使用OpenCVselenium解决滑块验证码

博观而约取，厚积而薄发。这篇文章主要讲述使用OpenCVselenium解决滑块验证码相关的知识，希望能为你提供帮助。
本次案例使用OpenCV和selenium来解决一下滑块验证码
先说一下思路：

弹出滑块验证码后使用selenium元素截图将验证码整个背景图截取出来
将需要滑动的小图单独截取出来，最好将小图与背景图顶部的像素距离获取到，这样可以将背景图上下多余的边框截取掉
使用OpenCV将背景图和小图进行灰度处理，并对小图再次进行二值化全局阈值，这样就可以利用OpenCV在背景图中找到小图所在的位置
用OpenCV获取到相差的距离后利用selenium的鼠标拖动方法进行拖拉至终点。

详细步骤：
先获取验证码背景图，selenium浏览器对象中使用screenshot方法可以将指定的元素图片截取出来

import os from selenium import webdriverbrowser = webdriver.Chrome() browser.get("https://www.toutiao.com/c/user/token/MS4wLjABAAAA4EKNlqVeNTTuEdWn0VytNS8cdODKTsNNwLTxOnigzZtclro2Kylvway5mTyTUKvz/")save_path = os.path.join(os.path.expanduser(\'~\'), "Desktop", "background.png") browser.find_element_by_id("element_id_name").screenshot(save_path)

【使用OpenCVselenium解决滑块验证码】
截取后的验证码背景图和需要滑动的小图如：

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再将小图与背景图顶部的像素距离获取到，指的是下面图中红边的高度：

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如果html元素中小图是单独存在时，那么它的高度在会定义在页面元素中，使用selenium页面元素对象的value_of_css_property方法可以获取到像素距离。
获取这个是因为要把背景图的上下两边多余部分进行切除，从而保留关键的图像部位，能够大幅度提高识别率。

element_object = browser.find_element_by_xpath("xpath_element") px = element_object.value_of_css_property("top")

接下来就要对图像进行灰度处理：

import numpy import cv2def make_threshold(img): """全局阈值将图片二值化,去除噪点,让其黑白分明""" x = numpy.ones(img.shape, numpy.uint8) * 255 y = img - x result, thresh = cv2.threshold(y, 127, 255, cv2.THRESH_BINARY_INV) # 将二值化后的结果返回 return threshclass ComputeDistance: """获取需要滑动的距离将验证码背景大图和需要滑动的小图进行处理,先在大图中找到相似的小图位置,再获取对应的像素偏移量""" def __init__(self, Background_path: str, image_to_move: str, offset_top_px: int): """ :param Background_path: 验证码背景大图 :param image_to_move: 需要滑动的小图 :param offset_top_px: 小图距离在大图上的顶部边距(像素偏移量) """ self.Background_img = cv2.imread(Background_path) self.offset_px = offset_top_px self.show_img = show_img small_img_data = https://www.songbingjia.com/android/cv2.imread(image_to_move, cv2.IMREAD_UNCHANGED) # 得到一个改变维度为50的乘以值 scaleX = 50 / small_img_data.shape[1] # 使用最近邻插值法缩放，让xy乘以scaleX，得到缩放后shape为50x50的图片 self.tpl_img = cv2.resize(small_img_data, (0, 0), fx=scaleX, fy=scaleX) self.Background_cutting = Nonedef tpl_op(self): # 将小图转换为灰色 tpl_gray = cv2.cvtColor(self.tpl_img, cv2.COLOR_BGR2GRAY) h, w = tpl_gray.shape # 将背景图转换为灰色 # Background_gray = cv2.cvtColor(self.Background_img, cv2.COLOR_BGR2GRAY) Background_gray = cv2.cvtColor(self.Background_cutting, cv2.COLOR_BGR2GRAY) # 得到二值化后的小图 threshold_img = make_threshold(tpl_gray) # 将小图与大图进行模板匹配,找到所对应的位置 result = cv2.matchTemplate(Background_gray, threshold_img, cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result) # 左上角位置 top_left = (max_loc[0] - 5, max_loc[1] + self.offset_px) # 右下角位置 bottom_right = (top_left[0] + w, top_left[1] + h) # 在源颜色大图中画出小图需要移动到的终点位置"""rectangle(图片源数据, 左上角, 右下角, 颜色, 画笔厚度)""" cv2.rectangle(self.Background_img, top_left, bottom_right, (0, 0, 255), 2)def cutting_background(self): """切割图片的上下边框""" height = self.tpl_img.shape[0] # 将大图中上下多余部分去除,如: Background_img[40:110, :] self.Background_cutting = self.Background_img[self.offset_px - 10: self.offset_px + height + 10, :]def run(self): # 如果小图的长度与大图的长度一致则不用将大图进行切割,可以将self.cutting_background()注释掉 self.cutting_background() return self.tpl_op()if __name__ == \'__main__\': image_path1 = "背景图路径" image_path2 = "小图路径" distance_px = "像素距离" main = ComputeDistance(image_path1, image_path2, distance_px) main.run()

上面代码可以返回小图到凹点的距离，现在我们可以看一下灰度处理中的图片样子：

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得到距离后还要对这个距离数字进行处理一下，要让它拆分成若干个小数，这么做的目的是在拖动的时候不能一下拖动到终点，
要模仿人类的手速缓缓向前行驶，不然很明显是机器在操控。
比如到终点的距离为100，那么要把它转为 [8, 6, 11, 10, 3, 6, 3, -2, 4, 0, 15, 1, 9, 6, -2, 4, 1, -2, 15, 6, -2] 类似的，列表中的数加起来正好为100.
最简单的转换：

def handle_distance(distance): """将直线距离转为缓慢的轨迹""" import random slow_distance = [] while sum(slow_distance) < = distance: slow_distance.append(random.randint(-2, 15))if sum(slow_distance) != distance: slow_distance.append(distance - sum(slow_distance)) return slow_distance

有了到终点的距离，接下来就开始拖动吧：

import time from random import randint from selenium.webdriver.common.action_chains import ActionChainsdef move_slider(website, slider, track, **kwargs): """将滑块移动到终点位置 :param website: selenium页面对象 :param slider: selenium页面中滑块元素对象 :param track: 到终点所需的距离 """ name = kwargs.get(\'name\', \'滑块\')try: if track[0] > 200: return track[0] # 点击滑块元素并拖拽 ActionChains(website).click_and_hold(slider).perform() time.sleep(0.15) for i in track: # 随机上下浮动鼠标 ActionChains(website).move_by_offset(xoffset=i, yoffset=randint(-2, 2)).perform() # 释放元素 time.sleep(1) ActionChains(website).release(slider).perform() time.sleep(1) # 随机拿开鼠标 ActionChains(website).move_by_offset(xoffset=randint(200, 300), yoffset=randint(200, 300)).perform() print(f\'[网页] 拖拽 {name}\') return True except Exception as e: print(f\'[网页] 拖拽 {name} 失败 {e}\')

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教程结束，让我们结合上面代码做一个案例吧。调用ComputeDistance类时，参数 show_img=True 可以在拖动验证码前进行展示背景图识别终点后的区域在哪里，如：
distance_obj = ComputeDistance(background_path, small_path, px, show_img=True)

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OK，下面为案例代码：

import os import time import requests import cv2 import numpy from random import randintfrom selenium import webdriver from selenium.webdriver.common.action_chains import ActionChainsdef show_image(img_array, name=\'img\', resize_flag=False): """展示图片""" maxHeight = 540 maxWidth = 960 scaleX = maxWidth / img_array.shape[1] scaleY = maxHeight / img_array.shape[0] scale = min(scaleX, scaleY) if resize_flag and scale < 1: img_array = cv2.resize(img_array, (0, 0), fx=scale, fy=scale) cv2.imshow(name, img_array) cv2.waitKey(0) cv2.destroyWindow(name)def make_threshold(img): """全局阈值将图片二值化,去除噪点,让其黑白分明""" x = numpy.ones(img.shape, numpy.uint8) * 255 y = img - x result, thresh = cv2.threshold(y, 127, 255, cv2.THRESH_BINARY_INV) # 将二值化后的结果返回 return threshdef move_slider(website, slider, track, **kwargs): """将滑块移动到终点位置 :param website: selenium页面对象 :param slider: selenium页面中滑块元素对象 :param track: 到终点所需的距离 """ name = kwargs.get(\'name\', \'滑块\')try: if track[0] > 200: return track[0] # 点击滑块元素并拖拽 ActionChains(website).click_and_hold(slider).perform() time.sleep(0.15) for i in track: # 随机上下浮动鼠标 ActionChains(website).move_by_offset(xoffset=i, yoffset=randint(-2, 2)).perform() # 释放元素 time.sleep(1) ActionChains(website).release(slider).perform() time.sleep(1) # 随机拿开鼠标 ActionChains(website).move_by_offset(xoffset=randint(200, 300), yoffset=randint(200, 300)).perform() print(f\'[网页] 拖拽 {name}\') return True except Exception as e: print(f\'[网页] 拖拽 {name} 失败 {e}\')class ComputeDistance: """获取需要滑动的距离将验证码背景大图和需要滑动的小图进行处理,先在大图中找到相似的小图位置,再获取对应的像素偏移量""" def __init__(self, Background_path: str, image_to_move: str, offset_top_px: int, show_img=False): """ :param Background_path: 验证码背景大图 :param image_to_move: 需要滑动的小图 :param offset_top_px: 小图距离在大图上的顶部边距(像素偏移量) :param show_img: 是否展示图片 """ self.Background_img = cv2.imread(Background_path) self.offset_px = offset_top_px self.show_img = show_img small_img_data = https://www.songbingjia.com/android/cv2.imread(image_to_move, cv2.IMREAD_UNCHANGED) # 得到一个改变维度为50的乘以值 scaleX = 50 / small_img_data.shape[1] # 使用最近邻插值法缩放，让xy乘以scaleX，得到缩放后shape为50x50的图片 self.tpl_img = cv2.resize(small_img_data, (0, 0), fx=scaleX, fy=scaleX) self.Background_cutting = Nonedef show(self, img): if self.show_img: show_image(img)def tpl_op(self): # 将小图转换为灰色 tpl_gray = cv2.cvtColor(self.tpl_img, cv2.COLOR_BGR2GRAY) h, w = tpl_gray.shape # 将背景图转换为灰色 # Background_gray = cv2.cvtColor(self.Background_img, cv2.COLOR_BGR2GRAY) Background_gray = cv2.cvtColor(self.Background_cutting, cv2.COLOR_BGR2GRAY) # 得到二值化后的小图 threshold_img = make_threshold(tpl_gray) # 将小图与大图进行模板匹配,找到所对应的位置 result = cv2.matchTemplate(Background_gray, threshold_img, cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result) # 左上角位置 top_left = (max_loc[0] - 5, max_loc[1] + self.offset_px) # 右下角位置 bottom_right = (top_left[0] + w, top_left[1] + h) # 在源颜色大图中画出小图需要移动到的终点位置"""rectangle(图片源数据, 左上角, 右下角, 颜色, 画笔厚度)""" cv2.rectangle(self.Background_img, top_left, bottom_right, (0, 0, 255), 2) if self.show_img: show_image(self.Background_img) return top_leftdef cutting_background(self): """切割图片的上下边框""" height = self.tpl_img.shape[0] # 将大图中上下多余部分去除,如: Background_img[40:110, :] self.Background_cutting = self.Background_img[self.offset_px - 10: self.offset_px + height + 10, :]def run(self): # 如果小图的长度与大图的长度一致则不用将大图进行切割,可以将self.cutting_background()注释掉 self.cutting_background() return self.tpl_op()class TodayNews(object): def __init__(self): self.url = "https://www.toutiao.com/c/user/token/" \\ "MS4wLjABAAAA4EKNlqVeNTTuEdWn0VytNS8cdODKTsNNwLTxOnigzZtclro2Kylvway5mTyTUKvz/" self.process_folder = os.path.join(os.path.expanduser(\'~\'), "Desktop", "today_news") self.background_path = os.path.join(self.process_folder, "background.png") self.small_path = os.path.join(self.process_folder, "small.png") self.small_px = None self.xpath = {} self.browser = Nonedef check_file_exist(self): """检查流程目录是否存在""" if not os.path.isdir(self.process_folder): os.mkdir(self.process_folder)def start_browser(self): """启动浏览器""" self.browser = webdriver.Chrome() self.browser.maximize_window()def close_browser(self): self.browser.quit()def wait_element_loaded(self, xpath: str, timeout=10, close_browser=True): """等待页面元素加载完成 :param xpath: xpath表达式 :param timeout: 最长等待超时时间 :param close_browser: 元素等待超时后是否关闭浏览器 :return: Boolean """ now_time = int(time.time()) while int(time.time()) - now_time < timeout: # noinspection PyBroadException try: element = self.browser.find_element_by_xpath(xpath) if element: return True time.sleep(1) except Exception: pass else: if close_browser: self.close_browser() # print("查找页面元素失败，如果不存在网络问题请尝试修改xpath表达式") return Falsedef add_page_element(self): self.xpath[\'background_img\'] = \'//div[@role="dialog"]/div[2]/img[1]\' self.xpath[\'small_img\'] = \'//div[@role="dialog"]/div[2]/img[2]\' self.xpath[\'slider_button\'] = \'//div[@id="secsdk-captcha-drag-wrapper"]/div[2]\'def process_main(self): """处理页面内容""" self.browser.get(self.url)for _ in range(10): if self.wait_element_loaded(self.xpath[\'background_img\'], timeout=5, close_browser=False): time.sleep(1) # 截图 self.browser.find_element_by_xpath(self.xpath[\'background_img\']).screenshot(self.background_path) small_img = self.browser.find_element_by_xpath(self.xpath[\'small_img\']) # 获取小图片的URL链接 small_url = small_img.get_attribute("src") # 获取小图片距离背景图顶部的像素距离 self.small_px = small_img.value_of_css_property("top").replace("px", "").split(".")[0]response = requests.get(small_url) if response.ok: with open(self.small_path, "wb") as file: file.write(response.content)time.sleep(1) # 如果没滑动成功则刷新页面重试 if not self.process_slider(): self.browser.refresh() continue else: break@staticmethod def handle_distance(distance): """将直线距离转为缓慢的轨迹""" import random slow_distance = [] while sum(slow_distance) < = distance: slow_distance.append(random.randint(-2, 15))if sum(slow_distance) != distance: slow_distance.append(distance - sum(slow_distance)) return slow_distancedef process_slider(self): """处理滑块验证码"""distance_obj = ComputeDistance(self.background_path, self.small_path, int(self.small_px), show_img=False) # 获取移动所需的距离 distance = distance_obj.run()track = self.handle_distance(distance[0]) track.append(-2) slider_element = self.browser.find_element_by_xpath(self.xpath[\'slider_button\'])move_slider(self.browser, slider_element, track) time.sleep(2)# 如果滑动完成则返回True if not self.wait_element_loaded(self.xpath[\'slider_button\'], timeout=2, close_browser=False): return True else: return Falsedef run(self): self.check_file_exist() self.start_browser() self.add_page_element() self.process_main() # self.close_browser()if __name__ == \'__main__\': main = TodayNews() main.run()