diff options
Diffstat (limited to 'Wrappers/Python/wip')
| -rwxr-xr-x | Wrappers/Python/wip/demo_ccpi_simple.py | 222 | ||||
| -rwxr-xr-x | Wrappers/Python/wip/simple_demo_ccpi.py | 228 |
2 files changed, 222 insertions, 228 deletions
diff --git a/Wrappers/Python/wip/demo_ccpi_simple.py b/Wrappers/Python/wip/demo_ccpi_simple.py new file mode 100755 index 0000000..a8265ce --- /dev/null +++ b/Wrappers/Python/wip/demo_ccpi_simple.py @@ -0,0 +1,222 @@ + +# This demo illustrates how CCPi 2D parallel-beam projectors can be used with +# the modular optimisation framework. The demo sets up a small 4-slice 3D test +# case and demonstrates reconstruction using CGLS, as well as FISTA for least +# squares and 1-norm regularisation and FBPD for 1-norm regularisation. + +# First make all imports +from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry + +from ccpi.optimisation.algs import FISTA, FBPD, CGLS +from ccpi.optimisation.funcs import Norm2sq, Norm1 + +from ccpi.plugins.ops import CCPiProjectorSimple + +import numpy as np +import matplotlib.pyplot as plt + +# Set up phantom size N x N x vert by creating ImageGeometry, initialising the +# ImageData object with this geometry and empty array and finally put some +# data into its array, and display one slice as image. + +# Image parameters +N = 128 +vert = 4 + +# Set up image geometry +ig = ImageGeometry(voxel_num_x=N, + voxel_num_y=N, + voxel_num_z=vert) + +# Set up empty image data +Phantom = ImageData(geometry=ig, + dimension_labels=['horizontal_x', + 'horizontal_y', + 'vertical']) + +# Populate image data by looping over and filling slices +i = 0 +while i < vert: + if vert > 1: + x = Phantom.subset(vertical=i).array + else: + x = Phantom.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)] = 0.98 + if vert > 1 : + Phantom.fill(x, vertical=i) + i += 1 + +# Display slice of phantom +if vert > 1: + plt.imshow(Phantom.subset(vertical=0).as_array()) +else: + plt.imshow(Phantom.as_array()) +plt.show() + + +# 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, set the width of a detector +# pixel relative to an object pixe and the number of detector pixels. +angles_num = 20 +det_w = 1.0 +det_num = N + +angles = np.linspace(0,np.pi,angles_num,endpoint=False,dtype=np.float32)*\ + 180/np.pi + +# Inputs: Geometry, 2D or 3D, angles, horz detector pixel count, +# horz detector pixel size, vert detector pixel count, +# vert detector pixel size. +ag = AcquisitionGeometry('parallel', + '3D', + angles, + N, + det_w, + vert, + det_w) + +# Set up Operator object combining the ImageGeometry and AcquisitionGeometry +# wrapping calls to CCPi projector. +Cop = CCPiProjectorSimple(ig, ag) + +# Forward and backprojection are available as methods direct and adjoint. Here +# generate test data b and do simple backprojection to obtain z. Display all +# data slices as images, and a single backprojected slice. +b = Cop.direct(Phantom) +z = Cop.adjoint(b) + +for i in range(b.get_dimension_size('vertical')): + plt.imshow(b.subset(vertical=i).array) + plt.show() + +plt.imshow(z.subset(vertical=0).array) +plt.title('Backprojected data') +plt.show() + +# Using the test data b, different reconstruction methods can now be set up as +# demonstrated in the rest of this file. In general all methods need an initial +# guess and some algorithm options to be set. Note that 100 iterations for +# some of the methods is a very low number and 1000 or 10000 iterations may be +# needed if one wants to obtain a converged solution. +x_init = ImageData(geometry=ig, + dimension_labels=['horizontal_x','horizontal_y','vertical']) +opt = {'tol': 1e-4, 'iter': 100} + +# First a CGLS reconstruction can be done: +x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Cop, b, opt=opt) + +plt.imshow(x_CGLS.subset(vertical=0).array) +plt.title('CGLS') +plt.show() + +plt.semilogy(criter_CGLS) +plt.title('CGLS criterion') +plt.show() + +# CGLS solves the simple least-squares problem. The same problem can be solved +# by FISTA by setting up explicitly a least squares function object and using +# no regularisation: + +# Create least squares object instance with projector, test data and a constant +# coefficient of 0.5: +f = Norm2sq(Cop,b,c=0.5) + +# Run FISTA for least squares without regularization +x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt=opt) + +plt.imshow(x_fista0.subset(vertical=0).array) +plt.title('FISTA Least squares') +plt.show() + +plt.semilogy(criter0) +plt.title('FISTA Least squares criterion') +plt.show() + +# FISTA can also solve regularised forms by specifying a second function object +# such as 1-norm regularisation with choice of regularisation parameter lam: + +# Create 1-norm function object +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) + +plt.imshow(x_fista1.subset(vertical=0).array) +plt.title('FISTA Least squares plus 1-norm regularisation') +plt.show() + +plt.semilogy(criter1) +plt.title('FISTA Least squares plus 1-norm regularisation criterion') +plt.show() + +# The least squares plus 1-norm regularisation problem can also be solved by +# other algorithms such as the Forward Backward Primal Dual algorithm. This +# algorithm minimises the sum of three functions and the least squares and +# 1-norm functions should be given as the second and third function inputs. +# In this test case, this algorithm requires more iterations to converge, so +# new options are specified. +x_fbpd1, it_fbpd1, timing_fbpd1, criter_fbpd1 = FBPD(x_init,None,f,g0,opt=opt) + +plt.imshow(x_fbpd1.subset(vertical=0).array) +plt.title('FBPD for least squares plus 1-norm regularisation') +plt.show() + +plt.semilogy(criter_fbpd1) +plt.title('FBPD for least squares plus 1-norm regularisation criterion') +plt.show() + + +# Compare all reconstruction and criteria + +clims = (0,1) +cols = 3 +rows = 2 +current = 1 + +fig = plt.figure() +a=fig.add_subplot(rows,cols,current) +a.set_title('phantom {0}'.format(np.shape(Phantom.as_array()))) +imgplot = plt.imshow(Phantom.subset(vertical=0).as_array(), + vmin=clims[0],vmax=clims[1]) +plt.axis('off') + +current = current + 1 +a=fig.add_subplot(rows,cols,current) +a.set_title('CGLS') +imgplot = plt.imshow(x_CGLS.subset(vertical=0).as_array(), + vmin=clims[0],vmax=clims[1]) +plt.axis('off') + +current = current + 1 +a=fig.add_subplot(rows,cols,current) +a.set_title('FISTA LS') +imgplot = plt.imshow(x_fista0.subset(vertical=0).as_array(), + vmin=clims[0],vmax=clims[1]) +plt.axis('off') + +current = current + 1 +a=fig.add_subplot(rows,cols,current) +a.set_title('FISTA LS+1') +imgplot = plt.imshow(x_fista1.subset(vertical=0).as_array(), + vmin=clims[0],vmax=clims[1]) +plt.axis('off') + +current = current + 1 +a=fig.add_subplot(rows,cols,current) +a.set_title('FBPD LS+1') +imgplot = plt.imshow(x_fbpd1.subset(vertical=0).as_array(), + vmin=clims[0],vmax=clims[1]) +plt.axis('off') + +fig = plt.figure() +b=fig.add_subplot(1,1,1) +b.set_title('criteria') +imgplot = plt.loglog(criter_CGLS, label='CGLS') +imgplot = plt.loglog(criter0 , label='FISTA LS') +imgplot = plt.loglog(criter1 , label='FISTA LS+1') +imgplot = plt.loglog(criter_fbpd1, label='FBPD LS+1') +b.legend(loc='lower left') +plt.show()
\ No newline at end of file diff --git a/Wrappers/Python/wip/simple_demo_ccpi.py b/Wrappers/Python/wip/simple_demo_ccpi.py deleted file mode 100755 index 3fdc2d4..0000000 --- a/Wrappers/Python/wip/simple_demo_ccpi.py +++ /dev/null @@ -1,228 +0,0 @@ -#import sys -#sys.path.append("..") - -from ccpi.framework import ImageData , AcquisitionData, ImageGeometry, AcquisitionGeometry - -from ccpi.optimisation.algs import FISTA, FBPD, CGLS -from ccpi.optimisation.funcs import Norm2sq, Norm1 , TV2D - -from ccpi.plugins.ops import CCPiProjectorSimple -from ccpi.plugins.processors import CCPiForwardProjector, CCPiBackwardProjector -from ccpi.reconstruction.parallelbeam import alg as pbalg - -import numpy as np -import matplotlib.pyplot as plt - -test_case = 1 # 1=parallel2D, 2=cone2D, 3=parallel3D - -# Set up phantom -N = 128 -vert = 4 -# Set up measurement geometry -angles_num = 20; # angles number -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,dtype=np.float32)*180/np.pi - #nangles = angles_num - #angles = np.linspace(0,360, nangles, dtype=np.float32) - -elif test_case==2: - angles = np.linspace(0,2*np.pi,angles_num,endpoint=False) -elif test_case == 3: - angles = np.linspace(0,np.pi,angles_num,endpoint=False) -else: - NotImplemented - -vg = ImageGeometry(voxel_num_x=N, - voxel_num_y=N, - voxel_num_z=vert) - -Phantom = ImageData(geometry=vg,dimension_labels=['horizontal_x','horizontal_y','vertical']) + 0.1 - -i = 0 -while i < vert: - if vert > 1: - x = Phantom.subset(vertical=i).array - else: - x = Phantom.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)] = 0.98 - if vert > 1 : - Phantom.fill(x, vertical=i) - i += 1 - -if vert > 1: - plt.imshow(Phantom.subset(vertical=0).as_array()) -else: - plt.imshow(Phantom.as_array()) -plt.show() - - - -# Parallelbeam geometry test -if test_case==1: - #Phantom_ccpi = Phantom.subset(['horizontal_x','horizontal_y','vertical']) - #Phantom_ccpi.geometry = vg.clone() - center_of_rotation = Phantom.get_dimension_size('horizontal_x') / 2 - - pg = AcquisitionGeometry('parallel', - '3D', - angles, - N , det_w, - vert , det_w #2D in 3D is a slice 1 pixel thick - ) -elif test_case==2: - raise NotImplemented('cone beam projector not yet available') - pg = AcquisitionGeometry('cone', - '2D', - angles, - det_num, - det_w, - vert, det_w, #2D in 3D is a slice 1 pixel thick - dist_source_center=SourceOrig, - dist_center_detector=OrigDetec) - -# ASTRA operator using volume and sinogram geometries -#Aop = AstraProjectorSimple(vg, pg, 'cpu') -Cop = CCPiProjectorSimple(vg, pg) - -# Try forward and backprojection -b = Cop.direct(Phantom) -out2 = Cop.adjoint(b) - -#%% -for i in range(b.get_dimension_size('vertical')): - plt.imshow(b.subset(vertical=i).array) - #plt.imshow(Phantom.subset( vertical=i).array) - #plt.imshow(b.array[:,i,:]) - plt.show() -#%% - -plt.imshow(out2.subset( vertical=0).array) -plt.show() - -# Create least squares object instance with projector and data. -f = Norm2sq(Cop,b,c=0.5) - -# 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) - -#%% -# Run FISTA for least squares without regularization -opt = {'tol': 1e-4, 'iter': 100} -x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt=opt) - -plt.imshow(x_fista0.subset(vertical=0).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) - -plt.imshow(x_fista0.subset(vertical=0).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) - -plt.imshow(x_fbpd1.subset(vertical=0).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, b, opt=opt) - -plt.imshow(x_CGLS.subset(vertical=0).array) -plt.title('CGLS') -plt.title('CGLS recon, compare FISTA0') -plt.show() - -plt.semilogy(criter_CGLS) -plt.title('CGLS criterion') -plt.show() - - -#%% - -clims = (0,1) -cols = 3 -rows = 2 -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.subset(vertical=0).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.subset(vertical=0).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.subset(vertical=0).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.subset(vertical=0).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.subset(vertical=0).as_array(),vmin=clims[0],vmax=clims[1]) - -plt.show() -#%% -#current = current + 1 -#a=fig.add_subplot(rows,cols,current) -#a.set_title('FBPD TV') -#imgplot = plt.imshow(x_fbpdtv.subset(vertical=0).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') -b.legend(loc='right') -plt.show() -#%%
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