requests登录并自动识别验证码（存数字）

import requests
from pytesseract import image_to_string
from PIL import Image,ImageEnhance
import re,time

session = requests.Session()
login_url = 'xxxxxxxxxxxx' #登录url
random_url = 'xxxxxxxxxxxxx' #验证码url
tessdata_dir_config = '--tessdata-dir "C:/Program Files (x86)/Tesseract-OCR/tessdata"'
loginname = '63001800'

#账号密码等
data = {
'username' :'root', 
'password':'123456'
}

def get_url(url):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.146 Safari/537.36',
    }
    response = session.get(url,headers=headers)
    request = response.content
    with open('code.png','wb') as f:
        f.write(request)

def post_url(url,data):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.146 Safari/537.36',
        }
    response = session.post(url, data=data)
    home = session.get('#登录成功后访问某个url',headers=headers)
    print(home.text)



def binaryzation(threshold=140):  #二值化
    table = []
    for i in range(256):
        if i < threshold:
            table.append(0)
        else:
            table.append(1)
    return table


def sum_9_region_new(img, x, y):
    '''确定噪点 '''
    cur_pixel = img.getpixel((x, y))  # 当前像素点的值
    width = img.width
    height = img.height

    if cur_pixel == 1:  # 如果当前点为白色区域,则不统计邻域值
        return 0

    # 因当前图片的四周都有黑点，所以周围的黑点可以去除
    if y < 3:  # 本例中，前两行的黑点都可以去除
        return 1
    elif y > height - 3:  # 最下面两行
        return 1
    else:  # y不在边界
        if x < 3:  # 前两列
            return 1
        elif x == width - 1:  # 右边非顶点
            return 1
        else:  # 具备9领域条件的
            sum = img.getpixel((x - 1, y - 1)) \
                  + img.getpixel((x - 1, y)) \
                  + img.getpixel((x - 1, y + 1)) \
                  + img.getpixel((x, y - 1)) \
                  + cur_pixel \
                  + img.getpixel((x, y + 1)) \
                  + img.getpixel((x + 1, y - 1)) \
                  + img.getpixel((x + 1, y)) \
                  + img.getpixel((x + 1, y + 1))
            return 9 - sum

def collect_noise_point(img):
    '''收集所有的噪点'''
    noise_point_list = []
    for x in range(img.width):
        for y in range(img.height):
            res_9 = sum_9_region_new(img, x, y)
            if (0 < res_9 < 3) and img.getpixel((x, y)) == 0:  # 找到孤立点
                pos = (x, y)
                noise_point_list.append(pos)
    return noise_point_list

def remove_noise_pixel(img, noise_point_list):
    '''根据噪点的位置信息，消除二值图片的黑点噪声'''
    for item in noise_point_list:
        img.putpixel((item[0], item[1]), 1)




def image_identification(img):
    im = Image.open(img)
    image = im.convert('L')
    image = image.point(binaryzation(), '1')
    noise_point_list = collect_noise_point(image)
    remove_noise_pixel(image, noise_point_list)
    image.show()
    content = image_to_string(image, lang='eng', config=tessdata_dir_config)
    print(content,'content')
    return content


def random_code(img): 
    get_url(random_url) #获取验证码并保存图片
    code = image_identification(img)
    code = re.sub(r"[a-z.A-Z |+-]+",'',code)  #过滤验证码
    return code



if __name__ == "__main__":
    flag = True
    while flag:
        code = random_code('code.png') 
        print('-----------------')
        print(code,type(code),len(code))
        if len(code) == 4:
            data['randomCode'] = code
            post_url(login_url, data) #登录
            print(data)
            flag = False