Welcome to pymoten!

What is pymoten?

pymoten is a python package that provides a convenient way to extract motion energy features from video using a pyramid of spatio-temporal Gabor filters [1] [2]. The filters are created at multiple spatial and temporal frequencies, directions of motion, x-y positions, and sizes. Each filter quadrature-pair is convolved with the video and their activation energy is computed for each frame. These features provide a good basis to model brain responses to natural movies [3] [4].

Installation

Using pip, install the latest version from git:

pip install git+https://github.com/gallantlab/pymoten.git

Or the most recent release:

pip install pymoten

Getting started

Example using synthetic data

import moten
import numpy as np

# Generate synthetic data
nimages, vdim, hdim = (100, 90, 180)
noise_movie = np.random.randn(nimages, vdim, hdim)

# Create a pyramid of spatio-temporal gabor filters
pyramid = moten.get_default_pyramid(vhsize=(vdim, hdim), fps=24)

# Compute motion energy features
moten_features = pyramid.project_stimulus(noise_movie)

Simple example using a video file

import moten

# Stream and convert the RGB video into a sequence of luminance images
video_file = 'http://anwarnunez.github.io/downloads/avsnr150s24fps_tiny.mp4'
luminance_images = moten.io.video2luminance(video_file, nimages=100)

# Create a pyramid of spatio-temporal gabor filters
nimages, vdim, hdim = luminance_images.shape
pyramid = moten.get_default_pyramid(vhsize=(vdim, hdim), fps=24)

# Compute motion energy features
moten_features = pyramid.project_stimulus(luminance_images)

Cite as

Nunez-Elizalde AO, Deniz F, Dupré la Tour T, Visconti di Oleggio Castello M, and Gallant JL (2021). pymoten: scientific python package for computing motion energy features from video. Zenodo. https://doi.org/10.5281/zenodo.6349625

References

[1]	Adelson, E. H., & Bergen, J. R. (1985). Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A, 2(2), 284-299.

[2]	Watson, A. B., & Ahumada, A. J. (1985). Model of human visual-motion sensing. Journal of the Optical Society of America A, 2(2), 322–342.

[3]	Nishimoto, S., & Gallant, J. L. (2011). A three-dimensional spatiotemporal receptive field model explains responses of area MT neurons to naturalistic movies. Journal of Neuroscience, 31(41), 14551-14564.

[4]	Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., & Gallant, J. L. (2011). Reconstructing visual experiences from brain activity evoked by natural movies. Current Biology, 21(19), 1641-1646.

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A MATLAB implementation can be found here.