python实现计算RGB图像协方差矩阵

代码如下:

import cv2
import numpy as np
from matplotlib import pyplot as plt

# 计算三波段协方差
'''
1.读取图像BGR三个波段的元素   
2.分别计算三个波段元素平均值
3.计算cov协方差
4.得出协方差矩阵

'''
def Cov(image):
    w = image.shape[0]
    h = image.shape[1]
    b = [0]*w*h
    g = [0]*w*h
    r = [0]*w*h

    for i in range(w):  # 获得BGR三个波段的像素值
        for j in range(h):
            for k in range(1):
                B = image[i,j,k+0]
                G = image[i,j,k+1]
                R = image[i,j,k+2]
                b[i*h+j] = B
                g[i*h+j] = G
                r[i*h+j] = R
# 计算平均值
    all_b = 0  # 像素值总和
    all_g = 0
    all_r = 0
    for i in range(w*h):
        all_b += b[i]
        all_g += g[i]
        all_r += r[i]
    mean_b = int(all_b/(w*h-1))    # 像素值平均值 取整
    mean_g = int(all_g/(w*h-1))
    mean_r = int(all_r/(w*h-1))

#计算协方差
    cov_bb = 0
    cov_bg = 0
    cov_br = 0
    cov_gb = 0
    cov_gg = 0
    cov_gr = 0
    cov_rb = 0
    cov_rg = 0
    cov_rr = 0
    for i in range(w*h):
        cov_bb += (b[i]-mean_b)*(b[i]-mean_b)
        cov_bg += (b[i]-mean_b)*(g[i]-mean_g)
        cov_br += (b[i]-mean_b)*(r[i]-mean_r)
        cov_gb += (g[i]-mean_g)*(b[i]-mean_b)
        cov_gg += (g[i]-mean_g)*(g[i]-mean_g)
        cov_gr += (g[i]-mean_g)*(r[i]-mean_r)
        cov_rb += (r[i]-mean_r)*(b[i]-mean_b)
        cov_rg += (r[i]-mean_r)*(g[i]-mean_g)
        cov_rr += (r[i]-mean_r)*(r[i]-mean_r)
    m = w*h-1
    print('协方差矩阵:')
    print(cov_bb/m,'\t',cov_bg/m,'\t',cov_br,'\n',cov_gb/m,'\t',cov_gg/m,'\t',cov_gr/m,'\n',cov_rb/m,'\t',cov_rg/m,'\t',cov_rr/m,'\n')

 

# 读入图像
image = cv2.imread('JuZi.jpg')
#cv2.imshow('Tu xiang',image)
Cov(image)
cv2.waitKey(0)
cv2.destroyAllWindows()