63 lines
2 KiB
Python
63 lines
2 KiB
Python
from os.path import splitext
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from os import listdir
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import numpy as np
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from glob import glob
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import torch
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from torch.utils.data import Dataset
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import logging
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from PIL import Image
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class BasicDataset(Dataset):
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def __init__(self, imgs_dir, masks_dir, scale=1):
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self.imgs_dir = imgs_dir
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self.masks_dir = masks_dir
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self.scale = scale
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assert 0 < scale <= 1, 'Scale must be between 0 and 1'
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self.ids = [splitext(file)[0] for file in listdir(imgs_dir)
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if not file.startswith('.')]
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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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@classmethod
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def preprocess(cls, pil_img, scale):
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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'
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pil_img = pil_img.resize((newW, newH))
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img_nd = np.array(pil_img)
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if len(img_nd.shape) == 2:
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img_nd = np.expand_dims(img_nd, axis=2)
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# HWC to CHW
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img_trans = img_nd.transpose((2, 0, 1))
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if img_trans.max() > 1:
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img_trans = img_trans / 255
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return img_trans
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def __getitem__(self, i):
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idx = self.ids[i]
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mask_file = glob(self.masks_dir + idx + '*')
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img_file = glob(self.imgs_dir + idx + '*')
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assert len(mask_file) == 1, \
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f'Either no mask or multiple masks found for the ID {idx}: {mask_file}'
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assert len(img_file) == 1, \
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f'Either no image or multiple images found for the ID {idx}: {img_file}'
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mask = Image.open(mask_file[0])
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img = Image.open(img_file[0])
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assert img.size == mask.size, \
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f'Image and mask {idx} should be the same size, but are {img.size} and {mask.size}'
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img = self.preprocess(img, self.scale)
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mask = self.preprocess(mask, self.scale)
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return {'image': torch.from_numpy(img), 'mask': torch.from_numpy(mask)}
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