9ix0SSKJr SSKJr SSjrSSjrg))ndimage) label_cythonNc ,SSKJn US:XaU)nUc URnSUs=::aUR::d+O [SURSURSUS35eU"SX0R5n[R "XS 9nU(aU$US $) zzFaster implementation of clabel for boolean input. See context: https://github.com/scikit-image/scikit-image/issues/4833 )_resolve_neighborhoodrNzConnectivity for zD image should be in [1, ..., z]. Got .) structurer)morphology._utilrndim ValueErrorrlabel)image background return_num connectivityr footprintresults V/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/measure/_label.py _label_boolrs 9Qzz * * |,#jj\a A  &dL**EI ]]5 6F aycZUR[:Xa [UUUUS9$[XX#5$)ao 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]] )rrr)dtypeboolrclabel) label_imagerrrs rrr!s;hD  !!%   kzHHr)NFN)scipyr_ccomprrrrrrr s*8\Ir