cars.applications.point_cloud_outliers_removing.statistical

this module contains the statistical points removing application class.

Module Contents

Classes

Statistical

PointCloudOutliersRemoving

Functions

statistical_removing_wrapper(cloud, statistical_k, ...)

Statistical outlier removing

class cars.applications.point_cloud_outliers_removing.statistical.Statistical(conf=None)

Bases: cars.applications.point_cloud_outliers_removing.pc_out_removing.PointCloudOutliersRemoving

PointCloudOutliersRemoving

check_conf(conf)

Check configuration

Parameters

conf (dict) – configuration to check

Returns

overloaded configuration

Return type

dict

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

get_method()

Get margins to use during point clouds fusion

Returns

algorithm method

Return type

string

get_on_ground_margin(resolution=0.5)

Get margins to use during point clouds fusion

Returns

margin

Return type

float

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. CarsDataset contains:

  • Z x W Delayed tiles. Each tile will be a future pandas DataFrame containing:

    • data : with keys : “x”, “y”, “z”, “corr_msk” optional: “clr”, “msk”, “data_valid”,”coord_epi_geom_i”, “coord_epi_geom_j”, “idx_im_epi”

    • attrs with keys: “epsg”

  • attributes containing “bounds”, “ysize”, “xsize”, “epsg”

• orchestrator – orchestrator used

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

Returns

filtered merged points cloud. CarsDataset contains:

• Z x W Delayed tiles. Each tile will be a future pandas DataFrame containing:

  • data : with keys “x”, “y”, “z”, “corr_msk” optional: “clr”, “msk”, “data_valid”, “coord_epi_geom_i”, “coord_epi_geom_j”, “idx_im_epi”

  • attrs with keys: “epsg”

• attributes containing “bounds”, “ysize”, “xsize”, “epsg”

:rtype : CarsDataset filled with xr.Dataset

cars.applications.point_cloud_outliers_removing.statistical.statistical_removing_wrapper(cloud, statistical_k, std_dev_factor, save_by_pair: bool = False, point_cloud_csv_file_name=None, point_cloud_laz_file_name=None, saving_info=None)

Statistical outlier removing

Parameters

• cloud (pandas DataFrame) – cloud to filter

• statistical_k (float) – k

• std_dev_factor (float) – std factor

• save_by_pair (bool) – save point cloud as pair

• point_cloud_csv_file_name (str) – write point cloud as CSV in filename (if None, the point cloud is not written as csv)

• point_cloud_laz_file_name (str) – write point cloud as laz in filename (if None, the point cloud is not written as laz)

• saving_info (dict) – saving infos

Returns

filtered cloud

Return type

pandas DataFrame