pandora.interval_tools

This module contains functions associated to confidence intervals.

Attributes

create_connected_graph
graph_regularization

Functions

interval_regularization(→ Tuple[numpy.ndarray, ...)

Regularize interval bounds in ambiguous zones.

Module Contents

pandora.interval_tools.create_connected_graph

pandora.interval_tools.graph_regularization

pandora.interval_tools.interval_regularization(interval_inf: numpy.ndarray, interval_sup: numpy.ndarray, ambiguity: numpy.ndarray, ambiguity_threshold: float, ambiguity_kernel_size: int, vertical_depth: int = 0, quantile_regularization: float = 1.0) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]

Regularize interval bounds in ambiguous zones.

Parameters:

• interval_inf – lower bound of the confidence interval

• interval_sup – upper bound of the confidence interval

• ambiguity – ambiguity confidence map

• ambiguity_threshold (float) – threshold used for detecting ambiguous zones

• ambiguity_kernel_size (int) – number of columns for the minimitive kernel applied to ambiguity

• vertical_depth (int >= 0) – The number of lines above and below to look for adjacent segment during the regularization

• quantile_regularization (float between 0 and 1) – The quantile used for selecting the disparity value in the regularization step

Returns:

the regularized infimum and supremum of the set containing the true disparity and the mask of pixel that have been regularized

Return type:

Tuple(2D np.array (row, col) dtype = float32, 2D np.array (row, col) dtype = float32, 2D np.array (row, col) dtype = np.bool)