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import cv2 |
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import os |
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import torch |
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from basicsr.utils import img2tensor, tensor2img |
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from basicsr.utils.download_util import load_file_from_url |
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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 gfpgan.archs.gfpganv1_arch import GFPGANv1 |
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from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean |
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ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) |
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class GFPGANer(): |
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"""Helper for restoration with GFPGAN. |
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It will detect and crop faces, and then resize the faces to 512x512. |
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GFPGAN is used to restored the resized faces. |
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The background is upsampled with the bg_upsampler. |
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Finally, the faces will be pasted back to the upsample background image. |
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Args: |
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model_path (str): The path to the GFPGAN model. It can be urls (will first download it automatically). |
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upscale (float): The upscale of the final output. Default: 2. |
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arch (str): The GFPGAN architecture. Option: clean | original. Default: clean. |
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channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. |
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bg_upsampler (nn.Module): The upsampler for the background. Default: None. |
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""" |
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def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None): |
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self.upscale = upscale |
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self.bg_upsampler = bg_upsampler |
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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if arch == 'clean': |
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self.gfpgan = GFPGANv1Clean( |
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out_size=512, |
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num_style_feat=512, |
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channel_multiplier=channel_multiplier, |
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decoder_load_path=None, |
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fix_decoder=False, |
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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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else: |
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self.gfpgan = GFPGANv1( |
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out_size=512, |
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num_style_feat=512, |
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channel_multiplier=channel_multiplier, |
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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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self.face_helper = FaceRestoreHelper( |
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upscale, |
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face_size=512, |
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crop_ratio=(1, 1), |
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det_model='retinaface_resnet50', |
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save_ext='png', |
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device=self.device) |
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if model_path.startswith('https://'): |
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model_path = load_file_from_url( |
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url=model_path, model_dir=os.path.join(ROOT_DIR, 'gfpgan/weights'), progress=True, file_name=None) |
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loadnet = torch.load(model_path) |
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if 'params_ema' in loadnet: |
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keyname = 'params_ema' |
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else: |
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keyname = 'params' |
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self.gfpgan.load_state_dict(loadnet[keyname], strict=True) |
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self.gfpgan.eval() |
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self.gfpgan = self.gfpgan.to(self.device) |
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@torch.no_grad() |
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def enhance(self, img, has_aligned=False, only_center_face=False, paste_back=True): |
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self.face_helper.clean_all() |
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if has_aligned: |
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img = cv2.resize(img, (512, 512)) |
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self.face_helper.cropped_faces = [img] |
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else: |
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self.face_helper.read_image(img) |
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self.face_helper.get_face_landmarks_5(only_center_face=only_center_face, eye_dist_threshold=5) |
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self.face_helper.align_warp_face() |
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for cropped_face in self.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(self.device) |
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try: |
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output = self.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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self.face_helper.add_restored_face(restored_face) |
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if not has_aligned and paste_back: |
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if self.bg_upsampler is not None: |
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bg_img = self.bg_upsampler.enhance(img, outscale=self.upscale)[0] |
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else: |
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bg_img = None |
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self.face_helper.get_inverse_affine(None) |
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restored_img = self.face_helper.paste_faces_to_input_image(upsample_img=bg_img) |
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return self.face_helper.cropped_faces, self.face_helper.restored_faces, restored_img |
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else: |
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return self.face_helper.cropped_faces, self.face_helper.restored_faces, None |
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