Warden Terminal Camera Truck Detection

wando-warden can watch terminal cameras (such as those at Wando Welch), read the timestamp from the image, and count the number of trucks.

Features

• OCR (pytesseract) to extract timestamps
• AI object detection using AWS Rekognition
• Flexible storage options

Dashboard & Data Visualization

wando-warden comes with a streamlit app that can be used to configure the app, manually scan cameras, and visualize the data.

Architecture

Installation

1. Clone this repository:

   git clone https://github.com/hunterjsb/wando-warden.git
   cd wando-warden

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

Configuration

1. Edit the terminals.yaml file to configure your terminals and cameras.

2. Set up environment variables for sensitive information. See .env.example.

Usage

Run the streamlit app dashboard with:

streamlit run app.py

Run the main script with the following command:

python -m warden --detect-trucks

Arguments

• --terminals: Path to the YAML file containing terminal configurations (default: '../terminals.yaml')
• --storage: Choose between 'local' or 's3' storage (default: 's3')
• --log-level: Set the logging level (default: 'INFO')
• --db: Database to store object detection results (default: ')

CLI Examples

1. Run with default settings (S3 for photos, DynamoDB for results):

   python -m warden

2. Use local photo storage and debug logging:

   python -m warden --storage local --log-level DEBUG

3. Detect trucks and store the results in a postgresql db:

   python -m warden --db postgres --detect-trucks

Truck Detection

The Warden system now supports truck detection using AWS Rekognition. To use this feature:

1. Ensure you have the necessary AWS permissions to use Rekognition.
2. Set the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables with your AWS credentials.
3. Use the --detect-trucks flag when running the script: python -m warden --detect-trucks
4. This will detect trucks in the captured images, count them, calculate the average confidence of the detections, and store this information in a SQLite database.

The truck detection data is stored in a SQLite database. By default, it's saved as truck_detections.db in the current directory. See DB options below.

Database Options for Truck Detection Results

The Warden system now supports multiple database options for storing truck detection results:

1. SQLite (default)
2. MySQL
3. PostgreSQL
4. DynamoDB

To specify the database type, use the --db flag: python -m warden --detect-trucks --db [sqlite|mysql|postgres|dynamodb]

For each database type, you need to set the appropriate environment variables, the names are found in .env.example.

Note: For DynamoDB, ensure that you have the necessary AWS permissions and credentials set up.

Development

To extend or modify the Warden system:

1. Add new camera types in terminal.py
2. Implement new storage backends in memory.py
3. Enhance OCR capabilities in ocr.py
4. Adjust labels and object detection settings in detection.py
5. Edit default configuration values in config.py

License

This project is licensed under MIT-0. See the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

If you encounter any problems or have any questions, please open an issue on the GitHub repository.