Image_Face_Upscale_Restoration-GFPGAN

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Image_Face_Upscale_Restoration-GFPGAN / app.py

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Duplicate from nightfury/Image_Face_Upscale_Restoration-GFPGAN

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	import os

	import cv2
	import gradio as gr
	import torch
	from basicsr.archs.srvgg_arch import SRVGGNetCompact
	from gfpgan.utils import GFPGANer
	from realesrgan.utils import RealESRGANer

	os.system("pip freeze")
	# download weights
	if not os.path.exists('realesr-general-x4v3.pth'):
	os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
	if not os.path.exists('GFPGANv1.2.pth'):
	os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
	if not os.path.exists('GFPGANv1.3.pth'):
	os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
	if not os.path.exists('GFPGANv1.4.pth'):
	os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
	if not os.path.exists('RestoreFormer.pth'):
	os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
	if not os.path.exists('CodeFormer.pth'):
	os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")

	torch.hub.download_url_to_file(
	'https://thumbs.dreamstime.com/b/tower-bridge-traditional-red-bus-black-white-colors-view-to-tower-bridge-london-black-white-colors-108478942.jpg',
	'a1.jpg')
	torch.hub.download_url_to_file(
	'https://media.istockphoto.com/id/523514029/photo/london-skyline-b-w.jpg?s=612x612&w=0&k=20&c=kJS1BAtfqYeUDaORupj0sBPc1hpzJhBUUqEFfRnHzZ0=',
	'a2.jpg')
	torch.hub.download_url_to_file(
	'https://i.guim.co.uk/img/media/06f614065ed82ca0e917b149a32493c791619854/0_0_3648_2789/master/3648.jpg?width=700&quality=85&auto=format&fit=max&s=05764b507c18a38590090d987c8b6202',
	'a3.jpg')
	torch.hub.download_url_to_file(
	'https://i.pinimg.com/736x/46/96/9e/46969eb94aec2437323464804d27706d--victorian-london-victorian-era.jpg',
	'a4.jpg')

	# 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 = 'realesr-general-x4v3.pth'
	half = True if torch.cuda.is_available() else False
	upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)

	os.makedirs('output', exist_ok=True)


	# def inference(img, version, scale, weight):
	def inference(img, version, scale):
	# weight /= 100
	print(img, version, scale)
	try:
	extension = os.path.splitext(os.path.basename(str(img)))[1]
	img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
	if len(img.shape) == 3 and img.shape[2] == 4:
	img_mode = 'RGBA'
	elif len(img.shape) == 2: # for gray inputs
	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 version == 'v1.2':
	face_enhancer = GFPGANer(
	model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
	elif version == 'v1.3':
	face_enhancer = GFPGANer(
	model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
	elif version == 'v1.4':
	face_enhancer = GFPGANer(
	model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
	elif version == 'RestoreFormer':
	face_enhancer = GFPGANer(
	model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
	elif version == 'CodeFormer':
	face_enhancer = GFPGANer(
	model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
	elif version == 'RealESR-General-x4v3':
	face_enhancer = GFPGANer(
	model_path='realesr-general-x4v3.pth', upscale=2, arch='realesr-general', channel_multiplier=2, bg_upsampler=upsampler)

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

	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'
	else:
	extension = 'jpg'
	save_path = f'output/out.{extension}'
	cv2.imwrite(save_path, output)

	output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
	return output, save_path
	except Exception as error:
	print('global exception', error)
	return None, None


	title = "Image Upscaling & Restoration(esp. Face) using GFPGAN Algorithm"
	description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration and Upscalling of the image with a Generative Facial Prior</b></a>.<br>
	Practically the algorithm is used to restore your old photos or improve AI-generated faces.<br>
	To use it, simply just upload the concerned image.<br>
	"""
	article = r"""
	[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
	[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN)
	[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2101.04061)
	<center><img src='https://visitor-badge.glitch.me/badge?page_id=dj_face_restoration_GFPGAN' alt='visitor badge'></center>
	"""
	demo = gr.Interface(
	inference, [
	gr.inputs.Image(type="filepath", label="Input"),
	# gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", default='v1.4', label='version'),
	gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer','CodeFormer','RealESR-General-x4v3'], type="value", default='v1.4', label='version'),
	gr.inputs.Number(label="Rescaling factor", default=2),
	# gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', default=50)
	], [
	gr.outputs.Image(type="numpy", label="Output (The whole image)"),
	gr.outputs.File(label="Download the output image")
	],
	title=title,
	description=description,
	article=article,
	# examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
	# ['10045.png', 'v1.4', 2, 50]]).launch()
	examples=[['a1.jpg', 'v1.4', 2], ['a2.jpg', 'v1.4', 2], ['a3.jpg', 'v1.4', 2],['a4.jpg', 'v1.4', 2]])

	demo.queue(concurrency_count=4)
	demo.launch()