mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 23:42:02 +08:00
Stefan van der Walt
241 lines
6.4 KiB
Cython
241 lines
6.4 KiB
Cython
#cython: cdivision=True
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#cython: boundscheck=False
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#cython: nonecheck=False
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#cython: wraparound=False
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import numpy as np
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import warnings
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cimport numpy as cnp
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"""
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See also:
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Christophe Fiorio and Jens Gustedt,
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"Two linear time Union-Find strategies for image processing",
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Theoretical Computer Science 154 (1996), pp. 165-181.
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Kensheng Wu, Ekow Otoo and Arie Shoshani,
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"Optimizing connected component labeling algorithms",
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Paper LBNL-56864, 2005,
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Lawrence Berkeley National Laboratory
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(University of California),
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http://repositories.cdlib.org/lbnl/LBNL-56864
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"""
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# Tree operations implemented by an array as described in Wu et al.
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# The term "forest" is used to indicate an array that stores one or more trees
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DTYPE = np.intp
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cdef DTYPE_t find_root(DTYPE_t *forest, DTYPE_t n):
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"""Find the root of node n.
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"""
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cdef DTYPE_t root = n
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while (forest[root] < root):
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root = forest[root]
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return root
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cdef inline void set_root(DTYPE_t *forest, DTYPE_t n, DTYPE_t root):
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"""
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Set all nodes on a path to point to new_root.
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"""
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cdef DTYPE_t j
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while (forest[n] < n):
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j = forest[n]
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forest[n] = root
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n = j
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forest[n] = root
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cdef inline void join_trees(DTYPE_t *forest, DTYPE_t n, DTYPE_t m):
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"""Join two trees containing nodes n and m.
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"""
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cdef DTYPE_t root = find_root(forest, n)
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cdef DTYPE_t root_m
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if (n != m):
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root_m = find_root(forest, m)
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if (root > root_m):
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root = root_m
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set_root(forest, n, root)
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set_root(forest, m, root)
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cdef inline void link_bg(DTYPE_t *forest, DTYPE_t n, DTYPE_t *background_node):
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"""
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Link a node to the background node.
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"""
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if background_node[0] == -999:
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background_node[0] = n
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join_trees(forest, n, background_node[0])
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# Connected components search as described in Fiorio et al.
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def label(input, DTYPE_t neighbors=8, background=None, return_num=False):
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"""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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They can be neighbors either in a 4- or 8-connected sense::
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4-connectivity 8-connectivity
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[ ] [ ] [ ] [ ]
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| \ | /
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[ ]--[ ]--[ ] [ ]--[ ]--[ ]
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| / | \\
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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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background : int, optional
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Consider all pixels with this value as background pixels, and label
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them as -1. (Note: background pixels will be labeled as 0 starting with
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version 0.12).
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return_num : bool, optional
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Whether to return the number of assigned labels.
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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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Examples
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--------
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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(m.label(x, neighbors=4))
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[[0 1 1]
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[2 3 1]
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[2 2 4]]
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>>> print(m.label(x, neighbors=8))
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[[0 1 1]
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[1 0 1]
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[1 1 0]]
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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(m.label(x, background=0))
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[[ 0 -1 -1]
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[ 0 0 1]
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[-1 -1 -1]]
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"""
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cdef DTYPE_t rows = input.shape[0]
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cdef DTYPE_t cols = input.shape[1]
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cdef cnp.ndarray[DTYPE_t, ndim=2] data = np.array(input, copy=True,
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dtype=DTYPE)
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cdef cnp.ndarray[DTYPE_t, ndim=2] forest
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forest = np.arange(data.size, dtype=DTYPE).reshape((rows, cols))
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cdef DTYPE_t *forest_p = <DTYPE_t*>forest.data
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cdef DTYPE_t *data_p = <DTYPE_t*>data.data
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cdef DTYPE_t i, j
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cdef DTYPE_t background_val
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if background is None:
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background_val = -1
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warnings.warn(DeprecationWarning(
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'The default value for `background` will change to 0 in v0.12'
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))
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else:
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background_val = background
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cdef DTYPE_t background_node = -999
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if neighbors != 4 and neighbors != 8:
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raise ValueError('Neighbors must be either 4 or 8.')
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# Initialize the first row
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if data[0, 0] == background_val:
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link_bg(forest_p, 0, &background_node)
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for j in range(1, cols):
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if data[0, j] == background_val:
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link_bg(forest_p, j, &background_node)
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if data[0, j] == data[0, j-1]:
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join_trees(forest_p, j, j-1)
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for i in range(1, rows):
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# Handle the first column
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if data[i, 0] == background_val:
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link_bg(forest_p, i * cols, &background_node)
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if data[i, 0] == data[i-1, 0]:
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join_trees(forest_p, i*cols, (i-1)*cols)
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if neighbors == 8:
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if data[i, 0] == data[i-1, 1]:
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join_trees(forest_p, i*cols, (i-1)*cols + 1)
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for j in range(1, cols):
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if data[i, j] == background_val:
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link_bg(forest_p, i * cols + j, &background_node)
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if neighbors == 8:
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if data[i, j] == data[i-1, j-1]:
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join_trees(forest_p, i*cols + j, (i-1)*cols + j - 1)
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if data[i, j] == data[i-1, j]:
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join_trees(forest_p, i*cols + j, (i-1)*cols + j)
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if neighbors == 8:
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if j < cols - 1:
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if data[i, j] == data[i - 1, j + 1]:
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join_trees(forest_p, i*cols + j, (i-1)*cols + j + 1)
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if data[i, j] == data[i, j-1]:
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join_trees(forest_p, i*cols + j, i*cols + j - 1)
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# Label output
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cdef DTYPE_t ctr = 0
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for i in range(rows):
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for j in range(cols):
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if (i*cols + j) == background_node:
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data[i, j] = -1
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elif (i*cols + j) == forest[i, j]:
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data[i, j] = ctr
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ctr = ctr + 1
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else:
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data[i, j] = data_p[forest[i, j]]
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# Work around a bug in ndimage's type checking on 32-bit platforms
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if data.dtype == np.int32:
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data = data.view(np.int32)
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if return_num:
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return data, ctr
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else:
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return data
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