增加一些设置。

faiss_vector_db/build_faiss_index.py

@@ -133,10 +133,12 @@ class FAISSIndexBuilder:
             print(f"类别 '{class_name}': {len(image_files)} 张图片")
             
             for image_file in image_files:
-                image_path = os.path.join(class_dir, image_file)
-                image_paths.append(image_path)
-                class_names.append(class_name)
-                labels.append(class_idx)
+                # 可以不添加那些增强的图片
+                if image_file.startswith("img"):
+                    image_path = os.path.join(class_dir, image_file)
+                    image_paths.append(image_path)
+                    class_names.append(class_name)
+                    labels.append(class_idx)
         
         print(f"总计扫描到 {len(image_paths)} 张图片")
         return image_paths, class_names, labels
@@ -477,9 +479,13 @@ class FAISSSearcher:
 def main():
     """主函数"""
     # 配置参数
-    MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth"
-    TRAIN_DIR = "../dataset/train"
-    OUTPUT_DIR = "faiss_index"
+    # MODEL_PATH = "../model/embedding_20251011_133653/best_embedding_model.pth"
+    MODEL_PATH = "../model/WholeIngredientRecognition/embedding_20251017_145836/best_embedding_model.pth"
+    # MODEL_PATH = "../model/DishClassification/embedding_20251011_133653/best_embedding_model.pth"
+    TRAIN_DIR = "../dataset/WholeIngredientRecognition/train"
+    # TRAIN_DIR = "../dataset/DishClassification/train"
+    OUTPUT_DIR = "WholeIngredientRecognition/faiss_index"
+    # OUTPUT_DIR = "DishClassification/faiss_index"
     BATCH_SIZE = 16
     INDEX_TYPE = 'flat'  # 'flat', 'ivf', 'hnsw'
     EMBEDDING_DIM = 512