import cv2 
import numpy as np
img = cv2.imread('./picture/1.jpg')

#将图片灰度化处理,降维,加权进行灰度化c
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow('min_gray',gray)

cv2.waitKey(0)

cv2.destroyAllWindows()

t,gray2 = cv2.threshold(gray,220,255,cv2.THRESH_BINARY)
cv2.imshow('threshold',gray2)

cv2.waitKey(0)

cv2.destroyAllWindows()

def remove_noise(img,k=4):
  
  img2 = img.copy()
  
#   img处理数据，k过滤条件
  w,h = img2.shape
  def get_neighbors(img3,r,c):
    count = 0
    for i in [r-1,r,r+1]:
      for j in [c-1,c,c+1]:
        if img3[i,j] > 10:#纯白色
          count+=1
    return count
#   两层for循环判断所有的点
  for x in range(w):
    for y in range(h):
      if x == 0 or y == 0 or x == w -1 or y == h -1:
        img2[x,y] = 255
      else:
        n = get_neighbors(img2,x,y)#获取邻居数量，纯白色的邻居
        if n > k:
          img2[x,y] = 255
  return img2
result = remove_noise(gray2)
cv2.imshow('8neighbors',result)

cv2.waitKey(0)

cv2.destroyAllWindows()

import cv2 
import numpy as np
img = cv2.imread('./picture/1.jpg')

#将图片灰度化处理,降维,加权进行灰度化c
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
t,gray2 = cv2.threshold(gray,200,255,cv2.THRESH_BINARY)
cv2.imshow('threshold',gray2)
result = remove_noise(gray2)
cv2.imshow('8neighbors',result)

cv2.waitKey(0)

cv2.destroyAllWindows()