forked from mrq/DL-Art-School
22 lines
662 B
Python
22 lines
662 B
Python
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import torch
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import torchvision
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from PIL import Image
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def load_img(path):
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im = Image.open(path)
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return torchvision.transforms.ToTensor()(im)
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def save_img(t, path):
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torchvision.utils.save_image(t, path)
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img = load_img("me.png")
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# add zeros to the imaginary component
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img = torch.stack([img, torch.zeros_like(img)], dim=-1)
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fft = torch.fft(img, signal_ndim=2)
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fft_d = torch.zeros_like(fft)
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for i in range(-5, 5):
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diag = torch.diagonal(fft, offset=i, dim1=1, dim2=2)
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diag_em = torch.diag_embed(diag, offset=i, dim1=1, dim2=2)
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fft_d += diag_em
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resamp_img = torch.ifft(fft_d, signal_ndim=2)[:, :, :, 0]
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save_img(resamp_img, "resampled.png")
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