94 lines
3.1 KiB
Python
94 lines
3.1 KiB
Python
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from ._ccomp import label_cython as clabel
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def label(input, neighbors=None, background=None, return_num=False,
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connectivity=None):
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r"""Label connected regions of an integer array.
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Two pixels are connected when they are neighbors and have the same value.
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In 2D, they can be neighbors either in a 1- or 2-connected sense.
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The value refers to the maximum number of orthogonal hops to consider a
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pixel/voxel a neighbor::
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1-connectivity 2-connectivity diagonal connection close-up
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[ ] [ ] [ ] [ ] [ ]
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| \ | / | <- hop 2
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[ ]--[x]--[ ] [ ]--[x]--[ ] [x]--[ ]
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| / | \ hop 1
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[ ] [ ] [ ] [ ]
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Parameters
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----------
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input : ndarray of dtype int
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Image to label.
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neighbors : {4, 8}, int, optional
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Whether to use 4- or 8-"connectivity".
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In 3D, 4-"connectivity" means connected pixels have to share face,
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whereas with 8-"connectivity", they have to share only edge or vertex.
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**Deprecated, use** ``connectivity`` **instead.**
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background : int, optional
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Consider all pixels with this value as background pixels, and label
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them as 0. By default, 0-valued pixels are considered as background
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pixels.
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return_num : bool, optional
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Whether to return the number of assigned labels.
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connectivity : int, optional
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Maximum number of orthogonal hops to consider a pixel/voxel
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as a neighbor.
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Accepted values are ranging from 1 to input.ndim. If ``None``, a full
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connectivity of ``input.ndim`` is used.
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Returns
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-------
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labels : ndarray of dtype int
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Labeled array, where all connected regions are assigned the
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same integer value.
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num : int, optional
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Number of labels, which equals the maximum label index and is only
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returned if return_num is `True`.
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See Also
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--------
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regionprops
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References
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----------
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.. [1] Christophe Fiorio and Jens Gustedt, "Two linear time Union-Find
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strategies for image processing", Theoretical Computer Science
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154 (1996), pp. 165-181.
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.. [2] Kensheng Wu, Ekow Otoo and Arie Shoshani, "Optimizing connected
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component labeling algorithms", Paper LBNL-56864, 2005,
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Lawrence Berkeley National Laboratory (University of California),
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http://repositories.cdlib.org/lbnl/LBNL-56864
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Examples
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--------
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>>> import numpy as np
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>>> x = np.eye(3).astype(int)
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>>> print(x)
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[[1 0 0]
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[0 1 0]
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[0 0 1]]
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>>> print(label(x, connectivity=1))
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[[1 0 0]
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[0 2 0]
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[0 0 3]]
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>>> print(label(x, connectivity=2))
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[[1 0 0]
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[0 1 0]
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[0 0 1]]
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>>> print(label(x, background=-1))
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[[1 2 2]
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[2 1 2]
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[2 2 1]]
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>>> x = np.array([[1, 0, 0],
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... [1, 1, 5],
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... [0, 0, 0]])
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>>> print(label(x))
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[[1 0 0]
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[1 1 2]
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[0 0 0]]
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"""
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return clabel(input, neighbors, background, return_num, connectivity)
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