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import gradio as gr
import torch
import torch.nn as nn
import torchvision
import torch.backends.cudnn as cudnn
import torch.optim
import os
import sys
import argparse
import time
import dataloader
import model
import numpy as np
from torchvision import transforms
from PIL import Image
import glob
import time
def lowlight(image):
os.environ['CUDA_VISIBLE_DEVICES']=''
data_lowlight = Image.open(image)
data_lowlight = (np.asarray(data_lowlight)/255.0)
data_lowlight = torch.from_numpy(data_lowlight).float()
data_lowlight = data_lowlight.permute(2,0,1)
data_lowlight = data_lowlight.cpu().unsqueeze(0)
DCE_net = model.enhance_net_nopool().cpu()
DCE_net.load_state_dict(torch.load('Epoch99.pth', map_location=torch.device('cpu')))
start = time.time()
_,enhanced_image,_ = DCE_net(data_lowlight)
end_time = (time.time() - start)
print(end_time)
torchvision.utils.save_image(enhanced_image, f'01.png')
return '01.png'
title = "Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement"
description = "Low-light image enhancement using Zero-DCE model. Full paper: http://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_Zero-Reference_Deep_Curve_Estimation_for_Low-Light_Image_Enhancement_CVPR_2020_paper.pdf"
article = "<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/Li-Chongyi/Zero-DCE' target='_blank'>Github Repo</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=52Hz_CMFNet_deblurring' alt='visitor badge'></center>"
examples = [['01.jpg'], ['02.jpg'], ['03.jpg'],]
gr.Interface(
lowlight,
[gr.inputs.Image(type="file", label="Input")],
outputs = "image",
title=title,
description=description,
article=article,
allow_flagging=False,
allow_screenshot=False,
examples=examples
).launch(debug=True)
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