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 \(m1\) colour array to \(m2\) colour array. The resulting colour fitting matrix is computed using multiple linear regression.

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

Parameters:	m1 (array_like, (3, n)) – Test array \(m1\) to fit onto array \(m2\). m2 (array_like, (3, n)) – Reference array the array \(m1\) will be colour fitted against.
Returns:	Colour fitting matrix.
Return type:	ndarray, (3, 3)

Examples

>>> m1 = 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]])
>>> m2 = 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]])
>>> first_order_colour_fit(m1, m2)  
array([[ 0.6982266...,  0.0307162...,  0.1621042...],
       [ 0.0689349...,  0.6757961...,  0.1643038...],
       [-0.0631495...,  0.0921247...,  0.9713415...]])