init

backend/inpaint/lama/bin/blur_predicts.py

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+#!/usr/bin/env python3
+
+import os
+
+import cv2
+import numpy as np
+import tqdm
+
+from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset
+from saicinpainting.evaluation.utils import load_yaml
+
+
+def main(args):
+    config = load_yaml(args.config)
+
+    if not args.predictdir.endswith('/'):
+        args.predictdir += '/'
+
+    dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs)
+
+    os.makedirs(os.path.dirname(args.outpath), exist_ok=True)
+
+    for img_i in tqdm.trange(len(dataset)):
+        pred_fname = dataset.pred_filenames[img_i]
+        cur_out_fname = os.path.join(args.outpath, pred_fname[len(args.predictdir):])
+        os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
+
+        sample = dataset[img_i]
+        img = sample['image']
+        mask = sample['mask']
+        inpainted = sample['inpainted']
+
+        inpainted_blurred = cv2.GaussianBlur(np.transpose(inpainted, (1, 2, 0)),
+                                             ksize=(args.k, args.k),
+                                             sigmaX=args.s, sigmaY=args.s,
+                                             borderType=cv2.BORDER_REFLECT)
+
+        cur_res = (1 - mask) * np.transpose(img, (1, 2, 0)) + mask * inpainted_blurred
+        cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
+        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
+        cv2.imwrite(cur_out_fname, cur_res)
+
+
+if __name__ == '__main__':
+    import argparse
+
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument('config', type=str, help='Path to evaluation config')
+    aparser.add_argument('datadir', type=str,
+                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')
+    aparser.add_argument('predictdir', type=str,
+                         help='Path to folder with predicts (e.g. predict_hifill_baseline.py)')
+    aparser.add_argument('outpath', type=str, help='Where to put results')
+    aparser.add_argument('-s', type=float, default=0.1, help='Gaussian blur sigma')
+    aparser.add_argument('-k', type=int, default=5, help='Kernel size in gaussian blur')
+
+    main(aparser.parse_args())