# 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='PWLS',# 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 CGLS reconstrucion algorithm
RecCGLS = Rectools.CGLS(data_norm[:,det_y_crop], iterations = 7)
plt.figure()
plt.imshow(RecCGLS, vmin=0, vmax=0.3, cmap="gray")
plt.title('CGLS reconstruction')
plt.show()
#%%
# Initialise FISTA-type PWLS reconstruction (run once)
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
device_projector='gpu')
# prepare dictionaries with parameters:
_data_ = {
'projection_norm_data': data_norm,
'projection_raw_data': data_raw,
'OS_number': 6
} # data dictionary
lc = Rectools.powermethod(
_data_) # calculate Lipschitz constant (run once to initialise)
_algorithm_ = {'iterations': 20, 'lipschitz_const': lc}
# Run CGLS reconstrucion algorithm
RecCGLS = Rectools.CGLS(_data_, _algorithm_)
plt.figure()
plt.imshow(RecCGLS, vmin=0, vmax=0.3, cmap="gray")
plt.title('CGLS reconstruction')
plt.show()
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("Reconstructing with FISTA PWLS-OS-TV method % %%%%%%%%%%%%%%")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# adding regularisation
_regularisation_ = {
'method': 'PD_TV',
'regul_param': 0.0012,
'iterations': 80,
'device_regulariser': 'gpu'
# 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='PWLS',# 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 CGLS reconstrucion algorithm
RecCGLS = Rectools.CGLS(np.transpose(data_norm[det_y_crop,:,0]), iterations = 7)
plt.figure()
plt.imshow(RecCGLS, vmin=0, vmax=0.3, cmap="gray")
plt.title('CGLS reconstruction')
plt.show()
#%%
# Initialise FISTA-type PWLS reconstruction (run once)
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