Quick Start

# Extract archive
unzip edgefirst-samples-linux-x86_64.zip
cd edgefirst-samples-linux-x86_64

# List available topics
./list-topics

# Run complete vision pipeline demo
./mega-sample

# Remote connection
./list-topics --remote 192.168.1.100:7447

Release version 0.1.1

First public release of EdgeFirst Perception Middleware samples repository.

This release represents the initial open-source publication of comprehensive Rust and Python examples demonstrating EdgeFirst Perception capabilities across camera, LiDAR, radar, and sensor fusion use cases.

Highlights

• Comprehensive Examples: 28+ Rust examples with parallel Python implementations

  • Camera: DMA, H.264, info, camera_info
  • LiDAR: points, depth, clusters, reflectivity
  • Radar: targets, clusters, cube, info
  • ML Inference: 2D boxes, masks, tracked objects
  • Sensor Fusion: radar fusion, lidar fusion, 3D boxes, occupancy grids
  • Navigation: IMU, GPS

• Production-Ready CI/CD

  • Multi-platform testing (Linux, Windows, macOS)
  • Rust: cargo fmt, clippy, build, test
  • Python: black, flake8, import verification
  • SBOM generation with license compliance checking
  • SonarQube integration for code quality

• Open Source Infrastructure

  • Apache-2.0 licensed with full SPDX headers
  • Comprehensive documentation (README, CONTRIBUTING, ARCHITECTURE, SECURITY)
  • GitHub Actions workflows optimized for cross-platform development
  • Community guidelines (Code of Conduct, issue templates, PR templates)

Added

• Zenoh-based pub/sub communication patterns for all sensor types
• Optional Rerun visualization integration (feature-gated)
• Cross-platform build support (Linux primary, Windows/macOS client apps)
• Automated SBOM generation and license policy enforcement
• Version management with cargo-release
• Python import verification script for CI/CD reliability

Documentation

• Architecture guide explaining Zenoh patterns and message schemas
• Contributing guide with Rust and Python development setup
• Security policy with vulnerability reporting process
• AGENTS.md guide for AI-assisted development with project conventions

Infrastructure

• GitHub Actions workflows for continuous integration
• SBOM generation using scancode-toolkit (22s optimized performance)
• License policy compliance with automated checking
• Support for edgefirst-schemas 1.4.0 (Apache-2.0)

Notes

• This is a samples repository for demonstration and learning
• Not published to crates.io (publish = false)
• Versions track sample evolution and documentation improvements
• Previous development history considered legacy and not detailed here