这一篇写完很久了，因为识别率一直很低，没办法拿出来见大家，所以一直隐藏着，今天终于可以拿出来见见阳光了。

哈喽，大家好，我是星星在线，我又来了，今天给大家带来的是极验验证码的selenium破解之法，是不是有点小激动呢，小伙伴们等不了了，让我们赶紧直入主题吧。

虎嗅网注册

这次我们是拿虎嗅开刀，注册账号的时候需要滑动图片到缺口位置，这种验证码我们现在也经常遇到，这个就不用详细介绍了吧
) full_image.save("full.jpg") # 根据两个图片计算距离 distance = self.get_offset_distance(cut_image, full_image) # 开始移动 self.start_move(distance) # 如果出现error try: WebDriverWait(self.driver, 5, 0.5).until(EC.element_to_be_clickable((By.XPATH, '//div[@class="gt_ajax_tip gt_error"]'))) print("验证失败") return except TimeoutException as e: pass # 判断是否验证成功 try: WebDriverWait(self.driver, 10, 0.5).until(EC.element_to_be_clickable((By.XPATH, '//div[@class="gt_ajax_tip gt_success"]'))) except TimeoutException: print("again times") time.sleep(5) # 失败后递归执行拖动 self.analog_drag() else: # 成功后输入手机号，发送验证码 self.register() # 获取图片和位置列表 def get_image_url(self, xpath): link = re.compile('background-image: url\("(.*?)"\); background-position: (.*?)px (.*?)px;') elements = self.driver.find_elements_by_xpath(xpath) image_url = None location = list() for element in elements: style = element.get_attribute("style") groups = link.search(style) url = groups[1] x_pos = groups[2] y_pos = groups[3] location.append((int(x_pos), int(y_pos))) image_url = url return image_url, location # 拼接图片 def mosaic_image(self, image_url, location): resq = requests.get(image_url) file = BytesIO(resq.content) img = Image.open(file) image_upper_lst = [] image_down_lst = [] for pos in location: if pos[1] == 0: # y值==0的图片属于上半部分，高度58 image_upper_lst.append(img.crop((abs(pos[0]), 0, abs(pos[0]) + 10, 58))) else: # y值==58的图片属于下半部分 image_down_lst.append(img.crop((abs(pos[0]), 58, abs(pos[0]) + 10, img.height))) x_offset = 0 # 创建一张画布，x_offset主要为新画布使用 new_img = Image.new("RGB", (260, img.height)) for img in image_upper_lst: new_img.paste(img, (x_offset, 58)) x_offset += img.width x_offset = 0 for img in image_down_lst: new_img.paste(img, (x_offset, 0)) x_offset += img.width return new_img # 判断颜色是否相近 def is_similar_color(self, x_pixel, y_pixel): for i, pixel in enumerate(x_pixel): if abs(y_pixel[i] - pixel) > 50: return False return True # 计算距离 def get_offset_distance(self, cut_image, full_image): for x in range(cut_image.width): for y in range(cut_image.height): cpx = cut_image.getpixel((x, y)) fpx = full_image.getpixel((x, y)) if not self.is_similar_color(cpx, fpx): img = cut_image.crop((x, y, x + 50, y + 40)) # 保存一下计算出来位置图片，看看是不是缺口部分 img.save("1.jpg") return x # 开始移动 def start_move(self, distance): element = self.driver.find_element_by_xpath('//div[@class="gt_slider_knob gt_show"]') # 这里就是根据移动进行调试，计算出来的位置不是百分百正确的，加上一点偏移 distance -= element.size.get('width') / 2 distance += 15 # 按下鼠标左键 ActionChains(self.driver).click_and_hold(element).perform() time.sleep(0.5) while distance > 0: if distance > 10: # 如果距离大于10，就让他移动快一点 span = random.randint(5, 8) else: # 快到缺口了，就移动慢一点 span = random.randint(2, 3) ActionChains(self.driver).move_by_offset(span, 0).perform() distance -= span time.sleep(random.randint(10,50)/100) ActionChains(self.driver).move_by_offset(distance, 1).perform() ActionChains(self.driver).release(on_element=element).perform() def register(self): element = self.driver.find_element_by_xpath('//input[@id="sms_username"]') element.clear() element.send_keys("手机号") ele_captcha = self.driver.find_element_by_xpath('//span[@class="js-btn-captcha btn-captcha"]') ele_captcha.click() if __name__ == "__main__": h = HuXiu() h.visit_index()