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80 lines
2.5 KiB
80 lines
2.5 KiB
import os
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import math
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import numpy as np
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from glob import glob
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from random import shuffle
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from PIL import Image, ImageFilter
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import torch
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import torchvision.transforms.functional as F
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import torchvision.transforms as transforms
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from torch.utils.data import Dataset, DataLoader
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class InpaintingData(Dataset):
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def __init__(self, args):
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super(Dataset, self).__init__()
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self.w = self.h = args.image_size
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self.mask_type = args.mask_type
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# image and mask
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self.image_path = []
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for ext in ['*.jpg', '*.png']:
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self.image_path.extend(glob(os.path.join(args.dir_image, args.data_train, ext)))
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self.mask_path = glob(os.path.join(args.dir_mask, args.mask_type, '*.png'))
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# augmentation
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self.img_trans = transforms.Compose([
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transforms.RandomResizedCrop(args.image_size),
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transforms.RandomHorizontalFlip(),
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transforms.ColorJitter(0.05, 0.05, 0.05, 0.05),
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transforms.ToTensor()])
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self.mask_trans = transforms.Compose([
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transforms.Resize(args.image_size, interpolation=transforms.InterpolationMode.NEAREST),
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transforms.RandomHorizontalFlip(),
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transforms.RandomRotation(
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(0, 45), interpolation=transforms.InterpolationMode.NEAREST),
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])
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def __len__(self):
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return len(self.image_path)
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def __getitem__(self, index):
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# load image
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image = Image.open(self.image_path[index]).convert('RGB')
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filename = os.path.basename(self.image_path[index])
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if self.mask_type == 'pconv':
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index = np.random.randint(0, len(self.mask_path))
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mask = Image.open(self.mask_path[index])
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mask = mask.convert('L')
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else:
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mask = np.zeros((self.h, self.w)).astype(np.uint8)
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mask[self.h//4:self.h//4*3, self.w//4:self.w//4*3] = 1
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mask = Image.fromarray(m).convert('L')
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# augment
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image = self.img_trans(image) * 2. - 1.
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mask = F.to_tensor(self.mask_trans(mask))
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return image, mask, filename
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if __name__ == '__main__':
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from attrdict import AttrDict
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args = {
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'dir_image': '../../../dataset',
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'data_train': 'places2',
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'dir_mask': '../../../dataset',
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'mask_type': 'pconv',
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'image_size': 512
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}
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args = AttrDict(args)
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data = InpaintingData(args)
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print(len(data), len(data.mask_path))
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img, mask, filename = data[0]
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print(img.size(), mask.size(), filename) |