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| import os, sys | |
| import argparse | |
| import cv2 | |
| import gradio as gr | |
| import torch | |
| from basicsr.archs.srvgg_arch import SRVGGNetCompact | |
| from realesrgan.utils import RealESRGANer | |
| from glob import glob | |
| from RestoreFormer import RestoreFormer | |
| if not os.path.exists('experiments/pretrained_models'): | |
| os.makedirs('experiments/pretrained_models') | |
| realesr_model_path = 'experiments/pretrained_models/RealESRGAN_x4plus.pth' | |
| if not os.path.exists(realesr_model_path): | |
| os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -O experiments/pretrained_models/RealESRGAN_x4plus.pth") | |
| if not os.path.exists('experiments/RestoreFormer/'): | |
| os.makedirs('experiments/RestoreFormer/') | |
| restoreformer_model_path = 'experiments/RestoreFormer/last.ckpt' | |
| if not os.path.exists(restoreformer_model_path): | |
| os.system("wget https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer.ckpt -O experiments/RestoreFormer/last.ckpt") | |
| if not os.path.exists('experiments/RestoreFormerPlusPlus/'): | |
| os.makedirs('experiments/RestoreFormerPlusPlus/') | |
| restoreformerplusplus_model_path = 'experiments/RestoreFormerPlusPlus/last.ckpt' | |
| if not os.path.exists(restoreformerplusplus_model_path): | |
| os.system("wget https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer++.ckpt -O experiments/RestoreFormerPlusPlus/last.ckpt") | |
| # 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') | |
| half = True if torch.cuda.is_available() else False | |
| upsampler = RealESRGANer(scale=4, model_path=realesr_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, aligned, 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 == 'RestoreFormer': | |
| face_enhancer = RestoreFormer( | |
| model_path=restoreformer_model_path, upscale=2, arch='RestoreFormer', bg_upsampler=upsampler) | |
| elif version == 'RestoreFormer++': | |
| face_enhancer = RestoreFormer( | |
| model_path=restoreformerplusplus_model_path, upscale=2, arch='RestoreFormer++', bg_upsampler=upsampler) | |
| try: | |
| # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight) | |
| has_aligned = True if aligned == 'aligned' else False | |
| _, restored_aligned, restored_img = face_enhancer.enhance(img, has_aligned=has_aligned, only_center_face=False, paste_back=True) | |
| if has_aligned: | |
| output = restored_aligned[0] | |
| else: | |
| output = restored_img | |
| 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 = "RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Paris" | |
| important_links=r''' | |
| <div align='center'> | |
| [](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf) | |
| | |
| [](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf) | |
| | |
| [](https://github.com/wzhouxiff/RestoreFormerPlusPlus) | |
| | |
| [](https://github.com/wzhouxiff/RestoreFormer) | |
| | |
| [](https://gradio.app/hub/wzhouxiff/RestoreFormerPlusPlus) | |
| </div> | |
| ''' | |
| description = r""" | |
| <div align='center'> | |
| <a target='_blank' href='https://arxiv.org/pdf/2308.07228.pdf' style='float: left'> | |
| <img src='https://img.shields.io/badge/TPAMI-RestorFormer%2B%2B-green' alt='paper_RestroeForemer++'> | |
| </a> | |
|        | |
| <a target='_blank' href='https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf' style='float: left'> | |
| <img src='https://img.shields.io/badge/CVPR22-RestorFormer-green' alt='paere_RestroeForemer' > | |
| </a> | |
|        | |
| <a target='_blank' href='https://github.com/wzhouxiff/RestoreFormerPlusPlus' style='float: left'> | |
| <img src='https://img.shields.io/badge/GitHub-RestoreFormer%2B%2B-red' alt='code_RestroeForemer++'> | |
| </a> | |
|        | |
| <a target='_blank' href='https://github.com/wzhouxiff/RestoreFormer' style='float: left'> | |
| <img src='https://img.shields.io/badge/GitHub-RestoreFormer-red' alt='code_RestroeForemer' > | |
| </a> | |
|        | |
| <a target='_blank' href='https://huggingface.co/spaces/wzhouxiff/RestoreFormerPlusPlus' style='float: left' > | |
| <img src='https://img.shields.io/badge/Demo-Gradio-orange' alt='demo' > | |
| </a> | |
|        | |
| </div> | |
| <br> | |
| Gradio demo for <a href='https://github.com/wzhouxiff/RestoreFormerPlusPlus' target='_blank'><b>RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Paris</b></a>. | |
| <br> | |
| It is used to restore your Old Photos. | |
| <br> | |
| To use it, simply upload your image.<br> | |
| """ | |
| article = r""" | |
| If the proposed algorithm is helpful, please help to ⭐ the GitHub Repositories: <a href='https://github.com/wzhouxiff/RestoreFormer' target='_blank'>RestoreFormer</a> and | |
| <a href='https://github.com/wzhouxiff/RestoreFormerPlusPlus' target='_blank'>RestoreFormer++</a>. Thanks! | |
| [](https://github.com/wzhouxiff/RestoreFormer) | |
| [](https://github.com/wzhouxiff/RestoreFormerPlusPlus) | |
| --- | |
| 📝 **Citation** | |
| <br> | |
| If our work is useful for your research, please consider citing: | |
| ```bibtex | |
| @article{wang2023restoreformer++, | |
| title={RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Paris}, | |
| author={Wang, Zhouxia and Zhang, Jiawei and Chen, Tianshui and Wang, Wenping and Luo, Ping}, | |
| booktitle={IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)}, | |
| year={2023} | |
| } | |
| @article{wang2022restoreformer, | |
| title={RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs}, | |
| author={Wang, Zhouxia and Zhang, Jiawei and Chen, Runjian and Wang, Wenping and Luo, Ping}, | |
| booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| year={2022} | |
| } | |
| ``` | |
| If you have any question, please email 📧 `wzhoux@connect.hku.hk`. | |
| """ | |
| css=r""" | |
| """ | |
| demo = gr.Interface( | |
| inference, [ | |
| gr.Image(type="filepath", label="Input"), | |
| gr.Radio(['RestoreFormer', 'RestoreFormer++'], type="value", value='RestoreFormer++', label='version'), | |
| gr.Radio(['aligned', 'unaligned'], type="value", value='unaligned', label='Image Alignment'), | |
| gr.Number(label="Rescaling factor", value=2), | |
| ], [ | |
| gr.Image(type="numpy", label="Output (The whole image)"), | |
| gr.File(label="Download the output image") | |
| ], | |
| title=title, | |
| description=description, | |
| article=article, | |
| ) | |
| demo.queue(max_size=20).launch() |