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