Vehicle-Anti-Theft-Face-Rec.../venv/Lib/site-packages/skimage/morphology/grey.py

489 lines
16 KiB
Python

"""
Grayscale morphological operations
"""
import functools
import numpy as np
from scipy import ndimage as ndi
from .misc import default_selem
from ..util import crop
__all__ = ['erosion', 'dilation', 'opening', 'closing', 'white_tophat',
'black_tophat']
def _shift_selem(selem, shift_x, shift_y):
"""Shift the binary image `selem` in the left and/or up.
This only affects 2D structuring elements with even number of rows
or columns.
Parameters
----------
selem : 2D array, shape (M, N)
The input structuring element.
shift_x, shift_y : bool
Whether to move `selem` along each axis.
Returns
-------
out : 2D array, shape (M + int(shift_x), N + int(shift_y))
The shifted structuring element.
"""
if selem.ndim != 2:
# do nothing for 1D or 3D or higher structuring elements
return selem
m, n = selem.shape
if m % 2 == 0:
extra_row = np.zeros((1, n), selem.dtype)
if shift_x:
selem = np.vstack((selem, extra_row))
else:
selem = np.vstack((extra_row, selem))
m += 1
if n % 2 == 0:
extra_col = np.zeros((m, 1), selem.dtype)
if shift_y:
selem = np.hstack((selem, extra_col))
else:
selem = np.hstack((extra_col, selem))
return selem
def _invert_selem(selem):
"""Change the order of the values in `selem`.
This is a patch for the *weird* footprint inversion in
`ndi.grey_morphology` [1]_.
Parameters
----------
selem : array
The input structuring element.
Returns
-------
inverted : array, same shape and type as `selem`
The structuring element, in opposite order.
Examples
--------
>>> selem = np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], np.uint8)
>>> _invert_selem(selem)
array([[1, 1, 0],
[1, 1, 0],
[0, 0, 0]], dtype=uint8)
References
----------
.. [1] https://github.com/scipy/scipy/blob/ec20ababa400e39ac3ffc9148c01ef86d5349332/scipy/ndimage/morphology.py#L1285
"""
inverted = selem[(slice(None, None, -1),) * selem.ndim]
return inverted
def pad_for_eccentric_selems(func):
"""Pad input images for certain morphological operations.
Parameters
----------
func : callable
A morphological function, either opening or closing, that
supports eccentric structuring elements. Its parameters must
include at least `image`, `selem`, and `out`.
Returns
-------
func_out : callable
The same function, but correctly padding the input image before
applying the input function.
See Also
--------
opening, closing.
"""
@functools.wraps(func)
def func_out(image, selem, out=None, *args, **kwargs):
pad_widths = []
padding = False
if out is None:
out = np.empty_like(image)
for axis_len in selem.shape:
if axis_len % 2 == 0:
axis_pad_width = axis_len - 1
padding = True
else:
axis_pad_width = 0
pad_widths.append((axis_pad_width,) * 2)
if padding:
image = np.pad(image, pad_widths, mode='edge')
out_temp = np.empty_like(image)
else:
out_temp = out
out_temp = func(image, selem, out=out_temp, *args, **kwargs)
if padding:
out[:] = crop(out_temp, pad_widths)
else:
out = out_temp
return out
return func_out
@default_selem
def erosion(image, selem=None, out=None, shift_x=False, shift_y=False):
"""Return greyscale 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.
selem : ndarray, optional
The neighborhood expressed as an array of 1's and 0's.
If None, use cross-shaped structuring element (connectivity=1).
out : ndarrays, optional
The array to store the result of the morphology. If None is
passed, a new array will be allocated.
shift_x, shift_y : bool, optional
shift structuring element about center point. This only affects
eccentric structuring elements (i.e. selem with even numbered sides).
Returns
-------
eroded : array, same shape as `image`
The result of the morphological erosion.
Notes
-----
For ``uint8`` (and ``uint16`` up to a certain bit-depth) data, the
lower algorithm complexity makes the `skimage.filters.rank.minimum`
function more efficient for larger images and structuring elements.
Examples
--------
>>> # Erosion shrinks bright regions
>>> import numpy as np
>>> from skimage.morphology import square
>>> 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, square(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)
"""
selem = np.array(selem)
selem = _shift_selem(selem, shift_x, shift_y)
if out is None:
out = np.empty_like(image)
ndi.grey_erosion(image, footprint=selem, output=out)
return out
@default_selem
def dilation(image, selem=None, out=None, shift_x=False, shift_y=False):
"""Return greyscale morphological dilation of an image.
Morphological dilation sets a pixel at (i,j) to the maximum over all pixels
in the neighborhood centered at (i,j). Dilation enlarges bright regions
and shrinks dark regions.
Parameters
----------
image : ndarray
Image array.
selem : ndarray, optional
The neighborhood expressed as a 2-D array of 1's and 0's.
If None, use cross-shaped structuring element (connectivity=1).
out : ndarray, optional
The array to store the result of the morphology. If None, is
passed, a new array will be allocated.
shift_x, shift_y : bool, optional
shift structuring element about center point. This only affects
eccentric structuring elements (i.e. selem with even numbered sides).
Returns
-------
dilated : uint8 array, same shape and type as `image`
The result of the morphological dilation.
Notes
-----
For `uint8` (and `uint16` up to a certain bit-depth) data, the lower
algorithm complexity makes the `skimage.filters.rank.maximum` function more
efficient for larger images and structuring elements.
Examples
--------
>>> # Dilation enlarges bright regions
>>> import numpy as np
>>> from skimage.morphology import square
>>> 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, square(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)
"""
selem = np.array(selem)
selem = _shift_selem(selem, shift_x, shift_y)
# Inside ndimage.grey_dilation, the structuring element is inverted,
# eg. `selem = selem[::-1, ::-1]` for 2D [1]_, for reasons unknown to
# this author (@jni). To "patch" this behaviour, we invert our own
# selem before passing it to `ndi.grey_dilation`.
# [1] https://github.com/scipy/scipy/blob/ec20ababa400e39ac3ffc9148c01ef86d5349332/scipy/ndimage/morphology.py#L1285
selem = _invert_selem(selem)
if out is None:
out = np.empty_like(image)
ndi.grey_dilation(image, footprint=selem, output=out)
return out
@default_selem
@pad_for_eccentric_selems
def opening(image, selem=None, out=None):
"""Return greyscale morphological opening of an image.
The morphological opening on 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.
selem : ndarray, optional
The neighborhood expressed as an array of 1's and 0's.
If None, use cross-shaped structuring element (connectivity=1).
out : ndarray, optional
The array to store the result of the morphology. If None
is passed, a new array will be allocated.
Returns
-------
opening : array, same shape and type as `image`
The result of the morphological opening.
Examples
--------
>>> # Open up gap between two bright regions (but also shrink regions)
>>> import numpy as np
>>> from skimage.morphology import square
>>> 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, square(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)
"""
eroded = erosion(image, selem)
# note: shift_x, shift_y do nothing if selem side length is odd
out = dilation(eroded, selem, out=out, shift_x=True, shift_y=True)
return out
@default_selem
@pad_for_eccentric_selems
def closing(image, selem=None, out=None):
"""Return greyscale morphological closing of an image.
The morphological closing on 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.
selem : ndarray, optional
The neighborhood expressed as an array of 1's and 0's.
If None, use cross-shaped structuring element (connectivity=1).
out : ndarray, optional
The array to store the result of the morphology. If None,
is passed, a new array will be allocated.
Returns
-------
closing : array, same shape and type as `image`
The result of the morphological closing.
Examples
--------
>>> # Close a gap between two bright lines
>>> import numpy as np
>>> from skimage.morphology import square
>>> 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, square(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)
"""
dilated = dilation(image, selem)
# note: shift_x, shift_y do nothing if selem side length is odd
out = erosion(dilated, selem, out=out, shift_x=True, shift_y=True)
return out
@default_selem
def white_tophat(image, selem=None, out=None):
"""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 structuring element.
Parameters
----------
image : ndarray
Image array.
selem : ndarray, optional
The neighborhood expressed as an array of 1's and 0's.
If None, use cross-shaped structuring element (connectivity=1).
out : ndarray, optional
The array to store the result of the morphology. If None
is passed, a new array will be allocated.
Returns
-------
out : array, same shape and type as `image`
The result of the morphological white top hat.
See also
--------
black_tophat
References
----------
.. [1] https://en.wikipedia.org/wiki/Top-hat_transform
Examples
--------
>>> # Subtract grey background from bright peak
>>> import numpy as np
>>> from skimage.morphology import square
>>> bright_on_grey = 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_grey, square(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)
"""
selem = np.array(selem)
if out is image:
opened = opening(image, selem)
if np.issubdtype(opened.dtype, np.bool_):
np.logical_xor(out, opened, out=out)
else:
out -= opened
return out
elif out is None:
out = np.empty_like(image)
# work-around for NumPy deprecation warning for arithmetic
# operations on bool arrays
if isinstance(image, np.ndarray) and image.dtype == np.bool:
image_ = image.view(dtype=np.uint8)
else:
image_ = image
if isinstance(out, np.ndarray) and out.dtype == np.bool:
out_ = out.view(dtype=np.uint8)
else:
out_ = out
out_ = ndi.white_tophat(image_, footprint=selem, output=out_)
return out
@default_selem
def black_tophat(image, selem=None, out=None):
"""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 structuring element. Note that dark spots in the
original image are bright spots after the black top hat.
Parameters
----------
image : ndarray
Image array.
selem : ndarray, optional
The neighborhood expressed as a 2-D array of 1's and 0's.
If None, use cross-shaped structuring element (connectivity=1).
out : ndarray, optional
The array to store the result of the morphology. If None
is passed, a new array will be allocated.
Returns
-------
out : array, same shape and type as `image`
The result of the morphological black top hat.
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 square
>>> dark_on_grey = 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_grey, square(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:
original = image.copy()
else:
original = image
out = closing(image, selem, out=out)
if np.issubdtype(out.dtype, np.bool_):
np.logical_xor(out, original, out=out)
else:
out -= original
return out