Image Processing20（Histogram Comparison )

Goal

In this tutorial you will learn how to:

Use the function cv::compareHist to get a numerical parameter that express how well two histograms match with each other.使用函数cv::compareHist。得到一个表示两个直方图相似程度的数值。
Use different metrics to compare histograms。使用不同的指标比较直方图

Theory

To compare two histograms ( H1 and H2 ), first we have to choose a metric ( d(H1,H2)) to express how well both histograms match.
比较直方图，首先需要选择一个指标来表示它们的相似程度。
OpenCV implements the function cv::compareHist to perform a comparison. It also offers 4 different metrics to compute the matching:
OpenCV函数提供了函数：cv::compareHist进行直方图的比较。这个函数提供了四种可用的指标。
1. Correlation ( CV_COMP_CORREL )相关性。
  
  $d (H 1, H 2) = \sum I ( H 1 ( I ) - H 1 ¯ ) ( H 2 ( I ) - H 2 ¯ ) \sum I ( H 1 ( I ) - H 1 ¯ ) 2 \sum I ( H 2 ( I ) - H 2 ¯ ) 2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - \sqrt$ where
  $H k ¯ = 1 N \sum J H k (J) 平均值$ and N is the total number of histogram bins.
2. Chi-Square ( CV_COMP_CHISQR )卡方。
  $d (H 1, H 2) = \sum I ( H 1 ( I ) - H 2 ( I ) ) 2 H 1 ( I )$
3. Intersection ( method=CV_COMP_INTERSECT )交集。
4. $d (H 1, H 2) = \sum I min (H 1 (I), H 2 (I))$ Bhattacharyya distance ( CV_COMP_BHATTACHARYYA )
  $d (H 1, H 2) = 1 - 1 H 1 ¯ H 2 ¯ N 2 - - - - - - - \sqrt \sum I H 1 (I) \cdot H 2 (I) - - - - - - - - - - - \sqrt - - - - - - - - - - - - - - - - - - - - - - - - - - - - \sqrt$

Code

What does this program do?
- Loads a base image and 2 test images to be compared with it.（加载一个基本图像，和两个测试图像）
- Generate 1 image that is the lower half of the base image（生成一个图像，是基本图像的下半部分）
- Convert the images to HSV format（转化为HSV格式）
- Calculate the H-S histogram for all the images and normalize them in order to compare them.（计算H-S直方图，并且归一化，然后比较他们）
- Compare the histogram of the base image with respect to the 2 test histograms, the histogram of the lower half base image and with the same base image histogram.（比较基本图像以及另外的两个的测试图像，下半部分的图像以及基本图像进行比较）
- Display the numerical matching parameters obtained.（显示得到的相似程度数据）

#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include <iostream>
using namespace std;
using namespace cv;
int main( int argc, char** argv )
{
    Mat src_base, hsv_base;
    Mat src_test1, hsv_test1;
    Mat src_test2, hsv_test2;
    Mat hsv_half_down;
//     if( argc < 4 )
//     {
//         printf("** Error. Usage: ./compareHist_Demo <image_settings0> <image_settings1> <image_settings2>\n");
//         return -1;
//     }
    src_base = imread( "lena.jpg", IMREAD_COLOR );
    src_test1 = imread( "test1.jpg", IMREAD_COLOR );
    src_test2 = imread( "test2.jpg", IMREAD_COLOR );
    if(src_base.empty() || src_test1.empty() || src_test2.empty())
    {
      cout << "Can't read one of the images" << endl;
      return -1;
    }
    cvtColor( src_base, hsv_base, COLOR_BGR2HSV );//HSV
    cvtColor( src_test1, hsv_test1, COLOR_BGR2HSV );
    cvtColor( src_test2, hsv_test2, COLOR_BGR2HSV );

    hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) );//行和列

    int h_bins = 50; int s_bins = 60;
    int histSize[] = { h_bins, s_bins };
    // hue varies from 0 to 179, saturation from 0 to 255
    float h_ranges[] = { 0, 180 };
    float s_ranges[] = { 0, 256 };
    const float* ranges[] = { h_ranges, s_ranges };
    // Use the o-th and 1-st channels
    int channels[] = { 0, 1 };
    MatND hist_base;
    MatND hist_half_down;
    MatND hist_test1;
    MatND hist_test2;
    calcHist( &hsv_base, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false );
    //输入，输入数据的个数，通道数，掩码，目标输出，直方图的维度，每个维度分割数据多少，每个维度的范围，默认，默认
    normalize( hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat() );

    calcHist( &hsv_half_down, 1, channels, Mat(), hist_half_down, 2, histSize, ranges, true, false );
    normalize( hist_half_down, hist_half_down, 0, 1, NORM_MINMAX, -1, Mat() );

    calcHist( &hsv_test1, 1, channels, Mat(), hist_test1, 2, histSize, ranges, true, false );
    normalize( hist_test1, hist_test1, 0, 1, NORM_MINMAX, -1, Mat() );

    calcHist( &hsv_test2, 1, channels, Mat(), hist_test2, 2, histSize, ranges, true, false );
    normalize( hist_test2, hist_test2, 0, 1, NORM_MINMAX, -1, Mat() );
    imshow("base",src_base);
    imshow("test1",src_test1);
    imshow("test2",src_test2);
    for( int i = 0; i < 4; i++ )
    {
        int compare_method = i;
        double base_base = compareHist( hist_base, hist_base, compare_method ); //输入1，输入2，方法
        double base_half = compareHist( hist_base, hist_half_down, compare_method );
        double base_test1 = compareHist( hist_base, hist_test1, compare_method );
        double base_test2 = compareHist( hist_base, hist_test2, compare_method );
        printf( " Method [%d] Perfect, Base-Half, Base-Test(1), Base-Test(2) : %f, %f, %f, %f \n", i, base_base, base_half , base_test1, base_test2 );
    }
    printf( "Done \n" );
//     imshow("base",src_base);
//     imshow("test1",src_test1);
//     imshow("test2",src_test2);
    waitKey(0);
    return 0;

}

CMakeLists.txt

cmake_minimum_required(VERSION 2.8)

set(CMAKE_CXX_FLAGS "-std=c++11")
project( DisplayImage )
find_package( OpenCV REQUIRED )
include_directories( ${OpenCV_INCLUDE_DIRS} )
add_executable( DisplayImage main.cpp )
target_link_libraries( DisplayImage ${OpenCV_LIBS} )

install(TARGETS DisplayImage RUNTIME DESTINATION bin