Coverage for colour/characterisation/datasets/colour_checkers/chromaticity_coordinates.py: 100%
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1"""
2Chromaticity Coordinates of the Colour Checkers
3===============================================
5Define the chromaticity coordinates of the colour checkers.
7Each colour checker data is in the form of a :class:`dict` class instance
8of 24 or more samples as follows::
10 {'name': 'xyY', ..., 'name': 'xyY'}
12The following colour checkers are available:
14- :attr:`colour.characterisation.datasets.colour_checkers.\
15chromaticity_coordinates.CCS_COLORCHECKER1976`: *ColorChecker Classic*
16 developed by *McCamy et al. (1976)* at Macbeth, a Division of Kollmorgen.
17- :attr:`colour.characterisation.datasets.colour_checkers.\
18chromaticity_coordinates.CCS_COLORCHECKER2005`: *ColorChecker Classic*
19 reference data from *GretagMacbeth* published in 2005.
20- :attr:`colour.characterisation.datasets.colour_checkers.\
21chromaticity_coordinates.CCS_BABELCOLOR_AVERAGE`: Average data derived from
22 measurements of 30 *ColorChecker Classic* charts.
23- :attr:`colour.characterisation.datasets.colour_checkers.\
24chromaticity_coordinates.CCS_COLORCHECKER24_BEFORE_NOV2014`:
25 *ColorChecker Classic* reference data from *X-Rite* published in 2016 and
26 matching the data from *GretagMacbeth* published in 2005.
27- :attr:`colour.characterisation.datasets.colour_checkers.\
28chromaticity_coordinates.CCS_COLORCHECKER24_AFTER_NOV2014`:
29 *ColorChecker Classic* reference data from *X-Rite* published in 2016 and
30 matching the *ColorChecker Classic* edition after November 2014.
31- :attr:`colour.characterisation.datasets.colour_checkers.\
32chromaticity_coordinates.CCS_COLORCHECKERSG_BEFORE_NOV2014`:
33 *ColorChecker SG* reference data from *X-Rite* published in 2016
34- :attr:`colour.characterisation.datasets.colour_checkers.\
35chromaticity_coordinates.CCS_COLORCHECKERSG_AFTER_NOV2014`:
36 *ColorChecker SG* reference data from *X-Rite* published in 2016
37- :attr:`colour.characterisation.datasets.colour_checkers.\
38chromaticity_coordinates. CCS_TE226_V2`: Reference data from *TE226 V2*.
40References
41----------
42- :cite:`BabelColor2012b` : BabelColor. (2012). The ColorChecker (since
43 1976!). Retrieved September 26, 2014, from
44 http://www.babelcolor.com/main_level/ColorChecker.htm
45- :cite:`BabelColor2012c` : BabelColor. (2012). ColorChecker RGB and
46 spectra.
47 http://www.babelcolor.com/download/ColorChecker_RGB_and_spectra.xls
48- :cite:`ImageEngineering2017` : Image Engineering. (2017). TE226 V2 data
49 sheet, from https://www.image-engineering.de/content/products/charts/\
50te226/downloads/TE226_D_data_sheet.pdf
51- :cite:`X-Rite2016` : X-Rite. (2016). New color specifications for
52 ColorChecker SG and Classic Charts. Retrieved October 29, 2018, from
53 http://xritephoto.com/ph_product_overview.aspx?ID=938&Action=Support&\
54SupportID=5884#
55"""
57from __future__ import annotations
59import typing
60from dataclasses import dataclass
62import numpy as np
64from colour.colorimetry import CCS_ILLUMINANTS
66if typing.TYPE_CHECKING:
67 from colour.hints import NDArrayFloat
69from colour.models import Lab_to_XYZ, XYZ_to_xyY
70from colour.utilities import CanonicalMapping
72__author__ = "Colour Developers, Danny Pascale "
73__copyright__ = "Copyright 2013 Colour Developers"
74__copyright__ += ", "
75__copyright__ += (
76 "BabelColor ColorChecker data: Copyright (C) 2004-2012 Danny Pascale "
77 "(www.babelcolor.com); used by permission."
78)
79__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause"
80__maintainer__ = "Colour Developers"
81__email__ = "colour-developers@colour-science.org"
82__status__ = "Production"
84__all__ = [
85 "ColourChecker",
86 "SAMPLE_LABELS_COLORCHECKER_CLASSIC",
87 "DATA_COLORCHECKER1976",
88 "CCS_ILLUMINANT_COLORCHECKER1976",
89 "CCS_COLORCHECKER1976",
90 "DATA_COLORCHECKER2005",
91 "CCS_ILLUMINANT_COLORCHECKER2005",
92 "CCS_COLORCHECKER2005",
93 "DATA_BABELCOLOR_AVERAGE",
94 "CCS_ILLUMINANT_BABELCOLOR_AVERAGE",
95 "CCS_BABELCOLOR_AVERAGE",
96 "DATA_COLORCHECKER24_BEFORE_NOV2014_CIE_LAB",
97 "DATA_COLORCHECKER24_BEFORE_NOV2014",
98 "CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014",
99 "CCS_COLORCHECKER24_BEFORE_NOV2014",
100 "DATA_COLORCHECKER24_AFTER_NOV2014_CIE_LAB",
101 "DATA_COLORCHECKER24_AFTER_NOV2014",
102 "CCS_ILLUMINANT_COLORCHECKER24_AFTER_NOV2014",
103 "CCS_COLORCHECKER24_AFTER_NOV2014",
104 "SAMPLE_LABELS_COLORCHECKER_SG",
105 "DATA_COLORCHECKERSG_BEFORE_NOV2014_CIE_LAB",
106 "DATA_COLORCHECKERSG_BEFORE_NOV2014",
107 "CCS_ILLUMINANT_COLORCHECKERSG_BEFORE_NOV2014",
108 "CCS_COLORCHECKERSG_BEFORE_NOV2014",
109 "DATA_COLORCHECKERSG_AFTER_NOV2014_CIE_LAB",
110 "DATA_COLORCHECKERSG_AFTER_NOV2014",
111 "CCS_ILLUMINANT_COLORCHECKERSG_AFTER_NOV2014",
112 "CCS_COLORCHECKERSG_AFTER_NOV2014",
113 "DATA_TE226_V2_CIE_XYZ",
114 "DATA_TE226_V2",
115 "CCS_ILLUMINANT_TE226_V2",
116 "CCS_TE226_V2",
117 "CCS_COLOURCHECKERS",
118]
121@dataclass(frozen=True)
122class ColourChecker:
123 """
124 *Colour Checker* data.
126 Parameters
127 ----------
128 name
129 *Colour Checker* name.
130 data
131 Chromaticity coordinates in *CIE xyY* colourspace.
132 illuminant
133 *Colour Checker* illuminant chromaticity coordinates.
134 rows
135 *Colour Checker* row count.
136 columns
137 *Colour Checker* column count.
138 """
140 name: str
141 data: dict[str, NDArrayFloat]
142 illuminant: NDArrayFloat
143 rows: int
144 columns: int
147SAMPLE_LABELS_COLORCHECKER_CLASSIC: tuple = (
148 "dark skin",
149 "light skin",
150 "blue sky",
151 "foliage",
152 "blue flower",
153 "bluish green",
154 "orange",
155 "purplish blue",
156 "moderate red",
157 "purple",
158 "yellow green",
159 "orange yellow",
160 "blue",
161 "green",
162 "red",
163 "yellow",
164 "magenta",
165 "cyan",
166 "white 9.5 (.05 D)",
167 "neutral 8 (.23 D)",
168 "neutral 6.5 (.44 D)",
169 "neutral 5 (.70 D)",
170 "neutral 3.5 (1.05 D)",
171 "black 2 (1.5 D)",
172)
173"""*ColorChecker Classic* sample labels."""
175DATA_COLORCHECKER1976: dict = {
176 label: np.array(data)
177 for label, data in zip(
178 SAMPLE_LABELS_COLORCHECKER_CLASSIC,
179 [
180 [0.4002, 0.3504, 0.1005],
181 [0.3773, 0.3446, 0.3582],
182 [0.2470, 0.2514, 0.1933],
183 [0.3372, 0.4220, 0.1329],
184 [0.2651, 0.2400, 0.2427],
185 [0.2608, 0.3430, 0.4306],
186 [0.5060, 0.4070, 0.3005],
187 [0.2110, 0.1750, 0.1200],
188 [0.4533, 0.3058, 0.1977],
189 [0.2845, 0.2020, 0.0656],
190 [0.3800, 0.4887, 0.4429],
191 [0.4729, 0.4375, 0.4306],
192 [0.1866, 0.1285, 0.0611],
193 [0.3046, 0.4782, 0.2339],
194 [0.5385, 0.3129, 0.1200],
195 [0.4480, 0.4703, 0.5910],
196 [0.3635, 0.2325, 0.1977],
197 [0.1958, 0.2519, 0.1977],
198 [0.3101, 0.3163, 0.9001],
199 [0.3101, 0.3163, 0.5910],
200 [0.3101, 0.3163, 0.3620],
201 [0.3101, 0.3163, 0.1977],
202 [0.3101, 0.3163, 0.0900],
203 [0.3101, 0.3163, 0.0313],
204 ],
205 strict=True,
206 )
207}
209CCS_ILLUMINANT_COLORCHECKER1976: NDArrayFloat = CCS_ILLUMINANTS[
210 "CIE 1931 2 Degree Standard Observer"
211]["C"]
212"""*ColorChecker Classic 1976* illuminant."""
214CCS_COLORCHECKER1976: ColourChecker = ColourChecker(
215 "ColorChecker 1976",
216 DATA_COLORCHECKER1976,
217 CCS_ILLUMINANT_COLORCHECKER1976,
218 4,
219 6,
220)
221"""
222*ColorChecker Classic* developed by *McCamy et al.* (1976) at Macbeth, a
223Division of Kollmorgen.
224"""
226DATA_COLORCHECKER2005: dict = {
227 label: np.array(data)
228 for label, data in zip(
229 SAMPLE_LABELS_COLORCHECKER_CLASSIC,
230 [
231 [0.4316, 0.3777, 0.1008],
232 [0.4197, 0.3744, 0.3495],
233 [0.2760, 0.3016, 0.1836],
234 [0.3703, 0.4499, 0.1325],
235 [0.2999, 0.2856, 0.2304],
236 [0.2848, 0.3911, 0.4178],
237 [0.5295, 0.4055, 0.3118],
238 [0.2305, 0.2106, 0.1126],
239 [0.5012, 0.3273, 0.1938],
240 [0.3319, 0.2482, 0.0637],
241 [0.3984, 0.5008, 0.4446],
242 [0.4957, 0.4427, 0.4357],
243 [0.2018, 0.1692, 0.0575],
244 [0.3253, 0.5032, 0.2318],
245 [0.5686, 0.3303, 0.1257],
246 [0.4697, 0.4734, 0.5981],
247 [0.4159, 0.2688, 0.2009],
248 [0.2131, 0.3023, 0.1930],
249 [0.3469, 0.3608, 0.9131],
250 [0.3440, 0.3584, 0.5894],
251 [0.3432, 0.3581, 0.3632],
252 [0.3446, 0.3579, 0.1915],
253 [0.3401, 0.3548, 0.0883],
254 [0.3406, 0.3537, 0.0311],
255 ],
256 strict=True,
257 )
258}
260CCS_ILLUMINANT_COLORCHECKER2005: NDArrayFloat = CCS_ILLUMINANTS[
261 "CIE 1931 2 Degree Standard Observer"
262]["ICC D50"]
263"""*ColorChecker Classic 2005* illuminant."""
265CCS_COLORCHECKER2005: ColourChecker = ColourChecker(
266 "ColorChecker 2005",
267 DATA_COLORCHECKER2005,
268 CCS_ILLUMINANT_COLORCHECKER2005,
269 4,
270 6,
271)
272"""*ColorChecker Classic* data from *GretagMacbeth (2005)*."""
273DATA_BABELCOLOR_AVERAGE: dict = {
274 label: np.array(data)
275 for label, data in zip(
276 SAMPLE_LABELS_COLORCHECKER_CLASSIC,
277 [
278 [0.4325, 0.3788, 0.1034],
279 [0.4191, 0.3748, 0.3525],
280 [0.2761, 0.3004, 0.1847],
281 [0.3700, 0.4501, 0.1335],
282 [0.3020, 0.2877, 0.2324],
283 [0.2856, 0.3910, 0.4174],
284 [0.5291, 0.4075, 0.3117],
285 [0.2339, 0.2155, 0.1140],
286 [0.5008, 0.3293, 0.1979],
287 [0.3326, 0.2556, 0.0644],
288 [0.3989, 0.4998, 0.4435],
289 [0.4962, 0.4428, 0.4358],
290 [0.2040, 0.1696, 0.0579],
291 [0.3270, 0.5033, 0.2307],
292 [0.5709, 0.3298, 0.1268],
293 [0.4694, 0.4732, 0.6081],
294 [0.4177, 0.2704, 0.2007],
295 [0.2151, 0.3037, 0.1903],
296 [0.3488, 0.3628, 0.9129],
297 [0.3451, 0.3596, 0.5885],
298 [0.3446, 0.3590, 0.3595],
299 [0.3438, 0.3589, 0.1912],
300 [0.3423, 0.3576, 0.0893],
301 [0.3439, 0.3565, 0.0320],
302 ],
303 strict=True,
304 )
305}
307CCS_ILLUMINANT_BABELCOLOR_AVERAGE: NDArrayFloat = CCS_ILLUMINANTS[
308 "CIE 1931 2 Degree Standard Observer"
309]["ICC D50"]
310"""*BabelColor Average* illuminant."""
312CCS_BABELCOLOR_AVERAGE: ColourChecker = ColourChecker(
313 "BabelColor Average",
314 DATA_BABELCOLOR_AVERAGE,
315 CCS_ILLUMINANT_BABELCOLOR_AVERAGE,
316 4,
317 6,
318)
319"""Average data derived from measurements of 30 *ColorChecker Classic* charts."""
321DATA_COLORCHECKER24_BEFORE_NOV2014_CIE_LAB: dict = {
322 label: np.array(data)
323 for label, data in zip(
324 SAMPLE_LABELS_COLORCHECKER_CLASSIC,
325 [
326 [37.986, 13.555, 14.059],
327 [65.711, 18.13, 17.81],
328 [49.927, -4.88, -21.905],
329 [43.139, -13.095, 21.905],
330 [55.112, 8.844, -25.399],
331 [70.719, -33.397, -0.199],
332 [62.661, 36.067, 57.096],
333 [40.02, 10.41, -45.964],
334 [51.124, 48.239, 16.248],
335 [30.325, 22.976, -21.587],
336 [72.532, -23.709, 57.255],
337 [71.941, 19.363, 67.857],
338 [28.778, 14.179, -50.297],
339 [55.261, -38.342, 31.37],
340 [42.101, 53.378, 28.19],
341 [81.733, 4.039, 79.819],
342 [51.935, 49.986, -14.574],
343 [51.038, -28.631, -28.638],
344 [96.539, -0.425, 1.186],
345 [81.257, -0.638, -0.335],
346 [66.766, -0.734, -0.504],
347 [50.867, -0.153, -0.27],
348 [35.656, -0.421, -1.231],
349 [20.461, -0.079, -0.973],
350 ],
351 strict=True,
352 )
353}
355DATA_COLORCHECKER24_BEFORE_NOV2014: dict = dict(
356 zip(
357 SAMPLE_LABELS_COLORCHECKER_CLASSIC,
358 XYZ_to_xyY(
359 Lab_to_XYZ(
360 list(DATA_COLORCHECKER24_BEFORE_NOV2014_CIE_LAB.values()),
361 CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["ICC D50"],
362 )
363 ),
364 strict=True,
365 )
366)
368CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014: NDArrayFloat = CCS_ILLUMINANTS[
369 "CIE 1931 2 Degree Standard Observer"
370]["ICC D50"]
371"""*ColorChecker24 - Before November 2014* illuminant."""
373CCS_COLORCHECKER24_BEFORE_NOV2014: ColourChecker = ColourChecker(
374 "ColorChecker24 - Before November 2014",
375 DATA_COLORCHECKER24_BEFORE_NOV2014,
376 CCS_ILLUMINANT_COLORCHECKER24_BEFORE_NOV2014,
377 4,
378 6,
379)
380"""
381Reference *ColorChecker Classic* data from *X-Rite (2016)*.
383Notes
384-----
385- The rounded *ColorChecker24 - Before November 2014* values should match the
386 *ColorChecker Classic 2005* values. They are specified for reference of the
387 original *CIE L\\*a\\*b\\** colourspace values.
388"""
390DATA_COLORCHECKER24_AFTER_NOV2014_CIE_LAB: dict = {
391 label: np.array(data)
392 for label, data in zip(
393 SAMPLE_LABELS_COLORCHECKER_CLASSIC,
394 [
395 [37.54, 14.37, 14.92],
396 [64.66, 19.27, 17.5],
397 [49.32, -3.82, -22.54],
398 [43.46, -12.74, 22.72],
399 [54.94, 9.61, -24.79],
400 [70.48, -32.26, -0.37],
401 [62.73, 35.83, 56.5],
402 [39.43, 10.75, -45.17],
403 [50.57, 48.64, 16.67],
404 [30.1, 22.54, -20.87],
405 [71.77, -24.13, 58.19],
406 [71.51, 18.24, 67.37],
407 [28.37, 15.42, -49.8],
408 [54.38, -39.72, 32.27],
409 [42.43, 51.05, 28.62],
410 [81.8, 2.67, 80.41],
411 [50.63, 51.28, -14.12],
412 [49.57, -29.71, -28.32],
413 [95.19, -1.03, 2.93],
414 [81.29, -0.57, 0.44],
415 [66.89, -0.75, -0.06],
416 [50.76, -0.13, 0.14],
417 [35.63, -0.46, -0.48],
418 [20.64, 0.07, -0.46],
419 ],
420 strict=True,
421 )
422}
424DATA_COLORCHECKER24_AFTER_NOV2014: dict = dict(
425 zip(
426 SAMPLE_LABELS_COLORCHECKER_CLASSIC,
427 XYZ_to_xyY(
428 Lab_to_XYZ(
429 list(DATA_COLORCHECKER24_AFTER_NOV2014_CIE_LAB.values()),
430 CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["ICC D50"],
431 )
432 ),
433 strict=True,
434 )
435)
437CCS_ILLUMINANT_COLORCHECKER24_AFTER_NOV2014: NDArrayFloat = CCS_ILLUMINANTS[
438 "CIE 1931 2 Degree Standard Observer"
439]["ICC D50"]
440"""*ColorChecker24 - After November 2014* illuminant."""
442CCS_COLORCHECKER24_AFTER_NOV2014: ColourChecker = ColourChecker(
443 "ColorChecker24 - After November 2014",
444 DATA_COLORCHECKER24_AFTER_NOV2014,
445 CCS_ILLUMINANT_COLORCHECKER24_AFTER_NOV2014,
446 4,
447 6,
448)
449"""
450Reference *ColorChecker Classic* data from *X-Rite (2016)* and matching the
451*ColorChecker Classic* edition after November 2014.
452"""
454SAMPLE_LABELS_COLORCHECKER_SG: tuple = (
455 "A1",
456 "A2",
457 "A3",
458 "A4",
459 "A5",
460 "A6",
461 "A7",
462 "A8",
463 "A9",
464 "A10",
465 "B1",
466 "B2",
467 "B3",
468 "B4",
469 "B5",
470 "B6",
471 "B7",
472 "B8",
473 "B9",
474 "B10",
475 "C1",
476 "C2",
477 "C3",
478 "C4",
479 "C5",
480 "C6",
481 "C7",
482 "C8",
483 "C9",
484 "C10",
485 "D1",
486 "D2",
487 "D3",
488 "D4",
489 "D5",
490 "D6",
491 "D7",
492 "D8",
493 "D9",
494 "D10",
495 "E1",
496 "E2",
497 "E3",
498 "E4",
499 "E5",
500 "E6",
501 "E7",
502 "E8",
503 "E9",
504 "E10",
505 "F1",
506 "F2",
507 "F3",
508 "F4",
509 "F5",
510 "F6",
511 "F7",
512 "F8",
513 "F9",
514 "F10",
515 "G1",
516 "G2",
517 "G3",
518 "G4",
519 "G5",
520 "G6",
521 "G7",
522 "G8",
523 "G9",
524 "G10",
525 "H1",
526 "H2",
527 "H3",
528 "H4",
529 "H5",
530 "H6",
531 "H7",
532 "H8",
533 "H9",
534 "H10",
535 "I1",
536 "I2",
537 "I3",
538 "I4",
539 "I5",
540 "I6",
541 "I7",
542 "I8",
543 "I9",
544 "I10",
545 "J1",
546 "J2",
547 "J3",
548 "J4",
549 "J5",
550 "J6",
551 "J7",
552 "J8",
553 "J9",
554 "J10",
555 "K1",
556 "K2",
557 "K3",
558 "K4",
559 "K5",
560 "K6",
561 "K7",
562 "K8",
563 "K9",
564 "K10",
565 "L1",
566 "L2",
567 "L3",
568 "L4",
569 "L5",
570 "L6",
571 "L7",
572 "L8",
573 "L9",
574 "L10",
575 "M1",
576 "M2",
577 "M3",
578 "M4",
579 "M5",
580 "M6",
581 "M7",
582 "M8",
583 "M9",
584 "M10",
585 "N1",
586 "N2",
587 "N3",
588 "N4",
589 "N5",
590 "N6",
591 "N7",
592 "N8",
593 "N9",
594 "N10",
595)
596"""*ColorChecker SG* sample labels."""
598DATA_COLORCHECKERSG_BEFORE_NOV2014_CIE_LAB: dict = {
599 label: np.array(data)
600 for label, data in zip(
601 SAMPLE_LABELS_COLORCHECKER_SG,
602 [
603 [96.55, -0.91, 0.57],
604 [6.43, -0.06, -0.41],
605 [49.7, -0.18, 0.03],
606 [96.5, -0.89, 0.59],
607 [6.5, -0.06, -0.44],
608 [49.66, -0.2, 0.01],
609 [96.52, -0.91, 0.58],
610 [6.49, -0.02, -0.28],
611 [49.72, -0.2, 0.04],
612 [96.43, -0.91, 0.67],
613 [49.72, -0.19, 0.02],
614 [32.6, 51.58, -10.85],
615 [60.75, 26.22, -18.69],
616 [28.69, 48.28, -39],
617 [49.38, -15.43, -48.48],
618 [60.63, -30.77, -26.23],
619 [19.29, -26.37, -6.15],
620 [60.15, -41.77, -12.6],
621 [21.42, 1.67, 8.79],
622 [49.69, -0.2, 0.01],
623 [6.5, -0.03, -0.67],
624 [21.82, 17.33, -18.35],
625 [41.53, 18.48, -37.26],
626 [19.99, -0.16, -36.29],
627 [60.16, -18.45, -31.42],
628 [19.94, -17.92, -20.96],
629 [60.68, -6.05, -32.81],
630 [50.81, -49.8, -9.63],
631 [60.65, -39.77, 20.76],
632 [6.53, -0.03, -0.43],
633 [96.56, -0.91, 0.59],
634 [84.19, -1.95, -8.23],
635 [84.75, 14.55, 0.23],
636 [84.87, -19.07, -0.82],
637 [85.15, 13.48, 6.82],
638 [84.17, -10.45, 26.78],
639 [61.74, 31.06, 36.42],
640 [64.37, 20.82, 18.92],
641 [50.4, -53.22, 14.62],
642 [96.51, -0.89, 0.65],
643 [49.74, -0.19, 0.03],
644 [31.91, 18.62, 21.99],
645 [60.74, 38.66, 70.97],
646 [19.35, 22.23, -58.86],
647 [96.52, -0.91, 0.62],
648 [6.66, 0, -0.3],
649 [76.51, 20.81, 22.72],
650 [72.79, 29.15, 24.18],
651 [22.33, -20.7, 5.75],
652 [49.7, -0.19, 0.01],
653 [6.53, -0.05, -0.61],
654 [63.42, 20.19, 19.22],
655 [34.94, 11.64, -50.7],
656 [52.03, -44.15, 39.04],
657 [79.43, 0.29, -0.17],
658 [30.67, -0.14, -0.53],
659 [63.6, 14.44, 26.07],
660 [64.37, 14.5, 17.05],
661 [60.01, -44.33, 8.49],
662 [6.63, -0.01, -0.47],
663 [96.56, -0.93, 0.59],
664 [46.37, -5.09, -24.46],
665 [47.08, 52.97, 20.49],
666 [36.04, 64.92, 38.51],
667 [65.05, 0, -0.32],
668 [40.14, -0.19, -0.38],
669 [43.77, 16.46, 27.12],
670 [64.39, 17, 16.59],
671 [60.79, -29.74, 41.5],
672 [96.48, -0.89, 0.64],
673 [49.75, -0.21, 0.01],
674 [38.18, -16.99, 30.87],
675 [21.31, 29.14, -27.51],
676 [80.57, 3.85, 89.61],
677 [49.71, -0.2, 0.03],
678 [60.27, 0.08, -0.41],
679 [67.34, 14.45, 16.9],
680 [64.69, 16.95, 18.57],
681 [51.12, -49.31, 44.41],
682 [49.7, -0.2, 0.02],
683 [6.67, -0.05, -0.64],
684 [51.56, 9.16, -26.88],
685 [70.83, -24.26, 64.77],
686 [48.06, 55.33, -15.61],
687 [35.26, -0.09, -0.24],
688 [75.16, 0.25, -0.2],
689 [44.54, 26.27, 38.93],
690 [35.91, 16.59, 26.46],
691 [61.49, -52.73, 47.3],
692 [6.59, -0.05, -0.5],
693 [96.58, -0.9, 0.61],
694 [68.93, -34.58, -0.34],
695 [69.65, 20.09, 78.57],
696 [47.79, -33.18, -30.21],
697 [15.94, -0.42, -1.2],
698 [89.02, -0.36, -0.48],
699 [63.43, 25.44, 26.25],
700 [65.75, 22.06, 27.82],
701 [61.47, 17.1, 50.72],
702 [96.53, -0.89, 0.66],
703 [49.79, -0.2, 0.03],
704 [85.17, 10.89, 17.26],
705 [89.74, -16.52, 6.19],
706 [84.55, 5.07, -6.12],
707 [84.02, -13.87, -8.72],
708 [70.76, 0.07, -0.35],
709 [45.59, -0.05, 0.23],
710 [20.3, 0.07, -0.32],
711 [61.79, -13.41, 55.42],
712 [49.72, -0.19, 0.02],
713 [6.77, -0.05, -0.44],
714 [21.85, 34.37, 7.83],
715 [42.66, 67.43, 48.42],
716 [60.33, 36.56, 3.56],
717 [61.22, 36.61, 17.32],
718 [62.07, 52.8, 77.14],
719 [72.42, -9.82, 89.66],
720 [62.03, 3.53, 57.01],
721 [71.95, -27.34, 73.69],
722 [6.59, -0.04, -0.45],
723 [49.77, -0.19, 0.04],
724 [41.84, 62.05, 10.01],
725 [19.78, 29.16, -7.85],
726 [39.56, 65.98, 33.71],
727 [52.39, 68.33, 47.84],
728 [81.23, 24.12, 87.51],
729 [81.8, 6.78, 95.75],
730 [71.72, -16.23, 76.28],
731 [20.31, 14.45, 16.74],
732 [49.68, -0.19, 0.05],
733 [96.48, -0.88, 0.68],
734 [49.69, -0.18, 0.03],
735 [6.39, -0.04, -0.33],
736 [96.54, -0.9, 0.67],
737 [49.72, -0.18, 0.05],
738 [6.49, -0.03, -0.41],
739 [96.51, -0.9, 0.69],
740 [49.7, -0.19, 0.07],
741 [6.47, 0, -0.38],
742 [96.46, -0.89, 0.71],
743 ],
744 strict=True,
745 )
746}
748_DATA_COLORCHECKERSG_BEFORE_NOV2014 = np.reshape(
749 np.transpose(
750 np.reshape(
751 np.array(
752 list(
753 zip(
754 SAMPLE_LABELS_COLORCHECKER_SG,
755 DATA_COLORCHECKERSG_BEFORE_NOV2014_CIE_LAB.values(),
756 strict=True,
757 )
758 ),
759 dtype=object,
760 ),
761 (14, 10, 2),
762 ),
763 [1, 0, 2],
764 ),
765 (-1, 2),
766)
768DATA_COLORCHECKERSG_BEFORE_NOV2014: dict = dict(
769 zip(
770 _DATA_COLORCHECKERSG_BEFORE_NOV2014[..., 0],
771 XYZ_to_xyY(
772 Lab_to_XYZ(
773 list(_DATA_COLORCHECKERSG_BEFORE_NOV2014[..., 1]),
774 CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["ICC D50"],
775 )
776 ),
777 strict=True,
778 )
779)
781del _DATA_COLORCHECKERSG_BEFORE_NOV2014
783CCS_ILLUMINANT_COLORCHECKERSG_BEFORE_NOV2014: NDArrayFloat = CCS_ILLUMINANTS[
784 "CIE 1931 2 Degree Standard Observer"
785]["ICC D50"]
786"""*ColorCheckerSG - Before November 2014* illuminant."""
788CCS_COLORCHECKERSG_BEFORE_NOV2014: ColourChecker = ColourChecker(
789 "ColorCheckerSG - Before November 2014",
790 DATA_COLORCHECKERSG_BEFORE_NOV2014,
791 CCS_ILLUMINANT_COLORCHECKERSG_BEFORE_NOV2014,
792 10,
793 14,
794)
795"""
796Reference *ColorChecker SG* data from *X-Rite (2016)*.
797"""
799DATA_COLORCHECKERSG_AFTER_NOV2014_CIE_LAB: dict = {
800 label: np.array(data)
801 for label, data in zip(
802 SAMPLE_LABELS_COLORCHECKER_SG,
803 [
804 [96.71, -0.62, 2.06],
805 [8.05, 0.17, -0.69],
806 [49.76, 0.11, 0.72],
807 [96.72, -0.63, 2.06],
808 [8.17, 0.15, -0.65],
809 [49.68, 0.14, 0.74],
810 [96.60, -0.62, 2.11],
811 [7.99, 0.21, -0.75],
812 [49.67, 0.15, 0.73],
813 [96.51, -0.63, 2.11],
814 [49.70, 0.12, 0.68],
815 [33.02, 52.00, -10.30],
816 [61.40, 27.14, -18.42],
817 [30.54, 50.39, -41.79],
818 [49.56, -13.90, -49.65],
819 [60.62, -29.91, -27.54],
820 [20.13, -24.81, -7.50],
821 [60.32, -40.29, -13.25],
822 [19.62, 1.77, 11.99],
823 [49.68, 0.15, 0.78],
824 [8.13, 0.15, -0.76],
825 [19.65, 20.42, -18.82],
826 [41.70, 18.90, -37.42],
827 [20.25, 0.26, -36.44],
828 [60.13, -17.88, -32.08],
829 [19.75, -17.79, -22.37],
830 [60.43, -5.12, -32.79],
831 [50.46, -47.90, -11.56],
832 [60.53, -40.75, 19.37],
833 [8.09, 0.19, -0.69],
834 [96.79, -0.66, 1.99],
835 [84.00, -1.70, -8.37],
836 [85.48, 15.15, 0.79],
837 [84.56, -19.74, -1.13],
838 [85.26, 13.37, 7.95],
839 [84.38, -11.97, 27.16],
840 [62.35, 29.94, 36.89],
841 [64.17, 21.34, 19.36],
842 [50.48, -53.21, 12.65],
843 [96.57, -0.64, 2.00],
844 [49.79, 0.13, 0.66],
845 [32.77, 19.91, 22.33],
846 [62.28, 37.56, 68.87],
847 [19.92, 25.07, -61.05],
848 [96.78, -0.66, 2.01],
849 [8.07, 0.12, -0.93],
850 [77.37, 20.28, 24.27],
851 [74.01, 29.00, 25.80],
852 [20.33, -23.98, 7.20],
853 [49.72, 0.14, 0.71],
854 [8.09, 0.19, -0.69],
855 [63.88, 20.34, 19.93],
856 [35.28, 12.93, -51.17],
857 [52.75, -44.12, 38.68],
858 [79.65, -0.08, 0.62],
859 [30.32, -0.10, 0.22],
860 [63.46, 13.53, 26.37],
861 [64.44, 14.31, 17.63],
862 [60.05, -44.00, 7.27],
863 [8.08, 0.18, -0.78],
864 [96.70, -0.66, 1.97],
865 [45.84, -3.74, -25.32],
866 [47.60, 53.66, 22.15],
867 [36.88, 65.72, 41.63],
868 [65.22, -0.27, 0.16],
869 [39.55, -0.37, -0.09],
870 [44.49, 16.06, 26.79],
871 [64.97, 15.89, 16.79],
872 [60.77, -30.19, 40.76],
873 [96.71, -0.64, 2.01],
874 [49.74, 0.14, 0.68],
875 [38.29, -17.44, 30.22],
876 [20.76, 31.66, -28.04],
877 [81.43, 2.41, 88.98],
878 [49.71, 0.12, 0.69],
879 [60.04, 0.09, 0.05],
880 [67.60, 14.47, 17.12],
881 [64.75, 17.30, 18.88],
882 [51.26, -50.65, 43.80],
883 [49.76, 0.14, 0.71],
884 [8.10, 0.19, -0.93],
885 [51.36, 9.52, -26.98],
886 [71.62, -24.77, 64.10],
887 [48.75, 57.24, -14.45],
888 [34.85, -0.21, 0.73],
889 [75.36, 0.35, 0.26],
890 [45.14, 26.38, 41.24],
891 [36.20, 16.70, 27.06],
892 [61.65, -54.33, 46.18],
893 [7.97, 0.14, -0.80],
894 [96.69, -0.67, 1.95],
895 [68.71, -35.41, -1.11],
896 [70.39, 19.37, 79.73],
897 [47.42, -30.91, -32.27],
898 [15.43, -0.24, -0.25],
899 [88.85, -0.59, 0.25],
900 [64.00, 25.09, 27.14],
901 [66.65, 22.21, 28.81],
902 [62.05, 16.45, 51.74],
903 [96.71, -0.64, 2.02],
904 [49.72, 0.12, 0.64],
905 [85.68, 10.75, 18.39],
906 [89.35, -16.38, 6.41],
907 [84.59, 5.21, -5.87],
908 [83.63, -12.47, -8.89],
909 [70.60, -0.24, 0.07],
910 [45.14, -0.04, 0.86],
911 [20.33, 0.40, -0.21],
912 [62.33, -14.54, 54.58],
913 [49.74, 0.14, 0.69],
914 [8.08, 0.13, -0.81],
915 [23.03, 33.95, 8.88],
916 [44.35, 67.94, 50.62],
917 [60.91, 36.55, 4.15],
918 [62.20, 37.45, 18.18],
919 [63.33, 51.30, 81.88],
920 [73.74, -11.45, 85.07],
921 [62.35, 1.96, 57.52],
922 [72.77, -29.09, 71.26],
923 [8.13, 0.15, -0.86],
924 [49.71, 0.12, 0.62],
925 [42.52, 63.55, 11.43],
926 [18.09, 32.61, -5.90],
927 [40.66, 65.54, 31.98],
928 [53.13, 68.44, 49.57],
929 [82.08, 23.39, 87.24],
930 [82.50, 5.29, 96.68],
931 [71.90, -17.32, 77.72],
932 [21.95, 13.41, 16.36],
933 [49.74, 0.12, 0.69],
934 [96.79, -0.67, 1.97],
935 [49.78, 0.12, 0.65],
936 [8.23, 0.18, -0.82],
937 [96.73, -0.67, 1.99],
938 [49.80, 0.11, 0.67],
939 [8.18, 0.15, -0.84],
940 [96.73, -0.65, 2.01],
941 [49.75, 0.13, 0.67],
942 [8.11, 0.15, -0.90],
943 [96.55, -0.64, 2.02],
944 ],
945 strict=True,
946 )
947}
949_DATA_COLORCHECKERSG_AFTER_NOV2014 = np.reshape(
950 np.transpose(
951 np.reshape(
952 np.array(
953 list(
954 zip(
955 SAMPLE_LABELS_COLORCHECKER_SG,
956 DATA_COLORCHECKERSG_AFTER_NOV2014_CIE_LAB.values(),
957 strict=True,
958 )
959 ),
960 dtype=object,
961 ),
962 (14, 10, 2),
963 ),
964 [1, 0, 2],
965 ),
966 (-1, 2),
967)
969DATA_COLORCHECKERSG_AFTER_NOV2014: dict = dict(
970 zip(
971 _DATA_COLORCHECKERSG_AFTER_NOV2014[..., 0],
972 XYZ_to_xyY(
973 Lab_to_XYZ(
974 list(_DATA_COLORCHECKERSG_AFTER_NOV2014[..., 1]),
975 CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["ICC D50"],
976 )
977 ),
978 strict=True,
979 )
980)
982del _DATA_COLORCHECKERSG_AFTER_NOV2014
984CCS_ILLUMINANT_COLORCHECKERSG_AFTER_NOV2014: NDArrayFloat = CCS_ILLUMINANTS[
985 "CIE 1931 2 Degree Standard Observer"
986]["ICC D50"]
987"""*ColorCheckerSG - After November 2014* illuminant."""
989CCS_COLORCHECKERSG_AFTER_NOV2014: ColourChecker = ColourChecker(
990 "ColorCheckerSG - After November 2014",
991 DATA_COLORCHECKERSG_AFTER_NOV2014,
992 CCS_ILLUMINANT_COLORCHECKERSG_AFTER_NOV2014,
993 10,
994 14,
995)
996"""
997Reference *ColorChecker SG* data from *X-Rite (2016)* and matching the
998*ColorChecker SG* edition after November 2014.
999"""
1001DATA_TE226_V2_CIE_XYZ: dict = {
1002 label: np.array(data)
1003 for label, data in {
1004 "dark skin": [0.1278, 0.1074, 0.0726],
1005 "light skin": [0.4945, 0.4484, 0.3586],
1006 "blue sky": [0.1459, 0.1690, 0.2925],
1007 "foliage": [0.0714, 0.1243, 0.0254],
1008 "blue flower": [0.4470, 0.4039, 0.7304],
1009 "bluish green": [0.3921, 0.5420, 0.6113],
1010 "orange": [0.4574, 0.3628, 0.0624],
1011 "purplish blue": [0.2979, 0.3180, 0.8481],
1012 "moderate red": [0.3884, 0.2794, 0.1886],
1013 "purple": [0.1324, 0.0796, 0.3824],
1014 "yellow green": [0.3399, 0.5786, 0.1360],
1015 "orange yellow": [0.5417, 0.4677, 0.0644],
1016 "blue": [0.0859, 0.0361, 0.4728],
1017 "green": [0.1000, 0.2297, 0.0530],
1018 "red": [0.3594, 0.1796, 0.0197],
1019 "yellow": [0.5236, 0.5972, 0.0368],
1020 "magenta": [0.4253, 0.2050, 0.5369],
1021 "cyan": [0.4942, 0.6119, 1.0304],
1022 "patch 19": [0.2646, 0.2542, 0.1631],
1023 "patch 20": [0.7921, 0.7560, 0.5988],
1024 "patch 21": [0.4409, 0.4004, 0.3366],
1025 "patch 22": [0.1546, 0.3395, 0.1016],
1026 "patch 23": [0.3182, 0.3950, 0.5857],
1027 "patch 24": [0.5920, 0.5751, 0.9892],
1028 "patch 25": [0.4287, 0.2583, 0.0444],
1029 "patch 26": [0.4282, 0.5757, 0.4770],
1030 "patch 27": [0.1697, 0.1294, 0.7026],
1031 "patch 28": [0.2143, 0.1564, 0.1908],
1032 "patch 29": [0.1659, 0.3876, 0.3945],
1033 "patch 30": [0.1869, 0.1093, 0.7069],
1034 "patch 31": [0.3316, 0.1596, 0.1714],
1035 "patch 32": [0.8298, 0.8910, 0.5199],
1036 "patch 33": [0.1412, 0.1758, 0.4643],
1037 "patch 34": [0.0153, 0.0668, 0.0694],
1038 "patch 35": [0.6053, 0.5088, 0.1593],
1039 "patch 36": [0.4217, 0.4459, 0.3173],
1040 "white": [0.9505, 1.0000, 1.0888],
1041 "neutral 87": [0.8331, 0.8801, 0.9576],
1042 "neutral 63": [0.6050, 0.6401, 0.6958],
1043 "neutral 44": [0.4119, 0.4358, 0.4724],
1044 "neutral 28": [0.2638, 0.2798, 0.3018],
1045 "neutral 15": [0.1405, 0.1489, 0.1598],
1046 "neutral 7": [0.0628, 0.0665, 0.0701],
1047 "neutral 2": [0.0190, 0.0202, 0.0202],
1048 "neutral < 0.1": [0.0000, 0.0001, 0.0000],
1049 }.items()
1050}
1052DATA_TE226_V2: dict = dict(
1053 zip(
1054 tuple(DATA_TE226_V2_CIE_XYZ.keys()),
1055 XYZ_to_xyY(list(DATA_TE226_V2_CIE_XYZ.values())),
1056 strict=True,
1057 )
1058)
1060CCS_ILLUMINANT_TE226_V2: NDArrayFloat = CCS_ILLUMINANTS[
1061 "CIE 1931 2 Degree Standard Observer"
1062]["D65"]
1063"""*TE226 V2* illuminant."""
1065CCS_TE226_V2: ColourChecker = ColourChecker(
1066 "TE226 V2", DATA_TE226_V2, CCS_ILLUMINANT_TE226_V2, 5, 9
1067)
1068"""
1069Reference data from *TE226 V2*. Transparent color rendition test chart
1070for HDTV cameras, in addition to known colors from "ColorChecker", the test
1071chart contains colors which are critical in reproduction.
1072"""
1074CCS_COLOURCHECKERS: CanonicalMapping = CanonicalMapping(
1075 {
1076 "ColorChecker 1976": CCS_COLORCHECKER1976,
1077 "ColorChecker 2005": CCS_COLORCHECKER2005,
1078 "BabelColor Average": CCS_BABELCOLOR_AVERAGE,
1079 "ColorChecker24 - Before November 2014": CCS_COLORCHECKER24_BEFORE_NOV2014,
1080 "ColorChecker24 - After November 2014": CCS_COLORCHECKER24_AFTER_NOV2014,
1081 "ColorCheckerSG - Before November 2014": CCS_COLORCHECKERSG_BEFORE_NOV2014,
1082 "ColorCheckerSG - After November 2014": CCS_COLORCHECKERSG_AFTER_NOV2014,
1083 "TE226 V2": CCS_TE226_V2,
1084 }
1085)
1086CCS_COLOURCHECKERS.__doc__ = """
1087Chromaticity coordinates of the colour checkers.
1089References
1090----------
1091:cite:`BabelColor2012b`, :cite:`BabelColor2012c`,
1092:cite:`ImageEngineering2017`, :cite:`X-Rite2016`
1094Aliases:
1096- 'babel_average': 'BabelColor Average'
1097- 'cc2005': 'ColorChecker 2005'
1098- 'ccb2014': 'ColorChecker24 - Before November 2014'
1099- 'cca2014': 'ColorChecker24 - After November 2014'
1100"""
1101CCS_COLOURCHECKERS["babel_average"] = CCS_COLOURCHECKERS["BabelColor Average"]
1102CCS_COLOURCHECKERS["cc2005"] = CCS_COLOURCHECKERS["ColorChecker 2005"]
1103CCS_COLOURCHECKERS["ccb2014"] = CCS_COLOURCHECKERS[
1104 "ColorChecker24 - Before November 2014"
1105]
1106CCS_COLOURCHECKERS["cca2014"] = CCS_COLOURCHECKERS[
1107 "ColorChecker24 - After November 2014"
1108]