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import os.path |
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from data.base_dataset import BaseDataset, get_params, get_transform |
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from PIL import Image |
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import linecache |
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class AlignedDataset(BaseDataset): |
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def initialize(self, opt): |
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self.opt = opt |
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self.root = opt.dataroot |
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self.fine_height=256 |
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self.fine_width=192 |
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self.dataset_size = len(open('demo.txt').readlines()) |
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dir_I = '_img' |
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self.dir_I = os.path.join(opt.dataroot, opt.phase + dir_I) |
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dir_C = '_clothes' |
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self.dir_C = os.path.join(opt.dataroot, opt.phase + dir_C) |
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dir_E = '_edge' |
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self.dir_E = os.path.join(opt.dataroot, opt.phase + dir_E) |
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def __getitem__(self, index): |
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file_path ='demo.txt' |
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im_name, c_name = linecache.getline(file_path, index+1).strip().split() |
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I_path = os.path.join(self.dir_I,im_name) |
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I = Image.open(I_path).convert('RGB') |
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I = I.resize((self.fine_width, self.fine_height)) |
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params = get_params(self.opt, I.size) |
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transform = get_transform(self.opt, params) |
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transform_E = get_transform(self.opt, params, method=Image.NEAREST, normalize=False) |
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I_tensor = transform(I) |
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C_path = os.path.join(self.dir_C,c_name) |
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C = Image.open(C_path).convert('RGB') |
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C_tensor = transform(C) |
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E_path = os.path.join(self.dir_E,c_name) |
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E = Image.open(E_path).convert('L') |
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E_tensor = transform_E(E) |
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input_dict = { 'image': I_tensor,'clothes': C_tensor, 'edge': E_tensor} |
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return input_dict |
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def __len__(self): |
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return self.dataset_size |
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def name(self): |
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return 'AlignedDataset' |
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