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Python selenium 破解腾讯滑块行为验证码

news 来源：原创 2024/9/19 9:17:53

直接上代码：

from selenium import webdriver
from selenium.webdriver.common.action_chains import ActionChains
import time,re,requests
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from PIL import Imageimport os,cv2
import sys
path = os.path.dirname(os.path.dirname(__file__))
sys.path.append(path)class main():def __init__(self):self.url = 'https://static-mp-dc3bab1b-06be-41ca-9070-ab7368c17ae5.next.bspapp.com/'self.distance = 0self.left = 0self.track = []# 启动浏览器def Launch_browser(self):options = webdriver.ChromeOptions()options.add_argument('--headless')# self.driver = webdriver.Chrome(options=options)self.driver = webdriver.Chrome()self.wait = WebDriverWait(self.driver, 10, 0.5)self.driver.get(self.url)self.driver.find_element(By.XPATH,'/html/body/button').click()# 等待className为geetest_slider_button的元素在元素表中出现time.sleep(5)element = WebDriverWait(self.driver, 10).until(EC.visibility_of_element_located((By.CLASS_NAME, 'tcaptcha-transform')))time.sleep(5)# 切换到iframe# 假设iframe有id或者其他属性，可以通过这些属性定位self.iframe = self.driver.find_element(By.ID,'tcaptcha_iframe_dy')self.driver.switch_to.frame(self.iframe)self.slider = self.driver.find_element(By.XPATH, '/html/body/div/div[3]/div[2]/div[7]')self.sliderImg = self.driver.find_element(By.XPATH, '/html/body/div/div[3]/div[2]/div[1]/div[2]/div')sliderImg_background_image_url = self.sliderImg.value_of_css_property('background-image')sliderImg_background_image_url = sliderImg_background_image_url[5:len(sliderImg_background_image_url) - 3]resp = requests.get(sliderImg_background_image_url)with open('./sliderImg.png', 'wb') as f:f.write(resp.content)slider_background_image_url = self.slider.value_of_css_property('background-image')slider_background_image_url = slider_background_image_url[5:len(slider_background_image_url) - 3]resp = requests.get(slider_background_image_url)with open('./slider.png', 'wb') as f:f.write(resp.content)# 150,270# 500,600image = Image.open('./slider.png')bg = image.crop([130, 479, 272, 622])bg.save('slider.png')import ddddocrdet = ddddocr.DdddOcr(det=False, ocr=True, show_ad=False)with open('slider.png', 'rb') as f:target_bytes = f.read()with open('sliderImg.png', 'rb') as f:background_bytes = f.read()res = det.slide_match(target_bytes, background_bytes, simple_target=True)print(res)self.distance = res['target'][0]self.left = self.slider.value_of_css_property('left').split('px')[0]self.left = eval(self.left)xoffset = int(self.distance * 0.51)print(xoffset)verify_img = cv2.imread('sliderImg.png')# 调用函数，得到x坐标x = get_pos(verify_img)x = int(x * 0.51) - 30# 实现拖拽滑动ActionChains(self.driver).click_and_hold(self.slider).perform()ActionChains(self.driver).move_by_offset(x, 0).perform()ActionChains(self.driver).release().perform()self.quit()# 关闭浏览器def quit(self):time.sleep(10)self.driver.quit()# main方法def main(self):self.Launch_browser()# self.cjy()# self.move()# self.quit()# 定义一个处理图片缺口的函数，最后是返回x坐标，滑块移动不需要y坐标
def get_pos(image):# 首先使用高斯模糊去噪，噪声会影响边缘检测的准确性，因此首先要将噪声过滤掉blurred = cv2.GaussianBlur(image, (5, 5), 0, 0)# 边缘检测，得到图片轮廓canny = cv2.Canny(blurred, 200, 400)  # 200为最小阈值，400为最大阈值，可以修改阈值达到不同的效果# 轮廓检测# cv2.findContours()函数接受的参数为二值图，即黑白的（不是灰度图），所以读取的图像要先转成灰度的，再转成二值图，此处canny已经是二值图# contours：所有的轮廓像素坐标数组，hierarchy 轮廓之间的层次关系contours, hierarchy = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# print(contours, hierarchy)for i, contour in enumerate(contours):  # 对所有轮廓进行遍历M = cv2.moments(contour)  # 并计算每一个轮廓的力矩(Moment)，就可以得出物体的质心位置# print(M)if M['m00'] == 0:cx = cy = 0else:# 得到质心位置，打印这个轮廓的面积和周长，用于过滤cx, cy = M['m10'] / M['m00'], M['m01'] / M['m00']print(cv2.contourArea(contour), cv2.arcLength(contour, True))# 判断这个轮廓是否在这个面积和周长的范围内if 5000 < cv2.contourArea(contour) < 8000 and 300 < cv2.arcLength(contour, True) < 500:print(cx)if cx < 300:continueprint(cv2.contourArea(contour))print(cv2.arcLength(contour, True))# 外接矩形，x，y是矩阵左上点的坐标，w，h是矩阵的宽和高x, y, w, h = cv2.boundingRect(contour)cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)  # 画出矩行# cv2.imshow('image', image)cv2.imwrite('111.jpg', image)  # 保存return xreturn 0
if __name__ == '__main__':ma = main()ma.main()

效果展示：