Python Learning Day7

破解极验滑动验证

　　博客园登录url:
　　　　https://account.cnblogs.com/signin?returnUrl=https%3A%2F%2Fwww.cnblogs.com%2F

　　代码逻辑:
　　　　1、输入用户名与密码，并点击登录
　　　　2、弹出滑动验证，获取有缺口与完整的图片
　　　　3、通过像素点进行比对，获取滑动位移距离
　　　　4、模拟人的行为轨迹
　　　　5、开始滑动

 from selenium import webdriver  # 用来驱动浏览器的
 from selenium.webdriver import ActionChains  # 破解滑动验证码的时候用的 可以拖动图片
 import time
 from PIL import Image  # pip3 install pillow
 import random

 # 截图图片函数
 def cut_image(driver):
     # 获取整个页面图片，图片名字为'snap.png'
     driver.save_screenshot('snap.png')

     # 获取滑动小画图
     image = driver.find_element_by_class_name('geetest_canvas_img')
     print(image.location)
     print(image.size)

     # 获取小图片的左上右下的位置
     left = image.location['x']
     top = image.location['y']
     right = left + image.size['width']
     buttom = top + image.size['height']
     print(left, top, right, buttom)

     # 调用open方法打开全屏图片并赋值给image_obj对象
     image_obj = Image.open('snap.png')

     # 通过image_obj对象对小图片进行截取
     # box: The crop rectangle, as a (left, upper, right, lower)-tuple.
     img = image_obj.crop((left, top, right, buttom))
     # 打开截取后的小图片
     # img.show()
     return img

 # 获取完整图片
 def get_image1(driver):
     time.sleep(2)

     # 修改document文档树，把完整图片的display属性修改为block
     js_code = '''
         var x = document.getElementsByClassName("geetest_canvas_fullbg")[0].style.display = "block";
     '''

     # 执行js代码
     driver.execute_script(js_code)

     # 截取图片
     image = cut_image(driver)

     return image

 # 获取有缺口图片
 def get_image2(driver):
     time.sleep(2)

     # 修改document文档树，把完整图片的display属性修改为block
     js_code = '''
         var x = document.getElementsByClassName("geetest_canvas_fullbg")[0].style.display = "none";
     '''

     # 执行js代码
     driver.execute_script(js_code)

     # 截取图片
     image = cut_image(driver)

     return image

 # 获取滑块滑动距离
 def get_distance(image1, image2):
     # 小滑块右侧位置
     start = 60

     # 像素差
     num = 60
     print(image1.size)
     for x in range(start, image1.size[0]):
         for y in range(image1.size[1]):

             # 获取image1完整图片每一个坐标的像素点
             rgb1 = image1.load()[x, y]

             # 获取image2缺口图片每一个坐标的像素点
             rgb2 = image2.load()[x, y]
             # (60, 86, 40) (60, 86, 40) rgb
             print(rgb1, rgb2)

             # abs获取绝对值， 像素点比较的值
             r = abs(rgb1[0] - rgb2[0])
             g = abs(rgb1[1] - rgb2[1])
             b = abs(rgb1[2] - rgb2[2])

             # 如果条件成立，则找到缺口位置
             if not (r < num and g < num and b < num):
                 # 有误差 - 7像素
                 return x - 7

 # 模拟人的滑动轨迹
 def get_strck_move(distance):
     distance += 20

     '''
     滑动行为轨迹
     加速公式:
         v = v0 + a * t

     路程公式:
         s = v0 * t + 0.5 * a * (t ** 2)
     '''

     # 初速度
     v0 = 0

     # 时间
     t = 0.2

     # 位置
     s = 0

     # 滑动轨迹列表 向前滑动列表
     move_list = []

     # 中间值,作为加减速度的位置
     mid = distance / 5 * 3

     # 加减速度列表
     v_list = [1, 2, 3, 4]

     # 循环位移
     while s < distance:
         if s < mid:
             # 随机获取一个加速度
             a = v_list[random.randint(0, len(v_list) - 1)]

         else:
             # 随机获取一个减速度
             a = -v_list[random.randint(0, len(v_list) - 1)]

         '''
         匀加速\减速运行
         v = v0 + a * t

         位移:
         s = v * t + 0.5 * a * (t**2)
         '''
         # 获取初始速度
         v = v0

         # 路程公式:
         s1 = v * t + 0.5 * a * (t ** 2)
         s1 = round(s1)  # 取整

         # 加速公式:
         # v = v0 + a * t
         m_v = v + a * t

         # 把当前加/减速度赋值给初始速度，以便下一次计算
         v0 = m_v

         # 把位移添加到滑动列表中
         move_list.append(s1)

         # 修改滑动初始距离
         s += s1

     # 后退列表, 自定义后退滑动轨迹,必须是负值
     back_list = [-1, -1, -2, -3, -2, -1, -1, -2, -3, -2, -1, -1]

     return {'move_list': move_list, 'back_list': back_list}

 def main():
     driver = webdriver.Chrome(r'D:\BaiduNetdiskDownload\chromedriver_win32\chromedriver.exe')
     driver.implicitly_wait(10)
     try:
         driver.get('https://account.cnblogs.com/signin?returnUrl=https%3A%2F%2Fwww.cnblogs.com%2F')

         # 1、输入用户名与密码，并点击登录
         user_input = driver.find_element_by_id('LoginName')
         user_input.send_keys('_tank_')
         time.sleep(0.2)

         pwd_input = driver.find_element_by_id('Password')
         pwd_input.send_keys('k46709394.')
         time.sleep(2)

         login_submit = driver.find_element_by_id('submitBtn')
         login_submit.click()

         # 2、获取完整的图片
         image1 = get_image1(driver)

         # 3、获取有缺口图片
         image2 = get_image2(driver)

         # 4、比对两张图片，获取滑动距离
         distance = get_distance(image1, image2)
         print(distance)

         # 5、模拟人的滑动轨迹
         move_dict = get_strck_move(distance)
         # 获取前进滑动轨迹
         move_list = move_dict['move_list']
         # 获取后退滑动轨迹
         back_list = move_dict['back_list']

         # 6、开始滑动
         move_tag = driver.find_element_by_class_name('geetest_slider_button')
         # 点击摁住滑动按钮
         ActionChains(driver).click_and_hold(move_tag).perform()

         # 向前滑动
         for move in move_list:
             ActionChains(driver).move_by_offset(xoffset=move, yoffset=0).perform()
             time.sleep(0.1)

         time.sleep(0.1)

         # 向后滑动
         for back in back_list:
             ActionChains(driver).move_by_offset(xoffset=back, yoffset=0).perform()
             time.sleep(0.1)

         # 制作微妙晃动
         ActionChains(driver).move_by_offset(xoffset=3, yoffset=0).perform()
         ActionChains(driver).move_by_offset(xoffset=-3, yoffset=0).perform()

         time.sleep(0.1)

         # 释放滑动按钮
         ActionChains(driver).release().perform()

         time.sleep(100)

     finally:
         driver.close()

 if __name__ == '__main__':
     main()from selenium import webdriver  # 用来驱动浏览器的
 from selenium.webdriver import ActionChains  # 破解滑动验证码的时候用的 可以拖动图片
 import time
 from PIL import Image  # pip3 install pillow
 import random

 # 截图图片函数
 def cut_image(driver):
     # 获取整个页面图片，图片名字为'snap.png'
     driver.save_screenshot('snap.png')

     # 获取滑动小画图
     image = driver.find_element_by_class_name('geetest_canvas_img')
     print(image.location)
     print(image.size)

     # 获取小图片的左上右下的位置
     left = image.location['x']
     top = image.location['y']
     right = left + image.size['width']
     buttom = top + image.size['height']
     print(left, top, right, buttom)

     # 调用open方法打开全屏图片并赋值给image_obj对象
     image_obj = Image.open('snap.png')

     # 通过image_obj对象对小图片进行截取
     # box: The crop rectangle, as a (left, upper, right, lower)-tuple.
     img = image_obj.crop((left, top, right, buttom))
     # 打开截取后的小图片
     # img.show()
     return img

 # 获取完整图片
 def get_image1(driver):
     time.sleep(2)

     # 修改document文档树，把完整图片的display属性修改为block
     js_code = '''
         var x = document.getElementsByClassName("geetest_canvas_fullbg")[0].style.display = "block";
     '''

     # 执行js代码
     driver.execute_script(js_code)

     # 截取图片
     image = cut_image(driver)

     return image

 # 获取有缺口图片
 def get_image2(driver):
     time.sleep(2)

     # 修改document文档树，把完整图片的display属性修改为block
     js_code = '''
         var x = document.getElementsByClassName("geetest_canvas_fullbg")[0].style.display = "none";
     '''

     # 执行js代码
     driver.execute_script(js_code)

     # 截取图片
     image = cut_image(driver)

     return image

 # 获取滑块滑动距离
 def get_distance(image1, image2):
     # 小滑块右侧位置
     start = 60

     # 像素差
     num = 60
     print(image1.size)
     for x in range(start, image1.size[0]):
         for y in range(image1.size[1]):

             # 获取image1完整图片每一个坐标的像素点
             rgb1 = image1.load()[x, y]

             # 获取image2缺口图片每一个坐标的像素点
             rgb2 = image2.load()[x, y]
             # (60, 86, 40) (60, 86, 40) rgb
             print(rgb1, rgb2)

             # abs获取绝对值， 像素点比较的值
             r = abs(rgb1[0] - rgb2[0])
             g = abs(rgb1[1] - rgb2[1])
             b = abs(rgb1[2] - rgb2[2])

             # 如果条件成立，则找到缺口位置
             if not (r < num and g < num and b < num):
                 # 有误差 - 7像素
                 return x - 7

 # 模拟人的滑动轨迹
 def get_strck_move(distance):
     distance += 20

     '''
     滑动行为轨迹
     加速公式:
         v = v0 + a * t

     路程公式:
         s = v0 * t + 0.5 * a * (t ** 2)
     '''

     # 初速度
     v0 = 0

     # 时间
     t = 0.2

     # 位置
     s = 0

     # 滑动轨迹列表 向前滑动列表
     move_list = []

     # 中间值,作为加减速度的位置
     mid = distance / 5 * 3

     # 加减速度列表
     v_list = [1, 2, 3, 4]

     # 循环位移
     while s < distance:
         if s < mid:
             # 随机获取一个加速度
             a = v_list[random.randint(0, len(v_list) - 1)]

         else:
             # 随机获取一个减速度
             a = -v_list[random.randint(0, len(v_list) - 1)]

         '''
         匀加速\减速运行
         v = v0 + a * t

         位移:
         s = v * t + 0.5 * a * (t**2)
         '''
         # 获取初始速度
         v = v0

         # 路程公式:
         s1 = v * t + 0.5 * a * (t ** 2)
         s1 = round(s1)  # 取整

         # 加速公式:
         # v = v0 + a * t
         m_v = v + a * t

         # 把当前加/减速度赋值给初始速度，以便下一次计算
         v0 = m_v

         # 把位移添加到滑动列表中
         move_list.append(s1)

         # 修改滑动初始距离
         s += s1

     # 后退列表, 自定义后退滑动轨迹,必须是负值
     back_list = [-1, -1, -2, -3, -2, -1, -1, -2, -3, -2, -1, -1]

     return {'move_list': move_list, 'back_list': back_list}

 def main():
     driver = webdriver.Chrome(r'D:\BaiduNetdiskDownload\chromedriver_win32\chromedriver.exe')
     driver.implicitly_wait(10)
     try:
         driver.get('https://account.cnblogs.com/signin?returnUrl=https%3A%2F%2Fwww.cnblogs.com%2F')

         # 1、输入用户名与密码，并点击登录
         user_input = driver.find_element_by_id('LoginName')
         user_input.send_keys('_tank_')
         time.sleep(0.2)

         pwd_input = driver.find_element_by_id('Password')
         pwd_input.send_keys('k46709394.')
         time.sleep(2)

         login_submit = driver.find_element_by_id('submitBtn')
         login_submit.click()

         # 2、获取完整的图片
         image1 = get_image1(driver)

         # 3、获取有缺口图片
         image2 = get_image2(driver)

         # 4、比对两张图片，获取滑动距离
         distance = get_distance(image1, image2)
         print(distance)

         # 5、模拟人的滑动轨迹
         move_dict = get_strck_move(distance)
         # 获取前进滑动轨迹
         move_list = move_dict['move_list']
         # 获取后退滑动轨迹
         back_list = move_dict['back_list']

         # 6、开始滑动
         move_tag = driver.find_element_by_class_name('geetest_slider_button')
         # 点击摁住滑动按钮
         ActionChains(driver).click_and_hold(move_tag).perform()

         # 向前滑动
         for move in move_list:
             ActionChains(driver).move_by_offset(xoffset=move, yoffset=0).perform()
             time.sleep(0.1)

         time.sleep(0.1)

         # 向后滑动
         for back in back_list:
             ActionChains(driver).move_by_offset(xoffset=back, yoffset=0).perform()
             time.sleep(0.1)

         # 制作微妙晃动
         ActionChains(driver).move_by_offset(xoffset=3, yoffset=0).perform()
         ActionChains(driver).move_by_offset(xoffset=-3, yoffset=0).perform()

         time.sleep(0.1)

         # 释放滑动按钮
         ActionChains(driver).release().perform()

         time.sleep(100)

     finally:
         driver.close()

 if __name__ == '__main__':
     main()