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import os |
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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 skimage import color |
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from PIL import Image |
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import numpy as np |
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import torchvision.transforms as transforms |
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class ColorizationDataset(BaseDataset): |
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"""This dataset class can load a set of natural images in RGB, and convert RGB format into (L, ab) pairs in Lab color space. |
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This dataset is required by pix2pix-based colorization model ('--model colorization') |
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""" |
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@staticmethod |
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def modify_commandline_options(parser, is_train): |
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"""Add new dataset-specific options, and rewrite default values for existing options. |
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Parameters: |
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parser -- original option parser |
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is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. |
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Returns: |
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the modified parser. |
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By default, the number of channels for input image is 1 (L) and |
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the number of channels for output image is 2 (ab). The direction is from A to B |
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""" |
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parser.set_defaults(input_nc=1, output_nc=2, direction='AtoB') |
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return parser |
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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.dir = os.path.join(opt.dataroot, opt.phase) |
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self.AB_paths = sorted(make_dataset(self.dir, opt.max_dataset_size)) |
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assert(opt.input_nc == 1 and opt.output_nc == 2 and opt.direction == 'AtoB') |
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self.transform = get_transform(self.opt, convert=False) |
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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, B, A_paths and B_paths |
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A (tensor) - - the L channel of an image |
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B (tensor) - - the ab channels of the same image |
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A_paths (str) - - image paths |
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B_paths (str) - - image paths (same as A_paths) |
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""" |
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path = self.AB_paths[index] |
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im = Image.open(path).convert('RGB') |
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im = self.transform(im) |
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im = np.array(im) |
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lab = color.rgb2lab(im).astype(np.float32) |
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lab_t = transforms.ToTensor()(lab) |
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A = lab_t[[0], ...] / 50.0 - 1.0 |
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B = lab_t[[1, 2], ...] / 110.0 |
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return {'A': A, 'B': B, 'A_paths': path, 'B_paths': 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.AB_paths) |
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