共2个文件。
""" web自动化的环境安装: 1、selenium: 安装:pip install selenium 2、安装chromedriver: 案例需求:解决web自动化过程中遇到的滑动验证码验证的问题: 解决方案:自动识别滑动距离,进行滑动验证 """ import time from selenium import webdriver from py0723_slide.slideVerfication import SlideVerificationCode # 第一步:打开qq空间登录页面: # 1.1启动浏览器 driver = webdriver.Chrome() driver.implicitly_wait(10) # 1.2访问qq空间登录页面 driver.get("https://qzone.qq.com/") # 1.3点击账号密码登陆按钮 # 1.3.1切换到登录iframe中 driver.switch_to.frame("login_frame") # 1.3.2点击打开账号密码登录 driver.find_element_by_id("switcher_plogin").click() # 第二步:输入账号密码,点击登录 # 2.1定位账号输入框,输入账号 driver.find_element_by_id('u').send_keys("350978786") # 2.2定位密码输入框,输入密码 driver.find_element_by_id('p').send_keys("12131311") # 2.3 点击登录 driver.find_element_by_id('login_button').click() # 第三步:进行滑动验证 # 3.1定位验证码所在的iframe,并进行切换 v_frame = driver.find_element_by_id('tcaptcha_iframe') driver.switch_to.frame(v_frame) # 3.2获取验证码滑块图元素 sli_ele = driver.find_element_by_id('slideBlock') # 3.3获取验证码背景图的元素 bg_ele = driver.find_element_by_id('slideBg') # 3.4 识别滑块需要滑动的距离 # 3.4.1识别背景缺口位置 sv = SlideVerificationCode() distance = sv.get_element_slide_distance(sli_ele,bg_ele ) # 3.4.2 根据页面的缩放比列调整滑动距离 dis = (distance * 280/680)-30 # 3.5 获取滑块按钮 sli_btn =driver.find_element_by_id('tcaptcha_drag_thumb') # 3.6拖动滑块进行验证 sv.slide_verification(driver,sli_btn,dis) # 关闭浏览 time.sleep(15) driver.close()
2.slideVerfication.py>
""" 本模块专门用来处理滑动验证码的问题, """ from selenium.webdriver import ActionChains import random, time, os import cv2 from PIL import Image as Im import numpy as np import requests class SlideVerificationCode(): """滑动验证码破解""" def __init__(self, slider_ele=None, background_ele=None, count=1, save_image=False): """ :param count: 验证重试的次数,默认为5次 :param save_image: 是否保存验证过程中的图片,默认不保存 """ self.count = count self.save_image = save_image self.slider_ele = slider_ele self.background_ele = background_ele def slide_verification(self, driver, slide_element, distance): """ :param driver: driver对象 :type driver:webdriver.Chrome :param slide_element: 滑块的元组 :type slider_ele: WebElement :param distance: 滑动的距离 :type: int :return: """ # 获取滑动前页面的url地址 start_url = driver.current_url print("需要滑动的距离为:", distance) # 根据滑动距离生成滑动轨迹 locus = self.get_slide_locus(distance) print("生成的滑动轨迹为:{},轨迹的距离之和为{}".format(locus, distance)) # 按下鼠标左键 ActionChains(driver).click_and_hold(slide_element).perform() time.sleep(0.5) # 遍历轨迹进行滑动 for loc in locus: time.sleep(0.01) ActionChains(driver).move_by_offset(loc, random.randint(-5, 5)).perform() ActionChains(driver).context_click(slide_element) # 释放鼠标 ActionChains(driver).release(on_element=slide_element).perform() # 判读是否验证通过,未通过的情况下重新滑动 time.sleep(2) # 滑动之后再次获取url地址 end_url = driver.current_url # 滑动失败的情况下,重试count次 if start_url == end_url and self.count > 0: print("第{}次验证失败,开启重试".format(6 - self.count)) self.count -= 1 self.slide_verification(driver, slide_element, distance) def onload_save_img(self, url, filename="image.png"): """ 下载图片保存 :param url:图片地址 :param filename: 保存的图片名 :return: """ try: response = requests.get(url=url) except(requests.exceptions.ConnectTimeout, requests.exceptions.ConnectionError)as e: print("图片下载失败") raise e else: with open(filename, "wb") as f: f.write(response.content) def get_element_slide_distance(self, slider_ele, background_ele, correct=0): """ 根据传入滑块,和背景的节点,计算滑块的距离 该方法只能计算 滑块和背景图都是一张完整图片的场景, 如果是通过多张小图拼接起来的背景图,该方法不适用,后续会补充一个专门针对处理该场景的方法 :param slider_ele: 滑块图片的节点 :type slider_ele: WebElement :param background_ele: 背景图的节点 :type background_ele:WebElement :param correct:滑块缺口截图的修正值,默认为0,调试截图是否正确的情况下才会用 :type: int :return: 背景图缺口位置的X轴坐标位置(缺口图片左边界位置) """ # 获取验证码的图片 slider_url = slider_ele.get_attribute("src") background_url = background_ele.get_attribute("src") # 下载验证码背景图,滑动图片 slider = "slider.jpg" background = "background.jpg" self.onload_save_img(slider_url, slider) self.onload_save_img(background_url, background) # 读取进行色度图片,转换为numpy中的数组类型数据, slider_pic = cv2.imread(slider, 0) background_pic = cv2.imread(background, 0) # 获取缺口图数组的形状 -->缺口图的宽和高 width, height = slider_pic.shape[::-1] # 将处理之后的图片另存 slider01 = "slider01.jpg" background_01 = "background01.jpg" cv2.imwrite(background_01, background_pic) cv2.imwrite(slider01, slider_pic) # 读取另存的滑块图 slider_pic = cv2.imread(slider01) # 进行色彩转换 slider_pic = cv2.cvtColor(slider_pic, cv2.COLOR_BGR2GRAY) # 获取色差的绝对值 slider_pic = abs(255 - slider_pic) # 保存图片 cv2.imwrite(slider01, slider_pic) # 读取滑块 slider_pic = cv2.imread(slider01) # 读取背景图 background_pic = cv2.imread(background_01) # 比较两张图的重叠区域 result = cv2.matchTemplate(slider_pic, background_pic, cv2.TM_CCOEFF_NORMED) # 通过数组运算,获取图片的缺口位置 top, left = np.unravel_index(result.argmax(), result.shape) # 背景图中的图片缺口坐标位置 print("当前滑块的缺口位置:", (left, top, left + width, top + height)) # 判读是否需求保存识别过程中的截图文件 if self.save_image: # 截图滑块保存 # 进行坐标修正 loc = (left + correct, top + correct, left + width - correct, top + height - correct) self.image_crop(background, loc) else: # 删除识别过程中保存的临时文件 os.remove(slider01) os.remove(background_01) os.remove(slider) os.remove(background) # 返回需要移动的位置距离 return left def get_image_slide_dictance(self, slider_image, background_image, correct=0): """ 根据传入滑块,和背景的图片,计算滑块的距离 该方法只能计算 滑块和背景图都是一张完整图片的场景, 如果是通过多张小图拼接起来的背景图,该方法不适用,后续会补充一个专门针对处理该场景的方法 :param slider_iamge: 滑块图的图片 :type slider_image: str :param background_image: 背景图的图片 :type background_image: str :param correct:滑块缺口截图的修正值,默认为0,调试截图是否正确的情况下才会用 :type: int :return: 背景图缺口位置的X轴坐标位置(缺口图片左边界位置) """ # 读取进行色度图片,转换为numpy中的数组类型数据, slider_pic = cv2.imread(slider_image, 0) background_pic = cv2.imread(background_image, 0) # 获取缺口图数组的形状 -->缺口图的宽和高 width, height = slider_pic.shape[::-1] # 将处理之后的图片另存 slider01 = "slider01.jpg" background_01 = "background01.jpg" cv2.imwrite(background_01, background_pic) cv2.imwrite(slider01, slider_pic) # 读取另存的滑块图 slider_pic = cv2.imread(slider01) # 进行色彩转换 slider_pic = cv2.cvtColor(slider_pic, cv2.COLOR_BGR2GRAY) # 获取色差的绝对值 slider_pic = abs(255 - slider_pic) # 保存图片 cv2.imwrite(slider01, slider_pic) # 读取滑块 slider_pic = cv2.imread(slider01) # 读取背景图 background_pic = cv2.imread(background_01) # 比较两张图的重叠区域 result = cv2.matchTemplate(slider_pic, background_pic, cv2.TM_CCOEFF_NORMED) # 获取图片的缺口位置 top, left = np.unravel_index(result.argmax(), result.shape) # 背景图中的图片缺口坐标位置 print("当前滑块的缺口位置:", (left, top, left + width, top + height)) # 判读是否需求保存识别过程中的截图文件 if self.save_image: # 截图滑块保存 # 进行坐标修正 loc = (left + correct, top + correct, left + width - correct, top + height - correct) self.image_crop(background_image, loc) else: # 删除识别过程中保存的临时文件 os.remove(slider01) os.remove(background_01) # 返回需要移动的位置距离 return left @classmethod def get_slide_locus(self, distance): """ 根据移动坐标位置构造移动轨迹,前期移动慢,中期块,后期慢 :param distance:移动距离 :type:int :return:移动轨迹 :rtype:list """ remaining_dist = distance locus = [] while remaining_dist > 0: ratio = remaining_dist / distance if ratio < 0.2: # 开始阶段移动较慢 span = random.randint(2, 8) elif ratio > 0.8: # 结束阶段移动较慢 span = random.randint(5, 8) else: # 中间部分移动快 span = random.randint(10, 16) locus.append(span) remaining_dist -= span return locus def image_crop(self, image, location, new_name="new_image.png"): """ 对图片的指定位置进行截图 :param image: 被截取图片的坐标位置 :param location:需要截图的坐标位置:(left,top,right,button) :type location: tuple :return: """ # 打开图片 image = Im.open(image) # 切割图片 imagecrop = image.crop(location) # 保存图片 imagecrop.save(new_name)