mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-13 04:26:57 +08:00
Juan Nunez-Iglesias
63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
import numpy as np
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import scipy.ndimage as nd
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def remove_small_connected_components(ar, min_size=64,
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connectivity=1, in_place=False):
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"""Remove connected components smaller than the specified size.
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Parameters
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----------
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ar : ndarray (arbitrary shape, int or bool type)
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The array containing the connected components of interest.
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min_size : int, optional (default: 64)
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The smallest allowable connected component size.
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connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1)
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The connectivity defining the neighborhood of a pixel.
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in_place : bool, optional (default: False)
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If `True`, remove the connected components in the input array itself.
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Otherwise, make a copy.
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Returns
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-------
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out : ndarray, same shape and type as input `ar`
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The input array with small connected components removed.
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Examples
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--------
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>>> import numpy as np
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>>> from skimage import morphology
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>>> from scipy import ndimage as nd
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>>> a = np.array([[0, 0, 0, 1, 0],
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... [1, 1, 1, 0, 0],
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... [1, 1, 1, 0, 1]], bool)
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>>> b = morphology.remove_small_connected_components(a, 6)
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>>> b
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array([[False, False, False, False, False],
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[ True, True, True, False, False],
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[ True, True, True, False, False]], dtype=bool)
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>>> c = morphology.remove_small_connected_components(a, 7, connectivity=2)
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>>> c
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array([[False, False, False, True, False],
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[ True, True, True, False, False],
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[ True, True, True, False, False]], dtype=bool)
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>>> d = morphology.remove_small_connected_components(a, 6, in_place=True)
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>>> d is a
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True
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"""
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structuring_element = nd.generate_binary_structure(ar.ndim, connectivity)
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if in_place:
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out = ar
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else:
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out = ar.copy()
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if min_size == 0: # shortcut for efficiency
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return out
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if out.dtype == bool:
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ccs = nd.label(ar, structuring_element)[0]
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else:
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ccs = out
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component_sizes = np.bincount(ccs.ravel())
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too_small = component_sizes < min_size
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too_small_mask = too_small[ccs]
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out[too_small_mask] = 0
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return out
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