import gradio as gr import os os.system("git clone https://github.com/megvii-research/NAFNet") os.system("mv NAFNet/* ./") os.system("mv *.pth experiments/pretrained_models/") os.system("python3 setup.py develop --no_cuda_ext --user") def inference(image, task): if not os.path.exists('tmp'): os.system('mkdir tmp') image.save("tmp/lq_image.png", "PNG") if task == 'Denoising': os.system("python basicsr/demo.py -opt options/test/SIDD/NAFNet-width64.yml --input_path ./tmp/lq_image.png --output_path ./tmp/image.png") if task == 'Deblurring': os.system("python basicsr/demo.py -opt options/test/REDS/NAFNet-width64.yml --input_path ./tmp/lq_image.png --output_path ./tmp/image.png") return 'tmp/image.png' title = "NAFNet" description = "Gradio demo for NAFNet: Nonlinear Activation Free Network for Image Restoration. NAFNet achieves state-of-the-art performance on three tasks: image denoising, image debluring and stereo image super-resolution (SR). See the paper and project page for detailed results below. Here, we provide a demo for image denoise and deblur. To use it, simply upload your image, or click one of the examples to load them." article = "

Simple Baselines for Image Restoration | NAFSSR: Stereo Image Super-Resolution Using NAFNet | Github Repo

" examples = [['demo/noisy.png', 'Denoising'], ['demo/blurry.jpg', 'Deblurring']] iface = gr.Interface( inference, [gr.inputs.Image(type="pil", label="Input"), gr.inputs.Radio(["Denoising", "Deblurring"], default="Denoising", label='task'),], gr.outputs.Image(type="file", label="Output"), title=title, description=description, article=article, enable_queue=True, examples=examples ) iface.launch(debug=True,enable_queue=True)