def test_axis_sym_projection(self):
tol = 1e-6
dir_path = os.path.dirname(os.path.realpath(__file__))
param = axitom.config_from_xtekct(dir_path +
"/example_data/settings.xtekct")
param.n_voxels_x = 100
param.n_voxels_y = 100
param.n_voxels_z = 100
#
param.object_size_x = 100.
param.object_size_y = 100.
param.object_size_z = 100.
param.n_pixels_u = 100
param.n_pixels_v = 100
param.detector_size_u = param.object_size_x
param.detector_size_v = param.object_size_y
param.source_to_detector_dist = 1.0e20
param.source_to_object_dist = 1.e20
param.update()
volume = barrel(100)
angles = [0.]
proj = axis_sym_projection(volume, param, angles=angles)
error = np.abs(np.sum(volume, axis=1) - proj)
if np.max(error) > tol:
self.fail()
def run_reconstruction_and_compare(self, path_to_projections, correct_body):
# Set the work path as the filepath to this file
dir_path = os.path.dirname(os.path.realpath(__file__))
# Set the same settings as used in the forward projection
param = axitom.config_from_xtekct(dir_path + "/example_data/settings.xtekct")
param.n_voxels_x = 500
param.n_voxels_y = 500
param.n_voxels_z = 500
param.n_pixels_u = 500
param.n_pixels_v = 500
param.update()
proj = np.load(dir_path + path_to_projections)
proj = np.exp(-proj)
reconstruction = axitom.fdk(proj, param)
# Note that the reconstruction give the radial slice only
deviation_field = np.abs(correct_body[:250, 250, :] - reconstruction)
# Sample some deviation values from the background and the interior
background_deviation = deviation_field[5:60, 75:-75]
interior_deviation = deviation_field[90:, 75:-75]
if np.max(background_deviation) > self.tol:
self.fail("The background was not reconstructed properly with largest deviation of %f" % np.max(
background_deviation))
if np.max(interior_deviation) > self.tol:
self.fail(
"The interior was not reconstructed properly with largest deviation of %f" % np.max(interior_deviation))
def __init__(self, dataset=None, xtekct=None, debug=None, **kwargs):
self.debug = debug
if dataset is not None:
self.dataset = sorted([
os.path.join(data, f) for f in os.listdir(data)
if f.endswith('.tif')
])
else:
self.dataset = sorted(glob.glob(".tif"))
if xtekct is not None:
self.config = axitom.config_from_xtekct(xtekct)
else:
self.config = axitom.config_from_xtekct(glob.glob("*.xtekct")[0])
if self.debug:
print(self.dataset)
print(self.config)
def main():
config = axitom.config_from_xtekct("./example_data/radiogram.xtekct")
config.n_voxels_x = 500
config.n_voxels_y = 500
config.n_voxels_z = 500
config.n_pixels_u = 500
config.n_pixels_v = 500
config.update()
radiograms = np.load("proj_barrel.npy")
radiograms = np.exp(-radiograms)
tomogram = axitom.fdk(radiograms, config)
return tomogram
def main():
config = axitom.config_from_xtekct(join(path_to_data, "radiogram.xtekct"))
config.n_voxels_x = 500
config.n_voxels_y = 500
config.n_voxels_z = 500
config.n_pixels_u = 500
config.n_pixels_v = 500
config.update()
radiograms = np.load(join(path_to_data, "proj_barrel.npy"))
radiograms = np.exp(-radiograms)
tomogram = axitom.fdk(radiograms, config)
return tomogram
def reconstruct_tomogram():
config = axitom.config_from_xtekct("./example_data/radiogram.xtekct")
radiogram = axitom.read_image(r"./example_data/radiogram.tif", flat_corrected=True)
# Remove some edges that are in field of view
radiogram[:250, :] = 0.95
radiogram[1800:, :] = 0.95
radiogram = median_filter(radiogram, size=20)
_, center_offset = axitom.object_center_of_rotation(radiogram, config, background_internsity=0.9)
config.center_of_rot_y = center_offset
config.update()
tomogram = axitom.fdk(radiogram, config)
return tomogram
def run_reconstruction():
dir_path = os.path.dirname(os.path.realpath(__file__))
config = axitom.config_from_xtekct(dir_path +
"/example_data/settings.xtekct")
radiogram = axitom.read_image(dir_path + "/example_data/radiogram.tif",
flat_corrected=True)
# Remove some edges that are in field of view
radiogram[:250, :] = 0.95
radiogram[1800:, :] = 0.95
radiogram = median_filter(radiogram, size=20)
_, center_offset = axitom.object_center_of_rotation(
radiogram, config, background_internsity=0.9)
config.center_of_rot_y = center_offset
config.update()
reconstruction = axitom.fdk(radiogram, config)
return reconstruction