| import glob |
| import os |
| from typing import List, Optional, Tuple |
|
|
| import logging |
| import numpy as np |
| import torchvision.transforms.functional as TF |
| import PIL |
| from PIL import Image |
| from torchvision.datasets import VisionDataset |
|
|
| logger = logging.getLogger(__name__) |
|
|
|
|
| class PathDataset(VisionDataset): |
| def __init__( |
| self, |
| root: List[str], |
| loader: None = None, |
| transform: Optional[str] = None, |
| extra_transform: Optional[str] = None, |
| mean: Optional[List[float]] = None, |
| std: Optional[List[float]] = None, |
| ): |
| super().__init__(root=root) |
|
|
| PIL.Image.MAX_IMAGE_PIXELS = 256000001 |
|
|
| self.files = [] |
| for folder in self.root: |
| self.files.extend( |
| sorted(glob.glob(os.path.join(folder, "**", "*.jpg"), recursive=True)) |
| ) |
| self.files.extend( |
| sorted(glob.glob(os.path.join(folder, "**", "*.png"), recursive=True)) |
| ) |
|
|
| self.transform = transform |
| self.extra_transform = extra_transform |
| self.mean = mean |
| self.std = std |
|
|
| self.loader = loader |
|
|
| logger.info(f"loaded {len(self.files)} samples from {root}") |
|
|
| assert (mean is None) == (std is None) |
|
|
| def __len__(self) -> int: |
| return len(self.files) |
|
|
| def __getitem__(self, idx) -> Tuple[np.ndarray, np.ndarray]: |
| path = self.files[idx] |
|
|
| if self.loader is not None: |
| return self.loader(path), None |
|
|
| img = Image.open(path).convert("RGB") |
| if self.transform is not None: |
| img = self.transform(img) |
| img = TF.to_tensor(img) |
| if self.mean is not None and self.std is not None: |
| img = TF.normalize(img, self.mean, self.std) |
| return img, None |
|
|
