mac使用python识别图形验证码功能

pip3 install pytesseract
pip3 install pillow

import pytesseract
from PIL import Image
import os
def binarizing(img, threshold):
 """传入image对象进行灰度、二值处理"""
 pixdata = img.load()
 w, h = img.size
 # 遍历所有像素，大于阈值的为黑色
 for y in range(h):
 for x in range(w):
  if pixdata[x, y] < threshold:
  pixdata[x, y] = 0
  else:
  pixdata[x, y] = 255
 return img
_temp = os.path.dirname(__file__)
file_path = os.path.join(_temp, 'code2.jpg')
print("file_path", file_path)
image = Image.open(file_path)
image = image.convert('L')
threshold = 157
table = []
# 接下来是二值化处理
# 遍历所有像素，大于阈值的为黑色，threshold是阀值
image = binarizing(image, threshold)
result = pytesseract.image_to_string(image)
print(result)

def binarizing(img, threshold):
 """传入image对象进行灰度、二值处理"""
 pixdata = img.load()
 w, h = img.size
 # 遍历所有像素，大于阈值的为黑色
 for y in range(h):
 for x in range(w):
  if pixdata[x, y] < threshold:
  pixdata[x, y] = 0 #小于阀值设为0，0是黑色
  else:
  pixdata[x, y] = 255 0 #大于阀值设为255，255是白色
 return img

# -*- coding: utf-8 -*-
import cv2
import numpy as np
import os
_temp = os.path.dirname(__file__)
file_path = os.path.join(_temp, 'code2.jpg')
def remove_noise(img, k=4):
 ###8领域过滤
 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
img = cv2.imread(file_path)
# 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()