Real-ESRGAN

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# flake8: noqa
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# This file is used for deploying replicate models
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# running: cog predict -i img=@inputs/00017_gray.png -i version='General - v3' -i scale=2 -i face_enhance=True -i tile=0
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# push: cog push r8.im/xinntao/realesrgan
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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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        # download weights
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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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    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':  # RGBA images should be saved in png format
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                extension = 'png'
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            # save_path = f'output/out.{extension}'
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            # cv2.imwrite(save_path, output)
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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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