#!/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)

# Define default ArLayer draw parameters
DEFAULT_ARLAYER_DRAW_PARAMETERS = {
    "draw_aoi_scene": {
        "draw_aoi": {
            "color": (255, 255, 255),
            "border_size": 1
        }
    },
    "draw_aoi_matching": {
        "draw_matched_fixation": {
            "deviation_circle_color": (255, 255, 255)
        },
        "draw_matched_fixation_positions": {
            "position_color": (0, 255, 255),
            "line_color": (0, 0, 0)
        },
        "draw_matched_region": {
            "color": (0, 255, 0),
            "border_size": 4
        }, 
        "draw_looked_aoi": {
            "color": (0, 255, 0),
            "border_size": 2
        },
        "looked_aoi_name_color": (255, 255, 255),
        "looked_aoi_name_offset": (0, -10)
    }
}

@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
		aoi_matcher: AOI matcher object
		aoi_scan_path: AOI scan path object
		aoi_scan_path_analyzers: dictionary of AOI scan path analyzers
		log: enable aoi scan path analysis logging
		draw_parameters: default parameters passed to draw method
	"""

	name: str
	aoi_color: tuple = field(default=(0, 0, 0))
	aoi_scene: AOIFeatures.AOIScene = field(default_factory=AOIFeatures.AOIScene)
	aoi_matcher: GazeFeatures.AOIMatcher = field(default_factory=GazeFeatures.AOIMatcher)
	aoi_scan_path: GazeFeatures.AOIScanPath = field(default_factory=GazeFeatures.AOIScanPath)
	aoi_scan_path_analyzers: dict = field(default_factory=dict)
	log: bool = field(default=False)
	draw_parameters: dict = field(default_factory=DEFAULT_ARLAYER_DRAW_PARAMETERS)

	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 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)

		# Prepare logging if needed
		self.__ts_logs = {}

		if self.log:

			# Create timestamped buffers to log each aoi scan path analysis
			for aoi_scan_path_analyzer_module_path in self.aoi_scan_path_analyzers.keys():

				self.__ts_logs[aoi_scan_path_analyzer_module_path] = DataStructures.TimeStampedBuffer()

	@classmethod
	def from_dict(self, layer_data: dict, 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()

		# Load aoi matcher
		try:

			aoi_matcher_value = layer_data.pop('aoi_matcher')

			aoi_matcher_module_path, aoi_matcher_parameters = aoi_matcher_value.popitem()

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

			aoi_matcher_module = importlib.import_module(aoi_matcher_module_path)
			new_aoi_matcher = aoi_matcher_module.AOIMatcher(**aoi_matcher_parameters)

		except KeyError:

			new_aoi_matcher = None

		# 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

		# Load log status
		try:

			new_layer_log = layer_data.pop('log')

		except KeyError:

			new_layer_log = False

		# Load image parameters
		try:

			new_layer_draw_parameters = layer_data.pop('draw_parameters')

		except KeyError:

			new_layer_draw_parameters = DEFAULT_ARLAYER_DRAW_PARAMETERS

		# Create layer
		return ArLayer(new_layer_name, \
						new_aoi_color, \
						new_aoi_scene, \
						new_aoi_matcher, \
						new_aoi_scan_path, \
						new_aoi_scan_path_analyzers, \
						new_layer_log, \
						new_layer_draw_parameters \
						)

	@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 logs(self):
		"""
		Get stored logs
		"""

		return self.__ts_logs

	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_name, looked_aoi = (None, None)

		# Init aoi scan path analysis report
		aoi_scan_path_analysis = {}

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

		# Catch any error
		exception = None

		try:

			# Check gaze movement validity
			if gaze_movement.valid:

				if self.aoi_matcher is not None:

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

					# Update looked aoi thanks to aoi matcher
					# Note: don't filter finished/unfinished fixation/saccade as we don't know how the aoi matcher works internally
					looked_aoi_name, looked_aoi = self.aoi_matcher.match(self.aoi_scene, gaze_movement, exclude=[self.name])

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

				# Finished gaze movement has been identified
				if gaze_movement.finished:

					if GazeFeatures.is_fixation(gaze_movement):

						# Append fixation to aoi scan path
						if self.aoi_scan_path is not None and looked_aoi_name is not None:

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

							# Is there a new step?
							if aoi_scan_step is not None 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

									# Log analysis
									if self.log:

										self.__ts_logs[aoi_scan_path_analyzer_module_path][timestamp] = aoi_scan_path_analyzer.analysis

					elif GazeFeatures.is_saccade(gaze_movement):

						# Append saccade to aoi scan path
						if self.aoi_scan_path is not None:

							self.aoi_scan_path.append_saccade(timestamp, gaze_movement)

		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_matcher']:

			total_execution_time += execution_times['aoi_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, draw_aoi_scene: dict = None, draw_aoi_matching: dict = None):
		"""
		Draw into image.

		Parameters:
			draw_aoi_scene: AreaOfInterest.AOI2DScene.draw parameters (if None, no aoi scene is drawn)
            draw_aoi_matching: AOIMatcher.draw parameters (which depends of the loaded aoi matcher module, if None, no aoi matching is drawn)
		"""

		# Use draw_parameters attribute if no parameters
		if draw_aoi_scene is None and draw_aoi_matching is None:

			return self.draw(image, **self.draw_parameters)

		# Lock frame exploitation
		self.__look_lock.acquire()

		# Draw aoi if required
		if draw_aoi_scene is not None:
		
			self.aoi_scene.draw(image, **draw_aoi_scene)

		# Draw aoi matching if required
		if draw_aoi_matching is not None and self.aoi_matcher is not None:

			self.aoi_matcher.draw(image, **draw_aoi_matching)

		# Unlock frame exploitation
		self.__look_lock.release()

# Define default ArFrame image parameters
DEFAULT_ARFRAME_IMAGE_PARAMETERS = {
    "background_weight": 1.,
    "heatmap_weight": 0.5, 
    "draw_scan_path": {
        "draw_fixations": {
            "deviation_circle_color": (255, 255, 255),
            "duration_border_color": (127, 127, 127),
            "duration_factor": 1e-2
        }, 
        "draw_saccades": {
            "line_color": (255, 255, 255)
        },
        "deepness": 0
    }, 
    "draw_gaze_position": {
        "color": (0, 255, 255),
        "size": 2
    }
}

@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.
		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
		background: picture to draw behind
		layers: dictionary of AOI layers
		log: enable scan path analysis logging
		image_parameters: default parameters passed to image method
	"""

	name: str
	size: tuple[int] = field(default=(1, 1))
	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)
	background: numpy.array = field(default_factory=numpy.array)
	layers: dict = field(default_factory=dict)
	log: bool = field(default=False)
	image_parameters: dict = field(default_factory=DEFAULT_ARFRAME_IMAGE_PARAMETERS)
	
	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()

		# Prepare logging if needed
		self.__ts_logs = {}

		if self.log:
			
			# Create timestamped buffers to log each aoi scan path analysis
			for scan_path_analyzer_module_path in self.scan_path_analyzers.keys():

				self.__ts_logs[scan_path_analyzer_module_path] = DataStructures.TimeStampedBuffer()

	@classmethod
	def from_dict(self, frame_data: dict, 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 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 = True

		# 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 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, 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 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)

				# Project 3D aoi scene layer to get only 2D aoi scene
				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

		# Load log status
		try:

			new_frame_log = frame_data.pop('log')

		except KeyError:

			new_frame_log = False

		# Load image parameters
		try:

			new_frame_image_parameters = frame_data.pop('image_parameters')

		except KeyError:

			new_frame_image_parameters = DEFAULT_ARFRAME_IMAGE_PARAMETERS

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

	@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 logs(self):
		"""
		Get stored logs
		"""

		return self.__ts_logs

	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 is not None:

				# 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 is not 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 is not 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

								# Log analysis
								if self.log:

									self.__ts_logs[scan_path_analyzer_module_path][timestamp] = scan_path_analyzer.analysis

			# No valid finished gaze movement: optionnaly stop in progress fixation filtering
			elif self.gaze_movement_identifier is not None 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 is not None:

				# 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 image(self, background_weight: float = None, heatmap_weight: float = None, draw_scan_path: dict = None, draw_layers: dict = None, draw_gaze_position: dict = None) -> numpy.array:
		"""
		Get background image with overlaid visualisations.

		Parameters:
			background_weight: weight of background overlay
			heatmap_weight: weight of heatmap overlay
			draw_scan_path: [GazeFeatures.ScanPath.draw](argaze.md/#argaze.GazeFeatures.ScanPath.draw) parameters (if None, no scan path is drawn)
			draw_layers: dictionary of [ArLayer.draw](argaze.md/#argaze.ArFeatures.ArLayer.draw) parameters per layer  (if None, no layer is drawn)
			draw_gaze_position: [GazeFeatures.GazePosition.draw](argaze.md/#argaze.GazeFeatures.GazePosition.draw) parameters (if None, no gaze position is drawn)
		"""

		# Use image_parameters attribute if no parameters
		if background_weight is None and heatmap_weight is None and draw_scan_path is None and draw_layers is None and draw_gaze_position is None:

			return self.image(**self.image_parameters)

		# Lock frame exploitation
		self.__look_lock.acquire()

		# Draw background only
		if background_weight is not None and heatmap_weight is None:

			image = self.background.copy()

		# Draw mix background and heatmap if required
		elif background_weight  is not None and heatmap_weight  is not None and self.heatmap:

			background_image = self.background.copy()
			heatmap_image = cv2.resize(self.heatmap.image, dsize=self.size, interpolation=cv2.INTER_LINEAR)
			image = cv2.addWeighted(heatmap_image, heatmap_weight, background_image, background_weight, 0)

		# Draw heatmap only
		elif background_weight is None and heatmap_weight is not None and self.heatmap:

			image = cv2.resize(self.heatmap.image, dsize=self.size, interpolation=cv2.INTER_LINEAR)

		# Draw black image
		else:

			image = numpy.full((self.size[1], self.size[0], 3), 0).astype(numpy.uint8)

		# Draw scan path if required
		if draw_scan_path is not None and self.scan_path is not None:

			self.scan_path.draw(image, **draw_scan_path)

		# Draw layers if required
		if draw_layers is not None:

			for layer_name, draw_layer in draw_layers.items():

				self.layers[layer_name].draw(image, **draw_layer)

		# Draw current gaze position if required
		if draw_gaze_position is not None:

			self.__gaze_position.draw(image, **draw_gaze_position)

		# Unlock frame exploitation
		self.__look_lock.release()

		return image

@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 is not 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)

# Define default ArEnvironment image_paremeters values
DEFAULT_ARENVIRONMENT_IMAGE_PARAMETERS = {
    "draw_detected_markers": {
		"color": (0, 255, 0),
		"draw_axes": {
			"thickness": 3
		}
	}
}

@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)
	image_parameters: dict = field(default_factory=DEFAULT_ARENVIRONMENT_IMAGE_PARAMETERS)

	def __post_init__(self):

		# Setup camera frame parent attribute
		if self.camera_frame is not 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 is not None:

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

					if camera_frame_layer.aoi_scan_path is not 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

		# Load environment image parameters
		try:

			new_environment_image_parameters = environment_data.pop('image_parameters')

		except KeyError:

			new_environment_image_parameters = DEFAULT_ARENVIRONMENT_IMAGE_PARAMETERS

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

	@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 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?: Should we prefer to use camera frame AOIMatcher object?
					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 image(self, draw_detected_markers: dict = None):
		"""Get camera frame projections with ArUco detection visualisation.

		Parameters:
            image: image where to draw
            draw_detected_markers: ArucoMarker.draw parameters (if None, no marker drawn)
		"""

		# Use image_parameters attribute if no parameters
		if draw_detected_markers is None:

			return self.image(**self.image_parameters)

		# 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()

		# Draw detected markers if required
		if draw_detected_markers is not None:

			self.aruco_detector.draw_detected_markers(image, draw_detected_markers)

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

		return image

	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)