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...]])