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import os |
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import cv2 |
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import numpy as np |
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import tqdm |
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from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset |
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from saicinpainting.evaluation.utils import load_yaml |
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def main(args): |
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config = load_yaml(args.config) |
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if not args.predictdir.endswith('/'): |
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args.predictdir += '/' |
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dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs) |
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os.makedirs(os.path.dirname(args.outpath), exist_ok=True) |
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for img_i in tqdm.trange(len(dataset)): |
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pred_fname = dataset.pred_filenames[img_i] |
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cur_out_fname = os.path.join(args.outpath, pred_fname[len(args.predictdir):]) |
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os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True) |
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sample = dataset[img_i] |
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img = sample['image'] |
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mask = sample['mask'] |
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inpainted = sample['inpainted'] |
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inpainted_blurred = cv2.GaussianBlur(np.transpose(inpainted, (1, 2, 0)), |
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ksize=(args.k, args.k), |
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sigmaX=args.s, sigmaY=args.s, |
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borderType=cv2.BORDER_REFLECT) |
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cur_res = (1 - mask) * np.transpose(img, (1, 2, 0)) + mask * inpainted_blurred |
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cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8') |
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cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR) |
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cv2.imwrite(cur_out_fname, cur_res) |
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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('config', type=str, help='Path to evaluation config') |
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aparser.add_argument('datadir', type=str, |
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help='Path to folder with images and masks (output of gen_mask_dataset.py)') |
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aparser.add_argument('predictdir', type=str, |
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help='Path to folder with predicts (e.g. predict_hifill_baseline.py)') |
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aparser.add_argument('outpath', type=str, help='Where to put results') |
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aparser.add_argument('-s', type=float, default=0.1, help='Gaussian blur sigma') |
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aparser.add_argument('-k', type=int, default=5, help='Kernel size in gaussian blur') |
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main(aparser.parse_args()) |
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