cars.applications.dense_matching.abstract_dense_matching_app

this module contains the abstract matching application class.

Attributes

AbstractDenseMatchingMethod

Classes

AbstractDenseMatchingApplication

AbstractDenseMatchingApplication

Module Contents

cars.applications.dense_matching.abstract_dense_matching_app.AbstractDenseMatchingMethod

class cars.applications.dense_matching.abstract_dense_matching_app.AbstractDenseMatchingApplication(conf=None)

Bases: cars.applications.application_template.ApplicationTemplate

AbstractDenseMatchingApplication

available_applications: Dict

default_application = 'basic'

default_method = 'pandora_auto'

classmethod __init_subclass__(short_name, **kwargs)

dense_matching_method

abstract get_optimal_tile_size(disp_range_grid, max_ram_per_worker)

Get the optimal tile size to use during dense matching.

Parameters:

• disp_range_grid – minimum and maximum disparity grid

• max_ram_per_worker – maximum ram per worker

Returns:

optimal tile size

abstract get_performance_map_parameters()

Get parameter linked to performance, that will be used in triangulation

Returns:

parameters to use

Type:

dict

abstract get_margins_fun(grid_left, disp_range_grid)

Get Margins function that generates margins needed by matching method, to use during resampling

Parameters:

• grid_left – left epipolar grid

• disp_range_grid – minimum and maximum disparity grid

Returns:

function that generates margin for given roi

abstract generate_disparity_grids(sensor_image_right, grid_right, geom_plugin_with_dem_and_geoid, dmin=None, dmax=None, dem_min=None, dem_max=None, pair_folder=None, orchestrator=None)

Generate disparity grids min and max, with given step

global mode: uses dmin and dmax local mode: uses dems

Parameters:

• sensor_image_right (dict) – sensor image

• grid_right (CarsDataset) – right epipolar grid

• geom_plugin_with_dem_and_geoid (GeometryPlugin) – geometry plugin with dem mean used to generate epipolar grids

• dmin (float) – minimum disparity

• dmax (float) – maximum disparity

• dem_min (str) – path to minimum dem

• dem_max (str) – path to maximum dem

• pair_folder (str) – folder used for current pair

• orchestrator (Orchestrator) – orchestrator to perform inverse locs

:return disparity grid range, containing grid min and max :rtype: CarsDataset

abstract run(epipolar_images_left, epipolar_images_right, local_tile_optimal_size_fun, orchestrator=None, pair_folder=None, pair_key='PAIR_0', disp_range_grid=None, compute_disparity_masks=False, margins_to_keep=0, texture_bands=None, classif_bands_to_mask=None)

Run Matching application.

Create CarsDataset filled with xarray.Dataset, corresponding to epipolar disparities, on the same geometry than epipolar_images_left.

Parameters:

• epipolar_images_left (CarsDataset) –

  tiled left epipolar CarsDataset contains:

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

    • data with keys : “im”, “msk”, “texture”

    • attrs with keys: “margins” with “disp_min” and “disp_max” “transform”, “crs”, “valid_pixels”, “no_data_mask”, “no_data_img”

  • attributes containing:

    ”largest_epipolar_region”,”opt_epipolar_tile_size”

• epipolar_images_right (CarsDataset) –

  tiled right epipolar CarsDataset contains:

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

    • data with keys : “im”, “msk”, “texture”

    • attrs with keys: “margins” with “disp_min” and “disp_max”

      ”transform”, “crs”, “valid_pixels”, “no_data_mask”, “no_data_img”

  • attributes containing:

    ”largest_epipolar_region”,”opt_epipolar_tile_size”

• local_tile_optimal_size_fun (func) – function to compute local optimal tile size

• orchestrator – orchestrator used

• pair_folder (str) – folder used for current pair

• pair_key (str) – pair id

• disp_range_grid (dict) – minimum and maximum disparity grid

• disp_to_alt_ratio (float) – disp to alti ratio used for performance map

• margins_to_keep (int) – margin to keep after dense matching

• texture_bands (list) – indices of bands from epipolar_images_left used for output texture

Returns:

disparity map: The 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”,

  ”disp_min_tiling”, “disp_max_tiling”

Return type:

CarsDataset