C++: void calcBackProject(const Mat* images, int nimages, const int* channels, InputArray hist, OutputArray backProject, const float** ranges, double scale=1, bool uniform=true )

images – Source arrays. They all should have the same depth, CV_8U or CV_32F , and the same size. Each of them can have an arbitrary number of channels. nimages – Number of source images. channels – The list of channels used to compute the back projection. The number of channels must match the histogram dimensionality. The first array channels are numerated from 0 to images[0].channels()-1 , the second array channels are counted from images[0].channels() to images[0].channels() + images[1].channels()-1, and so on. hist – Input histogram that can be dense or sparse. backProject – Destination back projection array that is a single-channel array of the same size and depth as images[0] . ranges – Array of arrays of the histogram bin boundaries in each dimension. See calcHist() . scale – Optional scale factor for the output back projection. uniform – Flag indicating whether the histogram is uniform or not (see above).

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

class Histogram1D {
private:
	int histSize[1]; // number of bins
	float hranges[2]; // min and max pixel value
	const float* ranges[1];
	int channels[1]; // only 1 channel used here
public:
	Histogram1D() {
		histSize[0]= 256;
		hranges[0]= 0.0;
		hranges[1]= 255.0;
		ranges[0]= hranges;
		channels[0]= 0;
	}
	
	Mat getHistogram(const cv::Mat &image) {
		Mat hist;
		calcHist(&image,1,channels,Mat(),hist,1,histSize,ranges);
		return hist;
	}
};

Mat applyLookUp(const cv::Mat& image,const cv::Mat& lookup) { 
	Mat result;
	cv::LUT(image,lookup,result);
	return result;
}


int main( int, char** argv )
{
  	Mat image,gray,backproj;
  	image = imread( argv[1], 1 );
  	if( !image.data )
  		return -1;
  	cvtColor(image, gray, CV_BGR2GRAY);
    
	Mat imageROI;
	imageROI= gray(cv::Rect(360,55,40,50)); // 模版图像

	Histogram1D h;
	Mat hist= h.getHistogram(imageROI);
	normalize(hist,hist,1.0);           //归一化，得到概率直方图
	float range[] = { 0, 255 };
  	const float* ranges = { range };
	calcBackProject( &gray, 1, 0, hist, backproj, &ranges, 255.0);  //反向投影，最重要的一步
	
	namedWindow("after project");
	imshow("after project",backproj);
	
	Mat lut(1,256,CV_8U);
	for (int i=0; i<256; i++) {
		lut.at<uchar>(i)= 255-i;     //reverse操作
	}
	Mat out = applyLookUp(backproj,lut);
	namedWindow("after reverse");
	imshow("after reverse",out);
	
	threshold(out, out, 240 ,255, THRESH_BINARY);    // 以240为分界线,<240为0，否则为255
	namedWindow("after threshold");
	imshow("after threshold",out);

  	waitKey(0);
  	return 0;
}

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.
	Mat getHistogram(const Mat &image) {

		Mat 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; 

		calcHist(&image, 1,	channels,Mat(),	hist,3,histSize,ranges);
		return hist;
	}

	Mat colorReduce(const Mat &image, int div=64) {
		int n= (int)(log((double)(div))/log(2.0));
		// mask used to round the pixel value
		uchar mask= 0xFF<<n; // e.g. for div=16, mask= 0xF0

	  	Mat_<Vec3b>::const_iterator it= image.begin<Vec3b>();
	  	Mat_<Vec3b>::const_iterator itend= image.end<Vec3b>();

	  	// Set output image (always 1-channel)
	 	Mat result(image.rows,image.cols,image.type());
	 	Mat_<Vec3b>::iterator itr= result.begin<Vec3b>();

	  	for ( ; it!= itend; ++it, ++itr) {
        	(*itr)[0]= ((*it)[0]&mask) + div/2;
        	(*itr)[1]= ((*it)[1]&mask) + div/2;
        	(*itr)[2]= ((*it)[2]&mask) + div/2;
	  	}

	  	return result;
	}

};

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;	
		hranges[1]= 255.0;
		channels[0]= 0;		
		channels[1]= 1; 
		channels[2]= 2; 
		ranges[0]= hranges;
		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;
	}
	
	//设置了直方图，并归一化，生成概率直方图
	void setHistogram(const Mat& h) {
		histogram= h;
		normalize(histogram,histogram,1.0);     
	}
	
	Mat find(const Mat& image) {
		Mat result;
		//最重要的一步，产生反向投影图	
		calcBackProject(&image, 1,channels,histogram,result,ranges,255.0);
                //二值化
		if (threshold>0.0)
			cv::threshold(result, result,255*threshold, 255, cv::THRESH_BINARY);
		return result;
	}

};

int main( int, char** argv )
{
  	Mat color;
  	color = imread( argv[1], 1 );
  	if( !color.data )
  		return -1;
  		
  	ColorHistogram hc;

	// reduce colors
	color = hc.colorReduce(color,32);
	// blue sky area
	Mat imageROI = color(Rect(0,0,165,75));
	Mat hist= hc.getHistogram(imageROI);
	ContentFinder finder;
	finder.setHistogram(hist);
	finder.setThreshold(0.05f);

	Mat result= finder.find(color);
	
	namedWindow("sample");
	imshow("sample",result);

  	waitKey(0);
  	return 0;
}