#include "opencv2/opencv.hpp"

 using namespace cv;

 void main()

 {
 6     Mat srcImg = imread("E://02.jpg", 0);  //以灰度方式打开，需要输入单通道图像

 7     imshow("src", srcImg);

 8     Mat dstImg;  //均衡化后的图像

 9     equalizeHist(srcImg, dstImg);

10     imshow("dst", dstImg);


     //绘制src直方图

     MatND dstHist;  //定义存储直方图变量

     int dims = ;  //需要统计的特征数目(只统计灰度值)

     float hranges[] = {, };  //范围[0,256)注意是最大值加1

     const float* ranges[] = {hranges};

     int bins = ;

     int channels = ;

     calcHist(&srcImg, , &channels, Mat(), dstHist, dims, &bins, ranges);

     int scale = ;

     Mat HistImg(bins * scale, bins*, CV_8UC3, Scalar());  //定义直方图输出图像

     double minValue = ;

     double maxValue = ;

     minMaxLoc(dstHist, &minValue, &maxValue, , );

     int hpt = saturate_cast<int>(0.9*bins);  //设置最大值并防止溢出

     int j=;

     for(int i=; i<; i++)

     {

         float binValue = dstHist.at<float>(i);

         int realValue = saturate_cast<int>(binValue*hpt/maxValue);  //归一化数据

         line(HistImg, Point(i*scale, bins-), Point(i*scale, bins-realValue), Scalar(, , ), , );

     }

     imshow("src_hist", HistImg);

     //绘制dst直方图

     calcHist(&dstImg, , &channels, Mat(), dstHist, dims, &bins, ranges);

     Mat HistImg2(bins * scale, bins*, CV_8UC3, Scalar());  //定义直方图输出图像

     for(int i=; i<; i++)

     {

         float binValue = dstHist.at<float>(i);

         int realValue = saturate_cast<int>(binValue*hpt/maxValue);  //归一化数据

         line(HistImg2, Point(i*scale, bins-), Point(i*scale, bins-realValue), Scalar(, , ), , );

     }

     imshow("dst_hist", HistImg2);

     waitKey();

 }

 #include "opencv2/opencv.hpp"

 using namespace cv;

 void main()

 {

     Mat src = imread("E://05.jpg");

     imshow("src", src);

     //分割通道

     vector<Mat>channels;

     split(src,channels);

     Mat blue,green,red,dst;

     blue=channels.at();

     green=channels.at();

     red=channels.at();

     //分别对BGR通道做直方图均衡化

     equalizeHist(blue,blue);

     equalizeHist(green,green);

     equalizeHist(red,red);

     //合并通道

     merge(channels,dst);

     imshow("dst", dst);

     waitKey();

 }

 #include "opencv2/opencv.hpp"

 #include<iostream>

 using namespace cv;

 using namespace std;

 void main()

 {

     Mat src1 = imread("E://a.jpg");

     Mat src2 = imread("E://b.jpg");

     imshow("src1", src1);

     imshow("src2", src2);

     MatND dstHist;  //定义存储直方图变量

     int dims = ;  //需要统计的特征数目(只统计灰度值)

     float hranges[] = {, };  //范围[0,256)注意是最大值加1

     const float* ranges[] = {hranges};

     int bins = ;

     int channels = ;

     calcHist(&src1, , &channels, Mat(), dstHist, dims, &bins, ranges);

     int scale = ;

     Mat HistImg(bins * scale, bins*, CV_8UC3, Scalar());  //定义直方图输出图像

     double minValue = ;

     double maxValue = ;

     minMaxLoc(dstHist, &minValue, &maxValue, , );

     int hpt = saturate_cast<int>(0.9*bins);  //设置最大值并防止溢出

     int j=;

     for(int i=; i<; i++)

     {

         float binValue = dstHist.at<float>(i);

         int realValue = saturate_cast<int>(binValue*hpt/maxValue);  //归一化数据

         line(HistImg, Point(i*scale, bins-), Point(i*scale, bins-realValue), Scalar(, , ), , );

     }

     imshow("src1_hist", HistImg);

     MatND dstHist2;  //定义存储直方图变量

     calcHist(&src2, , &channels, Mat(), dstHist2, dims, &bins, ranges);

     Mat HistImg2(bins * scale, bins*, CV_8UC3, Scalar());  //定义直方图输出图像

     minMaxLoc(dstHist2, &minValue, &maxValue, , );

     for(int i=; i<; i++)

     {

         float binValue = dstHist2.at<float>(i);

         int realValue = saturate_cast<int>(binValue*hpt/maxValue);  //归一化数据

         line(HistImg2, Point(i*scale, bins-), Point(i*scale, bins-realValue), Scalar(, , ), , );

     }

     imshow("src2_hist", HistImg2);

     double matchValue0 = compareHist(dstHist, dstHist2, CV_COMP_CORREL);  //值越大相似度越高

     double matchValue1 = compareHist(dstHist, dstHist2, CV_COMP_CHISQR);  //值越小相似度越高

     double matchValue2 = compareHist(dstHist, dstHist2, CV_COMP_INTERSECT); //值越大相似度越高

     double matchValue3 = compareHist(dstHist, dstHist2, CV_COMP_BHATTACHARYYA); //值越小相似度越高

     cout<<"matchValue0(max_best)="<<matchValue0<<endl;

     cout<<"matchValue1(min_best)="<<matchValue1<<endl;

     cout<<"matchValue2(max_best)="<<matchValue2<<endl;

     cout<<"matchValue3(min_best)="<<matchValue3<<endl;

     waitKey();

 }

 #include "opencv2/opencv.hpp"

 using namespace cv;

 #define WINDOW_NAME "【原始图】"

 Mat g_hueImage;

 int g_bins = ;//直方图组距

 void on_BinChange(int, void* )

 {

     MatND hist;

     int histSize = MAX( g_bins,  );

     float hue_range[] = { ,  };

     const float* ranges = { hue_range };

     calcHist( &g_hueImage, , , Mat(), hist, , &histSize, &ranges, true, false );

     normalize( hist, hist, , , NORM_MINMAX, -, Mat() );

     MatND backproj;

     calcBackProject( &g_hueImage, , , hist, backproj, &ranges, , true );

     imshow( "反向投影图", backproj );

     int w = ; int h = ;

     int bin_w = cvRound( (double) w / histSize );

     Mat histImg = Mat::zeros( w, h, CV_8UC3 );

     for( int i = ; i < g_bins; i ++ )

     {

         rectangle( histImg, Point( i*bin_w, h ), Point( (i+)*bin_w, h - cvRound( hist.at<float>(i)*h/255.0 ) ), Scalar( , ,  ), - );

     }

     imshow( "直方图", histImg );

 }

 void main()

 {

     Mat g_srcImage = imread( "E://1.jpg" );

     Mat g_hsvImage;

     resize(g_srcImage, g_srcImage, Size(g_srcImage.cols/, g_srcImage.rows/));//原图太大，进行缩放

     cvtColor( g_srcImage, g_hsvImage, CV_BGR2HSV );

     g_hueImage.create( g_hsvImage.size(), g_hsvImage.depth() );

     int ch[ ] = { ,  };

     mixChannels( &g_hsvImage, , &g_hueImage, , ch,  );//从输入中拷贝某通道到输出中特定的通道

     namedWindow( WINDOW_NAME , CV_WINDOW_AUTOSIZE );

     createTrackbar("色调组距 ", WINDOW_NAME , &g_bins, , on_BinChange );

     on_BinChange(, );

     imshow( WINDOW_NAME , g_srcImage );

     waitKey();

 }