Cards Object Detection

This project aims to detect strategic fruits cards using YOLOv8. It is implemented in Python and uses several libraries for data processing and model training.

Project Structure

The project has the following structure:

• card_detection_app.py: Main application script for card detection.
• environment.yml: Contains the conda environment configuration.
• setup.py: Contains the setup configuration for the Python package.

Getting Started

Prerequisites

Ensure you have the following installed on your system:

• Python 3.12.3
• Conda package manager
• Cuda Toolkit 12.3
• Suggested IDE:
  • VSCode with Python and Jupyter extensions
  • PyCharm Professional is also a good choice if you have a license

Installation

1. Clone the repository:

git clone https://github.com/MacMat01/yolo-detection-app.git

2. Navigate to the project directory:

cd yolo-detection-app

3. Create a new conda environment from the environment.yml file:

conda env create --name <your-environment-name>

4. Activate the conda environment:

conda activate <your-environment-name>

5. Install the build and pip tools:

pip install --upgrade build pip

6. Build a source distribution (sdist) and a binary distribution (wheel) of your package:

python -m build

7. Install the package from the wheel file:

pip install --user dist/*.whl # If it doesn't work, change </*.whl> to the name of the wheel file generated in step 6

Running the Application

For Using the Main Application

To run the main application, navigate to the src directory and run the card_detection_app.py script:

cd src

Before running the main application, in the CardDetectionApp’s init method, remember to change the video_path to the path of the video you want to use. Otherwise, by default will be used the video camera

For the main application, run the following command:

python card_detection_app.py

License

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