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import argparse
import cv2
import glob
import numpy as np
import os
import torch
from basicsr.utils import imwrite
from gfpgan import GFPGANer
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--upscale', type=int, default=2)
parser.add_argument('--arch', type=str, default='clean')
parser.add_argument('--channel', type=int, default=2)
parser.add_argument('--model_path', type=str, default='GFPGANCleanv1-NoCE-C2.pth')
parser.add_argument('--bg_upsampler', type=str, default='realesrgan')
parser.add_argument('--bg_tile', type=int, default=400)
parser.add_argument('--test_path', type=str, default='inputs/whole_imgs')
parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
parser.add_argument('--only_center_face', action='store_true')
parser.add_argument('--aligned', action='store_true')
parser.add_argument('--paste_back', action='store_false')
parser.add_argument('--save_root', type=str, default='results')
args = parser.parse_args()
if args.test_path.endswith('/'):
args.test_path = args.test_path[:-1]
os.makedirs(args.save_root, exist_ok=True)
# background upsampler
if args.bg_upsampler == 'realesrgan':
if not torch.cuda.is_available(): # CPU
import warnings
warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
'If you really want to use it, please modify the corresponding codes.')
bg_upsampler = None
else:
from realesrgan import RealESRGANer
bg_upsampler = RealESRGANer(
scale=2,
model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
tile=args.bg_tile,
tile_pad=10,
pre_pad=0,
half=True) # need to set False in CPU mode
else:
bg_upsampler = None
# set up GFPGAN restorer
restorer = GFPGANer(
model_path=args.model_path,
upscale=args.upscale,
arch=args.arch,
channel_multiplier=args.channel,
bg_upsampler=bg_upsampler)
img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))
for img_path in img_list:
# read image
img_name = os.path.basename(img_path)
print(f'Processing {img_name} ...')
basename, ext = os.path.splitext(img_name)
input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
cropped_faces, restored_faces, restored_img = restorer.enhance(
input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back)
# save faces
for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)):
# save cropped face
save_crop_path = os.path.join(args.save_root, f'{basename}_{idx:02d}.png')
imwrite(cropped_face, save_crop_path)
# save restored face
if args.suffix is not None:
save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png'
else:
save_face_name = f'{basename}_{idx:02d}.png'
save_restore_path = os.path.join(args.save_root, save_face_name)
imwrite(restored_face, save_restore_path)
# save cmp image
cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
imwrite(cmp_img, os.path.join(args.save_root, f'{basename}_{idx:02d}.png'))
# save restored img
if restored_img is not None:
if args.suffix is not None:
save_restore_path = os.path.join(args.save_root, f'{basename}_{args.suffix}{ext}')
else:
save_restore_path = os.path.join(args.save_root, img_name)
imwrite(restored_img, save_restore_path)
print(f'Results are in the [{args.save_root}] folder.')
if __name__ == '__main__':
main()
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