208 lines
8.7 KiB
Python
208 lines
8.7 KiB
Python
# coding=utf-8
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import asyncio
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import logging
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import uuid
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import os
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import sys
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import time
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from PIL import Image
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# 将项目根目录添加到 sys.path
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project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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if project_root not in sys.path:
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sys.path.append(project_root)
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import uiautomator2 as u2
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from Apps.AiTeJiYiChong import Kit
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from Apps.AiTeJiYiChong.Kit import take_screenshot
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from Apps.AiTeJiYiChong.ReadImageKit import ReadImageKit
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from Util.RedisKit import RedisKit
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from Apps.AiTeJiYiChong.Service import AiTeJiYiChongService
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from Config.Config import TEMP_IMAGE_DIR
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from Apps.AiTeJiYiChong.Config.Setting import (
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SCROLL_DISTANCE_RATIO,
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MAX_SCROLLS, REDIS_STATION_EXPIRE,
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WAIT_AFTER_SCROLL,
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MAX_CRAWL_DISTANCE
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)
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logger = logging.getLogger("AiTeJiYiChongCrawler")
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async def get_station_list(d, service, max_scrolls=MAX_SCROLLS):
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"""
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获取场站列表并处理翻页
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"""
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redis_kit = RedisKit()
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window_size = d.window_size()
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w, h = window_size[0], window_size[1]
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device_info = d.info
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device_info['width'] = w
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device_info['height'] = h
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logger.info(f"开始爬取列表，设备: {device_info.get('productName')} | 分辨率: {w}x{h}")
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for i in range(max_scrolls + 1):
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logger.info(f"正在处理第 {i + 1} 页...")
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# 1. 拍摄截图
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image_uuid = str(uuid.uuid4())
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screenshot_path = take_screenshot(d, image_uuid, save_dir=TEMP_IMAGE_DIR)
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logger.info(f"列表页截图已完成: {screenshot_path}")
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# 2. 执行图形学分析，生成 _flag.jpg, _vl.jpg 和 .json
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logger.info("正在执行图形学切片分析...")
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json_data = Kit.crop_cards_from_image(screenshot_path)
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# 3. 调用 VL 模型识别并保存数据
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stations = await service.process_station_list_vl(screenshot_path, device_info=device_info)
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logger.info(f"本页识别到 {len(stations)} 个场站")
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# 4. 匹配几何卡片与 VL 识别结果
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if json_data and json_data.get("cards") and stations:
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for card in json_data["cards"]:
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card_rect = card["rect"] # [x1, y1, x2, y2]
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for st in stations:
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st_bounds = st.get("bounds") # [x1, y1, x2, y2] (0-1000)
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if not st_bounds: continue
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# 转换 VL 坐标到像素坐标
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st_y1_px = st_bounds[1] * h / 1000
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st_y2_px = st_bounds[3] * h / 1000
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# 计算 y 轴重叠
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overlap = min(card_rect[3], st_y2_px) - max(card_rect[1], st_y1_px)
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if overlap > 50: # 有显著重叠
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card["station_name"] = st.get("station_name")
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logger.info(f"匹配成功: 几何卡片 {card_rect} -> 场站 {card['station_name']}")
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break
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# 5. 遍历处理本页所有场站
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if json_data and json_data.get("cards"):
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for card_idx, card in enumerate(json_data["cards"]):
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station_name = card.get("station_name")
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if not station_name:
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logger.warning(f"第 {card_idx + 1} 个几何卡片未匹配到场站名称，跳过。")
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continue
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# 检查 Redis 去重
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redis_key = f"crawled:aite:{station_name}"
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if await redis_kit.get_data(redis_key):
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logger.info(f"场站 {station_name} 已处理，跳过。")
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continue
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click_x, click_y = card["click_point"]
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logger.info(f"准备处理第 {card_idx + 1} 个场站: {station_name}, 点击坐标: ({click_x}, {click_y})")
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d.click(click_x, click_y)
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# 等待二级页面加载
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await asyncio.sleep(3)
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# 截取二级页面图
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detail_uuid = f"detail_{station_name}_{image_uuid}"
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detail_path = take_screenshot(d, detail_uuid, save_dir=TEMP_IMAGE_DIR)
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# 5. 调用详情页处理逻辑 (二级页面：提取地址)
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logger.info(f"正在解析详情页基础数据: {detail_path}")
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detail_data = await service.process_station_detail(detail_path, station_name=station_name)
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# 6. 寻找 timePrice.jpg 图标并进入三级页面 (分时价格页)
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time_price_template = os.path.join(os.path.dirname(__file__), "BiaoShi", "timePrice.jpg")
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coords = Kit.find_template_coords(detail_path, time_price_template)
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if coords:
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cx, cy, conf = coords
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logger.info(f"找到分时价格图标，进入三级页面...")
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d.click(cx, cy)
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await asyncio.sleep(3)
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# 截取三级页面并处理滑动价格
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price_detail_uuid = f"price_detail_{station_name}_{image_uuid}"
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price_detail_path = take_screenshot(d, price_detail_uuid, save_dir=TEMP_IMAGE_DIR)
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all_prices = []
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# 初始识别
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prices = await ReadImageKit.get_price_detail_from_image(price_detail_path)
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if prices: all_prices.extend(prices)
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last_md5 = Kit.get_file_md5(price_detail_path)
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# 滑动逻辑
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for scroll_idx in range(3):
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logger.info(f"执行第 {scroll_idx + 1} 次滑动以抓取更多价格...")
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d.swipe(w // 2, int(h * 0.7), w // 2, int(h * 0.3), duration=0.5)
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await asyncio.sleep(2)
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scroll_path = take_screenshot(d, f"price_scroll_{scroll_idx}_{station_name}", save_dir=TEMP_IMAGE_DIR)
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current_md5 = Kit.get_file_md5(scroll_path)
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if current_md5 == last_md5:
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logger.info("检测到屏幕未发生变化（已滑到底部），停止滑动识别。")
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break
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last_md5 = current_md5
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new_prices = await ReadImageKit.get_price_detail_from_image(scroll_path)
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if new_prices:
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for np_item in new_prices:
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if not any(np_i.get("start") == np_item.get("start") for np_i in all_prices):
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all_prices.append(np_item)
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else: break
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if all_prices:
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all_prices.sort(key=lambda x: x.get("start", "00:00"))
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hourly_schedule = ReadImageKit.expand_schedule_to_24h(all_prices)
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await service.process_price_detail_data(station_name, hourly_schedule)
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logger.info(f"场站 {station_name} 三级页面价格处理完成。")
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# 从三级页面返回二级页面
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logger.info("从三级页面返回二级页面...")
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d.press("back")
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await asyncio.sleep(1.5)
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else:
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logger.warning(f"场站 {station_name} 未找到分时价格入口。")
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# 从二级页面返回列表页
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logger.info("从二级页面返回列表页...")
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d.press("back")
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await asyncio.sleep(1.5)
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# 记录 Redis
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await redis_kit.set_data(redis_key, "1", expire=REDIS_STATION_EXPIRE)
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# 5. 翻页滑动 (如果没进入二级页面)
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logger.info("执行翻页滑动...")
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start_x, start_y = w // 2, int(h * 0.8)
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end_x, end_y = w // 2, int(h * (0.8 - SCROLL_DISTANCE_RATIO))
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d.swipe(start_x, start_y, end_x, end_y, duration=0.5)
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await asyncio.sleep(WAIT_AFTER_SCROLL)
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logger.info("达到最大翻页次数，爬取结束。")
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return True
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async def main(service=None, do_cleanup=True):
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"""
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爬虫主入口
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"""
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if do_cleanup:
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Kit.clear_temp_dir()
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if service is None:
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service = AiTeJiYiChongService()
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await service.init_db()
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d = u2.connect()
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try:
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await get_station_list(d, service)
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return True
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except Exception as e:
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logger.error(f"爬取过程中出现异常: {e}")
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return False
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finally:
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# 如果是内部初始化的 service，则在此关闭
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pass
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if __name__ == "__main__":
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asyncio.run(main())
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