plt.imshow(recFBP[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
plt.title('FBP Reconstruction, coronal (Y-Z) view', fontsize=19)
ax2.set_aspect('equal')
plt.show()
#plt.savefig('FBP_phantom.pdf', format='pdf', dpi=1600)
# calculate errors
Qtools = QualityTools(phantom, recFBP)
RMSE_fbp = Qtools.rmse()
print("Root Mean Square Error for FBP is {}".format(RMSE_fbp))
# SSIM measure
Qtools = QualityTools(phantom[128,:,:]*255, recFBP[128,:,:]*235)
win = np.array([gaussian(11, 1.5)])
win2d = win * (win.T)
ssim_fbp = Qtools.ssim(win2d)
print("Mean SSIM for FBP is {}".format(ssim_fbp[0]))
#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("Reconstructing with ADMM method using TomoRec software")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
# initialise TomoRec ITERATIVE reconstruction class ONCE
from tomorec.methodsIR import RecToolsIR
RectoolsIR = RecToolsIR(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
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
pars['regularisation_parameter'],
pars['number_of_iterations'],
pars['time_marching_parameter'],
pars['tolerance_constant'],'cpu')
toc=timeit.default_timer()
Run_time_rof = toc - tic
Qtools = QualityTools(phantom_tm, rof_cpu3D)
RMSE_rof = Qtools.rmse()
# SSIM measure
Qtools = QualityTools(phantom_tm[sliceSel,:,:]*255, rof_cpu3D[sliceSel,:,:]*235)
win = np.array([gaussian(11, 1.5)])
win2d = win * (win.T)
ssim_rof = Qtools.ssim(win2d)
print("ROF-TV (cpu) ____ RMSE: {}, MMSIM: {}, run time: {} sec".format(RMSE_rof,ssim_rof[0],Run_time_rof))
#%%
print ("#############ROF TV GPU####################")
# set parameters
pars = {'algorithm': ROF_TV, \
'input' : phantom_noise,\
'regularisation_parameter':0.06,\
'number_of_iterations': 8330,\
'time_marching_parameter': 0.00025,\
'tolerance_constant':0.0}
tic=timeit.default_timer()
(rof_gpu3D, infogpu) = ROF_TV(pars['input'],
pars['regularisation_parameter'],
