#!/usr/bin/env python

"""ArCamera based of ArUco markers technology."""

__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 time

from argaze import ArFeatures, DataStructures
from argaze.ArUcoMarkers import ArUcoMarkersDictionary, ArUcoDetector, ArUcoOpticCalibrator, ArUcoScene
from argaze.AreaOfInterest import AOI2DScene

import cv2
import numpy

ArUcoCameraType = TypeVar('ArUcoCamera', bound="ArUcoCamera")
# Type definition for type annotation convenience

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

@dataclass
class ArUcoCamera(ArFeatures.ArCamera):
	"""
	Define an ArCamera based on ArUco marker detection.

	Parameters:
		aruco_detector: ArUco marker detector
	"""

	aruco_detector: ArUcoDetector.ArUcoDetector = field(default_factory=ArUcoDetector.ArUcoDetector)

	def __post_init__(self):

		super().__post_init__()

		# Check optic parameters
		if self.aruco_detector.optic_parameters is not None:

			# Optic parameters dimensions should be equal to camera frame size 
			if self.aruco_detector.optic_parameters.dimensions != self.size:

				raise ArFeatures.LoadingFailed('ArUcoCamera: aruco_detector.optic_parameters.dimensions have to be equal to size.')

		# No optic parameters loaded
		else:

			# Create default optic parameters adapted to frame size
			# Note: The choice of 1000 for default focal length should be discussed...
			self.aruco_detector.optic_parameters = ArUcoOpticCalibrator.OpticParameters(rms=-1, dimensions=self.size, K=ArUcoOpticCalibrator.K0(focal_length=(1000., 1000.), width=self.size[0], height=self.size[1]))

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

		output = super().__str__()
		output += f'ArUcoDetector:\n{self.aruco_detector}\n'

		return output

	@classmethod
	def from_dict(self, aruco_camera_data: dict, working_directory: str = None) -> ArUcoCameraType:
		"""
		Load ArUcoCamera from dictionary.

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

		# Load ArUco detector
		new_aruco_detector = ArUcoDetector.ArUcoDetector.from_dict(aruco_camera_data.pop('aruco_detector'), working_directory)

		# Load ArUcoScenes
		new_scenes = {}

		try:

			for aruco_scene_name, aruco_scene_data in aruco_camera_data.pop('scenes').items():

				# Append name
				aruco_scene_data['name'] = aruco_scene_name

				# Create new aruco scene
				new_aruco_scene = ArUcoScene.ArUcoScene.from_dict(aruco_scene_data, working_directory)

				# Append new scene
				new_scenes[aruco_scene_name] = new_aruco_scene
				
		except KeyError:

			pass

		# Set image_parameters to default if there is not
		if 'image_parameters' not in aruco_camera_data.keys():

			aruco_camera_data['image_parameters'] = {**ArFeatures.DEFAULT_ARFRAME_IMAGE_PARAMETERS, **DEFAULT_ARUCOCAMERA_IMAGE_PARAMETERS}
			
			# Set draw_layers to default if there is not
			if 'draw_layers' not in aruco_camera_data['image_parameters'].keys():

				aruco_camera_data['image_parameters']['draw_layers'] = {}

				for layer_name, layer_data in aruco_camera_data['layers'].items():
					aruco_camera_data['image_parameters']['draw_layers'][layer_name] = ArFeatures.DEFAULT_ARLAYER_DRAW_PARAMETERS

		# Get values of temporary ar frame created from aruco_camera_data
		temp_ar_frame_values = DataStructures.as_dict(ArFeatures.ArFrame.from_dict(aruco_camera_data, working_directory))

		# Create new aruco camera using temporary ar frame values
		return ArUcoCamera(aruco_detector=new_aruco_detector, scenes=new_scenes, **temp_ar_frame_values)

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

		Parameters:
			json_filepath: path to json file
		"""

		with open(json_filepath) as configuration_file:

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

			return ArUcoCamera.from_dict(aruco_camera_data, working_directory)

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

		Returns:
			detection time: aruco marker detection time in ms.
			projection time: scenes projection time in ms.
			exception: dictionary with exception raised per scene.
		"""

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

		# Lock camera frame exploitation
		super().acquire()

		# Store projection execution start date
		projection_start = time.perf_counter()

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

		# Clear former layers projection into camera frame
		for layer_name, layer in self.layers.items():
		
			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.layers[??].aoi_2d_scene |= scene.build_aruco_aoi_scene(self.aruco_detector.detected_markers)

				except ArFeatures.PoseEstimationFailed:

					pass
			'''

			try:

				# 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, self.visual_hfov, self.visual_vfov):

					try:

						self.layers[layer_name].aoi_scene |= layer_projection

					except KeyError:

						pass

			# Store exceptions and continue
			except Exception as e:

				exceptions[scene_name] = e

		# Assess projection time in ms
		projection_time = (time.perf_counter() - projection_start) * 1e3

		# Unlock camera frame exploitation
		super().release()

		# Return detection time, projection time and exceptions
		return detection_time, projection_time, exceptions

	def __image(self, draw_detected_markers: dict = None, draw_scenes: dict = None, draw_optic_parameters_grid: dict = None, **kwargs: dict) -> numpy.array:
		"""Get frame image with ArUco detection visualisation.

		Parameters:
			draw_detected_markers: ArucoMarker.draw parameters (if None, no marker drawn)
			draw_scenes: ArUcoScene.draw parameters (if None, no scene drawn)
			draw_optic_parameters_grid: OpticParameter.draw parameters (if None, no grid drawn)
			kwargs: ArCamera.image parameters
		"""

		# Get camera frame image
		# Note: don't lock/unlock camera frame here as super().image manage it.
		image = super().image(**kwargs)

		# Draw optic parameters grid if required
		if draw_optic_parameters_grid is not None:

			self.aruco_detector.optic_parameters.draw(image, **draw_optic_parameters_grid)

		# Draw scenes if required
		if draw_scenes is not None:

			for scene_name, draw_scenes_parameters in draw_scenes.items():

				self.scenes[scene_name].draw(image, **draw_scenes_parameters)

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

			self.aruco_detector.draw_detected_markers(image, draw_detected_markers)

		return image

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

		Parameters:
			kwargs: ArUcoCamera.__image parameters
		"""

		# Use image_parameters attribute if no kwargs
		if kwargs:

			return self.__image(**kwargs)

		return self.__image(**self.image_parameters)