Welcome to the Optimal Camera Placement Problem Solver project. This project is dedicated to examining different solutions to the Optimal Camera Placement Problem (OCPP), a common challenge in the fields of surveillance and computer vision.

The aim of OCPP is to determine the best locations to place cameras in a given environment in order to maximize surveillance coverage while minimizing the number of cameras used. It's an NP-Hard problem with diverse applications in areas like public safety, event management, and even wildlife research.

In this project, we are developing a Flask-based application that visualizes and demonstrates different solutions to the OCPP, allowing users to interactively experiment with these solutions and understand their characteristics.

• Python 3.9+
• Pipenv

Clone the repository to your local machine:

git clone https://github.com/username/OptimalCameraPlacementProblemSolver.git

Navigate to the project directory:

cd OptimalCameraPlacementProblemSolver

Install dependencies using Pipenv:

pipenv install

Activate the Pipenv shell:

pipenv shell

The Flask application is initiated by running app.py.

python app.py

After running the application, you can visit http://localhost:5000/ on your preferred web browser to start using the application.

• Explore multiple algorithms to solve the OCPP, including greedy, genetic, and simulated annealing approaches.
• Interactive 2D visualization of the environment and camera placements.
• Adjustable parameters to test different scenarios and configurations.
• Export results for further analysis.

This project is licensed under the MIT License - see the LICENSE.md file for details.

Please feel free to raise an issue on Github or contact us directly if you have any questions, comments, or suggestions.

Thank you for your interest in our project! We hope you find it useful in understanding and solving the Optimal Camera Placement Problem.