path: root/src/argaze/ArFeatures.py

#!/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

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

import numpy
import cv2

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

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

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

class EnvironmentJSONLoadingFailed(Exception):
	"""
	Exception raised by ArEnvironment when JSON loading fails.
	"""

	def __init__(self, message):  

		super().__init__(message)

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)

@dataclass
class ArFrame():
	"""
	Define Augmented Reality frame as an AOI2DScene made from a projected then reframed parent AOI3DScene.

	Parameters:
		name: name of the frame
		size: frame dimension in pixel.
		background: image to draw behind
		aoi_2d_scene: AOI 2D scene description ... : see [orthogonal_projection][argaze.ArFeatures.ArScene.orthogonal_projection] and [reframe][argaze.AreaOfInterest.AOI2DScene.reframe] functions.
		...
	"""

	name: str
	size: tuple[int] = field(default=(1, 1))
	aoi_2d_scene: AOI2DScene.AOI2DScene = field(default_factory=AOI2DScene.AOI2DScene)
	background: numpy.array = field(default_factory=numpy.array)
	gaze_movement_identifier: GazeFeatures.GazeMovementIdentifier = field(default_factory=GazeFeatures.GazeMovementIdentifier)
	scan_path: GazeFeatures.ScanPath = field(default_factory=GazeFeatures.ScanPath)
	scan_path_analyzers: dict = field(default_factory=dict)
	aoi_scan_path: GazeFeatures.AOIScanPath = field(default_factory=GazeFeatures.AOIScanPath)
	aoi_scan_path_analyzers: dict = field(default_factory=dict)
	heatmap: AOIFeatures.Heatmap = field(default_factory=AOIFeatures.Heatmap)

	def __post_init__(self):

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

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

		# Init heatmap if required
		if self.heatmap:

			self.heatmap.init()

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

	@classmethod
	def from_dict(self, frame_data, working_directory: str = None) -> ArFrameType:

		# 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 aoi 2D scene
		try:

			new_aoi_2d_scene_value = frame_data.pop('aoi_2d_scene')

			# str: relative path to .json file
			if type(new_aoi_2d_scene_value) == str:

				json_filepath = os.path.join(working_directory, new_aoi_2d_scene_value)
				new_aoi_2d_scene = AOI2DScene.AOI2DScene.from_json(obj_filepath)

			# dict:
			else:

				new_aoi_2d_scene = AOI2DScene.AOI2DScene(new_aoi_2d_scene_value)

		except KeyError:

			new_aoi_2d_scene = AOI2DScene.AOI2DScene()

		# 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.zeros((new_frame_size[1], new_frame_size[0], 3)).astype(numpy.uint8)

		# Load gaze movement identifier
		try:

			gaze_movement_identifier_value = frame_data.pop('gaze_movement_identifier')

			gaze_movement_identifier_type, gaze_movement_identifier_parameters = gaze_movement_identifier_value.popitem()

			gaze_movement_identifier_module = importlib.import_module(f'argaze.GazeAnalysis.{gaze_movement_identifier_type}')
			finished_gaze_movement_identifier = gaze_movement_identifier_module.GazeMovementIdentifier(**gaze_movement_identifier_parameters)

		except KeyError:

			finished_gaze_movement_identifier = 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_type, scan_path_analyzer_parameters in new_scan_path_analyzers_value.items():

				scan_path_analyzer_module = importlib.import_module(f'argaze.GazeAnalysis.{scan_path_analyzer_type}')

				# 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 argaze.GazeAnalysis module
							parameter_module_path = parameter_type.__module__.split('.')

							if len(parameter_module_path) == 3:

								if parameter_module_path[0] == 'argaze' and parameter_module_path[1] == 'GazeAnalysis':

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

										scan_path_analyzer_parameters[parameter] = new_scan_path_analyzers[parameter_module_path[2]]

									except KeyError:

										raise EnvironmentJSONLoadingFailed(f'{scan_path_analyzer_type} scan path analyzer loading fails because {parameter_module_path[2]} scan path analyzer is missing.')

				scan_path_analyzer = scan_path_analyzer_module.ScanPathAnalyzer(**scan_path_analyzer_parameters)

				new_scan_path_analyzers[scan_path_analyzer_type] = scan_path_analyzer

		except KeyError:

			pass
			
		# Load AOI scan path analyzers
		new_aoi_scan_path_analyzers = {}

		try:

			new_aoi_scan_path_analyzers_value = frame_data.pop('aoi_scan_path_analyzers')

			for aoi_scan_path_analyzer_type, aoi_scan_path_analyzer_parameters in new_aoi_scan_path_analyzers_value.items():

				aoi_scan_path_analyzer_module = importlib.import_module(f'argaze.GazeAnalysis.{aoi_scan_path_analyzer_type}')

				# 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('.')

							if len(parameter_module_path) == 3:

								if parameter_module_path[0] == 'argaze' and parameter_module_path[1] == 'GazeAnalysis':

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

										aoi_scan_path_analyzer_parameters[parameter] = new_aoi_scan_path_analyzers[parameter_module_path[2]]

									except KeyError:

										raise EnvironmentJSONLoadingFailed(f'{aoi_scan_path_analyzer_type} aoi scan path analyzer loading fails because {parameter_module_path[2]} 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_type] = aoi_scan_path_analyzer

		except KeyError:

			pass

		# Load heatmap
		try:

			new_heatmap_value = frame_data.pop('heatmap')

		except KeyError:

			new_heatmap_value = False

		# Create frame
		return ArFrame(new_frame_name, \
						new_frame_size, \
						new_aoi_2d_scene, \
						new_frame_background, \
						finished_gaze_movement_identifier, \
						GazeFeatures.ScanPath() if len(new_scan_path_analyzers) > 0 else None, \
						new_scan_path_analyzers, \
						GazeFeatures.AOIScanPath(list(new_aoi_2d_scene.keys())) if len(new_aoi_scan_path_analyzers) > 0 else None, \
						new_aoi_scan_path_analyzers, \
						AOIFeatures.Heatmap(new_frame_size) if new_heatmap_value else None \
						)

	@classmethod
	def from_json(self, json_filepath: str) -> ArEnvironmentType:
		"""
		Load ArFrame 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:

			image = cv2.addWeighted(self.heatmap.image, 0.5, image, 1., 0)

		# Unlock frame exploitation
		self.__look_lock.release()

		return image

	def look(self, timestamp: int|float, inner_gaze_position: GazeFeatures.GazePosition) -> Tuple[GazeFeatures.GazeMovement, str, dict, dict, dict]:
		""" 
		
		GazeFeatures.AOIScanStepError

		Returns:
			fixation: identified fixation (if gaze_movement_identifier is instanciated)
			look at: when identified fixation looks at
			scan_step: new scan step (if scan_path is instanciated)
			aoi_scan_step: new scan step (if aoi_scan_path is instanciated)
		"""

		# Lock frame exploitation
		self.__look_lock.acquire()

		# Update current gaze position
		self.__gaze_position = inner_gaze_position

		# No fixation is identified by default
		fixation = GazeFeatures.UnvalidGazeMovement()

		# No aoi is looked by default
		look_at = None

		# Init scan path analysis report
		scan_step_analysis = {}
		aoi_scan_step_analysis = {}

		# Catch any error
		exception = None

		try:

			# Identify gaze movement
			if self.gaze_movement_identifier:

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

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

					if GazeFeatures.is_fixation(finished_gaze_movement):

						# Update current fixation
						fixation = finished_gaze_movement

						# Does the fixation match an aoi?
						for name, aoi in self.aoi_2d_scene.items():

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

							if circle_ratio > 0.25:

								if name != self.name:

									# Update current look at
									look_at = name
									break

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

							self.scan_path.append_fixation(timestamp, finished_gaze_movement)

						# Append fixation to aoi scan path
						if self.aoi_scan_path != None and look_at != None:

							aoi_scan_step = self.aoi_scan_path.append_fixation(timestamp, finished_gaze_movement, look_at)

							# Analyze aoi scan path
							if aoi_scan_step and len(self.aoi_scan_path) > 1:

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

									aoi_scan_path_analyzer.analyze(self.aoi_scan_path)

									aoi_scan_step_analysis[aoi_scan_path_analyzer_type] = aoi_scan_path_analyzer.analysis

					elif GazeFeatures.is_saccade(finished_gaze_movement):

						# Update current look at
						look_at = None

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

							# Analyze aoi scan path
							if scan_step and len(self.scan_path) > 1:

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

									scan_path_analyzer.analyze(self.scan_path)

									scan_step_analysis[scan_path_analyzer_type] = scan_path_analyzer.analysis

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

							self.aoi_scan_path.append_saccade(timestamp, finished_gaze_movement)

				# No valid finished gaze movement: check current fixation
				else:

					current_fixation = self.gaze_movement_identifier.current_fixation

					if current_fixation.valid:

						# Update current fixation
						fixation = current_fixation

						# Does the fixation match an aoi?
						for name, aoi in self.aoi_2d_scene.items():

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

							if circle_ratio > 0.25:

								if name != self.name:

									# Update current look at
									look_at = name
									break

			# Update heatmap
			if self.heatmap:

				self.heatmap.update(self.__gaze_position.value, sigma=0.05)

		except Exception as e:

			fixation = GazeFeatures.UnvalidGazeMovement()
			look_at = None
			scan_step_analysis = {}
			aoi_scan_step_analysis = {}
			exception = e

		# Unlock frame exploitation
		self.__look_lock.release()

		# Return look data
		return fixation, look_at, scan_step_analysis, aoi_scan_step_analysis, exception

	def draw(self, image:numpy.array, aoi_color=(0, 0, 0)):
		"""
		Draw frame into image.

		Parameters:
			image: where to draw
		"""

		# Lock frame exploitation
		self.__look_lock.acquire()

		# Draw aoi
		self.aoi_2d_scene.draw(image, color=aoi_color)

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

		# Draw current gaze movements
		if self.gaze_movement_identifier:

			current_fixation = self.gaze_movement_identifier.current_fixation

			if current_fixation.valid:

				current_fixation.draw(image, color=(0, 255, 255))
				current_fixation.draw_positions(image)

				# Draw looked AOI
				self.aoi_2d_scene.draw_circlecast(image, current_fixation.focus, current_fixation.deviation_max, base_color=(0, 0, 0), matching_color=(255, 255, 255))

			current_saccade = self.gaze_movement_identifier.current_saccade

			if current_saccade.valid:

				current_saccade.draw(image, color=(0, 255, 255))
				current_saccade.draw_positions(image)

		# Unlock frame exploitation
		self.__look_lock.release()

@dataclass
class ArScene():
	"""
	Define an Augmented Reality scene with ArUco markers and AOI scenes.

	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.

		aoi_3d_scene: AOI 3D scene description that will be projected onto estimated scene once its pose will be estimated : see [project][argaze.ArFeatures.ArScene.project] function below.

		aoi_frames: Optional dictionary to define AOI as ArFrame.

		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)
	aoi_3d_scene: AOI3DScene.AOI3DScene = field(default_factory=AOI3DScene.AOI3DScene)
	aoi_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 environment attribute: it will be setup by parent environment later
		self.__environment = None

		# Preprocess orthogonal projection to speed up further aruco aoi processings
		self.__orthogonal_projection_cache = self.aoi_3d_scene.orthogonal_projection

		# Setup aoi frame parent attribute
		for aoi_name, frame in self.aoi_frames.items():

			frame.parent = self

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

		output = f'ArEnvironment:\n{self.environment.name}\n'
		output += f'ArUcoScene:\n{self.aruco_scene}\n'
		output += f'AOI3DScene:\n{self.aoi_3d_scene}\n'

		return output

	@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 optional aoi filter
		try:

			aoi_exclude_list = scene_data.pop('aoi_exclude')

		except KeyError:

			aoi_exclude_list = []

		# Load aoi 3d scene
		try:

			# Check aoi_3d_scene value type
			aoi_3d_scene_value = scene_data.pop('aoi_3d_scene')

			# str: relative path to .obj file
			if type(aoi_3d_scene_value) == str:

				obj_filepath = os.path.join(working_directory, aoi_3d_scene_value)
				new_aoi_3d_scene = AOI3DScene.AOI3DScene.from_obj(obj_filepath).copy(exclude=aoi_exclude_list)

			# dict:
			else:

				new_aoi_3d_scene = AOI3DScene.AOI3DScene(aoi_3d_scene_value).copy(exclude=aoi_exclude_list)

		except KeyError:

			new_aoi_3d_scene = None

		# Load aoi frames
		new_aoi_frames = {}

		try:

			for aoi_name, aoi_frame_data in scene_data.pop('aoi_frames').items():

				# Create aoi frame
				new_aoi_frame = ArFrame.from_dict(aoi_frame_data, working_directory)

				# Setup aoi frame
				new_aoi_frame.name = aoi_name
				new_aoi_frame.aoi_2d_scene = new_aoi_3d_scene.orthogonal_projection.reframe(aoi_name, new_aoi_frame.size)

				if new_aoi_frame.aoi_scan_path != None:

					new_aoi_frame.aoi_scan_path.expected_aois = list(new_aoi_3d_scene.keys())

				# Append new aoi frame
				new_aoi_frames[aoi_name] = new_aoi_frame

		except KeyError:

			pass

		return ArScene(new_scene_name, new_aruco_scene, new_aoi_3d_scene, new_aoi_frames, **scene_data)

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

		return self.__environment

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

		self.__environment = environment
	
	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.) -> AOI2DScene.AOI2DScene:
		"""Project AOI scene 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:
			aoi_2d_scene: scene projection
		"""

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

			# Transform scene into camera referential
			aoi_3d_scene_camera_ref = self.aoi_3d_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_3d_scene_camera_ref.vision_cone(cone_vision_radius_cm, cone_vision_height_cm)

			# Keep only aoi inside vision cone field
			aoi_3d_scene_copy = self.aoi_3d_scene.copy(exclude=aoi_outside.keys())

		else:

			aoi_3d_scene_copy = self.aoi_3d_scene.copy()

		return aoi_3d_scene_copy.project(tvec, rvec, self.environment.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.environment.aruco_detector.optic_parameters.K, self.environment.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.environment.aruco_detector.optic_parameters.K, self.environment.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 environment attribute
		for name, scene in self.scenes.items():

			scene.environment = 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 new_scene_name, scene_data in environment_data.pop('scenes').items():

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

			# Setup new scene
			new_scene.name = new_scene_name

			# Append new scene
			new_scenes[new_scene_name] = new_scene

		# Setup expected aoi for camera frame aoi scan path
		if new_camera_frame != None:

			if new_camera_frame.aoi_scan_path != None:

				# List all environment aoi
				all_aoi_list = []
				for scene_name, scene in new_scenes.items():

					all_aoi_list.extend(list(scene.aoi_3d_scene.keys()))

				new_camera_frame.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 aoi_frames(self):
		"""Iterate over all environment scenes aoi frames"""

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

			# For each aoi frame
			for frame_name, aoi_frame in scene.aoi_frames.items():

				yield aoi_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 scenes projection into camera frame 
		self.camera_frame.aoi_2d_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
				self.camera_frame.aoi_2d_scene |= scene.project(tvec, rmat)

			# 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."""

		# 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 aoi frames if possible
		for aoi_frame in self.aoi_frames:

			# Is aoi frame projected into camera frame ?
			try:

				aoi_2d = self.camera_frame.aoi_2d_scene[aoi_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 aoi_frame, aoi_frame.look(timestamp, inner_gaze_position * aoi_frame.size)

			# Ignore missing aoi 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):
		"""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
		self.camera_frame.draw(image)

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