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import os |
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from data.pix2pix_dataset import Pix2pixDataset, BaseDataset |
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from data.image_folder import make_dataset |
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from torchvision import transforms |
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import os |
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from PIL import Image |
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class Summer2WinterYosemiteDataset(BaseDataset): |
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@staticmethod |
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def modify_commandline_options(parser, is_train): |
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parser = Pix2pixDataset.modify_commandline_options(parser, is_train) |
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parser.set_defaults(preprocess_mode='resize_and_crop') |
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parser.set_defaults(load_size=512) |
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parser.set_defaults(crop_size=256) |
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return parser |
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def initialize(self, opt): |
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self.opt = opt |
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label_paths, image_paths, instance_paths = self.get_paths(opt) |
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self.label_paths = label_paths[:opt.max_dataset_size] |
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self.image_paths = image_paths[:opt.max_dataset_size] |
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self.dataset_size = len(self.label_paths) |
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print(f"Number of labels: {len(self.label_paths)}, Number of images: {len(self.image_paths)}") |
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if len(self.label_paths) != len(self.image_paths): |
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raise ValueError("The number of labels and images do not match.") |
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def get_paths(self, opt): |
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croot = opt.croot |
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sroot = opt.sroot |
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c_image_dir = os.path.join(croot, f'{opt.phase}A') |
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s_image_dir = os.path.join(sroot, f'{opt.phase}B') |
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c_image_paths = sorted(make_dataset(c_image_dir, recursive=True)) |
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s_image_paths = sorted(make_dataset(s_image_dir, recursive=True)) |
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return c_image_paths, s_image_paths, [] |
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def __getitem__(self, index): |
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label_path = self.label_paths[index] |
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image_path = self.image_paths[index] |
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label = Image.open(label_path).convert('RGB') |
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image = Image.open(image_path).convert('RGB') |
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transform = transforms.Compose([ |
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transforms.Resize((self.opt.load_size, self.opt.load_size)), |
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transforms.RandomCrop(self.opt.crop_size), |
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transforms.ToTensor(), |
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) |
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]) |
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return {'image': transform(label), 'label': transform(image),"cpath":image_path} |
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def __len__(self): |
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return self.dataset_size |
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