# Source code for porespy.filters._snows

```
import dask.array as da
import inspect as insp
import logging
import numpy as np
from numba import njit, prange
from edt import edt
import scipy.ndimage as spim
import scipy.spatial as sptl
from skimage.segmentation import watershed
from skimage.morphology import ball, disk, square, cube
from porespy.tools import _check_for_singleton_axes
from porespy.tools import extend_slice, ps_rect, ps_round
from porespy.tools import Results
from porespy.tools import get_tqdm
from porespy.filters import chunked_func
from porespy import settings
tqdm = get_tqdm()
logger = logging.getLogger(__name__)
[docs]def snow_partitioning(im, dt=None, r_max=4, sigma=0.4, peaks=None):
r"""
Partition the void space into pore regions using a marker-based
watershed algorithm
Parameters
----------
im : array_like
A boolean image of the domain, with ``True`` indicating the pore
space and ``False`` elsewhere.
dt : array_like, optional
The distance transform of the pore space. This is done
automatically if not provided, but if the distance transform has
already been computed then supplying it can save some time.
r_max : int
The radius of the spherical structuring element to use in the
Maximum filter stage that is used to find peaks. The default is 4.
sigma : float
The standard deviation of the Gaussian filter used in step 1. The
default is 0.4. If 0 is given then the filter is not applied,
which is useful if a distance transform is supplied as the ``im``
argument that has already been processed.
peaks : ndarray, optional
Optionally, it is possible to supply an array containing peaks, which
are used as markers in the watershed segmentation. If a boolean array
is received (``True`` indicating peaks), then ``scipy.ndimage.label``
with cubic connectivity is used to label them. If an integer array is
received then it is assumed the peaks have already been labelled.
This allows for comparison of peak finding algorithms for instance.
If this argument is provided, then ``r_max`` and ``sigma`` are ignored
since these are specfically used in the peak finding process.
Returns
-------
results : Results object
A custom object with the following data as attributes:
============ ==========================================================
Item Description
============ ==========================================================
``im`` The binary image of the void space
``dt`` The distance transform of the image
``peaks`` The peaks of the distance transform after applying the
steps of the SNOW algorithm
``regions`` The void space partitioned into pores using a marker
based watershed with the peaks found by the SNOW algorithm
============ ==========================================================
Notes
-----
The SNOW network extraction algorithm (Sub-Network of an
Over-segmented Watershed) was designed to handle to perculiarities of
high porosity materials, but it applies well to other materials as
well.
References
----------
[1] Gostick, J. "A versatile and efficient network extraction
algorithm using marker-based watershed segmenation". Physical Review
E. (2017)
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/snow_partitioning.html>`_
to view online example.
"""
logger.info("Beginning SNOW algorithm")
im_shape = np.array(im.shape)
if im.dtype is not bool:
logger.info("Converting supplied image to boolean")
im = im > 0
if dt is None:
logger.info("Peforming distance transform")
if np.any(im_shape == 1):
dt = edt(im.squeeze())
dt = dt.reshape(im_shape)
else:
dt = edt(im)
if peaks is None:
if sigma > 0:
logger.info(f"Applying Gaussian blur with sigma = {sigma}")
dt_blurred = spim.gaussian_filter(input=dt, sigma=sigma)*im
else:
dt_blurred = np.copy(dt)
peaks = find_peaks(dt=dt_blurred, r_max=r_max)
logger.debug(f"Initial number of peaks: {spim.label(peaks)[1]}")
peaks = trim_saddle_points(peaks=peaks, dt=dt)
logger.debug(f"Peaks after trimming saddle points: {spim.label(peaks)[1]}")
peaks = trim_nearby_peaks(peaks=peaks, dt=dt)
logger.debug(f"Peaks after trimming nearby points: {spim.label(peaks)[1]}")
peaks, N = spim.label(peaks > 0, structure=ps_rect(3, im.ndim))
regions = watershed(image=-dt, markers=peaks)
tup = Results()
tup.im = im
tup.dt = dt
tup.peaks = peaks
tup.regions = regions * (im > 0)
return tup
[docs]def snow_partitioning_n(im, r_max=4, sigma=0.4, peaks=None):
r"""
This function partitions an imaging oontain an arbitrary number of
phases into regions using a marker-based watershed segmentation.
Parameters
----------
im : ndarray
Image of porous material where each phase is represented by unique
integer starting from 1 (0's are ignored).
r_max : scalar
The radius of the spherical structuring element to use in the
Maximum filter stage that is used to find peaks. The default is 4.
sigma : scalar
The standard deviation of the Gaussian filter used. The default is
0.4. If 0 is given then the filter is not applied.
peaks : ndarray, optional
Optionally, it is possible to supply an array containing peaks, which
are used as markers in the watershed segmentation. Must be a boolean
array with ``True`` indicating peaks; ``scipy.ndimage.label``
with cubic connectivity is used to label them. If this argument is
provided then ``r_max`` and ``sigma`` are ignored, since these are
specfically used in the peak finding process.
Returns
-------
results : Results object
A custom object with the following data as attributes:
- 'im'
The original image of the porous material
- 'dt'
The combined distance transform in alll phases of the image
- 'phase_max_label'
The list of max label of each phase in order to
distinguish between each other
- 'regions'
The partitioned regions of n phases using a marker
based watershed with the peaks found by the SNOW algorithm
References
----------
[1] Gostick, J. "A versatile and efficient network extraction
algorithm using marker-based watershed segmentation". Physical Review
E. (2017)
[2] Khan, ZA et al. "Dual network extraction algorithm to investigate
multiple transport processes in porous materials: Image-based modeling
of pore and grain-scale processes". Computers in Chemical Engineering.
(2019)
See Also
--------
snow_partitioning
Notes
-----
In principle it is possible to perform a distance transform on each
phase separately, merge these into a single image, then apply the
watershed only once. This, however, has been found to create edge
artifacts between regions arising from the way watershed handles
plateaus in the distance transform. To overcome this, this function
applies the watershed to each of the distance transforms separately,
then merges the segmented regions back into a single image.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/snow_partitioning_n.html>`_
to view online example.
"""
# Perform snow on each phase and merge all segmentation and dt together
phases_num = np.unique(im).astype(int)
phases_num = phases_num[phases_num > 0]
combined_dt = 0
combined_region = 0
_peaks = np.zeros_like(im, dtype=int)
num = [0]
for i, j in enumerate(phases_num):
logger.info(f"Processing Phase {j}")
# Isolate active phase from image
phase = im == j
# Limit peaks to active phase only
temp = peaks*phase if peaks is not None else None
phase_snow = snow_partitioning(phase, dt=None, r_max=r_max,
sigma=sigma, peaks=temp)
combined_dt += phase_snow.dt
phase_snow.regions *= phase_snow.im
phase_snow.regions += num[i]
phase_ws = phase_snow.regions * phase_snow.im
phase_ws[phase_ws == num[i]] = 0
combined_region += phase_ws
_peaks = _peaks + phase_snow.peaks + (phase_snow.peaks > 0)*num[i]
num.append(np.amax(combined_region))
tup = Results()
tup.im = im
tup.dt = combined_dt
tup.phase_max_label = num[1:]
tup.regions = combined_region
tup.peaks = _peaks
return tup
[docs]def find_peaks(dt, r_max=4, strel=None, sigma=None, divs=1):
r"""
Finds local maxima in the distance transform
Parameters
----------
dt : ndarray
The distance transform of the pore space. This may be calculated
and filtered using any means desired.
r_max : scalar
The radius of the spherical element used in the maximum filter.
This controls the localness of any maxima. The default is 4 voxels.
strel : ndarray
Instead of supplying ``r_max``, this argument allows a custom
structuring element allowing control over both size and shape.
sigma : float or list of floats
If given, then a gaussian filter is applied to the distance transform
using this value for the kernel
(i.e. ``scipy.ndimage.gaussian_filter(dt, sigma)``)
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
An array of booleans with ``True`` values at the location of any
local maxima.
Notes
-----
It is also possible ot the ``peak_local_max`` function from the
``skimage.feature`` module as follows:
``peaks = peak_local_max(image=dt, min_distance=r, exclude_border=0,
indices=False)``
The *skimage* function automatically uses a square structuring element
which is significantly faster than using a circular or spherical
element.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/find_peaks.html>`_
to view online example.
"""
im = dt > 0
_check_for_singleton_axes(im)
if strel is None:
strel = ps_round(r=r_max, ndim=im.ndim)
if sigma is not None:
dt = spim.gaussian_filter(dt, sigma=sigma)
parallel = False
if isinstance(divs, int):
divs = [divs]*len(im.shape)
if np.any(np.array(divs) > 1):
parallel = True
logger.info(f'Performing {insp.currentframe().f_code.co_name} in parallel')
if parallel:
overlap = max(strel.shape)
mx = chunked_func(func=spim.maximum_filter, overlap=overlap,
im_arg='input', input=dt + 2.0 * (~im),
footprint=strel,
cores=settings.ncores, divs=divs)
else:
# The "2 * (~im)" sets solid voxels to 2 so peaks are not found
# at the void/solid interface
mx = spim.maximum_filter(dt + 2.0 * (~im), footprint=strel)
peaks = (dt == mx) * im
return peaks
[docs]def reduce_peaks(peaks):
r"""
Any peaks that are broad or elongated are replaced with a single voxel
that is located at the center of mass of the original voxels.
Parameters
----------
peaks : ndarray
An image containing ``True`` values indicating peaks in the
distance transform
Returns
-------
image : ndarray
An array with the same number of isolated peaks as the original
image, but fewer total ``True`` voxels.
Notes
-----
The center of mass of a group of voxels is used as the new single
voxel, so if the group has an odd shape (like a horse shoe), the new
voxel may *not* lie on top of the original set.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/reduce_peaks.html>`_
to view online example.
"""
if peaks.ndim == 2:
strel = square
else:
strel = cube
markers, N = spim.label(input=peaks, structure=strel(3))
inds = spim.measurements.center_of_mass(
input=peaks, labels=markers, index=np.arange(1, N + 1)
)
inds = np.floor(inds).astype(int)
# Centroid may not be on old pixel, so create a new peaks image
peaks_new = np.zeros_like(peaks, dtype=bool)
peaks_new[tuple(inds.T)] = True
return peaks_new
[docs]def trim_saddle_points(peaks, dt, maxiter=20):
r"""
Removes peaks that were mistakenly identified because they lied on a
saddle or ridge in the distance transform that was not actually a true
local peak.
Parameters
----------
peaks : ndarray
A boolean image containing ``True`` values to mark peaks in the
distance transform (``dt``)
dt : ndarray
The distance transform of the pore space for which the peaks
are sought.
maxiter : int
The number of iteration to use when finding saddle points.
The default value is 20.
Returns
-------
image : ndarray
An image with fewer peaks than the input image
References
----------
[1] Gostick, J. "A versatile and efficient network extraction algorithm
using marker-based watershed segmentation". Physical Review E. (2017)
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/trim_saddle_points.html>`_
to view online example.
"""
new_peaks = np.zeros_like(peaks, dtype=bool)
if dt.ndim == 2:
from skimage.morphology import square as cube
else:
from skimage.morphology import cube
labels, N = spim.label(peaks > 0)
slices = spim.find_objects(labels)
for i, s in tqdm(enumerate(slices), **settings.tqdm):
sx = extend_slice(s, shape=peaks.shape, pad=maxiter)
peaks_i = labels[sx] == i + 1
dt_i = dt[sx]
im_i = dt_i > 0
iters = 0
while iters < maxiter:
iters += 1
peaks_dil = spim.binary_dilation(input=peaks_i, structure=cube(3))
peaks_max = peaks_dil * np.amax(dt_i * peaks_dil)
peaks_extended = (peaks_max == dt_i) * im_i
if np.all(peaks_extended == peaks_i):
new_peaks[sx] += peaks_i
break # Found a true peak
elif np.sum(peaks_extended * peaks_i) == 0:
break # Found a saddle point
peaks_i = peaks_extended
if iters >= maxiter:
logger.debug(
"Maximum number of iterations reached, consider "
+ "running again with a larger value of max_iters"
)
return new_peaks*peaks
def trim_saddle_points_legacy(peaks, dt, maxiter=10):
r"""
Removes peaks that were mistakenly identified because they lied on a
saddle or ridge in the distance transform that was not actually a true
local peak.
Parameters
----------
peaks : ND-array
A boolean image containing True values to mark peaks in the distance
transform (``dt``)
dt : ND-array
The distance transform of the pore space for which the true peaks are
sought.
maxiter : int
The maximum number of iterations to run while eroding the saddle
points. The default is 10, which is usually not reached; however,
a warning is issued if the loop ends prior to removing all saddle
points.
Returns
-------
image : ND-array
An image with fewer peaks than the input image
Notes
-----
This version of the function was included in versions of PoreSpy < 2. It
is too aggressive in trimming peaks, so was rewritten for PoreSpy >= 2.
This is included here for legacy reasons
References
----------
[1] Gostick, J. "A versatile and efficient network extraction algorithm
using marker-based watershed segmenation". Physical Review E. (2017)
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/trim_saddle_points_legacy.html>`_
to view online example.
"""
new_peaks = np.zeros_like(peaks, dtype=bool)
if dt.ndim == 2:
from skimage.morphology import square as cube
else:
from skimage.morphology import cube
labels, N = spim.label(peaks > 0)
slices = spim.find_objects(labels)
for i, s in tqdm(enumerate(slices), **settings.tqdm):
sx = extend_slice(s, shape=peaks.shape, pad=10)
peaks_i = labels[sx] == i + 1
dt_i = dt[sx]
im_i = dt_i > 0
iters = 0
while iters < maxiter:
iters += 1
peaks_dil = spim.binary_dilation(input=peaks_i, structure=cube(3))
peaks_max = peaks_dil * np.amax(dt_i * peaks_dil)
peaks_extended = (peaks_max == dt_i) * im_i
if np.all(peaks_extended == peaks_i):
break # Found a true peak
elif np.sum(peaks_extended * peaks_i) == 0:
peaks_i = False
break # Found a saddle point
# The following line was also missing from the original. It has
# no effect on the result but without this the "maxiters" is
# reached very often.
peaks_i = peaks_extended
# The following line is essentially a bug. It should be:
# peaks[s] += peaks_i. Without the += the peaks_i image overwrites
# the entire slice s, which may include other peaks that are within
# 10 voxels due to the padding of s with extend_slice.
new_peaks[sx] = peaks_i
if iters >= maxiter:
logger.debug(
"Maximum number of iterations reached, consider "
+ "running again with a larger value of max_iters"
)
return new_peaks*peaks
[docs]def trim_nearby_peaks(peaks, dt, f=1):
r"""
Removes peaks that are nearer to another peak than to solid
Parameters
----------
peaks : ndarray
A image containing nonzeros values indicating peaks in the distance
transform (``dt``). If ``peaks`` is boolean, a boolean is returned;
if ``peaks`` have already been labelled, then the original labels
are returned, missing the trimmed peaks.
dt : ndarray
The distance transform of the pore space
f : scalar
Controls how close peaks must be before they are considered near
to each other. Sets of peaks are tagged as too near if
``d_neighbor < f * d_solid``.
Returns
-------
image : ndarray
An array the same size and type as ``peaks`` containing a subset of
the peaks in the original image.
Notes
-----
Each pair of peaks is considered simultaneously, so for a triplet of nearby
peaks, each pair is considered. This ensures that only the single peak
that is furthest from the solid is kept. No iteration is required.
References
----------
[1] Gostick, J. "A versatile and efficient network extraction
algorithm using marker-based watershed segmenation". Physical Review
E. (2017)
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/trim_nearby_peaks.html>`_
to view online example.
"""
if dt.ndim == 2:
from skimage.morphology import square as cube
else:
from skimage.morphology import cube
labels, N = spim.label(peaks > 0, structure=cube(3))
crds = spim.measurements.center_of_mass(peaks > 0, labels=labels,
index=np.arange(1, N + 1))
crds = np.vstack(crds).astype(int) # Convert to numpy array of ints
L = dt[tuple(crds.T)] # Get distance to solid for each peak
# Add tiny amount to joggle points to avoid equal distances to solid
# arange was added instead of random values so the results are repeatable
L = L + np.arange(len(L))*1e-6
tree = sptl.KDTree(data=crds)
# Find list of nearest peak to each peak
temp = tree.query(x=crds, k=2)
nearest_neighbor = temp[1][:, 1]
dist_to_neighbor = temp[0][:, 1]
del temp, tree # Free-up memory
hits = np.where(dist_to_neighbor <= f * L)[0]
# Drop peak that is closer to the solid than it's neighbor
drop_peaks = []
for i in hits:
if L[i] < L[nearest_neighbor[i]]:
drop_peaks.append(i)
else:
drop_peaks.append(nearest_neighbor[i])
drop_peaks = np.unique(drop_peaks)
new_peaks = ~np.isin(labels, drop_peaks+1)*peaks
return new_peaks
def _estimate_overlap(im, mode='dt', zoom=0.25):
logger.info('Calculating overlap thickness')
if mode == 'watershed':
rev = spim.interpolation.zoom(im, zoom=zoom, order=0)
rev = rev > 0
dt = edt(rev, parallel=0)
rev_snow = snow_partitioning(rev, dt=dt)
labels, counts = np.unique(rev_snow, return_counts=True)
node = np.where(counts == counts[1:].max())[0][0]
slices = spim.find_objects(rev_snow)
overlap = max(rev_snow[slices[node - 1]].shape) / (zoom * 2.0)
if mode == 'dt':
dt = edt((im > 0), parallel=0)
overlap = dt.max()
return overlap
[docs]def snow_partitioning_parallel(im,
r_max=4,
sigma=0.4,
divs=2,
overlap=None,
cores=None,
):
r"""
Performs SNOW algorithm in parallel (or serial) to reduce time
(or memory usage) by geomertirc domain decomposition of large images.
Parameters
----------
im : ndarray
A binary image of porous media with 'True' values indicating
phase of interest.
overlap : float (optional)
The amount of overlap to apply between chunks. If not provided it
will be estiamted using ``porespy.tools.estimate_overlap`` with
``mode='dt'``.
divs : list or int
Number of domains each axis will be divided. Options are:
- scalar: it will be assigned to all axis.
- list: each respective axis will be divided by its
corresponding number in the list. For example [2, 3, 4] will
divide z, y and x axis to 2, 3, and 4 respectively.
cores : int or None
Number of cores that will be used to parallel process all domains.
If ``None`` then all cores will be used but user can specify any
integer values to control the memory usage. Setting value to 1
will effectively process the chunks in serial to minimize memory
usage.
Returns
-------
regions : ndarray
Partitioned image of segmentated regions with unique labels. Each
region correspond to pore body while intersection with other
region correspond throat area.
Examples
--------
`Click here
<https://porespy.org/examples/filters/reference/snow_partitioning_parallel.html>`_
to view online example.
"""
# Adjust image shape according to specified dimension
if isinstance(divs, int):
divs = [divs for i in range(im.ndim)]
shape = []
for i in range(im.ndim):
shape.append(divs[i] * (im.shape[i] // divs[i]))
if tuple(shape) != im.shape:
for i in range(im.ndim):
im = im.swapaxes(0, i)
im = im[:shape[i], ...]
im = im.swapaxes(i, 0)
logger.debug(f'Image was cropped to shape {shape}')
# Get overlap thickness from distance transform
chunk_shape = (np.array(shape) / np.array(divs)).astype(int)
logger.info('Beginning parallel SNOW algorithm...')
if overlap is None:
overlap = _estimate_overlap(im, mode='dt')
overlap = overlap / 2.0
logger.debug(f'Overlap thickness: {int(2 * overlap)} voxels')
dt = edt((im > 0), parallel=0)
# Get overlap and trim depth of all image dimension
depth = {}
trim_depth = {}
for i in range(im.ndim):
depth[i] = int(2.0 * overlap)
trim_depth[i] = int(2.0 * overlap) - 1
# Applying SNOW to image chunks
regions = da.from_array(dt, chunks=chunk_shape)
regions = da.overlap.overlap(regions, depth=depth, boundary='none')
regions = regions.map_blocks(_snow_chunked, r_max=r_max,
sigma=sigma, dtype=dt.dtype)
regions = da.overlap.trim_internal(regions, trim_depth, boundary='none')
# TODO: use dask ProgressBar once compatible w/ logging.
logger.info('Applying snow to image chunks')
regions = regions.compute(num_workers=cores)
# Relabelling watershed chunks
logger.info('Relabelling watershed chunks')
regions = relabel_chunks(im=regions, chunk_shape=chunk_shape)
# Stitching watershed chunks
logger.info('Stitching watershed chunks')
regions = _watershed_stitching(im=regions, chunk_shape=chunk_shape)
tup = Results()
tup.im = im
tup.dt = dt
tup.regions = regions
return tup
def _pad(im, pad_width=1, constant_value=0):
r"""
Pad the image with a constant values and width.
Parameters
----------
im : ndarray
The image that requires padding
pad_width : int
The number of values that will be padded from the edges. Default
values is 1.
contant_value : int
Pads with the specified constant value
Returns
-------
output: ndarray
Padded image with same dimnesions as provided image
"""
shape = np.array(im.shape)
pad_shape = shape + (2 * pad_width)
temp = np.zeros(pad_shape, dtype=np.uint32)
if constant_value != 0:
temp = temp + constant_value
if im.ndim == 3:
temp[pad_width: -pad_width,
pad_width: -pad_width,
pad_width: -pad_width] = im
elif im.ndim == 2:
temp[pad_width: -pad_width,
pad_width: -pad_width] = im
else:
temp[pad_width: -pad_width] = im
return temp
def relabel_chunks(im, chunk_shape):
r"""
Assign new labels to each chunk or sub-domain of actual image. This
prevents from two or more regions to have same label.
Parameters
----------
im: ndarray
Actual image that contains repeating labels in chunks/sub-domains.
chunk_shape: tuple
The shape of chunk that will be relabeled in actual image. Note
the chunk shape should be a multiple of actual image shape
otherwise some labels will not be relabeled.
Returns
-------
output : ndarray
Relabeled image with unique label assigned to each region.
"""
im = _pad(im, pad_width=1)
im_shape = np.array(im.shape, dtype=np.uint32)
max_num = 0
c = np.array(chunk_shape, dtype=np.uint32) + 2
num = (im_shape / c).astype(int)
if im.ndim == 3:
for z in range(num[0]):
for y in range(num[1]):
for x in range(num[2]):
chunk = im[z * c[0]: (z + 1) * c[0],
y * c[1]: (y + 1) * c[1],
x * c[2]: (x + 1) * c[2]]
chunk += max_num
chunk[chunk == max_num] = 0
max_num = chunk.max()
im[z * c[0]: (z + 1) * c[0],
y * c[1]: (y + 1) * c[1],
x * c[2]: (x + 1) * c[2]] = chunk
else:
for y in range(num[0]):
for x in range(num[1]):
chunk = im[y * c[0]: (y + 1) * c[0],
x * c[1]: (x + 1) * c[1]]
chunk += max_num
chunk[chunk == max_num] = 0
max_num = chunk.max()
im[y * c[0]: (y + 1) * c[0],
x * c[1]: (x + 1) * c[1]] = chunk
return im
def _trim_internal_slice(im, chunk_shape):
r"""
Delete extra slices from image that were used to stitch two or more
chunks together.
Parameters:
-----------
im : ndarray
image that contains extra slices in x, y, z direction.
chunk_shape : tuple
The shape of the chunk from which image is subdivided.
Return:
-------
output : ndarray
Image without extra internal slices. The shape of the image will
be same as input image provided for waterhsed segmentation.
"""
im_shape = np.array(im.shape, dtype=np.uint32)
c1 = np.array(chunk_shape, dtype=np.uint32) + 2
c2 = np.array(chunk_shape, dtype=np.uint32)
num = (im_shape / c1).astype(int)
out_shape = num * c2
out = np.empty((out_shape), dtype=np.uint32)
if im.ndim == 3:
for z in range(num[0]):
for y in range(num[1]):
for x in range(num[2]):
chunk = im[z * c1[0]: (z + 1) * c1[0],
y * c1[1]: (y + 1) * c1[1],
x * c1[2]: (x + 1) * c1[2]]
out[z * c2[0]: (z + 1) * c2[0],
y * c2[1]: (y + 1) * c2[1],
x * c2[2]: (x + 1) * c2[2]] = chunk[1:-1, 1:-1, 1:-1]
else:
for y in range(num[0]):
for x in range(num[1]):
chunk = im[y * c1[0]: (y + 1) * c1[0],
x * c1[1]: (x + 1) * c1[1]]
out[y * c2[0]: (y + 1) * c2[0],
x * c2[1]: (x + 1) * c2[1]] = chunk[1:-1, 1:-1]
return out
def _watershed_stitching(im, chunk_shape):
r"""
Stitch individual sub-domains of watershed segmentation into one big
segmentation with all boundary labels of each sub-domain relabeled to
merge boundary regions.
Parameters:
-----------
im : ndarray
A worked image with watershed segmentation performed on all
sub-domains individually.
chunk_shape: tuple
The shape of the sub-domain in which segmentation is performed.
Returns
-------
output : ndarray
Stitched watershed segmentation with all sub-domains merged to
form a single watershed segmentation.
"""
c_shape = np.array(chunk_shape)
cuts_num = (np.array(im.shape) / c_shape).astype(np.uint32)
for axis, num in enumerate(cuts_num):
keys = []
values = []
if num > 1:
im = im.swapaxes(0, axis)
for i in range(1, num):
sl = i * (chunk_shape[axis] + 3) - (i - 1)
sl1 = im[sl - 3, ...]
sl1_mask = sl1 > 0
sl2 = im[sl - 1, ...] * sl1_mask
sl1_labels = sl1.flatten()[sl1.flatten() > 0]
sl2_labels = sl2.flatten()[sl2.flatten() > 0]
if sl1_labels.size != sl2_labels.size:
raise Exception('The selected overlapping thickness is not '
'suitable for input image. Change '
'overlapping criteria '
'or manually input value.')
keys.append(sl1_labels)
values.append(sl2_labels)
im = _replace_labels(array=im, keys=keys, values=values)
im = im.swapaxes(axis, 0)
im = _trim_internal_slice(im=im, chunk_shape=chunk_shape)
im = _resequence_labels(array=im)
return im
@njit(parallel=True)
def _copy(im, output):
r"""
The function copy the input array and make output array that is
allocated in different memory space. This a numba version of copy
function of numpy. Because each element is copied using parallel
approach this implementation is faster than numpy version of copy.
Parameters
----------
array: ndarray
Array that needs to be copied.
Returns
-------
output: ndarray
Copied array.
"""
if im.ndim == 3:
for i in prange(im.shape[0]):
for j in prange(im.shape[1]):
for k in prange(im.shape[2]):
output[i, j, k] = im[i, j, k]
elif im.ndim == 2:
for i in prange(im.shape[0]):
for j in prange(im.shape[1]):
output[i, j] = im[i, j]
else:
for i in prange(im.shape[0]):
output[i] = im[i]
return output
@njit(parallel=True)
def _replace(array, keys, values, ind_sort):
r"""
This function replace keys elements in input array with new value
elements. This function is used as internal function of
replace_relabels.
Parameters
----------
array : ndarray
Array which requires replacing labels.
keys : array_like
1d array containing unique labels that need to be replaced.
values : array_like
1d array containing unique values that will be assigned to labels.
Returns
-------
array : ndarray
Array with replaced labels.
"""
# ind_sort = np.argsort(keys)
keys_sorted = keys[ind_sort]
values_sorted = values[ind_sort]
s_keys = set(keys)
for i in prange(array.shape[0]):
if array[i] in s_keys:
ind = np.searchsorted(keys_sorted, array[i])
array[i] = values_sorted[ind]
def _replace_labels(array, keys, values):
r"""
Replace labels in array provided as keys to values.
Parameter:
----------
array : ndarray
Array which requires replacing labels
keys : 1D-array
The unique labels that need to be replaced
values : 1D-array
The unique values that will be assigned to labels
return:
-------
array : ndarray
Array with replaced labels.
"""
a_shape = array.shape
array = array.flatten()
keys = np.concatenate(keys, axis=0)
values = np.concatenate(values, axis=0)
ind_sort = np.argsort(keys)
_replace(array, keys, values, ind_sort)
return array.reshape(a_shape)
@njit()
def _sequence(array, count):
r"""
Internal function of resequnce_labels method. This function resquence
array elements in an ascending order using numba technique which is
many folds faster than make contigious funcition.
Parameters
----------
array: ndarray
1d array that needs resquencing.
count: array_like
1d array of zeros having same size as array.
Returns
-------
array: 1d-array
The input array with elements resequenced in ascending order
Notes
-----
The output of this function is not same as make_contigous or
relabel_sequential function of scikit-image. This function resequence
and randomize the regions while other methods only do resequencing and
output sorted array.
"""
a = 1
i = 0
while i < (len(array)):
data = array[i]
if data != 0:
if count[data] == 0:
count[data] = a
a += 1
array[i] = count[data]
i += 1
@njit(parallel=True)
def _amax(array):
r"""
Find largest element in an array using fast parallel numba technique.
Parameter:
----------
array: ndarray
Array in which largest elements needs to be calcuted.
Returns
-------
scalar: float or int
The largest element value in the input array.
"""
return np.max(array)
def _resequence_labels(array):
r"""
Resequence the lablels to make them contigious.
Parameters
----------
array: ndarray
Array that requires resequencing.
Returns
-------
array : ndarray
Resequenced array with same shape as input array.
"""
a_shape = array.shape
array = array.ravel()
max_num = _amax(array) + 1
count = np.zeros(max_num, dtype=np.uint32)
_sequence(array, count)
return array.reshape(a_shape)
def _snow_chunked(dt, r_max=5, sigma=0.4):
r"""
This private version of snow is called during snow_parallel.
"""
dt2 = spim.gaussian_filter(input=dt, sigma=sigma)
peaks = find_peaks(dt=dt2, r_max=r_max)
peaks = trim_saddle_points(peaks=peaks, dt=dt)
peaks = trim_nearby_peaks(peaks=peaks, dt=dt)
peaks, N = spim.label(peaks > 0)
regions = watershed(image=-dt, markers=peaks)
return regions * (dt > 0)
```