GFPGAN

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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/whole_imgs/10045.png -i version='v1.4' -i scale=2
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# push: cog push r8.im/tencentarc/gfpgan
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# push (backup): cog push r8.im/xinntao/gfpgan
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import os
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os.system('python setup.py develop')
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os.system('pip install realesrgan')
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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.srvgg_arch import SRVGGNetCompact
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from gfpgan import GFPGANer
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try:
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    from cog import BasePredictor, Input, Path
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    from realesrgan.utils import RealESRGANer
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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('gfpgan/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 ./gfpgan/weights'
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            )
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        if not os.path.exists('gfpgan/weights/GFPGANv1.2.pth'):
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            os.system(
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                'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P ./gfpgan/weights')
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        if not os.path.exists('gfpgan/weights/GFPGANv1.3.pth'):
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            os.system(
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                'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P ./gfpgan/weights')
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        if not os.path.exists('gfpgan/weights/GFPGANv1.4.pth'):
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            os.system(
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                'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./gfpgan/weights')
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        if not os.path.exists('gfpgan/weights/RestoreFormer.pth'):
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            os.system(
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                'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P ./gfpgan/weights'
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            )
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        # background enhancer with RealESRGAN
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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 = 'gfpgan/weights/realesr-general-x4v3.pth'
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        half = True if torch.cuda.is_available() else False
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        self.upsampler = RealESRGANer(
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            scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
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        # Use GFPGAN for face enhancement
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        self.face_enhancer = GFPGANer(
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            model_path='gfpgan/weights/GFPGANv1.4.pth',
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            upscale=2,
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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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        self.current_version = 'v1.4'
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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='GFPGAN version. v1.3: better quality. v1.4: more details and better identity.',
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                choices=['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'],
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                default='v1.4'),
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            scale: float = Input(description='Rescaling factor', default=2),
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    ) -> Path:
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        weight = 0.5
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        print(img, version, scale, weight)
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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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            if self.current_version != version:
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                if version == 'v1.2':
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                    self.face_enhancer = GFPGANer(
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                        model_path='gfpgan/weights/GFPGANv1.2.pth',
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                        upscale=2,
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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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                    self.current_version = 'v1.2'
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                elif version == 'v1.3':
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                    self.face_enhancer = GFPGANer(
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                        model_path='gfpgan/weights/GFPGANv1.3.pth',
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                        upscale=2,
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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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                    self.current_version = 'v1.3'
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                elif version == 'v1.4':
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                    self.face_enhancer = GFPGANer(
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                        model_path='gfpgan/weights/GFPGANv1.4.pth',
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                        upscale=2,
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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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                    self.current_version = 'v1.4'
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                elif version == 'RestoreFormer':
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                    self.face_enhancer = GFPGANer(
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                        model_path='gfpgan/weights/RestoreFormer.pth',
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                        upscale=2,
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                        arch='RestoreFormer',
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                        channel_multiplier=2,
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                        bg_upsampler=self.upsampler)
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            try:
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                _, _, output = self.face_enhancer.enhance(
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                    img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
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            except RuntimeError as error:
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                print('Error', error)
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            try:
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                if scale != 2:
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                    interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
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                    h, w = img.shape[0:2]
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                    output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
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            except Exception as error:
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                print('wrong scale input.', error)
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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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