opencv之稀疏光流-KLT的对象跟踪

注:此教程是对贾志刚老师的opencv课程学习的一个记录,在此表示对贾老师的感谢.
稀疏光流-KLT检测的流程如下所示：

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

Mat frame, gray;
Mat prev_frame, prev_gray;
vector<Point2f> features;

vector<Point2f> iniPoints;
vector<Point2f> fpts[2];

vector<uchar> status;
vector<float> errors;

void drawFeature(Mat &inFrame);
void detectFeatures(Mat &inFrame, Mat &ingray);
void klTrackFeature();
void drawTrackLines();
int main(int argc, char** argv) {
    
    
	//VideoCapture capture(0);
	VideoCapture capture;
	capture.open("/home/fuhong/code/cpp/opencv_learning/src/object_tracing/video/video_006.mp4");
	if (!capture.isOpened()) {
    
    
		printf("could not load video file...\n");
		return -1;
	}

	namedWindow("camera input", CV_WINDOW_AUTOSIZE);
	while (capture.read(frame)) {
    
    
		//flip(frame, frame, 1);
		cvtColor(frame, gray, COLOR_BGR2GRAY);
		if (fpts[0].size() < 40) {
    
    
			detectFeatures(frame, gray);
			fpts[0].insert(fpts[0].end(), features.begin(), features.end());
			iniPoints.insert(iniPoints.end(), features.begin(), features.end());
		}
		else {
    
    
			printf("tttttttttttttttttttttttttttttttttttttttt...\n");
		}

		if (prev_gray.empty()) {
    
    
			gray.copyTo(prev_gray);
		}

		klTrackFeature();
		drawFeature(frame);

		gray.copyTo(prev_gray);
		frame.copyTo(prev_frame);
		imshow("camera input", frame);

		char c = waitKey(50);
		if (c == 27) {
    
    
			break;
		}
	}

	waitKey(0);
	return 0;
}

void detectFeatures(Mat &inFrame, Mat &ingray) {
    
    
	double maxCorners = 5000;
	double qualitylevel = 0.01;
	double minDistance = 10;
	double blockSize = 3;
	double k = 0.04;
	goodFeaturesToTrack(ingray, features, maxCorners, qualitylevel, minDistance, Mat(), blockSize, false, k);
	cout << "detect features : " << features.size() << endl;
}

void drawFeature(Mat &inFrame) {
    
    
	for (size_t t = 0; t < fpts[0].size(); t++) {
    
    
		circle(inFrame, fpts[0][t], 2, Scalar(0, 0, 255), 2, 8, 0);
	}
}

void klTrackFeature() {
    
    
	// KLT
	calcOpticalFlowPyrLK(prev_gray, gray, fpts[0], fpts[1], status, errors);
	int k = 0;
	for (int i = 0; i < fpts[1].size(); i++) {
    
    
		double dist = abs(fpts[0][i].x - fpts[1][i].x) + abs(fpts[0][i].y - fpts[1][i].y);
		if (dist > 2 && status[i]) {
    
    
			iniPoints[k] = iniPoints[i];
			fpts[1][k++] = fpts[1][i];
		}
	}
	iniPoints.resize(k);
	fpts[1].resize(k);
	drawTrackLines();

	std::swap(fpts[1], fpts[0]);
}

void drawTrackLines() {
    
    
	for (size_t t=0; t<fpts[1].size(); t++) {
    
    
		line(frame, iniPoints[t], fpts[1][t], Scalar(0, 255, 0), 1, 8, 0);
		circle(frame, fpts[1][t], 2, Scalar(0, 0, 255), 2, 8, 0);
	}
}

效果如下所示：