colour.models.XYZ_to_Iab#

colour.models.XYZ_to_Iab(XYZ: Domain1, LMS_to_LMS_p_callable: Callable, matrix_XYZ_to_LMS: ArrayLike, matrix_LMS_p_to_Iab: ArrayLike) → Range1[source]#

Convert from CIE XYZ tristimulus values to IPT-like \(Iab\) colour representation.

Perform conversion from CIE XYZ tristimulus values to IPT colourspace and other similar conversions. It implements a generic transformation from CIE XYZ tristimulus values to lightness \(I\), \(a\) representing the red-green dimension (the dimension lost by protanopes), and \(b\) representing the yellow-blue dimension (the dimension lost by tritanopes).

Parameters:

XYZ (Domain1) – CIE XYZ tristimulus values.
LMS_to_LMS_p_callable (Callable) – Callable applying the forward non-linearity to the \(LMS\) colourspace array.
matrix_XYZ_to_LMS (ArrayLike) – Matrix converting from CIE XYZ tristimulus values to \(LMS\) colourspace.
matrix_LMS_p_to_Iab (ArrayLike) – Matrix converting from non-linear \(LMS_p\) colourspace to IPT-like \(Iab\) colour representation.

Returns:

IPT-like \(Iab\) colour representation.

Return type:

numpy.ndarray

Notes

Domain	Scale - Reference	Scale - 1
`XYZ`	1	1

Range	Scale - Reference	Scale - 1
`Iab`	1	1

Examples

>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> LMS_to_LMS_p = lambda x: x**0.43
>>> M_XYZ_to_LMS = np.array(
...     [
...         [0.4002, 0.7075, -0.0807],
...         [-0.2280, 1.1500, 0.0612],
...         [0.0000, 0.0000, 0.9184],
...     ]
... )
>>> M_LMS_p_to_Iab = np.array(
...     [
...         [0.4000, 0.4000, 0.2000],
...         [4.4550, -4.8510, 0.3960],
...         [0.8056, 0.3572, -1.1628],
...     ]
... )
>>> XYZ_to_Iab(XYZ, LMS_to_LMS_p, M_XYZ_to_LMS, M_LMS_p_to_Iab)
...
array([ 0.3842619...,  0.3848730...,  0.1888683...])

colour.models.XYZ_to_Iab#

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