OpenCV实现目标跟踪

使用opencv3以上在opencv_contib中集成的跟踪算法，实现目标跟踪

在这里选择使用摄像头画面进行跟踪

python代码如下：

import cv2
import sys
 

print cv2.__version__
if __name__ == '__main__' :
 
    # Set up tracker.
    # Instead of MIL, you can also use
 
    tracker_types = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN']
    tracker_type = tracker_types[2]
 
    
    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()
 
    # Read video
    video = cv2.VideoCapture(0)
 
    # Exit if video not opened.
    if not video.isOpened():
        print "Could not open video"
        sys.exit()
 
    # Read first frame.
    ok, frame = video.read()
    if not ok:
        print 'Cannot read video file'
        sys.exit()
    
    # Define an initial bounding box
    #bbox = (287, 23, 86, 320)
 
    # Uncomment the line below to select a different bounding box
    bbox = cv2.selectROI(frame, False)
 
    # Initialize tracker with first frame and bounding box
    ok = tracker.init(frame, bbox)
 
    while True:
        # Read a new frame
        ok, frame = video.read()
        if not ok:
            break
        
        # Start timer
        timer = cv2.getTickCount()
 
        # Update tracker
        ok, bbox = tracker.update(frame)
 
        # Calculate Frames per second (FPS)
        fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer);
 
        # Draw bounding box
        if ok:
            # Tracking success
            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 :
            # Tracking failure
            cv2.putText(frame, "Tracking failure detected", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)
 
        # Display tracker type on frame
        cv2.putText(frame, tracker_type + " Tracker", (100,20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50),2);
    
        # Display FPS on frame
        cv2.putText(frame, "FPS : " + str(int(fps)), (100,50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50,170,50), 2);
 
        # Display result
        cv2.imshow("Tracking", frame)
 
        # Exit if ESC pressed
        k = cv2.waitKey(1) & 0xff
        if k == 27 : break
 

c++代码如下：

#include 
#include 
#include 
 
using namespace cv;
using namespace std;
 
// Convert to string
#define SSTR( x ) static_cast< std::ostringstream & >( \
( std::ostringstream() << std::dec << x ) ).str()
 
int main(int argc, char **argv)
{
    // List of tracker types in OpenCV 3.2
    // NOTE : GOTURN implementation is buggy and does not work.
    string trackerTypes[6] = {"BOOSTING", "MIL", "KCF", "TLD","MEDIANFLOW", "GOTURN"};
    // vector  trackerTypes(types, std::end(types));
 
    // Create a tracker
    string trackerType = trackerTypes[2];
 
    Ptr 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();
    }
    #endif
    // Read video
    VideoCapture video("videos/chaplin.mp4");
    
    // Exit if video is not opened
    if(!video.isOpened())
    {
        cout << "Could not read video file" << endl;
        return 1;
        
    }
    
    // Read first frame
    Mat frame;
    bool ok = video.read(frame);
    
    // Define initial boundibg box
    Rect2d bbox(287, 23, 86, 320);
    
    // Uncomment the line below to select a different bounding box
    bbox = selectROI(frame, false);
 
    // Display bounding box.
    rectangle(frame, bbox, Scalar( 255, 0, 0 ), 2, 1 );
    imshow("Tracking", frame);
    
    tracker->init(frame, bbox);
    
    while(video.read(frame))
    {     
        // Start timer
        double timer = (double)getTickCount();
        
        // Update the tracking result
        bool ok = tracker->update(frame, bbox);
        
        // Calculate Frames per second (FPS)
        float fps = getTickFrequency() / ((double)getTickCount() - timer);
        
        if (ok)
        {
            // Tracking success : Draw the tracked object
            rectangle(frame, bbox, Scalar( 255, 0, 0 ), 2, 1 );
        }
        else
        {
            // Tracking failure detected.
            putText(frame, "Tracking failure detected", Point(100,80), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0,0,255),2);
        }
        
        // Display tracker type on frame
        putText(frame, trackerType + " Tracker", Point(100,20), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(50,170,50),2);
        
        // Display FPS on frame
        putText(frame, "FPS : " + SSTR(int(fps)), Point(100,50), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(50,170,50), 2);
 
        // Display frame.
        imshow("Tracking", frame);
        
        // Exit if ESC pressed.
        int k = waitKey(1);
        if(k == 27)
        {
            break;
        }
 
    }
}

在这里选择的是KCF跟踪方法，效果如下图所示：

注意：在选择goturn方法时会报错，提示找不到goturn.prototxt:

goturn.prototx和goturn.caffemode可以在这里下载。下载后将他们拷贝到/opencv_contrib-master/modules/tracking/src/下，并确保对opencv目录具有读写权限。

经过测试我个人认为KCF算法效果比较好。

参考链接：

https://blog.csdn.net/qq_40239482/article/details/79015635

http://answers.opencv.org/question/123871/where-to-place-the-pretrained-data-for-goturn-tracker/