🏌️‍♂️ How I Built a Golf Swing Analyzer in Python Using AI Pose Detection (That Actually Works)

#webdev #ai #programming #javascript

⛳ Why This Project Matters

Golf has always been a game of inches , a micro-adjustment in wrist angle, a fraction of a second in timing, or a subtle shift in posture can be the difference between a 300-yard drive and a slice into the trees.

Traditionally, only elite players with access to swing coaches, motion capture systems, or $10,000 launch monitors could dissect their biomechanics. Everyone else? We just squint at slow-mo YouTube replays of Tiger and hope for the best.

That gap is what I set out to solve.

What if anyone, anywhere, with nothing more than a smartphone video and a Colab notebook, could access near-pro-level swing diagnostics?

That was the genesis of GolfPosePro , an AI-powered golf swing analyzer that:

Tracks your swing phases frame-by-frame with pose estimation.
Visualizes biomechanics (like wrist trajectory) in debug plots.
Compares your motion to PGA Tour pros , side-by-side.
Generates enhanced playback with slow motion, labeled overlays, and pro benchmarks.

All built with Python, MediaPipe, OpenCV, matplotlib, and Google Colab Pro.

This isn’t just about golf — it’s a case study in democratizing biomechanics through AI.

⚙️ What It Does

🧠 Extracts wrist motion from your swing video.
🪄 Segments swing phases dynamically: Address → Backswing → Top → Downswing → Impact → Follow-through
🔍 Overlays debug plots of wrist trajectory, velocity, and key checkpoints.
🎯 Runs side-by-side comparisons against PGA swings (downloaded with yt-dlp).
🐢 Encodes slow-motion video segments, highlighting your motion frame-by-frame.

👉 Imagine watching your swing next to Rory McIlroy’s — with a biomechanical plot showing exactly where your wrist path diverges.

🧱 How It Works

This project is really three systems working together:

Pose Estimation Engine (MediaPipe) → Converts pixels into biomechanical landmarks.
Signal Processing Layer (NumPy + matplotlib) → Smooths, filters, and segments motion.
Visualization Pipeline (OpenCV + FFmpeg) → Merges raw video with analytical overlays.

Let’s break that down.

🧍‍♂️ 1. Pose Estimation with MediaPipe
At the heart of the system is MediaPipe Pose — Google’s real-time human landmark detector.

It tracks 33 body landmarks at ~30 FPS, including wrists, shoulders, and hips.

results = pose.process(rgb_frame)
wrist_y = results.pose_landmarks.landmark[LEFT_WRIST].y
From a swing video, we extract wrist positions across time. Why wrists? Because they’re critical in determining swing path, lag, and release timing.

🧼 2. Trajectory Smoothing
Raw pose data is noisy (frames jitter, lighting shifts). To stabilize it, I apply a uniform moving average filter and compute velocity with NumPy gradients.


velocity = np.gradient(uniform_filter1d(wrist_y, size=5))

This transforms jittery landmarks into smooth curves that actually mean something.

Velocity spikes = transition points
Flat zones = posture holds

📐 3. Swing Phase Segmentation
Here’s the biomechanical magic:

Address → Backswing start = wrist first deviates upward.
Top of swing = lowest wrist point (relative to torso).
Impact = peak wrist acceleration crossing baseline.
Follow-through = velocity decay + posture stabilization. Each phase is dynamically detected, then color-coded on the debug plot.

🎥 4. Side-by-Side Video Overlays

A coach doesn’t just tell you where you’re off — they show you.
So with OpenCV and FFmpeg, I stack:

Your swing
A pro’s swing (downloaded via yt-dlp)
Trajectory plots with labeled swing checkpoints

combined_frame = np.hstack((frame, debug_plot_img))

The final output: a video file with slow-motion playback at impact, plus real-time analytical overlays.

🧪 Tools Used

🏌️ Built For

Amateurs → Upload iPhone swing clips, get coach-like insights.
Coaches → Use it as a feedback tool without expensive sensors.
Developers → A sandbox for exploring pose detection + video analytics. This notebook isn’t replacing coaches or TrackMan — but it’s democratizing access to biomechanics.

🙏 Credits

Pro swing footage: YouTube Shorts (Max Homa, Ludvig Åberg).
Frameworks: MediaPipe, OpenCV, matplotlib, FFmpeg.
Countless test swings (and slices) on the driving range.

🚀 What’s Next

🗣️ AI coach commentary overlay.
🏌️ Support for left-handed players (pose normalization).
🎥 Ball tracer integration.
📊 Automatic swing grading with ML classifiers.
📱 Mobile-friendly UI.

🏁 Final Thoughts
Golf is often said to be a battle between the player and themselves. By applying AI pose detection, we finally have a way to quantify the invisible — turning milliseconds of motion into data you can act on.
This project isn’t just about golf. It’s a glimpse of how AI can democratize performance analysis across all sports.
And for me? It’s about making practice smarter, not just longer.
⛳ Let’s bring AI to the range — one frame at a time
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