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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())
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