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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 facexlib.utils.face_restoration_helper import FaceRestoreHelper |
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from torchvision.transforms.functional import normalize |
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from archs.gfpganv1_arch import GFPGANv1 |
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from basicsr.utils import img2tensor, imwrite, tensor2img |
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def restoration(gfpgan, |
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face_helper, |
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img_path, |
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save_root, |
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has_aligned=False, |
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only_center_face=True, |
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suffix=None, |
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paste_back=False): |
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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, _ = os.path.splitext(img_name) |
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input_img = cv2.imread(img_path, cv2.IMREAD_COLOR) |
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face_helper.clean_all() |
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if has_aligned: |
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input_img = cv2.resize(input_img, (512, 512)) |
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face_helper.cropped_faces = [input_img] |
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else: |
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face_helper.read_image(input_img) |
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face_helper.get_face_landmarks_5(only_center_face=only_center_face, pad_blur=False) |
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save_crop_path = os.path.join(save_root, 'cropped_faces', img_name) |
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face_helper.align_warp_face(save_crop_path) |
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for idx, cropped_face in enumerate(face_helper.cropped_faces): |
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cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) |
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normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) |
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cropped_face_t = cropped_face_t.unsqueeze(0).to('cuda') |
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try: |
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with torch.no_grad(): |
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output = gfpgan(cropped_face_t, return_rgb=False)[0] |
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restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1)) |
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except RuntimeError as error: |
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print(f'\tFailed inference for GFPGAN: {error}.') |
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restored_face = cropped_face |
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restored_face = restored_face.astype('uint8') |
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face_helper.add_restored_face(restored_face) |
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if suffix is not None: |
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save_face_name = f'{basename}_{idx:02d}_{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(save_root, '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(save_root, 'cmp', f'{basename}_{idx:02d}.png')) |
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if not has_aligned and paste_back: |
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face_helper.get_inverse_affine(None) |
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save_restore_path = os.path.join(save_root, 'restored_imgs', img_name) |
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face_helper.paste_faces_to_input_image(save_restore_path) |
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if __name__ == '__main__': |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--upscale_factor', type=int, default=1) |
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parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANv1.pth') |
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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_true') |
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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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save_root = 'results/' |
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os.makedirs(save_root, exist_ok=True) |
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gfpgan = GFPGANv1( |
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out_size=512, |
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num_style_feat=512, |
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channel_multiplier=1, |
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decoder_load_path=None, |
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fix_decoder=True, |
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num_mlp=8, |
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input_is_latent=True, |
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different_w=True, |
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narrow=1, |
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sft_half=True) |
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gfpgan.to(device) |
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checkpoint = torch.load(args.model_path, map_location=lambda storage, loc: storage) |
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gfpgan.load_state_dict(checkpoint['params_ema']) |
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gfpgan.eval() |
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face_helper = FaceRestoreHelper( |
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args.upscale_factor, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png') |
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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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restoration( |
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gfpgan, |
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face_helper, |
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img_path, |
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save_root, |
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has_aligned=args.aligned, |
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only_center_face=args.only_center_face, |
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suffix=args.suffix, |
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paste_back=args.paste_back) |
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print('Results are in the <results> folder.') |
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