 'esc' key press to exit
        {
            break;
        }
 
        results.clear();
 
 
    }
    capture.release();
    video.release();
    cv::destroyAllWindows();
 
 
}