colour.matrix_colour_correction

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

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

Compute the colour correction matrix using multiple linear or polynomial regression with the specified method. The resulting matrix enables colour matching between two arrays, such as matching 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 (Literal['Cheung 2004', 'Finlayson 2015', 'Vandermonde'] | str) – Computation method.

• degree – {colour.characterisation.matrix_colour_correction_Finlayson2015(), colour.characterisation.matrix_colour_correction_Vandermonde()}, Expanded polynomial degree, must be one of [1, 2, 3, 4] for colour.characterisation.matrix_colour_correction_Finlayson2015() definition.

• root_polynomial_expansion – {colour.characterisation.matrix_colour_correction_Finlayson2015()}, Whether to use the root-polynomials set for the expansion.

• terms – {colour.characterisation.matrix_colour_correction_Cheung2004()}, Number of terms of the expanded polynomial.

• kwargs (Any)

Returns:

Colour correction matrix mapping expanded test colours to reference colours.

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...]])