OpenCV之Tracking追踪demo

OpenCV之Tracking追踪demo

纯属为了记录<—>

单目标的追踪基于OpenCV3.0

OpenCV中提供tracker中有BOOSTING,MIL,KCF,TLD,MEDIANFLOW 四种跟踪方法

通过提取前景进行图像的轮廓检测进行运动物体的框选实时跟踪

#include
#include
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#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include
#include

using namespace std;
using namespace cv;
using namespace cv::xfeatures2d;
int main()
{
	Mat frame;
	VideoCapture capture("E:/Images/video.avi");
	Ptr tracker = Tracker::create("KCF");   //BOOSTING,MIL,KCF,TLD,MEDIANFLOW 四种跟踪方法
	//Ptr tracker = Tracker::create("TLD");
	Rect2d rect;
	capture.read(frame);
	resize(frame, frame, Size(), 1.25, 1.25);
	imshow("frame", frame);
	rect = selectROI("frame", frame, false, false);
	tracker->init(frame, rect);
	cout << "starting tracking!" << endl;
	while (capture.read(frame))
	{
		resize(frame, frame, Size(), 1.25, 1.25);
		tracker->update(frame, rect);
		rectangle(frame, rect, Scalar(0, 0, 255), 2, 1);
		imshow("TRACK", frame);
		char c = waitKey(50);
		if (c == 27)
			break;
	}
	waitKey(0);
	return 0;
}

移动物体的侦测与统计

对运动物体的侦测以及在摄像头下的运动物体数量实时显示以及记录
通过背景建模进行前景的提取并对移动物体进行侦测

   //移动物体的侦测与统计
    #include 
    #include 
    using namespace cv;
    using namespace std;

int main() 
{
	VideoCapture capture;
	capture.open("E:/Images/video.avi");
	if (!capture.isOpened()) {
		printf("could not load video data...\n");
		return -1;
	}

	namedWindow("input video", CV_WINDOW_AUTOSIZE);
	namedWindow("motion objects", CV_WINDOW_AUTOSIZE);

	// 初始BS模型   背景建模取背景或前景图
	Ptr pMOG2 = createBackgroundSubtractorMOG2();
	//Ptr pMOG2 = createBackgroundSubtractorMOG2();
	//Ptr pMOG2 = createBackgroundSubtractorKNN();
	Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));

	vector> contours;
	//vector hireachy;
	int count = 0;
	int maxcount = 0;
	int mincount = 100;

	Mat frame, gray, mogMask, backImage;
	while (capture.read(frame)) {
		imshow("input video", frame);
		pMOG2->apply(frame, mogMask);   //背景建模取前景图
		pMOG2->getBackgroundImage(backImage);   //提取背景图,基本没什么用
		GaussianBlur(mogMask, mogMask, Size(3, 3), 0);
		threshold(mogMask, mogMask, 100, 255, THRESH_BINARY);   //THRESH_OTSU 表示取自适应中最合适的阈值范围
		//threshold(mogMask, mogMask, 100, 255, THRESH_BINARY | THRESH_OTSU);
		morphologyEx(mogMask, mogMask, MORPH_OPEN, kernel, Point(-1, -1));
		imshow("mogMask", mogMask);
		imshow("backImage", backImage);

		findContours(mogMask, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0));
		count = 0;
		char numText[8];
		for (size_t t = 0; t < contours.size(); t++) {
			double area = contourArea(contours[t]);
			if (area < 40) continue;
			Rect selection = boundingRect(contours[t]);
			if (selection.width < 5 || selection.height < 8) continue;
			count++;
			rectangle(frame, selection, Scalar(0, 0, 255), 2, 8);

			sprintf(numText, "%d", count);
			putText(frame, numText, Point(selection.x, selection.y), CV_FONT_NORMAL, FONT_HERSHEY_PLAIN, Scalar(255, 0, 0), 1, 8);
			if (count > maxcount)
			{
				maxcount = count;
				cout << "maxcount=" << maxcount << endl;
			}
			if (count < mincount)
			{
				mincount = count;
				cout << "mincount=" << mincount << endl;
			}
		}

		imshow("motion objects", frame);
		char c = waitKey(50);
		if (c == 27) {         // ESC 
			break;
		}
	}

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

效果不是很好，仅供参考！！！
欢迎指正批评！！！