mirror of
https://github.com/TencentARC/GFPGAN.git
synced 2025-05-15 14:50:11 -07:00
108 lines
4.2 KiB
Python
108 lines
4.2 KiB
Python
# flake8: noqa
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# This file is used for deploying replicate models
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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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# download weights
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if not os.path.exists('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 .')
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if not os.path.exists('GFPGANv1.2.pth'):
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os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .')
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if not os.path.exists('GFPGANv1.3.pth'):
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os.system('wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .')
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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 = 'realesr-general-x4v3.pth'
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half = True if torch.cuda.is_available() else False
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upsampler = RealESRGANer(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_v3 = GFPGANer(
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model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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self.face_enhancer_v2 = GFPGANer(
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model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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os.makedirs('output', exist_ok=True)
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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(description='GFPGAN version', choices=['v1.2', 'v1.3'], default='v1.3'),
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scale: float = Input(description='Rescaling factor', default=2)
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) -> Path:
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try:
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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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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 version == 'v1.2':
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face_enhancer = self.face_enhancer_v2
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else:
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face_enhancer = self.face_enhancer_v3
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try:
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_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
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except RuntimeError as error:
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print('Error', error)
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else:
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extension = 'png'
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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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else:
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extension = 'jpg'
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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 = os.path.join(tempfile.mkdtemp(), 'output.png')
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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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