drssionPage+ddddocr 滑块验证方案

1.环境

1. DrissionPage==4.1.0.18
2. ddddocr==1.5.6
3. python-3.1x

pip安装环境，不赘述，python环境建议3.10以上

drssionPage基于chrome协议开发，一般不会被检测。官网地址

2. 关键代码

1. 启动浏览器

def exec(url, proxies, port, user_agent):co = ChromiumOptions()# 随机端口if port is not None:co.set_local_port(port)else:co.auto_port(on_off=True)# co.headless()  # 无头模式# 匿名模式co.incognito()# 无沙盒模式co.set_argument('--no-sandbox')# DOM 就绪即停止加载co.set_load_mode('eager')# 无痕co.incognito(True)# 设置初始窗口大小co.set_argument('--window-size', '1500,800')co.set_argument('--proxy-server', proxies.get("http"))co.set_user_agent(user_agent)try:tab = page.latest_tab# 开始监听请求tab.listen.start(targets=True)tab.get(url)for step in tab.listen.steps(timeout=10):# 认证请求if step.url.find("/captcha/get") != -1:# 滑块验证if listen_verify(step, tab, proxies) == False:return {"code": 500}data = get_data(url, tab)page.close()return data# 正常请求if step.url.find(urlparse(url).path) != -1:data = step.response.bodyif data.find("路径") != -1:page.close()return {"code": 200, "data": data}except Exception as e:page.close()raise e

无头模式：在无桌面的环境运行，后台浏览器运行。

2. 识别认证图片

    # 认证图片background_bytes = http.request(method="GET", url=bg_url).datatarget_bytes = http.request(method="GET", url=target_url).dataimg_width = 552  # 下载宽度# 图片屏幕比率ratio = (img_width) / 800# ocr识别距离distance = get_distance(target_bytes, background_bytes) - deviationdistance = int(distance * ratio)time.sleep(1)tab.actions.move_to(tab.ele("@class=xxx"))# 移动滑块slide_div = tab.ele("@class=xxx")# 点击滑块位置tab.actions.move_to(slide_div, offset_x=random.randint(0, 20), duration=0.5)action_ele = tab.actions.hold(slide_div)current_position = 0time.sleep(1)# 随机滑动，滑块轨迹for x, y, t in get_tracks(distance):offset_x = (x - current_position)if offset_x == 0:time.sleep(0.02)continueaction_ele.move(offset_x=offset_x, offset_y=y, duration=t / 20000)current_position = xtime.sleep(0.2)tab.actions.release()

3. ocr识别滑块距离

def get_distance(target_bytes, background_bytes):"""ddddocr 识别滑动距离"""det = ddddocr.DdddOcr(det=False, ocr=False, show_ad=False)return int(det.slide_match(target_bytes, background_bytes)["target"][0])

4.轨迹模拟算法

def get_tracks(distance):"""轨迹算法"""tracks = list()y, v, t, current = 0, 0, 1, 0mid = distance * 3 / 4exceed = random.randint(40, 90)z = random.randint(30, 150)while current < (distance + exceed):if current < mid / 2:a = 2elif current < mid:a = 3else:a = -3a /= 2v0 = vs = v0 * t + 0.5 * a * (t * t)current += int(s)v = v0 + a * ty += random.randint(-3, 3)z = z + random.randint(5, 10)tracks.append([min(current, (distance + exceed)), y, z])while exceed > 0:exceed -= random.randint(0, 5)y += random.randint(-5, 5)z = z + random.randint(5, 9)tracks.append([min(current, (distance + exceed)), y, z])return tracks

3.后记

一般来说，不算太严格的滑块验证码都能过，轨迹模拟，和滑块点击滑动可自己改，drssionPage语法可在官网看。