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Oğuzcan Turan
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demo added
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LICENSE
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MIT License
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Copyright (c) 2022 Oğuzcan Turan
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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---
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title: Light Side
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emoji:
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colorFrom:
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colorTo:
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sdk: streamlit
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sdk_version: 1.10.0
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app_file: app.py
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license: mit
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---
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-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Light Side
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emoji: ⚡️
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colorFrom: black
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colorTo: white
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sdk: streamlit
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sdk_version: 1.10.0
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app_file: app.py
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license: mit
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---
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Check out the configuration reference at <https://huggingface.co/docs/hub/spaces-config-reference>
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app.py
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import random
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from datetime import datetime
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import av
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import light_side as ls
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import numpy as np
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import requests
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import streamlit as st
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from PIL import Image
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from streamlit_image_comparison import image_comparison
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from streamlit_webrtc import VideoProcessorBase, webrtc_streamer
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def main():
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# pylint: disable=no-member
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st.set_page_config(
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page_title="Light Side Demo Page",
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page_icon="⚡️",
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layout="centered",
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initial_sidebar_state="expanded",
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menu_items={
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"Get Help": "https://canturan10.github.io/light_side/",
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"About": "Low-Light Image Enhancement",
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},
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)
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st.title("Light Side Demo Page")
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url = "https://raw.githubusercontent.com/canturan10/light_side/master/src/light_side.png?raw=true"
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light_side = Image.open(requests.get(url, stream=True).raw)
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st.sidebar.image(light_side, width=100)
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st.sidebar.title("Light Side of the Night")
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st.sidebar.caption(ls.__description__)
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st.sidebar.write(
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"**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`."
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)
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st.sidebar.caption(f"Version: `{ls.__version__}`")
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st.sidebar.caption(f"License: `{ls.__license__}`")
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st.sidebar.caption("")
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st.sidebar.caption(f"[Website](https://lnkd.in/dKejmaH2)")
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st.sidebar.caption(f"[Docs](https://lnkd.in/dt-2T4ZN)")
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st.sidebar.caption(f"[Github](https://lnkd.in/dDttpiEG)")
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# st.sidebar.caption(f"[Demo Page](https://lnkd.in/dxy-632M)")
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st.sidebar.caption(f"[Hugging Face]( https://lnkd.in/d5-VVK62)")
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st.sidebar.caption(f"[Pypi](https://lnkd.in/d7eDNb-B)")
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st.sidebar.caption("")
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st.sidebar.caption(ls.__copyright__)
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selected_model = st.selectbox(
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"Select model",
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ls.available_models(),
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)
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selected_version = st.selectbox(
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"Select version",
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ls.get_model_versions(selected_model),
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)
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mode = st.radio("Select Inference Mode", ("Image", "Video (WebRTC)"))
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model = ls.Enhancer.from_pretrained(selected_model, selected_version)
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model.eval()
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if mode == "Image":
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uploaded_file = st.file_uploader(
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"", type=["png", "jpg", "jpeg"], accept_multiple_files=False
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)
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if uploaded_file is None:
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st.write("Default Image")
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# Default image.
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url = f"https://github.com/canturan10/light_side/blob/master/src/sample/{random_sample}?raw=true"
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image = Image.open(requests.get(url, stream=True).raw)
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else:
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# User-selected image.
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image = Image.open(uploaded_file)
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image = np.array(image.convert("RGB"))
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results = model.predict(image)[0]
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orj_img = results["image"]
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enh_img = results["enhanced"]
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image_comparison(
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img1=orj_img,
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img2=enh_img,
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label1="Dark Side",
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label2="Light Side",
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)
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else:
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st.write(
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"If video is not playing, please refresh the page. Depends on your browser and connection, it may take some time to load the video."
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)
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class VideoProcessor(VideoProcessorBase):
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def recv(self, frame):
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img = frame.to_ndarray(format="bgr24")
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results = model.predict(img)[0]
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orj_img = results["image"]
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enh_img = results["enhanced"]
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return av.VideoFrame.from_ndarray(
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np.concatenate((orj_img, enh_img), axis=1), format="bgr24"
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)
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ctx = webrtc_streamer(
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key="example",
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video_processor_factory=VideoProcessor,
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rtc_configuration={
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"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]
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},
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media_stream_constraints={
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"video": True,
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"audio": False,
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},
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)
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if __name__ == "__main__":
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samples = [
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"0_orj.png",
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"1_orj.png",
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"2_orj.png",
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"3_orj.png",
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"4_orj.png",
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"5_orj.png",
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]
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random.seed(datetime.now())
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random_sample = samples[random.randint(0, len(samples) - 1)]
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main()
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requirements.txt
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av~=9.2.0
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light_side
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streamlit-image-comparison~=0.0.2
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streamlit-webrtc~=0.42.0
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streamlit~=1.8.1
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