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48 lines
1.5 KiB
48 lines
1.5 KiB
import argparse
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import numpy as np
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from tqdm import tqdm
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from glob import glob
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from PIL import Image
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from multiprocessing import Pool
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from metric import metric as module_metric
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parser = argparse.ArgumentParser(description='Image Inpainting')
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parser.add_argument('--real_dir', required=True, type=str)
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parser.add_argument('--fake_dir', required=True, type=str)
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parser.add_argument("--metric", type=str, nargs="+")
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args = parser.parse_args()
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def read_img(name_pair):
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rname, fname = name_pair
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rimg = Image.open(rname)
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fimg = Image.open(fname)
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return np.array(rimg), np.array(fimg)
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def main(num_worker=8):
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real_names = sorted(list(glob(f'{args.real_dir}/*.png')))
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fake_names = sorted(list(glob(f'{args.fake_dir}/*.png')))
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print(f'real images: {len(real_names)}, fake images: {len(fake_names)}')
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real_images = []
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fake_images = []
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pool = Pool(num_worker)
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for rimg, fimg in tqdm(pool.imap_unordered(read_img, zip(real_names, fake_names)), total=len(real_names), desc='loading images'):
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real_images.append(rimg)
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fake_images.append(fimg)
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# metrics prepare for image assesments
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metrics = {met: getattr(module_metric, met) for met in args.metric}
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evaluation_scores = {key: 0 for key,val in metrics.items()}
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for key, val in metrics.items():
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evaluation_scores[key] = val(real_images, fake_images, num_worker=num_worker)
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print(' '.join(['{}: {:6f},'.format(key, val) for key,val in evaluation_scores.items()]))
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if __name__ == '__main__':
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main() |