def _adjust_hist_limits(tomo, theta, ind, mask, emission):
# Make an initial reconstruction to adjust histogram limits.
rec = recon(tomo, theta, emission=emission, algorithm='gridrec')
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Adjust histogram boundaries according to reconstruction.
return _adjust_hist_min(rec.min()), _adjust_hist_max(rec.max())
def _adjust_hist_limits(tomo, theta, ind, mask, emission):
# Make an initial reconstruction to adjust histogram limits.
rec = recon(tomo, theta, emission=emission, algorithm='gridrec')
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Adjust histogram boundaries according to reconstruction.
return _adjust_hist_min(rec.min()), _adjust_hist_max(rec.max())
def _adjust_hist_limits(tomo_ind, theta, mask, sinogram_order):
# Make an initial reconstruction to adjust histogram limits.
rec = recon(
tomo_ind,
theta,
sinogram_order=sinogram_order,
algorithm=algorithm,
filter_name=filter_name,
)
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Adjust histogram boundaries according to reconstruction.
return _adjust_hist_min(rec.min()), _adjust_hist_max(rec.max())
def _find_center_cost(
center, tomo, theta, ind, hmin, hmax, mask, ratio, emission):
"""
Cost function used for the ``find_center`` routine.
"""
logger.info('Trying center: %s', center)
center = np.array(center, dtype='float32')
rec = recon(
tomo[:, ind:ind + 1, :], theta, center,
emission=emission, algorithm='gridrec')
if mask is True:
rec = circ_mask(rec, axis=0)
hist, e = np.histogram(rec, bins=64, range=[hmin, hmax])
hist = hist.astype('float32') / rec.size + 1e-12
return -np.dot(hist, np.log2(hist))
def _find_center_cost(center, tomo, theta, ind, hmin, hmax, mask, ratio,
emission):
"""
Cost function used for the ``find_center`` routine.
"""
logger.info('Trying center: %s', center)
center = np.array(center, dtype='float32')
rec = recon(tomo[:, ind:ind + 1, :],
theta,
center,
emission=emission,
algorithm='gridrec')
if mask is True:
rec = circ_mask(rec, axis=0)
hist, e = np.histogram(rec, bins=64, range=[hmin, hmax])
hist = hist.astype('float32') / rec.size + 1e-12
return -np.dot(hist, np.log2(hist))
def _find_center_cost(
center, tomo_ind, theta, hmin, hmax, mask, ratio,
sinogram_order=False):
"""
Cost function used for the ``find_center`` routine.
"""
logger.info('Trying rotation center: %s', center)
center = np.array(center, dtype='float32')
rec = recon(
tomo_ind, theta, center,
sinogram_order=sinogram_order, algorithm='gridrec')
if mask is True:
rec = circ_mask(rec, axis=0)
hist, e = np.histogram(rec, bins=64, range=[hmin, hmax])
hist = hist.astype('float32') / rec.size + 1e-12
val = -np.dot(hist, np.log2(hist))
logger.info("Function value = %f"%val)
return val
def _find_center_cost(
center, tomo_ind, theta, hmin, hmax, mask, ratio,
sinogram_order=False):
"""
Cost function used for the ``find_center`` routine.
"""
logger.warn('Trying center: %s', center)
center = np.array(center, dtype='float32')
rec = recon(
tomo_ind, theta, center,
sinogram_order=sinogram_order, algorithm='gridrec')
if mask is True:
rec = circ_mask(rec, axis=0)
hist, e = np.histogram(rec, bins=64, range=[hmin, hmax])
hist = hist.astype('float32') / rec.size + 1e-12
val = -np.dot(hist, np.log2(hist))
logger.warn("val = %f"%val)
return val
def write_center(
tomo, theta, dpath='tmp/center', cen_range=None, ind=None,
emission=True, mask=False, ratio=1.):
"""
Save images reconstructed with a range of rotation centers.
Helps finding the rotation center manually by visual inspection of
images reconstructed with a set of different centers.The output
images are put into a specified folder and are named by the
center position corresponding to the image.
Parameters
----------
tomo : ndarray
3D tomographic data.
theta : array
Projection angles in radian.
dpath : str, optional
Folder name to save output images.
cen_range : list, optional
[start, end, step] Range of center values.
ind : int, optional
Index of the slice to be used for reconstruction.
emission : bool, optional
Determines whether data is emission or transmission type.
mask : bool, optional
If ``True``, apply a circular mask to the reconstructed image to
limit the analysis into a circular region.
ratio : float, optional
The ratio of the radius of the circular mask to the edge of the
reconstructed image.
"""
tomo = dtype.as_float32(tomo)
theta = dtype.as_float32(theta)
dx, dy, dz = tomo.shape
if ind is None:
ind = dy // 2
if cen_range is None:
center = np.arange(dz / 2 - 5, dz / 2 + 5, 0.5)
if len(cen_range) < 3:
cen_range[2] = 1
else:
center = np.arange(cen_range[0], cen_range[1], cen_range[2] / 2.)
stack = np.zeros((dx, len(center), dz))
for m in range(center.size):
stack[:, m, :] = tomo[:, ind, :]
# Reconstruct the same slice with a range of centers.
rec = recon(
stack, theta, center=center, emission=emission, algorithm='gridrec')
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Save images to a temporary folder.
for m in range(len(center)):
if m % 2 == 0: # 2 slices same bec of gridrec.
fname = os.path.join(
dpath, str('{0:.2f}'.format(center[m]) + '.tiff'))
write_tiff(rec[m:m + 1], fname=fname, overwrite=True)
def write_center(tomo,
theta,
dpath='tmp/center',
cen_range=None,
ind=None,
mask=False,
ratio=1.,
sinogram_order=False,
algorithm='gridrec',
filter_name='parzen'):
"""
Save images reconstructed with a range of rotation centers.
Helps finding the rotation center manually by visual inspection of
images reconstructed with a set of different centers.The output
images are put into a specified folder and are named by the
center position corresponding to the image.
Parameters
----------
tomo : ndarray
3D tomographic data.
theta : array
Projection angles in radian.
dpath : str, optional
Folder name to save output images.
cen_range : list, optional
[start, end, step] Range of center values.
ind : int, optional
Index of the slice to be used for reconstruction.
mask : bool, optional
If ``True``, apply a circular mask to the reconstructed image to
limit the analysis into a circular region.
ratio : float, optional
The ratio of the radius of the circular mask to the edge of the
reconstructed image.
sinogram_order: bool, optional
Determins whether data is a stack of sinograms (True, y-axis first axis)
or a stack of radiographs (False, theta first axis).
algorithm : {str, function}
One of the following string values.
'art'
Algebraic reconstruction technique :cite:`Kak:98`.
'bart'
Block algebraic reconstruction technique.
'fbp'
Filtered back-projection algorithm.
'gridrec'
Fourier grid reconstruction algorithm :cite:`Dowd:99`,
:cite:`Rivers:06`.
'mlem'
Maximum-likelihood expectation maximization algorithm
:cite:`Dempster:77`.
'osem'
Ordered-subset expectation maximization algorithm
:cite:`Hudson:94`.
'ospml_hybrid'
Ordered-subset penalized maximum likelihood algorithm with
weighted linear and quadratic penalties.
'ospml_quad'
Ordered-subset penalized maximum likelihood algorithm with
quadratic penalties.
'pml_hybrid'
Penalized maximum likelihood algorithm with weighted linear
and quadratic penalties :cite:`Chang:04`.
'pml_quad'
Penalized maximum likelihood algorithm with quadratic penalty.
'sirt'
Simultaneous algebraic reconstruction technique.
'tv'
Total Variation reconstruction technique
:cite:`Chambolle:11`.
'grad'
Gradient descent method with a constant step size
filter_name : str, optional
Name of the filter for analytic reconstruction.
'none'
No filter.
'shepp'
Shepp-Logan filter (default).
'cosine'
Cosine filter.
'hann'
Cosine filter.
'hamming'
Hamming filter.
'ramlak'
Ram-Lak filter.
'parzen'
Parzen filter.
'butterworth'
Butterworth filter.
'custom'
A numpy array of size `next_power_of_2(num_detector_columns)/2`
specifying a custom filter in Fourier domain. The first element
of the filter should be the zero-frequency component.
'custom2d'
A numpy array of size `num_projections*next_power_of_2(num_detector_columns)/2`
specifying a custom angle-dependent filter in Fourier domain. The first element
of each filter should be the zero-frequency component.
"""
tomo = dtype.as_float32(tomo)
theta = dtype.as_float32(theta)
if sinogram_order:
dy, dt, dx = tomo.shape
else:
dt, dy, dx = tomo.shape
if ind is None:
ind = dy // 2
if cen_range is None:
center = np.arange(dx / 2 - 5, dx / 2 + 5, 0.5)
else:
center = np.arange(*cen_range)
stack = dtype.empty_shared_array((len(center), dt, dx))
for m in range(center.size):
if sinogram_order:
stack[m] = tomo[ind]
else:
stack[m] = tomo[:, ind, :]
# Reconstruct the same slice with a range of centers.
rec = recon(stack,
theta,
center=center,
sinogram_order=True,
algorithm=algorithm,
filter_name=filter_name,
nchunk=1)
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Save images to a temporary folder.
for m in range(len(center)):
fname = os.path.join(dpath, str('{0:.2f}'.format(center[m]) + '.tiff'))
dxchange.write_tiff(rec[m], fname=fname, overwrite=True)
def write_center(
tomo, theta, dpath='tmp/center', cen_range=None, ind=None,
mask=False, ratio=1., sinogram_order=False):
"""
Save images reconstructed with a range of rotation centers.
Helps finding the rotation center manually by visual inspection of
images reconstructed with a set of different centers.The output
images are put into a specified folder and are named by the
center position corresponding to the image.
Parameters
----------
tomo : ndarray
3D tomographic data.
theta : array
Projection angles in radian.
dpath : str, optional
Folder name to save output images.
cen_range : list, optional
[start, end, step] Range of center values.
ind : int, optional
Index of the slice to be used for reconstruction.
mask : bool, optional
If ``True``, apply a circular mask to the reconstructed image to
limit the analysis into a circular region.
ratio : float, optional
The ratio of the radius of the circular mask to the edge of the
reconstructed image.
sinogram_order: bool, optional
Determins whether data is a stack of sinograms (True, y-axis first axis)
or a stack of radiographs (False, theta first axis).
"""
tomo = dtype.as_float32(tomo)
theta = dtype.as_float32(theta)
if sinogram_order:
dy, dt, dx = tomo.shape
else:
dt, dy, dx = tomo.shape
if ind is None:
ind = dy // 2
if cen_range is None:
center = np.arange(dx / 2 - 5, dx / 2 + 5, 0.5)
else:
center = np.arange(*cen_range)
stack = dtype.empty_shared_array((len(center), dt, dx))
for m in range(center.size):
if sinogram_order:
stack[m] = tomo[ind]
else:
stack[m] = tomo[:, ind, :]
# Reconstruct the same slice with a range of centers.
rec = recon(stack,
theta,
center=center,
sinogram_order=True,
algorithm='gridrec',
nchunk=1)
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Save images to a temporary folder.
for m in range(len(center)):
fname = os.path.join(
dpath, str('{0:.2f}'.format(center[m]) + '.tiff'))
dxchange.write_tiff(rec[m], fname=fname, overwrite=True)
def write_center(
tomo, theta, dpath='tmp/center', cen_range=None, ind=None,
mask=False, ratio=1., sinogram_order=False):
"""
Save images reconstructed with a range of rotation centers.
Helps finding the rotation center manually by visual inspection of
images reconstructed with a set of different centers.The output
images are put into a specified folder and are named by the
center position corresponding to the image.
Parameters
----------
tomo : ndarray
3D tomographic data.
theta : array
Projection angles in radian.
dpath : str, optional
Folder name to save output images.
cen_range : list, optional
[start, end, step] Range of center values.
ind : int, optional
Index of the slice to be used for reconstruction.
mask : bool, optional
If ``True``, apply a circular mask to the reconstructed image to
limit the analysis into a circular region.
ratio : float, optional
The ratio of the radius of the circular mask to the edge of the
reconstructed image.
sinogram_order: bool, optional
Determins whether data is a stack of sinograms (True, y-axis first axis)
or a stack of radiographs (False, theta first axis).
"""
tomo = dtype.as_float32(tomo)
theta = dtype.as_float32(theta)
if sinogram_order:
dy, dt, dx = tomo.shape
else:
dt, dy, dx = tomo.shape
if ind is None:
ind = dy // 2
if cen_range is None:
center = np.arange(dx / 2 - 5, dx / 2 + 5, 0.5)
else:
center = np.arange(*cen_range)
stack = dtype.empty_shared_array((len(center), dt, dx))
for m in range(center.size):
if sinogram_order:
stack[m] = tomo[ind]
else:
stack[m] = tomo[:, ind, :]
# Reconstruct the same slice with a range of centers.
rec = recon(stack,
theta,
center=center,
sinogram_order=True,
algorithm='gridrec',
nchunk=1)
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Save images to a temporary folder.
for m in range(len(center)):
fname = os.path.join(
dpath, str('{0:.2f}'.format(center[m]) + '.tiff'))
dxchange.write_tiff(rec[m], fname=fname, overwrite=True)
def write_center(
tomo, theta, dpath='tmp/center', cen_range=None, ind=None,
mask=False, ratio=1., sinogram_order=False, algorithm='gridrec', filter_name='parzen'):
"""
Save images reconstructed with a range of rotation centers.
Helps finding the rotation center manually by visual inspection of
images reconstructed with a set of different centers.The output
images are put into a specified folder and are named by the
center position corresponding to the image.
Parameters
----------
tomo : ndarray
3D tomographic data.
theta : array
Projection angles in radian.
dpath : str, optional
Folder name to save output images.
cen_range : list, optional
[start, end, step] Range of center values.
ind : int, optional
Index of the slice to be used for reconstruction.
mask : bool, optional
If ``True``, apply a circular mask to the reconstructed image to
limit the analysis into a circular region.
ratio : float, optional
The ratio of the radius of the circular mask to the edge of the
reconstructed image.
sinogram_order: bool, optional
Determins whether data is a stack of sinograms (True, y-axis first axis)
or a stack of radiographs (False, theta first axis).
algorithm : {str, function}
One of the following string values.
'art'
Algebraic reconstruction technique :cite:`Kak:98`.
'bart'
Block algebraic reconstruction technique.
'fbp'
Filtered back-projection algorithm.
'gridrec'
Fourier grid reconstruction algorithm :cite:`Dowd:99`,
:cite:`Rivers:06`.
'mlem'
Maximum-likelihood expectation maximization algorithm
:cite:`Dempster:77`.
'osem'
Ordered-subset expectation maximization algorithm
:cite:`Hudson:94`.
'ospml_hybrid'
Ordered-subset penalized maximum likelihood algorithm with
weighted linear and quadratic penalties.
'ospml_quad'
Ordered-subset penalized maximum likelihood algorithm with
quadratic penalties.
'pml_hybrid'
Penalized maximum likelihood algorithm with weighted linear
and quadratic penalties :cite:`Chang:04`.
'pml_quad'
Penalized maximum likelihood algorithm with quadratic penalty.
'sirt'
Simultaneous algebraic reconstruction technique.
'tv'
Total Variation reconstruction technique
:cite:`Chambolle:11`.
'grad'
Gradient descent method with a constant step size
filter_name : str, optional
Name of the filter for analytic reconstruction.
'none'
No filter.
'shepp'
Shepp-Logan filter (default).
'cosine'
Cosine filter.
'hann'
Cosine filter.
'hamming'
Hamming filter.
'ramlak'
Ram-Lak filter.
'parzen'
Parzen filter.
'butterworth'
Butterworth filter.
'custom'
A numpy array of size `next_power_of_2(num_detector_columns)/2`
specifying a custom filter in Fourier domain. The first element
of the filter should be the zero-frequency component.
'custom2d'
A numpy array of size `num_projections*next_power_of_2(num_detector_columns)/2`
specifying a custom angle-dependent filter in Fourier domain. The first element
of each filter should be the zero-frequency component.
"""
tomo = dtype.as_float32(tomo)
theta = dtype.as_float32(theta)
if sinogram_order:
dy, dt, dx = tomo.shape
else:
dt, dy, dx = tomo.shape
if ind is None:
ind = dy // 2
if cen_range is None:
center = np.arange(dx / 2 - 5, dx / 2 + 5, 0.5)
else:
center = np.arange(*cen_range)
stack = dtype.empty_shared_array((len(center), dt, dx))
for m in range(center.size):
if sinogram_order:
stack[m] = tomo[ind]
else:
stack[m] = tomo[:, ind, :]
# Reconstruct the same slice with a range of centers.
rec = recon(stack,
theta,
center=center,
sinogram_order=True,
algorithm=algorithm,
filter_name=filter_name,
nchunk=1)
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Save images to a temporary folder.
dpath = os.path.abspath(dpath)
if not os.path.exists(dpath):
os.makedirs(dpath)
for m in range(len(center)):
fname = os.path.join(
dpath, str('{0:.2f}'.format(center[m]) + '.tiff'))
tifffile.imsave(file=fname, data=rec[m])
def write_center(tomo,
theta,
dpath='tmp/center',
cen_range=None,
ind=None,
emission=True,
mask=False,
ratio=1.):
"""
Save images reconstructed with a range of rotation centers.
Helps finding the rotation center manually by visual inspection of
images reconstructed with a set of different centers.The output
images are put into a specified folder and are named by the
center position corresponding to the image.
Parameters
----------
tomo : ndarray
3D tomographic data.
theta : array
Projection angles in radian.
dpath : str, optional
Folder name to save output images.
cen_range : list, optional
[start, end, step] Range of center values.
ind : int, optional
Index of the slice to be used for reconstruction.
emission : bool, optional
Determines whether data is emission or transmission type.
mask : bool, optional
If ``True``, apply a circular mask to the reconstructed image to
limit the analysis into a circular region.
ratio : float, optional
The ratio of the radius of the circular mask to the edge of the
reconstructed image.
"""
tomo = dtype.as_float32(tomo)
theta = dtype.as_float32(theta)
dx, dy, dz = tomo.shape
if ind is None:
ind = dy // 2
if cen_range is None:
center = np.arange(dz / 2 - 5, dz / 2 + 5, 0.5)
if len(cen_range) < 3:
cen_range[2] = 1
else:
center = np.arange(cen_range[0], cen_range[1], cen_range[2] / 2.)
stack = np.zeros((dx, len(center), dz))
for m in range(center.size):
stack[:, m, :] = tomo[:, ind, :]
# Reconstruct the same slice with a range of centers.
rec = recon(stack,
theta,
center=center,
emission=emission,
algorithm='gridrec')
# Apply circular mask.
if mask is True:
rec = circ_mask(rec, axis=0)
# Save images to a temporary folder.
for m in range(len(center)):
if m % 2 == 0: # 2 slices same bec of gridrec.
fname = os.path.join(dpath,
str('{0:.2f}'.format(center[m]) + '.tiff'))
write_tiff(rec[m:m + 1], fname=fname, overwrite=True)
