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| import random | |
| from datetime import datetime | |
| import av | |
| 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 | |
| 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"[Demo Page](https://canturan10-light-side-streamlit-app-guxrpf.streamlitapp.com/)") | |
| # 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.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 (WebRTC)")) | |
| 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( | |
| "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() | |