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datasets.py
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70 lines (50 loc) · 1.97 KB
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import glob
import random
import os
import numpy as np
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
class ImageDataset(Dataset):
def __init__(self, root, transforms_=None, mode='train'):
self.transform = transforms.Compose(transforms_)
self.files = sorted(glob.glob(os.path.join(root, mode) + '/*.*'))
if mode == 'train':
self.files.extend(sorted(glob.glob(os.path.join(root, 'test') + '/*.*')))
def __getitem__(self, index):
file = self.files[index % len(self.files)]
img = Image.open(file)
w, h = img.size
img_A = img.crop((0, 0, w/2, h))
img_A = img_A.convert('RGB')
img_B = img.crop((w/2, 0, w, h))
img_B = img_B.convert('RGB')
if np.random.random() < 0.5:
img_A = Image.fromarray(np.array(img_A)[:, ::-1, :], 'RGB')
img_B = Image.fromarray(np.array(img_B)[:, ::-1, :], 'RGB')
img_A = self.transform(img_A)
img_B = self.transform(img_B)
img_C = img_A
img_A = img_B
img_B = img_C
return {'A': img_A, 'B': img_B}
def __len__(self):
return len(self.files)
class TestDataset(Dataset):
def __init__(self, root, transforms_=None, mode='test'):
self.transform = transforms.Compose(transforms_)
self.files = sorted(glob.glob(os.path.join(root, mode) + '/*.*'))
if mode == 'test':
self.files.extend(sorted(glob.glob(os.path.join(root, 'test') + '/*.*')))
def __getitem__(self, index):
img = Image.open(self.files[index % len(self.files)])
w, h = img.size
img_A = img.crop((0, 0, w/2, h))
img_A = img_A.convert('RGB')
img_B = img.crop((w/2, 0, w, h))
img_B = img_B.convert('RGB')
img_A = self.transform(img_A)
img_B = self.transform(img_B)
return {'A': img_A, 'B': img_B}
def __len__(self):
return len(self.files)