tv tomo 2D

2 files changed, 173 insertions, 0 deletions

diff --git a/Wrappers/Python/demos/PDHG_examples/Tomo/PDHG_TV_Tomo2D.py b/Wrappers/Python/demos/PDHG_examples/Tomo/PDHG_TV_Tomo2D.py
new file mode 100644
index 0000000..f179e95
--- /dev/null
+++ b/Wrappers/Python/demos/PDHG_examples/Tomo/PDHG_TV_Tomo2D.py
@@ -0,0 +1,173 @@
+#========================================================================
+# 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 2D Tomography Reconstruction using PDHG algorithm:
+
+
+Problem:     min_u  \alpha * ||\nabla u||_{2,1} + \frac{1}{2}||Au - g||^{2}
+             min_u, u>0  \alpha * ||\nabla u||_{2,1} + \int A u  - g log (Au + \eta)
+
+             \nabla: Gradient operator              
+             A: System Matrix
+             g: Noisy sinogram 
+             \eta: Background noise
+             
+             \alpha: Regularization parameter
+ 
+"""
+
+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, L2NormSquared, \
+                      MixedL21Norm, BlockFunction, KullbackLeibler, IndicatorBox
+                      
+from ccpi.astra.ops import AstraProjectorSimple
+from ccpi.framework import TestData
+from PIL import Image
+import os, sys
+if int(numpy.version.version.split('.')[1]) > 12:
+    from skimage.util import random_noise
+else:
+    from demoutil import random_noise
+
+import scipy.io
+
+# user supplied input
+if len(sys.argv) > 1:
+    which_noise = int(sys.argv[1])
+else:
+    which_noise = 1
+
+# Load 256 shepp-logan
+data256 = scipy.io.loadmat('phantom.mat')['phantom256']
+data = ImageData(numpy.array(Image.fromarray(data256).resize((256,256))))
+N, M = data.shape
+ig = ImageGeometry(voxel_num_x=N, voxel_num_y=M)
+
+# Add it to testdata or use tomophantom
+#loader = TestData(data_dir=os.path.join(sys.prefix, 'share','ccpi'))
+#data = loader.load(TestData.SIMPLE_PHANTOM_2D, size=(50, 50))
+#ig = data.geometry
+
+# Create acquisition data and geometry
+detectors = N
+angles = np.linspace(0, np.pi, 180)
+ag = AcquisitionGeometry('parallel','2D',angles, detectors)
+
+# Select device
+device = '0'
+#device = input('Available device: GPU==1 / CPU==0 ')
+if device=='1':
+    dev = 'gpu'
+else:
+    dev = 'cpu'
+    
+Aop = AstraProjectorSimple(ig, ag, dev)
+sin = Aop.direct(data)
+
+# Create noisy data. Apply Gaussian noise
+noises = ['gaussian', 'poisson']
+noise = noises[which_noise]
+
+if noise == 'poisson':
+    scale = 5
+    eta = 0
+    noisy_data = AcquisitionData(np.random.poisson( scale * (eta + sin.as_array()))/scale, ag)
+elif noise == 'gaussian':
+    n1 = np.random.normal(0, 1, size = ag.shape)
+    noisy_data = AcquisitionData(n1 + sin.as_array(), ag)
+else:
+    raise ValueError('Unsupported Noise ', noise)
+
+# Show Ground Truth and Noisy Data
+plt.figure(figsize=(10,10))
+plt.subplot(1,2,2)
+plt.imshow(data.as_array())
+plt.title('Ground Truth')
+plt.colorbar()
+plt.subplot(1,2,1)
+plt.imshow(noisy_data.as_array())
+plt.title('Noisy Data')
+plt.colorbar()
+plt.show()
+
+# Create operators
+op1 = Gradient(ig)
+op2 = Aop
+
+# Create BlockOperator
+operator = BlockOperator(op1, op2, shape=(2,1) ) 
+
+# Compute operator Norm
+normK = operator.norm()
+
+# Create functions
+if noise == 'poisson':
+    alpha = 3
+    f2 = KullbackLeibler(noisy_data)  
+    g =  IndicatorBox(lower=0) 
+    sigma = 1
+    tau = 1/(sigma*normK**2)    
+    
+elif noise == 'gaussian':   
+    alpha = 20
+    f2 = 0.5 * L2NormSquared(b=noisy_data)                                         
+    g = ZeroFunction()
+    sigma = 10
+    tau = 1/(sigma*normK**2)     
+    
+f1 = alpha * MixedL21Norm() 
+f = BlockFunction(f1, f2)    
+
+# Setup and run the PDHG algorithm
+pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma)
+pdhg.max_iteration = 2000
+pdhg.update_objective_interval = 200
+pdhg.run(2000)
+
+plt.figure(figsize=(15,15))
+plt.subplot(3,1,1)
+plt.imshow(data.as_array())
+plt.title('Ground Truth')
+plt.colorbar()
+plt.subplot(3,1,2)
+plt.imshow(noisy_data.as_array())
+plt.title('Noisy Data')
+plt.colorbar()
+plt.subplot(3,1,3)
+plt.imshow(pdhg.get_output().as_array())
+plt.title('TV Reconstruction')
+plt.colorbar()
+plt.show()
+plt.plot(np.linspace(0,ig.shape[1],ig.shape[1]), data.as_array()[int(N/2),:], label = 'GTruth')
+plt.plot(np.linspace(0,ig.shape[1],ig.shape[1]), pdhg.get_output().as_array()[int(N/2),:], label = 'TV reconstruction')
+plt.legend()
+plt.title('Middle Line Profiles')
+plt.show()
diff --git a/Wrappers/Python/demos/PDHG_examples/Tomo/phantom.mat b/Wrappers/Python/demos/PDHG_examples/Tomo/phantom.mat
new file mode 100755
index 0000000..c465bbe
--- /dev/null
+++ b/Wrappers/Python/demos/PDHG_examples/Tomo/phantom.mat