基于OpenCV4.2实现单目标跟踪

#include <opencv2/opencv.hpp>
#include <opencv2/tracking.hpp>
#include <opencv2/core/ocl.hpp>

using namespace cv;
using namespace std;

// 转换为字符串
#define SSTR( x ) static_cast< std::ostringstream & >( ( std::ostringstream() << std::dec << x ) ).str()

int main(int argc, char **argv)
{
    // OpenCV 3.4.1中的跟踪器类型列表
    string trackerTypes[8] = {"BOOSTING", "MIL", "KCF", "TLD","MEDIANFLOW", "GOTURN", "MOSSE", "CSRT"};
    // vector <string> trackerTypes(types, std::end(types));

    // 创建一个跟踪器
    string trackerType = trackerTypes[2];

    Ptr<Tracker> tracker;

    #if (CV_MINOR_VERSION < 3)
    {
        tracker = Tracker::create(trackerType);
    }
    #else
    {
        if (trackerType == "BOOSTING")
            tracker = TrackerBoosting::create();
        if (trackerType == "MIL")
            tracker = TrackerMIL::create();
        if (trackerType == "KCF")
            tracker = TrackerKCF::create();
        if (trackerType == "TLD")
            tracker = TrackerTLD::create();
        if (trackerType == "MEDIANFLOW")
            tracker = TrackerMedianFlow::create();
        if (trackerType == "GOTURN")
            tracker = TrackerGOTURN::create();
        if (trackerType == "MOSSE")
            tracker = TrackerMOSSE::create();
        if (trackerType == "CSRT")
            tracker = TrackerCSRT::create();
    }
    #endif
    // 读取视频
    VideoCapture video("videos/chaplin.mp4");
    
    // 如果视频没有打开，退出
    if(!video.isOpened())
    {
        cout << "Could not read video file" << endl; 
        return 1; 
    } 

    // 读第一帧
    Mat frame; 
    bool ok = video.read(frame); 

    // 定义初始边界框
    Rect2d bbox(287, 23, 86, 320); 

    // 取消注释下面的行以选择一个不同的边界框 
    // bbox = selectROI(frame, false); 
    // 显示边界框
    rectangle(frame, bbox, Scalar( 255, 0, 0 ), 2, 1 ); 

    imshow("Tracking", frame); 
    tracker->init(frame, bbox);
    
    while(video.read(frame))
    {     
        // 启动定时器
        double timer = (double)getTickCount();
        
        // 更新跟踪结果
        bool ok = tracker->update(frame, bbox);
        
        // 计算每秒帧数(FPS)
        float fps = getTickFrequency() / ((double)getTickCount() - timer);
        
        if (ok)
        {
            // 跟踪成功:绘制被跟踪对象
            rectangle(frame, bbox, Scalar( 255, 0, 0 ), 2, 1 );
        }
        else
        {
            // 跟踪失败
            putText(frame, "Tracking failure detected", Point(100,80), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0,0,255),2);
        }
        
        // 在帧上显示跟踪器类型
        putText(frame, trackerType + " Tracker", Point(100,20), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(50,170,50),2);
        
        // 帧显示FPS
        putText(frame, "FPS : " + SSTR(int(fps)), Point(100,50), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(50,170,50), 2);

        // 显示帧
        imshow("Tracking", frame);
        
        // 按ESC键退出。
        int k = waitKey(1);
        if(k == 27)
        {
            break;
        }

    }
}

import cv2
import sys

(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')?

if __name__ == '__main__' :

    # 建立追踪器
    # 除了MIL之外，您还可以使用

    tracker_types = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT']
    tracker_type = tracker_types[2]

    if int(minor_ver) < 3:
        tracker = cv2.Tracker_create(tracker_type)
    else:
        if tracker_type == 'BOOSTING':
            tracker = cv2.TrackerBoosting_create()
        if tracker_type == 'MIL':
            tracker = cv2.TrackerMIL_create()
        if tracker_type == 'KCF':
            tracker = cv2.TrackerKCF_create()
        if tracker_type == 'TLD':
            tracker = cv2.TrackerTLD_create()
        if tracker_type == 'MEDIANFLOW':
            tracker = cv2.TrackerMedianFlow_create()
        if tracker_type == 'GOTURN':
            tracker = cv2.TrackerGOTURN_create()
        if tracker_type == 'MOSSE':
            tracker = cv2.TrackerMOSSE_create()
        if tracker_type == "CSRT":
            tracker = cv2.TrackerCSRT_create()

    # 读取视频
    video = cv2.VideoCapture("videos/chaplin.mp4")

    # 如果视频没有打开，退出。
    if not video.isOpened():
        print "Could not open video"
        sys.exit()

    # 读第一帧。
    ok, frame = video.read()
    if not ok:
        print('Cannot read video file')
        sys.exit()
    
    # 定义一个初始边界框
    bbox = (287, 23, 86, 320)

    # 取消注释下面的行以选择一个不同的边界框
    # bbox = cv2.selectROI(frame, False)

    # 用第一帧和包围框初始化跟踪器
    ok = tracker.init(frame, bbox)

    while True:
        # 读取一个新的帧
        ok, frame = video.read()
        if not ok:
            break
        
        # 启动计时器
        timer = cv2.getTickCount()

        # 更新跟踪器
        ok, bbox = tracker.update(frame)

        # 计算帧率(FPS)
        fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);

        # 绘制包围框
        if ok:
            # 跟踪成功
            p1 = (int(bbox[0]), int(bbox[1]))
            p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
            cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1)
        else :
            # 跟踪失败
            cv2.putText(frame, "Tracking failure detected", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)

        # 在帧上显示跟踪器类型名字
        cv2.putText(frame, tracker_type + " Tracker", (100,20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50),2);
    
        # 在帧上显示帧率FPS
        cv2.putText(frame, "FPS : " + str(int(fps)), (100,50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50), 2);

        # 显示结果
        cv2.imshow("Tracking", frame)

        # 按ESC键退出
        k = cv2.waitKey(1) & 0xff
        if k == 27 : break