colour.colour_correction#

colour.colour_correction(RGB: ArrayLike, M_T: ArrayLike, M_R: ArrayLike, method: Literal['Cheung 2004', 'Finlayson 2015', 'Vandermonde'] | str = 'Cheung 2004', **kwargs: Any) → NDArrayFloat[source]#

Perform colour correction of given RGB colourspace array using the colour correction matrix from given \(M_T\) colour array to \(M_R\) colour array.

Parameters:

RGB (ArrayLike) – RGB colourspace array to colour correct.
M_T (ArrayLike) – Test array \(M_T\) to fit onto array \(M_R\).
M_R (ArrayLike) – Reference array the array \(M_T\) will be colour fitted against.
method (Literal['Cheung 2004', 'Finlayson 2015', 'Vandermonde'] | str) – Computation method.
degree – {colour.characterisation.polynomial_expansion_Finlayson2015(), colour.characterisation.polynomial_expansion_Vandermonde()}, Expanded polynomial degree, must be one of [1, 2, 3, 4] for colour.characterisation.polynomial_expansion_Finlayson2015() definition.
root_polynomial_expansion – {colour.characterisation.polynomial_expansion_Finlayson2015()}, Whether to use the root-polynomials set for the expansion.
terms – {colour.characterisation.matrix_augmented_Cheung2004()}, Number of terms of the expanded polynomial.
kwargs (Any)

Returns:

Colour corrected RGB colourspace array.

Return type:

numpy.ndarray

References

[CWCR04], [FMH15], [WR04], [Wikipedia03f]

Examples

>>> RGB = np.array([0.17224810, 0.09170660, 0.06416938])
>>> M_T = np.array(
...     [
...         [0.17224810, 0.09170660, 0.06416938],
...         [0.49189645, 0.27802050, 0.21923399],
...         [0.10999751, 0.18658946, 0.29938611],
...         [0.11666120, 0.14327905, 0.05713804],
...         [0.18988879, 0.18227649, 0.36056247],
...         [0.12501329, 0.42223442, 0.37027445],
...         [0.64785606, 0.22396782, 0.03365194],
...         [0.06761093, 0.11076896, 0.39779139],
...         [0.49101797, 0.09448929, 0.11623839],
...         [0.11622386, 0.04425753, 0.14469986],
...         [0.36867946, 0.44545230, 0.06028681],
...         [0.61632937, 0.32323906, 0.02437089],
...         [0.03016472, 0.06153243, 0.29014596],
...         [0.11103655, 0.30553067, 0.08149137],
...         [0.41162190, 0.05816656, 0.04845934],
...         [0.73339206, 0.53075188, 0.02475212],
...         [0.47347718, 0.08834792, 0.30310315],
...         [0.00000000, 0.25187016, 0.35062450],
...         [0.76809639, 0.78486240, 0.77808297],
...         [0.53822392, 0.54307997, 0.54710883],
...         [0.35458526, 0.35318419, 0.35524431],
...         [0.17976704, 0.18000531, 0.17991488],
...         [0.09351417, 0.09510603, 0.09675027],
...         [0.03405071, 0.03295077, 0.03702047],
...     ]
... )
>>> M_R = np.array(
...     [
...         [0.15579559, 0.09715755, 0.07514556],
...         [0.39113140, 0.25943419, 0.21266708],
...         [0.12824821, 0.18463570, 0.31508023],
...         [0.12028974, 0.13455659, 0.07408400],
...         [0.19368988, 0.21158946, 0.37955964],
...         [0.19957425, 0.36085439, 0.40678123],
...         [0.48896605, 0.20691688, 0.05816533],
...         [0.09775522, 0.16710693, 0.47147724],
...         [0.39358649, 0.12233400, 0.10526425],
...         [0.10780332, 0.07258529, 0.16151473],
...         [0.27502671, 0.34705454, 0.09728099],
...         [0.43980441, 0.26880559, 0.05430533],
...         [0.05887212, 0.11126272, 0.38552469],
...         [0.12705825, 0.25787860, 0.13566464],
...         [0.35612929, 0.07933258, 0.05118732],
...         [0.48131976, 0.42082843, 0.07120612],
...         [0.34665585, 0.15170714, 0.24969804],
...         [0.08261116, 0.24588716, 0.48707733],
...         [0.66054904, 0.65941137, 0.66376412],
...         [0.48051509, 0.47870296, 0.48230082],
...         [0.33045354, 0.32904184, 0.33228886],
...         [0.18001305, 0.17978567, 0.18004416],
...         [0.10283975, 0.10424680, 0.10384975],
...         [0.04742204, 0.04772203, 0.04914226],
...     ]
... )
>>> colour_correction(RGB, M_T, M_R)  
array([ 0.1334872...,  0.0843921...,  0.0599014...])

colour.colour_correction#

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