【opencv】教程代码 —Histograms_Matching（2）计算直方图、直方图比较、直方图均衡、模板匹配...

• 计算直方图
  

• 直方图比较

• 图像进行直方图均衡化处理

• 模板匹配

1. calcHist_Demo.cpp 计算直方图

这段代码的功能是加载图像，分离图像的三个颜色通道，然后分别计算这三个通道的直方图，绘制出来并显示结果。直方图是图像中像素值分布的图形表示，可以用于图像分析或图像处理中。

// 加载OpenCV库，并用cv和std命名空间
#include "opencv2/highgui.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
using namespace std;
using namespace cv;


// 主函数
int main(int argc, char** argv)
{
    // 从命令行参数中加载图像，如果不指定，则默认加载lena.jpg
    CommandLineParser parser( argc, argv, "{@input | lena.jpg | input image}" );
    Mat src = imread( samples::findFile( parser.get<String>( "@input" ) ), IMREAD_COLOR );
    // 如果图像为空，则返回退出失败
    if( src.empty() )
    {
        return EXIT_FAILURE;
    }


    // 分离图像的三个通道：蓝色、绿色和红色
    vector<Mat> bgr_planes;
    split( src, bgr_planes );


    // 确定直方图中的柱子数量（bin的数量），这里设置为256
    int histSize = 256;


    // 设置B、G、R通道的范围，由于直方图的上界是独占的，所以设置的范围是{0，256}
    float range[] = { 0, 256 };
    const float* histRange[] = { range };


    // 设置直方图参数，设置为均匀的，并且不累计
    bool uniform = true, accumulate = false;


    // 计算蓝色、绿色和红色通道的直方图
    Mat b_hist, g_hist, r_hist;
    calcHist( &bgr_planes[0], 1, 0, Mat(), b_hist, 1, &histSize, histRange, uniform, accumulate );
    calcHist( &bgr_planes[1], 1, 0, Mat(), g_hist, 1, &histSize, histRange, uniform, accumulate );
    calcHist( &bgr_planes[2], 1, 0, Mat(), r_hist, 1, &histSize, histRange, uniform, accumulate );


    // 创建一个用于绘制直方图的图像
    int hist_w = 512, hist_h = 400;
    int bin_w = cvRound( (double) hist_w/histSize );
    Mat histImage( hist_h, hist_w, CV_8UC3, Scalar( 0,0,0) );


    // 将直方图的结果归一化到(0, histImage.rows)
    normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );
    normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );
    normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat() );


    // 为每个通道绘制直方图
    for( int i = 1; i < histSize; i++ )
    {
        line( histImage, Point( bin_w*(i-1), hist_h - cvRound(b_hist.at<float>(i-1)) ),
              Point( bin_w*(i), hist_h - cvRound(b_hist.at<float>(i)) ),
              Scalar( 255, 0, 0), 2, 8, 0  );
        line( histImage, Point( bin_w*(i-1), hist_h - cvRound(g_hist.at<float>(i-1)) ),
              Point( bin_w*(i), hist_h - cvRound(g_hist.at<float>(i)) ),
              Scalar( 0, 255, 0), 2, 8, 0  );
        line( histImage, Point( bin_w*(i-1), hist_h - cvRound(r_hist.at<float>(i-1)) ),
              Point( bin_w*(i), hist_h - cvRound(r_hist.at<float>(i)) ),
              Scalar( 0, 0, 255), 2, 8, 0  );
    }


    // 显示源图像和计算的直方图
    imshow("Source image", src );
    imshow("calcHist Demo", histImage );
    waitKey();


    return EXIT_SUCCESS;
}

calcHist( &bgr_planes[0], 1, 0, Mat(), b_hist, 1, &histSize, histRange, uniform, accumulate );

normalize(b_hi