261 lines
8.5 KiB
Python
261 lines
8.5 KiB
Python
"""Utility functions used in the morphology subpackage."""
|
|
|
|
|
|
import numpy as np
|
|
from scipy import ndimage as ndi
|
|
|
|
|
|
def _validate_connectivity(image_dim, connectivity, offset):
|
|
"""Convert any valid connectivity to a structuring element and offset.
|
|
|
|
Parameters
|
|
----------
|
|
image_dim : int
|
|
The number of dimensions of the input image.
|
|
connectivity : int, array, or None
|
|
The neighborhood connectivity. An integer is interpreted as in
|
|
``scipy.ndimage.generate_binary_structure``, as the maximum number
|
|
of orthogonal steps to reach a neighbor. An array is directly
|
|
interpreted as a structuring element and its shape is validated against
|
|
the input image shape. ``None`` is interpreted as a connectivity of 1.
|
|
offset : tuple of int, or None
|
|
The coordinates of the center of the structuring element.
|
|
|
|
Returns
|
|
-------
|
|
c_connectivity : array of bool
|
|
The structuring element corresponding to the input `connectivity`.
|
|
offset : array of int
|
|
The offset corresponding to the center of the structuring element.
|
|
|
|
Raises
|
|
------
|
|
ValueError:
|
|
If the image dimension and the connectivity or offset dimensions don't
|
|
match.
|
|
"""
|
|
if connectivity is None:
|
|
connectivity = 1
|
|
|
|
if np.isscalar(connectivity):
|
|
c_connectivity = ndi.generate_binary_structure(image_dim, connectivity)
|
|
else:
|
|
c_connectivity = np.array(connectivity, bool)
|
|
if c_connectivity.ndim != image_dim:
|
|
raise ValueError("Connectivity dimension must be same as image")
|
|
|
|
if offset is None:
|
|
if any([x % 2 == 0 for x in c_connectivity.shape]):
|
|
raise ValueError("Connectivity array must have an unambiguous "
|
|
"center")
|
|
|
|
offset = np.array(c_connectivity.shape) // 2
|
|
|
|
return c_connectivity, offset
|
|
|
|
|
|
def _offsets_to_raveled_neighbors(image_shape, selem, center, order='C'):
|
|
"""Compute offsets to a samples neighbors if the image would be raveled.
|
|
|
|
Parameters
|
|
----------
|
|
image_shape : tuple
|
|
The shape of the image for which the offsets are computed.
|
|
selem : ndarray
|
|
A structuring element determining the neighborhood expressed as an
|
|
n-D array of 1's and 0's.
|
|
center : tuple
|
|
Tuple of indices to the center of `selem`.
|
|
order : {"C", "F"}, optional
|
|
Whether the image described by `image_shape` is in row-major (C-style)
|
|
or column-major (Fortran-style) order.
|
|
|
|
Returns
|
|
-------
|
|
raveled_offsets : ndarray
|
|
Linear offsets to a samples neighbors in the raveled image, sorted by
|
|
their distance from the center.
|
|
|
|
Notes
|
|
-----
|
|
This function will return values even if `image_shape` contains a dimension
|
|
length that is smaller than `selem`.
|
|
|
|
Examples
|
|
--------
|
|
>>> _offsets_to_raveled_neighbors((4, 5), np.ones((4, 3)), (1, 1))
|
|
array([-5, -1, 1, 5, -6, -4, 4, 6, 10, 9, 11])
|
|
>>> _offsets_to_raveled_neighbors((2, 3, 2), np.ones((3, 3, 3)), (1, 1, 1))
|
|
array([ 2, -6, 1, -1, 6, -2, 3, 8, -3, -4, 7, -5, -7, -8, 5, 4, -9,
|
|
9])
|
|
"""
|
|
if not selem.ndim == len(image_shape) == len(center):
|
|
raise ValueError(
|
|
"number of dimensions in image shape, structuring element and its"
|
|
"center index does not match"
|
|
)
|
|
|
|
selem_indices = np.array(np.nonzero(selem)).T
|
|
offsets = selem_indices - center
|
|
|
|
if order == 'F':
|
|
offsets = offsets[:, ::-1]
|
|
image_shape = image_shape[::-1]
|
|
elif order != 'C':
|
|
raise ValueError("order must be 'C' or 'F'")
|
|
|
|
# Scale offsets in each dimension and sum
|
|
ravel_factors = image_shape[1:] + (1,)
|
|
ravel_factors = np.cumprod(ravel_factors[::-1])[::-1]
|
|
raveled_offsets = (offsets * ravel_factors).sum(axis=1)
|
|
|
|
# Sort by distance
|
|
distances = np.abs(offsets).sum(axis=1)
|
|
raveled_offsets = raveled_offsets[np.argsort(distances)]
|
|
|
|
# If any dimension in image_shape is smaller than selem.shape
|
|
# duplicates might occur, remove them
|
|
if any(x < y for x, y in zip(image_shape, selem.shape)):
|
|
# np.unique reorders, which we don't want
|
|
_, indices = np.unique(raveled_offsets, return_index=True)
|
|
raveled_offsets = raveled_offsets[np.sort(indices)]
|
|
|
|
# Remove "offset to center"
|
|
raveled_offsets = raveled_offsets[1:]
|
|
|
|
return raveled_offsets
|
|
|
|
|
|
def _resolve_neighborhood(selem, connectivity, ndim):
|
|
"""Validate or create structuring element.
|
|
|
|
Depending on the values of `connectivity` and `selem` this function
|
|
either creates a new structuring element (`selem` is None) using
|
|
`connectivity` or validates the given structuring element (`selem` is not
|
|
None).
|
|
|
|
Parameters
|
|
----------
|
|
selem : ndarray
|
|
A structuring element used to determine the neighborhood of each
|
|
evaluated pixel (``True`` denotes a connected pixel). It must be a
|
|
boolean array and have the same number of dimensions as `image`. If
|
|
neither `selem` nor `connectivity` are given, all adjacent pixels are
|
|
considered as part of the neighborhood.
|
|
connectivity : int
|
|
A number used to determine the neighborhood of each evaluated pixel.
|
|
Adjacent pixels whose squared distance from the center is less than or
|
|
equal to `connectivity` are considered neighbors. Ignored if
|
|
`selem` is not None.
|
|
ndim : int
|
|
Number of dimensions `selem` ought to have.
|
|
|
|
Returns
|
|
-------
|
|
selem : ndarray
|
|
Validated or new structuring element specifying the neighborhood.
|
|
|
|
Examples
|
|
--------
|
|
>>> _resolve_neighborhood(None, 1, 2)
|
|
array([[False, True, False],
|
|
[ True, True, True],
|
|
[False, True, False]])
|
|
>>> _resolve_neighborhood(None, None, 3).shape
|
|
(3, 3, 3)
|
|
"""
|
|
if selem is None:
|
|
if connectivity is None:
|
|
connectivity = ndim
|
|
selem = ndi.generate_binary_structure(ndim, connectivity)
|
|
else:
|
|
# Validate custom structured element
|
|
selem = np.asarray(selem, dtype=np.bool)
|
|
# Must specify neighbors for all dimensions
|
|
if selem.ndim != ndim:
|
|
raise ValueError(
|
|
"number of dimensions in image and structuring element do not"
|
|
"match"
|
|
)
|
|
# Must only specify direct neighbors
|
|
if any(s != 3 for s in selem.shape):
|
|
raise ValueError("dimension size in structuring element is not 3")
|
|
|
|
return selem
|
|
|
|
|
|
def _set_border_values(image, value):
|
|
"""Set edge values along all axes to a constant value.
|
|
|
|
Parameters
|
|
----------
|
|
image : ndarray
|
|
The array to modify inplace.
|
|
value : scalar
|
|
The value to use. Should be compatible with `image`'s dtype.
|
|
|
|
Examples
|
|
--------
|
|
>>> image = np.zeros((4, 5), dtype=int)
|
|
>>> _set_border_values(image, 1)
|
|
>>> image
|
|
array([[1, 1, 1, 1, 1],
|
|
[1, 0, 0, 0, 1],
|
|
[1, 0, 0, 0, 1],
|
|
[1, 1, 1, 1, 1]])
|
|
"""
|
|
for axis in range(image.ndim):
|
|
# Index first and last element in each dimension
|
|
sl = (slice(None),) * axis + ((0, -1),) + (...,)
|
|
image[sl] = value
|
|
|
|
|
|
def _fast_pad(image, value, *, order="C"):
|
|
"""Pad an array on all axes by one with a value.
|
|
|
|
Parameters
|
|
----------
|
|
image : ndarray
|
|
Image to pad.
|
|
value : scalar
|
|
The value to use. Should be compatible with `image`'s dtype.
|
|
order : "C" or "F"
|
|
Specify the memory layout of the padded image (C or Fortran style).
|
|
|
|
Returns
|
|
-------
|
|
padded_image : ndarray
|
|
The new image.
|
|
|
|
Notes
|
|
-----
|
|
The output of this function is equivalent to::
|
|
|
|
np.pad(image, 1, mode="constant", constant_values=value)
|
|
|
|
Up to versions < 1.17 `numpy.pad` uses concatenation to create padded
|
|
arrays while this method needs to only allocate and copy once.
|
|
This can result in significant speed gains if `image` has a large number of
|
|
dimensions.
|
|
Thus this function may be safely removed once that version is the minimum
|
|
required by scikit-image.
|
|
|
|
Examples
|
|
--------
|
|
>>> _fast_pad(np.zeros((2, 3), dtype=int), 4)
|
|
array([[4, 4, 4, 4, 4],
|
|
[4, 0, 0, 0, 4],
|
|
[4, 0, 0, 0, 4],
|
|
[4, 4, 4, 4, 4]])
|
|
"""
|
|
# Allocate padded image
|
|
new_shape = np.array(image.shape) + 2
|
|
new_image = np.empty(new_shape, dtype=image.dtype, order=order)
|
|
|
|
# Copy old image into new space
|
|
sl = (slice(1, -1),) * image.ndim
|
|
new_image[sl] = image
|
|
# and set the edge values
|
|
_set_border_values(new_image, value)
|
|
|
|
return new_image
|