searchstims 2.4.0

Python package to make stimuli like those used in classic visual search experiments.

searchstims

Python package to make stimuli like those used in classic visual search experiments
https://en.wikipedia.org/wiki/Visual_search
... but with the exact size to feed them to your favorite neural network.

There are links to example configuration files below.

For a recent review of factors influencing visual search, please see:
http://search.bwh.harvard.edu/new/pubs/FiveFactors_Wolfe-Horowitz_2017.pdf

For a dataset of human subjects performing a similar visual search task, please see: http://search.bwh.harvard.edu/new/data_set_files.html

Installation

pip install searchstims

If you want to download and install locally into an environment with Anaconda: /home/you/Documents $ conda create -n searchstims-env python=3.6 numpy pygame
/home/you/Documents $ source activate searchstims-env
(searchstims-env) /home/you/Documents $ git clone
(searchstim) /home/you/Documents $ cd searchstims
(searchstim) /home/you/Documents/searchstims $ pip install -e .

Usage

The searchstims package installs itself so that you can run it from the command line. You will use a config.ini file to specify the visual search stimuli you want the package to generate.

/home/you/Documents $ searchstims config.ini

Running the example script will create a folder ~/output with visual search stimuli. For more detail on the structure of config.ini files used with this package, see ./doc/config.md.

For examples of config.ini files, see ./doc/configs/. These examples were used in this project:
https://github.com/NickleDave/visual-search-nets

.json output file

In addition to saving visual search stimuli in the output folder, searchstims saves information about stimuli in a .json output file. This .json file is provided to make it easier to work with the visual search image files, and analyze results obtained with them. For more detail, see ./doc/json.md

License

BSD-3

Citation

If you use this library, please cite this repository using the DOI:

Acknowledgments

• Research funded by the Lifelong Learning Machines program, DARPA/Microsystems Technology Office, DARPA cooperative agreement HR0011-18-2-0019