| 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] |
|
|
|
|
| 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) |
|
|
