"""Miscellaneous morphology functions.""" import numpy as np import functools from scipy import ndimage as ndi from .._shared.utils import warn from .selem import _default_selem # Our function names don't exactly correspond to ndimages. # This dictionary translates from our names to scipy's. funcs = ('erosion', 'dilation', 'opening', 'closing') skimage2ndimage = dict((x, 'grey_' + x) for x in funcs) # These function names are the same in ndimage. funcs = ('binary_erosion', 'binary_dilation', 'binary_opening', 'binary_closing', 'black_tophat', 'white_tophat') skimage2ndimage.update(dict((x, x) for x in funcs)) def default_selem(func): """Decorator to add a default structuring element to morphology functions. Parameters ---------- func : function A morphology function such as erosion, dilation, opening, closing, white_tophat, or black_tophat. Returns ------- func_out : function The function, using a default structuring element of same dimension as the input image with connectivity 1. """ @functools.wraps(func) def func_out(image, selem=None, *args, **kwargs): if selem is None: selem = _default_selem(image.ndim) return func(image, selem=selem, *args, **kwargs) return func_out def _check_dtype_supported(ar): # Should use `issubdtype` for bool below, but there's a bug in numpy 1.7 if not (ar.dtype == bool or np.issubdtype(ar.dtype, np.integer)): raise TypeError("Only bool or integer image types are supported. " "Got %s." % ar.dtype) def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False): """Remove connected components smaller than the specified size. Parameters ---------- ar : ndarray (arbitrary shape, int or bool type) The array containing the connected components of interest. If the array type is int, it is assumed that it contains already-labeled objects. The ints must be non-negative. min_size : int, optional (default: 64) The smallest allowable connected component size. connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1) The connectivity defining the neighborhood of a pixel. in_place : bool, optional (default: False) If `True`, remove the connected components in the input array itself. Otherwise, make a copy. Raises ------ TypeError If the input array is of an invalid type, such as float or string. ValueError If the input array contains negative values. Returns ------- out : ndarray, same shape and type as input `ar` The input array with small connected components removed. Examples -------- >>> from skimage import morphology >>> a = np.array([[0, 0, 0, 1, 0], ... [1, 1, 1, 0, 0], ... [1, 1, 1, 0, 1]], bool) >>> b = morphology.remove_small_objects(a, 6) >>> b array([[False, False, False, False, False], [ True, True, True, False, False], [ True, True, True, False, False]], dtype=bool) >>> c = morphology.remove_small_objects(a, 7, connectivity=2) >>> c array([[False, False, False, True, False], [ True, True, True, False, False], [ True, True, True, False, False]], dtype=bool) >>> d = morphology.remove_small_objects(a, 6, in_place=True) >>> d is a True """ # Raising type error if not int or bool _check_dtype_supported(ar) if in_place: out = ar else: out = ar.copy() if min_size == 0: # shortcut for efficiency return out if out.dtype == bool: selem = ndi.generate_binary_structure(ar.ndim, connectivity) ccs = np.zeros_like(ar, dtype=np.int32) ndi.label(ar, selem, output=ccs) else: ccs = out try: component_sizes = np.bincount(ccs.ravel()) except ValueError: raise ValueError("Negative value labels are not supported. Try " "relabeling the input with `scipy.ndimage.label` or " "`skimage.morphology.label`.") if len(component_sizes) == 2 and out.dtype != bool: warn("Only one label was provided to `remove_small_objects`. " "Did you mean to use a boolean array?") too_small = component_sizes < min_size too_small_mask = too_small[ccs] out[too_small_mask] = 0 return out def remove_small_holes(ar, area_threshold=64, connectivity=1, in_place=False, min_size=None): """Remove continguous holes smaller than the specified size. Parameters ---------- ar : ndarray (arbitrary shape, int or bool type) The array containing the connected components of interest. area_threshold : int, optional (default: 64) The maximum area, in pixels, of a contiguous hole that will be filled. Replaces `min_size`. connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1) The connectivity defining the neighborhood of a pixel. in_place : bool, optional (default: False) If `True`, remove the connected components in the input array itself. Otherwise, make a copy. Raises ------ TypeError If the input array is of an invalid type, such as float or string. ValueError If the input array contains negative values. Returns ------- out : ndarray, same shape and type as input `ar` The input array with small holes within connected components removed. Examples -------- >>> from skimage import morphology >>> a = np.array([[1, 1, 1, 1, 1, 0], ... [1, 1, 1, 0, 1, 0], ... [1, 0, 0, 1, 1, 0], ... [1, 1, 1, 1, 1, 0]], bool) >>> b = morphology.remove_small_holes(a, 2) >>> b array([[ True, True, True, True, True, False], [ True, True, True, True, True, False], [ True, False, False, True, True, False], [ True, True, True, True, True, False]], dtype=bool) >>> c = morphology.remove_small_holes(a, 2, connectivity=2) >>> c array([[ True, True, True, True, True, False], [ True, True, True, False, True, False], [ True, False, False, True, True, False], [ True, True, True, True, True, False]], dtype=bool) >>> d = morphology.remove_small_holes(a, 2, in_place=True) >>> d is a True Notes ----- If the array type is int, it is assumed that it contains already-labeled objects. The labels are not kept in the output image (this function always outputs a bool image). It is suggested that labeling is completed after using this function. """ _check_dtype_supported(ar) # Creates warning if image is an integer image if ar.dtype != bool: warn("Any labeled images will be returned as a boolean array. " "Did you mean to use a boolean array?", UserWarning) if min_size is not None: warn("the min_size argument is deprecated and will be removed in " + "0.16. Use area_threshold instead.") area_threshold = min_size if in_place: out = ar else: out = ar.copy() # Creating the inverse of ar if in_place: out = np.logical_not(out, out) else: out = np.logical_not(out) # removing small objects from the inverse of ar out = remove_small_objects(out, area_threshold, connectivity, in_place) if in_place: out = np.logical_not(out, out) else: out = np.logical_not(out) return out