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import os
os.system("pip install gfpgan")
os.system("pip install gradio==2.5.3")
os.system("pip freeze")
os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .")
import random
import gradio as gr
from PIL import Image
import torch
#torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg')
torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png')
torch.hub.download_url_to_file('https://api.curtisbrown.co.uk/media/70446/show/square', 'churchil.png')
import cv2
import glob
import numpy as np
from basicsr.utils import imwrite
from gfpgan import GFPGANer
import warnings
warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
'If you really want to use it, please modify the corresponding codes.')
bg_upsampler = None
# set up GFPGAN restorer
restorer = GFPGANer(
model_path='GFPGANCleanv1-NoCE-C2.pth',
upscale=2,
arch='clean',
channel_multiplier=2,
bg_upsampler=bg_upsampler)
def inference(img):
input_img = cv2.imread(img, cv2.IMREAD_COLOR)
cropped_faces, restored_faces, restored_img = restorer.enhance(
input_img, has_aligned=False, only_center_face=False, paste_back=True)
return Image.fromarray(restored_faces[0][:,:,::-1])
title = "GFP-GAN"
description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2101.04061'>Towards Real-World Blind Face Restoration with Generative Facial Prior</a> | <a href='https://github.com/TencentARC/GFPGAN'>Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>"
gr.Interface(
inference,
[gr.inputs.Image(type="filepath", label="Input")],
gr.outputs.Image(type="pil", label="Output"),
title=title,
description=description,
article=article,
examples=[
['lincoln.jpg'],
['einstein.png']
],
enable_queue=True
).launch() |