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This article mainly introduces C++ OpenCV how to achieve histogram comparison, the article is very detailed, has a certain reference value, interested friends must read it!

Histogram comparison

For the two input images, the histograms H1 and H2 are calculated and normalized to the same scale space, and then the similarity of the two histograms can be obtained by calculating the distance between H1 and H2, and then the similarity of the image itself can be compared.

OpenCV provides four methods of comparison:

Correlation correlation comparison

Chi-Square chi-square comparison

Intersection cross comparison

Bhattacharyya Babbitt distance comparison

Related API cv::compareHist

Code

# include "opencv2/highgui/highgui.hpp"

# include "opencv2/imgproc/imgproc.hpp"

# include

# include

Using namespace std

Using namespace cv

/ * @ function main * /

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

/ / load three images with different backgrounds

If (argc

< 4 ) { printf("** Error. Usage: ./compareHist_Demo \n"); return -1; } src_base = imread( argv[1], 1 ); src_test1 = imread( argv[2], 1 ); src_test2 = imread( argv[3], 1 ); /// 转换到 HSV cvtColor( src_base, hsv_base, CV_BGR2HSV ); cvtColor( src_test1, hsv_test1, CV_BGR2HSV ); cvtColor( src_test2, hsv_test2, CV_BGR2HSV ); hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) ); /// 对hue通道使用30个bin,对saturatoin通道使用32个bin int h_bins = 50; int s_bins = 60; int histSize[] = { h_bins, s_bins }; // hue的取值范围从0到256, saturation取值范围从0到180 float h_ranges[] = { 0, 256 }; float s_ranges[] = { 0, 180 }; const float* ranges[] = { h_ranges, s_ranges }; // 使用第0和第1通道 int channels[] = { 0, 1 }; /// 直方图 MatND hist_base; MatND hist_half_down; MatND hist_test1; MatND hist_test2; /// 计算HSV图像的直方图 calcHist( &hsv_base, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false ); 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() ); ///应用不同的直方图对比方法 for( int i = 0; i < 4; i++ ) { int compare_method = i; double base_base = compareHist( hist_base, hist_base, compare_method ); 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" ); return 0; } 输出的结果

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