File size: 4,111 Bytes
0378d2e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 |
import argparse
import glob
import os
from concurrent.futures import ThreadPoolExecutor, as_completed
from PIL import Image
from tqdm import tqdm
def process_image(path, output_dir, scale_list, shortest_edge):
"""Обрабатывает одно изображение, создавая масштабированные версии."""
basename = os.path.splitext(os.path.basename(path))[0]
# Проверяем, все ли файлы уже существуют
output_files = [os.path.join(output_dir, f'{basename}T{idx}.png') for idx in range(len(scale_list) + 1)]
if all(os.path.exists(f) for f in output_files):
return 'skipped'
img = Image.open(path)
width, height = img.size
for idx, scale in enumerate(scale_list):
output_path = os.path.join(output_dir, f'{basename}T{idx}.png')
if not os.path.exists(output_path):
rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS)
rlt.save(output_path)
# save the smallest image which the shortest edge is 400
output_path = os.path.join(output_dir, f'{basename}T{len(scale_list)}.png')
if not os.path.exists(output_path):
if width < height:
ratio = height / width
new_width = shortest_edge
new_height = int(new_width * ratio)
else:
ratio = width / height
new_height = shortest_edge
new_width = int(new_height * ratio)
rlt = img.resize((int(new_width), int(new_height)), resample=Image.LANCZOS)
rlt.save(output_path)
return 'processed'
def main(args):
# For DF2K, we consider the following three scales,
# and the smallest image whose shortest edge is 400
scale_list = [0.75, 0.5, 1 / 3]
shortest_edge = 400
path_list = sorted(glob.glob(os.path.join(args.input, '*')))
if not path_list:
print('Изображения не найдены')
return
# Используем ThreadPoolExecutor для параллельной обработки
with ThreadPoolExecutor(max_workers=args.workers) as executor:
futures = {
executor.submit(process_image, path, args.output, scale_list, shortest_edge): path
for path in path_list
}
# Ожидаем завершения всех задач с прогресс-баром
processed = 0
skipped = 0
errors = 0
with tqdm(total=len(path_list), desc='Обработка изображений', unit='img') as pbar:
for future in as_completed(futures):
try:
result = future.result()
if result == 'skipped':
skipped += 1
else:
processed += 1
pbar.set_postfix({'обработано': processed, 'пропущено': skipped, 'ошибок': errors})
except Exception as e:
path = futures[future]
errors += 1
tqdm.write(f'Ошибка при обработке {path}: {e}')
pbar.set_postfix({'обработано': processed, 'пропущено': skipped, 'ошибок': errors})
finally:
pbar.update(1)
if __name__ == '__main__':
"""Generate multi-scale versions for GT images with LANCZOS resampling.
It is now used for DF2K dataset (DIV2K + Flickr 2K)
Multithreaded version.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder')
parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder')
parser.add_argument('--workers', type=int, default=None, help='Number of worker threads (default: CPU count)')
args = parser.parse_args()
if args.workers is None:
import multiprocessing
args.workers = multiprocessing.cpu_count()
os.makedirs(args.output, exist_ok=True)
main(args)
|