704 lines
25 KiB
Python
704 lines
25 KiB
Python
"""
|
|
Grayscale morphological operations
|
|
"""
|
|
|
|
import warnings
|
|
|
|
import numpy as np
|
|
from scipy import ndimage as ndi
|
|
|
|
from .footprints import _footprint_is_sequence, mirror_footprint, pad_footprint
|
|
from .misc import default_footprint
|
|
from .._shared.utils import DEPRECATED
|
|
|
|
|
|
__all__ = ['erosion', 'dilation', 'opening', 'closing', 'white_tophat', 'black_tophat']
|
|
|
|
|
|
def _iterate_gray_func(gray_func, image, footprints, out, mode, cval):
|
|
"""Helper to call `gray_func` for each footprint in a sequence.
|
|
|
|
`gray_func` is a morphology function that accepts `footprint`, `output`,
|
|
`mode` and `cval` keyword arguments (e.g. `scipy.ndimage.grey_erosion`).
|
|
"""
|
|
fp, num_iter = footprints[0]
|
|
gray_func(image, footprint=fp, output=out, mode=mode, cval=cval)
|
|
for _ in range(1, num_iter):
|
|
gray_func(out.copy(), footprint=fp, output=out, mode=mode, cval=cval)
|
|
for fp, num_iter in footprints[1:]:
|
|
# Note: out.copy() because the computation cannot be in-place!
|
|
for _ in range(num_iter):
|
|
gray_func(out.copy(), footprint=fp, output=out, mode=mode, cval=cval)
|
|
return out
|
|
|
|
|
|
def _shift_footprint(footprint, shift_x, shift_y):
|
|
"""Shift the binary image `footprint` in the left and/or up.
|
|
|
|
This only affects 2D footprints with even number of rows
|
|
or columns.
|
|
|
|
Parameters
|
|
----------
|
|
footprint : 2D array, shape (M, N)
|
|
The input footprint.
|
|
shift_x, shift_y : bool or None
|
|
Whether to move `footprint` along each axis. If ``None``, the
|
|
array is not modified along that dimension.
|
|
|
|
Returns
|
|
-------
|
|
out : 2D array, shape (M + int(shift_x), N + int(shift_y))
|
|
The shifted footprint.
|
|
"""
|
|
footprint = np.asarray(footprint)
|
|
if footprint.ndim != 2:
|
|
# do nothing for 1D or 3D or higher footprints
|
|
return footprint
|
|
m, n = footprint.shape
|
|
if m % 2 == 0:
|
|
extra_row = np.zeros((1, n), footprint.dtype)
|
|
if shift_x:
|
|
footprint = np.vstack((footprint, extra_row))
|
|
else:
|
|
footprint = np.vstack((extra_row, footprint))
|
|
m += 1
|
|
if n % 2 == 0:
|
|
extra_col = np.zeros((m, 1), footprint.dtype)
|
|
if shift_y:
|
|
footprint = np.hstack((footprint, extra_col))
|
|
else:
|
|
footprint = np.hstack((extra_col, footprint))
|
|
return footprint
|
|
|
|
|
|
def _shift_footprints(footprint, shift_x, shift_y):
|
|
"""Shifts the footprints, whether it's a single array or a sequence.
|
|
|
|
See `_shift_footprint`, which is called for each array in the sequence.
|
|
"""
|
|
if shift_x is DEPRECATED and shift_y is DEPRECATED:
|
|
return footprint
|
|
|
|
warning_msg = (
|
|
"The parameters `shift_x` and `shift_y` are deprecated since v0.23 and "
|
|
"will be removed in v0.26. Use `pad_footprint` or modify the footprint"
|
|
"manually instead."
|
|
)
|
|
warnings.warn(warning_msg, FutureWarning, stacklevel=4)
|
|
|
|
if _footprint_is_sequence(footprint):
|
|
return tuple((_shift_footprint(fp, shift_x, shift_y), n) for fp, n in footprint)
|
|
return _shift_footprint(footprint, shift_x, shift_y)
|
|
|
|
|
|
def _min_max_to_constant_mode(dtype, mode, cval):
|
|
"""Replace 'max' and 'min' with appropriate 'cval' and 'constant' mode."""
|
|
if mode == "max":
|
|
mode = "constant"
|
|
if np.issubdtype(dtype, bool):
|
|
cval = True
|
|
elif np.issubdtype(dtype, np.integer):
|
|
cval = np.iinfo(dtype).max
|
|
else:
|
|
cval = np.inf
|
|
elif mode == "min":
|
|
mode = "constant"
|
|
if np.issubdtype(dtype, bool):
|
|
cval = False
|
|
elif np.issubdtype(dtype, np.integer):
|
|
cval = np.iinfo(dtype).min
|
|
else:
|
|
cval = -np.inf
|
|
return mode, cval
|
|
|
|
|
|
_SUPPORTED_MODES = {
|
|
"reflect",
|
|
"constant",
|
|
"nearest",
|
|
"mirror",
|
|
"wrap",
|
|
"max",
|
|
"min",
|
|
"ignore",
|
|
}
|
|
|
|
|
|
@default_footprint
|
|
def erosion(
|
|
image,
|
|
footprint=None,
|
|
out=None,
|
|
shift_x=DEPRECATED,
|
|
shift_y=DEPRECATED,
|
|
*,
|
|
mode="reflect",
|
|
cval=0.0,
|
|
):
|
|
"""Return grayscale morphological erosion of an image.
|
|
|
|
Morphological erosion sets a pixel at (i,j) to the minimum over all pixels
|
|
in the neighborhood centered at (i,j). Erosion shrinks bright regions and
|
|
enlarges dark regions.
|
|
|
|
Parameters
|
|
----------
|
|
image : ndarray
|
|
Image array.
|
|
footprint : ndarray or tuple, optional
|
|
The neighborhood expressed as a 2-D array of 1's and 0's.
|
|
If None, use a cross-shaped footprint (connectivity=1). The footprint
|
|
can also be provided as a sequence of smaller footprints as described
|
|
in the notes below.
|
|
out : ndarrays, optional
|
|
The array to store the result of the morphology. If None is
|
|
passed, a new array will be allocated.
|
|
mode : str, optional
|
|
The `mode` parameter determines how the array borders are handled.
|
|
Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap',
|
|
'max', 'min', or 'ignore'.
|
|
If 'max' or 'ignore', pixels outside the image domain are assumed
|
|
to be the maximum for the image's dtype, which causes them to not
|
|
influence the result. Default is 'reflect'.
|
|
cval : scalar, optional
|
|
Value to fill past edges of input if `mode` is 'constant'. Default
|
|
is 0.0.
|
|
|
|
.. versionadded:: 0.23
|
|
`mode` and `cval` were added in 0.23.
|
|
|
|
Returns
|
|
-------
|
|
eroded : array, same shape as `image`
|
|
The result of the morphological erosion.
|
|
|
|
Other Parameters
|
|
----------------
|
|
shift_x, shift_y : DEPRECATED
|
|
|
|
.. deprecated:: 0.23
|
|
|
|
Notes
|
|
-----
|
|
For ``uint8`` (and ``uint16`` up to a certain bit-depth) data, the
|
|
lower algorithm complexity makes the :func:`skimage.filters.rank.minimum`
|
|
function more efficient for larger images and footprints.
|
|
|
|
The footprint can also be a provided as a sequence of 2-tuples where the
|
|
first element of each 2-tuple is a footprint ndarray and the second element
|
|
is an integer describing the number of times it should be iterated. For
|
|
example ``footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]``
|
|
would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net
|
|
effect that is the same as ``footprint=np.ones((9, 9))``, but with lower
|
|
computational cost. Most of the builtin footprints such as
|
|
:func:`skimage.morphology.disk` provide an option to automatically generate
|
|
a footprint sequence of this type.
|
|
|
|
For even-sized footprints, :func:`skimage.morphology.binary_erosion` and
|
|
this function produce an output that differs: one is shifted by one pixel
|
|
compared to the other.
|
|
|
|
Examples
|
|
--------
|
|
>>> # Erosion shrinks bright regions
|
|
>>> import numpy as np
|
|
>>> from skimage.morphology import footprint_rectangle
|
|
>>> bright_square = np.array([[0, 0, 0, 0, 0],
|
|
... [0, 1, 1, 1, 0],
|
|
... [0, 1, 1, 1, 0],
|
|
... [0, 1, 1, 1, 0],
|
|
... [0, 0, 0, 0, 0]], dtype=np.uint8)
|
|
>>> erosion(bright_square, footprint_rectangle((3, 3)))
|
|
array([[0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0],
|
|
[0, 0, 1, 0, 0],
|
|
[0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0]], dtype=uint8)
|
|
|
|
"""
|
|
if out is None:
|
|
out = np.empty_like(image)
|
|
|
|
if mode not in _SUPPORTED_MODES:
|
|
raise ValueError(f"unsupported mode, got {mode!r}")
|
|
if mode == "ignore":
|
|
mode = "max"
|
|
mode, cval = _min_max_to_constant_mode(image.dtype, mode, cval)
|
|
|
|
footprint = _shift_footprints(footprint, shift_x, shift_y)
|
|
footprint = pad_footprint(footprint, pad_end=False)
|
|
if not _footprint_is_sequence(footprint):
|
|
footprint = [(footprint, 1)]
|
|
|
|
out = _iterate_gray_func(
|
|
gray_func=ndi.grey_erosion,
|
|
image=image,
|
|
footprints=footprint,
|
|
out=out,
|
|
mode=mode,
|
|
cval=cval,
|
|
)
|
|
return out
|
|
|
|
|
|
@default_footprint
|
|
def dilation(
|
|
image,
|
|
footprint=None,
|
|
out=None,
|
|
shift_x=DEPRECATED,
|
|
shift_y=DEPRECATED,
|
|
*,
|
|
mode="reflect",
|
|
cval=0.0,
|
|
):
|
|
"""Return grayscale morphological dilation of an image.
|
|
|
|
Morphological dilation sets the value of a pixel to the maximum over all
|
|
pixel values within a local neighborhood centered about it. The values
|
|
where the footprint is 1 define this neighborhood.
|
|
Dilation enlarges bright regions and shrinks dark regions.
|
|
|
|
Parameters
|
|
----------
|
|
image : ndarray
|
|
Image array.
|
|
footprint : ndarray or tuple, optional
|
|
The neighborhood expressed as a 2-D array of 1's and 0's.
|
|
If None, use a cross-shaped footprint (connectivity=1). The footprint
|
|
can also be provided as a sequence of smaller footprints as described
|
|
in the notes below.
|
|
out : ndarray, optional
|
|
The array to store the result of the morphology. If None is
|
|
passed, a new array will be allocated.
|
|
mode : str, optional
|
|
The `mode` parameter determines how the array borders are handled.
|
|
Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap',
|
|
'max', 'min', or 'ignore'.
|
|
If 'min' or 'ignore', pixels outside the image domain are assumed
|
|
to be the maximum for the image's dtype, which causes them to not
|
|
influence the result. Default is 'reflect'.
|
|
cval : scalar, optional
|
|
Value to fill past edges of input if `mode` is 'constant'. Default
|
|
is 0.0.
|
|
|
|
.. versionadded:: 0.23
|
|
`mode` and `cval` were added in 0.23.
|
|
|
|
Returns
|
|
-------
|
|
dilated : uint8 array, same shape and type as `image`
|
|
The result of the morphological dilation.
|
|
|
|
Other Parameters
|
|
----------------
|
|
shift_x, shift_y : DEPRECATED
|
|
|
|
.. deprecated:: 0.23
|
|
|
|
Notes
|
|
-----
|
|
For ``uint8`` (and ``uint16`` up to a certain bit-depth) data, the lower
|
|
algorithm complexity makes the :func:`skimage.filters.rank.maximum`
|
|
function more efficient for larger images and footprints.
|
|
|
|
The footprint can also be a provided as a sequence of 2-tuples where the
|
|
first element of each 2-tuple is a footprint ndarray and the second element
|
|
is an integer describing the number of times it should be iterated. For
|
|
example ``footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]``
|
|
would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net
|
|
effect that is the same as ``footprint=np.ones((9, 9))``, but with lower
|
|
computational cost. Most of the builtin footprints such as
|
|
:func:`skimage.morphology.disk` provide an option to automatically generate
|
|
a footprint sequence of this type.
|
|
|
|
For non-symmetric footprints, :func:`skimage.morphology.binary_dilation`
|
|
and :func:`skimage.morphology.dilation` produce an output that differs:
|
|
`binary_dilation` mirrors the footprint, whereas `dilation` does not.
|
|
|
|
Examples
|
|
--------
|
|
>>> # Dilation enlarges bright regions
|
|
>>> import numpy as np
|
|
>>> from skimage.morphology import footprint_rectangle
|
|
>>> bright_pixel = np.array([[0, 0, 0, 0, 0],
|
|
... [0, 0, 0, 0, 0],
|
|
... [0, 0, 1, 0, 0],
|
|
... [0, 0, 0, 0, 0],
|
|
... [0, 0, 0, 0, 0]], dtype=np.uint8)
|
|
>>> dilation(bright_pixel, footprint_rectangle((3, 3)))
|
|
array([[0, 0, 0, 0, 0],
|
|
[0, 1, 1, 1, 0],
|
|
[0, 1, 1, 1, 0],
|
|
[0, 1, 1, 1, 0],
|
|
[0, 0, 0, 0, 0]], dtype=uint8)
|
|
|
|
"""
|
|
if out is None:
|
|
out = np.empty_like(image)
|
|
|
|
if mode not in _SUPPORTED_MODES:
|
|
raise ValueError(f"unsupported mode, got {mode!r}")
|
|
if mode == "ignore":
|
|
mode = "min"
|
|
mode, cval = _min_max_to_constant_mode(image.dtype, mode, cval)
|
|
|
|
footprint = _shift_footprints(footprint, shift_x, shift_y)
|
|
footprint = pad_footprint(footprint, pad_end=False)
|
|
# Note that `ndi.grey_dilation` mirrors the footprint and this
|
|
# additional inversion should be removed in skimage2, see gh-6676.
|
|
footprint = mirror_footprint(footprint)
|
|
if not _footprint_is_sequence(footprint):
|
|
footprint = [(footprint, 1)]
|
|
|
|
out = _iterate_gray_func(
|
|
gray_func=ndi.grey_dilation,
|
|
image=image,
|
|
footprints=footprint,
|
|
out=out,
|
|
mode=mode,
|
|
cval=cval,
|
|
)
|
|
return out
|
|
|
|
|
|
@default_footprint
|
|
def opening(image, footprint=None, out=None, *, mode="reflect", cval=0.0):
|
|
"""Return grayscale morphological opening of an image.
|
|
|
|
The morphological opening of an image is defined as an erosion followed by
|
|
a dilation. Opening can remove small bright spots (i.e. "salt") and connect
|
|
small dark cracks. This tends to "open" up (dark) gaps between (bright)
|
|
features.
|
|
|
|
Parameters
|
|
----------
|
|
image : ndarray
|
|
Image array.
|
|
footprint : ndarray or tuple, optional
|
|
The neighborhood expressed as a 2-D array of 1's and 0's.
|
|
If None, use a cross-shaped footprint (connectivity=1). The footprint
|
|
can also be provided as a sequence of smaller footprints as described
|
|
in the notes below.
|
|
out : ndarray, optional
|
|
The array to store the result of the morphology. If None
|
|
is passed, a new array will be allocated.
|
|
mode : str, optional
|
|
The `mode` parameter determines how the array borders are handled.
|
|
Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap',
|
|
'max', 'min', or 'ignore'.
|
|
If 'ignore', pixels outside the image domain are assumed
|
|
to be the maximum for the image's dtype in the erosion, and minimum
|
|
in the dilation, which causes them to not influence the result.
|
|
Default is 'reflect'.
|
|
cval : scalar, optional
|
|
Value to fill past edges of input if `mode` is 'constant'. Default
|
|
is 0.0.
|
|
|
|
.. versionadded:: 0.23
|
|
`mode` and `cval` were added in 0.23.
|
|
|
|
Returns
|
|
-------
|
|
opening : array, same shape and type as `image`
|
|
The result of the morphological opening.
|
|
|
|
Notes
|
|
-----
|
|
The footprint can also be a provided as a sequence of 2-tuples where the
|
|
first element of each 2-tuple is a footprint ndarray and the second element
|
|
is an integer describing the number of times it should be iterated. For
|
|
example ``footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]``
|
|
would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net
|
|
effect that is the same as ``footprint=np.ones((9, 9))``, but with lower
|
|
computational cost. Most of the builtin footprints such as
|
|
:func:`skimage.morphology.disk` provide an option to automatically generate
|
|
a footprint sequence of this type.
|
|
|
|
Examples
|
|
--------
|
|
>>> # Open up gap between two bright regions (but also shrink regions)
|
|
>>> import numpy as np
|
|
>>> from skimage.morphology import footprint_rectangle
|
|
>>> bad_connection = np.array([[1, 0, 0, 0, 1],
|
|
... [1, 1, 0, 1, 1],
|
|
... [1, 1, 1, 1, 1],
|
|
... [1, 1, 0, 1, 1],
|
|
... [1, 0, 0, 0, 1]], dtype=np.uint8)
|
|
>>> opening(bad_connection, footprint_rectangle((3, 3)))
|
|
array([[0, 0, 0, 0, 0],
|
|
[1, 1, 0, 1, 1],
|
|
[1, 1, 0, 1, 1],
|
|
[1, 1, 0, 1, 1],
|
|
[0, 0, 0, 0, 0]], dtype=uint8)
|
|
|
|
"""
|
|
footprint = pad_footprint(footprint, pad_end=False)
|
|
eroded = erosion(image, footprint, mode=mode, cval=cval)
|
|
out = dilation(eroded, mirror_footprint(footprint), out=out, mode=mode, cval=cval)
|
|
return out
|
|
|
|
|
|
@default_footprint
|
|
def closing(image, footprint=None, out=None, *, mode="reflect", cval=0.0):
|
|
"""Return grayscale morphological closing of an image.
|
|
|
|
The morphological closing of an image is defined as a dilation followed by
|
|
an erosion. Closing can remove small dark spots (i.e. "pepper") and connect
|
|
small bright cracks. This tends to "close" up (dark) gaps between (bright)
|
|
features.
|
|
|
|
Parameters
|
|
----------
|
|
image : ndarray
|
|
Image array.
|
|
footprint : ndarray or tuple, optional
|
|
The neighborhood expressed as a 2-D array of 1's and 0's.
|
|
If None, use a cross-shaped footprint (connectivity=1). The footprint
|
|
can also be provided as a sequence of smaller footprints as described
|
|
in the notes below.
|
|
out : ndarray, optional
|
|
The array to store the result of the morphology. If None,
|
|
a new array will be allocated.
|
|
mode : str, optional
|
|
The `mode` parameter determines how the array borders are handled.
|
|
Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap',
|
|
'max', 'min', or 'ignore'.
|
|
If 'ignore', pixels outside the image domain are assumed
|
|
to be the maximum for the image's dtype in the erosion, and minimum
|
|
in the dilation, which causes them to not influence the result.
|
|
Default is 'reflect'.
|
|
cval : scalar, optional
|
|
Value to fill past edges of input if `mode` is 'constant'. Default
|
|
is 0.0.
|
|
|
|
.. versionadded:: 0.23
|
|
`mode` and `cval` were added in 0.23.
|
|
|
|
Returns
|
|
-------
|
|
closing : array, same shape and type as `image`
|
|
The result of the morphological closing.
|
|
|
|
Notes
|
|
-----
|
|
The footprint can also be a provided as a sequence of 2-tuples where the
|
|
first element of each 2-tuple is a footprint ndarray and the second element
|
|
is an integer describing the number of times it should be iterated. For
|
|
example ``footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]``
|
|
would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net
|
|
effect that is the same as ``footprint=np.ones((9, 9))``, but with lower
|
|
computational cost. Most of the builtin footprints such as
|
|
:func:`skimage.morphology.disk` provide an option to automatically generate
|
|
a footprint sequence of this type.
|
|
|
|
Examples
|
|
--------
|
|
>>> # Close a gap between two bright lines
|
|
>>> import numpy as np
|
|
>>> from skimage.morphology import footprint_rectangle
|
|
>>> broken_line = np.array([[0, 0, 0, 0, 0],
|
|
... [0, 0, 0, 0, 0],
|
|
... [1, 1, 0, 1, 1],
|
|
... [0, 0, 0, 0, 0],
|
|
... [0, 0, 0, 0, 0]], dtype=np.uint8)
|
|
>>> closing(broken_line, footprint_rectangle((3, 3)))
|
|
array([[0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0],
|
|
[1, 1, 1, 1, 1],
|
|
[0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0]], dtype=uint8)
|
|
|
|
"""
|
|
footprint = pad_footprint(footprint, pad_end=False)
|
|
dilated = dilation(image, footprint, mode=mode, cval=cval)
|
|
out = erosion(dilated, mirror_footprint(footprint), out=out, mode=mode, cval=cval)
|
|
return out
|
|
|
|
|
|
@default_footprint
|
|
def white_tophat(image, footprint=None, out=None, *, mode="reflect", cval=0.0):
|
|
"""Return white top hat of an image.
|
|
|
|
The white top hat of an image is defined as the image minus its
|
|
morphological opening. This operation returns the bright spots of the image
|
|
that are smaller than the footprint.
|
|
|
|
Parameters
|
|
----------
|
|
image : ndarray
|
|
Image array.
|
|
footprint : ndarray or tuple, optional
|
|
The neighborhood expressed as a 2-D array of 1's and 0's.
|
|
If None, use a cross-shaped footprint (connectivity=1). The footprint
|
|
can also be provided as a sequence of smaller footprints as described
|
|
in the notes below.
|
|
out : ndarray, optional
|
|
The array to store the result of the morphology. If None
|
|
is passed, a new array will be allocated.
|
|
mode : str, optional
|
|
The `mode` parameter determines how the array borders are handled.
|
|
Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap',
|
|
'max', 'min', or 'ignore'. See :func:`skimage.morphology.opening`.
|
|
Default is 'reflect'.
|
|
cval : scalar, optional
|
|
Value to fill past edges of input if `mode` is 'constant'. Default
|
|
is 0.0.
|
|
|
|
.. versionadded:: 0.23
|
|
`mode` and `cval` were added in 0.23.
|
|
|
|
Returns
|
|
-------
|
|
out : array, same shape and type as `image`
|
|
The result of the morphological white top hat.
|
|
|
|
Notes
|
|
-----
|
|
The footprint can also be a provided as a sequence of 2-tuples where the
|
|
first element of each 2-tuple is a footprint ndarray and the second element
|
|
is an integer describing the number of times it should be iterated. For
|
|
example ``footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]``
|
|
would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net
|
|
effect that is the same as ``footprint=np.ones((9, 9))``, but with lower
|
|
computational cost. Most of the builtin footprints such as
|
|
:func:`skimage.morphology.disk` provide an option to automatically generate
|
|
a footprint sequence of this type.
|
|
|
|
See Also
|
|
--------
|
|
black_tophat
|
|
|
|
References
|
|
----------
|
|
.. [1] https://en.wikipedia.org/wiki/Top-hat_transform
|
|
|
|
Examples
|
|
--------
|
|
>>> # Subtract gray background from bright peak
|
|
>>> import numpy as np
|
|
>>> from skimage.morphology import footprint_rectangle
|
|
>>> bright_on_gray = np.array([[2, 3, 3, 3, 2],
|
|
... [3, 4, 5, 4, 3],
|
|
... [3, 5, 9, 5, 3],
|
|
... [3, 4, 5, 4, 3],
|
|
... [2, 3, 3, 3, 2]], dtype=np.uint8)
|
|
>>> white_tophat(bright_on_gray, footprint_rectangle((3, 3)))
|
|
array([[0, 0, 0, 0, 0],
|
|
[0, 0, 1, 0, 0],
|
|
[0, 1, 5, 1, 0],
|
|
[0, 0, 1, 0, 0],
|
|
[0, 0, 0, 0, 0]], dtype=uint8)
|
|
|
|
"""
|
|
if out is image:
|
|
# We need a temporary image
|
|
opened = opening(image, footprint, mode=mode, cval=cval)
|
|
if np.issubdtype(opened.dtype, bool):
|
|
np.logical_xor(out, opened, out=out)
|
|
else:
|
|
out -= opened
|
|
return out
|
|
|
|
# Else write intermediate result into output image
|
|
out = opening(image, footprint, out=out, mode=mode, cval=cval)
|
|
if np.issubdtype(out.dtype, bool):
|
|
np.logical_xor(image, out, out=out)
|
|
else:
|
|
np.subtract(image, out, out=out)
|
|
return out
|
|
|
|
|
|
@default_footprint
|
|
def black_tophat(image, footprint=None, out=None, *, mode="reflect", cval=0.0):
|
|
"""Return black top hat of an image.
|
|
|
|
The black top hat of an image is defined as its morphological closing minus
|
|
the original image. This operation returns the dark spots of the image that
|
|
are smaller than the footprint. Note that dark spots in the
|
|
original image are bright spots after the black top hat.
|
|
|
|
Parameters
|
|
----------
|
|
image : ndarray
|
|
Image array.
|
|
footprint : ndarray or tuple, optional
|
|
The neighborhood expressed as a 2-D array of 1's and 0's.
|
|
If None, use a cross-shaped footprint (connectivity=1). The footprint
|
|
can also be provided as a sequence of smaller footprints as described
|
|
in the notes below.
|
|
out : ndarray, optional
|
|
The array to store the result of the morphology. If None
|
|
is passed, a new array will be allocated.
|
|
mode : str, optional
|
|
The `mode` parameter determines how the array borders are handled.
|
|
Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap',
|
|
'max', 'min', or 'ignore'. See :func:`skimage.morphology.closing`.
|
|
Default is 'reflect'.
|
|
cval : scalar, optional
|
|
Value to fill past edges of input if `mode` is 'constant'. Default
|
|
is 0.0.
|
|
|
|
.. versionadded:: 0.23
|
|
`mode` and `cval` were added in 0.23.
|
|
|
|
Returns
|
|
-------
|
|
out : array, same shape and type as `image`
|
|
The result of the morphological black top hat.
|
|
|
|
Notes
|
|
-----
|
|
The footprint can also be a provided as a sequence of 2-tuples where the
|
|
first element of each 2-tuple is a footprint ndarray and the second element
|
|
is an integer describing the number of times it should be iterated. For
|
|
example ``footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]``
|
|
would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net
|
|
effect that is the same as ``footprint=np.ones((9, 9))``, but with lower
|
|
computational cost. Most of the builtin footprints such as
|
|
:func:`skimage.morphology.disk` provide an option to automatically generate
|
|
a footprint sequence of this type.
|
|
|
|
See Also
|
|
--------
|
|
white_tophat
|
|
|
|
References
|
|
----------
|
|
.. [1] https://en.wikipedia.org/wiki/Top-hat_transform
|
|
|
|
Examples
|
|
--------
|
|
>>> # Change dark peak to bright peak and subtract background
|
|
>>> import numpy as np
|
|
>>> from skimage.morphology import footprint_rectangle
|
|
>>> dark_on_gray = np.array([[7, 6, 6, 6, 7],
|
|
... [6, 5, 4, 5, 6],
|
|
... [6, 4, 0, 4, 6],
|
|
... [6, 5, 4, 5, 6],
|
|
... [7, 6, 6, 6, 7]], dtype=np.uint8)
|
|
>>> black_tophat(dark_on_gray, footprint_rectangle((3, 3)))
|
|
array([[0, 0, 0, 0, 0],
|
|
[0, 0, 1, 0, 0],
|
|
[0, 1, 5, 1, 0],
|
|
[0, 0, 1, 0, 0],
|
|
[0, 0, 0, 0, 0]], dtype=uint8)
|
|
|
|
"""
|
|
if out is image:
|
|
# We need a temporary image
|
|
closed = closing(image, footprint, mode=mode, cval=cval)
|
|
if np.issubdtype(closed.dtype, bool):
|
|
np.logical_xor(closed, out, out=out)
|
|
else:
|
|
np.subtract(closed, out, out=out)
|
|
return out
|
|
|
|
out = closing(image, footprint, out=out, mode=mode, cval=cval)
|
|
if np.issubdtype(out.dtype, np.bool_):
|
|
np.logical_xor(out, image, out=out)
|
|
else:
|
|
out -= image
|
|
return out
|