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    <p>There is an excellent implementation in <a href="http://opencv.willowgarage.com/wiki/" rel="nofollow noreferrer">OpenCv</a> for Python. The name of the function is <a href="http://www.seas.upenn.edu/~bensapp/opencvdocs/ref/opencvref_cv.htm#decl_cvCalcEMD2" rel="nofollow noreferrer">CalcEMD2</a> and a simple code to compare histograms of two images would look like this:</p> <pre><code>#Import OpenCv library from cv2 import * ### HISTOGRAM FUNCTION ######################################################### def calcHistogram(src): # Convert to HSV hsv = cv.CreateImage(cv.GetSize(src), 8, 3) cv.CvtColor(src, hsv, cv.CV_BGR2HSV) # Extract the H and S planes size = cv.GetSize(src) h_plane = cv.CreateMat(size[1], size[0], cv.CV_8UC1) s_plane = cv.CreateMat(size[1], size[0], cv.CV_8UC1) cv.Split(hsv, h_plane, s_plane, None, None) planes = [h_plane, s_plane] #Define numer of bins h_bins = 30 s_bins = 32 #Define histogram size hist_size = [h_bins, s_bins] # hue varies from 0 (~0 deg red) to 180 (~360 deg red again */ h_ranges = [0, 180] # saturation varies from 0 (black-gray-white) to 255 (pure spectrum color) s_ranges = [0, 255] ranges = [h_ranges, s_ranges] #Create histogram hist = cv.CreateHist([h_bins, s_bins], cv.CV_HIST_ARRAY, ranges, 1) #Calc histogram cv.CalcHist([cv.GetImage(i) for i in planes], hist) cv.NormalizeHist(hist, 1.0) #Return histogram return hist ### EARTH MOVERS ############################################################ def calcEM(hist1,hist2,h_bins,s_bins): #Define number of rows numRows = h_bins*s_bins sig1 = cv.CreateMat(numRows, 3, cv.CV_32FC1) sig2 = cv.CreateMat(numRows, 3, cv.CV_32FC1) for h in range(h_bins): for s in range(s_bins): bin_val = cv.QueryHistValue_2D(hist1, h, s) cv.Set2D(sig1, h*s_bins+s, 0, cv.Scalar(bin_val)) cv.Set2D(sig1, h*s_bins+s, 1, cv.Scalar(h)) cv.Set2D(sig1, h*s_bins+s, 2, cv.Scalar(s)) bin_val = cv.QueryHistValue_2D(hist2, h, s) cv.Set2D(sig2, h*s_bins+s, 0, cv.Scalar(bin_val)) cv.Set2D(sig2, h*s_bins+s, 1, cv.Scalar(h)) cv.Set2D(sig2, h*s_bins+s, 2, cv.Scalar(s)) #This is the important line were the OpenCV EM algorithm is called return cv.CalcEMD2(sig1,sig2,cv.CV_DIST_L2) ### MAIN ######################################################################## if __name__=="__main__": #Load image 1 src1 = cv.LoadImage("image1.jpg") #Load image 1 src2 = cv.LoadImage("image2.jpg") # Get histograms histSrc1= calcHistogram(src1) histSrc2= calcHistogram(src2) # Compare histograms using earth mover's histComp = calcEM(histSrc1,histSrc2,30,32) #Print solution print(histComp) </code></pre> <p>I tested a code very similar to the previous code with Python 2.7 and Python(x,y). If you want to learn more about Earth Mover's and you want to see an implementation using OpenCV and C++, you can read "Chapter 7: Histograms an Matching" of the book "Learning OpenCV" by Gary Bradski &amp; Adrain Kaebler.</p>
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    1. USJaime Ivan Cervantes
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    1. COUpVoted,Can you see this question.it seems you can solve it.http://stackoverflow.com/questions/15706339/how-to-compute-emd-for-2-numpy-arrays-i-ehistogram-using-opencv-under-python
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    2. COThanks stranger .
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