From 5c3621aeb63e6465f1884be81ebd12d8a39dcdbd Mon Sep 17 00:00:00 2001 From: epapoutsellis Date: Wed, 12 Jun 2019 10:44:43 +0100 Subject: delete old demos from wip --- Wrappers/Python/wip/Demos/PDHG_TGV_Tomo2D.py | 124 ------------ .../Python/wip/Demos/PDHG_TV_Denoising_Gaussian.py | 225 --------------------- .../wip/Demos/PDHG_TV_Denoising_Gaussian_3D.py | 155 -------------- .../Python/wip/Demos/PDHG_TV_Denoising_Poisson.py | 207 ------------------- .../wip/Demos/PDHG_TV_Denoising_SaltPepper.py | 198 ------------------ .../Python/wip/Demos/PDHG_Tikhonov_Denoising.py | 176 ---------------- Wrappers/Python/wip/Demos/fista_test.py | 127 ------------ 7 files changed, 1212 deletions(-) delete mode 100644 Wrappers/Python/wip/Demos/PDHG_TGV_Tomo2D.py delete mode 100644 Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian.py delete mode 100644 Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian_3D.py delete mode 100644 Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py delete mode 100644 Wrappers/Python/wip/Demos/PDHG_TV_Denoising_SaltPepper.py delete mode 100644 Wrappers/Python/wip/Demos/PDHG_Tikhonov_Denoising.py delete mode 100644 Wrappers/Python/wip/Demos/fista_test.py (limited to 'Wrappers') diff --git a/Wrappers/Python/wip/Demos/PDHG_TGV_Tomo2D.py b/Wrappers/Python/wip/Demos/PDHG_TGV_Tomo2D.py deleted file mode 100644 index 49d4db6..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_TGV_Tomo2D.py +++ /dev/null @@ -1,124 +0,0 @@ -# -*- 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, Identity, \ - SymmetrizedGradient, ZeroOperator -from ccpi.optimisation.functions import ZeroFunction, KullbackLeibler, \ - MixedL21Norm, BlockFunction - -from ccpi.astra.ops import AstraProjectorSimple - -# Create phantom for TV 2D tomography -N = 75 - -data = np.zeros((N,N)) - -x1 = np.linspace(0, int(N/2), N) -x2 = np.linspace(int(N/2), 0., N) -xv, yv = np.meshgrid(x1, x2) - -xv[int(N/4):int(3*N/4)-1, int(N/4):int(3*N/4)-1] = yv[int(N/4):int(3*N/4)-1, int(N/4):int(3*N/4)-1].T -data = xv -data = ImageData(data/data.max()) - -ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) - -detectors = N -angles = np.linspace(0, np.pi, N, dtype=np.float32) - -ag = AcquisitionGeometry('parallel','2D',angles, detectors) -Aop = AstraProjectorSimple(ig, ag, 'gpu') -sin = Aop.direct(data) - -# Create noisy data. Apply Poisson noise -scale = 0.1 -np.random.seed(5) -n1 = scale * np.random.poisson(sin.as_array()/scale) -noisy_data = AcquisitionData(n1, ag) - - -plt.imshow(noisy_data.as_array()) -plt.show() -#%% -# Regularisation Parameters -alpha = 0.7 -beta = 2 - -# Create Operators -op11 = Gradient(ig) -op12 = Identity(op11.range_geometry()) - -op22 = SymmetrizedGradient(op11.domain_geometry()) -op21 = ZeroOperator(ig, op22.range_geometry()) - -op31 = Aop -op32 = ZeroOperator(op22.domain_geometry(), ag) - -operator = BlockOperator(op11, -1*op12, op21, op22, op31, op32, shape=(3,2) ) - -f1 = alpha * MixedL21Norm() -f2 = beta * MixedL21Norm() -f3 = KullbackLeibler(noisy_data) -f = BlockFunction(f1, f2, f3) -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 = 50 -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()[0].as_array()) -plt.title('TGV Reconstruction') -plt.colorbar() -plt.show() -## -plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth') -plt.plot(np.linspace(0,N,N), pdhg.get_output()[0].as_array()[int(N/2),:], label = 'TGV reconstruction') -plt.legend() -plt.title('Middle Line Profiles') -plt.show() - - diff --git a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian.py b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian.py deleted file mode 100644 index 5df02b1..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian.py +++ /dev/null @@ -1,225 +0,0 @@ -#======================================================================== -# 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 Denoising using PDHG algorithm: - - min_{x} max_{y} < K x, y > + g(x) - f^{*}(y) - - -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: K = [ \nabla, - Identity] - - Method = 1: K = \nabla - - -""" - -from ccpi.framework import ImageData, ImageGeometry - -import numpy as np -import numpy -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 Data import * - -#%% - - -data = ImageData(plt.imread('camera.png')) - -# -## Create phantom for TV Gaussian denoising -#N = 200 -# -#data = np.zeros((N,N)) -#data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -#data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 -#data = ImageData(data) -#ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) -#ag = ig -# -# -# -## Replace with http://sipi.usc.edu/database/database.php?volume=misc&image=36#top - - - -# Create noisy data. Add Gaussian noise -np.random.seed(10) -noisy_data = ImageData( data.as_array() + np.random.normal(0, 0.05, size=ig.shape) ) - -# Show Ground Truth and Noisy Data -plt.figure(figsize=(15,15)) -plt.subplot(2,1,1) -plt.imshow(data.as_array()) -plt.title('Ground Truth') -plt.colorbar() -plt.subplot(2,1,2) -plt.imshow(noisy_data.as_array()) -plt.title('Noisy Data') -plt.colorbar() -plt.show() - -# Regularisation Parameter -alpha = 2 - -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) -pdhg.max_iteration = 3000 -pdhg.update_objective_interval = 200 -pdhg.run(3000, verbose=False) - -# Show Results -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,N,N), data.as_array()[int(N/2),:], label = 'GTruth') -plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'TV reconstruction') -plt.legend() -plt.title('Middle Line Profiles') -plt.show() - - -#%% 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 - u = Variable(ig.shape) - - 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(u), DY.matrix() * vec(u)]), 2, axis = 0)) - fidelity = 0.5 * sum_squares(u - noisy_data.as_array()) - - # choose solver - if 'MOSEK' in installed_solvers(): - solver = MOSEK - else: - solver = SCS - - obj = Minimize( regulariser + fidelity) - prob = Problem(obj) - result = prob.solve(verbose = True, solver = MOSEK) - - diff_cvx = numpy.abs( pdhg.get_output().as_array() - u.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(u.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), u.value[int(N/2),:], label = 'CVX') - plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'Truth') - - 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_Denoising_Gaussian_3D.py b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian_3D.py deleted file mode 100644 index c86ddc9..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian_3D.py +++ /dev/null @@ -1,155 +0,0 @@ -# -*- 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 - -import numpy as np -import numpy -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) - -fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(10, 8)) - -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() - diff --git a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py deleted file mode 100644 index 70f6b9b..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Poisson.py +++ /dev/null @@ -1,207 +0,0 @@ -# -*- 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 STFC, 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 - -# 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 Denoising using PDHG algorithm: - - min_{x} max_{y} < K x, y > + g(x) - f^{*}(y) - - -Problem: min_x, x>0 \alpha * ||\nabla x||_{1} + \int x - g * log(x) - - \nabla: Gradient operator - g: Noisy Data with Poisson Noise - \alpha: Regularization parameter - - Method = 0: K = [ \nabla, - Identity] - - Method = 1: K = \nabla - - -""" - -from ccpi.framework import ImageData, ImageGeometry - -import numpy as np -import numpy -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, KullbackLeibler, \ - MixedL21Norm, BlockFunction - -from skimage.util import random_noise - -# Create phantom for TV Poisson denoising -N = 100 - -data = np.zeros((N,N)) -data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 -data = ImageData(data) -ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) -ag = ig - -# Create noisy data. Apply Poisson noise -n1 = random_noise(data.as_array(), mode = 'poisson', seed = 10) -noisy_data = ImageData(n1) - -# Regularisation Parameter -alpha = 2 - -method = '1' - -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 = KullbackLeibler(noisy_data) - f = BlockFunction(f1, f2) - - g = ZeroFunction() - -else: - - # Without the "Block Framework" - operator = Gradient(ig) - f = alpha * MixedL21Norm() - g = KullbackLeibler(noisy_data) - - -# Compute operator Norm -normK = operator.norm() - -# Primal & dual stepsizes -sigma = 1 -tau = 1/(sigma*normK**2) -opt = {'niter':2000, 'memopt': True} - -# 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 = 50 - -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)) -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,N,N), data.as_array()[int(N/2),:], label = 'GTruth') -plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'TV reconstruction') -plt.legend() -plt.title('Middle Line Profiles') -plt.show() - - -#%% 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])) - - - - - diff --git a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_SaltPepper.py b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_SaltPepper.py deleted file mode 100644 index f5d4ce4..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_SaltPepper.py +++ /dev/null @@ -1,198 +0,0 @@ -# -*- 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. - -""" - -Total Variation Denoising using PDHG algorithm: - - min_{x} max_{y} < K x, y > + g(x) - f^{*}(y) - - -Problem: min_x, x>0 \alpha * ||\nabla x||_{1} + ||x-g||_{1} - - \nabla: Gradient operator - g: Noisy Data with Salt & Pepper Noise - \alpha: Regularization parameter - - Method = 0: K = [ \nabla, - Identity] - - Method = 1: K = \nabla - - -""" - -from ccpi.framework import ImageData, ImageGeometry - -import numpy as np -import numpy -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, L1Norm, \ - MixedL21Norm, BlockFunction - -from skimage.util import random_noise - -# Create phantom for TV Salt & Pepper denoising -N = 100 - -data = np.zeros((N,N)) -data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 -data = ImageData(data) -ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) -ag = ig - -# Create noisy data. Apply Salt & Pepper noise -n1 = random_noise(data.as_array(), mode = 's&p', salt_vs_pepper = 0.9, amount=0.2) -noisy_data = ImageData(n1) - -# Regularisation Parameter -alpha = 2 - -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 = L1Norm(b = noisy_data) - f = BlockFunction(f1, f2) - - g = ZeroFunction() - -else: - - # Without the "Block Framework" - operator = Gradient(ig) - f = alpha * MixedL21Norm() - g = L1Norm(b = noisy_data) - - -# Compute operator Norm -normK = operator.norm() - -# Primal & dual stepsizes -sigma = 1 -tau = 1/(sigma*normK**2) -opt = {'niter':2000, 'memopt': True} - -# 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 = 50 -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,N,N), data.as_array()[int(N/2),:], label = 'GTruth') -plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'TV reconstruction') -plt.legend() -plt.title('Middle Line Profiles') -plt.show() - - -##%% 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 - u = Variable(ig.shape) - - 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(u), DY.matrix() * vec(u)]), 2, axis = 0)) - fidelity = pnorm( u - noisy_data.as_array(),1) - - # choose solver - if 'MOSEK' in installed_solvers(): - solver = MOSEK - else: - solver = SCS - - obj = Minimize( regulariser + fidelity) - prob = Problem(obj) - result = prob.solve(verbose = True, solver = solver) - - diff_cvx = numpy.abs( pdhg.get_output().as_array() - u.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(u.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), u.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_Tikhonov_Denoising.py b/Wrappers/Python/wip/Demos/PDHG_Tikhonov_Denoising.py deleted file mode 100644 index 041d4ee..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_Tikhonov_Denoising.py +++ /dev/null @@ -1,176 +0,0 @@ -# -*- 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 - -import numpy as np -import numpy -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, BlockFunction - -from skimage.util import random_noise - -# Create phantom for TV Salt & Pepper denoising -N = 100 - -data = np.zeros((N,N)) -data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 -data = ImageData(data) -ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) -ag = ig - -# Create noisy data. Apply Salt & Pepper noise -n1 = random_noise(data.as_array(), mode = 'gaussian', mean=0, var = 0.05, seed=10) -noisy_data = ImageData(n1) - -# Regularisation Parameter -alpha = 4 - -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 * L2NormSquared() - f2 = 0.5 * L2NormSquared(b = noisy_data) - f = BlockFunction(f1, f2) - g = ZeroFunction() - -else: - - # Without the "Block Framework" - operator = Gradient(ig) - f = alpha * L2NormSquared() - g = 0.5 * L2NormSquared(b = noisy_data) - - -# Compute operator Norm -normK = operator.norm() - -# Primal & dual stepsizes -sigma = 1 -tau = 1/(sigma*normK**2) -opt = {'niter':2000, 'memopt': True} - -# 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 = 50 -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('Tikhonov Reconstruction') -plt.colorbar() -plt.show() -## -plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth') -plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'Tikhonov reconstruction') -plt.legend() -plt.title('Middle Line Profiles') -plt.show() - - -##%% 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 - u = Variable(ig.shape) - - 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_squares(norm(vstack([DX.matrix() * vec(u), DY.matrix() * vec(u)]), 2, axis = 0)) - fidelity = 0.5 * sum_squares(u - noisy_data.as_array()) - - # choose solver - if 'MOSEK' in installed_solvers(): - solver = MOSEK - else: - solver = SCS - - obj = Minimize( regulariser + fidelity) - prob = Problem(obj) - result = prob.solve(verbose = True, solver = solver) - - diff_cvx = numpy.abs( pdhg.get_output().as_array() - u.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(u.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), u.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/fista_test.py b/Wrappers/Python/wip/Demos/fista_test.py deleted file mode 100644 index dd1f6fa..0000000 --- a/Wrappers/Python/wip/Demos/fista_test.py +++ /dev/null @@ -1,127 +0,0 @@ -# -*- 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 - -import numpy as np -import numpy -import matplotlib.pyplot as plt - -from ccpi.optimisation.algorithms import FISTA, PDHG - -from ccpi.optimisation.operators import BlockOperator, Gradient, Identity -from ccpi.optimisation.functions import L2NormSquared, L1Norm, \ - MixedL21Norm, FunctionOperatorComposition, BlockFunction, ZeroFunction - -from skimage.util import random_noise - -# Create phantom for TV Gaussian denoising -N = 100 - -data = np.zeros((N,N)) -data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 -data = ImageData(data) -ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) -ag = ig - -# Create noisy data. Add Gaussian noise -n1 = random_noise(data.as_array(), mode = 's&p', salt_vs_pepper = 0.9, amount=0.2) -noisy_data = ImageData(n1) - -# Regularisation Parameter -alpha = 5 - -operator = Gradient(ig) - -#fidelity = L1Norm(b=noisy_data) -#regulariser = FunctionOperatorComposition(alpha * L2NormSquared(), operator) - -fidelity = FunctionOperatorComposition(alpha * MixedL21Norm(), operator) -regulariser = 0.5 * L2NormSquared(b = noisy_data) - -x_init = ig.allocate() - -## Setup and run the PDHG algorithm -opt = {'tol': 1e-4, 'memopt':True} -fista = FISTA(x_init=x_init , f=regulariser, g=fidelity, opt=opt) -fista.max_iteration = 2000 -fista.update_objective_interval = 50 -fista.run(2000, verbose=True) - -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(fista.get_output().as_array()) -plt.title('TV Reconstruction') -plt.colorbar() -plt.show() - -# Compare with PDHG -method = '0' -# -if method == '0': -# -# # Create operators - op1 = Gradient(ig) - op2 = Identity(ig, ag) -# -# # Create BlockOperator - operator = BlockOperator(op1, op2, shape=(2,1) ) - f = BlockFunction(alpha * L2NormSquared(), fidelity) - 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 = 50 -pdhg.run(2000) -# -#%% -plt.figure(figsize=(15,15)) -plt.subplot(3,1,1) -plt.imshow(fista.get_output().as_array()) -plt.title('FISTA') -plt.colorbar() -plt.subplot(3,1,2) -plt.imshow(pdhg.get_output().as_array()) -plt.title('PDHG') -plt.colorbar() -plt.subplot(3,1,3) -plt.imshow(np.abs(pdhg.get_output().as_array()-fista.get_output().as_array())) -plt.title('Diff FISTA-PDHG') -plt.colorbar() -plt.show() - - -- cgit v1.2.3