Merge branch 'master' into parvec

8 files changed, 334 insertions, 23 deletions

diff --git a/samples/matlab/s010_supersampling.m b/samples/matlab/s010_supersampling.m
index 80f6f56..148f6ad 100644
--- a/samples/matlab/s010_supersampling.m
+++ b/samples/matlab/s010_supersampling.m
@@ -12,23 +12,15 @@ vol_geom = astra_create_vol_geom(256, 256);
 proj_geom = astra_create_proj_geom('parallel', 3.0, 128, linspace2(0,pi,180));
 P = phantom(256);
 
-% Because the astra_create_sino_gpu wrapper does not have support for
-% all possible algorithm options, we manually create a sinogram
-phantom_id = astra_mex_data2d('create', '-vol', vol_geom, P);
-sinogram_id = astra_mex_data2d('create', '-sino', proj_geom);
-cfg = astra_struct('FP_CUDA');
-cfg.VolumeDataId = phantom_id;
-cfg.ProjectionDataId = sinogram_id;
+% We create a projector set up to use 3 rays per detector element
+cfg_proj = astra_struct('cuda');
+cfg_proj.option.DetectorSuperSampling = 3;
+cfg_proj.ProjectionGeometry = proj_geom;
+cfg_proj.VolumeGeometry = vol_geom;
+proj_id = astra_mex_projector('create', cfg_proj);
 
-% Set up 3 rays per detector element
-cfg.option.DetectorSuperSampling = 3;
 
-alg_id = astra_mex_algorithm('create', cfg);
-astra_mex_algorithm('run', alg_id);
-astra_mex_algorithm('delete', alg_id);
-astra_mex_data2d('delete', phantom_id);
-
-sinogram3 = astra_mex_data2d('get', sinogram_id);
+[sinogram3 sinogram_id] = astra_create_sino(P, proj_id);
 
 figure(1); imshow(P, []);
 figure(2); imshow(sinogram3, []);
@@ -39,14 +31,14 @@ rec_id = astra_mex_data2d('create', '-vol', vol_geom);
 cfg = astra_struct('SIRT_CUDA');
 cfg.ReconstructionDataId = rec_id;
 cfg.ProjectionDataId = sinogram_id;
-% Set up 3 rays per detector element
-cfg.option.DetectorSuperSampling = 3;
+cfg.ProjectorId = proj_id;
+
 
 % There is also an option for supersampling during the backprojection step.
 % This should be used if your detector pixels are smaller than the voxels.
 
 % Set up 2 rays per image pixel dimension, for 4 rays total per image pixel.
-% cfg.option.PixelSuperSampling = 2;
+% cfg_proj.option.PixelSuperSampling = 2;
 
 
 alg_id = astra_mex_algorithm('create', cfg);
diff --git a/samples/matlab/s020_3d_multiGPU.m b/samples/matlab/s020_3d_multiGPU.m
new file mode 100644
index 0000000..bade325
--- /dev/null
+++ b/samples/matlab/s020_3d_multiGPU.m
@@ -0,0 +1,38 @@
+% -----------------------------------------------------------------------
+% This file is part of the ASTRA Toolbox
+% 
+% Copyright: 2010-2015, iMinds-Vision Lab, University of Antwerp
+%            2014-2015, CWI, Amsterdam
+% License: Open Source under GPLv3
+% Contact: astra@uantwerpen.be
+% Website: http://sf.net/projects/astra-toolbox
+% -----------------------------------------------------------------------
+
+
+% Set up multi-GPU usage.
+% This only works for 3D GPU forward projection and back projection.
+astra_mex('set_gpu_index', [0 1]);
+
+% Optionally, you can also restrict the amount of GPU memory ASTRA will use.
+% The line commented below sets this to 1GB.
+%astra_mex('set_gpu_index', [0 1], 'memory', 1024*1024*1024);
+
+vol_geom = astra_create_vol_geom(1024, 1024, 1024);
+
+angles = linspace2(0, pi, 1024);
+proj_geom = astra_create_proj_geom('parallel3d', 1.0, 1.0, 1024, 1024, angles);
+
+% Create a simple hollow cube phantom
+cube = zeros(1024,1024,1024);
+cube(129:896,129:896,129:896) = 1;
+cube(257:768,257:768,257:768) = 0;
+
+% Create projection data from this
+[proj_id, proj_data] = astra_create_sino3d_cuda(cube, proj_geom, vol_geom);
+
+% Backproject projection data
+[bproj_id, bproj_data] = astra_create_backprojection3d_cuda(proj_data, proj_geom, vol_geom);
+
+astra_mex_data3d('delete', proj_id);
+astra_mex_data3d('delete', bproj_id);
+
diff --git a/samples/python/s009_projection_matrix.py b/samples/python/s009_projection_matrix.py
index c4c4557..e20d58c 100644
--- a/samples/python/s009_projection_matrix.py
+++ b/samples/python/s009_projection_matrix.py
@@ -46,7 +46,7 @@ W = astra.matrix.get(matrix_id)
 # Manually use this projection matrix to do a projection:
 import scipy.io
 P = scipy.io.loadmat('phantom.mat')['phantom256']
-s = W.dot(P.flatten())
+s = W.dot(P.ravel())
 s = np.reshape(s, (len(proj_geom['ProjectionAngles']),proj_geom['DetectorCount']))
 
 import pylab
diff --git a/samples/python/s015_fp_bp.py b/samples/python/s015_fp_bp.py
index fa0bf86..ff0b30a 100644
--- a/samples/python/s015_fp_bp.py
+++ b/samples/python/s015_fp_bp.py
@@ -46,12 +46,12 @@ class astra_wrap(object):
     def matvec(self,v):
         sid, s = astra.create_sino(np.reshape(v,(vol_geom['GridRowCount'],vol_geom['GridColCount'])),self.proj_id)
         astra.data2d.delete(sid)
-        return s.flatten()
+        return s.ravel()
     
     def rmatvec(self,v):
         bid, b = astra.create_backprojection(np.reshape(v,(len(proj_geom['ProjectionAngles']),proj_geom['DetectorCount'],)),self.proj_id)
         astra.data2d.delete(bid)
-        return b.flatten()
+        return b.ravel()
 
 vol_geom = astra.create_vol_geom(256, 256)
 proj_geom = astra.create_proj_geom('parallel', 1.0, 384, np.linspace(0,np.pi,180,False))
@@ -65,7 +65,7 @@ proj_id = astra.create_projector('cuda',proj_geom,vol_geom)
 sinogram_id, sinogram = astra.create_sino(P, proj_id)
 
 # Reshape the sinogram into a vector
-b = sinogram.flatten()
+b = sinogram.ravel()
 
 # Call lsqr with ASTRA FP and BP
 import scipy.sparse.linalg
diff --git a/samples/python/s017_OpTomo.py b/samples/python/s017_OpTomo.py
index 967fa64..214e9a7 100644
--- a/samples/python/s017_OpTomo.py
+++ b/samples/python/s017_OpTomo.py
@@ -50,7 +50,7 @@ pylab.figure(2)
 pylab.imshow(sinogram)
 
 # Run the lsqr linear solver
-output = scipy.sparse.linalg.lsqr(W, sinogram.flatten(), iter_lim=150)
+output = scipy.sparse.linalg.lsqr(W, sinogram.ravel(), iter_lim=150)
 rec = output[0].reshape([256, 256])
 
 pylab.figure(3)
diff --git a/samples/python/s018_plugin.py b/samples/python/s018_plugin.py
new file mode 100644
index 0000000..85b5486
--- /dev/null
+++ b/samples/python/s018_plugin.py
@@ -0,0 +1,140 @@
+#-----------------------------------------------------------------------
+#Copyright 2015 Centrum Wiskunde & Informatica, Amsterdam
+#
+#Author: Daniel M. Pelt
+#Contact: D.M.Pelt@cwi.nl
+#Website: http://dmpelt.github.io/pyastratoolbox/
+#
+#
+#This file is part of the Python interface to the
+#All Scale Tomographic Reconstruction Antwerp Toolbox ("ASTRA Toolbox").
+#
+#The Python interface to 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 Python interface to 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 Python interface to the ASTRA Toolbox. If not, see <http://www.gnu.org/licenses/>.
+#
+#-----------------------------------------------------------------------
+
+import astra
+import numpy as np
+import six
+
+# Define the plugin class (has to subclass astra.plugin.base)
+# Note that usually, these will be defined in a separate package/module
+class LandweberPlugin(astra.plugin.base):
+    """Example of an ASTRA plugin class, implementing a simple 2D Landweber algorithm.
+
+    Options:
+
+    'Relaxation': relaxation factor (optional)
+    """
+
+    # The astra_name variable defines the name to use to
+    # call the plugin from ASTRA
+    astra_name = "LANDWEBER-PLUGIN"
+
+    def initialize(self,cfg, Relaxation = 1):
+        self.W = astra.OpTomo(cfg['ProjectorId'])
+        self.vid = cfg['ReconstructionDataId']
+        self.sid = cfg['ProjectionDataId']
+        self.rel = Relaxation
+
+    def run(self, its):
+        v = astra.data2d.get_shared(self.vid)
+        s = astra.data2d.get_shared(self.sid)
+        tv = np.zeros(v.shape, dtype=np.float32)
+        ts = np.zeros(s.shape, dtype=np.float32)
+        W = self.W
+        for i in range(its):
+            W.FP(v,out=ts)
+            ts -= s # ts = W*v - s
+
+            W.BP(ts,out=tv)
+            tv *= self.rel / s.size
+
+            v -= tv # v = v - rel * W'*(W*v-s) / s.size
+
+if __name__=='__main__':
+
+    vol_geom = astra.create_vol_geom(256, 256)
+    proj_geom = astra.create_proj_geom('parallel', 1.0, 384, np.linspace(0,np.pi,180,False))
+
+    # As before, create a sinogram from a phantom
+    import scipy.io
+    P = scipy.io.loadmat('phantom.mat')['phantom256']
+    proj_id = astra.create_projector('cuda',proj_geom,vol_geom)
+
+    # construct the OpTomo object
+    W = astra.OpTomo(proj_id)
+
+    sinogram = W * P
+    sinogram = sinogram.reshape([180, 384])
+
+    # Register the plugin with ASTRA
+    # First we import the package that contains the plugin
+    import s018_plugin
+    # Then, we register the plugin class with ASTRA
+    astra.plugin.register(s018_plugin.LandweberPlugin)
+
+    # Get a list of registered plugins
+    six.print_(astra.plugin.get_registered())
+
+    # To get help on a registered plugin, use get_help
+    six.print_(astra.plugin.get_help('LANDWEBER-PLUGIN'))
+
+    # Create data structures
+    sid = astra.data2d.create('-sino', proj_geom, sinogram)
+    vid = astra.data2d.create('-vol', vol_geom)
+
+    # Create config using plugin name
+    cfg = astra.astra_dict('LANDWEBER-PLUGIN')
+    cfg['ProjectorId'] = proj_id
+    cfg['ProjectionDataId'] = sid
+    cfg['ReconstructionDataId'] = vid
+
+    # Create algorithm object
+    alg_id = astra.algorithm.create(cfg)
+
+    # Run algorithm for 100 iterations
+    astra.algorithm.run(alg_id, 100)
+
+    # Get reconstruction
+    rec = astra.data2d.get(vid)
+
+    # Options for the plugin go in cfg['option']
+    cfg = astra.astra_dict('LANDWEBER-PLUGIN')
+    cfg['ProjectorId'] = proj_id
+    cfg['ProjectionDataId'] = sid
+    cfg['ReconstructionDataId'] = vid
+    cfg['option'] = {}
+    cfg['option']['Relaxation'] = 1.5
+    alg_id_rel = astra.algorithm.create(cfg)
+    astra.algorithm.run(alg_id_rel, 100)
+    rec_rel = astra.data2d.get(vid)
+
+    # We can also use OpTomo to call the plugin
+    rec_op = W.reconstruct('LANDWEBER-PLUGIN', sinogram, 100, extraOptions={'Relaxation':1.5})
+
+    import pylab as pl
+    pl.gray()
+    pl.figure(1)
+    pl.imshow(rec,vmin=0,vmax=1)
+    pl.figure(2)
+    pl.imshow(rec_rel,vmin=0,vmax=1)
+    pl.figure(3)
+    pl.imshow(rec_op,vmin=0,vmax=1)
+    pl.show()
+
+    # Clean up.
+    astra.projector.delete(proj_id)
+    astra.algorithm.delete([alg_id, alg_id_rel])
+    astra.data2d.delete([vid, sid])
diff --git a/samples/python/s019_experimental_multires.py b/samples/python/s019_experimental_multires.py
new file mode 100644
index 0000000..cf38e53
--- /dev/null
+++ b/samples/python/s019_experimental_multires.py
@@ -0,0 +1,84 @@
+#-----------------------------------------------------------------------
+#Copyright 2013 Centrum Wiskunde & Informatica, Amsterdam
+#
+#Author: Daniel M. Pelt
+#Contact: D.M.Pelt@cwi.nl
+#Website: http://dmpelt.github.io/pyastratoolbox/
+#
+#
+#This file is part of the Python interface to the
+#All Scale Tomographic Reconstruction Antwerp Toolbox ("ASTRA Toolbox").
+#
+#The Python interface to 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 Python interface to 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 Python interface to the ASTRA Toolbox. If not, see <http://www.gnu.org/licenses/>.
+#
+#-----------------------------------------------------------------------
+
+import astra
+import numpy as np
+from astra.experimental import do_composite_FP
+
+astra.log.setOutputScreen(astra.log.STDERR, astra.log.DEBUG)
+
+# low res part (voxels of 4x4x4)
+vol_geom1 = astra.create_vol_geom(32, 16, 32, -64, 0, -64, 64, -64, 64)
+
+# high res part (voxels of 1x1x1)
+vol_geom2 = astra.create_vol_geom(128, 64, 128, 0, 64, -64, 64, -64, 64)
+
+
+# Split the output in two parts as well, for demonstration purposes
+angles1 = np.linspace(0, np.pi/2, 90, False)
+angles2 = np.linspace(np.pi/2, np.pi, 90, False)
+proj_geom1 = astra.create_proj_geom('parallel3d', 1.0, 1.0, 128, 192, angles1)
+proj_geom2 = astra.create_proj_geom('parallel3d', 1.0, 1.0, 128, 192, angles2)
+
+# Create a simple hollow cube phantom
+cube1 = np.zeros((32,32,16))
+cube1[4:28,4:28,4:16] = 1
+
+cube2 = np.zeros((128,128,64))
+cube2[16:112,16:112,0:112] = 1
+cube2[33:97,33:97,4:28] = 0
+
+vol1 = astra.data3d.create('-vol', vol_geom1, cube1)
+vol2 = astra.data3d.create('-vol', vol_geom2, cube2)
+
+proj1 = astra.data3d.create('-proj3d', proj_geom1, 0)
+proj2 = astra.data3d.create('-proj3d', proj_geom2, 0)
+
+# The actual geometries don't matter for this composite FP/BP case
+projector = astra.create_projector('cuda3d', proj_geom1, vol_geom1)
+
+do_composite_FP(projector, [vol1, vol2], [proj1, proj2])
+
+proj_data1 = astra.data3d.get(proj1)
+proj_data2 = astra.data3d.get(proj2)
+
+# Display a single projection image
+import pylab
+pylab.gray()
+pylab.figure(1)
+pylab.imshow(proj_data1[:,0,:])
+pylab.figure(2)
+pylab.imshow(proj_data2[:,0,:])
+pylab.show()
+
+
+# Clean up. Note that GPU memory is tied up in the algorithm object,
+# and main RAM in the data objects.
+astra.data3d.delete(vol1)
+astra.data3d.delete(vol2)
+astra.data3d.delete(proj1)
+astra.data3d.delete(proj2)
+astra.projector3d.delete(projector)
diff --git a/samples/python/s020_3d_multiGPU.py b/samples/python/s020_3d_multiGPU.py
new file mode 100644
index 0000000..d6799c4
--- /dev/null
+++ b/samples/python/s020_3d_multiGPU.py
@@ -0,0 +1,57 @@
+#-----------------------------------------------------------------------
+#Copyright 2013 Centrum Wiskunde & Informatica, Amsterdam
+#
+#Author: Daniel M. Pelt
+#Contact: D.M.Pelt@cwi.nl
+#Website: http://dmpelt.github.io/pyastratoolbox/
+#
+#
+#This file is part of the Python interface to the
+#All Scale Tomographic Reconstruction Antwerp Toolbox ("ASTRA Toolbox").
+#
+#The Python interface to 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 Python interface to 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 Python interface to the ASTRA Toolbox. If not, see <http://www.gnu.org/licenses/>.
+#
+#-----------------------------------------------------------------------
+
+import astra
+import numpy as np
+
+# Set up multi-GPU usage.
+# This only works for 3D GPU forward projection and back projection.
+astra.astra.set_gpu_index([0,1])
+
+# Optionally, you can also restrict the amount of GPU memory ASTRA will use.
+# The line commented below sets this to 1GB.
+#astra.astra.set_gpu_index([0,1], memory=1024*1024*1024)
+
+vol_geom = astra.create_vol_geom(1024, 1024, 1024)
+
+angles = np.linspace(0, np.pi, 1024,False)
+proj_geom = astra.create_proj_geom('parallel3d', 1.0, 1.0, 1024, 1024, angles)
+
+# Create a simple hollow cube phantom
+cube = np.zeros((1024,1024,1024))
+cube[128:895,128:895,128:895] = 1
+cube[256:767,256:767,256:767] = 0
+
+# Create projection data from this
+proj_id, proj_data = astra.create_sino3d_gpu(cube, proj_geom, vol_geom)
+
+# Backproject projection data
+bproj_id, bproj_data = astra.create_backprojection3d_gpu(proj_data, proj_geom, vol_geom)
+
+# Clean up. Note that GPU memory is tied up in the algorithm object,
+# and main RAM in the data objects.
+astra.data3d.delete(proj_id)
+astra.data3d.delete(bproj_id)