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# -*- 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()
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