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| import os
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| os.system('pip install gfpgan')
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| os.system('python setup.py develop')
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| import cv2
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| import shutil
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| import tempfile
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| import torch
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| from basicsr.archs.rrdbnet_arch import RRDBNet
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| from basicsr.archs.srvgg_arch import SRVGGNetCompact
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| from realesrgan.utils import RealESRGANer
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| try:
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| from cog import BasePredictor, Input, Path
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| from gfpgan import GFPGANer
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| except Exception:
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| print('please install cog and realesrgan package')
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| class Predictor(BasePredictor):
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| def setup(self):
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| os.makedirs('output', exist_ok=True)
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| if not os.path.exists('weights/realesr-general-x4v3.pth'):
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| os.system(
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| 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./weights'
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| )
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| if not os.path.exists('weights/GFPGANv1.4.pth'):
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| os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./weights')
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| if not os.path.exists('weights/RealESRGAN_x4plus.pth'):
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| os.system(
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| 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P ./weights'
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| )
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| if not os.path.exists('weights/RealESRGAN_x4plus_anime_6B.pth'):
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| os.system(
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| 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P ./weights'
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| )
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| if not os.path.exists('weights/realesr-animevideov3.pth'):
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| os.system(
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| 'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth -P ./weights'
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| )
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| def choose_model(self, scale, version, tile=0):
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| half = True if torch.cuda.is_available() else False
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| if version == 'General - RealESRGANplus':
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| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
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| model_path = 'weights/RealESRGAN_x4plus.pth'
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| self.upsampler = RealESRGANer(
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| scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
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| elif version == 'General - v3':
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| model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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| model_path = 'weights/realesr-general-x4v3.pth'
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| self.upsampler = RealESRGANer(
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| scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
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| elif version == 'Anime - anime6B':
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| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
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| model_path = 'weights/RealESRGAN_x4plus_anime_6B.pth'
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| self.upsampler = RealESRGANer(
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| scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
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| elif version == 'AnimeVideo - v3':
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| model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
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| model_path = 'weights/realesr-animevideov3.pth'
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| self.upsampler = RealESRGANer(
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| scale=4, model_path=model_path, model=model, tile=tile, tile_pad=10, pre_pad=0, half=half)
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| self.face_enhancer = GFPGANer(
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| model_path='weights/GFPGANv1.4.pth',
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| upscale=scale,
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| arch='clean',
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| channel_multiplier=2,
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| bg_upsampler=self.upsampler)
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|
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| def predict(
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| self,
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| img: Path = Input(description='Input'),
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| version: str = Input(
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| description='RealESRGAN version. Please see [Readme] below for more descriptions',
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| choices=['General - RealESRGANplus', 'General - v3', 'Anime - anime6B', 'AnimeVideo - v3'],
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| default='General - v3'),
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| scale: float = Input(description='Rescaling factor', default=2),
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| face_enhance: bool = Input(
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| description='Enhance faces with GFPGAN. Note that it does not work for anime images/vidoes', default=False),
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| tile: int = Input(
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| description=
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| 'Tile size. Default is 0, that is no tile. When encountering the out-of-GPU-memory issue, please specify it, e.g., 400 or 200',
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| default=0)
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| ) -> Path:
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| if tile <= 100 or tile is None:
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| tile = 0
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| print(f'img: {img}. version: {version}. scale: {scale}. face_enhance: {face_enhance}. tile: {tile}.')
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| try:
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| extension = os.path.splitext(os.path.basename(str(img)))[1]
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| img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED)
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| if len(img.shape) == 3 and img.shape[2] == 4:
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| img_mode = 'RGBA'
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| elif len(img.shape) == 2:
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| img_mode = None
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| img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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| else:
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| img_mode = None
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| h, w = img.shape[0:2]
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| if h < 300:
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| img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
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| self.choose_model(scale, version, tile)
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| try:
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| if face_enhance:
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| _, _, output = self.face_enhancer.enhance(
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| img, has_aligned=False, only_center_face=False, paste_back=True)
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| else:
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| output, _ = self.upsampler.enhance(img, outscale=scale)
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| except RuntimeError as error:
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| print('Error', error)
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| print('If you encounter CUDA out of memory, try to set "tile" to a smaller size, e.g., 400.')
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| if img_mode == 'RGBA':
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| extension = 'png'
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| out_path = Path(tempfile.mkdtemp()) / f'out.{extension}'
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| cv2.imwrite(str(out_path), output)
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| except Exception as error:
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| print('global exception: ', error)
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| finally:
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| clean_folder('output')
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| return out_path
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| def clean_folder(folder):
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| for filename in os.listdir(folder):
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| file_path = os.path.join(folder, filename)
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| try:
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| if os.path.isfile(file_path) or os.path.islink(file_path):
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| os.unlink(file_path)
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| elif os.path.isdir(file_path):
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| shutil.rmtree(file_path)
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| except Exception as e:
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| print(f'Failed to delete {file_path}. Reason: {e}')
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|
