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import os |
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import cv2 |
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import gradio as gr |
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import torch |
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from basicsr.archs.srvgg_arch import SRVGGNetCompact |
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from realesrgan.utils import RealESRGANer |
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from RestoreFormer import RestoreFormer |
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os.system("pip freeze") |
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if not os.path.exists('realesr-general-x4v3.pth'): |
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os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .") |
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if not os.path.exists('RestoreFormer.ckpt'): |
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os.system("wget https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer.ckpt -P .") |
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if not os.path.exists('RestoreFormer++.pth'): |
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os.system("wget https://github.com/wzhouxiff/RestoreFormerPlusPlus/releases/download/v1.0.0/RestoreFormer++.ckpt -P .") |
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') |
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model_path = 'realesr-general-x4v3.pth' |
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half = True if torch.cuda.is_available() else False |
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upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half) |
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os.makedirs('output', exist_ok=True) |
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def inference(img, version, scale): |
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print(img, version, scale) |
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if scale > 4: |
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scale = 4 |
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try: |
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extension = os.path.splitext(os.path.basename(str(img)))[1] |
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img = cv2.imread(img, cv2.IMREAD_UNCHANGED) |
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if len(img.shape) == 3 and img.shape[2] == 4: |
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img_mode = 'RGBA' |
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elif len(img.shape) == 2: |
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img_mode = None |
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) |
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else: |
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img_mode = None |
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h, w = img.shape[0:2] |
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if h > 3500 or w > 3500: |
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print('too large size') |
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return None, None |
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if h < 300: |
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img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) |
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if version == 'RestoreFormer': |
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face_enhancer = RestoreFormer( |
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model_path='RestoreFormer.ckpt', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler) |
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elif version == 'RestoreFormer++': |
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face_enhancer = RestoreFormer( |
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model_path='RestoreFormer++.ckpt', upscale=2, arch='RestoreFormer++', channel_multiplier=2, bg_upsampler=upsampler) |
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try: |
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_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) |
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except RuntimeError as error: |
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print('Error', error) |
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try: |
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if scale != 2: |
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interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4 |
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h, w = img.shape[0:2] |
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output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation) |
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except Exception as error: |
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print('wrong scale input.', error) |
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if img_mode == 'RGBA': |
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extension = 'png' |
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else: |
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extension = 'jpg' |
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save_path = f'output/out.{extension}' |
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cv2.imwrite(save_path, output) |
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output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB) |
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return output, save_path |
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except Exception as error: |
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print('global exception', error) |
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return None, None |
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title = "RestoreFormer: Blind Face Restoration Algorithm" |
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description = r"""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> |
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It is used to restore your **old photos**.<br> |
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To use it, simply upload your image.<br> |
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""" |
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article = r""" |
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# [![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases) |
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# [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN) |
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[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/pdf/2308.07228.pdf) |
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[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf) |
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If you have any question, please email 📧 `wzhoux@connect.hku.hk`. |
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# <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center> |
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# <center><img src='https://visitor-badge.glitch.me/badge?page_id=Gradio_Xintao_GFPGAN' alt='visitor badge'></center> |
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""" |
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demo = gr.Interface( |
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inference, [ |
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gr.Image(type="filepath", label="Input"), |
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gr.Radio(['RestoreFormer', 'RestoreFormer++'], type="value", value='RestoreFormer++', label='version'), |
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gr.Number(label="Rescaling factor", value=2), |
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], [ |
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gr.Image(type="numpy", label="Output (The whole image)"), |
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gr.File(label="Download the output image") |
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], |
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title=title, |
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description=description, |
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article=article, |
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) |
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demo.queue().launch(share=True) |