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| import os | |
| import shutil | |
| import torch | |
| import cv2 | |
| import gradio as gr | |
| from PIL import Image | |
| os.chdir('Restormer') | |
| # Download sample images | |
| os.system("wget https://github.com/swz30/Restormer/releases/download/v1.0/sample_images.zip") | |
| shutil.unpack_archive('sample_images.zip') | |
| os.remove('sample_images.zip') | |
| examples = [['sample_images/Real_Denoising/degraded/117355.png', 'Denoising'], | |
| ['sample_images/Single_Image_Defocus_Deblurring/degraded/engagement.jpg', 'Defocus Deblurring'], | |
| ['sample_images/Motion_Deblurring/degraded/GoPro-GOPR0854_11_00-000090-input.jpg','Motion Deblurring'], | |
| ['sample_images/Deraining/degraded/Rain100H-77-input.jpg','Deraining']] | |
| title = "Restormer" | |
| description = """ | |
| Gradio demo for Restormer: Efficient Transformer for High-Resolution Image Restoration, CVPR 2022--ORAL. <a href='https://arxiv.org/abs/2111.09881'>[Paper]</a><a href='https://github.com/swz30/Restormer'>[Github Code]</a>\n | |
| With Restormer, you can perform: (1) Image Denoising, (2) Defocus Deblurring, (3) Motion Deblurring, and (4) Image Deraining. | |
| To use it, simply upload your own image, or click one of the examples provided below. | |
| """ | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.09881'>Restormer: Efficient Transformer for High-Resolution Image Restoration </a> | <a href='https://github.com/swz30/Restormer'>Github Repo</a></p>" | |
| def inference(img,task): | |
| os.system('mkdir temp') | |
| max_res = 904 | |
| width, height = img.size | |
| if max(width,height) > max_res: | |
| scale = min(width,height)/max(width,height) | |
| if width > max_res: | |
| width = max_res | |
| height = int(scale*max_res) | |
| if height > max_res: | |
| height = max_res | |
| width = int(scale*max_res) | |
| img = img.resize((width,height), Image.ANTIALIAS) | |
| img.save("temp/image.jpg", "JPEG") | |
| if task == 'Motion Deblurring': | |
| task = 'Motion_Deblurring' | |
| os.system("python demo_gradio.py --task 'Motion_Deblurring' --input_path 'temp/image.jpg' --result_dir './temp/'") | |
| if task == 'Defocus Deblurring': | |
| task = 'Single_Image_Defocus_Deblurring' | |
| os.system("python demo_gradio.py --task 'Single_Image_Defocus_Deblurring' --input_path 'temp/image.jpg' --result_dir './temp/'") | |
| if task == 'Denoising': | |
| task = 'Real_Denoising' | |
| os.system("python demo_gradio.py --task 'Real_Denoising' --input_path 'temp/image.jpg' --result_dir './temp/'") | |
| if task == 'Deraining': | |
| os.system("python demo_gradio.py --task 'Deraining' --input_path 'temp/image.jpg' --result_dir './temp/'") | |
| return f'temp/{task}/image.jpg' | |
| gr.Interface( | |
| inference, | |
| [ | |
| gr.inputs.Image(type="pil", label="Input"), | |
| gr.inputs.Radio(["Denoising", "Defocus Deblurring", "Motion Deblurring", "Deraining"], default="Denoising", label='task type') | |
| ], | |
| gr.outputs.Image(type="file", label="Output"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| allow_flagging=False, | |
| ).launch(debug=False,enable_queue=True) | |