cars.applications.point_cloud_outliers_removing.pc_out_removing
this module contains the abstract PointsCloudOutlierRemoving application class.
Module Contents
Classes
PointCloudOutliersRemoving |
- class cars.applications.point_cloud_outliers_removing.pc_out_removing.PointCloudOutliersRemoving(conf=None)
Bases:
cars.applications.application_template.ApplicationTemplatePointCloudOutliersRemoving
- 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_points_cloud_as_laz, save_points_cloud_as_csv, app_name=None)
Create dataset and registered the output in the orchestrator
- Parameters
merged_points_cloud (CarsDataset) – Merged point cloud
save_points_cloud_as_laz (bool) – true if save as laz
save_points_cloud_as_csv (bool) – true if save as csv
app_name (str) – application name for file names
- Returns
Filtered point cloud
- Return type
- abstract run(merged_points_cloud, orchestrator=None)
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
- Returns
filtered merged points cloud
- Return type
CarsDataset filled with xr.Dataset