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(): """Inference demo for GFPGAN (for users). """ parser = argparse.ArgumentParser() parser.add_argument( '-i', '--input', type=str, default='inputs/whole_imgs', help='Input image or folder. Default: inputs/whole_imgs') parser.add_argument('-o', '--output', type=str, default='results', help='Output folder. Default: results') # we use version to select models, which is more user-friendly parser.add_argument( '-v', '--version', type=str, default='1.3', help='GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3') parser.add_argument( '-s', '--upscale', type=int, default=2, help='The final upsampling scale of the image. Default: 2') parser.add_argument( '--bg_upsampler', type=str, default='realesrgan', help='background upsampler. Default: realesrgan') parser.add_argument( '--bg_tile', type=int, default=400, help='Tile size for background sampler, 0 for no tile during testing. Default: 400') parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces') parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face') parser.add_argument('--aligned', action='store_true', help='Input are aligned faces') parser.add_argument( '--ext', type=str, default='auto', help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto') parser.add_argument('-w', '--weight', type=float, default=0.5, help='Adjustable weights.') args = parser.parse_args() args = parser.parse_args() # ------------------------ input & output ------------------------ if args.input.endswith('/'): args.input = args.input[:-1] if os.path.isfile(args.input): img_list = [args.input] else: img_list = sorted(glob.glob(os.path.join(args.input, '*'))) os.makedirs(args.output, exist_ok=True) # ------------------------ set up background upsampler ------------------------ if args.bg_upsampler == 'realesrgan': if not torch.cuda.is_available(): # CPU import warnings warnings.warn('The unoptimized RealESRGAN is 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 basicsr.archs.rrdbnet_arch import RRDBNet from realesrgan import RealESRGANer model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2) bg_upsampler = RealESRGANer( scale=2, model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth', model=model, 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 ------------------------ if args.version == '1': arch = 'original' channel_multiplier = 1 model_name = 'GFPGANv1' url = 'https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth' elif args.version == '1.2': arch = 'clean' channel_multiplier = 2 model_name = 'GFPGANCleanv1-NoCE-C2' url = 'https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth' elif args.version == '1.3': arch = 'clean' channel_multiplier = 2 model_name = 'GFPGANv1.3' url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth' elif args.version == '1.4': arch = 'clean' channel_multiplier = 2 model_name = 'GFPGANv1.4' url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth' elif args.version == 'RestoreFormer': arch = 'RestoreFormer' channel_multiplier = 2 model_name = 'RestoreFormer' url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth' else: raise ValueError(f'Wrong model version {args.version}.') # determine model paths model_path = os.path.join('experiments/pretrained_models', model_name + '.pth') if not os.path.isfile(model_path): model_path = os.path.join('gfpgan/weights', model_name + '.pth') if not os.path.isfile(model_path): # download pre-trained models from url model_path = url restorer = GFPGANer( model_path=model_path, upscale=args.upscale, arch=arch, channel_multiplier=channel_multiplier, bg_upsampler=bg_upsampler) # ------------------------ restore ------------------------ 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) # restore faces and background if necessary cropped_faces, restored_faces, restored_img = restorer.enhance( input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=True, weight=args.weight) # 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.output, 'cropped_faces', 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.output, 'restored_faces', save_face_name) imwrite(restored_face, save_restore_path) # save comparison image cmp_img = np.concatenate((cropped_face, restored_face), axis=1) imwrite(cmp_img, os.path.join(args.output, 'cmp', f'{basename}_{idx:02d}.png')) # save restored img if restored_img is not None: if args.ext == 'auto': extension = ext[1:] else: extension = args.ext if args.suffix is not None: save_restore_path = os.path.join(args.output, 'restored_imgs', f'{basename}_{args.suffix}.{extension}') else: save_restore_path = os.path.join(args.output, 'restored_imgs', f'{basename}.{extension}') imwrite(restored_img, save_restore_path) print(f'Results are in the [{args.output}] folder.') if __name__ == '__main__': main()