roboflow--supervision/.github/copilot-instructions.md

GitHub Copilot Instructions for Supervision

This file provides context-aware guidance for GitHub Copilot when working in the Supervision repository.

---

📚 Repository Overview

Supervision is a Python library providing reusable computer vision utilities for working with object detection models (YOLO, SAM, etc.). It offers tools for detections processing, tracking, annotation, and dataset management.

• Languages: Python 3.10+
• Key Dependencies: NumPy, OpenCV, SciPy
• License: MIT

---

🏗️ Project Structure

supervision/
├── src/
│   └── supervision/     # Main library code
│       ├── detection/   # Detection utilities
│       ├── draw/        # Annotation and visualization
│       ├── tracker/     # Object tracking
│       ├── dataset/     # Dataset management
│       └── utils/       # Shared utilities
├── tests/               # Test suite (mirrors src/supervision/)
├── docs/                # MkDocs documentation
└── examples/            # Usage examples

---

🔧 Development Commands

Setup:

# Install dependencies
uv sync --group dev --group docs --extra metrics

# Install pre-commit hooks
uv run pre-commit install

Quality Checks:

# Run all pre-commit hooks (formatting, linting, type checking)
uv run pre-commit run --all-files

# Run tests with coverage
uv run pytest --cov=supervision

Documentation:

# Serve docs locally at http://127.0.0.1:8000
uv run mkdocs serve

---

💻 Code Conventions

General Guidelines

• Follow AGENTS.md for task-based development workflows
• Reference CONTRIBUTING.md for detailed contribution guidelines
• All code must pass pre-commit hooks before committing

Code Style

• Formatting: Enforced by ruff-format, prettier (pre-commit)
• Linting: Enforced by ruff-check (pre-commit)
• Type Hints: Required on all new code
• Docstrings: Required using Google Python style
  • Must include usage examples with primitive values
  • Serve as runnable documentation

Performance

• Avoid unnecessary NumPy array copies
• Prefer vectorized operations over Python loops
• Use OpenCV operations efficiently

API Design

• Follow existing naming patterns for consistency
• Maintain backward compatibility unless explicitly breaking
• Prefer functional utilities over complex classes

---

🧪 Testing Requirements

All new features must include:

• Unit tests covering happy path and edge cases
• Tests for None, empty inputs, large arrays, boundary conditions
• Clear test names describing what they validate
• Proper assertions (not just "no exception raised")

---

📝 Documentation Requirements

For new public functions/classes:

• Google-style docstrings with parameters, returns, exceptions
• Usage examples in docstrings
• Entry in appropriate docs/*.md file
• Reference in mkdocs.yml navigation

---

🔍 Pull Request Reviews

When reviewing PRs, follow the comprehensive PR Review Guidelines.

Quick checklist:

• Tests included and passing
• Docstrings follow Google style with examples
• Pre-commit hooks pass
• Breaking changes documented
• Score code quality, testing, docs (n/5 scale)
• Use inline comments + GitHub suggestion format

---

🌿 Branching & Commits

• Branch from develop using prefixes: feat/, fix/, docs/, refactor/, perf/, test/, chore/
• Use conventional commits: feat:, fix:, docs:, refactor:, perf:, test:, chore:
• All PRs target develop branch

---

🎯 Context-Aware Behavior

• For general development tasks: Follow AGENTS.md
• For pull request reviews: Follow PR Review Guidelines
• For detailed processes: Consult CONTRIBUTING.md