Python 爬虫新手教程：破解验证码技术，识别率高达百分之80！

import os
import pytesseract
from PIL import Image
from collections import defaultdict

# tesseract.exe所在的文件路径
pytesseract.pytesseract.tesseract_cmd = 'C://Program Files (x86)/Tesseract-OCR/tesseract.exe'

# 获取图片中像素点数量最多的像素
def get_threshold(image):
    pixel_dict = defaultdict(int)

    # 像素及该像素出现次数的字典
    rows, cols = image.size
    for i in range(rows):
        for j in range(cols):
            pixel = image.getpixel((i, j))
            pixel_dict[pixel] += 1

    count_max = max(pixel_dict.values()) # 获取像素出现出多的次数
    pixel_dict_reverse = {v:k for k,v in pixel_dict.items()}
    threshold = pixel_dict_reverse[count_max] # 获取出现次数最多的像素点

    return threshold

# 按照阈值进行二值化处理
# threshold: 像素阈值
def get_bin_table(threshold):
    # 获取灰度转二值的映射table
    table = []
    for i in range(256):
        rate = 0.1 # 在threshold的适当范围内进行处理
        if threshold*(1-rate)<= i <= threshold*(1+rate):
            table.append(1)
        else:
            table.append(0)
    return table

# 去掉二值化处理后的图片中的噪声点
def cut_noise(image):

    rows, cols = image.size # 图片的宽度和高度
    change_pos = [] # 记录噪声点位置

    # 遍历图片中的每个点，除掉边缘
    for i in range(1, rows-1):
        for j in range(1, cols-1):
            # pixel_set用来记录该店附近的黑色像素的数量
            pixel_set = []
            # 取该点的邻域为以该点为中心的九宫格
            for m in range(i-1, i+2):
                for n in range(j-1, j+2):
                    if image.getpixel((m, n)) != 1: # 1为白色,0位黑色
                        pixel_set.append(image.getpixel((m, n)))

            # 如果该位置的九宫内的黑色数量小于等于4，则判断为噪声
            if len(pixel_set) <= 4:
                change_pos.append((i,j))

    # 对相应位置进行像素修改，将噪声处的像素置为1（白色）
    for pos in change_pos:
        image.putpixel(pos, 1)

    return image # 返回修改后的图片

# 识别图片中的数字加字母
# 传入参数为图片路径，返回结果为：识别结果
def OCR_lmj(img_path):

    image = Image.open(img_path) # 打开图片文件
    imgry = image.convert('L')  # 转化为灰度图

    # 获取图片中的出现次数最多的像素，即为该图片的背景
    max_pixel = get_threshold(imgry)

    # 将图片进行二值化处理
    table = get_bin_table(threshold=max_pixel)
    out = imgry.point(table, '1')

    # 去掉图片中的噪声（孤立点）
    out = cut_noise(out)

    #保存图片
    # out.save('E://figures/img_gray.jpg')

    # 仅识别图片中的数字
    #text = pytesseract.image_to_string(out, config='digits')
    # 识别图片中的数字和字母
    text = pytesseract.image_to_string(out)

    # 去掉识别结果中的特殊字符
    exclude_char_list = ' .:\\|\'\"?![],()~@#$%^&*_+-={};<>/¥'
    text = ''.join([x for x in text if x not in exclude_char_list])
    #print(text)

    return text

def main():

    # 识别指定文件目录下的图片
    # 图片存放目录figures
    dir = 'E://figures'

    correct_count = 0  # 图片总数
    total_count = 0    # 识别正确的图片数量

    # 遍历figures下的png,jpg文件
    for file in os.listdir(dir):
        if file.endswith('.png') or file.endswith('.jpg'):
            # print(file)
            image_path = '%s/%s'%(dir,file) # 图片路径

            answer = file.split('.')[0]  # 图片名称，即图片中的正确文字
            recognizition = OCR_lmj(image_path) # 图片识别的文字结果

            print((answer, recognizition))
            if recognizition == answer: # 如果识别结果正确，则total_count加1
                correct_count += 1

            total_count += 1

    print('Total count: %d, correct: %d.'%(total_count, correct_count))
    '''
    # 单张图片识别
    image_path = 'E://figures/code (1).jpg'
    OCR_lmj(image_path)
    '''

main()

('101659', '101659')
('111073', '111073')
('114510', '114510')
('118235', '118235')
('124677', '124677')
('147291', '147291')
('169147', '169147')
('185302', '185302')
('23YB', '23YB')
('262051', '262051')
('2HED', '2MED')
('315386', '315386')
('3D7K', '3D7K')
('3DYH', '3DYH')
('3QG8', '30G8')
('3XNR', 'EXNR')
('44G5', '44G5')
('470259', '470259')
('515413', '515413')
('522351', '522351')
('539824', '539824')
('5CVL', 'SCVL')
('642689', '642689')
('671991', '671991')
('672838', '672838')
('6F5Y', '6F5Y')
('6USB', 'GUSB')
('703167', '703167')
('765120', '765120')
('779931', '779931')
('8UEF', '8SUEF')
('905857', '905857')
('9H4H', '9H4H')
('9SK1', 'OSK1')
('BDP4', 'BDP4')
('DXV3', 'DXV3')
('E78Y', 'E78Y')
('EAHR', 'EAHR')
('F585', 'Fss§')
('FBV8', 'FBV8')
('FJKK', 'FJKK')
('GXKQ', 'GXKQ')
('H7Y9', 'H7Y9')
('J4LJ', 'J4LJ')
('J8YH', 'J8YH')
('JCDL', 'JCDL')
('JTX2', 'JTX2')
('JYLH', 'JYLH')
('KFYA', 'KFYA')
('L3VZ', 'L3VZ')
('LCGV', 'LCGV')
('LKEK', 'LKEK')
('N3FJ', 'N3FJ')
('PJZN', 'PJZN')
('PNDQ', 'PNDQ')
('Q7HP', 'Q7HP')
('QSHU', 'QSHU')
('R1RN', 'RLRN')
('RPNX', 'RPNX')
('TUKG', 'TUKG')
('U9G3', 'U9G3')
('UZAH', 'UZAH')
('V6P9', 'very')
('Y18D', '18D')
('Y237', 'Y237')
('ZZT5', '2215')
Total count: 66, correct: 54.