diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Python/wip/demo_simple_RGLTK.py | 247 |
1 files changed, 105 insertions, 142 deletions
diff --git a/Wrappers/Python/wip/demo_simple_RGLTK.py b/Wrappers/Python/wip/demo_simple_RGLTK.py index 9f0a4c3..3831603 100644 --- a/Wrappers/Python/wip/demo_simple_RGLTK.py +++ b/Wrappers/Python/wip/demo_simple_RGLTK.py @@ -1,214 +1,177 @@ +# This demo illustrates how the CCPi Regularisation Toolkit can be used +# as TV regularisation for use with the FISTA algorithm of the modular +# optimisation framework and compares with the FBPD TV implementation. + +# All own imports from ccpi.framework import ImageData , ImageGeometry, AcquisitionGeometry from ccpi.optimisation.algs import FISTA, FBPD, CGLS from ccpi.optimisation.funcs import Norm2sq, Norm1, TV2D from ccpi.astra.ops import AstraProjectorSimple from ccpi.plugins.regularisers import _ROF_TV_, _FGP_TV_ +# All external imports import numpy as np import matplotlib.pyplot as plt -test_case = 1 # 1=parallel2D, 2=cone2D +# Choose either a parallel-beam (1=parallel2D) or fan-beam (2=cone2D) test case +test_case = 1 -# Set up phantom +# Set up phantom size NxN by creating ImageGeometry, initialising the +# ImageData object with this geometry and empty array and finally put some +# data into its array, and display as image. N = 128 - - -vg = ImageGeometry(voxel_num_x=N,voxel_num_y=N) -Phantom = ImageData(geometry=vg) +ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N) +Phantom = ImageData(geometry=ig) x = Phantom.as_array() x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 -#plt.imshow(x) -#plt.show() +plt.imshow(x) +plt.title('Phantom image') +plt.show() -# Set up measurement geometry -angles_num = 20; # angles number +# Set up AcquisitionGeometry object to hold the parameters of the measurement +# setup geometry: # Number of angles, the actual angles from 0 to +# pi for parallel beam and 0 to 2pi for fanbeam, set the width of a detector +# pixel relative to an object pixel, the number of detector pixels, and the +# source-origin and origin-detector distance (here the origin-detector distance +# set to 0 to simulate a "virtual detector" with same detector pixel size as +# object pixel size). +angles_num = 20 +det_w = 1.0 +det_num = N +SourceOrig = 200 +OrigDetec = 0 if test_case==1: angles = np.linspace(0,np.pi,angles_num,endpoint=False) + ag = AcquisitionGeometry('parallel', + '2D', + angles, + det_num,det_w) elif test_case==2: angles = np.linspace(0,2*np.pi,angles_num,endpoint=False) + ag = AcquisitionGeometry('cone', + '2D', + angles, + det_num, + det_w, + dist_source_center=SourceOrig, + dist_center_detector=OrigDetec) else: NotImplemented -det_w = 1.0 -det_num = N -SourceOrig = 200 -OrigDetec = 0 +# Set up Operator object combining the ImageGeometry and AcquisitionGeometry +# wrapping calls to ASTRA as well as specifying whether to use CPU or GPU. +Aop = AstraProjectorSimple(ig, ag, 'cpu') -# Parallelbeam geometry test -if test_case==1: - pg = AcquisitionGeometry('parallel', - '2D', - angles, - det_num,det_w) -elif test_case==2: - pg = AcquisitionGeometry('cone', - '2D', - angles, - det_num, - det_w, - dist_source_center=SourceOrig, - dist_center_detector=OrigDetec) - -# ASTRA operator using volume and sinogram geometries -Aop = AstraProjectorSimple(vg, pg, 'cpu') - -# Unused old astra projector without geometry -# Aop_old = AstraProjector(det_w, det_num, SourceOrig, -# OrigDetec, angles, -# N,'fanbeam','gpu') - -# Try forward and backprojection +# Forward and backprojection are available as methods direct and adjoint. Here +# generate test data b and do simple backprojection to obtain z. b = Aop.direct(Phantom) -out2 = Aop.adjoint(b) +z = Aop.adjoint(b) -#plt.imshow(b.array) -#plt.show() +plt.imshow(b.array) +plt.title('Simulated data') +plt.show() -#plt.imshow(out2.array) -#plt.show() +plt.imshow(z.array) +plt.title('Backprojected data') +plt.show() # Create least squares object instance with projector and data. f = Norm2sq(Aop,b,c=0.5) # Initial guess -x_init = ImageData(np.zeros(x.shape),geometry=vg) -#%% -# FISTA with ROF-TV regularisation -g_rof = _ROF_TV_(lambdaReg = 10.0,iterationsTV=50,tolerance=1e-5,time_marchstep=0.01,device='cpu') +x_init = ImageData(np.zeros(x.shape),geometry=ig) -opt = {'tol': 1e-4, 'iter': 100} - -x_fista_rof, it1, timing1, criter_rof = FISTA(x_init, f, g_rof,opt) +# Set up FBPD algorithm for TV reconstruction and solve +opt_FBPD = {'tol': 1e-4, 'iter': 10000} -plt.figure() -plt.subplot(121) -plt.imshow(x_fista_rof.array,cmap="BuPu") -plt.title('FISTA-ROF-TV') -plt.subplot(122) -plt.semilogy(criter_rof) -plt.show() -#%% -# FISTA with FGP-TV regularisation -g_fgp = _FGP_TV_(lambdaReg = 10.0,iterationsTV=50,tolerance=1e-5,methodTV=0,nonnegativity=0,printing=0,device='cpu') +lamtv = 1.0 +gtv = TV2D(lamtv) -x_fista_fgp, it1, timing1, criter_fgp = FISTA(x_init, f, g_fgp,opt) +x_fbpdtv, it_fbpdtv, timing_fbpdtv, criter_fbpdtv = FBPD(x_init, + None, + f, + gtv, + opt=opt_FBPD) plt.figure() plt.subplot(121) -plt.imshow(x_fista_fgp.array,cmap="BuPu") -plt.title('FISTA-FGP-TV') +plt.imshow(x_fbpdtv.array) +plt.title('FBPD TV') plt.subplot(122) -plt.semilogy(criter_fgp) -plt.show() -#%% -# Run FISTA for least squares without regularization -x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt) - -plt.imshow(x_fista0.array) -plt.title('FISTA0') +plt.semilogy(criter_fbpdtv) 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) +# Set up the ROF variant of TV from the CCPi Regularisation Toolkit and run +# TV-reconstruction using FISTA +g_rof = _ROF_TV_(lambdaReg = lamtv, + iterationsTV=50, + tolerance=1e-5, + time_marchstep=0.01, + device='cpu') -plt.imshow(x_fista1.array) -plt.title('FISTA1') -plt.show() - -plt.semilogy(criter1) -plt.show() -#%% -# Run FBPD=Forward Backward Primal Dual method on least squares plus 1-norm opt = {'tol': 1e-4, 'iter': 100} -x_fbpd1, it_fbpd1, timing_fbpd1, criter_fbpd1 = FBPD(x_init,None,f,g0,opt=opt) - -plt.imshow(x_fbpd1.array) -plt.title('FBPD1') -plt.show() - -plt.semilogy(criter_fbpd1) -plt.show() -#%% -opt_FBPD = {'tol': 1e-4, 'iter': 10000} -# Now FBPD for least squares plus TV -lamtv = 10.0 -gtv = TV2D(lamtv) -x_fbpdtv, it_fbpdtv, timing_fbpdtv, criter_fbpdtv = FBPD(x_init,None,f,gtv,opt=opt_FBPD) - -plt.imshow(x_fbpdtv.array) -plt.show() +x_fista_rof, it1, timing1, criter_rof = FISTA(x_init, f, g_rof,opt) -plt.semilogy(criter_fbpdtv) +plt.figure() +plt.subplot(121) +plt.imshow(x_fista_rof.array) +plt.title('FISTA ROF TV') +plt.subplot(122) +plt.semilogy(criter_rof) plt.show() +# Repeat for FGP variant. +g_fgp = _FGP_TV_(lambdaReg = lamtv, + iterationsTV=50, + tolerance=1e-5, + methodTV=0, + nonnegativity=0, + printing=0, + device='cpu') -# Run CGLS, which should agree with the FISTA0 -x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, b, opt ) - -plt.imshow(x_CGLS.array) -plt.title('CGLS') -#plt.title('CGLS recon, compare FISTA0') -plt.show() +x_fista_fgp, it1, timing1, criter_fgp = FISTA(x_init, f, g_fgp,opt) -plt.semilogy(criter_CGLS) -plt.title('CGLS criterion') +plt.figure() +plt.subplot(121) +plt.imshow(x_fista_fgp.array) +plt.title('FISTA FGP TV') +plt.subplot(122) +plt.semilogy(criter_fgp) plt.show() -#%% +# Compare all reconstruction and criteria clims = (0,1) cols = 3 -rows = 2 +rows = 1 current = 1 fig = plt.figure() -# projections row -a=fig.add_subplot(rows,cols,current) -a.set_title('phantom {0}'.format(np.shape(Phantom.as_array()))) - -imgplot = plt.imshow(Phantom.as_array(),vmin=clims[0],vmax=clims[1]) - -current = current + 1 -a=fig.add_subplot(rows,cols,current) -a.set_title('FISTA0') -imgplot = plt.imshow(x_fista0.as_array(),vmin=clims[0],vmax=clims[1]) -current = current + 1 a=fig.add_subplot(rows,cols,current) -a.set_title('FISTA1') -imgplot = plt.imshow(x_fista1.as_array(),vmin=clims[0],vmax=clims[1]) +a.set_title('FBPD TV') +imgplot = plt.imshow(x_fbpdtv.as_array(),vmin=clims[0],vmax=clims[1]) current = current + 1 a=fig.add_subplot(rows,cols,current) -a.set_title('FBPD1') -imgplot = plt.imshow(x_fbpd1.as_array(),vmin=clims[0],vmax=clims[1]) +a.set_title('FISTA ROF TV') +imgplot = plt.imshow(x_fista_rof.as_array(),vmin=clims[0],vmax=clims[1]) current = current + 1 a=fig.add_subplot(rows,cols,current) -a.set_title('CGLS') -imgplot = plt.imshow(x_CGLS.as_array(),vmin=clims[0],vmax=clims[1]) - -#current = current + 1 -#a=fig.add_subplot(rows,cols,current) -#a.set_title('FBPD TV') -#imgplot = plt.imshow(x_fbpdtv.as_array(),vmin=clims[0],vmax=clims[1]) +a.set_title('FISTA FGP TV') +imgplot = plt.imshow(x_fista_fgp.as_array(),vmin=clims[0],vmax=clims[1]) 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') +imgplot = plt.loglog(criter_rof , label='ROF TV') +imgplot = plt.loglog(criter_fgp, label='FGP TV') b.legend(loc='right') plt.show() -#%% |