cars.applications.point_cloud_fusion.mapping_to_terrain_tiles

this module contains the epipolar cloud fusion application class.

Module Contents

Classes

MappingToTerrainTiles

EpipolarCloudFusion

Functions

compute_point_cloud_wrapper(point_clouds, epsg[, ...])

Wrapper for point clouds fusion step :

class cars.applications.point_cloud_fusion.mapping_to_terrain_tiles.MappingToTerrainTiles(conf=None)

Bases: cars.applications.point_cloud_fusion.point_cloud_fusion.PointCloudFusion

EpipolarCloudFusion

check_conf(conf)

Check configuration

Parameters

conf (dict) – configuration to check

Returns

overloaded configuration

Return type

dict

run(list_epipolar_point_clouds, bounds, epsg, source_pc_names=None, orchestrator=None, margins=0, optimal_terrain_tile_width=500, roi=None, save_laz_output=False)

Run EpipolarCloudFusion application.

Creates a CarsDataset corresponding to the merged point clouds, tiled with the terrain grid used during rasterization.

Parameters

• list_epipolar_point_clouds (list(CarsDataset) filled with xr.Dataset) –

  list with point clouds Each CarsDataset contains:

  • N x M Delayed tiles. Each tile will be a future xarray Dataset containing:

    • data : with keys : “x”, “y”, “z”, “corr_msk” optional: “color”, “msk”,

    • attrs with keys: “margins”, “epi_full_size”, “epsg”

  • attributes containing: “disp_lower_bound”, “disp_upper_bound” “elevation_delta_lower_bound”, “elevation_delta_upper_bound”

• bounds (list) – terrain bounds

• epsg (str) – epsg to use

• source_pc_names (list[str]) – source pc names

• orchestrator (Orchestrator) – orchestrator used

• margins (float) – margins needed for tiles, meter or degree

• optimal_terrain_tile_width (int) – optimal terrain tile width

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

Returns

Merged point clouds

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”, “epsg”

Return type

CarsDataset filled with pandas.DataFrame

cars.applications.point_cloud_fusion.mapping_to_terrain_tiles.compute_point_cloud_wrapper(point_clouds, epsg, xmin: float = None, ymin: float = None, xmax: float = None, ymax: float = None, margins: float = 0, save_by_pair: bool = False, point_cloud_csv_file_name=None, point_cloud_laz_file_name=None, saving_info=None, source_pc_names=None)

Wrapper for point clouds fusion step : - Convert a list of clouds to correct epsg

Parameters

• point_clouds (list((xr.Dataset, int))) – list of clouds, list of (dataset, dataset_id) with : - cst.X - cst.Y - cst.Z - cst.EPI_COLOR

• epsg_code (int) – epsg code for the CRS of the output DSM

• stereo_out_epsg (int) – epsg code to convert point cloud to, if needed

• xmin – xmin of the rasterization grid (if None, will be estimated by the function)

• xmin – xmin of the rasterization grid (if None, will be estimated by the function)

• xmax – xmax of the rasterization grid (if None, will be estimated by the function)

• ymax – ymax of the rasterization grid (if None, will be estimated by the function)

• margins (float) – margins needed for tiles, meter or degree

• 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) – informations about CarsDataset ID.

• source_pc_names (list str) – source point cloud name (correspond to pair_key)

Returns

merged point cloud dataframe with: - cst.X - cst.Y - cst.Z - cst.EPI_COLOR - attrs : xmin, xmax, ymin, ymax, saving_info

Return type

pandas.DataFrame