一 介绍
一些网站会在正常的账号密码认证之外加一些验证码，以此来明确地区分人/机行为，从一定程度上达到反爬的效果，对于简单的校验码Tesserocr就可以搞定，如下
但一些网站加入了滑动验证码，最典型的要属于极验滑动认证了，极验官网：http://www.geetest.com/，下图是极验的登录界面
现在极验验证码已经更新到了 3.0 版本，截至 2017 年 7 月全球已有十六万家企业正在使用极验，每天服务响应超过四亿次，广泛应用于直播视频、金融服务、电子商务、游戏娱乐、政府企业等各大类型网站
对于这类验证，如果我们直接模拟表单请求，繁琐的认证参数与认证流程会让你蛋碎一地，我们可以用selenium驱动浏览器来解决这个问题，大致分为以下几个步骤
#步骤一:点击按钮，弹出没有缺口的图片 #步骤二：获取步骤一的图片 #步骤三：点击滑动按钮，弹出带缺口的图片 #步骤四：获取带缺口的图片 #步骤五：对比两张图片的所有RBG像素点，得到不一样像素点的x值，即要移动的距离 #步骤六：模拟人的行为习惯（先匀加速拖动后匀减速拖动），把需要拖动的总距离分成一段一段小的轨迹 #步骤七：按照轨迹拖动，完全验证 #步骤八：完成登录
二 实现
#安装：selenium+chrome/phantomjs #安装：Pillow Pillow:基于PIL，处理python 3.x的图形图像库.因为PIL只能处理到python 2.x，而这个模块能处理Python3.x，目前用它做图形的很多. http://www.cnblogs.com/apexchu/p/4231041.html C:UsersAdministrator>pip3 install pillow C:UsersAdministrator>python3 Python 3.6.1 (v3.6.1:69c0db5, Mar 21 2017, 18:41:36) [MSC v.1900 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> from PIL import Image >>>
from selenium import webdriver from selenium.webdriver import ActionChains from selenium.webdriver.common.by import By from selenium.webdriver.common.keys import Keys from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.support.wait import WebDriverWait from PIL import Image import time def get_snap(): ''' 对整个网页截图，保存成图片，然后用PIL.Image拿到图片对象 :return: 图片对象 ''' driver.save_screenshot('snap.png') page_snap_obj=Image.open('snap.png') return page_snap_obj def get_image(): ''' 从网页的网站截图中，截取验证码图片 :return: 验证码图片 ''' img=wait.until(EC.presence_of_element_located((By.CLASS_NAME,'geetest_canvas_img'))) time.sleep(2) #保证图片刷新出来 localtion=img.location size=img.size top=localtion['y'] bottom=localtion['y']+size['height'] left=localtion['x'] right=localtion['x']+size['width'] page_snap_obj=get_snap() crop_imag_obj=page_snap_obj.crop((left,top,right,bottom)) return crop_imag_obj def get_distance(image1,image2): ''' 拿到滑动验证码需要移动的距离 :param image1:没有缺口的图片对象 :param image2:带缺口的图片对象 :return:需要移动的距离 ''' threshold=60 left=57 for i in range(left,image1.size[0]): for j in range(image1.size[1]): rgb1=image1.load()[i,j] rgb2=image2.load()[i,j] res1=abs(rgb1[0]-rgb2[0]) res2=abs(rgb1[1]-rgb2[1]) res3=abs(rgb1[2]-rgb2[2]) if not (res1 < threshold and res2 < threshold and res3 < threshold): return i-7 #经过测试，误差为大概为7 return i-7 #经过测试，误差为大概为7 def get_tracks(distance): ''' 拿到移动轨迹，模仿人的滑动行为，先匀加速后匀减速 匀变速运动基本公式： ①v=v0+at ②s=v0t+½at² ③v²-v0²=2as :param distance: 需要移动的距离 :return: 存放每0.3秒移动的距离 ''' #初速度 v=0 #单位时间为0.2s来统计轨迹，轨迹即0.2内的位移 t=0.3 #位移/轨迹列表，列表内的一个元素代表0.2s的位移 tracks=[] #当前的位移 current=0 #到达mid值开始减速 mid=distance*4/5 while current < distance: if current < mid: # 加速度越小，单位时间的位移越小,模拟的轨迹就越多越详细 a= 2 else: a=-3 #初速度 v0=v #0.2秒时间内的位移 s=v0*t+0.5*a*(t**2) #当前的位置 current+=s #添加到轨迹列表 tracks.append(round(s)) #速度已经达到v,该速度作为下次的初速度 v=v0+a*t return tracks try: driver=webdriver.Chrome() driver.get('https://account.geetest.com/login') wait=WebDriverWait(driver,10) #步骤一：先点击按钮，弹出没有缺口的图片 button=wait.until(EC.presence_of_element_located((By.CLASS_NAME,'geetest_radar_tip'))) button.click() #步骤二：拿到没有缺口的图片 image1=get_image() #步骤三：点击拖动按钮，弹出有缺口的图片 button=wait.until(EC.presence_of_element_located((By.CLASS_NAME,'geetest_slider_button'))) button.click() #步骤四：拿到有缺口的图片 image2=get_image() # print(image1,image1.size) # print(image2,image2.size) #步骤五：对比两张图片的所有RBG像素点，得到不一样像素点的x值，即要移动的距离 distance=get_distance(image1,image2) #步骤六：模拟人的行为习惯（先匀加速拖动后匀减速拖动），把需要拖动的总距离分成一段一段小的轨迹 tracks=get_tracks(distance) print(tracks) print(image1.size) print(distance,sum(tracks)) #步骤七：按照轨迹拖动，完全验证 button=wait.until(EC.presence_of_element_located((By.CLASS_NAME,'geetest_slider_button'))) ActionChains(driver).click_and_hold(button).perform() for track in tracks: ActionChains(driver).move_by_offset(xoffset=track,yoffset=0).perform() else: ActionChains(driver).move_by_offset(xoffset=3,yoffset=0).perform() #先移过一点 ActionChains(driver).move_by_offset(xoffset=-3,yoffset=0).perform() #再退回来，是不是更像人了 time.sleep(0.5) #0.5秒后释放鼠标 ActionChains(driver).release().perform() #步骤八：完成登录 input_email=driver.find_element_by_id('email') input_password=driver.find_element_by_id('password') button=wait.until(EC.element_to_be_clickable((By.CLASS_NAME,'login-btn'))) input_email.send_keys('18611453110@163.com') input_password.send_keys('linhaifeng123') # button.send_keys(Keys.ENTER) button.click() import time time.sleep(200) finally: driver.close()
from selenium import webdriver from selenium.webdriver import ActionChains from selenium.webdriver.common.by import By from selenium.webdriver.common.keys import Keys from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.support.wait import WebDriverWait from PIL import Image import time def get_snap(): driver.save_screenshot('full_snap.png') page_snap_obj=Image.open('full_snap.png') return page_snap_obj def get_image(): img=driver.find_element_by_class_name('geetest_canvas_img') time.sleep(2) location=img.location size=img.size left=location['x'] top=location['y'] right=left+size['width'] bottom=top+size['height'] page_snap_obj=get_snap() image_obj=page_snap_obj.crop((left,top,right,bottom)) # image_obj.show() return image_obj def get_distance(image1,image2): start=57 threhold=60 for i in range(start,image1.size[0]): for j in range(image1.size[1]): rgb1=image1.load()[i,j] rgb2=image2.load()[i,j] res1=abs(rgb1[0]-rgb2[0]) res2=abs(rgb1[1]-rgb2[1]) res3=abs(rgb1[2]-rgb2[2]) # print(res1,res2,res3) if not (res1 < threhold and res2 < threhold and res3 < threhold): return i-7 return i-7 def get_tracks(distance): distance+=20 #先滑过一点，最后再反着滑动回来 v=0 t=0.2 forward_tracks=[] current=0 mid=distance*3/5 while current < distance: if current < mid: a=2 else: a=-3 s=v*t+0.5*a*(t**2) v=v+a*t current+=s forward_tracks.append(round(s)) #反着滑动到准确位置 back_tracks=[-3,-3,-2,-2,-2,-2,-2,-1,-1,-1] #总共等于-20 return {'forward_tracks':forward_tracks,'back_tracks':back_tracks} try: # 1、输入账号密码回车 driver = webdriver.Chrome() driver.implicitly_wait(3) driver.get('https://passport.cnblogs.com/user/signin') username = driver.find_element_by_id('input1') pwd = driver.find_element_by_id('input2') signin = driver.find_element_by_id('signin') username.send_keys('linhaifeng') pwd.send_keys('xxxxx') signin.click() # 2、点击按钮，得到没有缺口的图片 button = driver.find_element_by_class_name('geetest_radar_tip') button.click() # 3、获取没有缺口的图片 image1 = get_image() # 4、点击滑动按钮，得到有缺口的图片 button = driver.find_element_by_class_name('geetest_slider_button') button.click() # 5、获取有缺口的图片 image2 = get_image() # 6、对比两种图片的像素点，找出位移 distance = get_distance(image1, image2) # 7、模拟人的行为习惯，根据总位移得到行为轨迹 tracks = get_tracks(distance) print(tracks) # 8、按照行动轨迹先正向滑动，后反滑动 button = driver.find_element_by_class_name('geetest_slider_button') ActionChains(driver).click_and_hold(button).perform() # 正常人类总是自信满满地开始正向滑动，自信地表现是疯狂加速 for track in tracks['forward_tracks']: ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform() # 结果傻逼了，正常的人类停顿了一下，回过神来发现，卧槽，滑过了,然后开始反向滑动 time.sleep(0.5) for back_track in tracks['back_tracks']: ActionChains(driver).move_by_offset(xoffset=back_track, yoffset=0).perform() # 小范围震荡一下，进一步迷惑极验后台，这一步可以极大地提高成功率 ActionChains(driver).move_by_offset(xoffset=-3, yoffset=0).perform() ActionChains(driver).move_by_offset(xoffset=3, yoffset=0).perform() # 成功后，骚包人类总喜欢默默地欣赏一下自己拼图的成果，然后恋恋不舍地松开那只脏手 time.sleep(0.5) ActionChains(driver).release().perform() time.sleep(10) # 睡时间长一点，确定登录成功 finally: driver.close()
from selenium import webdriver from selenium.webdriver import ActionChains from selenium.webdriver.common.by import By from selenium.webdriver.common.keys import Keys from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.support.wait import WebDriverWait from PIL import Image import time def get_snap(driver): driver.save_screenshot('full_snap.png') page_snap_obj=Image.open('full_snap.png') return page_snap_obj def get_image(driver): img=driver.find_element_by_class_name('geetest_canvas_img') time.sleep(2) location=img.location size=img.size left=location['x'] top=location['y'] right=left+size['width'] bottom=top+size['height'] page_snap_obj=get_snap(driver) image_obj=page_snap_obj.crop((left,top,right,bottom)) # image_obj.show() return image_obj def get_distance(image1,image2): start=57 threhold=60 for i in range(start,image1.size[0]): for j in range(image1.size[1]): rgb1=image1.load()[i,j] rgb2=image2.load()[i,j] res1=abs(rgb1[0]-rgb2[0]) res2=abs(rgb1[1]-rgb2[1]) res3=abs(rgb1[2]-rgb2[2]) # print(res1,res2,res3) if not (res1 < threhold and res2 < threhold and res3 < threhold): return i-7 return i-7 def get_tracks(distance): distance+=20 #先滑过一点，最后再反着滑动回来 v=0 t=0.2 forward_tracks=[] current=0 mid=distance*3/5 while current < distance: if current < mid: a=2 else: a=-3 s=v*t+0.5*a*(t**2) v=v+a*t current+=s forward_tracks.append(round(s)) #反着滑动到准确位置 back_tracks=[-3,-3,-2,-2,-2,-2,-2,-1,-1,-1] #总共等于-20 return {'forward_tracks':forward_tracks,'back_tracks':back_tracks} def crack(driver): #破解滑动认证 # 1、点击按钮，得到没有缺口的图片 button = driver.find_element_by_class_name('geetest_radar_tip') button.click() # 2、获取没有缺口的图片 image1 = get_image(driver) # 3、点击滑动按钮，得到有缺口的图片 button = driver.find_element_by_class_name('geetest_slider_button') button.click() # 4、获取有缺口的图片 image2 = get_image(driver) # 5、对比两种图片的像素点，找出位移 distance = get_distance(image1, image2) # 6、模拟人的行为习惯，根据总位移得到行为轨迹 tracks = get_tracks(distance) print(tracks) # 7、按照行动轨迹先正向滑动，后反滑动 button = driver.find_element_by_class_name('geetest_slider_button') ActionChains(driver).click_and_hold(button).perform() # 正常人类总是自信满满地开始正向滑动，自信地表现是疯狂加速 for track in tracks['forward_tracks']: ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform() # 结果傻逼了，正常的人类停顿了一下，回过神来发现，卧槽，滑过了,然后开始反向滑动 time.sleep(0.5) for back_track in tracks['back_tracks']: ActionChains(driver).move_by_offset(xoffset=back_track, yoffset=0).perform() # 小范围震荡一下，进一步迷惑极验后台，这一步可以极大地提高成功率 ActionChains(driver).move_by_offset(xoffset=-3, yoffset=0).perform() ActionChains(driver).move_by_offset(xoffset=3, yoffset=0).perform() # 成功后，骚包人类总喜欢默默地欣赏一下自己拼图的成果，然后恋恋不舍地松开那只脏手 time.sleep(0.5) ActionChains(driver).release().perform() def login_cnblogs(username,password): driver = webdriver.Chrome() try: # 1、输入账号密码回车 driver.implicitly_wait(3) driver.get('https://passport.cnblogs.com/user/signin') input_username = driver.find_element_by_id('input1') input_pwd = driver.find_element_by_id('input2') signin = driver.find_element_by_id('signin') input_username.send_keys(username) input_pwd.send_keys(password) signin.click() # 2、破解滑动认证 crack(driver) time.sleep(10) # 睡时间长一点，确定登录成功 finally: driver.close() if __name__ == '__main__': login_cnblogs(username='linhaifeng',password='xxxx')
import time from selenium import webdriver from selenium.webdriver import ActionChains from PIL import Image def get_snap(tiga): tiga.save_screenshot('snap.png') snap_obj=Image.open('snap.png') return snap_obj def get_img(tiga): complete_img=tiga.find_element_by_class_name('geetest_canvas_img') time.sleep(2) size=complete_img.size location=complete_img.location left=location['x'] top=location['y'] right=left+size['width'] bottom=top+size['height'] snap_obj=get_snap(tiga) image_obj = snap_obj.crop((left, top, right, bottom)) #image_obj.show() return image_obj def get_distance(image1,image2): start_x=58 threhold=60 print(image1.size)#(258, 159) print(image2.size)#(258, 159) for x in range(start_x,image1.size[0]): for y in range(image1.size[1]): rgb1=image1.load()[x,y] rgb2=image2.load()[x,y] res1=abs(rgb1[0]-rgb2[0]) res2=abs(rgb1[1]-rgb2[1]) res3=abs(rgb1[2]-rgb2[2]) if not (res1 < threhold and res2 < threhold and res3 < threhold): return x-7 def get_tracks(distance): distance+=20 #v=v0+a*t #s=v*t+0.5*a*(t**2) v0=0 s=0 t=0.2 mid=distance*3/5 forward_tracks=[] while s < distance: if s < mid: a=2 else: a=-3 v=v0 track=v*t+0.5*a*(t**2) track=round(track) v0=v+a*t s+=track forward_tracks.append(track) back_tracks=[-1,-1,-1,-2,-2,-2,-3,-3,-2,-2,-1] #20 return {"forward_tracks":forward_tracks,'back_tracks':back_tracks} tiga=webdriver.Chrome() tiga.get('https://passport.cnblogs.com/user/signin') tiga.implicitly_wait(6) try: #1.输入账号密码,点击登录 username=tiga.find_element_by_id('input1') pwd=tiga.find_element_by_id('input2') btn=tiga.find_element_by_id('signin') username.send_keys('CTZ492745473') pwd.send_keys('CTZ1.2.3.DJATM') btn.click() #2.点击按钮弹出没有切口的图 button=tiga.find_element_by_class_name('geetest_radar_tip') button.click() #3.针对没有切口的图进行截图 image1=get_img(tiga) # 4、点击滑动按钮，弹出有缺口的图 slider_button=tiga.find_element_by_class_name('geetest_slider_button') slider_button.click() # 5、针对有缺口的图片进行截图 image2=get_img(tiga) #6、对比两张图片，找出缺口，即滑动的位移 distance=get_distance(image1,image2) print(distance) # 7、按照人的行为行为习惯，把总位移切成一段段小的位移 traks_dic = get_tracks(distance) # 8、按照位移移动 slider_button = tiga.find_element_by_class_name('geetest_slider_button') ActionChains(tiga).click_and_hold(slider_button).perform() # 先向前移动 forward_tracks = traks_dic["forward_tracks"] back_tracks = traks_dic["back_tracks"] for forward_track in forward_tracks: ActionChains(tiga).move_by_offset(xoffset=forward_track, yoffset=0).perform() # 短暂停顿，发现，移过了 time.sleep(0.2) # 先向后移动 for back_track in back_tracks: ActionChains(tiga).move_by_offset(xoffset=back_track, yoffset=0).perform() ActionChains(tiga).move_by_offset(xoffset=-3, yoffset=0).perform() ActionChains(tiga).move_by_offset(xoffset=3, yoffset=0).perform() time.sleep(0.3) ActionChains(tiga).release().perform() time.sleep(10) except Exception: pass finally: tiga.close()