# ============================================================================
# ============================================================================
# Copyright (c) 2021 Nghia T. Vo. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
# Author: Nghia T. Vo
# E-mail:
# Description: Python module of conversion techniques.
# Contributors:
# ============================================================================
"""
Module of conversion methods in the preprocessing stage:
- Stitching images.
- Joining images if there is no overlapping.
- Converting a 360-degree sinogram with offset center-of-rotation (COR)
to a 180-degree sinogram.
- Extending a 360-degree sinogram with offset COR for direct
reconstruction instead of converting it to a 180-degree sinogram.
- Converting a 180-degree sinogram to a 360-sinogram.
- Generating a sinogram from a helical data.
"""
import numpy as np
from scipy import interpolate
from scipy.ndimage import shift
import algotom.prep.removal as remo
import algotom.prep.calculation as calc
[docs]def make_weight_matrix(mat1, mat2, overlap, side):
"""
Generate a linear-ramp weighting matrix for image stitching.
Parameters
----------
mat1 : array_like
2D array. Projection image or sinogram image.
mat2 : array_like
2D array. Projection image or sinogram image.
overlap : int
Width of the overlap area between two images.
side : {0, 1}
Only two options: 0 or 1. It is used to indicate the overlap side
respects to image 1. "0" corresponds to the left side. "1" corresponds
to the right side.
"""
overlap = int(np.floor(overlap))
wei_mat1 = np.ones_like(mat1)
wei_mat2 = np.ones_like(mat2)
if side == 1:
list_down = np.linspace(1.0, 0.0, overlap)
list_up = 1.0 - list_down
wei_mat1[:, -overlap:] = np.float32(list_down)
wei_mat2[:, :overlap] = np.float32(list_up)
else:
list_down = np.linspace(1.0, 0.0, overlap)
list_up = 1.0 - list_down
wei_mat2[:, -overlap:] = np.float32(list_down)
wei_mat1[:, :overlap] = np.float32(list_up)
return wei_mat1, wei_mat2
[docs]def stitch_image(mat1, mat2, overlap, side, wei_mat1=None, wei_mat2=None,
norm=True, total_width=None):
"""
Stitch projection images or sinogram images using a linear ramp.
Parameters
----------
mat1 : array_like
2D array. Projection image or sinogram image.
mat2 : array_like
2D array. Projection image or sinogram image.
overlap : float
Width of the overlap area between two images.
side : {0, 1}
Only two options: 0 or 1. It is used to indicate the overlap side
respects to image 1. "0" corresponds to the left side. "1" corresponds
to the right side.
wei_mat1 : array_like, optional
Weighting matrix used for image 1.
wei_mat2 : array_like, optional
Weighting matrix used for image 2.
norm : bool, optional
Enable/disable normalization before stitching.
total_width : int, optional
Final width of the stitched image.
Returns
-------
array_like
Stitched image.
"""
(nrow1, ncol1) = mat1.shape
(nrow2, ncol2) = mat2.shape
overlap_int = int(np.floor(overlap))
sub_pixel = overlap - overlap_int
if sub_pixel > 0.0:
if side == 1:
mat1 = shift(mat1, (0, sub_pixel), mode='nearest')
mat2 = shift(mat2, (0, -sub_pixel), mode='nearest')
else:
mat1 = shift(mat1, (0, -sub_pixel), mode='nearest')
mat2 = shift(mat2, (0, sub_pixel), mode='nearest')
if nrow1 != nrow2:
raise ValueError("Two images are not at the same height!!!")
if (wei_mat1 is None) or (wei_mat2 is None):
(wei_mat1, wei_mat2) = make_weight_matrix(mat1, mat2,
overlap_int, side)
total_width0 = ncol1 + ncol2 - overlap_int
if (total_width is None) or (total_width < total_width0):
total_width = total_width0
mat_comb = np.zeros((nrow1, total_width0), dtype=np.float32)
if side == 1:
if norm is True:
factor1 = np.mean(mat1[:, -overlap_int:])
factor2 = np.mean(mat2[:, :overlap_int])
mat2 = mat2 * factor1 / factor2
mat_comb[:, 0:ncol1] = mat1 * wei_mat1
mat_comb[:, (ncol1 - overlap_int):total_width0] += mat2 * wei_mat2
else:
if norm is True:
factor2 = np.mean(mat2[:, -overlap_int:])
factor1 = np.mean(mat1[:, :overlap_int])
mat2 = mat2 * factor1 / factor2
mat_comb[:, 0:ncol2] = mat2 * wei_mat2
mat_comb[:, (ncol2 - overlap_int):total_width0] += mat1 * wei_mat1
if total_width > total_width0:
mat_comb = np.pad(
mat_comb, ((0, 0), (0, total_width - total_width0)), mode='edge')
return mat_comb
[docs]def join_image(mat1, mat2, joint_width, side, norm=True, total_width=None):
"""
Join projection images or sinogram images. This is useful for fixing the
problem of non-overlap between images.
Parameters
----------
mat1 : array_like
2D array. Projection image or sinogram image.
mat2 : array_like
2D array. Projection image or sinogram image.
joint_width : float
Width of the joint area between two images.
side : {0, 1}
Only two options: 0 or 1. It is used to indicate the overlap side
respects to image 1. "0" corresponds to the left side. "1" corresponds
to the right side.
norm : bool
Enable/disable normalization before joining.
total_width : int, optional
Final width of the joined image.
Returns
-------
array_like
Stitched image.
"""
(nrow1, ncol1) = mat1.shape
(nrow2, ncol2) = mat2.shape
joint_int = int(np.floor(joint_width))
sub_pixel = joint_width - joint_int
side = int(side)
if sub_pixel > 0.0:
if side == 1:
mat1 = shift(mat1, (0, sub_pixel), mode='nearest')
mat2 = shift(mat2, (0, -sub_pixel), mode='nearest')
else:
mat1 = shift(mat1, (0, -sub_pixel), mode='nearest')
mat2 = shift(mat2, (0, sub_pixel), mode='nearest')
if nrow1 != nrow2:
raise ValueError("Two images are not at the same height!!!")
total_width0 = ncol1 + ncol2 + joint_int
if (total_width is None) or (total_width < total_width0):
total_width = total_width0
mat_comb = np.zeros((nrow1, total_width0), dtype=np.float32)
if side == 1:
if norm is True:
factor1 = np.mean(mat1[:, -3:])
factor2 = np.mean(mat2[:, :3])
mat2 = mat2 * factor1 / factor2
mat_comb[:, 0:ncol1] = mat1
mat_comb[:, (ncol1 + joint_int):total_width0] += mat2
list_mask = np.zeros(total_width0, dtype=np.float32)
list_mask[ncol1 - 2:ncol1 + joint_int + 3] = 1.0
xlist = np.where(list_mask < 1.0)[0]
ylist = np.arange(nrow1)
finter = interpolate.RectBivariateSpline(ylist, xlist,
mat_comb[:, xlist],
kx=1, ky=1)
xlist_miss = np.where(list_mask > 0.0)[0]
if len(xlist_miss) > 0:
x_mat_miss, y_mat = np.meshgrid(xlist_miss, ylist)
output = finter.ev(np.ndarray.flatten(y_mat),
np.ndarray.flatten(x_mat_miss))
mat_comb[:, xlist_miss] = output.reshape(x_mat_miss.shape)
else:
if norm is True:
factor2 = np.mean(mat2[:, -3:])
factor1 = np.mean(mat1[:, :3])
mat2 = mat2 * factor1 / factor2
mat_comb[:, 0:ncol2] = mat2
mat_comb[:, (ncol2 + joint_int):total_width0] += mat1
list_mask = np.zeros(total_width0, dtype=np.float32)
list_mask[ncol2 - 2:ncol2 + joint_int + 3] = 1.0
xlist = np.where(list_mask < 1.0)[0]
ylist = np.arange(nrow1)
finter = interpolate.RectBivariateSpline(ylist, xlist,
mat_comb[:, xlist],
kx=1, ky=1)
xlist_miss = np.where(list_mask > 0.0)[0]
if len(xlist_miss) > 0:
x_mat_miss, y_mat = np.meshgrid(xlist_miss, ylist)
output = finter.ev(np.ndarray.flatten(y_mat),
np.ndarray.flatten(x_mat_miss))
mat_comb[:, xlist_miss] = output.reshape(x_mat_miss.shape)
if total_width > total_width0:
mat_comb = np.pad(
mat_comb, ((0, 0), (0, total_width - total_width0)), mode='edge')
return mat_comb
[docs]def stitch_image_multiple(list_mat, list_overlap, norm=True, total_width=None):
"""
Stitch list of projection images or sinogram images using a linear ramp.
Parameters
----------
list_mat : list of array_like
List of 2D array. Projection image or sinogram image.
list_overlap : list of tuple of floats
List of [overlap, side].
overlap : Width of the overlap area between two images.
side : Overlap side between two images.
norm : bool, optional
Enable/disable normalization before stitching.
total_width : int, optional
Final width of the stitched image.
Returns
-------
array_like
Stitched image.
"""
num_mat = len(list_mat)
mat_comb = np.copy(list_mat[0])
if num_mat > 1:
for i in range(1, num_mat):
(overlap, side) = list_overlap[i - 1][0:2]
mat_comb = stitch_image(mat_comb, list_mat[i], overlap, side, norm)
width = mat_comb.shape[1]
if total_width is None:
total_width = width
if total_width > width:
mat_comb = np.pad(
mat_comb, ((0, 0), (0, total_width - width)), mode='edge')
else:
raise ValueError("Need at least 2 images to work!!!")
return np.asarray(mat_comb)
[docs]def join_image_multiple(list_mat, list_joint, norm=True, total_width=None):
"""
Join list of projection images or sinogram images. This is useful for
fixing the problem of non-overlap between images.
Parameters
----------
list_mat : list of array_like
List of 2D array. Projection image or sinogram image.
list_joint : list of tuple of floats
List of [joint_width, side].
joint_width : Width of the joint area between two images.
side : Overlap side between two images.
norm : bool, optional
Enable/disable normalization before stitching.
total_width : int, optional
Final width of the stitched image.
Returns
-------
array_like
Stitched image.
"""
num_mat = len(list_mat)
if num_mat > 1:
mat_comb = np.copy(list_mat[0])
for i in range(1, num_mat):
(joint_width, side) = list_joint[i - 1][0:2]
mat_comb = join_image(mat_comb, list_mat[i], joint_width, side,
norm)
width = mat_comb.shape[1]
if total_width is None:
total_width = width
if total_width > width:
mat_comb = np.pad(
mat_comb, ((0, 0), (0, total_width - width)), mode='edge')
else:
raise ValueError("Need at least 2 images to work!!!")
return np.asarray(mat_comb)
[docs]def convert_sinogram_360_to_180(sino_360, cor, wei_mat1=None, wei_mat2=None,
norm=True, total_width=None):
"""
Convert a 360-degree sinogram to a 180-degree sinogram.
Parameters
----------
sino_360 : array_like
2D array. 360-degree sinogram.
cor : float or tuple of float
Center-of-rotation or (Overlap_area, overlap_side).
wei_mat1 : array_like, optional
Weighting matrix used for the 1st haft of the sinogram.
wei_mat2 : array_like, optional
Weighting matrix used for the 2nd haft of the sinogram.
norm : bool, optional
Enable/disable normalization before stitching.
total_width : int, optional
Final width of the stitched image.
Returns
-------
sino_stitch : array_like
Converted sinogram.
cor : float
Updated center-of-rotation referred to the converted sinogram.
"""
(nrow, ncol) = sino_360.shape
xcenter = (ncol - 1.0) * 0.5
nrow_180 = nrow // 2 + 1
sino_top = sino_360[0:nrow_180, :]
sino_bot = np.fliplr(sino_360[-nrow_180:, :])
if isinstance(cor, tuple):
(overlap, side) = cor
else:
if cor <= xcenter:
overlap = 2 * (cor + 1)
side = 0
else:
overlap = 2 * (ncol - cor - 1)
side = 1
sino_stitch = stitch_image(
sino_top, sino_bot, overlap, side, wei_mat1=wei_mat1,
wei_mat2=wei_mat2, norm=norm, total_width=total_width)
cor = (2 * ncol - np.floor(overlap) - 1.0) / 2.0
return sino_stitch, cor
[docs]def convert_sinogram_180_to_360(sino_180, center):
"""
Convert a 180-degree sinogram to a 360-degree sinogram (Ref. [1]).
Parameters
----------
sino_180 : array_like
2D array. 180-degree sinogram.
center : float
Center-of-rotation.
Returns
-------
array_like
360-degree sinogram.
References
----------
[1] : https://doi.org/10.1364/OE.22.019078
"""
(nrow, ncol) = sino_180.shape
xcenter = (ncol - 1.0) / 2.0
shift_x = xcenter - center
sino_flip = shift(np.fliplr(shift(sino_180, (0, shift_x), mode='nearest')),
(0, -shift_x), mode='nearest')
return np.vstack((sino_180, sino_flip[1:]))
[docs]def extend_sinogram(sino_360, cor, apply_log=True):
"""
Extend a 360-degree sinogram (with offset center-of-rotation) for
later reconstruction (Ref. [1]).
Parameters
----------
sino_360 : array_like
2D array. 360-degree sinogram.
cor : float or tuple of float
Center-of-rotation or (Overlap_area, overlap_side).
apply_log : bool, optional
Apply the logarithm function if True.
Returns
-------
sino_pad : array_like
Extended sinogram.
cor : float
Updated center-of-rotation referred to the converted sinogram.
References
----------
[1] : https://doi.org/10.1364/OE.418448
"""
if apply_log is True:
sino_360 = -np.log(sino_360)
else:
sino_360 = np.copy(sino_360)
(nrow, ncol) = sino_360.shape
xcenter = (ncol - 1.0) * 0.5
if isinstance(cor, tuple):
(overlap, side) = cor
else:
if cor <= xcenter:
overlap = 2 * (cor + 1)
side = 0
else:
overlap = 2 * (ncol - cor - 1)
side = 1
overlap_int = int(np.floor(overlap))
sub_pixel = overlap - overlap_int
if side == 1:
if sub_pixel > 0.0:
sino_360 = shift(sino_360, (0, sub_pixel), mode='nearest')
wei_list = np.linspace(1.0, 0.0, overlap_int)
wei_mat = np.tile(wei_list, (nrow, 1))
sino_360[:, -overlap_int:] = sino_360[:, -overlap_int:] * wei_mat
pad_wid = ncol - overlap_int
sino_pad = np.pad(sino_360, ((0, 0), (0, pad_wid)), mode='edge')
else:
if sub_pixel > 0.0:
sino_360 = shift(sino_360, (0, -sub_pixel), mode='nearest')
wei_list = np.linspace(0.0, 1.0, overlap_int)
wei_mat = np.tile(wei_list, (nrow, 1))
sino_360[:, :overlap_int] = sino_360[:, :overlap_int] * wei_mat
pad_wid = ncol - overlap_int
sino_pad = np.pad(sino_360, ((0, 0), (pad_wid, 0)), mode='edge')
cor = (sino_pad.shape[1] - 1.0) / 2.0
return sino_pad, cor
[docs]def generate_sinogram_helical_scan(index, tomo_data, num_proj, pixel_size,
y_start, y_stop, pitch, scan_type="180",
angles=None, flat=None, dark=None,
mask=None, crop=(0, 0, 0, 0)):
"""
Generate a 180-degree/360-degree sinogram from a helical-scan dataset
which is a hdf/nxs object (Ref. [1]).
Parameters
----------
index : int
Index of the sinogram.
tomo_data : hdf object.
3D array.
num_proj : int
Number of projections per 180-degree.
pixel_size : float
Pixel size. The unit must be the same as y-position.
y_start : float
Y-position of the stage at the beginning of the scan.
y_stop : float
Y-position of the stage at the end of the scan.
pitch : float
The distance which the y-stage is translated in one full rotation.
scan_type : {"180", "360"}
One of two options: "180" for generating a 180-degree sinogram or
"360" for generating a 360-degree sinogram.
angles : array_like, optional
1D array. Angles (degree) corresponding to acquired projections.
flat : array_like, optional
Flat-field image used for flat-field correction.
dark : array_like, optional
Dark-field image used for flat-field correction.
mask : array_like, optional
Used for removing streak artifacts caused by blobs in the flat-field
image.
crop : tuple of int, optional
Used for cropping images.
Returns
-------
sinogram : array_like
2D array. 180-degree sinogram or 360-degree sinogram.
list_angle : array_like
1D array. List of angles corresponding to the generated sinogram.
References
----------
[1] : https://doi.org/10.1364/OE.418448
"""
max_index = calc.calculate_maximum_index(y_start, y_stop, pitch,
pixel_size, scan_type)
(y_s, y_e) = calc.calculate_reconstructable_height(y_start, y_stop,
pitch, scan_type)
if index < 0 or index > max_index:
msg1 = "Requested index {0} is out of available index-range" \
" [0, {1}]\n".format(index, max_index)
msg2 = "corresponding to reconstructable heights" \
" [{0}, {1}]".format(y_s, y_e)
raise ValueError(msg1 + msg2)
(depth0, height0, width0) = tomo_data.shape
(crop_top, crop_bottom, crop_left, crop_right) = crop
top = crop_top
bottom = height0 - crop_bottom
left = crop_left
right = width0 - crop_right
width = right - left
height = bottom - top
if flat is None:
flat = np.ones((height0, width0), dtype=np.float32)
if dark is None:
dark = np.zeros((height0, width0), dtype=np.float32)
if angles is None:
step_angle = 180.0 / (num_proj - 1)
angles = np.arange(0, depth0) * step_angle
flat_dark = flat - dark
fov = pixel_size * height
y_step = pitch / (2.0 * (num_proj - 1))
if scan_type == "180":
num_proj_used = num_proj
else:
num_proj_used = 2 * (num_proj - 1) + 1
y_pos = (index - 1) * pixel_size + y_s
i0 = int(np.ceil((y_e - y_pos) / y_step))
if (i0 < 0) or (i0 >= depth0):
raise ValueError(
"Sinogram index {0} requests a projection index {1}"
" which is out of the data range [0, {2}]".format(
index, i0, depth0 - 1))
sinogram = np.zeros((num_proj_used, width), dtype=np.float32)
for i in range(i0, i0 + num_proj_used):
j0 = (y_e + fov - i * y_step - y_pos) / pixel_size - 1
if (j0 < 0) or (j0 >= height):
raise ValueError(
"Requested row index {0} of projection {1} is out of the"
" range [0, {2}]".format(j0, i0, height - 1))
j0 = np.clip(j0, 0, height - 1)
jd = int(np.floor(j0))
ju = int(np.ceil(j0))
list_down = (tomo_data[i, jd + crop_top, left: right]
- dark[jd + crop_top, left: right]) / flat_dark[
jd + crop_top,
left: right]
if mask is not None:
list_down = remo.remove_blob_1d(list_down,
mask[jd + crop_top, left: right])
if ju != jd:
list_up = (tomo_data[i, ju + crop_top, left: right]
- dark[ju + crop_top, left: right]) \
/ flat_dark[ju + crop_top, left: right]
if mask is not None:
list_up = remo.remove_blob_1d(list_up,
mask[ju + crop_top, left: right])
sinogram[i - i0] = list_down * (ju - j0) / (ju - jd) + list_up * (
j0 - jd) / (ju - jd)
else:
sinogram[i - i0] = list_down
list_angle = angles[i0:i0 + num_proj_used]
return sinogram, list_angle
[docs]def generate_full_sinogram_helical_scan(index, tomo_data, num_proj, pixel_size,
y_start, y_stop, pitch,
scan_type="180", angles=None,
flat=None, dark=None,
mask=None, crop=(0, 0, 0, 0)):
"""
Generate a full sinogram from a helical-scan dataset which is a hdf/nxs
object (Ref. [1]). Full sinogram is all 1D projections of the same slice
of a sample staying inside the field of view.
Parameters
----------
index : int
Index of the sinogram.
tomo_data : hdf object.
3D array.
num_proj : int
Number of projections per 180-degree.
pixel_size : float
Pixel size. The unit must be the same as y-position.
y_start : float
Y-position of the stage at the beginning of the scan.
y_stop : float
Y-position of the stage at the end of the scan.
pitch : float
The distance which the y-stage is translated in one full rotation.
scan_type : {"180", "360"}
Data acquired is the 180-degree type or 360-degree type [1].
angles : array_like, optional
1D array. Angles (degree) corresponding to acquired projections.
flat : array_like, optional
Flat-field image used for flat-field correction.
dark : array_like, optional
Dark-field image used for flat-field correction.
mask : array_like, optional
Used for removing streak artifacts caused by blobs in the flat-field
image.
crop : tuple of int, optional
Used for cropping images.
Returns
-------
sinogram : array_like
2D array. Full sinogram.
list_angle : array_like
1D array. List of angles corresponding to the generated sinogram.
References
----------
[1] : https://doi.org/10.1364/OE.418448
"""
(depth0, height0, width0) = tomo_data.shape
(crop_top, crop_bottom, crop_left, crop_right) = crop
top = crop_top
bottom = height0 - crop_bottom
left = crop_left
right = width0 - crop_right
width = right - left
height = bottom - top
if flat is None:
flat = np.ones((height0, width0), dtype=np.float32)
if dark is None:
dark = np.zeros((height0, width0), dtype=np.float32)
if angles is None:
step_angle = 180.0 / (num_proj - 1)
angles = np.arange(0, depth0) * step_angle
flat_dark = flat - dark
fov = pixel_size * height
y_step = pitch / (2.0 * (num_proj - 1))
if scan_type == "180":
y_e = y_stop - pitch / 2.0
y_s = y_start + pitch / 2.0
else:
y_e = y_stop - pitch
y_s = y_start + pitch
num_proj_used = int(np.floor(fov / y_step)) - 1
y_pos = (index - 1) * pixel_size + y_s
i0 = int(np.ceil((y_e - y_pos) / y_step))
if (i0 < 0) or (i0 >= depth0):
raise ValueError(
"Sinogram index {0} requests a projection index {1} which "
"is out of the projection range [0, {2}]".format(
index, i0, depth0 - 1))
if (i0 + num_proj_used) >= depth0:
raise ValueError(
"Sinogram index {0} requests projection-indices in the range of "
"[{1}, {2}] which is out of the data range [0, {3}]".format(
index, i0, i0 + num_proj_used, depth0 - 1))
sinogram = np.zeros((num_proj_used, width), dtype=np.float32)
for i in range(i0, i0 + num_proj_used):
j0 = (y_e + fov - i * y_step - y_pos) / pixel_size - 1
if (j0 < 0) or (j0 >= height):
raise ValueError(
"Requested row index {0} of projection {1} is out of"
" the range [0, {2}]".format(j0, i0, height))
j0 = np.clip(j0, 0, height - 1)
jd = int(np.floor(j0))
ju = int(np.ceil(j0))
list_down = (tomo_data[i, jd + crop_top, left: right]
- dark[jd + crop_top, left: right]) / flat_dark[
jd + crop_top,
left: right]
if mask is not None:
list_down = remo.remove_blob_1d(list_down,
mask[jd + crop_top, left: right])
if ju != jd:
list_up = (tomo_data[i, ju + crop_top, left: right]
- dark[ju + crop_top, left: right]) / flat_dark[
ju + crop_top,
left: right]
if mask is not None:
list_up = remo.remove_blob_1d(list_up,
mask[ju + crop_top, left: right])
sinogram[i - i0] = list_down * (ju - j0) / (ju - jd) + list_up * (
j0 - jd) / (ju - jd)
else:
sinogram[i - i0] = list_down
list_angle = angles[i0:i0 + num_proj_used]
return sinogram, list_angle