# -*- coding: utf-8 -*-
"""
Von Kries Chromatic Adaptation Model
====================================
Defines *Von Kries* chromatic adaptation model objects:
- :func:`colour.adaptation.chromatic_adaptation_matrix_VonKries`
- :func:`colour.adaptation.chromatic_adaptation_VonKries`
See Also
--------
`Chromatic Adaptation Jupyter Notebook
<http://nbviewer.jupyter.org/github/colour-science/colour-notebooks/\
blob/master/notebooks/adaptation/vonkries.ipynb>`_
References
----------
- :cite:`Fairchild2013t` : Fairchild, M. D. (2013). Chromatic Adaptation
Models. In Color Appearance Models (3rd ed., pp. 4179-4252). Wiley.
ISBN:B00DAYO8E2
"""
from __future__ import division, unicode_literals
import numpy as np
from colour.adaptation import CHROMATIC_ADAPTATION_TRANSFORMS
from colour.utilities import dot_matrix, dot_vector, row_as_diagonal
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2018 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = [
'chromatic_adaptation_matrix_VonKries', 'chromatic_adaptation_VonKries'
]
[docs]def chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform='CAT02'):
"""
Computes the *chromatic adaptation* matrix from test viewing conditions
to reference viewing conditions.
Parameters
----------
XYZ_w : array_like
Test viewing condition *CIE XYZ* tristimulus values of whitepoint.
XYZ_wr : array_like
Reference viewing condition *CIE XYZ* tristimulus values of whitepoint.
transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
Chromatic adaptation transform.
Returns
-------
ndarray
Chromatic adaptation matrix.
Raises
------
KeyError
If chromatic adaptation method is not defined.
References
----------
- :cite:`Fairchild2013t`
Examples
--------
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr)
... # doctest: +ELLIPSIS
array([[ 0.8687653..., -0.1416539..., 0.3871961...],
[-0.1030072..., 1.0584014..., 0.1538646...],
[ 0.0078167..., 0.0267875..., 2.9608177...]])
Using Bradford method:
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> method = 'Bradford'
>>> chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, method)
... # doctest: +ELLIPSIS
array([[ 0.8446794..., -0.1179355..., 0.3948940...],
[-0.1366408..., 1.1041236..., 0.1291981...],
[ 0.0798671..., -0.1349315..., 3.1928829...]])
"""
M = CHROMATIC_ADAPTATION_TRANSFORMS.get(transform)
if M is None:
raise KeyError(
'"{0}" chromatic adaptation transform is not defined! Supported '
'methods: "{1}".'.format(transform,
CHROMATIC_ADAPTATION_TRANSFORMS.keys()))
rgb_w = np.einsum('...i,...ij->...j', XYZ_w, np.transpose(M))
rgb_wr = np.einsum('...i,...ij->...j', XYZ_wr, np.transpose(M))
D = rgb_wr / rgb_w
D = row_as_diagonal(D)
cat = dot_matrix(np.linalg.inv(M), D)
cat = dot_matrix(cat, M)
return cat
[docs]def chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr, transform='CAT02'):
"""
Adapts given stimulus from test viewing conditions to reference viewing
conditions.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of stimulus to adapt.
XYZ_w : array_like
Test viewing condition *CIE XYZ* tristimulus values of whitepoint.
XYZ_wr : array_like
Reference viewing condition *CIE XYZ* tristimulus values of whitepoint.
transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
Chromatic adaptation transform.
Returns
-------
ndarray
*CIE XYZ_c* tristimulus values of the stimulus corresponding colour.
References
----------
- :cite:`Fairchild2013t`
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr) # doctest: +ELLIPSIS
array([ 0.0839746..., 0.1141321..., 0.2862554...])
Using Bradford method:
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> transform = 'Bradford'
>>> chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr, transform)
... # doctest: +ELLIPSIS
array([ 0.0854032..., 0.1140122..., 0.2972149...])
"""
cat = chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform)
XYZ_a = dot_vector(cat, XYZ)
return XYZ_a