Source code for stonesoup.types.detection

from typing import MutableMapping, Sequence

from .groundtruth import GroundTruthPath
from .state import CategoricalState, CompositeState
from .state import State, GaussianState, StateVector
from ..base import Property
from ..models.measurement import MeasurementModel

[docs] class Detection(State): """Detection type""" measurement_model: MeasurementModel = Property( default=None, doc="The measurement model used to generate the detection (the default is ``None``)") metadata: MutableMapping = Property( default=None, doc='Dictionary of metadata items for Detections.') def __init__(self, state_vector, *args, **kwargs): super().__init__(state_vector, *args, **kwargs) if self.metadata is None: self.metadata = {}
[docs] class GaussianDetection(Detection, GaussianState): """GaussianDetection type"""
[docs] class Clutter(Detection): """Clutter type for detections classed as clutter This is same as :class:`~.Detection`, but can be used to identify clutter for metrics and analysis purposes. """
[docs] class TrueDetection(Detection): """TrueDetection type for detections that come from ground truth This is same as :class:`~.Detection`, but can be used to identify true detections for metrics and analysis purposes. """ groundtruth_path: GroundTruthPath = Property( doc="Ground truth path that this detection came from")
[docs] class MissedDetection(Detection): """Detection type for a missed detection This is same as :class:`~.Detection`, but it is used in MultipleHypothesis to indicate the null hypothesis (no detections are associated with the specified track). """ state_vector: StateVector = Property(default=None, doc="State vector. Default `None`.") def __init__(self, state_vector=None, *args, **kwargs): super().__init__(state_vector, *args, **kwargs) def __bool__(self): return False
[docs] class CategoricalDetection(Detection, CategoricalState): """Categorical detection type."""
[docs] class TrueCategoricalDetection(TrueDetection, CategoricalDetection): """TrueCategoricalDetection type for categorical detections that come from ground truth."""
[docs] class CompositeDetection(CompositeState): """Composite detection type Composition of :class:`~.Detection`. """ sub_states: Sequence[Detection] = Property( doc="Sequence of sub-detections comprising the composite detection. All sub-detections " "must have matching timestamp. Must not be empty.") groundtruth_path: GroundTruthPath = Property( default=None, doc="Ground truth path that this detection came from.") mapping: Sequence[int] = Property( default=None, doc="Mapping of detections to composite state space. Defaults to `None`, where " "sub-detections map to sub-state spaces in order.") def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if self.mapping and len(self.mapping) != len(self.sub_states): raise ValueError("Mappings and sub-detections must have same count") elif self.mapping is None: self.mapping = list(range(len(self.sub_states))) @property def metadata(self): """Combined metadata of all sub-detections.""" metadata = dict() for sub_detection in self.sub_states: metadata.update(sub_detection.metadata) return metadata
Detection.register(CompositeDetection) # noqa: E305