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import numpy as np |
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import torch |
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from .data_process import DataProcess |
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class NormalizeImage(DataProcess): |
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RGB_MEAN = np.array([122.67891434, 116.66876762, 104.00698793]) |
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def process(self, data): |
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assert 'image' in data, '`image` in data is required by this process' |
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image = data['image'] |
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image -= self.RGB_MEAN |
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image /= 255. |
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image = torch.from_numpy(image).permute(2, 0, 1).float() |
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data['image'] = image |
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return data |
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@classmethod |
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def restore(self, image): |
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image = image.permute(1, 2, 0).to('cpu').numpy() |
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image = image * 255. |
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image += self.RGB_MEAN |
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image = image.astype(np.uint8) |
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return image |
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