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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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"""Inference demo for GFPGAN (for users). |
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""" |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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'-i', |
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'--input', |
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type=str, |
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default='inputs/whole_imgs', |
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help='Input image or folder. Default: inputs/whole_imgs') |
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parser.add_argument('-o', '--output', type=str, default='results', help='Output folder. Default: results') |
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parser.add_argument( |
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'-v', '--version', type=str, default='1.3', help='GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3') |
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parser.add_argument( |
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'-s', '--upscale', type=int, default=2, help='The final upsampling scale of the image. Default: 2') |
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parser.add_argument( |
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'--bg_upsampler', type=str, default='realesrgan', help='background upsampler. Default: realesrgan') |
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parser.add_argument( |
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'--bg_tile', |
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type=int, |
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default=400, |
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help='Tile size for background sampler, 0 for no tile during testing. Default: 400') |
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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', help='Only restore the center face') |
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parser.add_argument('--aligned', action='store_true', help='Input are aligned faces') |
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parser.add_argument( |
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'--ext', |
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type=str, |
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default='auto', |
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help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto') |
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parser.add_argument('-w', '--weight', type=float, default=0.5, help='Adjustable weights.') |
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args = parser.parse_args() |
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args = parser.parse_args() |
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if args.input.endswith('/'): |
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args.input = args.input[:-1] |
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if os.path.isfile(args.input): |
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img_list = [args.input] |
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else: |
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img_list = sorted(glob.glob(os.path.join(args.input, '*'))) |
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os.makedirs(args.output, exist_ok=True) |
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if args.bg_upsampler == 'realesrgan': |
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if not torch.cuda.is_available(): |
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import warnings |
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warnings.warn('The unoptimized RealESRGAN is 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 basicsr.archs.rrdbnet_arch import RRDBNet |
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from realesrgan import RealESRGANer |
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) |
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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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model=model, |
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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) |
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else: |
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bg_upsampler = None |
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if args.version == '1': |
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arch = 'original' |
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channel_multiplier = 1 |
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model_name = 'GFPGANv1' |
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url = 'https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth' |
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elif args.version == '1.2': |
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arch = 'clean' |
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channel_multiplier = 2 |
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model_name = 'GFPGANCleanv1-NoCE-C2' |
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url = 'https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth' |
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elif args.version == '1.3': |
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arch = 'clean' |
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channel_multiplier = 2 |
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model_name = 'GFPGANv1.3' |
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url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth' |
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elif args.version == '1.4': |
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arch = 'clean' |
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channel_multiplier = 2 |
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model_name = 'GFPGANv1.4' |
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url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth' |
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elif args.version == 'RestoreFormer': |
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arch = 'RestoreFormer' |
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channel_multiplier = 2 |
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model_name = 'RestoreFormer' |
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url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth' |
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else: |
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raise ValueError(f'Wrong model version {args.version}.') |
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model_path = os.path.join('gfpgan/weights', f'{model_name}.pth') |
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restorer = GFPGANer( |
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model_path=model_path, |
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upscale=args.upscale, |
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arch=arch, |
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channel_multiplier=channel_multiplier, |
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bg_upsampler=bg_upsampler) |
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for img_path in img_list: |
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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, |
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has_aligned=args.aligned, |
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only_center_face=args.only_center_face, |
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paste_back=True, |
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weight=args.weight) |
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for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)): |
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save_crop_path = os.path.join(args.output, 'cropped_faces', f'{basename}_{idx:02d}.png') |
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imwrite(cropped_face, save_crop_path) |
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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.output, 'restored_faces', save_face_name) |
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imwrite(restored_face, save_restore_path) |
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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.output, 'cmp', f'{basename}_{idx:02d}.png')) |
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if restored_img is not None: |
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if args.ext == 'auto': |
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extension = ext[1:] |
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else: |
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extension = args.ext |
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if args.suffix is not None: |
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save_restore_path = os.path.join(args.output, 'restored_imgs', f'{basename}_{args.suffix}.{extension}') |
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else: |
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save_restore_path = os.path.join(args.output, 'restored_imgs', f'{basename}.{extension}') |
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imwrite(restored_img, save_restore_path) |
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print(f'Results are in the [{args.output}] folder.') |
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if __name__ == '__main__': |
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main() |
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