Creation of objects: the creators module

astra.creators.astra_dict(intype)

Creates a dict to use with the ASTRA Toolbox.

Parameters:intype (string) – Type of the ASTRA object. Returns:dict – An ASTRA dict of type intype.

astra.creators.create_backprojection(data, proj_id, useCUDA=False, returnData=True)

Create a backprojection of a sinogram (2D).

Parameters:

• data (numpy.ndarray or int) – Sinogram data or ID.
• proj_id (int) – ID of the projector to use.
• useCUDA (bool) – If True, use CUDA for the calculation.
• returnData (bool) – If False, only return the ID of the backprojection.

Returns:

int or (int, 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.

astra.creators.create_backprojection3d_gpu(data, proj_geom, vol_geom, returnData=True)

Create a backprojection of a sinogram (3D) using CUDA.

Parameters:

• data (numpy.ndarray or int) – Sinogram data or ID.
• proj_geom (dict) – Projection geometry.
• vol_geom (dict) – Volume geometry.
• returnData (bool) – If False, only return the ID of the backprojection.

Returns:

int or (int, 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.

astra.creators.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):

Parameters:

• detector_spacing (float) – Distance between two adjacent detector pixels.
• det_count (int) – Number of detector pixels.
• angles (numpy.ndarray) – Array of angles in radians.

Returns:

A parallel projection geometry.

create_proj_geom('fanflat', det_width, det_count, angles, source_origin, source_det):

Parameters:

• det_width (float) – Size of a detector pixel.
• det_count (int) – Number of detector pixels.
• angles (numpy.ndarray) – Array of angles in radians.
• source_origin – Position of the source.
• source_det – Position of the detector

Returns:

A fan-beam projection geometry.

create_proj_geom('fanflat_vec', det_count, V):

Parameters:

• det_count (int) – Number of detector pixels.
• V (numpy.ndarray) – Vector array.

Returns:

A fan-beam projection geometry.

create_proj_geom('parallel3d', detector_spacing_x, detector_spacing_y, det_row_count, det_col_count, angles):

Parameters:

• detector_spacing_* (float) – Distance between two adjacent detector pixels.
• det_row_count (int) – Number of detector pixel rows.
• det_col_count (int) – Number of detector pixel columns.
• angles (numpy.ndarray) – Array of angles in radians.

Returns:

A parallel projection geometry.

create_proj_geom('cone', detector_spacing_x, detector_spacing_y, det_row_count, det_col_count, angles, source_origin, source_det):

Parameters:

• detector_spacing_* (float) – Distance between two adjacent detector pixels.
• det_row_count (int) – Number of detector pixel rows.
• det_col_count (int) – Number of detector pixel columns.
• angles (numpy.ndarray) – Array of angles in radians.
• source_origin (float) – Distance between point source and origin.
• source_det (float) – Distance between the detector and origin.

Returns:

A cone-beam projection geometry.

create_proj_geom('cone_vec', det_row_count, det_col_count, V):

Parameters:

• det_row_count (int) – Number of detector pixel rows.
• det_col_count (int) – Number of detector pixel columns.
• V (numpy.ndarray) – Vector array.

Returns:

A cone-beam projection geometry.

create_proj_geom('parallel3d_vec', det_row_count, det_col_count, V):

Parameters:

• det_row_count (int) – Number of detector pixel rows.
• det_col_count (int) – Number of detector pixel columns.
• V (numpy.ndarray) – Vector array.

Returns:

A parallel projection geometry.

create_proj_geom('sparse_matrix', det_width, det_count, angles, matrix_id):

Parameters:

• det_width (float) – Size of a detector pixel.
• det_count (int) – Number of detector pixels.
• angles (numpy.ndarray) – Array of angles in radians.
• matrix_id (int) – ID of the sparse matrix.

Returns:

A projection geometry based on a sparse matrix.

astra.creators.create_projector(proj_type, proj_geom, vol_geom)

Create a 2D projector.

Parameters:

• proj_type (string) – Projector type, such as 'line', 'linear', ...
• proj_geom (dict) – Projection geometry.
• vol_geom (dict) – Volume geometry.

Returns:

int – The ID of the projector.

astra.creators.create_reconstruction(rec_type, proj_id, sinogram, iterations=1, use_mask='no', mask=array([], dtype=float64), use_minc='no', minc=0, use_maxc='no', maxc=255, returnData=True, filterType=None, filterData=None)

Create a reconstruction of a sinogram (2D).

Parameters:

• rec_type (string) – Name of the reconstruction algorithm.
• proj_id (int) – ID of the projector to use.
• sinogram (numpy.ndarray or int) – Sinogram data or ID.
• iterations (int) – Number of iterations to run.
• use_mask ('yes' or 'no') – Whether to use a mask.
• mask (numpy.ndarray or int) – Mask data or ID
• use_minc ('yes' or 'no') – Whether to force a minimum value on the reconstruction pixels.
• minc (float) – Minimum value to use.
• use_maxc ('yes' or 'no') – Whether to force a maximum value on the reconstruction pixels.
• maxc (float) – Maximum value to use.
• returnData (bool) – If False, only return the ID of the reconstruction.
• filterType (string) – Which type of filter to use for filter-based methods.
• filterData (numpy.ndarray) – Optional filter data for filter-based methods.

Returns:

int or (int, 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.

astra.creators.create_sino(data, proj_id, useCUDA=False, returnData=True, gpuIndex=None)

Create a forward projection of an image (2D).

Parameters:

• data (numpy.ndarray or int) – Image data or ID.
• proj_id (int) – ID of the projector to use.
• useCUDA (bool) – If True, use CUDA for the calculation.
• returnData (bool) – If False, only return the ID of the forward projection.
• gpuIndex (int) – Optional GPU index.

Returns:

int or (int, 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.

astra.creators.create_sino3d_gpu(data, proj_geom, vol_geom, returnData=True, gpuIndex=None)

Create a forward projection of an image (3D).

Parameters:

• data (numpy.ndarray or int) – Image data or ID.
• proj_geom (dict) – Projection geometry.
• vol_geom (dict) – Volume geometry.
• returnData (bool) – If False, only return the ID of the forward projection.
• gpuIndex (int) – Optional GPU index.

Returns:

int or (int, 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.

astra.creators.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 \(N \times N\).

create_vol_geom((M, N)):

returns:A 2D volume geometry of size \(M \times N\).

create_vol_geom(M, N):

returns:A 2D volume geometry of size \(M \times N\).

create_vol_geom(M, N, minx, maxx, miny, maxy):

returns:A 2D volume geometry of size \(M \times N\), windowed as \(minx \leq x \leq maxx\) and \(miny \leq y \leq maxy\).

create_vol_geom((M, N, Z)):

returns:A 3D volume geometry of size \(M \times N \times Z\).

create_vol_geom(M, N, Z):

returns:A 3D volume geometry of size \(M \times N \times Z\).