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HuangHai
2026-01-12 08:26:13 +08:00
parent 4a45b27638
commit 7b4548c12c
13 changed files with 471 additions and 449 deletions
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Apps/XinDianTu/Config/__pycache__/Setting.cpython-310.pyc Normal file
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Apps/XinDianTu/Crawler.py
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@@ -16,23 +16,26 @@ if project_root not in sys.path:
import uiautomator2 as u2
from Core.BaseCrawler import BaseCrawler
from Util import Kit
from Util.Kit import take_screenshot
from Apps.XinDianTu import Kit
from Apps.XinDianTu.Kit import take_screenshot
from Util.ObsUtil import ObsUploader
from Util.RedisKit import RedisKit
from Util.PaddleOCRKit import get_ocr_kit
import cv2
from Apps.XinDianTu.Service import XinDianTuService
from Config.Config import (
OBS_TMP_PREFIX, CDN_DOMAIN, SCROLL_DISTANCE_RATIO,
OBS_TMP_PREFIX, CDN_DOMAIN, TEMP_IMAGE_DIR
)
from Apps.XinDianTu.Config.Setting import (
SCROLL_DISTANCE_RATIO,
MAX_SCROLLS, REDIS_STATION_EXPIRE,
WAIT_DETAIL_PAGE_LOAD, WAIT_BACK_TO_LIST, WAIT_AFTER_SCROLL,
MAX_CRAWL_DISTANCE, TEMP_IMAGE_DIR
MAX_CRAWL_DISTANCE
)
# --- 用户配置区域 ---
# 是否保留截图文件（True=保留备查，False=随用随删）
KEEP_SCREENSHOTS = True
KEEP_SCREENSHOTS = True
# [Testing] 是否在启动时清除 Redis 中的场站处理记录
# True: 每次运行前清除记录，方便反复测试同一个场站
@@ -57,14 +60,15 @@ logger = logging.getLogger("StationList")
# 强制设置所有相关模块的日志级别为 INFO，防止被第三方库干扰
logging.getLogger("PaddleOCRKit").setLevel(logging.INFO)
logging.getLogger("Util.Kit").setLevel(logging.INFO)
logging.getLogger("OpenXinDianTu").setLevel(logging.INFO)
logging.getLogger("FullProcess").setLevel(logging.INFO)
class XinDianTuCrawler(BaseCrawler):
"""
新电途小程序爬虫实现
"""
def __init__(self, service=None):
super().__init__(service)
# 初始化配置参数
@@ -90,6 +94,7 @@ class XinDianTuCrawler(BaseCrawler):
async def crawl_detail(self, station_info):
pass
async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
"""
获取场站列表并处理翻页
@@ -97,21 +102,21 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
all_stations = []
seen_names = set()
redis_kit = RedisKit()
# 确保在“找桩”或者首页列表界面
window_size = d.window_size()
w, h = window_size[0], window_size[1]
# 获取设备信息以用于坐标计算
device_info = d.info
device_info['width'] = w
device_info['height'] = h
logger.info(f"设备信息: {device_info.get('productName')} | 窗口分辨率: {w}x{h}")
max_distance_reached = False
stop_reason = "max_scrolls_reached" # 默认停止原因：达到最大翻页次数
# 存储后台异步任务
background_tasks = []
@@ -121,7 +126,7 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
stop_reason = "max_distance_reached"
break
logger.info(f"正在处理第 {i + 1} 页...")
# 1. 拍摄截图
logger.info(f"Step [1/6] 正在拍摄列表页截图...")
t_shot = time.time()
@@ -131,7 +136,7 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
save_dir = TEMP_IMAGE_DIR
screenshot_path = take_screenshot(d, image_uuid, save_dir=save_dir)
logger.info(f"Step [1/6] 列表页截图已完成: {screenshot_path} (耗时: {time.time() - t_shot:.2f}s)")
# 校验截图实际尺寸
img_w, img_h = 0, 0
try:
@@ -140,115 +145,119 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
logger.info(f"截图实际尺寸: {img_w}x{img_h}")
# 如果截图尺寸与窗口尺寸不符，更新 device_info 以适配坐标换算
if img_w != w or img_h != h:
logger.warning(f"检测到截图尺寸 ({img_w}x{img_h}) 与窗口尺寸 ({w}x{h}) 不一致，将以截图尺寸为准进行坐标换算。")
logger.warning(
f"检测到截图尺寸 ({img_w}x{img_h}) 与窗口尺寸 ({w}x{h}) 不一致，将以截图尺寸为准进行坐标换算。")
device_info['width'] = img_w
device_info['height'] = img_h
except Exception as e:
logger.warning(f"无法获取截图尺寸信息: {e}")
# 2. 使用 Kit.crop_cards_from_image 进行切片和生成 _vl.jpg
logger.info("Step [2/6] 正在执行本地图形学切片分析，识别场站卡片并生成绿色方框图...")
t_crop = time.time()
# 这会生成 _vl.jpg 和 .json
Kit.crop_cards_from_image(screenshot_path)
logger.info(f"Step [2/6] 图形学切片分析完成 (耗时: {time.time() - t_crop:.2f}s)")
# 读取生成的元数据
json_path = screenshot_path.replace(".jpg", ".json")
vl_img_path = screenshot_path.replace(".jpg", "_vl.jpg")
if not os.path.exists(json_path) or not os.path.exists(vl_img_path):
logger.error(f"❌ Step [2/6] 失败: 未生成有效的 JSON 或 VL 图片 (JSON: {os.path.exists(json_path)}, VL: {os.path.exists(vl_img_path)})")
logger.error(
f"❌ Step [2/6] 失败: 未生成有效的 JSON 或 VL 图片 (JSON: {os.path.exists(json_path)}, VL: {os.path.exists(vl_img_path)})")
logger.warning("可能未识别到任何卡片，跳过当前页")
continue
logger.info(f"Step [3/6] 绿色方框图已生成: {vl_img_path}")
with open(json_path, 'r', encoding='utf-8') as f:
json_metadata = json.load(f)
# --- New Logic: Local OCR via PaddleOCRKit ---
logger.info("Step [4/6] 正在开始本地 OCR 识别流程，将识别出的卡片逐一交给 OCR...")
t_ocr_total = time.time()
stations = []
try:
ocr_kit = get_ocr_kit()
# 读取原始截图进行裁剪 (比读取 _vl.jpg 更干净，没有绿框干扰)
original_img = cv2.imread(screenshot_path)
if "cards" in json_metadata and original_img is not None:
h_img, w_img = original_img.shape[:2]
num_cards = len(json_metadata['cards'])
logger.info(f"检测到 {num_cards} 个场站卡片，准备开始逐一 OCR 识别...")
for idx, card in enumerate(json_metadata["cards"]):
# card: {"id": 1, "rect": [x1, y1, x2, y2], "click_point": [cx, cy]}
rect = card.get("rect")
if not rect: continue
x1, y1, x2, y2 = rect
# 边界检查
x1 = max(0, min(x1, w_img))
x2 = max(0, min(x2, w_img))
y1 = max(0, min(y1, h_img))
y2 = max(0, min(y2, h_img))
if x2 <= x1 or y2 <= y1: continue
# 裁剪卡片
cropped_card = original_img[y1:y2, x1:x2]
# 识别
logger.info(f"正在识别第 {idx+1}/{num_cards} 个卡片 (区域: {rect})...")
logger.info(f"正在识别第 {idx + 1}/{num_cards} 个卡片 (区域: {rect})...")
t_card_start = time.time()
parsed_data = ocr_kit.recognize(cropped_card)
t_card_end = time.time()
# Log detailed OCR result
logger.info(f"卡片 {idx+1} OCR 识别耗时: {t_card_end - t_card_start:.2f}s")
logger.info(f"卡片 {idx + 1} OCR 识别耗时: {t_card_end - t_card_start:.2f}s")
if parsed_data and parsed_data.get("station_name"):
# 格式化数据以匹配原有结构
piles_list = parsed_data.get("piles", [])
piles_str_parts = []
for p in piles_list:
p_type = p.get("type", "")
p_free = p.get("free", 0)
p_total = p.get("total", 0)
piles_str_parts.append(f"{p_type}:{p_free}/{p_total}")
piles_str = " ".join(piles_str_parts)
station_info = {
"station_name": parsed_data.get("station_name"),
"price": str(parsed_data.get("price")) if parsed_data.get("price") is not None else "",
"pro_price": "", # 暂未解析
"piles": piles_str,
"distance": parsed_data.get("distance", ""),
"uia_center_x": card["click_point"][0],
"uia_center_y": card["click_point"][1],
"tags": parsed_data.get("tags", []),
"parking_info": parsed_data.get("parking", "")
}
logger.info(f"✅ 成功解析场站: {station_info['station_name']} | 距离: {station_info['distance']}")
# 立即记录场站基础状态信息 (电桩、价格等)
try:
await service.record_station_status(station_info)
except Exception as e:
logger.error(f"❌ 记录场站状态失败: {e}")
stations.append(station_info)
# 格式化数据以匹配原有结构
piles_list = parsed_data.get("piles", [])
piles_str_parts = []
for p in piles_list:
p_type = p.get("type", "")
p_free = p.get("free", 0)
p_total = p.get("total", 0)
piles_str_parts.append(f"{p_type}:{p_free}/{p_total}")
piles_str = " ".join(piles_str_parts)
station_info = {
"station_name": parsed_data.get("station_name"),
"price": str(parsed_data.get("price")) if parsed_data.get("price") is not None else "",
"pro_price": "", # 暂未解析
"piles": piles_str,
"distance": parsed_data.get("distance", ""),
"uia_center_x": card["click_point"][0],
"uia_center_y": card["click_point"][1],
"tags": parsed_data.get("tags", []),
"parking_info": parsed_data.get("parking", "")
}
logger.info(
f"✅ 成功解析场站: {station_info['station_name']} | 距离: {station_info['distance']}")
# 立即记录场站基础状态信息 (电桩、价格等)
try:
await service.record_station_status(station_info)
except Exception as e:
logger.error(f"❌ 记录场站状态失败: {e}")
stations.append(station_info)
else:
logger.warning(f"⚠️ 卡片 {idx+1} 未能识别出有效场站名称")
logger.warning(f"⚠️ 卡片 {idx + 1} 未能识别出有效场站名称")
except Exception as e:
logger.error(f"❌ Local OCR processing failed: {e}", exc_info=True)
logger.info(f"Step [5/6] 本地 OCR 解析完成 (总耗时: {time.time() - t_ocr_total:.2f}s) | 成功识别: {len(stations)}/{len(json_metadata.get('cards', []))}")
logger.info(
f"Step [5/6] 本地 OCR 解析完成 (总耗时: {time.time() - t_ocr_total:.2f}s) | 成功识别: {len(stations)}/{len(json_metadata.get('cards', []))}")
if not stations:
logger.warning("当前页面未检测到任何有效的场站信息")
else:
@@ -256,25 +265,25 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
new_stations_found = False
for s_idx, s in enumerate(stations):
name = s.get("station_name")
# 过滤掉明显的非场站名称
if not name:
continue
# --- 短期去重：检查 Redis 缓存 ---
# 2分钟内如果处理过该场站，则跳过
redis_key = f"processed_station:{name}"
if await redis_kit.exists(redis_key):
logger.info(f"场站 {name} 在 2 分钟内已处理过，跳过")
continue
if name in seen_names:
continue
# 使用从 JSON 元数据中恢复的点击坐标
x = s.get("uia_center_x")
y = s.get("uia_center_y")
if x is None or y is None:
logger.warning(f"场站 {name} 缺少坐标信息，无法进入详情页")
continue
@@ -282,7 +291,7 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
seen_names.add(name)
all_stations.append(s)
new_stations_found = True
# 打印基本信息
price = s.get("price")
pro_price = s.get("pro_price")
@@ -299,20 +308,22 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
dist_val = float(dist_match.group(1))
is_km = "km" in distance_str.lower()
dist_km = dist_val if is_km else dist_val / 1000.0
# 将距离解析结果和限制检查合并到日志中
dist_log_part = f" (解析: {dist_km:.2f}km / 限: {MAX_CRAWL_DISTANCE}km)"
if dist_km >= MAX_CRAWL_DISTANCE:
logger.info(f"当前场站 {name} 距离 {dist_km}km (原始: {distance_str}) 已达到或超过限制 {MAX_CRAWL_DISTANCE}km。")
logger.info(
f"当前场站 {name} 距离 {dist_km}km (原始: {distance_str}) 已达到或超过限制 {MAX_CRAWL_DISTANCE}km。")
max_distance_reached = True
except Exception as e:
logger.warning(f"解析距离出错: {distance_str}, {e}")
pro_info = f" | PRO会员价: {pro_price}" if pro_price else ""
# 确保距离始终显示，如果为空则显示 "未知"
display_distance = distance_str if distance_str else "未知"
logger.info(f"正在处理第 {s_idx+1} 个场站: {name} | 价格: {price}{pro_info} | 枪: {piles_str} | 距离: {display_distance}{dist_log_part}")
logger.info(
f"正在处理第 {s_idx + 1} 个场站: {name} | 价格: {price}{pro_info} | 枪: {piles_str} | 距离: {display_distance}{dist_log_part}")
if max_distance_reached:
break
@@ -328,7 +339,7 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
logger.info(f"👉 正在点击进入详情页: {name} (绝对坐标: {x}, {y})")
d.click(x, y)
await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD) # 等待详情页加载
await asyncio.sleep(WAIT_DETAIL_PAGE_LOAD) # 等待详情页加载
# 尝试获取并保存地址
address_recorded = False
@@ -338,21 +349,23 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
detail_uuid = str(uuid.uuid4())
detail_screenshot_path = take_screenshot(d, detail_uuid, save_dir=save_dir)
logger.info(f"Step [详情页截图] 耗时: {time.time() - t_d_shot:.4f}s")
# 2. 上传至 OBS
t_d_up = time.time()
detail_object_key = f"{OBS_TMP_PREFIX}/{detail_uuid}.jpg"
up_success, _ = uploader.upload_file(detail_object_key, detail_screenshot_path)
logger.info(f"Step [上传详情页截图] 耗时: {time.time() - t_d_up:.4f}s")
if up_success:
detail_url = f"https://{CDN_DOMAIN}/{detail_object_key}"
# 3. 解析并保存地址 (异步后台处理)
# 定义异步任务函数
async def _process_address_task(task_name, task_url, task_dev_info, task_redis_key):
t_addr_task = time.time()
try:
addr_res = await service.process_station_address(task_name, task_url, device_info=task_dev_info)
addr_res = await service.process_station_address(task_name, task_url,
device_info=task_dev_info)
logger.info(f"Step [解析并保存地址-后台] 耗时: {time.time() - t_addr_task:.4f}s")
if addr_res.get("address"):
logger.info(f"成功记录地址: {addr_res.get('address')}")
@@ -368,40 +381,41 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
# 标记为已记录以跳出 retry 循环
address_recorded = True
# 清理详情页临时截图
if not KEEP_SCREENSHOTS and os.path.exists(detail_screenshot_path):
os.remove(detail_screenshot_path)
break # 直接跳出 retry 循环
break # 直接跳出 retry 循环
# --- 抓取价格时段信息 (New Feature) ---
# 仅在测试模式下或特定需求下启用
try:
logger.info(f"开始抓取价格时段信息: {name}")
# 使用几何特征识别 "全部时段" 按钮
# 临时截图
temp_uuid = "temp_find_expand"
screenshot_path = take_screenshot(d, temp_uuid, save_dir=TEMP_IMAGE_DIR)
# 尝试识别，将调试图片保存到 Images 目录
t_find = time.time()
pos = Kit.find_expand_button_position(screenshot_path, debug_dir=save_dir, debug_filename_prefix=name)
pos = Kit.find_expand_button_position(screenshot_path, debug_dir=save_dir,
debug_filename_prefix=name)
logger.info(f"Step [识别展开按钮] 耗时: {time.time() - t_find:.4f}s")
# 清理截图
try:
os.remove(screenshot_path)
except:
pass
if pos:
x, y = pos
logger.info(f"通过几何特征找到 '全部时段' 按钮: ({x}, {y})")
d.click(x, y)
await asyncio.sleep(1.5)
# 抓取3屏截图
price_image_urls = []
for p_idx in range(1, 4):
@@ -411,7 +425,7 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
p_uuid = f"{name_md5}_price_{p_idx}"
p_path = take_screenshot(d, p_uuid, save_dir=save_dir)
logger.info(f"已保存价格时段截图 {p_idx} (Station: {name}, MD5: {name_md5}): {p_path}")
# 上传到 OBS
p_object_key = f"{OBS_TMP_PREFIX}/{p_uuid}.jpg"
success, _ = uploader.upload_file(p_object_key, p_path)
@@ -427,15 +441,16 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
if p_idx < 3:
d.swipe_ext("up", scale=0.7)
await asyncio.sleep(1.0)
# 调用服务解析价格时段 (异步后台处理)
if price_image_urls:
logger.info(f"正在调用接口解析电费时段数据 ({len(price_image_urls)} 张图片) - 转入后台...")
async def _process_price_task(task_name, task_urls, task_dev_info):
t_price_task = time.time()
try:
await service.process_price_schedule(task_name, task_urls, device_info=task_dev_info)
await service.process_price_schedule(task_name, task_urls,
device_info=task_dev_info)
logger.info(f"Step [解析电费时段数据-后台] 耗时: {time.time() - t_price_task:.4f}s")
logger.info(f"电费时段数据解析完成: {task_name}")
except Exception as e:
@@ -447,7 +462,7 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
logger.warning("未能获取到有效的价格时段截图，跳过解析")
else:
logger.warning(f"未能通过几何特征识别 '全部时段' 按钮")
except Exception as e:
logger.warning(f"抓取价格时段信息时发生异常: {e}")
@@ -457,8 +472,8 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
# 返回列表页
d.press("back")
await asyncio.sleep(WAIT_BACK_TO_LIST) # 等待列表页重新稳定
await asyncio.sleep(WAIT_BACK_TO_LIST) # 等待列表页重新稳定
if not new_stations_found and i > 0:
logger.info("未发现更多新场站，停止下拉。")
# 如果需要强行爬取所有，可以注释掉 break
@@ -472,17 +487,17 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
start_y = 0.5 + (SCROLL_DISTANCE_RATIO / 2)
end_y = 0.5 - (SCROLL_DISTANCE_RATIO / 2)
d.swipe(w * 0.5, h * start_y, w * 0.5, h * end_y, duration=0.5)
await asyncio.sleep(WAIT_AFTER_SCROLL) # 等待页面加载和列表稳定
await asyncio.sleep(WAIT_AFTER_SCROLL) # 等待页面加载和列表稳定
# 清理列表页截图
if not KEEP_SCREENSHOTS:
if os.path.exists(screenshot_path):
os.remove(screenshot_path)
os.remove(screenshot_path)
if os.path.exists(vl_img_path):
os.remove(vl_img_path)
os.remove(vl_img_path)
if os.path.exists(json_path):
os.remove(json_path)
os.remove(json_path)
logger.info(f"爬取任务结束，正在等待 {len(background_tasks)} 个后台处理任务完成...")
if background_tasks:
await asyncio.gather(*background_tasks)
@@ -491,22 +506,23 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
logger.info(f"任务结束，共采集到 {len(all_stations)} 个场站。停止原因: {stop_reason}")
return all_stations
def clean_images_dir():
"""清理 Images 目录下除保留文件外的所有文件"""
base_dir = os.path.dirname(os.path.abspath(__file__))
images_dir = os.path.join(base_dir, "Images")
if not os.path.exists(images_dir):
return
keep_files = {'1.jpg', '2.jpg', '3.jpg', '4.jpg'}
logger.info(f"正在清理 Images 目录 (保留: {keep_files})...")
deleted_count = 0
for filename in os.listdir(images_dir):
if filename in keep_files:
continue
file_path = os.path.join(images_dir, filename)
try:
if os.path.isfile(file_path):
@@ -514,9 +530,10 @@ def clean_images_dir():
deleted_count += 1
except Exception as e:
logger.warning(f"删除文件失败 {filename}: {e}")
logger.info(f"清理完成，共删除 {deleted_count} 个文件。")
async def clean_redis_data(redis_kit):
"""清除 Redis 中的场站处理记录"""
if TEST_CLEAR_REDIS:
@@ -528,10 +545,11 @@ async def clean_redis_data(redis_kit):
else:
logger.info("未发现旧的场站处理记录")
async def main(service=None, do_cleanup=True):
# 连接设备
d = u2.connect()
# 初始化服务
should_close_service = False
if service is None:
@@ -542,28 +560,28 @@ async def main(service=None, do_cleanup=True):
uploader = ObsUploader()
redis_kit = RedisKit()
try:
if do_cleanup:
# 清理图片目录
clean_images_dir()
# [Testing] 如果配置为测试模式，启动时清除 Redis 记录
await clean_redis_data(redis_kit)
# 清理过期数据
# await service.cleanup_old_data()
# 获取场站列表
stations = await get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS)
if stations:
logger.info("场站列表采集完成。")
else:
logger.warning("未采集到任何场站信息。")
return True
except Exception as e:
logger.exception(f"运行过程中出现异常: {e}")
return False
@@ -572,6 +590,7 @@ async def main(service=None, do_cleanup=True):
if should_close_service:
await service.close_db()
if __name__ == "__main__":
try:
asyncio.run(main())

412
Apps/XinDianTu/Kit.py
View File

File diff suppressed because it is too large Load Diff

11
Apps/XinDianTu/Opener.py
View File

@@ -1,14 +1,15 @@
# coding=utf-8
import asyncio
import logging
import os
import time
import uuid
import os
import uiautomator2 as u2
from Util.Kit import take_screenshot, detect_black_agree_button, click_image_template, detect_ad_close_x, detect_any_ad_close, detect_bottom_close_circle
from Util.ObsUtil import ObsUploader
from Util.XinDianTuReadImageKit import XinDianTuReadImageKit
from Config.Config import OBS_TMP_PREFIX, CDN_DOMAIN, TEMP_IMAGE_DIR
from Apps.XinDianTu.Kit import take_screenshot, detect_black_agree_button, detect_any_ad_close, \
detect_bottom_close_circle
from Config.Config import TEMP_IMAGE_DIR
# pip install adbutils
# 配置日志输出，方便调试和监控

31
Apps/XinDianTu/Run.py
View File

@@ -12,8 +12,6 @@ if project_root not in sys.path:
from Apps.XinDianTu.Service import XinDianTuService
from Apps.XinDianTu import Opener
from Apps.XinDianTu import Crawler
from Apps.XinDianTu import Coord as Y4_Coord
from Apps.XinDianTu import GetCookie as T2_GetAmapCookie
# 配置日志
logging.basicConfig(
@@ -59,35 +57,6 @@ async def main():
except Exception as e:
logger.error(f"❌ 步骤 2 执行异常: {e}")
return
# 步骤 3: 补全坐标 (调用 Y4_Coord)
logger.info(">>> 步骤 3: 启动高德地图坐标回填任务...")
try:
# 首次尝试，开启 Cookie 检查
await Y4_Coord.main(check_cookie_first=True)
except Y4_Coord.CookieInvalidError:
logger.warning("⚠️ 检测到高德地图 Cookie 失效或缺失。")
logger.info(">>> 正在启动 Cookie 获取工具 (T2)...")
logger.info("请在弹出的浏览器窗口中完成登录，然后在控制台按回车确认。")
# 调用同步的 T2 工具
# 由于 T2 包含 input() 阻塞，且这是客户端脚本，直接调用即可
try:
# 传入 force=True 跳过“文件已存在”的询问，因为我们确定它无效
# run_in_executor 用于避免完全阻塞事件循环（虽然在这里影响不大）
loop = asyncio.get_event_loop()
success = await loop.run_in_executor(None, T2_GetAmapCookie.run_login_tool, True)
if success:
logger.info("✅ Cookie 更新成功，正在重新启动坐标回填任务...")
# 再次尝试执行，这次可以跳过检查（刚获取完）或者继续检查
await Y4_Coord.main(check_cookie_first=False)
else:
logger.error("❌ Cookie 获取失败或用户取消，跳过坐标回填步骤。")
except Exception as e:
logger.error(f"❌ 调用 T2 工具失败: {e}")
except Exception as e:
logger.error(f"❌ 步骤 3 执行异常: {e}")
finally:
# 确保数据库连接关闭

10
Apps/XinDianTu/Service.py
View File

@@ -1,19 +1,17 @@
import hashlib
import logging
import os
import re
import sys
import uuid
import logging
import json
import re
from datetime import datetime, timedelta
from datetime import datetime
# Ensure sys path includes root for imports if not already
project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
if project_root not in sys.path:
sys.path.append(project_root)
from sqlalchemy import text
from Util.XinDianTuReadImageKit import XinDianTuReadImageKit
from Apps.XinDianTu.XinDianTuReadImageKit import XinDianTuReadImageKit
from DbKit.Db import Db
from Config.Config import DB_URL
from Model.StationProfile import StationProfile

207
Apps/XinDianTu/XinDianTuReadImageKit.py
View File

@@ -9,13 +9,16 @@ import logging
import base64
from openai import OpenAI, BadRequestError
from Config.Config import (
ALY_LLM_API_KEY, VL_MODEL_NAME, VL_MODEL_NAME_AD,
ALY_LLM_API_KEY, VL_MODEL_NAME, VL_MODEL_NAME_AD
)
from Apps.XinDianTu.Config.Setting import (
SAFE_EXCLUDE_RATIO, FALLBACK_WIDTH, FALLBACK_HEIGHT,
BOTTOM_SAFE_EXCLUDE_RATIO
)
logger = logging.getLogger(__name__)
class XinDianTuReadImageKit:
_client = OpenAI(
api_key=ALY_LLM_API_KEY,
@@ -105,7 +108,7 @@ class XinDianTuReadImageKit:
# 获取显示宽高，增加容错
display_width = float(device_info.get("displayWidth") or device_info.get("width") or 1080)
display_height = float(device_info.get("displayHeight") or device_info.get("height") or 2400)
bounds = obj.get("bounds")
bn = obj.get("bounds_norm")
text_bounds = obj.get("station_name_bounds")
@@ -116,7 +119,7 @@ class XinDianTuReadImageKit:
def get_pixel_coords(b_data, d_w, d_h):
if not b_data:
return None
# Extract values based on dict or list
if isinstance(b_data, list) and len(b_data) == 4:
v1, v2, v3, v4 = b_data
@@ -126,7 +129,7 @@ class XinDianTuReadImageKit:
v2 = b_data.get("top") if b_data.get("top") is not None else b_data.get("y1")
v3 = b_data.get("right") if b_data.get("right") is not None else b_data.get("x2")
v4 = b_data.get("bottom") if b_data.get("bottom") is not None else b_data.get("y2")
if any(v is None for v in (v1, v2, v3, v4)):
return None
else:
@@ -138,9 +141,9 @@ class XinDianTuReadImageKit:
return None
max_v = max(v1, v2, v3, v4)
# 1. 0-1 归一化坐标
if max_v <= 1.05:
if max_v <= 1.05:
x1, y1, x2, y2 = v1 * d_w, v2 * d_h, v3 * d_w, v4 * d_h
# 2. 0-1000 归一化坐标 (Qwen-VL 常用)
elif max_v <= 1005:
@@ -148,20 +151,24 @@ class XinDianTuReadImageKit:
# 3. 绝对像素坐标
else:
x1, y1, x2, y2 = v1, v2, v3, v4
return min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2)
# 优先使用场站名称区域进行点击 (最安全)
coords = get_pixel_coords(text_bn, display_width, display_height) or get_pixel_coords(text_bounds, display_width, display_height)
coords = get_pixel_coords(text_bn, display_width, display_height) or get_pixel_coords(text_bounds,
display_width,
display_height)
if coords:
x1, y1, x2, y2 = coords
uia_x = int(x1 + (x2 - x1) / 2)
uia_y = int(y1 + (y2 - y1) / 2)
logger.info(f"坐标计算: 使用文字区域 -> ({uia_x}, {uia_y}) | 区域: {coords} | 屏幕: {display_width}x{display_height}")
logger.info(
f"坐标计算: 使用文字区域 -> ({uia_x}, {uia_y}) | 区域: {coords} | 屏幕: {display_width}x{display_height}")
# 备选：使用整个卡片区域
if uia_x is None:
coords = get_pixel_coords(bn, display_width, display_height) or get_pixel_coords(bounds, display_width, display_height)
coords = get_pixel_coords(bn, display_width, display_height) or get_pixel_coords(bounds, display_width,
display_height)
if coords:
x1, y1, x2, y2 = coords
w, h = x2 - x1, y2 - y1
@@ -172,24 +179,27 @@ class XinDianTuReadImageKit:
# 默认中心点，但稍微偏上一点，避开底部可能的按钮
uia_x = int(x1 + w / 2)
uia_y = int(y1 + h * 0.4)
logger.info(f"坐标计算: 使用卡片区域 -> ({uia_x}, {uia_y}) | 区域: {coords} | 屏幕: {display_width}x{display_height}")
logger.info(
f"坐标计算: 使用卡片区域 -> ({uia_x}, {uia_y}) | 区域: {coords} | 屏幕: {display_width}x{display_height}")
# --- 安全过滤：过滤掉屏幕顶部的点击坐标 (通常是广告或菜单) ---
if uia_y is not None:
# 如果点击点在屏幕顶部 SAFE_EXCLUDE_RATIO 范围内，极大概率是误触广告位，将其排除
if uia_y < (display_height * SAFE_EXCLUDE_RATIO):
logger.warning(f"安全排除: 坐标 ({uia_x}, {uia_y}) 位于屏幕顶部 {int(SAFE_EXCLUDE_RATIO*100)}% 区域，疑似广告或菜单，已忽略。")
logger.warning(
f"安全排除: 坐标 ({uia_x}, {uia_y}) 位于屏幕顶部 {int(SAFE_EXCLUDE_RATIO * 100)}% 区域，疑似广告或菜单，已忽略。")
return None
# 如果点击点在屏幕底部 BOTTOM_SAFE_EXCLUDE_RATIO 范围内，极大概率是误触底部功能区（如扫码充电），将其排除
if uia_y > (display_height * (1 - BOTTOM_SAFE_EXCLUDE_RATIO)):
logger.warning(f"安全排除: 坐标 ({uia_x}, {uia_y}) 位于屏幕底部 {int(BOTTOM_SAFE_EXCLUDE_RATIO*100)}% 区域，疑似底部功能区，已忽略。")
logger.warning(
f"安全排除: 坐标 ({uia_x}, {uia_y}) 位于屏幕底部 {int(BOTTOM_SAFE_EXCLUDE_RATIO * 100)}% 区域，疑似底部功能区，已忽略。")
return None
if uia_x is not None and uia_y is not None:
obj["uia_center_x"] = uia_x
obj["uia_center_y"] = uia_y
# Clean up temporary fields
for k in ["bounds", "bounds_norm", "station_name_bounds", "station_name_bounds_norm"]:
if k in obj:
@@ -261,13 +271,13 @@ class XinDianTuReadImageKit:
device_info = XinDianTuReadImageKit._FALLBACK_DEVICE_INFO
prompt = (
"仅输出JSON对象（不含任何说明文字）。"
"请在图片中找到场站名称完全匹配“" + str(station_name) + "”的那一行/卡片，"
"返回该场站名称文字本身的区域坐标（优先返回归一化坐标）。"
"返回格式示例："
"{\"station_name_bounds\": {\"x1\": 100, \"y1\": 200, \"x2\": 300, \"y2\": 240}, "
"\"station_name_bounds_norm\": {\"left\": 0.1, \"top\": 0.2, \"right\": 0.3, \"bottom\": 0.24}}"
"注意：bounds使用0-1000归一化坐标空间；如果找不到，返回空JSON {}。"
"仅输出JSON对象（不含任何说明文字）。"
"请在图片中找到场站名称完全匹配“" + str(station_name) + "”的那一行/卡片，"
"返回该场站名称文字本身的区域坐标（优先返回归一化坐标）。"
"返回格式示例："
"{\"station_name_bounds\": {\"x1\": 100, \"y1\": 200, \"x2\": 300, \"y2\": 240}, "
"\"station_name_bounds_norm\": {\"left\": 0.1, \"top\": 0.2, \"right\": 0.3, \"bottom\": 0.24}}"
"注意：bounds使用0-1000归一化坐标空间；如果找不到，返回空JSON {}。"
)
loop = asyncio.get_event_loop()
@@ -293,7 +303,9 @@ class XinDianTuReadImageKit:
try:
data = json.loads(raw)
if isinstance(data, dict) and (data.get("station_name_bounds") or data.get("station_name_bounds_norm") or data.get("bounds") or data.get("bounds_norm")):
if isinstance(data, dict) and (
data.get("station_name_bounds") or data.get("station_name_bounds_norm") or data.get(
"bounds") or data.get("bounds_norm")):
data = XinDianTuReadImageKit._add_click_point(data, device_info, anchor="station_text")
return {
"uia_center_x": data.get("uia_center_x"),
@@ -339,7 +351,8 @@ class XinDianTuReadImageKit:
data = json.loads(raw)
if isinstance(data, list):
# 过滤掉 None (即被 _add_click_point 排除掉的顶部项)
data = [XinDianTuReadImageKit._add_click_point(x, device_info, anchor="station_text") for x in data if x.get("b_use", 1) == 1]
data = [XinDianTuReadImageKit._add_click_point(x, device_info, anchor="station_text") for x in data if
x.get("b_use", 1) == 1]
data = [x for x in data if x is not None]
elif isinstance(data, dict):
if data.get("b_use", 1) == 1:
@@ -389,7 +402,8 @@ class XinDianTuReadImageKit:
if tasks:
results = await asyncio.gather(*tasks, return_exceptions=True)
for i, r in zip(idxs, results):
if isinstance(r, dict) and r.get("uia_center_x") is not None and r.get("uia_center_y") is not None:
if isinstance(r, dict) and r.get("uia_center_x") is not None and r.get(
"uia_center_y") is not None:
data[i]["uia_center_x"] = r.get("uia_center_x")
data[i]["uia_center_y"] = r.get("uia_center_y")
@@ -418,11 +432,11 @@ class XinDianTuReadImageKit:
"""
if not json_metadata or "cards" not in json_metadata:
return []
cards_meta = json_metadata["cards"]
# 按 id 或 Y 坐标排序，确保顺序一致 (Kit 生成时已经是 top-down)
cards_meta.sort(key=lambda x: x["rect"][1])
cards_meta.sort(key=lambda x: x["rect"][1])
prompt = (
"图片中用绿色矩形框标记了若干个充电站卡片区域。\n"
"请按从上到下的顺序，依次识别每个绿色框内的场站信息，并返回一个JSON数组。\n"
@@ -440,7 +454,7 @@ class XinDianTuReadImageKit:
"\n"
"严格返回纯JSON格式。"
)
loop = asyncio.get_event_loop()
resp = await loop.run_in_executor(
None,
@@ -457,10 +471,10 @@ class XinDianTuReadImageKit:
],
)
)
content = resp.choices[0].message.content or ""
raw = XinDianTuReadImageKit._extract_json(content)
final_stations = []
try:
llm_data = json.loads(raw)
@@ -470,46 +484,48 @@ class XinDianTuReadImageKit:
for i in range(count):
item = llm_data[i]
meta = cards_meta[i]
if not item or not isinstance(item, dict):
continue
name = item.get("station_name")
if not name:
continue
# 注入元数据中的点击坐标
click_pt = meta.get("click_point", [0, 0])
cx, cy = click_pt
# 安全检查
img_h = 2400
if device_info:
img_h = float(device_info.get("displayHeight") or device_info.get("height") or 2400)
else:
img_h = json_metadata.get("height", 2400)
# 顶部安全排除
if cy < (img_h * SAFE_EXCLUDE_RATIO):
logger.warning(f"VL安全排除: 坐标 ({cx}, {cy}) 位于屏幕顶部 {int(SAFE_EXCLUDE_RATIO*100)}% 区域，已忽略。")
logger.warning(
f"VL安全排除: 坐标 ({cx}, {cy}) 位于屏幕顶部 {int(SAFE_EXCLUDE_RATIO * 100)}% 区域，已忽略。")
continue
# 底部安全排除
if cy > (img_h * (1 - BOTTOM_SAFE_EXCLUDE_RATIO)):
logger.warning(f"VL安全排除: 坐标 ({cx}, {cy}) 位于屏幕底部 {int(BOTTOM_SAFE_EXCLUDE_RATIO*100)}% 区域，疑似底部功能区，已忽略。")
logger.warning(
f"VL安全排除: 坐标 ({cx}, {cy}) 位于屏幕底部 {int(BOTTOM_SAFE_EXCLUDE_RATIO * 100)}% 区域，疑似底部功能区，已忽略。")
continue
item["uia_center_x"] = cx
item["uia_center_y"] = cy
item["rect"] = meta.get("rect")
final_stations.append(item)
else:
logger.warning(f"LLM return format error: expected list, got {type(llm_data)}")
except Exception as e:
logger.error(f"Error parsing VL response: {e}")
return final_stations
@staticmethod
@@ -530,7 +546,7 @@ class XinDianTuReadImageKit:
remote_path = f"tmp/patches/{os.path.basename(patch_path)}"
uploader.upload_file(patch_path, remote_path)
patch_url = f"{cdn_domain}/{remote_path}"
# 记录上传后的 URL 供识别使用
seg["patch_url"] = patch_url
tasks.append(XinDianTuReadImageKit.recognize_card_text(patch_url))
@@ -547,7 +563,7 @@ class XinDianTuReadImageKit:
res["x"] = seg["center_x"]
res["y"] = seg["center_y"]
final_stations.append(res)
# 4. 后置过滤 (复用原有过滤逻辑)
if final_stations:
processed_excluded_titles = {str(x).replace(" ", "").strip() for x in STATION_EXCLUDED_TITLES}
@@ -580,40 +596,40 @@ class XinDianTuReadImageKit:
img_data = np.array(img)
logger.info(f"页面分析 - 分辨率: {width}x{height}")
# 1. 行特征分析：识别分割线 (Divider)
# 分割线特征：横贯全屏，亮度均匀，通常比纯白(255)稍暗，比文字内容亮
# 典型值：Mean=242-247, Std<10
row_types = [] # 0: Unknown/Content, 1: Divider, 2: White/Empty
row_types = [] # 0: Unknown/Content, 1: Divider, 2: White/Empty
# 采样点：左中右
l_x, m_x, r_x = int(width * 0.05), int(width * 0.5), int(width * 0.95)
debug_rows = []
for y in range(height):
# 避开顶部和底部导航栏 (15% - 85%) - 扩大排除范围以避免误识别 Header/Footer
if y < height * 0.15 or y > height * 0.85:
row_types.append(1) # 视为无关区域 (标记为 Divider 以防止形成 Segment)
row_types.append(1) # 视为无关区域 (标记为 Divider 以防止形成 Segment)
continue
row = img_data[y]
row_mean = np.mean(row)
row_std = np.std(row)
# 判定逻辑：
# 1. 纯白行 (卡片底色) -> mean > 252 (允许微小噪点)
# 2. 分割线 (Divider) -> 230 < mean < 252 且 std < 15 (颜色均匀)
# 3. 内容行 (Content) -> mean <= 230 或 (mean > 230 且 std >= 15) (有文字/图片导致方差大)
if row_mean > 252:
r_type = 2 # White/Empty
r_type = 2 # White/Empty
elif 230 < row_mean <= 252 and row_std < 15:
r_type = 1 # Divider
r_type = 1 # Divider
else:
r_type = 0 # Content
r_type = 0 # Content
row_types.append(r_type)
# Debug log sampling
@@ -626,33 +642,33 @@ class XinDianTuReadImageKit:
# 2. 聚合连续的 Content 区域 (Type 0 or Type 2 sandwiched by Type 0)
# 实际上，卡片是由 Divider 分隔开的区域。
# 我们寻找两个 Divider 之间的区域，且该区域必须包含 Content (Type 0)。
segments = []
# 简化状态机：
# 寻找非 Divider 的连续段
in_segment = False
seg_start = -1
has_content = False
raw_blocks = []
for y, r_type in enumerate(row_types):
if r_type != 1: # Not Divider
if r_type != 1: # Not Divider
if not in_segment:
in_segment = True
seg_start = y
has_content = False
if r_type == 0:
has_content = True
else: # Is Divider
else: # Is Divider
if in_segment:
# 结束一段
if has_content: # 只有包含内容的段才算
if has_content: # 只有包含内容的段才算
raw_blocks.append((seg_start, y))
in_segment = False
# 处理最后一段
if in_segment and has_content:
raw_blocks.append((seg_start, len(row_types)))
@@ -660,52 +676,52 @@ class XinDianTuReadImageKit:
# 3. 过滤和后处理
# 合并距离很近的块？或者过滤太小的块
base_name = os.path.splitext(os.path.basename(image_path))[0]
valid_segments = []
logger.info(f"Initial raw blocks count: {len(raw_blocks)}")
for i, (y1, y2) in enumerate(raw_blocks):
h = y2 - y1
# 过滤太小的块 (可能是杂噪或单纯的文字行)
if h < 50:
if h < 50:
# logger.debug(f"Block {i} too small: {h}")
continue
# 过滤太大的块 (可能是全屏错误)
if h > 1000:
continue
# 再次确认内部是否有足够的“暗像素”（内容）
# 避免切出纯白的空隙
region = img_data[y1:y2]
region_mean = np.mean(region)
if region_mean > 254.5: # 整体太白 (放宽阈值，避免误杀大面积白色的卡片)
if region_mean > 254.5: # 整体太白 (放宽阈值，避免误杀大面积白色的卡片)
logger.info(f" [-] 忽略区域 {i}: Y({y1}-{y2}), H={h}, 整体太白 (Mean={region_mean:.1f} > 254.5)")
continue
# --- 优化：边缘背景检查与宽度裁剪 (Margin Check & Crop) ---
# 不再直接拒绝白边，而是尝试计算内容的有效宽度
# 假设：有效内容行的方差较高，或者亮度显著不同于背景
# 简单策略：保留全宽，但增加高度限制
if h < 60: # 稍微降低阈值以测试
if h < 60: # 稍微降低阈值以测试
logger.info(f" [-] 忽略区域 {i}: Y({y1}-{y2}), H={h}, 高度不足 (<60)")
continue
# 计算左右边距的平均亮度，辅助判断（仅记录日志，不强行过滤）
margin_w = max(5, int(width * 0.05))
l_margin = img_data[y1:y2, 0:margin_w]
r_margin = img_data[y1:y2, width-margin_w:width]
r_margin = img_data[y1:y2, width - margin_w:width]
l_mean = np.mean(l_margin)
r_mean = np.mean(r_margin)
# 如果是全宽卡片，左右边缘可能是白色的。
# 之前的逻辑: if l_mean > 252 or r_mean > 252: continue (导致漏检)
# 现在移除该逻辑。
# --- 新增：自动裁剪水平宽度 (Auto Horizontal Crop) ---
# 尝试找到内容的左右边界 (基于列的方差或亮度差异)
x1, x2 = 0, width
# 从左向右扫描
for x in range(0, int(width * 0.4), 2):
col = img_data[y1:y2, x]
@@ -713,22 +729,22 @@ class XinDianTuReadImageKit:
if np.std(col) > 5 or np.mean(col) < 245:
x1 = x
break
# 从右向左扫描
for x in range(width - 1, int(width * 0.6), -2):
col = img_data[y1:y2, x]
if np.std(col) > 5 or np.mean(col) < 245:
x2 = x + 1
break
# 增加一点 padding
x1 = max(0, x1 - 10)
x2 = min(width, x2 + 10)
# 如果裁剪后宽度太小，可能不是有效卡片
if (x2 - x1) < width * 0.5:
logger.info(f" [-] 忽略区域 {i}: 裁剪后宽度过小 ({x2-x1})")
continue
logger.info(f" [-] 忽略区域 {i}: 裁剪后宽度过小 ({x2 - x1})")
continue
# 保存
patch = img.crop((x1, y1, x2, y2))
@@ -743,8 +759,9 @@ class XinDianTuReadImageKit:
"center_y": center_y,
"y_range": (y1, y2)
})
logger.info(f" [+] 发现卡片 {i}: Y({y1}-{y2}), H={h}, Crop X({x1}-{x2}), 边缘(L={l_mean:.1f}, R={r_mean:.1f}), 已保存")
logger.info(
f" [+] 发现卡片 {i}: Y({y1}-{y2}), H={h}, Crop X({x1}-{x2}), 边缘(L={l_mean:.1f}, R={r_mean:.1f}), 已保存")
logger.info(f"分析完成：识别到 {len(valid_segments)} 个区域")
return valid_segments
@@ -767,7 +784,7 @@ class XinDianTuReadImageKit:
"role": "user",
"content": [
{
"type": "text",
"type": "text",
"text": "请识别图片中的充电站信息，并以 JSON 格式输出：{\"station_name\": \"...\", \"price\": \"...\", \"piles\": \"空闲数/总数\"}。只输出 JSON，不要有其他文字。"
},
{"type": "image_url", "image_url": {"url": patch_url}}
@@ -787,7 +804,7 @@ class XinDianTuReadImageKit:
return json.loads(json_match.group())
except Exception:
logger.warning(f"OCR 结果解析 JSON 失败: {content}")
return {"station_name": "未知"}
@staticmethod
@@ -940,8 +957,8 @@ class XinDianTuReadImageKit:
# 尝试捕获 DataInspection 错误并进行 Base64 回退
err_code = getattr(e, 'code', '') or ''
if not err_code and hasattr(e, 'body') and isinstance(e.body, dict):
err_code = e.body.get('code', '')
err_code = e.body.get('code', '')
# 如果是媒体格式或数据检查错误
if 'InvalidParameter.DataInspection' in str(err_code) or 'media format' in str(e).lower():
logger.warning(f"Image URL rejected ({err_code}). Attempting Base64 fallback: {image_url}")
@@ -956,7 +973,7 @@ class XinDianTuReadImageKit:
ext = "webp"
elif ".jpg" in lower_url or ".jpeg" in lower_url:
ext = "jpeg"
data_uri = f"data:image/{ext};base64,{base64_str}"
resp = await loop.run_in_executor(None, lambda: _do_request(data_uri))
else:
@@ -966,8 +983,8 @@ class XinDianTuReadImageKit:
logger.error(f"API BadRequestError: {e}")
return []
except Exception as e:
logger.error(f"API Unexpected Error: {e}")
return []
logger.error(f"API Unexpected Error: {e}")
return []
content = resp.choices[0].message.content or ""
raw = XinDianTuReadImageKit._extract_json(content)
@@ -1147,7 +1164,7 @@ class XinDianTuReadImageKit:
content = resp.choices[0].message.content or ""
raw = XinDianTuReadImageKit._extract_json(content)
result = {}
try:
data = json.loads(raw)
@@ -1155,7 +1172,7 @@ class XinDianTuReadImageKit:
result = data
except Exception as e:
logger.error(f"Error parsing address JSON: {e}")
# 等待并合并按钮坐标结果
try:
button_result = await button_task
@@ -1284,7 +1301,7 @@ async def test3(station_name: str):
if __name__ == "__main__":
#asyncio.run(test1())
# asyncio.run(test1())
station_name = '吉林省看守所充电站'
#asyncio.run(test2(station_name))
# asyncio.run(test2(station_name))
asyncio.run(test3(station_name))

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