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import os
import gradio as gr
from PIL import Image
os.system('wget https://github.com/FanChiMao/HWMNet/releases/download/v0.0/LOL_enhancement_HWMNet.pth -P experiments/pretrained_models')
os.system('wget https://github.com/FanChiMao/HWMNet/releases/download/v0.0/MIT5K_enhancement_HWMNet.pth -P experiments/pretrained_models')
def inference(img, model):
os.system('mkdir test')
#basewidth = 256
#wpercent = (basewidth / float(img.size[0]))
#hsize = int((float(img.size[1]) * float(wpercent)))
#img = img.resize((basewidth, hsize), Image.ANTIALIAS)
img.save("test/1.png", "PNG")
if model == 'LOL':
os.system('python main_test_HWMNet.py --input_dir test --weights experiments/pretrained_models/LOL_enhancement_HWMNet.pth')
elif model == 'MIT-5K':
os.system('python main_test_HWMNet.py --input_dir test --weights experiments/pretrained_models/MIT5K_enhancement_HWMNet.pth')
return 'result/1.png'
title = "Half Wavelet Attention on M-Net+ for Low-light Image Enhancement"
description = "Gradio demo for HWMNet. HWMNet has competitive performance results on two real-world low-light datasets in terms of quantitative metrics and visual quality. See the paper and project page for detailed results below. Here, we provide a demo for low-light image enhancement. To use it, simply upload your image, or click one of the examples to load them. Reference from: https://huggingface.co/akhaliq"
article = "<p style='text-align: center'><a href='https://' target='_blank'>Half Wavelet Attention on M-Net+ for Low-light Image Enhancement</a> | <a href='https://github.com/FanChiMao/HWMNet' target='_blank'>Github Repo</a></p>"
examples = [['low-light.png', 'LOL']]
gr.Interface(
inference,
[gr.inputs.Image(type="pil", label="Input"), gr.inputs.Dropdown(choices=['LOL', 'MIT-5K'], type="value", default='LOL', label="model")],
gr.outputs.Image(type="file", label="Output"),
title=title,
description=description,
article=article,
allow_flagging=False,
allow_screenshot=False,
examples=examples
).launch(debug=True, cache_examples=True) |