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ClaudioMartino
6a5884c04f
The Kullback-Leibler divergence works with histogram that have integral = 1, otherwise it can return negative values. The normalization of the histograms have been changed accordingly, and all the six comparison methods have been used in the histogram comparison tutorial.
97 lines
3.7 KiB
C++
97 lines
3.7 KiB
C++
/**
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* @file compareHist_Demo.cpp
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* @brief Sample code to use the function compareHist
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* @author OpenCV team
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*/
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#include "opencv2/imgcodecs.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include <iostream>
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using namespace std;
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using namespace cv;
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const char* keys =
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"{ help h| | Print help message. }"
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"{ @input1 |Histogram_Comparison_Source_0.jpg | Path to input image 1. }"
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"{ @input2 |Histogram_Comparison_Source_1.jpg | Path to input image 2. }"
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"{ @input3 |Histogram_Comparison_Source_2.jpg | Path to input image 3. }";
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/**
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* @function main
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*/
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int main( int argc, char** argv )
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{
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//! [Load three images with different environment settings]
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CommandLineParser parser( argc, argv, keys );
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samples::addSamplesDataSearchSubDirectory( "doc/tutorials/imgproc/histograms/histogram_comparison/images" );
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Mat src_base = imread(samples::findFile( parser.get<String>( "@input1" ) ) );
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Mat src_test1 = imread(samples::findFile( parser.get<String>( "@input2" ) ) );
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Mat src_test2 = imread(samples::findFile( parser.get<String>( "@input3" ) ) );
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if( src_base.empty() || src_test1.empty() || src_test2.empty() )
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{
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cout << "Could not open or find the images!\n" << endl;
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parser.printMessage();
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return -1;
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}
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//! [Load three images with different environment settings]
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//! [Convert to HSV]
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Mat hsv_base, hsv_test1, hsv_test2;
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cvtColor( src_base, hsv_base, COLOR_BGR2HSV );
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cvtColor( src_test1, hsv_test1, COLOR_BGR2HSV );
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cvtColor( src_test2, hsv_test2, COLOR_BGR2HSV );
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//! [Convert to HSV]
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//! [Convert to HSV half]
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Mat hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows ), Range( 0, hsv_base.cols ) );
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//! [Convert to HSV half]
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//! [Using 50 bins for hue and 60 for saturation]
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int h_bins = 50, s_bins = 60;
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int histSize[] = { h_bins, s_bins };
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// hue varies from 0 to 179, saturation from 0 to 255
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float h_ranges[] = { 0, 180 };
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float s_ranges[] = { 0, 256 };
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const float* ranges[] = { h_ranges, s_ranges };
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// Use the 0-th and 1-st channels
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int channels[] = { 0, 1 };
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//! [Using 50 bins for hue and 60 for saturation]
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//! [Calculate the histograms for the HSV images]
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Mat hist_base, hist_half_down, hist_test1, hist_test2;
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calcHist( &hsv_base, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false );
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normalize( hist_base, hist_base, 1, 0, NORM_L1, -1, Mat() );
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calcHist( &hsv_half_down, 1, channels, Mat(), hist_half_down, 2, histSize, ranges, true, false );
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normalize( hist_half_down, hist_half_down, 1, 0, NORM_L1, -1, Mat() );
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calcHist( &hsv_test1, 1, channels, Mat(), hist_test1, 2, histSize, ranges, true, false );
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normalize( hist_test1, hist_test1, 1, 0, NORM_L1, -1, Mat() );
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calcHist( &hsv_test2, 1, channels, Mat(), hist_test2, 2, histSize, ranges, true, false );
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normalize( hist_test2, hist_test2, 1, 0, NORM_L1, -1, Mat() );
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//! [Calculate the histograms for the HSV images]
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//! [Apply the histogram comparison methods]
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for( int compare_method = 0; compare_method < 6; compare_method++ )
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{
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double base_base = compareHist( hist_base, hist_base, compare_method );
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double base_half = compareHist( hist_base, hist_half_down, compare_method );
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double base_test1 = compareHist( hist_base, hist_test1, compare_method );
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double base_test2 = compareHist( hist_base, hist_test2, compare_method );
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cout << "Method " << compare_method << " Perfect, Base-Half, Base-Test(1), Base-Test(2) : "
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<< base_base << " / " << base_half << " / " << base_test1 << " / " << base_test2 << endl;
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}
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//! [Apply the histogram comparison methods]
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cout << "Done \n";
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return 0;
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}
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