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| Current File : //proc/thread-self/root/usr/local/lib/python3.8/dist-packages/skimage/morphology/footprints.py |
import numpy as np
from scipy import ndimage as ndi
from .. import draw
from .._shared.utils import deprecate_kwarg
def square(width, dtype=np.uint8):
"""Generates a flat, square-shaped footprint.
Every pixel along the perimeter has a chessboard distance
no greater than radius (radius=floor(width/2)) pixels.
Parameters
----------
width : int
The width and height of the square.
Other Parameters
----------------
dtype : data-type
The data type of the footprint.
Returns
-------
footprint : ndarray
A footprint consisting only of ones, i.e. every pixel belongs to the
neighborhood.
"""
return np.ones((width, width), dtype=dtype)
@deprecate_kwarg({'height': 'ncols', 'width': 'nrows'},
deprecated_version='0.18.0',
removed_version='0.20.0')
def rectangle(nrows, ncols, dtype=np.uint8):
"""Generates a flat, rectangular-shaped footprint.
Every pixel in the rectangle generated for a given width and given height
belongs to the neighborhood.
Parameters
----------
nrows : int
The number of rows of the rectangle.
ncols : int
The number of columns of the rectangle.
Other Parameters
----------------
dtype : data-type
The data type of the footprint.
Returns
-------
footprint : ndarray
A footprint consisting only of ones, i.e. every pixel belongs to the
neighborhood.
Notes
-----
- The use of ``width`` and ``height`` has been deprecated in
version 0.18.0. Use ``nrows`` and ``ncols`` instead.
"""
return np.ones((nrows, ncols), dtype=dtype)
def diamond(radius, dtype=np.uint8):
"""Generates a flat, diamond-shaped footprint.
A pixel is part of the neighborhood (i.e. labeled 1) if
the city block/Manhattan distance between it and the center of
the neighborhood is no greater than radius.
Parameters
----------
radius : int
The radius of the diamond-shaped footprint.
Other Parameters
----------------
dtype : data-type
The data type of the footprint.
Returns
-------
footprint : ndarray
The footprint where elements of the neighborhood are 1 and 0 otherwise.
"""
L = np.arange(0, radius * 2 + 1)
I, J = np.meshgrid(L, L)
return np.array(np.abs(I - radius) + np.abs(J - radius) <= radius,
dtype=dtype)
def disk(radius, dtype=np.uint8):
"""Generates a flat, disk-shaped footprint.
A pixel is within the neighborhood if the Euclidean distance between
it and the origin is no greater than radius.
Parameters
----------
radius : int
The radius of the disk-shaped footprint.
Other Parameters
----------------
dtype : data-type
The data type of the footprint.
Returns
-------
footprint : ndarray
The footprint where elements of the neighborhood are 1 and 0 otherwise.
"""
L = np.arange(-radius, radius + 1)
X, Y = np.meshgrid(L, L)
return np.array((X ** 2 + Y ** 2) <= radius ** 2, dtype=dtype)
def ellipse(width, height, dtype=np.uint8):
"""Generates a flat, ellipse-shaped footprint.
Every pixel along the perimeter of ellipse satisfies
the equation ``(x/width+1)**2 + (y/height+1)**2 = 1``.
Parameters
----------
width : int
The width of the ellipse-shaped footprint.
height : int
The height of the ellipse-shaped footprint.
Other Parameters
----------------
dtype : data-type
The data type of the footprint.
Returns
-------
footprint : ndarray
The footprint where elements of the neighborhood are 1 and 0 otherwise.
Examples
--------
>>> from skimage.morphology import footprints
>>> footprints.ellipse(5, 3)
array([[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]], dtype=uint8)
"""
footprint = np.zeros((2 * height + 1, 2 * width + 1), dtype=dtype)
rows, cols = draw.ellipse(height, width, height + 1, width + 1)
footprint[rows, cols] = 1
return footprint
def cube(width, dtype=np.uint8):
""" Generates a cube-shaped footprint.
This is the 3D equivalent of a square.
Every pixel along the perimeter has a chessboard distance
no greater than radius (radius=floor(width/2)) pixels.
Parameters
----------
width : int
The width, height and depth of the cube.
Other Parameters
----------------
dtype : data-type
The data type of the footprint.
Returns
-------
footprint : ndarray
A footprint consisting only of ones, i.e. every pixel belongs to the
neighborhood.
"""
return np.ones((width, width, width), dtype=dtype)
def octahedron(radius, dtype=np.uint8):
"""Generates a octahedron-shaped footprint.
This is the 3D equivalent of a diamond.
A pixel is part of the neighborhood (i.e. labeled 1) if
the city block/Manhattan distance between it and the center of
the neighborhood is no greater than radius.
Parameters
----------
radius : int
The radius of the octahedron-shaped footprint.
Other Parameters
----------------
dtype : data-type
The data type of the footprint.
Returns
-------
footprint : ndarray
The footprint where elements of the neighborhood are 1 and 0 otherwise.
"""
# note that in contrast to diamond(), this method allows non-integer radii
n = 2 * radius + 1
Z, Y, X = np.mgrid[-radius:radius:n * 1j,
-radius:radius:n * 1j,
-radius:radius:n * 1j]
s = np.abs(X) + np.abs(Y) + np.abs(Z)
return np.array(s <= radius, dtype=dtype)
def ball(radius, dtype=np.uint8):
"""Generates a ball-shaped footprint.
This is the 3D equivalent of a disk.
A pixel is within the neighborhood if the Euclidean distance between
it and the origin is no greater than radius.
Parameters
----------
radius : int
The radius of the ball-shaped footprint.
Other Parameters
----------------
dtype : data-type
The data type of the footprint.
Returns
-------
footprint : ndarray
The footprint where elements of the neighborhood are 1 and 0 otherwise.
"""
n = 2 * radius + 1
Z, Y, X = np.mgrid[-radius:radius:n * 1j,
-radius:radius:n * 1j,
-radius:radius:n * 1j]
s = X ** 2 + Y ** 2 + Z ** 2
return np.array(s <= radius * radius, dtype=dtype)
def octagon(m, n, dtype=np.uint8):
"""Generates an octagon shaped footprint.
For a given size of (m) horizontal and vertical sides
and a given (n) height or width of slanted sides octagon is generated.
The slanted sides are 45 or 135 degrees to the horizontal axis
and hence the widths and heights are equal.
Parameters
----------
m : int
The size of the horizontal and vertical sides.
n : int
The height or width of the slanted sides.
Other Parameters
----------------
dtype : data-type
The data type of the footprint.
Returns
-------
footprint : ndarray
The footprint where elements of the neighborhood are 1 and 0 otherwise.
"""
from . import convex_hull_image
footprint = np.zeros((m + 2 * n, m + 2 * n))
footprint[0, n] = 1
footprint[n, 0] = 1
footprint[0, m + n - 1] = 1
footprint[m + n - 1, 0] = 1
footprint[-1, n] = 1
footprint[n, -1] = 1
footprint[-1, m + n - 1] = 1
footprint[m + n - 1, -1] = 1
footprint = convex_hull_image(footprint).astype(dtype)
return footprint
def star(a, dtype=np.uint8):
"""Generates a star shaped footprint.
Start has 8 vertices and is an overlap of square of size `2*a + 1`
with its 45 degree rotated version.
The slanted sides are 45 or 135 degrees to the horizontal axis.
Parameters
----------
a : int
Parameter deciding the size of the star structural element. The side
of the square array returned is `2*a + 1 + 2*floor(a / 2)`.
Other Parameters
----------------
dtype : data-type
The data type of the footprint.
Returns
-------
footprint : ndarray
The footprint where elements of the neighborhood are 1 and 0 otherwise.
"""
from . import convex_hull_image
if a == 1:
bfilter = np.zeros((3, 3), dtype)
bfilter[:] = 1
return bfilter
m = 2 * a + 1
n = a // 2
footprint_square = np.zeros((m + 2 * n, m + 2 * n))
footprint_square[n: m + n, n: m + n] = 1
c = (m + 2 * n - 1) // 2
footprint_rotated = np.zeros((m + 2 * n, m + 2 * n))
footprint_rotated[0, c] = footprint_rotated[-1, c] = 1
footprint_rotated[c, 0] = footprint_rotated[c, -1] = 1
footprint_rotated = convex_hull_image(footprint_rotated).astype(int)
footprint = footprint_square + footprint_rotated
footprint[footprint > 0] = 1
return footprint.astype(dtype)
def _default_footprint(ndim):
"""Generates a cross-shaped footprint (connectivity=1).
This is the default footprint (footprint) if no footprint was
specified.
Parameters
----------
ndim : int
Number of dimensions of the image.
Returns
-------
footprint : ndarray
The footprint where elements of the neighborhood are 1 and 0 otherwise.
"""
return ndi.generate_binary_structure(ndim, 1)