colour.models.Iab_to_XYZ#

colour.models.Iab_to_XYZ(Iab: ArrayLike, LMS_p_to_LMS_callable: Callable, matrix_Iab_to_LMS_p: ArrayLike, matrix_LMS_to_XYZ: ArrayLike) ndarray[source]#

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

This definition is used to perform conversion from IPT colourspace to CIE XYZ tristimulus values and for other similar conversions. It implements a generic transformation from Lightness \(I\), \(a\) and \(b\) representing red-green dimension, i.e. the dimension lost by protanopes and the yellow-blue dimension, i.e. the dimension lost by tritanopes, respectively to CIE XYZ tristimulus values.

Parameters:
  • Iab (ArrayLike) – IPT-like \(Iab\) colour representation.

  • LMS_p_to_LMS_callable (Callable) – Callable applying the reverse non-linearity to the \(LMS_p\) colourspace array.

  • matrix_Iab_to_LMS_p (ArrayLike) – Matrix converting from IPT-like \(Iab\) colour representation to non-linear \(LMS_p\) colourspace.

  • matrix_LMS_to_XYZ (ArrayLike) – Matrix converting from \(LMS\) colourspace to CIE XYZ tristimulus values.

Returns:

CIE XYZ tristimulus values.

Return type:

numpy.ndarray

Notes

Domain

Scale - Reference

Scale - 1

Iab

I : [0, 1]

a : [-1, 1]

b : [-1, 1]

I : [0, 1]

a : [-1, 1]

b : [-1, 1]

Range

Scale - Reference

Scale - 1

XYZ

[0, 1]

[0, 1]

Examples

>>> Iab = np.array([0.38426191, 0.38487306, 0.18886838])
>>> LMS_p_to_LMS = lambda x: x ** (1 / 0.43)
>>> M_Iab_to_LMS_p = np.linalg.inv(
...     np.array(
...         [[0.4000, 0.4000, 0.2000],
...         [4.4550, -4.8510, 0.3960],
...         [0.8056, 0.3572, -1.1628]]))
>>> M_LMS_to_XYZ = np.linalg.inv(
...     np.array(
...         [[0.4002, 0.7075, -0.0807],
...         [-0.2280, 1.1500, 0.0612],
...         [0.0000, 0.0000, 0.9184]]))
>>> Iab_to_XYZ(Iab, LMS_p_to_LMS, M_Iab_to_LMS_p, M_LMS_to_XYZ)
... 
array([ 0.2065400...,  0.1219722...,  0.0513695...])