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#!/usr/bin/env python3
import os
import numpy as np
import tqdm
from scipy.ndimage.morphology import distance_transform_edt
from saicinpainting.evaluation.data import InpaintingDataset
from saicinpainting.evaluation.vis import save_item_for_vis
def main(args):
dataset = InpaintingDataset(args.datadir, img_suffix='.png')
area_bins = np.linspace(0, 1, args.area_bins + 1)
heights = []
widths = []
image_areas = []
hole_areas = []
hole_area_percents = []
known_pixel_distances = []
area_bins_count = np.zeros(args.area_bins)
area_bin_titles = [f'{area_bins[i] * 100:.0f}-{area_bins[i + 1] * 100:.0f}' for i in range(args.area_bins)]
bin2i = [[] for _ in range(args.area_bins)]
for i, item in enumerate(tqdm.tqdm(dataset)):
h, w = item['image'].shape[1:]
heights.append(h)
widths.append(w)
full_area = h * w
image_areas.append(full_area)
bin_mask = item['mask'] > 0.5
hole_area = bin_mask.sum()
hole_areas.append(hole_area)
hole_percent = hole_area / full_area
hole_area_percents.append(hole_percent)
bin_i = np.clip(np.searchsorted(area_bins, hole_percent) - 1, 0, len(area_bins_count) - 1)
area_bins_count[bin_i] += 1
bin2i[bin_i].append(i)
cur_dist = distance_transform_edt(bin_mask)
cur_dist_inside_mask = cur_dist[bin_mask]
known_pixel_distances.append(cur_dist_inside_mask.mean())
os.makedirs(args.outdir, exist_ok=True)
with open(os.path.join(args.outdir, 'summary.txt'), 'w') as f:
f.write(f'''Location: {args.datadir}
Number of samples: {len(dataset)}
Image height: min {min(heights):5d} max {max(heights):5d} mean {np.mean(heights):.2f}
Image width: min {min(widths):5d} max {max(widths):5d} mean {np.mean(widths):.2f}
Image area: min {min(image_areas):7d} max {max(image_areas):7d} mean {np.mean(image_areas):.2f}
Hole area: min {min(hole_areas):7d} max {max(hole_areas):7d} mean {np.mean(hole_areas):.2f}
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}
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}
Stats by hole area %:
''')
for bin_i in range(args.area_bins):
f.write(f'{area_bin_titles[bin_i]}%: '
f'samples number {area_bins_count[bin_i]}, '
f'{area_bins_count[bin_i] / len(dataset) * 100:.1f}%\n')
for bin_i in range(args.area_bins):
bindir = os.path.join(args.outdir, 'samples', area_bin_titles[bin_i])
os.makedirs(bindir, exist_ok=True)
bin_idx = bin2i[bin_i]
for sample_i in np.random.choice(bin_idx, size=min(len(bin_idx), args.samples_n), replace=False):
save_item_for_vis(dataset[sample_i], os.path.join(bindir, f'{sample_i}.png'))
if __name__ == '__main__':
import argparse
aparser = argparse.ArgumentParser()
aparser.add_argument('datadir', type=str,
help='Path to folder with images and masks (output of gen_mask_dataset.py)')
aparser.add_argument('outdir', type=str, help='Where to put results')
aparser.add_argument('--samples-n', type=int, default=10,
help='Number of sample images with masks to copy for visualization for each area bin')
aparser.add_argument('--area-bins', type=int, default=10, help='How many area bins to have')
main(aparser.parse_args())
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