In [2]:
%matplotlib inline
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import pylab as plt
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import time
import sys
import astra
import numpy as np
import pandas as pd
In [4]:
def create_test_cube(size):
# Create a simple hollow cube phantom
cube = np.zeros((size,size,size), dtype='float32')
x0 = int(128.*size/1024)
x1 = int(895.*size/1024)
y0 = int(256.*size/1024)
y1 = int(767.*size/1024)
cube[x0:x1,x0:x1,x0:x1] = 1
cube[y0:y1,y0:y1,y0:y1,] = 0
return cube
def test_projections_parallel(size, angles_count, gpus_list):
# Set up multi-GPU usage.
# This only works for 3D GPU forward projection and back projection.
astra.astra.set_gpu_index(gpus_list)
# 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)
angles = np.linspace(0, np.pi, angles_count, False)
proj_geom = astra.create_proj_geom('parallel3d', 1.0, 1.0, size, size, angles)
# Create a simple hollow cube phantom
cube = create_test_cube(size)
vol_geom = astra.create_vol_geom(cube.shape)
cube_id = astra.data3d.create('-vol', vol_geom, cube)
del cube
print('Start create_sino3d_gpu')
# Create projection data from this
t=time.time()
proj_id = astra.create_sino3d_gpu(cube_id, proj_geom, vol_geom, returnData=False)
astra.data3d.delete(cube_id)
t_proj = time.time()-t
print('Takes: {}'.format(t_proj))
print('Start astra.create_backprojection3d_gpu')
# Backproject projection data
t=time.time()
bproj_id = astra.create_backprojection3d_gpu(proj_id, proj_geom, vol_geom, returnData=False)
t_bproj = time.time()-t
print('Takes: {}'.format(t_bproj))
# Clean up. Note that GPU memory is tied up in the algorithm object,
# and main RAM in the data objects.
astra.data3d.info()
astra.data3d.delete(proj_id)
astra.data3d.delete(bproj_id)
return size, angles_count, len(gpus_list), t_proj, t_bproj
def test_projections_cone(size, angles_count, gpus_list):
# Set up multi-GPU usage.
# This only works for 3D GPU forward projection and back projection.
astra.astra.set_gpu_index(gpus_list)
# 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)
angles = np.linspace(0, np.pi, angles_count, False)
# Circular
# Parameters: width of detector column, height of detector row, #rows, #columns,
# angles, distance source-origin, distance origin-detector
# see example #5 from python samples
# All distances in [pixels]
pixel_size = 2.82473e-3
os_distance = 56.135 / pixel_size # object-sample distance
ds_distance = 225.082 / pixel_size # detector-sample distance
detector_size = size
# proj_geom = astra.create_proj_geom('cone', 1.0, 1.0, 32, 64, angles, 1000, 0)
proj_geom = astra.create_proj_geom('cone', ds_distance / os_distance,ds_distance / os_distance,
detector_size, detector_size, angles,
os_distance, (ds_distance - os_distance))
# proj_geom = astra.create_proj_geom('parallel3d', 1.0, 1.0, size, size, angles)
# Create a simple hollow cube phantom
cube = create_test_cube(size)
vol_geom = astra.create_vol_geom(cube.shape)
cube_id = astra.data3d.create('-vol', vol_geom, cube)
del cube
print('Start create_sino3d_gpu')
# Create projection data from this
t=time.time()
proj_id = astra.create_sino3d_gpu(cube_id, proj_geom, vol_geom, returnData=False)
astra.data3d.delete(cube_id)
t_proj = time.time()-t
print('Takes: {}'.format(t_proj))
print('Start astra.create_backprojection3d_gpu')
# Backproject projection data
t=time.time()
bproj_id = astra.create_backprojection3d_gpu(proj_id, proj_geom, vol_geom, returnData=False)
t_bproj = time.time()-t
print('Takes: {}'.format(t_bproj))
# Clean up. Note that GPU memory is tied up in the algorithm object,
# and main RAM in the data objects.
astra.data3d.info()
astra.data3d.delete(proj_id)
astra.data3d.delete(bproj_id)
return size, angles_count, len(gpus_list), t_proj, t_bproj
def test_reconstruction_cone_fdk(size, angles_count, gpus_list):
# Set up multi-GPU usage.
# This only works for 3D GPU forward projection and back projection.
astra.astra.set_gpu_index(gpus_list)
# 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)
angles = np.linspace(0, np.pi, angles_count, False)
# Circular
# Parameters: width of detector column, height of detector row, #rows, #columns,
# angles, distance source-origin, distance origin-detector
# see example #5 from python samples
# All distances in [pixels]
pixel_size = 2.82473e-3
os_distance = 56.135 / pixel_size # object-sample distance
ds_distance = 225.082 / pixel_size # detector-sample distance
detector_size = size
# proj_geom = astra.create_proj_geom('cone', 1.0, 1.0, 32, 64, angles, 1000, 0)
proj_geom = astra.create_proj_geom('cone', ds_distance / os_distance,ds_distance / os_distance,
detector_size, detector_size, angles,
os_distance, (ds_distance - os_distance))
# proj_geom = astra.create_proj_geom('parallel3d', 1.0, 1.0, size, size, angles)
# Create a simple hollow cube phantom
cube = create_test_cube(size)
vol_geom = astra.create_vol_geom(cube.shape)
cube_id = astra.data3d.create('-vol', vol_geom, cube)
del cube
print('Start create_sino3d_gpu')
# Create projection data from this
t=time.time()
proj_id = astra.create_sino3d_gpu(cube_id, proj_geom, vol_geom, returnData=False)
t_proj = time.time()-t
print('Takes: {}'.format(t_proj))
# Set up the parameters for a reconstruction algorithm using the GPU
cfg = astra.astra_dict('FDK_CUDA')
cfg['ReconstructionDataId'] = cube_id
cfg['ProjectionDataId'] = proj_id
# Create the algorithm object from the configuration structure
alg_id = astra.algorithm.create(cfg)
# print('Start astra reconstruction FDK_CUDA')
# # Backproject projection data
t=time.time()
astra.algorithm.run(alg_id, 1)
t_bproj = time.time()-t
print('Takes: {}'.format(t_bproj))
# rec = astra.data3d.get(cube_id)
# plt.figure()
# plt.imshow(rec[:,:,int(rec.shape[-1]/2)])
# plt.show()
# Clean up. Note that GPU memory is tied up in the algorithm object,
# and main RAM in the data objects.
astra.data3d.info()
astra.algorithm.delete(alg_id)
astra.data3d.delete(proj_id)
astra.data3d.delete(cube_id)
return size, angles_count, len(gpus_list), t_proj, t_bproj
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#-----------------------------------------------------------------------
#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
astra.astra.set_gpu_index([0,1])
vol_geom = astra.create_vol_geom(128, 128, 128)
angles = np.linspace(-1., np.pi, 180,False)
# proj_geom = astra.create_proj_geom('parallel3d', 1.0, 1.0, 128, 192, angles)
pixel_size = 2.82473e-3
os_distance = 56.135 / pixel_size # object-sample distance
ds_distance = 225.082 / pixel_size # detector-sample distance
detector_size = 128
# proj_geom = astra.create_proj_geom('cone', 1.0, 1.0, 32, 64, angles, 1000, 0)
proj_geom = astra.create_proj_geom('cone', ds_distance / os_distance,ds_distance / os_distance,
detector_size, detector_size, angles,
os_distance, (ds_distance - os_distance))
# Create a simple hollow cube phantom
cube = np.zeros((128,128,128))
cube[17:113,17:113,17:113] = 1
cube[33:97,33:97,33:97] = 0
# Create projection data from this
proj_id, proj_data = astra.create_sino3d_gpu(cube, proj_geom, vol_geom)
# Display a single projection image
import pylab
pylab.viridis()
pylab.figure(1)
pylab.imshow(proj_data[:,20,:])
# Create a data object for the reconstruction
rec_id = astra.data3d.create('-vol', vol_geom)
# Set up the parameters for a reconstruction algorithm using the GPU
cfg = astra.astra_dict('FDK_CUDA')
cfg['ReconstructionDataId'] = rec_id
cfg['ProjectionDataId'] = proj_id
cfg['options']={'ShortScan':True}
# Create the algorithm object from the configuration structure
alg_id = astra.algorithm.create(cfg)
# Run 150 iterations of the algorithm
# Note that this requires about 750MB of GPU memory, and has a runtime
# in the order of 10 seconds.
astra.algorithm.run(alg_id, 1000)
# Get the result
rec = astra.data3d.get(rec_id)
pylab.figure(2)
pylab.imshow(rec[:,:,64])
pylab.show()
# Clean up. Note that GPU memory is tied up in the algorithm object,
# and main RAM in the data objects.
astra.algorithm.delete(alg_id)
astra.data3d.delete(rec_id)
astra.data3d.delete(proj_id)
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astra.create_sino3d_gpu??
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