feat(detect): 全量推理可视化，预测框与真值框画在一起

scripts/visualize_detections.py：在整个数据集上推理，绿=真值、蓝=命中的预测、
红=没对上真值的预测，按结果类型分目录（ok / missed / extra / none），再配一份
index.csv（逐图的真值数、预测数、命中数、最高置信度）与 summary.json（逐类 TP/FP/FN）。
--only errors 只输出有问题的图。

匹配逻辑放在 engine/detection.py：按置信度贪心、IoU 阈值、类别需一致，与 mAP 的规则
一致，所以图上看到的漏检误检和指标算的是同一回事。same_class=False 可以单看定位准不准
——定位对但分类错，和完全没找到是两种问题。

train 与 val 分开放：训练集的图模型见过，那部分结果不能当泛化能力看。

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

engine/detection.py

@@ -0,0 +1,131 @@
+"""检测结果的匹配与绘制：把预测框和真值框放在一起看。
+
+匹配用标准的贪心策略——预测按置信度从高到低，each 认领 IoU 最高且未被认领的真值框。
+这与 mAP 的匹配规则一致，所以图上看到的"漏检/误检"和指标算的是同一回事。
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import NamedTuple
+
+#: 真值绿、命中的预测蓝、未命中的预测红。红绿对比最刺眼，留给"错"的那一类
+COLOR_GT = (0, 220, 0)
+COLOR_MATCHED = (0, 160, 255)
+COLOR_UNMATCHED = (255, 40, 40)
+
+
+class Box(NamedTuple):
+    """归一化的 xyxy 框。`score` 仅预测框有。"""
+
+    class_id: int
+    x1: float
+    y1: float
+    x2: float
+    y2: float
+    score: float | None = None
+
+
+def from_yolo(class_id: int, cx: float, cy: float, width: float, height: float) -> Box:
+    """YOLO 的中心点记法 -> xyxy。"""
+    return Box(class_id, cx - width / 2, cy - height / 2, cx + width / 2, cy + height / 2)
+
+
+def parse_label(text: str) -> list[Box]:
+    """读一个 YOLO 标签文件的内容；空文件返回空列表（负样本）。"""
+    boxes = []
+    for line in text.splitlines():
+        parts = line.split()
+        if len(parts) == 5:
+            boxes.append(from_yolo(int(parts[0]), *(float(v) for v in parts[1:])))
+    return boxes
+
+
+def iou(a: Box, b: Box) -> float:
+    """两个框的交并比。不相交时为 0。"""
+    left, top = max(a.x1, b.x1), max(a.y1, b.y1)
+    right, bottom = min(a.x2, b.x2), min(a.y2, b.y2)
+    if right <= left or bottom <= top:
+        return 0.0
+    overlap = (right - left) * (bottom - top)
+    area_a = max(a.x2 - a.x1, 0) * max(a.y2 - a.y1, 0)
+    area_b = max(b.x2 - b.x1, 0) * max(b.y2 - b.y1, 0)
+    union = area_a + area_b - overlap
+    return overlap / union if union > 0 else 0.0
+
+
+class Match(NamedTuple):
+    """一张图的匹配结果。下标分别指向 predictions 与 ground_truth。"""
+
+    pairs: list[tuple[int, int]]        # (预测下标, 真值下标)
+    unmatched_predictions: list[int]    # 误检
+    unmatched_ground_truth: list[int]   # 漏检
+
+
+def match_boxes(
+    predictions: Sequence[Box],
+    ground_truth: Sequence[Box],
+    iou_threshold: float = 0.5,
+    same_class: bool = True,
+) -> Match:
+    """贪心匹配，与 mAP 的规则一致。
+
+    Args:
+        iou_threshold: 低于它不算命中。
+        same_class: 是否要求类别一致。设 False 可以单看"位置对不对"——
+            定位准但分类错，和完全没找到是两种问题，值得分开看。
+    """
+    order = sorted(range(len(predictions)), key=lambda i: -(predictions[i].score or 0.0))
+    taken: set[int] = set()
+    pairs = []
+    unmatched_predictions = []
+    for index in order:
+        prediction = predictions[index]
+        best, best_iou = None, iou_threshold
+        for candidate, truth in enumerate(ground_truth):
+            if candidate in taken:
+                continue
+            if same_class and truth.class_id != prediction.class_id:
+                continue
+            overlap = iou(prediction, truth)
+            if overlap >= best_iou:
+                best, best_iou = candidate, overlap
+        if best is None:
+            unmatched_predictions.append(index)
+        else:
+            taken.add(best)
+            pairs.append((index, best))
+    return Match(
+        sorted(pairs),
+        sorted(unmatched_predictions),
+        [i for i in range(len(ground_truth)) if i not in taken],
+    )
+
+
+def draw_boxes(image, ground_truth: Sequence[Box], predictions: Sequence[Box],
+               classes: Sequence[str], match: Match, width: int = 2):
+    """把真值与预测画在图上（就地修改传入的 PIL 图像并返回）。
+
+    真值绿色、命中的预测蓝色、未命中的预测红色；预测标注类别与置信度。
+    """
+    from PIL import ImageDraw
+
+    draw = ImageDraw.Draw(image)
+    w, h = image.size
+    matched = {index for index, _ in match.pairs}
+
+    def rectangle(box: Box, color, label: str | None) -> None:
+        xy = [box.x1 * w, box.y1 * h, box.x2 * w, box.y2 * h]
+        draw.rectangle(xy, outline=color, width=width)
+        if label:
+            # 文字画在框上方，贴边时改画到框内，免得被裁掉
+            y = xy[1] - 11 if xy[1] > 12 else xy[1] + 1
+            draw.text((xy[0] + 1, y), label, fill=color)
+
+    for box in ground_truth:
+        rectangle(box, COLOR_GT, None)
+    for index, box in enumerate(predictions):
+        color = COLOR_MATCHED if index in matched else COLOR_UNMATCHED
+        name = classes[box.class_id] if box.class_id < len(classes) else str(box.class_id)
+        rectangle(box, color, f"{name} {box.score:.2f}" if box.score is not None else name)
+    return image