cars.applications.point_cloud_outliers_removing.outlier_removing_tools
This module contains functions used in outlier removing
Module Contents
Functions

Filter points cloud to remove small clusters of points 

Determine the indexes of the points of cloud_xyz to filter. 
Filter points cloud to remove statistical outliers 


Determine the indexes of the points of cloud_xyz to filter. 

Filter all points of the cloud DataFrame 

Add a uint16 mask labeled 'mask_label' to the clouds in clouds_list. 
 cars.applications.point_cloud_outliers_removing.outlier_removing_tools.small_components_filtering(cloud: pandas.DataFrame, connection_val: float, nb_pts_threshold: int, clusters_distance_threshold: float = None, filtered_elt_pos: bool = False) Tuple[pandas.DataFrame, Union[None, pandas.DataFrame]]
Filter points cloud to remove small clusters of points (see the detect_small_components function).
 Parameters
cloud – combined cloud as returned by the create_combined_cloud function
connection_val – distance to use to consider that two points are connected
nb_pts_threshold – number of points to use to identify small clusters to filter
clusters_distance_threshold – distance to use to consider if two points clusters are far from each other or not (set to None to deactivate this level of filtering)
filtered_elt_pos – if filtered_elt_pos is set to True, the removed points positions in their original epipolar images are returned, otherwise it is set to None
 Returns
Tuple made of the filtered cloud and the removed elements positions in their epipolar images
 cars.applications.point_cloud_outliers_removing.outlier_removing_tools.detect_small_components(cloud_xyz: numpy.ndarray, connection_val: float, nb_pts_threshold: int, clusters_distance_threshold: float = None) List[int]
Determine the indexes of the points of cloud_xyz to filter. The clusters are made of ‘connected’ points (2 connected points have a distance smaller than connection_val)
The removed clusters are composed of less than nb_pts_threshold points and are also far from other clusters (points are further than clusters_distance_threshold).
If clusters_distance_threshold is set to None, all the clusters that are composed of less than nb_pts_threshold points are filtered.
 Parameters
cloud_xyz – points kdTree
connection_val – distance to use to consider that two points are connected
nb_pts_threshold – number of points to use to identify small clusters to filter
clusters_distance_threshold – distance to use to consider if two points clusters are far from each other or not (set to None to deactivate this level of filtering)
 Returns
list of the points to filter indexes
 cars.applications.point_cloud_outliers_removing.outlier_removing_tools.statistical_outliers_filtering(cloud: pandas.DataFrame, k: int, std_factor: float, filtered_elt_pos: bool = False) Tuple[pandas.DataFrame, Union[None, pandas.DataFrame]]
Filter points cloud to remove statistical outliers (see the detect_statistical_outliers function).
 Parameters
cloud – combined cloud as returned by the create_combined_cloud function
k – number of neighbors
std_factor – multiplication factor to use to compute the distance threshold
filtered_elt_pos – if filtered_elt_pos is set to True, the removed points positions in their original epipolar images are returned, otherwise it is set to None
 Returns
Tuple made of the filtered cloud and the removed elements positions in their epipolar images
 cars.applications.point_cloud_outliers_removing.outlier_removing_tools.detect_statistical_outliers(cloud_xyz: numpy.ndarray, k: int, std_factor: float = 3.0) List[int]
Determine the indexes of the points of cloud_xyz to filter. The removed points have mean distances with their k nearest neighbors that are greater than a distance threshold (dist_thresh).
This threshold is computed from the mean (mean_distances) and standard deviation (stddev_distances) of all the points mean distances with their k nearest neighbors:
dist_thresh = mean_distances + std_factor * stddev_distances
 Parameters
cloud_xyz – points kdTree
k – number of neighbors
std_factor – multiplication factor to use to compute the distance threshold
 Returns
list of the points to filter indexes
 cars.applications.point_cloud_outliers_removing.outlier_removing_tools.filter_cloud(cloud: pandas.DataFrame, index_elt_to_remove: List[int], filtered_elt_pos: bool = False) Tuple[pandas.DataFrame, Union[None, pandas.DataFrame]]
Filter all points of the cloud DataFrame which index is in the index_elt_to_remove list.
If filtered_elt_pos is set to True, the information of the removed elements positions in their original epipolar images are returned.
To do so the cloud DataFrame has to be build with the ‘with_coords’ option activated.
 Parameters
cloud – combined cloud as returned by the create_combined_cloud function
index_elt_to_remove – indexes of lines to filter in the cloud DataFrame
filtered_elt_pos – if filtered_elt_pos is set to True, the removed points positions in their original epipolar images are returned, otherwise it is set to None
 Returns
Tuple composed of the filtered cloud DataFrame and the filtered elements epipolar position information (or None for the latter if filtered_elt_pos is set to False or if the cloud Dataframe has not been build with with_coords option)
 cars.applications.point_cloud_outliers_removing.outlier_removing_tools.add_cloud_filtering_msk(clouds_list: List[xarray.Dataset], elt_pos_infos: pandas.DataFrame, mask_label: str, mask_value: int = 255)
Add a uint16 mask labeled ‘mask_label’ to the clouds in clouds_list. (inline function)
 Parameters
clouds_list – Input list of clouds
elt_pos_infos – pandas dataframe composed of cst.POINTS_CLOUD_COORD_EPI_GEOM_I, cst.POINTS_CLOUD_COORD_EPI_GEOM_J, cst.POINTS_CLOUD_IDX_IM_EPI columns as computed in the create_combined_cloud function. Those information are used to retrieve the point position in its original epipolar image.
mask_label – label to give to the mask in the datasets
mask_value – filtered elements value in the mask