python+selenium实现自动验证滑动验证码功能，python破解滑块验证码

阳光穿透心脏的1/2处 2023-03-05 15:56 29阅读 0赞

使用python实现掘金站点[https://juejin.im/][https_juejin.im]滑动验证码自动验证功能

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTE0Nzc5MTQ_size_16_color_FFFFFF_t_70][]

完整代码如下（参考博主：[https://blog.csdn.net/Tracy\_LeBron/article/details/84567419][https_blog.csdn.net_Tracy_LeBron_article_details_84567419]），欢迎互相交流学习

# pip install opencv-python
    # pip install numpy
    # pip install selenium
    
    
    from selenium import webdriver
    from selenium.webdriver.common.action_chains import ActionChains
    from time import sleep
    from urllib import request
    import cv2
    import numpy as np
    
    
    # 这个函数是用来显示图片的。
    def show(name):
        cv2.imshow('Show', name)
        cv2.waitKey(0)
        cv2.destroyAllWindows()
    
    
    # 这里我用的google的驱动。
    driver = webdriver.Chrome()
    driver.maximize_window()
    driver.implicitly_wait(5)
    url = 'https://juejin.im/'
    
    
    #实现登录
    def get_login(driver, url):
        driver.get(url)
        driver.find_element_by_xpath('//button[@class="login-button"]').click()
        driver.find_element_by_xpath('//span[@class ="clickable"]').click()
        driver.find_element_by_xpath('//input[@name="loginPhoneOrEmail"]').send_keys('填入自己的账号')
        driver.find_element_by_xpath('//input[@name="loginPassword"]').send_keys('填入自己的密码')
        driver.find_element_by_xpath('//button[@class="btn"]').click()
        sleep(2)
        return driver
    
    
    driver = get_login(driver, url)
    
    # 块图片
    blockJpg = 'image/block.jpg'
    # 模板图片
    templateJpg = 'image/template.jpg'
    
    
    # 获取验证码中的图片
    def get_image(driver):
        # 获取背景图url
        bj = driver.find_element_by_xpath('//img[@class="sc-gZMcBi YkDbM sc-ifAKCX hCcViW"]').get_attribute('src')
        # 获取移动块url
        yd = driver.find_element_by_xpath('//img[@class="captcha_verify_img_slide react-draggable sc-gqjmRU irZaUl"]').get_attribute('src')
        req = request.Request(bj)
        bg = open(templateJpg, 'wb+')
        bg.write(request.urlopen(req).read())
        bg.close()
        req = request.Request(yd)
        bk = open(blockJpg, 'wb+')
        bk.write(request.urlopen(req).read())
        bk.close()
        return templateJpg, blockJpg
    
    
    bkg, blk = get_image(driver)
    
    
    # 计算缺口的位置，由于缺口位置查找偶尔会出现找不准的现象，这里进行判断，如果查找的缺口位置x坐标小于100，
    # 我们进行刷新验证码操作，重新计算缺口位置，知道满足条件位置。
    def get_distance(bkg, blk):
        # 读取灰度图
        block = cv2.imread(blk, 0)
        tp = cv2.imread(bkg, 0)
        # 保存图像
        cv2.imwrite(templateJpg, tp)
        cv2.imwrite(blockJpg, block)
    
        block = cv2.imread(blockJpg)
        block = cv2.cvtColor(block, cv2.COLOR_BGR2GRAY)
        block = abs(255 - block)
        cv2.imwrite(blockJpg, block)
        block = cv2.imread(blockJpg)
        tp = cv2.imread(templateJpg)
        ''' 
        模板匹配函数 cv2.matchTemplate(image, temp, method, result=None, mask=None)
        image：待搜索图像;temp：模板图像;result：匹配结果;method：计算匹配程度的方法
        '''
        result = cv2.matchTemplate(block, template, cv2.TM_CCOEFF_NORMED)
        mn_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
        # x, y = np.unravel_index(result.argmax(), result.shape)
        # x, y, w, h = cv2.boundingRect(GrayImage)
        print(min_loc, max_loc)
        # min_loc = (164, 58) # (y, x)
        # max_loc = (170, 103) # (y, x)
        # x取最小值(如果x<10，取最大值），y取最大值（如果y>200 ,重新获取)
        if min_loc[0] > max_loc[0]:
            y = min_loc[0]
        else:
            y = max_loc[0]
    
        if min_loc[1] > max_loc[1]:
            x = max_loc[1]
        else:
            x = min_loc[1]
    
        # x取最小值(如果x<10，取最大值）
        if x < 20:
            if min_loc[1] > max_loc[1]:
                x = min_loc[1]
            else:
                x = max_loc[1]
    
        # 这里就是下图中的绿色框框  50*50是移动块的大小
        cv2.rectangle(template, (y, x), (y + 50, x + 50), (7, 249, 151), 2)
        print('x坐标为：%d' % x)
        print('y坐标为：%d' % y)
        if y > 200:
            elem = driver.find_element_by_xpath('//a[@class="secsdk_captcha_refresh refresh-button___StyledA-sc-18f114n-0 jgMJRc"]')
            sleep(1)
            elem.click()
            bkg, blk = get_image(driver)
            y, tp = get_distance(bkg, blk)
        return y, tp
    
    
    distance, temp = get_distance(bkg, blk)
    
    
    # 这个是用来模拟人为拖动滑块行为，快到缺口位置时，减缓拖动的速度，服务器就是根据这个来判断是否是人为登录的。
    def get_tracks(dis):
        v = 0
        m = 0.3
        # 保存0.3内的位移
        tracks = []
        current = 0
        mid = distance*4/5
        while current <= dis:
            if current < mid:
                a = 2
            else:
                a = -3
            v0 = v
            s = v0*m+0.5*a*(m**2)
            current += s
            tracks.append(round(s))
            v = v0+a*m
        return tracks
    
    
    # 原图的像素是276*172，而网页的是340*212，图像放大了。  340/276 = 1.231884058
    double_distance = int(distance*1.231884058)
    tracks = get_tracks(double_distance)
    # 由于计算机计算的误差，导致模拟人类行为时，会出现分布移动总和大于真实距离，这里就把这个差添加到tracks中，也就是最后进行一步左移。
    tracks.append(-(sum(tracks)-double_distance))
    
    element = driver.find_element_by_xpath('//div[@class="secsdk-captcha-drag-icon sc-ckVGcZ gZcwqQ"]')
    ActionChains(driver).click_and_hold(on_element=element).perform()
    for track in tracks:
        ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform()
    sleep(0.5)
    ActionChains(driver).release(on_element=element).perform()
    show(temp)

[https_juejin.im]: https://juejin.im/
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTE0Nzc5MTQ_size_16_color_FFFFFF_t_70]: /images/20230208/a383541db9124950bb20ed49789098e3.png
[https_blog.csdn.net_Tracy_LeBron_article_details_84567419]: https://blog.csdn.net/Tracy_LeBron/article/details/84567419