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Running
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L40S
| import cv2 | |
| import os | |
| import torch | |
| from basicsr.utils import img2tensor, tensor2img | |
| from basicsr.utils.download_util import load_file_from_url | |
| from facexlib.utils.face_restoration_helper import FaceRestoreHelper | |
| from torchvision.transforms.functional import normalize | |
| from gfpgan.archs.gfpgan_bilinear_arch import GFPGANBilinear | |
| from gfpgan.archs.gfpganv1_arch import GFPGANv1 | |
| from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean | |
| ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) | |
| class GFPGANer(): | |
| """Helper for restoration with GFPGAN. | |
| It will detect and crop faces, and then resize the faces to 512x512. | |
| GFPGAN is used to restored the resized faces. | |
| The background is upsampled with the bg_upsampler. | |
| Finally, the faces will be pasted back to the upsample background image. | |
| Args: | |
| model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically). | |
| upscale (float): The upscale of the final output. Default: 2. | |
| arch (str): The GFPGAN architecture. Option: clean | original. Default: clean. | |
| channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. | |
| bg_upsampler (nn.Module): The upsampler for the background. Default: None. | |
| """ | |
| def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None, device=None): | |
| self.upscale = upscale | |
| self.bg_upsampler = bg_upsampler | |
| # initialize model | |
| self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device | |
| # initialize the GFP-GAN | |
| if arch == 'clean': | |
| self.gfpgan = GFPGANv1Clean( | |
| out_size=512, | |
| num_style_feat=512, | |
| channel_multiplier=channel_multiplier, | |
| decoder_load_path=None, | |
| fix_decoder=False, | |
| num_mlp=8, | |
| input_is_latent=True, | |
| different_w=True, | |
| narrow=1, | |
| sft_half=True) | |
| elif arch == 'bilinear': | |
| self.gfpgan = GFPGANBilinear( | |
| out_size=512, | |
| num_style_feat=512, | |
| channel_multiplier=channel_multiplier, | |
| decoder_load_path=None, | |
| fix_decoder=False, | |
| num_mlp=8, | |
| input_is_latent=True, | |
| different_w=True, | |
| narrow=1, | |
| sft_half=True) | |
| elif arch == 'original': | |
| self.gfpgan = GFPGANv1( | |
| out_size=512, | |
| num_style_feat=512, | |
| channel_multiplier=channel_multiplier, | |
| decoder_load_path=None, | |
| fix_decoder=True, | |
| num_mlp=8, | |
| input_is_latent=True, | |
| different_w=True, | |
| narrow=1, | |
| sft_half=True) | |
| elif arch == 'RestoreFormer': | |
| from gfpgan.archs.restoreformer_arch import RestoreFormer | |
| self.gfpgan = RestoreFormer() | |
| # initialize face helper | |
| self.face_helper = FaceRestoreHelper( | |
| upscale, | |
| face_size=512, | |
| crop_ratio=(1, 1), | |
| det_model='retinaface_resnet50', | |
| save_ext='png', | |
| use_parse=True, | |
| device=self.device, | |
| model_rootpath='gfpgan/weights') | |
| if model_path.startswith('https://'): | |
| model_path = load_file_from_url( | |
| url=model_path, model_dir=os.path.join(ROOT_DIR, 'gfpgan/weights'), progress=True, file_name=None) | |
| loadnet = torch.load(model_path) | |
| if 'params_ema' in loadnet: | |
| keyname = 'params_ema' | |
| else: | |
| keyname = 'params' | |
| self.gfpgan.load_state_dict(loadnet[keyname], strict=True) | |
| self.gfpgan.eval() | |
| self.gfpgan = self.gfpgan.to(self.device) | |
| def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True, weight=0.5): | |
| self.face_helper.clean_all() | |
| if has_aligned: # the inputs are already aligned | |
| img = cv2.resize(img, (512, 512)) | |
| self.face_helper.cropped_faces = [img] | |
| else: | |
| self.face_helper.read_image(img) | |
| # get face landmarks for each face | |
| self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5) | |
| # eye_dist_threshold=5: skip faces whose eye distance is smaller than 5 pixels | |
| # TODO: even with eye_dist_threshold, it will still introduce wrong detections and restorations. | |
| # align and warp each face | |
| self.face_helper.align_warp_face() | |
| # face restoration | |
| for cropped_face in self.face_helper.cropped_faces: | |
| # prepare data | |
| cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) | |
| normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) | |
| cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device) | |
| try: | |
| output = self.gfpgan(cropped_face_t, return_rgb=False, weight=weight)[0] | |
| # convert to image | |
| restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1)) | |
| except RuntimeError as error: | |
| print(f'\tFailed inference for GFPGAN: {error}.') | |
| restored_face = cropped_face | |
| restored_face = restored_face.astype('uint8') | |
| self.face_helper.add_restored_face(restored_face) | |
| if not has_aligned and paste_back: | |
| # upsample the background | |
| if self.bg_upsampler is not None: | |
| # Now only support RealESRGAN for upsampling background | |
| bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0] | |
| else: | |
| bg_img = None | |
| self.face_helper.get_inverse_affine(None) | |
| # paste each restored face to the input image | |
| restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img) | |
| return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img | |
| else: | |
| return self.face_helper.cropped_faces, self.face_helper.restored_faces, None | |