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import os |
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import numpy as np |
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import tqdm |
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from scipy.ndimage.morphology import distance_transform_edt |
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from saicinpainting.evaluation.data import InpaintingDataset |
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from saicinpainting.evaluation.vis import save_item_for_vis |
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def main(args): |
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dataset = InpaintingDataset(args.datadir, img_suffix='.png') |
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area_bins = np.linspace(0, 1, args.area_bins + 1) |
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heights = [] |
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widths = [] |
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image_areas = [] |
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hole_areas = [] |
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hole_area_percents = [] |
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known_pixel_distances = [] |
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area_bins_count = np.zeros(args.area_bins) |
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area_bin_titles = [f'{area_bins[i] * 100:.0f}-{area_bins[i + 1] * 100:.0f}' for i in range(args.area_bins)] |
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bin2i = [[] for _ in range(args.area_bins)] |
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for i, item in enumerate(tqdm.tqdm(dataset)): |
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h, w = item['image'].shape[1:] |
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heights.append(h) |
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widths.append(w) |
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full_area = h * w |
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image_areas.append(full_area) |
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bin_mask = item['mask'] > 0.5 |
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hole_area = bin_mask.sum() |
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hole_areas.append(hole_area) |
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hole_percent = hole_area / full_area |
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hole_area_percents.append(hole_percent) |
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bin_i = np.clip(np.searchsorted(area_bins, hole_percent) - 1, 0, len(area_bins_count) - 1) |
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area_bins_count[bin_i] += 1 |
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bin2i[bin_i].append(i) |
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cur_dist = distance_transform_edt(bin_mask) |
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cur_dist_inside_mask = cur_dist[bin_mask] |
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known_pixel_distances.append(cur_dist_inside_mask.mean()) |
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os.makedirs(args.outdir, exist_ok=True) |
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with open(os.path.join(args.outdir, 'summary.txt'), 'w') as f: |
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f.write(f'''Location: {args.datadir} |
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Number of samples: {len(dataset)} |
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Image height: min {min(heights):5d} max {max(heights):5d} mean {np.mean(heights):.2f} |
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Image width: min {min(widths):5d} max {max(widths):5d} mean {np.mean(widths):.2f} |
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Image area: min {min(image_areas):7d} max {max(image_areas):7d} mean {np.mean(image_areas):.2f} |
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Hole area: min {min(hole_areas):7d} max {max(hole_areas):7d} mean {np.mean(hole_areas):.2f} |
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Hole area %: min {min(hole_area_percents) * 100:2.2f} max {max(hole_area_percents) * 100:2.2f} mean {np.mean(hole_area_percents) * 100:2.2f} |
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Dist 2known: min {min(known_pixel_distances):2.2f} max {max(known_pixel_distances):2.2f} mean {np.mean(known_pixel_distances):2.2f} median {np.median(known_pixel_distances):2.2f} |
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Stats by hole area %: |
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''') |
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for bin_i in range(args.area_bins): |
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f.write(f'{area_bin_titles[bin_i]}%: ' |
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f'samples number {area_bins_count[bin_i]}, ' |
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f'{area_bins_count[bin_i] / len(dataset) * 100:.1f}%\n') |
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for bin_i in range(args.area_bins): |
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bindir = os.path.join(args.outdir, 'samples', area_bin_titles[bin_i]) |
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os.makedirs(bindir, exist_ok=True) |
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bin_idx = bin2i[bin_i] |
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for sample_i in np.random.choice(bin_idx, size=min(len(bin_idx), args.samples_n), replace=False): |
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save_item_for_vis(dataset[sample_i], os.path.join(bindir, f'{sample_i}.png')) |
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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('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('outdir', type=str, help='Where to put results') |
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aparser.add_argument('--samples-n', type=int, default=10, |
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help='Number of sample images with masks to copy for visualization for each area bin') |
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aparser.add_argument('--area-bins', type=int, default=10, help='How many area bins to have') |
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main(aparser.parse_args()) |
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