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.
Table of Contents
- 1 Draft Release Notes
- 2 Sponsors
- 3 Features
- 4 Installation
- 5 Documentation
- 5.1 Tutorial
- 5.2 How-To Guide
- 5.3 API Reference
- 5.4 Examples
- 5.4.1 Automatic Colour Conversion Graph -
colour.graph - 5.4.2 Chromatic Adaptation -
colour.adaptation - 5.4.3 Algebra -
colour.algebra - 5.4.4 Colour Appearance Models -
colour.appearance - 5.4.5 Colour Blindness -
colour.blindness - 5.4.6 Colour Correction -
colour characterisation - 5.4.7 ACES Input Transform -
colour characterisation - 5.4.8 Colorimetry -
colour.colorimetry - 5.4.9 Contrast Sensitivity Function -
colour.contrast - 5.4.10 Colour Difference -
colour.difference - 5.4.11 IO -
colour.io - 5.4.12 Colour Models -
colour.models - 5.4.13 Colour Notation Systems -
colour.notation - 5.4.14 Optical Phenomena -
colour.phenomena - 5.4.15 Light Quality -
colour.quality - 5.4.16 Spectral Up-Sampling & Reflectance Recovery -
colour.recovery - 5.4.17 Correlated Colour Temperature Computation Methods -
colour.temperature - 5.4.18 Colour Volume -
colour.volume - 5.4.19 Geometry Primitives Generation -
colour.geometry - 5.4.20 Plotting -
colour.plotting
- 5.4.1 Automatic Colour Conversion Graph -
- 6 Contributing
- 7 Changes
- 8 Bibliography
- 9 See Also
- 10 Code of Conduct
- 11 Contact & Social
- 12 Thank You!
- 13 About
The draft release notes of the develop branch are available at this url.
We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.
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Colour features a rich dataset and collection of objects, please see the features page for more information.
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-scienceThe 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-scienceThe static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.
The How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases.
The main technical reference for Colour and its API is the Colour Manual.
Most of the objects are available from the colour namespace:
>>> import colourStarting with version 0.3.14, Colour implements an automatic colour conversion graph enabling easier colour conversions.
>>> 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])>>> 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']>>> 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])>>> 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])>>> 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)>>> 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]])>>> 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']>>> sensitivities = colour.MSDS_CAMERA_SENSITIVITIES['Nikon 5100 (NPL)']
>>> illuminant = colour.SDS_ILLUMINANTS['D55']
>>> colour.matrix_idt(sensitivities, illuminant)
(array([[ 0.46579986, 0.13409221, 0.01935163],
[ 0.01786092, 0.77557268, -0.16775531],
[ 0.03458647, -0.16152923, 0.74270363]]), array([ 1.58214188, 1. , 1.28910346]))>>> 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']>>> 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']>>> 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})>>> 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]))>>> colour.lightness(12.19722535)
41.527875844653451
>>> sorted(colour.LIGHTNESS_METHODS)
['Abebe 2017'
'CIE 1976',
'Fairchild 2010',
'Fairchild 2011',
'Glasser 1958',
'Lstar1976',
'Wyszecki 1963']>>> colour.luminance(41.52787585)
12.197225353400775
>>> sorted(colour.LUMINANCE_METHODS)
['ASTM D1535',
'CIE 1976',
'Fairchild 2010',
'Fairchild 2011',
'Newhall 1943',
'astm2008',
'cie1976']>>> 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']>>> XYZ = [95.00000000, 100.00000000, 105.00000000]
>>> colour.yellowness(XYZ)
4.3400000000000034
>>> sorted(colour.YELLOWNESS_METHODS)
['ASTM D1925', 'ASTM E313', 'ASTM E313 Alternative']>>> 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>>> colour.contrast_sensitivity_function(u=4, X_0=60, E=65)
358.51180789884984
>>> sorted(colour.CONTRAST_SENSITIVITY_METHODS)
['Barten 1999']>>> 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']>>> RGB = colour.read_image('Ishihara_Colour_Blindness_Test_Plate_3.png')
>>> RGB.shape
(276, 281, 3)>>> 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])>>> colour.XYZ_to_xyY([0.20654008, 0.12197225, 0.05136952])
array([ 0.54369557, 0.32107944, 0.12197225])>>> colour.XYZ_to_Lab([0.20654008, 0.12197225, 0.05136952])
array([ 41.52787529, 52.63858304, 26.92317922])>>> colour.XYZ_to_Luv([0.20654008, 0.12197225, 0.05136952])
array([ 41.52787529, 96.83626054, 17.75210149])>>> colour.XYZ_to_UCS([0.20654008, 0.12197225, 0.05136952])
array([ 0.13769339, 0.12197225, 0.1053731 ])>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
>>> colour.XYZ_to_UVW(XYZ)
array([ 94.55035725, 11.55536523, 40.54757405])>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
>>> colour.XYZ_to_Hunter_Lab(XYZ)
array([ 34.92452577, 47.06189858, 14.38615107])>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
>>> colour.XYZ_to_Hunter_Rdab(XYZ)
array([ 12.197225 , 57.12537874, 17.46241341])>>> 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 ])
>>> XYZ = [0.20654008, 0.12197225, 0.05136952]
>>> XYZ_w = [95.05 / 100, 100.00 / 100, 108.88 / 100]
>>> colour.XYZ_to_CAM02UCS(XYZ, XYZ_w=XYZ_w, L_A=L_A, Y_b=Y_b)
array([ 47.16899898, 38.72623785, 15.8663383 ])>>> 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]
>>> XYZ = [0.20654008, 0.12197225, 0.05136952]
>>> XYZ_w = [95.05 / 100, 100.00 / 100, 108.88 / 100]
>>> colour.XYZ_to_CAM16UCS(XYZ, XYZ_w=XYZ_w, L_A=L_A, Y_b=Y_b)
array([ 46.55542238, 40.22460974, 14.25288392])>>> colour.XYZ_to_IgPgTg([0.20654008, 0.12197225, 0.05136952])
array([ 0.42421258, 0.18632491, 0.10689223])>>> colour.XYZ_to_IPT([0.20654008, 0.12197225, 0.05136952])
array([ 0.38426191, 0.38487306, 0.18886838])>>> Lab = [41.52787529, 52.63858304, 26.92317922]
>>> colour.Lab_to_DIN99(Lab)
array([ 53.22821988, 28.41634656, 3.89839552])>>> colour.XYZ_to_hdr_CIELab([0.20654008, 0.12197225, 0.05136952])
array([ 51.87002062, 60.4763385 , 32.14551912])>>> colour.XYZ_to_hdr_IPT([0.20654008, 0.12197225, 0.05136952])
array([ 25.18261761, -22.62111297, 3.18511729])>>> colour.XYZ_to_Oklab([0.20654008, 0.12197225, 0.05136952])
array([ 0.51634019, 0.154695 , 0.06289579])>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
>>> colour.XYZ_to_OSA_UCS(XYZ)
array([-3.0049979 , 2.99713697, -9.66784231])>>> colour.XYZ_to_JzAzBz([0.20654008, 0.12197225, 0.05136952])
array([ 0.00535048, 0.00924302, 0.00526007])>>> colour.RGB_to_YCbCr([1.0, 1.0, 1.0])
array([ 0.92156863, 0.50196078, 0.50196078])>>> colour.RGB_to_YCoCg([0.75, 0.75, 0.0])
array([ 0.5625, 0.375 , 0.1875])>>> colour.RGB_to_ICtCp([0.45620519, 0.03081071, 0.04091952])
array([ 0.07351364, 0.00475253, 0.09351596])>>> colour.RGB_to_HSV([0.45620519, 0.03081071, 0.04091952])
array([ 0.99603944, 0.93246304, 0.45620519])>>> colour.RGB_to_IHLS([0.45620519, 0.03081071, 0.04091952])
array([ 6.26236117, 0.12197943, 0.42539448])>>> colour.RGB_to_Prismatic([0.25, 0.50, 0.75])
array([ 0.75 , 0.16666667, 0.33333333, 0.5 ])>>> 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])>>> 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]])>>> 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',
'Blackmagic Wide Gamut',
'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',>>> sorted(colour.OETFS)
['ARIB STD-B67',
'Blackmagic Film Generation 5',
'DaVinci Intermediate',
'ITU-R BT.2100 HLG',
'ITU-R BT.2100 PQ',
'ITU-R BT.601',
'ITU-R BT.709',
'SMPTE 240M']>>> 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']>>> sorted(colour.OOTFS)
['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']>>> 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',
'N-Log',
'PLog',
'Panalog',
'Protune',
'REDLog',
'REDLogFilm',
'S-Log',
'S-Log2',
'S-Log3',
'T-Log',
'V-Log',
'ViperLog']>>> 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']>>> 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']>>> 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 ])>>> 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})>>> colour.colour_fidelity_index(colour.SDS_ILLUMINANTS['FL2'])
70.120825477833037
>>> sorted(colour.COLOUR_FIDELITY_INDEX_METHODS)
['ANSI/IES TM-30-18', 'CIE 2017']>>> colour.colour_quality_scale(colour.SDS_ILLUMINANTS['FL2'])
64.111703163816699
>>> sorted(colour.COLOUR_QUALITY_SCALE_METHODS)
['NIST CQS 7.4', 'NIST CQS 9.0']>>> colour.colour_rendering_index(colour.SDS_ILLUMINANTS['FL2'])
64.233724121664807>>> colour.spectral_similarity_index(colour.SDS_ILLUMINANTS['C'], colour.SDS_ILLUMINANTS['D65'])
94.0>>> 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']>>> 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']>>> colour.RGB_colourspace_volume_MonteCarlo(colour.RGB_COLOURSPACE_RGB['sRGB'])
821958.30000000005>>> 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']Most of the objects are available from the colour.plotting namespace:
>>> from colour.plotting import *
>>> colour_style()>>> plot_visible_spectrum('CIE 1931 2 Degree Standard Observer')
>>> plot_single_illuminant_sd('FL1')
>>> 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))
>>> plot_single_cmfs(
... 'Stockman & Sharpe 2 Degree Cone Fundamentals',
... y_label='Sensitivity',
... bounding_box=(390, 870, 0, 1.1))
>>> 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))
>>> 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'))
>>> plot_single_colour_checker(
... 'ColorChecker 2005', text_kwargs={'visible': False})
>>> plot_corresponding_chromaticities_prediction(
... 2, 'Von Kries', 'Bianco 2010')
>>> plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(['A', 'B', 'C'])
>>> 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'])
>>> plot_single_sd_colour_rendering_index_bars(
... colour.SDS_ILLUMINANTS['FL2'])
>>> plot_single_sd_colour_rendition_report(
... colour.SDS_ILLUMINANTS['FL2'])
If you would like to contribute to Colour, please refer to the following Contributing guide.
The changes are viewable on the Releases page.
The bibliography is available on the Bibliography page.
It is also viewable directly from the repository in BibTeX format.
Here is a list of notable colour science packages sorted by languages:
Python
- Colorio by Schlömer, N.
- ColorPy by Kness, M.
- Colorspacious by Smith, N. J., et al.
- python-colormath by Taylor, G., et al.
Go
- go-colorful by Beyer, L., et al.
.NET
- Colourful by Pažourek, T., et al.
Julia
- Colors.jl by Holy, T., et al.
Matlab & Octave
- COLORLAB by Malo, J., et al.
- Psychtoolbox by Brainard, D., et al.
- The Munsell and Kubelka-Munk Toolbox by Centore, P.
The Code of Conduct, adapted from the Contributor Covenant 1.4, is available on the Code of Conduct page.
The Colour Developers can be reached via different means:
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Colour by Colour Developers
Copyright © 2013-2021 – Colour Developers – [email protected]
This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause