Tools for Generating Synthetic Data [omni.isaac.synthetic_utils]
Warning
This extension is deprecated and will be removed in future releases. Please use omni.replicator.core and omni.syntheticdata annotators/writers or the Camera class in omni.isaac.sensor for accessing synthetic data
- class SyntheticDataHelper
- get_camera_params(viewport)
Get active camera intrinsic and extrinsic parameters.
- Returns
A dict of the active camera’s parameters.
pose (numpy.ndarray): camera position in world coordinates, fov (float): horizontal field of view in radians focal_length (float) horizontal_aperture (float) view_projection_matrix (numpy.ndarray(dtype=float64, shape=(4, 4))) resolution (dict): resolution as a dict with ‘width’ and ‘height’. clipping_range (tuple(float, float)): Near and Far clipping values.
- get_groundtruth(sensor_names, viewport_api, verify_sensor_init=True, wait_for_sensor_data=0.1)
Get groundtruth from specified gt_sensors.
- Parameters
sensor_names (list) – List of strings of sensor names. Valid sensors names: rgb, depth, instanceSegmentation, semanticSegmentation, boundingBox2DTight, boundingBox2DLoose, boundingBox3D, camera
viewport_api (Any) – Viewport from which to retrieve/create sensor.
verify_sensor_init (bool) – Additional check to verify creation and initialization of sensors.
wait_for_sensor_data (float) – Additional time to sleep before returning ground truth so are correctly filled. Default is 0.1 seconds
- Returns
Dict of sensor outputs
- get_mapped_semantic_data(semantic_data: list = [[]], user_semantic_label_map: dict = {}, remap_using_base_class=False) dict
[Deprecated] Map semantic segmentation data to IDs specified by user
- get_pose()
Get pose of all objects with a semantic label.
- get_semantic_id_map(semantic_labels: list = []) dict
[Deprecated] Get map of semantic ID from label
- get_semantic_ids(semantic_data: list = [[]]) List[int]
Returns unique id’s for a semantic image
- Parameters
semantic_data (list, optional) – Semantic Image. Defaults to [[]].
- Returns
List of unique semantic IDs in image
- Return type
List[int]
- get_semantic_label_map(semantic_ids: list = []) dict
[Deprecated] Get map of semantic label from ID
- initialize(sensor_names, viewport_api)
Initialize sensors in the list provided.
- Parameters
viewport_api (Any) – Viewport from which to retrieve/create sensor.
sensor_types (list of omni.syntheticdata._syntheticdata.SensorType) – List of sensor types to initialize.
- async initialize_async(sensor_names, viewport_api)
Initialize sensors in the list provided. Async version
- Parameters
viewport_api (Any) – Viewport from which to retrieve/create sensor.
sensor_types (list of omni.syntheticdata._syntheticdata.SensorType) – List of sensor types to initialize.
- class NumpyWriter(data_dir, num_worker_threads, max_queue_size=500, sensor_settings=None)
- create_output_folders(sensor_settings=None)
Checks if the sensor output folder corresponding to each viewport is created. If not, it creates them.
- worker()
Processes task from queue. Each tasks contains groundtruth data and metadata which is used to transform the output and write it to disk.
- class KittiWriter(data_dir='kitti_data', num_worker_threads=4, max_queue_size=500, train_size=10, classes=[], bbox_type='BBOX2DLOOSE')
- create_output_folders()
Checks if the output folders are created. If not, it creates them.
- worker()
Processes task from queue. Each tasks contains groundtruth data and metadata which is used to transform the output and write it to disk.
- colorize_bboxes(bboxes_2d_data, bboxes_2d_rgb, num_channels=3)
Colorizes 2D bounding box data for visualization.
- Parameters
bboxes_2d_data (numpy.ndarray) – 2D bounding box data from the sensor.
bboxes_2d_rgb (numpy.ndarray) – RGB data from the sensor to embed bounding box.
num_channels (int) – Specify number of channels i.e. 3 or 4.
- colorize_depth(depth_image, width, height, num_channels=3)
Colorizes depth data for visualization.
- Parameters
depth_image (numpy.ndarray) – Depth data from the sensor.
width (int) – Width of the viewport.
height (int) – Height of the viewport.
num_channels (int) – Specify number of channels i.e. 3 or 4.
- colorize_segmentation(segmentation_image, width, height, num_channels=3, num_colors=None)
Colorizes segmentation data for visualization.
- Parameters
segmentation_image (numpy.ndarray) – Segmentation data from the sensor.
width (int) – Width of the viewport.
height (int) – Height of the viewport.
num_channels (int) – Specify number of channels i.e. 3 or 4.
num_colors (int) – Specify number of colors for consistency across frames.
- random_colours(N, enable_random=True, num_channels=3)
Generate random colors. Generate visually distinct colours by linearly spacing the hue channel in HSV space and then convert to RGB space.