cars.applications.sparse_matching.abstract_sparse_matching_app

this module contains the abstract matching application class.

Attributes

AbstractSparseMatchingMethod

Classes

SparseMatching

SparseMatching

Module Contents

cars.applications.sparse_matching.abstract_sparse_matching_app.AbstractSparseMatchingMethod

class cars.applications.sparse_matching.abstract_sparse_matching_app.SparseMatching(conf=None)

Bases: cars.applications.application_template.ApplicationTemplate

SparseMatching

available_applications: Dict

default_application = 'basic'

default_method = 'sift'

classmethod __init_subclass__(short_name, **kwargs)

sparse_matching_method

abstract get_required_bands()

Get bands required by this application.

Returns:

required bands for left and right image

Return type:

dict

abstract get_margins_strip_fun(disp_min=None, disp_max=None, method='sift')

Get margins function to use in resampling

Parameters:

• disp_min – disp min for info

• disp_max – disp max for info

• method – method for the margins

Returns:

margins function

Return type:

function generating xr.Dataset

abstract get_margins_tile_fun(grid_left, disp_range_grid, method='sift')

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

Parameters:

• grid_left (dict) – left epipolar grid

• disp_range_grid – minimum and maximum disparity grid

Returns:

function that generates margin for given roi

abstract filter_matches(epipolar_matches_left, grid_left, grid_right, geom_plugin, orchestrator=None, pair_key='pair_0', pair_folder=None, save_matches=False)

Transform matches CarsDataset to numpy matches, and filters matches

Parameters:

• cars_orchestrator – orchestrator

• epipolar_matches_left (CarsDataset) –

  matches. 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”

• grid_left (dict) – left epipolar grid dict

• grid_right (dict) – right epipolar grid dict

• save_matches (bool) – true is matches needs to be saved

:return filtered matches :rtype: np.ndarray

abstract run(epipolar_image_left, epipolar_image_right, disp_to_alt_ratio, orchestrator=None, pair_folder=None, pair_key='PAIR_0', classif_bands_to_mask=None)

Run Matching application.

Create left and right CarsDataset filled with pandas.DataFrame , corresponding to epipolar 2D disparities, on the same geometry that epipolar_images_left and epipolar_images_right.