aboutsummaryrefslogtreecommitdiff
path: root/src/argaze/ArFeatures.py
blob: 5fa5cde3540c0491c53685777f9bb6e3509473ae (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
#!/usr/bin/env python

"""Manage AR environement assets."""

__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "BSD"

from typing import TypeVar, Tuple
from dataclasses import dataclass, field
import json
import os
import importlib
from inspect import getmembers
import threading
import time

from argaze import DataStructures, GazeFeatures
from argaze.ArUcoMarkers import *
from argaze.AreaOfInterest import *
from argaze.GazeAnalysis import *

import numpy
import cv2

ArLayerType = TypeVar('ArLayer', bound="ArLayer")
# Type definition for type annotation convenience

ArFrameType = TypeVar('ArFrame', bound="ArFrame")
# Type definition for type annotation convenience

ArSceneType = TypeVar('ArScene', bound="ArScene")
# Type definition for type annotation convenience

ArEnvironmentType = TypeVar('ArEnvironment', bound="ArEnvironment")
# Type definition for type annotation convenience

class PoseEstimationFailed(Exception):
	"""
	Exception raised by ArScene estimate_pose method when the pose can't be estimated due to unconsistencies.
	"""

	def __init__(self, message, unconsistencies=None):  

		super().__init__(message)

		self.unconsistencies = unconsistencies

class SceneProjectionFailed(Exception):
	"""
	Exception raised by ArEnvironment detect_and_project method when the scene can't be projected.
	"""

	def __init__(self, message):  

		super().__init__(message)

class LoadingFailed(Exception):
	"""
	Exception raised when attributes loading fails.
	"""

	def __init__(self, message):  

		super().__init__(message)

@dataclass
class ArLayer():
	"""
	Defines a space where to make matching of gaze movements and AOIs and inside which those matchings need to be analyzed.

	Parameters:
		name: name of the layer
		aoi_color: color to used in draw method
		aoi_scene: AOI scene description
		looked_aoi_covering_threshold: 
		aoi_scan_path: AOI scan path object
		aoi_scan_path_analyzers: dictionary of AOI scan path analyzers
	"""

	name: str
	aoi_color: tuple = field(default=(0, 0, 0))
	aoi_scene: AOIFeatures.AOIScene = field(default_factory=AOIFeatures.AOIScene)
	looked_aoi_covering_threshold: int = field(default=0)
	aoi_scan_path: GazeFeatures.AOIScanPath = field(default_factory=GazeFeatures.AOIScanPath)
	aoi_scan_path_analyzers: dict = field(default_factory=dict)
	
	def __post_init__(self):

		# Define parent attribute: it will be setup by parent later
		self.__parent = None

		# Init current gaze movement
		self.__gaze_movement = GazeFeatures.UnvalidGazeMovement()

		# Init looked aoi data
		self.__init_looked_aoi_data()

		# Init lock to share looking data with multiples threads
		self.__look_lock = threading.Lock()

		# Cast aoi scene to its effective dimension
		if self.aoi_scene.dimension == 2:

			self.aoi_scene = AOI2DScene.AOI2DScene(self.aoi_scene)

		elif self.aoi_scene.dimension == 3:

			self.aoi_scene = AOI3DScene.AOI3DScene(self.aoi_scene)

	@classmethod
	def from_dict(self, layer_data, working_directory: str = None) -> ArLayerType:
		"""Load attributes from dictionary.

		Parameters:
			layer_data: dictionary with attributes to load
			working_directory: folder path where to load files when a dictionary value is a relative filepath.
		"""

		# Load name
		try:

			new_layer_name = layer_data.pop('name')

		except KeyError:

			new_layer_name = None

		# Load aoi color
		try:

			new_aoi_color = layer_data.pop('aoi_color')

		except KeyError:

			new_aoi_color = (0, 0, 0)

		# Load optional aoi filter
		try:

			aoi_exclude_list = layer_data.pop('aoi_exclude')

		except KeyError:

			aoi_exclude_list = []

		# Load aoi scene
		try:

			new_aoi_scene_value = layer_data.pop('aoi_scene')

			# str: relative path to file
			if type(new_aoi_scene_value) == str:

				filepath = os.path.join(working_directory, new_aoi_scene_value)
				file_format = filepath.split('.')[-1]

				# JSON file format for 2D or 3D dimension
				if file_format == 'json':

					new_aoi_scene = AOIFeatures.AOIScene.from_json(filepath).copy(exclude=aoi_exclude_list)

				# OBJ file format for 3D dimension only
				elif file_format == 'obj':

					new_aoi_scene = AOI3DScene.AOI3DScene.from_obj(filepath).copy(exclude=aoi_exclude_list)

			# dict:
			else:

				new_aoi_scene = AOIFeatures.AOIScene.from_dict(new_aoi_scene_value)

		except KeyError:

			# Add AOI 2D Scene by default
			new_aoi_scene = AOI2DScene.AOI2DScene()

		# Looked aoi validity threshold
		try:

			looked_aoi_covering_threshold = layer_data.pop('looked_aoi_covering_threshold')

		except KeyError:

			looked_aoi_covering_threshold = 0

		# Edit expected AOI list by removing AOI with name equals to layer name
		expected_aois = list(new_aoi_scene.keys())

		if new_layer_name in expected_aois:
			expected_aois.remove(new_layer_name)

		# Load AOI scan path
		try:

			new_aoi_scan_path_data = layer_data.pop('aoi_scan_path')
			new_aoi_scan_path_data['expected_aois'] = expected_aois
			new_aoi_scan_path = GazeFeatures.AOIScanPath(**new_aoi_scan_path_data)

		except KeyError:

			new_aoi_scan_path_data = {}
			new_aoi_scan_path_data['expected_aois'] = expected_aois
			new_aoi_scan_path = None

		# Load AOI scan path analyzers
		new_aoi_scan_path_analyzers = {}

		try:

			new_aoi_scan_path_analyzers_value = layer_data.pop('aoi_scan_path_analyzers')

			for aoi_scan_path_analyzer_module_path, aoi_scan_path_analyzer_parameters in new_aoi_scan_path_analyzers_value.items():

				# Prepend argaze.GazeAnalysis path when a single name is provided
				if len(aoi_scan_path_analyzer_module_path.split('.')) == 1:
					aoi_scan_path_analyzer_module_path = f'argaze.GazeAnalysis.{aoi_scan_path_analyzer_module_path}'

				aoi_scan_path_analyzer_module = importlib.import_module(aoi_scan_path_analyzer_module_path)

				# Check aoi scan path analyzer parameters type
				members = getmembers(aoi_scan_path_analyzer_module.AOIScanPathAnalyzer)

				for member in members:

					if '__annotations__' in member:

						for parameter, parameter_type in member[1].items():

							# Check if parameter is part of argaze.GazeAnalysis module
							parameter_module_path = parameter_type.__module__.split('.')

							# Check if parameter is part of a package
							if len(parameter_type.__module__.split('.')) > 1:

								# Try get existing analyzer instance to append as parameter
								try:

									aoi_scan_path_analyzer_parameters[parameter] = new_aoi_scan_path_analyzers[parameter_type.__module__]

								except KeyError:

									raise LoadingFailed(f'{aoi_scan_path_analyzer_module_path} aoi scan path analyzer loading fails because {parameter_type.__module__} aoi scan path analyzer is missing.')

				aoi_scan_path_analyzer = aoi_scan_path_analyzer_module.AOIScanPathAnalyzer(**aoi_scan_path_analyzer_parameters)

				new_aoi_scan_path_analyzers[aoi_scan_path_analyzer_module_path] = aoi_scan_path_analyzer

			# Force AOI scan path creation
			if len(new_aoi_scan_path_analyzers) > 0 and new_aoi_scan_path == None:

				new_aoi_scan_path = GazeFeatures.AOIScanPath(**new_aoi_scan_path_data)

		except KeyError:

			pass

		# Create layer
		return ArLayer(new_layer_name, \
						new_aoi_color, \
						new_aoi_scene, \
						looked_aoi_covering_threshold, \
						new_aoi_scan_path, \
						new_aoi_scan_path_analyzers \
						)

	@classmethod
	def from_json(self, json_filepath: str) -> ArLayerType:
		"""
		Load attributes from .json file.

		Parameters:
			json_filepath: path to json file
		"""

		with open(json_filepath) as configuration_file:

			layer_data = json.load(configuration_file)
			working_directory = os.path.dirname(json_filepath)

			return ArLayer.from_dict(layer_data, working_directory)

	@property
	def parent(self):
		"""Get parent instance"""

		return self.__parent

	@parent.setter
	def parent(self, parent):
		"""Get parent instance"""

		self.__parent = parent

	@property
	def looked_aoi(self) -> str:
		"""Get most likely looked aoi name for current fixation (e.g. the aoi with the highest covering mean value)"""

		return self.__looked_aoi

	@property
	def looked_aoi_covering_mean(self) -> float:
		"""Get looked aoi covering mean for current fixation. 
		It represents the ratio of fixation deviation circle surface that used to cover the looked aoi."""

		return self.__looked_aoi_covering_mean

	@property
	def looked_aoi_covering(self) -> dict:
		"""Get all looked aois covering for current fixation."""

		return self.__looked_aoi_covering

	def __init_looked_aoi_data(self):
		"""Init looked aoi data."""

		self.__look_count = 0
		self.__looked_aoi = None
		self.__looked_aoi_covering_mean = 0
		self.__looked_aoi_covering = {}

	def __update_looked_aoi_data(self, fixation) -> str:
		"""Update looked aoi data."""

		self.__look_count += 1

		max_covering = 0.
		most_likely_looked_aoi = None

		for name, aoi in self.aoi_scene.items():

			_, _, circle_ratio = aoi.circle_intersection(fixation.focus, fixation.deviation_max)

			if name != self.name and circle_ratio > 0:

				# Sum circle ratio to update aoi covering
				try:

					self.__looked_aoi_covering[name] += circle_ratio

				except KeyError:

					self.__looked_aoi_covering[name] = circle_ratio

				# Update most likely looked aoi
				if self.__looked_aoi_covering[name] > max_covering:

					most_likely_looked_aoi = name
					max_covering = self.__looked_aoi_covering[name]

		# Update looked aoi
		self.__looked_aoi = most_likely_looked_aoi

		# Update looked aoi covering mean
		self.__looked_aoi_covering_mean = int(100 * max_covering / self.__look_count) / 100

		return self.__looked_aoi

	def look(self, timestamp: int|float, gaze_movement: GazeFeatures.GazePosition = GazeFeatures.UnvalidGazePosition()) -> dict:
		"""
		Project timestamped gaze movement into layer.

		!!! warning
			Be aware that gaze movement positions are in the same range of value than aoi_scene size attribute.

		Parameters:
			gaze_movement: gaze movement to project

		Returns:
			looked_aoi: most likely looked aoi name
			aoi_scan_path_analysis: aoi scan path analysis at each new scan step if aoi_scan_path is instanciated
			exception: error catched during gaze movement processing
		"""

		# Lock layer exploitation
		self.__look_lock.acquire()

		# Update current gaze movement
		self.__gaze_movement = gaze_movement

		# Init looked aoi
		looked_aoi = None

		# Init aoi scan path analysis report
		aoi_scan_path_analysis = {}

		# Assess pipeline execution times
		execution_times = {
			'aoi_fixation_matcher': None,
			'aoi_scan_step_analyzers': {}
		}

		# Catch any error
		exception = None

		try:

			# Valid and finished gaze movement has been identified
			if gaze_movement.valid and gaze_movement.finished:

				if GazeFeatures.is_fixation(gaze_movement):

					# Store aoi matching start date
					matching_start = time.perf_counter()

					# Does the finished fixation match an aoi?
					looked_aoi = self.__update_looked_aoi_data(gaze_movement)

					# Assess aoi matching time in ms
					execution_times['aoi_fixation_matcher'] = (time.perf_counter() - matching_start) * 1e3

					# Append fixation to aoi scan path
					if self.aoi_scan_path != None and self.looked_aoi != None and self.looked_aoi_covering_mean > self.looked_aoi_covering_threshold:

						aoi_scan_step = self.aoi_scan_path.append_fixation(timestamp, gaze_movement, self.looked_aoi)

						# Is there a new step?
						if aoi_scan_step and len(self.aoi_scan_path) > 1:

							for aoi_scan_path_analyzer_module_path, aoi_scan_path_analyzer in self.aoi_scan_path_analyzers.items():

								# Store aoi scan path analysis start date
								aoi_scan_path_analysis_start = time.perf_counter()

								# Analyze aoi scan path
								aoi_scan_path_analyzer.analyze(self.aoi_scan_path)

								# Assess aoi scan step analysis time in ms
								execution_times['aoi_scan_step_analyzers'][aoi_scan_path_analyzer_module_path] = (time.perf_counter() - aoi_scan_path_analysis_start) * 1e3

								# Store analysis
								aoi_scan_path_analysis[aoi_scan_path_analyzer_module_path] = aoi_scan_path_analyzer.analysis

				elif GazeFeatures.is_saccade(gaze_movement):

					# Reset looked aoi
					self.__init_looked_aoi_data()

					# Append saccade to aoi scan path
					if self.aoi_scan_path != None:

						self.aoi_scan_path.append_saccade(timestamp, gaze_movement)

			# Valid in progress fixation
			elif gaze_movement.valid and not gaze_movement.finished:

				if GazeFeatures.is_fixation(gaze_movement):

					# Store aoi matching start date
					matching_start = time.perf_counter()

					# Does the finished fixation match an aoi?
					looked_aoi = self.__update_looked_aoi_data(gaze_movement)

					# Assess aoi matching time in ms
					execution_times['aoi_fixation_matcher'] = (time.perf_counter() - matching_start) * 1e3

		except Exception as e:

			print('Warning: the following error occurs in ArLayer.look method:', e)

			looked_aoi = None
			aoi_scan_path_analysis = {}
			exception = e
		
		# Unlock layer exploitation
		self.__look_lock.release()

		# Sum all execution times
		total_execution_time = 0

		if execution_times['aoi_fixation_matcher']:

			total_execution_time += execution_times['aoi_fixation_matcher']

		for _, aoi_scan_path_analysis_time in execution_times['aoi_scan_step_analyzers'].items():

			total_execution_time += aoi_scan_path_analysis_time

		execution_times['total'] = total_execution_time

		# Return look data
		return looked_aoi, aoi_scan_path_analysis, execution_times, exception

	def draw(self, image:numpy.array) -> Exception:
		"""
		Draw layer into image.

		Parameters:
			image: where to draw
		"""

		# Lock frame exploitation
		self.__look_lock.acquire()

		# Catch any drawing error
		exception = None

		try:

			# Draw aoi
			self.aoi_scene.draw(image, color=self.aoi_color)

			# Draw current gaze movement
			if self.__gaze_movement.valid:

				if GazeFeatures.is_fixation(self.__gaze_movement):

					self.__gaze_movement.draw(image, color=(0, 255, 255))
					self.__gaze_movement.draw_positions(image)

					# Draw looked aoi
					if self.looked_aoi_covering_mean > self.looked_aoi_covering_threshold:

						self.aoi_scene.draw_circlecast(image, self.__gaze_movement.focus, self.__gaze_movement.deviation_max, matching_aoi = [self.__looked_aoi], base_color=(0, 0, 0), matching_color=(255, 255, 255))

				elif GazeFeatures.is_saccade(self.__gaze_movement):

					self.__gaze_movement.draw(image, color=(0, 255, 255))
					self.__gaze_movement.draw_positions(image)

		except Exception as e:

			# Store error to return it
			exception = e

		# Unlock frame exploitation
		self.__look_lock.release()

		# Return drawing error
		return exception

@dataclass
class ArFrame():
	"""
	Defines a rectangular area where to project in timestamped gaze positions and inside which they need to be analyzed.

	Parameters:
		name: name of the frame
		size: defines the dimension of the rectangular area where gaze positions are projected.
		background: image to draw behind
		gaze_movement_identifier: gaze movement identification algorithm
		filter_in_progress_fixation: ignore in progress fixation
		scan_path: scan path object
		scan_path_analyzers: dictionary of scan path analyzers
		heatmap: heatmap object
		aoi_layers: dictionary of AOI layers
	"""

	name: str
	size: tuple[int] = field(default=(1, 1))
	background: numpy.array = field(default_factory=numpy.array)
	gaze_movement_identifier: GazeFeatures.GazeMovementIdentifier = field(default_factory=GazeFeatures.GazeMovementIdentifier)
	filter_in_progress_fixation: bool = field(default=True)
	scan_path: GazeFeatures.ScanPath = field(default_factory=GazeFeatures.ScanPath)
	scan_path_analyzers: dict = field(default_factory=dict)
	heatmap: AOIFeatures.Heatmap = field(default_factory=AOIFeatures.Heatmap)
	layers: dict = field(default_factory=dict)

	def __post_init__(self):

		# Define parent attribute: it will be setup by parent later
		self.__parent = None

		# Setup layers parent attribute
		for name, layer in self.layers.items():

			layer.parent = self

		# Init current gaze position
		self.__gaze_position = GazeFeatures.UnvalidGazePosition()

		# Init lock to share looked data with multiples threads
		self.__look_lock = threading.Lock()

	@classmethod
	def from_dict(self, frame_data, working_directory: str = None) -> ArFrameType:
		"""Load attributes from dictionary.

		Parameters:
			frame_data: dictionary with attributes to load
			working_directory: folder path where to load files when a dictionary value is a relative filepath.
		"""

		# Load name
		try:

			new_frame_name = frame_data.pop('name')

		except KeyError:

			new_frame_name = None

		# Load size
		try:

			new_frame_size = frame_data.pop('size')

		except KeyError:

			new_frame_size = (0, 0)

		# Load background image
		try:

			new_frame_background_value = frame_data.pop('background')
			new_frame_background = cv2.imread(os.path.join(working_directory, new_frame_background_value))
			new_frame_background = cv2.resize(new_frame_background, dsize=(new_frame_size[0], new_frame_size[1]), interpolation=cv2.INTER_CUBIC)

		except KeyError:

			new_frame_background = numpy.full((new_frame_size[1], new_frame_size[0], 3), 127).astype(numpy.uint8)

		# Load gaze movement identifier
		try:

			gaze_movement_identifier_value = frame_data.pop('gaze_movement_identifier')

			gaze_movement_identifier_module_path, gaze_movement_identifier_parameters = gaze_movement_identifier_value.popitem()

			# Prepend argaze.GazeAnalysis path when a single name is provided
			if len(gaze_movement_identifier_module_path.split('.')) == 1:
				gaze_movement_identifier_module_path = f'argaze.GazeAnalysis.{gaze_movement_identifier_module_path}'

			gaze_movement_identifier_module = importlib.import_module(gaze_movement_identifier_module_path)
			new_gaze_movement_identifier = gaze_movement_identifier_module.GazeMovementIdentifier(**gaze_movement_identifier_parameters)

		except KeyError:

			new_gaze_movement_identifier = None

		# Current fixation matching
		try:

			filter_in_progress_fixation = frame_data.pop('filter_in_progress_fixation')

		except KeyError:

			filter_in_progress_fixation = False

		# Load scan path
		try:

			new_scan_path_data = frame_data.pop('scan_path')
			new_scan_path = GazeFeatures.ScanPath(**new_scan_path_data)

		except KeyError:

			new_scan_path_data = {}
			new_scan_path = None

		# Load scan path analyzers
		new_scan_path_analyzers = {}

		try:

			new_scan_path_analyzers_value = frame_data.pop('scan_path_analyzers')

			for scan_path_analyzer_module_path, scan_path_analyzer_parameters in new_scan_path_analyzers_value.items():

				# Prepend argaze.GazeAnalysis path when a single name is provided
				if len(scan_path_analyzer_module_path.split('.')) == 1:
					scan_path_analyzer_module_path = f'argaze.GazeAnalysis.{scan_path_analyzer_module_path}'

				scan_path_analyzer_module = importlib.import_module(scan_path_analyzer_module_path)

				# Check scan path analyzer parameters type
				members = getmembers(scan_path_analyzer_module.ScanPathAnalyzer)

				for member in members:

					if '__annotations__' in member:

						for parameter, parameter_type in member[1].items():

							# Check if parameter is part of a package
							if len(parameter_type.__module__.split('.')) > 1:

								# Try get existing analyzer instance to append as parameter
								try:

									scan_path_analyzer_parameters[parameter] = new_scan_path_analyzers[parameter_type.__module__]

								except KeyError:

									raise LoadingFailed(f'{scan_path_analyzer_module_path} scan path analyzer loading fails because {parameter_type.__module__} scan path analyzer is missing.')

				scan_path_analyzer = scan_path_analyzer_module.ScanPathAnalyzer(**scan_path_analyzer_parameters)

				new_scan_path_analyzers[scan_path_analyzer_module_path] = scan_path_analyzer

			# Force scan path creation
			if len(new_scan_path_analyzers) > 0 and new_scan_path == None:

				new_scan_path = GazeFeatures.ScanPath(**new_scan_path_data)

		except KeyError:

			pass

		# Load heatmap
		try:

			new_heatmap_data = frame_data.pop('heatmap')

			# Default heatmap size equals frame size
			if 'size' not in new_heatmap_data.keys():

				new_heatmap_data['size'] = new_frame_size

			new_heatmap = AOIFeatures.Heatmap(**new_heatmap_data)

		except KeyError:

			new_heatmap_data = {}
			new_heatmap = None

		# Load layers
		new_layers = {}

		try:

			for layer_name, layer_data in frame_data.pop('layers').items():

				# Append name
				layer_data['name'] = layer_name

				# Create layer
				new_layer = ArLayer.from_dict(layer_data, working_directory)

				# Setup layer
				if new_layer.aoi_scene.dimension == 3:

					new_layer.aoi_scene = new_layer.aoi_scene.orthogonal_projection * new_frame_size

				# Append new layer
				new_layers[layer_name] = new_layer

		except KeyError:

			pass

		# Create frame
		return ArFrame(new_frame_name, \
						new_frame_size, \
						new_frame_background, \
						new_gaze_movement_identifier, \
						filter_in_progress_fixation, \
						new_scan_path, \
						new_scan_path_analyzers, \
						new_heatmap, \
						new_layers \
						)

	@classmethod
	def from_json(self, json_filepath: str) -> ArFrameType:
		"""
		Load attributes from .json file.

		Parameters:
			json_filepath: path to json file
		"""

		with open(json_filepath) as configuration_file:

			frame_data = json.load(configuration_file)
			working_directory = os.path.dirname(json_filepath)

			return ArFrame.from_dict(frame_data, working_directory)

	@property
	def parent(self):
		"""Get parent instance"""

		return self.__parent

	@parent.setter
	def parent(self, parent):
		"""Get parent instance"""

		self.__parent = parent

	@property
	def image(self):
		"""
		Get background image + heatmap image
		"""

		# Lock frame exploitation
		self.__look_lock.acquire()

		image = self.background.copy()

		# Draw heatmap
		if self.heatmap:

			heatmap_image = cv2.resize(self.heatmap.image, dsize=(image.shape[1], image.shape[0]), interpolation=cv2.INTER_LINEAR)
			image = cv2.addWeighted(heatmap_image, 0.5, image, 1., 0)

		# Unlock frame exploitation
		self.__look_lock.release()

		return image

	def look(self, timestamp: int|float, gaze_position: GazeFeatures.GazePosition = GazeFeatures.UnvalidGazePosition()) -> Tuple[GazeFeatures.GazeMovement, dict, dict, dict]:
		"""
		Project gaze position into frame.

		!!! warning
			Be aware that gaze positions are in the same range of value than size attribute.

		Parameters:
			timestamp:
			gaze_position: gaze position to project

		Returns:
			identified_gaze_movement: identified gaze movement from incoming consecutive timestamped gaze positions if gaze_movement_identifier is instanciated. Current gaze movement if filter_in_progress_fixation is True.
			scan_path_analysis: scan path analysis at each new scan step if scan_path is instanciated
			
			exception: error catched during gaze position processing
		"""

		# Lock frame exploitation
		self.__look_lock.acquire()

		# Update current gaze position
		self.__gaze_position = gaze_position

		# No gaze movement identified by default
		identified_gaze_movement = GazeFeatures.UnvalidGazeMovement()

		# Init scan path analysis report
		scan_step_analysis = {}

		# Init layer analysis report
		layer_analysis = {}

		# Assess pipeline execution times
		execution_times = {
			'gaze_movement_identifier': None,
			'scan_step_analyzers':{},
			'heatmap': None,
			'layers': {}
		}

		# Catch any error
		exception = None

		try:

			# Identify gaze movement
			if self.gaze_movement_identifier:

				# Store movement identification start date
				identification_start = time.perf_counter()

				# Identify finished gaze movement
				identified_gaze_movement = self.gaze_movement_identifier.identify(timestamp, self.__gaze_position)

				# Assess movement identification time in ms
				execution_times['gaze_movement_identifier'] = (time.perf_counter() - identification_start) * 1e3

			# Valid and finished gaze movement has been identified
			if identified_gaze_movement.valid and identified_gaze_movement.finished:

				if GazeFeatures.is_fixation(identified_gaze_movement):

					# Append fixation to scan path
					if self.scan_path != None:

						self.scan_path.append_fixation(timestamp, identified_gaze_movement)

				elif GazeFeatures.is_saccade(identified_gaze_movement):

					# Append saccade to scan path
					if self.scan_path != None:
						
						scan_step = self.scan_path.append_saccade(timestamp, identified_gaze_movement)

						# Is there a new step?
						if scan_step and len(self.scan_path) > 1:

							for scan_path_analyzer_module_path, scan_path_analyzer in self.scan_path_analyzers.items():

								# Store scan step analysis start date
								scan_step_analysis_start = time.perf_counter()

								# Analyze aoi scan path
								scan_path_analyzer.analyze(self.scan_path)

								# Assess scan step analysis time in ms
								execution_times['scan_step_analyzers'][scan_path_analyzer_module_path] = (time.perf_counter() - scan_step_analysis_start) * 1e3

								# Store analysis
								scan_step_analysis[scan_path_analyzer_module_path] = scan_path_analyzer.analysis

			# No valid finished gaze movement: optionnaly stop in progress fixation filtering
			elif self.gaze_movement_identifier and not self.filter_in_progress_fixation:

				current_fixation = self.gaze_movement_identifier.current_fixation

				if current_fixation.valid:

					identified_gaze_movement = current_fixation

			# Update heatmap
			if self.heatmap:

				# Store heatmap start date
				heatmap_start = time.perf_counter()

				# Scale gaze position value
				scale = numpy.array([self.heatmap.size[0] / self.size[0], self.heatmap.size[1] / self.size[1]])

				# Update heatmap image
				self.heatmap.update(self.__gaze_position.value * scale)

				# Assess heatmap time in ms
				execution_times['heatmap'] = (time.perf_counter() - heatmap_start) * 1e3

			# Look layers
			for layer_name, layer in self.layers.items():

				looked_aoi, aoi_scan_path_analysis, layer_execution_times, layer_exception = layer.look(timestamp, identified_gaze_movement)

				layer_analysis[layer_name] = aoi_scan_path_analysis

				execution_times['layers'][layer_name] = layer_execution_times

				if layer_exception:

					raise(layer_exception)

		except Exception as e:

			print('Warning: the following error occurs in ArFrame.look method:', e)

			identified_gaze_movement = GazeFeatures.UnvalidGazeMovement()
			scan_step_analysis = {}
			layer_analysis = {}
			exception = e
		
		# Unlock frame exploitation
		self.__look_lock.release()

		# Sum all execution times
		total_execution_time = 0

		if execution_times['gaze_movement_identifier']:

			total_execution_time += execution_times['gaze_movement_identifier']

		for _, scan_step_analysis_time in execution_times['scan_step_analyzers'].items():

			total_execution_time += scan_step_analysis_time

		if execution_times['heatmap']:

			total_execution_time += execution_times['heatmap']

		for _, layer_execution_times in execution_times['layers'].items():

			total_execution_time += layer_execution_times['total']

		execution_times['total'] = total_execution_time

		# Return look data
		return identified_gaze_movement, scan_step_analysis, layer_analysis, execution_times, exception

	def draw(self, image:numpy.array) -> Exception:
		"""
		Draw frame into image.

		Parameters:
			image: where to draw
		"""

		# Lock frame exploitation
		self.__look_lock.acquire()

		# Catch any drawing error
		exception = None

		try:

			# Draw layers
			for layer_name, layer in self.layers.items():

				exception = layer.draw(image)

			# Draw current gaze position
			self.__gaze_position.draw(image, color=(255, 255, 255))

		except Exception as e:

			# Store error to return it
			exception = e

		# Unlock frame exploitation
		self.__look_lock.release()

		# Return drawing error
		return exception

@dataclass
class ArScene():
	"""
	Define an Augmented Reality scene with ArUcoMarkers, ArLayers and ArFrames inside.

	Parameters:
		
		name: name of the scene

		aruco_scene: ArUco markers 3D scene description used to estimate scene pose from detected markers: see [estimate_pose][argaze.ArFeatures.ArScene.estimate_pose] function below.

		layers: dictionary of ArLayers to project once the pose is estimated: see [project][argaze.ArFeatures.ArScene.project] function below.

		frames: dictionary to ArFrames to project once the pose is estimated: see [project][argaze.ArFeatures.ArScene.project] function below.

		aruco_axis: Optional dictionary to define orthogonal axis where each axis is defined by list of 3 markers identifier (first is origin). \
					This pose estimation strategy is used by [estimate_pose][argaze.ArFeatures.ArScene.estimate_pose] function when at least 3 markers are detected.

		aruco_aoi: Optional dictionary of AOI defined by list of markers identifier and markers corners index tuples: see [build_aruco_aoi_scene][argaze.ArFeatures.ArScene.build_aruco_aoi_scene] function below.

		angle_tolerance: Optional angle error tolerance to validate marker pose in degree used into [estimate_pose][argaze.ArFeatures.ArScene.estimate_pose] function.

		distance_tolerance: Optional distance error tolerance to validate marker pose in centimeter used into [estimate_pose][argaze.ArFeatures.ArScene.estimate_pose] function.
	"""
	name: str
	aruco_scene: ArUcoScene.ArUcoScene = field(default_factory=ArUcoScene.ArUcoScene)
	layers: dict = field(default_factory=dict)
	frames: dict = field(default_factory=dict)
	aruco_axis: dict = field(default_factory=dict)
	aruco_aoi: dict = field(default_factory=dict)
	angle_tolerance: float = field(default=0.)
	distance_tolerance: float = field(default=0.)

	def __post_init__(self):

		# Define parent attribute: it will be setup by parent object later
		self.__parent = None

		# Setup layer parent attribute
		for name, layer in self.layers.items():

			layer.parent = self

		# Setup frame parent attribute
		for name, frame in self.frames.items():

			frame.parent = self

		# Preprocess orthogonal projection to speed up further processings
		self.__orthogonal_projection_cache = {}

		for layer_name, layer in self.layers.items():

			self.__orthogonal_projection_cache[layer_name] = layer.aoi_scene.orthogonal_projection

	def __str__(self) -> str:
		"""
		Returns:
			String representation
		"""

		output = f'parent:\n{self.parent.name}\n'
		output += f'ArUcoScene:\n{self.aruco_scene}\n'

		if len(self.layers):
			output += f'ArLayers:\n'
			for name, layer in self.layers.items():
				output += f'{name}:\n{layer}\n'

		if len(self.frames):
			output += f'ArFrames:\n'
			for name, frame in self.frames.items():
				output += f'{name}:\n{frame}\n'

		return output

	@property
	def parent(self):
		"""Get parent instance"""

		return self.__parent

	@parent.setter
	def parent(self, parent):
		"""Get parent instance"""

		self.__parent = parent

	@classmethod
	def from_dict(self, scene_data, working_directory: str = None) -> ArSceneType:

		# Load name
		try:

			new_scene_name = scene_data.pop('name')

		except KeyError:

			new_scene_name = None

		# Load aruco scene
		try:

			# Check aruco_scene value type
			aruco_scene_value = scene_data.pop('aruco_scene')

			# str: relative path to .obj file
			if type(aruco_scene_value) == str:

				aruco_scene_value = os.path.join(working_directory, aruco_scene_value)
				new_aruco_scene = ArUcoScene.ArUcoScene.from_obj(aruco_scene_value)

			# dict:
			else:

				new_aruco_scene = ArUcoScene.ArUcoScene(**aruco_scene_value)

		except KeyError:

			new_aruco_scene = None

		# Load layers
		new_layers = {}

		try:

			for layer_name, layer_data in scene_data.pop('layers').items():

				# Append name
				layer_data['name'] = layer_name

				# Create layer
				new_layer = ArLayer.from_dict(layer_data, working_directory)

				# Append new layer
				new_layers[layer_name] = new_layer

		except KeyError:

			pass

		# Load frames
		new_frames = {}

		try:

			for frame_name, frame_data in scene_data.pop('frames').items():

				# Append name
				frame_data['name'] = frame_name

				# Create frame
				new_frame = ArFrame.from_dict(frame_data, working_directory)

				# Look for AOI with same frame name
				aoi_frame = None
				aoi_frame_found = False
				for layer_name, layer in new_layers.items():

					try:

						aoi_frame = layer.aoi_scene[frame_name]
						aoi_frame_found = True

					except KeyError:

						# AOI name should be unique
						break

				if aoi_frame_found:

					# Project and reframe each layers into corresponding frame layers
					for frame_layer_name, frame_layer in new_frame.layers.items():

						try:

							layer = new_layers[frame_layer_name]
							
							layer_aoi_scene_projection = layer.aoi_scene.orthogonal_projection
							aoi_frame_projection = layer_aoi_scene_projection[frame_name]

							frame_layer.aoi_scene = layer_aoi_scene_projection.reframe(aoi_frame_projection, new_frame.size)

							if frame_layer.aoi_scan_path != None:

								# Edit expected AOI list by removing AOI with name equals to frame layer name
								expected_aois = list(layer.aoi_scene.keys())

								if frame_layer_name in expected_aois:
									expected_aois.remove(frame_layer_name)

								frame_layer.aoi_scan_path.expected_aois = expected_aois

						except KeyError:

							continue

				# Append new frame
				new_frames[frame_name] = new_frame

		except KeyError:

			pass

		return ArScene(new_scene_name, new_aruco_scene, new_layers, new_frames, **scene_data)
	
	def estimate_pose(self, detected_markers) -> Tuple[numpy.array, numpy.array, str, dict]:
		"""Estimate scene pose from detected ArUco markers.

		Returns:
				scene translation vector
				scene rotation matrix
				pose estimation strategy
				dict of markers used to estimate the pose
		"""

		# Pose estimation fails when no marker is detected
		if len(detected_markers) == 0:

			raise PoseEstimationFailed('No marker detected')

		scene_markers, _ = self.aruco_scene.filter_markers(detected_markers)

		# Pose estimation fails when no marker belongs to the scene
		if len(scene_markers) == 0:

			raise PoseEstimationFailed('No marker belongs to the scene')

		# Estimate scene pose from unique marker transformations
		elif len(scene_markers) == 1:

			marker_id, marker = scene_markers.popitem()
			tvec, rmat = self.aruco_scene.estimate_pose_from_single_marker(marker)
			
			return tvec, rmat, 'estimate_pose_from_single_marker', {marker_id: marker}

		# Try to estimate scene pose from 3 markers defining an orthogonal axis
		elif len(scene_markers) >= 3 and len(self.aruco_axis) > 0:

			for axis_name, axis_markers in self.aruco_axis.items():

				try:

					origin_marker = scene_markers[axis_markers['origin_marker']]
					horizontal_axis_marker = scene_markers[axis_markers['horizontal_axis_marker']]
					vertical_axis_marker = scene_markers[axis_markers['vertical_axis_marker']]

					tvec, rmat = self.aruco_scene.estimate_pose_from_axis_markers(origin_marker, horizontal_axis_marker, vertical_axis_marker)

					return tvec, rmat, 'estimate_pose_from_axis_markers', {origin_marker.identifier: origin_marker, horizontal_axis_marker.identifier: horizontal_axis_marker, vertical_axis_marker.identifier: vertical_axis_marker}

				except:
					pass

			raise PoseEstimationFailed('No marker axis')

		# Otherwise, check markers consistency
		consistent_markers, unconsistent_markers, unconsistencies = self.aruco_scene.check_markers_consistency(scene_markers, self.angle_tolerance, self.distance_tolerance)

		# Pose estimation fails when no marker passes consistency checking
		if len(consistent_markers) == 0:

			raise PoseEstimationFailed('Unconsistent marker poses', unconsistencies)

		# Otherwise, estimate scene pose from all consistent markers pose
		tvec, rmat = self.aruco_scene.estimate_pose_from_markers(consistent_markers)

		return tvec, rmat, 'estimate_pose_from_markers', consistent_markers

	def project(self, tvec: numpy.array, rvec: numpy.array, visual_hfov: float = 0.) -> Tuple[str, AOI2DScene.AOI2DScene]:
		"""Project layers according estimated pose and optional horizontal field of view clipping angle.	

		Parameters:
			tvec: translation vector
			rvec: rotation vector
			visual_hfov: horizontal field of view clipping angle

		Returns:
			layer_name: name of projected layer
			layer_projection: AOI2DScene projection
		"""

		for name, layer in self.layers.items():

			# Clip AOI out of the visual horizontal field of view (optional)
			if visual_hfov > 0:

				# Transform layer aoi scene into camera referential
				aoi_scene_camera_ref = layer.aoi_scene.transform(tvec, rvec)

				# Get aoi inside vision cone field 
				cone_vision_height_cm = 200 # cm
				cone_vision_radius_cm = numpy.tan(numpy.deg2rad(visual_hfov / 2)) * cone_vision_height_cm

				_, aoi_outside = aoi_scene_camera_ref.vision_cone(cone_vision_radius_cm, cone_vision_height_cm)

				# Keep only aoi inside vision cone field
				aoi_scene_copy = layer.aoi_scene.copy(exclude=aoi_outside.keys())

			else:

				aoi_scene_copy = layer.aoi_scene.copy()

			# Project layer aoi scene
			yield name, aoi_scene_copy.project(tvec, rvec, self.parent.aruco_detector.optic_parameters.K)

	def build_aruco_aoi_scene(self, detected_markers) -> AOI2DScene.AOI2DScene:
		"""
		Build AOI scene from detected ArUco markers as defined in aruco_aoi dictionary.

		Returns:
			aoi_2d_scene: built AOI 2D scene
		"""

		# ArUco aoi must be defined
		assert(self.aruco_aoi)

		# AOI projection fails when no marker is detected
		if len(detected_markers) == 0:
			
			raise SceneProjectionFailed('No marker detected')

		aruco_aoi_scene = {}

		for aruco_aoi_name, aoi in self.aruco_aoi.items():

			# Each aoi's corner is defined by a marker's corner
			aoi_corners = []
			for corner in ["upper_left_corner", "upper_right_corner", "lower_right_corner", "lower_left_corner"]:

				marker_identifier = aoi[corner]["marker_identifier"]

				try:

					aoi_corners.append(detected_markers[marker_identifier].corners[0][aoi[corner]["marker_corner_index"]])

				except Exception as e:
					
					raise SceneProjectionFailed(f'Missing marker #{e} to build ArUco AOI scene')

			aruco_aoi_scene[aruco_aoi_name] = AOIFeatures.AreaOfInterest(aoi_corners)

			# Then each inner aoi is projected from the current aruco aoi
			for inner_aoi_name, inner_aoi in self.aoi_3d_scene.items():

				if aruco_aoi_name != inner_aoi_name:

					aoi_corners = [numpy.array(aruco_aoi_scene[aruco_aoi_name].outter_axis(inner)) for inner in self.__orthogonal_projection_cache[inner_aoi_name]]
					aruco_aoi_scene[inner_aoi_name] = AOIFeatures.AreaOfInterest(aoi_corners)

		return AOI2DScene.AOI2DScene(aruco_aoi_scene)

	def draw_axis(self, image: numpy.array):
		"""
		Draw scene axis into image.
		
		Parameters:
			image: where to draw
		"""

		self.aruco_scene.draw_axis(image, self.parent.aruco_detector.optic_parameters.K, self.parent.aruco_detector.optic_parameters.D)

	def draw_places(self, image: numpy.array):
		"""
		Draw scene places into image.

		Parameters:
			image: where to draw
		"""

		self.aruco_scene.draw_places(image, self.parent.aruco_detector.optic_parameters.K, self.parent.aruco_detector.optic_parameters.D)

@dataclass
class ArEnvironment():
	"""
	Define Augmented Reality environment based on ArUco marker detection.

	Parameters:
		name: environment name
		aruco_detector: ArUco marker detector
		camera_frame: where to project scenes
		scenes: all environment scenes
	"""

	name: str 
	aruco_detector: ArUcoDetector.ArUcoDetector = field(default_factory=ArUcoDetector.ArUcoDetector)
	camera_frame: ArFrame = field(default_factory=ArFrame)
	scenes: dict = field(default_factory=dict)

	def __post_init__(self):

		# Setup camera frame parent attribute
		if self.camera_frame != None:

			self.camera_frame.parent = self

		# Setup scenes parent attribute
		for name, scene in self.scenes.items():

			scene.parent = self

		# Init a lock to share AOI scene projections into camera frame between multiple threads
		self.__camera_frame_lock = threading.Lock()

		# Define public timestamp buffer to store ignored gaze positions
		self.ignored_gaze_positions = GazeFeatures.TimeStampedGazePositions()

	@classmethod
	def from_dict(self, environment_data, working_directory: str = None) -> ArEnvironmentType:

		new_environment_name = environment_data.pop('name')

		try:
			new_detector_data = environment_data.pop('aruco_detector')

			new_aruco_dictionary = ArUcoMarkersDictionary.ArUcoMarkersDictionary(**new_detector_data.pop('dictionary'))
			new_marker_size = new_detector_data.pop('marker_size')

			# Check optic_parameters value type
			optic_parameters_value = new_detector_data.pop('optic_parameters')

			# str: relative path to .json file
			if type(optic_parameters_value) == str:

				optic_parameters_value = os.path.join(working_directory, optic_parameters_value)
				new_optic_parameters = ArUcoOpticCalibrator.OpticParameters.from_json(optic_parameters_value)

			# dict:
			else:

				new_optic_parameters = ArUcoOpticCalibrator.OpticParameters(**optic_parameters_value)

			# Check detector parameters value type
			detector_parameters_value = new_detector_data.pop('parameters')

			# str: relative path to .json file
			if type(detector_parameters_value) == str:

				detector_parameters_value = os.path.join(working_directory, detector_parameters_value)
				new_aruco_detector_parameters = ArUcoDetector.DetectorParameters.from_json(detector_parameters_value)

			# dict:
			else:

				new_aruco_detector_parameters = ArUcoDetector.DetectorParameters(**detector_parameters_value)
			
			new_aruco_detector = ArUcoDetector.ArUcoDetector(new_aruco_dictionary, new_marker_size, new_optic_parameters, new_aruco_detector_parameters)

		except KeyError:

			new_aruco_detector = None

		# Load camera frame as large as aruco dectector optic parameters
		try:

			camera_frame_data = environment_data.pop('camera_frame')

			# Create camera frame
			new_camera_frame = ArFrame.from_dict(camera_frame_data, working_directory)

			# Setup camera frame
			new_camera_frame.name = new_environment_name
			new_camera_frame.size = new_optic_parameters.dimensions
			new_camera_frame.background = numpy.zeros((new_optic_parameters.dimensions[1], new_optic_parameters.dimensions[0], 3)).astype(numpy.uint8)

		except KeyError:

			new_camera_frame = None

		# Build scenes
		new_scenes = {}
		for scene_name, scene_data in environment_data.pop('scenes').items():

			# Append name
			scene_data['name'] = scene_name

			# Create new scene
			new_scene = ArScene.from_dict(scene_data, working_directory)

			# Append new scene
			new_scenes[scene_name] = new_scene

		# Setup expected aoi of each camera frame layer aoi scan path with the aoi of corresponding scene layer
		if new_camera_frame != None:

				for camera_frame_layer_name, camera_frame_layer in new_camera_frame.layers.items():

					if camera_frame_layer.aoi_scan_path != None:

						all_aoi_list = []

						for scene_name, scene in new_scenes.items():

							try:

								scene_layer = scene.layers[camera_frame_layer_name]

								all_aoi_list.extend(list(scene_layer.aoi_scene.keys()))

							except KeyError:

								continue

						camera_frame_layer.aoi_scan_path.expected_aois = all_aoi_list

		# Create new environment
		return ArEnvironment(new_environment_name, new_aruco_detector, new_camera_frame, new_scenes)

	@classmethod
	def from_json(self, json_filepath: str) -> ArEnvironmentType:
		"""
		Load ArEnvironment from .json file.

		Parameters:
			json_filepath: path to json file
		"""

		with open(json_filepath) as configuration_file:

			environment_data = json.load(configuration_file)
			working_directory = os.path.dirname(json_filepath)

			return ArEnvironment.from_dict(environment_data, working_directory)

	def __str__(self) -> str:
		"""
		Returns:
			String representation
		"""

		output = f'Name:\n{self.name}\n'
		output += f'ArUcoDetector:\n{self.aruco_detector}\n'

		for name, scene in self.scenes.items():
			output += f'\"{name}\" ArScene:\n{scene}\n'

		return output

	@property
	def image(self):
		"""Get camera frame image"""

		# Can't use camera frame when it is locked
		if self.__camera_frame_lock.locked():
			return

		# Lock camera frame exploitation
		self.__camera_frame_lock.acquire()

		# Get camera frame image
		image = self.camera_frame.image

		# Unlock camera frame exploitation
		self.__camera_frame_lock.release()

		return image

	@property
	def frames(self):
		"""Iterate over all environment scenes frames"""

		# For each scene
		for scene_name, scene in self.scenes.items():

			# For each frame
			for name, frame in scene.frames.items():

				yield frame

	def detect_and_project(self, image: numpy.array) -> Tuple[float, dict]:
		"""Detect environment aruco markers from image and project scenes into camera frame.

		Returns:
            - detection_time: aruco marker detection time in ms
            - exceptions: dictionary with exception raised per scene
        """

		# Detect aruco markers
		detection_time = self.aruco_detector.detect_markers(image)

		# Lock camera frame exploitation
		self.__camera_frame_lock.acquire()

		# Fill camera frame background with image
		self.camera_frame.background = image

		# Clear former layers projection into camera frame
		for came_layer_name, camera_layer in self.camera_frame.layers.items():
		
			camera_layer.aoi_scene = AOI2DScene.AOI2DScene()

		# Store exceptions for each scene
		exceptions = {}

		# Project each aoi 3d scene into camera frame
		for scene_name, scene in self.scenes.items():

			''' TODO: Enable aruco_aoi processing
			if scene.aruco_aoi:

				try:

					# Build AOI scene directly from detected ArUco marker corners
					self.camera_frame.aoi_2d_scene |= scene.build_aruco_aoi_scene(self.aruco_detector.detected_markers)

				except SceneProjectionFailed:

					pass
			'''

			try:

				# Estimate scene markers poses
				self.aruco_detector.estimate_markers_pose(scene.aruco_scene.identifiers)

				# Estimate scene pose from detected scene markers
				tvec, rmat, _, _ = scene.estimate_pose(self.aruco_detector.detected_markers)

				# Project scene into camera frame according estimated pose
				for layer_name, layer_projection in scene.project(tvec, rmat):

					try:

						self.camera_frame.layers[layer_name].aoi_scene |= layer_projection

					except KeyError:

						pass

			# Store exceptions and continue
			except Exception as e:

				exceptions[scene_name] = e

		# Unlock camera frame exploitation
		self.__camera_frame_lock.release()

		# Return dection time and exceptions
		return detection_time, exceptions

	def look(self, timestamp: int|float, gaze_position: GazeFeatures.GazePosition):
		"""Project timestamped gaze position into each frame.

		!!! warning detect_and_project method needs to be called first.
		"""

		# Can't use camera frame when it is locked
		if self.__camera_frame_lock.locked():

			# TODO: Store ignored timestamped gaze positions for further projections
			# PB: This would imply to also store frame projections !!!
			self.ignored_gaze_positions[timestamp] = gaze_position

			return

		# Lock camera frame exploitation
		self.__camera_frame_lock.acquire()

		# Project gaze position into camera frame
		yield self.camera_frame, self.camera_frame.look(timestamp, gaze_position)

		# Project gaze position into each frame if possible
		for frame in self.frames:

			# Is there an AOI inside camera frame layers projection which its name equals to a frame name?
			for camera_layer_name, camera_layer in self.camera_frame.layers.items():

				try:

					aoi_2d = camera_layer.aoi_scene[frame.name]

					# TODO: Add option to use gaze precision circle
					if aoi_2d.contains_point(gaze_position.value):

						inner_x, inner_y = aoi_2d.clockwise().inner_axis(gaze_position.value)

						# QUESTION: How to project gaze precision?
						inner_gaze_position = GazeFeatures.GazePosition((inner_x, inner_y))
						
						yield frame, frame.look(timestamp, inner_gaze_position * frame.size)

				# Ignore missing aoi in camera frame layer projection
				except KeyError:

					pass

		# Unlock camera frame exploitation
		self.__camera_frame_lock.release()

	def map(self):
		"""Project camera frame background into frames background.

		.. warning:: detect_and_project method needs to be called first.
		"""

		# Can't use camera frame when it is locked
		if self.__camera_frame_lock.locked():
			return

		# Lock camera frame exploitation
		self.__camera_frame_lock.acquire()

		# Project image into each frame if possible
		for frame in self.frames:

			# Is there an AOI inside camera frame layers projection which its name equals to a frame name?
			for camera_layer_name, camera_layer in self.camera_frame.layers.items():

				try:

					aoi_2d = camera_layer.aoi_scene[frame.name]

					# Apply perspective transform algorithm to fill aoi frame background
					width, height = frame.size
					destination = numpy.float32([[0, height],[width, height],[width, 0],[0, 0]])
					mapping = cv2.getPerspectiveTransform(aoi_2d.astype(numpy.float32), destination)
					frame.background = cv2.warpPerspective(self.camera_frame.background, mapping, (width, height))

				# Ignore missing frame projection
				except KeyError:

					pass

		# Unlock camera frame exploitation
		self.__camera_frame_lock.release()

	def to_json(self, json_filepath):
		"""Save environment to .json file."""

		with open(json_filepath, 'w', encoding='utf-8') as file:

			json.dump(self, file, ensure_ascii=False, indent=4, cls=DataStructures.JsonEncoder)

	def draw(self, image: numpy.array) -> Exception:
		"""Draw ArUco detection visualisation and camera frame projections."""

		# Draw detected markers
		self.aruco_detector.draw_detected_markers(image)

		# Can't use camera frame when it is locked
		if self.__camera_frame_lock.locked():
			return

		# Lock camera frame exploitation
		self.__camera_frame_lock.acquire()

		# Draw camera frame
		exception = self.camera_frame.draw(image)

		# Unlock camera frame exploitation
		self.__camera_frame_lock.release()

		# Return camera frame drawing error
		return exception