CamShift的原理还是比较简单的，跟踪直方图特征搜索出目标进行跟踪，相对于meanShift，解决的尺度问题。

代码如下：

#include "opencv2/video/tracking.hpp"#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/highgui/highgui.hpp"#include 
     
      #include 
      
       using namespace cv;using namespace std;Mat image;bool backprojMode = false;bool selectObject = false;int trackObject = 0;bool showHist = true;Point origin;Rect selection;int vmin = 10, vmax = 256, smin = 30;static void onMouse( int event, int x, int y, int, void* ){	if( selectObject )只有当鼠标左键按下去时才有效，然后通过if里面代码就可以确定所选择的矩形区域selection了	{		selection.x = MIN(x, origin.x);//矩形左上角顶点坐标		selection.y = MIN(y, origin.y);		selection.width = std::abs(x - origin.x);//矩形宽		selection.height = std::abs(y - origin.y);//矩形高		selection &= Rect(0, 0, image.cols, image.rows);//用于确保所选的矩形区域在图片范围内	}	switch( event )	{	case CV_EVENT_LBUTTONDOWN:		origin = Point(x,y);//鼠标初始点击坐标		selection = Rect(x,y,0,0);//鼠标刚按下去时初始化了一个矩形区域		selectObject = true;		break;	case CV_EVENT_LBUTTONUP:		selectObject = false;		if( selection.width > 0 && selection.height > 0 )			trackObject = -1;		break;	}}static void help(){	cout << "\nThis is a demo that shows mean-shift based tracking\n"		"You select a color objects such as your face and it tracks it.\n"		"This reads from video camera (0 by default, or the camera number the user enters\n"		"Usage: \n"		"   ./camshiftdemo [camera number]\n";	cout << "\n\nHot keys: \n"		"\tESC - quit the program\n"		"\tc - stop the tracking\n"		"\tb - switch to/from backprojection view\n"		"\th - show/hide object histogram\n"		"\tp - pause video\n"		"To initialize tracking, select the object with mouse\n";}const char* keys ={	"{c| camero | 0 | camera number}" //简称 | 全称 |值 |帮助说明	"{f| file   | F:/.mp4 | open avi files}"};int main( int argc, const char** argv ){		help();	VideoCapture cap;	Rect trackWindow;//跟踪窗的大小	int hsize = 16;	float hranges[] = {0,180}; //直方图的范围	const float* phranges = hranges;	CommandLineParser parser(argc, argv, keys);//命令行解析器	int camNum = parser.get
       
        ("c");		cap.open(camNum);	parser.printParams();	if( !cap.isOpened() )	{		help();		cout << "***Could not initialize capturing...***\n";		cout << "Current parameter's value: \n";		parser.printParams();  //打印出keys 		return -1;	}		namedWindow( "Histogram", 0 );	namedWindow( "CamShift Demo", 0 );	setMouseCallback( "CamShift Demo", onMouse, 0 );	createTrackbar( "Vmin", "CamShift Demo", &vmin, 256, 0 );	createTrackbar( "Vmax", "CamShift Demo", &vmax, 256, 0 );	createTrackbar( "Smin", "CamShift Demo", &smin, 256, 0 );	Mat frame, hsv, hue, mask, hist, histimg = Mat::zeros(200, 320, CV_8UC3), backproj;	bool paused = false;	for(;;)	{		if( !paused )  //多个相同条件的If语句，可以同步进行好几步操作		{			cap >> frame;			if( frame.empty() )				break;		}		frame.copyTo(image); //多个相同条件的If语句，可以同步进行好几步操作		if( !paused )		{			cvtColor(image, hsv, CV_BGR2HSV);			if( trackObject )			{				int _vmin = vmin, _vmax = vmax;				//inRange函数的功能是检查输入数组每个元素大小是否在2个给定数值之间，可以有多通道,mask保存0通道的最小值，也就是h分量				//这里利用了hsv的3个通道，比较h,0~180,s,smin~256,v,min(vmin,vmax),max(vmin,vmax)。如果3个通道都在对应的范围内，则				//mask对应的那个点的值全为1(0xff)，否则为0(0x00).				inRange(hsv, Scalar(0, smin, MIN(_vmin,_vmax)),					Scalar(180, 256, MAX(_vmin, _vmax)), mask);				int ch[] = {0, 0};//洗牌规则				hue.create(hsv.size(), hsv.depth());				mixChannels(&hsv, 1, &hue, 1, ch, 1);将hsv第一个通道(也就是色调)的数复制到hue中，0索引数组				//setMouseCallback( "CamShift Demo", NULL, 0 );  //注销鼠标事件				if( trackObject < 0 )//鼠标选择区域松开后，该函数内部又将其赋值1				{   					//此处的构造函数roi用的是Mat hue的矩阵头，且roi的数据指针指向hue，即共用相同的数据，					//select为其感兴趣的区域					mask保存的hsv的最小值					Mat roi(hue, selection), maskroi(mask, selection);					calcHist(&roi, 1, 0, maskroi, hist, 1, &hsize, &phranges);					normalize(hist, hist, 0, 255, CV_MINMAX);					trackWindow = selection;					trackObject = 1;//只要鼠标选完区域松开后，且没有按键盘清0键'c'，则trackObject一直保持为1，					               //因此该if函数只能执行一次，除非重新选择跟踪区域					//histimg是直方图图像					histimg = Scalar::all(0);//与按下'c'键是一样的，这里的all(0)表示的是标量全部清0					int binW = histimg.cols / hsize;					Mat buf(1, hsize, CV_8UC3);//定义一个缓冲单bin矩阵，不同的bin画出不同颜色					for( int i = 0; i < hsize; i++ )						buf.at
        
         (i) = Vec3b(saturate_cast
         
          (i*180./hsize), 255, 255);//saturate_case函数为从一个初始类型准确变换到另一个初始类型					cvtColor(buf, buf, CV_HSV2BGR);					for( int i = 0; i < hsize; i++ )					{						int val = saturate_cast
          
           (hist.at
           
            (i)*histimg.rows/255); rectangle( histimg, Point(i*binW,histimg.rows), Point((i+1)*binW,histimg.rows - val), Scalar(buf.at
            
             (i)), -1, 8 ); } } calcBackProject(&hue, 1, 0, hist, backproj, &phranges); //imshow("backproj",backproj); //waitKey(20); backproj &= mask; //超出范围的置零 //opencv2.0以后的版本函数命名前没有cv两字了，并且如果函数名是由2个意思的单词片段组成的话， //且前面那个片段不够成单词，则第一个字母要 //大写，比如Camshift，如果第一个字母是个单词，则小写，比如meanShift，但是第二个字母一定要大写 RotatedRect trackBox = CamShift(backproj, trackWindow, TermCriteria( CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1 )); if( trackWindow.area() <= 1 ) { int cols = backproj.cols, rows = backproj.rows, r = (MIN(cols, rows) + 5)/6; trackWindow = Rect(trackWindow.x - r, trackWindow.y - r, trackWindow.x + r, trackWindow.y + r) & Rect(0, 0, cols, rows); } if( backprojMode ) cvtColor( backproj, image, CV_GRAY2BGR ); ellipse( image, trackBox, Scalar(0,0,255), 3, CV_AA ); } } else if( trackObject < 0 ) paused = false; if( selectObject && selection.width > 0 && selection.height > 0 ) { Mat roi(image, selection); bitwise_not(roi, roi); } imshow( "CamShift Demo", image ); imshow( "Histogram", histimg ); char c = (char)waitKey(10); if( c == 27 ) break; switch(c) { case 'b': backprojMode = !backprojMode; break; case 'c': trackObject = 0; histimg = Scalar::all(0); break; case 'h': showHist = !showHist; if( !showHist ) destroyWindow( "Histogram" ); else namedWindow( "Histogram", 1 ); break; case 'p': paused = !paused; break; default: ; } } return 0;}
            
           
          
         
        
       
      
     

一下是上面程序涉及到的几个函数的用法的简单测试代码，以了解，函数的特性

包括 Mat(const Mat& m, const Rect& roi)用法测试，saturate_cast函数测试，RotatedRect类型测试代码，mixChannels用法测试

 

#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/highgui/highgui.hpp"#include 
     
      #include 
      
       using namespace cv;using namespace std;int main(){   	//Mat感兴趣区域设置， Mat(const Mat& m, const Rect& roi)用法测试	// 即： creates a matrix header for a part of the bigger matrix	Mat img=imread("./longtan.jpg");	Rect roi(100,100,100,100);	Mat img_roi(img,roi);//感兴趣区域选择，img_roi和img共用一个矩阵头，指向相同的值区域	namedWindow("img", WINDOW_AUTOSIZE);	namedWindow("img_roi",WINDOW_AUTOSIZE);	cout<<"img.cols ="<
       
        <
        
         <
         
          (-100); // a = 0 (UCHAR_MIN)安全转换	short b = saturate_cast
          
           (33333.33333); // b = 32767 (SHRT_MAX) cout<<"a ="<
           
            <<(int)a<