def transform_scalars(dataset, rot_center=0, tune_rot_center=True):
"""Reconstruct sinograms using the tomopy gridrec algorithm
Typically, a data exchange file would be loaded for this
reconstruction. This operation will attempt to perform
flat-field correction of the raw data using the dark and
white background data found in the data exchange file.
This operator also requires either the tomviz/tomopy-pipeline
docker image, or a python environment with tomopy installed.
"""
from tomviz import utils
import numpy as np
import tomopy
# Get the current volume as a numpy array.
array = utils.get_array(dataset)
dark = dataset.dark
white = dataset.white
angles = utils.get_tilt_angles(dataset)
tilt_axis = dataset.tilt_axis
# TomoPy wants the tilt axis to be zero, so ensure that is true
if tilt_axis == 2:
order = [2, 1, 0]
array = np.transpose(array, order)
if dark is not None and white is not None:
dark = np.transpose(dark, order)
white = np.transpose(white, order)
if angles is not None:
# tomopy wants radians
theta = np.radians(angles)
else:
# Assume it is equally spaced between 0 and 180 degrees
theta = tomopy.angles(array.shape[0])
# Perform flat-field correction of raw data
if white is not None and dark is not None:
array = tomopy.normalize(array, white, dark, cutoff=1.4)
if rot_center == 0:
# Try to find it automatically
init = array.shape[2] / 2.0
rot_center = tomopy.find_center(array,
theta,
init=init,
ind=0,
tol=0.5)
elif tune_rot_center:
# Tune the center
rot_center = tomopy.find_center(array,
theta,
init=rot_center,
ind=0,
tol=0.5)
# Calculate -log(array)
array = tomopy.minus_log(array)
# Remove nan, neg, and inf values
array = tomopy.remove_nan(array, val=0.0)
array = tomopy.remove_neg(array, val=0.00)
array[np.where(array == np.inf)] = 0.00
# Perform the reconstruction
array = tomopy.recon(array, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
array = tomopy.circ_mask(array, axis=0, ratio=0.95)
# Set the transformed array
child = utils.make_child_dataset(dataset)
utils.mark_as_volume(child)
utils.set_array(child, array)
return_values = {}
return_values['reconstruction'] = child
return return_values
def transform_scalars(self, dataset, Nrecon=None, filter=None,
interp=None, Nupdates=None):
"""
3D Reconstruct from a tilt series using Weighted Back-projection Method
"""
self.progress.maximum = 1
from tomviz import utils
interpolation_methods = ('linear', 'nearest', 'spline', 'cubic')
filter_methods = ('none', 'ramp', 'shepp-logan',
'cosine', 'hamming', 'hann')
# Get Tilt angles
tilt_angles = utils.get_tilt_angles(dataset)
tiltSeries = utils.get_array(dataset)
if tiltSeries is None:
raise RuntimeError("No scalars found!")
Nslice = tiltSeries.shape[0]
self.progress.maximum = Nslice
step = 0
recon = np.empty([Nslice, Nrecon, Nrecon], dtype=np.float32, order='F')
t0 = time.time()
counter = 1
etcMessage = 'Estimated time to complete: n/a'
child = utils.make_child_dataset(dataset) #create child for recon
utils.mark_as_volume(child)
for i in range(Nslice):
if self.canceled:
return
self.progress.message = 'Slice No.%d/%d. ' % (
i + 1, Nslice) + etcMessage
recon[i, :, :] = wbp2(tiltSeries[i, :, :], tilt_angles, Nrecon,
filter_methods[filter],
interpolation_methods[interp])
step += 1
self.progress.value = step
timeLeft = (time.time() - t0) / counter * (Nslice - counter)
counter += 1
timeLeftMin, timeLeftSec = divmod(timeLeft, 60)
timeLeftHour, timeLeftMin = divmod(timeLeftMin, 60)
etcMessage = 'Estimated time to complete: %02d:%02d:%02d' % (
timeLeftHour, timeLeftMin, timeLeftSec)
# Update only once every so many steps
if Nupdates != 0 and (i + 1) % (Nslice//Nupdates) == 0:
utils.set_array(child, recon) #add recon to child
# This copies data to the main thread
self.progress.data = child
# One last update of the child data.
utils.set_array(child, recon) #add recon to child
self.progress.data = child
returnValues = {}
returnValues["reconstruction"] = child
return returnValues
def transform_scalars(self,
dataset,
Niter=10,
stepSize=0.0001,
updateMethodIndex=0,
Nupdates=0):
"""
3D Reconstruct from a tilt series using Simultaneous Iterative
Reconstruction Techniques (SIRT)"""
self.progress.maximum = 1
update_methods = ('landweber', 'cimmino', 'component averaging')
#reference
"""L. Landweber, Amer. J. Math., 73 (1951), pp. 615–624"""
"""G. Cimmino, La Ric. Sci., XVI, Ser. II, Anno IX, 1 (1938),
pp. 326–333
"""
"""Y. Censor et al, Parallel Comput., 27 (2001), pp. 777–808"""
# Get Tilt angles
tiltAngles = utils.get_tilt_angles(dataset)
#remove zero tilt anlges
if np.count_nonzero(tiltAngles) < tiltAngles.size:
tiltAngles = tiltAngles + 0.001
# Get Tilt Series
tiltSeries = utils.get_array(dataset)
(Nslice, Nray, Nproj) = tiltSeries.shape
#Check if there's negative values, shift by minimum if true.
if np.any(tiltSeries < 0):
tiltSeries -= np.amin(tiltSeries)
if tiltSeries is None:
raise RuntimeError("No scalars found!")
# Determine slice for live updates
Nupdates = calc_Nupdates(Nupdates, Niter)
# Generate measurement matrix
self.progress.message = 'Generating measurement matrix'
A = parallelRay(Nray, 1.0, tiltAngles, Nray,
1.0) #A is a sparse matrix
recon = np.zeros([Nslice, Nray, Nray], dtype=np.float32, order='F')
self.progress.maximum = Nslice * Niter + 1
step = 0
#create a reconstruction object
r = SIRT(A, update_methods[updateMethodIndex], Nslice)
r.initialize()
step += 1
self.progress.value = step
t0 = time.time()
counter = 1
etcMessage = 'Estimated time to complete: n/a'
#create child for recon
child = utils.make_child_dataset(dataset)
utils.mark_as_volume(child)
for i in range(Niter):
for s in range(Nslice):
if self.canceled:
return
self.progress.message = 'Iteration No.%d/%d,Slice No.%d/%d.' % (
i + 1, Niter, s + 1, Nslice) + etcMessage
b = tiltSeries[s, :, :].transpose().flatten()
recon_slice = recon[s, :, :].flatten()
recon[s, :, :] = r.recon2(b, recon_slice, stepSize, s).reshape(
(Nray, Nray))
step += 1
self.progress.value = step
timeLeft = (time.time() - t0) / counter * (Nslice * Niter -
counter)
counter += 1
timeLeftMin, timeLeftSec = divmod(timeLeft, 60)
timeLeftHour, timeLeftMin = divmod(timeLeftMin, 60)
etcMessage = 'Estimated time to complete: %02d:%02d:%02d' % (
timeLeftHour, timeLeftMin, timeLeftSec)
# Give 4 updates for first iteration.
if Nupdates != 0 and i == 0 and (s + 1) % (Nslice // 4) == 0:
utils.set_array(child, recon)
self.progress.data = child
#Positivity constraint.
recon[recon < 0] = 0
#Update at the end of each iteration.
if Nupdates != 0 and (i + 1) % Nupdates == 0:
utils.set_array(child, recon)
self.progress.data = child
# One last update of the child data.
utils.set_array(child, recon) #add recon to child
self.progress.data = child
returnValues = {}
returnValues["reconstruction"] = child
return returnValues
def transform_scalars(self, dataset, Niter=1, Nupdates=0, beta=1.0):
"""
3D Reconstruction using Algebraic Reconstruction Technique (ART)
"""
self.progress.maximum = 1
# Get Tilt angles
tiltAngles = utils.get_tilt_angles(dataset)
# Get Tilt Series
tiltSeries = utils.get_array(dataset)
(Nslice, Nray, Nproj) = tiltSeries.shape
if tiltSeries is None:
raise RuntimeError("No scalars found!")
#Check if there's negative values, shift by minimum if true.
if np.any(tiltSeries < 0):
tiltSeries -= np.amin(tiltSeries)
# Determine the slices for live updates.
Nupdates = calc_Nupdates(Nupdates, Niter)
# Generate measurement matrix
self.progress.message = 'Generating measurement matrix'
A = parallelRay(Nray, 1.0, tiltAngles, Nray,
1.0) #A is a sparse matrix
recon = np.zeros([Nslice, Nray, Nray], dtype=np.float32, order='F')
A = A.tocsr()
(Nslice, Nray, Nproj) = tiltSeries.shape
(Nrow, Ncol) = A.shape
rowInnerProduct = np.zeros(Nrow, dtype=np.float32)
row = np.zeros(Ncol, dtype=np.float32)
f = np.zeros(Ncol, dtype=np.float32) # Placeholder for 2d image
beta = 1.0
# Calculate row inner product
for j in range(Nrow):
row[:] = A[j, :].toarray()
rowInnerProduct[j] = np.dot(row, row)
self.progress.maximum = Nslice * Niter
step = 0
t0 = time.time()
etcMessage = 'Estimated time to complete: n/a'
#create child for recon
child = utils.make_child_dataset(dataset)
utils.mark_as_volume(child)
counter = 1
for i in range(Niter):
for s in range(Nslice):
if self.canceled:
return
self.progress.message = 'Iteration No.%d/%d,Slice No.%d/%d.' % (
i + 1, Niter, s + 1, Nslice) + etcMessage
#Initialize slice as zeros on first iteration,
#or vectorize the slice for the next iter.
if (i == 0):
f[:] = 0
elif (i != 0):
f[:] = recon[s, :, :].flatten()
b = tiltSeries[s, :, :].transpose().flatten()
for j in range(Nrow):
row[:] = A[j, :].toarray()
a = (b[j] - np.dot(row, f)) / rowInnerProduct[j]
f = f + row * a * beta
recon[s, :, :] = f.reshape((Nray, Nray))
# Give 4 updates for first iteration.
if Nupdates != 0 and i == 0 and (s + 1) % (Nslice // 4) == 0:
utils.set_array(child, recon)
self.progress.data = child
step += 1
self.progress.value = step
timeLeft = (time.time() - t0) / counter * \
(Nslice * Niter - counter)
counter += 1
timeLeftMin, timeLeftSec = divmod(timeLeft, 60)
timeLeftHour, timeLeftMin = divmod(timeLeftMin, 60)
etcMessage = 'Estimated time to complete: %02d:%02d:%02d' % (
timeLeftHour, timeLeftMin, timeLeftSec)
recon[recon < 0] = 0 #Positivity constraint
#Update for XX iterations.
if Nupdates != 0 and (i + 1) % Nupdates == 0:
utils.set_array(child, recon)
self.progress.data = child
# One last update of the child data.
utils.set_array(child, recon) #add recon to child
self.progress.data = child
returnValues = {}
returnValues["reconstruction"] = child
return returnValues
def create_child_dataset(self):
new_data = utils.make_child_dataset(self._data_object)
return Dataset(new_data, self._data_source)
def transform_scalars(self, dataset, Niter=10, Nupdates=0):
"""3D Reconstruct from a tilt series using simple TV minimzation"""
self.progress.maximum = 1
# Get Tilt angles
tiltAngles = utils.get_tilt_angles(dataset)
#remove zero tilt anlges
if np.count_nonzero(tiltAngles) < tiltAngles.size:
tiltAngles = tiltAngles + 0.001
# Get Tilt Series
tiltSeries = utils.get_array(dataset)
(Nslice, Nray, Nproj) = tiltSeries.shape
# Determine the slices for live updates.
Nupdates = calc_Nupdates(Nupdates, Niter)
# Generate measurement matrix
A = parallelRay(Nray, 1.0, tiltAngles, Nray, 1.0) #A is a sparse matrix
recon = np.zeros([Nslice, Nray, Nray], dtype=np.float32, order='F')
A = A.tocsr()
(Nslice, Nray, Nproj) = tiltSeries.shape
(Nrow, Ncol) = A.shape
rowInnerProduct = np.zeros(Nrow, dtype=np.float32)
row = np.zeros(Ncol, dtype=np.float32)
f = np.zeros(Ncol, dtype=np.float32) # Placeholder for 2d image
ng = 5
beta = 1.0
r_max = 1.0
gamma_red = 0.8
# Calculate row inner product, preparation for ART recon
for j in range(Nrow):
row[:] = A[j, :].toarray()
rowInnerProduct[j] = np.dot(row, row)
self.progress.maximum = Niter * Nslice
t0 = time.time()
counter = 1
etcMessage = 'Estimated time to complete: n/a'
#Create child dataset for recon
child = utils.make_child_dataset(dataset)
utils.mark_as_volume(child)
for i in range(Niter): #main loop
recon_temp = recon.copy()
#ART recon
for s in range(Nslice): #
if self.canceled: #In case canceled during ART.
return
self.progress.message = 'Slice No.%d/%d, Iteration No.%d/%d. '\
% (s + 1, Nslice, i + 1, Niter) + etcMessage
if (i == 0):
f[:] = 0
elif (i != 0):
f[:] = recon[s, :, :].flatten()
b = tiltSeries[s, :, :].transpose().flatten()
for j in range(Nrow):
row[:] = A[j, :].toarray()
a = (b[j] - np.dot(row, f)) / rowInnerProduct[j]
f = f + row * a * beta
recon[s, :, :] = f.reshape((Nray, Nray))
self.progress.value = i*Nslice + s
timeLeft = (time.time() - t0) / counter * \
(Nslice * Niter - counter)
counter += 1
timeLeftMin, timeLeftSec = divmod(timeLeft, 60)
timeLeftHour, timeLeftMin = divmod(timeLeftMin, 60)
etcMessage = 'Estimated time to complete: %02d:%02d:%02d' % (
timeLeftHour, timeLeftMin, timeLeftSec)
recon[recon < 0] = 0 #Positivity constraint
#Update for XX iterations.
if Nupdates != 0 and (i + 1) % Nupdates == 0:
utils.set_array(child, recon)
self.progress.data = child
if i != (Niter - 1):
self.progress.message = 'Minimizating the Objects TV'
#calculate tomogram change due to POCS
dPOCS = np.linalg.norm(recon_temp - recon)
recon_temp = recon.copy()
#3D TV minimization
for j in range(ng):
R_0 = tv(recon)
v = tv_derivative(recon)
recon_prime = recon - dPOCS * v
recon_prime[recon_prime < 0] = 0
gamma = 1.0
R_f = tv(recon_prime)
#Projected Line search
while R_f > R_0:
gamma = gamma * gamma_red
recon_prime = recon - gamma * dPOCS * v
recon_prime[recon_prime < 0] = 0
R_f = tv(recon_prime)
recon = recon_prime
dg = np.linalg.norm(recon - recon_temp)
if dg > r_max*dPOCS:
recon = r_max*dPOCS/dg*(recon - recon_temp) + recon_temp
# One last update of the child data.
utils.set_array(child, recon) #add recon to child
self.progress.data = child
returnValues = {}
returnValues["reconstruction"] = child
return returnValues
def transform_scalars(self, dataset, Niter=10, stepSize=0.0001,
updateMethodIndex=0, Nupdates=0):
"""
3D Reconstruct from a tilt series using Simultaneous Iterative
Reconstruction Techniques (SIRT)"""
self.progress.maximum = 1
update_methods = ('landweber', 'cimmino', 'component averaging')
#reference
"""L. Landweber, Amer. J. Math., 73 (1951), pp. 615–624"""
"""G. Cimmino, La Ric. Sci., XVI, Ser. II, Anno IX, 1 (1938),
pp. 326–333
"""
"""Y. Censor et al, Parallel Comput., 27 (2001), pp. 777–808"""
# Get Tilt angles
tiltAngles = utils.get_tilt_angles(dataset)
#remove zero tilt anlges
if np.count_nonzero(tiltAngles) < tiltAngles.size:
tiltAngles = tiltAngles + 0.001
# Get Tilt Series
tiltSeries = utils.get_array(dataset)
(Nslice, Nray, Nproj) = tiltSeries.shape
#Check if there's negative values, shift by minimum if true.
if np.any(tiltSeries < 0):
tiltSeries -= np.amin(tiltSeries)
if tiltSeries is None:
raise RuntimeError("No scalars found!")
# Determine slice for live updates
Nupdates = calc_Nupdates(Nupdates, Niter)
# Generate measurement matrix
self.progress.message = 'Generating measurement matrix'
A = parallelRay(Nray, 1.0, tiltAngles, Nray, 1.0) #A is a sparse matrix
recon = np.zeros([Nslice, Nray, Nray], dtype=np.float32, order='F')
self.progress.maximum = Nslice*Niter + 1
step = 0
#create a reconstruction object
r = SIRT(A, update_methods[updateMethodIndex], Nslice)
r.initialize()
step += 1
self.progress.value = step
t0 = time.time()
counter = 1
etcMessage = 'Estimated time to complete: n/a'
#create child for recon
child = utils.make_child_dataset(dataset)
utils.mark_as_volume(child)
for i in range(Niter):
for s in range(Nslice):
if self.canceled:
return
self.progress.message = 'Iteration No.%d/%d,Slice No.%d/%d.' % (
i + 1, Niter, s + 1, Nslice) + etcMessage
b = tiltSeries[s, :, :].transpose().flatten()
recon_slice = recon[s, :, :].flatten()
recon[s, :, :] = r.recon2(b, recon_slice, stepSize,
s).reshape((Nray, Nray))
step += 1
self.progress.value = step
timeLeft = (time.time() - t0) / counter * (Nslice*Niter -
counter)
counter += 1
timeLeftMin, timeLeftSec = divmod(timeLeft, 60)
timeLeftHour, timeLeftMin = divmod(timeLeftMin, 60)
etcMessage = 'Estimated time to complete: %02d:%02d:%02d' % (
timeLeftHour, timeLeftMin, timeLeftSec)
# Give 4 updates for first iteration.
if Nupdates != 0 and i == 0 and (s + 1) % (Nslice//4) == 0:
utils.set_array(child, recon)
self.progress.data = child
#Positivity constraint.
recon[recon < 0] = 0
#Update at the end of each iteration.
if Nupdates != 0 and (i + 1) % Nupdates == 0:
utils.set_array(child, recon)
self.progress.data = child
# One last update of the child data.
utils.set_array(child, recon) #add recon to child
self.progress.data = child
returnValues = {}
returnValues["reconstruction"] = child
return returnValues
def transform_scalars(self,
dataset,
Nrecon=None,
filter=None,
interp=None):
"""
3D Reconstruct from a tilt series using Weighted Back-projection Method
"""
self.progress.maximum = 1
from tomviz import utils
interpolation_methods = ('linear', 'nearest', 'spline', 'cubic')
filter_methods = ('none', 'ramp', 'shepp-logan', 'cosine', 'hamming',
'hann')
# Get Tilt angles
tilt_angles = utils.get_tilt_angles(dataset)
tiltSeries = utils.get_array(dataset)
if tiltSeries is None:
raise RuntimeError("No scalars found!")
Nslice = tiltSeries.shape[0]
self.progress.maximum = Nslice
step = 0
recon = np.empty([Nslice, Nrecon, Nrecon], dtype=float, order='F')
t0 = time.time()
counter = 1
etcMessage = 'Estimated time to complete: n/a'
child = utils.make_child_dataset(dataset) #create child for recon
utils.mark_as_volume(child)
for i in range(Nslice):
if self.canceled:
return
self.progress.message = 'Slice No.%d/%d. ' % (i + 1,
Nslice) + etcMessage
recon[i, :, :] = wbp2(tiltSeries[i, :, :], tilt_angles, Nrecon,
filter_methods[filter],
interpolation_methods[interp])
step += 1
self.progress.value = step
timeLeft = (time.time() - t0) / counter * (Nslice - counter)
counter += 1
timeLeftMin, timeLeftSec = divmod(timeLeft, 60)
timeLeftHour, timeLeftMin = divmod(timeLeftMin, 60)
etcMessage = 'Estimated time to complete: %02d:%02d:%02d' % (
timeLeftHour, timeLeftMin, timeLeftSec)
# Update only once every so many steps
if (i + 1) % 40 == 0:
utils.set_array(child, recon) #add recon to child
# This copies data to the main thread
self.progress.data = child
# One last update of the child data.
utils.set_array(child, recon) #add recon to child
self.progress.data = child
returnValues = {}
returnValues["reconstruction"] = child
return returnValues
def transform_scalars(self, dataset, Niter=10, Nupdates=0):
"""3D Reconstruct from a tilt series using simple TV minimzation"""
self.progress.maximum = 1
# Get Tilt angles
tiltAngles = utils.get_tilt_angles(dataset)
#remove zero tilt anlges
if np.count_nonzero(tiltAngles) < tiltAngles.size:
tiltAngles = tiltAngles + 0.001
# Get Tilt Series
tiltSeries = utils.get_array(dataset)
(Nslice, Nray, Nproj) = tiltSeries.shape
# Determine the slices for live updates.
Nupdates = calc_Nupdates(Nupdates, Niter)
# Generate measurement matrix
A = parallelRay(Nray, 1.0, tiltAngles, Nray,
1.0) #A is a sparse matrix
recon = np.zeros([Nslice, Nray, Nray], dtype=np.float32, order='F')
A = A.tocsr()
(Nslice, Nray, Nproj) = tiltSeries.shape
(Nrow, Ncol) = A.shape
rowInnerProduct = np.zeros(Nrow, dtype=np.float32)
row = np.zeros(Ncol, dtype=np.float32)
f = np.zeros(Ncol, dtype=np.float32) # Placeholder for 2d image
ng = 5
beta = 1.0
r_max = 1.0
gamma_red = 0.8
# Calculate row inner product, preparation for ART recon
for j in range(Nrow):
row[:] = A[j, :].toarray()
rowInnerProduct[j] = np.dot(row, row)
self.progress.maximum = Niter * Nslice
t0 = time.time()
counter = 1
etcMessage = 'Estimated time to complete: n/a'
#Create child dataset for recon
child = utils.make_child_dataset(dataset)
utils.mark_as_volume(child)
for i in range(Niter): #main loop
recon_temp = recon.copy()
#ART recon
for s in range(Nslice): #
if self.canceled: #In case canceled during ART.
return
self.progress.message = 'Slice No.%d/%d, Iteration No.%d/%d. '\
% (s + 1, Nslice, i + 1, Niter) + etcMessage
if (i == 0):
f[:] = 0
elif (i != 0):
f[:] = recon[s, :, :].flatten()
b = tiltSeries[s, :, :].transpose().flatten()
for j in range(Nrow):
row[:] = A[j, :].toarray()
a = (b[j] - np.dot(row, f)) / rowInnerProduct[j]
f = f + row * a * beta
recon[s, :, :] = f.reshape((Nray, Nray))
self.progress.value = i * Nslice + s
timeLeft = (time.time() - t0) / counter * \
(Nslice * Niter - counter)
counter += 1
timeLeftMin, timeLeftSec = divmod(timeLeft, 60)
timeLeftHour, timeLeftMin = divmod(timeLeftMin, 60)
etcMessage = 'Estimated time to complete: %02d:%02d:%02d' % (
timeLeftHour, timeLeftMin, timeLeftSec)
recon[recon < 0] = 0 #Positivity constraint
#Update for XX iterations.
if Nupdates != 0 and (i + 1) % Nupdates == 0:
utils.set_array(child, recon)
self.progress.data = child
if i != (Niter - 1):
self.progress.message = 'Minimizating the Objects TV'
#calculate tomogram change due to POCS
dPOCS = np.linalg.norm(recon_temp - recon)
recon_temp = recon.copy()
#3D TV minimization
for j in range(ng):
R_0 = tv(recon)
v = tv_derivative(recon)
recon_prime = recon - dPOCS * v
recon_prime[recon_prime < 0] = 0
gamma = 1.0
R_f = tv(recon_prime)
#Projected Line search
while R_f > R_0:
gamma = gamma * gamma_red
recon_prime = recon - gamma * dPOCS * v
recon_prime[recon_prime < 0] = 0
R_f = tv(recon_prime)
recon = recon_prime
dg = np.linalg.norm(recon - recon_temp)
if dg > r_max * dPOCS:
recon = r_max * dPOCS / dg * (recon -
recon_temp) + recon_temp
# One last update of the child data.
utils.set_array(child, recon) #add recon to child
self.progress.data = child
returnValues = {}
returnValues["reconstruction"] = child
return returnValues