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
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
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...)