yidun滑块验证，python如何做到模拟手工拖动滑块一样丝滑？

Pojie1999.0909 发表于 2021-11-12 13:27

yidun滑块地址：http://app.miit-eidc.org.cn/miitxxgk/gonggao/xxgk/queryCpParamPage?dataTag=Z&gid=U3119671&pc=303](http://app.miit-eidc.org.cn/miitxxgk/gonggao/xxgk/queryCpParamPage?dataTag=Z&gid=U3119671&pc=303]http://app.miit-eidc.org.cn/miitxxgk/gonggao/xxgk/queryCpParamPage?dataTag=Z&gid=U3119671&pc=303)

我已找到一份python代码，可以准确识别出滑块的位置，效果如下（不能传gif，放阿里云盘了）：
我用阿里云盘分享了「Animation1.gif」，你可以不限速下载🚀复制这段内容打开「阿里云盘」App 即可获取链接：https://www.aliyundrive.com/s/NUe6jj686Z8

上面阿里云盘分享的gif图片中，可以将滑块准确拖动到对应的位置，但是依然不能通过验证。有没有大佬可以指点一下？

代码如下：
~~~
import os
import cv2
import time
import random
import requests
import numpy as np
from PIL import Image
from io import BytesIO
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver import ActionChains
from selenium.webdriver.support.wait import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

class CrackSlider():
def __init__(self):
self.browser = webdriver.Edge()
self.s2 = r'//*[@id="captcha_div"]/div/div/div/div/img'
self.s3 = r'//*[@id="captcha_div"]/div/div/div/div/img'
self.url = 'http://app.miit-eidc.org.cn/miitxxgk/gonggao/xxgk/queryCpParamPage?dataTag=Z&gid=U3119671&pc=303'# 测试网站
self.wait = WebDriverWait(self.browser, 20)
self.browser.get(self.url)

def get_img(self, target, template, xp):
   time.sleep(3)
   target_link = self.browser.find_element_by_xpath(self.s2).get_attribute("src")
   template_link = self.browser.find_element_by_xpath(self.s3).get_attribute("src")
   target_img = Image.open(BytesIO(requests.get(target_link).content))
   template_img = Image.open(BytesIO(requests.get(template_link).content))
   target_img.save(target)
   template_img.save(template)
   size_loc = target_img.size
   print('size_loc-----\n')
   print(size_loc)
   zoom = xp / int(size_loc)# 耦合像素
   print('zoom-----\n')
   print(zoom)
   return zoom

def change_size(self, file):
   image = cv2.imread(file, 1)# 读取图片 image_name应该是变量
   img = cv2.medianBlur(image, 5)# 中值滤波，去除黑色边际中可能含有的噪声干扰
   b = cv2.threshold(img, 15, 255, cv2.THRESH_BINARY)# 调整裁剪效果
   binary_image = b# 二值图--具有三通道
   binary_image = cv2.cvtColor(binary_image, cv2.COLOR_BGR2GRAY)
   x, y = binary_image.shape
   edges_x = []
   edges_y = []
   for i in range(x):
         for j in range(y):
            if binary_image == 255:
               edges_x.append(i)
               edges_y.append(j)

   left = min(edges_x)# 左边界
   right = max(edges_x)# 右边界
   width = right - left# 宽度
   bottom = min(edges_y)# 底部
   top = max(edges_y)# 顶部
   height = top - bottom# 高度
   pre1_picture = image# 图片截取
   return pre1_picture# 返回图片数据

def match(self, target, template):
   img_gray = cv2.imread(target, 0)
   img_rgb = self.change_size(template)
   template = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
   # cv2.imshow('template', template)
   # cv2.waitKey(0)
   res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
   run = 1

   # 使用二分法查找阈值的精确值
   L = 0
   R = 1
   while run < 20:
         run += 1
         threshold = (R + L) / 2
         if threshold < 0:
            print('Error')
            return None
         loc = np.where(res >= threshold)
         if len(loc) > 1:
            L += (R - L) / 2
         elif len(loc) == 1:
            break
         elif len(loc) < 1:
            R -= (R - L) / 2
   res = loc
   print('match distance-----\n')
   print(res)
   return res

def move_to_gap(self, tracks):
   slider = self.wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'yidun_slider')))
   ActionChains(self.browser).click_and_hold(slider).perform()
   #element = self.browser.find_element_by_xpath(self.s3)
   #ActionChains(self.browser).click_and_hold(on_element=element).perform()
   while tracks:
         x = tracks.pop(0)
         print('tracks.pop(0)-----\n')
         print(x)
         ActionChains(self.browser).move_by_offset(xoffset=x, yoffset=0).perform()
         #ActionChains(self.browser).move_to_element_with_offset(to_element=element, xoffset=x, yoffset=0).perform()
         #time.sleep(0.01)
   time.sleep(0.05)
   ActionChains(self.browser).release().perform()

def move_to_gap1(self, distance):
   distance += 46
   time.sleep(1)
   element = self.browser.find_element_by_xpath(self.s3)
   ActionChains(self.browser).click_and_hold(on_element=element).perform()
   ActionChains(self.browser).move_to_element_with_offset(to_element=element, xoffset=distance, yoffset=0).perform()
   #ActionChains(self.browser).release().perform()
   time.sleep(1.38)
   ActionChains(self.browser).release(on_element=element).perform()

def move_to_gap2(self, distance):
   element = self.browser.find_elements_by_class_name("yidun_slider")
   action = ActionChains(self.browser)
   mouse_action = action.click_and_hold(on_element=element)
   distance += 11
   distance = int(distance * 32/33)
   move_steps = int(distance/4)
   for i in range(0,move_steps):
         mouse_action.move_by_offset(4,random.randint(-5,5)).perform()
   time.sleep(0.1)
   mouse_action.release().perform()

def get_tracks(self, distance, seconds, ease_func):
   distance += 20
   tracks =
   offsets =
   for t in np.arange(0.0, seconds, 0.1):
         ease = ease_func
         print('ease-----\n')
         print(ease)
         offset = round(ease(t / seconds) * distance)
         print('offset-----\n')
         print(offset)
         tracks.append(offset - offsets[-1])
         print('offset - offsets[-1]-----\n')
         print(offset - offsets[-1])
         offsets.append(offset)
         print('offsets-----\n')
         print(offsets)
   tracks.extend([-3, -2, -3, -2, -2, -2, -2, -1, -0, -1, -1, -1])
   return tracks
def get_tracks1(self,distance):
   """
   根据偏移量获取移动轨迹
   :param distance: 偏移量
   :return: 移动轨迹
   """
   # 移动轨迹
   track = []
   # 当前位移
   current = 0
   # 减速阈值
   mid = distance * 4 / 5
   # 计算间隔
   t = 0.2
   # 初速度
   v = 0

   while current < distance:
         if current < mid:
            # 加速度为正 2
            a = 4
         else:
            # 加速度为负 3
            a = -3
         # 初速度 v0
         v0 = v
         # 当前速度 v = v0 + at
         v = v0 + a * t
         # 移动距离 x = v0t + 1/2 * a * t^2
         move = v0 * t + 1 / 2 * a * t * t
         # 当前位移
         current += move
         # 加入轨迹
         track.append(round(move))
   return track

def ease_out_quart(self, x):
   res = 1 - pow(1 - x, 4)
   print('ease_out_quart-----\n')
   print(res)
   return res

if __name__ == '__main__':
xp = 320# 验证码的像素-长
target = 'target.jpg'# 临时保存的图片名
template = 'template.png'# 临时保存的图片名

cs = CrackSlider()
zoom = cs.get_img(target, template, xp)
distance = cs.match(target, template)
track = cs.get_tracks((distance + 7) * zoom, random.randint(2, 4), cs.ease_out_quart)
#track = cs.get_tracks1(distance)
#track = cs.get_tracks((distance + 7) * zoom, random.randint(1, 2), cs.ease_out_quart)
cs.move_to_gap(track)
#cs.move_to_gap1(distance)
#cs.move_to_gap2(distance)
time.sleep(2)
#cs.browser.close()

~~~

Prozacs 发表于 2021-11-12 13:27

本帖最后由 Prozacs 于 2021-11-12 14:27 编辑

from pyppeteer import launch
import asyncio
import time
import json
import random
import cv2

def get_track(length):
list = []
x = random.randint(1,10)
while length:
   list.append(x)
   length -= x
   if length > 0 and length <= 5:
         break
   elif 5 < length < 25:
         x = random.randint(2,length)
   else:
         x = random.randint(5,25)
for i in range(length):
   list.append(1)
return list

def change_size( file):
image = cv2.imread(file, 1)# 读取图片 image_name应该是变量
img = cv2.medianBlur(image, 5)# 中值滤波，去除黑色边际中可能含有的噪声干扰
b = cv2.threshold(img, 15, 255, cv2.THRESH_BINARY)# 调整裁剪效果
binary_image = b# 二值图--具有三通道
binary_image = cv2.cvtColor(binary_image, cv2.COLOR_BGR2GRAY)
x, y = binary_image.shape
edges_x = []
edges_y = []
for i in range(x):
   for j in range(y):
         if binary_image == 255:
            edges_x.append(i)
            edges_y.append(j)

left = min(edges_x)# 左边界
right = max(edges_x)# 右边界
width = right - left# 宽度
bottom = min(edges_y)# 底部
top = max(edges_y)# 顶部
height = top - bottom# 高度
pre1_picture = image# 图片截取
return pre1_picture# 返回图片数据
async def main(url):
try:
   browser = await launch({'headless':False, 'dumpio': True, 'autoClose': False, 'args': ['--no-sandbox', '--window-size=1366,850']})
   page = await browser.newPage()
   await page.evaluateOnNewDocument('''() => {delete navigator.__proto__.webdriver;}''')
   await page.evaluateOnNewDocument('''() => {Object.defineProperty(navigator, 'webdriver', {get: () => undefined,});}''')
   await page.evaluateOnNewDocument('''() =>{ Object.defineProperty(navigator, 'languages', { get: () => ['en-CN', 'cn'] }); }''')
   await page.evaluateOnNewDocument('''() =>{ Object.defineProperty(navigator, 'plugins', { get: () => , }); }''')
   await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36')
   await page.setViewport({'width': 1366, 'height': 850})# 更改浏览器分辨率
   await page.goto(url, {'waitUntil': 'domcontentloaded'})# 进行第一次访问
   await page.waitFor(3000)
   authUrl = await page.content()
   if authUrl.find('向右拖动滑块填充拼图') != -1:
         el = await page.querySelector('#captcha_div > div > div.yidun_control')
         box = await el.boundingBox()
         while True:
            imageCard_list = []
            for imgItem in await page.xpath('//*[@id="captcha_div"]/div/div/div/div/img'):
               imageCard = await (await imgItem.getProperty('src')).jsonValue()
               imageCard_list.append(imageCard)
            from PIL import Image
            from io import BytesIO
            import requests
            import numpy as np
            target = 'target.jpg'# 临时保存的图片名
            template = 'template.png'
            target_img = Image.open(BytesIO(requests.get(imageCard_list).content))
            template_img = Image.open(BytesIO(requests.get(imageCard_list).content))
            target_img.save(target)
            template_img.save(template)
            img_gray = cv2.imread(target, 0)
            img_rgb = change_size(template)
            template = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
            res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
            run = 1
            # 使用二分法查找阈值的精确值
            L = 0
            R = 1
            while run < 20:
               run += 1
               threshold = (R + L) / 2
               if threshold < 0:
                     print('Error')
                     return None
               loc = np.where(res >= threshold)
               if len(loc) > 1:
                     L += (R - L) / 2
               elif len(loc) == 1:
                     break
               elif len(loc) < 1:
                     R -= (R - L) / 2
            res = loc
            await page.hover('#captcha_div > div > div.yidun_control > div.yidun_slider')# 鼠标移动方块上
            await page.mouse.down({'delay': random.randint(20, 100), 'steps': 30})# 鼠标拖动操作包括按下、移动、放开
            sixx = get_track(int(res+25))
            print(sixx)
            s = 0
            for i in sixx:
               s += i
               if i % 2:
                     await page.mouse.move(box['x'] + s, box['y'], {'delay': random.randint(20, 45), 'steps': 5})
               else:
                     await page.mouse.move(box['x'] + s, random.randint(int(box['y']) + 10, int(box['y']) + 20), {'delay': random.randint(20, 45), 'steps': 5})
            await page.waitFor(random.randint(50, 150))
            await page.mouse.up({'delay': random.randint(20, 100), 'steps': 20})
            await page.waitFor(1000)
            authUrl = await page.content()
            if '向右拖动滑块填充拼图' in authUrl:
               continue
            else:
               user_ck = await page.xpath(f'//*[@id="submit-btn"]')
               await user_ck.click()
               await page.waitFor(1000)
               authUrl = await page.content()
               print('成功')
except Exception as e:
   print(e)
finally:
   if page.isClosed():
         pass
   else:
         await page.close()
if __name__ == '__main__':
url ='http://app.miit-eidc.org.cn/miitxxgk/gonggao/xxgk/queryCpParamPage?dataTag=Z&gid=U3119671&pc=303'
asyncio.get_event_loop().run_until_complete(main(url))

#代码毕竟乱，你自己看着改吧，能成功，毕竟随手写的，可以结帖了

as614001 发表于 2021-11-12 13:44

get_tracks1好多年了还是熟悉的~

千百度° 发表于 2021-11-12 14:01

滑动到正确位置仍然不能通过，说明轨迹被识别到机器操作了，应该修改算法为更接近人为操作的轨迹

Prozacs 发表于 2021-11-12 14:09

用pyppetter可以过

偶尔平凡 发表于 2021-11-12 14:17

Pojie1999.0909 发表于 2021-11-12 14:17

千百度° 发表于 2021-11-12 14:01
滑动到正确位置仍然不能通过，说明轨迹被识别到机器操作了，应该修改算法为更接近人为操作的轨迹

我不会修改算法，让其更接近人为操作。大佬求解，CB不够全给您了:'(weeqw

Pojie1999.0909 发表于 2021-11-12 14:19

Prozacs 发表于 2021-11-12 14:09
用pyppetter可以过

大佬，可否再详细一点？{:1_893:}{:1_893:}

千百度° 发表于 2021-11-12 14:20

Pojie1999.0909 发表于 2021-11-12 14:17
我不会修改算法，让其更接近人为操作。大佬求解，CB不够全给您了

建议你学习一下贝尔曲线

Pojie1999.0909 发表于 2021-11-12 14:21

千百度° 发表于 2021-11-12 14:20
建议你学习一下贝尔曲线

{:1_893:}{:1_893:}多谢大佬！

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yidun滑块验证，python如何做到模拟手工拖动滑块一样丝滑？