cars.applications.rasterization.simple_gaussian
this module contains the dense_matching application class.
Module Contents
Classes
PointsCloudRasterisation |
Functions
|
Wrapper for rasterization step : |
- class cars.applications.rasterization.simple_gaussian.SimpleGaussian(conf=None)
Bases:
cars.applications.rasterization.point_cloud_rasterization.PointCloudRasterization
PointsCloudRasterisation
- check_conf(conf)
Check configuration
- Parameters
conf (dict) – configuration to check
- Returns
overloaded configuration
- Return type
dict
- get_resolution()
- get_margins()
- run(merged_points_cloud, epsg, orchestrator=None, dsm_file_name=None, color_file_name=None)
Run PointsCloudRasterisation application.
Creates a CarsDataset filled with dsm 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”, “ysize”, “xsize”, “xstart”, “ystart”
attributes containing “bounds”, “ysize”, “xsize”, “epsg”
epsg (str) – epsg of raster data
orchestrator – orchestrator used
dsm_file_name (str) – path of dsm
color_file_name (str) – path of color
- Returns
raster DSM. CarsDataset contains:
Z x W Delayed tiles. Each tile will be a future xarray Dataset containing:
data : with keys : “hgt”, “img”, “raster_msk”,optional : “n_pts”, “pts_in_cell”, “hgt_mean”, “hgt_stdev”
attrs with keys: “epsg”
attributes containing: None
:rtype : CarsDataset filled with xr.Dataset
- cars.applications.rasterization.simple_gaussian.rasterization_wrapper(cloud, resolution, epsg, window, profile, list_computed_layers: List[str] = None, saving_info=None, sigma: float = None, radius: int = 1, dsm_no_data: int = np.nan, color_no_data: int = np.nan, msk_no_data: int = 65535, grid_points_division_factor: int = None)
Wrapper for rasterization step : - Convert a list of clouds to correct epsg - Rasterize it with associated colors
- Parameters
cloud (pandas.DataFrame) – combined cloud
resolution (float) – Produced DSM resolution (meter, degree [EPSG dependent])
epsg_code (int) – epsg code for the CRS of the output DSM
window (int) – Window considered
profile (dict) – rasterio profile
list_computed_layers – list of computed output data
saving_info (dict) – information about CarsDataset ID.
sigma – sigma for gaussian interpolation. (If None, set to resolution)
radius – Radius for hole filling.
dsm_no_data – no data value to use in the final raster
color_no_data – no data value to use in the final colored raster
msk_no_data – no data value to use in the final mask image
grid_points_division_factor – number of blocks to use to divide the grid points (memory optimization, reduce the highest memory peak). If it is not set, the factor is automatically set to construct 700000 points blocks.
- Returns
digital surface model + projected colors
- Return type
xr.Dataset