407 lines
18 KiB
Python
407 lines
18 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 Util.EasyOcrKit import get_easyocr_reader
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from Apps.TeLaiDian.Kit import draw_rectangles, setup_logger, read_image, detect_price_info_container_cv
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from Apps.TeLaiDian.Config.Setting import SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO, MIN_CARD_HEIGHT
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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 find_price_entrance_ocr(self, image_path):
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"""
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使用 OCR 在详情页寻找价格入口 (全部时段 > 或 全天价格统一 >)
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不再使用 VLM 和 掩码 CV
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"""
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container_norm = detect_price_info_container_cv(image_path)
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if container_norm:
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# 找到矩形框 [x1, y1, x2, y2]
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x1, y1, x2, y2 = container_norm
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# 计算中心点
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center_x = (x1 + x2) // 2
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center_y = (y1 + y2) // 2
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# 绘制诊断图 (复用之前的逻辑)
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img = read_image(image_path)
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if img is not None:
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h, w = img.shape[:2]
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actual_p = [int(center_x * w / 1000), int(center_y * h / 1000)]
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bbox = [int(x1 * w / 1000), int(y1 * h / 1000), int(x2 * w / 1000), int(y2 * h / 1000)]
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draw_rectangles(image_path, bboxes=[bbox], click_points=[actual_p])
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logger.info(f"[OCR] 已定位价格入口并保存诊断图: {image_path.replace('.jpg', '_vl.jpg')}")
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return {
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"found": True,
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"reason": "OCR 精准定位到价格入口关键字",
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"point": [center_x, center_y]
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}
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# 记录警告日志
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logger.warning(f"⚠️ [OCR识别失败] 在图片中未发现价格入口关键字 (全部时段/全天价格统一): {image_path}")
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return {"found": False}
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async def find_close_button_vlm(self, image_path):
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"""
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使用 VLM 在页面上寻找关闭按钮（用于清理广告弹窗）
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"""
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prompt = """
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分析这张截图，判断是否存在弹窗广告或遮罩层。
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如果存在，请找到关闭按钮（通常是圆圈里的 X，或者写着“跳过”、“关闭”的按钮）。
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**重要警告**：
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1. 严禁选择屏幕最右上角的按钮（微信小程序的“胶囊按钮”，包含三个点和圆圈）。
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2. 广告关闭按钮通常在弹窗的边缘，或者是页面中心大弹窗的某个角落。
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输出格式为 JSON：
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{
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"has_ad": true/false,
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"reason": "简单描述发现的弹窗",
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"close_point": [x, y] // 归一化坐标 [0-1000]，如果不存在则为 null
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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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data = json.loads(json_str)
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# 保存诊断图片
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if data.get("has_ad") and data.get("close_point"):
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p = data["close_point"]
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# 转换坐标
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img = read_image(image_path)
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if img is not None:
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h, w = img.shape[:2]
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actual_p = [int(p[0] * w / 1000), int(p[1] * h / 1000)]
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# 生成虚拟 bbox
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bbox = [actual_p[0]-50, actual_p[1]-30, actual_p[0]+50, actual_p[1]+30]
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draw_rectangles(image_path, bboxes=[bbox], click_points=[actual_p])
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logger.info(f"已生成广告关闭诊断图片: {image_path.replace('.jpg', '_vl.jpg')}")
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return data
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except Exception as e:
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logger.error(f"VLM 寻找关闭按钮失败: {e}")
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return {"has_ad": False}
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async def check_warm_popup_vlm(self, image_path):
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"""
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检测详情页是否存在“温馨提示/下次再说”弹窗
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"""
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prompt = """
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分析这张特来电充电站详情页截图，判断是否存在带有“下次再说”或“下次现说”文案的温馨提示弹窗。
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要求：
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1. 只关注覆盖在详情页上方的弹窗或遮罩，其上包含“下次再说”“下次现说”等文字按钮。
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2. 不要将正常页面中的列表项、价格卡片、终端状态等区域误判为弹窗。
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3. 如果存在该弹窗，请给出“下次再说”按钮的大致点击中心位置。
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输出格式为 JSON：
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{
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"has_popup": true/false,
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"reason": "为什么认为有或没有温馨提示弹窗",
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"button_point": [x, y] // 归一化坐标，范围 [0-1000]，没有则为 null
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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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data = json.loads(json_str)
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if data.get("has_popup") and data.get("button_point"):
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p = data["button_point"]
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img = read_image(image_path)
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if img is not None:
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h, w = img.shape[:2]
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actual_p = [int(p[0] * w / 1000), int(p[1] * h / 1000)]
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bbox = [actual_p[0]-80, actual_p[1]-40, actual_p[0]+80, actual_p[1]+40]
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draw_rectangles(image_path, bboxes=[bbox], click_points=[actual_p])
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logger.info(f"已生成温馨提示弹窗诊断图片: {image_path.replace('.jpg', '_warm_vl.jpg')}")
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return data
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except Exception as e:
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logger.error(f"VLM 检测温馨提示弹窗失败: {e}")
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return {"has_popup": False}
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async def check_wrong_page_vlm(self, image_path):
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"""
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检查是否误触进入了错误的页面（如：新人福利、我的卡券、活动页等）
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"""
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prompt = """
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分析这张截图，判断这是否是一个真实的“充电站详情页”。
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**识别准则**：
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1. 真正的“详情页”必须包含：充电站的具体名称、电价列表、终端状态（空闲/占用）等信息。
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2. 如果页面标题是“新人福利专区”、“活动规则”、“我的卡券”、“领券中心”或类似的营销活动页，则判定为错误页面。
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输出格式为 JSON：
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{
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"is_detail_page": true/false,
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"page_type": "detail" / "marketing" / "coupons" / "other",
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"reason": "判断依据"
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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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data = json.loads(json_str)
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return data
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except Exception as e:
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logger.error(f"VLM 检查页面类型失败: {e}")
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return {"is_detail_page": True} # 默认认为是详情页，避免死循环
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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，包括优惠价、PLUS价和挂牌价
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"""
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prompt = """
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分析这张充电站价格详情页截图，提取**分时电价表**。
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对于每个时段，请识别并提取以下所有价格信息（如果存在）：
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1. 优惠价 (通常是红色或加粗的大字，作为默认 price)
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2. PLUS会员价 (标有 "PLUS" 标签的价格)
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3. 挂牌价 (标有 "挂牌价" 标签的价格)
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4. 电费 (Base electricity price)
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5. 服务费 (Service fee)
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请提取每个时段的：
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- start: 开始时间 (HH:MM)
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- end: 结束时间 (HH:MM)
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- price: 优惠价 (元/度)
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- plus_price: PLUS会员价 (元/度)
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- market_price: 挂牌价 (元/度)
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- elec_price: 电费 (元/度)
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- service_price: 服务费 (元/度)
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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": "16:00",
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"end": "21:00",
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"price": 1.3435,
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"plus_price": 1.3035,
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"market_price": 1.4435,
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"elec_price": 0.9435,
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"service_price": 0.4000
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},
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...
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]
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注意：
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- 如果某个字段缺失，请设为 null。
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- 确保 price 包含电费和服务费的总和。
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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、精确地址以及电桩状态信息
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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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3. 电桩状态信息：寻找包含“快充”、“慢充”、“空闲”、“总数”或类似描述的区域。
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- 提取快充桩的总数和空闲数
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- 提取慢充桩的总数和空闲数
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- 计算总桩数 (total_piles) 和总空闲数 (free_piles)
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4. 停车费信息：寻找“停车费”、“停车减免”、“免费停车”等相关描述。
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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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"total_piles": 10,
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"free_piles": 5,
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"piles_detail": [
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{"type": "快充", "total": 6, "free": 3},
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{"type": "慢充", "total": 4, "free": 2}
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],
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"parking_info": "前2小时免费，之后5元/小时"
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}
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注意：如果无法确定某个数值，请设为 null。
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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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prompt = """
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分析这张充电站列表截图，提取所有真实的充电站卡片。
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要求：
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1. 忽略页面上半部分（如顶部导航栏、搜索框、广告 Banner、筛选标签等）。
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2. 仅识别下半部分一条条“充电站卡片”，每张卡片通常包含：场站名称、评分、最近充电时间、距离（如 '5.3km' 或 '90m'）、价格、快/慢空闲数量等。
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3. 不要把同一张卡片拆成多块；每条场站只对应一个矩形框。
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对于每张卡片，请输出：
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- name: 场站名称
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- distance: 距离信息字符串（例如 '5.3km' 或 '90m'）
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- address: 场站地址（如果无法确定可置为 null）
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- point: 卡片中心点击坐标 [x, y]，使用归一化坐标 [0-1000]（0 表示最左/最上，1000 表示最右/最下）
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- bbox: 卡片外接矩形边界 [x1, y1, x2, y2]，同样使用归一化坐标 [0-1000]
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以 JSON 数组形式输出，例如：
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[
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{
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"name": "某某充电站",
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"distance": "1.2km",
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"address": "某某路 100 号",
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"point": [500, 750],
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"bbox": [50, 600, 950, 820]
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}
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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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vlm_results = json.loads(json_str)
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final_stations = []
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vlm_list = vlm_results if isinstance(vlm_results, list) else []
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img = read_image(image_path)
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h, w = img.shape[:2] if img is not None else (2400, 1080)
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y_threshold = h * SAFE_EXCLUDE_RATIO
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bottom_threshold = int(h * (1 - BOTTOM_SAFE_EXCLUDE_RATIO))
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def to_pixel(v, max_len):
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if v is None:
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return None
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try:
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fv = float(v)
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except:
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return None
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if fv <= 1000.0:
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return int(fv * max_len / 1000.0)
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return int(fv)
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for res in vlm_list:
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if not res:
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continue
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p = res.get("point")
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b = res.get("bbox")
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actual_p = None
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actual_bbox = None
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if p and len(p) == 2:
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px = to_pixel(p[0], w)
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py = to_pixel(p[1], h)
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if px is not None and py is not None:
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actual_p = [px, py]
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if b and len(b) == 4:
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x1 = to_pixel(b[0], w)
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y1 = to_pixel(b[1], h)
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x2 = to_pixel(b[2], w)
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y2 = to_pixel(b[3], h)
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if None not in (x1, y1, x2, y2):
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actual_bbox = [max(0, int(x1)), max(0, int(y1)), min(w, int(x2)), min(h, int(y2))]
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if actual_p is None and actual_bbox:
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actual_p = [(actual_bbox[0] + actual_bbox[2]) // 2, (actual_bbox[1] + actual_bbox[3]) // 2]
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if actual_p is None and actual_bbox is None:
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continue
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if actual_p and (actual_p[1] < y_threshold or actual_p[1] > bottom_threshold):
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logger.warning(f"过滤掉可能的误点击点 (y={actual_p[1]}): {res.get('name')}")
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continue
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if actual_bbox is None and actual_p:
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half_w = int(w * 0.4)
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half_h = max(MIN_CARD_HEIGHT // 2, 90)
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x1 = max(0, actual_p[0] - half_w)
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x2 = min(w, actual_p[0] + half_w)
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y1 = max(0, actual_p[1] - half_h)
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y2 = min(h, actual_p[1] + half_h)
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actual_bbox = [x1, y1, x2, y2]
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if actual_bbox is not None:
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bx1, by1, bx2, by2 = actual_bbox
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current_h = by2 - by1
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if current_h < MIN_CARD_HEIGHT * 0.8 or by2 > bottom_threshold:
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continue
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final_stations.append({
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"name": res.get("name"),
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"distance": res.get("distance"),
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"address": res.get("address"),
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"point": actual_p,
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"bbox": actual_bbox
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})
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if final_stations:
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final_stations.sort(key=lambda s: (s.get("point")[1] if s.get("point") else (s.get("bbox")[1] if s.get("bbox") else 0)))
|
||
|
||
# 保存诊断图片 (_vl.jpg, _flag.jpg)
|
||
if final_stations:
|
||
debug_bboxes = []
|
||
debug_points = []
|
||
for s in final_stations:
|
||
if s.get("bbox"):
|
||
debug_bboxes.append(s["bbox"])
|
||
if s.get("point"):
|
||
debug_points.append(s["point"])
|
||
|
||
# 如果没有 bbox，根据 point 生成一个虚拟 bbox 供可视化
|
||
if not debug_bboxes and debug_points:
|
||
for p in debug_points:
|
||
debug_bboxes.append([p[0]-50, p[1]-30, p[0]+50, p[1]+30])
|
||
|
||
if debug_bboxes or debug_points:
|
||
draw_rectangles(image_path, bboxes=debug_bboxes, click_points=debug_points)
|
||
logger.info(f"已生成诊断图片: {image_path.replace('.jpg', '_vl.jpg')} 和 _flag.jpg")
|
||
|
||
return final_stations
|
||
except Exception as e:
|
||
logger.error(f"分析列表页失败: {e}")
|
||
return []
|