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| import argparse | |
| import cv2 | |
| import glob | |
| import os | |
| from basicsr.archs.rrdbnet_arch import RRDBNet | |
| from realesrgan import RealESRGANer | |
| from realesrgan.archs.srvgg_arch import SRVGGNetCompact | |
| def main(): | |
| """Inference demo for Real-ESRGAN. | |
| """ | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder') | |
| parser.add_argument( | |
| '-n', | |
| '--model_name', | |
| type=str, | |
| default='RealESRGAN_x4plus', | |
| help=('Model names: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus' | |
| 'RealESRGANv2-anime-xsx2 | RealESRGANv2-animevideo-xsx2-nousm | RealESRGANv2-animevideo-xsx2' | |
| 'RealESRGANv2-anime-xsx4 | RealESRGANv2-animevideo-xsx4-nousm | RealESRGANv2-animevideo-xsx4')) | |
| parser.add_argument('-o', '--output', type=str, default='results', help='Output folder') | |
| parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image') | |
| parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image') | |
| parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing') | |
| parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding') | |
| parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border') | |
| parser.add_argument('--face_enhance', action='store_true', help='Use GFPGAN to enhance face') | |
| parser.add_argument('--half', action='store_true', help='Use half precision during inference') | |
| parser.add_argument( | |
| '--alpha_upsampler', | |
| type=str, | |
| default='realesrgan', | |
| help='The upsampler for the alpha channels. Options: realesrgan | bicubic') | |
| parser.add_argument( | |
| '--ext', | |
| type=str, | |
| default='auto', | |
| help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs') | |
| args = parser.parse_args() | |
| # determine models according to model names | |
| args.model_name = args.model_name.split('.')[0] | |
| if args.model_name in ['RealESRGAN_x4plus', 'RealESRNet_x4plus']: # x4 RRDBNet model | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) | |
| netscale = 4 | |
| elif args.model_name in ['RealESRGAN_x4plus_anime_6B']: # x4 RRDBNet model with 6 blocks | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4) | |
| netscale = 4 | |
| elif args.model_name in ['RealESRGAN_x2plus']: # x2 RRDBNet model | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) | |
| netscale = 2 | |
| elif args.model_name in [ | |
| 'RealESRGANv2-anime-xsx2', 'RealESRGANv2-animevideo-xsx2-nousm', 'RealESRGANv2-animevideo-xsx2' | |
| ]: # x2 VGG-style model (XS size) | |
| model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=2, act_type='prelu') | |
| netscale = 2 | |
| elif args.model_name in [ | |
| 'RealESRGANv2-anime-xsx4', 'RealESRGANv2-animevideo-xsx4-nousm', 'RealESRGANv2-animevideo-xsx4' | |
| ]: # x4 VGG-style model (XS size) | |
| model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu') | |
| netscale = 4 | |
| # determine model paths | |
| model_path = os.path.join('experiments/pretrained_models', args.model_name + '.pth') | |
| if not os.path.isfile(model_path): | |
| model_path = os.path.join('realesrgan/weights', args.model_name + '.pth') | |
| if not os.path.isfile(model_path): | |
| raise ValueError(f'Model {args.model_name} does not exist.') | |
| # restorer | |
| upsampler = RealESRGANer( | |
| scale=netscale, | |
| model_path=model_path, | |
| model=model, | |
| tile=args.tile, | |
| tile_pad=args.tile_pad, | |
| pre_pad=args.pre_pad, | |
| half=args.half) | |
| if args.face_enhance: # Use GFPGAN for face enhancement | |
| from gfpgan import GFPGANer | |
| face_enhancer = GFPGANer( | |
| model_path='https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth', | |
| upscale=args.outscale, | |
| arch='clean', | |
| channel_multiplier=2, | |
| bg_upsampler=upsampler) | |
| os.makedirs(args.output, exist_ok=True) | |
| if os.path.isfile(args.input): | |
| paths = [args.input] | |
| else: | |
| paths = sorted(glob.glob(os.path.join(args.input, '*'))) | |
| for idx, path in enumerate(paths): | |
| imgname, extension = os.path.splitext(os.path.basename(path)) | |
| print('Testing', idx, imgname) | |
| img = cv2.imread(path, cv2.IMREAD_UNCHANGED) | |
| if len(img.shape) == 3 and img.shape[2] == 4: | |
| img_mode = 'RGBA' | |
| else: | |
| img_mode = None | |
| try: | |
| if args.face_enhance: | |
| _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) | |
| else: | |
| output, _ = upsampler.enhance(img, outscale=args.outscale) | |
| except RuntimeError as error: | |
| print('Error', error) | |
| print('If you encounter CUDA out of memory, try to set --tile with a smaller number.') | |
| else: | |
| if args.ext == 'auto': | |
| extension = extension[1:] | |
| else: | |
| extension = args.ext | |
| if img_mode == 'RGBA': # RGBA images should be saved in png format | |
| extension = 'png' | |
| save_path = os.path.join(args.output, f'{imgname}_{args.suffix}.{extension}') | |
| cv2.imwrite(save_path, output) | |
| if __name__ == '__main__': | |
| main() | |