Module datatap.droplet.frame_annotation

Classes

class FrameAnnotation (*, classes: Mapping[str, ClassAnnotation])

A collection of class annotations that annotate a given image.

Expand source code

class FrameAnnotation:
        """
        A collection of class annotations that annotate a given image.
        """

        classes: Mapping[str, ClassAnnotation]
        """
        A mapping from class name to the annotations of that class.
        """

        @staticmethod
        def from_json(json: Mapping[str, Any]) -> FrameAnnotation:
                """
                Constructs an `FrameAnnotation` from an `FrameAnnotationJson`.
                """
                return FrameAnnotation(
                        classes = {
                                class_name: ClassAnnotation.from_json(json["classes"][class_name])
                                for class_name in json["classes"]
                        }
                )

        def __init__(
                self,
                *,
                classes: Mapping[str, ClassAnnotation],
        ):
                self.classes = classes

        def filter_detections(
                self,
                *,
                instance_filter: Callable[[Instance], bool],
                multi_instance_filter: Callable[[MultiInstance], bool]
        ) -> FrameAnnotation:
                """
                Returns a new image annotation consisting only of the instances and
                multi-instances that meet the given constraints.
                """
                return FrameAnnotation(
                        classes = {
                                class_name: class_annotation.filter_detections(
                                        instance_filter = instance_filter,
                                        multi_instance_filter = multi_instance_filter
                                )
                                for class_name, class_annotation in self.classes.items()
                        }
                )

        def apply_bounding_box_confidence_threshold(self, threshold: float) -> FrameAnnotation:
                """
                Returns a new image annotation consisting only of the instances and
                multi-instances that have bounding boxes which either do not have a
                confidence specified or which have a confience meeting the given
                threshold.
                """
                return self.filter_detections(
                        instance_filter = lambda instance: (
                                instance.bounding_box is not None
                                        and instance.bounding_box.meets_confidence_threshold(threshold)
                        ),
                        multi_instance_filter = lambda multi_instance: (
                                multi_instance.bounding_box is not None
                                        and multi_instance.bounding_box.meets_confidence_threshold(threshold)
                        )
                )

        def apply_segmentation_confidence_threshold(self, threshold: float) -> FrameAnnotation:
                """
                Returns a new image annotation consisting only of the instances and
                multi-instances that have segmentations which either do not have a
                confidence specified or which have a confience meeting the given
                threshold.
                """
                return self.filter_detections(
                        instance_filter = lambda instance: (
                                instance.segmentation is not None
                                        and instance.segmentation.meets_confidence_threshold(threshold)
                        ),
                        multi_instance_filter = lambda multi_instance: (
                                multi_instance.segmentation is not None
                                        and multi_instance.segmentation.meets_confidence_threshold(threshold)
                        )
                )

        def __repr__(self) -> str:
                return basic_repr(
                        "FrameAnnotation",
                        classes = self.classes
                )

        def __eq__(self, other: object) -> bool:
                if not isinstance(other, FrameAnnotation):
                        return NotImplemented
                return self.classes == other.classes

        def __add__(self, other: FrameAnnotation) -> FrameAnnotation:
                if not isinstance(other, FrameAnnotation): # type: ignore - pyright complains about the isinstance check being redundant
                        return NotImplemented

                classes: Dict[str, ClassAnnotation] = {}

                for key, value in self.classes.items():
                        classes[key] = value

                for key, value in other.classes.items():
                        if key in classes:
                                classes[key] += value
                        else:
                                classes[key] = value

                return FrameAnnotation(
                        classes = classes
                )

        def to_json(self) -> FrameAnnotationJson:
                """
                Serializes this image annotation into an `FrameAnnotationJson`.
                """
                json: FrameAnnotationJson = {
                        "classes": {
                                name: class_annotation.to_json()
                                for name, class_annotation in self.classes.items()
                        }
                }

                return json

Class variables

var classes : Mapping[str, ClassAnnotation]

A mapping from class name to the annotations of that class.

Static methods

def from_json(json: Mapping[str, Any]) ‑> FrameAnnotation

Constructs an FrameAnnotation from an FrameAnnotationJson.

Expand source code

@staticmethod
def from_json(json: Mapping[str, Any]) -> FrameAnnotation:
        """
        Constructs an `FrameAnnotation` from an `FrameAnnotationJson`.
        """
        return FrameAnnotation(
                classes = {
                        class_name: ClassAnnotation.from_json(json["classes"][class_name])
                        for class_name in json["classes"]
                }
        )

Methods

def apply_bounding_box_confidence_threshold(self, threshold: float) ‑> FrameAnnotation

Returns a new image annotation consisting only of the instances and multi-instances that have bounding boxes which either do not have a confidence specified or which have a confience meeting the given threshold.

Expand source code

def apply_bounding_box_confidence_threshold(self, threshold: float) -> FrameAnnotation:
        """
        Returns a new image annotation consisting only of the instances and
        multi-instances that have bounding boxes which either do not have a
        confidence specified or which have a confience meeting the given
        threshold.
        """
        return self.filter_detections(
                instance_filter = lambda instance: (
                        instance.bounding_box is not None
                                and instance.bounding_box.meets_confidence_threshold(threshold)
                ),
                multi_instance_filter = lambda multi_instance: (
                        multi_instance.bounding_box is not None
                                and multi_instance.bounding_box.meets_confidence_threshold(threshold)
                )
        )

def apply_segmentation_confidence_threshold(self, threshold: float) ‑> FrameAnnotation

Returns a new image annotation consisting only of the instances and multi-instances that have segmentations which either do not have a confidence specified or which have a confience meeting the given threshold.

Expand source code

def apply_segmentation_confidence_threshold(self, threshold: float) -> FrameAnnotation:
        """
        Returns a new image annotation consisting only of the instances and
        multi-instances that have segmentations which either do not have a
        confidence specified or which have a confience meeting the given
        threshold.
        """
        return self.filter_detections(
                instance_filter = lambda instance: (
                        instance.segmentation is not None
                                and instance.segmentation.meets_confidence_threshold(threshold)
                ),
                multi_instance_filter = lambda multi_instance: (
                        multi_instance.segmentation is not None
                                and multi_instance.segmentation.meets_confidence_threshold(threshold)
                )
        )

def filter_detections(self, *, instance_filter: Callable[[Instance], bool], multi_instance_filter: Callable[[MultiInstance], bool]) ‑> FrameAnnotation

Returns a new image annotation consisting only of the instances and multi-instances that meet the given constraints.

Expand source code

def filter_detections(
        self,
        *,
        instance_filter: Callable[[Instance], bool],
        multi_instance_filter: Callable[[MultiInstance], bool]
) -> FrameAnnotation:
        """
        Returns a new image annotation consisting only of the instances and
        multi-instances that meet the given constraints.
        """
        return FrameAnnotation(
                classes = {
                        class_name: class_annotation.filter_detections(
                                instance_filter = instance_filter,
                                multi_instance_filter = multi_instance_filter
                        )
                        for class_name, class_annotation in self.classes.items()
                }
        )

def to_json(self) ‑> FrameAnnotationJson

Serializes this image annotation into an FrameAnnotationJson.

Expand source code

def to_json(self) -> FrameAnnotationJson:
        """
        Serializes this image annotation into an `FrameAnnotationJson`.
        """
        json: FrameAnnotationJson = {
                "classes": {
                        name: class_annotation.to_json()
                        for name, class_annotation in self.classes.items()
                }
        }

        return json

class FrameAnnotationJson (*args, **kwargs)

The serialized JSON representation of an image annotation.

Expand source code

class FrameAnnotationJson(TypedDict):
        """
        The serialized JSON representation of an image annotation.
        """

        classes: Mapping[str, ClassAnnotationJson]

Ancestors

• builtins.dict

Class variables

var classes : Mapping[str, ClassAnnotationJson]