228 lines
7.6 KiB
Python
228 lines
7.6 KiB
Python
"""Miscellaneous morphology functions."""
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import numpy as np
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import functools
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from scipy import ndimage as ndi
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from .._shared.utils import warn
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from .selem import _default_selem
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# Our function names don't exactly correspond to ndimages.
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# This dictionary translates from our names to scipy's.
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funcs = ('erosion', 'dilation', 'opening', 'closing')
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skimage2ndimage = {x: 'grey_' + x for x in funcs}
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# These function names are the same in ndimage.
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funcs = ('binary_erosion', 'binary_dilation', 'binary_opening',
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'binary_closing', 'black_tophat', 'white_tophat')
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skimage2ndimage.update({x: x for x in funcs})
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def default_selem(func):
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"""Decorator to add a default structuring element to morphology functions.
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Parameters
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----------
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func : function
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A morphology function such as erosion, dilation, opening, closing,
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white_tophat, or black_tophat.
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Returns
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-------
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func_out : function
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The function, using a default structuring element of same dimension
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as the input image with connectivity 1.
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"""
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@functools.wraps(func)
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def func_out(image, selem=None, *args, **kwargs):
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if selem is None:
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selem = _default_selem(image.ndim)
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return func(image, selem=selem, *args, **kwargs)
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return func_out
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def _check_dtype_supported(ar):
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# Should use `issubdtype` for bool below, but there's a bug in numpy 1.7
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if not (ar.dtype == bool or np.issubdtype(ar.dtype, np.integer)):
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raise TypeError("Only bool or integer image types are supported. "
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"Got %s." % ar.dtype)
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def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False):
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"""Remove objects smaller than the specified size.
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Expects ar to be an array with labeled objects, and removes objects
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smaller than min_size. If `ar` is bool, the image is first labeled.
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This leads to potentially different behavior for bool and 0-and-1
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arrays.
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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 objects of interest. If the array type is
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int, the ints must be non-negative.
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min_size : int, optional (default: 64)
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The smallest allowable object 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. Used during
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labelling if `ar` is bool.
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in_place : bool, optional (default: False)
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If ``True``, remove the objects in the input array itself.
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Otherwise, make a copy.
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Raises
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------
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TypeError
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If the input array is of an invalid type, such as float or string.
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ValueError
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If the input array contains negative values.
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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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>>> from skimage import morphology
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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_objects(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]])
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>>> c = morphology.remove_small_objects(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]])
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>>> d = morphology.remove_small_objects(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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# Raising type error if not int or bool
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_check_dtype_supported(ar)
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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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selem = ndi.generate_binary_structure(ar.ndim, connectivity)
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ccs = np.zeros_like(ar, dtype=np.int32)
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ndi.label(ar, selem, output=ccs)
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else:
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ccs = out
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try:
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component_sizes = np.bincount(ccs.ravel())
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except ValueError:
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raise ValueError("Negative value labels are not supported. Try "
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"relabeling the input with `scipy.ndimage.label` or "
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"`skimage.morphology.label`.")
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if len(component_sizes) == 2 and out.dtype != bool:
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warn("Only one label was provided to `remove_small_objects`. "
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"Did you mean to use a boolean array?")
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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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def remove_small_holes(ar, area_threshold=64, connectivity=1, in_place=False):
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"""Remove contiguous holes 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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area_threshold : int, optional (default: 64)
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The maximum area, in pixels, of a contiguous hole that will be filled.
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Replaces `min_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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Raises
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------
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TypeError
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If the input array is of an invalid type, such as float or string.
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ValueError
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If the input array contains negative values.
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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 holes within connected components removed.
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Examples
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--------
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>>> from skimage import morphology
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>>> a = np.array([[1, 1, 1, 1, 1, 0],
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... [1, 1, 1, 0, 1, 0],
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... [1, 0, 0, 1, 1, 0],
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... [1, 1, 1, 1, 1, 0]], bool)
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>>> b = morphology.remove_small_holes(a, 2)
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>>> b
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array([[ True, True, True, True, True, False],
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[ True, True, True, True, True, False],
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[ True, False, False, True, True, False],
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[ True, True, True, True, True, False]])
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>>> c = morphology.remove_small_holes(a, 2, connectivity=2)
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>>> c
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array([[ True, True, True, True, True, False],
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[ True, True, True, False, True, False],
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[ True, False, False, True, True, False],
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[ True, True, True, True, True, False]])
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>>> d = morphology.remove_small_holes(a, 2, in_place=True)
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>>> d is a
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True
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Notes
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-----
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If the array type is int, it is assumed that it contains already-labeled
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objects. The labels are not kept in the output image (this function always
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outputs a bool image). It is suggested that labeling is completed after
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using this function.
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"""
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_check_dtype_supported(ar)
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# Creates warning if image is an integer image
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if ar.dtype != bool:
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warn("Any labeled images will be returned as a boolean array. "
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"Did you mean to use a boolean array?", UserWarning)
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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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# Creating the inverse of ar
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if in_place:
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np.logical_not(out, out=out)
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else:
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out = np.logical_not(out)
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# removing small objects from the inverse of ar
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out = remove_small_objects(out, area_threshold, connectivity, in_place)
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if in_place:
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np.logical_not(out, out=out)
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else:
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out = np.logical_not(out)
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return out
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