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