过滤手和空盘类别

core/common_classifier.py

@@ -511,6 +511,31 @@ class PartTemplateManager:
             return True, "·软阈值"
         return False, ""
 
+    @staticmethod
+    def _match_filter_names():
+        """模板比对后需丢弃的菜名（如手、空盘）。"""
+        try:
+            from dao.settings import DISH_MATCH_FILTER_NAMES
+            return {str(n).strip() for n in (DISH_MATCH_FILTER_NAMES or []) if str(n).strip()}
+        except Exception:
+            return {"手", "空盘"}
+
+    def _filtered_match_result(self, name, score, feat, top_k=None, scope="filtered"):
+        print(f"[match] filtered template hit: {name} score={score}")
+        return {
+            "text": f"已过滤：{name}",
+            "is_unknown": False,
+            "is_filtered": True,
+            "label": None,
+            "score": float(score) if score is not None else None,
+            "suggested_category": name,
+            "filtered_as": name,
+            "top_k": top_k or [],
+            "pending_id": None,
+            "feature": feat,
+            "match_scope": scope,
+        }
+
     def predict_from_feature(self, feat, image_path=None, auto_pending=True):
         """已有特征时直接匹配（避免待学习提交时重复提特征）。
 
@@ -567,25 +592,53 @@ class PartTemplateManager:
         if period_only:
             staple_names = self.staple_candidate_names()
             period_set = set(period_names or [])
-            allowed = set(staple_names) | period_set
+            filter_names = self._match_filter_names()
+            # 过滤类若本地有模板，纳入候选：先比中再丢弃，避免误认成其他菜
+            with self.lock:
+                lib_names = {str(k).strip() for k in (self.template_library or {}).keys() if str(k).strip()}
+            filter_in_lib = filter_names & lib_names
+            allowed = set(staple_names) | period_set | filter_in_lib
             ranked, _, _ = (
                 self._rank_categories(feat, allowed_names=allowed)
                 if allowed else ([], None, -1.0)
             )
 
-            # 从合并结果里拆出主食 / 餐段各自最高分
+            # 从合并结果里拆出主食 / 餐段 / 过滤类各自最高分
             best_st, max_st = None, -1.0
             best_p, max_p = None, -1.0
+            best_f, max_f = None, -1.0
             for name, score in ranked:
                 sc = float(score)
+                if name in filter_in_lib and sc > max_f:
+                    best_f, max_f = name, sc
                 if name in staple_names and sc > max_st:
                     best_st, max_st = name, sc
                 if name in period_set and sc > max_p:
                     best_p, max_p = name, sc
 
+            # 过滤类达标：直接丢弃该框（不再与菜品比优先）
+            f_hit, _ = self._score_hit(best_f, max_f, staple_thr, staple_soft)
+            # 过滤类用与主食相同阈值；也可用餐段阈值取较松者
+            if not f_hit:
+                f_hit, _ = self._score_hit(best_f, max_f, small_thr, soft_thr)
+
             st_hit, st_how = self._score_hit(best_st, max_st, staple_thr, staple_soft)
             p_hit, p_how = self._score_hit(best_p, max_p, small_thr, soft_thr)
 
+            top_k = [{"category": n, "score": s} for n, s in (ranked or [])[:3]]
+
+            if f_hit and best_f is not None:
+                # 过滤类分不低于当前菜/主食命中分 → 认定应丢弃
+                other_best = -1.0
+                if st_hit:
+                    other_best = max(other_best, float(max_st))
+                if p_hit:
+                    other_best = max(other_best, float(max_p))
+                if float(max_f) >= other_best - float(staple_margin):
+                    return self._filtered_match_result(
+                        str(best_f).strip(), float(max_f), feat, top_k=top_k, scope="filtered",
+                    )
+
             chosen = None
             how = ""
             scope = "period_miss"
@@ -597,10 +650,12 @@ class PartTemplateManager:
             elif st_hit:
                 chosen, how, scope = best_st, f"主食+餐段{st_how}", "staple"
 
-            top_k = [{"category": n, "score": s} for n, s in (ranked or [])[:3]]
-
             if chosen is not None:
                 score = float(max_st) if scope == "staple" else float(max_p)
+                if str(chosen).strip() in filter_names:
+                    return self._filtered_match_result(
+                        str(chosen).strip(), score, feat, top_k=top_k, scope=scope,
+                    )
                 text = f"{chosen} 相似度：{score:.3f}（{how}）"
                 return {
                     "text": text,
@@ -615,11 +670,19 @@ class PartTemplateManager:
                 }
 
             sug, sug_score = None, -1.0
+            if best_f is not None and float(max_f) > sug_score:
+                sug, sug_score = best_f, float(max_f)
             if best_st is not None and float(max_st) > sug_score:
                 sug, sug_score = best_st, float(max_st)
             if best_p is not None and float(max_p) > sug_score:
                 sug, sug_score = best_p, float(max_p)
 
+            # 最接近手/空盘：整框丢弃，不进未知/待学习
+            if sug is not None and str(sug).strip() in filter_names:
+                return self._filtered_match_result(
+                    str(sug).strip(), float(sug_score), feat, top_k=top_k, scope="period_miss",
+                )
+
             pending_id = None
             if auto_pending and image_path:
                 pending_id = self.pending_store.add(image_path, feat, sug, sug_score)

core/pending_learning_ui.py

@@ -738,6 +738,7 @@ class PendingLearningUIMixin:
         final_result = ""
         dish_labels = []
         dishes = []
+        kept_boxes = []
         unknown_count = 0
         for i in range(n):
             if progress_callback:
@@ -750,12 +751,17 @@ class PendingLearningUIMixin:
                     image_path=img_path if isinstance(img_path, str) else None,
                     auto_pending=auto_pending,
                 )
+                if detail.get("is_filtered"):
+                    print(f"[match] skip dish box filtered_as={detail.get('filtered_as')}")
+                    continue
                 if detail.get("is_unknown"):
                     unknown_count += 1
+                idx = len(dishes) + 1
                 dish_labels.append(format_dish_box_label(detail))
-                final_result += f"【菜品{i + 1}】{format_predict_detail(detail)}\n"
+                final_result += f"【菜品{idx}】{format_predict_detail(detail)}\n"
+                kept_boxes.append(box)
                 dishes.append({
-                    "index": i + 1,
+                    "index": idx,
                     "label": detail.get("label"),
                     "score": detail.get("score"),
                     "is_unknown": bool(detail.get("is_unknown")),
@@ -768,10 +774,12 @@ class PendingLearningUIMixin:
                     "feature": detail.get("feature"),
                 })
             except Exception as e:
+                idx = len(dishes) + 1
                 dish_labels.append("识别失败")
-                final_result += f"【菜品{i + 1}】加载/识别失败：{e}\n"
+                final_result += f"【菜品{idx}】加载/识别失败：{e}\n"
+                kept_boxes.append(box)
                 dishes.append({
-                    "index": i + 1,
+                    "index": idx,
                     "label": None,
                     "score": None,
                     "is_unknown": True,
@@ -792,7 +800,7 @@ class PendingLearningUIMixin:
                     (d.get("label") or d.get("suggested_category") or f"菜品{d.get('index') or i + 1}")
                     for i, d in enumerate(dishes)
                 ]
-                labeled_path = self.build_dish_labeled_image(source_image_path, boxes, viz_labels)
+                labeled_path = self.build_dish_labeled_image(source_image_path, kept_boxes, viz_labels)
                 _mark("batch: draw_labels done")
             except Exception as e:
                 _mark(f"batch: draw_labels fail {e}")

dao/settings.py

@@ -125,11 +125,13 @@ DISH_LIVE_ORDER_SAME_SCENE_FRAMES = int(os.environ.get('DISH_LIVE_ORDER_SAME_SCE
 DISH_LIVE_ORDER_LAYOUT_IOU = float(os.environ.get('DISH_LIVE_ORDER_LAYOUT_IOU', '0.40'))
 DISH_LIVE_ORDER_LAYOUT_MATCH_RATIO = float(os.environ.get('DISH_LIVE_ORDER_LAYOUT_MATCH_RATIO', '0.70'))
 
-# YOLO 检测需直接丢弃的类别（不裁剪、不识别、不上报），逗号分隔
+# 模板库比对后需丢弃的菜名（YOLO 无此类时仍生效）：命中「手」「空盘」等则整框过滤，不入清单/不上报
+# 环境变量名沿用 DISH_DETECT_FILTER_NAMES
 DISH_DETECT_FILTER_NAMES = [
     x.strip() for x in os.environ.get('DISH_DETECT_FILTER_NAMES', '手,空盘').split(',')
     if x.strip()
 ]
+DISH_MATCH_FILTER_NAMES = DISH_DETECT_FILTER_NAMES
 # 贴边不完整框过滤：托盘刚入画只露一角时 YOLO 常会检出，直接丢弃
 # 框任一边缘距画面边界 ≤ margin 像素，视为贴边
 DISH_DETECT_EDGE_MARGIN_PX = int(os.environ.get('DISH_DETECT_EDGE_MARGIN_PX', '8'))

ui/pending_learning_ui.py

@@ -1041,6 +1041,7 @@ class PendingLearningUIMixin:
         final_result = ""
         dish_labels = []
         dishes = []
+        kept_boxes = []
         unknown_count = 0
         for i in range(n):
             if progress_callback:
@@ -1054,6 +1055,11 @@ class PendingLearningUIMixin:
                     image_path=img_path if isinstance(img_path, str) else None,
                     auto_pending=auto_pending,
                 )
+                # 模板命中手/空盘等：整框丢弃
+                if detail.get("is_filtered"):
+                    filtered_as = detail.get("filtered_as") or detail.get("suggested_category")
+                    print(f"[match] skip dish box filtered_as={filtered_as}")
+                    continue
                 if detail.get("is_unknown"):
                     unknown_count += 1
                 match_scope = detail.get("match_scope")
@@ -1064,10 +1070,12 @@ class PendingLearningUIMixin:
                 label_src["det_conf"] = det_conf
                 label_src["in_today_menu"] = in_menu
                 label_src["match_scope"] = match_scope
+                idx = len(dishes) + 1
                 dish_labels.append(format_dish_box_label(label_src))
-                final_result += f"【菜品{i + 1}】{format_predict_detail(detail)}\n"
+                final_result += f"【菜品{idx}】{format_predict_detail(detail)}\n"
+                kept_boxes.append(box)
                 dishes.append({
-                    "index": i + 1,
+                    "index": idx,
                     "label": detail.get("label"),
                     "score": detail.get("score"),
                     "det_conf": det_conf,
@@ -1083,10 +1091,12 @@ class PendingLearningUIMixin:
                     "in_today_menu": in_menu,
                 })
             except Exception as e:
+                idx = len(dishes) + 1
                 dish_labels.append("识别失败")
-                final_result += f"【菜品{i + 1}】加载/识别失败：{e}\n"
+                final_result += f"【菜品{idx}】加载/识别失败：{e}\n"
+                kept_boxes.append(box)
                 dishes.append({
-                    "index": i + 1,
+                    "index": idx,
                     "label": None,
                     "score": None,
                     "det_conf": det_conf,
@@ -1109,7 +1119,7 @@ class PendingLearningUIMixin:
             ]
             viz_styles = [dish_overlay_style(d) for d in dishes]
             labeled_path = self.build_dish_labeled_image(
-                source_image_path, boxes, viz_labels, styles=viz_styles,
+                source_image_path, kept_boxes, viz_labels, styles=viz_styles,
             )
         except Exception:
             labeled_path = None