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#!/usr/bin/env python3
import os
import numpy as np
import tqdm
from skimage import io
from skimage.segmentation import mark_boundaries
from saicinpainting.evaluation.data import InpaintingDataset
from saicinpainting.evaluation.vis import save_item_for_vis
def save_mask_for_sidebyside(item, out_file):
mask = item['mask']# > 0.5
if mask.ndim == 3:
mask = mask[0]
mask = np.clip(mask * 255, 0, 255).astype('uint8')
io.imsave(out_file, mask)
def save_img_for_sidebyside(item, out_file):
img = np.transpose(item['image'], (1, 2, 0))
img = np.clip(img * 255, 0, 255).astype('uint8')
io.imsave(out_file, img)
def save_masked_img_for_sidebyside(item, out_file):
mask = item['mask']
img = item['image']
img = (1-mask) * img + mask
img = np.transpose(img, (1, 2, 0))
img = np.clip(img * 255, 0, 255).astype('uint8')
io.imsave(out_file, img)
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 = []
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)
hole_area = (item['mask'] == 1).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)
os.makedirs(args.outdir, exist_ok=True)
for bin_i in range(args.area_bins):
bindir = os.path.join(args.outdir, 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):
item = dataset[sample_i]
path = os.path.join(bindir, dataset.img_filenames[sample_i].split('/')[-1])
save_masked_img_for_sidebyside(item, path)
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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