DoorCam

Overview

The DoorCam project is designed to provide a security solution that integrates machine learning to recognize and log door entry events. It includes both the backend processing of data and the frontend for interacting with the system.

Features

• Real-time Door Monitoring: Capture and process door entry data.
• Machine Learning Integration: Models for predicting and analyzing entry events.
• Aggregation and Logging: Tools for aggregating data and maintaining logs of events.
• Training and Prediction: Scripts to train the machine learning models and make predictions based on new data.
• Web Interface: A user-friendly interface to interact with the DoorCam system.

Project Structure

DoorCam/
│
├── deploy/                     # Deployment configuration and scripts
│
├── doorcam/                    # Main application files
│   ├── aggregation.py          # Handles data aggregation
│   ├── models.py               # Defines and handles machine learning models
│   ├── entry_record.csv         # Log of door entry events
│   ├── client.py               # Handles client-side functionality
│   ├── predict.py              # Script to run predictions on new data
│   ├── config.yaml             # Configuration file for the system
│   ├── logs/                   # Log files
│   ├── train.py                # Script to train models
│   ├── app.py                  # Main application file (web or server interface)
│   ├── templates/              # HTML templates for web interface
│   └── utils.py                # Utility functions
│
├── tests/                      # Unit tests for the application
│
├── requirements.txt            # Python dependencies
├── README.md                   # Project documentation (this file)
└── .git/                       # Version control metadata

Getting Started

Prerequisites

• Python 3.8+
• Install dependencies with:

  pip install -r requirements.txt

Running the Application

1. Train the model: To train the machine learning model with available data:

   python doorcam/train.py

2. Make predictions: After training, you can run predictions using:

   python doorcam/predict.py

3. Start the application: To run the web interface:

   python doorcam/app.py

Configuration

Configuration for the system is stored in doorcam/config.yaml. You can modify this file to adjust parameters such as model settings, logging preferences, and more.

Testing

Unit tests are located in the tests/ directory. Run the tests using:

python -m unittest discover tests

Contributing

Feel free to open issues or submit pull requests if you would like to contribute to the project.

License

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