new phantom image

3 files changed, 373 insertions, 0 deletions

diff --git a/Wrappers/Python/data/shapes.png b/Wrappers/Python/data/shapes.png
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diff --git a/Wrappers/Python/demos/PDHG_examples/MultiChannel/PDHG_TV_Denoising_2D_time.py b/Wrappers/Python/demos/PDHG_examples/MultiChannel/PDHG_TV_Denoising_2D_time.py
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@@ -0,0 +1,192 @@
+#========================================================================
+# Copyright 2019 Science Technology Facilities Council
+# Copyright 2019 University of Manchester
+#
+# This work is part of the Core Imaging Library developed by Science Technology
+# Facilities Council and University of Manchester
+#
+# 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.txt
+#
+# 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, Identity
+from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \
+                      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 = 128 # 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)
+ag = ig
+                      
+# Create Noisy data. Add Gaussian noise
+np.random.seed(10)
+noisy_data = ImageData( data.as_array() + np.random.normal(0, 0.25, size=ig.shape) )
+
+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 = 0.3
+
+# Create operators
+#op1 = Gradient(ig)
+op1 = Gradient(ig, correlation='Space')
+op2 = Gradient(ig, correlation='SpaceChannels')
+
+op3 = Identity(ig, ag)
+
+# Create BlockOperator
+operator1 = BlockOperator(op1, op3, shape=(2,1) ) 
+operator2 = BlockOperator(op2, op3, shape=(2,1) )
+
+# Create functions
+      
+f1 = alpha * MixedL21Norm()
+f2 = 0.5 * L2NormSquared(b = noisy_data)    
+f = BlockFunction(f1, f2)  
+                                                                        
+g = ZeroFunction()
+    
+# Compute operator Norm
+normK1 = operator1.norm()
+normK2 = operator2.norm()
+
+#%%
+# Primal & dual stepsizes
+sigma1 = 1
+tau1 = 1/(sigma1*normK1**2)
+
+sigma2 = 1
+tau2 = 1/(sigma2*normK2**2)
+
+# Setup and run the PDHG algorithm
+pdhg1 = PDHG(f=f,g=g,operator=operator1, tau=tau1, sigma=sigma1)
+pdhg1.max_iteration = 2000
+pdhg1.update_objective_interval = 200
+pdhg1.run(2000)
+
+# Setup and run the PDHG algorithm
+pdhg2 = PDHG(f=f,g=g,operator=operator2, tau=tau2, sigma=sigma2)
+pdhg2.max_iteration = 2000
+pdhg2.update_objective_interval = 200
+pdhg2.run(2000)
+
+
+#%%
+
+tindex = [3, 6, 10]
+fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(10, 8))
+
+plt.subplot(3,3,1)
+plt.imshow(phantom_2Dt[tindex[0],:,:])
+plt.axis('off')
+plt.title('Time {}'.format(tindex[0]))
+
+plt.subplot(3,3,2)
+plt.imshow(phantom_2Dt[tindex[1],:,:])
+plt.axis('off')
+plt.title('Time {}'.format(tindex[1]))
+
+plt.subplot(3,3,3)
+plt.imshow(phantom_2Dt[tindex[2],:,:])
+plt.axis('off')
+plt.title('Time {}'.format(tindex[2]))
+
+plt.subplot(3,3,4)
+plt.imshow(pdhg1.get_output().as_array()[tindex[0],:,:])
+plt.axis('off')
+plt.subplot(3,3,5)
+plt.imshow(pdhg1.get_output().as_array()[tindex[1],:,:])
+plt.axis('off')
+plt.subplot(3,3,6)
+plt.imshow(pdhg1.get_output().as_array()[tindex[2],:,:])
+plt.axis('off')
+
+
+plt.subplot(3,3,7)
+plt.imshow(pdhg2.get_output().as_array()[tindex[0],:,:])
+plt.axis('off')
+plt.subplot(3,3,8)
+plt.imshow(pdhg2.get_output().as_array()[tindex[1],:,:])
+plt.axis('off')
+plt.subplot(3,3,9)
+plt.imshow(pdhg2.get_output().as_array()[tindex[2],:,:])
+plt.axis('off')
+
+#%%
+im = plt.imshow(pdhg1.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/demos/PDHG_examples/MultiChannel/PDHG_TV_Denoising_Gaussian_3D.py b/Wrappers/Python/demos/PDHG_examples/MultiChannel/PDHG_TV_Denoising_Gaussian_3D.py
new file mode 100644
index 0000000..03dc2ef
--- /dev/null
+++ b/Wrappers/Python/demos/PDHG_examples/MultiChannel/PDHG_TV_Denoising_Gaussian_3D.py
@@ -0,0 +1,181 @@
+#========================================================================
+# Copyright 2019 Science Technology Facilities Council
+# Copyright 2019 University of Manchester
+#
+# This work is part of the Core Imaging Library developed by Science Technology
+# Facilities Council and University of Manchester
+#
+# 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.txt
+#
+# 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.
+#
+#=========================================================================
+""" 
+
+Total Variation (3D) Denoising using PDHG algorithm:
+
+
+Problem:     min_{x} \alpha * ||\nabla x||_{2,1} + \frac{1}{2} * || x - g ||_{2}^{2}
+
+             \alpha: Regularization parameter
+             
+             \nabla: Gradient operator 
+             
+             g: Noisy Data with Gaussian Noise
+                          
+             Method = 0 ( PDHG - split ) :  K = [ \nabla,
+                                                 Identity]
+                          
+                                                                    
+             Method = 1 (PDHG - explicit ):  K = \nabla  
+                                                                
+"""
+
+from ccpi.framework import ImageData, ImageGeometry
+                         
+import matplotlib.pyplot as plt
+
+from ccpi.optimisation.algorithms import PDHG
+
+from ccpi.optimisation.operators import BlockOperator, Identity, Gradient
+from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \
+                      MixedL21Norm, BlockFunction
+
+from skimage.util import random_noise
+
+# Create phantom for TV Gaussian denoising
+import timeit
+import os
+from tomophantom import TomoP3D
+import tomophantom
+
+print ("Building 3D phantom using TomoPhantom software")
+tic=timeit.default_timer()
+model = 13 # select a model number from the library
+N = 64 # Define phantom dimensions using a scalar value (cubic phantom)
+path = os.path.dirname(tomophantom.__file__)
+path_library3D = os.path.join(path, "Phantom3DLibrary.dat")
+
+#This will generate a N x N x N phantom (3D)
+phantom_tm = TomoP3D.Model(model, N, path_library3D)
+
+#%%
+
+# Create noisy data. Add Gaussian noise
+ig = ImageGeometry(voxel_num_x=N, voxel_num_y=N, voxel_num_z=N)
+ag = ig
+n1 = random_noise(phantom_tm, mode = 'gaussian', mean=0, var = 0.001, seed=10)
+noisy_data = ImageData(n1)
+
+sliceSel = int(0.5*N)
+plt.figure(figsize=(15,15))
+plt.subplot(3,1,1)
+plt.imshow(noisy_data.as_array()[sliceSel,:,:],vmin=0, vmax=1)
+plt.title('Axial View')
+plt.colorbar()
+plt.subplot(3,1,2)
+plt.imshow(noisy_data.as_array()[:,sliceSel,:],vmin=0, vmax=1)
+plt.title('Coronal View')
+plt.colorbar()
+plt.subplot(3,1,3)
+plt.imshow(noisy_data.as_array()[:,:,sliceSel],vmin=0, vmax=1)
+plt.title('Sagittal View')
+plt.colorbar()
+plt.show()   
+
+#%%
+
+# Regularisation Parameter
+alpha = 0.05
+
+method = '0'
+
+if method == '0':
+
+    # Create operators
+    op1 = Gradient(ig)
+    op2 = Identity(ig, ag)
+
+    # Create BlockOperator
+    operator = BlockOperator(op1, op2, shape=(2,1) ) 
+
+    # Create functions
+      
+    f1 = alpha * MixedL21Norm()
+    f2 = 0.5 * L2NormSquared(b = noisy_data)    
+    f = BlockFunction(f1, f2)  
+                                      
+    g = ZeroFunction()
+    
+else:
+    
+    # Without the "Block Framework"
+    operator = Gradient(ig)
+    f =  alpha * MixedL21Norm()
+    g =  0.5 * L2NormSquared(b = noisy_data)
+        
+    
+# 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, verbose = True)
+
+
+#%%
+fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(10, 8))
+fig.suptitle('TV Reconstruction',fontsize=20)
+
+
+plt.subplot(2,3,1)
+plt.imshow(noisy_data.as_array()[sliceSel,:,:],vmin=0, vmax=1)
+plt.axis('off')
+plt.title('Axial View')
+
+plt.subplot(2,3,2)
+plt.imshow(noisy_data.as_array()[:,sliceSel,:],vmin=0, vmax=1)
+plt.axis('off')
+plt.title('Coronal View')
+
+plt.subplot(2,3,3)
+plt.imshow(noisy_data.as_array()[:,:,sliceSel],vmin=0, vmax=1)
+plt.axis('off')
+plt.title('Sagittal View')
+
+
+plt.subplot(2,3,4)
+plt.imshow(pdhg.get_output().as_array()[sliceSel,:,:],vmin=0, vmax=1)
+plt.axis('off')
+plt.subplot(2,3,5)
+plt.imshow(pdhg.get_output().as_array()[:,sliceSel,:],vmin=0, vmax=1)
+plt.axis('off')
+plt.subplot(2,3,6)
+plt.imshow(pdhg.get_output().as_array()[:,:,sliceSel],vmin=0, vmax=1)
+plt.axis('off')
+im = plt.imshow(pdhg.get_output().as_array()[:,:,sliceSel],vmin=0, vmax=1)
+
+
+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()
+