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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 .") | |
| torch.hub.download_url_to_file( | |
| 'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', | |
| 'lincoln.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg', | |
| 'AI-generate.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg', | |
| 'Blake_Lively.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): | |
| # 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 > 3500 or w > 3500: | |
| print('too large size') | |
| return None, None | |
| 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) | |
| _, _, 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 | |
| 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""" | |
| [](https://github.com/TencentARC/GFPGAN/releases) | |
| [](https://github.com/TencentARC/GFPGAN) | |
| [](https://arxiv.org/abs/2101.04061) | |
| If you have any question, please email 📧 `[email protected]` or `[email protected]`. | |
| <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center> | |
| <center><img src='https://visitor-badge.glitch.me/badge?page_id=Gradio_Xintao_GFPGAN' alt='visitor badge'></center> | |
| """ | |
| 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.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], 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)"), | |
| gr.File(label="Download the output image") | |
| ], | |
| title=title, | |
| description=description, | |
| article=article, | |
| # examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50], | |
| # ['10045.png', 'v1.4', 2, 50]]).launch() | |
| examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2], | |
| ['10045.png', 'v1.4', 2]]) | |
| demo.queue().launch() | |