time_limit = 180
# Max Number of iterations before next projection is collected.
max_iter = 125
#SNR
SNR = 100
noise = True
save = False # Save final reconstruction.
show_live_plot = 1 # Show intermediate results.
##########################################
#Read Image.
(file_name, original_volume) = load_data(vol_size, file_name)
file_name = 'au_sto'
(Nslice, Nray, _) = original_volume.shape
# Generate Tilt Angles.
tiltAngles = np.load('Tilt_Series/' + file_name + '_tiltAngles.npy')
Nproj = tiltAngles.shape[0]
# Initialize C++ Object..
tomo_obj = ctvlib.ctvlib(Nslice, Nray, Nproj)
# Generate measurement matrix
A = parallelRay(Nray, tiltAngles)
tomo_obj.load_A(A)
A = None
tomo_obj.rowInnerProduct()
time_limit = 180
# Max Number of iterations before next projection is collected.
max_iter = 125
#SNR
SNR = 100
noise = True
save = False # Save final reconstruction.
show_live_plot = 0 # Show intermediate results.
##########################################
# #Read Image.
(file_name, tiltSeries) = load_data(vol_size, file_name)
(Nslice, Nray, Nproj) = tiltSeries.shape
b = np.zeros([Nslice, Nray * Nproj])
# Initialize C++ Object..
tomo_obj = ctvlib.ctvlib(Nslice, Nray, Nproj)
for s in range(Nslice):
b[s, :] = tiltSeries[s, :, :].transpose().ravel()
tomo_obj.setTiltSeries(b)
tiltSeries = None
# Generate Tilt Angles.
tiltAngles = np.load('Tilt_Series/' + file_name + '_tiltAngles.npy')
# Generate measurement matrix