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import os.path
import torchvision.transforms as transforms
from data.base_dataset import BaseDataset
from data.image_folder import make_dataset
from PIL import Image
class SingleDataset(BaseDataset):
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.dir_A = os.path.join(opt.dataroot)
self.A_paths = make_dataset(self.dir_A)
self.A_paths = sorted(self.A_paths)
transform_list = []
if opt.resize_or_crop == 'resize_and_crop':
transform_list.append(transforms.Scale(opt.loadSize))
if opt.isTrain and not opt.no_flip:
transform_list.append(transforms.RandomHorizontalFlip())
if opt.resize_or_crop != 'no_resize':
transform_list.append(transforms.RandomCrop(opt.fineSize))
transform_list += [transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
self.transform = transforms.Compose(transform_list)
def __getitem__(self, index):
A_path = self.A_paths[index]
A_img = Image.open(A_path).convert('RGB')
A_img = self.transform(A_img)
return {'A': A_img, 'A_paths': A_path}
def __len__(self):
return len(self.A_paths)
def name(self):
return 'SingleImageDataset'
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