added demo from framework

1 files changed, 164 insertions, 0 deletions

diff --git a/Wrappers/Python/wip/demo_nexus.py b/Wrappers/Python/wip/demo_nexus.py
new file mode 100755
index 0000000..4dcc9f8
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+++ b/Wrappers/Python/wip/demo_nexus.py
@@ -0,0 +1,164 @@
+# -*- coding: utf-8 -*-

+"""

+Created on Wed Mar 21 14:26:21 2018

+

+@author: ofn77899

+"""

+

+from ccpi.framework import ImageData , AcquisitionData, ImageGeometry, AcquisitionGeometry

+from ccpi.optimisation.algs import FISTA, FBPD, CGLS

+from ccpi.optimisation.funcs import Norm2sq, Norm1

+from ccpi.reconstruction.ccpiops import CCPiProjectorSimple

+from ccpi.reconstruction.parallelbeam import alg as pbalg

+from ccpi.reconstruction.processors import CCPiForwardProjector, CCPiBackwardProjector , \

+Normalizer , CenterOfRotationFinder , AcquisitionDataPadder

+

+from ccpi.io.reader import NexusReader

+

+import numpy

+import matplotlib.pyplot as plt

+

+import os

+import pickle

+

+

+def avg_img(image):

+    shape = list(numpy.shape(image))

+    l = shape.pop(0)

+    avg = numpy.zeros(shape)

+    for i in range(l):

+        avg += image[i] / l

+    return avg

+    

+

+reader = NexusReader(os.path.join(".." ,".." ,".." , "data" , "24737_fd.nxs" ))

+

+dims = reader.get_projection_dimensions()

+print (dims)

+

+flat = avg_img(reader.load_flat())

+dark = avg_img(reader.load_dark())

+

+norm = Normalizer(flat_field=flat, dark_field=dark)

+

+norm.set_input(reader.get_acquisition_data())

+

+cor = CenterOfRotationFinder()

+cor.set_input(norm.get_output())

+center_of_rotation = cor.get_output()

+voxel_per_pixel = 1

+

+padder = AcquisitionDataPadder(center_of_rotation=center_of_rotation)

+padder.set_input(norm.get_output())

+padded_data = padder.get_output()

+

+pg = padded_data.geometry

+geoms = pbalg.pb_setup_geometry_from_acquisition(padded_data.as_array(),

+                                                pg.angles,

+                                                center_of_rotation,

+                                                voxel_per_pixel )

+vg = ImageGeometry(voxel_num_x=geoms['output_volume_x'],

+                   voxel_num_y=geoms['output_volume_y'], 

+                   voxel_num_z=geoms['output_volume_z'])

+#data = numpy.reshape(reader.getAcquisitionData())

+print ("define projector")

+Cop = CCPiProjectorSimple(vg, pg)

+# Create least squares object instance with projector and data.

+print ("Create least squares object instance with projector and data.")

+f = Norm2sq(Cop,padded_data,c=0.5)

+print ("Initial guess")

+# Initial guess

+x_init = ImageData(geometry=vg, dimension_labels=['horizontal_x','horizontal_y','vertical'])

+        

+#%%

+print ("run FISTA")

+# Run FISTA for least squares without regularization

+opt = {'tol': 1e-4, 'iter': 10}

+x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt=opt)

+pickle.dump(x_fista0, open("fista0.pkl", "wb"))

+

+

+plt.imshow(x_fista0.subset(horizontal_x=80).array)

+plt.title('FISTA0')

+#plt.show()

+

+# Now least squares plus 1-norm regularization

+lam = 0.1

+g0 = Norm1(lam)

+

+# Run FISTA for least squares plus 1-norm function.

+x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g0,opt=opt)

+pickle.dump(x_fista1, open("fista1.pkl", "wb"))

+

+plt.imshow(x_fista0.subset(horizontal_x=80).array)

+plt.title('FISTA1')

+#plt.show()

+

+plt.semilogy(criter1)

+#plt.show()

+

+# Run FBPD=Forward Backward Primal Dual method on least squares plus 1-norm

+x_fbpd1, it_fbpd1, timing_fbpd1, criter_fbpd1 = FBPD(x_init,None,f,g0,opt=opt)

+pickle.dump(x_fbpd1, open("fbpd1.pkl", "wb"))

+

+plt.imshow(x_fbpd1.subset(horizontal_x=80).array)

+plt.title('FBPD1')

+#plt.show()

+

+plt.semilogy(criter_fbpd1)

+#plt.show()

+

+# Run CGLS, which should agree with the FISTA0

+x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Cop, padded_data, opt=opt)

+pickle.dump(x_CGLS, open("cgls.pkl", "wb"))

+plt.imshow(x_CGLS.subset(horizontal_x=80).array)

+plt.title('CGLS')

+plt.title('CGLS recon, compare FISTA0')

+#plt.show()

+

+plt.semilogy(criter_CGLS)

+plt.title('CGLS criterion')

+#plt.show()

+

+

+cols = 4

+rows = 1

+current = 1

+fig = plt.figure()

+# projections row

+

+current = current 

+a=fig.add_subplot(rows,cols,current)

+a.set_title('FISTA0')

+imgplot = plt.imshow(x_fista0.subset(horizontal_x=80).as_array())

+

+current = current + 1

+a=fig.add_subplot(rows,cols,current)

+a.set_title('FISTA1')

+imgplot = plt.imshow(x_fista1.subset(horizontal_x=80).as_array())

+

+current = current + 1

+a=fig.add_subplot(rows,cols,current)

+a.set_title('FBPD1')

+imgplot = plt.imshow(x_fbpd1.subset(horizontal_x=80).as_array())

+

+current = current + 1

+a=fig.add_subplot(rows,cols,current)

+a.set_title('CGLS')

+imgplot = plt.imshow(x_CGLS.subset(horizontal_x=80).as_array())

+

+plt.show()

+

+

+#%%

+fig = plt.figure()

+# projections row

+b=fig.add_subplot(1,1,1)

+b.set_title('criteria')

+imgplot = plt.loglog(criter0 , label='FISTA0')

+imgplot = plt.loglog(criter1 , label='FISTA1')

+imgplot = plt.loglog(criter_fbpd1, label='FBPD1')

+imgplot = plt.loglog(criter_CGLS, label='CGLS')

+#imgplot = plt.loglog(criter_fbpdtv, label='FBPD TV')

+b.legend(loc='right')

+plt.show()
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