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Alexander Refsum Jensenius edited this page Aug 2, 2025 · 13 revisions

The Musical Gestures Toolbox for Python is a collection of high-level modules targeted at researchers working with video recordings. It includes visualization techniques such as motion videos, motion history images, and motiongrams; techniques that, in different ways, allow for looking at video recordings from different temporal and spatial perspectives. It also includes basic computer vision analysis, such as extracting the quantity and centroid of motion, and using such features in analysis.

The toolbox was initially developed to analyze music-related body motion (of musicians, dancers, and perceivers) but is equally helpful for other disciplines working with video recordings of humans, such as linguistics, pedagogy, psychology, and medicine.

Functionality

The Musical Gestures Toolbox contains functions to analyze and visualize video, audio, and motion capture data. There are three categories of functions:

  • Preprocessing (trimming, cropping, color adjustments, etc.)
  • Visualization (video playback, image display, plotting)
  • Processing (videograms, average images, motion images, etc.)

Getting Started Options

  1. Quick Start Tutorial
  2. Jupyter Notebook – can also be run in Colab
  3. Browse the detailed function documentation in this wiki
  4. Examples Overview
  5. Complete Documentation Site

Wiki Navigation

This wiki provides detailed documentation for individual MGT functions:

Backend

The speed and efficiency of the MGT are made possible by the excellent FFmpeg project. Many of the toolbox functions are Python wrappers/bindings on FFmpeg commands called in a subprocess.

Problems

Please help improve the toolbox by adding bugs and feature requests in the issues section.

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