update

README.MD

@@ -209,4 +209,6 @@ workalendar==17.0.0
 # pip3 install aliyun_python_sdk
 # pip3 install -U aliyun-log-python-sdk
 aliyun_python_sdk==2.2.0
+# pip3 install opencv-python / pip3 install opencv-contrib-python
+opencv-python~=4.8.0.74
 ```

dev/dev_script/xg_recommend.py

@@ -0,0 +1,342 @@
+# -*- coding: utf-8 -*-
+# @Author: wangkun
+# @Time: 2023/7/6
+import os
+import random
+import sys
+import time
+import cv2
+import numpy as np
+from selenium.webdriver.common.keys import Keys
+import requests
+import urllib3
+from selenium.webdriver import DesiredCapabilities, ActionChains
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.common.by import By
+sys.path.append(os.getcwd())
+from common.common import Common
+
+
+class XGRecommend(object):
+
+    def __init__(self, log_type, crawler, env):
+        """
+        本地启动 Chrome，指定端口号:12306
+        open -a "Google Chrome" --args --remote-debugging-port=12306
+        """
+        Common.logger(log_type, crawler).info("启动 Chrome 浏览器")
+        cmd = 'open -a "Google Chrome" --args --remote-debugging-port=12306'
+        os.system(cmd)
+
+        if env == "dev":
+            chromedriver = "/Users/wangkun/Downloads/chromedriver/chromedriver_v114/chromedriver"
+        else:
+            chromedriver = "/usr/bin/chromedriver"
+
+        # 打印请求配置
+        ca = DesiredCapabilities.CHROME
+        ca["goog:loggingPrefs"] = {"performance": "ALL"}
+        # 初始化浏览器
+        self.browser = webdriver.ChromeOptions()
+        self.browser.add_experimental_option("debuggerAddress", "127.0.0.1:12306")
+        # # 设置user-agent
+        # self.browser.add_argument(
+        #     f'user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.79 Safari/537.36')
+        # # 去掉提示：Chrome正收到自动测试软件的控制
+        # self.browser.add_argument('--disable-infobars')
+
+        # # 禁用GPU加速
+        # self.browser.add_argument('--disable-gpu')
+        # # 关闭开发者模式
+        # self.browser.add_experimental_option("useAutomationExtension", False)
+        # # 以键值对的形式加入参数
+        # self.browser.add_experimental_option('excludeSwitches', ['enable-automation'])
+        # # 禁用启用Blink运行时的功能
+        # self.browser.add_argument('--disable-blink-features=AutomationControlled')
+        # 不打开浏览器运行
+        # self.browser.add_argument("--headless")
+        # # linux 环境中，静默模式必须要加的参数
+        # self.browser.add_argument("--no-sandbox")
+        # # 设置浏览器size
+        # self.browser.add_argument("--window-size=1920,1080")
+
+        # driver初始化
+        self.driver = webdriver.Chrome(desired_capabilities=ca, options=self.browser, service=Service(chromedriver))
+        self.driver.implicitly_wait(10)
+        Common.logger(log_type, crawler).info("打开西瓜推荐页")
+        self.driver.get(f"https://www.ixigua.com/")
+        # 在当前页面打开新的标签页
+        self.driver.find_element(By.TAG_NAME, 'body').send_keys(Keys.COMMAND + 't')
+        # 切换到新打开的标签页
+        self.driver.switch_to.window(self.driver.window_handles[-1])
+        self.username = "19831265541"
+        self.password = "Test111111"
+        time.sleep(2)
+
+    def quit(self, log_type, crawler):
+        Common.logger(log_type, crawler).info("退出浏览器")
+        self.driver.quit()
+
+    #  传入滑块背景图片本地路径和滑块本地路径，返回滑块到缺口的距离
+    @staticmethod
+    def findPic(log_type, crawler, img_bg_path, img_slider_path):
+        """
+        找出图像中最佳匹配位置
+        :param log_type: log
+        :param crawler: 爬虫
+        :param img_bg_path: 滑块背景图本地路径
+        :param img_slider_path: 滑块图片本地路径
+        :return: 返回最差匹配、最佳匹配对应的x坐标
+        """
+
+        # 读取滑块背景图片，参数是图片路径，Opencv2默认使用BGR模式
+        # cv2.imread()是 image read的简写
+        # img_bg 是一个numpy库ndarray数组对象
+        img_bg = cv2.imread(img_bg_path)
+
+        # 对滑块背景图片进行处理，由BGR模式转为gray模式（即灰度模式，也就是黑白图片）
+        # 为什么要处理？ BGR模式（彩色图片）的数据比黑白图片的数据大，处理后可以加快算法的计算
+        # BGR模式：常见的是RGB模式
+        # R代表红，red; G代表绿，green;  B代表蓝，blue。
+        # RGB模式就是，色彩数据模式，R在高位，G在中间，B在低位。BGR正好相反。
+        # 如红色：RGB模式是(255,0,0)，BGR模式是(0,0,255)
+        img_bg_gray = cv2.cvtColor(img_bg, cv2.COLOR_BGR2GRAY)
+
+        # 读取滑块，参数1是图片路径，参数2是使用灰度模式
+        img_slider_gray = cv2.imread(img_slider_path, 0)
+
+        # 在滑块背景图中匹配滑块。参数cv2.TM_CCOEFF_NORMED是opencv2中的一种算法
+        res = cv2.matchTemplate(img_bg_gray, img_slider_gray, cv2.TM_CCOEFF_NORMED)
+
+        Common.logger(log_type, crawler).info(f"{'#' * 50}")
+        Common.logger(log_type, crawler).info(type(res))  # 打印：<class 'numpy.ndarray'>
+        Common.logger(log_type, crawler).info(res)
+        # 打印：一个二维的ndarray数组
+        # [[0.05604218  0.05557462  0.06844381... - 0.1784117 - 0.1811338 - 0.18415523]
+        #  [0.06151756  0.04408009  0.07010461... - 0.18493137 - 0.18440475 - 0.1843424]
+        # [0.0643926    0.06221284  0.0719175... - 0.18742703 - 0.18535161 - 0.1823346]
+        # ...
+        # [-0.07755355 - 0.08177952 - 0.08642308... - 0.16476074 - 0.16210903 - 0.15467581]
+        # [-0.06975575 - 0.07566144 - 0.07783117... - 0.1412715 - 0.15145643 - 0.14800543]
+        # [-0.08476129 - 0.08415948 - 0.0949327... - 0.1371379 - 0.14271489 - 0.14166716]]
+
+        Common.logger(log_type, crawler).info(f"{'#' * 50}")
+
+        # cv22.minMaxLoc() 从ndarray数组中找到最小值、最大值及他们的坐标
+        value = cv2.minMaxLoc(res)
+        # 得到的value，如：(-0.1653602570295334, 0.6102921366691589, (144, 1), (141, 56))
+
+        Common.logger(log_type, crawler).info(f"{value, '#' * 30}")
+
+        # 获取x坐标，如上面的144、141
+        return value[2:][0][0], value[2:][1][0]
+
+    # 返回两个数组：一个用于加速拖动滑块，一个用于减速拖动滑块
+    @staticmethod
+    def generate_tracks(distance):
+        # 给距离加上20，这20像素用在滑块滑过缺口后，减速折返回到缺口
+        distance += 20
+        v = 0
+        t = 0.2
+        forward_tracks = []
+        current = 0
+        mid = distance * 3 / 5  # 减速阀值
+        while current < distance:
+            if current < mid:
+                a = 2  # 加速度为+2
+            else:
+                a = -3  # 加速度-3
+            s = v * t + 0.5 * a * (t ** 2)
+            v = v + a * t
+            current += s
+            forward_tracks.append(round(s))
+
+        back_tracks = [-3, -3, -2, -2, -2, -2, -2, -1, -1, -1, -1]
+        return forward_tracks, back_tracks
+
+    # 获取距离值
+    @staticmethod
+    def get_tracks(distance):
+        """
+        模拟人的滑动行为，先匀加速后匀减速
+        匀变速基本公式
+        v=v0+at
+        s=vot+1/2at2
+        """
+        # 初速度
+        v = 0
+        # 设置时间
+        t = 0.3
+        # 存储每段距离值
+        tracks = []
+        # 当前距离
+        current = 0
+        # 中间位置为4/5距离处
+        mid = distance * 4 / 5
+        while current < distance:
+            if current < mid:
+                # 加速阶段
+                a = 2
+            else:
+                # 减速阶段
+                a = -3
+            # 当前速度
+            v0 = v
+            # 当前位移
+            s = v0 * t + 0.5 * a * t ** 2
+            # 更新当前速度
+            v = v0 + a * t
+            # 更新当前位移
+            current += s
+            # 添加到轨迹列表
+            tracks.append(round(s))
+        return tracks
+
+    # 使用图像处理库（例如 OpenCV）找到滑块在背景图片中的位置
+    @staticmethod
+    def get_slider_offset(log_type, crawler, image_bg):
+        image = cv2.imread(image_bg)
+        # 高斯滤波
+        blurred = cv2.GaussianBlur(image, (5, 5), 0)
+        # 边缘检测
+        canny = cv2.Canny(blurred, 200, 400)
+        # 轮廓检测
+        contours, hierarchy = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        for i, contour in enumerate(contours):
+            m = cv2.moments(contour)
+            if m['m00'] == 0:
+                cx = cy = 0
+            else:
+                cx, cy = m['m10'] / m['m00'], m['m01'] / m['m00']
+            if 6000 < cv2.contourArea(contour) < 8000 and 370 < cv2.arcLength(contour, True) < 390:
+                if cx < 400:
+                    continue
+                x, y, w, h = cv2.boundingRect(contour)  # 外接矩形
+                cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)
+                # cv.imshow('image', image)  # 显示识别结果
+                Common.logger(log_type, crawler).info('【缺口识别】 {x}px'.format(x=x / 2))
+                return x / 2
+        return 0
+
+    @staticmethod
+    def FindPic(log_type, crawler, target, template):
+        """
+        找出图像中最佳匹配位置
+        :param log_type: 日志
+        :param crawler: 爬虫
+        :param target: 目标即背景图
+        :param template: 模板即需要找到的图
+        :return: 返回最佳匹配及其最差匹配和对应的坐标
+        """
+        target_rgb = cv2.imread(target)
+        target_gray = cv2.cvtColor(target_rgb, cv2.COLOR_BGR2GRAY)
+        template_rgb = cv2.imread(template, 0)
+        res = cv2.matchTemplate(target_gray, template_rgb, cv2.TM_CCOEFF_NORMED)
+        value = cv2.minMaxLoc(res)
+        Common.logger(log_type, crawler).info(value)
+        # 计算缺口的 X 轴距离
+        x_val = int(value[3][0])
+        Common.logger(log_type, crawler).info(f"缺口的 X 轴距离:{x_val}")
+        # 获取模板图的宽高
+        template_height, template_width, template_c = cv2.imread(template).shape
+        Common.logger(log_type, crawler).info(f"模板高:{template_height}")
+        Common.logger(log_type, crawler).info(f"模板宽:{template_width}")
+        Common.logger(log_type, crawler).info(f"图片的通道数:{template_c}")
+        # 计算需要滑动的距离
+        move_val = x_val - template_width
+        Common.logger(log_type, crawler).info(f"需要滑动的距离:{move_val}")
+        return x_val
+
+    def login(self, log_type, crawler, env):
+        # Common.logger(log_type, crawler).info("点击登录")
+        # self.driver.find_element(By.XPATH, '//*[@class="xg-button xg-button-primary xg-button-middle loginButton"]').click()
+        # time.sleep(random.randint(1, 2))
+        # Common.logger(log_type, crawler).info("点击密码登录")
+        # self.driver.find_element(By.XPATH, '//*[@class="web-login-link-list__item__text"]').click()
+        # time.sleep(random.randint(1, 2))
+        # Common.logger(log_type, crawler).info("输入手机号")
+        # self.driver.find_element(By.XPATH, '//*[@class="web-login-normal-input__input"]').send_keys(self.username)
+        # time.sleep(random.randint(1, 2))
+        # Common.logger(log_type, crawler).info("输入密码")
+        # self.driver.find_element(By.XPATH, '//*[@class="web-login-button-input__input"]').send_keys(self.password)
+        # time.sleep(random.randint(1, 2))
+        # Common.logger(log_type, crawler).info("点击登录")
+        # self.driver.find_element(By.XPATH, '//*[@class="web-login-account-password__button-wrapper"]/*[1]').click()
+        # time.sleep(random.randint(1, 2))
+
+        # # 获取滑块
+        # Common.logger(log_type, crawler).info("获取滑块")
+        # move_btns = self.driver.find_elements(By.XPATH, '//*[@class="sc-kkGfuU bujTgx"]')
+        # if len(move_btns) == 0:
+        #     Common.logger(log_type, crawler).info("未发现滑块，3-5 秒后重试")
+        #     self.quit(log_type, crawler)
+        #     time.sleep(random.randint(3, 5))
+        #     self.__init__(log_type, crawler, env)
+        #     self.login(log_type, crawler, env)
+        # move_btn = move_btns[0]
+        #
+        # while True:
+        #
+        #     # 使用requests下载滑块
+        #     slide_url = self.driver.find_element(By.XPATH,
+        #                                          '//*[@class="captcha_verify_img_slide react-draggable sc-VigVT ggNWOG"]').get_attribute(
+        #         "src")
+        #     slide_dir = f"./{crawler}/photo/img_slide.png"
+        #     urllib3.disable_warnings()
+        #     slide_url_response = requests.get(slide_url, verify=False)
+        #     with open(slide_dir, "wb") as file:
+        #         file.write(slide_url_response.content)
+        #
+        #     # 使用urllib下载背景图
+        #     bg_image_url = self.driver.find_element(By.XPATH, '//*[@id="captcha-verify-image"]').get_attribute("src")
+        #     bg_image_dir = f"./{crawler}/photo/img_bg.png"
+        #     urllib3.disable_warnings()
+        #     bg_image_url_response = requests.get(bg_image_url, verify=False)
+        #     with open(bg_image_dir, "wb") as file:
+        #         file.write(bg_image_url_response.content)
+        #
+        #     offset = self.FindPic(log_type, crawler, bg_image_dir, slide_dir)
+        #     Common.logger(log_type, crawler).info(f"offset:{offset}")
+        #
+        #     # 在滑块上暂停
+        #     Common.logger(log_type, crawler).info("在滑块上暂停")
+        #     ActionChains(self.driver).click_and_hold(on_element=move_btn).perform()
+        #     # 拖动滑块
+        #     Common.logger(log_type, crawler).info("拖动滑块0.7*距离")
+        #     ActionChains(self.driver).move_to_element_with_offset(to_element=move_btn, xoffset=int(0.5*offset), yoffset=0).perform()
+        #     # 拖动剩余像素
+        #     Common.logger(log_type, crawler).info("拖动剩余像素")
+        #     tracks = self.get_tracks(int(0.15*offset))
+        #     # 遍历梅一段距离
+        #     for track in tracks:
+        #         # 滑块移动响应距离
+        #         ActionChains(self.driver).move_by_offset(xoffset=track, yoffset=0).perform()
+        #     # 休息1s
+        #     Common.logger(log_type, crawler).info("休息1s")
+        #     time.sleep(1)
+        #     # 释放滑块
+        #     Common.logger(log_type, crawler).info("释放滑块")
+        #     ActionChains(self.driver).release().perform()
+        #
+        #     if len(move_btns) != 0:
+        #         time.sleep(1)
+        #         continue
+        #     break
+        Common.logger(log_type, crawler).info("刷新页面")
+        self.driver.refresh()
+
+        Common.logger(log_type, crawler).info("关闭当前标签页")
+        time.sleep(5)
+        # 关闭当前标签页
+        self.driver.find_element(By.TAG_NAME, 'body').send_keys(Keys.COMMAND + 'w')
+        Common.logger(log_type, crawler).info("已关闭")
+        Common.logger(log_type, crawler).info("退出浏览器")
+        self.quit(log_type, crawler)
+
+
+
+if __name__ == "__main__":
+    Recommend = XGRecommend("search", "dev", "dev")
+    Recommend.login("search", "dev", "dev")
+    pass

dev/dev_script/xg_recommend2.py

@@ -0,0 +1,248 @@
+# -*- coding: utf-8 -*-
+# @Author: wangkun
+# @Time: 2023/7/6
+import os
+import random
+import sys
+import time
+import cv2
+import requests
+import urllib3
+from selenium.webdriver import DesiredCapabilities, ActionChains
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.common.by import By
+sys.path.append(os.getcwd())
+from common.common import Common
+
+
+class XGRecommend(object):
+
+    def __init__(self, log_type, crawler, env):
+        """
+        本地启动 Chrome，指定端口号:12306
+        open -a "Google Chrome" --args --remote-debugging-port=12306
+        """
+        # Common.logger(log_type, crawler).info("启动 Chrome 浏览器")
+        # cmd = 'open -a "Google Chrome" --args --remote-debugging-port=12306'
+        # os.system(cmd)
+
+        if env == "dev":
+            chromedriver = "/Users/wangkun/Downloads/chromedriver/chromedriver_v114/chromedriver"
+        else:
+            chromedriver = "/usr/bin/chromedriver"
+
+        # 打印请求配置
+        ca = DesiredCapabilities.CHROME
+        ca["goog:loggingPrefs"] = {"performance": "ALL"}
+        # 初始化浏览器
+        self.browser = webdriver.ChromeOptions()
+        # 设置user-agent
+        self.browser.add_argument(
+            f'user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.79 Safari/537.36')
+        # 去掉提示：Chrome正收到自动测试软件的控制
+        self.browser.add_argument('--disable-infobars')
+
+        # 禁用GPU加速
+        self.browser.add_argument('--disable-gpu')
+        # 关闭开发者模式
+        self.browser.add_experimental_option("useAutomationExtension", False)
+        # 以键值对的形式加入参数
+        self.browser.add_experimental_option('excludeSwitches', ['enable-automation'])
+        # 禁用启用Blink运行时的功能
+        self.browser.add_argument('--disable-blink-features=AutomationControlled')
+        # 不打开浏览器运行
+        # self.browser.add_argument("--headless")
+        # linux 环境中，静默模式必须要加的参数
+        # self.browser.add_argument("--no-sandbox")
+        # 设置浏览器size
+        self.browser.add_argument("--window-size=1920,1080")
+
+        # driver初始化
+        self.driver = webdriver.Chrome(desired_capabilities=ca, options=self.browser, service=Service(chromedriver))
+        self.driver.implicitly_wait(10)
+        Common.logger(log_type, crawler).info("打开西瓜推荐页")
+        self.driver.get(f"https://www.ixigua.com/")
+        self.username = "19831265541"
+        self.password = "Test111111"
+        time.sleep(2)
+
+    def quit(self, log_type, crawler):
+        Common.logger(log_type, crawler).info("退出浏览器")
+        self.driver.quit()
+
+    # 返回两个数组：一个用于加速拖动滑块，一个用于减速拖动滑块
+    @staticmethod
+    def generate_tracks(distance):
+        # 给距离加上20，这20像素用在滑块滑过缺口后，减速折返回到缺口
+        distance += 20
+        v = 0
+        t = 0.2
+        forward_tracks = []
+        current = 0
+        mid = distance * 3 / 5  # 减速阀值
+        while current < distance:
+            if current < mid:
+                a = 2  # 加速度为+2
+            else:
+                a = -3  # 加速度-3
+            s = v * t + 0.5 * a * (t ** 2)
+            v = v + a * t
+            current += s
+            forward_tracks.append(round(s))
+
+        back_tracks = [-3, -3, -2, -2, -2, -2, -2, -1, -1, -1, -1]
+        return forward_tracks, back_tracks
+
+    # 获取距离值
+    @staticmethod
+    def get_tracks(distance):
+        """
+        模拟人的滑动行为，先匀加速后匀减速
+        匀变速基本公式
+        v=v0+at
+        s=vot+1/2at2
+        """
+        # 初速度
+        v = 0
+        # 设置时间
+        t = 0.3
+        # 存储每段距离值
+        tracks = []
+        # 当前距离
+        current = 0
+        # 中间位置为4/5距离处
+        mid = distance * 4 / 5
+        while current < distance:
+            if current < mid:
+                # 加速阶段
+                a = 2
+            else:
+                # 减速阶段
+                a = -3
+            # 当前速度
+            v0 = v
+            # 当前位移
+            s = v0 * t + 0.5 * a * t ** 2
+            # 更新当前速度
+            v = v0 + a * t
+            # 更新当前位移
+            current += s
+            # 添加到轨迹列表
+            tracks.append(round(s))
+        return tracks
+
+    @staticmethod
+    def FindPic(log_type, crawler, target, template):
+        """
+        找出图像中最佳匹配位置
+        :param log_type: 日志
+        :param crawler: 爬虫
+        :param target: 目标即背景图
+        :param template: 模板即需要找到的图
+        :return: 返回最佳匹配及其最差匹配和对应的坐标
+        """
+        target_rgb = cv2.imread(target)
+        target_gray = cv2.cvtColor(target_rgb, cv2.COLOR_BGR2GRAY)
+        template_rgb = cv2.imread(template, 0)
+        res = cv2.matchTemplate(target_gray, template_rgb, cv2.TM_CCOEFF_NORMED)
+        value = cv2.minMaxLoc(res)
+        Common.logger(log_type, crawler).info(value)
+        # 计算缺口的 X 轴距离
+        x_val = int(value[3][0])
+        Common.logger(log_type, crawler).info(f"缺口的 X 轴距离:{x_val}")
+        # 获取模板图的宽高
+        template_height, template_width, template_c = cv2.imread(template).shape
+        Common.logger(log_type, crawler).info(f"模板高:{template_height}")
+        Common.logger(log_type, crawler).info(f"模板宽:{template_width}")
+        Common.logger(log_type, crawler).info(f"图片的通道数:{template_c}")
+        # 计算需要滑动的距离
+        move_val = x_val - template_width
+        Common.logger(log_type, crawler).info(f"需要滑动的距离:{move_val}")
+        return x_val
+
+    def login(self, log_type, crawler, env):
+        Common.logger(log_type, crawler).info("点击登录")
+        self.driver.find_element(By.XPATH, '//*[@class="xg-button xg-button-primary xg-button-middle loginButton"]').click()
+        time.sleep(random.randint(1, 2))
+        Common.logger(log_type, crawler).info("点击密码登录")
+        self.driver.find_element(By.XPATH, '//*[@class="web-login-link-list__item__text"]').click()
+        time.sleep(random.randint(1, 2))
+        Common.logger(log_type, crawler).info("输入手机号")
+        self.driver.find_element(By.XPATH, '//*[@class="web-login-normal-input__input"]').send_keys(self.username)
+        time.sleep(random.randint(1, 2))
+        Common.logger(log_type, crawler).info("输入密码")
+        self.driver.find_element(By.XPATH, '//*[@class="web-login-button-input__input"]').send_keys(self.password)
+        time.sleep(random.randint(1, 2))
+        Common.logger(log_type, crawler).info("点击登录")
+        self.driver.find_element(By.XPATH, '//*[@class="web-login-account-password__button-wrapper"]/*[1]').click()
+        time.sleep(random.randint(1, 2))
+
+        # 获取滑块
+        Common.logger(log_type, crawler).info("获取滑块")
+        move_btns = self.driver.find_elements(By.XPATH, '//*[@class="sc-kkGfuU bujTgx"]')
+        if len(move_btns) == 0:
+            Common.logger(log_type, crawler).info("未发现滑块，3-5 秒后重试")
+            self.quit(log_type, crawler)
+            time.sleep(random.randint(3, 5))
+            self.__init__(log_type, crawler, env)
+            self.login(log_type, crawler, env)
+        move_btn = move_btns[0]
+
+        while True:
+
+            # 使用requests下载滑块
+            slide_url = self.driver.find_element(By.XPATH,
+                                                 '//*[@class="captcha_verify_img_slide react-draggable sc-VigVT ggNWOG"]').get_attribute(
+                "src")
+            slide_dir = f"./{crawler}/photo/img_slide.png"
+            urllib3.disable_warnings()
+            slide_url_response = requests.get(slide_url, verify=False)
+            with open(slide_dir, "wb") as file:
+                file.write(slide_url_response.content)
+
+            # 使用urllib下载背景图
+            bg_image_url = self.driver.find_element(By.XPATH, '//*[@id="captcha-verify-image"]').get_attribute("src")
+            bg_image_dir = f"./{crawler}/photo/img_bg.png"
+            urllib3.disable_warnings()
+            bg_image_url_response = requests.get(bg_image_url, verify=False)
+            with open(bg_image_dir, "wb") as file:
+                file.write(bg_image_url_response.content)
+
+            offset = self.FindPic(log_type, crawler, bg_image_dir, slide_dir)
+            Common.logger(log_type, crawler).info(f"offset:{offset}")
+
+            # 在滑块上暂停
+            Common.logger(log_type, crawler).info("在滑块上暂停")
+            ActionChains(self.driver).click_and_hold(on_element=move_btn).perform()
+            # 拖动滑块
+            Common.logger(log_type, crawler).info("拖动滑块0.7*距离")
+            ActionChains(self.driver).move_to_element_with_offset(to_element=move_btn, xoffset=int(0.5*offset), yoffset=0).perform()
+            # 拖动剩余像素
+            Common.logger(log_type, crawler).info("拖动剩余像素")
+            tracks = self.get_tracks(int(0.15*offset))
+            # 遍历梅一段距离
+            for track in tracks:
+                # 滑块移动响应距离
+                ActionChains(self.driver).move_by_offset(xoffset=track, yoffset=0).perform()
+            # 休息1s
+            Common.logger(log_type, crawler).info("休息1s")
+            time.sleep(1)
+            # 释放滑块
+            Common.logger(log_type, crawler).info("释放滑块")
+            ActionChains(self.driver).release().perform()
+
+            if len(move_btns) != 0:
+                time.sleep(1)
+                continue
+            break
+        time.sleep(5)
+        Common.logger(log_type, crawler).info("退出浏览器")
+        self.quit(log_type, crawler)
+
+
+
+if __name__ == "__main__":
+    Recommend = XGRecommend("search", "dev", "dev")
+    Recommend.login("search", "dev", "dev")
+    pass

dev/photo/__init__.py

@@ -0,0 +1,3 @@
+# -*- coding: utf-8 -*-
+# @Author: wangkun
+# @Time: 2023/7/6

requirements.txt

@@ -1,17 +1,15 @@
-aliyun_python_sdk==2.2.0
-Appium_Python_Client==2.11.0
 atomac==1.2.0
 ffmpeg==1.4
 jieba==0.42.1
 loguru==0.6.0
 lxml==4.9.1
-mq_http_sdk==1.0.3
 oss2==2.15.0
 psutil==5.9.2
-PyExecJS==1.5.1
 PyMySQL==1.0.2
 redis==4.5.1
 requests==2.27.1
-selenium==4.10.0
+selenium~=4.2.0
 urllib3==1.26.9
 workalendar==17.0.0
+opencv-python~=4.8.0.74
+Appium-Python-Client~=2.8.1

zhongmiaoyinxin/zhongmiaoyinxin_recommend/zhongmiaoyinxin_recommend.py

@@ -101,8 +101,8 @@ class ZhongmiaoyinxinRecommend:
                      int(size['width'] * 0.5), int(size['height'] * 0.8), 200)
         # 打开小程序"众妙之上"
         time.sleep(5)
-        Common.logger(log_type, crawler).info('打开小程序"西瓜悦"')
-        driver.find_elements(By.XPATH, '//*[@text="西瓜悦"]')[-1].click()
+        Common.logger(log_type, crawler).info('打开小程序"西瓜说说"')
+        driver.find_elements(By.XPATH, '//*[@text="西瓜说说"]')[-1].click()
 
         time.sleep(5)
         cls.get_videoList(log_type, crawler, driver, env)