Merge branch 'demo_ccpi' of https://github.com/vais-ral/CCPi-Framework into demo_ccpi

5 files changed, 253 insertions, 71 deletions

diff --git a/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py b/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py
index bd48e13..c923a30 100755
--- a/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py
+++ b/Wrappers/Python/ccpi/optimisation/algorithms/Algorithm.py
@@ -149,14 +149,12 @@ class Algorithm(object):
         i = 0
         
         for _ in self:
-            if verbose and (self.iteration -1) % self.update_objective_interval == 0:
-                print ("Iteration {}/{}, = {}".format(self.iteration-1, 
+            if (self.iteration -1) % self.update_objective_interval == 0:                
+                if verbose:
+                    print ("Iteration {}/{}, = {}".format(self.iteration-1, 
                        self.max_iteration, self.get_last_objective()) )
-                
-                
-            else:
                 if callback is not None:
-                    callback(self.iteration, self.get_last_objective(), self.x)
+                    callback(self.iteration -1, self.get_last_objective(), self.x)
             i += 1
             if i == iterations:
                 break
diff --git a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
index eb26596..8740376 100644
--- a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
+++ b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py
@@ -136,7 +136,13 @@ class L2NormSquared(Function):
                         
         '''
         
-        return ScaledFunction(self, scalar)        
+        return ScaledFunction(self, scalar)  
+
+
+    def operator_composition(self, operator):
+        
+        return FunctionOperatorComposition
+      
 
 
 if __name__ == '__main__':
diff --git a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py
index 482b3b4..ccdabb2 100644
--- a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py
+++ b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py
@@ -87,7 +87,17 @@ opt = {'niter':2000, 'memopt': True}
 pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True)
 pdhg.max_iteration = 2000
 pdhg.update_objective_interval = 50
-pdhg.run(2000)
+
+def pdgap_objectives(niter, objective, solution):
+    
+
+     print( "{:04}/{:04} {:<5} {:.4f} {:<5} {:.4f} {:<5} {:.4f}".\
+                      format(niter, pdhg.max_iteration,'', \
+                             objective[0],'',\
+                             objective[1],'',\
+                             objective[2]))
+
+pdhg.run(2000, callback = pdgap_objectives)
 
 
 plt.figure(figsize=(15,15))
@@ -112,66 +122,65 @@ plt.title('Middle Line Profiles')
 plt.show()
 
 
-##%% Check with CVX solution
 #%% Check with CVX solution
 
-from ccpi.optimisation.operators import SparseFiniteDiff
-
-try:
-    from cvxpy import *
-    cvx_not_installable = True
-except ImportError:
-    cvx_not_installable = False
-
-
-if cvx_not_installable:
-
-    ##Construct problem    
-    u1 = Variable(ig.shape)
-    q = Variable()
-    
-    DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann')
-    DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann')
-    
-    # Define Total Variation as a regulariser
-    regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u1), DY.matrix() * vec(u1)]), 2, axis = 0))
-    
-    fidelity = sum( u1 - multiply(noisy_data.as_array(), log(u1)) )
-    constraints = [q>= fidelity, u1>=0]
-        
-    solver = ECOS
-    obj =  Minimize( regulariser +  q)
-    prob = Problem(obj, constraints)
-    result = prob.solve(verbose = True, solver = solver)
-
-    
-    diff_cvx = numpy.abs( pdhg.get_output().as_array() - u1.value )
-        
-    plt.figure(figsize=(15,15))
-    plt.subplot(3,1,1)
-    plt.imshow(pdhg.get_output().as_array())
-    plt.title('PDHG solution')
-    plt.colorbar()
-    plt.subplot(3,1,2)
-    plt.imshow(u1.value)
-    plt.title('CVX solution')
-    plt.colorbar()
-    plt.subplot(3,1,3)
-    plt.imshow(diff_cvx)
-    plt.title('Difference')
-    plt.colorbar()
-    plt.show()    
-    
-    plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG')
-    plt.plot(np.linspace(0,N,N), u1.value[int(N/2),:], label = 'CVX')
-    plt.legend()
-    plt.title('Middle Line Profiles')
-    plt.show()
-            
-    print('Primal Objective (CVX) {} '.format(obj.value))
-    print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0]))
-
-
-
-
-
+#from ccpi.optimisation.operators import SparseFiniteDiff
+#
+#try:
+#    from cvxpy import *
+#    cvx_not_installable = True
+#except ImportError:
+#    cvx_not_installable = False
+#
+#
+#if cvx_not_installable:
+#
+#    ##Construct problem    
+#    u1 = Variable(ig.shape)
+#    q = Variable()
+#    
+#    DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann')
+#    DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann')
+#    
+#    # Define Total Variation as a regulariser
+#    regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u1), DY.matrix() * vec(u1)]), 2, axis = 0))
+#    
+#    fidelity = sum( u1 - multiply(noisy_data.as_array(), log(u1)) )
+#    constraints = [q>= fidelity, u1>=0]
+#        
+#    solver = ECOS
+#    obj =  Minimize( regulariser +  q)
+#    prob = Problem(obj, constraints)
+#    result = prob.solve(verbose = True, solver = solver)
+#
+#    
+#    diff_cvx = numpy.abs( pdhg.get_output().as_array() - u1.value )
+#        
+#    plt.figure(figsize=(15,15))
+#    plt.subplot(3,1,1)
+#    plt.imshow(pdhg.get_output().as_array())
+#    plt.title('PDHG solution')
+#    plt.colorbar()
+#    plt.subplot(3,1,2)
+#    plt.imshow(u1.value)
+#    plt.title('CVX solution')
+#    plt.colorbar()
+#    plt.subplot(3,1,3)
+#    plt.imshow(diff_cvx)
+#    plt.title('Difference')
+#    plt.colorbar()
+#    plt.show()    
+#    
+#    plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG')
+#    plt.plot(np.linspace(0,N,N), u1.value[int(N/2),:], label = 'CVX')
+#    plt.legend()
+#    plt.title('Middle Line Profiles')
+#    plt.show()
+#            
+#    print('Primal Objective (CVX) {} '.format(obj.value))
+#    print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0]))
+#
+#
+#
+#
+#
diff --git a/Wrappers/Python/wip/Demos/PDHG_TV_Tomo2D_time.py b/Wrappers/Python/wip/Demos/PDHG_TV_Tomo2D_time.py
new file mode 100644
index 0000000..045458a
--- /dev/null
+++ b/Wrappers/Python/wip/Demos/PDHG_TV_Tomo2D_time.py
@@ -0,0 +1,169 @@
+# -*- coding: utf-8 -*-
+#   This work is part of the Core Imaging Library developed by
+#   Visual Analytics and Imaging System Group of the Science Technology
+#   Facilities Council, STFC
+
+#   Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca
+
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+
+#       http://www.apache.org/licenses/LICENSE-2.0
+
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, AcquisitionData
+
+import numpy as np 
+import numpy                          
+import matplotlib.pyplot as plt
+
+from ccpi.optimisation.algorithms import PDHG
+
+from ccpi.optimisation.operators import BlockOperator, Gradient
+from ccpi.optimisation.functions import ZeroFunction, KullbackLeibler, \
+                      MixedL21Norm, BlockFunction
+
+from ccpi.astra.ops import AstraProjectorMC
+
+import os
+import tomophantom
+from tomophantom import TomoP2D
+
+# Create phantom for TV 2D dynamic tomography 
+
+model = 102  # note that the selected model is temporal (2D + time)
+N = 50 # set dimension of the phantom
+# one can specify an exact path to the parameters file
+# path_library2D = '../../../PhantomLibrary/models/Phantom2DLibrary.dat'
+path = os.path.dirname(tomophantom.__file__)
+path_library2D = os.path.join(path, "Phantom2DLibrary.dat")
+#This will generate a N_size x N_size x Time frames phantom (2D + time)
+phantom_2Dt = TomoP2D.ModelTemporal(model, N, path_library2D)
+
+plt.close('all')
+plt.figure(1)
+plt.rcParams.update({'font.size': 21})
+plt.title('{}''{}'.format('2D+t phantom using model no.',model))
+for sl in range(0,np.shape(phantom_2Dt)[0]):
+    im = phantom_2Dt[sl,:,:]
+    plt.imshow(im, vmin=0, vmax=1)
+    plt.pause(.1)
+    plt.draw
+
+    
+ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N, channels = np.shape(phantom_2Dt)[0])
+data = ImageData(phantom_2Dt, geometry=ig)
+
+detectors = N
+angles = np.linspace(0,np.pi,N)
+
+ag = AcquisitionGeometry('parallel','2D', angles, detectors, channels = np.shape(phantom_2Dt)[0])
+Aop = AstraProjectorMC(ig, ag, 'gpu')
+sin = Aop.direct(data)
+
+scale = 2
+n1 = scale * np.random.poisson(sin.as_array()/scale)
+noisy_data = AcquisitionData(n1, ag)
+
+tindex = [3, 6, 10]
+
+fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 10))
+plt.subplot(1,3,1)
+plt.imshow(noisy_data.as_array()[tindex[0],:,:])
+plt.axis('off')
+plt.title('Time {}'.format(tindex[0]))
+plt.subplot(1,3,2)
+plt.imshow(noisy_data.as_array()[tindex[1],:,:])
+plt.axis('off')
+plt.title('Time {}'.format(tindex[1]))
+plt.subplot(1,3,3)
+plt.imshow(noisy_data.as_array()[tindex[2],:,:])
+plt.axis('off')
+plt.title('Time {}'.format(tindex[2]))
+
+fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,
+                    wspace=0.02, hspace=0.02)
+
+plt.show()   
+
+#%%
+# Regularisation Parameter
+alpha = 5
+
+# Create operators
+#op1 = Gradient(ig)
+op1 = Gradient(ig, correlation='SpaceChannels')
+op2 = Aop
+
+# Create BlockOperator
+operator = BlockOperator(op1, op2, shape=(2,1) ) 
+
+# Create functions
+      
+f1 = alpha * MixedL21Norm()
+f2 = KullbackLeibler(noisy_data)    
+f = BlockFunction(f1, f2)  
+                                      
+g = ZeroFunction()
+    
+# Compute operator Norm
+normK = operator.norm()
+
+# Primal & dual stepsizes
+sigma = 1
+tau = 1/(sigma*normK**2)
+
+
+# Setup and run the PDHG algorithm
+pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True)
+pdhg.max_iteration = 2000
+pdhg.update_objective_interval = 200
+pdhg.run(2000)
+
+
+#%%
+fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(10, 8))
+
+plt.subplot(2,3,1)
+plt.imshow(phantom_2Dt[tindex[0],:,:],vmin=0, vmax=1)
+plt.axis('off')
+plt.title('Time {}'.format(tindex[0]))
+
+plt.subplot(2,3,2)
+plt.imshow(phantom_2Dt[tindex[1],:,:],vmin=0, vmax=1)
+plt.axis('off')
+plt.title('Time {}'.format(tindex[1]))
+
+plt.subplot(2,3,3)
+plt.imshow(phantom_2Dt[tindex[2],:,:],vmin=0, vmax=1)
+plt.axis('off')
+plt.title('Time {}'.format(tindex[2]))
+
+
+plt.subplot(2,3,4)
+plt.imshow(pdhg.get_output().as_array()[tindex[0],:,:])
+plt.axis('off')
+plt.subplot(2,3,5)
+plt.imshow(pdhg.get_output().as_array()[tindex[1],:,:])
+plt.axis('off')
+plt.subplot(2,3,6)
+plt.imshow(pdhg.get_output().as_array()[tindex[2],:,:])
+plt.axis('off')
+im = plt.imshow(pdhg.get_output().as_array()[tindex[0],:,:])
+
+
+fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,
+                    wspace=0.02, hspace=0.02)
+
+cb_ax = fig.add_axes([0.83, 0.1, 0.02, 0.8])
+cbar = fig.colorbar(im, cax=cb_ax)
+
+
+plt.show()
+
diff --git a/Wrappers/Python/wip/pdhg_TV_tomography2D_time.py b/Wrappers/Python/wip/pdhg_TV_tomography2D_time.py
index dea8e5c..5423b22 100644
--- a/Wrappers/Python/wip/pdhg_TV_tomography2D_time.py
+++ b/Wrappers/Python/wip/pdhg_TV_tomography2D_time.py
@@ -16,7 +16,7 @@ import matplotlib.pyplot as plt
 from ccpi.optimisation.algorithms import PDHG, PDHG_old
 
 from ccpi.optimisation.operators import BlockOperator, Identity, Gradient
-from ccpi.optimisation.functions import ZeroFun, L2NormSquared, \
+from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \
                       MixedL21Norm, BlockFunction, ScaledFunction
 
 from ccpi.astra.ops import AstraProjectorSimple, AstraProjectorMC
@@ -100,7 +100,7 @@ operator = BlockOperator(op1, op2, shape=(2,1) )
 alpha = 50
 f = BlockFunction( alpha * MixedL21Norm(), \
                    0.5 * L2NormSquared(b = noisy_data) )
-g = ZeroFun()
+g = ZeroFunction()
 
 # Compute operator Norm
 normK = operator.norm()