from tomobar.methodsIR import RecToolsIR
# set parameters and initiate a class object
Rectools = RecToolsIR(
DetectorsDimH=
detectorHoriz, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
CenterRotOffset=None, # Center of Rotation (CoR) scalar (for 3D case only)
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
datafidelity='LS', # data fidelity, choose LS, PWLS, GH (wip), Student (wip)
nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
OS_number=
None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
tolerance=1e-08, # tolerance to stop outer iterations earlier
device='gpu')
# Run ADMM-LS-TV reconstrucion algorithm
RecADMM_LS_TV = Rectools.ADMM(data_norm[:,:,slice_to_recon], \
rho_const = 500.0, \
iterationsADMM = 5,\
regularisation = 'FGP_TV', \
regularisation_parameter = 0.005,\
regularisation_iterations = 100)
fig = plt.figure()
plt.imshow(RecADMM_LS_TV[150:550, 150:550], vmin=0, vmax=0.003, cmap="gray")
#plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('ADMM LS-TV reconstruction')
plt.show()
#fig.savefig('dendr_TV.png', dpi=200)
RecFISTA_regNLTV = Rectools.FISTA(_data_, _algorithm_, _regularisation_)
fig = plt.figure()
plt.imshow(RecFISTA_regNLTV, vmin=0, vmax=0.2, cmap="gray")
plt.title('FISTA PWLS-OS-NLTV reconstruction')
plt.show()
#fig.savefig('ice_NLTV.png', dpi=200)
#%%
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("%%%%%%Reconstructing with ADMM LS-NLTV method %%%%%%%%%%%%%%%%")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
_algorithm_ = {'iterations': 5, 'ADMM_rho_const': 500.0}
_regularisation_ = {
'method': 'PD_TV',
'regul_param': 0.015,
'iterations': 80,
'device_regulariser': 'gpu'
}
# Run ADMM-LS-TV reconstrucion algorithm
RecADMM_LS_TV = Rectools.ADMM(_data_, _algorithm_, _regularisation_)
fig = plt.figure()
plt.imshow(RecADMM_LS_TV, vmin=0, vmax=0.2, cmap="gray")
#plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('ADMM LS-TV reconstruction')
plt.show()
#%%
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
CenterRotOffset=None, # Center of Rotation (CoR) scalar (for 3D case only)
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
datafidelity=
'LS', # data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
OS_number=
None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
tolerance=1e-06, # tolerance to stop OUTER iterations earlier
device='gpu')
# Run ADMM reconstrucion algorithm with regularisation
RecADMM_reg = Rectools.ADMM(noisy_sino,
rho_const = 4000.0, \
iterationsADMM = 15, \
regularisation = 'SB_TV', \
regularisation_parameter = 0.06,\
regularisation_iterations = 50)
plt.figure()
plt.imshow(RecADMM_reg, vmin=0, vmax=1, cmap="gray")
plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('ADMM reconstruction')
plt.show()
# calculate errors
Qtools = QualityTools(phantom_2D, RecADMM_reg)
RMSE_ADMM = Qtools.rmse()
print("RMSE for regularised ADMM is {}".format(RMSE_ADMM))
#%%
# set parameters and initiate a class object
Rectools = RecToolsIR(DetectorsDimH = Horiz_det, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV = Vert_det, # DetectorsDimV # detector dimension (vertical) for 3D case only
CenterRotOffset = None, # Center of Rotation (CoR) scalar (for 3D case only)
AnglesVec = angles_rad, # array of angles in radians
ObjSize = N_size, # a scalar to define reconstructed object dimensions
datafidelity='LS',# data fidelity, choose LS, PWLS, GH (wip), Student (wip)
nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
tolerance = 1e-06, # tolerance to stop outer iterations earlier
device='gpu')
# Run ADMM reconstrucion algorithm with regularisation
RecADMM_reg = Rectools.ADMM(projData3D_analyt_noise,
rho_const = 2000.0, \
iterationsADMM = 20, \
regularisation = 'FGP_TV', \
regularisation_parameter = 0.0035,\
regularisation_iterations = 200)
sliceSel = int(0.5*N_size)
max_val = 1
plt.figure()
plt.subplot(131)
plt.imshow(RecADMM_reg[sliceSel,:,:],vmin=0, vmax=max_val)
plt.title('3D ADMM Reconstruction, axial view')
plt.subplot(132)
plt.imshow(RecADMM_reg[:,sliceSel,:],vmin=0, vmax=max_val)
plt.title('3D ADMM Reconstruction, coronal view')
DetectorsDimV=
100, # 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
datafidelity=
'LS', # data fidelity, choose LS, PWLS, GH (wip), Students t (wip)
nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
OS_number=
None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
tolerance=0.0, # tolerance to stop inner (regularisation) iterations earlier
device='gpu')
#%%
print("Reconstructing with ADMM method using SB-TV penalty")
RecADMM_reg_sbtv = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
rho_const = 2000.0, \
iterationsADMM = 15, \
regularisation = 'SB_TV', \
regularisation_parameter = 0.00085,\
regularisation_iterations = 50)
sliceSel = 50
max_val = 0.003
plt.figure()
plt.subplot(131)
plt.imshow(RecADMM_reg_sbtv[sliceSel, :, :], vmin=0, vmax=max_val, cmap="gray")
plt.title('3D ADMM-SB-TV Reconstruction, axial view')
plt.subplot(132)
plt.imshow(RecADMM_reg_sbtv[:, sliceSel, :], vmin=0, vmax=max_val, cmap="gray")
plt.title('3D ADMM-SB-TV Reconstruction, coronal view')
plt.subplot(133)
# calculate errors
Qtools = QualityTools(phantom_2D, RecFISTA)
RMSE_FISTA = Qtools.rmse()
Qtools = QualityTools(phantom_2D, RecFISTA_reg)
RMSE_FISTA_reg = Qtools.rmse()
print("RMSE for FISTA is {}".format(RMSE_FISTA))
print("RMSE for regularised FISTA is {}".format(RMSE_FISTA_reg))
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing using ADMM method (tomobar)")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# Run ADMM reconstrucion algorithm with regularisation
RecADMM_reg = RectoolsIR.ADMM(noisy_zing_stripe,
rho_const=5000.0,
iterationsADMM=10,
regularisation='ROF_TV',
regularisation_parameter=0.3)
plt.figure()
plt.imshow(RecADMM_reg, vmin=0, vmax=1, cmap="gray")
plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('ADMM reconstruction')
plt.show()
# calculate errors
Qtools = QualityTools(phantom_2D, RecADMM_reg)
RMSE_ADMM_reg = Qtools.rmse()
print("RMSE for regularised ADMM is {}".format(RMSE_ADMM_reg))
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing using ADMM method (tomobar)")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# Run ADMM reconstrucion algorithm with regularisation
_data_ = {'projection_norm_data': noisy_zing_stripe} # data dictionary
_algorithm_ = {'iterations': 15, 'ADMM_rho_const': 5000.0}
# adding regularisation using the CCPi regularisation toolkit
_regularisation_ = {
'method': 'PD_TV',
'regul_param': 0.1,
'iterations': 150,
'device_regulariser': 'gpu'
}
RecADMM_reg = RectoolsIR.ADMM(_data_, _algorithm_, _regularisation_)
plt.figure()
plt.imshow(RecADMM_reg, vmin=0, vmax=2, cmap="gray")
plt.colorbar(ticks=[0, 0.5, 3], orientation='vertical')
plt.title('ADMM reconstruction')
plt.show()
# calculate errors
Qtools = QualityTools(phantom_2D, RecADMM_reg)
RMSE_ADMM_reg = Qtools.rmse()
print("RMSE for regularised ADMM is {}".format(RMSE_ADMM_reg))
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing using FISTA method (tomobar)")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("%%%%%%Reconstructing with ADMM LS-TV method %%%%%%%%%%%%%%%%")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
from tomobar.methodsIR import RecToolsIR
# set parameters and initiate a class object
Rectools = RecToolsIR(DetectorsDimH = np.size(det_y_crop), # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV = None, # DetectorsDimV # detector dimension (vertical) for 3D case only
CenterRotOffset = None, # Center of Rotation (CoR) scalar (for 3D case only)
AnglesVec = angles_rad, # array of angles in radians
ObjSize = N_size, # a scalar to define reconstructed object dimensions
datafidelity='LS',# data fidelity, choose LS, PWLS, GH (wip), Student (wip)
nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
tolerance = 1e-08, # tolerance to stop outer iterations earlier
device='gpu')
# Run ADMM-LS-TV reconstrucion algorithm
RecADMM_LS_TV = Rectools.ADMM(data_norm[:,det_y_crop], \
rho_const = 500.0, \
iterationsADMM = 3,\
regularisation = 'FGP_TV', \
regularisation_parameter = 0.05,\
regularisation_iterations = 100)
fig = plt.figure()
plt.imshow(RecADMM_LS_TV, vmin=0, vmax=0.2, cmap="gray")
#plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('ADMM LS-TV reconstruction')
plt.show()
#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("%%%%%%Reconstructing with ADMM LS-TV method %%%%%%%%%%%%%%%%")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
from tomobar.methodsIR import RecToolsIR
# set parameters and initiate a class object
Rectools = RecToolsIR(DetectorsDimH = np.size(det_y_crop), # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV = None, # DetectorsDimV # detector dimension (vertical) for 3D case only
CenterRotOffset = None, # Center of Rotation (CoR) scalar (for 3D case only)
AnglesVec = angles_rad, # array of angles in radians
ObjSize = N_size, # a scalar to define reconstructed object dimensions
datafidelity='LS',# data fidelity, choose LS, PWLS, GH (wip), Student (wip)
nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
tolerance = 1e-08, # tolerance to stop outer iterations earlier
device='gpu')
# Run ADMM-LS-TV reconstrucion algorithm
RecADMM_LS_TV = Rectools.ADMM(np.transpose(data_norm[det_y_crop,:,0]), \
rho_const = 500.0, \
iterationsADMM = 3,\
regularisation = 'FGP_TV', \
regularisation_parameter = 0.05,\
regularisation_iterations = 100)
fig = plt.figure()
plt.imshow(RecADMM_LS_TV, vmin=0, vmax=0.2, cmap="gray")
#plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('ADMM LS-TV reconstruction')
plt.show()
#%%
ObjSize = N_size, # a scalar to define reconstructed object dimensions
datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
tolerance = 0.0, # tolerance to stop inner (regularisation) iterations earlier
device='gpu')
#%%
param_space = 30
reg_param_sb_vec = np.linspace(0.03,0.15,param_space,dtype='float32') # a vector of parameters
erros_vec_sbtv = np.zeros((param_space)) # a vector of errors
print ("Reconstructing with ADMM method using SB-TV penalty")
for i in range(0,param_space):
RecADMM_reg_sbtv = RectoolsIR.ADMM(projdata_norm,
rho_const = 2000.0, \
iterationsADMM = 15, \
regularisation = 'SB_TV', \
regularisation_parameter = reg_param_sb_vec[i],\
regularisation_iterations = 50)
# calculate errors
Qtools = QualityTools(phantom, RecADMM_reg_sbtv)
erros_vec_sbtv[i] = Qtools.rmse()
print("RMSE for regularisation parameter {} for ADMM-SB-TV is {}".format(reg_param_sb_vec[i],erros_vec_sbtv[i]))
plt.figure()
plt.plot(erros_vec_sbtv)
# Saving generated data with a unique time label
h5f = h5py.File('Optim_admm_sbtv.h5', 'w')
h5f.create_dataset('reg_param_sb_vec', data=reg_param_sb_vec)
h5f.create_dataset('erros_vec_sbtv', data=erros_vec_sbtv)
h5f.close()
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
datafidelity=
'LS', # data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
OS_number=
None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
tolerance=1e-06, # tolerance to stop outer -ADMM iterations earlier
device='gpu')
#%%
print("Reconstructing with ADMM method using SB-TV penalty")
RecADMM_reg_sbtv = RectoolsIR.ADMM(projdata_norm,
rho_const = 2000.0, \
iterationsADMM = 25, \
regularisation = 'SB_TV', \
regularisation_parameter = optimReg_sbtv,\
regularisation_iterations = 50)
sliceSel = int(0.5 * N_size)
max_val = 1
plt.figure(figsize=(20, 3))
gs1 = gridspec.GridSpec(1, 4)
gs1.update(wspace=0.02, hspace=0.01) # set the spacing between axes.
ax1 = plt.subplot(gs1[0])
plt.plot(reg_param_sb_vec, erros_vec_sbtv, color='k', linewidth=2)
plt.xlabel('Regularisation parameter', fontsize=16)
plt.ylabel('RMSE value', fontsize=16)
plt.title('Regularisation selection', fontsize=19)
ax2 = plt.subplot(gs1[1])
plt.imshow(RecADMM_reg_sbtv[sliceSel, :, :], vmin=0, vmax=max_val, cmap="PuOr")