增加了数据增强离线的程序（可扩充数据集），将图片压缩从32*32，调整为224*224.

classifier/food_classifier_app.py

@@ -30,7 +30,7 @@ class FoodClassifierApp:
         self.root.geometry("1400x800")
         
         # 食物类别（根据您的数据集）
-        self.food_classes = ["回锅肉", "西红柿鸡蛋","麻辣小面"]
+        self.food_classes = ['回锅肉', '炒细面', '西红柿鸡蛋', '麻辣小面']
         
         # 设备设置
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

data_expansion/data_expansion.py

@@ -0,0 +1,50 @@
+import os
+import random
+from PIL import Image
+from torchvision import transforms
+
+# 原始数据目录
+input_root = "../dataset/train"
+
+# 增强后保存目录
+output_root = "../dataset/train_aug"
+
+# 每类目标张数
+target_num = 500
+
+# 定义数据增强
+transform = transforms.Compose([
+    transforms.RandomHorizontalFlip(p=0.5),
+    transforms.RandomRotation(15),
+    transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.02),
+    transforms.RandomResizedCrop(size=(224, 224), scale=(0.8, 1.0)),
+])
+
+os.makedirs(output_root, exist_ok=True)
+
+# 遍历每个类别文件夹
+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)
+
+    # 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]
+
+    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
+
+    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！")

net/food_net.py

@@ -17,9 +17,9 @@ class FoodCNN(nn.Module):
         super(FoodCNN, self).__init__()
         self.use_internal_preprocess = use_internal_preprocess
         
-        # 图片预处理变换（仅在推理时使用）
+        # 图片预处理变换（仅在推理时使用）（32）改为（224）
         self.preprocess = transforms.Compose([
-            transforms.Resize((32, 32),interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.Resize((224, 224),interpolation=transforms.InterpolationMode.BILINEAR),
             transforms.ToTensor(),
             transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
         ])
@@ -49,7 +49,8 @@ class FoodCNN(nn.Module):
         self.dropout3 = nn.Dropout2d(0.25)
         
         # 全连接层
-        self.fc1 = nn.Linear(128 * 4 * 4, 512)
+        # self.fc1 = nn.Linear(128 * 4 * 4, 512)
+        self.fc1 = nn.Linear(128 * 28 * 28, 512)
         self.dropout4 = nn.Dropout(0.5)
         self.fc2 = nn.Linear(512, settings.NUM_CLASSES)
         
@@ -76,8 +77,9 @@ class FoodCNN(nn.Module):
         tensor = tensor.unsqueeze(0)
         
         # 使用与移动端相同的插值方法缩放到32x32
-        tensor = F.interpolate(tensor, size=(32, 32), mode='bilinear', align_corners=False)
-        
+        # tensor = F.interpolate(tensor, size=(32, 32), mode='bilinear', align_corners=False)
+        tensor = F.interpolate(tensor, size=(224, 224), mode='bilinear', align_corners=False)
+
         # ImageNet标准化
         mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
         std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)
@@ -100,7 +102,8 @@ class FoodCNN(nn.Module):
         # x = x.float() / 255.0
         
         # 缩放到 32x32
-        x = F.interpolate(x, size=(32, 32), mode='bilinear', align_corners=False)
+        # x = F.interpolate(x, size=(32, 32), mode='bilinear', align_corners=False)
+        x = F.interpolate(x, size=(224, 224), mode='bilinear', align_corners=False)
         # 缩放到 32x32 - 使用 align_corners=True 来匹配 PIL 的默认行为
         # x = F.interpolate(x, size=(32, 32), mode='bilinear', align_corners=True)
 
@@ -140,8 +143,10 @@ class FoodCNN(nn.Module):
         x = self.dropout3(x)
         
         # 展平
-        x = x.view(-1, 128 * 4 * 4)
-        
+        # x = x.view(-1, 128 * 4 * 4)
+        x = x.view(-1, 128 * 28 * 28)
+        # x = x.view(-1, 65536)
+
         # 全连接层
         x = F.relu(self.fc1(x))
         x = self.dropout4(x)

settings/settings.py

@@ -15,7 +15,7 @@ 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', '12')
+MODEL_DIR = os.path.join(BASE_DIR, 'model', '15')
 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')
@@ -26,6 +26,7 @@ INFERENCE_BEST_MODEL_PATH = BEST_MODEL_PATH
 # 训练参数
 NUM_EPOCHS = 100
 BATCH_SIZE = 32
+# BATCH_SIZE = 128
 LEARNING_RATE = 0.001
 WEIGHT_DECAY = 1e-4
 

train/train_food_classifier.py

@@ -30,17 +30,27 @@ print(f"使用设备: {device}")
 
 
 # 数据预处理 - 必须包含Resize以保证batch中tensor尺寸一致，只有Normalize由模型内部完成
+# 把32，32换成224，224
+# transform_train = transforms.Compose([
+#     transforms.Resize((224, 224)),
+#     transforms.RandomHorizontalFlip(p=0.5),
+#     transforms.RandomRotation(10),
+#     transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
+#     transforms.ToTensor(),
+#     # 注意：只有Normalize由模型内部处理
+# ])
 transform_train = transforms.Compose([
-    transforms.Resize((32, 32)),  # 必须保留，确保batch中tensor尺寸一致
+    transforms.Resize((224, 224)),
     transforms.RandomHorizontalFlip(p=0.5),
     transforms.RandomRotation(10),
-    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),
+    transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.02),
     transforms.ToTensor(),
     # 注意：只有Normalize由模型内部处理
 ])
 
+# 把缩放成32*32，修改为224*224
 transform_test = transforms.Compose([
-    transforms.Resize((32, 32)),  # 必须保留，确保batch中tensor尺寸一致
+    transforms.Resize((224, 224)),  # 必须保留，确保batch中tensor尺寸一致
     transforms.ToTensor(),
     # 注意：只有Normalize由模型内部处理
 ])
@@ -129,7 +139,7 @@ def test(model, test_loader, device, class_names):
             })
     
     print(f'\n测试集总体准确率: {100.*correct/total:.2f}%')
-    for i in range(3):
+    for i in range(settings.NUM_CLASSES):
         if class_total[i] > 0:
             print(f'{class_names[i]} 准确率: {100.*class_correct[i]/class_total[i]:.2f}%')