如何计算2个numpy数组i的“EMD”。使用opencv e“直方图”?

Since I'm new to opencv, I don't know how to use the cv.CalcEMD2 function with numpy arrays.
I have two arrays:

因为我是opencv的新手，所以我不知道如何使用cv。具有numpy数组的CalcEMD2函数。我有两个数组:

a=[1,2,3,4,5]  
b=[1,2,3,4]

How can I transfer numpy array to CVhistogram and from Cvhistogram to the function parameter signature?

如何将numpy阵列传输到CVhistogram，以及从CVhistogram传输到函数参数签名?

I would like anyone who answers the question to explain any used opencv functions through the provided solution.

我希望任何回答这个问题的人都能通过提供的解决方案来解释任何使用过的opencv函数。

"EMD" == earth mover's distance.

“EMD”== earth mover的距离。

Update:-
also ,It will be helpful if anyone can tell me how to set the cv.CalcEMD2 parameter i.e"signature" using numpy array!!

更新:-同时，如果有人能告诉我如何设置简历，这将是很有帮助的。我CalcEMD2参数。e“签名”使用numpy数组! !

Note:-
* For those who may be interested in this question ,This answer needs more testing.

注意:- *对于那些可能对这个问题感兴趣的人，这个答案需要更多的测试。

3 个解决方案

#1

You have to define your arrays in terms of weights and coordinates. If you have two arrays a = [1,1,0,0,1] and b = [0,1,0,1] that represent one dimensional histograms, then the numpy arrays should look like this:

你必须用权重和坐标来定义数组。如果有两个数组a =[1,1,0,0,0,1]和b =[0,1,0,1]表示一维直方图，那么numpy数组应该是这样的:

a = [[1 1]
     [1 2]
     [0 3]
     [0 4]
     [1 5]]

b = [[0 1]
     [1 2]
     [0 3]
     [1 4]]

Notice that the number of rows can be different. The number of columns should be the dimensions + 1. The first column contains the weights, and the second column contains the coordinates.

注意，行数可以是不同的。列的数量应该是维数+ 1。第一列包含权重，第二列包含坐标。

The next step is to convert your arrays to a CV_32FC1 Mat before you input the numpy array as a signature to the CalcEMD2 function. The code would look like this:

下一步是将数组转换为CV_32FC1席，然后将numpy数组作为CalcEMD2函数的签名输入。代码是这样的:

from cv2 import *
import numpy as np

# Initialize a and b numpy arrays with coordinates and weights
a = np.zeros((5,2))

for i in range(0,5):
    a[i][1] = i+1

a[0][0] = 1
a[1][0] = 1
a[2][0] = 0
a[3][0] = 0
a[4][0] = 1

b = np.zeros((4,2))

for i in range(0,4):
    b[i][1] = i+1

b[0][0] = 0
b[1][0] = 1
b[2][0] = 0
b[3][0] = 1    

# Convert from numpy array to CV_32FC1 Mat
a64 = cv.fromarray(a)
a32 = cv.CreateMat(a64.rows, a64.cols, cv.CV_32FC1)
cv.Convert(a64, a32)

b64 = cv.fromarray(b)
b32 = cv.CreateMat(b64.rows, b64.cols, cv.CV_32FC1)
cv.Convert(b64, b32)

# Calculate Earth Mover's
print cv.CalcEMD2(a32,b32,cv.CV_DIST_L2)

# Wait for key
cv.WaitKey(0)

Notice that the third parameter of CalcEMD2 is the Euclidean Distance CV_DIST_L2. Another option for the third parameter is the Manhattan Distance CV_DIST_L1.

注意，CalcEMD2的第三个参数是欧几里得距离CV_DIST_L2。第三个参数的另一个选项是曼哈顿距离CV_DIST_L1。

I would also like to mention that I wrote the code to calculate the Earth Mover's distance of two 2D histograms in Python. You can find this code here.

我还想提一下，我写了一段代码，用来计算在Python中，地球Mover的两个二维直方图的距离。你可以在这里找到这个代码。

#2

CV.CalcEMD2 expects arrays that also include the weight for each signal according to the documentation.

简历。根据文档，CalcEMD2期望数组中还包含每个信号的权重。

I would suggest defining your arrays with a weight of 1, like so:

我建议定义你的数组的权重为1，比如:

a=array([1,1],[2,1],[3,1],[4,1],[5,1])
b=array([1,1],[2,1],[3,1],[4,1])

#3

I know the OP wanted to measure Earth Mover's Distance using OpenCV, but if you'd like to do so using Scipy, you can use the following (Wasserstein Distance is also known as Earth Mover's Distance):

我知道OP想用OpenCV测量地球搬运工的距离，但是如果你想用Scipy来测量，你可以用以下方法(Wasserstein距离也被称为地球搬运工的距离):

from scipy.stats import wasserstein_distance
from scipy.ndimage import imread
import numpy as np

def get_histogram(img):
  '''
  Get the histogram of an image. For an 8-bit, grayscale image, the
  histogram will be a 256 unit vector in which the nth value indicates
  the percent of the pixels in the image with the given darkness level.
  The histogram's values sum to 1.
  '''
  h, w = img.shape
  hist = [0.0] * 256
  for i in range(h):
    for j in range(w):
      hist[img[i, j]] += 1
  return np.array(hist) / (h * w)

a = imread('a.jpg')
b = imread('b.jpg')
a_hist = get_histogram(a)
b_hist = get_histogram(b)
dist = wasserstein_distance(a_hist, b_hist)
print(dist)

#1