colour.RGB_to_XYZ

colour.RGB_to_XYZ(RGB, illuminant_RGB, illuminant_XYZ, RGB_to_XYZ_matrix, chromatic_adaptation_transform='CAT02', cctf_decoding=None, **kwargs)[source]

Converts given RGB colourspace array to CIE XYZ tristimulus values.

Parameters:
  • RGB (array_like) – RGB colourspace array.
  • illuminant_RGB (array_like) – RGB colourspace illuminant chromaticity coordinates or CIE xyY colourspace array.
  • illuminant_XYZ (array_like) – CIE XYZ tristimulus values illuminant chromaticity coordinates or CIE xyY colourspace array.
  • RGB_to_XYZ_matrix (array_like) – Normalised primary matrix.
  • chromatic_adaptation_transform (unicode, optional) – {‘CAT02’, ‘XYZ Scaling’, ‘Von Kries’, ‘Bradford’, ‘Sharp’, ‘Fairchild’, ‘CMCCAT97’, ‘CMCCAT2000’, ‘CAT02_BRILL_CAT’, ‘Bianco’, ‘Bianco PC’, None}, Chromatic adaptation transform, if None no chromatic adaptation is performed.
  • cctf_decoding (object, optional) – Decoding colour component transfer function (Decoding CCTF) or electro-optical transfer function (EOTF / EOCF).
Other Parameters:
 

**kwargs (dict, optional) – Keywords arguments for deprecation management.

Returns:

CIE XYZ tristimulus values.

Return type:

ndarray

Notes

Domain Scale - Reference Scale - 1
RGB [0, 1] [0, 1]
illuminant_XYZ [0, 1] [0, 1]
illuminant_RGB [0, 1] [0, 1]
Range Scale - Reference Scale - 1
XYZ [0, 1] [0, 1]

Examples

>>> RGB = np.array([0.45595571, 0.03039702, 0.04087245])
>>> illuminant_RGB = np.array([0.31270, 0.32900])
>>> illuminant_XYZ = np.array([0.34570, 0.35850])
>>> chromatic_adaptation_transform = 'Bradford'
>>> RGB_to_XYZ_matrix = np.array(
...     [[0.41240000, 0.35760000, 0.18050000],
...      [0.21260000, 0.71520000, 0.07220000],
...      [0.01930000, 0.11920000, 0.95050000]]
... )
>>> RGB_to_XYZ(RGB, illuminant_RGB, illuminant_XYZ, RGB_to_XYZ_matrix,
...            chromatic_adaptation_transform)  # doctest: +ELLIPSIS
array([ 0.2163881...,  0.1257    ,  0.0384749...])