dwg_review/config.py

@@ -122,10 +122,36 @@ IMAGE_FORMAT = os.environ.get("IMAGE_FORMAT", "png").strip().lower()
 IMAGE_JPEG_QUALITY = int(os.environ.get("IMAGE_JPEG_QUALITY", "80"))
 
 # 取证阶段每图框的局部截图数（0=仅整体；4=整体+2x2 象限）
+# —— 仅在自适应瓦片规划不可用时作为**回退**路径参数
 EVIDENCE_DETAIL_TILES = int(os.environ.get("EVIDENCE_DETAIL_TILES", "4"))
 # 每图框图片硬上限（整体 + 局部总张数）
 EVIDENCE_MAX_IMAGES = int(os.environ.get("EVIDENCE_MAX_IMAGES", "5"))
 
+# ---------------------------------------------------------------------------
+# 自适应瓦片规划（按实体分布决定截几张、截哪里）
+# ---------------------------------------------------------------------------
+# 是否启用瓦片规划；False = 退回固定 EVIDENCE_DETAIL_TILES（行为与旧版一致）
+TILING_ENABLED = _read_bool("TILING_ENABLED", True)
+# 纸面字高（mm）。国标标注字高 2~3mm，取保守下限
+TEXT_HEIGHT_MM = float(os.environ.get("TEXT_HEIGHT_MM", "2.0"))
+# VLM 可读的**最小像素字高**。
+# 离线标定（verify_vlm_resolution_offline.py，repeat=2）：黑底 12px / 白底 10px 全行正确。
+# CAD 模型空间默认黑底 → 取 12；离线合成图偏干净，故以 B2 CAD 实测为最终依据。
+TEXT_READABLE_PX = int(os.environ.get("TEXT_READABLE_PX", "12"))
+# VLM 可分辨的**最小线间距像素**。
+# 同上标定：4~5px 有噪声、≥6px 稳定（1 条 vs 2 条二选一）。取 6 保守。
+LINE_READABLE_PX = int(os.environ.get("LINE_READABLE_PX", "6"))
+# 单图框瓦片数硬上界（成本兜底）
+TILE_MAX_COUNT = int(os.environ.get("TILE_MAX_COUNT", "36"))
+# 相邻瓦片重叠比例（防边界切字）
+TILE_OVERLAP = float(os.environ.get("TILE_OVERLAP", "0.0"))
+# 热力图网格划分（长边切几份）
+HEATMAP_CELL_DIV = int(os.environ.get("HEATMAP_CELL_DIV", "32"))
+# 热力图聚类前的膨胀格数（合并相近内容；0=不膨胀）
+HEATMAP_CLUSTER_DILATE = int(os.environ.get("HEATMAP_CLUSTER_DILATE", "0"))
+# 丢弃 bbox 面积超过图框该比例的实体（去图框边框/图签块）
+HEATMAP_MAX_AREA_FRAC = float(os.environ.get("HEATMAP_MAX_AREA_FRAC", "0.5"))
+
 
 def has_api_key() -> bool:
     return bool(API_KEY.strip())

dwg_review/llm_client.py

@@ -67,6 +67,21 @@ def downsample_image_b64(b64, mime="image/png", max_edge=None, fmt=None, quality
         return b64, mime
 
 
+def image_size_b64(b64, mime="image/png"):
+    """解码图片并返回 `(宽, 高)`；缺 Pillow / 解码失败返回 None。
+
+    用于瓦片规划前测量**绘图区实际像素**（决定单张截图能覆盖多大世界范围）。
+    """
+    if not HAS_PIL or not b64:
+        return None
+    try:
+        img = Image.open(io.BytesIO(base64.b64decode(b64)))
+        w, h = img.size
+        return (int(w), int(h))
+    except Exception:
+        return None
+
+
 class LlmClient:
     """统一的 LLM 调用入口。"""
 

dwg_review/phases/evidence.py

@@ -10,7 +10,8 @@
 import math
 import time
 
-from .. import config, evidence_model
+from .. import config, evidence_model, heatmap, tiling
+from ..llm_client import image_size_b64
 from ..mcp_client import result_images, result_json
 
 _LABEL_OVERALL = "overall"
@@ -66,10 +67,80 @@ def _zoom_and_shot(ctx, window, margin=0.02):
     return images[0]
 
 
-def capture_images(ctx, window, *, tiles=0, max_images=None):
+def _is_truncated(*envelopes):
-    """采集视觉证据：整体一张 + 局部 tiles 张。
+    """任一信封 `total > len(items)` 即视为**被截断**（数据不全）。"""
+    for env in envelopes:
+        if not isinstance(env, dict):
+            continue
+        items = env.get("items")
+        if not isinstance(items, list):
+            continue
+        try:
+            total = int(env.get("total") or len(items))
+        except (TypeError, ValueError):
+            total = len(items)
+        if total > len(items):
+            return True
+    return False
+
+
+def _text_boxes(entities, texts, dimensions):
+    """收集「文字类」实体 bbox。
+
+    标准文字 / 标注信封（`get_text_content` / `get_dimensions`）全收；
+    再加**类名含 text/label/dim 等关键词**的自定义实体（`tiling.is_text_type`）。
+    """
+    boxes = list(heatmap.entity_boxes(texts))
+    boxes.extend(heatmap.entity_boxes(dimensions))
+    items = entities.get("items") if isinstance(entities, dict) else None
+    for it in items or []:
+        if isinstance(it, dict) and tiling.is_text_type(it.get("type")):
+            b = heatmap.norm_box(it.get("bbox"))
+            if b is not None:
+                boxes.append(b)
+    return boxes
+
+
+def _plan_tiles(window, frame, boxes, text_boxes, truncated, view_px):
+    """尝试按实体分布规划瓦片窗口；任何条件不满足都返回 []（调用方退回固定策略）。
+
+    降级条件：规划被关闭 / 实体数据被截断 / 缺图框 / 无实体 / 拿不到绘图区像素 /
+    比例未知（`frame_scale` 返 0）。
+    """
+    if not config.TILING_ENABLED or truncated or not frame or not boxes or not view_px:
+        return []
+    scale = tiling.frame_scale(frame)
+    s_text = tiling.world_per_px(scale, config.TEXT_HEIGHT_MM, config.TEXT_READABLE_PX)
+    # 线条区只需看清走向：同以「最小关注特征尺寸」（纸面 TEXT_HEIGHT_MM）为基准，
+    # 但像素门槛更低（R_line < R_text）→ 覆盖范围更大、瓦片更少。
+    s_line = tiling.world_per_px(scale, config.TEXT_HEIGHT_MM, config.LINE_READABLE_PX)
+    if s_text <= 0 or s_line <= 0:
+        return []
+    eff = tiling.effective_px(view_px, config.IMAGE_MAX_EDGE)
+    if eff[0] <= 0 or eff[1] <= 0:
+        return []
+
+    cell = heatmap.div_to_cell(window, config.HEATMAP_CELL_DIV)
+    grid = heatmap.build_grid(window, boxes, cell)
+    clusters = heatmap.cluster_cells(grid, dilate=config.HEATMAP_CLUSTER_DILATE)
+    if not clusters:
+        return []
+    flags = tiling.cluster_text_flags(clusters, text_boxes)
+    text_idx = {i for i, is_text in enumerate(flags) if is_text}
+    return tiling.plan_regions(
+        frame, clusters,
+        text_clusters=text_idx, s_text=s_text, s_line=s_line, view_px=eff,
+        max_tiles=config.TILE_MAX_COUNT, overlap=config.TILE_OVERLAP,
+    )
+
+
+def capture_images(ctx, window, *, tiles=0, max_images=None, frame=None,
+                   boxes=None, text_boxes=None, truncated=False):
+    """采集视觉证据：整体一张 + 局部若干张（自适应瓦片优先，否则等分象限回退）。
 
     - 整体 label="overall"，局部 label="tile-1"..；行优先。
+    - 局部窗口优先由 `_plan_tiles` 按**实体分布**给出（空白区不出图）；
+      不可用时退回 `sub_windows(window, tiles)`（行为与旧版完全一致）。
     - 结束时**复位到整体视口**，保证后续流程从整幅视图起手。
     - 受 max_images（默认 config.EVIDENCE_MAX_IMAGES）截断。
     """
@@ -81,11 +152,17 @@ def capture_images(ctx, window, *, tiles=0, max_images=None):
         if shot and len(images) < max_images:
             images.append(evidence_model.make_image(shot[0], shot[1], label))
 
-    # 整体
+    # 整体图：既是证据，也用于测量绘图区**实际像素**（决定单张截图的世界覆盖范围）
-    _add(_zoom_and_shot(ctx, window), _LABEL_OVERALL)
+    overall = _zoom_and_shot(ctx, window)
+    view_px = image_size_b64(overall[0]) if overall else None
+    _add(overall, _LABEL_OVERALL)
 
-    # 局部（等分象限）
+    # 局部：优先自适应瓦片，失败退回等分象限
-    for i, sub in enumerate(sub_windows(window, tiles), 1):
+    regions = _plan_tiles(window, frame, boxes, text_boxes, truncated, view_px)
+    if not regions:
+        regions = sub_windows(window, tiles)
+
+    for i, sub in enumerate(regions, 1):
         if len(images) >= max_images:
             break
         _add(_zoom_and_shot(ctx, sub), f"tile-{i}")
@@ -115,8 +192,19 @@ def collect_frame(ctx, index, frame):
         ctx.tools.call("get_polylines_info", {"window": win, "limit": limit}), {}
     )
 
-    # b. 视觉：整体 + 可选局部（多图）
+    # b. 视觉：整体 + 局部（优先按实体分布自适应瓦片；数据截断则退回固定策略）
-    images = capture_images(ctx, win, tiles=config.EVIDENCE_DETAIL_TILES)
+    truncated = _is_truncated(entities, texts, dimensions, blocks, polylines)
+    boxes = heatmap.drop_oversized(
+        heatmap.entity_boxes(entities), win, config.HEATMAP_MAX_AREA_FRAC
+    )
+    images = capture_images(
+        ctx, win,
+        tiles=config.EVIDENCE_DETAIL_TILES,
+        frame=frame,
+        boxes=boxes,
+        text_boxes=_text_boxes(entities, texts, dimensions),
+        truncated=truncated,
+    )
 
     return {
         "source_type": evidence_model.source_type_of(frame),

dwg_review/tiling.py

@@ -0,0 +1,284 @@
+"""自适应瓦片规划器：按实体分布把图框切成若干 zoom 窗口，使 VLM 能看清细节。
+
+纯函数模块（仅依赖 `math`），不依赖 Pillow / CAD。
+
+核心思路（用户方案）：
+  1. 由图框纸张 + 比例换算「CAD 图形单位 / 像素」s；
+  2. 由 VLM 可读下限（文字 `R_text` px、线条 `R_line` px）定每张截图的**世界覆盖范围**；
+  3. 复用热力图（`heatmap.py`）的**占用网格 + 聚类**得到簇包围盒；簇内是否含**文字类实体**
+     决定用细（`s_text`）/ 粗（`s_line`）尺度；
+  4. 簇 ≤ 单屏 → 1 张；超出 → 行优先细分；**空白区不出图**（整体图已覆盖）；
+     受 `max_tiles` 截断，**文字簇优先保留**。
+
+坐标约定：与 `heatmap.py` / CAD 一致——x 向右、y **向上**；bbox=(min_x,min_y,max_x,max_y)；
+瓦片行优先且 row 0 在**最上方**（与 `phases/evidence.py::sub_windows` 一致）。
+"""
+
+import math
+
+# 纸张尺寸（mm，长边在前）：GB/T 14689 基本幅面
+SHEET_SIZES_MM = {
+    "A0": (1189.0, 841.0),
+    "A1": (841.0, 594.0),
+    "A2": (594.0, 420.0),
+    "A3": (420.0, 297.0),
+    "A4": (297.0, 210.0),
+}
+
+# 标准实体名：本身就是文字/标注类
+_STD_TEXT_TYPES = frozenset({"TEXT", "MTEXT", "ATTRIB", "ATTDEF", "DIMENSION"})
+
+# 自定义实体类名关键词（用户经验：类名常含 text/label/dim 等）
+_TEXT_KEYWORDS = ("TEXT", "LABEL", "DIM", "NOTE", "TAG", "ANNO", "TITLE")
+
+# 瓦片重叠上限（防边界切字；过大会显著增加张数）
+_MAX_OVERLAP = 0.5
+
+
+def norm_box(b):
+    """归一 bbox 为 (minx,miny,maxx,maxy)；非法返回 None。"""
+    if not isinstance(b, (list, tuple)) or len(b) != 4:
+        return None
+    try:
+        x1, y1, x2, y2 = (float(v) for v in b)
+    except (TypeError, ValueError):
+        return None
+    if not all(math.isfinite(v) for v in (x1, y1, x2, y2)):
+        return None
+    if x1 > x2:
+        x1, x2 = x2, x1
+    if y1 > y2:
+        y1, y2 = y2, y1
+    return (x1, y1, x2, y2)
+
+
+def frame_bbox(frame):
+    """图框 bbox 元组；非法返回 None。"""
+    if not isinstance(frame, dict):
+        return None
+    box = norm_box(frame.get("bbox"))
+    if box is None or box[2] <= box[0] or box[3] <= box[1]:
+        return None
+    return box
+
+
+def frame_world_size(frame):
+    """图框在 CAD 中的世界尺寸 (W, H)；bbox 非法返回 (0.0, 0.0)。"""
+    box = frame_bbox(frame)
+    if box is None:
+        return (0.0, 0.0)
+    return (box[2] - box[0], box[3] - box[1])
+
+
+def frame_scale(frame, default=0.0):
+    """图框出图比例（1:100 → 100.0）。
+
+    优先级：
+      1. `sheet_scale`（工具已识别，>0 直接用）；
+      2. 由「图框世界尺寸 / 纸张尺寸」反算（需 `sheet_size` 命中纸张表）；
+      3. `default`（拿不到 → 调用方退回固定策略）。
+    """
+    if not isinstance(frame, dict):
+        return default
+    try:
+        s = float(frame.get("sheet_scale") or 0.0)
+    except (TypeError, ValueError):
+        s = 0.0
+    if s > 0:
+        return s
+
+    w, h = frame_world_size(frame)
+    if w <= 0 or h <= 0:
+        return default
+    sheet = SHEET_SIZES_MM.get(str(frame.get("sheet_size") or "").strip().upper())
+    if not sheet:
+        return default
+    sw, sh = sheet
+    # 判断横放 / 纵放：取长宽比更接近的朝向
+    ar = w / h
+    if abs(ar - sw / sh) <= abs(ar - sh / sw):
+        return w / sw  # 横放：世界宽 ↔ 纸宽
+    return w / sh      # 纵放：世界宽 ↔ 纸高
+
+
+def effective_px(view_px, max_edge):
+    """按长边下采样后的**有效像素** (ew, eh)。
+
+    与 `llm_client.downsample_image_b64` 的缩放规则一致（长边缩到 max_edge）。
+    非法输入返回 (0, 0)；max_edge<=0 表示不缩放。
+    """
+    if not view_px or len(view_px) != 2:
+        return (0, 0)
+    try:
+        w, h = int(view_px[0]), int(view_px[1])
+    except (TypeError, ValueError):
+        return (0, 0)
+    if w <= 0 or h <= 0:
+        return (0, 0)
+    if max_edge and max_edge > 0 and max(w, h) > max_edge:
+        k = max_edge / float(max(w, h))
+        return (max(1, int(round(w * k))), max(1, int(round(h * k))))
+    return (w, h)
+
+
+def world_per_px(scale, text_mm, readable_px):
+    """每像素代表的 CAD 图形单位：s = (text_mm * scale) / readable_px。
+
+    `text_mm` 为纸面字高（国标 2~3mm），`scale` 为出图比例（1:100 → 100），
+    `readable_px` 为 VLM 可读的**最小像素字高**。任一非正 → 返回 0.0（表示无法规划）。
+    """
+    try:
+        scale, text_mm, readable_px = float(scale), float(text_mm), float(readable_px)
+    except (TypeError, ValueError):
+        return 0.0
+    if scale <= 0 or text_mm <= 0 or readable_px <= 0:
+        return 0.0
+    return text_mm * scale / readable_px
+
+
+def is_text_type(type_name):
+    """实体类型名是否为「文字/标注类」。
+
+    标准实体给 DXF 名（TEXT/MTEXT/ATTRIB/ATTDEF/DIMENSION）→ 精确命中；
+    自定义实体兜底为**类名去 AcDb 前缀并大写**，故按**关键词包含**判定
+    （TEXT/LABEL/DIM/NOTE/TAG/ANNO/TITLE）。非字符串/空 → False。
+    """
+    if not isinstance(type_name, str):
+        return False
+    t = type_name.strip().upper()
+    if not t:
+        return False
+    if t in _STD_TEXT_TYPES:
+        return True
+    return any(k in t for k in _TEXT_KEYWORDS)
+
+
+def boxes_intersect(a, b, eps=0.0):
+    """两个 bbox 是否相交（接触视为相交；eps 可放宽）。"""
+    if a is None or b is None:
+        return False
+    return not (
+        a[2] < b[0] - eps or b[2] < a[0] - eps
+        or a[3] < b[1] - eps or b[3] < a[1] - eps
+    )
+
+
+def cluster_text_flags(clusters, text_boxes):
+    """标记每个簇是否含文字类实体（bbox 相交即算，保守）。
+
+    返回与 `clusters` 等长的 bool 列表。
+    """
+    boxes = [b for b in (norm_box(x) for x in (text_boxes or [])) if b]
+    flags = []
+    for cl in clusters or []:
+        b = norm_box(cl.get("bbox")) if isinstance(cl, dict) else None
+        flags.append(bool(b) and any(boxes_intersect(b, tb) for tb in boxes))
+    return flags
+
+
+def tiles_for_box(box, cap_w, cap_h, overlap=0.0):
+    """覆盖 box 所需的 (cols, rows)。
+
+    `cap_w`/`cap_h` 为单张截图的世界覆盖范围；`overlap` 为相邻瓦片重叠比例
+    （>0 时有效步长缩短，张数增加）。非法输入返回 (1, 1)。
+    """
+    if box is None:
+        return (1, 1)
+    w, h = box[2] - box[0], box[3] - box[1]
+    if w <= 0 or h <= 0:
+        return (1, 1)
+    ov = min(max(float(overlap or 0.0), 0.0), _MAX_OVERLAP)
+    step_w = float(cap_w) * (1.0 - ov)
+    step_h = float(cap_h) * (1.0 - ov)
+    if step_w <= 0 or step_h <= 0:
+        return (1, 1)
+    return (max(1, int(math.ceil(w / step_w))), max(1, int(math.ceil(h / step_h))))
+
+
+def _clamp_window(win, frame_win):
+    """把窗口夹到图框范围内；夹后退化（宽/高<=0）返回 None。"""
+    if frame_win is None:
+        return win
+    fx1, fy1, fx2, fy2 = frame_win
+    x1 = max(win["min_x"], fx1)
+    y1 = max(win["min_y"], fy1)
+    x2 = min(win["max_x"], fx2)
+    y2 = min(win["max_y"], fy2)
+    if x2 <= x1 or y2 <= y1:
+        return None
+    return {"min_x": x1, "min_y": y1, "max_x": x2, "max_y": y2}
+
+
+def subdivide(box, cols, rows, overlap=0.0, frame_win=None):
+    """把 box 等分为 cols×rows 个 zoom 窗口（行优先，row 0 在最上方）。
+
+    每张按 `overlap` 比例向外扩张（防边界切字），再夹到 `frame_win`。
+    """
+    x1, y1, x2, y2 = box
+    cols, rows = max(1, int(cols)), max(1, int(rows))
+    w = (x2 - x1) / cols
+    h = (y2 - y1) / rows
+    ov = min(max(float(overlap or 0.0), 0.0), _MAX_OVERLAP)
+    mx, my = w * ov, h * ov
+    out = []
+    for r in range(rows):
+        for c in range(cols):
+            sx1 = x1 + c * w
+            sy2 = y2 - r * h          # row 0 在最上方
+            win = {
+                "min_x": sx1 - mx, "min_y": sy2 - h - my,
+                "max_x": sx1 + w + mx, "max_y": sy2 + my,
+            }
+            clamped = _clamp_window(win, frame_win)
+            if clamped:
+                out.append(clamped)
+    return out
+
+
+def plan_regions(frame, clusters, *, text_clusters=(), s_text=0.0, s_line=0.0,
+                 view_px=(0, 0), max_tiles=36, overlap=0.0):
+    """把图框内的簇规划成一组 zoom 窗口（自适应瓦片）。
+
+    参数：
+      - `frame`        ：图框（含 bbox / sheet_size / sheet_scale）；
+      - `clusters`     ：`heatmap.cluster_cells` 的输出（已按实体计数降序）；
+      - `text_clusters`：含文字类实体的**簇下标**集合（决定用 s_text 还是 s_line）；
+      - `s_text`/`s_line`：CAD 图形单位 / 像素（`world_per_px` 的输出）；
+      - `view_px`      ：单张截图的**有效像素** (ew, eh)（`effective_px` 的输出）；
+      - `max_tiles`    ：返回窗口数硬上界（文字簇优先保留）；
+      - `overlap`      ：相邻瓦片重叠比例。
+
+    返回行优先的 window 列表；**无法规划时返回 []**（调用方退回固定策略）。
+    """
+    frame_win = frame_bbox(frame)
+    if frame_win is None:
+        return []
+    ew, eh = view_px[0] if view_px else 0, view_px[1] if view_px and len(view_px) > 1 else 0
+    if ew <= 0 or eh <= 0:
+        return []
+
+    text_idx = set(text_clusters or ())
+    # 文字簇优先（同级内保持输入的计数降序）
+    ordered = sorted(
+        enumerate(clusters or []),
+        key=lambda p: (0 if p[0] in text_idx else 1, p[0]),
+    )
+
+    regions = []
+    for i, cl in ordered:
+        if len(regions) >= max_tiles:
+            break
+        box = norm_box(cl.get("bbox")) if isinstance(cl, dict) else None
+        if box is None:
+            continue
+        s = s_text if i in text_idx else s_line
+        if s <= 0:
+            continue
+        cap_w, cap_h = s * ew, s * eh
+        cols, rows = tiles_for_box(box, cap_w, cap_h, overlap)
+        # 单屏可覆盖 → 1 张（subdivide 仍会按 overlap 外扩并夹到图框）
+        for win in subdivide(box, cols, rows, overlap, frame_win):
+            regions.append(win)
+            if len(regions) >= max_tiles:
+                break
+    return regions[:max_tiles]

tests/test_config.py

@@ -79,6 +79,28 @@ def test_credibility_constants_have_expected_types():
     assert isinstance(config.DUP_GROUP_LIMIT, int)
 
 
+def test_tiling_constants_have_expected_types():
+    assert isinstance(config.TILING_ENABLED, bool)
+    assert isinstance(config.TEXT_HEIGHT_MM, float)
+    assert isinstance(config.TEXT_READABLE_PX, int)
+    assert isinstance(config.LINE_READABLE_PX, int)
+    assert isinstance(config.TILE_MAX_COUNT, int)
+    assert isinstance(config.TILE_OVERLAP, float)
+    assert isinstance(config.HEATMAP_CELL_DIV, int)
+    assert isinstance(config.HEATMAP_CLUSTER_DILATE, int)
+    assert isinstance(config.HEATMAP_MAX_AREA_FRAC, float)
+
+
+def test_tiling_defaults_are_sane():
+    # 默认启用瓦片规划；可读下限为正；成本上界为正
+    assert config.TILING_ENABLED is True
+    assert config.TEXT_HEIGHT_MM > 0
+    assert config.TEXT_READABLE_PX > 0
+    assert config.LINE_READABLE_PX > 0
+    assert config.TILE_MAX_COUNT > 0
+    assert config.HEATMAP_CELL_DIV > 0
+
+
 def test_credibility_defaults_are_on():
     # 默认开启，保证默认拿到可信度收益
     assert config.DETERMINISTIC_CHECKS is True

tests/test_evidence.py

@@ -1,8 +1,17 @@
 """`dwg_review.phases.evidence` 单元测试。"""
 
+import base64
+import io
+
 import pytest
 
-from dwg_review.phases.evidence import bbox_to_window, collect_frame
+from dwg_review.phases.evidence import (
+    _is_truncated,
+    _plan_tiles,
+    _text_boxes,
+    bbox_to_window,
+    collect_frame,
+)
 from tests.fakes import FakeMcpClient, frame, make_ctx, ok_image, ok_text
 
 
@@ -141,3 +150,144 @@ def test_collect_returns_none_for_unsupported_source():
 
     ctx = make_ctx(client=FakeMcpClient())
     assert collect(ctx, 1, {"source_type": "pdf", "bbox": [0, 0, 1, 1]}) is None
+
+
+# ---------------------------------------------------------------------------
+#  自适应瓦片规划
+# ---------------------------------------------------------------------------
+@pytest.fixture
+def tiling_env(monkeypatch):
+    """把瓦片相关配置钉死，使用例不受外部环境变量影响。"""
+    from dwg_review import config
+
+    for name, value in {
+        "TILING_ENABLED": True,
+        "TEXT_HEIGHT_MM": 2.0,
+        "TEXT_READABLE_PX": 12,
+        "LINE_READABLE_PX": 6,
+        "TILE_MAX_COUNT": 36,
+        "TILE_OVERLAP": 0.0,
+        "HEATMAP_CELL_DIV": 32,
+        "HEATMAP_CLUSTER_DILATE": 0,
+        "HEATMAP_MAX_AREA_FRAC": 0.5,
+        "IMAGE_MAX_EDGE": 1280,
+        "EVIDENCE_MAX_IMAGES": 5,
+        "EVIDENCE_DETAIL_TILES": 4,
+    }.items():
+        monkeypatch.setattr(config, name, value)
+    return config
+
+
+def _png_b64(w=2000, h=1414):
+    from PIL import Image
+
+    buf = io.BytesIO()
+    Image.new("RGB", (w, h), "black").save(buf, format="PNG")
+    return base64.b64encode(buf.getvalue()).decode("ascii")
+
+
+def _tiling_client(b64, entity_type="LINE"):
+    return FakeMcpClient(
+        responses={
+            "query_entities": ok_text(
+                {"total": 1, "items": [{"handle": "A", "type": entity_type,
+                                        "bbox": [0, 0, 60000, 20000]}]}
+            ),
+            "get_text_content": ok_text({"total": 0, "items": []}),
+            "get_dimensions": ok_text({"total": 0, "items": []}),
+            "get_block_references": ok_text({"total": 0, "items": []}),
+            "get_polylines_info": ok_text({"total": 0, "items": []}),
+            "get_viewport_screenshot": ok_image(b64),
+        }
+    )
+
+
+def test_is_truncated():
+    assert _is_truncated({"total": 5, "items": [1]}) is True
+    assert _is_truncated({"total": 1, "items": [1]}) is False
+    assert _is_truncated({"items": [1, 2]}) is False
+    assert _is_truncated(None, {}, {"total": 3, "items": []}) is True
+    assert _is_truncated() is False
+
+
+def test_text_boxes_includes_std_and_custom():
+    entities = {"items": [
+        {"type": "LINE", "bbox": [0, 0, 1, 1]},
+        {"type": "AECDLABEL", "bbox": [2, 2, 3, 3]},
+        {"type": "TEXT", "bbox": [4, 4, 5, 5]},
+    ]}
+    texts = {"items": [{"bbox": [6, 6, 7, 7]}]}
+    dimensions = {"items": [{"bbox": [8, 8, 9, 9]}]}
+    boxes = _text_boxes(entities, texts, dimensions)
+    for expected in ((2, 2, 3, 3), (4, 4, 5, 5), (6, 6, 7, 7), (8, 8, 9, 9)):
+        assert tuple(float(v) for v in expected) in boxes
+    assert (0.0, 0.0, 1.0, 1.0) not in boxes  # 普通线不算文字
+
+
+def test_plan_tiles_degrades(tiling_env):
+    win = {"min_x": 0.0, "min_y": 0.0, "max_x": 100.0, "max_y": 50.0}
+    f = frame([0, 0, 100, 50], sheet_size="A0", sheet_scale=100.0)
+    boxes = [(0.0, 0.0, 10.0, 10.0)]
+    assert _plan_tiles(win, f, boxes, [], False, (100, 50)) != []  # 正常路径非空
+    assert _plan_tiles(win, None, boxes, [], False, (100, 50)) == []          # 缺图框
+    assert _plan_tiles(win, f, [], [], False, (100, 50)) == []                # 无实体
+    assert _plan_tiles(win, f, boxes, [], False, None) == []                  # 无像素
+    assert _plan_tiles(win, f, boxes, [], True, (100, 50)) == []              # 数据截断
+    # 比例未知（sheet_scale=0 且 sheet_size 不在纸张表）→ 无法规划
+    unknown = frame([0, 0, 100, 50], sheet_size="ZZ", sheet_scale=0.0)
+    assert _plan_tiles(win, unknown, boxes, [], False, (100, 50)) == []
+
+
+def test_plan_tiles_disabled_by_config(tiling_env, monkeypatch):
+    tiling_env.TILING_ENABLED = False
+    win = {"min_x": 0.0, "min_y": 0.0, "max_x": 100.0, "max_y": 50.0}
+    f = frame([0, 0, 100, 50], sheet_size="A0", sheet_scale=100.0)
+    assert _plan_tiles(win, f, [(0.0, 0.0, 10.0, 10.0)], [], False, (100, 50)) == []
+
+
+def test_capture_images_tiles_line_cluster(tiling_env):
+    """线条簇：单张可覆盖 → 2 张瓦片（行优先）。"""
+    pytest.importorskip("PIL")
+    ctx = make_ctx(client=_tiling_client(_png_b64()))
+    ev = collect_frame(
+        ctx, 1, frame([0, 0, 120000, 40000], sheet_size="A0", sheet_scale=100.0)
+    )
+    assert [i["label"] for i in ev["images"]] == ["overall", "tile-1", "tile-2"]
+
+
+def test_capture_images_tiles_text_cluster_is_finer(tiling_env):
+    """文字簇用更细尺度 → 瓦片更多，并受 EVIDENCE_MAX_IMAGES 截断。"""
+    pytest.importorskip("PIL")
+    ctx = make_ctx(client=_tiling_client(_png_b64(), entity_type="AECDLABEL"))
+    ev = collect_frame(
+        ctx, 1, frame([0, 0, 120000, 40000], sheet_size="A0", sheet_scale=100.0)
+    )
+    labels = [i["label"] for i in ev["images"]]
+    assert labels == ["overall", "tile-1", "tile-2", "tile-3", "tile-4"]  # 上限 5 张
+
+
+def test_capture_images_falls_back_without_scale(tiling_env):
+    """缺 sheet_size/sheet_scale → 退回固定 2x2 象限。"""
+    pytest.importorskip("PIL")
+    ctx = make_ctx(client=_tiling_client(_png_b64()))
+    ev = collect_frame(ctx, 1, frame([0, 0, 120000, 40000], sheet_size="ZZ"))
+    assert [i["label"] for i in ev["images"]] == [
+        "overall", "tile-1", "tile-2", "tile-3", "tile-4",
+    ]
+
+
+def test_capture_images_falls_back_when_truncated(tiling_env):
+    """实体被截断 → 禁用内容过滤，退回固定策略。"""
+    pytest.importorskip("PIL")
+    client = _tiling_client(_png_b64())
+    client.responses["query_entities"] = ok_text(
+        {"total": 500, "items": [{"handle": "A", "type": "LINE",
+                                  "bbox": [0, 0, 60000, 20000]}]}
+    )
+    ctx = make_ctx(client=client)
+    ev = collect_frame(
+        ctx, 1, frame([0, 0, 120000, 40000], sheet_size="A0", sheet_scale=100.0)
+    )
+    assert [i["label"] for i in ev["images"]] == [
+        "overall", "tile-1", "tile-2", "tile-3", "tile-4",
+    ]

tests/test_llm_client.py

@@ -186,3 +186,31 @@ def test_downsample_bad_base64_returns_original():
     pytest.importorskip("PIL")
     bad = base64.b64encode(b"this is not an image").decode("ascii")
     assert downsample_image_b64(bad, "image/png", max_edge=10) == (bad, "image/png")
+
+
+# ---------------------------------------------------------------------------
+#  图片尺寸探测（瓦片规划用）
+# ---------------------------------------------------------------------------
+def test_image_size_returns_none_without_pil(monkeypatch):
+    monkeypatch.setattr(llm_module, "HAS_PIL", False)
+    assert llm_module.image_size_b64("QUJD") is None
+
+
+def test_image_size_returns_none_on_empty():
+    assert llm_module.image_size_b64("") is None
+
+
+def test_image_size_returns_none_on_bad_data():
+    pytest.importorskip("PIL")
+    bad = base64.b64encode(b"not an image").decode("ascii")
+    assert llm_module.image_size_b64(bad) is None
+
+
+def test_image_size_reads_dimensions():
+    pytest.importorskip("PIL")
+    from PIL import Image
+
+    buf = io.BytesIO()
+    Image.new("RGB", (321, 123), "white").save(buf, format="PNG")
+    b64 = base64.b64encode(buf.getvalue()).decode("ascii")
+    assert llm_module.image_size_b64(b64) == (321, 123)

tests/test_tiling.py

@@ -0,0 +1,208 @@
+"""`dwg_review.tiling` 单元测试（纯函数，不依赖 CAD / LLM / Pillow）。"""
+
+from dwg_review import tiling as tl
+
+
+def _frame(bbox, sheet_size="A0", sheet_scale=0.0):
+    return {"bbox": list(bbox), "sheet_size": sheet_size, "sheet_scale": sheet_scale}
+
+
+def _cluster(bbox, count=10):
+    return {"bbox": tuple(float(v) for v in bbox), "cells": [], "count": count}
+
+
+# --- norm_box / frame_bbox --------------------------------------------------
+
+def test_norm_box_valid_and_invalid():
+    assert tl.norm_box([0, 0, 10, 20]) == (0.0, 0.0, 10.0, 20.0)
+    assert tl.norm_box([10, 20, 0, 0]) == (0.0, 0.0, 10.0, 20.0)
+    assert tl.norm_box([1, 2, 3]) is None
+    assert tl.norm_box(None) is None
+    assert tl.norm_box([0, 0, float("nan"), 1]) is None
+
+
+def test_frame_bbox_rejects_degenerate_and_non_dict():
+    assert tl.frame_bbox(_frame((0, 0, 100, 50))) == (0.0, 0.0, 100.0, 50.0)
+    assert tl.frame_bbox(_frame((0, 0, 0, 50))) is None   # 零宽
+    assert tl.frame_bbox(_frame((0, 0, 100, 0))) is None   # 零高
+    assert tl.frame_bbox({"bbox": [1, 2, 3]}) is None
+    assert tl.frame_bbox(None) is None
+
+
+def test_frame_world_size():
+    assert tl.frame_world_size(_frame((0, 0, 100, 50))) == (100.0, 50.0)
+    assert tl.frame_world_size(_frame((0, 0, 0, 0))) == (0.0, 0.0)
+
+
+# --- frame_scale：三条路径 --------------------------------------------------
+
+def test_frame_scale_prefers_sheet_scale():
+    assert tl.frame_scale(_frame((0, 0, 100, 50), sheet_scale=100.0)) == 100.0
+
+
+def test_frame_scale_derived_landscape():
+    # A0 横放：世界 118900 x 84100 → 118900/1189 = 100
+    f = _frame((0, 0, 118900, 84100), sheet_size="A0")
+    assert tl.frame_scale(f) == 100.0
+
+
+def test_frame_scale_derived_portrait():
+    # A0 纵放：世界宽 ↔ 纸高 841
+    f = _frame((0, 0, 84100, 118900), sheet_size="A0")
+    assert tl.frame_scale(f) == 100.0
+
+
+def test_frame_scale_falls_back_to_default():
+    assert tl.frame_scale(_frame((0, 0, 100, 50), sheet_size="ZZ"), default=7.0) == 7.0
+    assert tl.frame_scale(_frame((0, 0, 0, 0)), default=7.0) == 7.0
+    assert tl.frame_scale(None, default=7.0) == 7.0
+
+
+# --- effective_px / world_per_px -------------------------------------------
+
+def test_effective_px_no_resize_when_small():
+    assert tl.effective_px((800, 600), 1280) == (800, 600)
+
+
+def test_effective_px_downsamples_long_edge():
+    # 实测 CAD 绘图区 2110x462 → 长边缩到 1280
+    assert tl.effective_px((2110, 462), 1280) == (1280, 280)
+
+
+def test_effective_px_invalid():
+    assert tl.effective_px(None, 1280) == (0, 0)
+    assert tl.effective_px((0, 100), 1280) == (0, 0)
+    assert tl.effective_px((100, 100), 0) == (100, 100)  # 0 = 不缩放
+
+
+def test_world_per_px():
+    # 1:100、纸面 2mm、VLM 需 10px → 每像素 20 图形单位
+    assert tl.world_per_px(100.0, 2.0, 10.0) == 20.0
+    assert tl.world_per_px(0, 2.0, 10.0) == 0.0
+    assert tl.world_per_px(100.0, 2.0, 0) == 0.0
+
+
+# --- is_text_type -----------------------------------------------------------
+
+def test_is_text_type_standard():
+    for t in ("TEXT", "MTEXT", "ATTRIB", "ATTDEF", "DIMENSION", "text"):
+        assert tl.is_text_type(t) is True
+
+
+def test_is_text_type_custom_keywords():
+    for t in ("AECDLABEL", "AECDDIMTEXT", "MYNOTE", "PARTTAG", "ANNOTATION", "TITLEBLK"):
+        assert tl.is_text_type(t) is True
+
+
+def test_is_text_type_negative():
+    for t in ("AECDBEWPIPE", "LINE", "LWPOLYLINE", "INSERT", "HATCH", "", None, 123):
+        assert tl.is_text_type(t) is False
+
+
+# --- 相交 / 文字簇标记 -------------------------------------------------------
+
+def test_boxes_intersect():
+    assert tl.boxes_intersect((0, 0, 10, 10), (5, 5, 15, 15)) is True
+    assert tl.boxes_intersect((0, 0, 10, 10), (10, 10, 20, 20)) is True  # 接触算相交
+    assert tl.boxes_intersect((0, 0, 10, 10), (11, 11, 20, 20)) is False
+    assert tl.boxes_intersect(None, (0, 0, 1, 1)) is False
+
+
+def test_cluster_text_flags():
+    clusters = [_cluster((0, 0, 10, 10)), _cluster((100, 100, 110, 110))]
+    flags = tl.cluster_text_flags(clusters, [(5, 5, 6, 6)])
+    assert flags == [True, False]
+    assert tl.cluster_text_flags(clusters, []) == [False, False]
+    assert tl.cluster_text_flags([], [(0, 0, 1, 1)]) == []
+
+
+# --- tiles_for_box / subdivide ---------------------------------------------
+
+def test_tiles_for_box_fits_single():
+    assert tl.tiles_for_box((0, 0, 1000, 500), 1000, 500) == (1, 1)
+
+
+def test_tiles_for_box_dense():
+    assert tl.tiles_for_box((0, 0, 2000, 1000), 1000, 500) == (2, 2)
+
+
+def test_tiles_for_box_overlap_increases_count():
+    assert tl.tiles_for_box((0, 0, 1000, 500), 1000, 500, overlap=0.1) == (2, 2)
+
+
+def test_tiles_for_box_invalid():
+    assert tl.tiles_for_box(None, 10, 10) == (1, 1)
+    assert tl.tiles_for_box((0, 0, 0, 0), 10, 10) == (1, 1)
+    assert tl.tiles_for_box((0, 0, 10, 10), 0, 10) == (1, 1)
+
+
+def test_subdivide_row_major_top_first():
+    wins = tl.subdivide((0, 0, 100, 100), 2, 2)
+    assert len(wins) == 4
+    assert wins[0] == {"min_x": 0.0, "min_y": 50.0, "max_x": 50.0, "max_y": 100.0}
+    assert wins[1] == {"min_x": 50.0, "min_y": 50.0, "max_x": 100.0, "max_y": 100.0}
+    assert wins[2] == {"min_x": 0.0, "min_y": 0.0, "max_x": 50.0, "max_y": 50.0}
+    assert wins[3] == {"min_x": 50.0, "min_y": 0.0, "max_x": 100.0, "max_y": 50.0}
+
+
+def test_subdivide_overlap_expands_then_clamps():
+    wins = tl.subdivide((0, 0, 100, 100), 2, 2, overlap=0.1, frame_win=(0, 0, 100, 100))
+    # 每格 50x50，外扩 5 → 左上格：x 0..55（左被夹到 0），y 45..100（上被夹到 100）
+    assert wins[0] == {"min_x": 0.0, "min_y": 45.0, "max_x": 55.0, "max_y": 100.0}
+
+
+# --- plan_regions -----------------------------------------------------------
+
+def test_plan_regions_sparse_single_tile():
+    frame = _frame((0, 0, 1000, 500))
+    clusters = [_cluster((100, 100, 400, 300))]
+    wins = tl.plan_regions(frame, clusters, s_line=10.0, view_px=(100, 50))
+    assert len(wins) == 1
+    assert wins[0]["min_x"] <= 100 and wins[0]["max_x"] >= 400
+
+
+def test_plan_regions_dense_splits():
+    frame = _frame((0, 0, 2000, 1000))
+    clusters = [_cluster((0, 0, 2000, 1000))]
+    wins = tl.plan_regions(frame, clusters, s_line=10.0, view_px=(100, 50))
+    assert len(wins) == 4  # 2x2
+
+
+def test_plan_regions_text_uses_finer_scale():
+    frame = _frame((0, 0, 1000, 500))
+    clusters = [_cluster((0, 0, 1000, 500))]
+    line_only = tl.plan_regions(frame, clusters, s_line=10.0, view_px=(100, 50))
+    text = tl.plan_regions(frame, clusters, text_clusters={0}, s_text=5.0,
+                           s_line=10.0, view_px=(100, 50))
+    assert len(line_only) == 1
+    assert len(text) == 4  # 半径减半 → 2x2
+
+
+def test_plan_regions_text_cluster_prioritized_on_truncation():
+    frame = _frame((0, 0, 4000, 2000))
+    clusters = [_cluster((0, 0, 2000, 1000), count=99),   # 非文字，排前面（输入序）
+                _cluster((2000, 1000, 4000, 2000), count=1)]  # 文字
+    wins = tl.plan_regions(frame, clusters, text_clusters={1},
+                           s_text=10.0, s_line=10.0, view_px=(100, 50), max_tiles=2)
+    assert len(wins) == 2
+    # 文字簇（下标 1，位于右上）应优先出图
+    assert wins[0]["min_x"] >= 2000 and wins[0]["min_y"] >= 1000
+
+
+def test_plan_regions_degrades():
+    frame = _frame((0, 0, 1000, 500))
+    clusters = [_cluster((0, 0, 1000, 500))]
+    assert tl.plan_regions(frame, clusters, s_line=0.0, view_px=(100, 50)) == []
+    assert tl.plan_regions(frame, clusters, s_line=10.0, view_px=(0, 0)) == []
+    assert tl.plan_regions({"bbox": [0, 0, 0, 0]}, clusters, s_line=10.0,
+                           view_px=(100, 50)) == []
+    assert tl.plan_regions(frame, [], s_line=10.0, view_px=(100, 50)) == []
+
+
+def test_plan_regions_clamps_to_frame():
+    frame = _frame((0, 0, 1000, 500))
+    clusters = [_cluster((0, 0, 2000, 1000))]  # 超出图框
+    wins = tl.plan_regions(frame, clusters, s_line=10.0, view_px=(100, 50), max_tiles=4)
+    for w in wins:
+        assert w["min_x"] >= 0 and w["max_x"] <= 1000
+        assert w["min_y"] >= 0 and w["max_y"] <= 500

verify_vlm_resolution_offline.py

@@ -0,0 +1,415 @@
+"""离线标定 VLM 读图下限（不碰 CAD，纯 PIL 合成）。
+
+目的：在接入自适应瓦片规划前，先离线测出视觉模型（deepseek-flash）的
+「最小可读文字像素高 `R_text`」与「最小可分辨线间距 `R_line`」，为 `tiling.py`
+的 `world_per_px` 提供**区间参考**。
+
+做法：PIL 合成"类图纸"图（可控背景极性），**精确控制像素字高**（先用
+`font.getbbox` 二分出目标 ink 高度对应的字号），加可控间距/线宽的平行线，
+以及实线 / 虚线对比。每档发 VLM，提示词强约束 JSON，与真值比对。
+
+重要前提：**合成图比真实 CAD 渲染干净 → 结论偏乐观**，只作区间；最终以
+`verify_text_resolution.py`（CAD 临时加字实测）为准。
+
+用法：
+    python verify_vlm_resolution_offline.py --dry-run          # 只出图，不调 VLM
+    python verify_vlm_resolution_offline.py --repeat 2         # 每档重复 2 次
+    python verify_vlm_resolution_offline.py --heights 4,6,8,10,12 --spacings 2,3,4,6,8
+    python verify_vlm_resolution_offline.py --polarity light   # 白底黑线
+
+前置：需要 Pillow；调 VLM 需要 DEEPSEEK_API_KEY（--dry-run 不需要密钥）。
+"""
+
+import argparse
+import base64
+import io
+import json
+import math
+import random
+import re
+from pathlib import Path
+
+from dwg_review import config
+from dwg_review.llm_client import HAS_PIL, LlmClient, downsample_image_b64
+
+# 画布宽固定为 1280（= IMAGE_MAX_EDGE），使发给 VLM 前**不发生下采样**，
+# 与真实管线「截图长边缩到 1280」后的条件一致。
+_CANVAS_W = 1280
+_MIN_CANVAS_H = 280
+
+# 易混字符集：去掉 I / O，避免"真值本身歧义"污染标定
+_ALPHABET = "ABCDEFGHJKLMNPQRSTUVWXYZ23456789"
+
+_DARK = {"bg": (0, 0, 0), "fg": (255, 255, 255)}
+_LIGHT = {"bg": (255, 255, 255), "fg": (0, 0, 0)}
+_POLARITY = {"dark": _DARK, "light": _LIGHT}
+
+_FONT_CANDIDATES = (
+    r"C:\Windows\Fonts\consola.ttf",  # 等宽，字高最稳
+    r"C:\Windows\Fonts\cour.ttf",
+    r"C:\Windows\Fonts\lucon.ttf",
+    r"C:\Windows\Fonts\arial.ttf",
+    r"/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf",
+    r"/System/Library/Fonts/Menlo.ttc",
+)
+
+_PROBE = "K7Q2M9"  # 全为大写/数字，ink 高度一致，用于二分字号
+
+_SYS_TEXT = (
+    "你是图像文字识别器。只输出一个 JSON 对象，不要任何解释、不要 markdown 代码块。"
+)
+_SYS_LINE = (
+    "你是工程图纸线型识别器。只输出一个 JSON 对象，不要任何解释、不要 markdown 代码块。"
+)
+
+
+# ---------------------------------------------------------------------------
+# 字体与像素字高
+# ---------------------------------------------------------------------------
+def find_font_path(explicit=None):
+    if explicit:
+        p = Path(explicit)
+        if p.exists():
+            return str(p)
+        raise SystemExit(f"指定的字体不存在：{explicit}")
+    for cand in _FONT_CANDIDATES:
+        if Path(cand).exists():
+            return cand
+    raise SystemExit("未找到可用 TTF 字体，请用 --font 指定")
+
+
+def font_for_ink_height(target_h, font_path, probe=_PROBE):
+    """二分出使 `probe` 的 ink 高度最接近 target_h 的字体。
+
+    返回 (font, size, actual_h)。ink 高度 = getbbox 的 (bottom - top)。
+    """
+    from PIL import ImageFont
+
+    target_h = max(1, int(target_h))
+    lo, hi = 1, 400
+    best = None  # (font, size, h)
+    while lo <= hi:
+        mid = (lo + hi) // 2
+        font = ImageFont.truetype(font_path, mid)
+        bb = font.getbbox(probe)
+        h = bb[3] - bb[1]
+        if best is None or abs(h - target_h) < abs(best[2] - target_h):
+            best = (font, mid, h)
+        if h == target_h:
+            break
+        if h < target_h:
+            lo = mid + 1
+        else:
+            hi = mid - 1
+    return best
+
+
+# ---------------------------------------------------------------------------
+# 合成图
+# ---------------------------------------------------------------------------
+def _canvas(h, polarity):
+    from PIL import Image
+
+    pal = _POLARITY[polarity]
+    return Image.new("RGB", (_CANVAS_W, max(_MIN_CANVAS_H, int(h))), pal["bg"])
+
+
+def synth_text_image(height_px, tokens, font_path, *, polarity="dark", left=90):
+    """合成 N 行等高文字（每行一个随机串）。返回 (image, actual_h)。"""
+    from PIL import ImageDraw
+
+    font, _size, actual_h = font_for_ink_height(height_px, font_path)
+    n = len(tokens)
+    pitch = max(actual_h + 3, int(round(actual_h * 3)))
+    block_h = pitch * (n - 1) + actual_h
+    canvas_h = block_h + 4 * actual_h + 60
+    img = _canvas(canvas_h, polarity)
+    draw = ImageDraw.Draw(img)
+    fg = _POLARITY[polarity]["fg"]
+
+    y_top = (img.height - block_h) // 2
+    for i, tok in enumerate(tokens):
+        bb = font.getbbox(tok)
+        # getbbox 的 top 可能为负（字体 ascent 内留白），补偿使 ink 顶对齐 y_top
+        draw.text((left, y_top - bb[1]), tok, font=font, fill=fg)
+        y_top += pitch
+    return img, actual_h
+
+
+def synth_lines_image(spacing_px, count, *, width=1, polarity="dark",
+                      x0=200, x1=1080, dashed=False, dash=(6, 4)):
+    """合成 count 条水平平行线，间距 spacing_px，线宽 width。返回 image。"""
+    from PIL import ImageDraw
+
+    spacing_px = max(1, int(spacing_px))
+    block_h = (count - 1) * spacing_px
+    canvas_h = max(block_h + 8 * max(spacing_px, 4), 2 * block_h + 60)
+    img = _canvas(canvas_h, polarity)
+    draw = ImageDraw.Draw(img)
+    fg = _POLARITY[polarity]["fg"]
+
+    y0 = (img.height - block_h) // 2
+    for i in range(count):
+        y = y0 + i * spacing_px
+        if dashed:
+            _draw_dashed(draw, x0, x1, y, dash[0], dash[1], fg, width)
+        else:
+            draw.line([(x0, y), (x1, y)], fill=fg, width=width)
+    return img
+
+
+def _draw_dashed(draw, x0, x1, y, on, off, color, width):
+    x = x0
+    while x < x1:
+        draw.line([(x, y), (min(x + on, x1), y)], fill=color, width=width)
+        x += on + off
+
+
+def _to_b64(img, max_edge=None):
+    buf = io.BytesIO()
+    img.save(buf, format="PNG")
+    b64 = base64.b64encode(buf.getvalue()).decode("ascii")
+    return downsample_image_b64(b64, "image/png", max_edge=max_edge)
+
+
+# ---------------------------------------------------------------------------
+# VLM 交互
+# ---------------------------------------------------------------------------
+def _extract_json(text):
+    """从模型输出里抠出第一个 JSON 对象（容忍 ```json 包裹与前后噪声）。"""
+    if not text:
+        return None
+    m = re.search(r"\{.*\}", text, re.S)
+    if not m:
+        return None
+    try:
+        return json.loads(m.group(0))
+    except json.JSONDecodeError:
+        return None
+
+
+def ask_vlm(client, img, system, prompt, *, max_edge=None):
+    """发一张图问 VLM，返回解析后的 dict（失败返回 None）。"""
+    b64, mime = _to_b64(img, max_edge)
+    resp = client.chat([
+        {"role": "system", "content": system},
+        {"role": "user", "content": [
+            {"type": "text", "text": prompt},
+            LlmClient.image_content(b64, mime),
+        ]},
+    ])
+    return _extract_json(resp.choices[0].message.content)
+
+
+def _norm_token(s):
+    return re.sub(r"[^A-Z0-9]", "", str(s).upper())
+
+
+def _lenient(s):
+    """宽松归一：常见混淆 0<->O、1<->I<->L。"""
+    s = _norm_token(s)
+    return s.replace("0", "O").replace("1", "I").replace("L", "I")
+
+
+# ---------------------------------------------------------------------------
+# 各子测试
+# ---------------------------------------------------------------------------
+def run_text_level(client, height_px, font_path, polarity, repeat, out_dir, dry_run):
+    rng = random.Random(1000 + height_px)
+    tokens = ["".join(rng.choice(_ALPHABET) for _ in range(6)) for _ in range(5)]
+    img, actual_h = synth_text_image(height_px, tokens, font_path, polarity=polarity)
+    png = out_dir / f"text_h{height_px}_{polarity}.png"
+    img.save(str(png))
+
+    prompt = (
+        "图中是若干行**等高的白色字符**（黑底）。请逐行读出，按从上到下顺序输出 JSON："
+        '{"texts":["第一行","第二行",...]}。无法辨认的行填空字符串。'
+    )
+    if polarity == "light":
+        prompt = prompt.replace("白色字符**（黑底）", "黑色字符**（白底）")
+
+    trials = []
+    if not dry_run:
+        for _ in range(max(1, repeat)):
+            data = ask_vlm(client, img, _SYS_TEXT, prompt)
+            got = list(data.get("texts") or []) if isinstance(data, dict) else []
+            got = [str(x) for x in got]
+            exact = sum(1 for i, t in enumerate(tokens) if i < len(got) and _norm_token(got[i]) == t)
+            lenient = sum(1 for i, t in enumerate(tokens) if i < len(got) and _lenient(got[i]) == _lenient(t))
+            trials.append({"got": got, "exact": exact, "lenient": lenient})
+
+    n = len(tokens)
+    best_exact = max((t["exact"] for t in trials), default=0)
+    return {
+        "height_px": height_px, "actual_h": actual_h, "tokens": tokens,
+        "trials": trials, "n": n, "best_exact": best_exact,
+        "readable": best_exact == n,          # 全部行都对 → 可读
+        "mostly": best_exact >= n - 1,        # 容错 1 行
+        "png": png.name,
+    }
+
+
+def _ask_count(client, img, prompt):
+    data = ask_vlm(client, img, _SYS_LINE, prompt)
+    if not isinstance(data, dict):
+        return None
+    try:
+        return int(data.get("count"))
+    except (TypeError, ValueError):
+        return None
+
+
+def run_line_level(client, spacing, polarity, repeat, out_dir, dry_run):
+    """1 vs 2 **二选一**（比计数稳健得多）：间距 spacing 的两条细线 vs 一条等宽粗线。
+
+    实测该模型**无法可靠计数**（6 条线在 2~10px 间距下答 4/10/4/5/4/4），
+    故线条判据只测「相邻线能否被分辨开」这一最核心的细节能力。
+    """
+    pair = synth_lines_image(spacing, 2, width=1, polarity=polarity)
+    single = synth_lines_image(20, 1, width=max(1, int(spacing)), polarity=polarity)
+    pair_png = out_dir / f"pair_s{spacing}_{polarity}.png"
+    single_png = out_dir / f"single_s{spacing}_{polarity}.png"
+    pair.save(str(pair_png))
+    single.save(str(single_png))
+
+    prompt = (
+        "图中是**水平直线**（黑底白线）。只判断线条数量是 1 还是 2，"
+        '输出 JSON：{"count": 1 或 2}。'
+    )
+    if polarity == "light":
+        prompt = prompt.replace("黑底白线", "白底黑线")
+
+    trials = []
+    if not dry_run:
+        for _ in range(max(1, repeat)):
+            g_pair = _ask_count(client, pair, prompt)
+            g_single = _ask_count(client, single, prompt)
+            trials.append({"pair": g_pair, "single": g_single,
+                           "ok": g_pair == 2 and g_single == 1})
+    return {
+        "spacing": spacing, "truth_pair": 2, "truth_single": 1,
+        "trials": trials, "resolved": any(t["ok"] for t in trials),
+        "png_pair": pair_png.name, "png_single": single_png.name,
+    }
+
+
+def run_dashed_test(client, polarity, repeat, out_dir, dry_run):
+    """虚线 vs 实线识别（两条线，避开计数干扰）。"""
+    out = []
+    for dashed in (False, True):
+        img = synth_lines_image(12, 2, width=1, polarity=polarity,
+                                dashed=dashed, dash=(8, 6))
+        png = out_dir / f"dashed_{'on' if dashed else 'off'}_{polarity}.png"
+        img.save(str(png))
+        prompt = (
+            "图中是两条**水平直线**（黑底白线）。判断它们是否为虚线，"
+            '输出 JSON：{"dashed": true 或 false}。'
+        )
+        if polarity == "light":
+            prompt = prompt.replace("黑底白线", "白底黑线")
+        trials = []
+        if not dry_run:
+            for _ in range(max(1, repeat)):
+                data = ask_vlm(client, img, _SYS_LINE, prompt)
+                got = data.get("dashed") if isinstance(data, dict) else None
+                if isinstance(got, str):
+                    got = got.strip().lower() in ("true", "1", "yes", "是")
+                trials.append({"got_dashed": got, "ok": got is dashed})
+        out.append({"truth_dashed": dashed, "trials": trials,
+                    "correct": any(t["ok"] for t in trials), "png": png.name})
+    return out
+
+
+# ---------------------------------------------------------------------------
+# 主流程
+# ---------------------------------------------------------------------------
+def _parse_ints(s):
+    return [int(x) for x in str(s).split(",") if x.strip()]
+
+
+def main(argv=None):
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--heights", default="4,6,8,10,12,16,20,24", help="文字像素高扫描档")
+    ap.add_argument("--spacings", default="2,3,4,5,6,8,10", help="线间距(px)扫描档")
+    ap.add_argument("--polarity", choices=("dark", "light", "both"), default="dark",
+                    help="背景极性：dark=黑底白线（CAD 模型空间默认），light=白底黑线")
+    ap.add_argument("--repeat", type=int, default=1, help="每档重复次数（取多数/任一成功）")
+    ap.add_argument("--font", default=None, help="TTF 字体路径（默认自动查找等宽字体）")
+    ap.add_argument("--max-edge", type=int, default=None, help="发给 VLM 前长边上限（默认 config）")
+    ap.add_argument("--dry-run", action="store_true", help="只合成图并落盘，不调用 VLM")
+    ap.add_argument("--out", default="reports/_probe/vlm_calib")
+    args = ap.parse_args(argv)
+
+    if not HAS_PIL:
+        raise SystemExit("离线标定需要 Pillow：pip install pillow")
+
+    font_path = find_font_path(args.font)
+    out_dir = Path(config.PROJECT_ROOT) / args.out
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    polarities = ("dark", "light") if args.polarity == "both" else (args.polarity,)
+    client = None
+    if not args.dry_run:
+        client = LlmClient()
+        print(f"模型：{client.model}    字体：{font_path}")
+
+    heights = _parse_ints(args.heights)
+    spacings = _parse_ints(args.spacings)
+    results = {"polarity": list(polarities), "text": {}, "line": {}, "dashed": {}}
+
+    for pol in polarities:
+        print(f"\n===== 极性 {pol} =====")
+
+        print("-- 文字可读性 --")
+        text_rows = []
+        for h in heights:
+            row = run_text_level(client, h, font_path, pol, args.repeat, out_dir, args.dry_run)
+            text_rows.append(row)
+            if args.dry_run:
+                print(f"  字高 {h:>3}px (实际 {row['actual_h']}px) -> {row['png']}")
+            else:
+                print(f"  字高 {h:>3}px (实际 {row['actual_h']}px) 命中 "
+                      f"{row['best_exact']}/{row['n']} 可读={row['readable']}")
+        results["text"][pol] = text_rows
+
+        print("-- 线间距可分辨性（1 vs 2 二选一） --")
+        line_rows = []
+        for s in spacings:
+            row = run_line_level(client, s, pol, args.repeat, out_dir, args.dry_run)
+            line_rows.append(row)
+            if args.dry_run:
+                print(f"  间距 {s:>3}px -> {row['png_pair']} / {row['png_single']}")
+            else:
+                print(f"  间距 {s:>3}px 可分辨={row['resolved']} {row['trials']}")
+        results["line"][pol] = line_rows
+
+        print("-- 虚线识别 --")
+        d = run_dashed_test(client, pol, args.repeat, out_dir, args.dry_run)
+        results["dashed"][pol] = d
+        if not args.dry_run:
+            for item in d:
+                print(f"  虚线={item['truth_dashed']} 识别正确={item['correct']}")
+
+    # 汇总（取两极性中更优者作为"区间参考"）
+    summary = {}
+    for pol in polarities:
+        text_rows = results["text"].get(pol, [])
+        line_rows = results["line"].get(pol, [])
+        r_text = next((r["height_px"] for r in text_rows if r["readable"]), None)
+        r_text_mostly = next((r["height_px"] for r in text_rows if r["mostly"]), None)
+        r_line = next((r["spacing"] for r in line_rows if r["resolved"]), None)
+        summary[pol] = {"R_text": r_text, "R_text_mostly": r_text_mostly, "R_line": r_line}
+        print(f"\n[{pol}] R_text(全部行正确)={r_text}  R_text(容错1行)={r_text_mostly}  "
+              f"R_line={r_line}")
+
+    results["summary"] = summary
+    (out_dir / "summary.json").write_text(
+        json.dumps(results, ensure_ascii=False, indent=2), encoding="utf-8"
+    )
+    print(f"\n输出目录: {out_dir}")
+    print("注意：合成图偏干净，结论偏乐观，仅作区间；以 CAD 实测 (verify_text_resolution.py) 为准。")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())