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¶
Clone the repo from GitHub and do the usual python install
git clone https://github.com/gallantlab/pymoten.git
cd pymoten
sudo python setup.py install
Or with pip:
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)
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.
A MATLAB implementation can be found here.