`skimage.measure._label:label`

source: /dev/scikit-image/src/skimage/measure/_label.py :33

Signature

   def     label (    label_image    ,    background    =  None   ,    return_num    =  False   ,    connectivity    =  None     )

Summary

Label connected regions of an integer array.

Extended Summary

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

label_image : ndarray of dtype int: Image to label.
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)
print(label(x, connectivity=1))
print(label(x, connectivity=2))
print(label(x, background=-1))
x = np.array([[1, 0, 0],
              [1, 1, 5],
              [0, 0, 0]])
print(label(x))

✓

Aliases

skimage.morphology.label

Referenced by

This package

skimage.segmentation._watershed:watershed