'commit'

Apps/XinDianTu/Config/Setting.py

@@ -13,7 +13,10 @@ WAIT_AFTER_SCROLL = 1.0
 
 # 坐标计算与安全防护
 SAFE_EXCLUDE_RATIO = 0.30
-BOTTOM_SAFE_EXCLUDE_RATIO = 0.12
+BOTTOM_SAFE_EXCLUDE_RATIO = 0.08
+MIN_CARD_HEIGHT_RATIO = 0.12
+STATION_BOX_WIDTH_RATIO = 0.95
+STATION_BOX_HEIGHT_RATIO = 0.18
 FALLBACK_WIDTH = 1080
 FALLBACK_HEIGHT = 2400
 

Apps/XinDianTu/FirstPageKit.py

@@ -0,0 +1,483 @@
+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.XinDianTu.Kit import setup_logger, get_ocr_reader
+from Apps.XinDianTu.ReadImageKit import ReadImageKit
+from Apps.XinDianTu.Config.Setting import SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO, STATION_BOX_WIDTH_RATIO, STATION_BOX_HEIGHT_RATIO
+from Util.LlmUtil import get_llm_response
+
+
+logger = setup_logger("XinDianTu.FirstPageKit")
+NON_STATION_KEYWORDS = [
+    "首页",
+    "找桩",
+    "会员",
+    "我的",
+    "扫码充电",
+    "扫码",
+    "新电途 到哪都能充",
+    "搜索场站或目的地",
+    "签到",
+    "畅充会员",
+    "组团充电",
+    "微信社群",
+    "申卡有礼",
+    "新电途招募城市合伙人",
+    "去赚钱",
+    "综合排序",
+    "停车费",
+    "充电速度",
+    "筛选",
+    "不限车长",
+    "不限车高",
+    "夜间免停",
+    "洗手间",
+]
+STATION_TITLE_KEYWORDS = [
+    "充电站",
+    "超快充",
+    "快充",
+    "换电站",
+    "加电站",
+    "超级充电",
+    "超充",
+    "充电",
+]
+TOP_ZONE_STATION_HINT_KEYWORDS = [
+    "充电站",
+    "公共充电",
+    "超级充电",
+    "慢充站",
+    "慢充",
+    "超快充",
+    "快充",
+    "充电",
+    "超充",
+]
+
+
+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
+
+
+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 < 0.4:
+            status = "drop"
+            reasons.append("prob<0.4")
+        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":
+            for kw in NON_STATION_KEYWORDS:
+                if kw and kw in text:
+                    status = "drop"
+                    reasons.append("non_station_keyword")
+                    break
+        if status == "drop" and "prob<0.4" in reasons and text:
+            for kw in STATION_TITLE_KEYWORDS:
+                if kw and kw in text:
+                    status = "keep"
+                    reasons.append("force_keep_station_title")
+                    break
+        if status == "drop" and "top_safe_zone" in reasons and text:
+            if SAFE_EXCLUDE_RATIO - 0.06 <= cy_norm < SAFE_EXCLUDE_RATIO:
+                for kw in TOP_ZONE_STATION_HINT_KEYWORDS:
+                    if kw and kw in text:
+                        status = "keep"
+                        reasons.append("force_keep_top_station_title")
+                        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 = (
+        "下面是新电途列表页整张截图的 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_info: 忙闲信息对象，格式为 {\"mode\": \"快|慢|超|普通\", \"idle\": number, \"total\": number}。\n"
+        "   例如：\"快 闲24/32\" => {\"mode\": \"快\", \"idle\": 24, \"total\": 32}；\n"
+        "         \"慢 闲0/10\"  => {\"mode\": \"慢\", \"idle\": 0,  \"total\": 10}；\n"
+        "         \"超 闲1/3\"   => {\"mode\": \"超\", \"idle\": 1,  \"total\": 3}；\n"
+        "         \"闲5/10\"    => {\"mode\": \"慢\", \"idle\": 5,  \"total\": 10}。\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红色或绿色小块中，例如 \"快 闲24/32\"、\"慢 闲0/10\"、\"超 闲1/3\"、\"闲5/10\"；\n"
+        "- 即使 station_name 中不包含“充电站”“超快充”等字样，但只要与某个 \"x.xkm\" 行在同一卡片区域内，也应视为一个完整的场站名称。\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 = ReadImageKit._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") or item.get("center_norm")
+        if not anchor:
+            log_detail(f"LLM item[{idx}] 缺少 anchor 信息, 跳过, content={item}")
+            continue
+        try:
+            ax = float(anchor.get("x"))
+            ay = float(anchor.get("y"))
+        except Exception:
+            log_detail(f"LLM item[{idx}] anchor 不可解析, content={anchor}")
+            continue
+        if not (0 <= ax <= 1 and 0 <= ay <= 1):
+            log_detail(
+                f"LLM item[{idx}] anchor 超出范围, x={ax}, y={ay}, 跳过"
+            )
+            continue
+        px = int(ax * w)
+        py = int(ay * h) - 20
+        if py < 0:
+            py = 0
+
+        distance_text = item.get("distance_text") or item.get("distance")
+        busy_raw = item.get("busy_info") or item.get("busy")
+        busy_mode = None
+        busy_idle = None
+        busy_total = None
+        if isinstance(busy_raw, dict):
+            busy_mode = busy_raw.get("mode") or busy_raw.get("type")
+            try:
+                if busy_raw.get("idle") is not None:
+                    busy_idle = int(busy_raw.get("idle"))
+            except Exception:
+                busy_idle = None
+            try:
+                if busy_raw.get("total") is not None:
+                    busy_total = int(busy_raw.get("total"))
+            except Exception:
+                busy_total = None
+
+        stations.append(
+            {
+                "name": name,
+                "ax": ax,
+                "ay": ay,
+                "px": px,
+                "py": py,
+                "distance_text": distance_text,
+                "busy_mode": busy_mode,
+                "busy_idle": busy_idle,
+                "busy_total": busy_total,
+            }
+        )
+        log_detail(
+            f"LLM anchor 规范化[{len(stations)}] name={name} ax={ax:.4f} ay={ay:.4f} py={py} "
+            f"distance={distance_text} busy=({busy_mode},{busy_idle},{busy_total})"
+        )
+
+    if not stations:
+        log_detail("LLM 解析后没有可用的场站锚点, 结束当前图片处理")
+        return
+
+    stations.sort(key=lambda s: s["py"])
+
+    overlay = img.copy()
+    results = []
+
+    box_w = int(w * STATION_BOX_WIDTH_RATIO)
+    box_h_conf = int(h * STATION_BOX_HEIGHT_RATIO)
+    box_h = box_h_conf
+
+    if len(stations) >= 2:
+        gaps = []
+        for i in range(len(stations) - 1):
+            dy = stations[i + 1]["py"] - stations[i]["py"]
+            if dy > 0:
+                gaps.append(dy)
+        if gaps:
+            min_gap = min(gaps)
+            max_no_overlap = max(min_gap - 4, 40)
+            if max_no_overlap < box_h_conf * 0.6:
+                box_h = box_h_conf
+                log_detail(
+                    f"场站间距过小(min_gap={min_gap}), 使用配置高度 box_h={box_h_conf}"
+                )
+            else:
+                box_h = min(box_h_conf, max_no_overlap)
+                log_detail(
+                    f"根据最小锚点间距调整 box_h: conf={box_h_conf}, "
+                    f"min_gap={min_gap}, final={box_h}"
+                )
+        else:
+            box_h = box_h_conf
+            log_detail("未找到有效间距, 使用配置高度 box_h={box_h_conf}")
+    else:
+        box_h = box_h_conf
+        log_detail(f"仅一个场站, 使用配置高度 box_h={box_h_conf}")
+
+    x1_fixed = max(0, (w - box_w) // 2)
+    x2_fixed = min(w, x1_fixed + box_w)
+    effective_top = int(h * SAFE_EXCLUDE_RATIO)
+    effective_bottom = int(h * (1 - BOTTOM_SAFE_EXCLUDE_RATIO))
+
+    log_detail(
+        f"固定绿框参数: box_w={box_w}, box_h={box_h}, "
+        f"x1_fixed={x1_fixed}, x2_fixed={x2_fixed}, "
+        f"effective_top={effective_top}, effective_bottom={effective_bottom}"
+    )
+
+    prev_y2 = None
+
+    for idx, s in enumerate(stations):
+        name = s["name"]
+        ax = s["ax"]
+        ay = s["ay"]
+        px = s["px"]
+        py = s["py"]
+
+        y1 = py - int(box_h * 0.35)
+        y2 = y1 + box_h
+        orig_y1 = y1
+        orig_y2 = y2
+        if y1 < 0:
+            y1 = 0
+            y2 = box_h
+        if y2 > h:
+            y2 = h
+            y1 = h - box_h
+
+        if ay >= SAFE_EXCLUDE_RATIO and y1 < effective_top:
+            y1 = effective_top
+            y2 = y1 + box_h
+            if y2 > h:
+                y2 = h
+                y1 = h - box_h
+        if y2 > effective_bottom:
+            y2 = effective_bottom
+            y1 = y2 - box_h
+            if y1 < 0:
+                y1 = 0
+                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:
+                log_detail(
+                    f"Station[{idx + 1}] {name} 因避免重叠无法放入有效区域, 被丢弃"
+                )
+                continue
+
+        prev_y2 = y2
+
+        click_x = int((x1_fixed + x2_fixed) / 2)
+        click_y = int((y1 + y2) / 2)
+
+        log_detail(
+            f"Station[{idx + 1}] name={name} anchor=({ax:.4f},{ay:.4f}) "
+            f"px={px}, py={py}, box_orig=({orig_y1},{orig_y2}), "
+            f"box_adj=({y1},{y2}), click=({click_x},{click_y})"
+        )
+
+        cv2.rectangle(overlay, (x1_fixed, y1), (x2_fixed, y2), (0, 255, 0), 2)
+        cv2.circle(overlay, (click_x, click_y), 8, (0, 0, 255), -1)
+
+        results.append(
+            {
+                "index": idx + 1,
+                "station_name": name,
+                "rect": [x1_fixed, y1, x2_fixed, y2],
+                "click_point": [click_x, click_y],
+                "distance_text": s.get("distance_text"),
+                "busy_mode": s.get("busy_mode"),
+                "busy_idle": s.get("busy_idle"),
+                "busy_total": s.get("busy_total"),
+            }
+        )
+
+    stem, ext = os.path.splitext(image_path)
+    out_path = f"{stem}_ocr_rect{ext}"
+    cv2.imwrite(out_path, overlay)
+
+    log_detail(f"输入图片: {image_path}")
+    log_detail(f"调试输出图片: {out_path}")
+    log_detail("识别到的场站及矩形坐标如下:")
+    for item in results:
+        log_detail(json.dumps(item, ensure_ascii=False))
+
+    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}")
+
+
+async def run_batch_in_dir(base_dir, log_path=None):
+    files = sorted(os.listdir(base_dir))
+    jpg_files = []
+    for name in files:
+        lower = name.lower()
+        if not lower.endswith(".jpg"):
+            continue
+        if lower.endswith("_ocr_rect.jpg"):
+            continue
+        jpg_files.append(os.path.join(base_dir, name))
+
+    if not jpg_files:
+        logger.info(f"目录中未找到待处理的 JPG 文件: {base_dir}")
+        return
+
+    logger.info(f"即将批量处理 {len(jpg_files)} 张图片:")
+    for p in jpg_files:
+        logger.info(f"  - {p}")
+
+    for p in jpg_files:
+        await run_ocr_rect(p, log_path=log_path)
+
+
+if __name__ == "__main__":
+    base_dir = os.path.dirname(os.path.abspath(__file__))
+    asyncio.run(run_batch_in_dir(base_dir))
+

Apps/XinDianTu/Kit.py

@@ -4,7 +4,7 @@ import cv2
 import numpy as np
 import time
 import hashlib
-from Apps.XinDianTu.Config.Setting import SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO
+from Apps.XinDianTu.Config.Setting import SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO, MIN_CARD_HEIGHT_RATIO
 from Config.Config import TEMP_IMAGE_DIR
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
@@ -862,12 +862,21 @@ def crop_cards_from_image(img_path, output_dir=None, save_debug=True):
             max_h = max(heights)
             median_h = np.median(heights)
 
-            threshold_h = median_h * 0.80
+            min_abs_h = h * MIN_CARD_HEIGHT_RATIO
+            base_threshold = max(median_h * 0.90, min_abs_h)
 
             for (y1, y2), card_h in zip(temp_valid_segments, heights):
-                if card_h < threshold_h:
+                center_y = (y1 + y2) / 2.0
+
+                local_threshold = base_threshold
+
+                if center_y > h * 0.70:
+                    local_threshold = median_h * 0.70
+
+                if card_h < local_threshold:
                     logger.info(
-                        f"  Filtering out segment Y={y1}-{y2} (H={card_h}) because it's too short (Threshold={threshold_h:.1f}).")
+                        f"  Filtering out segment Y={y1}-{y2} (H={card_h}) because it's too short "
+                        f"(Threshold={local_threshold:.1f}, center_y={center_y:.1f}).")
                 else:
                     final_cards.append((y1, y2, final_x1, final_x2))
                     logger.info(f"  Card: Y={y1}-{y2}, X={final_x1}-{final_x2}, H={card_h}")
@@ -908,18 +917,24 @@ def crop_cards_from_image(img_path, output_dir=None, save_debug=True):
     # 准备 _vl.jpg (只画框，不画红点)
     vl_img = img.copy()
 
-    logger.info(f"  Step [2.1/VL] 准备在 VL 图片上绘制 {len(final_cards)} 个场站的绿色方框...")
+    logger.info(f"  Step [2.1/VL] 准备在 VL 图片上绘制 {len(final_cards)} 个场站的绿色方框...")
 
-    for idx, (y1, y2, x1, x2) in enumerate(final_cards):
-        PAD_TOP = 5
-        draw_y1 = max(0, y1 - PAD_TOP)
-        draw_y2 = y2
-
-        w_card = x2 - x1
-        h_card = draw_y2 - draw_y1
-
-        click_x = int((x1 + x2) / 2)
-        click_y = int((draw_y1 + draw_y2) / 2)
+    for idx, (y1, y2, x1, x2) in enumerate(final_cards):
+        segment_height = y2 - y1
+        pad_top = int(segment_height * 0.20)
+        if pad_top < 10:
+            pad_top = 10
+        if pad_top > 60:
+            pad_top = 60
+
+        draw_y1 = max(0, y1 - pad_top)
+        draw_y2 = y2
+
+        w_card = x2 - x1
+        h_card = draw_y2 - draw_y1
+
+        click_x = int((x1 + x2) / 2)
+        click_y = int((draw_y1 + draw_y2) / 2)
 
         # [修改] 不再保存单张子图，只记录元数据
         # card = img[y1:y2, x1:x2]