import os, sys import argparse import cv2 import gradio as gr import torch # from basicsr.archs.srvgg_arch import SRVGGNetCompact from 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'''
[![paper_RestroeForemer++](https://img.shields.io/badge/TPAMI-Restorformer%2B%2B-green )](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf)   [![paere_RestroeForemer](https://img.shields.io/badge/CVPR22-Restorformer-green)](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf)   [![code_RestroeForemer++](https://img.shields.io/badge/GitHub-RestoreFormer%2B%2B-red )](https://github.com/wzhouxiff/RestoreFormerPlusPlus)   [![code_RestroeForemer](https://img.shields.io/badge/GitHub-RestoreFormer-red)](https://github.com/wzhouxiff/RestoreFormer)   [![demo](https://img.shields.io/badge/Demo-Gradio-orange )](https://gradio.app/hub/wzhouxiff/RestoreFormerPlusPlus)
''' description = r"""
paper_RestroeForemer++        paere_RestroeForemer        code_RestroeForemer++        code_RestroeForemer        demo       

Gradio demo for RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Paris.
It is used to restore your Old Photos.
To use it, simply upload your image.
""" article = r""" If the proposed algorithm is helpful, please help to ⭐ the GitHub Repositories: RestoreFormer and RestoreFormer++. Thanks! [![GitHub Stars](https://img.shields.io/github/stars/wzhouxiff%2FRestoreFormer )](https://github.com/wzhouxiff/RestoreFormer) [![GitHub Stars](https://img.shields.io/github/stars/wzhouxiff%2FRestoreFormerPlusPlus )](https://github.com/wzhouxiff/RestoreFormerPlusPlus) --- 📝 **Citation**
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()