增加了训练时间！

classifier/food_classifier_app.py

@@ -12,6 +12,7 @@ from torchvision import transforms
 from typing import List, Optional, Tuple
 from tkinterdnd2 import DND_FILES, TkinterDnD
 import threading
+import time
 
 # 设置customtkinter的外观
 ctk.set_appearance_mode("System")
@@ -90,6 +91,10 @@ class FoodClassifierApp:
         self.uploaded_images = []
         self.current_results = []
         
+        # 识别时间记录
+        self.recognition_start_time = None
+        self.recognition_duration = 0
+        
         # 模型相关
         self.model = None
         self.transform = None
@@ -500,6 +505,9 @@ class FoodClassifierApp:
             if result:
                 self.uploaded_images.clear()
                 self.current_results.clear()
+                # 重置识别时间
+                self.recognition_start_time = None
+                self.recognition_duration = 0
                 self.update_images_display()
                 self.update_recognize_button()
                 self.update_results_display()
@@ -518,6 +526,9 @@ class FoodClassifierApp:
             messagebox.showinfo("提示", "请先上传图片")
             return
         
+        # 记录识别开始时间
+        self.recognition_start_time = time.time()
+        
         # 在新线程中执行识别，避免界面卡顿
         self.recognize_button.configure(state="disabled", text="识别中...")
         threading.Thread(target=self.recognize_images, daemon=True).start()
@@ -603,9 +614,13 @@ class FoodClassifierApp:
     
     def recognition_completed(self):
         """识别完成"""
+        # 计算识别耗时
+        if self.recognition_start_time is not None:
+            self.recognition_duration = time.time() - self.recognition_start_time
+        
         self.recognize_button.configure(state="normal", text="开始识别")
         self.update_stats()
-        messagebox.showinfo("完成", "所有图片识别完成！")
+        messagebox.showinfo("完成", f"所有图片识别完成！识别耗时: {self.recognition_duration:.2f}秒")
     
     def resize_image_for_display(self, image, max_width, max_height):
         """调整图片大小用于显示"""
@@ -756,11 +771,17 @@ class FoodClassifierApp:
             correct_count = sum(1 for r in auto_judged_results if r['is_correct'])
             accuracy = (correct_count / len(auto_judged_results)) * 100
             
-            # 更新统计标签
-            stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: {len(auto_judged_results)} | 平均准确率: {accuracy:.1f}%"
+            # 构建统计文本，包含识别时间
+            if self.recognition_duration > 0:
+                stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: {len(auto_judged_results)} | 识别时间: {self.recognition_duration:.2f}秒 | 平均准确率: {accuracy:.1f}%"
+            else:
+                stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: {len(auto_judged_results)} | 平均准确率: {accuracy:.1f}%"
         else:
             # 如果没有可自动判断的结果
-            stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: 0 | 平均准确率: 0%"
+            if self.recognition_duration > 0:
+                stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: 0 | 识别时间: {self.recognition_duration:.2f}秒 | 平均准确率: 0%"
+            else:
+                stats_text = f"总图片: {total_images} | 已识别: {recognized_images} | 可判断: 0 | 平均准确率: 0%"
         
         self.stats_label.configure(text=stats_text)