'commit'

Apps/TeLaiDian/Crawler.py

@@ -10,6 +10,7 @@ from Apps.TeLaiDian.Kit import (
     setup_logger, read_image, save_image, detect_warm_popup_xczs_cv
 )
 from Apps.TeLaiDian.ReadImageKit import ReadImageKit
+from Apps.TeLaiDian.FirstPageKit import run_ocr_rect
 from Apps.TeLaiDian.Service import TeLaiDianService
 from Apps.TeLaiDian.Config.Setting import (
     SCROLL_DISTANCE_RATIO, WAIT_AFTER_SCROLL, MAX_STATIONS_COUNT,
@@ -157,10 +158,8 @@ class TeLaiDianCrawler(BaseCrawler):
         last_md5 = None
         
         while processed_count < max_to_crawl:
-            # 1. 截图并分析
             screenshot_path = take_screenshot(d, f"tld_list_{int(time.time())}.jpg")
             
-            # 检测是否滚动到底部
             curr_md5 = get_image_content_md5(screenshot_path, top_ratio=SAFE_EXCLUDE_RATIO, bottom_ratio=BOTTOM_SAFE_EXCLUDE_RATIO)
             if last_md5 == curr_md5:
                 logger.info("内容无变化，判定已到底部")
@@ -168,9 +167,8 @@ class TeLaiDianCrawler(BaseCrawler):
                 break
             last_md5 = curr_md5
             
-            stations = await self.read_image_kit.analyze_station_list(screenshot_path)
-            if not stations:
-                # 检查是否意外退出了小程序
+            stations_page = await run_ocr_rect(screenshot_path)
+            if not stations_page:
                 is_wrong_page = await self.check_wrong_page(d, screenshot_path)
                 if is_wrong_page:
                     logger.error("检测到已退出详情列表页（可能回到了搜索页），尝试重新进入...")
@@ -183,21 +181,23 @@ class TeLaiDianCrawler(BaseCrawler):
                 await asyncio.sleep(WAIT_AFTER_SCROLL)
                 continue
             
-            for station in stations:
+            for st in stations_page:
                 if processed_count >= max_to_crawl:
                     break
                 
-                name = station.get("name")
-                status_text = station.get("status_text") or ""
-                if station.get("is_closed") is True or ("歇业中" in status_text):
-                    logger.info(f"列表标记为歇业中，跳过场站: {name}")
-                    continue
-                point = station.get("point")
+                name = st.get("station_name") or st.get("name")
+                rect = st.get("rect") or []
+                click_point = st.get("click_point") or st.get("click") or []
+                distance = st.get("distance_text") or st.get("distance")
+                piles = st.get("busy_list") or []
                 
-                if not name or not point:
+                if not name or not isinstance(rect, (list, tuple)) or len(rect) < 4:
+                    continue
+                if not isinstance(click_point, (list, tuple)) or len(click_point) < 2:
                     continue
                 
-                # [优化] 使用 Redis 进行跨运行去重
+                point = [int(click_point[0]), int(click_point[1])]
+                
                 cleaned_name = clean_station_name(name)
                 redis_key = f"crawled:tld:{cleaned_name}"
                 if await self.redis_kit.get_data(redis_key):
@@ -206,7 +206,7 @@ class TeLaiDianCrawler(BaseCrawler):
                 
                 current_idx = processed_count + 1
                 remaining = max_to_crawl - current_idx
-                logger.info(f"--- [进度: {current_idx}/{max_to_crawl}, 剩余: {remaining}] 处理场站: {name} (坐标: {point}, 距离: {station.get('distance')}) ---")
+                logger.info(f"--- [进度: {current_idx}/{max_to_crawl}, 剩余: {remaining}] 处理场站: {name} (坐标: {point}, 距离: {distance}) ---")
                 
                 # 点击进入详情
                 d.click(point[0], point[1])

Apps/TeLaiDian/FirstPageKit.py

@@ -0,0 +1,433 @@
+import asyncio
+import json
+import os
+import sys
+
+import cv2
+import numpy as np
+
+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 Apps.TeLaiDian.Kit import setup_logger, get_ocr_reader, draw_rectangles
+from Apps.TeLaiDian.Config.Setting import SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO
+from Util.LlmUtil import get_llm_response
+
+
+logger = setup_logger("TeLaiDian.FirstPageKit")
+
+NON_STATION_KEYWORDS = [
+    "地图",
+    "目的地",
+    "电站名",
+    "充电礼",
+    "再充",
+    "注册礼",
+    "元券",
+    "PLUS会员",
+    "我的收藏",
+    "最近充电",
+    "我的卡券",
+    "我的订单",
+    "充电券",
+    "电信积分兑换",
+    "确认",
+    "广告",
+    "距离/区域",
+    "综合排序",
+    "偏好",
+    "星级站",
+    "停车减免",
+    "重卡可用",
+    "首页",
+    "特省钱",
+    "扫码",
+    "输入",
+    "商城",
+    "推荐",
+]
+
+def _load_image(path):
+    if not os.path.exists(path):
+        raise FileNotFoundError(path)
+    img = cv2.imread(path)
+    if img is None:
+        raise RuntimeError(f"无法读取图片: {path}")
+    h, w = img.shape[:2]
+    return img, w, h
+
+
+def _extract_json(text: str) -> str:
+    if not text:
+        return "[]"
+    cleaned = text.strip()
+    if "```" in cleaned:
+        lines = []
+        for line in cleaned.splitlines():
+            if line.strip().startswith("```"):
+                continue
+            lines.append(line)
+        cleaned = "\n".join(lines).strip()
+    decoder = json.JSONDecoder()
+    pos = 0
+    while pos < len(cleaned):
+        idx_dict = cleaned.find("{", pos)
+        idx_list = cleaned.find("[", pos)
+        candidates = [i for i in (idx_dict, idx_list) if i != -1]
+        if not candidates:
+            break
+        start = min(candidates)
+        snippet = cleaned[start:]
+        try:
+            _, end = decoder.raw_decode(snippet)
+            return snippet[:end]
+        except json.JSONDecodeError:
+            pos = start + 1
+            continue
+    return "[]"
+
+
+async def run_ocr_rect(image_path, log_path=None):
+    log_lines = []
+
+    def log_detail(msg):
+        logger.info(msg)
+        log_lines.append(msg)
+
+    img, w, h = _load_image(image_path)
+    log_detail(f"开始处理图片: {image_path}, 宽={w}, 高={h}")
+
+    reader = get_ocr_reader()
+    ocr_results = reader.read_text(img)
+    log_detail(f"OCR 原始结果数量: {len(ocr_results)}")
+
+    entries = []
+    for idx, (quad, text, prob) in enumerate(ocr_results):
+        pts = np.array(quad).astype(int)
+        x_min = int(np.min(pts[:, 0]))
+        y_min = int(np.min(pts[:, 1]))
+        x_max = int(np.max(pts[:, 0]))
+        y_max = int(np.max(pts[:, 1]))
+        cx = (x_min + x_max) / 2.0
+        cy = (y_min + y_max) / 2.0
+        cx_norm = cx / w
+        cy_norm = cy / h
+
+        status = "keep"
+        reasons = []
+        if prob < 0.3:
+            status = "drop"
+            reasons.append("prob<0.3")
+        if not text:
+            status = "drop"
+            reasons.append("empty_text")
+        if cy_norm < SAFE_EXCLUDE_RATIO:
+            status = "drop"
+            reasons.append("top_safe_zone")
+        if cy_norm > (1 - BOTTOM_SAFE_EXCLUDE_RATIO):
+            status = "drop"
+            reasons.append("bottom_safe_zone")
+        if status == "keep" and text:
+            for kw in NON_STATION_KEYWORDS:
+                if kw and kw in text:
+                    status = "drop"
+                    reasons.append("non_station_keyword")
+                    break
+
+        log_detail(
+            f"OCR[{idx + 1}] text={repr(text)} prob={prob:.3f} "
+            f"cx_norm={cx_norm:.4f} cy_norm={cy_norm:.4f} "
+            f"status={status} reasons={','.join(reasons) if reasons else '-'}"
+        )
+
+        if status != "keep":
+            continue
+
+        entries.append(
+            {
+                "text": text,
+                "prob": float(prob),
+                "cx_norm": cx_norm,
+                "cy_norm": cy_norm,
+            }
+        )
+
+    log_detail(f"OCR 通过过滤的有效文本数量: {len(entries)}")
+
+    if not entries:
+        log_detail("无有效 OCR 文本, 结束当前图片处理")
+        return
+
+    indexed_entries = []
+    for idx, e in enumerate(entries):
+        indexed_entries.append(
+            {
+                "id": idx + 1,
+                "text": e["text"],
+                "prob": e["prob"],
+                "cx_norm": round(e["cx_norm"], 4),
+                "cy_norm": round(e["cy_norm"], 4),
+            }
+        )
+
+    payload_json = json.dumps(indexed_entries, ensure_ascii=False)
+    log_detail(f"传给 LLM 的 OCR 条目数: {len(indexed_entries)}")
+
+    query_text = (
+        "下面是特来电列表页整张截图的 OCR 结果，每一项代表一行文字，包含其中心点的归一化坐标：\n"
+        "ocr_items = " + payload_json + "\n"
+        "请你根据这些文本，将它们聚合成若干个“充电场站卡片”。输出一个 JSON 数组，每个元素必须包含:\n"
+        "1) station_name: 场站名称，只能是卡片标题中的名称，不允许是筛选标签、导航按钮、底部功能区等。\n"
+        "2) anchor_point_norm: 一个对象 {\"x\": number, \"y\": number}，表示该场站名称文字所在行的中心点坐标，取值范围 0-1。\n"
+        "并且尽量补充以下可选字段（找不到时可以省略或设为 null）:\n"
+        "3) distance_text: 距离字符串，例如 \"6.9km\"、\"500m\"，从对应卡片中的距离行提取。\n"
+        "4) busy_list: 忙闲信息数组，数组中的每一项是 {\"mode\": \"快|慢|超|普通\", \"idle\": number, \"total\": number}。\n"
+        "   在特来电 UI 中，忙闲文本通常是 \"空闲x/总y\" 或 \"快 空闲x/总y\" 这种形式，\n"
+        "   请从相应行中解析出模式和空闲/总数。\n"
+        "额外提示：\n"
+        "- 每个场站卡片通常包含一行类似 \"1.4km\"、\"3.6km\" 的距离文本；\n"
+        "- 该距离文本所在行的左侧、且在同一卡片中的那一行文字，就是对应的场站标题 station_name；\n"
+        "- 忙闲信息通常出现在卡片右侧的彩色小块中，例如 \"快 空闲24/32\"、\"慢 空闲0/10\" 等；\n"
+        "要求：\n"
+        "- 场站按从上到下排序；\n"
+        "- station_name 不能取距离行本身（如 \"1.4km\"），而是要取与之成一对的标题行；\n"
+        "- 如果某些 OCR 文本显然不属于任何场站卡片，可以忽略；\n"
+        "- 只输出 JSON 数组，不要输出其它任何文字。"
+    )
+
+    chunks = []
+    async for part in get_llm_response(
+        query_text=query_text,
+        stream=False,
+        system_prompt="你是一个帮助整理 OCR 文本的助手，只输出 JSON。",
+        chat_history=None,
+        temperature=0,
+    ):
+        chunks.append(part)
+
+    full_text = "".join(chunks)
+    log_detail("LLM 原始返回内容开始")
+    log_lines.append(full_text)
+    log_detail("LLM 原始返回内容结束")
+
+    raw = _extract_json(full_text)
+    log_detail(f"从 LLM 返回内容中抽取出的 JSON 片段: {raw}")
+
+    try:
+        data = json.loads(raw)
+    except Exception as e:
+        log_detail(f"解析 LLM 返回 JSON 失败: {e}")
+        logs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Logs")
+        os.makedirs(logs_dir, exist_ok=True)
+        if log_path is None:
+            final_log_path = os.path.join(
+                logs_dir, os.path.basename(image_path).replace(".jpg", ".log")
+            )
+            mode = "w"
+        else:
+            final_log_path = log_path
+            mode = "a"
+        with open(final_log_path, mode, encoding="utf-8") as f:
+            for line in log_lines:
+                f.write(line + "\n")
+        log_detail(f"已写入详细日志到: {final_log_path}")
+        return
+
+    if not isinstance(data, list):
+        log_detail(f"期望 LLM 返回 JSON 数组, 实际类型: {type(data)}")
+        return
+
+    if not data:
+        log_detail("LLM 返回空数组, 结束当前图片处理")
+        return
+
+    stations = []
+    for idx, item in enumerate(data):
+        if not isinstance(item, dict):
+            log_detail(f"LLM item[{idx}] 不是对象类型, 跳过")
+            continue
+        name = item.get("station_name") or item.get("name")
+        anchor = item.get("anchor_point_norm") or item.get("anchor")
+        distance = item.get("distance_text") or item.get("distance")
+        busy_list = item.get("busy_list") or []
+        if not name or not isinstance(anchor, dict):
+            log_detail(f"LLM item[{idx}] 缺少必要字段, 跳过: {item}")
+            continue
+        ax = float(anchor.get("x", 0.5))
+        ay = float(anchor.get("y", 0.5))
+        if not (0 <= ax <= 1 and 0 <= ay <= 1):
+            log_detail(f"LLM item[{idx}] anchor_point_norm 超出范围, 跳过: {anchor}")
+            continue
+        px = int(ax * w)
+        py = int(ay * h)
+        stations.append(
+            {
+                "station_name": name,
+                "anchor_point_norm": {"x": ax, "y": ay},
+                "distance_text": distance,
+                "busy_list": busy_list,
+                "anchor_px": px,
+                "anchor_py": py,
+            }
+        )
+        log_detail(
+            f"LLM anchor 规范化[{len(stations)}] name={name} ax={ax:.4f} ay={ay:.4f} "
+            f"px={px} py={py} distance={distance} busy_list={busy_list}"
+        )
+
+    if not stations:
+        log_detail("LLM 解析后无有效场站, 结束当前图片处理")
+        return
+    filtered = []
+    for s in stations:
+        dl = s.get("distance_text")
+        bl = s.get("busy_list") or []
+        ok_dist = isinstance(dl, str) and (("km" in dl) or ("m" in dl))
+        ok_busy = isinstance(bl, list) and len(bl) > 0
+        if ok_dist:
+            filtered.append(s)
+            if not ok_busy:
+                log_detail(f"场站缺少忙闲信息但保留: {s.get('station_name')}")
+        else:
+            log_detail(f"丢弃缺少距离信息的条目: {s.get('station_name')}")
+    stations = filtered
+    if not stations:
+        log_detail("过滤后无有效场站, 结束当前图片处理")
+        return
+    stations.sort(key=lambda s: s["anchor_py"])
+
+    min_gap = None
+    if len(stations) >= 2:
+        gaps = []
+        for i in range(1, len(stations)):
+            gaps.append(stations[i]["anchor_py"] - stations[i - 1]["anchor_py"])
+        min_gap = min(gaps)
+        box_h = min_gap
+        if box_h < int(h * 0.20):
+            box_h = int(h * 0.20)
+        if box_h > int(h * 0.32):
+            box_h = int(h * 0.32)
+        log_detail(f"根据最小锚点间距调整 box_h: min_gap={min_gap}, final={box_h}")
+    else:
+        box_h = int(h * 0.22)
+        log_detail(f"仅有一个场站, 使用默认 box_h={box_h}")
+
+    box_w = int(w * 0.90)
+    x1_fixed = int((w - box_w) / 2)
+    x2_fixed = x1_fixed + box_w
+
+    result = []
+    rects = []
+    click_points = []
+    effective_top = int(h * SAFE_EXCLUDE_RATIO)
+    effective_bottom = int(h * (1 - BOTTOM_SAFE_EXCLUDE_RATIO))
+    prev_y2 = None
+    anchor_ratio = 0.15
+    for idx, st in enumerate(stations, start=1):
+        py = st["anchor_py"]
+        if py < effective_top:
+            log_detail(f"锚点位于顶部保护区，丢弃: {st.get('station_name')} py={py}")
+            continue
+        y1 = int(py - box_h * anchor_ratio)
+        y2 = y1 + box_h
+        if min_gap is not None:
+            downward_limit = int(min_gap * 0.75)
+            max_y2 = py + downward_limit
+            if y2 > max_y2:
+                y2 = max_y2
+                y1 = y2 - box_h
+        if y1 < 0:
+            y1 = 0
+            y2 = y1 + box_h
+        if y2 > h:
+            y2 = h
+            y1 = y2 - box_h
+        if y1 < effective_top:
+            y1 = effective_top
+            y2 = y1 + box_h
+        if y2 > effective_bottom:
+            y2 = effective_bottom
+            y1 = y2 - box_h
+        if prev_y2 is not None and y1 <= prev_y2:
+            shift = prev_y2 - y1 + 1
+            y1 += shift
+            y2 += shift
+            if y2 > effective_bottom:
+                if idx == len(stations):
+                    min_h = int(box_h * 0.5)
+                    new_y1 = prev_y2 + 1
+                    new_y2 = effective_bottom
+                    if new_y2 - new_y1 >= min_h:
+                        y1 = new_y1
+                        y2 = new_y2
+                    else:
+                        log_detail(f"底部空间不足，丢弃: {st.get('station_name')}")
+                        continue
+                else:
+                    log_detail(f"避免重叠无法放置，丢弃: {st.get('station_name')}")
+                    continue
+        rect = [x1_fixed, y1, x2_fixed, y2]
+        cx = int((rect[0] + rect[2]) / 2)
+        cy = int((rect[1] + rect[3]) / 2)
+
+        click_points.append([cx, cy])
+        rects.append(rect)
+        prev_y2 = y2
+
+        item = {
+            "index": idx,
+            "station_name": st["station_name"],
+            "rect": rect,
+            "click_point": [cx, cy],
+            "distance_text": st.get("distance_text"),
+            "busy_list": st.get("busy_list") or [],
+        }
+        result.append(item)
+        log_detail(
+            f"Station[{idx}] name={item['station_name']} "
+            f"rect={rect} click={item['click_point']} "
+            f"distance={item['distance_text']} busy_list={item['busy_list']}"
+        )
+
+    try:
+        draw_rectangles(image_path, rects, click_points)
+    except Exception as e:
+        log_detail(f"绘制调试矩形失败: {e}")
+
+    logs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Logs")
+    os.makedirs(logs_dir, exist_ok=True)
+    if log_path is None:
+        final_log_path = os.path.join(
+            logs_dir, os.path.basename(image_path).replace(".jpg", ".log")
+        )
+        mode = "w"
+    else:
+        final_log_path = log_path
+        mode = "a"
+    with open(final_log_path, mode, encoding="utf-8") as f:
+        for line in log_lines:
+            f.write(line + "\n")
+    log_detail(f"已写入详细日志到: {final_log_path}")
+
+    return result
+
+
+async def run_batch_in_dir(image_dir, log_file=None):
+    img_files = []
+    for name in os.listdir(image_dir):
+        if name.lower().endswith(".jpg") or name.lower().endswith(".png"):
+            img_files.append(os.path.join(image_dir, name))
+    img_files.sort()
+
+    if not img_files:
+        logger.info(f"目录下未找到图片: {image_dir}")
+        return
+
+    for idx, path in enumerate(img_files, start=1):
+        logger.info(f"[批处理] 开始处理第 {idx} 张图片: {path}")
+        try:
+            await run_ocr_rect(path, log_path=log_file)
+        except Exception as e:
+            logger.exception(f"[批处理] 处理图片失败: {path}, {e}")
+