source/tomopy/misc/corr.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
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"""
Module for data correction and masking functions.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import scipy.ndimage
import tomopy.util.mproc as mproc
import tomopy.util.dtype as dtype
import tomopy.util.extern as extern
import logging
import warnings
import numexpr as ne
import concurrent.futures as cf
from scipy.signal import medfilt2d
logger = logging.getLogger(__name__)
__author__ = "Doga Gursoy, William Judge"
__credits__ = "Mark Rivers, Xianghui Xiao"
__copyright__ = "Copyright (c) 2015, UChicago Argonne, LLC."
__docformat__ = "restructuredtext en"
__all__ = [
"adjust_range",
"circ_mask",
"gaussian_filter",
"median_filter",
"median_filter_cuda",
"median_filter_nonfinite",
"median_filter3d",
"remove_outlier3d",
"sobel_filter",
"remove_nan",
"remove_neg",
"remove_outlier",
"remove_outlier1d",
"remove_outlier_cuda",
"remove_ring",
"enhance_projs_aps_1id",
"inpainter_morph",
]
def adjust_range(arr, dmin=None, dmax=None):
"""
Change dynamic range of values in an array.
Parameters
----------
arr : ndarray
Input array.
dmin, dmax : float, optional
Mininum and maximum values to rescale data.
Returns
-------
ndarray
Output array.
"""
if dmax is None:
dmax = np.max(arr)
if dmin is None:
dmin = np.min(arr)
if dmax < np.max(arr):
arr[arr > dmax] = dmax
if dmin > np.min(arr):
arr[arr < dmin] = dmin
return arr
def gaussian_filter(arr, sigma=3, order=0, axis=0, ncore=None):
"""
Apply Gaussian filter to 3D array along specified axis.
Parameters
----------
arr : ndarray
Input array.
sigma : scalar or sequence of scalars
Standard deviation for Gaussian kernel. The standard deviations
of the Gaussian filter are given for each axis as a sequence, or
as a single number, in which case it is equal for all axes.
order : {0, 1, 2, 3} or sequence from same set, optional
Order of the filter along each axis is given as a sequence
of integers, or as a single number. An order of 0 corresponds
to convolution with a Gaussian kernel. An order of 1, 2, or 3
corresponds to convolution with the first, second or third
derivatives of a Gaussian. Higher order derivatives are not
implemented
axis : int, optional
Axis along which median filtering is performed.
ncore : int, optional
Number of cores that will be assigned to jobs.
Returns
-------
ndarray
3D array of same shape as input.
"""
arr = dtype.as_float32(arr)
out = np.empty_like(arr)
if ncore is None:
ncore = mproc.mp.cpu_count()
with cf.ThreadPoolExecutor(ncore) as e:
slc = [slice(None)] * arr.ndim
for i in range(arr.shape[axis]):
slc[axis] = i
e.submit(
scipy.ndimage.gaussian_filter,
arr[tuple(slc)],
sigma,
order=order,
output=out[tuple(slc)],
)
return out
def median_filter(arr, size=3, axis=0, ncore=None):
"""
Apply median filter to 3D array along specified axis.
Parameters
----------
arr : ndarray
Input array.
size : int, optional
The size of the filter.
axis : int, optional
Axis along which median filtering is performed.
ncore : int, optional
Number of cores that will be assigned to jobs.
Returns
-------
ndarray
Median filtered 3D array.
"""
arr = dtype.as_float32(arr)
out = np.empty_like(arr)
if ncore is None:
ncore = mproc.mp.cpu_count()
with cf.ThreadPoolExecutor(ncore) as e:
slc = [slice(None)] * arr.ndim
for i in range(arr.shape[axis]):
slc[axis] = i
e.submit(
scipy.ndimage.median_filter,
arr[tuple(slc)],
size=(size, size),
output=out[tuple(slc)],
)
return out
def median_filter_cuda(arr, size=3, axis=0):
"""
Apply median filter to 3D array along 0 axis with GPU support.
The winAllow is for A6000, Tian X support 3 to 8
Parameters
----------
arr : ndarray
Input array.
size : int, optional
The size of the filter.
axis : int, optional
Axis along which median filtering is performed.
Returns
-------
ndarray
Median filtered 3D array.
Example
-------
import tomocuda
tomocuda.remove_outlier_cuda(arr, dif, 5)
For more information regarding install and using tomocuda, check
https://github.com/kyuepublic/tomocuda for more information
"""
try:
import tomocuda
winAllow = range(2, 16)
if axis != 0:
arr = np.swapaxes(arr, 0, axis)
if size in winAllow:
loffset = int(size / 2)
roffset = int((size - 1) / 2)
prjsize = arr.shape[0]
imsizex = arr.shape[2]
imsizey = arr.shape[1]
filter = tomocuda.mFilter(imsizex, imsizey, prjsize, size)
out = np.zeros(shape=(prjsize, imsizey, imsizex), dtype=np.float32)
for step in range(prjsize):
# im_noisecu = arr[:][step][:].astype(np.float32)
im_noisecu = arr[step].astype(np.float32)
im_noisecu = np.lib.pad(
im_noisecu,
((loffset, roffset), (loffset, roffset)),
"symmetric",
)
im_noisecu = im_noisecu.flatten()
filter.setCuImage(im_noisecu)
filter.run2DFilter(size)
results = filter.retreive()
results = results.reshape(imsizey, imsizex)
out[step] = results
if axis != 0:
out = np.swapaxes(out, 0, axis)
else:
warnings.warn("Window size not support, using cpu median filter")
out = median_filter(arr, size, axis)
except ImportError:
warnings.warn("The tomocuda is not support, using cpu median filter")
out = median_filter(arr, size, axis)
return out
def median_filter_nonfinite(arr, size=3, callback=None):
"""
Remove nonfinite values from a 3D array using an in-place 2D median filter.
The 2D selective median filter is applied along the last two axes of
the array.
.. versionadded:: 1.11
Parameters
----------
arr : ndarray
The 3D array with nonfinite values in it.
size : int, optional
The size of the filter.
callback : func(total, description, unit)
A function called after every internal loop iteration.
total is number of loop iterations.
description is 'Nonfinite median filter'.
unit is ' prjs'.
Returns
-------
ndarray
The corrected 3D array with all nonfinite values removed based upon the
local median value defined by the kernel size.
Raises
------
ValueError
If the filter comes across a kernel only containing non-finite values a
ValueError is raised for the user to increase their kernel size.
"""
# Defining a callback function if None is provided
if callback is None:
def callback(total, description, unit):
pass
# Iterating throug each projection to save on RAM
for projection in arr:
nonfinite_idx = np.nonzero(~np.isfinite(projection))
projection_copy = projection.copy()
# Iterating through each bad value and replace it with finite median
for x_idx, y_idx in zip(*nonfinite_idx):
# Determining the lower and upper bounds for kernel
x_lower = max(0, x_idx - (size // 2))
x_higher = min(arr.shape[1], x_idx + (size // 2) + 1)
y_lower = max(0, y_idx - (size // 2))
y_higher = min(arr.shape[2], y_idx + (size // 2) + 1)
# Extracting kernel data and fining finite median
kernel_cropped_arr = projection_copy[x_lower:x_higher, y_lower:y_higher]
if len(kernel_cropped_arr[np.isfinite(kernel_cropped_arr)]) == 0:
raise ValueError(
"Found kernel containing only non-finite values.\
Please increase kernel size"
)
median_corrected_arr = np.median(
kernel_cropped_arr[np.isfinite(kernel_cropped_arr)]
)
# Replacing bad data with finite median
projection[x_idx, y_idx] = median_corrected_arr
callback(arr.shape[0], "Nonfinite median filter", " prjs")
return arr
def median_filter3d(arr, size=3, ncore=None):
"""
Apply 3D median filter to a 3D array.
.. versionadded:: 1.13
Parameters
----------
arr : ndarray
Input 3D array either float32 or uint16 data type.
size : int, optional
The size of the filter's kernel.
ncore : int, optional
Number of cores that will be assigned to jobs. All cores will be used
if unspecified.
Returns
-------
ndarray
Median filtered 3D array either float32 or uint16 data type.
Raises
------
ValueError
If the input array is not three dimensional.
"""
if ncore is None:
ncore = mproc.mp.cpu_count()
input_type = arr.dtype
if (input_type != "float32") and (input_type != "uint16"):
arr = dtype.as_float32(arr) # silent convertion to float32 data type
out = np.empty_like(arr)
dif = 0.0 # set to 0 to avoid selective filtering
# convert the full kernel size (odd int) to a half size as the C function requires it
kernel_half_size = (max(int(size), 3) - 1) // 2
if arr.ndim == 3:
dz, dy, dx = arr.shape
if (dz == 0) or (dy == 0) or (dx == 0):
raise ValueError("The length of one of dimensions is equal to zero")
else:
raise ValueError("The input array must be a 3D array")
# perform full 3D filtering
if input_type == "float32":
extern.c_median_filt3d_float32(
np.ascontiguousarray(arr), out, kernel_half_size, dif, ncore, dx, dy, dz
)
else:
extern.c_median_filt3d_uint16(
np.ascontiguousarray(arr), out, kernel_half_size, dif, ncore, dx, dy, dz
)
return out
def remove_outlier3d(arr, dif, size=3, ncore=None):
"""
Selectively applies 3D median filter to a 3D array to remove outliers. Also
called a dezinger.
.. versionadded:: 1.13
Parameters
----------
arr : ndarray
Input 3D array either float32 or uint16 data type.
dif : float
Expected difference value between outlier value and the median value of
the array.
size : int, optional
The size of the filter's kernel.
ncore : int, optional
Number of cores that will be assigned to jobs. All cores will be used
if unspecified.
Returns
-------
ndarray
Dezingered 3D array either float32 or uint16 data type.
Raises
------
ValueError
If the input array is not three dimensional.
"""
if ncore is None:
ncore = mproc.mp.cpu_count()
input_type = arr.dtype
if (input_type != "float32") and (input_type != "uint16"):
arr = dtype.as_float32(arr) # silent convertion to float32 data type
out = np.copy(arr, order="C")
# convert the full kernel size (odd int) to a half size as the C function requires it
kernel_half_size = (max(int(size), 3) - 1) // 2
if arr.ndim == 3:
dz, dy, dx = arr.shape
if (dz == 0) or (dy == 0) or (dx == 0):
raise ValueError("The length of one of dimensions is equal to zero")
else:
raise ValueError("The input array must be a 3D array")
# perform full 3D filtering
if input_type == "float32":
extern.c_median_filt3d_float32(
np.ascontiguousarray(arr), out, kernel_half_size, dif, ncore, dx, dy, dz
)
else:
extern.c_median_filt3d_uint16(
np.ascontiguousarray(arr), out, kernel_half_size, dif, ncore, dx, dy, dz
)
return out
def sobel_filter(arr, axis=0, ncore=None):
"""
Apply Sobel filter to 3D array along specified axis.
Parameters
----------
arr : ndarray
Input array.
axis : int, optional
Axis along which sobel filtering is performed.
ncore : int, optional
Number of cores that will be assigned to jobs.
Returns
-------
ndarray
3D array of same shape as input.
"""
arr = dtype.as_float32(arr)
out = np.empty_like(arr)
if ncore is None:
ncore = mproc.mp.cpu_count()
with cf.ThreadPoolExecutor(ncore) as e:
slc = [slice(None)] * arr.ndim
for i in range(arr.shape[axis]):
slc[axis] = i
e.submit(filters.sobel, arr[slc], output=out[slc])
return out
def remove_nan(arr, val=0.0, ncore=None):
"""
Replace NaN values in array with a given value.
Parameters
----------
arr : ndarray
Input array.
val : float, optional
Values to be replaced with NaN values in array.
ncore : int, optional
Number of cores that will be assigned to jobs.
Returns
-------
ndarray
Corrected array.
"""
arr = dtype.as_float32(arr)
val = np.float32(val)
with mproc.set_numexpr_threads(ncore):
ne.evaluate("where(arr!=arr, val, arr)", out=arr)
return arr
def remove_neg(arr, val=0.0, ncore=None):
"""
Replace negative values in array with a given value.
Parameters
----------
arr : ndarray
Input array.
val : float, optional
Values to be replaced with negative values in array.
ncore : int, optional
Number of cores that will be assigned to jobs.
Returns
-------
ndarray
Corrected array.
"""
arr = dtype.as_float32(arr)
val = np.float32(val)
with mproc.set_numexpr_threads(ncore):
ne.evaluate("where(arr<0, val, arr)", out=arr)
return arr
def remove_outlier(arr, dif, size=3, axis=0, ncore=None, out=None):
"""
Remove high intensity bright spots from a N-dimensional array by chunking
along the specified dimension, and performing (N-1)-dimensional median
filtering along the other dimensions.
Parameters
----------
arr : ndarray
Input array.
dif : float
Expected difference value between outlier value and
the median value of the array.
size : int
Size of the median filter.
axis : int, optional
Axis along which to chunk.
ncore : int, optional
Number of cores that will be assigned to jobs.
out : ndarray, optional
Output array for result. If same as arr, process
will be done in-place.
Returns
-------
ndarray
Corrected array.
"""
tmp = np.empty_like(arr)
ncore, chnk_slices = mproc.get_ncore_slices(arr.shape[axis], ncore=ncore)
filt_size = [size] * arr.ndim
filt_size[axis] = 1
with cf.ThreadPoolExecutor(ncore) as e:
slc = [slice(None)] * arr.ndim
for i in range(ncore):
slc[axis] = chnk_slices[i]
e.submit(
scipy.ndimage.median_filter,
arr[tuple(slc)],
size=filt_size,
output=tmp[tuple(slc)],
)
arr = dtype.as_float32(arr)
tmp = dtype.as_float32(tmp)
dif = np.float32(dif)
with mproc.set_numexpr_threads(ncore):
out = ne.evaluate("where(arr-tmp>=dif,tmp,arr)", out=out)
return out
def remove_outlier1d(arr, dif, size=3, axis=0, ncore=None, out=None):
"""
Remove high intensity bright spots from an array, using a one-dimensional
median filter along the specified axis.
Parameters
----------
arr : ndarray
Input array.
dif : float
Expected difference value between outlier value and
the median value of the array.
size : int
Size of the median filter.
axis : int, optional
Axis along which median filtering is performed.
ncore : int, optional
Number of cores that will be assigned to jobs.
out : ndarray, optional
Output array for result. If same as arr, process
will be done in-place.
Returns
-------
ndarray
Corrected array.
"""
arr = dtype.as_float32(arr)
dif = np.float32(dif)
tmp = np.empty_like(arr)
other_axes = [i for i in range(arr.ndim) if i != axis]
largest = np.argmax([arr.shape[i] for i in other_axes])
lar_axis = other_axes[largest]
ncore, chnk_slices = mproc.get_ncore_slices(arr.shape[lar_axis], ncore=ncore)
filt_size = [1] * arr.ndim
filt_size[axis] = size
with cf.ThreadPoolExecutor(ncore) as e:
slc = [slice(None)] * arr.ndim
for i in range(ncore):
slc[lar_axis] = chnk_slices[i]
e.submit(
scipy.ndimage.median_filter,
arr[slc],
size=filt_size,
output=tmp[slc],
mode="mirror",
)
with mproc.set_numexpr_threads(ncore):
out = ne.evaluate("where(arr-tmp>=dif,tmp,arr)", out=out)
return out
def remove_outlier_cuda(arr, dif, size=3, axis=0):
"""
Remove high intensity bright spots from a 3D array along axis 0
dimension using GPU.
Parameters
----------
arr : ndarray
Input array.
dif : float
Expected difference value between outlier value and
the median value of the array.
size : int
Size of the median filter.
axis : int, optional
Axis along which outlier removal is performed.
Returns
-------
ndarray
Corrected array.
Example
-------
>>> import tomocuda
>>> tomocuda.remove_outlier_cuda(arr, dif, 5)
For more information regarding install and using tomocuda, check
https://github.com/kyuepublic/tomocuda for more information
"""
arr = dtype.as_float32(arr)
dif = np.float32(dif)
try:
import tomocuda
winAllow = range(2, 16)
if axis != 0:
arr = np.swapaxes(arr, 0, axis)
if size in winAllow:
prjsize = arr.shape[0]
loffset = int(size / 2)
roffset = int((size - 1) / 2)
imsizex = arr.shape[2]
imsizey = arr.shape[1]
filter = tomocuda.mFilter(imsizex, imsizey, prjsize, size)
out = np.zeros(shape=(prjsize, imsizey, imsizex), dtype=np.float32)
for step in range(prjsize):
im_noisecu = arr[step].astype(np.float32)
im_noisecu = np.lib.pad(
im_noisecu, ((loffset, roffset), (loffset, roffset)), "symmetric"
)
im_noisecu = im_noisecu.flatten()
filter.setCuImage(im_noisecu)
filter.run2DRemoveOutliner(size, dif)
results = filter.retreive()
results = results.reshape(imsizey, imsizex)
out[step] = results
if axis != 0:
out = np.swapaxes(out, 0, axis)
else:
warnings.warn("Window size not support, using cpu outlier removal")
out = remove_outlier(arr, dif, size)
except ImportError:
warnings.warn("The tomocuda is not support, using cpu outlier removal")
out = remove_outlier(arr, dif, size)
return out
def remove_ring(
rec,
center_x=None,
center_y=None,
thresh=300.0,
thresh_max=300.0,
thresh_min=-100.0,
theta_min=30,
rwidth=30,
int_mode="WRAP",
ncore=None,
nchunk=None,
out=None,
):
"""
Remove ring artifacts from images in the reconstructed domain.
Descriptions of parameters need to be more clear for sure.
Parameters
----------
arr : ndarray
Array of reconstruction data
center_x : float, optional
abscissa location of center of rotation
center_y : float, optional
ordinate location of center of rotation
thresh : float, optional
maximum value of an offset due to a ring artifact
thresh_max : float, optional
max value for portion of image to filter
thresh_min : float, optional
min value for portion of image to filer
theta_min : int, optional
Features larger than twice this angle (degrees) will be considered
a ring artifact. Must be less than 180 degrees.
rwidth : int, optional
Maximum width of the rings to be filtered in pixels
int_mode : str, optional
'WRAP' for wrapping at 0 and 360 degrees, 'REFLECT' for reflective
boundaries at 0 and 180 degrees.
ncore : int, optional
Number of cores that will be assigned to jobs.
nchunk : int, optional
Chunk size for each core.
out : ndarray, optional
Output array for result. If same as arr, process
will be done in-place.
Returns
-------
ndarray
Corrected reconstruction data
"""
rec = dtype.as_float32(rec)
if out is None:
out = rec.copy()
else:
out = dtype.as_float32(out)
dz, dy, dx = rec.shape
if center_x is None:
center_x = (dx - 1.0) / 2.0
if center_y is None:
center_y = (dy - 1.0) / 2.0
if int_mode.lower() == "wrap":
int_mode = 0
elif int_mode.lower() == "reflect":
int_mode = 1
else:
raise ValueError("int_mode should be WRAP or REFLECT")
if not 0 <= theta_min < 180:
raise ValueError("theta_min should be in the range [0 - 180)")
args = (
center_x,
center_y,
dx,
dy,
dz,
thresh_max,
thresh_min,
thresh,
theta_min,
rwidth,
int_mode,
)
axis_size = rec.shape[0]
ncore, nchunk = mproc.get_ncore_nchunk(axis_size, ncore, nchunk)
with cf.ThreadPoolExecutor(ncore) as e:
for offset in range(0, axis_size, nchunk):
slc = np.s_[offset : offset + nchunk]
e.submit(extern.c_remove_ring, out[slc], *args)
return out
def circ_mask(arr, axis, ratio=1, val=0.0, ncore=None):
"""
Apply circular mask to a 3D array.
Parameters
----------
arr : ndarray
Arbitrary 3D array.
axis : int
Axis along which mask will be performed.
ratio : int, optional
Ratio of the mask's diameter in pixels to
the smallest edge size along given axis.
val : int, optional
Value for the masked region.
Returns
-------
ndarray
Masked array.
"""
arr = dtype.as_float32(arr)
val = np.float32(val)
_arr = arr.swapaxes(0, axis)
dx, dy, dz = _arr.shape
mask = _get_mask(dy, dz, ratio)
with mproc.set_numexpr_threads(ncore):
ne.evaluate("where(mask, _arr, val)", out=_arr)
return _arr.swapaxes(0, axis)
def _get_mask(dx, dy, ratio):
"""
Calculate 2D boolean circular mask.
Parameters
----------
dx, dy : int
Dimensions of the 2D mask.
ratio : int
Ratio of the circle's diameter in pixels to
the smallest mask dimension.
Returns
-------
ndarray
2D boolean array.
"""
rad1 = dx / 2.0
rad2 = dy / 2.0
if dx < dy:
r2 = rad1 * rad1
else:
r2 = rad2 * rad2
y, x = np.ogrid[0.5 - rad1 : 0.5 + rad1, 0.5 - rad2 : 0.5 + rad2]
return x * x + y * y < ratio * ratio * r2
def enhance_projs_aps_1id(imgstack, median_ks=5, ncore=None):
"""
Enhance the projection images with weak contrast collected at APS 1ID
This filter uses a median fileter (will be switched to enhanced recursive
median fileter, ERMF, in the future) for denoising, and a histogram
equalization for dynamic range adjustment to bring out the details.
Parameters
----------
imgstack : np.ndarray
tomopy images stacks (axis_0 is the oemga direction)
median_ks : int, optional
2D median filter kernel size for local noise suppresion
ncore : int, optional
number of cores used for speed up
Returns
-------
ndarray
3D enhanced image stacks.
"""
ncore = mproc.mp.cpu_count() - 1 if ncore is None else ncore
# need to use multiprocessing to speed up the process
tmp = []
with cf.ProcessPoolExecutor(ncore) as e:
for n_img in range(imgstack.shape[0]):
tmp.append(
e.submit(
_enhance_img,
imgstack[n_img, :, :],
median_ks,
)
)
return np.stack([me.result() for me in tmp], axis=0)
def _enhance_img(img, median_ks, normalized=True):
"""
Enhance the projection image from aps 1ID to counter its weak contrast
nature
Parameters
----------
img : ndarray
original projection image collected at APS 1ID
median_ks: int
kernel size of the 2D median filter, must be odd
normalized: bool, optional
specify whether the enhanced image is normalized between 0 and 1,
default is True
Returns
-------
ndarray
enhanced projection image
"""
wgt = _calc_histequal_wgt(img)
img = medfilt2d(img, kernel_size=median_ks).astype(np.float64)
img = ne.evaluate("(img**2)*wgt", out=img)
return img / img.max() if normalized else img
def _calc_histequal_wgt(img):
"""
Calculate the histogram equalization weight for a given image
Parameters
----------
img : ndarray
2D images
Returns
-------
ndarray
histogram euqalization weights (0-1) in the same shape as original
image
"""
return (np.sort(img.flatten()).searchsorted(img) + 1) / np.prod(img.shape)
def inpainter_morph(
arr,
mask,
size=5,
iterations=2,
inpainting_type="random",
axis=None,
ncore=None,
):
"""
Apply 3D or 2D morphological inpainter (extrapolator) to a given missing
data array using provided mask (boolean). The algorithm is presented in the
paper :cite:`Kazantsev:23`.
If applied to 3D tomographic data, one can use the 2D module applied to a
sinogram by specifing the axis in the list of parameters bellow.
Alternatively, one can use the 3D module with symmetric 3D kernels
(axis=None). The latter is more recommended for 3D data as it ensures the
smoother intensity transition in every direction.
.. versionadded:: 1.15
Parameters
----------
arr : ndarray
Input 3D or 2D array of float32 data type with the missing data.
mask : ndarray, bool
Boolean mask array of the same size as arr. True values in the mask
indicate the region that needs to be inpainted.
size : int, optional
The size of the searching window (a kernel).
iterations : int, optional
Iterations of the algorithm after the inpainted region was processed
fully. The larger numbers usually lead to oversmoothing of the area, we
recommend 2.
inpainting_type : str, optional
The type of the inpainting technique. Choose between 'mean', 'median'
or 'random' neighbour selection. We suggest using 'random' to minimise
the "spilling" of intensities inside the inpainted areas.
axis : int, optional
Split the data along this axis, and perform inpainting in the remaining
dimensions. If set to `None` then the 3D inpainting is enabled, which
is recommended to use for 3D data. If the 2D inpainting used for 3D
data, it is best to apply it in sinogram space.
ncore : int, optional
Number of cores that will be assigned to jobs. All cores will be used
if unspecified.
Returns
-------
ndarray
Inpainted array of float32 data type.
Raises
------
ValueError
If the input array is not float32.
If the input mask array is not bool.
If inpainting_type is not 'mean', 'median' or 'random'.
If axis is an integer for 2D input data.
If axis is outside of the accepted range.
"""
if ncore is None:
ncore = mproc.mp.cpu_count()
if arr.dtype != "float32":
arr = dtype.as_float32(arr) # silent convertion to float32 data type
if mask.dtype != "bool":
raise ValueError(
"Please make sure that the mask provided is boolean where true values define the missing data regions!"
)
out = np.empty_like(arr, order="C")
if inpainting_type not in ["mean", "median", "random"]:
raise ValueError(
"Inpainting type should be chosen as 'mean', 'median' or 'random'"
)
if inpainting_type == "mean":
inp_type_int = 0
if inpainting_type == "median":
inp_type_int = 1
if inpainting_type == "random":
inp_type_int = 2
if axis is not None:
if arr.ndim == 2:
raise ValueError("The axis number is valid only for 3D data, please set axis to None for 2D data")
else:
if axis < 0 or axis > 2:
raise ValueError("The accepted axes range for 3D data is [0:2]")
if arr.ndim == 3:
dz, dy, dx = arr.shape
if (dz == 0) or (dy == 0) or (dx == 0):
raise ValueError("The length of one of dimensions is equal to zero")
if axis is not None:
# perform 2d inpainting for 3d data using the provided axis
slices_list = [slice(None), slice(None), slice(None)]
for m in range(arr.shape[axis]):
slices_list[axis] = slice(m,m+1)
slice2d = arr[tuple(slices_list)].squeeze()
mask2d = mask[tuple(slices_list)].squeeze()
dy, dx = slice2d.shape
out2d = np.empty_like(slice2d, order="C")
extern.c_inpainter(
np.ascontiguousarray(slice2d),
np.ascontiguousarray(mask2d),
out2d,
iterations,
size,
inp_type_int,
ncore,
dx,
dy,
1,
)
if axis == 0:
out[m, :, :] = out2d
elif axis == 1:
out[:, m, :] = out2d
else:
out[:, :, m] = out2d
else:
# 3d inpaiting for 3d data
extern.c_inpainter(
np.ascontiguousarray(arr),
np.ascontiguousarray(mask),
out,
iterations,
size,
inp_type_int,
ncore,
dx,
dy,
dz,
)
else:
dy, dx = arr.shape
if (dy == 0) or (dx == 0):
raise ValueError("The length of one of dimensions is equal to zero")
extern.c_inpainter(
np.ascontiguousarray(arr),
np.ascontiguousarray(mask),
out,
iterations,
size,
inp_type_int,
ncore,
dx,
dy,
1,
)
return out