import cv2 as cv

 import numpy as np

 def big_image_binary(image):

     print(image.shape)

     cw = 213

     ch = 547

     h,w = image.shape[:2]

     gray = cv.cvtColor(image,cv.COLOR_BGR2GRAY)

     for row in range(0,h,ch):

         for col in range(0,w,cw):

             roi = gray[row:row+ch,col:col+cw]    #进行分块

             print(np.std(roi),np.mean(roi))

             #全局阈值方法

             # ret,dst = cv.threshold(roi,127,256,cv.THRESH_BINARY|cv.THRESH_OTSU)

             # gray[row:row + ch, col:col + cw] = dst

             # 全局阈值过滤噪点方法

             if np.std(roi) < 20:

                 gray[row:row + ch, col:col + cw] = 255     #块的二维数组平方差小于20时 将其设为255-变白

             else:

                 ret,dst = cv.threshold(roi,127,256,cv.THRESH_BINARY|cv.THRESH_OTSU)

                 gray[row:row + ch, col:col + cw] = dst

             #局部阈值方法

             # dst = cv.adaptiveThreshold(roi,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,127,20)

             # gray[row:row + ch, col:col + cw] = dst

             # print(np.std(dst),np.mean(dst))   #np.std(dst)计算矩阵的标准差 np.mean(dst) 均值

     cv.imwrite('new_big_image3.jpg',gray)

 src = cv.imread('BigImage_Binary1.jpg')

 # cv.namedWindow('input_image',cv.WINDOW_AUTOSIZE)

 # cv.imshow('input_image',src)

 big_image_binary(src)

 cv.waitKey(0)

 cv.destroyAllWindows()

#np.std()  标准差 
#np.mean() 均值

>>> a = np.array([[1, 2], [3, 4]])

>>> np.std(a) # 计算矩阵全局标准差

1.1180339887498949

>>> np.std(a) # 计算矩阵全局标准差
2.5

>>> np.std(a, axis=0) # axis=0计算每一列的标准差  
array([ 1., 1.]) 
>>> np.std(a, axis=1) # 计算每一行的标准差  array([ 0.5, 0.5])