plt.show()
# Projection geometry related parameters:
Horiz_det = int(np.sqrt(2) * N_size) # detector column count (horizontal)
Vert_det = N_size # detector row count (vertical) (no reason for it to be > N)
angles_num = int(0.5 * np.pi * N_size)
# angles number
angles = np.linspace(0.0, 179.9, angles_num, dtype='float32') # in degrees
angles_rad = angles * (np.pi / 180.0)
#%%
# initialise TomoRec DIRECT reconstruction class ONCE
from tomorec.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(
DetectorsDimH=Horiz_det, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
Vert_det, # DetectorsDimV # detector dimension (vertical) for 3D case only
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
device='gpu')
#%%
print("Building 3D analytical projection data with TomoPhantom")
projData3D_analyt = TomoP3D.ModelSino(model, N_size, Horiz_det, Vert_det,
angles, path_library3D)
intens_max = 70
sliceSel = 150
plt.figure()
plt.subplot(131)
plt.imshow(projData3D_analyt[:, sliceSel, :], vmin=0, vmax=intens_max)
plt.title('2D Projection (analytical)')
plt.subplot(132)
plt.xlabel('X-detector', fontsize=16)
plt.ylabel('Projection angle', fontsize=16)
plt.title('Sinogram (X-Y) view', fontsize=19)
plt.subplot(133)
plt.imshow(projdata_norm[:,:,sliceSel],vmin=0, vmax=intens_max, cmap="PuOr")
plt.xlabel('Projection angle', fontsize=16)
plt.ylabel('Z-detector', fontsize=16)
plt.title('Vertical (Y-Z) view', fontsize=19)
plt.show()
#plt.savefig('projdata.pdf', format='pdf', dpi=1200)
#%%
# initialise TomoRec DIRECT reconstruction class ONCE
from tomorec.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(DetectorsDimH = Horiz_det, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV = Vert_det, # DetectorsDimV # detector dimension (vertical) for 3D case only
AnglesVec = proj_angles, # array of angles in radians
ObjSize = N_size, # a scalar to define reconstructed object dimensions
device = 'gpu')
#%%
print ("Reconstruction using FBP from TomoRec")
recFBP= RectoolsDIR.FBP(projdata_norm) # FBP reconstruction
#%%
x0, y0 = 0, 127 # These are in _pixel_ coordinates!!
x1, y1 = 255, 127
sliceSel = int(0.5*N_size)
max_val = 1
plt.figure(figsize = (20,5))
gs1 = gridspec.GridSpec(1, 3)
gs1.update(wspace=0.1, hspace=0.05) # set the spacing between axes.
ax1 = plt.subplot(gs1[0])
#%%
# using normaliser from TomoRec package
data_norm = normaliser(data_raw[:, :, 150:160],
flats[:, :, 150:160],
darks[:, :, 150:160],
log='log')
# remove nan's
data_norm = np.where(np.isfinite(data_norm), data_norm, 0)
#%%
# Reconstructing normalised data
from tomorec.methodsDIR import RecToolsDIR
N_size = 1000
detectHoriz, anglesNum, slices = np.shape(data_norm)
det_y_crop = [i for i in range(115, detectHoriz)]
RectoolsDIR = RecToolsDIR(
DetectorsDimH=np.size(
det_y_crop), # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
device='gpu')
FBP = RectoolsDIR.FBP(np.transpose(data_norm[det_y_crop, :, 0]))
plt.figure()
plt.imshow(FBP, vmin=0, vmax=0.003, cmap="gray")
plt.title('FBP reconstruction')
plt.show()
print("Numerical (ASTRA) sinogram has been generated in {} seconds".format(
Run_time))
plt.figure()
plt.rcParams.update({'font.size': 21})
plt.imshow(sino_num_ASTRA, cmap="BuPu")
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}' '{}'.format('Numerical sinogram (ASTRA) of model no.', model))
#%%
###################################################################
# initialise TomoRec reconstruction class ONCE
from tomorec.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(
DetectorsDimH=P, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
device='cpu')
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing analytical sinogram using Fourier Slice method")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
RecFourier = RectoolsDIR.fourier(sino_an, 'linear')
plt.figure()
plt.imshow(RecFourier, vmin=0, vmax=1, cmap="BuPu")
plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('Fourier slice reconstruction')
#%%
ObjSize=N_size, # a scalar to define reconstructed object dimensions
datafidelity=
'LS', # data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
OS_number=
None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
tolerance=1e-06, # tolerance to stop outer iterations earlier
device='gpu')
lc = Rectools.powermethod(
) # calculate Lipschitz constant (run once to initilise)
from tomorec.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(
DetectorsDimH=P, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
device='gpu')
FBPrec = RectoolsDIR.FBP(noisy_sino) #perform FBP
plt.figure()
plt.imshow(FBPrec, vmin=0, vmax=1, cmap="BuPu")
plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('FBP reconstruction')
#%%
print("Pre-calculating weights for non-local patches using FBP image...")
pars = {'algorithm' : PatchSelect, \
noisy_zing_stripe = artifacts_add.stripes(percentage=1, maxthickness=1)
noisy_zing_stripe[noisy_zing_stripe < 0] = 0
plt.figure()
plt.rcParams.update({'font.size': 21})
plt.imshow(noisy_zing_stripe, cmap="gray")
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}' '{}'.format('Analytical noisy sinogram with artifacts.', model))
#%%
# initialise TomoRec DIRECT reconstruction class ONCE
from tomorec.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(
DetectorsDimH=P, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
device='cpu')
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing analytical sinogram using Fourier Slice method")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
RecFourier = RectoolsDIR.fourier(sino_an, 'linear')
plt.figure()
plt.imshow(RecFourier, vmin=0, vmax=1, cmap="BuPu")
plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('Fourier slice reconstruction')
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing analytical sinogram using FBP (TomoRec)...")
angles_rad = angles*(np.pi/180)
P = int(np.sqrt(2)*N_size) #detectors
sino = TomoP2D.ModelSinoTemporal(model, N_size, P, angles, path_library2D)
plt.figure(2)
plt.rcParams.update({'font.size': 21})
plt.title('{}''{}'.format('2D+t sinogram of model no.',model))
for sl in range(0,np.shape(phantom_2Dt)[0]):
im = sino[sl,:,:].transpose()
plt.imshow(im, vmin=0, vmax=180)
plt.pause(.1)
plt.draw
#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("Reconstructing analytical sinogram using FBP (TomoRec)...")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# initialise TomoRec reconstruction class ONCE
from tomorec.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(DetectorsDimH = P, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV = None, # DetectorsDimV # detector dimension (vertical) for 3D case only
AnglesVec = angles_rad, # array of angles in radians
ObjSize = N_size, # a scalar to define reconstructed object dimensions
device='cpu')
FBPrec = RectoolsDIR.FBP(sino[15,:,:].transpose()) # reconstruct one frame
plt.figure(3)
plt.imshow(FBPrec, vmin=0, vmax=1)
plt.title('FBP Reconstructed Phantom')
#%%
plt.show()
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("%%%%%%%%%%%%Reconstructing with FBP method %%%%%%%%%%%%%%%%%")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
from tomorec.methodsDIR import RecToolsDIR
N_size = 950
detectHoriz, anglesNum, timeframe_no = np.shape(data_norm)
FBP_timeframes = np.zeros((N_size, N_size, timeframe_no), dtype='float32')
for i in range(0, timeframe_no):
RectoolsDIR = RecToolsDIR(
DetectorsDimH=
detectHoriz, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
AnglesVec=angles_rad[i, :], # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
device='gpu')
FBP_timeframes[:, :, i] = RectoolsDIR.FBP(np.transpose(data_norm[:, :, i]))
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing with FISTA PWLS-OS-TV method % %%%%%%%%%%%%%%")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
from tomorec.methodsIR import RecToolsIR
N_size = 950
detectHoriz, anglesNum, timeframe_no = np.shape(data_norm)
FISTA_TV_timeframes = np.zeros((N_size, N_size, timeframe_no), dtype='float32')
for i in range(0, timeframe_no):
plt.figure(2)
plt.rcParams.update({'font.size': 21})
plt.imshow(sino_an, cmap="BuPu")
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}'.format('Analytical sinogram of an object'))
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing analytical sinogram using FBP (TomoRec)...")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# initialise TomoRec reconstruction class ONCE
from tomorec.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(
DetectorsDimH=P, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
device='cpu')
FBPrec = RectoolsDIR.FBP(sino_an)
plt.figure(3)
plt.imshow(FBPrec, vmin=0, vmax=1, cmap="BuPu")
plt.title('FBP Reconstructed Model')
#%%
"""
Main difference from DemoModel.py is that we extract all parameters from the
library file using Python and then pass it to the Object function instead of model.
This can be helpful if one would like to avoid using library files and can
pass parameters directly into object function