deep-learninginpaintingcomputer-visionimage-inpaintingcolab-notebookhigh-resolutioncolabgenerative-adversarial-networkscnngenerative-adversarial-networkganfourier-transformfourier-convolutionspytorchfourierinpainting-methodsdeep-neural-networksinpainting-algorithm
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64 lines
1.6 KiB
64 lines
1.6 KiB
3 years ago
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import PIL.Image as Image
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import numpy as np
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import os
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def main(args):
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if not args.indir.endswith('/'):
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args.indir += '/'
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os.makedirs(args.outdir, exist_ok=True)
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src_images = [
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args.indir+fname for fname in os.listdir(args.indir)]
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tgt_masks = [
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args.outdir+fname[:-4] + f'_mask000.png'
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for fname in os.listdir(args.indir)]
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for img_name, msk_name in zip(src_images, tgt_masks):
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#print(img)
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#print(msk)
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image = Image.open(img_name).convert('RGB')
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image = np.transpose(np.array(image), (2, 0, 1))
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mask = (image == 255).astype(int)
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print(mask.dtype, mask.shape)
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Image.fromarray(
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np.clip(mask[0,:,:] * 255, 0, 255).astype('uint8'),mode='L'
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).save(msk_name)
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'''
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for infile in src_images:
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try:
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file_relpath = infile[len(indir):]
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img_outpath = os.path.join(outdir, file_relpath)
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os.makedirs(os.path.dirname(img_outpath), exist_ok=True)
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image = Image.open(infile).convert('RGB')
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mask =
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Image.fromarray(
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np.clip(
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cur_mask * 255, 0, 255).astype('uint8'),
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mode='L'
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).save(cur_basename + f'_mask{i:03d}.png')
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'''
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if __name__ == '__main__':
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import argparse
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aparser = argparse.ArgumentParser()
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aparser.add_argument('--indir', type=str, help='Path to folder with images')
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aparser.add_argument('--outdir', type=str, help='Path to folder to store aligned images and masks to')
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main(aparser.parse_args())
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