345 lines
11 KiB
Python
345 lines
11 KiB
Python
"""
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SegFormer训练数据集类
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功能说明：
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1. 加载图像和对应的分割mask
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2. 数据增强（翻转、旋转、颜色变换等）
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3. 预处理和标准化
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4. 批量数据加载
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"""
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import os
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import numpy as np
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from PIL import Image
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import torch
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from torch.utils.data import Dataset
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from pathlib import Path
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from typing import Optional, Tuple, List
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import albumentations as A
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from albumentations.pytorch import ToTensorV2
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class FoodSegmentationDataset(Dataset):
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"""
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食物分割数据集
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数据格式：
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- 图像：RGB图像 (.jpg, .png等)
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- 标注：PNG格式mask，像素值为类别ID (0=背景, 1=食物)
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"""
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def __init__(
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self,
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data_root: str,
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split: str = 'train',
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image_size: int = 512,
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num_classes: int = 2,
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augmentation: bool = True
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):
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"""
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初始化数据集
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Args:
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data_root: 数据集根目录（包含images和annotations文件夹）
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split: 'train' 或 'val'
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image_size: 输入图像尺寸（将resize到此尺寸）
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num_classes: 类别数（包括背景）
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augmentation: 是否使用数据增强（仅训练集）
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"""
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self.data_root = Path(data_root)
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self.split = split
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self.image_size = image_size
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self.num_classes = num_classes
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self.augmentation = augmentation and (split == 'train')
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# 构建图像和mask的路径
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self.images_dir = self.data_root / "images" / split
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self.masks_dir = self.data_root / "annotations" / split
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# 获取所有图像文件
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self.image_files = sorted(list(self.images_dir.glob("*.*")))
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# 过滤：只保留有对应mask的图像
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self.valid_samples = []
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for img_path in self.image_files:
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mask_path = self.masks_dir / (img_path.stem + '.png')
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if mask_path.exists():
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self.valid_samples.append((img_path, mask_path))
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print(f"✓ {split}集加载完成: {len(self.valid_samples)} 个样本")
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# 构建数据增强pipeline
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self.transform = self._build_transforms()
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def _build_transforms(self):
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"""
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构建数据增强和预处理pipeline
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使用albumentations库进行高效的数据增强
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注意：对于分割任务，增强操作需要同时应用到图像和mask
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"""
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if self.augmentation:
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# 训练集：激进的数据增强（因为数据量小）
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transform = A.Compose([
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# 1. 尺寸调整
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A.Resize(self.image_size, self.image_size),
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# 2. 几何变换（同时作用于图像和mask）
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A.HorizontalFlip(p=0.5), # 50%概率水平翻转
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A.VerticalFlip(p=0.3), # 30%概率垂直翻转
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A.Rotate(limit=30, p=0.5), # ±30度旋转
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A.ShiftScaleRotate(
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shift_limit=0.1, # 平移±10%
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scale_limit=0.2, # 缩放±20%
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rotate_limit=20, # 旋转±20度
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p=0.5
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),
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# 3. 颜色增强（仅作用于图像）
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A.RandomBrightnessContrast(
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brightness_limit=0.2,
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contrast_limit=0.2,
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p=0.5
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),
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A.HueSaturationValue(
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hue_shift_limit=20,
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sat_shift_limit=30,
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val_shift_limit=20,
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p=0.5
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),
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# 4. 模糊和噪声
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A.OneOf([
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A.GaussianBlur(blur_limit=(3, 5), p=1.0),
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A.MedianBlur(blur_limit=5, p=1.0),
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], p=0.3),
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# 5. 标准化（使用ImageNet均值和标准差）
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A.Normalize(
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mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225],
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),
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# 6. 转换为Tensor
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ToTensorV2(),
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])
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else:
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# 验证集：仅resize和标准化
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transform = A.Compose([
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A.Resize(self.image_size, self.image_size),
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A.Normalize(
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mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225],
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),
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ToTensorV2(),
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])
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return transform
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def __len__(self) -> int:
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"""返回数据集大小"""
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return len(self.valid_samples)
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def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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获取一个样本
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Args:
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idx: 样本索引
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Returns:
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image: 图像Tensor (3, H, W)
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mask: 分割mask Tensor (H, W)，像素值为类别ID
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"""
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img_path, mask_path = self.valid_samples[idx]
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# 1. 读取图像和mask
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image = np.array(Image.open(img_path).convert('RGB'))
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mask = np.array(Image.open(mask_path))
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# 2. 应用数据增强
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# albumentations会自动将增强同时应用到image和mask
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transformed = self.transform(image=image, mask=mask)
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image = transformed['image'] # Tensor (3, H, W)
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mask = transformed['mask'] # ndarray (H, W)
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# 3. 将mask转换为Tensor
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mask = torch.from_numpy(mask).long()
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# 4. 检查mask的有效性
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# 确保mask的值在[0, num_classes-1]范围内
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if mask.max() >= self.num_classes:
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print(f"警告: {mask_path.name} 包含无效类别ID {mask.max()}")
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mask = torch.clamp(mask, 0, self.num_classes - 1)
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return image, mask
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def get_sample_info(self, idx: int) -> dict:
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"""
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获取样本的元信息（用于调试和可视化）
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Args:
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idx: 样本索引
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Returns:
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info: 包含文件名、路径等信息的字典
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"""
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img_path, mask_path = self.valid_samples[idx]
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return {
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'image_name': img_path.name,
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'mask_name': mask_path.name,
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'image_path': str(img_path),
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'mask_path': str(mask_path),
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}
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def get_dataloaders(
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data_root: str,
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batch_size: int = 4,
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image_size: int = 512,
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num_workers: int = 0,
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num_classes: int = 2
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) -> Tuple[torch.utils.data.DataLoader, torch.utils.data.DataLoader]:
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"""
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创建训练集和验证集的DataLoader
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Args:
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data_root: 数据集根目录
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batch_size: 批大小
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image_size: 图像尺寸
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num_workers: 数据加载进程数（CPU训练时设为0）
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num_classes: 类别数
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Returns:
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train_loader: 训练集DataLoader
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val_loader: 验证集DataLoader
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"""
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# 创建训练集
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train_dataset = FoodSegmentationDataset(
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data_root=data_root,
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split='train',
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image_size=image_size,
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num_classes=num_classes,
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augmentation=True # 训练集使用数据增强
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)
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# 创建验证集
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val_dataset = FoodSegmentationDataset(
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data_root=data_root,
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split='val',
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image_size=image_size,
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num_classes=num_classes,
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augmentation=False # 验证集不使用数据增强
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)
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# 创建DataLoader
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train_loader = torch.utils.data.DataLoader(
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train_dataset,
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batch_size=batch_size,
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shuffle=True, # 训练集打乱顺序
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num_workers=num_workers,
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pin_memory=False, # CPU训练时设为False
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drop_last=True if len(train_dataset) > batch_size else False
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)
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val_loader = torch.utils.data.DataLoader(
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val_dataset,
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batch_size=batch_size,
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shuffle=False, # 验证集不打乱
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num_workers=num_workers,
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pin_memory=False,
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drop_last=False
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)
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print(f"\n✓ DataLoader创建完成")
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print(f" 训练集: {len(train_dataset)} 样本, {len(train_loader)} 批次")
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print(f" 验证集: {len(val_dataset)} 样本, {len(val_loader)} 批次")
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return train_loader, val_loader
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def test_dataset():
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"""
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测试数据集加载是否正常
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用于开发调试，验证：
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1. 数据能否正确加载
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2. 数据增强是否正常工作
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3. 数据的shape和类型是否正确
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"""
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import matplotlib.pyplot as plt
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print("="*60)
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print("数据集测试")
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print("="*60)
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# 配置
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DATA_ROOT = "d:/MyProjects/PythonProjects/FoodClassifier/SegFormer/data/segformer_format"
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# 创建数据集
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dataset = FoodSegmentationDataset(
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data_root=DATA_ROOT,
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split='train',
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image_size=512,
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augmentation=True
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)
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# 测试读取第一个样本
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image, mask = dataset[0]
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info = dataset.get_sample_info(0)
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print(f"\n样本信息:")
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print(f" 文件名: {info['image_name']}")
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print(f" 图像shape: {image.shape}") # 应该是 (3, 512, 512)
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print(f" Mask shape: {mask.shape}") # 应该是 (512, 512)
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print(f" Mask唯一值: {torch.unique(mask).numpy()}") # 应该是 [0, 1]
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print(f" 图像数据范围: [{image.min():.3f}, {image.max():.3f}]")
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# 可视化前3个样本（含数据增强效果）
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fig, axes = plt.subplots(3, 3, figsize=(12, 12))
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for i in range(3):
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image, mask = dataset[i]
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# 反标准化用于显示
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mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
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std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
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image_denorm = image * std + mean
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image_denorm = torch.clamp(image_denorm, 0, 1)
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# 转换为numpy用于显示
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image_np = image_denorm.permute(1, 2, 0).numpy()
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mask_np = mask.numpy()
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# 显示图像
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axes[i, 0].imshow(image_np)
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axes[i, 0].set_title(f"样本{i+1}: 图像")
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axes[i, 0].axis('off')
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# 显示mask
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axes[i, 1].imshow(mask_np, cmap='gray', vmin=0, vmax=1)
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axes[i, 1].set_title(f"样本{i+1}: Mask")
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axes[i, 1].axis('off')
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# 显示叠加
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overlay = image_np.copy()
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red_mask = np.zeros_like(overlay)
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red_mask[mask_np == 1] = [1, 0, 0]
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overlay = overlay * 0.6 + red_mask * 0.4
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axes[i, 2].imshow(overlay)
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axes[i, 2].set_title(f"样本{i+1}: 叠加")
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axes[i, 2].axis('off')
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plt.tight_layout()
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plt.savefig("dataset_test_result.png", dpi=120)
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print(f"\n✓ 测试结果已保存: dataset_test_result.png")
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plt.show()
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print("\n" + "="*60)
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print("✓ 数据集测试通过!")
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print("="*60)
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if __name__ == "__main__":
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test_dataset()
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