grid_to_graph (original) (raw)

Graph of the pixel-to-pixel connections.

Edges exist if 2 voxels are connected.

Read more in the User Guide.

Parameters:

n_xint

Dimension in x axis.

n_yint

Dimension in y axis.

n_zint, default=1

Dimension in z axis.

maskndarray of shape (n_x, n_y, n_z), dtype=bool, default=None

An optional mask of the image, to consider only part of the pixels.

return_asnp.ndarray or a sparse matrix class, default=sparse.coo_matrix

The class to use to build the returned adjacency matrix.

dtypedtype, default=int

The data of the returned sparse matrix. By default it is int.

Returns:

graphnp.ndarray or a sparse matrix class

The computed adjacency matrix.

Examples

import numpy as np from sklearn.feature_extraction.image import grid_to_graph shape_img = (4, 4, 1) mask = np.zeros(shape=shape_img, dtype=bool) mask[[1, 2], [1, 2], :] = True graph = grid_to_graph(*shape_img, mask=mask) print(graph) <COOrdinate sparse matrix of dtype 'int64' with 2 stored elements and shape (2, 2)> Coords Values (0, 0) 1 (1, 1) 1