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. """ parser = argparse.ArgumentParser() parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image') parser.add_argument('--arch', type=str, default='clean', help='The GFPGAN architecture. Option: clean | original') parser.add_argument('--channel', type=int, default=2, help='Channel multiplier for large networks of StyleGAN2') parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANv1.3.pth') parser.add_argument('--bg_upsampler', type=str, default='realesrgan', help='background upsampler') parser.add_argument( '--bg_tile', type=int, default=400, help='Tile size for background sampler, 0 for no tile during testing') parser.add_argument('--test_path', type=str, default='inputs/whole_imgs', help='Input folder') 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('--paste_back', action='store_false', help='Paste the restored faces back to images') parser.add_argument('--save_root', type=str, default='results', help='Path to save root') 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() 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 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 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) # 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=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, '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.save_root, '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.save_root, '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.save_root, 'restored_imgs', f'{basename}_{args.suffix}.{extension}') else: save_restore_path = os.path.join(args.save_root, 'restored_imgs', f'{basename}.{extension}') imwrite(restored_img, save_restore_path) print(f'Results are in the [{args.save_root}] folder.') if __name__ == '__main__': main()