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|
"""ArGaze pipeline assets.
This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with
this program. If not, see <https://www.gnu.org/licenses/>.
"""
__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "GPLv3"
import logging
import math
import os
import ast
from typing import Iterator, Union
import cv2
import numpy
from argaze import DataFeatures, GazeFeatures
from argaze.AreaOfInterest import *
from argaze.utils import UtilsFeatures
class PoseEstimationFailed(Exception):
"""
Exception raised by ArScene estimate_pose method when the pose can't be estimated due to inconsistencies.
"""
def __init__(self, message, inconsistencies=None):
super().__init__(message)
self.inconsistencies = inconsistencies
class SceneProjectionFailed(Exception):
"""
Exception raised by ArCamera watch method when the scene can't be projected.
"""
def __init__(self, message):
super().__init__(message)
class DrawingFailed(Exception):
"""
Exception raised when drawing 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)
}
}
class ArLayer(DataFeatures.SharedObject, DataFeatures.PipelineStepObject):
"""
Defines a space where to make matching of gaze movements and AOI and inside which those matching need to be analyzed.
!!! note
Inherits from DataFeatures.SharedObject class to be shared by multiple threads.
"""
@DataFeatures.PipelineStepInit
def __init__(self, **kwargs):
"""Initialize ArLayer."""
# Init parent classes
DataFeatures.SharedObject.__init__(self)
# Init private attributes
self.__aoi_scene = None
self.__aoi_matcher = None
self.__aoi_scan_path = None
self.__aoi_scan_path_analyzers = []
self.__gaze_movement = GazeFeatures.GazeMovement()
self.__looked_aoi_name = None
self.__aoi_scan_path_analyzed = False
# Init pipeline step object attributes
self.draw_parameters = DEFAULT_ARLAYER_DRAW_PARAMETERS
@property
def aoi_scene(self) -> AOIFeatures.AOIScene:
"""AOI scene description."""
return self.__aoi_scene
@aoi_scene.setter
def aoi_scene(self, aoi_scene_value: AOIFeatures.AOIScene | str | dict):
new_aoi_scene = None
if issubclass(type(aoi_scene_value), AOIFeatures.AOIScene):
new_aoi_scene = aoi_scene_value
# str: relative path to file
elif type(aoi_scene_value) is str:
filepath = os.path.join(DataFeatures.get_working_directory(), 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:
elif type(aoi_scene_value) is dict:
new_aoi_scene = AOIFeatures.AOIScene.from_dict(aoi_scene_value)
else:
raise ValueError("Bad aoi scene value")
# Cast aoi scene to its effective dimension
if new_aoi_scene.dimension == 2:
self.__aoi_scene = AOI2DScene.AOI2DScene(new_aoi_scene)
elif new_aoi_scene.dimension == 3:
self.__aoi_scene = AOI3DScene.AOI3DScene(new_aoi_scene)
# Edit parent
if self.__aoi_scene is not None:
self.__aoi_scene.parent = self
@property
def aoi_matcher(self) -> GazeFeatures.AOIMatcher:
"""Select AOI matcher object."""
return self.__aoi_matcher
@aoi_matcher.setter
@DataFeatures.PipelineStepAttributeSetter
def aoi_matcher(self, aoi_matcher: GazeFeatures.AOIMatcher):
assert (issubclass(type(aoi_matcher), GazeFeatures.AOIMatcher))
self.__aoi_matcher = aoi_matcher
# Edit parent
if self.__aoi_matcher is not None:
self.__aoi_matcher.parent = self
@property
def aoi_scan_path(self) -> GazeFeatures.AOIScanPath:
"""AOI scan path object."""
return self.__aoi_scan_path
@aoi_scan_path.setter
@DataFeatures.PipelineStepAttributeSetter
def aoi_scan_path(self, aoi_scan_path: GazeFeatures.AOIScanPath):
assert (isinstance(aoi_scan_path, GazeFeatures.AOIScanPath))
self.__aoi_scan_path = aoi_scan_path
# Update expected AOI
self._update_expected_aoi()
# Edit parent
if self.__aoi_scan_path is not None:
self.__aoi_scan_path.parent = self
@property
def aoi_scan_path_analyzers(self) -> list:
"""AOI scan path analyzers list."""
return self.__aoi_scan_path_analyzers
# noinspection PyUnresolvedReferences
@aoi_scan_path_analyzers.setter
@DataFeatures.PipelineStepAttributeSetter
def aoi_scan_path_analyzers(self, aoi_scan_path_analyzers: list):
self.__aoi_scan_path_analyzers = aoi_scan_path_analyzers
# Connect analyzers if required
for analyzer in self.__aoi_scan_path_analyzers:
assert (issubclass(type(analyzer), GazeFeatures.AOIScanPathAnalyzer))
# Check scan path analyzer properties type
for name, item in type(analyzer).__dict__.items():
if isinstance(item, property) and item.fset is not None:
# Check setter annotations to get expected value type
try:
property_type = list(item.fset.__annotations__.values())[0]
except KeyError:
raise (ValueError(f'Missing annotations in {item.fset.__name__}: {item.fset.__annotations__}'))
if issubclass(property_type, GazeFeatures.AOIScanPathAnalyzer):
# Search for analyzer instance to set property
found = False
for a in self.__aoi_scan_path_analyzers:
if type(a) is property_type:
setattr(analyzer, name, a)
found = True
if not found:
raise DataFeatures.PipelineStepLoadingFailed(
f'{type(analyzer)} analyzer loading fails because {property_type} analyzer is missing.')
# Force scan path creation
if len(self.__aoi_scan_path_analyzers) > 0 and self.aoi_scan_path is None:
self.__aoi_scan_path = GazeFeatures.ScanPath()
# Edit parent
for analyzer in self.__aoi_scan_path_analyzers:
analyzer.parent = self
def last_looked_aoi_name(self) -> str:
"""Get last looked aoi name."""
return self.__looked_aoi_name
def is_analysis_available(self) -> bool:
"""Are aoi scan path analysis ready?"""
return self.__aoi_scan_path_analyzed
def analysis(self) -> dict:
"""Get all aoi scan path analysis into dictionary."""
analysis = {}
for analyzer in self.__aoi_scan_path_analyzers:
analysis[DataFeatures.get_class_path(analyzer)] = analyzer.analysis()
return analysis
def as_dict(self) -> dict:
"""Export ArLayer properties as dictionary."""
return {
**DataFeatures.PipelineStepObject.as_dict(self),
"aoi_scene": self.__aoi_scene,
"aoi_matcher": self.__aoi_matcher,
"aoi_scan_path": self.__aoi_scan_path,
"aoi_scan_path_analyzers": self.__aoi_scan_path_analyzers,
"draw_parameters": self._draw_parameters
}
def _update_expected_aoi(self):
"""Update expected AOI of AOI scan path considering AOI scene and layer name."""
if self.__aoi_scene is None:
logging.debug('ArLayer._update_expected_aoi %s (parent: %s): missing aoi scene', self.name, self.parent)
return
logging.debug('ArLayer._update_expected_aoi %s (parent: %s)', self.name, self.parent)
# Get aoi names from aoi scene
expected_aoi = list(self.__aoi_scene.keys())
# Remove layer name from expected aoi
if self.name in expected_aoi:
expected_aoi.remove(self.name)
# Update expected aoi: this will clear the scan path
self.__aoi_scan_path.expected_aoi = expected_aoi
@DataFeatures.PipelineStepMethod
def look(self, gaze_movement: GazeFeatures.GazeMovement = None):
"""
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
"""
if gaze_movement is None:
gaze_movement = GazeFeatures.GazeMovement()
# Use layer lock feature
with self._lock:
logging.debug('ArLayer.look %s (parent: %s)', self.name, self.parent.name)
# Update current gaze movement
self.__gaze_movement = gaze_movement
# No looked aoi by default
self.__looked_aoi_name = None
# Reset aoi scan path analyzed state
self.__aoi_scan_path_analyzed = False
if self.__aoi_matcher is not None and self.__aoi_scene is not None:
# Update looked aoi thanks to aoi matcher
# Note: don't filter valid/invalid and finished/unfinished fixation/saccade as we don't know how the aoi matcher works internally
self.__looked_aoi_name, _ = self.__aoi_matcher.match(gaze_movement, self.__aoi_scene)
logging.debug('\t> looked aoi name: %s', self.__looked_aoi_name)
# Valid and finished gaze movement has been identified
if gaze_movement and gaze_movement.is_finished():
if GazeFeatures.is_fixation(gaze_movement):
# Append fixation to aoi scan path
# TODO: add an option to filter None looked_aoi_name or not
if self.__aoi_scan_path is not None:
logging.debug('\t> append fixation')
aoi_scan_step = self.__aoi_scan_path.append_fixation(gaze_movement, self.__looked_aoi_name)
# Is there a new step?
if aoi_scan_step is not None and len(self.__aoi_scan_path) > 1:
logging.debug('\t> analyse aoi scan path')
# Analyze aoi scan path
for aoi_scan_path_analyzer in self.__aoi_scan_path_analyzers:
aoi_scan_path_analyzer.analyze(self.__aoi_scan_path, timestamp=gaze_movement.timestamp)
# Update aoi scan path analyzed state
self.__aoi_scan_path_analyzed = True
elif GazeFeatures.is_saccade(gaze_movement):
# Append saccade to aoi scan path
if self.__aoi_scan_path is not None:
logging.debug('\t> append saccade')
self.__aoi_scan_path.append_saccade(gaze_movement)
@DataFeatures.PipelineStepDraw
def draw(self, image: numpy.array, draw_aoi_scene: dict = None, draw_aoi_matching: dict = None):
"""
Draw into image.
Parameters:
image: image where to draw.
draw_aoi_scene: [AOI2DScene.draw][argaze.AreaOfInterest.AOI2DScene.AOI2DScene.draw] parameters (if None, no aoi scene is drawn)
draw_aoi_matching: [AOIMatcher.draw][argaze.GazeFeatures.AOIMatcher.draw] parameters (which depends on the loaded aoi matcher module,
if None, no aoi matching is drawn)
"""
# Use layer lock feature
with self._lock:
# Draw aoi if required
if draw_aoi_scene is not None and self.__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)
# 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
}
}
class ArFrame(DataFeatures.SharedObject, DataFeatures.PipelineStepObject):
"""
Defines a rectangular area where to project in timestamped gaze positions and inside which they need to be analyzed.
!!! note
Inherits from DataFeatures.SharedObject class to be shared by multiple threads
"""
@DataFeatures.PipelineStepInit
def __init__(self, **kwargs):
""" Initialize ArFrame."""
# Init parent classes
DataFeatures.SharedObject.__init__(self)
# Init private attributes
self.__size = (1, 1)
self.__gaze_position_calibrator = None
self.__gaze_movement_identifier = None
self.__filter_in_progress_identification = True
self.__scan_path = None
self.__scan_path_analyzers = []
self.__background = DataFeatures.TimestampedImage(numpy.full((1, 1, 3), 127).astype(numpy.uint8))
self.__heatmap = None
self.__calibrated_gaze_position = GazeFeatures.GazePosition()
self.__identified_gaze_movement = GazeFeatures.GazeMovement()
self.__scan_path_analyzed = False
# Init protected attributes
self._layers = {}
self._image_parameters = DEFAULT_ARFRAME_IMAGE_PARAMETERS
@property
def size(self) -> tuple[int, int]:
"""Defines the dimension of the rectangular area where gaze positions are projected."""
return self.__size
@size.setter
def size(self, size: tuple[int, int]):
self.__size = size
@property
def gaze_position_calibrator(self) -> GazeFeatures.GazePositionCalibrator:
"""Select gaze position calibration algorithm."""
return self.__gaze_position_calibrator
@gaze_position_calibrator.setter
@DataFeatures.PipelineStepAttributeSetter
def gaze_position_calibrator(self, gaze_position_calibrator: GazeFeatures.GazePositionCalibrator):
assert (issubclass(type(gaze_position_calibrator), GazeFeatures.GazePositionCalibrator))
self.__gaze_position_calibrator = gaze_position_calibrator
# Edit parent
if self.__gaze_position_calibrator is not None:
self.__gaze_position_calibrator.parent = self
@property
def gaze_movement_identifier(self) -> GazeFeatures.GazeMovementIdentifier:
"""Select gaze movement identification algorithm."""
return self.__gaze_movement_identifier
@gaze_movement_identifier.setter
@DataFeatures.PipelineStepAttributeSetter
def gaze_movement_identifier(self, gaze_movement_identifier: GazeFeatures.GazeMovementIdentifier):
assert (issubclass(type(gaze_movement_identifier), GazeFeatures.GazeMovementIdentifier))
self.__gaze_movement_identifier = gaze_movement_identifier
# Edit parent
if self.__gaze_movement_identifier is not None:
self.__gaze_movement_identifier.parent = self
@property
def filter_in_progress_identification(self) -> bool:
"""Is frame ignores in progress gaze movement identification?"""
return self.__filter_in_progress_identification
@filter_in_progress_identification.setter
@DataFeatures.PipelineStepAttributeSetter
def filter_in_progress_identification(self, filter_in_progress_identification: bool = True):
self.__filter_in_progress_identification = filter_in_progress_identification
@property
def scan_path(self) -> GazeFeatures.ScanPath:
"""Scan path object."""
return self.__scan_path
@scan_path.setter
@DataFeatures.PipelineStepAttributeSetter
def scan_path(self, scan_path: GazeFeatures.ScanPath):
assert (isinstance(scan_path, GazeFeatures.ScanPath))
self.__scan_path = scan_path
# Edit parent
if self.__scan_path is not None:
self.__scan_path.parent = self
@property
def scan_path_analyzers(self) -> list:
"""Scan path analyzers list."""
return self.__scan_path_analyzers
# noinspection PyUnresolvedReferences
@scan_path_analyzers.setter
@DataFeatures.PipelineStepAttributeSetter
def scan_path_analyzers(self, scan_path_analyzers: list):
self.__scan_path_analyzers = scan_path_analyzers
# Connect analyzers if required
for analyzer in self.__scan_path_analyzers:
assert (issubclass(type(analyzer), GazeFeatures.ScanPathAnalyzer))
# Check scan path analyzer properties type
for name, item in type(analyzer).__dict__.items():
if isinstance(item, property) and item.fset is not None:
# Check setter annotations to get expected value type
try:
property_type = list(item.fset.__annotations__.values())[0]
except KeyError:
raise (ValueError(f'Missing annotations in {item.fset.__name__}: {item.fset.__annotations__}'))
if issubclass(property_type, GazeFeatures.AOIScanPathAnalyzer):
# Search for analyzer instance to set property
found = False
for a in self.__scan_path_analyzers:
if type(a) is property_type:
setattr(analyzer, name, a)
found = True
if not found:
raise DataFeatures.PipelineStepLoadingFaile(
f'{type(analyzer)} analyzer loading fails because {property_type} analyzer is missing.')
# Force scan path creation
if len(self.__scan_path_analyzers) > 0 and self.__scan_path is None:
self.__scan_path = GazeFeatures.ScanPath()
# Edit parent
for analyzer in self.__scan_path_analyzers:
analyzer.parent = self
@property
def background(self) -> numpy.array:
"""Picture to draw behind."""
return self.__background
@background.setter
@DataFeatures.PipelineStepAttributeSetter
def background(self, background: DataFeatures.TimestampedImage):
assert (isinstance(background, DataFeatures.TimestampedImage))
if background.size != self.size:
# Resize image to frame size
self.__background = DataFeatures.TimestampedImage(
cv2.resize(background, dsize=self.size, interpolation=cv2.INTER_CUBIC), background.timestamp)
else:
self.__background = background
@property
def heatmap(self) -> AOIFeatures.Heatmap:
"""Heatmap object."""
return self.__heatmap
@heatmap.setter
@DataFeatures.PipelineStepAttributeSetter
def heatmap(self, heatmap: AOIFeatures.Heatmap):
assert (isinstance(heatmap, AOIFeatures.Heatmap))
self.__heatmap = heatmap
# Default heatmap size equals frame size
if self.__heatmap.size == (1, 1):
self.__heatmap.size = self.size
# Edit parent
if self.__heatmap is not None:
self.__heatmap.parent = self
@property
def layers(self) -> dict:
"""Layers dictionary."""
return self._layers
@layers.setter
@DataFeatures.PipelineStepAttributeSetter
def layers(self, layers: dict):
self._layers = {}
for layer_name, layer_data in layers.items():
self._layers[layer_name] = ArLayer(name=layer_name, **layer_data)
# Edit parent
for name, layer in self._layers.items():
layer.parent = self
def last_gaze_position(self) -> object:
"""Get last calibrated gaze position"""
return self.__calibrated_gaze_position
def last_gaze_movement(self) -> object:
"""Get last identified gaze movement"""
return self.__identified_gaze_movement
def is_analysis_available(self) -> bool:
"""Are scan path analysis ready?"""
return self.__scan_path_analyzed
def analysis(self) -> dict:
"""Get all scan path analysis into dictionary."""
analysis = {}
for analyzer in self.__scan_path_analyzers:
analysis[DataFeatures.get_class_path(analyzer)] = analyzer.analysis()
return analysis
def as_dict(self) -> dict:
"""Export ArFrame attributes as dictionary.
Returns:
frame_data: dictionary with frame attributes values.
"""
d = {
**DataFeatures.PipelineStepObject.as_dict(self),
"size": self.__size,
"gaze_position_calibrator": self.__gaze_position_calibrator,
"gaze_movement_identifier": self.__gaze_movement_identifier,
"filter_in_progress_identification": self.__filter_in_progress_identification,
"scan_path": self.__scan_path,
"scan_path_analyzers": self.__scan_path_analyzers,
"background": self.__background,
"heatmap": self.__heatmap,
"layers": self._layers,
"image_parameters": self._image_parameters
}
return d
@DataFeatures.PipelineStepMethod
def look(self, timestamped_gaze_position: GazeFeatures.GazePosition = GazeFeatures.GazePosition()):
"""
Project timestamped gaze position into frame.
!!! warning
Be aware that gaze positions are in the same range of value than size attribute.
Parameters:
timestamped_gaze_position: gaze position to project
"""
# Use frame lock feature
with self._lock:
# No gaze movement identified by default
self.__identified_gaze_movement = GazeFeatures.GazeMovement()
# Reset scan path analyzed state
self.__scan_path_analyzed = False
# Apply gaze position calibration
if self.__gaze_position_calibrator is not None:
self.__calibrated_gaze_position = self.__gaze_position_calibrator.apply(timestamped_gaze_position)
# Or update gaze position at least
else:
self.__calibrated_gaze_position = timestamped_gaze_position
# Identify gaze movement
if self.__gaze_movement_identifier is not None:
# Identify finished gaze movement
self.__identified_gaze_movement = self.__gaze_movement_identifier.identify(
self.__calibrated_gaze_position)
# Valid and finished gaze movement has been identified
if self.__identified_gaze_movement and self.__identified_gaze_movement.is_finished():
if GazeFeatures.is_fixation(self.__identified_gaze_movement):
# Append fixation to scan path
if self.__scan_path is not None:
self.__scan_path.append_fixation(self.__identified_gaze_movement)
elif GazeFeatures.is_saccade(self.__identified_gaze_movement):
# Append saccade to scan path
if self.__scan_path is not None:
scan_step = self.__scan_path.append_saccade(self.__identified_gaze_movement)
# Is there a new step?
if scan_step and len(self.__scan_path) > 1:
# Analyze aoi scan path
for scan_path_analyzer in self.__scan_path_analyzers:
scan_path_analyzer.analyze(self.__scan_path, timestamp=self.__identified_gaze_movement.timestamp)
# Update scan path analyzed state
self.__scan_path_analyzed = True
# No valid finished gaze movement: optionally stop in progress identification filtering
elif self.__gaze_movement_identifier is not None and not self.__filter_in_progress_identification:
self.__identified_gaze_movement = self.__gaze_movement_identifier.current_gaze_movement()
# Update heatmap
if self.__heatmap is not None:
# 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.__calibrated_gaze_position * scale, timestamp=self.__calibrated_gaze_position.timestamp)
# Look layers with valid identified gaze movement
# Note: don't filter valid/invalid finished/unfinished gaze movement to allow layers to reset internally
for layer_name, layer in self._layers.items():
layer.look(self.__identified_gaze_movement)
@DataFeatures.PipelineStepImage
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 visualizations.
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)
"""
logging.debug('ArFrame.image %s', self.name)
# Use frame lock feature
with self._lock:
# Draw background only
if background_weight is not None and (heatmap_weight is None or self.__heatmap is None):
logging.debug('\t> drawing background only')
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:
logging.debug('\t> drawing background and 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:
logging.debug('\t> drawing heatmap only')
image = cv2.resize(self.__heatmap.image, dsize=self.__size, interpolation=cv2.INTER_LINEAR)
# Draw black image
else:
logging.debug('\t> drawing black image')
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:
logging.debug('\t> drawing gaze position calibrator')
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:
logging.debug('\t> drawing scan path')
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:
if self.__gaze_movement_identifier.current_fixation():
logging.debug('\t> drawing current fixation')
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:
if self.__gaze_movement_identifier.current_saccade():
logging.debug('\t> drawing current saccade')
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():
try:
logging.debug('\t> drawing %s layer', layer_name)
self._layers[layer_name].draw(image, **draw_layer)
except KeyError:
raise (DrawingFailed(f'\'{layer_name}\' layer doesn\'t exist.'))
# Draw current gaze position if required
if draw_gaze_positions is not None:
logging.debug('\t> drawing current gaze position')
self.__calibrated_gaze_position.draw(image, **draw_gaze_positions)
logging.debug('\t> returning image (%i x %i)', image.shape[1], image.shape[0])
return DataFeatures.TimestampedImage(image, timestamp=self.__background.timestamp)
class ArScene(DataFeatures.PipelineStepObject):
"""
Define abstract Augmented Reality scene with ArLayers and ArFrames inside.
"""
# noinspection PyMissingConstructor
@DataFeatures.PipelineStepInit
def __init__(self, **kwargs):
"""Initialize ArScene"""
# Init private attributes
self._layers = {}
self.__frames = {}
self.__angle_tolerance = 0.
self.__distance_tolerance = 0.
@property
def layers(self) -> dict:
"""Dictionary of ArLayers to project once the pose is estimated.
See [project][argaze.ArFeatures.ArScene.project] function below."""
return self._layers
@layers.setter
@DataFeatures.PipelineStepAttributeSetter
def layers(self, layers: dict):
self._layers = {}
for layer_name, layer_data in layers.items():
if type(layer_data) is dict:
self._layers[layer_name] = ArLayer(name=layer_name, **layer_data)
# str: relative path to JSON file
elif type(layer_data) is str:
self._layers[layer_name] = DataFeatures.from_json(
os.path.join(DataFeatures.get_working_directory(), layer_data))
# Edit parent
for name, layer in self._layers.items():
layer.parent = self
@property
def frames(self) -> dict:
"""Dictionary of ArFrames to project once the pose is estimated.
See [project][argaze.ArFeatures.ArScene.project] function below."""
return self.__frames
@frames.setter
@DataFeatures.PipelineStepAttributeSetter
def frames(self, frames: dict):
self.__frames = {}
for frame_name, frame_data in frames.items():
if type(frame_data) is dict:
new_frame = ArFrame(name=frame_name, **frame_data)
# str: relative path to JSON file
elif type(frame_data) is str:
new_frame = DataFeatures.from_json(os.path.join(DataFeatures.get_working_directory(), frame_data))
# Loaded frame name have to be equals to dictionary key
assert (new_frame.name == frame_name)
else:
raise ValueError("Bad frame data.")
# Look for a scene layer with an AOI named like the frame
for scene_layer_name, scene_layer in self.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)
except KeyError as e:
print('ArScene.from_dict: KeyError', e)
# Append new frame
self.__frames[frame_name] = new_frame
# Edit parent
for name, frame in self.__frames.items():
frame.parent = self
@property
def angle_tolerance(self) -> float:
"""Angle error tolerance to validate marker pose in degree used into [estimate_pose][argaze.ArFeatures.ArScene.estimate_pose] function."""
return self.__angle_tolerance
@angle_tolerance.setter
def angle_tolerance(self, value: float):
self.__angle_tolerance = value
@property
def distance_tolerance(self) -> float:
"""Distance error tolerance to validate marker pose in centimeter used into [estimate_pose][argaze.ArFeatures.ArScene.estimate_pose] function."""
return self.__distance_tolerance
@distance_tolerance.setter
def distance_tolerance(self, value: float):
self.__distance_tolerance = value
def as_dict(self) -> dict:
"""Export ArScene properties as dictionary."""
return {
**DataFeatures.PipelineStepObject.as_dict(self),
"layers": self._layers,
"frames": self.__frames,
"angle_tolerance": self.__angle_tolerance,
"distance_tolerance": self.__distance_tolerance
}
@DataFeatures.PipelineStepMethod
def estimate_pose(self, detected_features: any) -> tuple[numpy.array, numpy.array, any]:
"""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
extra: any data about pose estimation
"""
raise NotImplementedError('estimate_pose() method not implemented')
@DataFeatures.PipelineStepMethod
def project(self, tvec: numpy.array, rvec: numpy.array, visual_hfov: float = 0., visual_vfov: float = 0.) -> Iterator[Union[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:
iterator: name of projected layer and 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
# noinspection PyUnresolvedReferences
yield name, aoi_scene_copy.project(tvec, rvec, self.parent.aruco_detector.optic_parameters.K)
class ArCamera(ArFrame):
"""
Define abstract Augmented Reality camera as ArFrame with ArScenes inside.
"""
@DataFeatures.PipelineStepInit
def __init__(self, **kwargs):
"""Initialize ArCamera."""
# Init ArFrame class
super().__init__()
# Init private attributes
self.__visual_hfov = 0.
self.__visual_vfov = 0.
self.__projection_cache = None
self.__projection_cache_writer = None
self.__projection_cache_reader = None
self.__projection_cache_data = None
# Init protected attributes
self._scenes = {}
@ArFrame.layers.setter
@DataFeatures.PipelineStepAttributeSetter
def layers(self, layers: dict):
self._layers = {}
for layer_name, layer_data in layers.items():
self._layers[layer_name] = ArLayer(name=layer_name, **layer_data)
# Edit parent
for name, layer in self._layers.items():
layer.parent = self
# Update expected and excluded aoi
self._update_expected_and_excluded_aoi()
@property
def scenes(self) -> dict:
"""All scenes to project into camera frame."""
return self._scenes
@scenes.setter
@DataFeatures.PipelineStepAttributeSetter
def scenes(self, scenes: dict):
self._scenes = {}
for scene_name, scene_data in scenes.items():
self._scenes[scene_name] = ArScene(name=scene_name, **scene_data)
# Edit parent
for name, scene in self._scenes.items():
scene.parent = self
# Update expected and excluded aoi
self._update_expected_and_excluded_aoi()
@property
def visual_hfov(self) -> float:
"""Angle in degree to clip scenes projection according visual horizontal field of view (HFOV)."""
return self.__visual_hfov
@visual_hfov.setter
def visual_hfov(self, value: float):
"""Set camera's visual horizontal field of view."""
self.__visual_hfov = value
@property
def visual_vfov(self) -> float:
"""Angle in degree to clip scenes projection according visual vertical field of view (VFOV)."""
return self.__visual_vfov
@visual_vfov.setter
def visual_vfov(self, value: float):
"""Set camera's visual vertical field of view."""
self.__visual_vfov = value
@property
def projection_cache(self) -> str:
"""file path to store/read layers projections into/from a cache."""
return self.__projection_cache
@projection_cache.setter
def projection_cache(self, projection_cache: str):
self.__projection_cache = projection_cache
# The file doesn't exist yet: store projections into the cache
if not os.path.exists(os.path.join( DataFeatures.get_working_directory(), self.__projection_cache) ):
self.__projection_cache_writer = UtilsFeatures.FileWriter(path=self.__projection_cache)
self.__projection_cache_reader = None
logging.info('ArCamera %s writes projection into %s', self.name, self.__projection_cache)
# The file exist: read projection from the cache
else:
self.__projection_cache_writer = None
self.__projection_cache_reader = UtilsFeatures.FileReader(path=self.__projection_cache)
logging.info('ArCamera %s reads projection from %s', self.name, self.__projection_cache)
def _clear_projection(self):
"""Clear layers projection."""
logging.debug('ArCamera._clear_projection %s', self.name)
for layer_name, layer in self.layers.items():
# Initialize layer if needed
if layer.aoi_scene is None:
layer.aoi_scene = AOI2DScene.AOI2DScene()
else:
layer.aoi_scene.clear()
def _write_projection_cache(self, timestamp: int|float, exception = None):
"""Write layers aoi scene into the projection cache.
Parameters:
timestamp: cache time
"""
if self.__projection_cache_writer is not None:
logging.debug('ArCamera._write_projection_cache %s %f', self.name, timestamp)
if exception is None:
projection = {}
for layer_name, layer in self.layers.items():
projection[layer_name] = layer.aoi_scene
self.__projection_cache_writer.write( (timestamp, projection) )
else:
self.__projection_cache_writer.write( (timestamp, exception) )
def _read_projection_cache(self, timestamp: int|float):
"""Read layers aoi scene from the projection cache.
Parameters:
timestamp: cache time.
Returns:
success: False if there is no projection cache, True otherwise.
"""
if self.__projection_cache_reader is None:
return False
logging.debug('ArCamera._read_projection_cache %s %f', self.name, timestamp)
# Clear former projection
self._clear_projection()
try:
# Read first data if not done yet
if self.__projection_cache_data is None:
self.__projection_cache_data = self.__projection_cache_reader.read()
# Continue reading cache until correct timestamped projection
while float(self.__projection_cache_data[0]) < timestamp:
self.__projection_cache_data = self.__projection_cache_reader.read()
# No more projection in the cache
except EOFError:
raise DataFeatures.TimestampedException("Projection cache is empty", timestamp=timestamp)
# Correct timestamped projection is found
if float(self.__projection_cache_data[0]) == timestamp:
# When correct timestamped projection is found
projection = {}
try:
projection = ast.literal_eval(self.__projection_cache_data[1])
for layer_name, aoi_scene in projection.items():
self._layers[layer_name].aoi_scene = AOI2DScene.AOI2DScene(aoi_scene)
self._layers[layer_name].timestamp = timestamp
logging.debug('> reading %s projection from cache', layer_name)
except SyntaxError as e:
raise DataFeatures.TimestampedException(self.__projection_cache_data[1], timestamp=timestamp)
return True
def scene_frames(self) -> Iterator[ArFrame]:
"""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 as_dict(self) -> dict:
"""Export ArCamera properties as dictionary."""
return {
**ArFrame.as_dict(self),
"scenes": self._scenes,
"visual_hfov": self.__visual_hfov,
"visual_vfov": self.__visual_vfov
}
@DataFeatures.PipelineStepEnter
def __enter__(self):
if self.__projection_cache_writer is not None:
self.__projection_cache_writer.__enter__()
if self.__projection_cache_reader is not None:
self.__projection_cache_reader.__enter__()
@DataFeatures.PipelineStepExit
def __exit__(self, exception_type, exception_value, exception_traceback):
if self.__projection_cache_writer is not None:
self.__projection_cache_writer.__exit__(exception_type, exception_value, exception_traceback)
if self.__projection_cache_reader is not None:
self.__projection_cache_reader.__exit__(exception_type, exception_value, exception_traceback)
def _update_expected_and_excluded_aoi(self):
"""Edit expected aoi of each layer aoi scan path with the aoi of corresponding scene layer.
Edit excluded aoi to ignore frame aoi from aoi matching.
"""
if not self._layers or not self._scenes:
logging.debug('ArCamera._update_expected_and_excluded_aoi %s: missing layers or scenes', self.name)
return
logging.debug('ArCamera._update_expected_and_excluded_aoi %s', self.name)
for layer_name, layer in self._layers.items():
expected_aoi_list = []
excluded_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)
excluded_aoi_list.append(frame_name)
except ValueError:
continue
if layer.aoi_scan_path is not None:
layer.aoi_scan_path.expected_aoi = expected_aoi_list
if layer.aoi_matcher is not None:
layer.aoi_matcher.exclude = excluded_aoi_list
@DataFeatures.PipelineStepMethod
def watch(self, image: numpy.array):
"""Detect AR features from image and project scenes into camera frame.
Parameters:
image: image where to extract AR features
"""
raise NotImplementedError('watch() method not implemented')
@DataFeatures.PipelineStepMethod
def look(self, timestamped_gaze_position: GazeFeatures.GazePosition):
"""Project timestamped gaze position into each scene frames.
!!! warning
watch method needs to be called first.
Parameters:
timestamped_gaze_position: gaze position to project
"""
# Project timestamped gaze position into camera frame
# NOTE: the call to super().look method uses unwrap option to disable observers notification
# as they are already notified that this look method is called. Cf DataFeatures.PipelineStepMethod.wrapper.
super().look(timestamped_gaze_position, unwrap=True)
# Use camera frame lock feature
with self._lock:
# 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():
if camera_layer.aoi_scene:
try:
aoi_2d = camera_layer.aoi_scene[scene_frame.name]
if timestamped_gaze_position:
# TODO?: Should we prefer to use camera frame AOIMatcher object?
if aoi_2d.contains_point(timestamped_gaze_position):
inner_x, inner_y = aoi_2d.clockwise().inner_axis(*timestamped_gaze_position)
# QUESTION: How to project gaze precision?
inner_gaze_position = GazeFeatures.GazePosition((inner_x, inner_y), timestamp=timestamped_gaze_position.timestamp)
# Project inner gaze position into scene frame
scene_frame.look(inner_gaze_position * scene_frame.size)
# Ignore missing aoi in camera frame layer projection
except KeyError:
pass
@DataFeatures.PipelineStepMethod
def map(self):
"""Project camera frame background into scene frames background.
!!! warning
watch method needs to be called first.
"""
# Use camera frame lock feature
with self._lock:
# 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 = DataFeatures.TimestampedImage(
cv2.warpPerspective(self.background, mapping, (width, height)),
timestamp=self.background.timestamp)
# Ignore missing frame projection
except KeyError:
pass
# Define default ArContext image parameters
DEFAULT_ARCONTEXT_IMAGE_PARAMETERS = {
"draw_times": True,
"draw_exceptions": True
}
class ArContext(DataFeatures.PipelineStepObject):
"""
Define class to ...
"""
# noinspection PyMissingConstructor
@DataFeatures.PipelineStepInit
def __init__(self, **kwargs):
# Init private attributes
self.__pipeline = None
self.__catch_exceptions = True
self.__exceptions = DataFeatures.TimestampedExceptions()
# Init gaze position processing assessment
self.__process_gaze_position_chrono = UtilsFeatures.TimeProbe()
self.__process_gaze_position_frequency = 0
# Init camera image processing assessment
self.__process_camera_image_chrono = UtilsFeatures.TimeProbe()
self.__process_camera_image_frequency = 0
# Init protected attributes
self._image_parameters = DEFAULT_ARCONTEXT_IMAGE_PARAMETERS
@property
def pipeline(self) -> DataFeatures.PipelineStepObject:
"""ArFrame used to process gaze data or ArCamera used to process gaze data and video of environment."""
return self.__pipeline
@pipeline.setter
@DataFeatures.PipelineStepAttributeSetter
def pipeline(self, pipeline: DataFeatures.PipelineStepObject):
assert (issubclass(type(pipeline), DataFeatures.PipelineStepObject))
self.__pipeline = pipeline
@property
def catch_exceptions(self) -> bool:
"""Catch pipeline exception to display them instead of crashing execution."""
return self.__catch_exceptions
@catch_exceptions.setter
def catch_exceptions(self, catch_exceptions: bool):
self.__catch_exceptions = catch_exceptions
def exceptions(self) -> DataFeatures.TimestampedExceptions:
"""Get exceptions list"""
return self.__exceptions
def as_dict(self) -> dict:
"""Export ArContext properties as dictionary."""
return {
**DataFeatures.PipelineStepObject.as_dict(self),
"pipeline": self.__pipeline,
"catch_exceptions": self.__catch_exceptions,
"image_parameters": self._image_parameters
}
@DataFeatures.PipelineStepEnter
def __enter__(self):
"""Enter into ArContext."""
self.__process_gaze_position_chrono.start()
self.__process_camera_image_chrono.start()
return self
@DataFeatures.PipelineStepExit
def __exit__(self, exception_type, exception_value, exception_traceback):
"""Exit from ArContext."""
pass
def _process_gaze_position(self, timestamp: int | float, x: int | float = None, y: int | float = None, precision: int | float = None):
"""Request pipeline to process new gaze position at a timestamp."""
logging.debug('ArContext._process_gaze_position %s', self.name)
# Assess gaze position processing frequency
lap_time, nb_laps, elapsed_time = self.__process_gaze_position_chrono.lap()
if elapsed_time > 1e3:
self.__process_gaze_position_frequency = nb_laps
self.__process_gaze_position_chrono.restart()
if issubclass(type(self.__pipeline), ArFrame):
try:
if x is None and y is None:
# Edit empty gaze position
self.__pipeline.look(GazeFeatures.GazePosition(timestamp=timestamp), catch_exceptions=self.__catch_exceptions)
else:
# Edit gaze position
self.__pipeline.look(GazeFeatures.GazePosition((x, y), precision=precision, timestamp=timestamp), catch_exceptions=self.__catch_exceptions)
except DataFeatures.TimestampedException as e:
self.__exceptions.append(e)
else:
raise (TypeError('Pipeline is not ArFrame instance.'))
def _process_camera_image(self, timestamp: int | float, image: numpy.array):
"""Request pipeline to process new camera image at a timestamp."""
logging.debug('ArContext._process_camera_image %s', self.name)
# Assess camera image processing frequency
lap_time, nb_laps, elapsed_time = self.__process_camera_image_chrono.lap()
if elapsed_time > 1e3:
self.__process_camera_image_frequency = nb_laps
self.__process_camera_image_chrono.restart()
if issubclass(type(self.__pipeline), ArCamera):
height, width, _ = image.shape
# Compare image size with ArCamera frame size
if list(image.shape[0:2][::-1]) != self.__pipeline.size:
logging.warning('%s._process_camera_image: image size (%i x %i) is different of ArCamera frame size (%i x %i)',
DataFeatures.get_class_path(self), width, height, self.__pipeline.size[0], self.__pipeline.size[1])
return
try:
logging.debug('\t> watch image (%i x %i)', width, height)
self.__pipeline.watch(DataFeatures.TimestampedImage(image, timestamp=timestamp), catch_exceptions=self.__catch_exceptions)
# TODO: make this step optional
self.__pipeline.map(timestamp=timestamp, catch_exceptions=self.__catch_exceptions)
except DataFeatures.TimestampedException as e:
logging.warning('%s._process_camera_image: %s', DataFeatures.get_class_path(self), e)
self.__exceptions.append(e)
else:
raise (TypeError('Pipeline is not ArCamera instance.'))
@DataFeatures.PipelineStepImage
def image(self, draw_times: bool = None, draw_exceptions: bool = None):
"""
Get pipeline image with execution information.
Parameters:
draw_times: draw pipeline execution times
draw_exceptions: draw pipeline exception messages
"""
logging.debug('ArContext.image %s', self.name)
image = self.__pipeline.image()
height, width, _ = image.shape
logging.debug('\t> get image (%i x %i)', width, height)
last_position = self.__pipeline.last_gaze_position()
info_stack = 0
if draw_times:
if image.is_timestamped():
info_stack += 1
cv2.putText(image, f'Frame at {image.timestamp}ms', (20, info_stack * 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)
if issubclass(type(self.__pipeline), ArCamera):
try:
watch_time = int(self.__pipeline.execution_times['watch'])
except KeyError:
watch_time = math.nan
info_stack += 1
cv2.putText(image, f'Watch {watch_time}ms at {self.__process_camera_image_frequency}Hz', (20, info_stack * 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)
if last_position is not None:
info_stack += 1
cv2.putText(image, f'Position at {last_position.timestamp}ms', (20, info_stack * 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)
if issubclass(type(self.__pipeline), ArFrame):
try:
look_time = self.__pipeline.execution_times['look']
except KeyError:
look_time = math.nan
info_stack += 1
cv2.putText(image, f'Look {look_time:.2f}ms at {self.__process_gaze_position_frequency}Hz', (20, info_stack * 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)
if draw_exceptions:
# Write exceptions
while self.__exceptions:
e = self.__exceptions.pop()
i = len(self.__exceptions)
cv2.rectangle(image, (0, height - (i + 1) * 50), (width, height - i * 50), (0, 0, 127), -1)
cv2.putText(image, f'error: {e}', (20, height - (i + 1) * 50 + 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)
return image
@DataFeatures.PipelineStepMethod
def pause(self):
"""Pause pipeline processing."""
raise NotImplementedError('pause() method not implemented')
def is_paused(self) -> bool:
"""Is pipeline processing paused?"""
raise NotImplementedError('is_paused() method not implemented')
@DataFeatures.PipelineStepMethod
def resume(self):
"""Resume pipeline processing."""
raise NotImplementedError('resume() method not implemented')
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