Source code for astra.creators

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# Copyright: 2010-2016, iMinds-Vision Lab, University of Antwerp
#            2013-2016, CWI, Amsterdam
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import six
import numpy as np
import math
from . import data2d
from . import data3d
from . import projector
from . import projector3d
from . import algorithm

[docs]def astra_dict(intype): """Creates a dict to use with the ASTRA Toolbox. :param intype: Type of the ASTRA object. :type intype: :class:`string` :returns: :class:`dict` -- An ASTRA dict of type ``intype``. """ if intype == 'SIRT_CUDA2': intype = 'SIRT_CUDA' six.print_('SIRT_CUDA2 has been deprecated. Use SIRT_CUDA instead.') elif intype == 'FP_CUDA2': intype = 'FP_CUDA' six.print_('FP_CUDA2 has been deprecated. Use FP_CUDA instead.') return {'type': intype}
[docs]def create_vol_geom(*varargin): """Create a volume geometry structure. This method can be called in a number of ways: ``create_vol_geom(N)``: :returns: A 2D volume geometry of size :math:`N \\times N`. ``create_vol_geom((Y, X))``: :returns: A 2D volume geometry of size :math:`Y \\times X`. ``create_vol_geom(Y, X)``: :returns: A 2D volume geometry of size :math:`Y \\times X`. ``create_vol_geom(Y, X, minx, maxx, miny, maxy)``: :returns: A 2D volume geometry of size :math:`Y \\times X`, windowed as :math:`minx \\leq x \\leq maxx` and :math:`miny \\leq y \\leq maxy`. ``create_vol_geom((Y, X, Z))``: :returns: A 3D volume geometry of size :math:`Y \\times X \\times Z`. ``create_vol_geom(Y, X, Z)``: :returns: A 3D volume geometry of size :math:`Y \\times X \\times Z`. ``create_vol_geom(Y, X, Z, minx, maxx, miny, maxy, minz, maxz)``: :returns: A 3D volume geometry of size :math:`Y \\times X \\times Z`, windowed as :math:`minx \\leq x \\leq maxx` and :math:`miny \\leq y \\leq maxy` and :math:`minz \\leq z \\leq maxz` . """ vol_geom = {'option': {}} # astra_create_vol_geom(row_count) if len(varargin) == 1 and isinstance(varargin[0], int) == 1: vol_geom['GridRowCount'] = varargin[0] vol_geom['GridColCount'] = varargin[0] vol_geom['option']['WindowMinX'] = -varargin[0] / 2. vol_geom['option']['WindowMaxX'] = varargin[0] / 2. vol_geom['option']['WindowMinY'] = -varargin[0] / 2. vol_geom['option']['WindowMaxY'] = varargin[0] / 2. # astra_create_vol_geom([row_count col_count]) elif len(varargin) == 1 and len(varargin[0]) == 2: vol_geom['GridRowCount'] = varargin[0][0] vol_geom['GridColCount'] = varargin[0][1] vol_geom['option']['WindowMinX'] = -varargin[0][1] / 2. vol_geom['option']['WindowMaxX'] = varargin[0][1] / 2. vol_geom['option']['WindowMinY'] = -varargin[0][0] / 2. vol_geom['option']['WindowMaxY'] = varargin[0][0] / 2. # astra_create_vol_geom([row_count col_count slice_count]) elif len(varargin) == 1 and len(varargin[0]) == 3: vol_geom['GridRowCount'] = varargin[0][0] vol_geom['GridColCount'] = varargin[0][1] vol_geom['GridSliceCount'] = varargin[0][2] vol_geom['option']['WindowMinX'] = -varargin[0][1] / 2. vol_geom['option']['WindowMaxX'] = varargin[0][1] / 2. vol_geom['option']['WindowMinY'] = -varargin[0][0] / 2. vol_geom['option']['WindowMaxY'] = varargin[0][0] / 2. vol_geom['option']['WindowMinZ'] = -varargin[0][2] / 2. vol_geom['option']['WindowMaxZ'] = varargin[0][2] / 2. # astra_create_vol_geom(row_count, col_count) elif len(varargin) == 2: vol_geom['GridRowCount'] = varargin[0] vol_geom['GridColCount'] = varargin[1] vol_geom['option']['WindowMinX'] = -varargin[1] / 2. vol_geom['option']['WindowMaxX'] = varargin[1] / 2. vol_geom['option']['WindowMinY'] = -varargin[0] / 2. vol_geom['option']['WindowMaxY'] = varargin[0] / 2. # astra_create_vol_geom(row_count, col_count, min_x, max_x, min_y, max_y) elif len(varargin) == 6: vol_geom['GridRowCount'] = varargin[0] vol_geom['GridColCount'] = varargin[1] vol_geom['option']['WindowMinX'] = varargin[2] vol_geom['option']['WindowMaxX'] = varargin[3] vol_geom['option']['WindowMinY'] = varargin[4] vol_geom['option']['WindowMaxY'] = varargin[5] # astra_create_vol_geom(row_count, col_count, slice_count) elif len(varargin) == 3: vol_geom['GridRowCount'] = varargin[0] vol_geom['GridColCount'] = varargin[1] vol_geom['GridSliceCount'] = varargin[2] # astra_create_vol_geom(row_count, col_count, slice_count, min_x, max_x, min_y, max_y, min_z, max_z) elif len(varargin) == 9: vol_geom['GridRowCount'] = varargin[0] vol_geom['GridColCount'] = varargin[1] vol_geom['GridSliceCount'] = varargin[2] vol_geom['option']['WindowMinX'] = varargin[3] vol_geom['option']['WindowMaxX'] = varargin[4] vol_geom['option']['WindowMinY'] = varargin[5] vol_geom['option']['WindowMaxY'] = varargin[6] vol_geom['option']['WindowMinZ'] = varargin[7] vol_geom['option']['WindowMaxZ'] = varargin[8] return vol_geom
[docs]def create_proj_geom(intype, *args): """Create a projection geometry. This method can be called in a number of ways: ``create_proj_geom('parallel', detector_spacing, det_count, angles)``: :param detector_spacing: Distance between two adjacent detector pixels. :type detector_spacing: :class:`float` :param det_count: Number of detector pixels. :type det_count: :class:`int` :param angles: Array of angles in radians. :type angles: :class:`numpy.ndarray` :returns: A parallel projection geometry. ``create_proj_geom('fanflat', det_width, det_count, angles, source_origin, origin_det)``: :param det_width: Size of a detector pixel. :type det_width: :class:`float` :param det_count: Number of detector pixels. :type det_count: :class:`int` :param angles: Array of angles in radians. :type angles: :class:`numpy.ndarray` :param source_origin: Position of the source. :param origin_det: Position of the detector :returns: A fan-beam projection geometry. ``create_proj_geom('fanflat_vec', det_count, V)``: :param det_count: Number of detector pixels. :type det_count: :class:`int` :param V: Vector array. :type V: :class:`numpy.ndarray` :returns: A fan-beam projection geometry. ``create_proj_geom('parallel3d', detector_spacing_x, detector_spacing_y, det_row_count, det_col_count, angles)``: :param detector_spacing_*: Distance between two adjacent detector pixels. :type detector_spacing_*: :class:`float` :param det_row_count: Number of detector pixel rows. :type det_row_count: :class:`int` :param det_col_count: Number of detector pixel columns. :type det_col_count: :class:`int` :param angles: Array of angles in radians. :type angles: :class:`numpy.ndarray` :returns: A parallel projection geometry. ``create_proj_geom('cone', detector_spacing_x, detector_spacing_y, det_row_count, det_col_count, angles, source_origin, origin_det)``: :param detector_spacing_*: Distance between two adjacent detector pixels. :type detector_spacing_*: :class:`float` :param det_row_count: Number of detector pixel rows. :type det_row_count: :class:`int` :param det_col_count: Number of detector pixel columns. :type det_col_count: :class:`int` :param angles: Array of angles in radians. :type angles: :class:`numpy.ndarray` :param source_origin: Distance between point source and origin. :type source_origin: :class:`float` :param origin_det: Distance between the detector and origin. :type origin_det: :class:`float` :returns: A cone-beam projection geometry. ``create_proj_geom('cone_vec', det_row_count, det_col_count, V)``: :param det_row_count: Number of detector pixel rows. :type det_row_count: :class:`int` :param det_col_count: Number of detector pixel columns. :type det_col_count: :class:`int` :param V: Vector array. :type V: :class:`numpy.ndarray` :returns: A cone-beam projection geometry. ``create_proj_geom('parallel3d_vec', det_row_count, det_col_count, V)``: :param det_row_count: Number of detector pixel rows. :type det_row_count: :class:`int` :param det_col_count: Number of detector pixel columns. :type det_col_count: :class:`int` :param V: Vector array. :type V: :class:`numpy.ndarray` :returns: A parallel projection geometry. ``create_proj_geom('sparse_matrix', det_width, det_count, angles, matrix_id)``: :param det_width: Size of a detector pixel. :type det_width: :class:`float` :param det_count: Number of detector pixels. :type det_count: :class:`int` :param angles: Array of angles in radians. :type angles: :class:`numpy.ndarray` :param matrix_id: ID of the sparse matrix. :type matrix_id: :class:`int` :returns: A projection geometry based on a sparse matrix. """ if intype == 'parallel': if len(args) < 3: raise Exception( 'not enough variables: astra_create_proj_geom(parallel, detector_spacing, det_count, angles)') return {'type': 'parallel', 'DetectorWidth': args[0], 'DetectorCount': args[1], 'ProjectionAngles': args[2]} elif intype == 'fanflat': if len(args) < 5: raise Exception('not enough variables: astra_create_proj_geom(fanflat, det_width, det_count, angles, source_origin, origin_det)') return {'type': 'fanflat', 'DetectorWidth': args[0], 'DetectorCount': args[1], 'ProjectionAngles': args[2], 'DistanceOriginSource': args[3], 'DistanceOriginDetector': args[4]} elif intype == 'fanflat_vec': if len(args) < 2: raise Exception('not enough variables: astra_create_proj_geom(fanflat_vec, det_count, V)') if not args[1].shape[1] == 6: raise Exception('V should be a Nx6 matrix, with N the number of projections') return {'type':'fanflat_vec', 'DetectorCount':args[0], 'Vectors':args[1]} elif intype == 'parallel3d': if len(args) < 5: raise Exception('not enough variables: astra_create_proj_geom(parallel3d, detector_spacing_x, detector_spacing_y, det_row_count, det_col_count, angles)') return {'type':'parallel3d', 'DetectorSpacingX':args[0], 'DetectorSpacingY':args[1], 'DetectorRowCount':args[2], 'DetectorColCount':args[3],'ProjectionAngles':args[4]} elif intype == 'cone': if len(args) < 7: raise Exception('not enough variables: astra_create_proj_geom(cone, detector_spacing_x, detector_spacing_y, det_row_count, det_col_count, angles, source_origin, origin_det)') return {'type': 'cone','DetectorSpacingX':args[0], 'DetectorSpacingY':args[1], 'DetectorRowCount':args[2],'DetectorColCount':args[3],'ProjectionAngles':args[4],'DistanceOriginSource': args[5],'DistanceOriginDetector':args[6]} elif intype == 'cone_vec': if len(args) < 3: raise Exception('not enough variables: astra_create_proj_geom(cone_vec, det_row_count, det_col_count, V)') if not args[2].shape[1] == 12: raise Exception('V should be a Nx12 matrix, with N the number of projections') return {'type': 'cone_vec','DetectorRowCount':args[0],'DetectorColCount':args[1],'Vectors':args[2]} elif intype == 'parallel3d_vec': if len(args) < 3: raise Exception('not enough variables: astra_create_proj_geom(parallel3d_vec, det_row_count, det_col_count, V)') if not args[2].shape[1] == 12: raise Exception('V should be a Nx12 matrix, with N the number of projections') return {'type': 'parallel3d_vec','DetectorRowCount':args[0],'DetectorColCount':args[1],'Vectors':args[2]} elif intype == 'sparse_matrix': if len(args) < 4: raise Exception( 'not enough variables: astra_create_proj_geom(sparse_matrix, det_width, det_count, angles, matrix_id)') return {'type': 'sparse_matrix', 'DetectorWidth': args[0], 'DetectorCount': args[1], 'ProjectionAngles': args[2], 'MatrixID': args[3]} else: raise Exception('Error: unknown type ' + intype)
[docs]def create_backprojection(data, proj_id, returnData=True): """Create a backprojection of a sinogram (2D). :param data: Sinogram data or ID. :type data: :class:`numpy.ndarray` or :class:`int` :param proj_id: ID of the projector to use. :type proj_id: :class:`int` :param returnData: If False, only return the ID of the backprojection. :type returnData: :class:`bool` :returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`) -- If ``returnData=False``, returns the ID of the backprojection. Otherwise, returns a tuple containing the ID of the backprojection and the backprojection itself, in that order. """ proj_geom = projector.projection_geometry(proj_id) vol_geom = projector.volume_geometry(proj_id) if isinstance(data, np.ndarray): sino_id = data2d.create('-sino', proj_geom, data) else: sino_id = data vol_id = data2d.create('-vol', vol_geom, 0) if projector.is_cuda(proj_id): algString = 'BP_CUDA' else: algString = 'BP' cfg = astra_dict(algString) cfg['ProjectorId'] = proj_id cfg['ProjectionDataId'] = sino_id cfg['ReconstructionDataId'] = vol_id alg_id = algorithm.create(cfg) algorithm.run(alg_id) algorithm.delete(alg_id) if isinstance(data, np.ndarray): data2d.delete(sino_id) if returnData: return vol_id, data2d.get(vol_id) else: return vol_id
[docs]def create_backprojection3d_gpu(data, proj_geom, vol_geom, returnData=True): """Create a backprojection of a sinogram (3D) using CUDA. :param data: Sinogram data or ID. :type data: :class:`numpy.ndarray` or :class:`int` :param proj_geom: Projection geometry. :type proj_geom: :class:`dict` :param vol_geom: Volume geometry. :type vol_geom: :class:`dict` :param returnData: If False, only return the ID of the backprojection. :type returnData: :class:`bool` :returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`) -- If ``returnData=False``, returns the ID of the backprojection. Otherwise, returns a tuple containing the ID of the backprojection and the backprojection itself, in that order. """ if isinstance(data, np.ndarray): sino_id = data3d.create('-sino', proj_geom, data) else: sino_id = data vol_id = data3d.create('-vol', vol_geom, 0) cfg = astra_dict('BP3D_CUDA') cfg['ProjectionDataId'] = sino_id cfg['ReconstructionDataId'] = vol_id alg_id = algorithm.create(cfg) algorithm.run(alg_id) algorithm.delete(alg_id) if isinstance(data, np.ndarray): data3d.delete(sino_id) if returnData: return vol_id, data3d.get(vol_id) else: return vol_id
[docs]def create_sino(data, proj_id, returnData=True, gpuIndex=None): """Create a forward projection of an image (2D). :param data: Image data or ID. :type data: :class:`numpy.ndarray` or :class:`int` :param proj_id: ID of the projector to use. :type proj_id: :class:`int` :param returnData: If False, only return the ID of the forward projection. :type returnData: :class:`bool` :param gpuIndex: Optional GPU index. :type gpuIndex: :class:`int` :returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`) If ``returnData=False``, returns the ID of the forward projection. Otherwise, returns a tuple containing the ID of the forward projection and the forward projection itself, in that order. """ proj_geom = projector.projection_geometry(proj_id) vol_geom = projector.volume_geometry(proj_id) if isinstance(data, np.ndarray): volume_id = data2d.create('-vol', vol_geom, data) else: volume_id = data sino_id = data2d.create('-sino', proj_geom, 0) if projector.is_cuda(proj_id): algString = 'FP_CUDA' else: algString = 'FP' cfg = astra_dict(algString) cfg['ProjectorId'] = proj_id if gpuIndex is not None: cfg['option'] = {'GPUindex': gpuIndex} cfg['ProjectionDataId'] = sino_id cfg['VolumeDataId'] = volume_id alg_id = algorithm.create(cfg) algorithm.run(alg_id) algorithm.delete(alg_id) if isinstance(data, np.ndarray): data2d.delete(volume_id) if returnData: return sino_id, data2d.get(sino_id) else: return sino_id
[docs]def create_sino3d_gpu(data, proj_geom, vol_geom, returnData=True, gpuIndex=None): """Create a forward projection of an image (3D). :param data: Image data or ID. :type data: :class:`numpy.ndarray` or :class:`int` :param proj_geom: Projection geometry. :type proj_geom: :class:`dict` :param vol_geom: Volume geometry. :type vol_geom: :class:`dict` :param returnData: If False, only return the ID of the forward projection. :type returnData: :class:`bool` :param gpuIndex: Optional GPU index. :type gpuIndex: :class:`int` :returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`) -- If ``returnData=False``, returns the ID of the forward projection. Otherwise, returns a tuple containing the ID of the forward projection and the forward projection itself, in that order. """ if isinstance(data, np.ndarray): volume_id = data3d.create('-vol', vol_geom, data) else: volume_id = data sino_id = data3d.create('-sino', proj_geom, 0) algString = 'FP3D_CUDA' cfg = astra_dict(algString) if not gpuIndex==None: cfg['option']={'GPUindex':gpuIndex} cfg['ProjectionDataId'] = sino_id cfg['VolumeDataId'] = volume_id alg_id = algorithm.create(cfg) algorithm.run(alg_id) algorithm.delete(alg_id) if isinstance(data, np.ndarray): data3d.delete(volume_id) if returnData: return sino_id, data3d.get(sino_id) else: return sino_id
[docs]def create_reconstruction(rec_type, proj_id, sinogram, iterations=1, use_mask='no', mask=np.array([]), use_minc='no', minc=0, use_maxc='no', maxc=255, returnData=True, filterType=None, filterData=None): """Create a reconstruction of a sinogram (2D). :param rec_type: Name of the reconstruction algorithm. :type rec_type: :class:`string` :param proj_id: ID of the projector to use. :type proj_id: :class:`int` :param sinogram: Sinogram data or ID. :type sinogram: :class:`numpy.ndarray` or :class:`int` :param iterations: Number of iterations to run. :type iterations: :class:`int` :param use_mask: Whether to use a mask. :type use_mask: ``'yes'`` or ``'no'`` :param mask: Mask data or ID :type mask: :class:`numpy.ndarray` or :class:`int` :param use_minc: Whether to force a minimum value on the reconstruction pixels. :type use_minc: ``'yes'`` or ``'no'`` :param minc: Minimum value to use. :type minc: :class:`float` :param use_maxc: Whether to force a maximum value on the reconstruction pixels. :type use_maxc: ``'yes'`` or ``'no'`` :param maxc: Maximum value to use. :type maxc: :class:`float` :param returnData: If False, only return the ID of the reconstruction. :type returnData: :class:`bool` :param filterType: Which type of filter to use for filter-based methods. :type filterType: :class:`string` :param filterData: Optional filter data for filter-based methods. :type filterData: :class:`numpy.ndarray` :returns: :class:`int` or (:class:`int`, :class:`numpy.ndarray`) -- If ``returnData=False``, returns the ID of the reconstruction. Otherwise, returns a tuple containing the ID of the reconstruction and reconstruction itself, in that order. """ proj_geom = projector.projection_geometry(proj_id) if isinstance(sinogram, np.ndarray): sino_id = data2d.create('-sino', proj_geom, sinogram) else: sino_id = sinogram vol_geom = projector.volume_geometry(proj_id) recon_id = data2d.create('-vol', vol_geom, 0) cfg = astra_dict(rec_type) cfg['ProjectorId'] = proj_id cfg['ProjectionDataId'] = sino_id cfg['ReconstructionDataId'] = recon_id cfg['options'] = {} if use_mask == 'yes': if isinstance(mask, np.ndarray): mask_id = data2d.create('-vol', vol_geom, mask) else: mask_id = mask cfg['options']['ReconstructionMaskId'] = mask_id if not filterType == None: cfg['FilterType'] = filterType if not filterData == None: if isinstance(filterData, np.ndarray): nexpow = int( pow(2, math.ceil(math.log(2 * proj_geom['DetectorCount'], 2)))) filtSize = nexpow / 2 + 1 filt_proj_geom = create_proj_geom( 'parallel', 1.0, filtSize, proj_geom['ProjectionAngles']) filt_id = data2d.create('-sino', filt_proj_geom, filterData) else: filt_id = filterData cfg['FilterSinogramId'] = filt_id cfg['options']['UseMinConstraint'] = use_minc cfg['options']['MinConstraintValue'] = minc cfg['options']['UseMaxConstraint'] = use_maxc cfg['options']['MaxConstraintValue'] = maxc cfg['options']['ProjectionOrder'] = 'random' alg_id = algorithm.create(cfg) algorithm.run(alg_id, iterations) algorithm.delete(alg_id) if isinstance(sinogram, np.ndarray): data2d.delete(sino_id) if use_mask == 'yes' and isinstance(mask, np.ndarray): data2d.delete(mask_id) if not filterData == None: if isinstance(filterData, np.ndarray): data2d.delete(filt_id) if returnData: return recon_id, data2d.get(recon_id) else: return recon_id
[docs]def create_projector(proj_type, proj_geom, vol_geom): """Create a 2D or 3D projector. :param proj_type: Projector type, such as ``'line'``, ``'linear'``, ... :type proj_type: :class:`string` :param proj_geom: Projection geometry. :type proj_geom: :class:`dict` :param vol_geom: Volume geometry. :type vol_geom: :class:`dict` :returns: :class:`int` -- The ID of the projector. """ if proj_type == 'blob': raise Exception('Blob type not yet implemented') cfg = astra_dict(proj_type) cfg['ProjectionGeometry'] = proj_geom cfg['VolumeGeometry'] = vol_geom types3d = ['linear3d', 'linearcone', 'cuda3d'] if proj_type in types3d: return projector3d.create(cfg) else: return projector.create(cfg)