52Hz/CMFNet_dehazing

1import os2import gradio as gr3from PIL import Image4import torch5 6os.system(7    'wget https://github.com/FanChiMao/CMFNet/releases/download/v0.0/dehaze_I_OHaze_CMFNet.pth -P experiments/pretrained_models')8 9 10def inference(img):11    if not os.path.exists('test'):12        os.system('mkdir test')13    14    basewidth = 51215    wpercent = (basewidth / float(img.size[0]))16    hsize = int((float(img.size[1]) * float(wpercent)))17    img = img.resize((basewidth, hsize), Image.BILINEAR)18    img.save("test/1.png", "PNG")19    os.system(20        'python main_test_CMFNet.py --input_dir test --weights experiments/pretrained_models/dehaze_I_OHaze_CMFNet.pth')21    return 'results/1.png'22 23 24title = "Compound Multi-branch Feature Fusion for Image Restoration (Dehaze)"25description = "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 dehaze. To use it, simply upload your image, or click one of the examples to load them. Reference from: https://huggingface.co/akhaliq"26article = "<p style='text-align: center'><a href='https://' target='_blank'>Compound Multi-branch Feature Fusion for Real Image Restoration</a> | <a href='https://github.com/FanChiMao/CMFNet' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=52Hz_CMFNet_dehazing' alt='visitor badge'></center>"27 28examples = [['Haze.png']]29gr.Interface(30    inference,31    [gr.components.Image(type="pil", label="Input")],32    gr.components.Image(type="filepath", label="Output"),33    title=title,34    description=description,35    examples=examples36).launch(debug=True)