Human Body Part Segmentation Toolkit

Welcome to the Human Body Part Segmentation Toolkit repository, a robust tool leveraging a pretrained model to perform detailed segmentation of human body parts from images. This project aims to facilitate advanced image analysis and applications in areas such as medical imaging, augmented reality, and motion capture.

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Features

• Accurate Segmentation: Utilizes a pretrained deep learning model to accurately segment human body parts.
• High Performance: Optimized for both speed and accuracy on standard hardware.
• Easy Integration: Designed for easy integration with existing projects needing human body part segmentation.

Built With

• Python 3.8 or higher
• OpenCV

Source Repository

This project utilizes models and code adapted from [(https://github.com/source-username/source-repository)]. Visit the original repository for more details on the model architecture and its implementation.

Getting Started

To get a local copy up and running, follow these simple steps.

Prerequisites

Ensure you have Python 3.8 or higher installed on your machine, along with pip for installing packages.

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/CoolFeature)
3. Commit your Changes (git commit -m 'Add some CoolFeature')
4. Push to the Branch (git push origin feature/CoolFeature)
5. Open a Pull Request

License

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

Contact

[email protected]

Acknowledgments

• Hat tip to anyone whose code was used
• Inspiration
• etc