readme

README.md

@@ -0,0 +1,25 @@
+## Yolov5s+SORT(卡尔曼+匈牙利)
+
+利用目标跟踪对检测框进行预测的技术，对目标检测任务进行加速，在yolov5检测的帧之间穿插使用卡尔曼滤波进行目标检测框的预测，减少对模型的调用，实现目标检测帧率提升的目的。
+
+#### 代码运行环境：
+
+| 开发环境 | Visual Studio 2022   |
+| -------- | -------------------- |
+| OpenCV   | opencv-4.8.0-windows |
+| 调用模型 | YOLOv5s 6.0          |
+
+
+
+#### 主要代码设置：
+
+main.py中
+
+![image-20241014170146823](C:\Users\FengHua\AppData\Roaming\Typora\typora-user-images\image-20241014170146823.png)
+
+使用到的模型以及检测视频的路径。
+
+![image-20241014170213505](C:\Users\FengHua\AppData\Roaming\Typora\typora-user-images\image-20241014170213505.png)
+
+模型的调用频率设置，设置为3表示帧率为3的倍数时进行模型的目标检测，其他时候使用预测的目标框。
+

yoloSORT/main.cpp

@@ -11,7 +11,7 @@ using namespace cv::dnn;
 
 int main()
 {
-    string img_path = "D:/VS/yoloSORT/images/1.mp4";
+    string img_path = "D:/VS/yoloSORT/images/test.mp4";
     string model_path = "D:/VS/yoloSORT/models/yolov5s.onnx";
 
     // 创建Yolov5模型和跟踪器
@@ -43,7 +43,7 @@ int main()
     }
 
     VideoWriter writer;
-    writer = VideoWriter("D:/VS/yoloSORT/images/test.mp4", CAP_OPENCV_MJPEG, 20, Size(2560, 1440), true);
+    writer = VideoWriter("D:/VS/yoloSORT/images/detect.mp4", CAP_OPENCV_MJPEG, 20, Size(2560, 1440), true);
 
     clock_t start, end; //计时器
     start = clock();
@@ -149,3 +149,118 @@ int main()
 
     return 0;
 }
+
+
+/*
+#include "yolo.h"
+#include "kalmanboxtracker.h"
+#include <iostream>
+#include <opencv2/opencv.hpp>
+#include <vector>
+#include <time.h>
+#include "sort.h"
+using namespace std;
+using namespace cv;
+using namespace cv::dnn;
+
+int main()
+{
+string img_path = "D:/VS/yoloTest/images/1.mp4";
+string model_path = "D:/VS/yoloTest/models/yolov5s.onnx";
+
+// 创建Yolov5模型和跟踪器
+Yolov5 yolo;
+Net net;
+Sort mot_tracker = Sort(1, 3, 0.3); // 创建Sort跟踪器
+
+// 读取Yolo模型
+if (!yolo.readModel(net, model_path, true)) {
+    cout << "无法加载Yolo模型" << endl;
+    return -1;
+}
+
+// 生成随机颜色
+srand(time(0));
+vector<Scalar> colors;
+for (int i = 0; i < 80; i++) {
+    int b = rand() % 256;
+    int g = rand() % 256;
+    int r = rand() % 256;
+    colors.push_back(Scalar(b, g, r));
+}
+
+// 读取视频
+VideoCapture cap(img_path);
+if (!cap.isOpened()) {
+    cout << "无法打开视频文件" << endl;
+    return -1;
+}
+
+VideoWriter writer;
+writer = VideoWriter("D:/VS/yoloTest/images/test.mp4", CAP_OPENCV_MJPEG, 20, Size(2560, 1440), true);
+
+clock_t start, end; //计时器
+start = clock();
+int num = 0;
+
+Mat frame;
+while (cap.read(frame)) {
+    cap >> frame;
+
+    if (frame.empty()) {
+        cout << "视频已结束" << endl;
+        break;
+    }
+    // 进行目标检测
+    vector<Output> result;
+    if (yolo.Detect(frame, net, result)) {
+        // 跟踪已检测到的目标
+        vector<Rect> detection_rects; // 存储检测结果的矩形框
+        for (int i = 0; i < result.size(); i++) {
+            int x = result[i].box.x;
+            int y = result[i].box.y;
+            int w = result[i].box.width;
+            int h = result[i].box.height;
+            Rect rect(x, y, w, h);
+            detection_rects.push_back(rect);
+        }
+        // 更新目标跟踪器
+        vector<vector<float>> trackers = mot_tracker.update(detection_rects);//x,y,w,h,id
+        // 绘制跟踪结果
+        for (int i = 0; i < trackers.size(); i++) {
+            Rect rect(trackers[i][0], trackers[i][1], trackers[i][2] - trackers[i][0], trackers[i][3] - trackers[i][1]);
+            //cout << "0:" << trackers[i][0] << endl; //跟踪目标的 x 坐标（左上角的 x 坐标）。
+            //cout << "1:" << trackers[i][1] << endl; //跟踪目标的 y 坐标（左上角的 y 坐标）。
+            //cout << "2:" << trackers[i][2] << endl; //跟踪目标的宽度。
+            //cout << "3:" << trackers[i][3] << endl; //跟踪目标的高度。
+            //cout << "4:" << trackers[i][4] << endl; //跟踪目标的 ID。
+            int id = static_cast<int>(trackers[i][4]);
+            //cout << "id:" << id << endl;
+            //string label = yolo.className[result[id].id] + ":" + to_string(result[id].confidence);
+            rectangle(frame, rect, Scalar(0, 255, 0), 2);
+            cout << trackers[i][5] << endl;
+            putText(frame, "id:" + to_string(int(trackers[i][4])), Point(rect.x, rect.y), FONT_HERSHEY_SIMPLEX, 1, Scalar(0, 255, 0), 2);
+        }
+
+    }
+
+    imshow("img", frame);
+    writer << frame;
+    if (waitKey(10) == 27) {
+        break; // 退出循环
+    }
+
+    num++;
+}
+
+end = clock();
+cout << "time = " << double(end - start) / CLOCKS_PER_SEC << "s" << endl;  //输出时间（单位：ｓ）
+cout << "frame num: " << num << endl;
+cout << "Speed = " << double(end - start) * 1000 / CLOCKS_PER_SEC / num << "ms" << endl;  //输出时间（单位：mｓ）
+
+cap.release();
+destroyAllWindows();
+
+return 0;
+}
+*/

yoloSORT/x64/Release/yoloSORT.log

@@ -76,8 +76,8 @@ D:\VS\yoloSORT\yoloSORT\main.cpp(111,40): warning C4244:         [
 D:\VS\yoloSORT\yoloSORT\main.cpp(111,40): warning C4244:             _Tp=int
 D:\VS\yoloSORT\yoloSORT\main.cpp(111,40): warning C4244:         ]
   正在生成代码
-  1 of 1116 functions (<0.1%) were compiled, the rest were copied from previous compilation.
+  0 of 1116 functions ( 0.0%) were compiled, the rest were copied from previous compilation.
     0 functions were new in current compilation
-    1 functions had inline decision re-evaluated but remain unchanged
+    0 functions had inline decision re-evaluated but remain unchanged
   已完成代码的生成
   yoloSORT.vcxproj -> D:\VS\yoloSORT\x64\Release\yoloSORT.exe