图像跟踪算法demo的总结附源码_多物体追踪demo

图像跟踪算法demo的总结附源码

将最近写过的一些跟踪算法的demo做个总结吧，目前都是基于传统算法，模型的话实在是太多了！！！下面的都是源码可是实际运行成功的，不需要更改！可做为参考使用！

跟踪算法demo头文件:tracking.h

#pragma once
#ifndef _TRACK_H_
#define _TRACK_H_

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

// 跟踪应用demo
class TRACK
{
public:
	/** 背景的滤除,前景的浮现   背景建模实现 */
	static void backgroundsubtractor();
	// 以动物体的侦测
	static void obj_detected();
	// 追踪
	static void tracker();
	// 稀疏光流
	static void klTrack();
	// 稠密光流
	static void denseTrack();
	// 三帧差法跟踪
	static void ThreeFrameSubtract();
	// test
	void print_f();
};

#endif // !_TRACK_H_

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跟踪算法demo的主要算法文件.cpp:tracking.cpp

#include "tracking.h"

void TRACK::backgroundsubtractor()
{
	cv::VideoCapture capture;
	capture.open("D:/Images/video01.avi");
	//VideoCapture capture(0);
	if (!capture.isOpened())
	{
		std::cout << "could not load video data..." << std::endl;
		return;
	}
	cv::Mat backmog2, backknn;
	cv::Ptr<cv::BackgroundSubtractor> MOG2 = cv::createBackgroundSubtractorMOG2();
	cv::Ptr<cv::BackgroundSubtractor> KNN = cv::createBackgroundSubtractorKNN();
	double fps = capture.get(CV_CAP_PROP_FPS);
	cv::Size size = cv::Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT));
	double width = capture.get(CV_CAP_PROP_FRAME_WIDTH);
	double height = capture.get(CV_CAP_PROP_FRAME_HEIGHT);
	std::cout << "width=" << width << std::endl << "height=" << height << std::endl;
	std::cout << "FPS:" << fps << std::endl;
	//VideoWriter write("E:/Images/vidoe.mp4", CV_FOURCC('D', 'I', 'V', 'X'), 15.0, size, true);
	cv::Mat frame, gray, binary;
	cv::namedWindow("video-demo", CV_WINDOW_AUTOSIZE);
	cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3), cv::Point(-1, -1));
	std::vector<cv::Mat> bgr;
	while (capture.read(frame))
	{
		resize(frame, frame, cv::Size(), 0.25, 0.25);
		//inRange(frame, Scalar(0, 127, 0), Scalar(127, 255, 127), gray);   //大于和小于的阈值为零，在范围内的为255
		cvtColor(frame, gray, CV_BGR2GRAY);
		threshold(gray, binary, 0, 255, cv::THRESH_BINARY | cv::THRESH_OTSU);
		//bitwise_not(frame, frame);
		//flip(frame, frame, 1);  //水平翻转
		MOG2->apply(frame, backmog2);  //有点滤除背景的意思  apply()函数
									   //morphologyEx(backmog2, backmog2, MORPH_OPEN, kernel, Point(-1, -1));
									   //dilate(backmog2, backmog2, kernel, Point(-1, -1), 3);
		KNN->apply(frame, backknn);
		cv::imshow("KNN-apply", backknn);
		morphologyEx(backknn, backknn, cv::MORPH_OPEN, kernel, cv::Point(-1, -1));
		erode(backknn, backknn, kernel, cv::Point(-1, -1));
		//dilate(backknn, backknn, kernel, Point(-1, -1), 3);
		std::vector<std::vector<cv::Point>> contours;
		cv::Rect rect;
		findContours(backknn, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE, cv::Point());
		double areamax = 0;
		for (int i = 0; i < contours.size(); i++)
		{
			double area = contourArea(contours[i]);
			std::cout << "area=" << area << std::endl;
			if (area > areamax)
			{
				rect = boundingRect(contours[i]);
			}
		}
		rectangle(frame, rect, cv::Scalar(0, 0, 255), 2, 8, 0);
		cv::imshow("video-demo", frame);
		cv::imshow("backmog2", backmog2);
		cv::imshow("backnn", backknn);
		//write.write(frame);
		char c = cv::waitKey(100);
		if (c == 27)
			break;
	}
	cv::waitKey(0);
	return;
}

void TRACK::print_f()
{
	std::cout << "hello OpenCV... coming...!" << std::endl;
}


//***********************************************移动侦测物体***********************************************
//移动物体的侦测与统计

void TRACK::obj_detected() 
{
	cv::VideoCapture capture;
	capture.open("D:/Images/video01.avi");
	if (!capture.isOpened()) {
		printf("could not load video data...\n");
		return;
	}

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

	// 初始BS模型   背景建模取背景或前景图
	//cv::Ptr<cv::BackgroundSubtractor> pMOG2 = cv::createBackgroundSubtractorMOG2();
	//Ptr<BackgroundSubtractorMOG2> pMOG2 = createBackgroundSubtractorMOG2();
	cv::Ptr<cv::BackgroundSubtractorKNN> pMOG2 = cv::createBackgroundSubtractorKNN();
	cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3), cv::Point(-1, -1));

	std::vector<std::vector<cv::Point>> contours;
	//vector<Vec4i> hireachy;
	int count = 0;
	int maxcount = 0;
	int mincount = 100;

	cv::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
		GaussianBlur(mogMask, mogMask, cv::Size(3, 3), 0);
		threshold(mogMask, mogMask, 100, 255, cv::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, cv::MORPH_OPEN, kernel, cv::Point(-1, -1));
		imshow("mogMask", mogMask);
		imshow("backImage", backImage);

		findContours(mogMask, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE, cv::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;
			cv::Rect selection = boundingRect(contours[t]);
			if (selection.width < 5 || selection.height < 8) continue;
			count++;
			rectangle(frame, selection, cv::Scalar(0, 0, 255), 2, 8);

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

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

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

// 追踪运动物体
void TRACK::tracker()
{
	cv::Mat frame;
	cv::VideoCapture capture("D:/Images/video01.avi");
	//Ptr<Tracker> tracker = Tracker::create("KCF");   //BOOSTING,MIL,KCF,TLD,MEDIANFLOW 四种跟踪方法
	//Ptr<Tracker> tracker = Tracker::create("TLD");
	cv::Ptr<cv::Tracker> tracker = cv::TrackerBoosting::create();
	cv::Rect2d rect;
	capture.read(frame);
	resize(frame, frame, cv::Size(), 1.25, 1.25);
	imshow("frame", frame);
	rect = selectROI("frame", frame, false, false);
	tracker->init(frame, rect);
	std::cout << "starting tracking!" << std::endl;
	while (capture.read(frame))
	{
		resize(frame, frame, cv::Size(), 1.25, 1.25);
		tracker->update(frame, rect);
		rectangle(frame, rect, cv::Scalar(0, 0, 255), 2, 1);
		imshow("TRACK", frame);
		char c = cv::waitKey(50);
		if (c == 27)
			break;
	}
	cv::waitKey(0);
	return;
}


using namespace std;
using namespace cv;
//稀疏光流检测&&运动目标的检测
cv::Mat frame, gray;
cv::Mat prev_frame, prev_gray;
std::vector<cv::Point2f> features;  //shi-tomasi角点检测-特征数据
std::vector<cv::Point2f> iniPoints;  //初始化特征数据
std::vector<cv::Point2f> fpts[2];   //保持当前帧和前一帧的特征点位置
std::vector<uchar> status;    //特征点跟踪成功标志位
std::vector<float> errors;   //跟踪时候区域误差和
void drawTrackLines();
void detectFeatures(cv::Mat& ingray)   //特征点检测(角点)
{
	goodFeaturesToTrack(ingray, features, 5000, 0.01, 10, Mat(), 3, false, 0.04);
	cout << "detect features=" << features.size() << endl;
}

void drawFeature(Mat &inFrame)   //画特征点
{
	for (int t = 0; t < fpts[0].size(); ++t)
	{
		circle(inFrame, fpts[0][t], 2, Scalar(0, 0, 255), 2, 8, 0);
	}
}
void klTrackFeature()    //光流函数及检测光流
{
	calcOpticalFlowPyrLK(prev_gray, gray, fpts[0], fpts[1], status, errors);  //KLT   将检测的光流送入数组中保存
	int k = 0;
	//特征点过滤
	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];
		}
	}
	//保存特征点并绘制跟踪轨迹
	iniPoints.resize(k);   //裁剪不要的特征点，更新集合大小
	fpts[1].resize(k);
	drawTrackLines();
	swap(fpts[1], fpts[0]);   //更新帧的特征点
}
void drawTrackLines()   //画光流形成的前后的过程
{
	for (int i = 0; i < fpts[1].size(); i++)
	{
		line(frame, iniPoints[i], fpts[1][i], Scalar(0, 255, 0), 1, 8, 0);
		circle(frame, fpts[1][i], 2, Scalar(0, 0, 255), 2, 8, 0);
	}
}
// 稀疏光流
void TRACK::klTrack()
{
	VideoCapture capture("D:/Images/video01.avi");
	while (capture.read(frame))
	{
		//resize(frame, frame, Size(), 0.25, 0.25);
		cvtColor(frame, gray, CV_BGR2GRAY);
		if (fpts[0].size() < 40)
		{
			detectFeatures(gray);
			fpts[0].insert(fpts[0].end(), features.begin(), features.end());
			iniPoints.insert(iniPoints.end(), features.begin(), features.end());
		}
		else
		{
			cout << "NO detect!" << endl;
		}
		if (prev_gray.empty())
		{
			gray.copyTo(prev_gray);
		}
		klTrackFeature();
		drawFeature(frame);
		//更新前一帧数据
		gray.copyTo(prev_gray);
		frame.copyTo(prev_frame);
		imshow("frame", frame);
		char c = waitKey(50);
		if (c == 27)
		{
			break;
		}
	}
	waitKey(0);
	return;
}


//稠密光流的检测
void drawlines(Mat& flowdata, Mat& Image)
{
	for (int i = 0; i < Image.rows; i++)
	{
		for (int j = 0; j < Image.cols; j++)
		{
			const Point2f fxy = flowdata.at<Point2f>(i, j);
			if (fxy.x > 1 || fxy.y > 1)
			{
				line(Image, Point(j, i), Point((j + fxy.x), (i + fxy.y)), Scalar(0, 0, 255), 1, 8, 0);
				circle(Image, Point(j, i), 2, Scalar(0, 255, 0), 2, 8, 0);
			}
		}
	}
}
void TRACK::denseTrack()
{
	Mat frame, gray, prev_gray, flowdata, flowresult;
	VideoCapture capture("D:/Images/video01.avi");
	capture.read(frame);
	//resize(frame, frame, Size(), 0.25, 0.25);
	cvtColor(frame, prev_gray, CV_BGR2GRAY);
	//从第二帧数据开始
	while (capture.read(frame))
	{
		//resize(frame, frame, Size(), 0.25, 0.25);
		cvtColor(frame, gray, CV_BGR2GRAY);
		if (!prev_gray.empty())
		{
			calcOpticalFlowFarneback(prev_gray, gray, flowdata, 0.5, 3, 15, 3, 5, 1.2, 0);   //稠密光流的检测函数
			cvtColor(prev_gray, flowresult, CV_GRAY2BGR);   //灰度图,转不过来
			drawlines(flowdata, flowresult);
			imshow("flowresult", flowresult);
			imshow("frame", frame);
		}
		char c = waitKey(50);
		if (c == 27)
			break;
	}
	waitKey(0);
	return;
}


int FORE_GROUD = 255; int thresh = 10;
void TRACK::ThreeFrameSubtract()
{
	VideoCapture video("D:/Images/video01.avi"); //判断如果video是否可以打开 
	if (!video.isOpened())
		return;

	//用于保存当前帧的图片 

	Mat currentBGRFrame;
	//用来保存上一帧和当前帧的灰度图片 
	Mat previousSecondGrayFrame; Mat previousFirstGrayFrame; Mat currentGaryFrame;
	//保存两次的帧差 
	Mat previousFrameDifference; //previousFrameFirst - previousFrameSecond的差分 
	Mat currentFrameDifference;  //currentFrame - previousFrameFirst; 
								 //用来保存帧差的绝对值 
	Mat absFrameDifferece;
	//用来显示前景 
	Mat previousSegmentation; Mat currentSegmentation; Mat segmentation;
	//显示前景 
	namedWindow("segmentation", 1);
	createTrackbar("阈值:", "segmentation", &thresh, FORE_GROUD, NULL);
	//帧数 
	int numberFrame = 0;
	//形态学处理用到的算子 
	Mat morphologyKernel = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
	for (;;)
	{  
		long tStart = getTickCount();
		//读取当前帧  
	   //video >> currentBGRFrame;
		video.read(currentBGRFrame);
		//判断当前帧是否存在  
		if (!currentBGRFrame.data)
			break;
		numberFrame++;
		//cout << "numberFrame=" << numberFrame << endl;
		//颜色空间的转换  
		cvtColor(currentBGRFrame, currentGaryFrame, COLOR_BGR2GRAY);
		if (numberFrame == 1) {
			//保存当前帧的灰度图   
			previousSecondGrayFrame = currentGaryFrame.clone();
			//显示视频  
			imshow("video", currentBGRFrame);   continue;
		}
		else if (numberFrame == 2)
		{
			//保存当前帧的灰度图  
			previousFirstGrayFrame = currentGaryFrame.clone();
			//previousFirst - previousSecond   
			subtract(previousFirstGrayFrame, previousSecondGrayFrame, previousFrameDifference, Mat(), CV_16SC1);
			//取绝对值   
			absFrameDifferece = abs(previousFrameDifference);
			//位深的改变   
			absFrameDifferece.convertTo(absFrameDifferece, CV_8UC1, 1, 0);
			//阈值处理  
			threshold(absFrameDifferece, previousSegmentation, double(thresh), double(FORE_GROUD), THRESH_BINARY);
			//显示视频  
			imshow("video", currentBGRFrame);
			continue;
		}
		else
		{
			//src1-src2   
			subtract(currentGaryFrame, previousFirstGrayFrame, currentFrameDifference, Mat(), CV_16SC1);
			//取绝对值  
			absFrameDifferece = abs(currentFrameDifference);
			//位深的改变   
			absFrameDifferece.convertTo(absFrameDifferece, CV_8UC1, 1, 0);
			//阈值处理   
			threshold(absFrameDifferece, currentSegmentation, double(thresh), double(FORE_GROUD), THRESH_BINARY);  //与运算   
			bitwise_and(previousSegmentation, currentSegmentation, segmentation);    //中值滤波   
			medianBlur(segmentation, segmentation, 3); //形态学处理(开闭运算)   
													   //morphologyEx(segmentation,segmentation,MORPH_OPEN,morphologyKernel,Point(-1,-1),1,BORDER_REPLICATE);   
			morphologyEx(segmentation, segmentation, MORPH_CLOSE, morphologyKernel, Point(-1, -1), 2, BORDER_REPLICATE);
			//找边界   
			vector< vector<Point> > contours;
			vector<Vec4i> hierarchy;
			//复制segmentation  
			Mat tempSegmentation = segmentation.clone();
			findContours(segmentation, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));//CV_RETR_TREE  
			vector< vector<Point> > contours_poly(contours.size());
			/*存储运动物体*/
			vector<Rect> boundRect;
			boundRect.clear();
			//画出运动物体   
			for (int index = 0; index < contours.size(); index++)
			{
				approxPolyDP(Mat(contours[index]), contours_poly[index], 3, true);
				Rect rect = boundingRect(Mat(contours_poly[index]));
				rectangle(currentBGRFrame, rect, Scalar(0, 255, 255), 2);
			}
			//显示视频  
			imshow("video", currentBGRFrame);
			//前景检测   
			imshow("segmentation", segmentation);
			//保存当前帧的灰度图   
			previousFirstGrayFrame = currentGaryFrame.clone();
			//保存当前的前景检测   
			previousSegmentation = currentSegmentation.clone();
		}
		long tEnd = getTickCount() - tStart;
		long Time = (tEnd / getTickFrequency()) * 1000;
		printf("Running time once again:%.2f\n", Time);

		if (waitKey(33) == 'q')
			break;
	}
}
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留在这里了，具体的实验就得自己亲自去测试了！！！