prj = dxchange.read_tiff_stack(f'{file_name[:-3]}/data/d_00000.tiff',
ind=range(ntheta))
theta = np.load(file_name[:-3] + '/data/theta.npy')
nz, n = prj.shape[1:]
niteradmm = [96, 48, 24, 12] # number of iterations in the ADMM scheme
# niteradmm = [2,2,2] # number of iterations in the ADMM scheme
startwin = [256, 128, 64,
32] # starting window size in optical flow estimation
stepwin = [2, 2, 2,
2] # step for decreasing the window size in optical flow estimtion
res = tomoalign.admm_of_levels(prj,
theta,
pnz,
ptheta,
center,
ngpus,
niteradmm,
startwin,
stepwin,
file_name[:-3] + '/tmp/',
padding=True)
dxchange.write_tiff_stack(res['u'],
file_name[:-3] + '/results_admm/u/r',
overwrite=True)
dxchange.write_tiff_stack(res['psi'],
file_name[:-3] + '/results_admm/psi/r',
overwrite=True)
ptheta = 10
niteradmm = [80, 72, 32, 24] # number of iterations in the ADMM scheme
# niteradmm = [0, 0, 0, 2] # number of iterations in the ADMM scheme
# starting window size in optical flow estimation
startwin = [128, 96, 48, 32]
# step for decreasing the window size in optical flow estimtion
stepwin = [1, 1, 1, 1]
center = 1158.5
name += '/fw' + str(len(theta)) + '_' + str(start) + '/'
res = tomoalign.admm_of_levels(data,
theta,
pnz,
ptheta,
center,
ngpus,
niteradmm,
startwin,
stepwin,
name,
padding=True)
dxchange.write_tiff_stack(res['u'],
name + '/results_admm/u/r',
overwrite=True)
dxchange.write_tiff_stack(res['psi'],
name + '/results_admm/psi/r',
overwrite=True)
np.save(name + '/results_admm/flow.npy', res['flow'])
if __name__ == "__main__":
n = 1024 + 512
ntheta = 1920
pprot = 96
# adef = 10
# tomoalign.gen_cyl_data(n, ntheta, pprot, adef)
data = dxchange.read_tiff('data/deformed_data.tiff')
data = ndimage.zoom(
data,
[ntheta // data.shape[0], n // data.shape[1], n // data.shape[2]],
order=0)
[ntheta, nz, n] = data.shape
theta = np.linspace(0, 4 * np.pi, ntheta).astype('float32')
center = n / 2
pnz = 32 # number of slice for simultaneus processing by one GPU in the tomography sub-problem
ptheta = 128 # number of projections for simultaneus processing by one GPU in the alignment sub-problem
# step for decreasing window size (increase resolution) in Farneback's algorithm on each ADMM iteration
ngpus = 8 # number of gpus
niteradmm = [5, 5, 5] # number of iterations in the ADMM scheme
startwin = [n, n // 2, n // 4]
stepwin = [2, 2, 2]
fname = 'data/tmp'
uof = tomoalign.admm_of_levels(data, theta, pnz, ptheta, center, ngpus,
niteradmm, startwin, stepwin, fname)
dxchange.write_tiff(uof['u'],
'data/of_recon/recon/iter' + str(niteradmm),
overwrite=True)
ptheta = 2
niteradmm = [128] # number of iterations in the ADMM scheme
# starting window size in optical flow estimation
startwin = [256]
# step for decreasing the window size in optical flow estimtion
stepwin = [2]
center = 192
# data = dxchange.read_tiff_stack(data_prefix+'/reccrop_align_azat/psiangle/r_00000.tiff',ind=range(ntheta))
data_prefix += 'rectomoalign_azat/'
res = tomoalign.admm_of_levels(data,
theta,
pnz,
ptheta,
center,
ngpus,
niteradmm,
startwin,
stepwin,
data_prefix,
padding=False)
dxchange.write_tiff_stack(res['u'],
data_prefix + '/results_admm/u/r',
overwrite=True)
dxchange.write_tiff_stack(res['psi'],
data_prefix + '/results_admm/psi/r',
overwrite=True)
np.save(data_prefix + '/results_admm/flow.npy', res['flow'])