Source code for colour.appearance.atd95

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
ATD (1995) Colour Vision Model
==============================

Defines ATD (1995) colour vision model objects:

-   :class:`ATD95_Specification`
-   :func:`XYZ_to_ATD95`

See Also
--------
`ATD (1995) Colour Vision Model IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/appearance/atd95.ipynb>`_  # noqa

Notes
-----
-   According to CIE TC1-34 definition of a colour appearance model, the
    *ATD95* model cannot be considered as a colour appearance model. It was
    developed with different aims and is described as a model of colour vision.

References
----------
.. [1]  Fairchild, M. D. (2013). ATD Model. In Color Appearance Models
        (3rd ed., pp. 5852–5991). Wiley. ASIN:B00DAYO8E2
.. [2]  Guth, S. L. (1995). Further applications of the ATD model for color
        vision. In E. Walowit (Ed.), IS&T/SPIE’s Symposium on Electronic …
        (Vol. 2414, pp. 12–26). doi:10.1117/12.206546
"""

from __future__ import division, unicode_literals

import numpy as np
from collections import namedtuple

from colour.utilities.array import tsplit, tstack

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2015 - Colour Developers'
__license__ = 'GPL V3.0 - http://www.gnu.org/licenses/'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'

__all__ = ['ATD95_ReferenceSpecification',
           'ATD95_Specification',
           'XYZ_to_ATD95',
           'luminance_to_retinal_illuminance',
           'XYZ_to_LMS_ATD95',
           'opponent_colour_dimensions',
           'final_response']


[docs]class ATD95_ReferenceSpecification( namedtuple('ATD95_ReferenceSpecification', ('H', 'C', 'Br', 'A_1', 'T_1', 'D_1', 'A_2', 'T_2', 'D_2'))): """ Defines the ATD (1995) colour vision model reference specification. This specification has field names consistent with Fairchild (2013) reference. Parameters ---------- H : numeric or array_like *Hue* angle :math:`H` in degrees. C : numeric or array_like Correlate of *saturation* :math:`C`. Guth (1995) incorrectly uses the terms saturation and chroma interchangeably. However, :math:`C` is here a measure of saturation rather than chroma since it is measured relative to the achromatic response for the stimulus rather than that of a similarly illuminated white. Br : numeric or array_like Correlate of *brightness* :math:`Br`. A_1 : numeric or array_like First stage :math:`A_1` response. T_1 : numeric or array_like First stage :math:`T_1` response. D_1 : numeric or array_like First stage :math:`D_1` response. A_2 : numeric or array_like Second stage :math:`A_2` response. T_2 : numeric or array_like Second stage :math:`A_2` response. D_2 : numeric or array_like Second stage :math:`D_2` response. """
[docs]class ATD95_Specification( namedtuple('ATD95_Specification', ('h', 'C', 'Q', 'A_1', 'T_1', 'D_1', 'A_2', 'T_2', 'D_2'))): """ Defines the ATD (1995) colour vision model specification. This specification has field names consistent with the remaining colour appearance models in :mod:`colour.appearance` but diverge from Fairchild (2013) reference. Notes ----- - This specification is the one used in the current model implementation. Parameters ---------- h : numeric or array_like *Hue* angle :math:`H` in degrees. C : numeric or array_like Correlate of *saturation* :math:`C`. Guth (1995) incorrectly uses the terms saturation and chroma interchangeably. However, :math:`C` is here a measure of saturation rather than chroma since it is measured relative to the achromatic response for the stimulus rather than that of a similarly illuminated white. Q : numeric or array_like Correlate of *brightness* :math:`Br`. A_1 : numeric or array_like First stage :math:`A_1` response. T_1 : numeric or array_like First stage :math:`T_1` response. D_1 : numeric or array_like First stage :math:`D_1` response. A_2 : numeric or array_like Second stage :math:`A_2` response. T_2 : numeric or array_like Second stage :math:`A_2` response. D_2 : numeric or array_like Second stage :math:`D_2` response. """
[docs]def XYZ_to_ATD95(XYZ, XYZ_0, Y_0, k_1, k_2, sigma=300): """ Computes the ATD (1995) colour vision model correlates. Parameters ---------- XYZ : array_like *CIE XYZ* tristimulus values of test sample / stimulus in domain [0, 100]. XYZ_0 : array_like *CIE XYZ* tristimulus values of reference white in domain [0, 100]. Y_0 : numeric or array_like Absolute adapting field luminance in :math:`cd/m^2`. k_1 : numeric or array_like Application specific weight :math:`k_1`. k_2 : numeric or array_like Application specific weight :math:`k_2`. sigma : numeric or array_like, optional Constant :math:`\sigma` varied to predict different types of data. Returns ------- ATD95_Specification ATD (1995) colour vision model specification. Warning ------- The input domain of that definition is non standard! Notes ----- - Input *CIE XYZ* tristimulus values are in domain [0, 100]. - Input *CIE XYZ_0* tristimulus values are in domain [0, 100]. - For unrelated colors, there is only self-adaptation, and :math:`k_1` is set to 1.0 while :math:`k_2` is set to 0.0. For related colors such as typical colorimetric applications, :math:`k_1` is set to 0.0 and :math:`k_2` is set to a value between 15 and 50 *(Guth, 1995)*. Examples -------- >>> XYZ = np.array([19.01, 20.00, 21.78]) >>> XYZ_0 = np.array([95.05, 100.00, 108.88]) >>> Y_0 = 318.31 >>> k_1 = 0.0 >>> k_2 = 50.0 >>> XYZ_to_ATD95(XYZ, XYZ_0, Y_0, k_1, k_2) # doctest: +ELLIPSIS ATD95_Specification(h=1.9089869..., C=1.2064060..., Q=0.1814003..., A_1=0.1787931... T_1=0.0286942..., D_1=0.0107584..., A_2=0.0192182..., T_2=0.0205377..., D_2=0.0107584...) """ Y_0 = np.asarray(Y_0) k_1 = np.asarray(k_1) k_2 = np.asarray(k_2) sigma = np.asarray(sigma) XYZ = luminance_to_retinal_illuminance(XYZ, Y_0) XYZ_0 = luminance_to_retinal_illuminance(XYZ_0, Y_0) # Computing adaptation model. LMS = XYZ_to_LMS_ATD95(XYZ) XYZ_a = k_1[..., np.newaxis] * XYZ + k_2[..., np.newaxis] * XYZ_0 LMS_a = XYZ_to_LMS_ATD95(XYZ_a) LMS_g = LMS * (sigma[..., np.newaxis] / (sigma[..., np.newaxis] + LMS_a)) # Computing opponent colour dimensions. A_1, T_1, D_1, A_2, T_2, D_2 = tsplit( opponent_colour_dimensions(LMS_g)) # ------------------------------------------------------------------------- # Computing the correlate of *brightness* :math:`Br`. # ------------------------------------------------------------------------- Br = (A_1 ** 2 + T_1 ** 2 + D_1 ** 2) ** 0.5 # ------------------------------------------------------------------------- # Computing the correlate of *saturation* :math:`C`. # ------------------------------------------------------------------------- C = (T_2 ** 2 + D_2 ** 2) ** 0.5 / A_2 # ------------------------------------------------------------------------- # Computing the *hue* :math:`H`. # ------------------------------------------------------------------------- H = T_2 / D_2 return ATD95_Specification(H, C, Br, A_1, T_1, D_1, A_2, T_2, D_2)
[docs]def luminance_to_retinal_illuminance(XYZ, Y_c): """ Converts from luminance in :math:`cd/m^2` to retinal illuminance in trolands. Parameters ---------- XYZ : array_like *CIE XYZ* tristimulus values. Y_c : numeric or array_like Absolute adapting field luminance in :math:`cd/m^2`. Returns ------- ndarray Converted *CIE XYZ* tristimulus values in trolands. Examples -------- >>> XYZ = np.array([19.01, 20.00, 21.78]) >>> Y_0 = 318.31 >>> luminance_to_retinal_illuminance(XYZ, Y_0) # doctest: +ELLIPSIS array([ 479.4445924..., 499.3174313..., 534.5631673...]) """ XYZ = np.asarray(XYZ) Y_c = np.asarray(Y_c) return 18 * (Y_c[..., np.newaxis] * XYZ / 100.) ** 0.8
[docs]def XYZ_to_LMS_ATD95(XYZ): """ Converts from *CIE XYZ* tristimulus values to *LMS* cone responses. Parameters ---------- XYZ : array_like *CIE XYZ* tristimulus values. Returns ------- ndarray *LMS* cone responses. Examples -------- >>> XYZ = np.array([19.01, 20.00, 21.78]) >>> XYZ_to_LMS_ATD95(XYZ) # doctest: +ELLIPSIS array([ 6.2283272..., 7.4780666..., 3.8859772...]) """ X, Y, Z = tsplit(XYZ) L = ((0.66 * (0.2435 * X + 0.8524 * Y - 0.0516 * Z)) ** 0.7) + 0.024 M = ((-0.3954 * X + 1.1642 * Y + 0.0837 * Z) ** 0.7) + 0.036 S = ((0.43 * (0.04 * Y + 0.6225 * Z)) ** 0.7) + 0.31 LMS = tstack((L, M, S)) return LMS
[docs]def opponent_colour_dimensions(LMS_g): """ Returns opponent colour dimensions from given post adaptation cone signals. Parameters ---------- LMS_g : array_like Post adaptation cone signals. Returns ------- ndarray Opponent colour dimensions. Examples -------- >>> LMS_g = np.array([6.95457922, 7.08945043, 6.44069316]) >>> opponent_colour_dimensions(LMS_g) # doctest: +ELLIPSIS array([ 0.1787931..., 0.0286942..., 0.0107584..., 0.0192182..., 0.0205377..., 0.0107584...]) """ L_g, M_g, S_g = tsplit(LMS_g) A_1i = 3.57 * L_g + 2.64 * M_g T_1i = 7.18 * L_g - 6.21 * M_g D_1i = -0.7 * L_g + 0.085 * M_g + S_g A_2i = 0.09 * A_1i T_2i = 0.43 * T_1i + 0.76 * D_1i D_2i = D_1i A_1 = final_response(A_1i) T_1 = final_response(T_1i) D_1 = final_response(D_1i) A_2 = final_response(A_2i) T_2 = final_response(T_2i) D_2 = final_response(D_2i) return tstack((A_1, T_1, D_1, A_2, T_2, D_2))
[docs]def final_response(value): """ Returns the final response of given opponent colour dimension. Parameters ---------- value : numeric or array_like Opponent colour dimension. Returns ------- numeric or ndarray Final response of opponent colour dimension. Examples -------- >>> final_response(43.54399695501678) # doctest: +ELLIPSIS 0.1787931... """ value = np.asarray(value) return value / (200 + np.abs(value))