colour-science-蟒蛇的色彩科學-Colour Developers
發佈:2020-12-23 17:30:38.032425
做者:Colour Developers
做者郵箱:colour-developers@colour-science.org
首頁:https://www.colour-science.org/
文檔:None
下載連接
.. image:: https://raw.githubusercontent.com/colour-science/colour-branding/master/images/ColourLogoMedium_001.pnghtml
.. start-badgespython
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Colour <https://github.com/colour-science/colour>_ is an open-source Python <https://www.python.org/>_ package providing a comprehensive number of algorithms and datasets for colour science.bash
It is freely available under the New BSD License <https://opensource.org/licenses/BSD-3-Clause>__ terms.app
Colour is an affiliated project of NumFOCUS <https://numfocus.org/>__, a 501(c)(3) nonprofit in the United States.dom
.. contents:: Table of Contents :backlinks: none :depth: 3ide
.. sectnum::svg
Draft Release Notes
The draft release notes of the develop <https://github.com/colour-science/colour/tree/develop>_ branch are available at this url <https://gist.github.com/KelSolaar/4a6ebe9ec3d389f0934b154fec8df51d>_.
Sponsors
We are grateful 💖 for the support of our sponsors <https://github.com/colour-science/colour/blob/develop/SPONSORS.rst>. If you'd like to join them, please consider becoming a sponsor on OpenCollective <https://opencollective.com/colour-science>.
Features
Colour features a rich dataset and collection of objects, please see the features <https://www.colour-science.org/features/>__ page for more information.
Installation
Colour and its primary dependencies can be easily installed from the Python Package Index <https://pypi.org/project/colour-science/>__ by issuing this command in a shell:
.. code-block:: bash
$ pip install --user colour-science
The detailed installation procedure for the secondary dependencies is described in the Installation Guide <https://www.colour-science.org/installation-guide/>__.
Colour is also available for Anaconda <https://www.continuum.io/downloads>_ from *Continuum Analytics* via conda-forge <https://conda-forge.org/>_:
.. code-block:: bash
$ conda install -c conda-forge colour-science
Documentation
Tutorial ~~~~~~~~
The static tutorial <https://colour.readthedocs.io/en/develop/tutorial.html>_ provides an introduction to Colour. An interactive version is available via Google Colab <https://colab.research.google.com/notebook#fileId=1Im9J7or9qyClQCv5sPHmKdyiQbG4898K&offline=true&sandboxMode=true>_.
How-To Guide ~~~~~~~~~~~~
The How-To <https://colab.research.google.com/notebook#fileId=1NRcdXSCshivkwoU2nieCvC3y14fx1X4X&offline=true&sandboxMode=true>__ guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases.
API Reference ~~~~~~~~~~~~~
The main technical reference for Colour and its API is the Colour Manual <https://colour.readthedocs.io/en/latest/manual.html>__.
Examples ~~~~~~~~
Most of the objects are available from the colour namespace:
.. code-block:: python
>>> import colour
Automatic Colour Conversion Graph - colour.graph ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Starting with version 0.3.14, Colour implements an automatic colour conversion graph enabling easier colour conversions.
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesColourAutomaticConversion_Graph.png
.. code-block:: python
>>> 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])
.. code-block:: python
>>> illuminant = colour.SDS_ILLUMINANTS['FL2']
>>> colour.convert(sd, 'Spectral Distribution', 'sRGB', sd_to_XYZ={'illuminant': illuminant})
array([ 0.47924575, 0.31676968, 0.17362725])
Chromatic Adaptation - colour.adaptation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> 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']
Algebra - colour.algebra ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Kernel Interpolation
.. code-block:: python
>>> 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])
Sprague (1880) Interpolation
.. code-block:: python
>>> 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])
Colour Appearance Models - colour.appearance ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> 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)
Colour Blindness - colour.blindness ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> 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]])
Colour Correction - colour characterisation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> 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']
ACES Input Transform - colour characterisation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> 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]])
Colorimetry - colour.colorimetry ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Spectral Computations
.. code-block:: python
>>> 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']
Multi-Spectral Computations
.. code-block:: python
>>> 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']
Blackbody Spectral Radiance Computation
.. code-block:: python
>>> 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})
Dominant, Complementary Wavelength & Colour Purity Computation
.. code-block:: python
>>> 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]))
Lightness Computation
.. code-block:: python
>>> colour.lightness(12.19722535) 41.527875844653451 >>> sorted(colour.LIGHTNESS_METHODS) ['CIE 1976', 'Fairchild 2010', 'Fairchild 2011', 'Glasser 1958', 'Lstar1976', 'Wyszecki 1963']
Luminance Computation
.. code-block:: python
>>> colour.luminance(41.52787585) 12.197225353400775 >>> sorted(colour.LUMINANCE_METHODS) ['ASTM D1535', 'CIE 1976', 'Fairchild 2010', 'Fairchild 2011', 'Newhall 1943', 'astm2008', 'cie1976']
Whiteness Computation
.. code-block:: python
>>> 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']
Yellowness Computation
.. code-block:: python
>>> XYZ = [95.00000000, 100.00000000, 105.00000000] >>> colour.yellowness(XYZ) 11.065000000000003 >>> sorted(colour.YELLOWNESS_METHODS) ['ASTM D1925', 'ASTM E313']
Luminous Flux, Efficiency & Efficacy Computation
.. code-block:: python
>>> 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
Contrast Sensitivity Function - colour.contrast ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> colour.contrast_sensitivity_function(u=4, X_0=60, E=65) 358.51180789884984 >>> sorted(colour.CONTRAST_SENSITIVITY_METHODS) ['Barten 1999']
Colour Difference - colour.difference ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> 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']
IO - colour.io ^^^^^^^^^^^^^^^^^^
Images
.. code-block:: python
>>> RGB = colour.read_image('Ishihara_Colour_Blindness_Test_Plate_3.png')
>>> RGB.shape
(276, 281, 3)
Look Up Table (LUT) Data
.. code-block:: python
>>> 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)
.. code-block:: python
>>> RGB = [0.17224810, 0.09170660, 0.06416938] >>> LUT.apply(RGB) array([ 0.00575674, 0.00181493, 0.00121419])
Colour Models - colour.models ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
CIE xyY Colourspace
.. code-block:: python
>>> colour.XYZ_to_xyY([0.20654008, 0.12197225, 0.05136952]) array([ 0.54369557, 0.32107944, 0.12197225])
CIE Lab* Colourspace
.. code-block:: python
>>> colour.XYZ_to_Lab([0.20654008, 0.12197225, 0.05136952]) array([ 41.52787529, 52.63858304, 26.92317922])
CIE Luv* Colourspace
.. code-block:: python
>>> colour.XYZ_to_Luv([0.20654008, 0.12197225, 0.05136952]) array([ 41.52787529, 96.83626054, 17.75210149])
CIE 1960 UCS Colourspace
.. code-block:: python
>>> colour.XYZ_to_UCS([0.20654008, 0.12197225, 0.05136952]) array([ 0.13769339, 0.12197225, 0.1053731 ])
CIE 1964 UVW* Colourspace
.. code-block:: python
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100] >>> colour.XYZ_to_UVW(XYZ) array([ 94.55035725, 11.55536523, 40.54757405])
Hunter L,a,b Colour Scale
.. code-block:: python
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100] >>> colour.XYZ_to_Hunter_Lab(XYZ) array([ 34.92452577, 47.06189858, 14.38615107])
Hunter Rd,a,b Colour Scale
.. code-block:: python
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100] >>> colour.XYZ_to_Hunter_Rdab(XYZ) array([ 12.197225 , 57.12537874, 17.46241341])
CAM02-LCD, CAM02-SCD, and CAM02-UCS Colourspaces - Luo, Cui and Li (2006)
.. code-block:: python
>>> 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 ])
CAM16-LCD, CAM16-SCD, and CAM16-UCS Colourspaces - Li et al. (2017)
.. code-block:: python
>>> 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]
IGPGTG Colourspace
.. code-block:: python
>>> colour.XYZ_to_IGPGTG([0.20654008, 0.12197225, 0.05136952]) array([ 0.42421258, 0.18632491, 0.10689223])
IPT Colourspace
.. code-block:: python
>>> colour.XYZ_to_IPT([0.20654008, 0.12197225, 0.05136952]) array([ 0.38426191, 0.38487306, 0.18886838])
DIN99 Colourspace
.. code-block:: python
>>> Lab = [41.52787529, 52.63858304, 26.92317922] >>> colour.Lab_to_DIN99(Lab) array([ 53.22821988, 28.41634656, 3.89839552])
hdr-CIELAB Colourspace
.. code-block:: python
>>> colour.XYZ_to_hdr_CIELab([0.20654008, 0.12197225, 0.05136952]) array([ 51.87002062, 60.4763385 , 32.14551912])
hdr-IPT Colourspace
.. code-block:: python
>>> colour.XYZ_to_hdr_IPT([0.20654008, 0.12197225, 0.05136952]) array([ 25.18261761, -22.62111297, 3.18511729])
OSA UCS Colourspace
.. code-block:: python
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100] >>> colour.XYZ_to_OSA_UCS(XYZ) array([-3.0049979 , 2.99713697, -9.66784231])
JzAzBz Colourspace
.. code-block:: python
>>> colour.XYZ_to_JzAzBz([0.20654008, 0.12197225, 0.05136952]) array([ 0.00535048, 0.00924302, 0.00526007])
Y'CbCr Colour Encoding
.. code-block:: python
>>> colour.RGB_to_YCbCr([1.0, 1.0, 1.0]) array([ 0.92156863, 0.50196078, 0.50196078])
YCoCg Colour Encoding
.. code-block:: python
>>> colour.RGB_to_YCoCg([0.75, 0.75, 0.0]) array([ 0.5625, 0.375 , 0.1875])
ICTCP Colour Encoding
.. code-block:: python
>>> colour.RGB_to_ICTCP([0.45620519, 0.03081071, 0.04091952]) array([ 0.07351364, 0.00475253, 0.09351596])
HSV Colourspace
.. code-block:: python
>>> colour.RGB_to_HSV([0.45620519, 0.03081071, 0.04091952]) array([ 0.99603944, 0.93246304, 0.45620519])
Prismatic Colourspace
.. code-block:: python
>>> colour.RGB_to_Prismatic([0.25, 0.50, 0.75]) array([ 0.75 , 0.16666667, 0.33333333, 0.5 ])
RGB Colourspace and Transformations
.. code-block:: python
>>> 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])
RGB Colourspace Derivation
.. code-block:: python
>>> 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]])
RGB Colourspaces
.. code-block:: python
>>> 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',
OETFs
.. code-block:: python
>>> 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']
OETFs Inverse
.. code-block:: python
>>> 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']
EOTFs
.. code-block:: python
>>> 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']
EOTFs Inverse
.. code-block:: python
>>> 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']
OOTFs
.. code-block:: python
>>> sorted(colour.OOTFS) ['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']
OOTFs Inverse
.. code-block:: python
>>> sorted(colour.OOTF_INVERSES) ['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']
Log Encoding / Decoding
.. code-block:: python
>>> 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']
CCTFs Encoding / Decoding
.. code-block:: python
>>> 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 Notation Systems - colour.notation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Munsell Value
.. code-block:: python
>>> 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']
Munsell Colour
.. code-block:: python
>>> 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 ])
Optical Phenomena - colour.phenomena ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> 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})
Light Quality - colour.quality ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Colour Fidelity Index
.. code-block:: python
>>> colour.colour_fidelity_index(colour.SDS_ILLUMINANTS['FL2']) 70.120825477833037 >>> sorted(colour.COLOUR_FIDELITY_INDEX_METHODS) ['ANSI/IES TM-30-18', 'CIE 2017']
Colour Rendering Index
.. code-block:: python
>>> 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 Quality Scale
.. code-block:: python
>>> colour.colour_rendering_index(colour.SDS_ILLUMINANTS['FL2']) 64.233724121664807
Academy Spectral Similarity Index (SSI)
.. code-block:: python
>>> colour.spectral_similarity_index(colour.SDS_ILLUMINANTS['C'], colour.SDS_ILLUMINANTS['D65']) 94.0
Spectral Up-Sampling & Reflectance Recovery - colour.recovery ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> 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']
Correlated Colour Temperature Computation Methods - colour.temperature ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> 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 Volume - colour.volume ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> colour.RGB_colourspace_volume_MonteCarlo(colour.RGB_COLOURSPACE_RGB['sRGB']) 821958.30000000005
Geometry Primitives Generation - colour.geometry ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: python
>>> 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']
Plotting - colour.plotting ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Most of the objects are available from the colour.plotting namespace:
.. code-block:: python
>>> from colour.plotting import * >>> colour_style()
Visible Spectrum
.. code-block:: python
>>> plot_visible_spectrum('CIE 1931 2 Degree Standard Observer')
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlottingVisibleSpectrum.png
Spectral Distribution
.. code-block:: python
>>> plot_single_illuminant_sd('FL1')
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlottingIlluminantF1_SD.png
Blackbody
.. code-block:: python
>>> 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))
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlotting_Blackbodies.png
Colour Matching Functions
.. code-block:: python
>>> plot_single_cmfs( ... 'Stockman & Sharpe 2 Degree Cone Fundamentals', ... y_label='Sensitivity', ... bounding_box=(390, 870, 0, 1.1))
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlottingConeFundamentals.png
Luminous Efficiency
.. code-block:: python
>>> 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))
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlottingLuminousEfficiency.png
Colour Checker
.. code-block:: python
>>> 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'))
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlottingBabelColorAverage.png
.. code-block:: python
>>> plot_single_colour_checker(
... 'ColorChecker 2005', text_kwargs={'visible': False})
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlottingColorChecker2005.png
Chromaticities Prediction
.. code-block:: python
>>> plot_corresponding_chromaticities_prediction( ... 2, 'Von Kries', 'Bianco 2010')
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlottingChromaticitiesPrediction.png
Colour Temperature
.. code-block:: python
>>> plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(['A', 'B', 'C'])
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlottingCCTCIE1960UCSChromaticityDiagram.png
Chromaticities
.. code-block:: python
>>> 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'])
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlottingChromaticitiesCIE1931Chromaticity_Diagram.png
Colour Rendering Index
.. code-block:: python
>>> plot_single_sd_colour_rendering_index_bars( ... colour.SDS_ILLUMINANTS['FL2'])
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlotting_CRI.png
ANSI/IES TM-30-18 Colour Rendition Report
.. code-block:: python
>>> plot_single_sd_colour_rendition_report( ... colour.SDS_ILLUMINANTS['FL2'])
.. image:: https://colour.readthedocs.io/en/develop/static/ExamplesPlottingColourRendition_Report.png
Contributing
If you would like to contribute to Colour, please refer to the following Contributing <https://www.colour-science.org/contributing/>__ guide.
Changes
The changes are viewable on the Releases <https://github.com/colour-science/colour/releases>__ page.
Bibliography
The bibliography is available on the Bibliography <https://www.colour-science.org/bibliography/>__ page.
It is also viewable directly from the repository in BibTeX <https://github.com/colour-science/colour/blob/develop/BIBLIOGRAPHY.bib>__ format.
See Also
Here is a list of notable colour science packages sorted by languages:
Python
Colorio <https://github.com/nschloe/colorio/>__ by Schlömer, N.ColorPy <http://markkness.net/colorpy/ColorPy.html>__ by Kness, M.Colorspacious <https://colorspacious.readthedocs.io/>__ by Smith, N. J., et al.python-colormath <https://python-colormath.readthedocs.io/>__ by Taylor, G., et al.
Go
go-colorful <https://github.com/lucasb-eyer/go-colorful/>__ by Beyer, L., et al.
.NET
Colourful <https://github.com/tompazourek/Colourful>__ by Pažourek, T., et al.
Julia
Colors.jl <https://github.com/JuliaGraphics/Colors.jl>__ by Holy, T., et al.
Matlab & Octave
COLORLAB <https://www.uv.es/vista/vistavalencia/software/colorlab.html>__ by Malo, J., et al.Psychtoolbox <http://psychtoolbox.org/>__ by Brainard, D., et al.The Munsell and Kubelka-Munk Toolbox <http://www.munsellcolourscienceforpainters.com/MunsellAndKubelkaMunkToolbox/MunsellAndKubelkaMunkToolbox.html>__ by Centore, P.
Code of Conduct
The Code of Conduct, adapted from the Contributor Covenant 1.4 <https://www.contributor-covenant.org/version/1/4/code-of-conduct.html>, is available on the Code of Conduct <https://www.colour-science.org/code-of-conduct/> page.
About
| Colour by Colour Developers | Copyright © 2013-2020 – Colour Developers – colour-developers@colour-science.org <colour-developers@colour-science.org>_ | This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause | https://github.com/colour-science/colour <https://github.com/colour-science/colour>_
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