我正在寻找Python中的地球移动器的距离(或快速EMD)实现。关于在哪里找到它的任何线索,我在网上已经看够了。我想在我正在做的图像检索项目中使用它。谢谢。
编辑:我发现了使用纸浆库的一个非常好的解决方案。该页面还包含设置所需的说明。
OpenCv for Python中有一个出色的实现。该函数的名称为CalcEMD2,用于比较两个图像直方图的简单代码如下所示:
#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)
我使用Python 2.7和Python(x,y)测试了与之前的代码非常相似的代码。如果您想了解有关Earth Mover的更多信息,并希望了解使用OpenCV和C ++的实现,则可以阅读Gary Bradski和Adrain Kaebler的“学习OpenCV”一书的“第7章:直方图匹配”。