colour.matrix_colour_correction¶

colour.matrix_colour_correction(M_T: ArrayLike, M_R: ArrayLike, method: Union[Literal['Cheung 2004', 'Finlayson 2015', 'Vandermonde'], str] = 'Cheung 2004', **kwargs: Any) → numpy.ndarray[source]¶

Compute a colour correction matrix from given \(M_T\) colour array to \(M_R\) colour array.

The resulting colour correction matrix is computed using multiple linear or polynomial regression using given method. The purpose of that object is for example the matching of two ColorChecker colour rendition charts together.

Parameters

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 (Union[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 correction matrix.

Return type

numpy.ndarray

References

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

Examples

>>> 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]]
... )
>>> matrix_colour_correction(M_T, M_R)  
array([[ 0.6982266...,  0.0307162...,  0.1621042...],
       [ 0.0689349...,  0.6757961...,  0.1643038...],
       [-0.0631495...,  0.0921247...,  0.9713415...]])

colour.MATRIX_COLOUR_CORRECTION_METHODS

colour.COLOUR_CORRECTION_METHODS