Source code for colour.volume.rgb

# -*- coding: utf-8 -*-
"""
RGB Colourspace Volume Computation
==================================

Defines various RGB colourspace volume computation objects:

-   :func:`colour.RGB_colourspace_limits`
-   :func:`colour.RGB_colourspace_volume_MonteCarlo`
-   :func:`colour.RGB_colourspace_volume_coverage_MonteCarlo`
-   :func:`colour.RGB_colourspace_pointer_gamut_coverage_MonteCarlo`
-   :func:`colour.RGB_colourspace_visible_spectrum_coverage_MonteCarlo`
"""

from __future__ import division, unicode_literals

import itertools
import multiprocessing
import numpy as np

from colour.algebra import random_triplet_generator
from colour.colorimetry import ILLUMINANTS
from colour.constants import DEFAULT_INT_DTYPE
from colour.models import (Lab_to_XYZ, RGB_to_XYZ, XYZ_to_Lab, XYZ_to_RGB)
from colour.volume import is_within_pointer_gamut, is_within_visible_spectrum
from colour.utilities import as_float_array, multiprocessing_pool

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2020 - Colour Developers'
__license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-developers@colour-science.org'
__status__ = 'Production'

__all__ = [
    'sample_RGB_colourspace_volume_MonteCarlo', 'RGB_colourspace_limits',
    'RGB_colourspace_volume_MonteCarlo',
    'RGB_colourspace_volume_coverage_MonteCarlo',
    'RGB_colourspace_pointer_gamut_coverage_MonteCarlo',
    'RGB_colourspace_visible_spectrum_coverage_MonteCarlo'
]


def _wrapper_RGB_colourspace_volume_MonteCarlo(arguments):
    """
    Convenient wrapper to be able to call
    :func:`colour.volume.rgb.sample_RGB_colourspace_volume_MonteCarlo`:
    definition with multiple arguments.

    Parameters
    ----------
    arguments : array_like, optional
        Arguments.

    Returns
    -------
    integer
        Inside *RGB* colourspace volume samples count.
    """

    return sample_RGB_colourspace_volume_MonteCarlo(*arguments)


def sample_RGB_colourspace_volume_MonteCarlo(
        colourspace,
        samples=10e6,
        limits=np.array([[0, 100], [-150, 150], [-150, 150]]),
        illuminant_Lab=ILLUMINANTS['CIE 1931 2 Degree Standard Observer'][
            'D65'],
        chromatic_adaptation_method='CAT02',
        random_generator=random_triplet_generator,
        random_state=None):
    """
    Randomly samples the *CIE L\\*a\\*b\\** colourspace volume and returns the
    ratio of samples within the given *RGB* colourspace volume.

    Parameters
    ----------
    colourspace : RGB_Colourspace
        *RGB* colourspace to compute the volume of.
    samples : numeric, optional
        Samples count.
    limits : array_like, optional
        *CIE L\\*a\\*b\\** colourspace volume.
    illuminant_Lab : array_like, optional
        *CIE L\\*a\\*b\\** colourspace *illuminant* chromaticity coordinates.
    chromatic_adaptation_method : unicode, optional
        **{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
        'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
        'Bianco PC'}**,
        *Chromatic adaptation* method.
    random_generator : generator, optional
        Random triplet generator providing the random samples within the
        *CIE L\\*a\\*b\\** colourspace volume.
    random_state : RandomState, optional
        Mersenne Twister pseudo-random number generator to use in the random
        number generator.

    Returns
    -------
    integer
        Within *RGB* colourspace volume samples count.

    Notes
    -----
    -   The doctest is assuming that :func:`np.random.RandomState` definition
        will return the same sequence no matter which *OS* or *Python*
        version is used. There is however no formal promise about the *prng*
        sequence reproducibility of either *Python* or *Numpy*
        implementations: Laurent. (2012). Reproducibility of python
        pseudo-random numbers across systems and versions? Retrieved January
        20, 2015, from http://stackoverflow.com/questions/8786084/\
reproducibility-of-python-pseudo-random-numbers-across-systems-and-versions

    Examples
    --------
    >>> from colour.models import sRGB_COLOURSPACE as sRGB
    >>> prng = np.random.RandomState(2)
    >>> sample_RGB_colourspace_volume_MonteCarlo(sRGB, 10e3, random_state=prng)
    ... # doctest: +ELLIPSIS
    9...
    """

    random_state = (random_state
                    if random_state is not None else np.random.RandomState())

    Lab = as_float_array(list(random_generator(samples, limits, random_state)))
    RGB = XYZ_to_RGB(
        Lab_to_XYZ(Lab, illuminant_Lab),
        illuminant_Lab,
        colourspace.whitepoint,
        colourspace.XYZ_to_RGB_matrix,
        chromatic_adaptation_transform=chromatic_adaptation_method)
    RGB_w = RGB[np.logical_and(
        np.min(RGB, axis=-1) >= 0,
        np.max(RGB, axis=-1) <= 1)]
    return len(RGB_w)


[docs]def RGB_colourspace_limits( colourspace, illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']): """ Computes given *RGB* colourspace volume limits in *CIE L\\*a\\*b\\** colourspace. Parameters ---------- colourspace : RGB_Colourspace *RGB* colourspace to compute the volume of. illuminant : array_like, optional *CIE L\\*a\\*b\\** colourspace *illuminant* chromaticity coordinates. Returns ------- ndarray *RGB* colourspace volume limits. Examples -------- >>> from colour.models import sRGB_COLOURSPACE as sRGB >>> RGB_colourspace_limits(sRGB) # doctest: +ELLIPSIS array([[ 0. ..., 100. ...], [ -86.182855 ..., 98.2563272...], [-107.8503557..., 94.4894974...]]) """ Lab = [] for combination in list(itertools.product([0, 1], repeat=3)): Lab.append( XYZ_to_Lab( RGB_to_XYZ(combination, colourspace.whitepoint, illuminant, colourspace.RGB_to_XYZ_matrix))) Lab = np.array(Lab) limits = [] for i in np.arange(3): limits.append((np.min(Lab[..., i]), np.max(Lab[..., i]))) return np.array(limits)
[docs]def RGB_colourspace_volume_MonteCarlo( colourspace, samples=10e6, limits=np.array([[0, 100], [-150, 150], [-150, 150]], dtype=np.float), illuminant_Lab=ILLUMINANTS['CIE 1931 2 Degree Standard Observer'][ 'D65'], chromatic_adaptation_method='CAT02', random_generator=random_triplet_generator, random_state=None): """ Performs given *RGB* colourspace volume computation using *Monte Carlo* method and multiprocessing. Parameters ---------- colourspace : RGB_Colourspace *RGB* colourspace to compute the volume of. samples : numeric, optional Samples count. limits : array_like, optional *CIE L\\*a\\*b\\** colourspace volume. illuminant_Lab : array_like, optional *CIE L\\*a\\*b\\** colourspace *illuminant* chromaticity coordinates. chromatic_adaptation_method : unicode, optional **{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp', 'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco', 'Bianco PC'}**, *Chromatic adaptation* method. random_generator : generator, optional Random triplet generator providing the random samples within the *CIE L\\*a\\*b\\** colourspace volume. random_state : RandomState, optional Mersenne Twister pseudo-random number generator to use in the random number generator. Returns ------- float *RGB* colourspace volume. Notes ----- - The doctest is assuming that :func:`np.random.RandomState` definition will return the same sequence no matter which *OS* or *Python* version is used. There is however no formal promise about the *prng* sequence reproducibility of either *Python* or *Numpy* implementations: Laurent. (2012). Reproducibility of python pseudo-random numbers across systems and versions? Retrieved January 20, 2015, from http://stackoverflow.com/questions/8786084/\ reproducibility-of-python-pseudo-random-numbers-across-systems-and-versions Examples -------- >>> from colour.models import sRGB_COLOURSPACE as sRGB >>> from colour.utilities import disable_multiprocessing >>> prng = np.random.RandomState(2) >>> with disable_multiprocessing(): ... RGB_colourspace_volume_MonteCarlo(sRGB, 10e3, random_state=prng) ... # doctest: +ELLIPSIS 8... """ processes = multiprocessing.cpu_count() process_samples = DEFAULT_INT_DTYPE(np.round(samples / processes)) arguments = (colourspace, process_samples, limits, illuminant_Lab, chromatic_adaptation_method, random_generator, random_state) with multiprocessing_pool() as pool: results = pool.map(_wrapper_RGB_colourspace_volume_MonteCarlo, [arguments for _ in range(processes)]) Lab_volume = np.product([np.sum(np.abs(x)) for x in limits]) return Lab_volume * np.sum(results) / (process_samples * processes)
[docs]def RGB_colourspace_volume_coverage_MonteCarlo( colourspace, coverage_sampler, samples=10e6, random_generator=random_triplet_generator, random_state=None): """ Returns given *RGB* colourspace percentage coverage of an arbitrary volume. Parameters ---------- colourspace : RGB_Colourspace *RGB* colourspace to compute the volume coverage percentage. coverage_sampler : object Python object responsible for checking the volume coverage. samples : numeric, optional Samples count. random_generator : generator, optional Random triplet generator providing the random samples. random_state : RandomState, optional Mersenne Twister pseudo-random number generator to use in the random number generator. Returns ------- float Percentage coverage of volume. Examples -------- >>> from colour.models import sRGB_COLOURSPACE as sRGB >>> prng = np.random.RandomState(2) >>> RGB_colourspace_volume_coverage_MonteCarlo( ... sRGB, is_within_pointer_gamut, 10e3, random_state=prng) ... # doctest: +ELLIPSIS 81... """ random_state = (random_state if random_state is not None else np.random.RandomState()) # TODO: Investigate for generator yielding directly a ndarray. XYZ = as_float_array( list(random_generator(samples, random_state=random_state))) XYZ_vs = XYZ[coverage_sampler(XYZ)] RGB = XYZ_to_RGB(XYZ_vs, colourspace.whitepoint, colourspace.whitepoint, colourspace.XYZ_to_RGB_matrix) RGB_c = RGB[np.logical_and( np.min(RGB, axis=-1) >= 0, np.max(RGB, axis=-1) <= 1)] return 100 * RGB_c.size / XYZ_vs.size
[docs]def RGB_colourspace_pointer_gamut_coverage_MonteCarlo( colourspace, samples=10e6, random_generator=random_triplet_generator, random_state=None): """ Returns given *RGB* colourspace percentage coverage of Pointer's Gamut volume using *Monte Carlo* method. Parameters ---------- colourspace : RGB_Colourspace *RGB* colourspace to compute the *Pointer's Gamut* coverage percentage. samples : numeric, optional Samples count. random_generator : generator, optional Random triplet generator providing the random samples. random_state : RandomState, optional Mersenne Twister pseudo-random number generator to use in the random number generator. Returns ------- float Percentage coverage of *Pointer's Gamut* volume. Examples -------- >>> from colour.models import sRGB_COLOURSPACE as sRGB >>> prng = np.random.RandomState(2) >>> RGB_colourspace_pointer_gamut_coverage_MonteCarlo( ... sRGB, 10e3, random_state=prng) # doctest: +ELLIPSIS 81... """ return RGB_colourspace_volume_coverage_MonteCarlo( colourspace, is_within_pointer_gamut, samples, random_generator, random_state)
[docs]def RGB_colourspace_visible_spectrum_coverage_MonteCarlo( colourspace, samples=10e6, random_generator=random_triplet_generator, random_state=None): """ Returns given *RGB* colourspace percentage coverage of visible spectrum volume using *Monte Carlo* method. Parameters ---------- colourspace : RGB_Colourspace *RGB* colourspace to compute the visible spectrum coverage percentage. samples : numeric, optional Samples count. random_generator : generator, optional Random triplet generator providing the random samples. random_state : RandomState, optional Mersenne Twister pseudo-random number generator to use in the random number generator. Returns ------- float Percentage coverage of visible spectrum volume. Examples -------- >>> from colour.models import sRGB_COLOURSPACE as sRGB >>> prng = np.random.RandomState(2) >>> RGB_colourspace_visible_spectrum_coverage_MonteCarlo( ... sRGB, 10e3, random_state=prng) # doctest: +ELLIPSIS 46... """ return RGB_colourspace_volume_coverage_MonteCarlo( colourspace, is_within_visible_spectrum, samples, random_generator, random_state)