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-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

>>> 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]]

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

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

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

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