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Contributing to SecureEye

Thank you for your interest in contributing.

Project Scope

SecureEye is a modern reimplementation of facial authentication for Linux systems. The project focuses on maintainability, modularity, and security.

Getting Started

Requirements

  • Python 3.12+
  • Git
  • uv (see the install instructions in the uv docs)

Setup

git clone https://github.com/vhrabar/secureEye
cd secureEye
uv sync
source .venv/bin/activate

Development Guidelines

Code Style

  • Python: Follow PEP8
  • Python: Use type hints where appropriate
  • Python: Format code using black
  • Python: Lint using ruff
  • C++: Use C++20+, prefer standard library features
  • C++: 4-space indentation, max 100 characters per line
  • C++: Snake_case for functions/variables, PascalCase for classes, UPPER_CASE for constants
  • C++: Trailing _ for private members (e.g., buffer_)
  • C++: Prefer RAII and smart pointers, avoid raw pointers
  • C++: Open braces on the same line
  • C++: Always check return values/errors; use std::optional or exceptions carefully

Architecture Principles

  • Keep modules small and focused
  • Avoid global state
  • Design for testability
  • Keep recognition pipeline modular

Commit Guidelines

  • Use clear, descriptive commit messages
  • Keep commits focused and minimal
  • Reference issues where applicable

Example:

feat(recognition): add MediaPipe-based face detector

Pull Requests

Before submitting a PR:

  • Ensure code builds and runs
  • Add or update tests if applicable
  • Keep PRs focused on a single concern

PRs may be rejected if they:

  • Introduce unnecessary complexity
  • Break modular design
  • Add unmaintained dependencies

Testing

  • Use pytest
  • Avoid reliance on physical hardware (mock camera input)
  • Ensure reproducible results

Use of Generative AI

Contributions assisted by generative AI tools are permitted, but must meet strict quality standards.

  • All submitted code must be fully understood by the contributor.

  • Generated code must be reviewed, tested, and validated before submission.

  • Submissions that contain low-quality, unverified, or irrelevant generated output (“AI slop”) will be rejected.

  • Contributors are responsible for ensuring:

    • correctness
    • security
    • adherence to project architecture and standards

Large, uncurated AI-generated changes that do not demonstrate clear intent or understanding are not acceptable.

Attribution

This project is inspired by the Howdy project (MIT License). Original authors are credited via the preserved Git history. See the NOTICE file for details.

Security Considerations

This project interacts with system authentication (PAM).

  • Do not introduce unsafe system calls
  • Validate all external inputs
  • Avoid insecure defaults

Questions

Open an issue for discussion before making large changes.