Utilizing the power of Deep Learning, Python, OpenCV, and Streamlit, we present an elegant and intuitive web application designed to detect violence in real-time. Featuring a stunning user interface with seamless functionality, this app stands at the forefront of safety and technology.
- Real-Time Violence Detection: Leveraging a sophisticated deep learning model to analyze and detect violent behaviors in video streams.
- Frame Capture & Storage: Automatically captures frames from the video stream, storing them in a dedicated folder for further analysis.
- Batch Processing: Utilizes batches of 16 frames for efficient and accurate violence detection.
- Interactive UI: A beautifully crafted user interface with Start, Stop, Predict, and Delete buttons for easy control and management.
- Streamlit WebApp: Built with Streamlit, ensuring a smooth and responsive experience.
- Clone the Repository
git clone https://github.com/siddhu1919/Realtime-Violence-Detection_Using-DeepLearning-OpenCV-Streamlit.git
- Install Requirements
Ensure you have Python installed, then run:
python -m pip install -r requirements.txt
- **Launch the WebApp **
streamlit run main3.py
Navigate to the URL provided in the command line output to access the web app.
main3.py: The main Streamlit application file.model/: Directory containing the deep learning model files.frames/: Folder where captured frames are stored.requirements.txt: Lists all the Python dependencies.
Download: The Models folder and Paste in root directory.- Drive Link 🚗
- Python - The programming language used.
- OpenCV - For capturing and processing video frames.
- Deep Learning Model - The core algorithm for violence detection.
- Streamlit - For creating the web application.
- Beautiful UI - Designed with attention to detail for a great user experience.
This project is licensed under the MIT License - see the LICENSE.md file for details.
Contributions, issues, and feature requests are welcome! Feel free to check .
Follow This Contributors :
1. Siddhant G Singh ⭐
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Made with ❤️ and 🚀 by S I D D H A R T H . G . S I N G H