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author | Jakob Jorgensen <jakob.jorgensen@manchester.ac.uk> | 2018-04-12 16:12:21 +0100 |
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committer | Jakob Jorgensen <jakob.jorgensen@manchester.ac.uk> | 2018-04-12 16:12:21 +0100 |
commit | 1ee5e4ca7d69fadbdf209e847cf6bf30a20fc734 (patch) | |
tree | 29a2977a509fddc19580cdf6f25f02b72b839ad8 /Wrappers/Python | |
parent | 697c0bdd118ba844d65d6fb87ca82363efb94cfc (diff) | |
download | astra-wrapper-1ee5e4ca7d69fadbdf209e847cf6bf30a20fc734.tar.gz astra-wrapper-1ee5e4ca7d69fadbdf209e847cf6bf30a20fc734.tar.bz2 astra-wrapper-1ee5e4ca7d69fadbdf209e847cf6bf30a20fc734.tar.xz astra-wrapper-1ee5e4ca7d69fadbdf209e847cf6bf30a20fc734.zip |
Tidied sophieabeads demo
Diffstat (limited to 'Wrappers/Python')
-rwxr-xr-x | Wrappers/Python/wip/demo_sophiabeads.py | 12 |
1 files changed, 10 insertions, 2 deletions
diff --git a/Wrappers/Python/wip/demo_sophiabeads.py b/Wrappers/Python/wip/demo_sophiabeads.py index e3c7f3a..8c72964 100755 --- a/Wrappers/Python/wip/demo_sophiabeads.py +++ b/Wrappers/Python/wip/demo_sophiabeads.py @@ -1,4 +1,12 @@ +# This demo shows how to load a Nikon XTek micro-CT data set and reconstruct +# the central slice using the CGLS method. The SophiaBeads dataset with 256 +# projections is used as test data and can be obtained from here: +# https://zenodo.org/record/16474 +# The filename with full path to the .xtekct file should be given as string +# input to XTEKReader to load in the data. + +# Do all imports from ccpi.io.reader import XTEKReader import numpy as np import matplotlib.pyplot as plt @@ -7,7 +15,7 @@ from ccpi.astra.astra_ops import AstraProjectorSimple from ccpi.reconstruction.algs import CGLS # Set up reader object and read the data -datareader = XTEKReader("C:/Users/mbbssjj2/Documents/SophiaBeads_256_averaged/SophiaBeads_256_averaged.xtekct") +datareader = XTEKReader("REPLACE_THIS_BY_PATH_TO_DATASET/SophiaBeads_256_averaged.xtekct") data = datareader.getAcquisitionData() # Extract central slice, scale and negative-log transform @@ -56,7 +64,7 @@ Aop = AstraProjectorSimple(ig2d, ag2d,"gpu") # Set initial guess for CGLS reconstruction x_init = ImageData(np.zeros((N,N)),geometry=ig2d) -# Run CGLS reconstruction +# Run 50-iteration CGLS reconstruction num_iter = 50 x, it, timing, criter = CGLS(Aop,data2d,num_iter,x_init) |