app.py

import os
 
os.system("pip uninstall -y gradio")
os.system("pip install gradio==3.50")

os.system('wget "https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/RetinaFace-R50.pth" -O weights/RetinaFace-R50.pth')
os.system('wget "https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-512.pth" -O weights/GPEN-512.pth')
os.system('wget "https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-Colorization-1024.pth" -O weights/GPEN-1024-Color.pth ')
os.system('wget "https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/realesrnet_x2.pth" -O weights/realesrnet_x2.pth ')
os.system('wget "https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-Inpainting-1024.pth" -O weights/GPEN-Inpainting-1024.pth ')
jksp= os.environ['SELFIE']
os.system(f'wget "{jksp}" -O weights/GPEN-BFR-2048.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
import glob
import time
import math
import imutils
import argparse
import numpy as np
from PIL import Image, ImageDraw
import __init_paths
from face_enhancement import FaceEnhancement 
from face_colorization import FaceColorization 
from face_inpainting import FaceInpainting 
  
def brush_stroke_mask(img, color=(255,255,255)):
    min_num_vertex = 8
    max_num_vertex = 28
    mean_angle = 2*math.pi / 5
    angle_range = 2*math.pi / 15
    min_width = 12
    max_width = 80
    def generate_mask(H, W, img=None):
        average_radius = math.sqrt(H*H+W*W) / 8
        mask = Image.new('RGB', (W, H), 0)
        if img is not None: mask = img #Image.fromarray(img)

        for _ in range(np.random.randint(1, 4)):
            num_vertex = np.random.randint(min_num_vertex, max_num_vertex)
            angle_min = mean_angle - np.random.uniform(0, angle_range)
            angle_max = mean_angle + np.random.uniform(0, angle_range)
            angles = []
            vertex = []
            for i in range(num_vertex):
                if i % 2 == 0:
                    angles.append(2*math.pi - np.random.uniform(angle_min, angle_max))
                else:
                    angles.append(np.random.uniform(angle_min, angle_max))

            h, w = mask.size
            vertex.append((int(np.random.randint(0, w)), int(np.random.randint(0, h))))
            for i in range(num_vertex):
                r = np.clip(
                    np.random.normal(loc=average_radius, scale=average_radius//2),
                    0, 2*average_radius)
                new_x = np.clip(vertex[-1][0] + r * math.cos(angles[i]), 0, w)
                new_y = np.clip(vertex[-1][1] + r * math.sin(angles[i]), 0, h)
                vertex.append((int(new_x), int(new_y)))

            draw = ImageDraw.Draw(mask)
            width = int(np.random.uniform(min_width, max_width))
            draw.line(vertex, fill=color, width=width)
            for v in vertex:
                draw.ellipse((v[0] - width//2,
                              v[1] - width//2,
                              v[0] + width//2,
                              v[1] + width//2),
                             fill=color)

        return mask

    width, height = img.size
    mask = generate_mask(height, width, img)
    return mask

def resize(image, width = 1024):
    aspect_ratio = float(image.shape[1])/float(image.shape[0])
    height = width/aspect_ratio
    image = cv2.resize(image, (int(height),int(width)))
    return image

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='cpu', u=False)
    img, orig_faces, enhanced_faces = faceenhancer.process(im)
    cv2.imwrite(os.path.join("e.png"), img)
    
    
    if mode == "enhance":
        return os.path.join("e.png")
    elif mode == "colorize":
        model = {'name':'GPEN-1024-Color', 'size':1024}
        grayf = cv2.imread("e.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='cpu')
        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")
    elif mode == "inpainting":
        im1 = cv2.imread(file, cv2.IMREAD_COLOR)
        im2 = resize(im1, width = 1024)
        model = {'name':'GPEN-Inpainting-1024', 'size':1024}
        faceinpainter = FaceInpainting(size=model['size'], model=model['name'], channel_multiplier=2, device='cpu')
        im3 = np.asarray(brush_stroke_mask(Image.fromarray(im2)))
        inpaint = faceinpainter.process(im3)

        cv2.imwrite(os.path.join("output.png"), inpaint)
        return os.path.join("output.png")
    elif mode == "selfie":
        model = {'name':'GPEN-BFR-2048', 'size':2048}
        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:
        faceenhancer = FaceEnhancement(size=512, model='GPEN-512', channel_multiplier=2, device='cpu', u=True)
        img, orig_faces, enhanced_faces = faceenhancer.process(im)
        cv2.imwrite(os.path.join("output.png"), img)
        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><p style='text-align: center;'><img src='https://img.shields.io/badge/Hugging%20Face-Original%20demo-blue' alt='https://huggingface.co/spaces/akhaliq/GPEN' width='172' height='20' /></p>"


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