python计算图像的Hu矩和皮尔逊相关系数

用python 加opencv计算两张图像的hu矩和皮尔逊相关系数，输出其相似度。

import cv2
import math
import numpy as np
from PIL import Image

im1 = cv2.imread('xuetaggujia.png',cv2.IMREAD_GRAYSCALE)
im2 = cv2.imread('xuegengujia.png',cv2.IMREAD_GRAYSCALE)

_,im1 = cv2.threshold(im1, 128, 255, cv2.THRESH_BINARY)
_,im2 = cv2.threshold(im2, 128, 255, cv2.THRESH_BINARY)
# Calculate Moments
moments1 = cv2.moments(im1)
# Calculate Hu Moments
huMoments1 = cv2.HuMoments(moments1)


for i in range(0,7):
    huMoments1[i] = -1 * math.copysign(1.0, huMoments1[i]) * math.log10(abs(huMoments1[i]))

huMoments1 = np.array(huMoments1)
huMoments1 = huMoments1.astype(int)
print(huMoments1)

# Calculate Moments
moments2 = cv2.moments(im2)
# Calculate Hu Moments
huMoments2 = cv2.HuMoments(moments2)

for i in range(0,7):
    huMoments2[i] = -1 * math.copysign(1.0, huMoments2[i]) * math.log10(abs(huMoments2[i]))

huMoments2 = np.array(huMoments2 )

huMoments2 = huMoments2.astype(int)

print(huMoments2)



def calcMean(x,y):
    sum_x = sum(x)
    sum_y = sum(y)
    n = len(x)
    x_mean = float(sum_x+0.0)/n
    y_mean = float(sum_y+0.0)/n
    return x_mean,y_mean

#计算Pearson系数
def calcPearson(x,y):
    x_mean= np.mean(x,axis=0)	#计算x,y向量平均值
    y_mean = np.mean(y,axis=0)
    n = len(x)
    sumTop = 0.0
    sumBottom = 0.0
    x_pow = 0.0
    y_pow = 0.0
    for i in range(n):
        sumTop += (x[i]-x_mean)*(y[i]-y_mean)
    for i in range(n):
        x_pow += math.pow(x[i]-x_mean,2)
    for i in range(n):
        y_pow += math.pow(y[i]-y_mean,2)
    sumBottom = math.sqrt(x_pow*y_pow)
    p = sumTop/sumBottom
    return p

k = calcPearson(huMoments1,huMoments2)
print(k)