`cars.applications.dense_match_filling.cpp.dense_match_filling_cpp`

this module contains the headers of the dense_match_filling_cpp module.

Module Contents

Functions

`fill_disp_pandora`(→ Tuple[numpy.ndarray, numpy.ndarray])	Interpolation of the left disparity map to fill holes.
`find_valid_neighbors`(dirs, disp, valid, row, col, ...)	Find valid neighbors along directions

cars.applications.dense_match_filling.cpp.dense_match_filling_cpp.fill_disp_pandora(disp: numpy.ndarray, msk_fill_disp: numpy.ndarray, nb_directions: int) → Tuple[numpy.ndarray, numpy.ndarray][source]

Interpolation of the left disparity map to fill holes. Interpolate invalid pixel by finding the nearest correct pixels in 8/16 different directions and use the median of their disparities. ?bontar, J., & LeCun, Y. (2016). Stereo matching by training a convolutional neural network to compare image patches. The journal of machine learning research, 17(1), 2287-2318. HIRSCHMULLER, Heiko. Stereo processing by semiglobal matching and mutual information. IEEE Transactions on pattern analysis and machine intelligence, 2007, vol. 30, no 2, p. 328-341.

Copied/adapted fct from pandora/validation/interpolated_disparity.py

Parameters

disp (2D np.array (row, col)) – disparity map
msk_fill_disp (2D np.array (row, col)) – validity mask
nb_directions (integer) – nb directions to explore

Returns

the interpolate left disparity map, with the validity mask update :

Return type

tuple(2D np.array (row, col), 2D np.array (row, col))

cars.applications.dense_match_filling.cpp.dense_match_filling_cpp.find_valid_neighbors(dirs: numpy.ndarray, disp: numpy.ndarray, valid: numpy.ndarray, row: int, col: int, nb_directions: int)[source]

Find valid neighbors along directions