colour.XYZ_to_LLAB#

colour.XYZ_to_LLAB(XYZ: Union[numpy.typing._array_like._SupportsArray[numpy.dtype], numpy.typing._nested_sequence._NestedSequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], XYZ_0: Union[numpy.typing._array_like._SupportsArray[numpy.dtype], numpy.typing._nested_sequence._NestedSequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], Y_b: Union[float, numpy.typing._array_like._SupportsArray[numpy.dtype], numpy.typing._nested_sequence._NestedSequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], bool, int, complex, str, bytes, numpy.typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], L: Union[float, numpy.typing._array_like._SupportsArray[numpy.dtype], numpy.typing._nested_sequence._NestedSequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], bool, int, complex, str, bytes, numpy.typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], surround: colour.appearance.llab.InductionFactors_LLAB = VIEWING_CONDITIONS_LLAB['Reference Samples & Images, Average Surround, Subtending < 4']) → colour.appearance.llab.CAM_Specification_LLAB[source]#

Compute the :math:`LLAB(l:c)` colour appearance model correlates.

Parameters

XYZ (Union[numpy.typing._array_like._SupportsArray[numpy.dtype], numpy.typing._nested_sequence._NestedSequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]) – CIE XYZ tristimulus values of test sample / stimulus.
XYZ_0 (Union[numpy.typing._array_like._SupportsArray[numpy.dtype], numpy.typing._nested_sequence._NestedSequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]) – CIE XYZ tristimulus values of reference white.
Y_b (Union[float, numpy.typing._array_like._SupportsArray[numpy.dtype], numpy.typing._nested_sequence._NestedSequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], bool, int, complex, str, bytes, numpy.typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]) – Luminance factor of the background in \(cd/m^2\).
L (Union[float, numpy.typing._array_like._SupportsArray[numpy.dtype], numpy.typing._nested_sequence._NestedSequence[numpy.typing._array_like._SupportsArray[numpy.dtype]], bool, int, complex, str, bytes, numpy.typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]) – Absolute luminance \(L\) of reference white in \(cd/m^2\).
surround (colour.appearance.llab.InductionFactors_LLAB) – Surround viewing conditions induction factors.

Returns

:math:`LLAB(l:c)` colour appearance model specification.

Return type

colour.CAM_Specification_LLAB

Notes

Domain	Scale - Reference	Scale - 1
`XYZ`	[0, 100]	[0, 1]
`XYZ_0`	[0, 100]	[0, 1]

Range	Scale - Reference	Scale - 1
`CAM_Specification_LLAB.h`	[0, 360]	[0, 1]

References

[Fai13h], [LLK96], [LM96]

Examples

>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_0 = np.array([95.05, 100.00, 108.88])
>>> Y_b = 20.0
>>> L = 318.31
>>> surround = VIEWING_CONDITIONS_LLAB['ref_average_4_minus']
>>> XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L, surround)  
CAM_Specification_LLAB(J=37.3668650..., C=0.0089496..., h=270..., s=0.0002395..., M=0.0190185..., HC=None, a=..., b=-0.0190185...)

colour.appearance.MediaParameters_Kim2009

colour.CAM_Specification_LLAB