Class containing batched data of a detected bodies/persons from the body tracking module. More...

Functions
int	id (self)
	Id of the batch.

OBJECT_TRACKING_STATE	tracking_state (self)
	Bodies/persons tracking state.

np.array[float][float]	positions (self)
	NumPy array of positions for each body/person.

np.array[float][float]	position_covariances (self)
	NumPy array of positions' covariances for each body/person.

np.array[float][float]	velocities (self)
	NumPy array of 3D velocities for each body/person.

list[Timestamp]	timestamps (self)
	List of timestamps for each body/person.

np.array[float][float][float]	bounding_boxes (self)
	NumPy array of 3D bounding boxes for each body/person. More...

np.array[int][int][int]	bounding_boxes_2d (self)
	NumPy array of 2D bounding boxes for each body/person. More...

np.array[float]	confidences (self)
	NumPy array of confidences for each body/person.

list[OBJECT_ACTION_STATE]	action_states (self)
	List of action states for each body/person.

np.array[int][int][int]	keypoints_2d (self)
	NumPy array of 2D keypoints for each body/person. More...

np.array[float][float][float]	keypoints (self)
	NumPy array of 3D keypoints for each body/person. More...

np.array[int][int][int]	head_bounding_boxes_2d (self)
	NumPy array of 2D bounding box of the head for each body/person. More...

np.array[float][float][float]	head_bounding_boxes (self)
	NumPy array of 3D bounding box of the head for each body/person. More...

np.array[float][float]	head_positions (self)
	NumPy array of 3D centroid of the head for each body/person. More...

np.array[float][float]	keypoint_confidences (self)
	NumPy array of detection confidences NumPy array for each keypoint for each body/person. More...

Detailed Description

Class containing batched data of a detected bodies/persons from the body tracking module.

Functions

◆ bounding_boxes()

np.array[float][float][float] bounding_boxes ( self )

NumPy array of 3D bounding boxes for each body/person.

Note: They are defined in sl.InitParameters.coordinate_units and expressed in sl.RuntimeParameters.measure3D_reference_frame.
1 ------ 2

/ /|

0 ------ 3 |

| Object | 6

| |/

4 ------ 7

◆ bounding_boxes_2d()

np.array[int][int][int] bounding_boxes_2d ( self )

NumPy array of 2D bounding boxes for each body/person.

Note: Expressed in pixels on the original image resolution, [0, 0] is the top left corner.
A ------ B

| Object |

D ------ C

◆ keypoints_2d()

np.array[int][int][int] keypoints_2d ( self )

NumPy array of 2D keypoints for each body/person.

Warning: In some cases, eg. body partially out of the image or missing depth data, some keypoints can not be detected. They will have non finite values.

◆ keypoints()

np.array[float][float][float] keypoints ( self )

NumPy array of 3D keypoints for each body/person.

Warning: In some cases, eg. body partially out of the image or missing depth data, some keypoints can not be detected. They will have non finite values.

◆ head_bounding_boxes_2d()

np.array[int][int][int] head_bounding_boxes_2d ( self )

NumPy array of 2D bounding box of the head for each body/person.

Note: Expressed in pixels on the original image resolution, [0, 0] is the top left corner.

◆ head_bounding_boxes()

np.array[float][float][float] head_bounding_boxes ( self )

NumPy array of 3D bounding box of the head for each body/person.

Note: They are defined in sl.InitParameters.coordinate_units and expressed in sl.RuntimeParameters.measure3D_reference_frame.

◆ head_positions()

np.array[float][float] head_positions ( self )

NumPy array of 3D centroid of the head for each body/person.

Note: They are defined in sl.InitParameters.coordinate_units and expressed in sl.RuntimeParameters.measure3D_reference_frame.

◆ keypoint_confidences()

np.array[float][float] keypoint_confidences ( self )

NumPy array of detection confidences NumPy array for each keypoint for each body/person.

Note: They can not be lower than the sl.BodyTrackingRuntimeParameters.detection_confidence_threshold.

Warning: In some cases, eg. body partially out of the image or missing depth data, some keypoints can not be detected. They will have non finite values.