对于类别中样本超过30个的采用K-Means算法截取前30个，存入向量数据库

classifier/embedding_food_classifier_app.py

@@ -71,7 +71,7 @@ class EmbeddingFoodClassifierApp:
             # model_path = os.path.join(BASE_DIR, "../model/ProcessedIngredientRecognition/embedding_20251029_170904/best_embedding_model.pth")
             # model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/cosface_20251106_134718/best_cosface_model.pth")
             # model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/grid_search_20251204_140816/model_s64.0_m0.4.pth")
-            model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/grid_search_20260302_092600/best_model_s64.0_m0.4.pth")
+            model_path = os.path.join(BASE_DIR, "../model/WholeIngredientRecognition/grid_search_20260302_182102/best_model_s60.0_m0.42.pth")
             # model_path = os.path.join(BASE_DIR, "../model/DishClassification/cosface_20251111_153649/best_cosface_model.pth")
 
             # FAISS索引目录

faiss_vector_db/build_faiss_index.py

@@ -317,8 +317,89 @@ class FAISSIndexBuilder:
 
         print(f"特征向量已保存到: {embeddings_path}")
     
+    def select_representative_samples(
+            self,
+            embeddings: np.ndarray,
+            image_paths: List[str],
+            labels: List[int],
+            max_per_class: int = 30) -> Tuple[np.ndarray, List[str], List[int]]:
+        """
+        使用 K-Means 为每个类别筛选代表性样本，消除样本数量不均衡带来的密度偏差。
+
+        原理：
+          - 样本数 <= max_per_class：全部保留
+          - 样本数 >  max_per_class：对该类向量做 K-Means(k=max_per_class)
+            然后为每个聚类中心找最近的真实图片，只保留这些代表性样本
+
+        Args:
+            embeddings   : 全量嵌入向量数组 (N, dim)
+            image_paths  : 图片路径列表
+            labels       : 标签索引列表
+            max_per_class: 每类最多保留的样本数（默认 30）
+
+        Returns:
+            Tuple[np.ndarray, List[str], List[int]]: 筛选后的 (embeddings, paths, labels)
+        """
+        print(f"\n{'='*60}")
+        print(f"K-Means 代表性样本筛选 (max_per_class={max_per_class})")
+        print(f"{'='*60}")
+
+        embeddings_array = embeddings if isinstance(embeddings, np.ndarray) else np.array(embeddings)
+        labels_array = np.array(labels)
+        selected_global_indices: List[int] = []
+
+        for class_idx in np.unique(labels_array):
+            class_name = self.idx_to_class[int(class_idx)]
+            global_indices = np.where(labels_array == class_idx)[0]
+            count = len(global_indices)
+
+            if count <= max_per_class:
+                # 样本数不超过上限，全部保留
+                selected_global_indices.extend(global_indices.tolist())
+                print(f"  [{class_name}]: {count} 张, 全部保留")
+            else:
+                # 样本数超过上限 -> K-Means 聚类后选代表
+                class_embs = embeddings_array[global_indices].astype(np.float32)
+                dim = class_embs.shape[1]
+                k   = max_per_class
+
+                # 1. 用 FAISS 内置 K-Means 聚类（无需 sklearn）
+                kmeans = faiss.Kmeans(dim, k, niter=30, verbose=False)
+                kmeans.train(class_embs)
+                centroids = kmeans.centroids  # shape: (k, dim)
+
+                # 2. 为每个幽灵中心，找离它最近的真实图片（幽灵变真身）
+                temp_index = faiss.IndexFlatL2(dim)
+                temp_index.add(class_embs)
+                _, nearest_local = temp_index.search(centroids, 1)  # (k, 1)
+                nearest_local = nearest_local[:, 0].tolist()
+
+                # 3. 去重（极少情况下两个中心可能最近邻相同）
+                unique_local = list(set(nearest_local))
+
+                # 4. 映射回全局索引
+                selected_global = global_indices[unique_local].tolist()
+                selected_global_indices.extend(selected_global)
+                print(f"  [{class_name}]: {count} 张 -> K-Means 筛选为 {len(selected_global)} 张")
+
+        # 按索引排序，确保顺序稳定
+        selected_global_indices = sorted(selected_global_indices)
+
+        filtered_embeddings = embeddings_array[selected_global_indices]
+        filtered_paths  = [image_paths[i]  for i in selected_global_indices]
+        filtered_labels = [labels[i]        for i in selected_global_indices]
+
+        total_before = len(image_paths)
+        total_after  = len(filtered_paths)
+        print(f"\nK-Means 筛选完成: {total_before} -> {total_after} 个向量"
+              f"（减少了 {total_before - total_after} 个）")
+        print(f"{'='*60}\n")
+
+        return filtered_embeddings, filtered_paths, filtered_labels
+
     def build_complete_index(self, train_dir: str, output_dir: str,
-                           batch_size: int = 32, index_type: str = 'flat'):
+                           batch_size: int = 32, index_type: str = 'flat',
+                           max_per_class: int = 30):
         """
         完整的索引构建流程
         
@@ -329,30 +410,36 @@ class FAISSIndexBuilder:
             index_type: 索引类型
         """
         print("=" * 60)
-        print("开始构建FAISS索引")
+        print("\u5f00始构建 FAISS 索引")
         print("=" * 60)
 
-        # 1. 扫描训练数据
+        # 1. 扫描训练数据（全量，不做任何过滤）
         image_paths, class_names, labels = self.scan_training_data(train_dir)
+
+        # 2. 提取全量特征向量（K-Means 聚类需要看到所有向量）
+        embeddings = self.extract_features_batch(image_paths, batch_size)
+
+        # 3. K-Means 代表性样本筛选（每类最多保留 max_per_class 个向量）
+        embeddings, image_paths, labels = self.select_representative_samples(
+            embeddings, image_paths, labels, max_per_class
+        )
         self.image_paths = image_paths
         self.labels = labels
-        
-        # 2. 提取特征向量
-        embeddings = self.extract_features_batch(image_paths, batch_size)
         self.embeddings = embeddings
 
-        # 3. 构建FAISS索引
+        # 4. 构建 FAISS 索引（使用筛选后的向量）
         index = self.build_faiss_index(embeddings, index_type)
 
-        # 4. 保存索引和元数据
+        # 5. 保存索引和元数据
         self.save_index_and_metadata(index, output_dir)
 
         print("=" * 60)
-        print("FAISS索引构建完成!")
+        print("FAISS 索引构建完成!")
-        print(f"输出目录: {output_dir}")
+        print(f"输出目录    : {output_dir}")
-        print(f"总图片数: {len(image_paths)}")
+        print(f"入库向量数  : {len(image_paths)}（K-Means 筛选后）")
-        print(f"类别数: {len(self.class_names)}")
+        print(f"类别数      : {len(self.class_names)}")
-        print(f"特征维度: {self.embedding_dim}")
+        print(f"每类上限    : {max_per_class} 张")
+        print(f"特征维度    : {self.embedding_dim}")
         print("=" * 60)
 
         return index
@@ -564,7 +651,8 @@ def main():
     #MODEL_PATH = "../model/WholeIngredientRecognition/cosface_20260121_133207/best_cosface_model.pth"
     #grid_search最佳模型 s=68, m=0.42 1.22
     # MODEL_PATH = "../model/WholeIngredientRecognition/grid_search_20260122_112545/best_model_s68.0_m0.42.pth"
-    MODEL_PATH = "../model/WholeIngredientRecognition/grid_search_20260302_092600/best_model_s64.0_m0.4.pth"
+    # MODEL_PATH = "../model/WholeIngredientRecognition/grid_search_20260302_092600/best_model_s64.0_m0.4.pth"
+    MODEL_PATH = "../model/WholeIngredientRecognition/grid_search_20260302_182102/best_model_s60.0_m0.42.pth"
     # MODEL_PATH = "../model/DishClassification/grid_search_20251121_102723/model_s56.0_m0.4.pth"
     # MODEL_PATH = "../model/DishClassification/cosface_20251111_153649/best_cosface_model.pth"
     # TRAIN_DIR = "../dataset/ProcessedIngredientRecognition/train"
@@ -575,8 +663,9 @@ def main():
     OUTPUT_DIR = "WholeIngredientRecognition/faiss_index"
     # OUTPUT_DIR = "DishClassification/faiss_index"
     BATCH_SIZE = 16
-    INDEX_TYPE = 'flat'  # 'flat', 'ivf', 'hnsw'
+    INDEX_TYPE = 'flat'      # 'flat', 'ivf', 'hnsw'
     EMBEDDING_DIM = 512
+    MAX_PER_CLASS = 30       # 每类最多保留的代表性样本数（K-Means 筛选上限）
 
     try:
         # 构建索引
@@ -585,7 +674,8 @@ def main():
             train_dir=TRAIN_DIR,
             output_dir=OUTPUT_DIR,
             batch_size=BATCH_SIZE,
-            index_type=INDEX_TYPE
+            index_type=INDEX_TYPE,
+            max_per_class=MAX_PER_CLASS
         )
         
         print("\n" + "=" * 60)

toAndroid/toAndroidEmbedding.py

@@ -19,10 +19,8 @@ def main():
     base_model = create_mobile_resnet50_embedding(embedding_dim=512, pretrained=True)
     # model_path = "../model/DishClassification/grid_search_20251121_102723/model_s56.0_m0.4.pth"
     # model_path = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_cosface_model.pth"
-    #原本路径，原本模型
+    model_path = "../model/WholeIngredientRecognition/grid_search_20260302_182102/best_model_s60.0_m0.42.pth"
-    #model_path = "../model/WholeIngredientRecognition/grid_search_20251204_140816/model_s64.0_m0.4.pth"
+    # model_path = "../model/WholeIngredientRecognition/cosface_20260121_133207/best_cosface_model.pth"
-    #新路径，新模型 （模型不可用）
-    model_path = "../model/WholeIngredientRecognition/cosface_20260121_133207/best_cosface_model.pth"
     # model_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model.pth"
 
     if not os.path.exists(model_path):
@@ -98,9 +96,9 @@ def main():
         # 保存模型,一定不要乱改，防止模型覆盖!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
         # output_path = "../model/DishClassification/grid_search_20251121_102723/best_embedding_model_mobile.pt"
         #原本路径
-        #output_path = "../model/WholeIngredientRecognition/grid_search_20251204_140816/best_embedding_model_mobile.pt"
+        output_path = "../model/WholeIngredientRecognition/grid_search_20260302_182102/best_embedding_model_mobile.pt"
         #新路径
-        output_path = "../model/WholeIngredientRecognition/cosface_20260121_133207/best_cosface_model_mobile.pt"
+        # output_path = "../model/WholeIngredientRecognition/cosface_20260121_133207/best_cosface_model_mobile.pt"
         # output_path = "../model/WholeIngredientRecognition/cosface_20251106_134718/best_embedding_model_mobile.pt"
         # output_path = "../model/ProcessedIngredientRecognition/embedding_20251029_173607/best_embedding_model_mobile.pt"
         traced_model.save(output_path)

train/grid_search_cosface.py

@@ -57,8 +57,8 @@ GRID_PARAMS = {
         'm': [0.32, 0.35, 0.38, 0.40,0.45,0.50],  # margin参数
     },
     'whole_ingredient': {
-        's': [64.0],
+        's': [60.0,64.0],
-        'm': [0.40, 0.42, 0.43, 0.45],
+        'm': [0.42,  0.45],
     },
     'processed_ingredient': {
         's': [56.0, 60.0, 64.0, 68.0],