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| import os.path | |
| from data.base_dataset import BaseDataset, get_transform | |
| from data.image_folder import make_dataset | |
| from PIL import Image | |
| import random | |
| class UnalignedDataset(BaseDataset): | |
| def modify_commandline_options(parser, is_train): | |
| return parser | |
| def initialize(self, opt): | |
| self.opt = opt | |
| self.root = opt.dataroot | |
| self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A') | |
| self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') | |
| self.A_paths = make_dataset(self.dir_A) | |
| self.B_paths = make_dataset(self.dir_B) | |
| self.A_paths = sorted(self.A_paths) | |
| self.B_paths = sorted(self.B_paths) | |
| self.A_size = len(self.A_paths) | |
| self.B_size = len(self.B_paths) | |
| self.transform = get_transform(opt) | |
| def __getitem__(self, index): | |
| A_path = self.A_paths[index % self.A_size] | |
| if self.opt.serial_batches: | |
| index_B = index % self.B_size | |
| else: | |
| index_B = random.randint(0, self.B_size - 1) | |
| B_path = self.B_paths[index_B] | |
| # print('(A, B) = (%d, %d)' % (index_A, index_B)) | |
| A_img = Image.open(A_path).convert('RGB') | |
| B_img = Image.open(B_path).convert('RGB') | |
| A = self.transform(A_img) | |
| B = self.transform(B_img) | |
| if self.opt.which_direction == 'BtoA': | |
| input_nc = self.opt.output_nc | |
| output_nc = self.opt.input_nc | |
| else: | |
| input_nc = self.opt.input_nc | |
| output_nc = self.opt.output_nc | |
| if input_nc == 1: # RGB to gray | |
| tmp = A[0, ...] * 0.299 + A[1, ...] * 0.587 + A[2, ...] * 0.114 | |
| A = tmp.unsqueeze(0) | |
| if output_nc == 1: # RGB to gray | |
| tmp = B[0, ...] * 0.299 + B[1, ...] * 0.587 + B[2, ...] * 0.114 | |
| B = tmp.unsqueeze(0) | |
| return {'A': A, 'B': B, | |
| 'A_paths': A_path, 'B_paths': B_path} | |
| def __len__(self): | |
| return max(self.A_size, self.B_size) | |
| def name(self): | |
| return 'UnalignedDataset' | |