Python实现京东自动登录（自动完成滑块验证）

╰半橙微兮° 2024-04-18 19:01 52阅读 0赞

前提要配置好webdriver，不会的可以百度一下，我用的是Chrome的。  
Python自动登录京东，这里需要填写自己的账号和密码

id = "*********" # 用户账号
    passwd = "******" # 用户密码
    loadpage(id, passwd)

需要说明的是，这几个库都要pip install 安装一下哦。

import cv2
    import time
    import numpy as np
    from selenium import webdriver
    from urllib import request
    from selenium.webdriver.common.action_chains import ActionChains

源码如下，

import cv2
    import time
    import numpy as np
    from selenium import webdriver
    from urllib import request
    from selenium.webdriver.common.action_chains import ActionChains
    brower = webdriver.Chrome()
    def loadpage(userid, password):
        url = "https://passport.jd.com/new/login.aspx?"
        brower.get(url)
        time.sleep(3)
        s1 = r'//div/div[@class="login-tab login-tab-r"]/a'
        userlogin = brower.find_element_by_xpath(s1)
        userlogin.click()
        # time.sleep(5)
        username = brower.find_element_by_id("loginname")
        username.send_keys(userid)
        userpswd = brower.find_element_by_id("nloginpwd")
        userpswd.send_keys(password)
        # time.sleep(5)
        brower.find_element_by_id("loginsubmit").click()
        time.sleep(3)
        while True:
            try:
                getPic()
            except:
                print("登陆成功----")
                break
        time.sleep(5)
    def getPic():
        # 用于找到登录图片的大图
        s2 = r'//div/div[@class="JDJRV-bigimg"]/img'
        # 用来找到登录图片的小滑块
        s3 = r'//div/div[@class="JDJRV-smallimg"]/img'
        bigimg = brower.find_element_by_xpath(s2).get_attribute("src")
        smallimg = brower.find_element_by_xpath(s3).get_attribute("src")
        # print(smallimg + '\n')
        # print(bigimg)
        # 背景大图命名
        backimg = "backimg.png"
        # 滑块命名
        slideimg = "slideimg.png"
        # 下载背景大图保存到本地
        request.urlretrieve(bigimg, backimg)
        # 下载滑块保存到本地
        request.urlretrieve(smallimg, slideimg)
        # 获取图片并灰度化
        block = cv2.imread(slideimg, 0)
        template = cv2.imread(backimg, 0)
        # 二值化后的图片名称
        blockName = "block.jpg"
        templateName = "template.jpg"
        # 将二值化后的图片进行保存
        cv2.imwrite(blockName, block)
        cv2.imwrite(templateName, template)
        block = cv2.imread(blockName)
        block = cv2.cvtColor(block, cv2.COLOR_RGB2GRAY)
        block = abs(255 - block)
        cv2.imwrite(blockName, block)
        block = cv2.imread(blockName)
        template = cv2.imread(templateName)
        # 获取偏移量
        result = cv2.matchTemplate(block, template, cv2.TM_CCOEFF_NORMED) # 查找block在template中的位置，返回result是一个矩阵，是每个点的匹配结果
        x, y = np.unravel_index(result.argmax(), result.shape)
        # print("x方向的偏移", int(y * 0.4 + 18), 'x:', x, 'y:', y)
        # 获取滑块
        element = brower.find_element_by_xpath(s3)
        ActionChains(brower).click_and_hold(on_element=element).perform()
        ActionChains(brower).move_to_element_with_offset(to_element=element, xoffset=y, yoffset=0).perform()
        ActionChains(brower).release(on_element=element).perform()
        time.sleep(3)
     
     
    if __name__ == '__main__':
        id = "*********" # 用户账号
        passwd = "******" # 用户密码
        loadpage(id, passwd)