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CONTRIBUTING.md

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Contributing

We're thrilled you're considering contributing to homr! This document outlines the process for contributing to the homr project.

Getting Started

Before you begin, make sure you have a GitHub account and have familiarized yourself with the homr project by reading through the documentation and exploring the codebase.

Issues

  • Before submitting a new issue, please search existing issues to avoid duplicates.
  • When creating an issue, provide as much relevant information as possible to help us understand the problem or feature request.

Pull Requests

  1. Fork the Repository: Start by forking the homr repository to your GitHub account.
  2. Create a Branch: Create a branch in your forked repository for your contribution.
  3. Make Your Changes: Implement your changes, run make format to apply the code style and make ci to run code analysis and tests
  4. Commit Your Changes: Make sure your commit messages are clear and follow best practices.
  5. Push Your Changes: Push your changes to your forked repository on GitHub.
  6. Submit a Pull Request: Open a pull request from your forked repository to the main homr repository. Provide a clear description of the problem you're solving or the feature you're adding.

Contribution Guidelines

Code Style

  • Follow the coding style and conventions already present in the homr codebase.
  • Use meaningful variable names and comments to make the code as readable as possible.
  • Format your code by running make format.

Testing

  • Add unit tests for new features or bug fixes whenever possible.
  • Ensure that all tests pass before submitting your pull request, run make ci.

Documentation

  • Update the README.md or documentation if your changes require it.
  • Add comments to your code where necessary to explain complex or non-obvious parts of your implementation.

Detection Algorithm Changes

If your contribution impacts the detection algorithm, please include examples that showcase the enhancements. This should cover both qualitative examples and, where possible, quantitative metrics to illustrate the improvements. Utilize the rate_validation_result.py script to generate metrics across multiple images, provided you have access to corresponding groundtruth MusicXML files.

Training Model Updates

If your contribution involves modifications to the model training process, please include a link to the updated trained model. This ensures that reviewers and users can easily access and evaluate the impact of your changes.

Licensing

By contributing to homr, you agree that your contributions will be licensed under both the AGPL and Apache 2 licenses. This ensures that your contributions can be used in a wide range of applications while maintaining the project's open-source nature.

Questions?

If you have any questions or need further clarification on the contribution process, feel free to open an issue for discussion.

Thank you for contributing to homr!