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import os
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')
import argparse
import cv2
import glob
import numpy as np
import os
import torch
from basicsr.utils import imwrite
from gfpgan import GFPGANer
# background upsampler
if args.bg_upsampler == 'realesrgan':
if not torch.cuda.is_available(): # CPU
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
else:
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
bg_upsampler = RealESRGANer(
scale=2,
model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
model=model,
tile=args.bg_tile,
tile_pad=10,
pre_pad=0,
half=True) # need to set False in CPU mode
else:
bg_upsampler = None
# set up GFPGAN restorer
restorer = GFPGANer(
model_path=args.model_path,
upscale=args.upscale,
arch=args.arch,
channel_multiplier=args.channel,
bg_upsampler=bg_upsampler)
img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))
os.system("pip install gfpgan")
os.system("pip freeze")
os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .")
def inference(img):
input_img = cv2.imread(img, cv2.IMREAD_COLOR)
# restore faces and background if necessary
cropped_faces, restored_faces, restored_img = restorer.enhance(
input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back)
# save faces
# save cropped face
# save comparison image
cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
return Image.fromarray(cmp_img)
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="pil", label="Input")],
gr.outputs.Image(type="file", label="Output"),
title=title,
description=description,
article=article,
examples=[
['lincoln.jpg'],
['einstein.png']
],
enable_queue=True
).launch(debug=True) |