def setUp(self):
# Timing
self.start_time = time.time()
# DATA
self.d = ctdata.sets[14]
self.d.load()
# Parameters
det_row_count, num_proj, det_col_count = self.d.shape
num_voxel = (det_col_count, det_col_count, det_row_count)
voxel_size = 2 * self.d.roi_cubic_width_mm / num_voxel[0]
source_origin = self.d.distance_source_origin_mm / voxel_size
origin_detector = self.d.distance_origin_detector_mm / voxel_size
angles = self.d.angles_rad
det_col_spacing = self.d.detector_width_mm / det_col_count / voxel_size
det_row_spacing = det_col_spacing
# PROJECTOR
self.projector = Projector(
num_voxel=num_voxel,
det_row_count=det_row_count, det_col_count=det_col_count,
source_origin=source_origin, origin_detector=origin_detector,
det_row_spacing=det_row_spacing, det_col_spacing=det_col_spacing,
angles=angles)
# ALGORITHM
self.pc = ChambollePock(projections=self.d.projections,
projector=self.projector)
self.u_shape = num_voxel
def test_gui(self):
num_iter = 8
u, p, cpd, l2_atp = ChambollePock(
projections=np.ones((100, 180, 100)),
projector=Projector()).least_squares(
num_iterations=num_iter,
L=363.569641113,
verbose=True,
non_negativiy_constraint=True)
self.assertEqual(u.__class__.__name__, 'ndarray')
self.assertEqual(cpd.size, num_iter)
self.assertEqual(l2_atp.size, num_iter)
class ChambollePockTestCase(unittest.TestCase):
"""Test case for primal-dual method Chambolle-Pock algorithm."""
def setUp(self):
# Timing
self.start_time = time.time()
# DATA
self.d = ctdata.sets[14]
self.d.load()
# Parameters
det_row_count, num_proj, det_col_count = self.d.shape
num_voxel = (det_col_count, det_col_count, det_row_count)
voxel_size = 2 * self.d.roi_cubic_width_mm / num_voxel[0]
source_origin = self.d.distance_source_origin_mm / voxel_size
origin_detector = self.d.distance_origin_detector_mm / voxel_size
angles = self.d.angles_rad
det_col_spacing = self.d.detector_width_mm / det_col_count / voxel_size
det_row_spacing = det_col_spacing
# PROJECTOR
self.projector = Projector(
num_voxel=num_voxel,
det_row_count=det_row_count, det_col_count=det_col_count,
source_origin=source_origin, origin_detector=origin_detector,
det_row_spacing=det_row_spacing, det_col_spacing=det_col_spacing,
angles=angles)
# ALGORITHM
self.pc = ChambollePock(projections=self.d.projections,
projector=self.projector)
self.u_shape = num_voxel
def tearDown(self):
# Timing
t = time.time() - self.start_time
print "%s: %.3f" % (self.id(), t)
# Clean ASTRA memory
self.projector.clear()
def test_projection_data(self):
d = self.d.projections
print 'min:', d.min(), 'max:', d.max(), 'mean:', np.mean(d)
self.assertTrue(self.d.projections.min() > 0)
self.assertTrue(self.d.projections.max() < np.inf)
def test_initialization(self):
self.assertTrue(self.d.projections.shape > 0)
self.assertTrue(self.pc.K.volume_data)
self.pc.K.backward()
# self.assertEqual(str(self.d.dtype), 'uint16')
self.assertEqual(str(self.d.dtype), 'float32')
self.assertEqual(self.pc.K.volume_data.dtype.__str__(), 'float32')
self.assertTrue(self.pc.K.volume_shape > 0)
self.assertTrue(issubclass(type(self.pc), object))
self.assertEqual(self.pc.K, self.projector)
def test_matrix_norm(self):
# Start computation of matrix
num_iter = 2
mat_norm_list = self.pc.matrix_norm(
num_iter, vol_init=1, intermediate_results=True)
self.assertEqual(np.size(mat_norm_list), num_iter)
# Continue iteration of matrix, starting from the above results
mat_norm = self.pc.matrix_norm(3, continue_iteration=True)
self.assertEqual(np.size(mat_norm), 1)
self.assertTrue(mat_norm > 0)
self.assertNotEqual(mat_norm_list[-1], mat_norm)
mat_norm = self.pc.matrix_norm(20, vol_init=1,
intermediate_results=True)
self.pc.K.clear()
print mat_norm
def test_least_squares(self):
num_iter = 10
u, p, cpd, l2_atp = self.pc.least_squares(
num_iterations=num_iter,
L=363.569641113,
verbose=True,
non_negativiy_constraint=False)
self.assertEqual(u.__class__.__name__, 'ndarray')
self.assertEqual(cpd.size, num_iter)
self.assertEqual(l2_atp.size, num_iter)
self.pc.K.clear()
def test_least_squares_with_non_negativity_constraint(self):
num_iter = 4
u, p, cpd, l2_atp = self.pc.least_squares(
num_iterations=num_iter,
L=363.569641113,
verbose=True,
non_negativiy_constraint=True)
self.assertEqual(u.__class__.__name__, 'ndarray')
self.assertEqual(cpd.size, num_iter)
self.assertEqual(l2_atp.size, num_iter)
self.pc.K.clear()