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| import os | |
| import cv2 | |
| 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 RestoreFormer_arch import VQVAEGANMultiHeadTransformer | |
| ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) | |
| class RestoreFormer(): | |
| """Helper for restoration with RestoreFormer. | |
| It will detect and crop faces, and then resize the faces to 512x512. | |
| RestoreFormer 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 RestoreFormer architecture. Option: RestoreFormer | RestoreFormer++. Default: RestoreFormer++. | |
| bg_upsampler (nn.Module): The upsampler for the background. Default: None. | |
| """ | |
| def __init__(self, model_path, upscale=2, arch='RestoreFromerPlusPlus', bg_upsampler=None, device=None): | |
| self.upscale = upscale | |
| self.bg_upsampler = bg_upsampler | |
| self.arch = arch | |
| self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device | |
| if arch == 'RestoreFormer': | |
| self.RF = VQVAEGANMultiHeadTransformer(head_size = 8, ex_multi_scale_num = 0) | |
| elif arch == 'RestoreFormer++': | |
| self.RF = VQVAEGANMultiHeadTransformer(head_size = 4, ex_multi_scale_num = 1) | |
| else: | |
| raise NotImplementedError(f'Not support arch: {arch}.') | |
| # 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=None) | |
| if model_path.startswith('https://'): | |
| model_path = load_file_from_url( | |
| url=model_path, model_dir=os.path.join(ROOT_DIR, 'experiments/weights'), progress=True, file_name=None) | |
| loadnet = torch.load(model_path) | |
| strict=False | |
| weights = loadnet['state_dict'] | |
| new_weights = {} | |
| for k, v in weights.items(): | |
| if k.startswith('vqvae.'): | |
| k = k.replace('vqvae.', '') | |
| new_weights[k] = v | |
| self.RF.load_state_dict(new_weights, strict=strict) | |
| self.RF.eval() | |
| self.RF = self.RF.to(self.device) | |
| def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True): | |
| 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) | |
| 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.RF(cropped_face_t)[0] | |
| restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1)) | |
| except RuntimeError as error: | |
| print(f'\tFailed inference for RestoreFormer: {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 | |