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Reference

.. currentmodule:: colorspacious

Conversion functions

.. autofunction:: cspace_convert

.. autofunction:: cspace_converter

Specifying colorspaces

Colorspacious knows about a wide variety of colorspaces, some of which take additional parameters, and it can convert freely between any of them. Here's an image showing all the known spaces, and the conversion paths used. (This graph is generated directly from the source code: when you request a conversion between two spaces, :func:`cspace_convert` automatically traverses this graph to find the best conversion path. This makes it very easy to add support for new colorspaces.)

.. ipython:: python
   :suppress:

   import colorspacious
   with open("_static/colorspacious-graph.dot", "w") as f:
       colorspacious.conversion.GRAPH.dump_dot(f)
   import subprocess
   subprocess.check_call(["dot", "-Tsvg", "_static/colorspacious-graph.dot",
                          "-o", "_static/colorspacious-graph.svg"])

The most general and primitive way to specify a colorspace is via a dict, e.g., all the following are valid arguments that can be passed to :func:`cspace_convert`:

{"name": "XYZ100"}
{"name": "CIELab", "XYZ100_w": "D65"}
{"name": "CIELab", "XYZ100_w": [95.047, 100, 108.883]}

These dictionaries always have a "name" key specifying the colorspace. Every bold-faced string in the above image is a recognized colorspace name. Some spaces take additional parameters beyond the name, such as the CIELab whitepoint above. These additional parameters are indicated by the italicized strings in the image above.

There are also several shorthands accepted, to let you avoid writing out long dicts in most cases. In particular:

  • Any :class:`CIECAM02Space` object myspace is expanded to:

    {"name": "CIECAM02",
 "ciecam02_space": myspace}

  • Any :class:`LuoEtAl2006UniformSpace` object myspace is expanded to:

    {"name": "J'a'b'",
 "ciecam02_space": CIECAM02.sRGB,
 "luoetal2006_space": myspace}

  • The string "CIELab" expands to: {"name": "CIELab", "XYZ100_w": "D65"}

  • The string "CIELCh" expands to: {"name": "CIELCh", "XYZ100_w": "D65"}

  • the string "CIECAM02" expands to CIECAM02Space.sRGB, which in turn expands to {"name": "CIECAM02", "ciecam02_space": CIECAM02Space.sRGB}.

  • The strings "CAM02-UCS", "CAM02-SCD", "CAM02-LCD" expand to the global instance objects :data:`CAM02UCS`, :data:`CAM02SCD`, :data:`CAM02LCD`, which in turn expand to "J'a'b'" dicts as described above.

  • Any string consisting only of characters from the set "JChQMsH" is expanded to:

    {"name": "CIECAM02-subset",
 "axes": <the string provided>
 "ciecam02_space": CIECAM02.sRGB}

    This allows you to directly use common shorthands like "JCh" or "JMh" as first-class colorspaces.

Any other string "foo" expands to {"name": "foo"}. So for any space that doesn't take parameters, you can simply say "sRGB1" or "XYZ100" or whatever and ignore all these complications.

And, as one final trick, any alias can also be used as the "name" field in a colorspace dict, in which case its normal expansion is used to provide overrideable defaults for parameters. For example:

# You write:
{"name": "CAM02-UCS",
 "ciecam02_space": my_ciecam02_space}

# Colorspacious expands this to:
{"name": "J'a'b'",
 "ciecam02_space": my_ciecam02_space,
 "luoetal2006_space": CAM02UCS}

Or:

# You write:
{"name": "JCh",
 "ciecam02_space": my_ciecam02_space}

# Colorspacious expands this to:
{"name": "CIECAM02-subset",
 "axes": "JCh",
 "ciecam02_space": my_ciecam02_space}

Well-known colorspaces

sRGB1, sRGB100: The standard sRGB colorspace. If you have generic "RGB" values with no further information specified, then usually the right thing to do is to assume that they are in the sRGB space; the sRGB space was originally designed to match the behavior of common consumer monitors, and these days common consumer monitors are designed to match sRGB. Use sRGB1 if you have or want values that are normalized to fall between 0 and 1, and use sRGB255 if you have or want values that are normalized to fall between 0 and 255.

XYZ100, XYZ1: The standard CIE 1931 XYZ color space. Use XYZ100 if you have or want values that are normalized to fall between 0 and 100 (roughly speaking -- values greater than 100 are valid in certain cases). Use XYZ1 if you have or want values that are normalized to fall between 0 and 1 (roughly). This is a space which is "linear-light", i.e. related by a linear transformation to the photon counts in a spectral power distribution. In particular, this means that linear interpolation in this space is a valid way to simulate physical mixing of lights.

sRGB1-linear: A linear-light version of sRGB1, i.e., it has not had gamma correction applied, but is still represented in terms of the standard sRGB primaries.

xyY100, xyY1: The standard CIE 1931 xyY color space. The x and y values are always normalized to fall between 0 and 1. Use xyY100 if you have or want a Y value that falls between 0 and 100, and use xyY1 if you have or want a Y value that falls between 0 and 1.

CIELab: The standard CIE 1976 L*a*b* color space. L* is scaled to vary from 0 to 100, and in most settings the values of a* and b* will be roughly in the range -100 to 100, though you can certainly get larger values if your inputs are sufficiently extreme. This space takes a parameter, XYZ100_w, which sets the reference white point, and may be specified either directly as a tristimulus value or as a string naming one of the well-known standard illuminants like "D65".

CIELCh: Cylindrical version of CIELab. Accepts the same parameters. h* is in degrees.

Simulation of color vision deficiency

We provide simulation of common (and not so common) forms of color vision deficiency (also known as "colorblindness"), using the model described by :cite:`Machado-CVD`.

This is generally done by specifying a colorspace like:

{"name": "sRGB1+CVD",
 "cvd_type": <type>,
 "severity": <severity>}

where <type> is one of the following strings:

  • "protanomaly": A common form of red-green colorblindness; affects ~2% of white men to some degree (less common among other ethnicities, much less common among women, see Tables 1.5 and 1.6 in :cite:`Sharpe-CVD`).
  • "deuteranomaly": The most common form of red-green colorblindness; affects ~6% of white men to some degree (less common among other ethnicities, much less common among women, see Tables 1.5 and 1.6 in :cite:`Sharpe-CVD`).
  • "tritanomaly": A very rare form of colorblindness affecting blue/yellow discrimination -- so rare that its detailed effects and even rate of occurrence are not well understood. Affects <0.1% of people, possibly much less (:cite:`Sharpe-CVD`, page 47). Also, the name we use here is somewhat misleading because only full tritanopia has been documented, and partial tritanomaly likely does not exist (:cite:`Sharpe-CVD`, page 45). What this means is that while Colorspacious will happily allow any severity value to be passed, probably only severity = 100 corresponds to any real people.

And <severity> is any number between 0 (indicating regular vision) and 100 (indicating complete dichromacy).

Warning

If you have an image, e.g. a photo, and you want to "convert it to simulate colorblindness", then this is done with an incantation like:

cspace_convert(img, some_cvd_space, "sRGB1")

Notice that these arguments are given in the opposite order from what you might naively expect. See :ref:`tutorial-cvd` for explanation and worked examples.

CIECAM02

CIECAM02 is a standardized, rather complex, state-of-the-art color appearance model, i.e., it's not useful for describing the voltage that should be applied to a phosphorescent element in your monitor (like RGB was originally designed to do), and it's not useful for modelling physical properties of light (like XYZ), but it is very useful to tell you what a color will look like subjectively to a human observer, under a certain set of viewing conditions. Unfortunately this makes it rather complicated, because human vision is rather complicated.

If you just want a better replacement for traditional ad hoc spaces like "Hue/Saturation/Value", then use the string "JCh" for your colorspace (see :ref:`tutorial-perception` for a tutorial) and be happy.

If you want the full power of CIECAM02, or just to understand what exactly is happening when you type "JCh", then read on.

First, you need to specify your viewing conditions. For many purposes, you can use the default :attr:`CIECAM02Space.sRGB` object. Or if you want to specify different viewing conditions, you can instantiate your own :class:`CIECAM02Space` object:

.. autoclass:: CIECAM02Space

   .. attribute:: sRGB

      A class-level constant representing the viewing conditions
      specified in the sRGB standard. (The sRGB standard defines two
      things: how a standard monitor should respond to different RGB
      values, and a standard set of viewing conditions in which you
      are supposed to look at such a monitor, and that attempt to
      approximate the average conditions in which people actually do
      look at such monitors. This object encodes the latter.)

   The CIECAM02Space object has some low-level methods you can use
   directly if you want, though usually it'll be easier to just use
   :func:`cspace_convert`:

   .. automethod:: XYZ100_to_CIECAM02
   .. automethod:: CIECAM02_to_XYZ100


.. autoclass:: CIECAM02Surround

   A namedtuple holding the CIECAM02 surround parameters, :math:`F`,
   :math:`c`, and :math:`N_c`.

   The CIECAM02 standard surrounds are available as constants defined
   on this class; for most purposes you'll just want to use one of
   them:

   * :data:`CIECAM02Surround.AVERAGE`
   * :data:`CIECAM02Surround.DIM`
   * :data:`CIECAM02Surround.DARK`


.. autoclass:: NegativeAError

Now that you have a :class:`CIECAM02Space` object, what can you do with it?

First, you can pass it directly to :func:`cspace_convert` as an input or output space (which is a shorthand for using a space like {"name": "CIECAM02", "ciecam02_space": <whatever>}).

The plain vanilla "CIECAM02" space is weird and special: unlike all the other spaces supported by colorspacious, it does not represent values with ordinary NumPy arrays. This is because there are just too many perceptual correlates, and trying to keep track of whether M is at index 4 or 5 would be way too obnoxious. Instead, it returns an object of class :class:`JChQMsH`:

.. autoclass:: JChQMsH

   A namedtuple with a mnemonic name: it has attributes ``J``, ``C``,
   ``h``, ``Q``, ``M``, ``s``, and ``H``, each of which holds a scalar
   or NumPy array representing lightness, chroma, hue angle,
   brightness, colorfulness, saturation, and hue composition,
   respectively.

Alternatively, because you usually only want a subset of these, you can take advantage of the "CIECAM02-subset" space, which takes the perceptual correlates you want as a parameter. So for example if you just want JCh, you can write:

{"name": "CIECAM02-subset",
 "axes": "JCh",
 "ciecam02_space": CIECAM02.sRGB}

When using "CIECAM02-subset", you don't have to worry about :class:`JChQMsH` -- it just takes and returns regular NumPy arrays, like all the other colorspaces.

And as a convenience, all strings composed of the character JChQMsH are automatically treated as specifying CIECAM02-subset spaces, so you can write:

"JCh"

and it expands to:

{"name": "CIECAM02-subset",
 "axes": "JCh",
 "ciecam02_space": CIECAM02.sRGB}

or you can write:

{"name": "JCh",
 "ciecam02_space": my_space}

and it expands to:

{"name": "CIECAM02-subset",
 "axes": "JCh",
 "ciecam02_space": my_space}

Perceptually uniform colorspaces based on CIECAM02

The J'a'b' spaces proposed by :cite:`CAM02-UCS` are high-quality, approximately perceptually uniform spaces based on CIECAM02. They propose three variants: CAM02-LCD optimized for "large color differences" (e.g., estimating the similarity between blue and green), CAM02-SCD optimized for "small color differences" (e.g., estimating the similarity between light blue with a faint greenish cast and light blue with a faint purpleish cast), and CAM02-UCS which attempts to provide a single "uniform color space" that is less optimized for either case but provides acceptable performance in general.

Colorspacious represents these spaces as instances of :class:`LuoEtAl2006UniformSpace`:

.. autoclass:: LuoEtAl2006UniformSpace

Because these spaces are defined as transformations from CIECAM02, to have a fully specified color space you must also provide some particular CIECAM02 viewing conditions, e.g.:

{"name": "J'a'b'",
 "ciecam02_space": CIECAM02.sRGB,
 "luoetal2006_space": CAM02UCS}

As usual, you can also pass any instance of :class:`LuoEtAl2006UniformSpace` and it will be expanded into a dict like the above, or for the three common variants you can pass the strings "CAM02-UCS", "CAM02-LCD", or "CAM02-SCD".

.. versionchanged:: 1.1.0

   In v1.0.0 and earlier, colorspacious's definitions of the
   ``CAM02-LCD`` and ``CAM02-SCD`` spaces were swapped compared to
   what they should have been based on the :cite:`CAM02-UCS` – i.e.,
   if you asked for LCD, you got SCD, and vice-versa. (``CAM02-UCS``
   was correct, though). Starting in 1.1.0, all three spaces are now
   correct.

Color difference computation

.. autofunction:: deltaE

For examples, see :ref:`tutorial-deltaE` in the tutorial.

Utilities

You probably won't need these, but just in case they're useful:

.. autofunction:: standard_illuminant_XYZ100

.. autofunction:: as_XYZ100_w


.. autofunction:: machado_et_al_2009_matrix