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VSL Image Appearance Library

Python module for working with color spaces and stuff

Usage

Color space conversion:

  • Convert from sRGB to libRGB
from vsl_ial.cs import convert, sRGB, linRGB

convert(sRGB(), linRGB(), color=[0.12412, 0.07493, 0.3093])

Available color spaces

Function name Color space description
XYZ() CIE XYZ
CIExyY() CIE xyY
CIELUV() CIELUV
CIELAB() CIELAB
ProLab() proLab
Oklab() Oklab
ICaCb() ICaCb
ICtCp() ICtCp
JzAzBz() Jzazbz
CAM02LCD() CAM02-LCD
CAM02SCD() CAM02-SCD
CAM02UCS() CAM02-UCS
CAM16LCD() CAM16-LCD
CAM16SCD() CAM16-SCD
CAM16UCS() CAM16-UCS
sRGB() sRGB
linRGB() linear sRGB
LMS() LMS
Opponent() Opponent color space by Zhang and Wandell
PCS23-UCS() Uniform Color Space with Advanced Hue Linearity

Available measures of the consistency between perceived and computed color differences

  • CV
  • PF/3
  • STRESS
  • Mean STRESS
  • Group STRESS
  • Weighted group STRESS

Model evaluation

Run

python -m vsl_ial.eval

To get uniformity table

group_stress

COMBVD BFD-P D65 BFD-P M BFD-P C RIT-DuPont Witt Leeds Munsell-3.3
CAM16-SCD 0.295 0.254 0.346 0.283 0.237 0.306 0.219 0.218
CAM16-UCS 0.305 0.271 0.35 0.297 0.206 0.31 0.245 0.205
CAM16-LCD 0.339 0.311 0.372 0.366 0.214 0.372 0.292 0.217
CAM02-SCD 0.296 0.266 0.338 0.303 0.244 0.303 0.221 0.266
CAM02-UCS 0.306 0.28 0.343 0.321 0.213 0.305 0.246 0.267
CAM02-LCD 0.339 0.318 0.366 0.404 0.223 0.366 0.296 0.333
PCS23-UCS 0.311 0.289 0.325 0.379 0.3 0.381 0.332 0.253
CIELAB 0.426 0.41 0.433 0.543 0.334 0.517 0.401 0.308
ProLab 0.441 0.451 0.429 0.485 0.302 0.519 0.394 0.289
Oklab 0.471 0.515 0.424 0.416 0.318 0.452 0.45 0.132
JzAzBz 0.418 0.404 0.424 0.494 0.385 0.474 0.451 0.244
ICaCb 0.391 0.396 0.38 0.424 0.248 0.474 0.373 0.192
ICtCp 0.463 0.481 0.441 0.636 0.423 0.559 0.397 0.34

For module developers

  • How to make whl file:
python -m pip install --upgrade build
python -m build
  • How to run unit tests:
python -m unittest
  • How to run coverage:
python -m coverage run -m unittest
python -m coverage html

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VSL Image Apperance Library

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