for(s=top;s<=down;s++){
     				for(t=left;t<=right;t++)
     				{
      					ws=(s-m)*(s-m)+(t-n)*(t-n);//spatial information
      					ws/=(hs*hs);
      					ws=exp(-ws);
      
      					wr=(data[s*step+t*channels]-data[m*step+n*channels])*(data[s*step+t*channels]－data[m*step+n*channels]);
      					wr+=(data[s*step+t*channels+1]-data[m*step+n*channels+1])*(data[s*step+t*channels+1]-data[m*step+n*channels+1]);
      					wr+=(data[s*step+t*channels+2]-data[m*step+n*channels+2])*(data[s*step+t*channels+2]-data[m*step+n*channels+2]);

      					wr/=(hr*hr);

      					if(wr>1)
       						wr=0.;
      					else
       						wr=exp(-wr);
       						

      					sumw+=wr*ws;
      					for(k=0;k<5;k++)//try            
       						y[1][k]+=oridata[s*width+t][k]*wr*ws;
     				}
    			}

    			for(k=0;k<5;k++) //try     
     				y[1][k]/=sumw;

    			//下一个要到的点
    			m=(int)(y[1][0]+0.5);
    			n=(int)(y[1][1]+0.5);

//#include <stdafx.h>
#include <cxcore.h>
#include <cv.h>   
#include <highgui.h>   
#include <stdio.h>
#include <vector>
#include <utility>
 
using namespace cv;
using namespace std;

#define hs 25
#define hr 25
#define maxstep 100
#define EPSLON 0.01
#define width 1920
#define height 1080	
float oridata[height*width][5];//try
int visited[height][width];

int main(int argc,char **argv)
{
	IplImage *oriImg,*luvImg,*fltImg,*afterImg; 
 
	char *filename = argv[1];
	oriImg=cvLoadImage(filename,1); 
 
	if(!oriImg){
		printf("cannot load the file.\n");
  		return -1;
 	}

 	luvImg=cvCreateImage(cvSize(width,height),oriImg->depth,oriImg->nChannels); 
 	fltImg=cvCreateImage(cvSize(width,height),oriImg->depth,oriImg->nChannels); 
 	afterImg=cvCreateImage(cvSize(width,height),oriImg->depth,oriImg->nChannels); 

 	cvCvtColor(oriImg,luvImg,CV_RGB2Luv); 
 
 	uchar *data,*newdata;
 	int channels, step,depth;
 
 	depth=luvImg->depth;
 	step=luvImg->widthStep;
 	channels=luvImg->nChannels;
 
 	data=http://www.mamicode.com/(uchar *) luvImg->imageData;>

C++: int meanShift(InputArray probImage, Rect& window, TermCriteria criteria)

	probImage – 反向投影矩阵 window – 一开始的窗口,就是自己用矩形圈起来的地方 criteria – Stop criteria for the iterative search algorithm.

TermCriteria::TermCriteria

The constructors.

C++: TermCriteria::TermCriteria()

C++: TermCriteria::TermCriteria(int type, int maxCount, double epsilon)

C++: TermCriteria::TermCriteria(const CvTermCriteria& criteria)

	type – The type of termination criteria: TermCriteria::COUNT, TermCriteria::EPS or TermCriteria::COUNT +TermCriteria::EPS. maxCount – The maximum number of iterations or elements to compute. epsilon – The desired accuracy or change in parameters at which the iterative algorithm stops. criteria – Termination criteria in the deprecated CvTermCriteria format.

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/video/tracking.hpp"
#include <iostream>
using namespace cv;
using namespace std;

class ColorHistogram {
private:
    int histSize[3];
	float hranges[2];
    const float* ranges[3];
    int channels[3];

public:
	ColorHistogram() {
		// Prepare arguments for a color histogram
		histSize[0]= histSize[1]= histSize[2]= 256;
		hranges[0]= 0.0;    // BRG range
		hranges[1]= 255.0;
		ranges[0]= hranges; // all channels have the same range 
		ranges[1]= hranges; 
		ranges[2]= hranges; 
		channels[0]= 0;		// the three channels 
		channels[1]= 1; 
		channels[2]= 2; 
	}
	//这里获取HSV的第一个通道的直方图
	Mat getHueHistogram(const cv::Mat &image,int minSaturation=0) {
		Mat hist;
		//色彩空间转换
		Mat hsv;
		cvtColor(image, hsv, CV_BGR2HSV);
		// mask,要注意哦
		Mat mask;
		if (minSaturation>0) {
			// 三通道分开
			vector<Mat> v;
			split(hsv,v);
			// 去除低饱和的值
			threshold(v[1],mask,minSaturation,255,THRESH_BINARY);
		} 
		hranges[0]= 0.0;    //hue的range是[0,180]
		hranges[1]= 180.0;
		channels[0]= 0;
		calcHist(&hsv,1,channels,mask,hist,1,histSize,ranges);//注意，mask不为0的地方计算在内，也就是高饱和的Hue计算在内
		return hist;
	}

};

class ContentFinder {
private:
	float hranges[2];
	const float* ranges[3];
	int channels[3];
	float threshold;
	Mat histogram;
	
public:
	ContentFinder() : threshold(-1.0f) {
		hranges[0]= 0.0;	// range [0,255]
		hranges[1]= 255.0;
		channels[0]= 0;		// the three channels 
		channels[1]= 1; 
		channels[2]= 2; 
		ranges[0]= hranges; // all channels have same range
		ranges[1]= hranges;
		ranges[2]= hranges;
	}
	
	// Sets the reference histogram
	void setHistogram(const Mat& h) {
		histogram= h;
		normalize(histogram,histogram,1.0);
	}
	
	cv::Mat find(const cv::Mat& image, float minValue, float maxValue, int *channels, int dim) {
		cv::Mat result;

		hranges[0]= minValue;
		hranges[1]= maxValue;
		ranges[0]= hranges; // all channels have same range
		ranges[1]= hranges;
		ranges[2]= hranges;

		calcBackProject(&image,1,channels,histogram,result,ranges,255.0);
		return result;
	}
	
};

int main( int, char** argv )
{
	Mat image= cv::imread("baboon1.jpg");
	// Baboon‘s face ROI
	Mat imageROI= image(cv::Rect(110,260,35,40));
	// 获得Hue直方图
	int minSat=65;
	ColorHistogram hc;
	Mat colorhist = hc.getHueHistogram(imageROI,minSat);

        ContentFinder finder;
	finder.setHistogram(colorhist);

	image= cv::imread("baboon3.jpg");
	// 色彩空间转换
	Mat hsv;
	cvtColor(image, hsv, CV_BGR2HSV);
	
	vector<Mat> v;
	split(hsv,v);
	//除去低饱和度
	threshold(v[1],v[1],minSat,255,cv::THRESH_BINARY);
	
	// 获得反向投影
	int ch[1]={0};
	Mat result= finder.find(hsv,0.0f,180.0f,ch,1);
	// 除去低饱和度的点
	bitwise_and(result,v[1],result);
	Rect rect(110,260,35,40);
	rectangle(image, rect, Scalar(0,0,255));
	TermCriteria criteria(TermCriteria::MAX_ITER,10,0.01);
	meanShift(result,rect,criteria);

	rectangle(image, rect, cv::Scalar(0,255,0));
	namedWindow("result");
	imshow("result",image);

  	waitKey(0);
  	return 0;
}	

看下效果