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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 gfpgan.utils import GFPGANer |
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from realesrgan.utils import RealESRGANer |
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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('GFPGANv1.2.pth'): |
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .") |
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if not os.path.exists('GFPGANv1.3.pth'): |
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .") |
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if not os.path.exists('GFPGANv1.4.pth'): |
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .") |
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if not os.path.exists('RestoreFormer.pth'): |
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .") |
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if not os.path.exists('CodeFormer.pth'): |
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .") |
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torch.hub.download_url_to_file( |
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'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', |
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'lincoln.jpg') |
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torch.hub.download_url_to_file( |
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'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg', |
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'AI-generate.jpg') |
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torch.hub.download_url_to_file( |
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'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg', |
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'Blake_Lively.jpg') |
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torch.hub.download_url_to_file( |
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'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png', |
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'10045.png') |
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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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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 < 300: |
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img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) |
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if version == 'v1.2': |
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face_enhancer = GFPGANer( |
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model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) |
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elif version == 'v1.3': |
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face_enhancer = GFPGANer( |
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model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) |
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elif version == 'v1.4': |
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face_enhancer = GFPGANer( |
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model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) |
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elif version == 'RestoreFormer': |
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face_enhancer = GFPGANer( |
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model_path='RestoreFormer.pth', 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 = "Image Upscaling & Restoration(esp. Face) using GFPGAN Algorithm" |
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description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration and Upscalling of the image with a Generative Facial Prior</b></a>.<br> |
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Practically the algorithm is used to restore your **old photos** or improve **AI-generated faces**.<br> |
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To use it, simply just upload the concerned image.<br> |
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""" |
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article = r""" |
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[](https://github.com/TencentARC/GFPGAN/releases) |
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[](https://github.com/TencentARC/GFPGAN) |
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[](https://arxiv.org/abs/2101.04061) |
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<center><img src='https://visitor-badge.glitch.me/badge?page_id=dj_face_restoration_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.inputs.Image(type="filepath", label="Input"), |
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gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", default='v1.4', label='version'), |
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gr.inputs.Number(label="Rescaling factor", default=2), |
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], [ |
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gr.outputs.Image(type="numpy", label="Output (The whole image)"), |
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gr.outputs.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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examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2], |
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['10045.png', 'v1.4', 2]]) |
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demo.queue(concurrency_count=4) |
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demo.launch() |
