Added IMAT white-beam demo loading summed fits files

1 files changed, 128 insertions, 0 deletions

diff --git a/Wrappers/Python/wip/demo_imat_whitebeam.py b/Wrappers/Python/wip/demo_imat_whitebeam.py
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+# This script demonstrates how to load IMAT fits data
+# into the CIL optimisation framework and run reconstruction methods.
+#
+# This demo loads the summedImg files which are the non-spectral images 
+# resulting from summing projections over all spectral channels.
+
+# needs dxchange: conda install -c conda-forge dxchange
+# needs astropy: conda install astropy
+
+
+# All third-party imports.
+import numpy
+from scipy.io import loadmat
+import matplotlib.pyplot as plt
+from dxchange.reader import read_fits
+
+# All own imports.
+from ccpi.framework import AcquisitionData, AcquisitionGeometry, ImageGeometry, ImageData
+from ccpi.astra.ops import AstraProjectorSimple, AstraProjector3DSimple
+from ccpi.optimisation.algs import CGLS, FISTA
+from ccpi.optimisation.funcs import Norm2sq, Norm1
+
+# Load and display a couple of summed projection as examples
+pathname0 = '/media/jakob/050d8d45-fab3-4285-935f-260e6c5f162c1/Data/neutrondata/PSI_phantom_IMAT/DATA/Sample/angle0/'
+filename0 = 'IMAT00004675_Tomo_test_000_SummedImg.fits'
+
+data0 = read_fits(pathname0 + filename0)
+
+pathname10 = '/media/jakob/050d8d45-fab3-4285-935f-260e6c5f162c1/Data/neutrondata/PSI_phantom_IMAT/DATA/Sample/angle10/'
+filename10 = 'IMAT00004685_Tomo_test_000_SummedImg.fits'
+
+data10 = read_fits(pathname10 + filename10)
+
+# Load a flat field (more are available, should we average over them?)
+flat1 = read_fits('/media/jakob/050d8d45-fab3-4285-935f-260e6c5f162c1/Data/neutrondata/PSI_phantom_IMAT/DATA/OpenBeam_aft1/IMAT00004932_Tomo_test_000_SummedImg.fits')
+
+# Apply flat field and display after flat-field correction and negative log
+data0_rel = data0 / flat1
+data10_rel = data10 / flat1
+
+plt.imshow(data0_rel)
+plt.colorbar()
+plt.show()
+
+plt.imshow(-numpy.log(data0_rel))
+plt.colorbar()
+plt.show()
+
+plt.imshow(data10_rel)
+plt.colorbar()
+plt.show()
+
+plt.imshow(-numpy.log(data10_rel))
+plt.colorbar()
+plt.show()
+
+# Set up for loading all summed images at 250 angles.
+pathname = '/media/jakob/050d8d45-fab3-4285-935f-260e6c5f162c1/Data/neutrondata/PSI_phantom_IMAT/DATA/Sample/angle{}/'
+filename = 'IMAT0000{}_Tomo_test_000_SummedImg.fits'
+
+# Dimensions
+num_angles = 250
+imsize = 512
+
+# Initialise array
+data = numpy.zeros((num_angles,imsize,imsize))
+
+# Load only 0-249, as 250 is at repetition of zero degrees just like image 0
+for i in range(0,250):
+    curimfile = (pathname + filename).format(i, i+4675)
+    data[i,:,:] = read_fits(curimfile)
+
+# Apply flat field and take negative log
+data_rel = -numpy.log(data/flat1)
+
+# Permute order to get: angles, vertical, horizontal, as default in framework.
+data_rel = numpy.transpose(data_rel,(0,2,1))
+
+# Set angles to use
+angles = numpy.linspace(-numpy.pi,numpy.pi,num_angles,endpoint=False)
+
+# Create 3D acquisition geometry and acquisition data
+ag = AcquisitionGeometry('parallel',
+                         '3D',
+                         angles,
+                         pixel_num_h=imsize,
+                         pixel_num_v=imsize)
+b = AcquisitionData(data_rel, geometry=ag)
+
+# Reduce to single (noncentral) slice by extracting relevant parameters from data and its
+# geometry. Perhaps create function to extract central slice automatically?
+b2d = b.subset(vertical=128)
+ag2d = AcquisitionGeometry('parallel',
+                         '2D',
+                         ag.angles,
+                         pixel_num_h=ag.pixel_num_h)
+b2d.geometry = ag2d
+
+# Create 2D image geometry
+ig2d = ImageGeometry(voxel_num_x=ag2d.pixel_num_h, 
+                     voxel_num_y=ag2d.pixel_num_h)
+
+# Create GPU projector/backprojector operator with ASTRA.
+Aop = AstraProjectorSimple(ig2d,ag2d,'gpu')
+
+# Demonstrate operator is working by applying simple backprojection.
+z = Aop.adjoint(b2d)
+plt.imshow(z.array)
+plt.title('Simple backprojection')
+plt.colorbar()
+plt.show()
+
+# Set initial guess ImageData with zeros for algorithms, and algorithm options.
+x_init = ImageData(numpy.zeros((imsize,imsize)),
+                   geometry=ig2d)
+opt_CGLS = {'tol': 1e-4, 'iter': 20}
+
+# Run CGLS algorithm and display reconstruction.
+x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, b2d, opt_CGLS)
+
+plt.imshow(x_CGLS.array)
+plt.title('CGLS')
+plt.colorbar()
+plt.show()
+
+plt.semilogy(criter_CGLS)
+plt.title('CGLS Criterion vs iterations')
+plt.show()
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