But processing time is slow.
For using the EMD compare, we should make signature value.
The EMD method compares two signatures value.
Firstly, we prepare histograms of 2 images.
And convert values of histrogram to signature.
A configuration of signature values is very simple.
bins value, x index, y index.
bins value, x index, y index.
bins value, x index, y index.
bins value, x index, y index.
bins value, x index, y index.
....
Of course this type is in case of 2d histogram.
More detail, see the source code.
In here I cannot explain earth mover distance algorithm.
please refer to internet information.
thank you.
origin images
result
...
#include < iostream>
#include < vector>
#include < stdio.h>
#include < opencv2\opencv.hpp>
#ifdef _DEBUG
#pragma comment(lib, "opencv_core249d.lib")
#pragma comment(lib, "opencv_imgproc249d.lib") //MAT processing
#pragma comment(lib, "opencv_highgui249d.lib")
#else
#pragma comment(lib, "opencv_core249.lib")
#pragma comment(lib, "opencv_imgproc249.lib")
#pragma comment(lib, "opencv_highgui249.lib")
#endif
using namespace cv;
using namespace std;
int main()
{
//read 2 images for histogram comparing
///////////////////////////////////////////////////////////////////////////////////////////////////////////////
Mat imgA, imgB;
imgA = imread(".\\image1.jpg");
imgB = imread(".\\image2.jpg");
imshow("img1", imgA);
imshow("img2", imgB);
//variables preparing
///////////////////////////////////////////////////////////////////////////////////////////////////////////////
int hbins = 30, sbins = 32;
int channels[] = {0, 1};
int histSize[] = {hbins, sbins};
float hranges[] = { 0, 180 };
float sranges[] = { 0, 255 };
const float* ranges[] = { hranges, sranges};
Mat patch_HSV;
MatND HistA, HistB;
//cal histogram & normalization
///////////////////////////////////////////////////////////////////////////////////////////////////////////////
cvtColor(imgA, patch_HSV, CV_BGR2HSV);
calcHist( &patch_HSV, 1, channels, Mat(), // do not use mask
HistA, 2, histSize, ranges,
true, // the histogram is uniform
false );
normalize(HistA, HistA, 0, 1, CV_MINMAX);
cvtColor(imgB, patch_HSV, CV_BGR2HSV);
calcHist( &patch_HSV, 1, channels, Mat(),// do not use mask
HistB, 2, histSize, ranges,
true, // the histogram is uniform
false );
normalize(HistB, HistB, 0, 1, CV_MINMAX);
//compare histogram
///////////////////////////////////////////////////////////////////////////////////////////////////////////////
int numrows = hbins * sbins;
//make signature
Mat sig1(numrows, 3, CV_32FC1);
Mat sig2(numrows, 3, CV_32FC1);
//fill value into signature
for(int h=0; h< hbins; h++)
{
for(int s=0; s< sbins; ++s)
{
float binval = HistA.at< float>(h,s);
sig1.at< float>( h*sbins + s, 0) = binval;
sig1.at< float>( h*sbins + s, 1) = h;
sig1.at< float>( h*sbins + s, 2) = s;
binval = HistB.at< float>(h,s);
sig2.at< float>( h*sbins + s, 0) = binval;
sig2.at< float>( h*sbins + s, 1) = h;
sig2.at< float>( h*sbins + s, 2) = s;
}
}
//compare similarity of 2images using emd.
float emd = cv::EMD(sig1, sig2, CV_DIST_L2); //emd 0 is best matching.
printf("similarity %5.5f %%\n", (1-emd)*100 );
waitKey(0);
return 0;
}
...
