Acknowledgements

This work was supported by a 2019 grant from the National Science Foundation to Simona Buetti (PI) under award number BCS1921735 (Hummel and Lleras, Co-PIs), CompCog: Template Contrast and Saliency (TCAS) Toolbox: a tool to visualize parallel attentive evaluation of scenes.

This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) the State of Illinois and the National Geospatial-Intelligence Agency. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.

V1-salience-model

Conceptualization: Simona Buetti, John E Hummel, Alejandro Lleras, and Rachel F Heaton . Software: John E Hummel and Rachel F Heaton. This model was benchmarked on the MIT/Tuebingen Saliency benchmarks as CASPER V1 Salience https://saliency.tuebingen.ai/results.html

If this work is used in academic research, please cite

Heaton, R. F., Buetti, S., Lleras, A., & Hummel, J. E. (2021). Visual salience maps based on V1 blob cells. 62nd Annual Meeting of the Psychonomic Society. Heaton, R. F., Hummel, J. E., Lleras, A., & Buetti, S. (2023). Cortical Magnification, Not Summary Statistics, Explains Information Loss in Peripheral Vision. 31st Annual meeting of the Object Perception, Attention, and Memory conference.

This code dynamically links Pillow which requires the following information to be included in any redistributions or uses:

The Python Imaging Library (PIL) is Copyright © 1997-2011 by Secret Labs AB Copyright © 1995-2011 by Fredrik Lundh Pillow is the friendly PIL fork. It is Copyright © 2010-2023 by Jeffrey A. Clark (Alex) and contributors. Like PIL, Pillow is licensed under the open source HPND License: By obtaining, using, and/or copying this software and/or its associated documentation, you agree that you have read, understood, and will comply with the following terms and conditions: Permission to use, copy, modify and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appears in all copies, and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Secret Labs AB or the author not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission.

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This code dynamically links MPI which requires the following information to be included in any redistributions or uses:

=======================

LICENSE: MPI for Python

:Author: Lisandro Dalcin :Contact: dalcinl@gmail.com :Web Site: http://mpi4py.googlecode.com/ :Organization: CIMEC http://www.cimec.org.ar :Address: CCT CONICET, 3000 Santa Fe, Argentina

Copyright (c) 2013, Lisandro Dalcin. All rights reserved.

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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

To run this code:

1. Install Python 3

(This code was developed using Python 3.7)

Make sure the following modules/libraries are installed and available: os, sys, math, datetime, numpy, Pillow, mpi4py

2. Update the input and output path information for the images you want to run

These are found in the main() method at the bottom of runfilters_contrast.py The default paths are

inpath = './data/input_images/'

outpath = './data/output_images/'

Put the files you want to analyze in the 'inpath' directory

3. Open a terminal window and execute the following command in the directory where the code is located

mpirun -np 2 python3 -u -m mpi4py.futures ./runfilters_contrast.py 2