#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
CMCCAT2000 Chromatic Adaptation Model
=====================================
Defines CMCCAT2000 chromatic adaptation model objects:
- :class:`CMCCAT2000_InductionFactors`
- :class:`CMCCAT2000_VIEWING_CONDITIONS`
- :func:`CMCCAT2000_forward`
- :func:`CMCCAT2000_reverse`
- :func:`chromatic_adaptation_CMCCAT2000`
See Also
--------
`CMCCAT2000 Chromatic Adaptation Model IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/adaptation/cmccat2000.ipynb>`_ # noqa
References
----------
.. [1] Li, C., Luo, M. R., Rigg, B., & Hunt, R. W. G. (2002). CMC 2000
chromatic adaptation transform: CMCCAT2000. Color Research & …, 27(1),
49–58. doi:10.1002/col.10005
.. [2] Westland, S., Ripamonti, C., & Cheung, V. (2012). CMCCAT2000. In
Computational Colour Science Using MATLAB (2nd ed., pp. 83–86).
ISBN:978-0-470-66569-5
"""
from __future__ import division, unicode_literals
import numpy as np
from collections import namedtuple
from colour.adaptation import CMCCAT2000_CAT
from colour.utilities import CaseInsensitiveMapping, dot_vector
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2015 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['CMCCAT2000_INVERSE_CAT',
'CMCCAT2000_InductionFactors',
'CMCCAT2000_VIEWING_CONDITIONS',
'CMCCAT2000_forward',
'CMCCAT2000_reverse',
'chromatic_adaptation_CMCCAT2000']
CMCCAT2000_INVERSE_CAT = np.linalg.inv(CMCCAT2000_CAT)
"""
Inverse CMCCAT2000_CAT chromatic adaptation transform.
CMCCAT2000_INVERSE_CAT : array_like, (3, 3)
"""
[docs]class CMCCAT2000_InductionFactors(
namedtuple('CMCCAT2000_InductionFactors',
('F',))):
"""
CMCCAT2000 chromatic adaptation model induction factors.
Parameters
----------
F : numeric or array_like
:math:`F` surround condition.
"""
CMCCAT2000_VIEWING_CONDITIONS = CaseInsensitiveMapping(
{'Average': CMCCAT2000_InductionFactors(1.),
'Dim': CMCCAT2000_InductionFactors(0.8),
'Dark': CMCCAT2000_InductionFactors(0.8)})
"""
Reference CMCCAT2000 chromatic adaptation model viewing conditions.
CMCCAT2000_VIEWING_CONDITIONS : CaseInsensitiveMapping
('Average', 'Dim', 'Dark')
"""
[docs]def CMCCAT2000_forward(XYZ,
XYZ_w,
XYZ_wr,
L_A1,
L_A2,
surround=CMCCAT2000_VIEWING_CONDITIONS.get('Average')):
"""
Adapts given stimulus *CIE XYZ* tristimulus values from test viewing
conditions to reference viewing conditions using CMCCAT2000 forward
chromatic adaptation model.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of the stimulus to adapt.
XYZ_w : array_like
Test viewing condition *CIE XYZ* tristimulus values of the whitepoint.
XYZ_wr : array_like
Reference viewing condition *CIE XYZ* tristimulus values of the
whitepoint.
L_A1 : numeric or array_like
Luminance of test adapting field :math:`L_{A1}` in :math:`cd/m^2`.
L_A2 : numeric or array_like
Luminance of reference adapting field :math:`L_{A2}` in :math:`cd/m^2`.
surround : CMCCAT2000_InductionFactors, optional
Surround viewing conditions induction factors.
Returns
-------
ndarray
*CIE XYZ_c* tristimulus values of the stimulus corresponding colour.
Warning
-------
The input and output domains of that definition are non standard!
Notes
-----
- Input *CIE XYZ*, *CIE XYZ_w* and *CIE XYZ_wr* tristimulus values are in
domain [0, 100].
- Output *CIE XYZ_c* tristimulus values are in domain [0, 100].
Examples
--------
>>> XYZ = np.array([22.48, 22.74, 8.54])
>>> XYZ_w = np.array([111.15, 100.00, 35.20])
>>> XYZ_wr = np.array([94.81, 100.00, 107.30])
>>> L_A1 = 200
>>> L_A2 = 200
>>> CMCCAT2000_forward( # doctest: +ELLIPSIS
... XYZ, XYZ_w, XYZ_wr, L_A1, L_A2)
array([ 19.5269832..., 23.0683396..., 24.9717522...])
"""
XYZ = np.asarray(XYZ)
XYZ_w = np.asarray(XYZ_w)
XYZ_wr = np.asarray(XYZ_wr)
L_A1 = np.asarray(L_A1)
L_A2 = np.asarray(L_A2)
RGB = dot_vector(CMCCAT2000_CAT, XYZ)
RGB_w = dot_vector(CMCCAT2000_CAT, XYZ_w)
RGB_wr = dot_vector(CMCCAT2000_CAT, XYZ_wr)
D = (surround.F *
(0.08 * np.log10(0.5 * (L_A1 + L_A2)) +
0.76 - 0.45 * (L_A1 - L_A2) / (L_A1 + L_A2)))
D = np.clip(D, 0, 1)
a = D * XYZ_w[..., 1] / XYZ_wr[..., 1]
RGB_c = (RGB *
(a[..., np.newaxis] * (RGB_wr / RGB_w) + 1 - D[..., np.newaxis]))
XYZ_c = dot_vector(CMCCAT2000_INVERSE_CAT, RGB_c)
return XYZ_c
[docs]def CMCCAT2000_reverse(XYZ_c,
XYZ_w,
XYZ_wr,
L_A1,
L_A2,
surround=CMCCAT2000_VIEWING_CONDITIONS.get('Average')):
"""
Adapts given stimulus corresponding colour *CIE XYZ* tristimulus values
from reference viewing conditions to test viewing conditions using
CMCCAT2000 reverse chromatic adaptation model.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of the stimulus to adapt.
XYZ_w : array_like
Test viewing condition *CIE XYZ* tristimulus values of the whitepoint.
XYZ_wr : array_like
Reference viewing condition *CIE XYZ* tristimulus values of the
whitepoint.
L_A1 : numeric or array_like
Luminance of test adapting field :math:`L_{A1}` in :math:`cd/m^2`.
L_A2 : numeric or array_like
Luminance of reference adapting field :math:`L_{A2}` in :math:`cd/m^2`.
surround : CMCCAT2000_InductionFactors, optional
Surround viewing conditions induction factors.
Returns
-------
ndarray
*CIE XYZ_c* tristimulus values of the adapted stimulus.
Warning
-------
The input and output domains of that definition are non standard!
Notes
-----
- Input *CIE XYZ_c*, *CIE XYZ_w* and *CIE XYZ_wr* tristimulus values
are in domain [0, 100].
- Output *CIE XYZ* tristimulus values are in domain [0, 100].
Examples
--------
>>> XYZ_c = np.array([19.53, 23.07, 24.97])
>>> XYZ_w = np.array([111.15, 100.00, 35.20])
>>> XYZ_wr = np.array([94.81, 100.00, 107.30])
>>> L_A1 = 200
>>> L_A2 = 200
>>> CMCCAT2000_reverse( # doctest: +ELLIPSIS
... XYZ_c, XYZ_w, XYZ_wr, L_A1, L_A2)
array([ 22.4839876..., 22.7419485..., 8.5393392...])
"""
XYZ_c = np.asarray(XYZ_c)
XYZ_w = np.asarray(XYZ_w)
XYZ_wr = np.asarray(XYZ_wr)
L_A1 = np.asarray(L_A1)
L_A2 = np.asarray(L_A2)
RGB_c = dot_vector(CMCCAT2000_CAT, XYZ_c)
RGB_w = dot_vector(CMCCAT2000_CAT, XYZ_w)
RGB_wr = dot_vector(CMCCAT2000_CAT, XYZ_wr)
D = (surround.F *
(0.08 * np.log10(0.5 * (L_A1 + L_A2)) +
0.76 - 0.45 * (L_A1 - L_A2) / (L_A1 + L_A2)))
D = np.clip(D, 0, 1)
a = D * XYZ_w[..., 1] / XYZ_wr[..., 1]
RGB = (RGB_c /
(a[..., np.newaxis] * (RGB_wr / RGB_w) + 1 - D[..., np.newaxis]))
XYZ = dot_vector(CMCCAT2000_INVERSE_CAT, RGB)
return XYZ
[docs]def chromatic_adaptation_CMCCAT2000(
XYZ,
XYZ_w,
XYZ_wr,
L_A1,
L_A2,
surround=CMCCAT2000_VIEWING_CONDITIONS.get('Average'),
method='Forward'):
"""
Adapts given stimulus *CIE XYZ* tristimulus values using given viewing
conditions.
This definition is a convenient wrapper around :func:`CMCCAT2000_forward`
and :func:`CMCCAT2000_reverse`.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values of the stimulus to adapt.
XYZ_w : array_like
Source viewing condition *CIE XYZ* tristimulus values of the
whitepoint.
XYZ_wr : array_like
Target viewing condition *CIE XYZ* tristimulus values of the
whitepoint.
L_A1 : numeric or array_like
Luminance of test adapting field :math:`L_{A1}` in :math:`cd/m^2`.
L_A2 : numeric or array_like
Luminance of reference adapting field :math:`L_{A2}` in :math:`cd/m^2`.
surround : CMCCAT2000_InductionFactors, optional
Surround viewing conditions induction factors.
method : unicode, optional
**{'Forward', 'Reverse'}**,
Chromatic adaptation method.
Returns
-------
ndarray
Adapted stimulus *CIE XYZ* tristimulus values.
Warning
-------
The input and output domains of that definition are non standard!
Notes
-----
- Input *CIE XYZ*, *CIE XYZ_w* and *CIE XYZ_wr* tristimulus values are in
domain [0, 100].
- Output *CIE XYZ* tristimulus values are in domain [0, 100].
Examples
--------
>>> XYZ = np.array([22.48, 22.74, 8.54])
>>> XYZ_w = np.array([111.15, 100.00, 35.20])
>>> XYZ_wr = np.array([94.81, 100.00, 107.30])
>>> L_A1 = 200
>>> L_A2 = 200
>>> chromatic_adaptation_CMCCAT2000( # doctest: +ELLIPSIS
... XYZ, XYZ_w, XYZ_wr, L_A1, L_A2, method='Forward')
array([ 19.5269832..., 23.0683396..., 24.9717522...])
Using the CMCCAT2000 reverse model:
>>> XYZ = np.array([19.52698326, 23.06833960, 24.97175229])
>>> XYZ_w = np.array([111.15, 100.00, 35.20])
>>> XYZ_wr = np.array([94.81, 100.00, 107.30])
>>> L_A1 = 200
>>> L_A2 = 200
>>> chromatic_adaptation_CMCCAT2000( # doctest: +ELLIPSIS
... XYZ, XYZ_w, XYZ_wr, L_A1, L_A2, method='Reverse')
array([ 22.48, 22.74, 8.54])
"""
if method.lower() == 'forward':
return CMCCAT2000_forward(XYZ, XYZ_w, XYZ_wr, L_A1, L_A2, surround)
else:
return CMCCAT2000_reverse(XYZ, XYZ_w, XYZ_wr, L_A1, L_A2, surround)
Colour 0.3