准备工具
建议指定清华源下载速度会更快点
使用方法 : pip3 install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple/opencv-python/
谷歌驱动
谷歌驱动下载链接 :http://npm.taobao.org/mirrors/chromedriver/
前言
本篇文章采用的是cv2的Canny边缘检测算法进行图像识别匹配。
Canny边缘检测算法参考链接:/article/185336.htm
具体使用的是Canny的matchTemplate方法进行模糊匹配,匹配方法用CV_TM_CCOEFF_NORMED归一化相关系数匹配。得出的max_loc就是匹配出来的位置信息。从而达到位置的距离。
难点
流程
1.实例化谷歌浏览器 ,并打开哔哩哔哩登入页面。
2.点击登陆,弹出滑动验证框。
3.全屏截图、后按照尺寸裁剪各两张。
5.模糊匹配两张图片,从而获取匹配结果以及位置信息 。
6.将位置信息与页面上的位移距离转化,并尽可能少的减少误差 。
7.变速的拖动滑块到指定位置,从而达到模拟登入。
效果图
代码实例
库安装好后,然后填写配置区域后即可运行。
from PIL import Image from time import sleep from selenium import webdriver from selenium.webdriver import ActionChains from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC import cv2 import numpy as np import math ############ 配置区域 ######### zh='' #账号 pwd='' #密码 # chromedriver的路径 chromedriver_path = "C:\Program Files (x86)\Google\Chrome\Application\chromedriver.exe" ####### end ######### options = webdriver.ChromeOptions() options.add_argument('--no-sandbox') options.add_argument('--window-size=1020,720') # options.add_argument('--start-maximized') # 浏览器窗口最大化 options.add_argument('--disable-gpu') options.add_argument('--hide-scrollbars') options.add_argument('test-type') options.add_experimental_option("excludeSwitches", ["ignore-certificate-errors", "enable-automation"]) # 设置为开发者模式 driver = webdriver.Chrome(options=options, executable_path=chromedriver_path) driver.get('https://passport.bilibili.com/login') # 登入 def login(): driver.find_element_by_id("login-username").send_keys(zh) driver.find_element_by_id("login-passwd").send_keys(pwd) driver.find_element_by_css_selector("#geetest-wrap > div > div.btn-box > a.btn.btn-login").click() print("点击登入") # 整个图,跟滑块整个图 def screen(screenXpath): img = WebDriverWait(driver, 20).until( EC.visibility_of_element_located((By.XPATH, screenXpath)) ) driver.save_screenshot("allscreen.png") # 对整个浏览器页面进行截图 left = img.location['x']+160 #往右 top = img.location['y']+60 # 往下 right = img.location['x'] + img.size['width']+230 # 往左 bottom = img.location['y'] + img.size['height']+110 # 往上 im = Image.open('allscreen.png') im = im.crop((left, top, right, bottom)) # 对浏览器截图进行裁剪 im.save('1.png') print("截图完成1") screen_two(screenXpath) screen_th(screenXpath) matchImg('3.png','2.png') # 滑块部分图 def screen_two(screenXpath): img = WebDriverWait(driver, 20).until( EC.visibility_of_element_located((By.XPATH, screenXpath)) ) left = img.location['x'] + 160 top = img.location['y'] + 80 right = img.location['x'] + img.size['width']-30 bottom = img.location['y'] + img.size['height'] + 90 im = Image.open('allscreen.png') im = im.crop((left, top, right, bottom)) # 对浏览器截图进行裁剪 im.save('2.png') print("截图完成2") # 滑块剩余部分图 def screen_th(screenXpath): img = WebDriverWait(driver, 20).until( EC.visibility_of_element_located((By.XPATH, screenXpath)) ) left = img.location['x'] + 220 top = img.location['y'] + 60 right = img.location['x'] + img.size['width']+230 bottom = img.location['y'] + img.size['height'] +110 im = Image.open('allscreen.png') im = im.crop((left, top, right, bottom)) # 对浏览器截图进行裁剪 im.save('3.png') print("截图完成3") #图形匹配 def matchImg(imgPath1,imgPath2): imgs = [] #展示 sou_img1= cv2.imread(imgPath1) sou_img2 = cv2.imread(imgPath2) # 最小阈值100,最大阈值500 img1 = cv2.imread(imgPath1, 0) blur1 = cv2.GaussianBlur(img1, (3, 3), 0) canny1 = cv2.Canny(blur1, 100, 500) cv2.imwrite('temp1.png', canny1) img2 = cv2.imread(imgPath2, 0) blur2 = cv2.GaussianBlur(img2, (3, 3), 0) canny2 = cv2.Canny(blur2, 100, 500) cv2.imwrite('temp2.png', canny2) target = cv2.imread('temp1.png') template = cv2.imread('temp2.png') # 调整大小 target_temp = cv2.resize(sou_img1, (350, 200)) target_temp = cv2.copyMakeBorder(target_temp, 5, 5, 5, 5, cv2.BORDER_CONSTANT, value=[255, 255, 255]) template_temp = cv2.resize(sou_img2, (200, 200)) template_temp = cv2.copyMakeBorder(template_temp, 5, 5, 5, 5, cv2.BORDER_CONSTANT, value=[255, 255, 255]) imgs.append(target_temp) imgs.append(template_temp) theight, twidth = template.shape[:2] # 匹配跟拼图 result = cv2.matchTemplate(target, template, cv2.TM_CCOEFF_NORMED) cv2.normalize( result, result, 0, 1, cv2.NORM_MINMAX, -1 ) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result) # 画圈 cv2.rectangle(target,max_loc,(max_loc[0]+twidth,max_loc[1]+theight),(0,0,255),2) target_temp_n = cv2.resize(target, (350, 200)) target_temp_n = cv2.copyMakeBorder(target_temp_n, 5, 5, 5, 5, cv2.BORDER_CONSTANT, value=[255, 255, 255]) imgs.append(target_temp_n) imstack = np.hstack(imgs) cv2.imshow('windows'+str(max_loc), imstack) cv2.waitKey(0) cv2.destroyAllWindows() # 计算距离 print(max_loc) dis=str(max_loc).split()[0].split('(')[1].split(',')[0] x_dis=int(dis)+135 t(x_dis) #拖动滑块 def t(distances): draggable = driver.find_element_by_css_selector('div.geetest_slider.geetest_ready > div.geetest_slider_button') ActionChains(driver).click_and_hold(draggable).perform() #抓住 print(driver.title) num=getNum(distances) sleep(3) for distance in range(1,int(num)): print('移动的步数: ',distance) ActionChains(driver).move_by_offset(xoffset=distance, yoffset=0).perform() sleep(0.25) ActionChains(driver).release().perform() #松开 # 计算步数 def getNum(distances): p = 1+4*distances x1 = (-1 + math.sqrt(p)) / 2 x2 = (-1 - math.sqrt(p)) / 2 print(x1,x2) if x1>=0 and x2<0: return x1+2 elif(x1<0 and x2>=0): return x2+2 else: return x1+2 def main(): login() sleep(5) screenXpath = '/html/body/div[2]/div[2]/div[6]/div/div[1]/div[1]/div/a/div[1]/div/canvas[2]' screen(screenXpath) sleep(5) if __name__ == '__main__': main()
有能力的可以研究一下思路,然后写出更好的解决办法。
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