label
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skimage.measure.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 ofinput.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]]
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