from abc import abstractmethod, ABC
from typing import Set, Union, Sequence
import numpy as np
from .actionable import Actionable
from .base import PlatformMountable
from ..base import Property
from ..models.clutter.clutter import ClutterModel
from ..types.detection import TrueDetection
from ..types.groundtruth import GroundTruthState
[docs]class Sensor(PlatformMountable, Actionable):
"""Sensor Base class for general use.
Most properties and methods are inherited from :class:`~.PlatformMountable`.
Notes
-----
* Sensors must have a measure function.
* Attributes that are modifiable via actioning the sensor should be
:class:`~.ActionableProperty` types.
* The sensor has a timestamp property that should be updated via its
:meth:`~Actionable.act` method.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.timestamp = None
[docs] def validate_timestamp(self):
if self.timestamp:
return True
try:
self.timestamp = self.movement_controller.state.timestamp
except AttributeError:
return False
if self.timestamp is None:
return False
return True
[docs] @abstractmethod
def measure(self, ground_truths: Set[GroundTruthState], noise: Union[np.ndarray, bool] = True,
**kwargs) -> Set[TrueDetection]:
"""Generate a measurement for a given state
Parameters
----------
ground_truths : Set[:class:`~.GroundTruthState`]
A set of :class:`~.GroundTruthState`
noise: :class:`numpy.ndarray` or bool
An externally generated random process noise sample (the default is `True`, in which
case :meth:`~.Model.rvs` is used; if `False`, no noise will be added)
Returns
-------
Set[:class:`~.TrueDetection`]
A set of measurements generated from the given states. The timestamps of the
measurements are set equal to that of the corresponding states that they were
calculated from. Each measurement stores the ground truth path that it was produced
from.
"""
raise NotImplementedError
@property
@abstractmethod
def measurement_model(self):
"""Measurement model of the sensor, describing general sensor model properties"""
raise NotImplementedError
[docs]class SimpleSensor(Sensor, ABC):
clutter_model: ClutterModel = Property(
default=None,
doc="An optional clutter generator that adds a set of simulated "
":class:`Clutter` objects to the measurements at each time step. "
"The clutter is simulated according to the provided distribution.")
[docs] def measure(self, ground_truths: Set[GroundTruthState], noise: Union[np.ndarray, bool] = True,
**kwargs) -> Set[TrueDetection]:
measurement_model = self.measurement_model
detectable_ground_truths = [truth for truth in ground_truths
if self.is_detectable(truth)]
if noise is True:
if len(detectable_ground_truths) > 1:
noise_vectors_iter = iter(measurement_model.rvs(len(detectable_ground_truths),
**kwargs))
else:
noise_vectors_iter = iter([measurement_model.rvs(**kwargs)])
detections = set()
for truth in detectable_ground_truths:
measurement_vector = measurement_model.function(truth, noise=False, **kwargs)
if noise is True:
measurement_noise = next(noise_vectors_iter)
else:
measurement_noise = noise
# Add in measurement noise to the measurement vector
measurement_vector += measurement_noise
detection = TrueDetection(measurement_vector,
measurement_model=measurement_model,
timestamp=truth.timestamp,
groundtruth_path=truth)
detections.add(detection)
# Generate clutter at this time step
if self.clutter_model is not None:
self.clutter_model.measurement_model = measurement_model
clutter = self.clutter_model.function(ground_truths)
detections = set.union(detections, clutter)
return detections
@abstractmethod
def is_detectable(self, state: GroundTruthState) -> bool:
raise NotImplementedError
[docs]class SensorSuite(Sensor):
"""Sensor composition type
Models a suite of sensors all returning detections at the same 'time'. Returns all detections
in one go.
Can append information of the sensors to the metadata of their corresponding detections.
"""
sensors: Sequence[Sensor] = Property(doc="Suite of sensors to get detections from.")
attributes_inform: Set[str] = Property(
doc="Names of attributes to store the value of at time of detection."
)
[docs] def measure(self, ground_truths: Set[GroundTruthState], noise: Union[bool, np.ndarray] = True,
**kwargs) -> Set[TrueDetection]:
"""Call each sub-sensor's measure method in turn. Key word arguments are passed to the
measure method of each sensor.
Append additional metadata to each sensor's set of detections. Which keys are appended is
dictated by :attr:`attributes_inform`."""
all_detections = set()
for sensor in self.sensors:
detections = sensor.measure(ground_truths, noise, **kwargs)
attributes_dict = {attribute_name: sensor.__getattribute__(attribute_name)
for attribute_name in self.attributes_inform}
for detection in detections:
detection.metadata.update(attributes_dict)
all_detections.update(detections)
return all_detections
@property
def measurement_model(self):
"""Measurement model of the sensor, describing general sensor model properties"""
raise NotImplementedError