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| from data.base_dataset import BaseDataset, get_transform | |
| from data.image_folder import make_dataset | |
| from PIL import Image | |
| class SingleDataset(BaseDataset): | |
| """This dataset class can load a set of images specified by the path --dataroot /path/to/data. | |
| It can be used for generating CycleGAN results only for one side with the model option '-model test'. | |
| """ | |
| def __init__(self, opt): | |
| """Initialize this dataset class. | |
| Parameters: | |
| opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions | |
| """ | |
| BaseDataset.__init__(self, opt) | |
| self.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size)) | |
| input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc | |
| self.transform = get_transform(opt, grayscale=(input_nc == 1)) | |
| def __getitem__(self, index): | |
| """Return a data point and its metadata information. | |
| Parameters: | |
| index - - a random integer for data indexing | |
| Returns a dictionary that contains A and A_paths | |
| A(tensor) - - an image in one domain | |
| A_paths(str) - - the path of the image | |
| """ | |
| A_path = self.A_paths[index] | |
| A_img = Image.open(A_path).convert('RGB') | |
| A = self.transform(A_img) | |
| return {'A': A, 'A_paths': A_path} | |
| def __len__(self): | |
| """Return the total number of images in the dataset.""" | |
| return len(self.A_paths) | |