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| # flake8: noqa | |
| # This file is used for deploying replicate models | |
| # running: cog predict -i img=@inputs/whole_imgs/10045.png -i version='v1.4' -i scale=2 | |
| # push: cog push r8.im/tencentarc/gfpgan | |
| # push (backup): cog push r8.im/xinntao/gfpgan | |
| import os | |
| os.system('python setup.py develop') | |
| os.system('pip install realesrgan') | |
| import cv2 | |
| import shutil | |
| import tempfile | |
| import torch | |
| from basicsr.archs.srvgg_arch import SRVGGNetCompact | |
| from gfpgan import GFPGANer | |
| try: | |
| from cog import BasePredictor, Input, Path | |
| from realesrgan.utils import RealESRGANer | |
| except Exception: | |
| print('please install cog and realesrgan package') | |
| class Predictor(BasePredictor): | |
| def setup(self): | |
| os.makedirs('output', exist_ok=True) | |
| # download weights | |
| if not os.path.exists('gfpgan/weights/realesr-general-x4v3.pth'): | |
| os.system( | |
| 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./gfpgan/weights' | |
| ) | |
| if not os.path.exists('gfpgan/weights/GFPGANv1.2.pth'): | |
| os.system( | |
| 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P ./gfpgan/weights') | |
| if not os.path.exists('gfpgan/weights/GFPGANv1.3.pth'): | |
| os.system( | |
| 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P ./gfpgan/weights') | |
| if not os.path.exists('gfpgan/weights/GFPGANv1.4.pth'): | |
| os.system( | |
| 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./gfpgan/weights') | |
| if not os.path.exists('gfpgan/weights/RestoreFormer.pth'): | |
| os.system( | |
| 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P ./gfpgan/weights' | |
| ) | |
| # background enhancer with RealESRGAN | |
| model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') | |
| model_path = 'gfpgan/weights/realesr-general-x4v3.pth' | |
| half = True if torch.cuda.is_available() else False | |
| self.upsampler = RealESRGANer( | |
| scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half) | |
| # Use GFPGAN for face enhancement | |
| self.face_enhancer = GFPGANer( | |
| model_path='gfpgan/weights/GFPGANv1.4.pth', | |
| upscale=2, | |
| arch='clean', | |
| channel_multiplier=2, | |
| bg_upsampler=self.upsampler) | |
| self.current_version = 'v1.4' | |
| def predict( | |
| self, | |
| img: Path = Input(description='Input'), | |
| version: str = Input( | |
| description='GFPGAN version. v1.3: better quality. v1.4: more details and better identity.', | |
| choices=['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], | |
| default='v1.4'), | |
| scale: float = Input(description='Rescaling factor', default=2), | |
| ) -> Path: | |
| weight = 0.5 | |
| print(img, version, scale, weight) | |
| try: | |
| extension = os.path.splitext(os.path.basename(str(img)))[1] | |
| img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED) | |
| if len(img.shape) == 3 and img.shape[2] == 4: | |
| img_mode = 'RGBA' | |
| elif len(img.shape) == 2: | |
| img_mode = None | |
| img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) | |
| else: | |
| img_mode = None | |
| h, w = img.shape[0:2] | |
| if h < 300: | |
| img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) | |
| if self.current_version != version: | |
| if version == 'v1.2': | |
| self.face_enhancer = GFPGANer( | |
| model_path='gfpgan/weights/GFPGANv1.2.pth', | |
| upscale=2, | |
| arch='clean', | |
| channel_multiplier=2, | |
| bg_upsampler=self.upsampler) | |
| self.current_version = 'v1.2' | |
| elif version == 'v1.3': | |
| self.face_enhancer = GFPGANer( | |
| model_path='gfpgan/weights/GFPGANv1.3.pth', | |
| upscale=2, | |
| arch='clean', | |
| channel_multiplier=2, | |
| bg_upsampler=self.upsampler) | |
| self.current_version = 'v1.3' | |
| elif version == 'v1.4': | |
| self.face_enhancer = GFPGANer( | |
| model_path='gfpgan/weights/GFPGANv1.4.pth', | |
| upscale=2, | |
| arch='clean', | |
| channel_multiplier=2, | |
| bg_upsampler=self.upsampler) | |
| self.current_version = 'v1.4' | |
| elif version == 'RestoreFormer': | |
| self.face_enhancer = GFPGANer( | |
| model_path='gfpgan/weights/RestoreFormer.pth', | |
| upscale=2, | |
| arch='RestoreFormer', | |
| channel_multiplier=2, | |
| bg_upsampler=self.upsampler) | |
| try: | |
| _, _, output = self.face_enhancer.enhance( | |
| img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight) | |
| except RuntimeError as error: | |
| print('Error', error) | |
| try: | |
| if scale != 2: | |
| interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4 | |
| h, w = img.shape[0:2] | |
| output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation) | |
| except Exception as error: | |
| print('wrong scale input.', error) | |
| if img_mode == 'RGBA': # RGBA images should be saved in png format | |
| extension = 'png' | |
| # save_path = f'output/out.{extension}' | |
| # cv2.imwrite(save_path, output) | |
| out_path = Path(tempfile.mkdtemp()) / f'out.{extension}' | |
| cv2.imwrite(str(out_path), output) | |
| except Exception as error: | |
| print('global exception: ', error) | |
| finally: | |
| clean_folder('output') | |
| return out_path | |
| def clean_folder(folder): | |
| for filename in os.listdir(folder): | |
| file_path = os.path.join(folder, filename) | |
| try: | |
| if os.path.isfile(file_path) or os.path.islink(file_path): | |
| os.unlink(file_path) | |
| elif os.path.isdir(file_path): | |
| shutil.rmtree(file_path) | |
| except Exception as e: | |
| print(f'Failed to delete {file_path}. Reason: {e}') | |