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| 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 <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 😊<br> | |
| This demo was forked by [vicalloy](https://github.com/vicalloy), add `face blur` param to optimize painting face enhance. | |
| """ | |
| article = r""" | |
| [](https://github.com/TencentARC/GFPGAN/releases) | |
| [](https://github.com/TencentARC/GFPGAN) | |
| [](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("<center><h1>%s</h1></center>" % 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() | |