A fast, clean, production-ready face detection + detailed facial landmark pipeline built from scratch in pure Python.
Runs at 120–140 FPS on a regular laptop (no dedicated GPU needed).
All 5 parts are now live. Finish the whole series in ~2 hours and walk away with a complete, real-time app.
Series Contents
| Part | Title | Link |
|---|---|---|
| 1 | Project Setup & Clean Architecture | Part 1 → |
| 2 | ConfigModel – Load YOLO Only Once | Part 2 → |
| 3 | OpenCVBase – Eliminate Duplicate Code | Part 3 → |
| 4 | YOLOv9 + MediaPipe FaceMesh (539 refined landmarks) | Part 4 → |
| 5 | Final Rendering + Complete Real-Time App | Part 5 → |
Full Source Code → GitHub
Demo → LinkedIn
Tech Stack
- Python 3.11+
- Ultralytics YOLOv9t-face (lindevs model)
- MediaPipe FaceMesh with
refine_landmarks=True - Pure OpenCV (no heavy extra dependencies)
Why this series stands out
- Zero code duplication
- Models loaded only once → maximum FPS
- Fully modular OOP design – extend in minutes
- Perfect portfolio / resume / startup prototype project
Prerequisites: Basic Python + some OpenCV knowledge (Part 1 covers the rest).
If you enjoyed this series — star the repo, give this post 50 claps (really helps!), and follow for more production-grade computer vision content.
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