def diff_from(self, image):
handle = pystir.cSTIR_imagesDifference\
(self.handle, image.handle, self.rimsize)
_check_status(handle)
diff = pyiutil.doubleDataFromHandle(handle)
pyiutil.deleteDataHandle(handle)
return diff
def gradient(self, image, subset):
grad = Image()
pyiutil.deleteDataHandle(grad.handle)
grad.handle = pystir.cSTIR_objectiveFunctionGradient\
(self.handle, image.handle, subset)
_check_status(grad.handle)
return grad
def get_gradient_plus_sensitivity_no_penalty(self, image, subset):
grad = Image()
pyiutil.deleteDataHandle(grad.handle)
grad.handle = pystir.cSTIR_objectiveFunctionGradientNotDivided\
(self.handle, image.handle, subset)
_check_status(grad.handle)
return grad
def get_empty_copy(self, value = 1.0):
image = Image()
pyiutil.deleteDataHandle(image.handle)
image.handle = pystir.cSTIR_imageFromImage(self.handle)
_check_status(image.handle)
image.fill(value)
return image
def initialise\
(self, arg1, arg2 = 0, arg3 = 0, arg4 = 1, arg5 = 1, arg6 = 1, \
arg7 = 0, arg8 = 0, arg9 = 0):
if type(arg1) == type((0,0,0)):
dim = arg1
if arg2 == 0:
vsize = (1, 1, 1)
origin = (0, 0, 0)
else:
vsize = arg2
if arg3 == 0:
origin = (0, 0, 0)
else:
origin = arg3
else:
dim = (arg1, arg2, arg3)
vsize = (arg4, arg5, arg6)
origin = (arg7, arg8, arg9)
if self.handle is not None:
pyiutil.deleteDataHandle(self.handle)
self.handle = None
voxels = pystir.cSTIR_voxels3DF\
(dim[0], dim[1], dim[2], \
vsize[0], vsize[1], vsize[2], \
origin[0], origin[1], origin[2])
_check_status(voxels)
self.handle = pystir.cSTIR_imageFromVoxels(voxels)
_check_status(self.handle)
pyiutil.deleteDataHandle(voxels)
def get_acquisition_model(self):
am = AcquisitionModelUsingMatrix()
if am.handle is not None:
pyiutil.deleteDataHandle(am.handle)
am.handle = pystir.cSTIR_parameter\
(self.handle, self.name, 'acquisition_model')
_check_status(am.handle)
return am
def set_up(self, acq_templ, img_templ):
''' Prepares this object for performing forward and backward
projections;
acq_templ: an AcquisitionData object used as a template for
creating an AcquisitionData object to store forward
projection;
img_templ: an Image object used as a template for creating an
Image object to store backward projection.
'''
handle = pystir.cSTIR_setupAcquisitionModel\
(self.handle, acq_templ.handle, img_templ.handle)
_check_status(handle)
pyiutil.deleteDataHandle(handle)
def create_empty_image(self, value = 0):
''' Creates Image object containing PET image of dimensions
and voxel sizes compatible with the scanner geometry stored
in this AcquisitionData object and assigns a given value
to all voxels;
value: a Python float.
'''
image = Image()
pyiutil.deleteDataHandle(image.handle)
image.handle = pystir.cSTIR_imageFromAcquisitionData(self.handle)
_check_status(image.handle)
image.fill(value)
return image
def as_array(self):
''' Returns a copy of acquisition data stored in this object as a
NumPy ndarray of 3 dimensions (in default C ordering of data):
- number of sinograms
- number of views
- number of tangential positions.
'''
dim = numpy.ndarray((3,), dtype = numpy.int32)
handle = pystir.cSTIR_getAcquisitionsDimensions\
(self.handle, dim.ctypes.data)
_check_status(handle)
pyiutil.deleteDataHandle(handle)
nx = dim[0]
ny = dim[1]
nz = dim[2]
if nx == 0 or ny == 0 or nz == 0:
raise error('density data not available')
array = numpy.ndarray((nz, ny, nx), dtype = numpy.float64)
handle = pystir.cSTIR_getAcquisitionsData\
(self.handle, array.ctypes.data)
_check_status(handle)
pyiutil.deleteDataHandle(handle)
return array
def set_up(self, image):
handle = pystir.cSTIR_setupReconstruction(self.handle, image.handle)
_check_status(handle)
pyiutil.deleteDataHandle(handle)
def reconstruct(self, image):
handle = pystir.cSTIR_runReconstruction(self.handle, image.handle)
_check_status(handle)
pyiutil.deleteDataHandle(handle)
def read_from_file(self, filename):
if self.handle is not None:
pyiutil.deleteDataHandle(self.handle)
self.handle = pystir.cSTIR_objectFromFile('Image', filename)
_check_status(self.handle)
def add_shape(self, shape, scale):
if self.handle is None:
raise error('cannot add shapes to uninitialised image')
handle = pystir.cSTIR_addShape(self.handle, shape.handle, scale)
_check_status(handle)
pyiutil.deleteDataHandle(handle)
def apply(self, image):
handle = pystir.cSTIR_applyDataProcessor\
(self.handle, image.handle)
_check_status(handle)
pyiutil.deleteDataHandle(handle)
def get_subset_sensitivity(self, subset):
ss = Image()
pyiutil.deleteDataHandle(ss.handle)
ss.handle = pystir.cSTIR_subsetSensitivity(self.handle, subset)
_check_status(ss.handle)
return ss
def get_gradient(self, image):
grad = Image()
pyiutil.deleteDataHandle(grad.handle)
grad.handle = pystir.cSTIR_priorGradient(self.handle, image.handle)
_check_status(grad.handle)
return grad
def _float_par(handle, set, par):
h = pystir.cSTIR_parameter(handle, set, par)
_check_status(h)
value = pyiutil.floatDataFromHandle(h)
pyiutil.deleteDataHandle(h)
return value
def __del__(self):
pyiutil.deleteDataHandle(self.handle)
def clone(self):
image = Image()
pyiutil.deleteDataHandle(image.handle)
image.handle = pystir.cSTIR_imageFromImage(self.handle)
_check_status(image.handle)
return image
def process(self):
if self.image is None:
raise error('current estimate not set')
handle = pystir.cSTIR_runReconstruction(self.handle, self.image.handle)
_check_status(handle)
pyiutil.deleteDataHandle(handle)
def _setParameter(hs, set, par, hv):
h = pystir.cSTIR_setParameter(hs, set, par, hv)
_check_status(h)
pyiutil.deleteDataHandle(h)
def _set_char_par(handle, set, par, value):
h = pyiutil.charDataHandle(value)
_setParameter(handle, set, par, h)
pyiutil.deleteDataHandle(h)
def __del__(self):
if self.handle is not None:
pyiutil.deleteDataHandle(self.handle)
def _set_float_par(handle, set, par, value):
h = pyiutil.floatDataHandle(value)
_setParameter(handle, set, par, h)
pyiutil.deleteDataHandle(h)
