def prealign(data):
mmin,mmax = find_min_max(data)
pars = [0.5,1, 2*n, 4, 5, 1.1, 4]
for k in range(4):
with dc.SolverDeform(720, nz, n, 20) as dslv:
flow = dslv.registration_flow_batch(
data[k*720:(k+1)*720], data[0*720:(0+1)*720], mmin[:720], mmax[:720], None, pars, 20)
data[k*720:(k+1)*720] = dslv.apply_flow_gpu_batch(data[k*720:(k+1)*720], flow)
return data
ptheta = 40
dxchange.write_tiff_stack(data, name + '/data/d', overwrite=True)
#exit()
#if(il==0):
u = np.zeros([nz, ne, ne], dtype='float32')
psi = data.copy() * 0
lamd = np.zeros([ntheta, nz, n], dtype='float32')
flow = np.zeros([ntheta, nz, n, 2], dtype='float32')
#else:
# u, psi, lamd, flow = interpdense(u,psi,lamd,flow)
# optical flow parameters
pars = [0.5, 1, w[il], 4, 5, 1.1, 4]
with tc.SolverTomo(theta, ntheta, nz, ne, pnz,
center / pow(2, binning), ngpus) as tslv:
with dc.SolverDeform(ntheta, nz, n, ptheta) as dslv:
rho = 0.5
h0 = psi
for k in range(niter[il]):
# registration
# print(np.linalg.norm(psi-data))
tic()
flow = dslv.registration_flow_batch(
psi, data, mmin, mmax, flow.copy(), pars, 40)
print(toc())
# Tpsi = dslv.apply_flow_gpu_batch(psi, flow)
# print(np.linalg.norm(Tpsi-data))
# deformation subproblem
tic()
psi = dslv.cg_deform_gpu_batch(
# initial guess
u = np.zeros([det, det, det], dtype='float32')
psi1 = data.copy()
psi2 = np.zeros([3, det, det, det], dtype='float32')
lamd1 = np.zeros([ntheta, det, det], dtype='float32')
lamd2 = np.zeros([3, det, det, det], dtype='float32')
flow = np.zeros([ntheta, det, det, 2], dtype='float32')
# optical flow parameters
pars = [0.5, 1, 1024, 4, 5, 1.1, 4]
niter = 257
alpha = 8e-14
with lcg.SolverLam(det, det, det, det, ntheta, phi, 1e-2) as tslv:
with dc.SolverDeform(ntheta, det, det, 42) as dslv:
rho1 = 0.5
rho2 = 0.5
h01 = psi1
h02 = psi2
for k in range(niter):
# registration
tic()
flow = dslv.registration_flow_batch(psi1,
data,
mmin,
mmax,
flow.copy(),
nz = 128 # object size in z
ntheta = 1 # number of angles (rotations)
ptheta = 1
# Load object
u0 = dxchange.read_tiff('data/delta-chip-128.tiff')[:, 64:64 +
ntheta].swapaxes(0, 1)
# deform it
u = deform(u0)
mmin = np.min(u, axis=(1, 2))
mmax = np.max(u, axis=(1, 2))
flow = np.zeros([ntheta, nz, n, 2], dtype='float32')
with df.SolverDeform(ntheta, nz, n, ptheta) as slv:
flow = slv.registration_flow_batch(u0,
u,
mmin,
mmax,
flow,
pars=[0.5, 1, 16, 20, 5, 1.1, 0])
rec = slv.cg_deform(u, u * 0, flow, 12, dbg=False)
dxchange.write_tiff(u0[ntheta // 2],
'res/delta-chip-128.tiff',
overwrite=True)
dxchange.write_tiff(u[ntheta // 2],
'res/defdelta-chip-128.tiff',
overwrite=True)
dxchange.write_tiff(rec[ntheta // 2],
