OpenCV结合selenium实现滑块验证码 OpenCV结合selenium实现滑块验证码

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 osfrom 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)

截取后的验证码背景图和需要滑动的小图如：

文章图片

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：

文章图片

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 numpyimport 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) * 255y = img - xresult, 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_pxself.show_img = show_imgsmall_img_data = https://www.it610.com/article/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：

文章图片

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 randomslow_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 timefrom random import randintfrom 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 Trueexcept Exception as e:print(f'[网页] 拖拽 {name} 失败 {e}')

教程结束，让我们结合上面代码做一个案例吧。
访问今日头条某博主的主页，直接打开主页的链接会出现验证码。
下面代码使用pip安装好相关依赖库后可直接运行：
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)

文章图片

OK，下面为案例代码：

import osimport timeimport requestsimport cv2import numpyfrom random import randintfrom selenium import webdriverfrom selenium.webdriver.common.action_chains import ActionChainsdef show_image(img_array, name='img', resize_flag=False):"""展示图片"""maxHeight = 540maxWidth = 960scaleX = 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) * 255y = img - xresult, 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 Trueexcept 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_pxself.show_img = show_imgsmall_img_data = https://www.it610.com/article/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 = Noneself.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 PyBroadExceptiontry:element = self.browser.find_element_by_xpath(xpath)if element:return Truetime.sleep(1)except Exception:passelse: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()continueelse:break@staticmethoddef handle_distance(distance):"""将直线距离转为缓慢的轨迹"""import randomslow_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Trueif not self.wait_element_loaded(self.xpath['slider_button'], timeout=2, close_browser=False):return Trueelse: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()

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