cars.applications.point_cloud_outliers_removing.pc_out_removing

this module contains the abstract PointsCloudOutlierRemoving application class.

Module Contents

Classes

PointCloudOutliersRemoving

PointCloudOutliersRemoving
class cars.applications.point_cloud_outliers_removing.pc_out_removing.PointCloudOutliersRemoving(conf=None)

Bases: cars.applications.application_template.ApplicationTemplate

PointCloudOutliersRemoving

available_applications: Dict
default_application = 'statistical'
classmethod __init_subclass__(short_name, **kwargs)
abstract get_on_ground_margin(resolution=0.5)

Get margins to use during point clouds fusion

Returns

margin

Return type

float

abstract get_method()

Get margins to use during point clouds fusion

Returns

algorithm method

Return type

string

abstract get_optimal_tile_size(max_ram_per_worker, superposing_point_clouds=1, point_cloud_resolution=0.5)

Get the optimal tile size to use, depending on memory available

Parameters

  • max_ram_per_worker (int) – maximum ram available

  • superposing_point_clouds (int) – number of point clouds superposing

  • point_cloud_resolution (float) – resolution of point cloud

Returns

optimal tile size in meter

Return type

float

__register_dataset__(merged_points_cloud, save_laz_output=False, app_name=None)

Create dataset and registered the output in the orchestrator. The point cloud dataset can be saved as laz using the save_laz_output option. Alternatively, the point cloud will be saved as laz and csv in the dump directory if the application save_intermediate data configuration parameter is set.

Parameters

  • merged_points_cloud (CarsDataset) – Merged point cloud

  • save_laz_output (bool) – true if save to laz as official product

  • app_name (str) – application name for file names

Returns

Filtered point cloud

Return type

CarsDataset

abstract run(merged_points_cloud, orchestrator=None, save_laz_output=False)

Run PointCloudOutliersRemoving application.

Creates a CarsDataset filled with new point cloud tiles.

Parameters

  • merged_points_cloud (CarsDataset filled with pandas.DataFrame) – merged point cloud

  • orchestrator – orchestrator used

  • save_laz_output (bool) – save output point cloud as laz

Returns

filtered merged points cloud

Return type

CarsDataset filled with xr.Dataset