最后，进行了一次二值化

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).

对于參数就不多作解释了，由于和计算直方图的參数差点儿一摸一样，scale是个扩大系数，常常取255.我们通过以下的样例来进一步巩固。

#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;

}

之前有提到过，灰度反向投影太不准确，非常多错误的点都被弄进来了，以下，我们实现彩色反向投影。

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;

	}

};

最须要注意的是colorReduce这种方法

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;

	}

};

然后，看下我们的main函数

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;

}

我们来看一下效果，先看原图，我们选取了左上角的蓝天作模版图