菜品识别程序，增加向量输出到控制台的功能！

classifier/food_classifier_app.py

@@ -66,7 +66,7 @@ class FoodClassifierApp:
                 
                 # 定义图像预处理（与训练时相同）
                 self.transform = transforms.Compose([
-                    transforms.Resize((32, 32)),
+                    transforms.Resize((32, 32),interpolation=transforms.InterpolationMode.BILINEAR),
                     transforms.ToTensor(),
                     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
                 ])
@@ -114,25 +114,26 @@ class FoodClassifierApp:
             image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
             
             if image is not None:
+                # image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
                 return image
             
-            # 方法2：如果方法1失败，尝试使用PIL
-            from PIL import Image as PILImage
-            pil_image = PILImage.open(file_path)
-            
-            # 转换为RGB（如果是RGBA）
-            if pil_image.mode == 'RGBA':
-                pil_image = pil_image.convert('RGB')
-            elif pil_image.mode == 'L':  # 灰度图
-                pil_image = pil_image.convert('RGB')
-            
-            # 转换为numpy数组
-            image_array = np.array(pil_image)
-            
-            # PIL使用RGB，OpenCV使用BGR，需要转换
-            image = cv2.cvtColor(image_array, cv2.COLOR_RGB2BGR)
-            
-            return image
+            # # 方法2：如果方法1失败，尝试使用PIL
+            # from PIL import Image as PILImage
+            # pil_image = PILImage.open(file_path)
+            #
+            # # 转换为RGB（如果是RGBA）
+            # if pil_image.mode == 'RGBA':
+            #     pil_image = pil_image.convert('RGB')
+            # elif pil_image.mode == 'L':  # 灰度图
+            #     pil_image = pil_image.convert('RGB')
+            #
+            # # 转换为numpy数组
+            # image_array = np.array(pil_image)
+            #
+            # # PIL使用RGB，OpenCV使用BGR，需要转换
+            # image = cv2.cvtColor(image_array, cv2.COLOR_RGB2BGR)
+            #
+            # return image
             
         except Exception as e:
             print(f"加载图片失败: {e}")
@@ -549,12 +550,30 @@ class FoodClassifierApp:
             pil_image = Image.fromarray(image_rgb)
             
             # 应用预处理
+            resize_transform = transforms.Resize((32,32),interpolation=transforms.InterpolationMode.BICUBIC)
+            resized_image = resize_transform(pil_image)
+            if isinstance(resized_image, Image.Image):
+                # 转换为tensor但不归一化
+                to_tensor = transforms.ToTensor()
+                resized_tensor = to_tensor(resized_image)
+                print(f"缩放后tensor形状: {resized_tensor.shape}")
+
+                # 打印前5个像素值（每个通道）
+                print("前5个像素值 (R, G, B):")
+                for i in range(min(5, resized_tensor.shape[1])):
+                    r_val = resized_tensor[0, 0, i].item() * 255  # Red通道 (转换回0-255范围)
+                    g_val = resized_tensor[1, 0, i].item() * 255  # Green通道
+                    b_val = resized_tensor[2, 0, i].item() * 255  # Blue通道
+                    print(f"  像素[0,{i}]: R={r_val:.2f}, G={g_val:.2f}, B={b_val:.2f}")
+
+
             input_tensor = self.transform(pil_image).unsqueeze(0)  # 添加batch维度
             input_tensor = input_tensor.to(self.device)
             
             # 进行预测
             with torch.no_grad():
                 outputs = self.model(input_tensor)
+                print('outputs',outputs)
                 probabilities = F.softmax(outputs, dim=1)
                 confidence, predicted = torch.max(probabilities, 1)
                 

settings/settings.py

@@ -15,17 +15,16 @@ VAL_DATA_DIR = os.path.join(DATASET_DIR, 'val')
 TEST_DATA_DIR = os.path.join(DATASET_DIR, 'test')
 
 # 模型保存路径
-MODEL_DIR = os.path.join(BASE_DIR, 'model', '05')
+MODEL_DIR = os.path.join(BASE_DIR, 'model', '06')
 BEST_MODEL_PATH = os.path.join(MODEL_DIR, 'best_food_model.pth')
 TRAINING_CURVES_PATH = os.path.join(MODEL_DIR, 'training_curves.png')
 TRAINING_RESULTS_PATH = os.path.join(MODEL_DIR, 'training_results.txt')
 
-# INFERENCE_BEST_MODEL_PATH = os.path.join(BASE_DIR, 'model', '03','best_food_model.pth')
+# INFERENCE_BEST_MODEL_PATH = os.path.join(BASE_DIR, 'model', '05','best_food_model.pth')
 INFERENCE_BEST_MODEL_PATH = BEST_MODEL_PATH
 
 # 训练参数
-# NUM_EPOCHS = 100
-NUM_EPOCHS = 13
+NUM_EPOCHS = 100
 BATCH_SIZE = 32
 LEARNING_RATE = 0.001
 WEIGHT_DECAY = 1e-4

toAndroid/toAndroid.py

@@ -7,7 +7,7 @@ from net import create_food_cnn
 # 1. 初始化模型
 model = create_food_cnn()
 # 2. 加载训练好的权重
-model.load_state_dict(torch.load("../model/02/best_food_model.pth", map_location='cpu'))
+model.load_state_dict(torch.load("../model/06/best_food_model.pth", map_location='cpu'))
 model.eval()  # 设置为推理模式
 
 # 3. 创建示例输入 (假设输入是 3x224x224 的图片)
@@ -15,4 +15,4 @@ example_input = torch.randn(1, 3, 224, 224)
 
 # 4. 转换为 TorchScript
 traced_script_module = torch.jit.trace(model, example_input)
-traced_script_module.save("../model/02/best_food_model_mobile.pt")
+traced_script_module.save("../model/06/best_food_model_mobile.pt")

train/train_food_classifier.py

@@ -14,8 +14,9 @@ import time
 
 # 添加net目录到路径
 # sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'net'))
-# from food_net import create_food_cnn
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
 from net import create_food_cnn
+# from net import create_food_cnn
 from settings import settings
 
 # 设置matplotlib支持中文显示