"""Miscellaneous morphology functions.""" import numpy as np import functools from scipy import ndimage as ndi from .._shared.utils import warn # Our function names don't exactly correspond to ndimages. # This dictionary translates from our names to scipy's. funcs = ('erosion', 'dilation', 'opening', 'closing') skimage2ndimage = {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({x: x for x in funcs}) def default_footprint(func): """Decorator to add a default footprint 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 footprint of same dimension as the input image with connectivity 1. """ @functools.wraps(func) def func_out(image, footprint=None, *args, **kwargs): if footprint is None: footprint = ndi.generate_binary_structure(image.ndim, 1) return func(image, footprint=footprint, *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. " f"Got {ar.dtype}.") def remove_small_objects(ar, min_size=64, connectivity=1, *, out=None): """Remove objects smaller than the specified size. Expects ar to be an array with labeled objects, and removes objects smaller than min_size. If `ar` is bool, the image is first labeled. This leads to potentially different behavior for bool and 0-and-1 arrays. Parameters ---------- ar : ndarray (arbitrary shape, int or bool type) The array containing the objects of interest. If the array type is int, the ints must be non-negative. min_size : int, optional (default: 64) The smallest allowable object size. connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1) The connectivity defining the neighborhood of a pixel. Used during labelling if `ar` is bool. out : ndarray Array of the same shape as `ar`, into which the output is placed. By default, a new array is created. 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]]) >>> 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]]) >>> d = morphology.remove_small_objects(a, 6, out=a) >>> d is a True """ # Raising type error if not int or bool _check_dtype_supported(ar) if out is None: out = ar.copy() else: out[:] = ar if min_size == 0: # shortcut for efficiency return out if out.dtype == bool: footprint = ndi.generate_binary_structure(ar.ndim, connectivity) ccs = np.zeros_like(ar, dtype=np.int32) ndi.label(ar, footprint, 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, *, out=None): """Remove contiguous 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. out : ndarray Array of the same shape as `ar` and bool dtype, into which the output is placed. By default, a new array is created. 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]]) >>> 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]]) >>> d = morphology.remove_small_holes(a, 2, out=a) >>> 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 out is not None: if out.dtype != bool: raise TypeError("out dtype must be bool") else: out = ar.astype(bool, copy=True) # Creating the inverse of ar np.logical_not(ar, out=out) # removing small objects from the inverse of ar out = remove_small_objects(out, area_threshold, connectivity, out=out) np.logical_not(out, out=out) return out