/hand-tracking-pong

Real-time Hand-Tracking Pong Game

Primary LanguagePythonMIT LicenseMIT

Hand-Tracking Pong

"Real-time" Hand-Tracking Pong. As of the moment, object detection inference time is the bottleneck, at 1-2 FPS on a Macbook Pro (i5, 2.5GHz, 8GB) with detections on 160x90 images.

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The original hand tracker utilities and model are taken from Victor Dibia's Hand Tracking repository.

Installation

  1. Setup a Python virtual environment with Python 3.5.

  2. Install all Python dependencies.

pip install -r requirements.txt

For Ubuntu users, install libsm6 for OpenCV to work:

apt install libsm6

Installation is complete. To get started, launch the demo. I recommend running the multi-threaded version.

python main_multi.py