Metadata-Version: 2.1
Name: pymoten
Version: 0.0.3
Summary: Extract motion energy features from video using spatio-temporal Gabors
Home-page: https://gallantlab.github.io/pymoten/
Author: Anwar O. Nunez-Elizalde
Author-email: anwarnunez@gmail.com
License: UNKNOWN
Description: =====================
         Welcome to pymoten!
        =====================
        
        |Build Status| |Github| |codecov| |Python| 
        
        
        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
        
        .. code-block:: bash
        
           git clone https://github.com/gallantlab/pymoten.git
           cd pymoten
           sudo python setup.py install
        
        Or with pip:
        
        .. code-block:: bash
        
           pip install pymoten
           
        
        Getting started
        ===============
        
        Example using synthetic data
        
        .. code-block:: python
        
           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
        
        .. code-block:: python
        
           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)
        
        
        .. |Build Status| image:: https://travis-ci.org/gallantlab/pymoten.svg?branch=master
            :target: https://travis-ci.org/gallantlab/pymoten
            
        .. |Github| image:: https://img.shields.io/badge/github-pymoten-blue
           :target: https://github.com/gallantlab/pymoten
        
        .. |Python| image:: https://img.shields.io/badge/python-3.7%2B-blue
           :target: https://www.python.org/downloads/release/python-370
        
        .. |Codecov| image:: https://codecov.io/gh/gallantlab/pymoten/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/gallantlab/pymoten
        
        
        
        
        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 <https://github.com/gallantlab/motion_energy_matlab/>`_.
        
Platform: UNKNOWN
Description-Content-Type: text/x-rst
