mirror of
https://github.com/wassname/scikit-image.git
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Olivia
9ea085fd6f
This is currently working with **kwargs except for inplace which I cannot get working. It works with arrays of type int but returns an array of type bool. Possibly in future add labelling for arrays of type int. A UserWarning is produced when using arrays of type int which seems to work normally but the test created for this does not pick up the warning. Any assistance on these issues would be helpful. I started this at EuroScipy 2015 and this fixes issue #1642
207 lines
7.3 KiB
Python
207 lines
7.3 KiB
Python
import numpy as np
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import functools
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import warnings
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from scipy import ndimage as ndi
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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 = dict((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(dict((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 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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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. If the array
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type is int, it is assumed that it contains already-labeled objects.
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The ints must be non-negative.
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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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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]], dtype=bool)
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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]], dtype=bool)
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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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# 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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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:
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warnings.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, min_size=64, connectivity=1, in_place=False):
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"""Remove connected components smaller than the specified size within a
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larger connected object.
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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. If the array
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type is int, it is assumed that it contains already-labeled objects. The
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labels are not kept in the output image (this function always outputs
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a bool image. It is suggested that labeling is completed after using
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this function.
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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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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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"""
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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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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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#Creates warning if image is an integer image
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if out.dtype != bool:
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print("I'm about to warn")
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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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#Creating the inverse of ar
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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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out = np.logical_not(out)
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return out |