angles,
det_num,
det_w,
dist_source_center=SourceOrig,
dist_center_detector=OrigDetec)
else:
NotImplemented
# Set up Operator object combining the ImageGeometry and AcquisitionGeometry
# wrapping calls to ASTRA as well as specifying whether to use CPU or GPU.
Aop = AstraProjectorSimple(ig, ag, 'gpu')
# Forward and backprojection are available as methods direct and adjoint. Here
# generate test data b and do simple backprojection to obtain z.
b = Aop.direct(Phantom)
z = Aop.adjoint(b)
plt.imshow(b.array)
plt.title('Simulated data')
plt.show()
plt.imshow(z.array)
plt.title('Backprojected data')
plt.colorbar()
plt.show()
# Using the test data b, different reconstruction methods can now be set up as
# demonstrated in the rest of this file. In general all methods need an initial
# guess and some algorithm options to be set:
x_init = ig.allocate()
opt = {'tol': 1e-4, 'iter': 100}
angles = np.linspace(0, 2 * np.pi, angles_num)
ag = AcquisitionGeometry('cone',
'2D',
angles,
detectors,
det_w,
dist_source_center=SourceOrig,
dist_center_detector=OrigDetec)
else:
NotImplemented
Aop = AstraProjectorSimple(ig, ag, dev)
sin = Aop.direct(data)
eta = 0
noisy_data = AcquisitionData(sin.as_array() + np.random.normal(0, 1, ag.shape))
back_proj = Aop.adjoint(noisy_data)
# Define Least Squares
f = FunctionOperatorComposition(L2NormSquared(b=noisy_data), Aop)
# Allocate solution
x_init = ig.allocate()
# Run FISTA for least squares
fista = FISTA(x_init=x_init, f=f, g=ZeroFunction())
fista.max_iteration = 10
fista.update_objective_interval = 2
fista.run(100, verbose=True)
# Run FISTA for least squares with lower/upper bound
fista0 = FISTA(x_init=x_init, f=f, g=IndicatorBox(lower=0, upper=1))