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import random
from datetime import datetime
import light_side as ls
import numpy as np
import requests
import streamlit as st
from PIL import Image
from streamlit_image_comparison import image_comparison
# import av
# from streamlit_webrtc import VideoProcessorBase, webrtc_streamer
def main():
# pylint: disable=no-member
st.set_page_config(
page_title="Light Side Demo Page",
page_icon="⚡️",
layout="centered",
initial_sidebar_state="expanded",
menu_items={
"Get Help": "https://canturan10.github.io/light_side/",
"About": "Low-Light Image Enhancement",
},
)
st.title("Light Side Demo Page")
url = "https://raw.githubusercontent.com/canturan10/light_side/master/src/light_side.png?raw=true"
light_side = Image.open(requests.get(url, stream=True).raw)
st.sidebar.image(
light_side,
width=100,
)
st.sidebar.title("Light Side of the Night")
st.sidebar.caption(ls.__description__)
st.sidebar.write(
"**Light Side** is an low-light image enhancement library that consist state-of-the-art deep learning methods. The light side of the Force is referenced. The aim is to create a light structure that will find the `Light Side of the Night`."
)
st.sidebar.caption(f"Version: `{ls.__version__}`")
st.sidebar.caption(f"License: `{ls.__license__}`")
st.sidebar.caption("")
st.sidebar.caption(f"[Website](https://canturan10.github.io/light_side/)")
st.sidebar.caption(f"[Docs](https://light-side.readthedocs.io/)")
st.sidebar.caption(f"[Github](https://github.com/canturan10/light_side)")
# st.sidebar.caption(f"[Hugging Face](https://huggingface.co/spaces/canturan10/light_side)")
st.sidebar.caption(f"[Pypi](https://pypi.org/project/light-side/)")
st.sidebar.caption("")
st.sidebar.markdown(
"[](https://www.buymeacoffee.com/canturan10)"
)
st.sidebar.caption("")
st.sidebar.caption(ls.__copyright__)
selected_model = st.selectbox(
"Select model",
ls.available_models(),
)
selected_version = st.selectbox(
"Select version",
ls.get_model_versions(selected_model),
)
mode = st.radio("Select Inference Mode", ("Image", "Video"))
model = ls.Enhancer.from_pretrained(selected_model, selected_version)
model.eval()
if mode == "Image":
uploaded_file = st.file_uploader(
"", type=["png", "jpg", "jpeg"], accept_multiple_files=False
)
if uploaded_file is None:
st.write("Default Image")
# Default image.
url = f"https://github.com/canturan10/light_side/blob/master/src/sample/{random_sample}?raw=true"
image = Image.open(requests.get(url, stream=True).raw)
else:
# User-selected image.
image = Image.open(uploaded_file)
image = np.array(image.convert("RGB"))
results = model.predict(image)[0]
orj_img = results["image"]
enh_img = results["enhanced"]
image_comparison(
img1=orj_img,
img2=enh_img,
label1="Dark Side",
label2="Light Side",
)
else:
st.write(
"This feature has been suspended for now. Errors may occur during service free of charge due to limited resources. If there is support for the project, I can activate this feature again by increasing the limit."
)
st.markdown(
"[](https://www.buymeacoffee.com/canturan10)"
)
# st.write(
# "If video is not playing, please refresh the page. Depends on your browser and connection, it may take some time to load the video."
# )
# class VideoProcessor(VideoProcessorBase):
# def recv(self, frame):
# img = frame.to_ndarray(format="bgr24")
# results = model.predict(img)[0]
# orj_img = results["image"]
# enh_img = results["enhanced"]
# return av.VideoFrame.from_ndarray(
# np.concatenate((orj_img, enh_img), axis=1), format="bgr24"
# )
# ctx = webrtc_streamer(
# key="example",
# video_processor_factory=VideoProcessor,
# rtc_configuration={
# "iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]
# },
# media_stream_constraints={
# "video": True,
# "audio": False,
# },
# )
if __name__ == "__main__":
samples = [
"0_orj.png",
"1_orj.png",
"2_orj.png",
"3_orj.png",
"4_orj.png",
"5_orj.png",
]
random.seed(datetime.now())
random_sample = samples[random.randint(0, len(samples) - 1)]
main()
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