colour.characterisation.fitting Module¶

Colour Fitting¶

Defines various objects for colour fitting, like colour matching two images.

colour.characterisation.fitting.first_order_colour_fit(m1, m2)[source]¶

Performs a first order colour fit from given \(m2\) colour matrix to \(m1\) colour matrix. The resulting colour matrix is calculated using multiple linear regression.

The purpose of that object is for example matching of two ColorChecker colour rendition charts together.

Parameters:	m1 (array_like, (3, n)) – Reference matrix the matrix \(m2\) will be colour fitted against. m2 (array_like, (3, n)) – Matrix to fit.
Returns:	Fitting colour matrix.
Return type:	ndarray, (3, 3)

Examples

>>> m1 = np.array([
...     [0.1722481, 0.0917066, 0.06416938],
...     [0.49189645, 0.2780205, 0.21923399],
...     [0.10999751, 0.18658946, 0.29938611],
...     [0.1166612, 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, 0.4454523, 0.06028681],
...     [0.61632937, 0.32323906, 0.02437089],
...     [0.03016472, 0.06153243, 0.29014596],
...     [0.11103655, 0.30553067, 0.08149137],
...     [0.4116219, 0.05816656, 0.04845934],
...     [0.73339206, 0.53075188, 0.02475212],
...     [0.47347718, 0.08834792, 0.30310315],
...     [0., 0.25187016, 0.3506245],
...     [0.76809639, 0.7848624, 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]])
>>> m2 = np.array([
...     [0.15579559, 0.09715755, 0.07514556],
...     [0.3911314, 0.25943419, 0.21266708],
...     [0.12824821, 0.1846357, 0.31508023],
...     [0.12028974, 0.13455659, 0.074084],
...     [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, 0.122334, 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, 0.2578786, 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, 0.1042468, 0.10384975],
...     [0.04742204, 0.04772203, 0.04914226]])
>>> first_order_colour_fit(m1, m2)  
array([[ 1.4043128...,  0.0112806..., -0.2029710...],
       [-0.0998911...,  1.5012214..., -0.1856479...],
       [ 0.2248369..., -0.0767236...,  1.0496013...]])