raillabel_providerkit.format.understand_ai package

Submodules

raillabel_providerkit.format.understand_ai.bounding_box_2d module

class raillabel_providerkit.format.understand_ai.bounding_box_2d.BoundingBox2d(id: UUID, object_id: UUID, class_name: str, attributes: dict, sensor: SensorReference, x_min: float, y_min: float, x_max: float, y_max: float)

Bases: _Annotation

A 2d bounding box.

Parameters:

  • id (uuid.UUID) – Unique identifier of the annotation.

  • object_id (uuid.UUID) – Unique identifier of the object this annotation refers to. Used for tracking.

  • class_name (str) – Name of the class this annotation belongs to.

  • attributes (dict[str, str or list]) – Key value pairs of attributes with the keys beeing the friendly identifier of the attribute and the value beeing the attribute value.

  • sensor (raillabel.format.understand_ai.SensorReference) – Information about the sensor this annotation is labeled in.

  • x_min (float) – Left corner of the bounding box in pixels.

  • y_min (float) – Top corner of the bounding box in pixels.

  • x_max (float) – Right corner of the bounding box in pixels.

  • y_max (float) – Bottom corner of the bounding box in pixels.

OPENLABEL_ID = 'bbox'
classmethod fromdict(data_dict: dict) → BoundingBox2d

Generate a BoundingBox2d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 2d bounding box.

Return type:

BoundingBox2d

x_max: float
x_min: float
y_max: float
y_min: float

raillabel_providerkit.format.understand_ai.bounding_box_3d module

class raillabel_providerkit.format.understand_ai.bounding_box_3d.BoundingBox3d(id: UUID, object_id: UUID, class_name: str, attributes: dict, sensor: SensorReference, center: Point3d, size: Size3d, quaternion: Quaternion)

Bases: _Annotation

A 3d bounding box.

Parameters:

  • id (uuid.UUID) – Unique identifier of the annotation.

  • object_id (uuid.UUID) – Unique identifier of the object this annotation refers to. Used for tracking.

  • class_name (str) – Name of the class this annotation belongs to.

  • attributes (dict[str, str or list]) – Key value pairs of attributes with the keys beeing the friendly identifier of the attribute and the value beeing the attribute value.

  • sensor (raillabel.format.understand_ai.SensorReference) – Information about the sensor this annotation is labeled in.

  • center (raillabel.format.understand_ai.Point3d) – Center position of the bounding box.

  • size (raillabel.format.understand_ai.Size3d) – 3d size of the bounding box.

  • quaternion (raillabel.format.understand_ai.Quaternion) – Rotation quaternion of the bounding box.

OPENLABEL_ID = 'cuboid'
center: Point3d
classmethod fromdict(data_dict: dict) → BoundingBox3d

Generate a BoundingBox3d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 3d bounding box.

Return type:

BoundingBox3d

quaternion: Quaternion
size: Size3d

raillabel_providerkit.format.understand_ai.coordinate_system module

class raillabel_providerkit.format.understand_ai.coordinate_system.CoordinateSystem(uid: str, topic: str, frame_id: str, position: list[float], rotation_quaternion: list[float], rotation_matrix: list[float], angle_axis_rotation: list[float], homogeneous_transform: list[float] | None = None, measured_position: list[float] | None = None, camera_matrix: list[float] | None = None, dist_coeffs: list[float] | None = None)

Bases: object

Global information for a sensor regarding calibration.

Parameters:

  • uid (str) – Friendly name of the sensor as well as its identifier. Must be unique

  • topic (str) – Rostopic of the sensor.

  • frame_id (str) – Name of the directory containing the files from this sensor.

  • position (list of float) – 3D translation with regards to the origin.

  • rotation_quaternion (list of float) – Rotation quaternion with regards to the origin.

  • rotation_matrix (list of float) – Rotation matrix with regards to the origin.

  • angle_axis_rotation (list of float) – Angle axis rotation with regards to the origin.

  • homogeneous_transform (list of float, optional) – Homogeneous transformation matrix with regards to the origin. Default is None.

  • measured_position (list of float, optional)

  • camera_matrix (list of float, optional) – Camera matrix of the sensor. Only applies to sensors of type camera. Default is None.

  • dist_coeffs (list of float, optional) – Distortion coefficients of the sensor. Only applies to sensors of type camera. Default is None.

angle_axis_rotation: list[float]
camera_matrix: list[float] | None = None
dist_coeffs: list[float] | None = None
frame_id: str
classmethod fromdict(data_dict: dict) → CoordinateSystem

Generate a CoordinateSystem from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data.

Returns:

coordinate_system – Converted coordinate_system.

Return type:

CoordinateSystem

homogeneous_transform: list[float] | None = None
measured_position: list[float] | None = None
position: list[float]
rotation_matrix: list[float]
rotation_quaternion: list[float]
to_raillabel() → tuple[dict, dict]

Convert to a raillabel compatible dict.

Returns:

  • coordinate_system_dict (dict) – Dictionary of the raillabel coordinate system.

  • stream_dict (dict) – Dictionary of the raillabel stream.

topic: str
property translated_uid: str

Return uid translated to raillabel.

uid: str

raillabel_providerkit.format.understand_ai.frame module

class raillabel_providerkit.format.understand_ai.frame.Frame(id: int, timestamp: Decimal, bounding_box_2ds: dict[str, BoundingBox2d], bounding_box_3ds: dict[str, BoundingBox3d], polygon_2ds: dict[str, Polygon2d], polyline_2ds: dict[str, Polyline2d], segmentation_3ds: dict[str, Segmentation3d], _annotation_uids: set[str] = None)

Bases: object

A container of dynamic, timewise, information.

Parameters:

  • id (int) – Numerical identifier of the frame. Must be unique within the scene.

  • timestamp (decimal.Decimal) – Timestamp containing the Unix epoch time of the frame with up to nanosecond precision.

  • annotations (dict) – Dictionary containing all annotations. The keys are the uids of the annotations and the values are objects of type BoundingBox2d, BoundingBox3d, Polygon2d, Polyline2d or Segementation3d.

property annotations: dict

Return all annotations of this frame in one dict.

bounding_box_2ds: dict[str, BoundingBox2d]
bounding_box_3ds: dict[str, BoundingBox3d]
classmethod fromdict(data_dict: dict) → Frame

Generate a Frame from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted frame.

Return type:

Frame

id: int
polygon_2ds: dict[str, Polygon2d]
polyline_2ds: dict[str, Polyline2d]
segmentation_3ds: dict[str, Segmentation3d]
timestamp: Decimal
to_raillabel() → dict

Generate a Frame from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted frame.

Return type:

Frame

property translated_objects: dict

Return all objects in this frame and translate them.

Returns:

Dictionary containing all objects. Keys are the object IDs and values are the translated class names.

Return type:

dict

property translated_sensors: dict

Return all sensors in this frame and translates them.

Returns:

Dictionary containing all sensors. Keys are the translated sensor IDs and values are the SensorReference objects.

Return type:

dict

raillabel_providerkit.format.understand_ai.metadata module

class raillabel_providerkit.format.understand_ai.metadata.Metadata(clip_id: str, external_clip_id: str, project_id: str, export_time: str, exporter_version: str, coordinate_system_3d: str, coordinate_system_reference: str, folder_name: str)

Bases: object

Container for metadata information about the scene itself.

Parameters:

  • clip_id (str) – Identifier of the scene for internal purposes.

  • external_clip_id (str) – Identifier of the scene for external purposes.

  • project_id (str) – Identifier of the annotation project.

  • export_time (str) – Timestamp of the export in the format ‘YYYY-MM-DD hh:mm UTC’.

  • exporter_version (str) – Version of the Understand.AI-exporter.

  • coordinate_system_3d (str)

  • coordinate_system_reference (str)

  • folder_name (str) – Directory with the exported reference data (e.g. images, point clouds).

clip_id: str
coordinate_system_3d: str
coordinate_system_reference: str
export_time: str
exporter_version: str
external_clip_id: str
folder_name: str
classmethod fromdict(data_dict: dict) → Metadata

Generate a Metadata from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

metadata – Converted metadata.

Return type:

Metadata

project_id: str
to_raillabel() → dict

Convert to a raillabel compatible dict.

Returns:

metadata – Converted metadata.

Return type:

dict

raillabel_providerkit.format.understand_ai.point_3d module

class raillabel_providerkit.format.understand_ai.point_3d.Point3d(x: float, y: float, z: float)

Bases: object

Dimensional information of an object in 3d.

Parameters:

  • x (float) – Position of the object in the x-dimension.

  • y (float) – Position of the object in the y-dimension.

  • z (float) – Position of the object in the z-dimension.

classmethod fromdict(data_dict: dict) → Point3d

Generate a Point3d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 3d point.

Return type:

Point3d

x: float
y: float
z: float

raillabel_providerkit.format.understand_ai.polygon_2d module

class raillabel_providerkit.format.understand_ai.polygon_2d.Polygon2d(id: UUID, object_id: UUID, class_name: str, attributes: dict, sensor: SensorReference, points: list[tuple[float, float]])

Bases: _Annotation

A 2d polygon.

Parameters:

  • id (uuid.UUID) – Unique identifier of the annotation.

  • object_id (uuid.UUID) – Unique identifier of the object this annotation refers to. Used for tracking.

  • class_name (str) – Name of the class this annotation belongs to.

  • attributes (dict[str, str or list]) – Key value pairs of attributes with the keys beeing the friendly identifier of the attribute and the value beeing the attribute value.

  • sensor (raillabel.format.understand_ai.SensorReference) – Information about the sensor this annotation is labeled in.

  • points (list[tuple[float, float]]) – 2d points belonging to the polygon.

OPENLABEL_ID = 'poly2d'
classmethod fromdict(data_dict: dict) → Polygon2d

Generate a Polygon2d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 2d polygon.

Return type:

Polygon2d

points: list[tuple[float, float]]
to_raillabel() → tuple[dict, str, str, dict]

Convert to a raillabel compatible dict.

Returns:

  • annotation (dict) – Dictionary valid for the raillabel schema.

  • object_id (str) – Friendly identifier of the object this sensor belongs to.

  • class_name (str) – Friendly identifier of the class the annotated object belongs to.

  • sensor_reference (dict) – Dictionary of the sensor reference.

raillabel_providerkit.format.understand_ai.polyline_2d module

class raillabel_providerkit.format.understand_ai.polyline_2d.Polyline2d(id: UUID, object_id: UUID, class_name: str, attributes: dict, sensor: SensorReference, points: list[tuple[float, float]])

Bases: _Annotation

A 2d polyline.

Parameters:

  • id (uuid.UUID) – Unique identifier of the annotation.

  • object_id (uuid.UUID) – Unique identifier of the object this annotation refers to. Used for tracking.

  • class_name (str) – Name of the class this annotation belongs to.

  • attributes (dict[str, str or list]) – Key value pairs of attributes with the keys beeing the friendly identifier of the attribute and the value beeing the attribute value.

  • sensor (raillabel.format.understand_ai.SensorReference) – Information about the sensor this annotation is labeled in.

  • points (list[tuple[float, float]]) – 2d points belonging to the polyline.

OPENLABEL_ID = 'poly2d'
classmethod fromdict(data_dict: dict) → Polyline2d

Generate a Polyline2d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 2d polyline.

Return type:

Polyline2d

points: list[tuple[float, float]]
to_raillabel() → tuple[dict, str, str, dict]

Convert to a raillabel compatible dict.

Returns:

  • annotation (dict) – Dictionary valid for the raillabel schema.

  • object_id (str) – Friendly identifier of the object this sensor belongs to.

  • class_name (str) – Friendly identifier of the class the annotated object belongs to.

  • sensor_reference (dict) – Dictionary of the sensor reference.

raillabel_providerkit.format.understand_ai.quaternion module

class raillabel_providerkit.format.understand_ai.quaternion.Quaternion(x: float, y: float, z: float, w: float)

Bases: object

Dimensional information of an object in 3d.

Parameters:

  • x (float) – The x component of the quaternion.

  • y (float) – The y component of the quaternion.

  • z (float) – The z component of the quaternion.

  • w (float) – The w component of the quaternion.

classmethod fromdict(data_dict: dict) → Quaternion

Generate a Quaternion from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted quaternion.

Return type:

Quaternion

w: float
x: float
y: float
z: float

raillabel_providerkit.format.understand_ai.scene module

class raillabel_providerkit.format.understand_ai.scene.Scene(metadata: Metadata, coordinate_systems: dict[str, CoordinateSystem], frames: dict[int, Frame])

Bases: object

The root Understand.Ai class, which contains all data.

Parameters:

  • metadata (raillabel.format.understand_ai.Metadata) – Container for metadata information about the scene itself.

  • coordinate_systems (dict[str, raillabel.format.understand_ai.CoordinateSystem]) – Global information for sensors regarding calibration.

  • frames (dict[int, raillabel.format.understand_ai.Frame])

coordinate_systems: dict[str, CoordinateSystem]
frames: dict[int, Frame]
classmethod fromdict(data_dict: dict) → Scene

Generate a Scene from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted scene.

Return type:

Scene

metadata: Metadata
to_raillabel() → dict

Convert to a raillabel compatible dict.

Returns:

Dictionary of the raillabel scene.

Return type:

dict

raillabel_providerkit.format.understand_ai.segmentation_3d module

class raillabel_providerkit.format.understand_ai.segmentation_3d.Segmentation3d(id: UUID, object_id: UUID, class_name: str, attributes: dict, sensor: SensorReference, associated_points: list[int], number_of_points: int)

Bases: _Annotation

The 3D segmentation of a lidar pointcloud.

Parameters:

  • id (uuid.UUID) – Unique identifier of the annotation.

  • object_id (uuid.UUID) – Unique identifier of the object this annotation refers to. Used for tracking.

  • class_name (str) – Name of the class this annotation belongs to.

  • attributes (dict[str, str or list]) – Key value pairs of attributes with the keys beeing the friendly identifier of the attribute and the value beeing the attribute value.

  • sensor (raillabel.format.understand_ai.SensorReference) – Information about the sensor this annotation is labeled in.

  • associated_points (list[int]) – List of point indices of the lidar pointcloud.

  • number_of_points (int) – Total number of points in the associated_points.

OPENLABEL_ID = 'vec'
associated_points: list[int]
classmethod fromdict(data_dict: dict) → Segmentation3d

Generate a Segmentation3d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 3d segmentation.

Return type:

Segmentation3d

number_of_points: int

raillabel_providerkit.format.understand_ai.sensor_reference module

class raillabel_providerkit.format.understand_ai.sensor_reference.SensorReference(type: str, uri: str, timestamp: Decimal)

Bases: object

Information for a sensor in a frame.

Parameters:

  • type (str) – Friendly name of the sensor and its unique identifier.

  • uri (str) – URI to the file containing the frame specific sensor output from the project directory.

  • timestamp (decimal.Decimal) – Unix timestamp of the sensor recording.

classmethod fromdict(data_dict: dict) → SensorReference

Generate a SensorReference from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted sensor reference.

Return type:

SensorReference

timestamp: Decimal
to_raillabel() → tuple[str, dict]

Convert to a raillabel compatible dict.

Returns:

  • sensor_id (str) – Friendly identifier of the sensor.

  • sensor_reference (dict) – Dictionary valid for the raillabel schema.

type: str
uri: str

raillabel_providerkit.format.understand_ai.size_3d module

class raillabel_providerkit.format.understand_ai.size_3d.Size3d(width: float, length: float, height: float)

Bases: object

Dimensional information of an object in 3d.

Parameters:

  • width (float) – Size of the object in the x-dimension.

  • length (float) – Size of the object in the y-dimension.

  • height (float) – Size of the object in the z-dimension.

classmethod fromdict(data_dict: dict) → Size3d

Generate a Size3d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 3d size.

Return type:

Size3d

height: float
length: float
width: float

Module contents

Module containing all relevant understand.ai format classes.

class raillabel_providerkit.format.understand_ai.BoundingBox2d(id: UUID, object_id: UUID, class_name: str, attributes: dict, sensor: SensorReference, x_min: float, y_min: float, x_max: float, y_max: float)

Bases: _Annotation

A 2d bounding box.

Parameters:

  • id (uuid.UUID) – Unique identifier of the annotation.

  • object_id (uuid.UUID) – Unique identifier of the object this annotation refers to. Used for tracking.

  • class_name (str) – Name of the class this annotation belongs to.

  • attributes (dict[str, str or list]) – Key value pairs of attributes with the keys beeing the friendly identifier of the attribute and the value beeing the attribute value.

  • sensor (raillabel.format.understand_ai.SensorReference) – Information about the sensor this annotation is labeled in.

  • x_min (float) – Left corner of the bounding box in pixels.

  • y_min (float) – Top corner of the bounding box in pixels.

  • x_max (float) – Right corner of the bounding box in pixels.

  • y_max (float) – Bottom corner of the bounding box in pixels.

OPENLABEL_ID = 'bbox'
attributes: dict
class_name: str
classmethod fromdict(data_dict: dict) → BoundingBox2d

Generate a BoundingBox2d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 2d bounding box.

Return type:

BoundingBox2d

id: UUID
object_id: UUID
sensor: SensorReference
x_max: float
x_min: float
y_max: float
y_min: float
class raillabel_providerkit.format.understand_ai.BoundingBox3d(id: UUID, object_id: UUID, class_name: str, attributes: dict, sensor: SensorReference, center: Point3d, size: Size3d, quaternion: Quaternion)

Bases: _Annotation

A 3d bounding box.

Parameters:

  • id (uuid.UUID) – Unique identifier of the annotation.

  • object_id (uuid.UUID) – Unique identifier of the object this annotation refers to. Used for tracking.

  • class_name (str) – Name of the class this annotation belongs to.

  • attributes (dict[str, str or list]) – Key value pairs of attributes with the keys beeing the friendly identifier of the attribute and the value beeing the attribute value.

  • sensor (raillabel.format.understand_ai.SensorReference) – Information about the sensor this annotation is labeled in.

  • center (raillabel.format.understand_ai.Point3d) – Center position of the bounding box.

  • size (raillabel.format.understand_ai.Size3d) – 3d size of the bounding box.

  • quaternion (raillabel.format.understand_ai.Quaternion) – Rotation quaternion of the bounding box.

OPENLABEL_ID = 'cuboid'
attributes: dict
center: Point3d
class_name: str
classmethod fromdict(data_dict: dict) → BoundingBox3d

Generate a BoundingBox3d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 3d bounding box.

Return type:

BoundingBox3d

id: UUID
object_id: UUID
quaternion: Quaternion
sensor: SensorReference
size: Size3d
class raillabel_providerkit.format.understand_ai.CoordinateSystem(uid: str, topic: str, frame_id: str, position: list[float], rotation_quaternion: list[float], rotation_matrix: list[float], angle_axis_rotation: list[float], homogeneous_transform: list[float] | None = None, measured_position: list[float] | None = None, camera_matrix: list[float] | None = None, dist_coeffs: list[float] | None = None)

Bases: object

Global information for a sensor regarding calibration.

Parameters:

  • uid (str) – Friendly name of the sensor as well as its identifier. Must be unique

  • topic (str) – Rostopic of the sensor.

  • frame_id (str) – Name of the directory containing the files from this sensor.

  • position (list of float) – 3D translation with regards to the origin.

  • rotation_quaternion (list of float) – Rotation quaternion with regards to the origin.

  • rotation_matrix (list of float) – Rotation matrix with regards to the origin.

  • angle_axis_rotation (list of float) – Angle axis rotation with regards to the origin.

  • homogeneous_transform (list of float, optional) – Homogeneous transformation matrix with regards to the origin. Default is None.

  • measured_position (list of float, optional)

  • camera_matrix (list of float, optional) – Camera matrix of the sensor. Only applies to sensors of type camera. Default is None.

  • dist_coeffs (list of float, optional) – Distortion coefficients of the sensor. Only applies to sensors of type camera. Default is None.

angle_axis_rotation: list[float]
camera_matrix: list[float] | None = None
dist_coeffs: list[float] | None = None
frame_id: str
classmethod fromdict(data_dict: dict) → CoordinateSystem

Generate a CoordinateSystem from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data.

Returns:

coordinate_system – Converted coordinate_system.

Return type:

CoordinateSystem

homogeneous_transform: list[float] | None = None
measured_position: list[float] | None = None
position: list[float]
rotation_matrix: list[float]
rotation_quaternion: list[float]
to_raillabel() → tuple[dict, dict]

Convert to a raillabel compatible dict.

Returns:

  • coordinate_system_dict (dict) – Dictionary of the raillabel coordinate system.

  • stream_dict (dict) – Dictionary of the raillabel stream.

topic: str
property translated_uid: str

Return uid translated to raillabel.

uid: str
class raillabel_providerkit.format.understand_ai.Frame(id: int, timestamp: Decimal, bounding_box_2ds: dict[str, BoundingBox2d], bounding_box_3ds: dict[str, BoundingBox3d], polygon_2ds: dict[str, Polygon2d], polyline_2ds: dict[str, Polyline2d], segmentation_3ds: dict[str, Segmentation3d], _annotation_uids: set[str] = None)

Bases: object

A container of dynamic, timewise, information.

Parameters:

  • id (int) – Numerical identifier of the frame. Must be unique within the scene.

  • timestamp (decimal.Decimal) – Timestamp containing the Unix epoch time of the frame with up to nanosecond precision.

  • annotations (dict) – Dictionary containing all annotations. The keys are the uids of the annotations and the values are objects of type BoundingBox2d, BoundingBox3d, Polygon2d, Polyline2d or Segementation3d.

property annotations: dict

Return all annotations of this frame in one dict.

bounding_box_2ds: dict[str, BoundingBox2d]
bounding_box_3ds: dict[str, BoundingBox3d]
classmethod fromdict(data_dict: dict) → Frame

Generate a Frame from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted frame.

Return type:

Frame

id: int
polygon_2ds: dict[str, Polygon2d]
polyline_2ds: dict[str, Polyline2d]
segmentation_3ds: dict[str, Segmentation3d]
timestamp: Decimal
to_raillabel() → dict

Generate a Frame from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted frame.

Return type:

Frame

property translated_objects: dict

Return all objects in this frame and translate them.

Returns:

Dictionary containing all objects. Keys are the object IDs and values are the translated class names.

Return type:

dict

property translated_sensors: dict

Return all sensors in this frame and translates them.

Returns:

Dictionary containing all sensors. Keys are the translated sensor IDs and values are the SensorReference objects.

Return type:

dict

class raillabel_providerkit.format.understand_ai.Metadata(clip_id: str, external_clip_id: str, project_id: str, export_time: str, exporter_version: str, coordinate_system_3d: str, coordinate_system_reference: str, folder_name: str)

Bases: object

Container for metadata information about the scene itself.

Parameters:

  • clip_id (str) – Identifier of the scene for internal purposes.

  • external_clip_id (str) – Identifier of the scene for external purposes.

  • project_id (str) – Identifier of the annotation project.

  • export_time (str) – Timestamp of the export in the format ‘YYYY-MM-DD hh:mm UTC’.

  • exporter_version (str) – Version of the Understand.AI-exporter.

  • coordinate_system_3d (str)

  • coordinate_system_reference (str)

  • folder_name (str) – Directory with the exported reference data (e.g. images, point clouds).

clip_id: str
coordinate_system_3d: str
coordinate_system_reference: str
export_time: str
exporter_version: str
external_clip_id: str
folder_name: str
classmethod fromdict(data_dict: dict) → Metadata

Generate a Metadata from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

metadata – Converted metadata.

Return type:

Metadata

project_id: str
to_raillabel() → dict

Convert to a raillabel compatible dict.

Returns:

metadata – Converted metadata.

Return type:

dict

class raillabel_providerkit.format.understand_ai.Point3d(x: float, y: float, z: float)

Bases: object

Dimensional information of an object in 3d.

Parameters:

  • x (float) – Position of the object in the x-dimension.

  • y (float) – Position of the object in the y-dimension.

  • z (float) – Position of the object in the z-dimension.

classmethod fromdict(data_dict: dict) → Point3d

Generate a Point3d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 3d point.

Return type:

Point3d

x: float
y: float
z: float
class raillabel_providerkit.format.understand_ai.Polygon2d(id: UUID, object_id: UUID, class_name: str, attributes: dict, sensor: SensorReference, points: list[tuple[float, float]])

Bases: _Annotation

A 2d polygon.

Parameters:

  • id (uuid.UUID) – Unique identifier of the annotation.

  • object_id (uuid.UUID) – Unique identifier of the object this annotation refers to. Used for tracking.

  • class_name (str) – Name of the class this annotation belongs to.

  • attributes (dict[str, str or list]) – Key value pairs of attributes with the keys beeing the friendly identifier of the attribute and the value beeing the attribute value.

  • sensor (raillabel.format.understand_ai.SensorReference) – Information about the sensor this annotation is labeled in.

  • points (list[tuple[float, float]]) – 2d points belonging to the polygon.

OPENLABEL_ID = 'poly2d'
attributes: dict
class_name: str
classmethod fromdict(data_dict: dict) → Polygon2d

Generate a Polygon2d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 2d polygon.

Return type:

Polygon2d

id: UUID
object_id: UUID
points: list[tuple[float, float]]
sensor: SensorReference
to_raillabel() → tuple[dict, str, str, dict]

Convert to a raillabel compatible dict.

Returns:

  • annotation (dict) – Dictionary valid for the raillabel schema.

  • object_id (str) – Friendly identifier of the object this sensor belongs to.

  • class_name (str) – Friendly identifier of the class the annotated object belongs to.

  • sensor_reference (dict) – Dictionary of the sensor reference.

class raillabel_providerkit.format.understand_ai.Polyline2d(id: UUID, object_id: UUID, class_name: str, attributes: dict, sensor: SensorReference, points: list[tuple[float, float]])

Bases: _Annotation

A 2d polyline.

Parameters:

  • id (uuid.UUID) – Unique identifier of the annotation.

  • object_id (uuid.UUID) – Unique identifier of the object this annotation refers to. Used for tracking.

  • class_name (str) – Name of the class this annotation belongs to.

  • attributes (dict[str, str or list]) – Key value pairs of attributes with the keys beeing the friendly identifier of the attribute and the value beeing the attribute value.

  • sensor (raillabel.format.understand_ai.SensorReference) – Information about the sensor this annotation is labeled in.

  • points (list[tuple[float, float]]) – 2d points belonging to the polyline.

OPENLABEL_ID = 'poly2d'
attributes: dict
class_name: str
classmethod fromdict(data_dict: dict) → Polyline2d

Generate a Polyline2d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 2d polyline.

Return type:

Polyline2d

id: UUID
object_id: UUID
points: list[tuple[float, float]]
sensor: SensorReference
to_raillabel() → tuple[dict, str, str, dict]

Convert to a raillabel compatible dict.

Returns:

  • annotation (dict) – Dictionary valid for the raillabel schema.

  • object_id (str) – Friendly identifier of the object this sensor belongs to.

  • class_name (str) – Friendly identifier of the class the annotated object belongs to.

  • sensor_reference (dict) – Dictionary of the sensor reference.

class raillabel_providerkit.format.understand_ai.Quaternion(x: float, y: float, z: float, w: float)

Bases: object

Dimensional information of an object in 3d.

Parameters:

  • x (float) – The x component of the quaternion.

  • y (float) – The y component of the quaternion.

  • z (float) – The z component of the quaternion.

  • w (float) – The w component of the quaternion.

classmethod fromdict(data_dict: dict) → Quaternion

Generate a Quaternion from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted quaternion.

Return type:

Quaternion

w: float
x: float
y: float
z: float
class raillabel_providerkit.format.understand_ai.Scene(metadata: Metadata, coordinate_systems: dict[str, CoordinateSystem], frames: dict[int, Frame])

Bases: object

The root Understand.Ai class, which contains all data.

Parameters:

  • metadata (raillabel.format.understand_ai.Metadata) – Container for metadata information about the scene itself.

  • coordinate_systems (dict[str, raillabel.format.understand_ai.CoordinateSystem]) – Global information for sensors regarding calibration.

  • frames (dict[int, raillabel.format.understand_ai.Frame])

coordinate_systems: dict[str, CoordinateSystem]
frames: dict[int, Frame]
classmethod fromdict(data_dict: dict) → Scene

Generate a Scene from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted scene.

Return type:

Scene

metadata: Metadata
to_raillabel() → dict

Convert to a raillabel compatible dict.

Returns:

Dictionary of the raillabel scene.

Return type:

dict

class raillabel_providerkit.format.understand_ai.Segmentation3d(id: UUID, object_id: UUID, class_name: str, attributes: dict, sensor: SensorReference, associated_points: list[int], number_of_points: int)

Bases: _Annotation

The 3D segmentation of a lidar pointcloud.

Parameters:

  • id (uuid.UUID) – Unique identifier of the annotation.

  • object_id (uuid.UUID) – Unique identifier of the object this annotation refers to. Used for tracking.

  • class_name (str) – Name of the class this annotation belongs to.

  • attributes (dict[str, str or list]) – Key value pairs of attributes with the keys beeing the friendly identifier of the attribute and the value beeing the attribute value.

  • sensor (raillabel.format.understand_ai.SensorReference) – Information about the sensor this annotation is labeled in.

  • associated_points (list[int]) – List of point indices of the lidar pointcloud.

  • number_of_points (int) – Total number of points in the associated_points.

OPENLABEL_ID = 'vec'
associated_points: list[int]
attributes: dict
class_name: str
classmethod fromdict(data_dict: dict) → Segmentation3d

Generate a Segmentation3d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 3d segmentation.

Return type:

Segmentation3d

id: UUID
number_of_points: int
object_id: UUID
sensor: SensorReference
class raillabel_providerkit.format.understand_ai.SensorReference(type: str, uri: str, timestamp: Decimal)

Bases: object

Information for a sensor in a frame.

Parameters:

  • type (str) – Friendly name of the sensor and its unique identifier.

  • uri (str) – URI to the file containing the frame specific sensor output from the project directory.

  • timestamp (decimal.Decimal) – Unix timestamp of the sensor recording.

classmethod fromdict(data_dict: dict) → SensorReference

Generate a SensorReference from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted sensor reference.

Return type:

SensorReference

timestamp: Decimal
to_raillabel() → tuple[str, dict]

Convert to a raillabel compatible dict.

Returns:

  • sensor_id (str) – Friendly identifier of the sensor.

  • sensor_reference (dict) – Dictionary valid for the raillabel schema.

type: str
uri: str
class raillabel_providerkit.format.understand_ai.Size3d(width: float, length: float, height: float)

Bases: object

Dimensional information of an object in 3d.

Parameters:

  • width (float) – Size of the object in the x-dimension.

  • length (float) – Size of the object in the y-dimension.

  • height (float) – Size of the object in the z-dimension.

classmethod fromdict(data_dict: dict) → Size3d

Generate a Size3d from a dictionary in the UAI format.

Parameters:

data_dict (dict) – Understand.AI T4 format dictionary containing the data_dict.

Returns:

Converted 3d size.

Return type:

Size3d

height: float
length: float
width: float