def _get_algorithm_kwargs(shape):
dy, dt, dx = shape
return {
'num_gridx': dx,
'num_gridy': dx,
'filter_name': 'shepp',
'filter_par': np.array([0.5, 8], dtype='float32'),
'num_iter': dtype.as_int32(1),
'reg_par': np.ones(10, dtype='float32'),
'num_block': dtype.as_int32(1),
'ind_block':
np.arange(0, dt, dtype=np.float32), # TODO: I think this should be int
'options': {},
'accelerated': False,
'pool_size':
0, # if zero, calculate based on threads started at Python level
'interpolation':
'NN', # interpolation method (NN = nearest-neighbor, LINEAR, CUBIC)
'device': 'gpu',
'grid_size': np.array(
[0, 0, 0],
dtype='int32'), # CUDA grid size. If zero, dynamically computed
'block_size': np.array([32, 32, 1],
dtype='int32'), # CUDA threads per block
}
def _get_algorithm_kwargs(shape):
dx, dy, dz = shape
return {
'num_gridx': dz,
'num_gridy': dz,
'filter_name': np.array('shepp', dtype=(str, 16)),
'num_iter': dtype.as_int32(1),
'reg_par': np.ones(10, dtype='float32'),
'num_block': dtype.as_int32(1),
'ind_block': np.arange(0, dx, dtype='float32'),
}
def _get_algorithm_kwargs(shape):
dx, dy, dz = shape
return {
'num_gridx': dz,
'num_gridy': dz,
'filter_name': np.array('shepp', dtype=(str, 16)),
'num_iter': dtype.as_int32(1),
'reg_par': np.ones(10, dtype='float32'),
'num_block': dtype.as_int32(1),
'ind_block': np.arange(0, dx, dtype='float32'),
'options': {},
}
def _get_algorithm_kwargs(shape):
dy, dt, dx = shape
return {
'num_gridx': dx,
'num_gridy': dx,
'filter_name': np.array('shepp', dtype=(str, 16)),
'filter_par': np.array([0.5, 8], dtype='float32'),
'num_iter': dtype.as_int32(1),
'reg_par': np.ones(10, dtype='float32'),
'num_block': dtype.as_int32(1),
'ind_block': np.arange(0, dt, dtype=np.float32), #TODO: I think this should be int
'options': {},
}
def _get_algorithm_kwargs(shape):
dy, dt, dx = shape
return {
"num_gridx": dx,
"num_gridy": dx,
"filter_name": "shepp",
"filter_par": np.array([0.5, 8], dtype="float32"),
"num_iter": dtype.as_int32(1),
"reg_par": np.ones(10, dtype="float32"),
"num_block": dtype.as_int32(1),
"ind_block": np.arange(0, dt, dtype=np.float32), # TODO: I think this should be int
"options": {},
}
def _get_algorithm_kwargs(shape):
dy, dt, dx = shape
return {
'num_gridx': dx,
'num_gridy': dx,
'filter_name': 'shepp',
'filter_par': np.array([0.5, 8], dtype='float32'),
'num_iter': dtype.as_int32(1),
'reg_par': np.ones(10, dtype='float32'),
'num_block': dtype.as_int32(1),
'ind_block': np.arange(0, dt, dtype=np.float32), #TODO: I think this should be int
'options': {},
}
def normalize_bg(tomo, air=1, ncore=None, nchunk=None):
"""
Normalize 3D tomgraphy data based on background intensity.
Weight sinogram such that the left and right image boundaries
(i.e., typically the air region around the object) are set to one
and all intermediate values are scaled linearly.
Parameters
----------
tomo : ndarray
3D tomographic data.
air : int, optional
Number of pixels at each boundary to calculate the scaling factor.
ncore : int, optional
Number of cores that will be assigned to jobs.
nchunk : int, optional
Chunk size for each core.
Returns
-------
ndarray
Corrected 3D tomographic data.
"""
tomo = dtype.as_float32(tomo)
air = dtype.as_int32(air)
arr = mproc.distribute_jobs(
tomo,
func=extern.c_normalize_bg,
args=(air,),
axis=0,
ncore=ncore,
nchunk=nchunk)
return arr
def normalize_bg(tomo, air=1, ncore=None, nchunk=None):
"""
Normalize 3D tomgraphy data based on background intensity.
Weight sinogram such that the left and right image boundaries
(i.e., typically the air region around the object) are set to one
and all intermediate values are scaled linearly.
Parameters
----------
tomo : ndarray
3D tomographic data.
air : int, optional
Number of pixels at each boundary to calculate the scaling factor.
ncore : int, optional
Number of cores that will be assigned to jobs.
nchunk : int, optional
Chunk size for each core.
Returns
-------
ndarray
Corrected 3D tomographic data.
"""
tomo = dtype.as_float32(tomo)
air = dtype.as_int32(air)
dx, dy, dz = tomo.shape
arr = mproc.distribute_jobs(tomo,
func=extern.c_normalize_bg,
args=(dx, dy, dz, air),
axis=0,
ncore=ncore,
nchunk=nchunk)
return arr
def _get_algorithm_kwargs(shape):
dy, dt, dx = shape
return {
'num_gridx': dx,
'num_gridy': dx,
'filter_name': 'shepp',
'filter_par': np.array([0.5, 8], dtype='float32'),
'num_iter': dtype.as_int32(1),
'reg_par': np.ones(10, dtype='float32'),
'num_block': dtype.as_int32(1),
'ind_block': np.arange(0, dt, dtype=np.float32), # TODO: I think this should be int
'options': {},
'accelerated': False,
'pool_size': 0, # if zero, calculate based on threads started at Python level
'interpolation': 'NN', # interpolation method (NN = nearest-neighbor, LINEAR, CUBIC)
'device': 'gpu',
'grid_size': np.array([0, 0, 0], dtype='int32'), # CUDA grid size. If zero, dynamically computed
'block_size': np.array([32, 32, 1], dtype='int32'), # CUDA threads per block
}