#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
RLAB Colour Appearance Model
============================
Defines *RLAB* colour appearance model objects:
- :attr:`RLAB_VIEWING_CONDITIONS`
- :attr:`RLAB_D_FACTOR`
- :class:`RLAB_Specification`
- :func:`XYZ_to_RLAB`
See Also
--------
`RLAB Colour Appearance Model IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/appearance/rlab.ipynb>`_ # noqa
References
----------
.. [1] **Mark D. Fairchild**,
*Refinement of the RLAB color space*,
*Color Research & Application, Volume 21, Issue 5, pages 338–346,
October 1996*,
https://ritdml.rit.edu/bitstream/handle/1850/7857/MFairchildArticle12-06-1998.pdf # noqa
(Last accessed 16 August 2014)
.. [2] **Mark D. Fairchild**, *Color Appearance Models, 3nd Edition*,
The Wiley-IS&T Series in Imaging Science and Technology,
published June 2013, ASIN: B00DAYO8E2,
locations 6019-6178.
"""
from __future__ import division, unicode_literals
import math
import numpy as np
from collections import namedtuple
from colour.appearance.hunt import XYZ_to_rgb
from colour.appearance.hunt import XYZ_TO_HPE_MATRIX
from colour.utilities import CaseInsensitiveMapping
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2014 - Colour Developers'
__license__ = 'GPL V3.0 - http://www.gnu.org/licenses/'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['R_MATRIX',
'RLAB_VIEWING_CONDITIONS',
'RLAB_D_FACTOR',
'RLAB_ReferenceSpecification',
'RLAB_Specification',
'XYZ_to_RLAB']
R_MATRIX = np.array(
[[1.9569, -1.1882, 0.2313],
[0.3612, 0.6388, 0.0000],
[0.0000, 0.0000, 1.0000]])
"""
*RLAB* colour appearance model precomputed helper matrix.
R_MATRIX : array_like, (3, 3)
"""
RLAB_VIEWING_CONDITIONS = CaseInsensitiveMapping(
{'Average': 1 / 2.3,
'Dim': 1 / 2.9,
'Dark': 1 / 3.5})
"""
Reference *RLAB* colour appearance model viewing conditions.
RLAB_VIEWING_CONDITIONS : dict
('Average', 'Dim', 'Dark')
"""
RLAB_D_FACTOR = CaseInsensitiveMapping(
{'Hard Copy Images': 1,
'Soft Copy Images': 0,
'Projected Transparencies, Dark Room': 0.5})
"""
*RLAB* colour appearance model *Discounting-the-Illuminant* factor values.
RLAB_D_FACTOR : dict
('Hard Copy Images',
'Soft Copy Images',
'Projected Transparencies, Dark Room')
Aliases:
- 'hard_cp_img': 'Hard Copy Images'
- 'soft_cp_img': 'Soft Copy Images'
- 'projected_dark': 'Projected Transparencies, Dark Room'
"""
RLAB_D_FACTOR['hard_cp_img'] = (
RLAB_D_FACTOR['Hard Copy Images'])
RLAB_D_FACTOR['soft_cp_img'] = (
RLAB_D_FACTOR['Soft Copy Images'])
RLAB_D_FACTOR['projected_dark'] = (
RLAB_D_FACTOR['Projected Transparencies, Dark Room'])
[docs]class RLAB_ReferenceSpecification(
namedtuple('RLAB_ReferenceSpecification',
('LR', 'CR', 'hR', 'sR', 'HR', 'aR', 'bR'))):
"""
Defines the *RLAB* colour appearance model reference specification.
This specification has field names consistent with **Mark D. Fairchild**
reference.
Parameters
----------
LR : numeric
Correlate of *Lightness* :math:`L^R`.
CR : numeric
Correlate of *achromatic chroma* :math:`C^R`.
hR : numeric
*Hue* angle :math:`h^R` in degrees.
sR : numeric
Correlate of *saturation* :math:`s^R`.
HR : numeric
*Hue* :math:`h` composition :math:`H^R`.
aR : numeric
Red–green chromatic response :math:`a^R`.
bR : numeric
Yellow–blue chromatic response :math:`b^R`.
"""
[docs]class RLAB_Specification(
namedtuple('RLAB_Specification',
('J', 'C', 'h', 's', 'HC', 'a', 'b'))):
"""
Defines the *RLAB* colour appearance model specification.
This specification has field names consistent with the remaining colour
appearance models in :mod:`colour.appearance` but diverge from
**Mark D. Fairchild** reference.
Parameters
----------
J : numeric
Correlate of *Lightness* :math:`L^R`.
C : numeric
Correlate of *achromatic chroma* :math:`C^R`.
h : numeric
*Hue* angle :math:`h^R` in degrees.
s : numeric
Correlate of *saturation* :math:`s^R`.
HC : numeric
*Hue* :math:`h` composition :math:`H^C`.
a : numeric
Red–green chromatic response :math:`a^R`.
b : numeric
Yellow–blue chromatic response :math:`b^R`.
"""
[docs]def XYZ_to_RLAB(XYZ,
XYZ_n,
Y_n,
sigma=RLAB_VIEWING_CONDITIONS.get('Average'),
D=RLAB_D_FACTOR.get('Hard Copy Images')):
"""
Computes the RLAB model color appearance correlates.
Parameters
----------
XYZ : array_like, (3, n)
*CIE XYZ* colourspace matrix of test sample / stimulus in domain
[0, 100].
XYZ_n : array_like, (3,)
*CIE XYZ* colourspace matrix of reference white in domain [0, 100].
Y_n : numeric
Absolute adapting luminance in :math:`cd/m^2`.
sigma : numeric, optional
Relative luminance of the surround, see :attr:`RLAB_VIEWING_CONDITIONS`
for reference.
D : numeric, optional
*Discounting-the-Illuminant* factor in domain [0, 1].
Returns
-------
RLAB_Specification
*RLAB* colour appearance model specification.
Warning
-------
The input domain of that definition is non standard!
Notes
-----
- Input *CIE XYZ* colourspace matrix is in domain [0, 100].
- Input *CIE XYZ_n* colourspace matrix is in domain [0, 100].
Examples
--------
>>> XYZ = np.array([19.01, 20, 21.78])
>>> XYZ_n = np.array([109.85, 100, 35.58])
>>> Y_n = 31.83
>>> sigma = RLAB_VIEWING_CONDITIONS['Average']
>>> D = RLAB_D_FACTOR['Hard Copy Images']
>>> XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma, D) # doctest: +ELLIPSIS
RLAB_Specification(J=49.8347069..., C=54.8700585..., h=286.4860208..., s=1.1010410..., HC=None, a=15.5711021..., b=-52.6142956...)
"""
X, Y, Z = np.ravel(XYZ)
# Converting to cone responses.
LMS = XYZ_to_rgb(XYZ)
LMS_n = XYZ_to_rgb(XYZ_n)
# Computing the :math:`A` matrix.
LMS_l_E = (3 * LMS_n) / (LMS_n[0] + LMS_n[1] + LMS_n[2])
LMS_p_L = ((1 + (Y_n ** (1 / 3)) + LMS_l_E) /
(1 + (Y_n ** (1 / 3)) + (1 / LMS_l_E)))
LMS_a_L = (LMS_p_L + D * (1 - LMS_p_L)) / LMS_n
# Special handling here to allow *array_like* variable input.
if len(np.shape(X)) == 0:
# *numeric* case.
# Implementation as per reference.
aR = np.diag(LMS_a_L)
XYZ_ref = R_MATRIX.dot(aR).dot(XYZ_TO_HPE_MATRIX).dot(XYZ)
else:
# *array_like* case.
# Constructing huge multidimensional arrays might not be the best idea,
# we handle each input dimension separately.
# First figure out how many values we have to deal with.
dimension = len(X)
# Then create the output array that will be filled layer by layer.
XYZ_ref = np.zeros((3, dimension))
for layer in range(dimension):
aR = np.diag(LMS_a_L[..., layer])
XYZ_ref[..., layer] = (
R_MATRIX.dot(aR).dot(XYZ_TO_HPE_MATRIX).dot(XYZ[..., layer]))
X_ref, Y_ref, Z_ref = XYZ_ref
# -------------------------------------------------------------------------
# Computing the correlate of *Lightness* :math:`L^R`.
# -------------------------------------------------------------------------
LR = 100 * (Y_ref ** sigma)
# Computing opponent colour dimensions :math:`a^R` and :math:`b^R`.
aR = 430 * ((X_ref ** sigma) - (Y_ref ** sigma))
bR = 170 * ((Y_ref ** sigma) - (Z_ref ** sigma))
# -------------------------------------------------------------------------
# Computing the *hue* angle :math:`h^R`.
# -------------------------------------------------------------------------
hR = math.degrees(np.arctan2(bR, aR)) % 360
# TODO: Implement hue composition computation.
# -------------------------------------------------------------------------
# Computing the correlate of *chroma* :math:`C^R`.
# -------------------------------------------------------------------------
CR = np.sqrt((aR ** 2) + (bR ** 2))
# -------------------------------------------------------------------------
# Computing the correlate of *saturation* :math:`s^R`.
# -------------------------------------------------------------------------
sR = CR / LR
return RLAB_Specification(LR, CR, hR, sR, None, aR, bR)
Colour 0.3