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bestfy / cog_predict.py

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0d11573 over 1 year ago

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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')

	# 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'],
	default='v1.4'),
	scale: float = Input(description='Rescaling factor', default=2)
	) -> Path:
	print(img, version, scale)
	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'

	try:
	_, _, output = self.face_enhancer.enhance(
	img, has_aligned=False, only_center_face=False, paste_back=True)
	except RuntimeError as error:
	print('Error', error)
	else:
	extension = 'png'

	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}')