def process(self):
projections = self.get_input()
projections_axes = projections.get_data_axes_order(new_order=self.default_acquisition_axes_order)
if not projections_axes == [0,1,2]:
projections.array = numpy.transpose(projections.array, projections_axes)
pixel_per_voxel = 1 # should be estimated from image_geometry and acquisition_geometry
image_geometry = ImageGeometry(voxel_num_x = self.acquisition_geometry.pixel_num_h,
voxel_num_y = self.acquisition_geometry.pixel_num_h,
voxel_num_z = self.acquisition_geometry.pixel_num_v)
# input centered/padded acquisitiondata
center_of_rotation = projections.get_dimension_size('horizontal') / 2
if self.acquisition_geometry.geom_type == 'parallel':
back = pbalg.pb_backward_project(
projections.as_array(),
self.acquisition_geometry.angles,
center_of_rotation,
pixel_per_voxel
)
out = ImageData(geometry=self.image_geometry,
dimension_labels=self.default_image_axes_order)
out_axes = out.get_data_axes_order(new_order=self.output_axes_order)
if not out_axes == [0,1,2]:
back = numpy.transpose(back, out_axes)
out.fill(back)
return out
else:
raise ValueError('Cannot process cone beam')
def test_FISTA(self):
print ("Test FISTA")
ig = ImageGeometry(127,139,149)
x_init = ImageData(geometry=ig)
b = x_init.copy()
# fill with random numbers
b.fill(numpy.random.random(x_init.shape))
x_init = ImageData(geometry=ig)
x_init.fill(numpy.random.random(x_init.shape))
identity = TomoIdentity(geometry=ig)
norm2sq = Norm2sq(identity, b)
opt = {'tol': 1e-4, 'memopt':False}
alg = FISTA(x_init=x_init, f=norm2sq, g=None, opt=opt)
alg.max_iteration = 2
alg.run(20, verbose=True)
self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
alg.run(20, verbose=True)
self.assertNumpyArrayAlmostEqual(alg.x.as_array(), b.as_array())
def test_BlockDataContainer_fill(self):
print("test block data container")
ig0 = ImageGeometry(2, 3, 4)
ig1 = ImageGeometry(2, 3, 5)
data0 = ImageData(geometry=ig0)
data1 = ImageData(geometry=ig1) + 1
data2 = ImageData(geometry=ig0) + 2
data3 = ImageData(geometry=ig1) + 3
cp0 = BlockDataContainer(data0, data1)
#cp1 = BlockDataContainer(data2,data3)
cp2 = BlockDataContainer(data0 + 1, data1 + 1)
data0.fill(data2)
self.assertNumpyArrayEqual(data0.as_array(), data2.as_array())
data0 = ImageData(geometry=ig0)
for el, ot in zip(cp0, cp2):
print(el.shape, ot.shape)
cp0.fill(cp2)
self.assertBlockDataContainerEqual(cp0, cp2)
def create_image_data(self):
x0 = ImageData(geometry = self.volume_geometry,
dimension_labels=self.bp.output_axes_order)#\
#.subset(['horizontal_x','horizontal_y','vertical'])
x0.fill(numpy.random.randn(*x0.shape))
return x0
# Set up empty image data
Phantom = ImageData(
geometry=ig, dimension_labels=['horizontal_x', 'horizontal_y', 'vertical'])
# Populate image data by looping over and filling slices
i = 0
while i < vert:
if vert > 1:
x = Phantom.subset(vertical=i).array
else:
x = Phantom.array
x[round(N / 4):round(3 * N / 4), round(N / 4):round(3 * N / 4)] = 0.5
x[round(N / 8):round(7 * N / 8), round(3 * N / 8):round(5 * N / 8)] = 0.98
if vert > 1:
Phantom.fill(x, vertical=i)
i += 1
# Display slice of phantom
if vert > 1:
plt.imshow(Phantom.subset(vertical=0).as_array())
else:
plt.imshow(Phantom.as_array())
plt.show()
# Set up AcquisitionGeometry object to hold the parameters of the measurement
# setup geometry: # Number of angles, the actual angles from 0 to
# pi for parallel beam, set the width of a detector
# pixel relative to an object pixe and the number of detector pixels.
angles_num = 20
det_w = 1.0