from scipy import ndimage
from ._ccomp import label_cython as clabel
def _label_bool(image, background=None, return_num=False, connectivity=None):
"""Faster implementation of clabel for boolean input.
See context: https://github.com/scikit-image/scikit-image/issues/4833
"""
from ..morphology._util import _resolve_neighborhood
if background == 1:
image = ~image
if connectivity is None:
connectivity = image.ndim
if not 1 <= connectivity <= image.ndim:
raise ValueError(
f'Connectivity for {image.ndim}D image should '
f'be in [1, ..., {image.ndim}]. Got {connectivity}.'
)
footprint = _resolve_neighborhood(None, connectivity, image.ndim)
result = ndimage.label(image, structure=footprint)
if return_num:
return result
else:
return result[0]
[文档]
def label(label_image, background=None, return_num=False, connectivity=None):
r"""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
----------
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`.
See Also
--------
skimage.measure.regionprops
skimage.measure.regionprops_table
References
----------
.. [1] Christophe Fiorio and Jens Gustedt, "Two linear time Union-Find
strategies for image processing", Theoretical Computer Science
154 (1996), pp. 165-181.
.. [2] Kensheng Wu, Ekow Otoo and Arie Shoshani, "Optimizing connected
component labeling algorithms", Paper LBNL-56864, 2005,
Lawrence Berkeley National Laboratory (University of California),
http://repositories.cdlib.org/lbnl/LBNL-56864
Examples
--------
>>> import numpy as np
>>> x = np.eye(3).astype(int)
>>> print(x)
[[1 0 0]
[0 1 0]
[0 0 1]]
>>> 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]]
"""
if label_image.dtype == bool:
return _label_bool(
label_image,
background=background,
return_num=return_num,
connectivity=connectivity,
)
else:
return clabel(label_image, background, return_num, connectivity)
