增加了相似菜品预览功能，更完整了，交互更方便了。

classifier/embedding_food_classifier_app.py

@@ -61,7 +61,7 @@ class EmbeddingFoodClassifierApp:
         """加载训练好的embedding模型和FAISS索引"""
         try:
             # 模型路径
-            model_path = "../model/embedding_20250917_145342/best_embedding_model.pth"
+            model_path = "../model/embedding_20250929_151102/best_embedding_model.pth"
             
             # FAISS索引目录
             index_dir = "../faiss_vector_db/faiss_index"
@@ -744,11 +744,13 @@ class EmbeddingFoodClassifierApp:
             # 保存图片路径
             paths_path = os.path.join(index_dir, 'image_paths.pkl')
             with open(paths_path, 'wb') as f:
+                # 重新写入图片路径
                 pickle.dump(self.image_paths, f)
             
             # 保存标签
             labels_path = os.path.join(index_dir, 'labels.pkl')
             with open(labels_path, 'wb') as f:
+                # 重新写入标签
                 pickle.dump(self.labels, f)
             
             # 更新类别信息
@@ -1309,18 +1311,153 @@ class EmbeddingFoodClassifierApp:
                 )
                 similar_label.pack(fill="x", padx=5, pady=(5, 2))
                 
-                # 显示前3张最相似的图片信息
+                # 显示前5张最相似的图片信息
-                for j, sim_img in enumerate(similar_images[:3]):
+                for j, sim_img in enumerate(similar_images[:5]):
+                    # 创建一个水平框架来放置信息和按钮
+                    sim_frame = ctk.CTkFrame(info_frame)
+                    sim_frame.pack(fill="x", padx=15, pady=1)
+                    
+                    # 相似图片信息
                     sim_info = f"  {j+1}. {sim_img['class']} (相似度: {sim_img['score']:.3f})"
                     sim_info_label = ctk.CTkLabel(
-                        info_frame,
+                        sim_frame,
                         text=sim_info,
                         anchor="w",
                         font=("Arial", 9),
                         text_color="gray"
                     )
-                    sim_info_label.pack(fill="x", padx=15, pady=1)
+                    sim_info_label.pack(side="left", fill="x", expand=True, padx=5, pady=2)
+                    
+                    # 查看按钮（使用眼睛图标）
+                    view_btn = ctk.CTkButton(
+                        sim_frame,
+                        text="👁",  # 眼睛图标
+                        width=25,
+                        height=20,
+                        font=("Arial", 12),
+                        command=lambda path=sim_img['path']: self.show_similar_image(path),
+                        fg_color="#2196F3",
+                        hover_color="#1976D2",
+                        corner_radius=10
+                    )
+                    view_btn.pack(side="right", padx=5, pady=2)
     
+    def show_similar_image(self, image_path):
+        """显示相似图片的窗口"""
+        try:
+            # 创建新窗口
+            similar_window = ctk.CTkToplevel(self.root)
+            similar_window.title("相似图片查看")
+            similar_window.geometry("600x500")
+            similar_window.resizable(True, True)
+            
+            # 设置窗口属性，确保在主窗口上方
+            similar_window.transient(self.root)  # 设置为主窗口的子窗口
+            similar_window.grab_set()  # 设置为模态窗口
+            similar_window.lift()  # 提升到最前面
+            similar_window.focus_set()  # 设置焦点
+            
+            # 居中显示
+            similar_window.update_idletasks()
+            x = (similar_window.winfo_screenwidth() // 2) - (600 // 2)
+            y = (similar_window.winfo_screenheight() // 2) - (500 // 2)
+            similar_window.geometry(f"600x500+{x}+{y}")
+            
+            # 主框架
+            main_frame = ctk.CTkFrame(similar_window)
+            main_frame.pack(fill="both", expand=True, padx=10, pady=10)
+            
+            # 标题
+            title_label = ctk.CTkLabel(
+                main_frame,
+                text="相似图片详情",
+                font=("Arial", 16, "bold")
+            )
+            title_label.pack(pady=(10, 20))
+            
+            # 图片路径信息
+            path_label = ctk.CTkLabel(
+                main_frame,
+                text=f"图片路径: {image_path}",
+                font=("Arial", 10),
+                text_color="gray",
+                wraplength=550
+            )
+            path_label.pack(pady=(0, 10))
+            
+            # 图片显示框架
+            image_frame = ctk.CTkFrame(main_frame)
+            image_frame.pack(fill="both", expand=True, padx=10, pady=10)
+            
+            # 加载并显示图片
+            try:
+                # 打开图片
+                pil_image = Image.open(image_path)
+                
+                # 计算合适的显示尺寸
+                max_width, max_height = 500, 350
+                img_width, img_height = pil_image.size
+                
+                # 计算缩放比例
+                scale = min(max_width / img_width, max_height / img_height, 1.0)
+                new_width = int(img_width * scale)
+                new_height = int(img_height * scale)
+                
+                # 调整图片大小
+                pil_image = pil_image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+                
+                # 转换为CTk图片
+                ctk_image = ctk.CTkImage(
+                    light_image=pil_image,
+                    dark_image=pil_image,
+                    size=(new_width, new_height)
+                )
+                
+                # 显示图片
+                image_label = ctk.CTkLabel(
+                    image_frame,
+                    image=ctk_image,
+                    text=""
+                )
+                image_label.pack(expand=True, pady=20)
+                
+                # 图片信息
+                info_text = f"原始尺寸: {img_width} × {img_height}显示尺寸: {new_width} × {new_height}"
+                info_label = ctk.CTkLabel(
+                    main_frame,
+                    text=info_text,
+                    font=("Arial", 9),
+                    text_color="gray"
+                )
+                info_label.pack(pady=(0, 10))
+                
+            except Exception as e:
+                error_label = ctk.CTkLabel(
+                    image_frame,
+                    text=f"无法加载图片: {str(e)}",
+                    font=("Arial", 12),
+                    text_color="red"
+                )
+                error_label.pack(expand=True)
+            
+            # 关闭按钮
+            close_btn = ctk.CTkButton(
+                main_frame,
+                text="关闭",
+                width=100,
+                command=similar_window.destroy,
+                fg_color="#f44336",
+                hover_color="#da190b"
+            )
+            close_btn.pack(pady=(10, 10))
+            
+            # 让窗口获得焦点
+            similar_window.focus()
+            similar_window.lift()
+            
+        except Exception as e:
+            messagebox.showerror("错误", f"显示相似图片时出错: {str(e)}")
+
     def update_stats(self):
         """更新统计信息"""
         total_images = len(self.uploaded_images)

data_expansion/data_expansion.py

@@ -27,24 +27,24 @@ for class_name in os.listdir(input_root):
     input_dir = os.path.join(input_root, class_name)
     output_dir = os.path.join(output_root, class_name)
     os.makedirs(output_dir, exist_ok=True)
+    if class_name == "麻婆豆腐":
+        # 读取类别下所有图片路径
+        img_files = [f for f in os.listdir(input_dir) if f.lower().endswith(('.jpg', '.png', '.jpeg'))]
+        img_paths = [os.path.join(input_dir, f) for f in img_files]
 
-    # 读取类别下所有图片路径
+        print(f"类别 {class_name} 原始图片数: {len(img_paths)}")
-    img_files = [f for f in os.listdir(input_dir) if f.lower().endswith(('.jpg', '.png', '.jpeg'))]
-    img_paths = [os.path.join(input_dir, f) for f in img_files]
 
-    print(f"类别 {class_name} 原始图片数: {len(img_paths)}")
+        count = 0
+        while count < target_num:
+            img_path = random.choice(img_paths)
+            img = Image.open(img_path).convert("RGB")
+            # 生成增强图
+            aug_img = transform(img)
+            # 保存
+            save_path = os.path.join(output_dir, f"aug_{count:03d}.jpg")
+            aug_img.save(save_path)
+            count += 1
 
-    count = 0
+        print(f"类别 {class_name} 已扩充到 {target_num} 张，保存于 {output_dir}")
-    while count < target_num:
-        img_path = random.choice(img_paths)
-        img = Image.open(img_path).convert("RGB")
-        # 生成增强图
-        aug_img = transform(img)
-        # 保存
-        save_path = os.path.join(output_dir, f"aug_{count:03d}.jpg")
-        aug_img.save(save_path)
-        count += 1
-
-    print(f"类别 {class_name} 已扩充到 {target_num} 张，保存于 {output_dir}")
 
 print("✅ 数据增强完成！")

faiss_vector_db/build_faiss_index.py

@@ -477,7 +477,7 @@ class FAISSSearcher:
 def main():
     """主函数"""
     # 配置参数
-    MODEL_PATH = "../model/embedding_20250917_145342/best_embedding_model.pth"
+    MODEL_PATH = "../model/embedding_20250929_151102/best_embedding_model.pth"
     TRAIN_DIR = "../dataset/train"
     OUTPUT_DIR = "faiss_index"
     BATCH_SIZE = 16

train/train_embedding.py

@@ -79,9 +79,9 @@ class TripletDataset(Dataset):
             image_files = [f for f in os.listdir(class_path) 
                           if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
             
-            # 限制每个类别的样本数
+            # 限制每个类别的样本数,没有必要限制
-            if len(image_files) > self.samples_per_class:
+            # if len(image_files) > self.samples_per_class:
-                image_files = random.sample(image_files, self.samples_per_class)
+            #     image_files = random.sample(image_files, self.samples_per_class)
             
             for img_file in image_files:
                 img_path = os.path.join(class_path, img_file)
@@ -551,7 +551,7 @@ def main():
     train_dataset = TripletDataset(
         dataset_path=settings.TRAIN_DATA_DIR,
         transform=transform_train,
-        samples_per_class=200
+        samples_per_class=550
     )
     
     val_dataset = TripletDataset(