地球移动器距离的Python代码 [英] Python code for Earth mover's Distance

问题描述

我在python中寻找Earth Mover的距离（或快速EMD）实现。
关于在何处找到它的任何线索，我在网上看得已经足够了。
我想在我正在做的图像检索项目中使用它。
谢谢。

I am looking for an Earth Mover's distance(or Fast EMD) implementation in python. Any clues on where to find it, I have looked enough on the web. I want to use it in an image retrieval project that I am doing. Thanks.

编辑：
我使用纸浆图书馆。
此页面还包含设置所需的说明。

I found a very nice solution using the pulp libararies. This page also has the instruction required to set it up.

推荐答案

OpenCv for Python。该函数的名称是 CalcEMD2 和用于比较两个图像的直方图的简单代码如下所示：

There is an excellent implementation in OpenCv for Python. The name of the function is CalcEMD2 and a simple code to compare histograms of two images would look like this:

#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）测试了与前面代码非常相似的代码。如果您想了解有关Earth Mover的更多信息，并希望看到使用OpenCV和C ++的实现，您可以阅读Gary Bradski和他的Learning OpenCV一书中的第7章：直方图匹配。 Adrain Kaebler。

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 & Adrain Kaebler.

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