极验
#
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) # 步骤八:完成登录 input_email = driver.find_element_by_xpath('//*[@id="base"]/div[2]/div/div[2]/div[3]/div/form/div[1]/div/div[1]/div/input') input_password = driver.find_element_by_xpath('//*[@id="base"]/div[2]/div/div[2]/div[3]/div/form/div[2]/div/div[1]/div/input') input_email.send_keys('18611453110@163.com') input_password.send_keys('linhaifeng123') # 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() button = wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'geetest-btn'))) # button.send_keys(Keys.ENTER) 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() # import time # time.sleep(200) finally: print(1) # driver.close()