Real-ESRGAN

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import argparse
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import glob
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import os
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from PIL import Image
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def main(args):
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    # For DF2K, we consider the following three scales,
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    # and the smallest image whose shortest edge is 400
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    scale_list = [0.75, 0.5, 1 / 3]
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    shortest_edge = 400
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    path_list = sorted(glob.glob(os.path.join(args.input, '*')))
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    for path in path_list:
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        print(path)
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        basename = os.path.splitext(os.path.basename(path))[0]
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        img = Image.open(path)
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        width, height = img.size
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        for idx, scale in enumerate(scale_list):
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            print(f'\t{scale:.2f}')
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            rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
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            rlt.save(os.path.join(args.output, f'{basename}T{idx}.png'))
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        # save the smallest image which the shortest edge is 400
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        if width < height:
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            ratio = height / width
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            width = shortest_edge
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            height = int(width * ratio)
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        else:
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            ratio = width / height
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            height = shortest_edge
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            width = int(height * ratio)
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        rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS)
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        rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png'))
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if __name__ == '__main__':
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    """Generate multi-scale versions for GT images with LANCZOS resampling.
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    It is now used for DF2K dataset (DIV2K + Flickr 2K)
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    """
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    parser = argparse.ArgumentParser()
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    parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder')
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    parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder')
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    args = parser.parse_args()
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    os.makedirs(args.output, exist_ok=True)
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    main(args)
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