pandora.cpp.interval_tools_cpp

Functions

create_connected_graph(→ numpy.ndarray)

Create a boolean connection matrix from segment coordinates

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

Regularize the intervals based on quantiles and a given connection graph.

Module Contents

pandora.cpp.interval_tools_cpp.create_connected_graph(border_left: numpy.ndarray, border_right: numpy.ndarray, depth: int) → numpy.ndarray[source]

Create a boolean connection matrix from segment coordinates

Parameters:

  • border_left ((n, 2) np.ndarray where n is the number of segments) – array containing the coordinates of segments left border

  • border_right ((n, 2) np.ndarray where n is the number of segments) – array containing the coordinates of segments right border

  • depth – the depth for regularization. It corresponds to the number of rows to explore below and above.

Returns:

A symmetric boolean matrix of shape (n, n). 1 indicating that the segment are connected

Return type:

np.ndarray of shape (n, n)

pandora.cpp.interval_tools_cpp.graph_regularization(interval_inf: numpy.ndarray, interval_sup: numpy.ndarray, border_left: numpy.ndarray, border_right: numpy.ndarray, connection_graph: numpy.ndarray, quantile: float) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]

Regularize the intervals based on quantiles and a given connection graph.

Parameters:

  • interval_inf ((row, col) np.ndarray) – The lower estimation of the disparity to regularize

  • interval_sup ((row, col) np.ndarray) – The upper estimation of the disparity to regularize

  • border_left ((n, 2) np.ndarray where n is the number of segments) – array containing the coordinates of segments left border

  • border_right ((n, 2) np.ndarray where n is the number of segments) – array containing the coordinates of segments right border

  • graph (connection) – A matrix indicating if the segments (n in total) are connected

  • quantile (float. 0 <= quantile <= 1) – Which quantile to select for the regularized value

Returns:

The regularized inf and sup of the disparity, and a boolean mask indicating regularization

Return type:

Tuple[np.ndarray, np.ndarray, np.ndarray]