81 lines
2.9 KiB
Python
81 lines
2.9 KiB
Python
import logging
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from os import listdir
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from os.path import splitext
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from pathlib import Path
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import numpy as np
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import torch
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from PIL import Image
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from torch.utils.data import Dataset
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class BasicDataset(Dataset):
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def __init__(self, images_dir: str, masks_dir: str, scale: float = 1.0, mask_suffix: str = ''):
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self.images_dir = Path(images_dir)
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self.masks_dir = Path(masks_dir)
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assert 0 < scale <= 1, 'Scale must be between 0 and 1'
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self.scale = scale
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self.mask_suffix = mask_suffix
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self.ids = [splitext(file)[0] for file in listdir(images_dir) if not file.startswith('.')]
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if not self.ids:
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raise RuntimeError(f'No input file found in {images_dir}, make sure you put your images there')
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logging.info(f'Creating dataset with {len(self.ids)} examples')
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def __len__(self):
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return len(self.ids)
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@staticmethod
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def preprocess(pil_img, scale, is_mask):
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w, h = pil_img.size
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newW, newH = int(scale * w), int(scale * h)
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assert newW > 0 and newH > 0, 'Scale is too small, resized images would have no pixel'
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pil_img = pil_img.resize((newW, newH), resample=Image.NEAREST if is_mask else Image.BICUBIC)
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img_ndarray = np.asarray(pil_img)
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if not is_mask:
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if img_ndarray.ndim == 2:
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img_ndarray = img_ndarray[np.newaxis, ...]
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else:
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img_ndarray = img_ndarray.transpose((2, 0, 1))
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img_ndarray = img_ndarray / 255
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return img_ndarray
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@staticmethod
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def load(filename):
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ext = splitext(filename)[1]
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if ext in ['.npz', '.npy']:
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return Image.fromarray(np.load(filename))
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elif ext in ['.pt', '.pth']:
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return Image.fromarray(torch.load(filename).numpy())
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else:
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return Image.open(filename)
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def __getitem__(self, idx):
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name = self.ids[idx]
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mask_file = list(self.masks_dir.glob(name + self.mask_suffix + '.*'))
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img_file = list(self.images_dir.glob(name + '.*'))
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assert len(mask_file) == 1, f'Either no mask or multiple masks found for the ID {name}: {mask_file}'
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assert len(img_file) == 1, f'Either no image or multiple images found for the ID {name}: {img_file}'
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mask = self.load(mask_file[0])
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img = self.load(img_file[0])
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assert img.size == mask.size, \
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f'Image and mask {name} should be the same size, but are {img.size} and {mask.size}'
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img = self.preprocess(img, self.scale, is_mask=False)
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mask = self.preprocess(mask, self.scale, is_mask=True)
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return {
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'image': torch.as_tensor(img.copy()).float().contiguous(),
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'mask': torch.as_tensor(mask.copy()).long().contiguous()
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}
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class CarvanaDataset(BasicDataset):
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def __init__(self, images_dir, masks_dir, scale=1):
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super().__init__(images_dir, masks_dir, scale, mask_suffix='_mask')
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