When visualizing categorical data we often want to encode each category with a distinct color. The set of colors may be generated randomly, however more visually pleasing results can be achieved if special care is taken to generate a maximally distinct set of colors.
This repository contains an implementation of a method proposed by Glasbey et al. [1] that is capable of identifying such a set. Dissimilarity between colors is computed in the state-of-the-art perceptually uniform color space CAM02-UCS [2].
A palette with 30 colors, based on the "Set1" palette from ColorBrewer2, with colors similar to black excluded.
A palette with 30 colors. First one was fixed to be white; no restrictions regarding similarity to black.
To install glasbey run
pip install path/to/glasbeyTo show a progress bar during palette generation run
pip install path/to/glasbey[progressbar]To be able to visualize generated palettes run
pip install path/to/glasbey[view_palette]For both run
pip install path/to/glasbey[progressbar,view_palette]usage: glasbey.py [-h] [--base-palette BASE_PALETTE] [--no-black] [--view]
[--format FORMAT]
size output
Generate a palette with maximally disticts colors using the sequential
method of Glasbey et al.¹
(Dis)similarity between colors is computed in the state-of-the-art
perceptually uniform color space CAM02-UCS.²
This script needs an RGB to CAM02-UCS color lookup table. Generation of
this table is a time-consuming process, therefore the first run of this
script will take some time. The generated table will be stored in the
working directory of the script and automatically used in next invocations
of the script. Note that the approximate size of the table is 363 Mb.
The palette generation method allows the user to supply a base palette. The
output palette will begin with the colors from the supplied set. If no base
palette is given, then white will be used as the first base color. The base
palette should be given as a text file where each line contains a color
description in RGB255 format with components separated with commas. (See
files in the 'palettes/' folder for an example.)
If having black (and colors close to black) is undesired, then `--no-black`
option may be used to prevent the algorithm from inserting such colors into
the palette. In addition to that, the range of colors considered for
inclusion in the palette can be limited by lightness, chroma, or hue.
¹) Glasbey, C., van der Heijden, G., Toh, V. F. K. and Gray, A. (2007),
Colour Displays for Categorical Images.
Color Research and Application, 32: 304-309
²) Luo, M. R., Cui, G. and Li, C. (2006),
Uniform Colour Spaces Based on CIECAM02 Colour Appearance Model.
Color Research and Application, 31: 320–330
positional arguments:
size number of colors in the palette
output output palette filename
optional arguments:
-h, --help show this help message and exit
--base-palette BASE_PALETTE
file with base palette
--no-black avoid black and similar colors
--lightness-range LIGHTNESS_RANGE
set min and max for lightness (e.g. 0,90)
--chroma-range CHROMA_RANGE
set min and max for chroma (e.g. 10,100)
--hue-range HUE_RANGE
set start and end for hue (e.g. 315,45)
--view view generated palette
--format {byte,float}
output format
>>> from glasbey import Glasbey
>>> gb = Glasbey(base_palette="palettes/set1.txt", overwrite_base_palette=True, lightness_range=(10,100), hue_range=(10,100), chroma_range=(10,100), no_black=True) # complicated example (demonstrate syntax)
>>> gb = Glasbey(base_palette=[(255, 0, 0), (0, 255, 0), (0, 0, 255)]) # base_palette can also be rgb-list
>>> gb = Glasbey() # simplest example, as all init parameters are optional
Generating color table: 100% |################################| Time: 0:00:34
>>> gb.generate_palette(size=3)
Generating palette: 100% |####################################| Time: 0:00:01
array([[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00],
[ 5.88229881e-42, 5.64082875e-42, 5.59612427e-42],
[ 8.43137255e-01, 1.43440815e-15, -6.27553565e-16]])
>>> gb.generate_palette(size=5) # calculates colors 4-5
Generating palette: 100% |####################################| Time: 0:00:02
array([[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00],
[ 5.88229881e-42, 5.64082875e-42, 5.59612427e-42],
[ 8.43137255e-01, 1.43440815e-15, -6.27553565e-16],
[ 5.49019608e-01, 2.35294118e-01, 1.00000000e+00],
[ 7.84313725e-03, 5.33333333e-01, -6.05140937e-16]])
>>> p = gb.generate_palette(size=5) # instantaneous because these colors were already calculated before
>>> gb.convert_palette_to_rgb(p)
[(255, 255, 255), (0, 0, 0), (215, 0, 0), (140, 60, 255), (2, 136, 0)]
>>> gb.save_palette(p, "out.txt") # save palette to file
>>> gb.view_palette(p) # opens imagemagick window
-
Glasbey, C., van der Heijden, G., Toh, V. F. K. and Gray, A. (2007), Colour Displays for Categorical Images. Color Research and Application, 32: 304-309
-
Luo, M. R., Cui, G. and Li, C. (2006), Uniform Colour Spaces Based on CIECAM02 Colour Appearance Model. Color Research and Application, 31: 320–330
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