diff options
| -rwxr-xr-x | Wrappers/Python/wip/test_reader_reconstr.py | 181 |
1 files changed, 0 insertions, 181 deletions
diff --git a/Wrappers/Python/wip/test_reader_reconstr.py b/Wrappers/Python/wip/test_reader_reconstr.py deleted file mode 100755 index 65a3381..0000000 --- a/Wrappers/Python/wip/test_reader_reconstr.py +++ /dev/null @@ -1,181 +0,0 @@ -# -*- 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'])
-#invL = 0.5
-#g = f.grad(x_init)
-#print (g)
-#u = x_init - invL*f.grad(x_init)
-
-#%%
-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()
-
-# Now FBPD for least squares plus TV
-#lamtv = 1
-#gtv = TV2D(lamtv)
-
-#x_fbpdtv, it_fbpdtv, timing_fbpdtv, criter_fbpdtv = FBPD(x_init,None,f,gtv,opt=opt)
-
-#plt.imshow(x_fbpdtv.subset(vertical=0).array)
-#plt.show()
-
-#plt.semilogy(criter_fbpdtv)
-#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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