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import os
import gradio as gr
import numpy as np
import torch
from torchvision.utils import make_grid
from huggingface_hub import snapshot_download
from zero_dce import enhance_net_nopool
os.system("pip freeze")
REPO_ID = "leonelhs/lowlight"
MODEL_NAME = "Epoch99.pth"
model = enhance_net_nopool().cpu()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
snapshot_folder = snapshot_download(repo_id=REPO_ID)
model_path = os.path.join(snapshot_folder, MODEL_NAME)
state = torch.load(model_path, map_location=device)
model.load_state_dict(state)
def tensor_to_ndarray(tensor, nrow=1, padding=0, normalize=True):
grid = make_grid(tensor, nrow, padding, normalize)
return grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
def predict(image):
image = (np.asarray(image) / 255.0)
image = torch.from_numpy(image).float()
image = image.permute(2, 0, 1)
image = image.cpu().unsqueeze(0)
_, enhanced_image, _ = model(image)
return tensor_to_ndarray(enhanced_image, nrow=8, padding=2, normalize=False)
title = "Zero-DCE"
description = r"""
## Low-Light Image Enhancement using Zero-DCE
The model improves the quality of images that have poor contrast, low brightness, and suboptimal exposure.
This is an implementation of <a href='https://github.com/Li-Chongyi/Zero-DCE' target='_blank'>Zero-DCE</a>.
It has no any particular purpose than start research on AI models.
"""
article = r"""
Questions, doubts, comments, please email 📧 `[email protected]`
This demo is running on a CPU, if you like this project please make us a donation to run on a GPU or just give us a <a href='https://github.com/leonelhs/Zero-DCE' target='_blank'>Github ⭐</a>
<a href="https://www.buymeacoffee.com/leonelhs"><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=leonelhs&button_colour=FFDD00&font_colour=000000&font_family=Cookie&outline_colour=000000&coffee_colour=ffffff" /></a>
<center><img src='https://visitor-badge.glitch.me/badge?page_id=zerodce.visitor-badge' alt='visitor badge'></center>
"""
demo = gr.Interface(
predict, [
gr.Image(type="pil", label="Image low light"),
], [
gr.Image(type="numpy", label="Image enhanced")
],
title=title,
description=description,
article=article)
demo.queue().launch()
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