增加多模态模型开放集识别。

exp_multimodal/exp_multimodal2.py

@@ -10,8 +10,11 @@ if __name__ == "__main__" and __package__ is None:
 
 # 统一使用绝对导入，避免相对导入在脚本直跑时失败
 from exp_multimodal.labels import build_labels
-from exp_multimodal.vlm_classifier import classify_image
+from exp_multimodal.vlm_classifier import classify_image, classify_image_openset
 from exp_multimodal.ollama_client import OLLAMA_URL, DEFAULT_MODEL
+from exp_multimodal.text_embedder import OllamaEmbedder
+from exp_multimodal.vector_matcher import DishNameMatcher
+from exp_multimodal.vlm_providers.ollama_provider import OllamaVLMProvider
 
 
 FEWSHOT_HINTS: Dict[str, str] = {
@@ -24,9 +27,14 @@ FEWSHOT_HINTS: Dict[str, str] = {
 
 def main() -> None:
     ap = argparse.ArgumentParser()
-    ap.add_argument("--mode", choices=["dish", "whole", "processed"], default="dish")
+    ap.add_argument("--mode", choices=["dish", "whole", "processed", "openset_dish"], default="openset_dish")
     ap.add_argument("--image", default=r"D:\MyProjects\PythonProjects\FoodClassifier\dataset\DishClassification\test\红烧肉\img04.png")
     ap.add_argument("--alias_map", default=None)
+    
+    # 开放式识别专用参数
+    ap.add_argument("--openset-top-k", type=int, default=3, help="开放式识别：向量检索Top-K候选数")
+    ap.add_argument("--openset-min-score", type=float, default=0.5, help="开放式识别：最低匹配分数阈值")
+    ap.add_argument("--openset-index-path", default="exp_multimodal/dish_name_index", help="开放式识别：向量索引路径")
 
     args = ap.parse_args()
 
@@ -37,24 +45,59 @@ def main() -> None:
     )
     print(f"[Main] Image exists={os.path.exists(args.image)} size={os.path.getsize(args.image) if os.path.exists(args.image) else 'N/A'}")
 
-    labels = build_labels(args.mode, args.alias_map)
-    ingredient_only = args.mode in {"whole", "processed"}
-    fewshot = FEWSHOT_HINTS if args.mode == "dish" else None
-    print(f"[Main] Built labels count={len(labels)} ingredient_only={ingredient_only} fewshot={bool(fewshot)}")
+    # 开放式识别分支
+    if args.mode == "openset_dish":
+        print(f"[Main] Openset mode: top_k={args.openset_top_k} min_score={args.openset_min_score} index_path={args.openset_index_path}")
+        
+        # 初始化Provider、Embedder和Matcher
+        provider = OllamaVLMProvider(base_url=OLLAMA_URL, model_name=DEFAULT_MODEL)
+        embedder = OllamaEmbedder(base_url=OLLAMA_URL, model_name="bge-large-zh-v1.5")
+        matcher = DishNameMatcher(index_dir=args.openset_index_path)
+        
+        print("[Main] Loading openset components...")
+        matcher.load()
+        print(f"[Main] Loaded {len(matcher.dish_names)} dish names from index")
+        
+        res = classify_image_openset(
+            image_path=args.image,
+            provider=provider,
+            embedder=embedder,
+            matcher=matcher,
+            top_k=args.openset_top_k,
+            min_match_score=args.openset_min_score,
+        )
+        
+        print(
+            json.dumps(
+                {"mode": args.mode, "result": res},
+                ensure_ascii=False,
+                indent=2,
+            )
+        )
+    
+    # 封闭式识别分支（原有逻辑）
+    else:
+        labels = build_labels(args.mode, args.alias_map)
+        ingredient_only = args.mode in {"whole", "processed"}
+        fewshot = FEWSHOT_HINTS if args.mode == "dish" else None
+        print(f"[Main] Built labels count={len(labels)} ingredient_only={ingredient_only} fewshot={bool(fewshot)}")
 
-    res = classify_image(
-        args.image,
-        labels,
-        fewshot_hints=fewshot,
-        ingredient_only=ingredient_only,
-    )
+        provider = OllamaVLMProvider(base_url=OLLAMA_URL, model_name=DEFAULT_MODEL)
+        
+        res = classify_image(
+            args.image,
+            labels,
+            provider=provider,
+            fewshot_hints=fewshot,
+            ingredient_only=ingredient_only,
+        )
 
-    print(
-        json.dumps(
-            {"mode": args.mode, "result": res, "num_labels": len(labels)},
-            ensure_ascii=False,
+        print(
+            json.dumps(
+                {"mode": args.mode, "result": res, "num_labels": len(labels)},
+                ensure_ascii=False,
+            )
         )
-    )
 
 
 if __name__ == "__main__":