app.py

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 -q -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 -q -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 -q -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 -q -P .")
if not os.path.exists('RestoreFormer.pth'):
    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -q -P .")
if not os.path.exists('CodeFormer.pth'):
    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -q -P .")


# 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):
    weight /= 100
    print(img, version, scale, weight)
    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)

        try:
            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
        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 = "GFPGAN: Practical Face Restoration Algorithm"
description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>
It can be used to restore your **old photos** or improve **AI-generated faces**.<br>
To use it, simply upload your image.<br>
If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a> and recommend it to your friends 😊
"""
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)
"""

demo = gr.Interface(
        inference, [
            gr.Image(type="filepath", label="Input"),
            gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", value='v1.4', label='version'),
            gr.Number(label="Rescaling factor", value=2),
            gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', value=50)
        ], [
            gr.Image(type="numpy", label="Output (The whole image)", format="png"),
            gr.File(label="Download the output image")
        ],
        title=title,
        description=description,
        article=article,
        examples=[['AI-generate.jpg', 'v1.4', 2, 50], ],
        flagging_mode="never",
        cache_mode="lazy",
        delete_cache=(1800, 3600),)
demo.queue()
demo.launch(show_error=True)