`cars.applications.triangulation.line_of_sight_intersection`

this module contains the dense_matching application class.

Module Contents

Classes

LineOfSightIntersection

Triangulation

Functions

triangulation_wrapper(→ Dict[str, ...)

Compute points clouds from image objects and disparity objects.

class cars.applications.triangulation.line_of_sight_intersection.LineOfSightIntersection(conf=None)

Bases: cars.applications.triangulation.triangulation.Triangulation

Triangulation

check_conf(conf)

Check configuration

Parameters: conf (dict) – configuration to check
Returns: overloaded configuration
Return type: dict

run(sensor_image_left, sensor_image_right, epipolar_image, grid_left, grid_right, epipolar_disparity_map, epsg, geometry_plugin, denoising_overload_fun=None, source_pc_names=None, orchestrator=None, pair_folder=None, pair_key='PAIR_0', uncorrected_grid_right=None, geoid_path=None, cloud_id=None, intervals=None)

Run Triangulation application.

Created left and right CarsDataset filled with xarray.Dataset, corresponding to 3D points clouds, stored on epipolar geometry grid.

Parameters

sensor_image_left (CarsDataset) – tiled sensor left image Dict Must contain keys : “image”, “color”, “geomodel”, “no_data”, “mask”. Paths must be absolutes
sensor_image_right (CarsDataset) – tiled sensor right image Dict Must contain keys : “image”, “color”, “geomodel”, “no_data”, “mask”. Paths must be absolutes
epipolar_image (CarsDataset) – tiled epipolar left image
grid_left –
left epipolar grid. Grid CarsDataset contains :
- A single tile stored in [0,0], containing a (N, M, 2) shape array in xarray Dataset
- Attributes containing: “grid_spacing”, “grid_origin”, “epipolar_size_x”, epipolar_size_y”, “epipolar_origin_x”, “epipolar_origin_y”,”epipolar_spacing_x”, “epipolar_spacing”, “disp_to_alt_ratio”, :type grid_left: CarsDataset
grid_right (CarsDataset) –
right epipolar grid. Grid CarsDataset contains :
- A single tile stored in [0,0], containing a (N, M, 2) shape
  array in xarray Dataset
- Attributes containing: “grid_spacing”, “grid_origin”,
  ”epipolar_size_x”, epipolar_size_y”, “epipolar_origin_x”, “epipolar_origin_y”,”epipolar_spacing_x”, “epipolar_spacing”, “disp_to_alt_ratio”,
epipolar_disparity_map (CarsDataset) –
tiled left disparity map or sparse matches:
- if CarsDataset is instance of “arrays”, CarsDataset contains:
  - N x M Delayed tiles Each tile will be a future xarray Dataset containing:
    
    data with keys : “disp”, “disp_msk”
    
    attrs with keys: profile, window, overlaps
  - attributes containing:”largest_epipolar_region” “opt_epipolar_tile_size”
- if CarsDataset is instance of “points”, CarsDataset contains:
  - N x M Delayed tiles Each tile will be a future pandas DataFrame containing:
    
    data : (L, 4) shape matches
  - attributes containing:”disp_lower_bound”,”disp_upper_bound”, “elevation_delta_lower_bound”,”elevation_delta_upper_bound”
denoising_overload_fun (fun) – function to overload dataset
source_pc_names (list[str]) – source pc names
orchestrator – orchestrator used
pair_folder (str) – folder used for current pair
pair_key (str) – pair key id
uncorrected_grid_right (CarsDataset) – not corrected right epipolar grid used if self.snap_to_img1
geoid_path (str) – geoid path
intervals (None or [str, str]) – Either None or a List of 2 intervals indicators

Returns

points cloud The 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”, “z_inf”, “z_sup”
- attrs with keys: “margins”, “epi_full_size”, “epsg”
attributes containing: “disp_lower_bound”, “disp_upper_bound”, “elevation_delta_lower_bound”,”elevation_delta_upper_bound”

Return type

Tuple(CarsDataset, CarsDataset)

cars.applications.triangulation.line_of_sight_intersection.triangulation_wrapper(disparity_object: xarray.Dataset, sensor1, sensor2, geomodel1, geomodel2, grid1, grid2, geometry_plugin, epsg, geoid_data: xarray.Dataset = None, denoising_overload_fun=None, cloud_id=None, intervals=None, saving_info=None) → Dict[str, Tuple[xarray.Dataset, xarray.Dataset]]