Skip to main content

Colour Science for Python

Project description

https://raw.githubusercontent.com/colour-science/colour-branding/master/images/Colour_Logo_Medium_001.png

Powered by NumFOCUS Develop Build Status Coverage Status Code Grade Package Version DOI

Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science.

It is freely available under the New BSD License terms.

Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States.

1   Draft Release Notes

The draft release notes of the develop branch are available at this url.

2   Sponsors

We are grateful 💖 for the support of our sponsors. If you’d like to join them, please consider becoming a sponsor on OpenCollective.

3   Features

Colour features a rich dataset and collection of objects, please see the features page for more information.

4   Installation

Colour and its primary dependencies can be easily installed from the Python Package Index by issuing this command in a shell:

$ pip install --user colour-science

The detailed installation procedure for the secondary dependencies is described in the Installation Guide.

Colour is also available for Anaconda from Continuum Analytics via conda-forge:

$ conda install -c conda-forge colour-science

5   Documentation

5.1   Tutorial

The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.

5.2   How-To Guide

The How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases.

5.3   API Reference

The main technical reference for Colour and its API is the Colour Manual.

5.4   Examples

Most of the objects are available from the colour namespace:

>>> import colour

5.4.1   Automatic Colour Conversion Graph - colour.graph

Starting with version 0.3.14, Colour implements an automatic colour conversion graph enabling easier colour conversions.

https://colour.readthedocs.io/en/develop/_static/Examples_Colour_Automatic_Conversion_Graph.png
>>> sd = colour.SDS_COLOURCHECKERS['ColorChecker N Ohta']['dark skin']
>>> colour.convert(sd, 'Spectral Distribution', 'sRGB', verbose={'mode': 'Short'})
===============================================================================
*                                                                             *
*   [ Conversion Path ]                                                       *
*                                                                             *
*   "sd_to_XYZ" --> "XYZ_to_sRGB"                                             *
*                                                                             *
===============================================================================
array([ 0.45675795,  0.30986982,  0.24861924])
>>> illuminant = colour.SDS_ILLUMINANTS['FL2']
>>> colour.convert(sd, 'Spectral Distribution', 'sRGB', sd_to_XYZ={'illuminant': illuminant})
array([ 0.47924575,  0.31676968,  0.17362725])

5.4.2   Chromatic Adaptation - colour.adaptation

>>> XYZ = [0.20654008, 0.12197225, 0.05136952]
>>> D65 = colour.CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']
>>> A = colour.CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['A']
>>> colour.chromatic_adaptation(
...     XYZ, colour.xy_to_XYZ(D65), colour.xy_to_XYZ(A))
array([ 0.2533053 ,  0.13765138,  0.01543307])
>>> sorted(colour.CHROMATIC_ADAPTATION_METHODS)
['CIE 1994', 'CMCCAT2000', 'Fairchild 1990', 'Von Kries']

5.4.3   Algebra - colour.algebra

5.4.3.1   Kernel Interpolation
>>> y = [5.9200, 9.3700, 10.8135, 4.5100, 69.5900, 27.8007, 86.0500]
>>> x = range(len(y))
>>> colour.KernelInterpolator(x, y)([0.25, 0.75, 5.50])
array([  6.18062083,   8.08238488,  57.85783403])
5.4.3.2   Sprague (1880) Interpolation
>>> y = [5.9200, 9.3700, 10.8135, 4.5100, 69.5900, 27.8007, 86.0500]
>>> x = range(len(y))
>>> colour.SpragueInterpolator(x, y)([0.25, 0.75, 5.50])
array([  6.72951612,   7.81406251,  43.77379185])

5.4.4   Colour Appearance Models - colour.appearance

>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
>>> XYZ_w = [95.05, 100.00, 108.88]
>>> L_A = 318.31
>>> Y_b = 20.0
>>> colour.XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b)
CAM_Specification_CIECAM02(J=34.434525727858997, C=67.365010921125915, h=22.279164147957076, s=62.814855853327131, Q=177.47124941102123, M=70.024939419291385, H=2.689608534423904, HC=None)

5.4.5   Colour Blindness - colour.blindness

>>> import numpy as np
>>> cmfs = colour.LMS_CMFS['Stockman & Sharpe 2 Degree Cone Fundamentals']
>>> colour.msds_cmfs_anomalous_trichromacy_Machado2009(cmfs, np.array([15, 0, 0]))[450]
array([ 0.08912884,  0.0870524 ,  0.955393  ])
>>> primaries = colour.MSDS_DISPLAY_PRIMARIES['Apple Studio Display']
>>> d_LMS = (15, 0, 0)
>>> colour.matrix_anomalous_trichromacy_Machado2009(cmfs, primaries, d_LMS)
array([[-0.27774652,  2.65150084, -1.37375432],
       [ 0.27189369,  0.20047862,  0.52762768],
       [ 0.00644047,  0.25921579,  0.73434374]])

5.4.6   Colour Correction - colour characterisation

>>> import numpy as np
>>> RGB = [0.17224810, 0.09170660, 0.06416938]
>>> M_T = np.random.random((24, 3))
>>> M_R = M_T + (np.random.random((24, 3)) - 0.5) * 0.5
>>> colour.colour_correction(RGB, M_T, M_R)
array([ 0.1806237 ,  0.07234791,  0.07848845])
>>> sorted(colour.COLOUR_CORRECTION_METHODS)
['Cheung 2004', 'Finlayson 2015', 'Vandermonde']

5.4.7   ACES Input Transform - colour characterisation

>>> sensitivities = colour.MSDS_CAMERA_SENSITIVITIES['Nikon 5100 (NPL)']
>>> illuminant = colour.SDS_ILLUMINANTS['D55']
>>> colour.matrix_idt(sensitivities, illuminant)
array([[ 0.46579991,  0.13409239,  0.01935141],
       [ 0.01786094,  0.77557292, -0.16775555],
       [ 0.03458652, -0.16152926,  0.74270359]])

5.4.8   Colorimetry - colour.colorimetry

5.4.8.1   Spectral Computations
>>> colour.sd_to_XYZ(colour.SDS_LIGHT_SOURCES['Neodimium Incandescent'])
array([ 36.94726204,  32.62076174,  13.0143849 ])
>>> sorted(colour.SPECTRAL_TO_XYZ_METHODS)
['ASTM E308', 'Integration', 'astm2015']
5.4.8.2   Multi-Spectral Computations
>>> msds = np.array([
...     [[0.01367208, 0.09127947, 0.01524376, 0.02810712, 0.19176012, 0.04299992],
...      [0.00959792, 0.25822842, 0.41388571, 0.22275120, 0.00407416, 0.37439537],
...      [0.01791409, 0.29707789, 0.56295109, 0.23752193, 0.00236515, 0.58190280]],
...     [[0.01492332, 0.10421912, 0.02240025, 0.03735409, 0.57663846, 0.32416266],
...      [0.04180972, 0.26402685, 0.03572137, 0.00413520, 0.41808194, 0.24696727],
...      [0.00628672, 0.11454948, 0.02198825, 0.39906919, 0.63640803, 0.01139849]],
...     [[0.04325933, 0.26825359, 0.23732357, 0.05175860, 0.01181048, 0.08233768],
...      [0.02484169, 0.12027161, 0.00541695, 0.00654612, 0.18603799, 0.36247808],
...      [0.03102159, 0.16815442, 0.37186235, 0.08610666, 0.00413520, 0.78492409]],
...     [[0.11682307, 0.78883040, 0.74468607, 0.83375293, 0.90571451, 0.70054168],
...      [0.06321812, 0.41898224, 0.15190357, 0.24591440, 0.55301750, 0.00657664],
...      [0.00305180, 0.11288624, 0.11357290, 0.12924391, 0.00195315, 0.21771573]],
... ])
>>> colour.msds_to_XYZ(msds, method='Integration',
...                    shape=colour.SpectralShape(400, 700, 60))
array([[[  7.68544647,   4.09414317,   8.49324254],
        [ 17.12567298,  27.77681821,  25.52573685],
        [ 19.10280411,  34.45851476,  29.76319628]],
       [[ 18.03375827,   8.62340812,   9.71702574],
        [ 15.03110867,   6.54001068,  24.53208465],
        [ 37.68269495,  26.4411103 ,  10.66361816]],
       [[  8.09532373,  12.75333339,  25.79613956],
        [  7.09620297,   2.79257389,  11.15039854],
        [  8.933163  ,  19.39985815,  17.14915636]],
       [[ 80.00969553,  80.39810464,  76.08184429],
        [ 33.27611427,  24.38947838,  39.34919287],
        [  8.89425686,  11.05185138,  10.86767594]]])
>>> sorted(colour.MSDS_TO_XYZ_METHODS)
['ASTM E308', 'Integration', 'astm2015']
5.4.8.3   Blackbody Spectral Radiance Computation
>>> colour.sd_blackbody(5000)
SpectralDistribution([[  3.60000000e+02,   6.65427827e+12],
                      [  3.61000000e+02,   6.70960528e+12],
                      [  3.62000000e+02,   6.76482512e+12],
                      ...
                      [  7.78000000e+02,   1.06068004e+13],
                      [  7.79000000e+02,   1.05903327e+13],
                      [  7.80000000e+02,   1.05738520e+13]],
                     interpolator=SpragueInterpolator,
                     interpolator_args={},
                     extrapolator=Extrapolator,
                     extrapolator_args={'right': None, 'method': 'Constant', 'left': None})
5.4.8.4   Dominant, Complementary Wavelength & Colour Purity Computation
>>> xy = [0.54369557, 0.32107944]
>>> xy_n = [0.31270000, 0.32900000]
>>> colour.dominant_wavelength(xy, xy_n)
(array(616.0),
 array([ 0.68354746,  0.31628409]),
 array([ 0.68354746,  0.31628409]))
5.4.8.5   Lightness Computation
>>> colour.lightness(12.19722535)
41.527875844653451
>>> sorted(colour.LIGHTNESS_METHODS)
['CIE 1976',
 'Fairchild 2010',
 'Fairchild 2011',
 'Glasser 1958',
 'Lstar1976',
 'Wyszecki 1963']
5.4.8.6   Luminance Computation
>>> colour.luminance(41.52787585)
12.197225353400775
>>> sorted(colour.LUMINANCE_METHODS)
['ASTM D1535',
 'CIE 1976',
 'Fairchild 2010',
 'Fairchild 2011',
 'Newhall 1943',
 'astm2008',
 'cie1976']
5.4.8.7   Whiteness Computation
>>> XYZ = [95.00000000, 100.00000000, 105.00000000]
>>> XYZ_0 = [94.80966767, 100.00000000, 107.30513595]
>>> colour.whiteness(XYZ, XYZ_0)
array([ 93.756     ,  -1.33000001])
>>> sorted(colour.WHITENESS_METHODS)
['ASTM E313',
 'Berger 1959',
 'CIE 2004',
 'Ganz 1979',
 'Stensby 1968',
 'Taube 1960',
 'cie2004']
5.4.8.8   Yellowness Computation
>>> XYZ = [95.00000000, 100.00000000, 105.00000000]
>>> colour.yellowness(XYZ)
11.065000000000003
>>> sorted(colour.YELLOWNESS_METHODS)
['ASTM D1925', 'ASTM E313']
5.4.8.9   Luminous Flux, Efficiency & Efficacy Computation
>>> sd = colour.SDS_LIGHT_SOURCES['Neodimium Incandescent']
>>> colour.luminous_flux(sd)
23807.655527367202
>>> sd = colour.SDS_LIGHT_SOURCES['Neodimium Incandescent']
>>> colour.luminous_efficiency(sd)
0.19943935624521045
>>> sd = colour.SDS_LIGHT_SOURCES['Neodimium Incandescent']
>>> colour.luminous_efficacy(sd)
136.21708031547874

5.4.9   Contrast Sensitivity Function - colour.contrast

>>> colour.contrast_sensitivity_function(u=4, X_0=60, E=65)
358.51180789884984
>>> sorted(colour.CONTRAST_SENSITIVITY_METHODS)
['Barten 1999']

5.4.10   Colour Difference - colour.difference

>>> Lab_1 = [100.00000000, 21.57210357, 272.22819350]
>>> Lab_2 = [100.00000000, 426.67945353, 72.39590835]
>>> colour.delta_E(Lab_1, Lab_2)
94.035649026659485
>>> sorted(colour.DELTA_E_METHODS)
['CAM02-LCD',
 'CAM02-SCD',
 'CAM02-UCS',
 'CAM16-LCD',
 'CAM16-SCD',
 'CAM16-UCS',
 'CIE 1976',
 'CIE 1994',
 'CIE 2000',
 'CMC',
 'DIN99',
 'cie1976',
 'cie1994',
 'cie2000']

5.4.11   IO - colour.io

5.4.11.1   Images
>>> RGB = colour.read_image('Ishihara_Colour_Blindness_Test_Plate_3.png')
>>> RGB.shape
(276, 281, 3)
5.4.11.2   Look Up Table (LUT) Data
>>> LUT = colour.read_LUT('ACES_Proxy_10_to_ACES.cube')
>>> print(LUT)
LUT3x1D - ACES Proxy 10 to ACES
-------------------------------
Dimensions : 2
Domain     : [[0 0 0]
              [1 1 1]]
Size       : (32, 3)
>>> RGB = [0.17224810, 0.09170660, 0.06416938]
>>> LUT.apply(RGB)
array([ 0.00575674,  0.00181493,  0.00121419])

5.4.12   Colour Models - colour.models

5.4.12.1   CIE xyY Colourspace
>>> colour.XYZ_to_xyY([0.20654008, 0.12197225, 0.05136952])
array([ 0.54369557,  0.32107944,  0.12197225])
5.4.12.2   CIE L*a*b* Colourspace
>>> colour.XYZ_to_Lab([0.20654008, 0.12197225, 0.05136952])
array([ 41.52787529,  52.63858304,  26.92317922])
5.4.12.3   CIE L*u*v* Colourspace
>>> colour.XYZ_to_Luv([0.20654008, 0.12197225, 0.05136952])
array([ 41.52787529,  96.83626054,  17.75210149])
5.4.12.4   CIE 1960 UCS Colourspace
>>> colour.XYZ_to_UCS([0.20654008, 0.12197225, 0.05136952])
array([ 0.13769339,  0.12197225,  0.1053731 ])
5.4.12.5   CIE 1964 U*V*W* Colourspace
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_UVW(XYZ)
array([ 94.55035725,  11.55536523,  40.54757405])
5.4.12.6   Hunter L,a,b Colour Scale
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_Hunter_Lab(XYZ)
array([ 34.92452577,  47.06189858,  14.38615107])
5.4.12.7   Hunter Rd,a,b Colour Scale
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_Hunter_Rdab(XYZ)
array([ 12.197225  ,  57.12537874,  17.46241341])
5.4.12.8   CAM02-LCD, CAM02-SCD, and CAM02-UCS Colourspaces - Luo, Cui and Li (2006)
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> XYZ_w = [95.05, 100.00, 108.88]
>>> L_A = 318.31
>>> Y_b = 20.0
>>> surround = colour.VIEWING_CONDITIONS_CIECAM02['Average']
>>> specification = colour.XYZ_to_CIECAM02(
        XYZ, XYZ_w, L_A, Y_b, surround)
>>> JMh = (specification.J, specification.M, specification.h)
>>> colour.JMh_CIECAM02_to_CAM02UCS(JMh)
array([ 47.16899898,  38.72623785,  15.8663383 ])
5.4.12.9   CAM16-LCD, CAM16-SCD, and CAM16-UCS Colourspaces - Li et al. (2017)
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> XYZ_w = [95.05, 100.00, 108.88]
>>> L_A = 318.31
>>> Y_b = 20.0
>>> surround = colour.VIEWING_CONDITIONS_CAM16['Average']
>>> specification = colour.XYZ_to_CAM16(
        XYZ, XYZ_w, L_A, Y_b, surround)
>>> JMh = (specification.J, specification.M, specification.h)
>>> colour.JMh_CAM16_to_CAM16UCS(JMh)
array([ 46.55542238,  40.22460974,  14.25288392]
5.4.12.10   IGPGTG Colourspace
>>> colour.XYZ_to_IGPGTG([0.20654008, 0.12197225, 0.05136952])
array([ 0.42421258,  0.18632491,  0.10689223])
5.4.12.11   IPT Colourspace
>>> colour.XYZ_to_IPT([0.20654008, 0.12197225, 0.05136952])
array([ 0.38426191,  0.38487306,  0.18886838])
5.4.12.12   DIN99 Colourspace
>>> Lab = [41.52787529, 52.63858304, 26.92317922]
>>> colour.Lab_to_DIN99(Lab)
array([ 53.22821988,  28.41634656,   3.89839552])
5.4.12.13   hdr-CIELAB Colourspace
>>> colour.XYZ_to_hdr_CIELab([0.20654008, 0.12197225, 0.05136952])
array([ 51.87002062,  60.4763385 ,  32.14551912])
5.4.12.14   hdr-IPT Colourspace
>>> colour.XYZ_to_hdr_IPT([0.20654008, 0.12197225, 0.05136952])
array([ 25.18261761, -22.62111297,   3.18511729])
5.4.12.15   OSA UCS Colourspace
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_OSA_UCS(XYZ)
array([-3.0049979 ,  2.99713697, -9.66784231])
5.4.12.16   JzAzBz Colourspace
>>> colour.XYZ_to_JzAzBz([0.20654008, 0.12197225, 0.05136952])
array([ 0.00535048,  0.00924302,  0.00526007])
5.4.12.17   Y’CbCr Colour Encoding
>>> colour.RGB_to_YCbCr([1.0, 1.0, 1.0])
array([ 0.92156863,  0.50196078,  0.50196078])
5.4.12.18   YCoCg Colour Encoding
>>> colour.RGB_to_YCoCg([0.75, 0.75, 0.0])
array([ 0.5625,  0.375 ,  0.1875])
5.4.12.19   ICTCP Colour Encoding
>>> colour.RGB_to_ICTCP([0.45620519, 0.03081071, 0.04091952])
array([ 0.07351364,  0.00475253,  0.09351596])
5.4.12.20   HSV Colourspace
>>> colour.RGB_to_HSV([0.45620519, 0.03081071, 0.04091952])
array([ 0.99603944,  0.93246304,  0.45620519])
5.4.12.21   Prismatic Colourspace
>>> colour.RGB_to_Prismatic([0.25, 0.50, 0.75])
array([ 0.75      ,  0.16666667,  0.33333333,  0.5       ])
5.4.12.22   RGB Colourspace and Transformations
>>> XYZ = [0.21638819, 0.12570000, 0.03847493]
>>> illuminant_XYZ = [0.34570, 0.35850]
>>> illuminant_RGB = [0.31270, 0.32900]
>>> chromatic_adaptation_transform = 'Bradford'
>>> matrix_XYZ_to_RGB = [
         [3.24062548, -1.53720797, -0.49862860],
         [-0.96893071, 1.87575606, 0.04151752],
         [0.05571012, -0.20402105, 1.05699594]]
>>> colour.XYZ_to_RGB(
         XYZ,
         illuminant_XYZ,
         illuminant_RGB,
         matrix_XYZ_to_RGB,
         chromatic_adaptation_transform)
array([ 0.45595571,  0.03039702,  0.04087245])
5.4.12.23   RGB Colourspace Derivation
>>> p = [0.73470, 0.26530, 0.00000, 1.00000, 0.00010, -0.07700]
>>> w = [0.32168, 0.33767]
>>> colour.normalised_primary_matrix(p, w)
array([[  9.52552396e-01,   0.00000000e+00,   9.36786317e-05],
       [  3.43966450e-01,   7.28166097e-01,  -7.21325464e-02],
       [  0.00000000e+00,   0.00000000e+00,   1.00882518e+00]])
5.4.12.24   RGB Colourspaces
>>> sorted(colour.RGB_COLOURSPACES)
['ACES2065-1',
 'ACEScc',
 'ACEScct',
 'ACEScg',
 'ACESproxy',
 'ALEXA Wide Gamut',
 'Adobe RGB (1998)',
 'Adobe Wide Gamut RGB',
 'Apple RGB',
 'Best RGB',
 'Beta RGB',
 'CIE RGB',
 'Cinema Gamut',
 'ColorMatch RGB',
 'DaVinci Wide Gamut',
 'DCDM XYZ',
 'DCI-P3',
 'DCI-P3+',
 'DJI D-Gamut',
 'DRAGONcolor',
 'DRAGONcolor2',
 'Display P3',
 'Don RGB 4',
 'ECI RGB v2',
 'ERIMM RGB',
 'Ekta Space PS 5',
 'F-Gamut',
 'FilmLight E-Gamut',
 'ITU-R BT.2020',
 'ITU-R BT.470 - 525',
 'ITU-R BT.470 - 625',
 'ITU-R BT.709',
 'Max RGB',
 'NTSC (1953)',
 'NTSC (1987)',
 'P3-D65',
 'Pal/Secam',
 'ProPhoto RGB',
 'Protune Native',
 'REDWideGamutRGB',
 'REDcolor',
 'REDcolor2',
 'REDcolor3',
 'REDcolor4',
 'RIMM RGB',
 'ROMM RGB',
 'Russell RGB',
 'S-Gamut',
 'S-Gamut3',
 'S-Gamut3.Cine',
 'SMPTE 240M',
 'SMPTE C',
 'Sharp RGB',
 'V-Gamut',
 'Venice S-Gamut3',
 'Venice S-Gamut3.Cine',
 'Xtreme RGB',
 'aces',
 'adobe1998',
 'prophoto',
5.4.12.25   OETFs
>>> sorted(colour.OETFS)
['ARIB STD-B67',
 'ITU-R BT.2100 HLG',
 'ITU-R BT.2100 PQ',
 'ITU-R BT.601',
 'ITU-R BT.709',
 'SMPTE 240M']
5.4.12.26   OETFs Inverse
>>> sorted(colour.OETF_INVERSES)
['ARIB STD-B67',
 'ITU-R BT.2100 HLG',
 'ITU-R BT.2100 PQ',
 'ITU-R BT.601',
 'ITU-R BT.709']
5.4.12.27   EOTFs
>>> sorted(colour.EOTFS)
['DCDM',
 'DICOM GSDF',
 'ITU-R BT.1886',
 'ITU-R BT.2020',
 'ITU-R BT.2100 HLG',
 'ITU-R BT.2100 PQ',
 'SMPTE 240M',
 'ST 2084',
 'sRGB']
5.4.12.28   EOTFs Inverse
>>> sorted(colour.EOTF_INVERSES)
['DCDM',
 'DICOM GSDF',
 'ITU-R BT.1886',
 'ITU-R BT.2020',
 'ITU-R BT.2100 HLG',
 'ITU-R BT.2100 PQ',
 'ST 2084',
 'sRGB']
5.4.12.29   OOTFs
>>> sorted(colour.OOTFS)
['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']
5.4.12.30   OOTFs Inverse
>>> sorted(colour.OOTF_INVERSES)
['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']
5.4.12.31   Log Encoding / Decoding
>>> sorted(colour.LOG_ENCODINGS)
['ACEScc',
 'ACEScct',
 'ACESproxy',
 'ALEXA Log C',
 'Canon Log',
 'Canon Log 2',
 'Canon Log 3',
 'Cineon',
 'D-Log',
 'ERIMM RGB',
 'F-Log',
 'Filmic Pro 6',
 'Log2',
 'Log3G10',
 'Log3G12',
 'PLog',
 'Panalog',
 'Protune',
 'REDLog',
 'REDLogFilm',
 'S-Log',
 'S-Log2',
 'S-Log3',
 'T-Log',
 'V-Log',
 'ViperLog']
5.4.12.32   CCTFs Encoding / Decoding
>>> sorted(colour.CCTF_ENCODINGS)
['ACEScc',
 'ACEScct',
 'ACESproxy',
 'ALEXA Log C',
 'ARIB STD-B67',
 'Canon Log',
 'Canon Log 2',
 'Canon Log 3',
 'Cineon',
 'D-Log',
 'DCDM',
 'DICOM GSDF',
 'ERIMM RGB',
 'F-Log',
 'Filmic Pro 6',
 'Gamma 2.2',
 'Gamma 2.4',
 'Gamma 2.6',
 'ITU-R BT.1886',
 'ITU-R BT.2020',
 'ITU-R BT.2100 HLG',
 'ITU-R BT.2100 PQ',
 'ITU-R BT.601',
 'ITU-R BT.709',
 'Log2',
 'Log3G10',
 'Log3G12',
 'PLog',
 'Panalog',
 'ProPhoto RGB',
 'Protune',
 'REDLog',
 'REDLogFilm',
 'RIMM RGB',
 'ROMM RGB',
 'S-Log',
 'S-Log2',
 'S-Log3',
 'SMPTE 240M',
 'ST 2084',
 'T-Log',
 'V-Log',
 'ViperLog',
 'sRGB']

5.4.13   Colour Notation Systems - colour.notation

5.4.13.1   Munsell Value
>>> colour.munsell_value(12.23634268)
4.0824437076525664
>>> sorted(colour.MUNSELL_VALUE_METHODS)
['ASTM D1535',
 'Ladd 1955',
 'McCamy 1987',
 'Moon 1943',
 'Munsell 1933',
 'Priest 1920',
 'Saunderson 1944',
 'astm2008']
5.4.13.2   Munsell Colour
>>> colour.xyY_to_munsell_colour([0.38736945, 0.35751656, 0.59362000])
'4.2YR 8.1/5.3'
>>> colour.munsell_colour_to_xyY('4.2YR 8.1/5.3')
array([ 0.38736945,  0.35751656,  0.59362   ])

5.4.14   Optical Phenomena - colour.phenomena

>>> colour.rayleigh_scattering_sd()
SpectralDistribution([[  3.60000000e+02,   5.99101337e-01],
                      [  3.61000000e+02,   5.92170690e-01],
                      [  3.62000000e+02,   5.85341006e-01],
                      ...
                      [  7.78000000e+02,   2.55208377e-02],
                      [  7.79000000e+02,   2.53887969e-02],
                      [  7.80000000e+02,   2.52576106e-02]],
                     interpolator=SpragueInterpolator,
                     interpolator_args={},
                     extrapolator=Extrapolator,
                     extrapolator_args={'right': None, 'method': 'Constant', 'left': None})

5.4.15   Light Quality - colour.quality

5.4.15.1   Colour Fidelity Index
>>> colour.colour_fidelity_index(colour.SDS_ILLUMINANTS['FL2'])
70.120825477833037
>>> sorted(colour.COLOUR_FIDELITY_INDEX_METHODS)
['ANSI/IES TM-30-18', 'CIE 2017']
5.4.15.2   Colour Rendering Index
>>> colour.colour_quality_scale(colour.SDS_ILLUMINANTS['FL2'])
64.111703163816699
>>> sorted(colour.COLOUR_QUALITY_SCALE_METHODS)
['NIST CQS 7.4', 'NIST CQS 9.0']
5.4.15.3   Colour Quality Scale
>>> colour.colour_rendering_index(colour.SDS_ILLUMINANTS['FL2'])
64.233724121664807
5.4.15.4   Academy Spectral Similarity Index (SSI)
>>> colour.spectral_similarity_index(colour.SDS_ILLUMINANTS['C'], colour.SDS_ILLUMINANTS['D65'])
94.0

5.4.16   Spectral Up-Sampling & Reflectance Recovery - colour.recovery

>>> colour.XYZ_to_sd([0.20654008, 0.12197225, 0.05136952])
SpectralDistribution([[  3.60000000e+02,   8.37868873e-02],
                      [  3.65000000e+02,   8.39337988e-02],
                      ...
                      [  7.70000000e+02,   4.46793405e-01],
                      [  7.75000000e+02,   4.46872853e-01],
                      [  7.80000000e+02,   4.46914431e-01]],
                     interpolator=SpragueInterpolator,
                     interpolator_kwargs={},
                     extrapolator=Extrapolator,
                     extrapolator_kwargs={'method': 'Constant', 'left': None, 'right': None})

>>> sorted(colour.REFLECTANCE_RECOVERY_METHODS)
['Jakob 2019', 'Mallett 2019', 'Meng 2015', 'Otsu 2018', 'Smits 1999']

5.4.17   Correlated Colour Temperature Computation Methods - colour.temperature

>>> colour.uv_to_CCT([0.1978, 0.3122])
array([  6.50751282e+03,   3.22335875e-03])
>>> sorted(colour.UV_TO_CCT_METHODS)
['Krystek 1985', 'Ohno 2013', 'Robertson 1968', 'ohno2013', 'robertson1968']
>>> sorted(colour.XY_TO_CCT_METHODS)
['CIE Illuminant D Series',
 'Hernandez 1999',
 'Kang 2002',
 'McCamy 1992',
 'daylight',
 'hernandez1999',
 'kang2002',
 'mccamy1992']

5.4.18   Colour Volume - colour.volume

>>> colour.RGB_colourspace_volume_MonteCarlo(colour.RGB_COLOURSPACE_RGB['sRGB'])
821958.30000000005

5.4.19   Geometry Primitives Generation - colour.geometry

>>> colour.primitive('Grid')
(array([ ([-0.5,  0.5,  0. ], [ 0.,  1.], [ 0.,  0.,  1.], [ 0.,  1.,  0.,  1.]),
       ([ 0.5,  0.5,  0. ], [ 1.,  1.], [ 0.,  0.,  1.], [ 1.,  1.,  0.,  1.]),
       ([-0.5, -0.5,  0. ], [ 0.,  0.], [ 0.,  0.,  1.], [ 0.,  0.,  0.,  1.]),
       ([ 0.5, -0.5,  0. ], [ 1.,  0.], [ 0.,  0.,  1.], [ 1.,  0.,  0.,  1.])],
      dtype=[('position', '<f4', (3,)), ('uv', '<f4', (2,)), ('normal', '<f4', (3,)), ('colour', '<f4', (4,))]), array([[0, 2, 1],
       [2, 3, 1]], dtype=uint32), array([[0, 2],
       [2, 3],
       [3, 1],
       [1, 0]], dtype=uint32))
>>> sorted(colour.PRIMITIVE_METHODS)
['Cube', 'Grid']
>>> colour.primitive_vertices('Quad MPL')
array([[ 0.,  0.,  0.],
       [ 1.,  0.,  0.],
       [ 1.,  1.,  0.],
       [ 0.,  1.,  0.]])
>>> sorted(colour.PRIMITIVE_VERTICES_METHODS)
['Cube MPL', 'Grid MPL', 'Quad MPL', 'Sphere']

5.4.20   Plotting - colour.plotting

Most of the objects are available from the colour.plotting namespace:

>>> from colour.plotting import *
>>> colour_style()
5.4.20.1   Visible Spectrum
>>> plot_visible_spectrum('CIE 1931 2 Degree Standard Observer')
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Visible_Spectrum.png
5.4.20.2   Spectral Distribution
>>> plot_single_illuminant_sd('FL1')
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Illuminant_F1_SD.png
5.4.20.3   Blackbody
>>> blackbody_sds = [
...     colour.sd_blackbody(i, colour.SpectralShape(0, 10000, 10))
...     for i in range(1000, 15000, 1000)
... ]
>>> plot_multi_sds(
...     blackbody_sds,
...     y_label='W / (sr m$^2$) / m',
...     plot_kwargs={
...         use_sd_colours=True,
...         normalise_sd_colours=True,
...     },
...     legend_location='upper right',
...     bounding_box=(0, 1250, 0, 2.5e15))
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Blackbodies.png
5.4.20.4   Colour Matching Functions
>>> plot_single_cmfs(
...     'Stockman & Sharpe 2 Degree Cone Fundamentals',
...     y_label='Sensitivity',
...     bounding_box=(390, 870, 0, 1.1))
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Cone_Fundamentals.png
5.4.20.5   Luminous Efficiency
>>> sd_mesopic_luminous_efficiency_function = (
...     colour.sd_mesopic_luminous_efficiency_function(0.2))
>>> plot_multi_sds(
...     (sd_mesopic_luminous_efficiency_function,
...      colour.PHOTOPIC_LEFS['CIE 1924 Photopic Standard Observer'],
...      colour.SCOTOPIC_LEFS['CIE 1951 Scotopic Standard Observer']),
...     y_label='Luminous Efficiency',
...     legend_location='upper right',
...     y_tighten=True,
...     margins=(0, 0, 0, .1))
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Luminous_Efficiency.png
5.4.20.6   Colour Checker
>>> from colour.characterisation.dataset.colour_checkers.sds import (
...     COLOURCHECKER_INDEXES_TO_NAMES_MAPPING)
>>> plot_multi_sds(
...     [
...         colour.SDS_COLOURCHECKERS['BabelColor Average'][value]
...         for key, value in sorted(
...             COLOURCHECKER_INDEXES_TO_NAMES_MAPPING.items())
...     ],
...     plot_kwargs={
...         use_sd_colours=True,
...     },
...     title=('BabelColor Average - '
...            'Spectral Distributions'))
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_BabelColor_Average.png
>>> plot_single_colour_checker(
...     'ColorChecker 2005', text_kwargs={'visible': False})
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_ColorChecker_2005.png
5.4.20.7   Chromaticities Prediction
>>> plot_corresponding_chromaticities_prediction(
...     2, 'Von Kries', 'Bianco 2010')
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Chromaticities_Prediction.png
5.4.20.8   Colour Temperature
>>> plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(['A', 'B', 'C'])
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png
5.4.20.9   Chromaticities
>>> import numpy as np
>>> RGB = np.random.random((32, 32, 3))
>>> plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
...     RGB, 'ITU-R BT.709',
...     colourspaces=['ACEScg', 'S-Gamut', 'Pointer Gamut'])
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png
5.4.20.10   Colour Rendering Index
>>> plot_single_sd_colour_rendering_index_bars(
...     colour.SDS_ILLUMINANTS['FL2'])
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_CRI.png
5.4.20.11   ANSI/IES TM-30-18 Colour Rendition Report
>>> plot_single_sd_colour_rendition_report(
...     colour.SDS_ILLUMINANTS['FL2'])
https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Colour_Rendition_Report.png

6   Contributing

If you would like to contribute to Colour, please refer to the following Contributing guide.

7   Changes

The changes are viewable on the Releases page.

8   Bibliography

The bibliography is available on the Bibliography page.

It is also viewable directly from the repository in BibTeX format.

9   See Also

Here is a list of notable colour science packages sorted by languages:

Python

Go

.NET

Julia

Matlab & Octave

10   Code of Conduct

The Code of Conduct, adapted from the Contributor Covenant 1.4, is available on the Code of Conduct page.

11   About

Colour by Colour Developers
Copyright © 2013-2020 – Colour Developers – colour-developers@colour-science.org
This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for colour-science, version 0.3.16
Filename, size File type Python version Upload date Hashes
Filename, size colour_science-0.3.16-py2.py3-none-any.whl (2.1 MB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size colour-science-0.3.16.tar.gz (1.7 MB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page