cars.applications.sparse_matching.sparse_matching_wrappers

Sparse matching Sift module: contains sift sparse matching method

Functions

euclidean_matrix_distance(descr1, descr2)	Compute a matrix containing cross euclidean distance
remove_epipolar_outliers(matches[, percent])	This function will filter the match vector
compute_disparity_range(matches[, percent])	This function will compute the disparity range
compute_disp_min_disp_max(pd_cloud, orchestrator[, ...])	Compute disp min and disp max from triangulated and filtered matches
transform_triangulated_matches_to_dataframe(...)
get_margins(margin_left, margin_right, disp_min, disp_max)	Get margins for the dense matching steps

Module Contents

cars.applications.sparse_matching.sparse_matching_wrappers.euclidean_matrix_distance(descr1: numpy.array, descr2: numpy.array)

Compute a matrix containing cross euclidean distance :param descr1: first keypoints descriptor :type descr1: numpy.ndarray :param descr2: second keypoints descriptor :type descr2: numpy.ndarray :return euclidean matrix distance :rtype: float

cars.applications.sparse_matching.sparse_matching_wrappers.remove_epipolar_outliers(matches, percent=0.1)

This function will filter the match vector according to a quantile of epipolar error used for testing compute_disparity_range sparse method

Parameters:

• matches (numpy array) – the [4,N] matches array

• percent (float) – the quantile to remove at each extrema

Returns:

the filtered match array

Return type:

numpy array

cars.applications.sparse_matching.sparse_matching_wrappers.compute_disparity_range(matches, percent=0.1)

This function will compute the disparity range from matches by filtering percent outliers

Parameters:

• matches (numpy array) – the [4,N] matches array

• percent (float) – the quantile to remove at each extrema (in %)

Returns:

the disparity range

Return type:

float, float

cars.applications.sparse_matching.sparse_matching_wrappers.compute_disp_min_disp_max(pd_cloud, orchestrator, disp_margin=0.1, pair_key=None, disp_to_alt_ratio=None)

Compute disp min and disp max from triangulated and filtered matches

Parameters:

• pd_cloud (pandas Dataframe) – triangulated_matches

• orchestrator (Orchestrator) – orchestrator used

• disp_margin (float) – disparity margin

• disp_to_alt_ratio (float) – used for logging info

Returns:

disp min and disp max

Return type:

float, float

cars.applications.sparse_matching.sparse_matching_wrappers.transform_triangulated_matches_to_dataframe(triangulated_matches)

Parameters:

triangulated_matches – triangulated matches

Type:

cars_dataset

cars.applications.sparse_matching.sparse_matching_wrappers.get_margins(margin_left, margin_right, disp_min, disp_max)

Get margins for the dense matching steps

Parameters:

• margin (Margins) – margins object

• disp_min (int) – Minimum disparity

• disp_max (int) – Maximum disparity

Returns:

margins of the matching algorithm used