opecv2 MeanShift 使用均值漂移算法查找物体

#if !defined OFINDER
#define OFINDER

#include <opencv2\core\core.hpp>
#include <opencv2\imgproc\imgproc.hpp>

class ContentFinder {

private:

	float hranges[2];
    const float* ranges[3];
    int channels[3];

	float threshold;
	cv::MatND histogram;
	cv::SparseMat shistogram;
	bool isSparse;

  public:

	ContentFinder() : threshold(0.1f), isSparse(false) {

		ranges[0]= hranges; // all channels have the same range
		ranges[1]= hranges;
		ranges[2]= hranges;
	}

	// Sets the threshold on histogram values [0,1]
	void setThreshold(float t) {

		threshold= t;
	}

	// Gets the threshold
	float getThreshold() {

		return threshold;
	}

	// Sets the reference histogram
	void setHistogram(const cv::MatND& h) {

		isSparse= false;
		histogram= h;
		cv::normalize(histogram,histogram,1.0);
	}

	// Sets the reference histogram
	void setHistogram(const cv::SparseMat& h) {

		isSparse= true;
		shistogram= h;
		cv::normalize(shistogram,shistogram,1.0,cv::NORM_L2);
	}

cv::Mat find(const cv::Mat& image) {

		cv::Mat result;

		hranges[0]= 0.0;	// range [0,255]
		hranges[1]= 255.0;
		channels[0]= 0;		// the three channels
		channels[1]= 1;
		channels[2]= 2; 

		if (isSparse) { // call the right function based on histogram type

		   cv::calcBackProject(&image,
                      1,            // one image
                      channels,     // vector specifying what histogram dimensions belong to what image channels
                      shistogram,   // the histogram we are using
                      result,       // the resulting back projection image
                      ranges,       // the range of values, for each dimension
                      255.0         // the scaling factor is chosen such that a histogram value of 1 maps to 255
		   );

		} else {

		   cv::calcBackProject(&image,
                      1,            // one image
                      channels,     // vector specifying what histogram dimensions belong to what image channels
                      histogram,    // the histogram we are using
                      result,       // the resulting back projection image
                      ranges,       // the range of values, for each dimension
                      255.0         // the scaling factor is chosen such that a histogram value of 1 maps to 255
		   );
		}

        // Threshold back projection to obtain a binary image
		if (threshold>0.0)
			cv::threshold(result, result, 255*threshold, 255, cv::THRESH_BINARY);

		return result;
	}

cv::Mat find(const cv::Mat& image, float minValue, float maxValue, int *channels, int dim) {

		cv::Mat result;

		hranges[0]= minValue;
		hranges[1]= maxValue;

		for (int i=0; i<dim; i++)
			this->channels[i]= channels[i];

		if (isSparse) { // call the right function based on histogram type

		   cv::calcBackProject(&image,
                      1,            // we only use one image at a time
                      channels,     // vector specifying what histogram dimensions belong to what image channels
                      shistogram,   // the histogram we are using
                      result,       // the resulting back projection image
                      ranges,       // the range of values, for each dimension
                      255.0         // the scaling factor is chosen such that a histogram value of 1 maps to 255
		   );

		} else {

		   cv::calcBackProject(&image,
                      1,            // we only use one image at a time
                      channels,     // vector specifying what histogram dimensions belong to what image channels
                      histogram,    // the histogram we are using
                      result,       // the resulting back projection image
                      ranges,       // the range of values, for each dimension
                      255.0         // the scaling factor is chosen such that a histogram value of 1 maps to 255
		   );
		}

        // Threshold back projection to obtain a binary image
		if (threshold>0.0)
			cv::threshold(result, result, 255*threshold, 255, cv::THRESH_BINARY);

		return result;
	}

};

#endif

#if !defined COLHISTOGRAM
#define COLHISTOGRAM

#include <opencv2\core\core.hpp>
#include <opencv2\imgproc\imgproc.hpp>
#include<opencv2/highgui/highgui.hpp>
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;
	}

	// Computes the histogram.
	cv::MatND getHistogram(const cv::Mat &image) {

		cv::MatND hist;

		// BGR color histogram
		hranges[0]= 0.0;    // BRG range
		hranges[1]= 255.0;
		channels[0]= 0;		// the three channels
		channels[1]= 1;
		channels[2]= 2; 

		// Compute histogram
		cv::calcHist(&image,
			1,			// histogram of 1 image only
			channels,	// the channel used
			cv::Mat(),	// no mask is used
			hist,		// the resulting histogram
			3,			// it is a 3D histogram
			histSize,	// number of bins
			ranges		// pixel value range
		);

		return hist;
	}

	// Computes the 1D Hue histogram with a mask.
	// BGR source image is converted to HSV
	cv::MatND getHueHistogram(const cv::Mat &image) {

		cv::MatND hist;

		// Convert to Lab color space
		cv::Mat hue;
		cv::cvtColor(image, hue, CV_BGR2HSV);

		// Prepare arguments for a 1D hue histogram
		hranges[0]= 0.0;
		hranges[1]= 180.0;
		channels[0]= 0; // the hue channel 

		// Compute histogram
		cv::calcHist(&hue,
			1,			// histogram of 1 image only
			channels,	// the channel used
			cv::Mat(),	// no mask is used
			hist,		// the resulting histogram
			1,			// it is a 1D histogram
			histSize,	// number of bins
			ranges		// pixel value range
		);

		return hist;
	}

cv::MatND getHueHistogram(const cv::Mat &image,int minSaturation)
	{
		cv::MatND hist;
		cv::Mat hsv;
		cv::cvtColor(image,hsv,CV_BGR2HSV);
		cv::Mat mask;
		if(minSaturation>0)
		{
			std::vector<cv::Mat>v;
			cv::split(hsv,v);
			cv::threshold(v[1],mask,minSaturation,255,cv::THRESH_BINARY);
		}
		hranges[0]=0.0;
		hranges[1]=180.0;
		channels[0]=0;
		calcHist(&hsv,1,channels,mask,hist,1,histSize,ranges);
		return hist;
	}

};

#endif

#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/video/video.hpp>
#include<iostream>
#include"colorhistogram.h"
#include"ContentFinder.h"

using namespace std;
using namespace cv;

int main()
{
	Mat image=imread("d:/test/opencv/baboon1.jpg");
	Mat imageROI=image(Rect(110,260,35,40));
	int minSat=65;
	ColorHistogram hc;
	MatND colorhist=hc.getHueHistogram(imageROI,minSat);

	namedWindow("image 1");
	imshow("image 1",image);

	ContentFinder finder;
	finder.setHistogram(colorhist);
	Mat hsv;
	image=imread("d:/test/opencv/baboon3.jpg");
	namedWindow("image 2");
	imshow("image 2",image);
	cvtColor(image,hsv,CV_BGR2HSV);
	vector<Mat>v;
	split(hsv,v);
	threshold(v[1],v[1],minSat,255,THRESH_BINARY);
	cv::namedWindow("Saturation");
	cv::imshow("Saturation",v[1]);
	int channel[1]={0};
	Mat result=finder.find(hsv,0.0f,180.0f,channel,1);

	cv::namedWindow("Result Hue");
	cv::imshow("Result Hue",result);

	cv::bitwise_and(result,v[1],result);
	cv::namedWindow("Result Hue and");
	cv::imshow("Result Hue and",result);

	finder.setThreshold(-1.0f);//
	result= finder.find(hsv,0.0f,180.0f,channel,1);
	cv::bitwise_and(result,v[1],result);
	cv::namedWindow("Result Hue and raw");
	cv::imshow("Result Hue and raw",result);

	cv::Rect rect(110,260,35,40);
	cv::rectangle(image, rect, cv::Scalar(0,0,255));

	cv::TermCriteria criteria(cv::TermCriteria::MAX_ITER,10,0.01);
	cout << "meanshift= " << cv::meanShift(result,rect,criteria) << endl;//

	cv::rectangle(image, rect, cv::Scalar(0,255,0));//

	// Display image
	cv::namedWindow("Image 2 result");
	cv::imshow("Image 2 result",image);

	cv::waitKey();
	return 0;

}