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
- 3.1 Automatic Colour Conversion Graph -
colour.graph - 3.2 Chromatic Adaptation -
colour.adaptation - 3.3 Algebra -
colour.algebra - 3.4 Colour Appearance Models -
colour.appearance - 3.5 Colour Blindness -
colour.blindness - 3.6 Colour Correction -
colour characterisation - 3.7 ACES Input Transform -
colour characterisation - 3.8 Colorimetry -
colour.colorimetry - 3.9 Contrast Sensitivity Function -
colour.contrast - 3.10 Colour Difference -
colour.difference - 3.11 IO -
colour.io - 3.12 Colour Models -
colour.models - 3.13 Colour Notation Systems -
colour.notation - 3.14 Optical Phenomena -
colour.phenomena - 3.15 Light Quality -
colour.quality - 3.16 Spectral Up-Sampling & Recovery -
colour.recovery - 3.17 Correlated Colour Temperature Computation Methods -
colour.temperature - 3.18 Colour Volume -
colour.volume - 3.19 Geometry Primitives Generation -
colour.geometry - 3.20 Plotting -
colour.plotting
- 3.1 Automatic Colour Conversion Graph -
- 4 User Guide
- 5 API Reference
- 6 See Also
- 7 Code of Conduct
- 8 Contact & Social
- 9 Thank You!
- 10 About
1 Draft Release Notes
The draft release notes of the develop branch are available at this url.
2 Sponsors
We are grateful
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Donations & Special Sponsors
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3 Features
Most of the objects are available from the colour namespace:
>>> import colour3.1 Automatic Colour Conversion Graph - colour.graph
Starting 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])3.2 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', 'Zhai 2018']3.3 Algebra - colour.algebra
3.3.1 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])3.3.2 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])3.4 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=67.365010921125943, h=22.279164147957065, s=62.81485585332716, Q=177.47124941102123, M=70.024939419291414, H=2.6896085344238898, HC=None)
>>> colour.XYZ_to_CIECAM16(XYZ, XYZ_w, L_A, Y_b)
CAM_Specification_CIECAM16(J=34.434525727858997, C=67.365010921125943, h=22.279164147957065, s=62.81485585332716, Q=177.47124941102123, M=70.024939419291414, H=2.6896085344238898, HC=None)
>>> colour.XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b)
CAM_Specification_CAM16(J=33.880368498111686, C=69.444353357408033, h=19.510887327451748, s=64.03612114840314, Q=176.03752758512178, M=72.18638534116765, H=399.52975599115319, HC=None)
>>> colour.XYZ_to_Hellwig2022(XYZ, XYZ_w, L_A)
CAM_Specification_Hellwig2022(J=33.880368498111686, C=40.347043294550311, h=19.510887327451748, s=117.38555017188679, Q=45.34489577734751, M=53.228355383108031, H=399.52975599115319, HC=None)
>>> colour.XYZ_to_Kim2009(XYZ, XYZ_w, L_A)
CAM_Specification_Kim2009(J=19.879918542450902, C=55.839055250876946, h=22.013388165090046, s=112.97979354939129, Q=36.309026130161449, M=46.346415858227864, H=2.3543198369639931, HC=None)
>>> colour.XYZ_to_ZCAM(XYZ, XYZ_w, L_A, Y_b)
CAM_Specification_ZCAM(J=38.347186278956357, C=21.12138989208518, h=33.711578931095197, s=81.444585609489536, Q=76.986725284523772, M=42.403805833900506, H=0.45779200212219573, HC=None, V=43.623590687423544, K=43.20894953152817, W=34.829588380192149)3.5 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]])3.6 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']3.7 ACES Input Transform - colour characterisation
>>> sensitivities = colour.MSDS_CAMERA_SENSITIVITIES["Nikon 5100 (NPL)"]
>>> illuminant = colour.SDS_ILLUMINANTS["D55"]
>>> colour.matrix_idt(sensitivities, illuminant)
(array([[ 0.59368175, 0.30418371, 0.10213454],
[ 0.00457979, 1.14946003, -0.15403982],
[ 0.03552213, -0.16312291, 1.12760077]]), array([ 1.58214188, 1. , 1.28910346]))3.8 Colorimetry - colour.colorimetry
3.8.1 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']3.8.2 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']3.8.3 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})3.8.4 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]))3.8.5 Lightness Computation
>>> colour.lightness(12.19722535)
41.527875844653451
>>> sorted(colour.LIGHTNESS_METHODS)
['Abebe 2017'
'CIE 1976',
'Fairchild 2010',
'Fairchild 2011',
'Glasser 1958',
'Lstar1976',
'Wyszecki 1963']3.8.6 Luminance Computation
>>> colour.luminance(41.52787585)
12.197225353400775
>>> sorted(colour.LUMINANCE_METHODS)
['ASTM D1535',
'CIE 1976',
'Fairchild 2010',
'Fairchild 2011',
'Newhall 1943',
'astm2008',
'cie1976']3.8.7 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']3.8.8 Yellowness Computation
>>> XYZ = [95.00000000, 100.00000000, 105.00000000]
>>> colour.yellowness(XYZ)
4.3400000000000034
>>> sorted(colour.YELLOWNESS_METHODS)
['ASTM D1925', 'ASTM E313', 'ASTM E313 Alternative']3.8.9 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)
136.217080315478743.9 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']3.10 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',
'ITP',
'cie1976',
'cie1994',
'cie2000']3.11 IO - colour.io
3.11.1 Images
>>> RGB = colour.read_image("Ishihara_Colour_Blindness_Test_Plate_3.png")
>>> RGB.shape
(276, 281, 3)3.11.2 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])3.12 Colour Models - colour.models
3.12.1 CIE xyY Colourspace
>>> colour.XYZ_to_xyY([0.20654008, 0.12197225, 0.05136952])
array([ 0.54369557, 0.32107944, 0.12197225])3.12.2 CIE L*a*b* Colourspace
>>> colour.XYZ_to_Lab([0.20654008, 0.12197225, 0.05136952])
array([ 41.52787529, 52.63858304, 26.92317922])3.12.3 CIE L*u*v* Colourspace
>>> colour.XYZ_to_Luv([0.20654008, 0.12197225, 0.05136952])
array([ 41.52787529, 96.83626054, 17.75210149])3.12.4 CIE 1960 UCS Colourspace
>>> colour.XYZ_to_UCS([0.20654008, 0.12197225, 0.05136952])
array([ 0.13769339, 0.12197225, 0.1053731 ])3.12.5 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])3.12.6 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 ])
>>> 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 ])3.12.7 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]
>>> 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])3.12.8 DIN99 Colourspace and DIN99b, DIN99c, DIN99d Refined Formulas
>>> Lab = [41.52787529, 52.63858304, 26.92317922]
>>> colour.Lab_to_DIN99(Lab)
array([ 53.22821988, 28.41634656, 3.89839552])3.12.9 ICaCb Colourspace
>>> XYZ_to_ICaCb(np.array([0.20654008, 0.12197225, 0.05136952]))
array([ 0.06875297, 0.05753352, 0.02081548])3.12.10 IgPgTg Colourspace
>>> colour.XYZ_to_IgPgTg([0.20654008, 0.12197225, 0.05136952])
array([ 0.42421258, 0.18632491, 0.10689223])3.12.11 IPT Colourspace
>>> colour.XYZ_to_IPT([0.20654008, 0.12197225, 0.05136952])
array([ 0.38426191, 0.38487306, 0.18886838])3.12.12 Jzazbz Colourspace
>>> colour.XYZ_to_Jzazbz([0.20654008, 0.12197225, 0.05136952])
array([ 0.00535048, 0.00924302, 0.00526007])3.12.13 hdr-CIELAB Colourspace
>>> colour.XYZ_to_hdr_CIELab([0.20654008, 0.12197225, 0.05136952])
array([ 51.87002062, 60.4763385 , 32.14551912])3.12.14 hdr-IPT Colourspace
>>> colour.XYZ_to_hdr_IPT([0.20654008, 0.12197225, 0.05136952])
array([ 25.18261761, -22.62111297, 3.18511729])3.12.15 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])3.12.16 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])3.12.17 Oklab Colourspace
>>> colour.XYZ_to_Oklab([0.20654008, 0.12197225, 0.05136952])
array([ 0.51634019, 0.154695 , 0.06289579])3.12.18 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])3.12.19 ProLab Colourspace
>>> colour.XYZ_to_ProLab([0.51634019, 0.15469500, 0.06289579])
array([1.24610688, 2.39525236, 0.41902126])3.12.20 Ragoo and Farup (2021) Optimised IPT Colourspace
>>> colour.XYZ_to_IPT_Ragoo2021([0.20654008, 0.12197225, 0.05136952])
array([ 0.42248243, 0.2910514 , 0.20410663])3.12.21 Yrg Colourspace - Kirk (2019)
>>> colour.XYZ_to_Yrg([0.20654008, 0.12197225, 0.05136952])
array([ 0.13137801, 0.49037645, 0.37777388])3.12.22 Y'CbCr Colour Encoding
>>> colour.RGB_to_YCbCr([1.0, 1.0, 1.0])
array([ 0.92156863, 0.50196078, 0.50196078])3.12.23 YCoCg Colour Encoding
>>> colour.RGB_to_YCoCg([0.75, 0.75, 0.0])
array([ 0.5625, 0.375 , 0.1875])3.12.24 ICtCp Colour Encoding
>>> colour.RGB_to_ICtCp([0.45620519, 0.03081071, 0.04091952])
array([ 0.07351364, 0.00475253, 0.09351596])3.12.25 HSV Colourspace
>>> colour.RGB_to_HSV([0.45620519, 0.03081071, 0.04091952])
array([ 0.99603944, 0.93246304, 0.45620519])3.12.26 IHLS Colourspace
>>> colour.RGB_to_IHLS([0.45620519, 0.03081071, 0.04091952])
array([ 6.26236117, 0.12197943, 0.42539448])3.12.27 Prismatic Colourspace
>>> colour.RGB_to_Prismatic([0.25, 0.50, 0.75])
array([ 0.75 , 0.16666667, 0.33333333, 0.5 ])3.12.28 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])3.12.29 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]])3.12.30 RGB Colourspaces
>>> sorted(colour.RGB_COLOURSPACES)
['ACES2065-1',
'ACEScc',
'ACEScct',
'ACEScg',
'ACESproxy',
'ARRI Wide Gamut 3',
'ARRI Wide Gamut 4',
'Adobe RGB (1998)',
'Adobe Wide Gamut RGB',
'Apple RGB',
'Best RGB',
'Beta RGB',
'Blackmagic Wide Gamut',
'CIE RGB',
'Cinema Gamut',
'ColorMatch RGB',
'DCDM XYZ',
'DCI-P3',
'DCI-P3-P',
'DJI D-Gamut',
'DRAGONcolor',
'DRAGONcolor2',
'DaVinci Wide Gamut',
'Display P3',
'Don RGB 4',
'EBU Tech. 3213-E',
'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',
'ITU-T H.273 - 22 Unspecified',
'ITU-T H.273 - Generic Film',
'Max RGB',
'N-Gamut',
'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',
'sRGB']3.12.31 OETFs
>>> sorted(colour.OETFS)
['ARIB STD-B67',
'Blackmagic Film Generation 5',
'DaVinci Intermediate',
'ITU-R BT.2020',
'ITU-R BT.2100 HLG',
'ITU-R BT.2100 PQ',
'ITU-R BT.601',
'ITU-R BT.709',
'ITU-T H.273 IEC 61966-2',
'ITU-T H.273 Log',
'ITU-T H.273 Log Sqrt',
'SMPTE 240M']3.12.32 EOTFs
>>> sorted(colour.EOTFS)
['DCDM',
'DICOM GSDF',
'ITU-R BT.1886',
'ITU-R BT.2100 HLG',
'ITU-R BT.2100 PQ',
'ITU-T H.273 ST.428-1',
'SMPTE 240M',
'ST 2084',
'sRGB']3.12.33 OOTFs
>>> sorted(colour.OOTFS)
['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']3.12.34 Log Encoding / Decoding
>>> sorted(colour.LOG_ENCODINGS)
['ACEScc',
'ACEScct',
'ACESproxy',
'ARRI LogC3',
'ARRI LogC4',
'Canon Log',
'Canon Log 2',
'Canon Log 3',
'Cineon',
'D-Log',
'ERIMM RGB',
'F-Log',
'F-Log2',
'Filmic Pro 6',
'L-Log',
'Log2',
'Log3G10',
'Log3G12',
'N-Log',
'PLog',
'Panalog',
'Protune',
'REDLog',
'REDLogFilm',
'S-Log',
'S-Log2',
'S-Log3',
'T-Log',
'V-Log',
'ViperLog']3.12.35 CCTFs Encoding / Decoding
>>> sorted(colour.CCTF_ENCODINGS)
['ACEScc',
'ACEScct',
'ACESproxy',
'ARRI LogC3',
'ARRI LogC4',
'ARIB STD-B67',
'Canon Log',
'Canon Log 2',
'Canon Log 3',
'Cineon',
'D-Log',
'DCDM',
'DICOM GSDF',
'ERIMM RGB',
'F-Log',
'F-Log2',
'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']3.12.36 Recommendation ITU-T H.273 Code points for Video Signal Type Identification
>>> colour.COLOUR_PRIMARIES_ITUTH273.keys()
dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 22, 23])
>>> colour.COLOUR_PRIMARIES_ITUTH273.keys()
>>> description = colour.models.describe_video_signal_colour_primaries(1)
===============================================================================
* *
* Colour Primaries: 1 *
* ------------------- *
* *
* Primaries : [[ 0.64 0.33] *
* [ 0.3 0.6 ] *
* [ 0.15 0.06]] *
* Whitepoint : [ 0.3127 0.329 ] *
* Whitepoint Name : D65 *
* NPM : [[ 0.4123908 0.35758434 0.18048079] *
* [ 0.21263901 0.71516868 0.07219232] *
* [ 0.01933082 0.11919478 0.95053215]] *
* NPM -1 : [[ 3.24096994 -1.53738318 -0.49861076] *
* [-0.96924364 1.8759675 0.04155506] *
* [ 0.05563008 -0.20397696 1.05697151]] *
* FFmpeg Constants : ['AVCOL_PRI_BT709', 'BT709'] *
* *
===============================================================================
>>> colour.TRANSFER_CHARACTERISTICS_ITUTH273.keys()
dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
>>> description = (
... colour.models.describe_video_signal_transfer_characteristics(1)
... )
===============================================================================
* *
* Transfer Characteristics: 1 *
* --------------------------- *
* *
* Function : <function oetf_BT709 at 0x165bb3550> *
* FFmpeg Constants : ['AVCOL_TRC_BT709', 'BT709'] *
* *
===============================================================================
>>> colour.MATRIX_COEFFICIENTS_ITUTH273.keys()
dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])
>>> description = colour.models.describe_video_signal_matrix_coefficients(
... 1
... )
===============================================================================
* *
* Matrix Coefficients: 1 *
* ---------------------- *
* *
* Matrix Coefficients : [ 0.2126 0.0722] *
* FFmpeg Constants : ['AVCOL_SPC_BT709', 'BT709'] *
* *
===============================================================================3.13 Colour Notation Systems - colour.notation
3.13.1 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']3.13.2 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 ])3.14 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})3.15 Light Quality - colour.quality
3.15.1 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']3.15.2 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']3.15.3 Colour Quality Scale
>>> colour.colour_rendering_index(colour.SDS_ILLUMINANTS["FL2"])
64.2337241216648073.15.4 Academy Spectral Similarity Index (SSI)
>>> colour.spectral_similarity_index(
... colour.SDS_ILLUMINANTS["C"], colour.SDS_ILLUMINANTS["D65"]
... )
94.03.16 Spectral Up-Sampling & Recovery - colour.recovery
3.16.1 Reflectance Recovery
>>> colour.XYZ_to_sd([0.20654008, 0.12197225, 0.05136952])
SpectralDistribution([[ 3.60000000e+02, 8.40144095e-02],
[ 3.65000000e+02, 8.41264236e-02],
[ 3.70000000e+02, 8.40057597e-02],
...
[ 7.70000000e+02, 4.46743012e-01],
[ 7.75000000e+02, 4.46817187e-01],
[ 7.80000000e+02, 4.46857696e-01]],
SpragueInterpolator,
{},
Extrapolator,
{'method': 'Constant', 'left': None, 'right': None})
>>> sorted(colour.REFLECTANCE_RECOVERY_METHODS)
['Jakob 2019', 'Mallett 2019', 'Meng 2015', 'Otsu 2018', 'Smits 1999']3.16.2 Camera RGB Sensitivities Recovery
>>> illuminant = colour.colorimetry.SDS_ILLUMINANTS["D65"] >>> sensitivities = colour.characterisation.MSDS_CAMERA_SENSITIVITIES[ ... "Nikon 5100 (NPL)" ... ] >>> reflectances = [ ... sd.copy().align( ... colour.recovery.SPECTRAL_SHAPE_BASIS_FUNCTIONS_DYER2017 ... ) ... for sd in colour.characterisation.SDS_COLOURCHECKERS[ ... "BabelColor Average" ... ].values() ... ] >>> reflectances = colour.colorimetry.sds_and_msds_to_msds(reflectances) >>> RGB = colour.colorimetry.msds_to_XYZ( ... reflectances, ... method="Integration", ... cmfs=sensitivities, ... illuminant=illuminant, ... k=0.01, ... shape=colour.recovery.SPECTRAL_SHAPE_BASIS_FUNCTIONS_DYER2017, ... ) >>> colour.recovery.RGB_to_msds_camera_sensitivities_Jiang2013( ... RGB, ... illuminant, ... reflectances, ... colour.recovery.BASIS_FUNCTIONS_DYER2017, ... colour.recovery.SPECTRAL_SHAPE_BASIS_FUNCTIONS_DYER2017, ... ) RGB_CameraSensitivities([[ 4.00000000e+02, 7.22815777e-03, 9.22506480e-03, -9.88368972e-03], [ 4.10000000e+02, -8.50457609e-03, 1.12777480e-02, 3.86248655e-03], [ 4.20000000e+02, 4.58191132e-02, 7.15520948e-02, 4.04068293e-01], ... [ 6.80000000e+02, 4.08276173e-02, 5.55290476e-03, 1.39907862e-03], [ 6.90000000e+02, -3.71437574e-03, 2.50935640e-03, 3.97652622e-04], [ 7.00000000e+02, -5.62256563e-03, 1.56433970e-03, 5.84726936e-04]], ['red', 'green', 'blue'], SpragueInterpolator, {}, Extrapolator, {'method': 'Constant', 'left': None, 'right': None})
3.17 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', 'Planck 1900', 'Robertson 1968', 'ohno2013', 'robertson1968']
>>> sorted(colour.XY_TO_CCT_METHODS)
['CIE Illuminant D Series',
'Hernandez 1999',
'Kang 2002',
'McCamy 1992',
'daylight',
'hernandez1999',
'kang2002',
'mccamy1992']3.18 Colour Volume - colour.volume
>>> colour.RGB_colourspace_volume_MonteCarlo(
... colour.RGB_COLOURSPACE_RGB["sRGB"]
... )
821958.300000000053.19 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']3.20 Plotting - colour.plotting
Most of the objects are available from the colour.plotting namespace:
>>> from colour.plotting import *
>>> colour_style()3.20.1 Visible Spectrum
>>> plot_visible_spectrum("CIE 1931 2 Degree Standard Observer")
3.20.2 Spectral Distribution
>>> plot_single_illuminant_sd("FL1")
3.20.3 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, 2.5e6),
... )
3.20.4 Colour Matching Functions
>>> plot_single_cmfs(
... "Stockman & Sharpe 2 Degree Cone Fundamentals",
... y_label="Sensitivity",
... bounding_box=(390, 870, 0, 1.1),
... )
3.20.5 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, 0.1),
... )
3.20.6 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"),
... )
>>> plot_single_colour_checker(
... "ColorChecker 2005", text_kwargs={"visible": False}
... )
3.20.7 Chromaticities Prediction
>>> plot_corresponding_chromaticities_prediction(
... 2, "Von Kries", "Bianco 2010"
... )
3.20.8 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"],
... )
3.20.9 Colour Rendering Index
>>> plot_single_sd_colour_rendering_index_bars(
... colour.SDS_ILLUMINANTS["FL2"]
... )
3.20.10 ANSI/IES TM-30-18 Colour Rendition Report
>>> plot_single_sd_colour_rendition_report(colour.SDS_ILLUMINANTS["FL2"])
3.20.11 Gamut Section
>>> plot_visible_spectrum_section(
... section_colours="RGB", section_opacity=0.15
... )
>>> plot_RGB_colourspace_section(
... "sRGB", section_colours="RGB", section_opacity=0.15
... )
3.20.12 Colour Temperature
>>> plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(
... ["A", "B", "C"]
... )
4 User Guide
4.1 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-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-science4.2 Tutorial
The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.
4.3 How-To
The Google Colab How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases.
4.4 Contributing
If you would like to contribute to Colour, please refer to the following Contributing guide.
4.5 Changes
The changes are viewable on the Releases page.
4.6 Bibliography
The bibliography is available on the Bibliography page.
It is also viewable directly from the repository in BibTeX format.
5 API Reference
The main technical reference for Colour is the API Reference.
6 See Also
6.1 Software
Python
- 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.
7 Code of Conduct
The Code of Conduct, adapted from the Contributor Covenant 1.4, is available on the Code of Conduct page.
8 Contact & Social
The Colour Developers can be reached via different means:
9 Thank You!
Coffee Sponsors
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10 About
Colour by Colour Developers
Copyright 2013 Colour Developers – [email protected]
This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause