init

backend/inpaint/lama/bin/side_by_side.py

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+#!/usr/bin/env python3
+import os
+import random
+
+import cv2
+import numpy as np
+
+from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset
+from saicinpainting.evaluation.utils import load_yaml
+from saicinpainting.training.visualizers.base import visualize_mask_and_images
+
+
+def main(args):
+    config = load_yaml(args.config)
+
+    datasets = [PrecomputedInpaintingResultsDataset(args.datadir, cur_predictdir, **config.dataset_kwargs)
+                for cur_predictdir in args.predictdirs]
+    assert len({len(ds) for ds in datasets}) == 1
+    len_first = len(datasets[0])
+
+    indices = list(range(len_first))
+    if len_first > args.max_n:
+        indices = sorted(random.sample(indices, args.max_n))
+
+    os.makedirs(args.outpath, exist_ok=True)
+
+    filename2i = {}
+
+    keys = ['image'] + [i for i in range(len(datasets))]
+    for img_i in indices:
+        try:
+            mask_fname = os.path.basename(datasets[0].mask_filenames[img_i])
+            if mask_fname in filename2i:
+                filename2i[mask_fname] += 1
+                idx = filename2i[mask_fname]
+                mask_fname_only, ext = os.path.split(mask_fname)
+                mask_fname = f'{mask_fname_only}_{idx}{ext}'
+            else:
+                filename2i[mask_fname] = 1
+
+            cur_vis_dict = datasets[0][img_i]
+            for ds_i, ds in enumerate(datasets):
+                cur_vis_dict[ds_i] = ds[img_i]['inpainted']
+
+            vis_img = visualize_mask_and_images(cur_vis_dict, keys,
+                                                last_without_mask=False,
+                                                mask_only_first=True,
+                                                black_mask=args.black)
+            vis_img = np.clip(vis_img * 255, 0, 255).astype('uint8')
+
+            out_fname = os.path.join(args.outpath, mask_fname)
+
+
+
+            vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR)
+            cv2.imwrite(out_fname, vis_img)
+        except Exception as ex:
+            print(f'Could not process {img_i} due to {ex}')
+
+
+if __name__ == '__main__':
+    import argparse
+
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument('--max-n', type=int, default=100, help='Maximum number of images to print')
+    aparser.add_argument('--black', action='store_true', help='Whether to fill mask on GT with black')
+    aparser.add_argument('config', type=str, help='Path to evaluation config (e.g. configs/eval1.yaml)')
+    aparser.add_argument('outpath', type=str, help='Where to put results')
+    aparser.add_argument('datadir', type=str,
+                         help='Path to folder with images and masks')
+    aparser.add_argument('predictdirs', type=str,
+                         nargs='+',
+                         help='Path to folders with predicts')
+
+
+    main(aparser.parse_args())