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 = 1;  //需要统计的特征数目(只统计灰度值)
    float hranges[] = {0, 256};  //范围[0,256)注意是最大值加1
    const float* ranges[] = {hranges};
    int bins = 256;
    int channels = 0;
    calcHist(&srcImg, 1, &channels, Mat(), dstHist, dims, &bins, ranges);
    int scale = 1;
    Mat HistImg(bins * scale, bins*1, CV_8UC3, Scalar(0));  //定义直方图输出图像
    double minValue = 0;
    double maxValue = 0;
    minMaxLoc(dstHist, &minValue, &maxValue, 0, 0);
    int hpt = saturate_cast<int>(0.9*bins);  //设置最大值并防止溢出
    int j=0;
    for(int i=0; i<256; 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-1), Point(i*scale, bins-realValue), Scalar(0, 255, 0), 1, 8);
    }
    imshow("src_hist", HistImg);

    //绘制dst直方图
    calcHist(&dstImg, 1, &channels, Mat(), dstHist, dims, &bins, ranges);
    Mat HistImg2(bins * scale, bins*1, CV_8UC3, Scalar(0));  //定义直方图输出图像
    for(int i=0; i<256; 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-1), Point(i*scale, bins-realValue), Scalar(0, 255, 0), 1, 8);
    }
    imshow("dst_hist", HistImg2);

    waitKey(0);
}

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(0);
    green=channels.at(1);
    red=channels.at(2);
    //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(0);
}

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 = 1;  //需要统计的特征数目(只统计灰度值)
    float hranges[] = {0, 256};  //范围[0,256)注意是最大值加1
    const float* ranges[] = {hranges};
    int bins = 256;
    int channels = 0;
    calcHist(&src1, 1, &channels, Mat(), dstHist, dims, &bins, ranges);
    int scale = 1;
    Mat HistImg(bins * scale, bins*1, CV_8UC3, Scalar(0));  //定义直方图输出图像
    double minValue = 0;
    double maxValue = 0;
    minMaxLoc(dstHist, &minValue, &maxValue, 0, 0);
    int hpt = saturate_cast<int>(0.9*bins);  //设置最大值并防止溢出
    int j=0;
    for(int i=0; i<256; 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-1), Point(i*scale, bins-realValue), Scalar(0, 255, 0), 1, 8);
    }
    imshow("src1_hist", HistImg);

    MatND dstHist2;  //定义存储直方图变量
    calcHist(&src2, 1, &channels, Mat(), dstHist2, dims, &bins, ranges);
    Mat HistImg2(bins * scale, bins*1, CV_8UC3, Scalar(0));  //定义直方图输出图像
    minMaxLoc(dstHist2, &minValue, &maxValue, 0, 0);
    for(int i=0; i<256; 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-1), Point(i*scale, bins-realValue), Scalar(0, 255, 0), 1, 8);
    }
    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(0);
}

三、反向投影

 

#include "opencv2/opencv.hpp"
using namespace cv;

#define WINDOW_NAME "【原始图】" 
Mat g_hueImage;
int g_bins = 30;//直方图组距

void on_BinChange(int, void* )
{
    MatND hist;
    int histSize = MAX( g_bins, 2 );
    float hue_range[] = { 0, 180 };
    const float* ranges = { hue_range };
    calcHist( &g_hueImage, 1, 0, Mat(), hist, 1, &histSize, &ranges, true, false );
    normalize( hist, hist, 0, 255, NORM_MINMAX, -1, Mat() );

    MatND backproj;
    calcBackProject( &g_hueImage, 1, 0, hist, backproj, &ranges, 1, true );
    imshow( "反向投影图", backproj );

    int w = 400; int h = 400;
    int bin_w = cvRound( (double) w / histSize );
    Mat histImg = Mat::zeros( w, h, CV_8UC3 );
    for( int i = 0; i < g_bins; i ++ )
    { 
        rectangle( histImg, Point( i*bin_w, h ), Point( (i+1)*bin_w, h - cvRound( hist.at<float>(i)*h/255.0 ) ), Scalar( 100, 123, 255 ), -1 ); 
    }
    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/4, g_srcImage.rows/4));//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[ ] = { 0, 0 };
    mixChannels( &g_hsvImage, 1, &g_hueImage, 1, ch, 1 );//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, 180, on_BinChange );
    on_BinChange(0, 0);

    imshow( WINDOW_NAME , g_srcImage );
    waitKey(0);
}