opencv学习之路（20）、直方图应用

一、直方图均衡化--equalizeHist()

 #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();
 }

注意：红色部分为均衡化的主要代码

彩色图像直方图均衡化

 #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();
 }

二、直方图对比

 #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();
 }

三、反向投影

 #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();
 }