robokudo.annotators.static_object_detector

Robokudo Static Object Detector Module

This module provides functionality for detecting objects at predefined locations using either manually configured bounding boxes or object knowledge from a database. The detector can create object hypotheses with poses, masks, and class labels.

Note

All poses are defined relative to the camera frame by default unless pose_in_world_coordinates is True.

Classes

StaticObjectMode	Generic enumeration.
StaticObjectDetectorAnnotator	Find a cluster based on a preconfigured Bounding Box, Pose and Class name.

Module Contents

class robokudo.annotators.static_object_detector.StaticObjectMode

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

BOUNDING_BOX = 'bounding_box'

OBJECT_KNOWLEDGE_INSTANCE = 'object_knowledge_instance'

OBJECT_KNOWLEDGE_BASE = 'object_knowledge_base'

class robokudo.annotators.static_object_detector.StaticObjectDetectorAnnotator(name='StaticObjectDetector', descriptor=Descriptor())

Bases: robokudo.annotators.core.BaseAnnotator

Find a cluster based on a preconfigured Bounding Box, Pose and Class name.

This annotator can:

• Create object hypotheses at fixed locations using manual bounding box coordinates

• Load object knowledge from a database to automatically infer bounding boxes

• Generate pose annotations in either camera or world coordinates

• Create masks for the detected regions

Note

The detector supports both Euler angles and quaternions for rotation specification. Parameters can be dynamically reconfigured through ROS dynamic reconfigure.

class Descriptor

Bases: robokudo.annotators.core.BaseAnnotator.Descriptor

Configuration descriptor for the StaticObjectDetectorAnnotator.

Defines all configurable parameters including:

• Bounding box dimensions and position

• Object knowledge database settings

• Pose generation options

• Coordinate frame settings

class Parameters

Parameters controlling the static object detection behavior.

bounding_box_x = 1

bounding_box_y = 1

bounding_box_width = 10

bounding_box_height = 10

mode

class_name = 'unknown'

class_names = []

create_pose_annotation = False

create_bounding_box_annotation = False

create_mask = True

object_knowledge_base_ros_package = 'robokudo'

object_knowledge_base_name = 'object_knowledge_iai_kitchen'

object_knowledge_instance: robokudo.object_knowledge_base.ObjectKnowledge | None = None

parameters

color = None

depth = None

cloud = None

cam_intrinsics = None

object_kb = None

object_knowledge = None

detect_from_bb_descriptor(color_rgb: numpy.ndarray) → robokudo.types.scene.ObjectHypothesis

Detect only based on the BB.

Parameters:

color_rgb (np.ndarray) – Image in RGB order

Returns:

robokudo.types.scene.ObjectHypothesis

detect_from_object_knowledge(object_knowledge: robokudo.object_knowledge_base.ObjectKnowledge, object_id: int = 0) → robokudo.types.scene.ObjectHypothesis | None

Detect from singular, passed ObjectKnowledge instance

Returns:

robokudo.types.scene.ObjectHypothesis

detect_from_object_knowledge_base() → list[robokudo.types.scene.ObjectHypothesis]

Detect from a completed object knowledge base

Returns:

robokudo.types.scene.ObjectHypothesis

update()

Process current scene to detect configured static objects.

Steps:

• Gets current color image, depth image and point cloud

• If using object knowledge, validates object class exists

• Scales color image to match depth image

• Creates object hypothesis with:

  • Bounding box from configuration or object knowledge

  • Pose annotation if enabled

  • Point cloud from ROI

  • Mask if enabled

Returns:

SUCCESS if detection completed, FAILURE if required transforms not found

Return type:

py_trees.common.Status

Raises:

Exception – If camera parameters are invalid or missing

static add_classification_annotation(object_hypothesis: robokudo.types.scene.ObjectHypothesis, class_name: str)

get_rotation_list_based_on_parameters(rotation_x: float, rotation_y: float, rotation_z: float, rotation_w: float) → list

Return a quaternion based on the parametrization of the Annotator.

Parameters:

• rotation_x –

• rotation_y –

• rotation_z –

• rotation_w –

Returns:

4-dim list with Quaternion

get_cloud_from_2d_bb_roi(color_rgb)