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 -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 -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 -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 -P .") if not os.path.exists('RestoreFormer.pth'): os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .") if not os.path.exists('CodeFormer.pth'): os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .") if not os.path.exists('HanamichiSakuragi.jpg'): torch.hub.download_url_to_file( 'https://haoluobo.com/wp-content/uploads/2023/01/%E6%A8%B1%E6%9C%A8%E8%8A%B1%E9%81%93.jpg', 'HanamichiSakuragi.jpg') torch.hub.download_url_to_file( 'https://haoluobo.com/wp-content/uploads/2023/01/%E6%9D%8E%E4%B8%96%E6%B0%91.jpg', 'LiShiming.jpg') torch.hub.download_url_to_file( 'https://haoluobo.com/wp-content/uploads/2023/01/%E4%B9%BE%E9%9A%86.jpg', 'QianLong.jpg') torch.hub.download_url_to_file( 'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png', '10045.png') # 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): def inference(img, version, scale, blur_face): scale = int(scale) blur_face = int(blur_face) if blur_face % 2 != 1: blur_face += 1 if blur_face < 3: blur_face = 0 # weight /= 100 print(img, version, scale) if scale > 4: scale = 4 # avoid too large scale value 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) face_helper = face_enhancer.face_helper align_warp_face = face_helper.align_warp_face def new_align_warp_face(*args, **kwargs): align_warp_face(*args, **kwargs) # save_cropped_path face_helper.org_cropped_faces = face_helper.cropped_faces if blur_face >= 3: face_helper.cropped_faces = [cv2.GaussianBlur(e, (blur_face, blur_face), 0) for e in face_helper.cropped_faces] print("find face count:", len(face_helper.cropped_faces)) face_helper.align_warp_face = new_align_warp_face _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) 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, [cv2.cvtColor(e, cv2.COLOR_BGR2RGB) for e in face_enhancer.face_helper.org_cropped_faces], [cv2.cvtColor(e, cv2.COLOR_BGR2RGB) for e in face_enhancer.face_helper.restored_faces] ) except Exception as error: print('global exception', error) return None, None title = "GFPGAN: Practical Face Restoration Algorithm" description = r"""Gradio demo for GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
It can be used to restore your **old photos** or improve **AI-generated faces**.
To use it, simply upload your image.
If GFPGAN is helpful, please help to ⭐ the Github Repo and recommend it to your friends 😊
This demo was forked by [vicalloy](https://github.com/vicalloy), add `face blur` param to optimize painting face enhance. """ 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-.svg)](https://arxiv.org/abs/2101.04061) If you have any question, please email 📧 `xintao.wang@outlook.com` or `xintaowang@tencent.com`. """ with gr.Blocks() as demo: gr.Markdown("

%s

" % title) gr.Markdown(description) with gr.Row(equal_height=False): with gr.Column(): file_path = gr.components.Image(type="filepath", label="Input") version = gr.components.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version') rescaling_factor = gr.components.Radio(['1', '2', '4'], type="value", value='2', label='Rescaling factor') blur_face = gr.Slider(label='Blur face', minimum=0, maximum=55, value=0, step=1) submit = gr.Button("Submit") with gr.Column(): output_img = gr.components.Image(type="numpy", label="Output (The whole image)") download = gr.components.File(label="Download the output image") with gr.Row(): with gr.Column(): input_faces = gr.Gallery(label="Input faces").style(height="auto") with gr.Column(): output_faces = gr.Gallery(label="Output faces").style(height="auto") gr.Examples([['HanamichiSakuragi.jpg', 'v1.4', '2', 31], ['LiShiming.jpg', 'v1.4', '2', 3], ['QianLong.jpg', 'v1.4', '2', 3], ['10045.png', 'v1.4', '2', 0]], [file_path, version, rescaling_factor, blur_face]) gr.Markdown(article) submit.click( inference, inputs=[file_path, version, rescaling_factor, blur_face], outputs=[output_img, download, input_faces, output_faces] ) demo.queue(concurrency_count=4) demo.launch()