OpenCV之视频分析与对象跟踪(三) 对象检测与跟踪(基于颜色)

/*
1.基于颜色跟踪的实现步骤
2.使用inRange的方法过滤出绿色，
3.形态学造作提取
4.轮廓查找
5.外界矩形获取
6.位置标定

*/


#include 
#include 

using namespace std;
using namespace cv;

Rect roi;
void processFrame(Mat &binary, Rect &rect);
int Count = 0;
int main(int argc, char* argv) {
	// load video
	VideoCapture capture;
	capture.open("video_006.mp4");
	if (!capture.isOpened()) {
		printf("could not find video file");
		return -1;
	}

	Mat frame, mask;
	Mat kernel1 = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
	Mat kernel2 = getStructuringElement(MORPH_RECT, Size(5, 5), Point(-1, -1));

	namedWindow("input video", CV_WINDOW_AUTOSIZE);
	namedWindow("track mask", CV_WINDOW_AUTOSIZE);
	double time0,time1=0;
	
	while (capture.read(frame)) {
		Count++;
		time0 = getTickCount();
		
		inRange(frame, Scalar(0, 127, 0), Scalar(120, 255, 120), mask); // 过滤
		morphologyEx(mask, mask, MORPH_OPEN, kernel1, Point(-1, -1), 1); // 开操作
		dilate(mask, mask, kernel2, Point(-1, -1), 4);// 膨胀
		imshow("track mask", mask);

		processFrame(mask, roi); // 轮廓发现与位置标定
		rectangle(frame, roi, Scalar(0, 0, 255), 3, 8, 0);
		imshow("input video", frame);
		printf("第 %d 次的时间是%lf\n",Count, (getTickCount() - time0) / getTickFrequency());
		time1 += (getTickCount() - time0) / getTickFrequency();
		// trigger exit
		char c = waitKey(1);
		if (c == 27) {
			break;
		}
		if (Count == 361)
			break;
	}
	
	cout << 1.0*Count/time1<<'\n';
	printf("%lf\n\n", time1);

	capture.release();
	waitKey(0);
	return 0;
}

void processFrame(Mat &binary, Rect &rect) {
	vector> contours;
	vector hireachy;
	findContours(binary, contours, hireachy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));
	if (contours.size() > 0) {
		double maxArea = 0.0;
		for (size_t t = 0; t < contours.size(); t++) {
			double area = contourArea(contours[static_cast(t)]);
			if (area > maxArea) {
				maxArea = area;
				rect = boundingRect(contours[static_cast(t)]);
			}
		}
	}
	else {
		rect.x = rect.y = rect.width = rect.height = 0;
	}

}

效果图：