增加一些设置。

faiss_vector_db/README_FAISS.md

@@ -111,7 +111,7 @@ builder = FAISSIndexBuilder(
 # 构建完整索引
 index = builder.build_complete_index(
     train_dir="../dataset/train",
-    output_dir="faiss_index",
+    output_dir="DishClassification/faiss_index",
     batch_size=16,
     index_type='flat'  # 'flat', 'ivf', 'hnsw'
 )

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

faiss_vector_db/run_faiss_demo.py

@@ -87,7 +87,7 @@ def run_search_demo():
         from faiss_vector_db.build_faiss_index import FAISSSearcher
         
         # 配置参数
-        index_dir = "faiss_index"
+        index_dir = "DishClassification/faiss_index"
         model_path = "../model/embedding_20250917_145342/best_embedding_model.pth"
         
         # 检查索引是否存在
@@ -206,7 +206,7 @@ def main():
     print("✓ 文件检查通过")
     
     # 检查是否已有索引
-    index_exists = os.path.exists("faiss_index/faiss_index.bin")
+    index_exists = os.path.exists("DishClassification/faiss_index/faiss_index.bin")
     
     if index_exists:
         print("✓ 发现已存在的FAISS索引")

faiss_vector_db/visualize_embeddings.py

@@ -162,8 +162,10 @@ def plot_2d(Z: np.ndarray, y: np.ndarray, title: str, out_path: Optional[str] =
 
 def main():
     parser = argparse.ArgumentParser(description="可视化高维 embedding 并进行坍塌诊断")
-    parser.add_argument("--embeddings", type=str, default=os.path.join( "faiss_index", "embeddings.json"), help="embeddings.json 路径")
-    parser.add_argument("--labels", type=str, default=os.path.join("faiss_index", "labels.json"), help="labels.json 路径")
+    # parser.add_argument("--embeddings", type=str, default=os.path.join("DishClassification/faiss_index", "embeddings.json"), help="embeddings.json 路径")
+    parser.add_argument("--embeddings", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "embeddings.json"), help="embeddings.json 路径")
+    # parser.add_argument("--labels", type=str, default=os.path.join("DishClassification/faiss_index", "labels.json"), help="labels.json 路径")
+    parser.add_argument("--labels", type=str, default=os.path.join("WholeIngredientRecognition/faiss_index", "labels.json"), help="labels.json 路径")
     parser.add_argument("--method", type=str, default="pca", choices=["pca", "tsne", "umap"], help="降维方法")
     parser.add_argument("--seed", type=int, default=42)
     parser.add_argument("--max_points", type=int, default=None, help="抽样上限，避免t-SNE/UMAP过慢；None为全量")