label

label

skimage.morphology.label(input, neighbors=None, background=None, return_num=False, connectivity=None) [source]

Label connected regions of an integer array.

Two pixels are connected when they are neighbors and have the same value. In 2D, they can be neighbors either in a 1- or 2-connected sense. The value refers to the maximum number of orthogonal hops to consider a pixel/voxel a neighbor:

1 2 3 4 5 6 7

1-connectivity      2-connectivity     diagonal connection close-up        [ ]           [ ]  [ ]  [ ]         [ ]       |               \  |  /             |  <- hop 2 [ ]--[x]--[ ]      [ ]--[x]--[ ]    [x]--[ ]       |               /  |  \         hop 1      [ ]           [ ]  [ ]  [ ]

Parameters:

input : ndarray of dtype int Image to label. neighbors : {4, 8}, int, optional Whether to use 4- or 8-“connectivity”. In 3D, 4-“connectivity” means connected pixels have to share face, whereas with 8-“connectivity”, they have to share only edge or vertex. Deprecated, use ``connectivity`` instead. background : int, optional Consider all pixels with this value as background pixels, and label them as 0. By default, 0-valued pixels are considered as background pixels. return_num : bool, optional Whether to return the number of assigned labels. connectivity : int, optional Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor. Accepted values are ranging from 1 to input.ndim. If None, a full connectivity of input.ndim is used.

Returns:

labels : ndarray of dtype int Labeled array, where all connected regions are assigned the same integer value. num : int, optional Number of labels, which equals the maximum label index and is only returned if return_num is True.

Examples

1 2 3 4 5 6 7 8 9 10 11

>>> import numpy as np >>> x = np.eye(3).astype(int) >>> print(x) [[1 0 0]  [0 1 0]  [0 0 1]] >>> from skimage.measure import label >>> print(label(x, connectivity=1)) [[1 0 0]  [0 2 0]  [0 0 3]]

1 2 3 4

>>> print(label(x, connectivity=2)) [[1 0 0]  [0 1 0]  [0 0 1]]

1 2 3 4

>>> print(label(x, background=-1)) [[1 2 2]  [2 1 2]  [2 2 1]]

1 2 3

>>> x = np.array([[1, 0, 0], ...               [1, 1, 5], ...               [0, 0, 0]])

1 2 3 4

>>> print(label(x)) [[1 0 0]  [1 1 2]  [0 0 0]]