Setting the Context

This project was under a collaborative effort of Aneesh and Hemanth for the Hackathon Hosted at Lords Insitutute of Technology, Hyderabad on the 8th and 9th of June, 2024.

Weapon Detection Streamlit App

This project demonstrates a real-time weapon detection application using Streamlit and OpenCV. Users can log in with a username and password (plain text for demonstration, not recommended for production) to access the live video stream with weapon detection functionality.

Features:

• Login functionality (username and password)
• Real-time weapon detection from a webcam stream (using a pre-trained classifier)
• Live video display with bounding boxes around detected weapons

Prerequisites:

• Python 3.12.3
• Streamlit library (pip install streamlit)
• OpenCV library (pip install opencv-python)
• imutils library (pip install imutils)
• numpy library (pip install numpy)

Instructions:

1. Clone or Download the Repository:

   • Clone the repository to your local machine using Git (recommended):

     git clone https://github.com/lhcee3/Weapon-Detection-Opencv.git

2. Set Up the Environment:

   • Create a virtual environment to isolate project dependencies (recommended):

     python -m venv venv  
     source venv/bin/activate  # Activate the virtual environment (Linux/macOS)
     venv\Scripts\activate.bat  # Activate on Windows

   • Install the required libraries:

     pip install streamlit opencv-python imutils numpy

3. Replace Classifier File Path (Optional):

   • The code assumes a weapon classifier file named cascade.xml. If you have a pre-trained classifier, replace the path in the code:

     gun_cascade = cv2.CascadeClassifier('path/to/your/cascade.xml')  # Update the path

   • Be cautious when downloading pre-trained classifiers, as their accuracy and limitations vary. Consider evaluating them for your specific use case.

4. Run the Application:

   • Open a terminal or command prompt and navigate to your project directory.
   • Activate your virtual environment if you created one (see step 2).
   • Run the application using:

     streamlit run check.py  

   • Access the application in your web browser at http://localhost:8501.

Disclaimer:

• This project is for demonstration purposes only.
• Pre-trained weapon classifiers might have limitations and inaccuracies. Evaluate their performance on your specific use case before relying on them for critical applications.
• Using plain text passwords in the code is highly discouraged for real-world deployments. Implement secure password hashing and storage if required.

Further Considerations:

• For production deployment, consider using a cloud platform like Heroku or Google Cloud Run. These platforms offer streamlined deployment processes and web hosting capabilities.
• Secure your deployed application by implementing user authentication and authorization mechanisms.
• Regularly update your dependencies to address security vulnerabilities and ensure compatibility with the latest Streamlit and OpenCV versions.