vis4d.data.const

Defines data related constants.

While the datasets can hold arbitrary data types and formats, this file provides some constants that are used to define a common data format which is helpful to use for better data transformation.

Classes

AxisMode(value)

Enum for choosing among different coordinate frame conventions.

CommonKeys()

Common supported keys for DictData.
class AxisMode(value)[source]

Enum for choosing among different coordinate frame conventions.

ROS: The coordinate frame aligns with the right hand rule:

x axis points forward y axis points left z axis points up

See also: https://www.ros.org/reps/rep-0103.html#axis-orientation

OpenCV: The coordinate frame aligns with a camera coordinate system:

x axis points right y axis points down z axis points forward

See also: https://docs.opencv.org/3.4/d9/d0c/group__calib3d.html

LiDAR: The coordinate frame aligns with a LiDAR coordinate system:

x axis points right y axis points forward z axis points up

See also: https://www.nuscenes.org/nuscenes#data-collection

class CommonKeys[source]

Common supported keys for DictData.

While DictData can hold arbitrary keys of data, we define a common set of keys where we expect a pre-defined format to enable the usage of common data pre-processing operations among different datasets.

images (NDArrayF32): Image of shape [1, H, W, C]. input_hw (Tuple[int, int]): Shape of image in (height, width) after

transformations.

original_images (NDArrayF32): Original image of shape [1, H, W, C]. original_hw (Tuple[int, int]): Original shape of image in (height, width).

sample_names (str): Name of the current sample. sequence_names (str): If the dataset contains videos, this field indicates

the name of the current sequence.

frame_ids (int): If the dataset contains videos, this field indicates the

temporal frame index of the current image / sample.

categories (NDArrayF32): Class labels of shape [C, ].

boxes2d (NDArrayF32): 2D bounding boxes of shape [N, 4] in xyxy format. boxes2d_classes (NDArrayI64): Semantic classes of 2D bounding boxes, shape

[N,].

boxes2d_track_ids (NDArrayI64): Tracking IDs of 2D bounding boxes,

shape [N,].

instance_masks (NDArrayUI8): Instance segmentation masks of shape

[N, H, W].

seg_masks (NDArrayUI8): Semantic segmentation masks [H, W]. panoptic_masks (NDArrayI64): Panoptic segmentation masks [H, W]. deph_maps (NDArrayF32): Depth maps of shape [H, W].

intrinsics (NDArrayF32): Intrinsic sensor calibration. Shape [3, 3]. extrinsics (NDArrayF32): Extrinsic sensor calibration, transformation of

sensor to world coordinate frame. Shape [4, 4].

axis_mode (AxisMode): Coordinate convention of the current sensor. timestamp (int): Sensor timestamp in Unix format.

points3d (NDArrayF32): 3D pointcloud data, assumed to be [N, 3] and in

sensor frame.

colors3d (NDArrayF32): Associated color values for each point, [N, 3].

semantics3d: TODO complete instances3d: TODO complete boxes3d (NDArrayF32): [N, 10], each row consists of center (XYZ),

dimensions (WLH), and orientation quaternion (WXYZ).

boxes3d_classes (NDArrayI64): Associated semantic classes of 3D bounding

boxes, [N,].