# Source code for porespy.filters._funcs

```
import inspect as insp
import dask
import numpy as np
from edt import edt
import operator as op
import scipy.ndimage as spim
from skimage.morphology import reconstruction
from skimage.segmentation import clear_border
from skimage.morphology import ball, disk, square, cube, diamond, octahedron
from porespy.tools import _check_for_singleton_axes
from porespy.tools import get_border, subdivide, recombine
from porespy.tools import unpad, extract_subsection
from porespy.tools import ps_disk, ps_ball
from porespy import settings
from porespy.tools import get_tqdm
from loguru import logger
tqdm = get_tqdm()
[docs]def apply_padded(im, pad_width, func, pad_val=1, **kwargs):
r"""
Applies padding to an image before sending to ``func``, then extracts
the result corresponding to the original image shape.
Parameters
----------
im : ndarray
The image to which ``func`` should be applied
pad_width : int or list of ints
The amount of padding to apply to each axis. Refer to
``numpy.pad`` documentation for more details.
pad_val : scalar
The value to place into the padded voxels. The default is 1 (or
``True``) which extends the pore space.
func : function handle
The function to apply to the padded image.
kwargs
Additional keyword arguments are collected and passed to ``func``.
Notes
-----
A use case for this is when using ``skimage.morphology.skeletonize_3d``
to ensure that the skeleton extends beyond the edges of the image.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/apply_padded.html>`_
to view online example.
"""
padded = np.pad(im, pad_width=pad_width,
mode='constant', constant_values=pad_val)
temp = func(padded, **kwargs)
result = unpad(im=temp, pad_width=pad_width)
return result
[docs]def trim_small_clusters(im, size=1):
r"""
Remove isolated voxels or clusters of a given size or smaller
Parameters
----------
im : ndarray
The binary image from which voxels are to be removed.
size : scalar
The threshold size of clusters to trim. As clusters with this
many voxels or fewer will be trimmed. The default is 1 so only
single voxels are removed.
Returns
-------
im : ndarray
A copy of ``im`` with clusters of voxels smaller than the given
``size`` removed.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/trim_small_clusters.html>`_
to view online example.
"""
if im.ndim == 2:
strel = disk(1)
elif im.ndim == 3:
strel = ball(1)
else:
raise Exception("Only 2D or 3D images are accepted")
filtered_array = np.copy(im)
labels, N = spim.label(filtered_array, structure=strel)
id_sizes = np.array(spim.sum(im, labels, range(N + 1)))
area_mask = id_sizes <= size
filtered_array[area_mask[labels]] = 0
return filtered_array
[docs]def hold_peaks(im, axis=-1, ascending=True):
r"""
Replaces each voxel with the highest value along the given axis.
Parameters
----------
im : ndarray
A greyscale image whose peaks are to be found.
axis : int
The axis along which the operation is to be applied.
ascending : bool
If ``True`` (default) the given ``axis`` is scanned from 0 to end.
If ``False``, it is scanned in reverse order from end to 0.
Returns
-------
result : ndarray
A copy of ``im`` with each voxel is replaced with the highest value along
the given axis.
Notes
-----
"im" must be a greyscale image. In case a Boolean image is fed into this
method, it will be converted to float values [0.0,1.0] before proceeding.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/hold_peaks.html>`_
to view online example.
"""
A = im.astype(float)
B = np.swapaxes(A, axis, -1)
if ascending is False: # Flip the axis of interest (-1)
B = np.flip(B, axis=-1)
updown = np.empty((*B.shape[:-1], B.shape[-1] + 1), B.dtype)
updown[..., 0], updown[..., -1] = -1, -1
np.subtract(B[..., 1:], B[..., :-1], out=updown[..., 1:-1])
chnidx = np.where(updown)
chng = updown[chnidx]
(pkidx,) = np.where((chng[:-1] > 0) & (chng[1:] < 0) | (chnidx[-1][:-1] == 0))
pkidx = (*map(op.itemgetter(pkidx), chnidx),)
out = np.zeros_like(A)
aux = out.swapaxes(axis, -1)
aux[(*map(op.itemgetter(slice(1, None)), pkidx),)] = np.diff(B[pkidx])
aux[..., 0] = B[..., 0]
result = out.cumsum(axis=axis)
if ascending is False: # Flip it back
result = np.flip(result, axis=-1)
return result
[docs]def distance_transform_lin(im, axis=0, mode="both"):
r"""
Replaces each void voxel with the linear distance to the nearest solid
voxel along the specified axis.
Parameters
----------
im : ndarray
The image of the porous material with ``True`` values indicating
the void phase (or phase of interest).
axis : int
The direction along which the distance should be measured, the
default is 0 (i.e. along the x-direction).
mode : str
Controls how the distance is measured. Options are:
'forward'
Distances are measured in the increasing direction
along the specified axis
'reverse'
Distances are measured in the reverse direction.
'backward' is also accepted.
'both'
Distances are calculated in both directions (by
recursively calling itself), then reporting the minimum value
of the two results.
Returns
-------
image : ndarray
A copy of ``im`` with each foreground voxel containing the
distance to the nearest background along the specified axis.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/distance_transform_lin.html>`_
to view online example.
"""
_check_for_singleton_axes(im)
if mode in ["backward", "reverse"]:
im = np.flip(im, axis)
im = distance_transform_lin(im=im, axis=axis, mode="forward")
im = np.flip(im, axis)
return im
elif mode in ["both"]:
im_f = distance_transform_lin(im=im, axis=axis, mode="forward")
im_b = distance_transform_lin(im=im, axis=axis, mode="backward")
return np.minimum(im_f, im_b)
b = np.cumsum(im > 0, axis=axis)
c = np.diff(b * (im == 0), axis=axis)
d = np.minimum.accumulate(c, axis=axis)
if im.ndim == 1:
e = np.pad(d, pad_width=[1, 0], mode="constant", constant_values=0)
elif im.ndim == 2:
ax = [[[1, 0], [0, 0]], [[0, 0], [1, 0]]]
e = np.pad(d, pad_width=ax[axis], mode="constant", constant_values=0)
elif im.ndim == 3:
ax = [
[[1, 0], [0, 0], [0, 0]],
[[0, 0], [1, 0], [0, 0]],
[[0, 0], [0, 0], [1, 0]],
]
e = np.pad(d, pad_width=ax[axis], mode="constant", constant_values=0)
f = im * (b + e)
return f
[docs]def find_disconnected_voxels(im, conn=None, surface=False):
r"""
Identifies all voxels that are not connected to the edge of the image.
Parameters
----------
im : ndarray
A Boolean image, with ``True`` values indicating the phase for which
disconnected voxels are sought.
conn : int
For 2D the options are 4 and 8 for square and diagonal neighbors,
while for the 3D the options are 6 and 26, similarily for square
and diagonal neighbors. The default is the maximum option.
surface : bool
If ``True`` any isolated regions touching the edge of the image are
considered disconnected.
Returns
-------
image : ndarray
An ndarray the same size as ``im``, with ``True`` values indicating
voxels of the phase of interest (i.e. ``True`` values in the original
image) that are not connected to the outer edges.
See Also
--------
fill_blind_pores, trim_floating_solid
Notes
-----
This function is just a convenient wrapper around the ``clear_border``
function of ``scikit-image``.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/find_disconnected_voxels.html>`_
to view online example.
"""
_check_for_singleton_axes(im)
if im.ndim == 2:
if conn == 4:
strel = disk(1)
elif conn in [None, 8]:
strel = square(3)
else:
raise Exception("Received conn is not valid")
elif im.ndim == 3:
if conn == 6:
strel = ball(1)
elif conn in [None, 26]:
strel = cube(3)
else:
raise Exception("Received conn is not valid")
labels, N = spim.label(input=im, structure=strel)
if not surface:
holes = clear_border(labels=labels) > 0
else:
counts = np.bincount(labels.flatten())[1:]
keep = np.where(counts == counts.max())[0] + 1
holes = (labels != keep)*im
return holes
[docs]def fill_blind_pores(im, conn=None, surface=False):
r"""
Fills all blind pores that are isolated from the main void space.
Parameters
----------
im : ndarray
The image of the porous material
Returns
-------
im : ndarray
A version of ``im`` but with all the disconnected pores removed.
conn : int
For 2D the options are 4 and 8 for square and diagonal neighbors,
while for the 3D the options are 6 and 26, similarily for square
and diagonal neighbors. The default is the maximum option.
surface : bool
If ``True``, any isolated pore regions that are connected to the
sufaces of the image are but not connected to the main percolating
path are also removed. When this is enabled, only the voxels
belonging to the largest region are kept. This can be
problematic if image contains non-intersecting tube-like structures,
for instance, since only the largest tube will be preserved.
See Also
--------
find_disconnected_voxels
trim_nonpercolating_paths
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/fill_blind_pores.html>`_
to view online example.
"""
im = np.copy(im)
holes = find_disconnected_voxels(im, conn=conn, surface=surface)
im[holes] = False
return im
[docs]def trim_floating_solid(im, conn=None, surface=False):
r"""
Removes all solid that that is not attached to main solid structure.
Parameters
----------
im : ndarray
The image of the porous material
conn : int
For 2D the options are 4 and 8 for square and diagonal neighbors,
while for the 3D the options are 6 and 26, similarily for square
and diagonal neighbors. The default is the maximum option.
surface : bool
If ``True``, any isolated solid regions that are connected to the
surfaces of the image but not the main body of the solid are also
removed. When this is enabled, only the voxels belonging to the
largest region are kept. This can be problematic if the image
contains non-intersecting tube-like structures, for instance,
since only the largest tube will be preserved.
Returns
-------
image : ndarray
A version of ``im`` but with all the disconnected solid removed.
See Also
--------
find_disconnected_voxels
trim_nonpercolating_paths
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/trim_floating_solid.html>`_
to view online example.
"""
im = np.copy(im)
holes = find_disconnected_voxels(~im, conn=conn, surface=surface)
im[holes] = True
return im
[docs]def trim_nonpercolating_paths(im, inlets, outlets, strel=None):
r"""
Remove all nonpercolating paths between specified locations
Parameters
----------
im : ndarray
The image of the porous material with ```True`` values indicating the
phase of interest
inlets : ndarray
A boolean mask indicating locations of inlets, such as produced by
``porespy.generators.faces``.
outlets : ndarray
A boolean mask indicating locations of outlets, such as produced by
``porespy.generators.faces``.
strel : ndarray
The structuring element to use when determining if regions are
connected. This is passed to ``scipiy.ndimage.label``.
Returns
-------
image : ndarray
A copy of ``im`` with all the nonpercolating paths removed
Notes
-----
This function is essential when performing transport simulations on an
image since regions that do not span between the desired inlet and
outlet do not contribute to the transport.
See Also
--------
find_disconnected_voxels
trim_floating_solid
trim_blind_pores
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/trim_nonpercolating_paths.html>`_
to view online example.
"""
labels = spim.label(im, structure=strel)[0]
IN = np.unique(labels * inlets)
OUT = np.unique(labels * outlets)
hits = np.array(list(set(IN).intersection(set(OUT))))
new_im = np.isin(labels, hits[hits > 0])
return new_im
[docs]def trim_extrema(im, h, mode="maxima"):
r"""
Trims local extrema in greyscale values by a specified amount.
This essentially decapitates peaks and/or floods valleys.
Parameters
----------
im : ndarray
The image whose extrema are to be removed
h : float
The height to remove from each peak or fill in each valley
mode : string {'maxima' | 'minima' | 'extrema'}
Specifies whether to remove maxima or minima or both
Returns
-------
image : ndarray
A copy of the input image with all the peaks and/or valleys
removed.
Notes
-----
(1) This function is referred to as **imhmax** or **imhmin** in Matlab.
(2) If the provided ``h`` is larger than ALL peaks in the array, then the
baseline values of the array are changed as well.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/trim_extrema.html>`_
to view online example.
"""
mask = np.copy(im)
im = np.copy(im)
if mode == 'maxima':
result = reconstruction(seed=im - h, mask=mask, method='dilation')
elif mode == 'minima':
result = reconstruction(seed=im + h, mask=mask, method='erosion')
elif mode == 'extrema':
result = reconstruction(seed=im - h, mask=mask, method='dilation')
result = reconstruction(seed=result + h, mask=result, method='erosion')
return result
[docs]def flood(im, labels, mode="max"):
r"""
Floods/fills each region in an image with a single value based on the
specific values in that region.
This function calls the various functions in ``scipy.ndimage.measurements``
but instead of returning a list of values, it fills each region with its
value. This is useful for visualization and statistics.
Parameters
----------
im : array_like
An image with the numerical values of interest in each voxel,
and 0's elsewhere.
labels : array_like
An array the same shape as ``im`` with each region labeled.
mode : string
Specifies how to determine the value to flood each region. Options
taken from the ``scipy.ndimage.measurements`` functions:
'maximum'
Floods each region with the local max in that region. The
keyword ``max`` is also accepted.
'minimum'
Floods each region the local minimum in that region. The
keyword ``min`` is also accepted.
'median'
Floods each region the local median in that region
'mean'
Floods each region the local mean in that region
'size'
Floods each region with the size of that region. This is
actually accomplished with ``scipy.ndimage.sum`` by converting
``im`` to a boolean image (``im = im > 0``).
'standard_deviation'
Floods each region with the value of the standard deviation
of the voxels in ``im``.
'variance'
Floods each region with the value of the variance of the voxels
in ``im``.
Returns
-------
flooded : ndarray
A copy of ``im`` with new values placed in each forground voxel
based on the ``mode``.
See Also
--------
prop_to_image, flood_func, region_size
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/flood.html>`_
to view online example.
"""
mask = im > 0
N = labels.max()
mode = "sum" if mode == "size" else mode
mode = "maximum" if mode == "max" else mode
mode = "minimum" if mode == "min" else mode
f = getattr(spim, mode)
vals = f(input=im, labels=labels, index=range(0, N + 1))
flooded = vals[labels]
flooded = flooded * mask
return flooded
[docs]def flood_func(im, func, labels=None):
r"""
Flood each isolated region in an image with a constant value calculated by
the given function.
Parameters
----------
im : ndarray
An image with the numerical values of interest in each voxel,
and 0's elsewhere.
func : Numpy function handle
The function to be applied to each region in the image. Any Numpy
function that returns a scalar value can be passed, such as ``amin``,
``amax``, ``sum``, ``mean``, ``median``, etc.
labels : ndarray
An array containing labels identifying each individual region to be
flooded. If not provided then ``scipy.ndimage.label`` is applied to
``im > 0``.
Returns
-------
flooded : ndarray
An image the same size as ``im`` with each isolated region flooded
with a constant value based on the given ``func`` and the values
in ``im``.
See Also
--------
flood, region_size
Notes
-----
Many of the functions in ``scipy.ndimage`` can be applied to
individual regions using the ``index`` argument. This function extends
that behavior to all numpy function, in the event you wanted to compute
the cosine of the values in each region for some reason. This function
also floods the original image instead of returning a list of values for
each region.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/flood_func.html>`_
to view online example.
"""
if labels is None:
labels = spim.label(im > 0)[0]
slices = spim.find_objects(labels)
flooded = np.zeros_like(im, dtype=float)
for i, s in enumerate(slices):
sub_im = labels[s] == (i + 1)
val = func(im[s][sub_im])
flooded[s] += sub_im*val
return flooded
[docs]def find_dt_artifacts(dt):
r"""
Label points in a distance transform that are closer to image boundary
than solid
These points could *potentially* be erroneously high since their
distance values do not reflect the possibility that solid may have
been present beyond the border of the image but was lost by trimming.
Parameters
----------
dt : ndarray
The distance transform of the phase of interest.
Returns
-------
image : ndarray
An ndarray the same shape as ``dt`` with numerical values
indicating the maximum amount of error in each volxel, which is
found by subtracting the distance to nearest edge of image from
the distance transform value. In other words, this is the error
that would be found if there were a solid voxel lurking just
beyond the nearest edge of the image. Obviously, voxels with a
value of zero have no error.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/find_dt_artifacts.html>`_
to view online example.
"""
temp = np.ones(shape=dt.shape) * np.inf
for ax in range(dt.ndim):
dt_lin = distance_transform_lin(np.ones_like(temp, dtype=bool),
axis=ax, mode="both")
temp = np.minimum(temp, dt_lin)
result = np.clip(dt - temp, a_min=0, a_max=np.inf)
return result
[docs]def region_size(im):
r"""
Replace each voxel with the size of the region to which it belongs
Parameters
----------
im : ndarray
Either a boolean image wtih ``True`` indicating the features of
interest, in which case ``scipy.ndimage.label`` will be applied to
find regions, or a greyscale image with integer values indicating
regions.
Returns
-------
image : ndarray
A copy of ``im`` with each voxel value indicating the size of the
region to which it belongs. This is particularly useful for
finding chord sizes on the image produced by ``apply_chords``.
See Also
--------
flood
Notes
-----
This function provides the same result as ``flood`` with ``mode='size'``,
although does the computation in a different way.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/region_size.html>`_
to view online example.
"""
if im.dtype == bool:
im = spim.label(im)[0]
counts = np.bincount(im.flatten())
counts[0] = 0
return counts[im]
[docs]def apply_chords(im, spacing=1, axis=0, trim_edges=True, label=False):
r"""
Adds chords to the void space in the specified direction.
Parameters
----------
im : ndarray
An image of the porous material with void marked as ``True``.
spacing : int
Separation between chords. The default is 1 voxel. This can be
decreased to 0, meaning that the chords all touch each other,
which automatically sets to the ``label`` argument to ``True``.
axis : int (default = 0)
The axis along which the chords are drawn.
trim_edges : bool (default = ``True``)
Whether or not to remove chords that touch the edges of the image.
These chords are artifically shortened, so skew the chord length
distribution.
label : bool (default is ``False``)
If ``True`` the chords in the returned image are each given a
unique label, such that all voxels lying on the same chord have
the same value. This is automatically set to ``True`` if spacing
is 0, but is ``False`` otherwise.
Returns
-------
image : ndarray
A copy of ``im`` with non-zero values indicating the chords.
See Also
--------
apply_chords_3D
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/apply_chords.html>`_
to view online example.
"""
_check_for_singleton_axes(im)
if spacing < 0:
raise Exception("Spacing cannot be less than 0")
if spacing == 0:
label = True
result = np.zeros(im.shape, dtype=int) # Will receive chords at end
slxyz = [slice(None, None, spacing * (axis != i) + 1) for i in [0, 1, 2]]
slices = tuple(slxyz[: im.ndim])
s = [[0, 1, 0], [0, 1, 0], [0, 1, 0]] # Straight-line structuring element
if im.ndim == 3: # Make structuring element 3D if necessary
s = np.pad(np.atleast_3d(s), pad_width=((0, 0), (0, 0), (1, 1)),
mode="constant", constant_values=0)
im = im[slices]
s = np.swapaxes(s, 0, axis)
chords = spim.label(im, structure=s)[0]
if trim_edges: # Label on border chords will be set to 0
chords = clear_border(chords)
result[slices] = chords # Place chords into empty image created at top
if label is False: # Remove label if not requested
result = result > 0
return result
[docs]def apply_chords_3D(im, spacing=0, trim_edges=True):
r"""
Adds chords to the void space in all three principle directions.
Chords in the X, Y and Z directions are labelled 1, 2 and 3 resepctively.
Parameters
----------
im : ndarray
A 3D image of the porous material with void space marked as True.
spacing : int (default = 0)
Chords are automatically separed by 1 voxel on all sides, and this
argument increases the separation.
trim_edges : bool (default is ``True``)
Whether or not to remove chords that touch the edges of the image.
These chords are artifically shortened, so skew the chord length
distribution
Returns
-------
image : ndarray
A copy of ``im`` with values of 1 indicating x-direction chords,
2 indicating y-direction chords, and 3 indicating z-direction
chords.
Notes
-----
The chords are separated by a spacing of at least 1 voxel so that
tools that search for connected components, such as
``scipy.ndimage.label`` can detect individual chords.
See Also
--------
apply_chords
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/apply_chords_3D.html>`_
to view online example.
"""
_check_for_singleton_axes(im)
if im.ndim < 3:
raise Exception("Must be a 3D image to use this function")
if spacing < 0:
raise Exception("Spacing cannot be less than 0")
ch = np.zeros_like(im, dtype=int)
ch[:, :: 4 + 2 * spacing, :: 4 + 2 * spacing] = 1 # X-direction
ch[:: 4 + 2 * spacing, :, 2::4 + 2 * spacing] = 2 # Y-direction
ch[2::4 + 2 * spacing, 2::4 + 2 * spacing, :] = 3 # Z-direction
chords = ch * im
if trim_edges:
temp = clear_border(spim.label(chords > 0)[0]) > 0
chords = temp * chords
return chords
[docs]def local_thickness(im, sizes=25, mode="hybrid", divs=1):
r"""
For each voxel, this function calculates the radius of the largest
sphere that both engulfs the voxel and fits entirely within the
foreground.
This is not the same as a simple distance transform, which finds the
largest sphere that could be *centered* on each voxel.
Parameters
----------
im : ndarray
A binary image with the phase of interest set to True
sizes : array_like or scalar
The sizes to invade. If a list of values of provided they are
used directly. If a scalar is provided then that number of points
spanning the min and max of the distance transform are used.
mode : str
Controls with method is used to compute the result. Options are:
'hybrid'
(default) Performs a distance transform of the void
space, thresholds to find voxels larger than ``sizes[i]``, trims
the resulting mask if ``access_limitations`` is ``True``, then
dilates it using the efficient fft-method to obtain the
non-wetting fluid configuration.
'dt'
Same as 'hybrid', except uses a second distance transform,
relative to the thresholded mask, to find the invading fluid
configuration. The choice of 'dt' or 'hybrid' depends on speed,
which is system and installation specific.
'mio'
Using a single morphological image opening step to obtain
the invading fluid confirguration directly, *then* trims if
``access_limitations`` is ``True``. This method is not ideal and
is included for comparison purposes.
divs : int or array_like
The number of times to divide the image for parallel processing. If ``1``
then parallel processing does not occur. ``2`` is equivalent to
``[2, 2, 2]`` for a 3D image. The number of cores used is specified in
``porespy.settings.ncores`` and defaults to all cores.
Returns
-------
image : ndarray
A copy of ``im`` with the pore size values in each voxel.
See Also
--------
porosimetry
Notes
-----
The term *foreground* is used since this function can be applied to
both pore space or the solid, whichever is set to ``True``.
This function is identical to ``porosimetry`` with ``access_limited``
set to ``False``.
The way local thickness is found in PoreSpy differs from the
traditional method (i.e. used in ImageJ
`<https://imagej.net/Local_Thickness>`_). Our approach is probably
slower, but it allows for the same code to be used for
``local_thickness`` and ``porosimetry``, since we can 'trim' invaded
regions that are not connected to the inlets in the ``porosimetry``
function. This is not needed in ``local_thickness`` however.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/local_thickness.html>`_
to view online example.
"""
im_new = porosimetry(im=im, sizes=sizes, access_limited=False, mode=mode,
divs=divs)
return im_new
[docs]def porosimetry(im, sizes=25, inlets=None, access_limited=True, mode='hybrid',
divs=1):
r"""
Performs a porosimetry simulution on an image.
Parameters
----------
im : ndarray
An ND image of the porous material containing ``True`` values in the
pore space.
sizes : array_like or scalar
The sizes to invade. If a list of values of provided they are
used directly. If a scalar is provided then that number of points
spanning the min and max of the distance transform are used.
inlets : ndarray, boolean
A boolean mask with ``True`` values indicating where the invasion
enters the image. By default all faces are considered inlets,
akin to a mercury porosimetry experiment. Users can also apply
solid boundaries to their image externally before passing it in,
allowing for complex inlets like circular openings, etc.
This argument is only used if ``access_limited`` is ``True``.
access_limited : bool
This flag indicates if the intrusion should only occur from the
surfaces (``access_limited`` is ``True``, which is the default),
or if the invading phase should be allowed to appear in the core
of the image. The former simulates experimental tools like
mercury intrusion porosimetry, while the latter is useful for
comparison to gauge the extent of shielding effects in the sample.
mode : str
Controls with method is used to compute the result. Options are:
'hybrid'
(default) Performs a distance tranform of the void
space, thresholds to find voxels larger than ``sizes[i]``,
trims the resulting mask if ``access_limitations`` is ``True``,
then dilates it using the efficient fft-method to obtain the
non-wetting fluid configuration.
'dt'
Same as 'hybrid', except uses a second distance
transform, relative to the thresholded mask, to find the
invading fluid configuration. The choice of 'dt' or 'hybrid'
depends on speed, which is system and installation specific.
'mio'
Uses bindary erosion followed by dilation to obtain the invading
fluid confirguration directly. If ``access_limitations`` is
``True`` then disconnected blobs are trimmmed before the dilation.
This is the only method that can be parallelized by chunking (see
``divs`` and ``cores``).
divs : int or array_like
The number of times to divide the image for parallel processing. If ``1``
then parallel processing does not occur. ``2`` is equivalent to
``[2, 2, 2]`` for a 3D image. The number of cores used is specified in
``porespy.settings.ncores`` and defaults to all cores.
Returns
-------
image : ndarray
A copy of ``im`` with voxel values indicating the sphere radius at
which it becomes accessible from the inlets. This image can be
used to find invading fluid configurations as a function of
applied capillary pressure by applying a boolean comparison:
``inv_phase = im > r`` where ``r`` is the radius (in voxels) of
the invading sphere. Of course, ``r`` can be converted to
capillary pressure using a preferred model.
Notes
-----
There are many ways to perform this filter, and PoreSpy offers 3,
which users can choose between via the ``mode`` argument. These
methods all work in a similar way by finding which foreground voxels
can accomodate a sphere of a given radius, then repeating for smaller
radii.
See Also
--------
local_thickness
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/porosimetry.html>`_
to view online example.
"""
from porespy.filters import fftmorphology
im = np.squeeze(im)
dt = edt(im > 0)
if inlets is None:
inlets = get_border(im.shape, mode="faces")
if isinstance(sizes, int):
sizes = np.logspace(start=np.log10(np.amax(dt)), stop=0, num=sizes)
else:
sizes = np.unique(sizes)[-1::-1]
if im.ndim == 2:
strel = ps_disk
strel_2 = disk
else:
strel = ps_ball
strel_2 = ball
parallel = False
if isinstance(divs, int):
divs = [divs]*im.ndim
if max(divs) > 1:
logger.info(f'Performing {insp.currentframe().f_code.co_name} in parallel')
parallel = True
if mode == "mio":
pw = int(np.floor(dt.max()))
impad = np.pad(im, mode="symmetric", pad_width=pw)
inlets = np.pad(inlets, mode="symmetric", pad_width=pw)
# sizes = np.unique(np.around(sizes, decimals=0).astype(int))[-1::-1]
imresults = np.zeros(np.shape(impad))
for r in tqdm(sizes, **settings.tqdm):
if parallel:
imtemp = chunked_func(func=fftmorphology,
im=impad, strel=strel(r),
overlap=int(r) + 1, mode='erosion',
cores=settings.ncores, divs=divs)
else:
imtemp = fftmorphology(im=impad, strel=strel(r), mode='erosion')
if access_limited:
imtemp = trim_disconnected_blobs(imtemp, inlets,
strel=strel_2(1))
if parallel:
imtemp = chunked_func(func=fftmorphology,
im=imtemp, strel=strel(r),
overlap=int(r) + 1, mode='dilation',
cores=settings.ncores, divs=divs)
else:
imtemp = fftmorphology(im=imtemp, strel=strel(r), mode='dilation')
if np.any(imtemp):
imresults[(imresults == 0) * imtemp] = r
imresults = extract_subsection(imresults, shape=im.shape)
elif mode == "dt":
imresults = np.zeros(np.shape(im))
for r in tqdm(sizes, **settings.tqdm):
imtemp = dt >= r
if access_limited:
imtemp = trim_disconnected_blobs(imtemp, inlets,
strel=strel_2(1))
if np.any(imtemp):
if parallel:
imtemp = chunked_func(func=edt,
data=~imtemp, im_arg='data',
overlap=int(r) + 1, parallel=0,
cores=settings.ncores, divs=divs) < r
else:
imtemp = edt(~imtemp) < r
imresults[(imresults == 0) * imtemp] = r
elif mode == "hybrid":
imresults = np.zeros(np.shape(im))
for r in tqdm(sizes, **settings.tqdm):
imtemp = dt >= r
if access_limited:
imtemp = trim_disconnected_blobs(imtemp, inlets,
strel=strel_2(1))
if np.any(imtemp):
if parallel:
imtemp = chunked_func(func=fftmorphology, mode='dilation',
im=imtemp, strel=strel(r),
overlap=int(r) + 1,
cores=settings.ncores, divs=divs)
else:
imtemp = fftmorphology(imtemp, strel(r),
mode="dilation")
imresults[(imresults == 0) * imtemp] = r
else:
raise Exception("Unrecognized mode " + mode)
return imresults
[docs]def trim_disconnected_blobs(im, inlets, strel=None):
r"""
Removes foreground voxels not connected to specified inlets.
Parameters
----------
im : ndarray
The image containing the blobs to be trimmed
inlets : ndarray or tuple of indices
The locations of the inlets. Can either be a boolean mask the
same shape as ``im``, or a tuple of indices such as that returned
by the ``where`` function. Any voxels *not* connected directly to
the inlets will be trimmed.
strel : array-like
The neighborhood over which connectivity should be checked. It
must be symmetric and the same dimensionality as the image. It is
passed directly to the ``scipy.ndimage.label`` function as the
``structure`` argument so refer to that docstring for additional
info.
Returns
-------
image : ndarray
An array of the same shape as ``im``, but with all foreground
voxels not connected to the ``inlets`` removed.
See Also
--------
find_disconnected_voxels, find_nonpercolating_paths
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/trim_disconnected_blobs.html>`_
to view online example.
"""
if type(inlets) == tuple:
temp = np.copy(inlets)
inlets = np.zeros_like(im, dtype=bool)
inlets[temp] = True
elif (inlets.shape == im.shape) and (inlets.max() == 1):
inlets = inlets.astype(bool)
else:
raise Exception("inlets not valid, refer to docstring for info")
if strel is None:
if im.ndim == 3:
strel = cube(3)
else:
strel = square(3)
labels = spim.label(inlets + (im > 0), structure=strel)[0]
keep = np.unique(labels[inlets])
keep = keep[keep > 0]
im2 = np.isin(labels, keep)
im2 = im2 * im
return im2
def _get_axial_shifts(ndim=2, include_diagonals=False):
r"""
Helper function to generate the axial shifts that will be performed on
the image to identify bordering pixels/voxels
"""
if ndim == 2:
if include_diagonals:
neighbors = square(3)
else:
neighbors = diamond(1)
neighbors[1, 1] = 0
x, y = np.where(neighbors)
x -= 1
y -= 1
return np.vstack((x, y)).T
else:
if include_diagonals:
neighbors = cube(3)
else:
neighbors = octahedron(1)
neighbors[1, 1, 1] = 0
x, y, z = np.where(neighbors)
x -= 1
y -= 1
z -= 1
return np.vstack((x, y, z)).T
def _make_stack(im, include_diagonals=False):
r"""
Creates a stack of images with one extra dimension to the input image
with length equal to the number of borders to search + 1.
Image is rolled along the axial shifts so that the border pixel is
overlapping the original pixel. First image in stack is the original.
Stacking makes direct vectorized array comparisons possible.
"""
ndim = len(np.shape(im))
axial_shift = _get_axial_shifts(ndim, include_diagonals)
if ndim == 2:
stack = np.zeros([np.shape(im)[0], np.shape(im)[1], len(axial_shift) + 1])
stack[:, :, 0] = im
for i in range(len(axial_shift)):
ax0, ax1 = axial_shift[i]
temp = np.roll(np.roll(im, ax0, 0), ax1, 1)
stack[:, :, i + 1] = temp
return stack
elif ndim == 3:
stack = np.zeros(
[np.shape(im)[0], np.shape(im)[1], np.shape(im)[2], len(axial_shift) + 1]
)
stack[:, :, :, 0] = im
for i in range(len(axial_shift)):
ax0, ax1, ax2 = axial_shift[i]
temp = np.roll(np.roll(np.roll(im, ax0, 0), ax1, 1), ax2, 2)
stack[:, :, :, i + 1] = temp
return stack
[docs]def nphase_border(im, include_diagonals=False):
r"""
Identifies the voxels in regions that border *N* other regions.
Useful for finding triple-phase boundaries.
Parameters
----------
im : ndarray
An ND image of the porous material containing discrete values in
the pore space identifying different regions. e.g. the result of a
snow-partition
include_diagonals : bool
When identifying bordering pixels (2D) and voxels (3D) include
those shifted along more than one axis
Returns
-------
image : ndarray
A copy of ``im`` with voxel values equal to the number of uniquely
different bordering values
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/nphase_border.html>`_
to view online example.
"""
_check_for_singleton_axes(im)
# Get dimension of image
ndim = len(np.shape(im))
if ndim not in [2, 3]:
raise NotImplementedError("Function only works for 2d and 3d images")
# Pad image to handle edges
im = np.pad(im, pad_width=1, mode="edge")
# Stack rolled images for each neighbor to be inspected
stack = _make_stack(im, include_diagonals)
# Sort the stack along the last axis
stack.sort()
out = np.ones_like(im)
# Run through stack recording when neighbor id changes
# Number of changes is number of unique bordering regions
for k in range(np.shape(stack)[ndim])[1:]:
if ndim == 2:
mask = stack[:, :, k] != stack[:, :, k - 1]
elif ndim == 3:
mask = stack[:, :, :, k] != stack[:, :, :, k - 1]
out += mask
# Un-pad
if ndim == 2:
return out[1:-1, 1:-1].copy()
else:
return out[1:-1, 1:-1, 1:-1].copy()
[docs]def prune_branches(skel, branch_points=None, iterations=1):
r"""
Remove all dangling ends or tails of a skeleton
Parameters
----------
skel : ndarray
A image of a full or partial skeleton from which the tails should
be trimmed.
branch_points : ndarray, optional
An image the same size ``skel`` with ``True`` values indicating the
branch points of the skeleton. If this is not provided it is
calculated automatically.
iterations : int
The number of times to recursively repeat the process. The default is
1.
Returns
-------
array
An ndarray containing the skeleton with tails removed.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/prune_branches.html>`_
to view online example.
"""
skel = skel > 0
if skel.ndim == 2:
from skimage.morphology import square as cube
else:
from skimage.morphology import cube
# Create empty image to house results
im_result = np.zeros_like(skel)
# If branch points are not supplied, attempt to find them
if branch_points is None:
branch_points = spim.convolve(skel * 1.0, weights=cube(3)) > 3
branch_points = branch_points * skel
# Store original branch points before dilating
pts_orig = branch_points
# Find arcs of skeleton by deleting branch points
arcs = skel * (~branch_points)
# Label arcs
arc_labels = spim.label(arcs, structure=cube(3))[0]
# Dilate branch points so they overlap with the arcs
branch_points = spim.binary_dilation(branch_points, structure=cube(3))
pts_labels = spim.label(branch_points, structure=cube(3))[0]
# Now scan through each arc to see if it's connected to two branch points
slices = spim.find_objects(arc_labels)
label_num = 0
for s in slices:
label_num += 1
# Find branch point labels the overlap current arc
hits = pts_labels[s] * (arc_labels[s] == label_num)
# If image contains 2 branch points, then it's not a tail.
if len(np.unique(hits)) == 3:
im_result[s] += arc_labels[s] == label_num
# Add missing branch points back to arc image to make complete skeleton
im_result += skel * pts_orig
if iterations > 1:
iterations -= 1
im_temp = np.copy(im_result)
im_result = prune_branches(skel=im_result,
branch_points=None,
iterations=iterations)
if np.all(im_temp == im_result):
iterations = 0
return im_result
[docs]def chunked_func(func,
overlap=None,
divs=2,
cores=None,
im_arg=["input", "image", "im"],
strel_arg=["strel", "structure", "footprint"],
**kwargs):
r"""
Performs the specfied operation "chunk-wise" in parallel using ``dask``.
This can be used to save memory by doing one chunk at a time
(``cores=1``) or to increase computation speed by spreading the work
across multiple cores (e.g. ``cores=8``)
This function can be used with any operation that applies a
structuring element of some sort, since this implies that the
operation is local and can be chunked.
Parameters
----------
func : function handle
The function which should be applied to each chunk, such as
``spipy.ndimage.binary_dilation``.
overlap : scalar or list of scalars, optional
The amount of overlap to include when dividing up the image. This
value will almost always be the size (i.e. raduis) of the
structuring element. If not specified then the amount of overlap
is inferred from the size of the structuring element, in which
case the ``strel_arg`` must be specified.
divs : scalar or list of scalars (default = [2, 2, 2])
The number of chunks to divide the image into in each direction.
The default is 2 chunks in each direction, resulting in a
quartering of the image and 8 total chunks (in 3D). A scalar is
interpreted as applying to all directions, while a list of scalars
is interpreted as applying to each individual direction.
cores : scalar
The number of cores which should be used. By default, all cores
will be used, or as many are needed for the given number of
chunks, which ever is smaller.
im_arg : str
The keyword used by ``func`` for the image to be operated on. By
default this function will look for ``image``, ``input``, and
``im`` which are commonly used by *scipy.ndimage* and *skimage*.
strel_arg : str
The keyword used by ``func`` for the structuring element to apply.
This is only needed if ``overlap`` is not specified. By default
this function will look for ``strel``, ``structure``, and
``footprint`` which are commonly used by *scipy.ndimage* and
*skimage*.
kwargs
All other arguments are passed to ``func`` as keyword arguments.
Note that PoreSpy will fetch the image from this list of keywords
using the value provided to ``im_arg``.
Returns
-------
result : ndarray
An image the same size as the input image, with the specified
filter applied as though done on a single large image. There
should be *no* difference.
Notes
-----
This function divides the image into the specified number of chunks,
but also applies a padding to each chunk to create an overlap with
neighboring chunks. This way the operation does not have any edge
artifacts. The amount of padding is usually equal to the radius of the
structuring element but some functions do not use one, such as the
distance transform and Gaussian blur. In these cases the user can
specify ``overlap``.
See Also
--------
scikit-image.util.apply_parallel
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/chunked_func.html>`_
to view online example.
"""
@dask.delayed
def apply_func(func, **kwargs):
# Apply function on sub-slice of overall image
return func(**kwargs)
# Determine the value for im_arg
if type(im_arg) == str:
im_arg = [im_arg]
for item in im_arg:
if item in kwargs.keys():
im = kwargs[item]
im_arg = item
break
# Fetch image from the kwargs dict
im = kwargs[im_arg]
# Determine the number of divisions to create
divs = np.ones((im.ndim,), dtype=int) * np.array(divs)
if cores is None:
cores = settings.ncores
# If overlap given then use it, otherwise search for strel in kwargs
if overlap is not None:
overlap = overlap * (divs > 1)
else:
if type(strel_arg) == str:
strel_arg = [strel_arg]
for item in strel_arg:
if item in kwargs.keys():
strel = kwargs[item]
break
overlap = np.array(strel.shape) * (divs > 1)
slices = subdivide(im=im, divs=divs, overlap=overlap)
# Apply func to each subsection of the image
res = []
for s in slices:
# Extract subsection from image and input into kwargs
kwargs[im_arg] = dask.delayed(np.ascontiguousarray(im[tuple(s)]))
res.append(apply_func(func=func, **kwargs))
# Have dask actually compute the function on each subsection in parallel
# with ProgressBar():
# ims = dask.compute(res, num_workers=cores)[0]
ims = dask.compute(res, num_workers=cores)[0]
# Finally, put the pieces back together into a single master image, im2
im2 = recombine(ims=ims, slices=slices, overlap=overlap)
return im2
```