path: root/src/argaze/ArFeatures.py

#!/usr/bin/env python

"""ArGaze pipeline assets."""

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

from typing import TypeVar, Tuple, Any
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.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

ArCameraType = TypeVar('ArCamera', bound="ArCamera")
# 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 ArCamera watch 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 AOI and inside which those matchings need to be analyzed.

	Parameters:
		name: name of the layer
		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_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 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)

				# SVG file format for 2D dimension only
				if file_format == 'svg':

					new_aoi_scene = AOI2DScene.AOI2DScene.from_svg(filepath)

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

					new_aoi_scene = AOI3DScene.AOI3DScene.from_obj(filepath)

			# dict:
			else:

				new_aoi_scene = AOIFeatures.AOIScene.from_dict(new_aoi_scene_value)

		except KeyError:

			pass

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

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

		if new_layer_name in expected_aoi:
			expected_aoi.remove(new_layer_name)

		# 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

		# Load AOI scan path
		try:

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

		except KeyError:

			new_aoi_scan_path_data = {}
			new_aoi_scan_path_data['expected_aoi'] = expected_aoi
			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_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()

		# Store look execution start date
		look_start = time.perf_counter()

		# 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:

			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 valid/unvalid and 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)

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

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

		# Assess total execution time in ms
		execution_times['total'] = (time.perf_counter() - look_start) * 1e3
		
		# Unlock layer exploitation
		self.__look_lock.release()

		# 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, self.aoi_scene, **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_positions": {
        "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_position_calibrator: gaze position calibration algoritm
		gaze_movement_identifier: gaze movement identification algorithm
		filter_in_progress_identification: ignore in progress gaze movement identification
		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_position_calibrator: GazeFeatures.GazePositionCalibrator = field(default_factory=GazeFeatures.GazePositionCalibrator)
	gaze_movement_identifier: GazeFeatures.GazeMovementIdentifier = field(default_factory=GazeFeatures.GazeMovementIdentifier)
	filter_in_progress_identification: 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=lambda : 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 position calibrator
		try:

			gaze_position_calibrator_value = frame_data.pop('gaze_position_calibrator')

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

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

				# JSON file format
				if file_format == 'json':

					new_gaze_position_calibrator = GazeFeatures.GazePositionCalibrator.from_json(filepath)

			# dict:
			else:

				new_gaze_position_calibrator = GazeFeatures.GazePositionCalibrator.from_dict(gaze_position_calibrator_value)

		except KeyError:

			new_gaze_position_calibrator = None

		# 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_identification = frame_data.pop('filter_in_progress_identification')

		except KeyError:

			filter_in_progress_identification = 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)

				# 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_position_calibrator, \
						new_gaze_movement_identifier, \
						filter_in_progress_identification, \
						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.GazePosition, GazeFeatures.GazeMovement, dict, dict, dict, Exception]:
		"""
		Project gaze position into frame.

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

		Parameters:
			timestamp: any number used to know when the given gaze position occurs
			gaze_position: gaze position to project

		Returns:
			current_gaze_position: calibrated gaze position if gaze_position_calibrator is instanciated else, given gaze position.
			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_identification is False.
			scan_path_analysis: scan path analysis at each new scan step if scan_path is instanciated.
			layers_analysis: aoi scan path analysis at each new aoi scan step for each instanciated layers aoi scan path.
			execution_times: all pipeline steps execution times.
			exception: error catched during gaze position processing.
		"""

		# Lock frame exploitation
		self.__look_lock.acquire()

		# Store look execution start date
		look_start = time.perf_counter()

		# 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:

			# Apply gaze position calibration
			if self.gaze_position_calibrator is not None:

				self.__gaze_position = self.gaze_position_calibrator.apply(gaze_position)

			# Or update gaze position at least
			else:

				self.__gaze_position = gaze_position

			# 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 identification filtering
			elif self.gaze_movement_identifier is not None and not self.filter_in_progress_identification:

				identified_gaze_movement = self.gaze_movement_identifier.current_gaze_movement

			# 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 with valid identified gaze movement
			# Note: don't filter valid/unvalid finished/unfished gaze movement to allow layers to reset internally
			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)

			self.__gaze_position = GazeFeatures.UnvalidGazePosition()
			identified_gaze_movement = GazeFeatures.UnvalidGazeMovement()
			scan_step_analysis = {}
			layer_analysis = {}
			exception = e

		# Assess total execution time in ms
		execution_times['total'] = (time.perf_counter() - look_start) * 1e3

		# Unlock frame exploitation
		self.__look_lock.release()

		# Return look data
		return self.__gaze_position, identified_gaze_movement, scan_step_analysis, layer_analysis, execution_times, exception

	def __image(self, background_weight: float = None, heatmap_weight: float = None, draw_gaze_position_calibrator: dict = None, draw_scan_path: dict = None, draw_layers: dict = None, draw_gaze_positions: dict = None, draw_fixations: dict = None, draw_saccades: dict = None) -> numpy.array:
		"""
		Get background image with overlaid visualisations.

		Parameters:
			background_weight: weight of background overlay
			heatmap_weight: weight of heatmap overlay
			draw_gaze_position_calibrator: [GazeFeatures.GazePositionCalibrator.draw](argaze.md/#argaze.GazeFeatures.GazePositionCalibrator.draw) parameters (if None, nothing is drawn)
			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_positions: [GazeFeatures.GazePosition.draw](argaze.md/#argaze.GazeFeatures.GazePosition.draw) parameters (if None, no gaze position is drawn)
			draw_fixations: [GazeFeatures.Fixation.draw](argaze.md/#argaze.GazeFeatures.Fixation.draw) parameters (if None, no fixation is drawn)
			draw_saccades: [GazeFeatures.Saccade.draw](argaze.md/#argaze.GazeFeatures.Saccade.draw) parameters (if None, no saccade is drawn)
		"""

		# Lock frame exploitation
		self.__look_lock.acquire()

		# Draw background only
		if background_weight is not None and (heatmap_weight is None or self.heatmap 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 gaze position calibrator
		if draw_gaze_position_calibrator is not None:

			self.gaze_position_calibrator.draw(image, size=self.size, **draw_gaze_position_calibrator)

		# 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 current fixation if required
		if draw_fixations is not None and self.gaze_movement_identifier is not None:

			self.gaze_movement_identifier.current_fixation.draw(image, **draw_fixations)

		# Draw current saccade if required
		if draw_saccades is not None and self.gaze_movement_identifier is not None:

			self.gaze_movement_identifier.current_saccade.draw(image, **draw_saccades)

		# 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_positions is not None:

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

		# Unlock frame exploitation
		self.__look_lock.release()

		return image

	def image(self, **kwargs: dict) -> numpy.array:
		"""
		Get frame image.

		Parameters:
			kwargs: ArFrame.__image parameters
		"""
		# Use image_parameters attribute if no kwargs
		if kwargs:

			return self.__image(**kwargs)

		return self.__image(**self.image_parameters)

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

	Parameters:
		name: name of the scene
		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.
		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
	layers: dict = field(default_factory=dict)
	frames: 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

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

		output = f'parent:\n{self.parent.name}\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: dict, working_directory: str = None) -> ArSceneType:
		"""
		Load ArScene from dictionary.

		Parameters:
			scene_data: dictionary
			working_directory: folder path where to load files when a dictionary value is a relative filepath.
		"""
		
		# Load name
		try:

			new_scene_name = scene_data.pop('name')

		except KeyError:

			new_scene_name = 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():

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

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

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

						new_frame = ArFrame.from_json(filepath)

				# dict:
				else:

					# Append name
					frame_data['name'] = frame_name

					new_frame = ArFrame.from_dict(frame_data, working_directory)

				# Look for a scene layer with an AOI named like the frame
				for scene_layer_name, scene_layer in new_layers.items():

					try:

						frame_3d = scene_layer.aoi_scene[frame_name]

						# Check that the frame have a layer named like this scene layer
						aoi_2d_scene = new_frame.layers[scene_layer_name].aoi_scene

						# Transform 2D frame layer AOI into 3D scene layer AOI 
						# Then, add them to scene layer
						scene_layer.aoi_scene |= aoi_2d_scene.dimensionalize(frame_3d, new_frame.size)

						'''DEPRECATED: but maybe still usefull?
						# 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_aoi = list(layer.aoi_scene.keys())

									if frame_layer_name in expected_aoi:
										expected_aoi.remove(frame_layer_name)

									frame_layer.aoi_scan_path.expected_aoi = expected_aoi

							except KeyError:

								continue
						'''

					except KeyError as e:

						print(e)

				# Append new frame
				new_frames[frame_name] = new_frame

		except KeyError:

			pass

		return ArScene(new_scene_name, new_layers, new_frames, **scene_data)
	
	def estimate_pose(self, detected_features: Any) -> Tuple[numpy.array, numpy.array]:
		"""Define abstract estimate scene pose method.

		Parameters:
			detected_features: any features detected by parent ArCamera that will help in scene pose estimation.

		Returns:
			tvec: scene translation vector
			rvec: scene rotation matrix
		"""

		raise NotImplementedError('estimate_pose() method not implemented')

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

		Parameters:
			tvec: translation vector
			rvec: rotation vector
			visual_hfov: horizontal field of view clipping angle
			visual_vfov: vertical 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)
			# TODO: use HFOV and VFOV and don't use vision_cone method
			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 draw(self, image: numpy.array, **kwargs: dict):
		"""
		Draw scene into image.
		
		Parameters:
			image: where to draw
		"""

		raise NotImplementedError('draw() method not implemented')

@dataclass
class ArCamera(ArFrame):
	"""
	Define abstract Augmented Reality camera as ArFrame with ArScenes inside.

	Parameters:
		scenes: all scenes to project into camera frame
		visual_hfov: Optional angle in degree to clip scenes projection according visual horizontal field of view (HFOV).
		visual_vfov: Optional angle in degree to clip scenes projection according visual vertical field of view (VFOV).
	"""

	scenes: dict = field(default_factory=dict)
	visual_hfov: float = field(default=0.)
	visual_vfov: float = field(default=0.)

	def __post_init__(self):

		# Init ArFrame
		super().__post_init__()

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

			scene.parent = self

		# Setup expected aoi of each layer aoi scan path with the aoi of corresponding scene layer
		# Edit aoi matcher exclude attribute to ignore frame aoi
		for layer_name, layer in self.layers.items():

			if layer.aoi_scan_path is not None:

				expected_aoi_list = []
				exclude_aoi_list = []

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

					# Append scene layer aoi to corresponding expected camera layer aoi
					try:

						scene_layer = scene.layers[layer_name]

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

					except KeyError:

						continue

					# Remove scene frame from expected camera layer aoi
					# Exclude scene frame from camera layer aoi matching
					for frame_name, frame in scene.frames.items():

						try:
							
							expected_aoi_list.remove(frame_name)
							exclude_aoi_list.append(frame_name)

						except ValueError:

							continue

				layer.aoi_scan_path.expected_aoi = expected_aoi_list
				layer.aoi_matcher.exclude = exclude_aoi_list

		# Init a lock to share scene projections into camera frame between multiple threads
		self._frame_lock = threading.Lock()
	
	def __str__(self) -> str:
		"""
		Returns:
			String representation
		"""

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

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

		return output

	@classmethod
	def from_dict(self, camera_data: dict, working_directory: str = None) -> ArCameraType:
		"""
		Load ArCamera from dictionary.

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

		raise NotImplementedError('from_dict() method not implemented')

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

		Parameters:
			json_filepath: path to json file
		"""

		raise NotImplementedError('from_json() method not implemented')

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

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

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

				yield scene_frame

	def watch(self, image: numpy.array) -> Tuple[float, dict]:
		"""Detect AR features from image and project scenes into camera frame.

		Returns:
			detection time: AR features detection time in ms.
			exception: dictionary with exception raised per scene.
        """

		raise NotImplementedError('watch() method not implemented')

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

		Parameters:
			timestamp: gaze position time stamp (unit does'nt matter)
			gaze_position: GazePosition object

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

		# Can't use camera frame while it is locked
		wait_start = time.perf_counter()
		waiting_time = 0

		while self._frame_lock.locked():

			time.sleep(1e-6)
			waiting_time = (time.perf_counter() - wait_start) * 1e3

			# TODO? return waiting time?

			# TODO? add timeout parameter?
			#if waiting_time > timeout:
			#	return None, None

		# DEBUG
		#if waiting_time > 0:
		#	print(f'ArCamera: waiting {waiting_time:.3f} ms before to process gaze position at {timestamp} time.')

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

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

		# Project gaze position into each scene frames if possible
		for scene_frame in self.scene_frames:

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

				try:

					aoi_2d = camera_layer.aoi_scene[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 scene_frame, scene_frame.look(timestamp, inner_gaze_position * scene_frame.size)

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

					pass

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

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

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

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

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

		# Project camera frame background into each scene frame if possible
		for frame in self.scene_frames:

			# Is there an AOI inside camera frame layers projection which its name equals to a scene frame name?
			for camera_layer_name, camera_layer in self.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, 0], [width, 0], [width, height], [0, height]])
					mapping = cv2.getPerspectiveTransform(aoi_2d.astype(numpy.float32), destination)
					frame.background = cv2.warpPerspective(self.background, mapping, (width, height))

				# Ignore missing frame projection
				except KeyError:

					pass

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

	def image(self, **kwargs: dict) -> numpy.array:
		"""
		Get frame image.

		Parameters:
			kwargs: ArFrame.image parameters
		"""

		return super().image(**kwargs)

	def to_json(self, json_filepath):
		"""Save camera 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)