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https://github.com/wassname/scikit-image.git
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Merge pull request #1689 from oew1v07/remove_small_holes
Remove small holes
This commit is contained in:
Juan Nunez-Iglesias
@@ -8,7 +8,7 @@ from .watershed import watershed
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from ._skeletonize import skeletonize, medial_axis
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from .convex_hull import convex_hull_image, convex_hull_object
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from .greyreconstruct import reconstruction
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from .misc import remove_small_objects
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from .misc import remove_small_objects, remove_small_holes
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from ..measure._label import label
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from .._shared.utils import deprecated as _deprecated
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@@ -40,4 +40,5 @@ __all__ = ['binary_erosion',
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'convex_hull_image',
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'convex_hull_object',
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'reconstruction',
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'remove_small_objects']
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'remove_small_objects',
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'remove_small_holes']
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@@ -37,7 +37,12 @@ def default_selem(func):
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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 connected components smaller than the specified size.
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@@ -88,10 +93,8 @@ def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False):
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>>> d is a
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True
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"""
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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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# 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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@@ -124,3 +127,89 @@ def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False):
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out[too_small_mask] = 0
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return out
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def remove_small_holes(ar, min_size=64, connectivity=1, in_place=False):
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"""Remove continguous 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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min_size : int, optional (default: 64)
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The hole 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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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]], dtype=bool)
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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]], dtype=bool)
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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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warnings.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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out = np.logical_not(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, min_size, connectivity, in_place)
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if in_place:
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out = np.logical_not(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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@@ -1,7 +1,8 @@
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import numpy as np
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from numpy.testing import (assert_array_equal, assert_equal, assert_raises,
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assert_warns)
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from skimage.morphology import remove_small_objects
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from skimage.morphology import remove_small_objects, remove_small_holes
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from ..._shared._warnings import expected_warnings
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test_image = np.array([[0, 0, 0, 1, 0],
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[1, 1, 1, 0, 0],
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@@ -60,7 +61,8 @@ def test_single_label_warning():
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image = np.array([[0, 0, 0, 1, 0],
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[1, 1, 1, 0, 0],
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[1, 1, 1, 0, 0]], int)
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assert_warns(UserWarning, remove_small_objects, image, min_size=6)
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with expected_warnings(['use a boolean array?']):
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remove_small_objects(image, min_size=6)
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def test_float_input():
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@@ -72,6 +74,102 @@ def test_negative_input():
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negative_int = np.random.randint(-4, -1, size=(5, 5))
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assert_raises(ValueError, remove_small_objects, negative_int)
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test_holes_image = np.array([[0,0,0,0,0,0,1,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,0,0,1,1,0,0,0,0],
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[0,1,1,1,0,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,0,0,0,0,0,0,1,1,1],
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[0,0,0,0,0,0,0,1,0,1],
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[0,0,0,0,0,0,0,1,1,1]], bool)
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def test_one_connectivity_holes():
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expected = np.array([[0,0,0,0,0,0,1,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,0,0,0,0,0,0,1,1,1],
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[0,0,0,0,0,0,0,1,1,1],
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[0,0,0,0,0,0,0,1,1,1]], bool)
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observed = remove_small_holes(test_holes_image, min_size=3)
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assert_array_equal(observed, expected)
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def test_two_connectivity_holes():
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expected = np.array([[0,0,0,0,0,0,1,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,0,0,1,1,0,0,0,0],
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[0,1,1,1,0,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,0,0,0,0,0,0,1,1,1],
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[0,0,0,0,0,0,0,1,1,1],
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[0,0,0,0,0,0,0,1,1,1]], bool)
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observed = remove_small_holes(test_holes_image, min_size=3, connectivity=2)
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assert_array_equal(observed, expected)
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def test_in_place_holes():
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observed = remove_small_holes(test_holes_image, min_size=3, in_place=True)
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assert_equal(observed is test_holes_image, True,
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"remove_small_holes in_place argument failed.")
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def test_labeled_image_holes():
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labeled_holes_image = np.array([[0,0,0,0,0,0,1,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,0,0,1,1,0,0,0,0],
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[0,1,1,1,0,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,0,0,0,0,0,0,2,2,2],
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[0,0,0,0,0,0,0,2,0,2],
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[0,0,0,0,0,0,0,2,2,2]], dtype=int)
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expected = np.array([[0,0,0,0,0,0,1,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,0,0,0,0,0,0,1,1,1],
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[0,0,0,0,0,0,0,1,1,1],
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[0,0,0,0,0,0,0,1,1,1]], dtype=bool)
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observed = remove_small_holes(labeled_holes_image, min_size=3)
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assert_array_equal(observed, expected)
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def test_uint_image_holes():
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labeled_holes_image = np.array([[0,0,0,0,0,0,1,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,0,0,1,1,0,0,0,0],
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[0,1,1,1,0,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,0,0,0,0,0,0,2,2,2],
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[0,0,0,0,0,0,0,2,0,2],
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[0,0,0,0,0,0,0,2,2,2]], dtype=np.uint8)
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expected = np.array([[0,0,0,0,0,0,1,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,0,0,0,0,0,0,1,1,1],
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[0,0,0,0,0,0,0,1,1,1],
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[0,0,0,0,0,0,0,1,1,1]], dtype=bool)
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observed = remove_small_holes(labeled_holes_image, min_size=3)
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assert_array_equal(observed, expected)
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def test_label_warning_holes():
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labeled_holes_image = np.array([[0,0,0,0,0,0,1,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,1,0,0,1,1,0,0,0,0],
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[0,1,1,1,0,1,0,0,0,0],
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[0,1,1,1,1,1,0,0,0,0],
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[0,0,0,0,0,0,0,2,2,2],
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[0,0,0,0,0,0,0,2,0,2],
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[0,0,0,0,0,0,0,2,2,2]], dtype=int)
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with expected_warnings(['use a boolean array?']):
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remove_small_holes(labeled_holes_image, min_size=3)
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def test_float_input_holes():
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float_test = np.random.rand(5, 5)
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assert_raises(TypeError, remove_small_holes, float_test)
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if __name__ == "__main__":
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np.testing.run_module_suite()
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