Tools for color models.
All methods in colorio are fully vectorized.
All color spaces implement the two methods
vals = colorspace.from_xyz100(xyz) xyz = colorspace.to_xyz100(vals)
for conversion from and to XYZ100. Adding new color spaces is as easy as writing a class that provides those two methods.
The following color spaces are implemented:
- XYZ (
- xyY (
- Linear SRGB (
colorio.SrgbLinear()) This class has the two additional methods
for conversion from and to standard RGB.
- CIELAB (
- CIELUV (
- ICtCp (
- CIECAM02 / CAM02-UCS
The implementation contains a few improvements over the CIECAM02 specification (see here).
import colorio L_A = 64 / numpy.pi / 5 ciecam02 = colorio.CIECAM02(0.69, 20, L_A) cam02 = colorio.CAM02('UCS', 0.69, 20, L_A)
- CAM16 / CAM16-UCS
The implementation contains a few improvements over the CAM16 specification (see here).
L_A = 64 / numpy.pi / 5 cam16 = colorio.CAM16(0.69, 20, L_A) cam16ucs = colorio.CAM16UCS(0.69, 20, L_A)
colorio provides a number of useful tools for analyzing and visualizing color spaces.
Visualizing the SRGB gamut
The SRGB gamut is a perfect cube in SRGB space, and takes curious shapes when translated into other color spaces. The above image shows the SRGB gamut in XYZ space. The image data was created with
colorspace = colorio.CIELAB() colorio.show_srgb_gamut(colorspace, "out.vtk", n=50, cut_000=False)
The VTK file can then be opened in, e.g., ParaView, where the following instructions apply:
- Open the file in ParaView and execute the following steps in the Properties tab to the left.
- Press the Apply button.
- Under the Coloring section, change
- If necessary, press the gear button to the right of the search field to activate advanced options.
- Under the Scalar Coloring section, uncheck Map Scalars.
More images are in the gh-pages branch.
The data can be written in most formats supported by meshio. (You might have to install additional packages for some formats.)
Visualizing the visible gamut
Same as above, but with the gamut visible under a given illuminant.
colorspace = colorio.XYZ() illuminant = colorio.illuminants.d65() observer = colorio.observers.cie_1931_2() colorio.show_visible_gamut(colorspace, observer, illuminant, "visible.vtk")
The gamut is shown in grey since SRGB screens are not able to display the colors anyway.
Show the classical visible gamut in xy with Planckian locus and the SRGB colors (at maximum luminosity).
Show experimental data
colorio contains lots of experimental data sets some of which can be used to assess certain properties of color spaces.
The famous MacAdam ellipses (from this article) can be plotted with
colorio.show_macadam( scaling=10, plot_filter_positions=False, plot_standard_deviations=False )
colorspace = colorio.JzAzBz() colorio.show_ebner_fairchild(colorspace)
shows constant-hue data from the Ebner-Fairchild experiments in the azbz-plane of the Jzazbz color space. (Ideally, all colors in one set sit on a line.)
Likewise for Hung-Berns:
colorspace = colorio.JzAzBz() colorio.show_hung_berns(colorspace)
Xiao et al.
Likewise for Xiao et al.:
colorspace = colorio.CIELAB() colorio.show_xiao(colorspace)
Munsell color data is visualized with
colorspace = colorio.CIELUV() colorio.show_munsell(colorspace, V=5)
Color differences in any space can be computed with
colorio is available from the Python Package Index, so with
pip install -U colorio
you can install/upgrade.
To run the tests, simply check out this repository and run
To create a new release
publish to PyPi and GitHub:
$ make publish
colorio is published under the MIT license.