cars.applications.sparse_matching.sparse_matching_tools

Sparse matching Sift module: contains sift sparse matching method

Module Contents

Functions

dataset_matching(ds1, ds2[, matching_threshold, ...])

Compute sift matches between two datasets

remove_epipolar_outliers(matches[, percent])

This function will filter the match vector

compute_disparity_range(matches[, percent])

This function will compute the disparity range

derive_disparity_range_from_matches(corrected_matches)

Compute disp min and disp max from matches
cars.applications.sparse_matching.sparse_matching_tools.dataset_matching(ds1, ds2, matching_threshold=0.6, n_octave=8, n_scale_per_octave=3, dog_threshold=20, edge_threshold=5, magnification=2.0, backmatching=True)

Compute sift matches between two datasets produced by stereo.epipolar_rectify_images

Parameters

  • ds1 (xarray.Dataset as produced by stereo.epipolar_rectify_images) – Left image dataset

  • ds2 (xarray.Dataset as produced by stereo.epipolar_rectify_images) – Right image dataset

  • threshold (float) – Threshold for matches

  • backmatching (bool) – Also check that right vs. left gives same match

Returns

matches

Return type

numpy buffer of shape (nb_matches,4)

cars.applications.sparse_matching.sparse_matching_tools.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_tools.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_tools.derive_disparity_range_from_matches(corrected_matches, orchestrator=None, disparity_margin=0.1, pair_key='PAIR_0', pair_folder=None, disp_to_alt_ratio=None, disparity_outliers_rejection_percent=0.1, save_matches=False)

Compute disp min and disp max from matches

Parameters

  • cars_orchestrator – orchestrator : used for info writting

  • corrected_matches (np.ndarray) – matches

  • disparity_margin (float) – disparity margin

  • pair_key (int) – id of pair : only used for info writting

  • disp_to_alt_ratio (float) – used for logging info

  • disparity_outliers_rejection_percent (float) – percentage of outliers to reject

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

Returns

disp min and disp max

Return type

float, float