simple_demo tidied, TV not working

1 files changed, 120 insertions, 91 deletions

diff --git a/Wrappers/Python/wip/simple_demo_astra.py b/Wrappers/Python/wip/simple_demo_astra.py
index e9586b0..369bc99 100755
--- a/Wrappers/Python/wip/simple_demo_astra.py
+++ b/Wrappers/Python/wip/simple_demo_astra.py
@@ -1,189 +1,218 @@
-#import sys
-#sys.path.append("..")
 
+# This demo illustrates how ASTRA 2D projectors can be used with
+# the modular optimisation framework. The demo sets up a 2D test case and 
+# demonstrates reconstruction using CGLS, as well as FISTA for least squares 
+# and 1-norm regularisation and FBPD for 1-norm and TV 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 , TV2D
+from ccpi.optimisation.funcs import Norm2sq, Norm1, TV2D
 from ccpi.astra.astra_ops import AstraProjectorSimple
-
 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.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, 'gpu')
 
-# 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.title('Simulated data')
 plt.show()
 
-plt.imshow(out2.array)
+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)
+# 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:
+x_init = ImageData(np.zeros(x.shape),geometry=ig)
+opt = {'tol': 1e-4, 'iter': 1000}
+
+# First a CGLS reconstruction can be done:
+x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, b, opt)
+
+plt.imshow(x_CGLS.array)
+plt.title('CGLS')
+plt.show()
+
+plt.semilogy(criter_CGLS)
+plt.title('CGLS criterion')
+plt.show()
 
-# Initial guess
-x_init = ImageData(np.zeros(x.shape),geometry=vg)
+# 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(Aop,b,c=0.5)
 
 # Run FISTA for least squares without regularization
-x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None)
+x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None,opt)
 
 plt.imshow(x_fista0.array)
-plt.title('FISTA0')
+plt.title('FISTA Least squares')
+plt.show()
+
+plt.semilogy(criter0)
+plt.title('FISTA Least squares criterion')
 plt.show()
 
-# Now least squares plus 1-norm regularization
+# 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)
+x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g0, opt)
 
 plt.imshow(x_fista1.array)
-plt.title('FISTA1')
+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()
 
-# 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)
+# 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.
+opt_FBPD = {'tol': 1e-4, 'iter': 7000}
+x_fbpd1, it_fbpd1, timing_fbpd1, criter_fbpd1 = FBPD(x_init,None,f,g0,opt_FBPD)
 
 plt.imshow(x_fbpd1.array)
-plt.title('FBPD1')
+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()
 
-# Now FBPD for least squares plus TV
+# The FBPD algorithm can also be used conveniently for TV regularisation:
+
+# Specify TV function object
 #lamtv = 1
 #gtv = TV2D(lamtv)
-
-#x_fbpdtv, it_fbpdtv, timing_fbpdtv, criter_fbpdtv = FBPD(x_init,None,f,gtv,opt=opt)
-
+#
+#x_fbpdtv,it_fbpdtv,timing_fbpdtv,criter_fbpdtv=FBPD(x_init,None,f,gtv,opt_FBPD)
+#
 #plt.imshow(x_fbpdtv.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, Aop, b, opt )
-
-plt.imshow(x_CGLS.array)
-plt.title('CGLS')
-#plt.title('CGLS recon, compare FISTA0')
-plt.show()
-
-plt.semilogy(criter_CGLS)
-plt.title('CGLS criterion')
-plt.show()
-
-
-#%%
-
+# Compare all reconstruction and criteria
 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.as_array(),vmin=clims[0],vmax=clims[1])
+plt.axis('off')
 
 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])
+a.set_title('CGLS')
+imgplot = plt.imshow(x_CGLS.as_array(),vmin=clims[0],vmax=clims[1])
+plt.axis('off')
 
 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('FISTA LS')
+imgplot = plt.imshow(x_fista0.as_array(),vmin=clims[0],vmax=clims[1])
+plt.axis('off')
 
 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 LS+1')
+imgplot = plt.imshow(x_fista1.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.as_array(),vmin=clims[0],vmax=clims[1])
+a.set_title('FBPD LS+1')
+imgplot = plt.imshow(x_fbpd1.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 TV')
 #imgplot = plt.imshow(x_fbpdtv.as_array(),vmin=clims[0],vmax=clims[1])
+#plt.axis('off')
 
 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(criter0 , label='FISTA LS')
+imgplot = plt.loglog(criter1 , label='FISTA LS+1')
+imgplot = plt.loglog(criter_fbpd1, label='FBPD LS+1')
 #imgplot = plt.loglog(criter_fbpdtv, label='FBPD TV')
-b.legend(loc='right')
+b.legend(loc='lower left')
 plt.show()
-#%%
\ No newline at end of file