Source code for astra.functions

# -----------------------------------------------------------------------
# Copyright: 2010-2016, iMinds-Vision Lab, University of Antwerp
#            2013-2016, CWI, Amsterdam
#
# Contact: astra@uantwerpen.be
# Website: http://www.astra-toolbox.com/
#
# This file is part of the ASTRA Toolbox.
#
#
# The ASTRA Toolbox is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# The ASTRA Toolbox is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with the ASTRA Toolbox. If not, see <http://www.gnu.org/licenses/>.
#
# -----------------------------------------------------------------------

"""Additional functions for PyAstraToolbox.

.. moduleauthor:: Daniel M. Pelt <D.M.Pelt@cwi.nl>


"""

from . import creators as ac
import numpy as np
try:
    from six.moves import range
except ImportError:
    # six 1.3.0
    from six.moves import xrange as range

from . import data2d
from . import data3d
from . import projector
from . import algorithm
from . import pythonutils



[docs]def clear(): """Clears all used memory of the ASTRA Toolbox. .. note:: This is irreversible. """ data2d.clear() data3d.clear() projector.clear() algorithm.clear()
[docs]def data_op(op, data, scalar, gpu_core, mask=None): """Perform data operation on data. :param op: Operation to perform. :param data: Data to perform operation on. :param scalar: Scalar argument to data operation. :param gpu_core: GPU core to perform operation on. :param mask: Optional mask. """ cfg = ac.astra_dict('DataOperation_CUDA') cfg['Operation'] = op cfg['Scalar'] = scalar cfg['DataId'] = data if not mask == None: cfg['MaskId'] = mask cfg['option']['GPUindex'] = gpu_core alg_id = algorithm.create(cfg) algorithm.run(alg_id) algorithm.delete(alg_id)
[docs]def add_noise_to_sino(sinogram_in, I0, seed=None): """Adds Poisson noise to a sinogram. :param sinogram_in: Sinogram to add noise to. :type sinogram_in: :class:`numpy.ndarray` :param I0: Background intensity. Lower values lead to higher noise. :type I0: :class:`float` :returns: :class:`numpy.ndarray` -- the sinogram with added noise. """ if not seed==None: curstate = np.random.get_state() np.random.seed(seed) if isinstance(sinogram_in, np.ndarray): sinogramRaw = sinogram_in else: sinogramRaw = data2d.get(sinogram_in) max_sinogramRaw = sinogramRaw.max() sinogramRawScaled = sinogramRaw / max_sinogramRaw # to detector count sinogramCT = I0 * np.exp(-sinogramRawScaled) # add poison noise sinogramCT_C = np.zeros_like(sinogramCT) for i in range(sinogramCT_C.shape[0]): for j in range(sinogramCT_C.shape[1]): sinogramCT_C[i, j] = np.random.poisson(sinogramCT[i, j]) # to density sinogramCT_D = sinogramCT_C / I0 sinogram_out = -max_sinogramRaw * np.log(sinogramCT_D) if not isinstance(sinogram_in, np.ndarray): data2d.store(sinogram_in, sinogram_out) if not seed==None: np.random.set_state(curstate) return sinogram_out
[docs]def move_vol_geom(geom, pos, is_relative=False): """Moves center of volume geometry to new position. :param geom: Input volume geometry :type geom: :class:`dict` :param pos: Tuple (x,y[,z]) for new position, with the center of the image at (0,0[,0]) :type pos: :class:`tuple` :param is_relative: Whether new position is relative to the old position :type is_relative: :class:`bool` :returns: :class:`dict` -- Volume geometry with the new center """ ret_geom = geom.copy() ret_geom['option'] = geom['option'].copy() if not is_relative: ret_geom['option']['WindowMinX'] = -geom['GridColCount']/2. ret_geom['option']['WindowMaxX'] = geom['GridColCount']/2. ret_geom['option']['WindowMinY'] = -geom['GridRowCount']/2. ret_geom['option']['WindowMaxY'] = geom['GridRowCount']/2. if len(pos)>2: ret_geom['option']['WindowMinZ'] = -geom['GridSliceCount']/2. ret_geom['option']['WindowMaxZ'] = geom['GridSliceCount']/2. ret_geom['option']['WindowMinX'] += pos[0] ret_geom['option']['WindowMaxX'] += pos[0] ret_geom['option']['WindowMinY'] += pos[1] ret_geom['option']['WindowMaxY'] += pos[1] if len(pos)>2: ret_geom['option']['WindowMinZ'] += pos[2] ret_geom['option']['WindowMaxZ'] += pos[2] return ret_geom
[docs]def geom_size(geom, dim=None): """Returns the size of a volume or sinogram, based on the projection or volume geometry. :param geom: Geometry to calculate size from :type geometry: :class:`dict` :param dim: Optional axis index to return :type dim: :class:`int` """ return pythonutils.geom_size(geom,dim)
[docs]def geom_2vec(proj_geom): """Returns a vector-based projection geometry from a basic projection geometry. :param proj_geom: Projection geometry to convert :type proj_geom: :class:`dict` """ if proj_geom['type'] == 'fanflat': angles = proj_geom['ProjectionAngles'] vectors = np.zeros((len(angles), 6)) for i in range(len(angles)): # source vectors[i, 0] = np.sin(angles[i]) * proj_geom['DistanceOriginSource'] vectors[i, 1] = -np.cos(angles[i]) * proj_geom['DistanceOriginSource'] # center of detector vectors[i, 2] = -np.sin(angles[i]) * proj_geom['DistanceOriginDetector'] vectors[i, 3] = np.cos(angles[i]) * proj_geom['DistanceOriginDetector'] # vector from detector pixel 0 to 1 vectors[i, 4] = np.cos(angles[i]) * proj_geom['DetectorWidth'] vectors[i, 5] = np.sin(angles[i]) * proj_geom['DetectorWidth'] proj_geom_out = ac.create_proj_geom( 'fanflat_vec', proj_geom['DetectorCount'], vectors) elif proj_geom['type'] == 'cone': angles = proj_geom['ProjectionAngles'] vectors = np.zeros((len(angles), 12)) for i in range(len(angles)): # source vectors[i, 0] = np.sin(angles[i]) * proj_geom['DistanceOriginSource'] vectors[i, 1] = -np.cos(angles[i]) * proj_geom['DistanceOriginSource'] vectors[i, 2] = 0 # center of detector vectors[i, 3] = -np.sin(angles[i]) * proj_geom['DistanceOriginDetector'] vectors[i, 4] = np.cos(angles[i]) * proj_geom['DistanceOriginDetector'] vectors[i, 5] = 0 # vector from detector pixel (0,0) to (0,1) vectors[i, 6] = np.cos(angles[i]) * proj_geom['DetectorSpacingX'] vectors[i, 7] = np.sin(angles[i]) * proj_geom['DetectorSpacingX'] vectors[i, 8] = 0 # vector from detector pixel (0,0) to (1,0) vectors[i, 9] = 0 vectors[i, 10] = 0 vectors[i, 11] = proj_geom['DetectorSpacingY'] proj_geom_out = ac.create_proj_geom( 'cone_vec', proj_geom['DetectorRowCount'], proj_geom['DetectorColCount'], vectors) # PARALLEL elif proj_geom['type'] == 'parallel3d': angles = proj_geom['ProjectionAngles'] vectors = np.zeros((len(angles), 12)) for i in range(len(angles)): # ray direction vectors[i, 0] = np.sin(angles[i]) vectors[i, 1] = -np.cos(angles[i]) vectors[i, 2] = 0 # center of detector vectors[i, 3] = 0 vectors[i, 4] = 0 vectors[i, 5] = 0 # vector from detector pixel (0,0) to (0,1) vectors[i, 6] = np.cos(angles[i]) * proj_geom['DetectorSpacingX'] vectors[i, 7] = np.sin(angles[i]) * proj_geom['DetectorSpacingX'] vectors[i, 8] = 0 # vector from detector pixel (0,0) to (1,0) vectors[i, 9] = 0 vectors[i, 10] = 0 vectors[i, 11] = proj_geom['DetectorSpacingY'] proj_geom_out = ac.create_proj_geom( 'parallel3d_vec', proj_geom['DetectorRowCount'], proj_geom['DetectorColCount'], vectors) else: raise ValueError( 'No suitable vector geometry found for type: ' + proj_geom['type']) return proj_geom_out