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Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Python/wip/demo_colourbay.py | 137 |
1 files changed, 137 insertions, 0 deletions
diff --git a/Wrappers/Python/wip/demo_colourbay.py b/Wrappers/Python/wip/demo_colourbay.py new file mode 100644 index 0000000..5dbf2e1 --- /dev/null +++ b/Wrappers/Python/wip/demo_colourbay.py @@ -0,0 +1,137 @@ +# This script demonstrates how to load a mat-file with UoM colour-bay data +# into the CIL optimisation framework and run (simple) multichannel +# reconstruction methods. + +# All third-party imports. +import numpy +from scipy.io import loadmat +import matplotlib.pyplot as plt + +# All own imports. +from ccpi.framework import AcquisitionData, AcquisitionGeometry, ImageGeometry, ImageData +from ccpi.astra.ops import AstraProjectorMC +from ccpi.optimisation.algs import CGLS, FISTA +from ccpi.optimisation.funcs import Norm2sq, Norm1 + +# Load full data and permute to expected ordering. Change path as necessary. +# The loaded X has dims 80x60x80x150, which is pix x angle x pix x channel. +# Permute (numpy.transpose) puts into our default ordering which is +# (channel, angle, vertical, horizontal). + +pathname = '/media/jakob/050d8d45-fab3-4285-935f-260e6c5f162c1/Data/ColourBay/spectral_data_sets/CarbonPd/' +filename = 'carbonPd_full_sinogram_stripes_removed.mat' + +X = loadmat(pathname + filename) +X = numpy.transpose(X['SS'],(3,1,2,0)) + +# Store geometric variables for reuse +num_channels = X.shape[0] +num_pixels_h = X.shape[3] +num_pixels_v = X.shape[2] +num_angles = X.shape[1] + +# Display a single projection in a single channel +plt.imshow(X[100,5,:,:]) +plt.title('Example of a projection image in one channel' ) +plt.show() + +# Set angles to use +angles = numpy.linspace(-numpy.pi,numpy.pi,num_angles,endpoint=False) + +# Define full 3D acquisition geometry and data container. +# Geometric info is taken from the txt-file in the same dir as the mat-file +ag = AcquisitionGeometry('cone', + '3D', + angles, + pixel_num_h=num_pixels_h, + pixel_size_h=0.25, + pixel_num_v=num_pixels_v, + pixel_size_v=0.25, + dist_source_center=233.0, + dist_center_detector=245.0, + channels=num_channels) +data = AcquisitionData(X, geometry=ag) + +# Reduce to central slice by extracting relevant parameters from data and its +# geometry. Perhaps create function to extract central slice automatically? +data2d = data.subset(vertical=40) +ag2d = AcquisitionGeometry('cone', + '2D', + ag.angles, + pixel_num_h=ag.pixel_num_h, + pixel_size_h=ag.pixel_size_h, + pixel_num_v=1, + pixel_size_v=ag.pixel_size_h, + dist_source_center=ag.dist_source_center, + dist_center_detector=ag.dist_center_detector, + channels=ag.channels) +data2d.geometry = ag2d + +# Set up 2D Image Geometry. +# First need the geometric magnification to scale the voxel size relative +# to the detector pixel size. +mag = (ag.dist_source_center + ag.dist_center_detector)/ag.dist_source_center +ig2d = ImageGeometry(voxel_num_x=ag2d.pixel_num_h, + voxel_num_y=ag2d.pixel_num_h, + voxel_size_x=ag2d.pixel_size_h/mag, + voxel_size_y=ag2d.pixel_size_h/mag, + channels=X.shape[0]) + +# Create GPU multichannel projector/backprojector operator with ASTRA. +Aall = AstraProjectorMC(ig2d,ag2d,'gpu') + +# Compute and simple backprojction and display one channel as image. +Xbp = Aall.adjoint(data2d) +plt.imshow(Xbp.subset(channel=100).array) +plt.show() + +# Set initial guess ImageData with zeros for algorithms, and algorithm options. +x_init = ImageData(numpy.zeros((num_channels,num_pixels_v,num_pixels_h)), + geometry=ig2d, + dimension_labels=['channel','horizontal_y','horizontal_x']) +opt_CGLS = {'tol': 1e-4, 'iter': 5} + +# Run CGLS algorithm and display one channel. +x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aall, data2d, opt_CGLS) + +plt.imshow(x_CGLS.subset(channel=100).array) +plt.title('CGLS') +plt.show() + +plt.semilogy(criter_CGLS) +plt.title('CGLS Criterion vs iterations') +plt.show() + +# Create least squares object instance with projector, test data and a constant +# coefficient of 0.5. Note it is least squares over all channels. +f = Norm2sq(Aall,data2d,c=0.5) + +# Options for FISTA algorithm. +opt = {'tol': 1e-4, 'iter': 100} + +# Run FISTA for least squares without regularization and display one channel +# reconstruction as image. +x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt) + +plt.imshow(x_fista0.subset(channel=100).array) +plt.title('FISTA LS') +plt.show() + +plt.semilogy(criter0) +plt.title('FISTA LS Criterion vs iterations') +plt.show() + +# Set up 1-norm regularisation (over all channels), solve with FISTA, and +# display one channel of reconstruction. +lam = 0.1 +g0 = Norm1(lam) + +x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g0, opt) + +plt.imshow(x_fista1.subset(channel=100).array) +plt.title('FISTA LS+1') +plt.show() + +plt.semilogy(criter1) +plt.title('FISTA LS+1 Criterion vs iterations') +plt.show()
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