def setUp(self):
"""Initialise the common tests attributes."""
self._shape = SPECTRAL_SHAPE_OTSU2018
self._cmfs, self._sd_D65 = handle_spectral_arguments(
shape_default=self._shape
)
self._reflectances = sds_and_msds_to_msds(
list(SDS_COLOURCHECKERS["ColorChecker N Ohta"].values())
+ list(SDS_COLOURCHECKERS["BabelColor Average"].values())
)
self._tree = Tree_Otsu2018(
self._reflectances, self._cmfs, self._sd_D65
)
self._XYZ_D65 = sd_to_XYZ(self._sd_D65)
self._xy_D65 = XYZ_to_xy(self._XYZ_D65)
self._temporary_directory = tempfile.mkdtemp()
self._path = os.path.join(
self._temporary_directory, "Test_Otsu2018.npz"
)
def setUp(self):
"""Initialise the common tests attributes."""
self._shape = SPECTRAL_SHAPE_OTSU2018
self._cmfs, self._sd_D65 = handle_spectral_arguments(
shape_default=self._shape
)
self._reflectances = sds_and_msds_to_msds(
SDS_COLOURCHECKERS["ColorChecker N Ohta"].values()
)
self._tree = Tree_Otsu2018(self._reflectances)
self._tree.optimise()
for leaf in self._tree.leaves:
if len(leaf.parent.children) == 2:
self._node_a = leaf.parent
self._node_b, self._node_c = self._node_a.children
break
self._data_a = Data_Otsu2018(
np.transpose(reshape_msds(self._reflectances, self._shape).values),
self._cmfs,
self._sd_D65,
)
self._data_b = self._node_b.data
self._partition_axis = self._node_a.partition_axis
def read_training_data_rawtoaces_v1() -> MultiSpectralDistributions:
"""
Read the *RAW to ACES* v1 190 patches.
Returns
-------
:class:`colour.MultiSpectralDistributions`
*RAW to ACES* v1 190 patches.
References
----------
:cite:`Dyer2017`
Examples
--------
>>> len(read_training_data_rawtoaces_v1().labels)
190
"""
global _TRAINING_DATA_RAWTOACES_V1
if _TRAINING_DATA_RAWTOACES_V1 is not None:
training_data = _TRAINING_DATA_RAWTOACES_V1
else:
path = os.path.join(RESOURCES_DIRECTORY_RAWTOACES, "190_Patches.csv")
training_data = sds_and_msds_to_msds(
list(read_sds_from_csv_file(path).values()))
_TRAINING_DATA_RAWTOACES_V1 = training_data
return training_data
def test_reflectances(self):
"""
Test :attr:`colour.recovery.otsu2018.Tree_Otsu2018.reflectances`
property.
"""
np.testing.assert_almost_equal(
self._tree.reflectances,
np.transpose(
reshape_msds(
sds_and_msds_to_msds(self._reflectances), self._shape
).values
),
decimal=7,
)
def setUp(self):
"""Initialise the common tests attributes."""
self._shape = SPECTRAL_SHAPE_OTSU2018
self._cmfs, self._sd_D65 = handle_spectral_arguments(
shape_default=self._shape
)
self._reflectances = np.transpose(
reshape_msds(
sds_and_msds_to_msds(
SDS_COLOURCHECKERS["ColorChecker N Ohta"].values()
),
self._shape,
).values
)
self._data = Data_Otsu2018(
self._reflectances, self._cmfs, self._sd_D65
)
def test_matrix_idt(self):
"""
Test :func:`colour.characterisation.aces_it.matrix_idt`
definition.
"""
# The *RAW to ACES* v1 matrix for the same camera and optimized by
# `Ceres Solver <http://ceres-solver.org/>`__ is as follows:
#
# 0.864994 -0.026302 0.161308
# 0.056527 1.122997 -0.179524
# 0.023683 -0.202547 1.178864
np.testing.assert_allclose(
matrix_idt(MSDS_CANON_EOS_5DMARK_II, SDS_ILLUMINANTS["D55"])[0],
np.array([
[0.84993207, -0.01605594, 0.15143504],
[0.05090392, 1.12559930, -0.18498249],
[0.02006825, -0.19445149, 1.16206549],
]),
rtol=0.0001,
atol=0.0001,
)
# The *RAW to ACES* v1 matrix for the same camera and optimized by
# `Ceres Solver <http://ceres-solver.org/>`__ is as follows:
#
# 0.888492 -0.077505 0.189014
# 0.021805 1.066614 -0.088418
# -0.019718 -0.206664 1.226381
np.testing.assert_allclose(
matrix_idt(MSDS_CANON_EOS_5DMARK_II,
SD_AMPAS_ISO7589_STUDIO_TUNGSTEN)[0],
np.array([
[0.85895300, -0.04381920, 0.15978620],
[0.01024800, 1.08825364, -0.11392229],
[-0.02327674, -0.18044292, 1.15903609],
]),
rtol=0.0001,
atol=0.0001,
)
M, RGB_w = matrix_idt(
MSDS_CANON_EOS_5DMARK_II,
SDS_ILLUMINANTS["D55"],
optimisation_factory=optimisation_factory_Jzazbz,
)
np.testing.assert_allclose(
M,
np.array([
[0.84841492, -0.01569765, 0.15799332],
[0.05333075, 1.11428542, -0.17523500],
[0.02262287, -0.22527728, 1.19646895],
]),
rtol=0.0001,
atol=0.0001,
)
np.testing.assert_allclose(
RGB_w,
np.array([2.34141541, 1.00000000, 1.51633759]),
rtol=0.0001,
atol=0.0001,
)
M, RGB_w = matrix_idt(
MSDS_CANON_EOS_5DMARK_II,
SDS_ILLUMINANTS["D55"],
optimisation_kwargs={"method": "Nelder-Mead"},
)
np.testing.assert_allclose(
M,
np.array([
[0.71327381, 0.19213397, 0.11115511],
[-0.05788252, 1.31165598, -0.21730625],
[-0.05913103, -0.02787107, 1.10737947],
]),
rtol=0.0001,
atol=0.0001,
)
np.testing.assert_allclose(
RGB_w,
np.array([2.34141541, 1.00000000, 1.51633759]),
rtol=0.0001,
atol=0.0001,
)
training_data = sds_and_msds_to_msds(
SDS_COLOURCHECKERS["BabelColor Average"].values())
# pylint: disable=E1102
np.testing.assert_allclose(
matrix_idt(
reshape_msds(
MSDS_CAMERA_SENSITIVITIES["Nikon 5100 (NPL)"],
SpectralShape(400, 700, 10),
),
SD_AMPAS_ISO7589_STUDIO_TUNGSTEN,
training_data=training_data,
)[0],
np.array([
[0.74041064, 0.10951105, 0.11963256],
[-0.00467360, 1.09238438, -0.11398966],
[0.06728533, -0.29530438, 1.18589793],
]),
rtol=0.0001,
atol=0.0001,
)
np.testing.assert_allclose(
matrix_idt(
MSDS_CANON_EOS_5DMARK_II,
SDS_ILLUMINANTS["D55"],
chromatic_adaptation_transform="Bradford",
)[0],
np.array([
[0.85020607, -0.01371074, 0.14907913],
[0.05074081, 1.12898863, -0.18800656],
[0.02095822, -0.20110079, 1.16769711],
]),
rtol=0.0001,
atol=0.0001,
)
_M, RGB_w, XYZ, RGB = matrix_idt(
MSDS_CANON_EOS_5DMARK_II,
SDS_ILLUMINANTS["D55"],
additional_data=True,
)
np.testing.assert_almost_equal(
RGB_w, np.array([2.34141541, 1.00000000, 1.51633759]))
np.testing.assert_almost_equal(
XYZ[:5, ...],
np.array([
[0.01743160, 0.01794927, 0.01960625],
[0.08556139, 0.08957352, 0.09017387],
[0.74560311, 0.78175547, 0.78350814],
[0.19005289, 0.19950000, 0.20126062],
[0.56264334, 0.59145486, 0.58950505],
]),
)
np.testing.assert_almost_equal(
RGB[:5, ...],
np.array([
[0.02075823, 0.01968577, 0.02139352],
[0.08957758, 0.08919227, 0.08910910],
[0.78102307, 0.78019384, 0.77643020],
[0.19950000, 0.19950000, 0.19950000],
[0.58984787, 0.59040152, 0.58510766],
]),
)
def test_training_data_sds_to_XYZ(self):
"""
Test :func:`colour.characterisation.aces_it.training_data_sds_to_XYZ`
definition.
"""
np.testing.assert_almost_equal(
training_data_sds_to_XYZ(
read_training_data_rawtoaces_v1(),
MSDS_CMFS["CIE 1931 2 Degree Standard Observer"],
SDS_ILLUMINANTS["D55"],
),
np.array([
[0.01743541, 0.01795040, 0.01961110],
[0.08556071, 0.08957352, 0.09017032],
[0.74558770, 0.78175495, 0.78343383],
[0.19005289, 0.19950000, 0.20126062],
[0.56263167, 0.59145443, 0.58944868],
[0.40708229, 0.42774653, 0.42813199],
[0.28533739, 0.29945717, 0.29732644],
[0.18670375, 0.19575576, 0.19612855],
[0.10734487, 0.11290543, 0.11381239],
[0.06188310, 0.06524694, 0.06594260],
[0.02905436, 0.03045954, 0.03111642],
[0.25031624, 0.22471846, 0.12599982],
[0.20848487, 0.18072652, 0.08216289],
[0.28173081, 0.26937432, 0.19943363],
[0.15129458, 0.13765872, 0.08086671],
[0.07854243, 0.07274480, 0.05123870],
[0.46574583, 0.43948749, 0.34501135],
[0.33111608, 0.29368033, 0.21379720],
[0.04596029, 0.04443836, 0.03115443],
[0.28422646, 0.15495892, 0.11586479],
[0.47490187, 0.41497780, 0.33505853],
[0.29452546, 0.20003225, 0.13705453],
[0.06905269, 0.04421818, 0.03449201],
[0.13040440, 0.06239791, 0.04175606],
[0.43838730, 0.29962261, 0.18439668],
[0.13390118, 0.08356608, 0.04956679],
[0.08356733, 0.05794634, 0.03910007],
[0.21637988, 0.12469189, 0.04842559],
[0.37899204, 0.22130821, 0.07365608],
[0.07733610, 0.04256869, 0.02300063],
[0.25696432, 0.14119282, 0.04740500],
[0.51960474, 0.41409496, 0.25643556],
[0.32241564, 0.19954021, 0.08051276],
[0.05811798, 0.03389661, 0.02553745],
[0.03192572, 0.02139972, 0.01894908],
[0.24605476, 0.17854962, 0.09147038],
[0.20624731, 0.10555152, 0.01675508],
[0.31255107, 0.19334840, 0.05143990],
[0.11006219, 0.06057155, 0.01700794],
[0.20509764, 0.12555310, 0.03594860],
[0.38058683, 0.30396093, 0.16256996],
[0.34354473, 0.23964048, 0.06111316],
[0.62251344, 0.54770879, 0.34634977],
[0.21294652, 0.14470338, 0.03492000],
[0.22064317, 0.19656587, 0.11907643],
[0.23955073, 0.19768225, 0.08595970],
[0.12377361, 0.08353105, 0.01434151],
[0.52378659, 0.40757502, 0.10242337],
[0.09732322, 0.07735501, 0.03254246],
[0.41081884, 0.30127969, 0.04240016],
[0.32946008, 0.27129095, 0.05232655],
[0.19870991, 0.18701769, 0.09764509],
[0.31867743, 0.25717029, 0.02158054],
[0.67745549, 0.64283785, 0.31268426],
[0.43182429, 0.39425828, 0.13198410],
[0.19075096, 0.16573196, 0.01845293],
[0.47578930, 0.43714747, 0.07974541],
[0.08420865, 0.08615579, 0.06605406],
[0.47306132, 0.43488423, 0.05262924],
[0.28242654, 0.28638349, 0.19186089],
[0.37367384, 0.38524079, 0.13498637],
[0.49536547, 0.51027091, 0.15645211],
[0.63680942, 0.67272132, 0.19642820],
[0.43790684, 0.39093965, 0.02518505],
[0.63216527, 0.66425603, 0.07124985],
[0.28682848, 0.29807036, 0.14308787],
[0.78666095, 0.83181391, 0.53110094],
[0.54475049, 0.57280425, 0.43240766],
[0.65555915, 0.68992930, 0.10030198],
[0.10560623, 0.10992647, 0.06863885],
[0.40588908, 0.43345904, 0.08589490],
[0.69824760, 0.76446843, 0.23843395],
[0.27951451, 0.30869595, 0.13310650],
[0.28351930, 0.32278417, 0.09130925],
[0.51144946, 0.58985649, 0.11409286],
[0.16769668, 0.19357639, 0.04824163],
[0.64027510, 0.74864980, 0.24145602],
[0.51533750, 0.64418491, 0.09390029],
[0.10903312, 0.13420204, 0.04403235],
[0.03916991, 0.04755109, 0.02410291],
[0.12726285, 0.16825903, 0.03705646],
[0.34079923, 0.44119883, 0.10621489],
[0.08299513, 0.10226271, 0.04607974],
[0.10117617, 0.12690940, 0.05211600],
[0.20673305, 0.25456362, 0.11244267],
[0.05040081, 0.06702198, 0.02944861],
[0.05809758, 0.07896803, 0.03312583],
[0.07202711, 0.09383365, 0.03453490],
[0.06392748, 0.07896740, 0.03860393],
[0.08851258, 0.11174080, 0.04873213],
[0.09821259, 0.13743849, 0.03901353],
[0.12253000, 0.18989034, 0.03327101],
[0.15082798, 0.25948217, 0.03805919],
[0.41476613, 0.56455709, 0.26988900],
[0.25043710, 0.40869656, 0.12211755],
[0.17536685, 0.28765326, 0.10166502],
[0.12038544, 0.19242328, 0.07754636],
[0.14661345, 0.23524743, 0.09334793],
[0.29469553, 0.41056592, 0.23093160],
[0.13015693, 0.19492122, 0.09333495],
[0.04081181, 0.08280292, 0.03122401],
[0.06569736, 0.13553353, 0.05266408],
[0.12177383, 0.20160583, 0.11621774],
[0.08354206, 0.11970984, 0.08207175],
[0.02834645, 0.06259404, 0.03135058],
[0.20884161, 0.29927365, 0.20553553],
[0.23180119, 0.33870071, 0.24267407],
[0.04413521, 0.05398934, 0.04862030],
[0.13068910, 0.19470885, 0.15073584],
[0.16108644, 0.18484544, 0.17474649],
[0.06206737, 0.12873462, 0.09368693],
[0.05126858, 0.06722639, 0.05961970],
[0.25534374, 0.31335090, 0.27780291],
[0.48369629, 0.63319069, 0.57347864],
[0.06066266, 0.09712274, 0.09253437],
[0.27940216, 0.41909220, 0.39351159],
[0.44664100, 0.54665344, 0.55342931],
[0.03590889, 0.06959304, 0.07535965],
[0.16621092, 0.30339106, 0.29722885],
[0.12909138, 0.22008859, 0.22690521],
[0.31015553, 0.42498221, 0.42044232],
[0.33970423, 0.42779997, 0.43883150],
[0.10000582, 0.19440825, 0.23393750],
[0.16694758, 0.26056864, 0.32541934],
[0.43598087, 0.55484571, 0.63089871],
[0.10305166, 0.13633951, 0.16650820],
[0.12725465, 0.19404057, 0.30068226],
[0.44450660, 0.54666776, 0.64220554],
[0.25312549, 0.31346831, 0.38485942],
[0.24557618, 0.34698805, 0.51328941],
[0.13585660, 0.18761687, 0.36302217],
[0.32288492, 0.39652004, 0.54579104],
[0.08400465, 0.11889755, 0.34519851],
[0.06038029, 0.07936884, 0.16393180],
[0.47840043, 0.53070661, 0.64043584],
[0.16727376, 0.19048161, 0.27055547],
[0.14740952, 0.19227205, 0.44545300],
[0.03953792, 0.04540593, 0.10766386],
[0.16200092, 0.18995251, 0.41003367],
[0.53147895, 0.57554326, 0.74787983],
[0.17107460, 0.19285623, 0.48157477],
[0.11394187, 0.12139868, 0.21928748],
[0.10838799, 0.11193347, 0.34884682],
[0.10390937, 0.10854555, 0.22459293],
[0.28493924, 0.30349174, 0.54832107],
[0.13572090, 0.13988801, 0.43412229],
[0.36141619, 0.37929776, 0.62919317],
[0.04527113, 0.04612919, 0.09028801],
[0.05164102, 0.04505136, 0.17732932],
[0.18148861, 0.19085005, 0.29528314],
[0.37792382, 0.39238764, 0.61357669],
[0.08148672, 0.06054619, 0.27321036],
[0.13431208, 0.12118937, 0.35762939],
[0.19932157, 0.19328547, 0.37878896],
[0.09456787, 0.08094285, 0.25785832],
[0.29868476, 0.28967149, 0.54786550],
[0.09582629, 0.06156148, 0.27163852],
[0.25053785, 0.23630807, 0.40751054],
[0.56821117, 0.57452018, 0.72419232],
[0.16116009, 0.13379410, 0.28760107],
[0.37816205, 0.32564214, 0.64945876],
[0.19440630, 0.16599850, 0.31684298],
[0.24229817, 0.19698372, 0.51538353],
[0.08104904, 0.06295569, 0.15738669],
[0.48808364, 0.46372832, 0.69336648],
[0.01983575, 0.01538929, 0.03252398],
[0.13468770, 0.08473328, 0.25136965],
[0.08762890, 0.06560340, 0.13804375],
[0.20192043, 0.12939477, 0.36343630],
[0.24231283, 0.19018859, 0.36604686],
[0.28784724, 0.21105155, 0.46114703],
[0.12549222, 0.07471177, 0.17126268],
[0.20910983, 0.18235419, 0.22475458],
[0.43032307, 0.32727171, 0.49574549],
[0.39105442, 0.32475758, 0.42885925],
[0.60567491, 0.57928897, 0.64030251],
[0.15645417, 0.12986348, 0.17171885],
[0.50025055, 0.32646202, 0.51899239],
[0.29822363, 0.19839451, 0.27397060],
[0.63136923, 0.55375993, 0.63816664],
[0.10261977, 0.05754107, 0.07473368],
[0.30325538, 0.21976283, 0.29171854],
[0.46794841, 0.39368920, 0.44286306],
[0.54326558, 0.36319029, 0.41127862],
[0.52355493, 0.42261205, 0.43529051],
[0.39852212, 0.37568122, 0.37825751],
[0.10892106, 0.06698290, 0.07939788],
[0.68780223, 0.58022018, 0.54422258],
[0.18984448, 0.09051898, 0.12104133],
[0.41991006, 0.29457037, 0.40780639],
]),
decimal=7,
)
training_data = sds_and_msds_to_msds(
SDS_COLOURCHECKERS["BabelColor Average"].values())
np.testing.assert_almost_equal(
training_data_sds_to_XYZ(
training_data,
MSDS_CMFS["CIE 1931 2 Degree Standard Observer"],
SDS_ILLUMINANTS["D55"],
),
np.array([
[0.11386016, 0.10184316, 0.06318332],
[0.38043230, 0.34842093, 0.23582246],
[0.17359019, 0.18707491, 0.31848244],
[0.10647823, 0.13300376, 0.06486355],
[0.24658643, 0.23417740, 0.40546447],
[0.30550003, 0.42171110, 0.41928361],
[0.38409200, 0.30325611, 0.05955461],
[0.13149767, 0.11720378, 0.35673016],
[0.28717811, 0.19215580, 0.12514286],
[0.08401031, 0.06423349, 0.12782115],
[0.33990604, 0.44124555, 0.10834694],
[0.46443889, 0.42686462, 0.07340585],
[0.07650085, 0.06051409, 0.26167301],
[0.14598990, 0.23185071, 0.09380297],
[0.20642710, 0.12162691, 0.04673088],
[0.57371755, 0.59896814, 0.08930486],
[0.30208819, 0.19714705, 0.28492050],
[0.14184323, 0.19554336, 0.36653731],
[0.86547610, 0.91241348, 0.88583082],
[0.55802432, 0.58852191, 0.59042758],
[0.34102067, 0.35951875, 0.36251375],
[0.18104441, 0.19123509, 0.19353380],
[0.08461047, 0.08944605, 0.09150081],
[0.03058273, 0.03200953, 0.03277947],
]),
decimal=7,
)
np.testing.assert_almost_equal(
training_data_sds_to_XYZ(
training_data,
MSDS_CMFS["CIE 1931 2 Degree Standard Observer"],
SDS_ILLUMINANTS["D55"],
chromatic_adaptation_transform="Bradford",
),
np.array([
[0.11386557, 0.10185906, 0.06306965],
[0.38044920, 0.34846911, 0.23548776],
[0.17349711, 0.18690409, 0.31901794],
[0.10656174, 0.13314825, 0.06450454],
[0.24642109, 0.23388536, 0.40625776],
[0.30564803, 0.42194543, 0.41894818],
[0.38414010, 0.30337780, 0.05881558],
[0.13128440, 0.11682332, 0.35780551],
[0.28707604, 0.19200780, 0.12518610],
[0.08392779, 0.06409174, 0.12816180],
[0.34028525, 0.44190577, 0.10665985],
[0.46462806, 0.42722924, 0.07207641],
[0.07631823, 0.06018898, 0.26258457],
[0.14620929, 0.23222248, 0.09296807],
[0.20635082, 0.12152088, 0.04669974],
[0.57410962, 0.59968182, 0.08713069],
[0.30185180, 0.19675858, 0.28565273],
[0.14177898, 0.19541060, 0.36711242],
[0.86550834, 0.91247072, 0.88567193],
[0.55803077, 0.58853268, 0.59040518],
[0.34102300, 0.35952246, 0.36250826],
[0.18104563, 0.19123690, 0.19353274],
[0.08461039, 0.08944568, 0.09150425],
[0.03058222, 0.03200864, 0.03278183],
]),
decimal=7,
)
def test_training_data_sds_to_RGB(self):
"""
Test :func:`colour.characterisation.aces_it.training_data_sds_to_RGB`
definition.
"""
RGB, RGB_w = training_data_sds_to_RGB(
read_training_data_rawtoaces_v1(),
MSDS_CANON_EOS_5DMARK_II,
SDS_ILLUMINANTS["D55"],
)
np.testing.assert_almost_equal(
RGB,
np.array([
[42.00296381, 39.83290349, 43.28842394],
[181.25453293, 180.47486885, 180.30657630],
[1580.35041765, 1578.67251435, 1571.05703787],
[403.67553672, 403.67553672, 403.67553672],
[1193.51958332, 1194.63985124, 1183.92806238],
[862.07824054, 863.30644583, 858.29863779],
[605.42274304, 602.94953701, 596.61414309],
[395.70687930, 394.67167942, 392.97719777],
[227.27502116, 228.33554705, 227.96959477],
[130.97735082, 132.12395139, 131.97239271],
[61.79308820, 61.85572037, 62.40560537],
[592.29430914, 383.93309398, 282.70032306],
[504.67305022, 294.69245978, 193.90976423],
[640.93167741, 494.91914821, 421.68337308],
[356.53952646, 239.77610719, 181.18147755],
[179.58569818, 130.00540238, 109.23999883],
[1061.07297514, 818.29727750, 730.13362169],
[765.75936417, 522.06805938, 456.59355601],
[104.70554060, 80.35106922, 65.75667232],
[694.19925422, 161.06849749, 214.20170991],
[1054.83161580, 709.41713619, 668.10329523],
[697.35479081, 276.20032105, 275.86226833],
[183.26315174, 65.93801513, 74.60775905],
[359.74416854, 59.73576149, 89.81296522],
[1043.53760601, 405.48081521, 376.37298474],
[344.35374209, 111.26727966, 109.10587712],
[215.18064862, 87.41152853, 85.18152727],
[555.37005673, 134.76016985, 111.54658160],
[931.71846961, 210.02605133, 150.65312210],
[211.01186324, 50.73939233, 54.55750662],
[654.45781665, 132.73694874, 107.20009737],
[1193.89772859, 625.60766645, 521.51066476],
[802.65730883, 228.94887565, 178.30864097],
[149.82853589, 44.31839648, 55.29195048],
[80.88083928, 33.78936351, 41.73438243],
[579.50157840, 240.80755019, 188.50864121],
[537.09280420, 80.41714202, 48.28907694],
[777.62363031, 205.11587061, 122.43126732],
[292.65436510, 59.53457252, 44.27126512],
[511.68625012, 134.76897130, 85.73242441],
[903.64947615, 462.49015529, 350.74183199],
[852.95457070, 291.64071698, 151.51871958],
[1427.59841722, 907.54863477, 724.29520203],
[527.68979414, 169.76114596, 89.48561902],
[496.62188809, 317.11827387, 243.77642038],
[554.39017413, 284.77453644, 181.92376325],
[310.50669032, 96.25812545, 41.22765558],
[1246.49891599, 522.05121993, 238.28646123],
[240.19646249, 118.57745244, 82.68426681],
[1005.98836135, 355.93514762, 118.60457241],
[792.31376787, 369.56509398, 143.27388201],
[459.04590557, 315.46594358, 215.53901098],
[806.50918893, 352.20277469, 97.69239677],
[1574.11778922, 1078.61331515, 697.02647383],
[1015.45155837, 598.98507153, 301.94169280],
[479.68722930, 242.23619637, 72.60351059],
[1131.70538515, 628.32510627, 213.67910327],
[185.86573238, 162.55033903, 137.59385867],
[1131.77074807, 603.89218698, 153.83160203],
[638.14148862, 527.18090248, 410.12394346],
[884.58039320, 655.09236879, 329.23967927],
[1172.73094356, 840.43080883, 380.90114088],
[1490.24223350, 1111.18491878, 482.33357611],
[1054.70234779, 513.29967197, 91.55980977],
[1532.99674295, 1035.15868150, 253.21942988],
[662.35328287, 528.52354760, 326.56458987],
[1769.55456145, 1557.58571488, 1155.79098414],
[1196.62083017, 1079.28012658, 888.47017893],
[1578.73591185, 1089.40083172, 314.45691871],
[252.98204345, 206.56788008, 153.62801631],
[973.59975800, 714.51185344, 251.12884859],
[1661.01720988, 1340.46809762, 619.61710815],
[656.66179353, 566.61547800, 322.22788098],
[676.69663303, 571.86743785, 249.62031449],
[1229.28626315, 1020.14702709, 353.11090960],
[390.76190378, 324.36051944, 119.31108035],
[1524.10495708, 1366.72397704, 633.03830849],
[1264.54750712, 1149.12002542, 335.25348483],
[265.96753330, 260.89397210, 130.78590008],
[90.15969432, 90.72350914, 55.12008388],
[298.22463247, 300.48700028, 101.95760063],
[813.34391710, 820.12623357, 313.17818415],
[186.96402165, 190.38042094, 104.27515726],
[230.34939258, 235.91900919, 120.77815429],
[469.57926615, 472.51064145, 256.40912347],
[117.81249486, 129.17019984, 69.78861213],
[133.39581196, 151.50390168, 77.66255652],
[164.19259747, 172.13159331, 80.92295294],
[146.12230124, 149.32536508, 87.48300520],
[201.93215173, 208.89885695, 111.84447436],
[248.41427850, 282.34047722, 122.55482010],
[304.35509339, 377.38986207, 118.66130122],
[381.85533606, 530.40398972, 150.83506876],
[967.19810669, 1161.33086750, 663.54746741],
[613.98437237, 865.41677370, 362.92357557],
[410.21304405, 611.89683658, 284.09389273],
[279.50447144, 416.01646348, 213.18049093],
[334.48807624, 487.46571814, 235.49134434],
[664.04349337, 867.87454943, 549.71146455],
[311.66934673, 431.38058636, 256.13307806],
[110.04404638, 203.88196409, 104.63331585],
[153.35857585, 312.67834716, 149.90942505],
[273.46344514, 462.41992197, 292.50571823],
[184.77058437, 267.46361125, 193.71894670],
[75.79805899, 163.84071881, 95.67465270],
[461.73803707, 668.68797906, 484.77687282],
[523.01992144, 790.69326153, 598.73122243],
[105.89414085, 124.92341127, 113.03925656],
[279.33299507, 446.45128537, 344.73426977],
[340.57250119, 381.28610429, 353.83182947],
[141.00956904, 329.50139051, 228.90179483],
[117.29728945, 156.88993944, 139.49878229],
[565.12438106, 696.52297174, 615.88218349],
[1046.73447319, 1446.22424473, 1277.47338963],
[133.87404291, 253.25944193, 224.75872956],
[586.52626500, 1015.43013448, 885.49907251],
[927.08412116, 1197.93784752, 1140.76612264],
[81.29463446, 202.46201173, 186.35209411],
[350.90699453, 788.82959642, 669.10307704],
[278.88231719, 581.42068355, 526.82554470],
[642.66176703, 990.64038619, 907.64284280],
[689.10344984, 942.49383066, 900.33073076],
[190.62073977, 540.21088595, 523.62573562],
[322.35685764, 676.02683754, 692.94583013],
[896.29532467, 1289.90474463, 1311.34615018],
[204.06785020, 321.83261403, 337.01923114],
[237.10512554, 549.97044011, 646.06486244],
[907.26703197, 1252.44260107, 1309.50173432],
[504.74103065, 728.27088424, 782.27808125],
[470.91049729, 912.49116456, 1059.41083523],
[231.75497961, 539.14727494, 732.41647792],
[624.91135978, 943.51709467, 1086.48492282],
[104.84186738, 398.05825469, 663.96030581],
[100.47632953, 226.41423139, 323.51675153],
[998.19560093, 1168.81108673, 1283.07267859],
[350.74519746, 457.74100518, 552.52270183],
[223.19531677, 560.14850077, 855.05346039],
[66.92044931, 128.18947830, 205.30719728],
[280.63458798, 518.51069955, 784.38948897],
[1071.24122457, 1267.16339790, 1467.81704311],
[271.47257445, 553.57609491, 914.33723598],
[211.86582477, 295.18643027, 418.51776463],
[153.86457460, 342.06625645, 649.82579665],
[179.59188635, 265.25370235, 413.68135787],
[529.77485058, 737.79030218, 1046.29865466],
[208.71936449, 421.30392624, 796.71281168],
[685.50294808, 879.76243717, 1195.00892794],
[85.02189613, 113.33360860, 171.03209018],
[72.06980264, 139.42600347, 315.97906141],
[349.57868286, 426.82308690, 556.49647978],
[726.50329821, 882.48411184, 1163.20130103],
[102.62158777, 177.73895468, 467.26740089],
[208.63097281, 322.84137064, 639.30554347],
[377.19498209, 456.13180268, 706.44272480],
[149.91131672, 218.16462984, 455.15510078],
[556.80606655, 673.96774240, 1020.98785748],
[172.19546054, 181.38617476, 478.69666973],
[494.98572332, 534.88874559, 773.75255591],
[1166.31475206, 1207.81829513, 1411.04368728],
[324.81131421, 298.91188334, 521.96994638],
[731.58631467, 725.95113189, 1192.71141630],
[376.70584074, 352.06184423, 572.37854429],
[421.32413767, 465.07677606, 910.85999527],
[155.65680826, 145.82096629, 282.56390371],
[982.43736509, 991.65710582, 1312.39630323],
[41.37244888, 33.41882583, 59.48460827],
[282.61535563, 188.37255834, 441.62967707],
[182.28936533, 136.29152918, 248.30801310],
[398.28853814, 281.28601665, 641.78038278],
[494.34030557, 393.91395210, 664.96627121],
[579.86630787, 449.57878986, 836.64303806],
[281.30892711, 142.60663373, 309.93723963],
[439.97606151, 345.13329865, 425.68615785],
[887.17712876, 583.53811414, 886.88440975],
[841.97939219, 617.28846790, 810.67002861],
[1280.60242984, 1139.62066080, 1255.46929276],
[336.77846782, 246.82877629, 324.48823631],
[1070.92080733, 527.41599474, 913.93600561],
[676.57753460, 329.48235976, 509.56020035],
[1353.12934453, 1048.28092139, 1227.42851889],
[248.56120754, 78.30056642, 137.39216268],
[675.76876164, 381.60749713, 545.08703142],
[1008.57884369, 704.64042514, 836.94311729],
[1207.19931876, 527.74482440, 737.30284625],
[1157.60714894, 736.24734736, 846.01278626],
[861.62204402, 714.70913295, 747.29294390],
[255.83324360, 94.08214754, 147.60127564],
[1522.93390177, 1017.14491217, 1073.23488749],
[460.59077351, 93.73852735, 210.75844436],
[909.87331348, 498.83253656, 750.09672276],
]),
decimal=7,
)
np.testing.assert_almost_equal(
RGB_w, np.array([2.34141541, 1.00000000, 1.51633759]), decimal=7)
training_data = sds_and_msds_to_msds(
SDS_COLOURCHECKERS["BabelColor Average"].values())
RGB, RGB_w = training_data_sds_to_RGB(training_data,
MSDS_CANON_EOS_5DMARK_II,
SDS_ILLUMINANTS["D55"])
np.testing.assert_almost_equal(
RGB,
np.array([
[263.80361607, 170.29412869, 132.71463416],
[884.07936328, 628.44083126, 520.43504675],
[324.17856150, 443.95092266, 606.43750890],
[243.82059773, 253.22111395, 144.98600653],
[481.54199203, 527.96925768, 764.50624747],
[628.07015143, 979.73104655, 896.85237907],
[927.63600544, 391.11468312, 150.73047156],
[203.13259862, 317.65395368, 639.54581080],
[686.28955864, 260.78688114, 254.89963998],
[174.05857536, 132.16684952, 230.54054095],
[806.50094411, 817.35481419, 312.91902292],
[1111.20280010, 608.82554576, 194.31984092],
[94.99792206, 185.04148229, 456.53592437],
[340.60457483, 498.62910631, 254.08356415],
[531.53679194, 136.11844274, 109.19876416],
[1387.37113491, 952.84382040, 286.23152122],
[681.97933172, 326.66634506, 526.23078660],
[244.90739217, 554.88866566, 741.21522946],
[1841.80020583, 1834.49277300, 1784.07500285],
[1179.76201558, 1189.84138939, 1182.25520674],
[720.27089899, 726.91855632, 724.84766858],
[382.16849234, 387.41521539, 386.87510339],
[178.43859184, 181.76108810, 182.71062184],
[64.77754952, 64.80020759, 65.45515287],
]),
decimal=7,
)
np.testing.assert_almost_equal(
RGB_w, np.array([2.34141541, 1.00000000, 1.51633759]), decimal=7)
"TestGenerateIlluminantsRawtoacesV1",
"TestWhiteBalanceMultipliers",
"TestBestIlluminant",
"TestNormaliseIlluminant",
"TestTrainingDataSdsToRGB",
"TestTrainingDataSdsToXYZ",
"TestOptimizationFactoryRawtoacesV1",
"TestOptimizationFactoryJzazbz",
"TestMatrixIdt",
"TestCamera_RGB_to_ACES2065_1",
]
MSDS_CANON_EOS_5DMARK_II: MultiSpectralDistributions = sds_and_msds_to_msds(
list(
read_sds_from_csv_file(
os.path.join(
RESOURCES_DIRECTORY_RAWTOACES,
"CANON_EOS_5DMark_II_RGB_Sensitivities.csv",
)).values()))
SD_AMPAS_ISO7589_STUDIO_TUNGSTEN: SpectralDistribution = (
read_sds_from_csv_file(
os.path.join(RESOURCES_DIRECTORY_RAWTOACES,
"AMPAS_ISO_7589_Tungsten.csv"))["iso7589"])
class TestSpectralToAcesRelativeExposureValues(unittest.TestCase):
"""
Define :func:`colour.characterisation.aces_it.\
sd_to_aces_relative_exposure_values` definition unit tests methods.
"""
def test_matrix_idt(self):
"""
Tests :func:`colour.characterisation.aces_it.matrix_idt`
definition.
"""
# The *RAW to ACES* v1 matrix for the same camera and optimized by
# `Ceres Solver <http://ceres-solver.org/>`__ is as follows:
#
# 0.864994 -0.026302 0.161308
# 0.056527 1.122997 -0.179524
# 0.023683 -0.202547 1.178864
np.testing.assert_allclose(matrix_idt(MSDS_CANON_EOS_5DMARK_II,
SDS_ILLUMINANTS['D55']),
np.array([
[0.84993207, -0.01605594, 0.15143504],
[0.05090392, 1.12559930, -0.18498249],
[0.02006825, -0.19445149, 1.16206549],
]),
rtol=0.0001,
atol=0.0001)
# The *RAW to ACES* v1 matrix for the same camera and optimized by
# `Ceres Solver <http://ceres-solver.org/>`__ is as follows:
#
# 0.888492 -0.077505 0.189014
# 0.021805 1.066614 -0.088418
# -0.019718 -0.206664 1.226381
np.testing.assert_allclose(matrix_idt(
MSDS_CANON_EOS_5DMARK_II, SD_AMPAS_ISO7589_STUDIO_TUNGSTEN),
np.array([
[0.85895300, -0.04381920, 0.15978620],
[0.01024800, 1.08825364, -0.11392229],
[-0.02327674, -0.18044292, 1.15903609],
]),
rtol=0.0001,
atol=0.0001)
np.testing.assert_allclose(matrix_idt(
MSDS_CANON_EOS_5DMARK_II,
SDS_ILLUMINANTS['D55'],
optimisation_factory=optimisation_factory_JzAzBz),
np.array([
[0.84841492, -0.01569765, 0.15799332],
[0.05333075, 1.11428542, -0.17523500],
[0.02262287, -0.22527728, 1.19646895],
]),
rtol=0.0001,
atol=0.0001)
np.testing.assert_allclose(matrix_idt(
MSDS_CANON_EOS_5DMARK_II,
SDS_ILLUMINANTS['D55'],
optimisation_kwargs={'method': 'Nelder-Mead'}),
np.array([
[0.71327381, 0.19213397, 0.11115511],
[-0.05788252, 1.31165598, -0.21730625],
[-0.05913103, -0.02787107, 1.10737947],
]),
rtol=0.0001,
atol=0.0001)
training_data = sds_and_msds_to_msds(
SDS_COLOURCHECKERS['BabelColor Average'].values())
np.testing.assert_allclose(matrix_idt(
MSDS_CAMERA_SENSITIVITIES['Nikon 5100 (NPL)'].copy().align(
SpectralShape(400, 700, 10)),
SD_AMPAS_ISO7589_STUDIO_TUNGSTEN,
training_data=training_data),
np.array([
[0.74041064, 0.10951105, 0.11963256],
[-0.00467360, 1.09238438, -0.11398966],
[0.06728533, -0.29530438, 1.18589793],
]),
rtol=0.0001,
atol=0.0001)
__maintainer__ = 'Colour Developers'
__email__ = '*****@*****.**'
__status__ = 'Production'
__all__ = [
'MSDS_CANON_EOS_5DMARK_II', 'SD_AMPAS_ISO7589_STUDIO_TUNGSTEN',
'TestSpectralToAcesRelativeExposureValues',
'TestReadTrainingDataRawtoacesV1', 'TestGenerateIlluminantsRawtoacesV1',
'TestWhiteBalanceMultipliers', 'TestBestIlluminant',
'TestNormaliseIlluminant', 'TestTrainingDataSdsToRGB',
'TestTrainingDataSdsToXYZ', 'TestOptimizationFactoryRawtoacesV1',
'TestOptimizationFactoryJzAzBz', 'TestMatrixIdt'
]
MSDS_CANON_EOS_5DMARK_II = sds_and_msds_to_msds(
read_sds_from_csv_file(
os.path.join(RESOURCES_DIRECTORY_RAWTOACES,
'CANON_EOS_5DMark_II_RGB_Sensitivities.csv')).values())
SD_AMPAS_ISO7589_STUDIO_TUNGSTEN = read_sds_from_csv_file(
os.path.join(RESOURCES_DIRECTORY_RAWTOACES,
'AMPAS_ISO_7589_Tungsten.csv'))['iso7589']
class TestSpectralToAcesRelativeExposureValues(unittest.TestCase):
"""
Defines :func:`colour.characterisation.aces_it.\
sd_to_aces_relative_exposure_values` definition unit tests methods.
"""
def test_spectral_to_aces_relative_exposure_values(self):
"""
Tests :func:`colour.characterisation.aces_it.
def spectral_uniformity(
sds: Union[
List[Union[SpectralDistribution, MultiSpectralDistributions]],
MultiSpectralDistributions,
],
use_second_order_derivatives: Boolean = False,
) -> NDArray:
"""
Compute the *spectral uniformity* (or *spectral flatness*) of given
spectral distributions.
Spectral uniformity :math:`(r')^2` is computed as follows:
:math:`mean((r'_1)^2, (r'_2)^2, ..., (r'_n)^2)`
where :math:`(r'_i)^2` is the first-order derivative, squared, of the
reflectance :math:`r_i` of a test sample.
Parameters
----------
sds
Spectral distributions or multi-spectral distributions to
compute the spectral uniformity of. `sds` can be a single
:class:`colour.MultiSpectralDistributions` class instance, a list
of :class:`colour.MultiSpectralDistributions` class instances or a
list of :class:`colour.SpectralDistribution` class instances.
use_second_order_derivatives
Whether to use the second-order derivatives in the computations.
Returns
-------
:class:`numpy.ndarray`
Spectral uniformity.
Warnings
--------
The spectral distributions must have the same spectral shape.
References
----------
:cite:`David2015a`
Examples
--------
>>> from colour.quality.datasets import SDS_TCS
>>> spectral_uniformity(SDS_TCS.values()) # doctest: +ELLIPSIS
array([ 9.5514285...e-06, 1.1482142...e-05, 1.8784285...e-05,
2.8711428...e-05, 3.1971428...e-05, 3.2342857...e-05,
3.3850000...e-05, 3.9925714...e-05, 4.1333571...e-05,
2.4002142...e-05, 5.7621428...e-06, 1.4757142...e-06,
9.7928571...e-07, 2.0057142...e-06, 3.7157142...e-06,
5.7678571...e-06, 7.5557142...e-06, 7.4635714...e-06,
5.7492857...e-06, 3.8692857...e-06, 3.5407142...e-06,
4.4742857...e-06, 5.6435714...e-06, 7.6371428...e-06,
1.0171428...e-05, 1.2254285...e-05, 1.4810000...e-05,
1.6517142...e-05, 1.5430714...e-05, 1.4536428...e-05,
1.4037857...e-05, 1.1587857...e-05, 1.0743571...e-05,
1.0979285...e-05, 1.0398571...e-05, 8.2971428...e-06,
6.3057142...e-06, 5.0942857...e-06, 4.8500000...e-06,
5.5371428...e-06, 6.4128571...e-06, 7.2592857...e-06,
7.7750000...e-06, 7.1607142...e-06, 6.6635714...e-06,
6.7328571...e-06, 7.5307142...e-06, 1.0733571...e-05,
1.6234285...e-05, 2.2570714...e-05, 2.7056428...e-05,
2.7781428...e-05, 2.5025714...e-05, 1.7966428...e-05,
1.0505000...e-05, 5.9657142...e-06, 3.6421428...e-06,
2.1664285...e-06, 1.2935714...e-06, 8.3642857...e-07,
7.2500000...e-07, 6.3928571...e-07, 6.6285714...e-07,
8.5571428...e-07, 1.4507142...e-06, 2.2542857...e-06,
3.4142857...e-06, 4.9864285...e-06, 6.4907142...e-06,
7.8928571...e-06, 9.1664285...e-06, 9.9521428...e-06,
9.7664285...e-06, 9.3150000...e-06, 8.9092857...e-06,
8.1578571...e-06, 6.8935714...e-06, 5.5721428...e-06,
4.4592857...e-06, 3.4778571...e-06, 2.7650000...e-06,
2.3114285...e-06, 1.7092857...e-06, 1.1771428...e-06,
9.8428571...e-07, 8.8285714...e-07, 7.4142857...e-07,
7.0142857...e-07, 7.0857142...e-07, 6.6642857...e-07,
7.5928571...e-07, 8.7000000...e-07, 8.2714285...e-07,
7.1714285...e-07, 6.6000000...e-07])
"""
msds = sds_and_msds_to_msds(sds)
interval = msds.shape.interval
r_i = np.gradient(np.transpose(msds.values), axis=1) / interval
if use_second_order_derivatives:
r_i = np.gradient(r_i, axis=1) / interval
return np.mean(r_i**2, axis=0)
