app.py

import os

os.system('wget "https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/RetinaFace-R50.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116085&Signature=GlUNW6%2B8FxvxWmE9jKIZYOOciKQ%3D" -O weights/RetinaFace-R50.pth')
os.system('wget "https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-512.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116208&Signature=hBgvVvKVSNGeXqT8glG%2Bd2t2OKc%3D" -O weights/GPEN-512.pth')
os.system('wget "https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-Colorization-1024.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1961116315&Signature=9tPavW2h%2F1LhIKiXj73sTQoWqcc%3D" -O weights/GPEN-1024-Color.pth ')
os.system('wget "https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/realesrnet_x2.pth?OSSAccessKeyId=LTAI4G6bfnyW4TA4wFUXTYBe&Expires=1962694780&Signature=lI%2FolhA%2FyigiTRvoDIVbtMIyhjI%3D" -O weights/realesrnet_x2.pth ')

import gradio as gr

'''
@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
@author: yangxy (yangtao9009@gmail.com)
'''
import os
import cv2
from face_enhancement import FaceEnhancement 
from face_colorization import FaceColorization 
  

def inference(file, mode):
    im = cv2.imread(file, cv2.IMREAD_COLOR)
    im = cv2.resize(im, (0,0), fx=2, fy=2)
    faceenhancer = FaceEnhancement(size=512, model='GPEN-512', channel_multiplier=2, device='cuda')
    img, orig_faces, enhanced_faces = faceenhancer.process(im)
    
    if mode == "enhance":
        # model = {'name':'GPEN-512', 'size':512}
        # im = cv2.imread(file, cv2.IMREAD_COLOR)
        # im = cv2.resize(im, (0,0), fx=2, fy=2)
        # faceenhancer = FaceEnhancement(size=model['size'], model=model['name'], channel_multiplier=2, device='cpu')
        # img, orig_faces, enhanced_faces = faceenhancer.process(im)
        cv2.imwrite(os.path.join("output.png"), img)
        return os.path.join("output.png")
    else:
        model = {'name':'GPEN-1024-Color', 'size':1024}
        grayf = cv2.imread("enhanced.png", cv2.IMREAD_GRAYSCALE)
        grayf = cv2.cvtColor(grayf, cv2.COLOR_GRAY2BGR) # channel: 1->3
        facecolorizer = FaceColorization(size=model['size'], model=model['name'], channel_multiplier=2, device='cuda')
        colorf = facecolorizer.process(grayf)

        colorf = cv2.resize(colorf, (grayf.shape[1], grayf.shape[0]))
        cv2.imwrite(os.path.join("output.png"), colorf)
        return os.path.join("output.png")
        
title = "GPEN"
description = "Gradio demo for GAN Prior Embedded Network for Blind Face Restoration in the Wild. This version of gradio demo includes face colorization from GPEN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."

article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2105.06070' target='_blank'>GAN Prior Embedded Network for Blind Face Restoration in the Wild</a> | <a href='https://github.com/yangxy/GPEN' target='_blank'>Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GPEN' alt='visitor badge'></center>"


gr.Interface(
    inference, 
    [gr.inputs.Image(type="filepath", label="Input"),gr.inputs.Radio(["enhance","colorize"], type="value", default="enhance", label="Type")], 
    gr.outputs.Image(type="file", label="Output"),
    title=title,
    description=description,
    article=article,
    examples=[
    ['enhance.png', 'Enhance'],
    ['color.png', 'Colorization']
    ],
    enable_queue=True
    ).launch()