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#!/usr/bin/env python3
import glob
import logging
import os
import shutil
import sys
import traceback
from saicinpainting.evaluation.data import load_image
from saicinpainting.evaluation.utils import move_to_device
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['OPENBLAS_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'
os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
os.environ['NUMEXPR_NUM_THREADS'] = '1'
import cv2
import hydra
import numpy as np
import torch
import tqdm
import yaml
from omegaconf import OmegaConf
from torch.utils.data._utils.collate import default_collate
from saicinpainting.training.data.datasets import make_default_val_dataset
from saicinpainting.training.trainers import load_checkpoint
from saicinpainting.utils import register_debug_signal_handlers
LOGGER = logging.getLogger(__name__)
def main(args):
try:
if not args.indir.endswith('/'):
args.indir += '/'
for in_img in glob.glob(os.path.join(args.indir, '**', '*' + args.img_suffix), recursive=True):
if 'mask' in os.path.basename(in_img):
continue
out_img_path = os.path.join(args.outdir, os.path.splitext(in_img[len(args.indir):])[0] + '.png')
out_mask_path = f'{os.path.splitext(out_img_path)[0]}_mask.png'
os.makedirs(os.path.dirname(out_img_path), exist_ok=True)
img = load_image(in_img)
height, width = img.shape[1:]
pad_h, pad_w = int(height * args.coef / 2), int(width * args.coef / 2)
mask = np.zeros((height, width), dtype='uint8')
if args.expand:
img = np.pad(img, ((0, 0), (pad_h, pad_h), (pad_w, pad_w)))
mask = np.pad(mask, ((pad_h, pad_h), (pad_w, pad_w)), mode='constant', constant_values=255)
else:
mask[:pad_h] = 255
mask[-pad_h:] = 255
mask[:, :pad_w] = 255
mask[:, -pad_w:] = 255
# img = np.pad(img, ((0, 0), (pad_h * 2, pad_h * 2), (pad_w * 2, pad_w * 2)), mode='symmetric')
# mask = np.pad(mask, ((pad_h * 2, pad_h * 2), (pad_w * 2, pad_w * 2)), mode = 'symmetric')
img = np.clip(np.transpose(img, (1, 2, 0)) * 255, 0, 255).astype('uint8')
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.imwrite(out_img_path, img)
cv2.imwrite(out_mask_path, mask)
except KeyboardInterrupt:
LOGGER.warning('Interrupted by user')
except Exception as ex:
LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
sys.exit(1)
if __name__ == '__main__':
import argparse
aparser = argparse.ArgumentParser()
aparser.add_argument('indir', type=str, help='Root directory with images')
aparser.add_argument('outdir', type=str, help='Where to store results')
aparser.add_argument('--img-suffix', type=str, default='.png', help='Input image extension')
aparser.add_argument('--expand', action='store_true', help='Generate mask by padding (true) or by cropping (false)')
aparser.add_argument('--coef', type=float, default=0.2, help='How much to crop/expand in order to get masks')
main(aparser.parse_args())
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