156 lines
6.1 KiB
Python
156 lines
6.1 KiB
Python
# coding=utf-8
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import logging
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import os
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import sys
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import json
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# Ensure sys path includes root for imports if not already
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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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from Util.VLMKit import VLMKit
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from Apps.TeLaiDian.Kit import draw_rectangles, detect_cards_cv, setup_logger
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from Apps.TeLaiDian.Config.Setting import SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO
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# 初始化日志
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logger = setup_logger("ReadImageKit")
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class ReadImageKit:
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def __init__(self):
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self.vlm = VLMKit()
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async def analyze_detail_price(self, image_path):
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"""
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分析详情页截图，提取电价信息
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"""
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prompt = """
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分析这张充电站详情页截图，提取**电价时段表**。
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请仔细寻找包含“时段”、“电价”、“服务费”或“总价”的表格或列表。
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请提取每个时段的：
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1. 开始时间 (HH:MM)
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2. 结束时间 (HH:MM)
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3. 总电价 (元/度，包含电费和服务费)
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输出格式为 JSON 数组：
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[
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{
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"start": "00:00",
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"end": "08:00",
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"price": 1.23
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},
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...
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]
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如果无法识别任何价格信息，请返回空数组 []。
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"""
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try:
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res_text = await self.vlm.analyze_image(image_path, prompt)
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logger.info(f"VLM Price Analysis Result for {os.path.basename(image_path)}: {res_text[:200]}...")
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json_str = self.vlm.extract_json(res_text)
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prices = json.loads(json_str)
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normalized_prices = []
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if isinstance(prices, list):
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for p in prices:
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new_p = p.copy()
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if 'time_range' in p and ('start' not in p or 'end' not in p):
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tr = p['time_range'].replace('~', '-').replace(' ', '')
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parts = tr.split('-')
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if len(parts) >= 2:
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new_p['start'] = parts[0]
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new_p['end'] = parts[1]
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if 'price' not in p:
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if 'total_price' in p:
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new_p['price'] = p['total_price']
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elif 'elec_price' in p and 'service_price' in p:
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try:
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new_p['price'] = float(p['elec_price']) + float(p['service_price'])
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except:
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pass
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normalized_prices.append(new_p)
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return normalized_prices
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return []
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except Exception as e:
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logger.error(f"分析电价详情失败: {e}")
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return []
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async def analyze_detail_basic_info(self, image_path):
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"""
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分析详情页首屏截图，提取场站名称和精确地址
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"""
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prompt = """
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分析这张充电站详情页首屏截图，提取：
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1. 场站名称 (通常在页面中部，大字体)
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2. 详细地址 (通常在名称下方或页面下半部分，伴有地址图标)
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输出格式为 JSON：
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{
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"name": "xxx充电站",
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"address": "xxx省xxx市xxx区xxx路xxx号"
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}
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"""
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try:
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res_text = await self.vlm.analyze_image(image_path, prompt)
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json_str = self.vlm.extract_json(res_text)
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return json.loads(json_str)
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except Exception as e:
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logger.error(f"分析详情页基础信息失败: {e}")
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return {}
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async def analyze_station_list(self, image_path):
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"""
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分析场站列表页图片，提取场站位置和基本信息
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"""
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cv_bboxes = detect_cards_cv(image_path, top_ratio=SAFE_EXCLUDE_RATIO, bottom_ratio=BOTTOM_SAFE_EXCLUDE_RATIO)
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if cv_bboxes:
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draw_rectangles(image_path, cv_bboxes)
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prompt = f"""
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图片中已经用绿色矩形框标记了 {len(cv_bboxes)} 个可能的充电站卡片。
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请按从上到下的顺序，识别每个绿色框内的场站信息。
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输出格式为 JSON 数组，长度必须为 {len(cv_bboxes)}。
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每个对象包含：
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- "name": 场站名称
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- "address": 场站地址
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- "is_valid": true/false (是否为真实的场站卡片)
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"""
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else:
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prompt = """
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分析这张充电站列表截图，提取所有充电站卡片信息。
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忽略顶部的筛选栏，仅提取下方重复出现的场站卡片。
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输出格式为 JSON 数组，每个对象包含：
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- "name": 场站名称
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- "address": 场站地址
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- "point": 场站卡片的中心点击坐标 [x, y]
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- "bbox": 场站卡片的边界框 [x1, y1, x2, y2]
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"""
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try:
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res_text = await self.vlm.analyze_image(image_path, prompt)
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json_str = self.vlm.extract_json(res_text)
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vlm_results = json.loads(json_str)
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final_stations = []
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if cv_bboxes and isinstance(vlm_results, list):
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for i, res in enumerate(vlm_results):
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if i < len(cv_bboxes):
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bbox = cv_bboxes[i]
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if res and (res.get("is_valid") is True or (res.get("name") and res.get("is_valid") is not False)):
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final_stations.append({
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"name": res.get("name"),
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"address": res.get("address"),
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"point": [(bbox[0] + bbox[2]) // 2, (bbox[1] + bbox[3]) // 2],
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"bbox": bbox
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})
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elif not cv_bboxes:
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final_stations = vlm_results if isinstance(vlm_results, list) else []
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return final_stations
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except Exception as e:
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logger.error(f"分析列表页失败: {e}")
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return []
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