TurtleBot Detection with a Custom YOLOv5 Model

This project provides a Flask API for detecting TurtleBot objects using a custom-trained YOLOv5 model.

The API accepts an image file via a POST request and returns detected TurtleBots along with their bounding boxes, class IDs, and confidence scores.

The main repository for the course project can be found here: GitHub - TurtleBot Laser Tag.

Installation

1. Clone the repository

2. Install the necessary dependencies by running:

   pip install -r requirements.txt

3. (Optional) If you have your own model, place it in the same directory as the script, or update the model_path variable to reflect the correct path.

Usage

1. Start the Flask server:

   gunicorn --bind 0.0.0.0:5000 app:app

2. The API will be available at http://localhost:5000.
3. To detect TurtleBots, send a POST request to the /detect endpoint with an image file attached. Example usage:

   curl -X POST -F "file=@your_image.jpg" http://localhost:5000/detect

Response

The API will return a JSON response with the following structure:

{
  "detections": [
    {
      "class_id": 0,
      "confidence": 0.95,
      "bounding_box": {
        "x_center": 0.5,
        "y_center": 0.5,
        "width": 0.2,
        "height": 0.3
      }
    }
  ]
}

Or if there are no detections:

{
  "message": "No objects detected." 
}

Dataset

The model was trained on a dataset of TurtleBot images. It is available here: Dataset Link.