Kinpzz / QuickCSF

A fast, adaptive approach to estimating contrast sensitivity function parameters

QuickCSF

A fast, adaptive approach to estimating contrast sensitivity function parameters.

This implmentation is based on:

Lesmes, L. A., Lu, Z. L., Baek, J., & Albright, T. D. (2010). Bayesian adaptive estimation of the contrast sensitivity function: The quick CSF method. Journal of vision, 10(3), 17-17.

Special thanks to Dr. Tianshi Lu at Wichita State University for providing a Matlab implemenation of the fundamental algorithm and Dr. Rui Ni at Wichita State University for the motivation.

Dependencies

$ pip3 install -e .

Requires:

• numpy
• qtpy
• Qt bindings (via PySide2, PyQt5, PySide, or PyQt)

Optional (for simulation visuals):

• matplotlib

Usage

Measuring CSF

Run:

$ python -m QuickCSF.app

A settings dialog will appear; session ID and viewing distance are required. Arguments can also be specified on the command line. Use the --help flag to see all options:

$ python -m QuickCSF.app --help

Simulate and visualize an evaluation

Run:

$ python -m QuickCSF.simulate

A settings dialog will appear; the number of trials is required. Arguments can also be specified on the command line. use the --help flag to see all options:

$ python -m QuickCSF.simulate --help