import os import gradio as gr from PIL import Image import torch os.system( 'wget https://github.com/FanChiMao/CMFNet/releases/download/v0.0/deblur_GoPro_CMFNet.pth -P experiments/pretrained_models') def inference(img): os.makedirs("./test", exist_ok=True) basewidth = 512 wpercent = (basewidth / float(img.size[0])) hsize = int((float(img.size[1]) * float(wpercent))) img = img.resize((basewidth, hsize), Image.BILINEAR) img.save("test/1.png", "PNG") os.system( 'python main_test_CMFNet.py --input_dir test --weights experiments/pretrained_models/deblur_GoPro_CMFNet.pth') return 'results/1.png' title = "Compound Multi-branch Feature Fusion for Image Restoration (Deblur)" description = "Gradio demo for CMFNet. CMFNet achieves competitive performance on three tasks: image deblurring, image dehazing and image deraindrop. Here, we provide a demo for image deblur. To use it, simply upload your image, or click one of the examples to load them. Reference from: https://huggingface.co/akhaliq" article = "

Compound Multi-branch Feature Fusion for Real Image Restoration | Github Repo

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" examples = [['images/Blur1.png'], ['images/Blur2.png'], ['images/Blur5.png'],] gr.Interface( inference, [gr.components.Image(type="pil", label="Input")], gr.components.Image(type="filepath", label="Output"), title=title, description=description, article=article, examples=examples ).launch(debug=True)