diff options
| author | Edoardo Pasca <edo.paskino@gmail.com> | 2019-07-02 10:50:59 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2019-07-02 10:50:59 +0100 |
| commit | d63f2e879162b32936556fa59bb9f89cb5d5f645 (patch) | |
| tree | 992acfbc335fcc292c5d69f62fc7491c64a0a011 | |
| parent | c890ffb05a36fde36093a19b35921c5a2d84b2a3 (diff) | |
| parent | d27e46190afe9d7901afcd1442a798b7382a01f0 (diff) | |
| download | framework-d63f2e879162b32936556fa59bb9f89cb5d5f645.tar.gz framework-d63f2e879162b32936556fa59bb9f89cb5d5f645.tar.bz2 framework-d63f2e879162b32936556fa59bb9f89cb5d5f645.tar.xz framework-d63f2e879162b32936556fa59bb9f89cb5d5f645.zip | |
Merge pull request #339 from vais-ral/remove_wip
removed wip directory
35 files changed, 0 insertions, 6593 deletions
diff --git a/Wrappers/Python/wip/CGLS_tikhonov.py b/Wrappers/Python/wip/CGLS_tikhonov.py deleted file mode 100644 index e9bbcd9..0000000 --- a/Wrappers/Python/wip/CGLS_tikhonov.py +++ /dev/null @@ -1,196 +0,0 @@ -from ccpi.optimisation.algorithms import CGLS - -from ccpi.plugins.ops import CCPiProjectorSimple -from ccpi.optimisation.ops import PowerMethodNonsquare -from ccpi.optimisation.ops import TomoIdentity -from ccpi.optimisation.funcs import Norm2sq, Norm1 -from ccpi.framework import ImageGeometry, AcquisitionGeometry, ImageData, AcquisitionData -from ccpi.optimisation.algorithms import GradientDescent -#from ccpi.optimisation.algorithms import CGLS -import matplotlib.pyplot as plt -import numpy -from ccpi.framework import BlockDataContainer -from ccpi.optimisation.operators import BlockOperator - -# Set up phantom size N x N x vert by creating ImageGeometry, initialising the -# ImageData object with this geometry and empty array and finally put some -# data into its array, and display one slice as image. - -# Image parameters -N = 128 -vert = 4 - -# Set up image geometry -ig = ImageGeometry(voxel_num_x=N, - voxel_num_y=N, - voxel_num_z=vert) - -# Set up empty image data -Phantom = ImageData(geometry=ig, - dimension_labels=['horizontal_x', - 'horizontal_y', - 'vertical']) -Phantom += 0.05 -# Populate image data by looping over and filling slices -i = 0 -while i < vert: - if vert > 1: - x = Phantom.subset(vertical=i).array - else: - x = Phantom.array - x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 - x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 0.94 - if vert > 1 : - Phantom.fill(x, vertical=i) - i += 1 - - -perc = 0.02 -# Set up empty image data -noise = ImageData(numpy.random.normal(loc = 0.04 , - scale = perc , - size = Phantom.shape), geometry=ig, - dimension_labels=['horizontal_x', - 'horizontal_y', - 'vertical']) -Phantom += noise - -# Set up AcquisitionGeometry object to hold the parameters of the measurement -# setup geometry: # Number of angles, the actual angles from 0 to -# pi for parallel beam, set the width of a detector -# pixel relative to an object pixe and the number of detector pixels. -angles_num = 20 -det_w = 1.0 -det_num = N - -angles = numpy.linspace(0,numpy.pi,angles_num,endpoint=False,dtype=numpy.float32)*\ - 180/numpy.pi - -# Inputs: Geometry, 2D or 3D, angles, horz detector pixel count, -# horz detector pixel size, vert detector pixel count, -# vert detector pixel size. -ag = AcquisitionGeometry('parallel', - '3D', - angles, - N, - det_w, - vert, - det_w) - -# Set up Operator object combining the ImageGeometry and AcquisitionGeometry -# wrapping calls to CCPi projector. -A = CCPiProjectorSimple(ig, ag) - -# Forward and backprojection are available as methods direct and adjoint. Here -# generate test data b and some noise - -b = A.direct(Phantom) - - -#z = A.adjoint(b) - - -# Using the test data b, different reconstruction methods can now be set up as -# demonstrated in the rest of this file. In general all methods need an initial -# guess and some algorithm options to be set. Note that 100 iterations for -# some of the methods is a very low number and 1000 or 10000 iterations may be -# needed if one wants to obtain a converged solution. -x_init = ImageData(geometry=ig, - dimension_labels=['horizontal_x','horizontal_y','vertical']) -X_init = BlockDataContainer(x_init) -B = BlockDataContainer(b, - ImageData(geometry=ig, dimension_labels=['horizontal_x','horizontal_y','vertical'])) - -# setup a tomo identity -Ibig = 1e5 * TomoIdentity(geometry=ig) -Ismall = 1e-5 * TomoIdentity(geometry=ig) -Iok = 1e1 * TomoIdentity(geometry=ig) - -# composite operator -Kbig = BlockOperator(A, Ibig, shape=(2,1)) -Ksmall = BlockOperator(A, Ismall, shape=(2,1)) -Kok = BlockOperator(A, Iok, shape=(2,1)) - -#out = K.direct(X_init) - -f = Norm2sq(Kbig,B) -f.L = 0.00003 - -fsmall = Norm2sq(Ksmall,B) -fsmall.L = 0.00003 - -fok = Norm2sq(Kok,B) -fok.L = 0.00003 - -simplef = Norm2sq(A, b) -simplef.L = 0.00003 - -gd = GradientDescent( x_init=x_init, objective_function=simplef, - rate=simplef.L) -gd.max_iteration = 50 - -Kbig.direct(X_init) -Kbig.adjoint(B) -cg = CGLS() -cg.set_up(X_init, Kbig, B ) -cg.max_iteration = 10 - -cgsmall = CGLS() -cgsmall.set_up(X_init, Ksmall, B ) -cgsmall.max_iteration = 10 - - -cgs = CGLS() -cgs.set_up(x_init, A, b ) -cgs.max_iteration = 10 - -cgok = CGLS() -cgok.set_up(X_init, Kok, B ) -cgok.max_iteration = 10 -# # -#out.__isub__(B) -#out2 = K.adjoint(out) - -#(2.0*self.c)*self.A.adjoint( self.A.direct(x) - self.b ) - -for _ in gd: - print ("iteration {} {}".format(gd.iteration, gd.get_last_loss())) - -cg.run(10, lambda it,val: print ("iteration {} objective {}".format(it,val))) - -cgs.run(10, lambda it,val: print ("iteration {} objective {}".format(it,val))) - -cgsmall.run(10, lambda it,val: print ("iteration {} objective {}".format(it,val))) -cgsmall.run(10, lambda it,val: print ("iteration {} objective {}".format(it,val))) -cgok.run(10, verbose=True) -# # for _ in cg: -# print ("iteration {} {}".format(cg.iteration, cg.get_current_loss())) -# # -# # fig = plt.figure() -# # plt.imshow(cg.get_output().get_item(0,0).subset(vertical=0).as_array()) -# # plt.title('Composite CGLS') -# # plt.show() -# # -# # for _ in cgs: -# print ("iteration {} {}".format(cgs.iteration, cgs.get_current_loss())) -# # -fig = plt.figure() -plt.subplot(2,3,1) -plt.imshow(Phantom.subset(vertical=0).as_array()) -plt.title('Simulated Phantom') -plt.subplot(2,3,2) -plt.imshow(gd.get_output().subset(vertical=0).as_array()) -plt.title('Simple Gradient Descent') -plt.subplot(2,3,3) -plt.imshow(cgs.get_output().subset(vertical=0).as_array()) -plt.title('Simple CGLS') -plt.subplot(2,3,5) -plt.imshow(cg.get_output().get_item(0).subset(vertical=0).as_array()) -plt.title('Composite CGLS\nbig lambda') -plt.subplot(2,3,6) -plt.imshow(cgsmall.get_output().get_item(0).subset(vertical=0).as_array()) -plt.title('Composite CGLS\nsmall lambda') -plt.subplot(2,3,4) -plt.imshow(cgok.get_output().get_item(0).subset(vertical=0).as_array()) -plt.title('Composite CGLS\nok lambda') -plt.show() diff --git a/Wrappers/Python/wip/CreatePhantom.py b/Wrappers/Python/wip/CreatePhantom.py deleted file mode 100644 index 4bf6ea4..0000000 --- a/Wrappers/Python/wip/CreatePhantom.py +++ /dev/null @@ -1,242 +0,0 @@ -import numpy -import tomophantom -from tomophantom import TomoP3D -from tomophantom.supp.artifacts import ArtifactsClass as Artifact -import os - -import matplotlib.pyplot as plt - -from ccpi.optimisation.algorithms import CGLS -from ccpi.plugins.ops import CCPiProjectorSimple -from ccpi.optimisation.ops import PowerMethodNonsquare -from ccpi.optimisation.ops import TomoIdentity -from ccpi.optimisation.funcs import Norm2sq, Norm1 -from ccpi.framework import ImageGeometry, AcquisitionGeometry, ImageData, AcquisitionData -from ccpi.optimisation.algorithms import GradientDescent -from ccpi.framework import BlockDataContainer -from ccpi.optimisation.operators import BlockOperator - - -model = 13 # select a model number from tomophantom library -N_size = 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_size x N_size x N_size phantom (3D) -phantom_tm = TomoP3D.Model(model, N_size, path_library3D) - -# detector column count (horizontal) -detector_horiz = int(numpy.sqrt(2)*N_size) -# detector row count (vertical) (no reason for it to be > N) -detector_vert = N_size -# number of projection angles -angles_num = int(0.5*numpy.pi*N_size) -# angles are expressed in degrees -angles = numpy.linspace(0.0, 179.9, angles_num, dtype='float32') - - -acquisition_data_array = TomoP3D.ModelSino(model, N_size, - detector_horiz, detector_vert, - angles, - path_library3D) - -tomophantom_acquisition_axes_order = ['vertical', 'angle', 'horizontal'] - -artifacts = Artifact(acquisition_data_array) - - -tp_acq_data = AcquisitionData(artifacts.noise(0.2, 'Gaussian'), - dimension_labels=tomophantom_acquisition_axes_order) -#print ("size", acquisition_data.shape) -print ("horiz", detector_horiz) -print ("vert", detector_vert) -print ("angles", angles_num) - -tp_acq_geometry = AcquisitionGeometry(geom_type='parallel', dimension='3D', - angles=angles, - pixel_num_h=detector_horiz, - pixel_num_v=detector_vert, - channels=1, - ) - -acq_data = tp_acq_geometry.allocate() -#print (tp_acq_geometry) -print ("AcquisitionData", acq_data.shape) -print ("TomoPhantom", tp_acq_data.shape, tp_acq_data.dimension_labels) - -default_acquisition_axes_order = ['angle', 'vertical', 'horizontal'] - -acq_data2 = tp_acq_data.subset(dimensions=default_acquisition_axes_order) -print ("AcquisitionData", acq_data2.shape, acq_data2.dimension_labels) -print ("AcquisitionData {} TomoPhantom {}".format(id(acq_data2.as_array()), - id(acquisition_data_array))) - -fig = plt.figure() -plt.subplot(1,2,1) -plt.imshow(acquisition_data_array[20]) -plt.title('Sinogram') -plt.subplot(1,2,2) -plt.imshow(tp_acq_data.as_array()[20]) -plt.title('Sinogram + noise') -plt.show() - -# Set up Operator object combining the ImageGeometry and AcquisitionGeometry -# wrapping calls to CCPi projector. - -ig = ImageGeometry(voxel_num_x=detector_horiz, - voxel_num_y=detector_horiz, - voxel_num_z=detector_vert) -A = CCPiProjectorSimple(ig, tp_acq_geometry) -# Forward and backprojection are available as methods direct and adjoint. Here -# generate test data b and some noise - -#b = A.direct(Phantom) -b = acq_data2 - -#z = A.adjoint(b) - - -# Using the test data b, different reconstruction methods can now be set up as -# demonstrated in the rest of this file. In general all methods need an initial -# guess and some algorithm options to be set. Note that 100 iterations for -# some of the methods is a very low number and 1000 or 10000 iterations may be -# needed if one wants to obtain a converged solution. -x_init = ImageData(geometry=ig, - dimension_labels=['horizontal_x','horizontal_y','vertical']) -X_init = BlockDataContainer(x_init) -B = BlockDataContainer(b, - ImageData(geometry=ig, dimension_labels=['horizontal_x','horizontal_y','vertical'])) - -# setup a tomo identity -Ibig = 4e1 * TomoIdentity(geometry=ig) -Ismall = 1e-3 * TomoIdentity(geometry=ig) -Iok = 7.6e0 * TomoIdentity(geometry=ig) - -# composite operator -Kbig = BlockOperator(A, Ibig, shape=(2,1)) -Ksmall = BlockOperator(A, Ismall, shape=(2,1)) -Kok = BlockOperator(A, Iok, shape=(2,1)) - -#out = K.direct(X_init) -#x0 = x_init.copy() -#x0.fill(numpy.random.randn(*x0.shape)) -#lipschitz = PowerMethodNonsquare(A, 5, x0) -#print("lipschitz", lipschitz) - -#%% - -simplef = Norm2sq(A, b, memopt=False) -#simplef.L = lipschitz[0]/3000. -simplef.L = 0.00003 - -f = Norm2sq(Kbig,B) -f.L = 0.00003 - -fsmall = Norm2sq(Ksmall,B) -fsmall.L = 0.00003 - -fok = Norm2sq(Kok,B) -fok.L = 0.00003 - -print("setup gradient descent") -gd = GradientDescent( x_init=x_init, objective_function=simplef, - rate=simplef.L) -gd.max_iteration = 5 -simplef2 = Norm2sq(A, b, memopt=True) -#simplef.L = lipschitz[0]/3000. -simplef2.L = 0.00003 -print("setup gradient descent") -gd2 = GradientDescent( x_init=x_init, objective_function=simplef2, - rate=simplef2.L) -gd2.max_iteration = 5 - -Kbig.direct(X_init) -Kbig.adjoint(B) -print("setup CGLS") -cg = CGLS() -cg.set_up(X_init, Kbig, B ) -cg.max_iteration = 10 - -print("setup CGLS") -cgsmall = CGLS() -cgsmall.set_up(X_init, Ksmall, B ) -cgsmall.max_iteration = 10 - - -print("setup CGLS") -cgs = CGLS() -cgs.set_up(x_init, A, b ) -cgs.max_iteration = 10 - -print("setup CGLS") -cgok = CGLS() -cgok.set_up(X_init, Kok, B ) -cgok.max_iteration = 10 -# # -#out.__isub__(B) -#out2 = K.adjoint(out) - -#(2.0*self.c)*self.A.adjoint( self.A.direct(x) - self.b ) - - -for _ in gd: - print ("GradientDescent iteration {} {}".format(gd.iteration, gd.get_last_loss())) -#gd2.run(5,verbose=True) -print("CGLS block lambda big") -cg.run(10, lambda it,val: print ("CGLS big iteration {} objective {}".format(it,val))) - -print("CGLS standard") -cgs.run(10, lambda it,val: print ("CGLS standard iteration {} objective {}".format(it,val))) - -print("CGLS block lambda small") -cgsmall.run(10, lambda it,val: print ("CGLS small iteration {} objective {}".format(it,val))) -print("CGLS block lambdaok") -cgok.run(10, verbose=True) -# # for _ in cg: -# print ("iteration {} {}".format(cg.iteration, cg.get_current_loss())) -# # -# # fig = plt.figure() -# # plt.imshow(cg.get_output().get_item(0,0).subset(vertical=0).as_array()) -# # plt.title('Composite CGLS') -# # plt.show() -# # -# # for _ in cgs: -# print ("iteration {} {}".format(cgs.iteration, cgs.get_current_loss())) -# # -Phantom = ImageData(phantom_tm) - -theslice=40 - -fig = plt.figure() -plt.subplot(2,3,1) -plt.imshow(numpy.flip(Phantom.subset(vertical=theslice).as_array(),axis=0), cmap='gray') -plt.clim(0,0.7) -plt.title('Simulated Phantom') -plt.subplot(2,3,2) -plt.imshow(gd.get_output().subset(vertical=theslice).as_array(), cmap='gray') -plt.clim(0,0.7) -plt.title('Simple Gradient Descent') -plt.subplot(2,3,3) -plt.imshow(cgs.get_output().subset(vertical=theslice).as_array(), cmap='gray') -plt.clim(0,0.7) -plt.title('Simple CGLS') -plt.subplot(2,3,5) -plt.imshow(cg.get_output().get_item(0).subset(vertical=theslice).as_array(), cmap='gray') -plt.clim(0,0.7) -plt.title('Composite CGLS\nbig lambda') -plt.subplot(2,3,6) -plt.imshow(cgsmall.get_output().get_item(0).subset(vertical=theslice).as_array(), cmap='gray') -plt.clim(0,0.7) -plt.title('Composite CGLS\nsmall lambda') -plt.subplot(2,3,4) -plt.imshow(cgok.get_output().get_item(0).subset(vertical=theslice).as_array(), cmap='gray') -plt.clim(0,0.7) -plt.title('Composite CGLS\nok lambda') -plt.show() - - -#Ibig = 7e1 * TomoIdentity(geometry=ig) -#Kbig = BlockOperator(A, Ibig, shape=(2,1)) -#cg2 = CGLS(x_init=X_init, operator=Kbig, data=B) -#cg2.max_iteration = 10 -#cg2.run(10, verbose=True) diff --git a/Wrappers/Python/wip/Demos/FISTA_vs_CGLS.py b/Wrappers/Python/wip/Demos/FISTA_vs_CGLS.py deleted file mode 100644 index 2dcaa89..0000000 --- a/Wrappers/Python/wip/Demos/FISTA_vs_CGLS.py +++ /dev/null @@ -1,119 +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 FISTA, CGLS - -from ccpi.optimisation.operators import Gradient -from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, FunctionOperatorComposition -from skimage.util import random_noise -from ccpi.astra.ops import AstraProjectorSimple - -#%% - -N = 75 -x = np.zeros((N,N)) -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -data = ImageData(x) -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, 'cpu') -sin = Aop.direct(data) - -noisy_data = sin - -fidelity = FunctionOperatorComposition(L2NormSquared(b=noisy_data), Aop) -regularizer = ZeroFunction() - -x_init = ig.allocate() - -## Setup and run the FISTA algorithm -opt = {'tol': 1e-4, 'memopt':True} -fista = FISTA(x_init=x_init , f=fidelity, g=regularizer, opt=opt) -fista.max_iteration = 500 -fista.update_objective_interval = 50 -fista.run(500, verbose=True) - -## Setup and run the CGLS algorithm -cgls = CGLS(x_init=x_init, operator=Aop, data=noisy_data) -cgls.max_iteration = 500 -cgls.update_objective_interval = 50 -cgls.run(500, verbose=True) - -diff = fista.get_output() - cgls.get_output() - - -#%% -print( diff.norm()) - -plt.figure(figsize=(15,15)) -plt.subplot(3,1,1) -plt.imshow(fista.get_output().as_array()) -plt.title('FISTA reconstruction') -plt.colorbar() -plt.subplot(3,1,2) -plt.imshow(cgls.get_output().as_array()) -plt.title('CGLS reconstruction') -plt.colorbar() -plt.subplot(3,1,3) -plt.imshow(diff.abs().as_array()) -plt.title('Difference reconstruction') -plt.colorbar() -plt.show() - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/Wrappers/Python/wip/Demos/FISTA_vs_PDHG.py b/Wrappers/Python/wip/Demos/FISTA_vs_PDHG.py deleted file mode 100644 index b7777ef..0000000 --- a/Wrappers/Python/wip/Demos/FISTA_vs_PDHG.py +++ /dev/null @@ -1,120 +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) - -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 -# 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() - - diff --git a/Wrappers/Python/wip/Demos/IMAT_Reconstruction/TV_WhiteBeam_reconstruction.py b/Wrappers/Python/wip/Demos/IMAT_Reconstruction/TV_WhiteBeam_reconstruction.py deleted file mode 100644 index e67bdb1..0000000 --- a/Wrappers/Python/wip/Demos/IMAT_Reconstruction/TV_WhiteBeam_reconstruction.py +++ /dev/null @@ -1,164 +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 ImageGeometry, AcquisitionGeometry, AcquisitionData -from astropy.io import fits -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, L2NormSquared,\ - MixedL21Norm, BlockFunction - -from ccpi.astra.ops import AstraProjectorSimple - - -# load IMAT file -#filename_sino = '/media/newhd/shared/DataProcessed/IMAT_beamtime_Feb_2019/preprocessed_test_flat/sino/rebin_slice_350/sino_log_rebin_141.fits' -filename_sino = '/media/newhd/shared/DataProcessed/IMAT_beamtime_Feb_2019/preprocessed_test_flat/sino/rebin_slice_350/sino_log_rebin_564.fits' - -sino_handler = fits.open(filename_sino) -sino_tmp = numpy.array(sino_handler[0].data, dtype=float) -# reorder sino coordinate: channels, angles, detectors -sinogram = numpy.rollaxis(sino_tmp, 2) -sino_handler.close() -#%% -# white beam data -sinogram_wb = sinogram.sum(axis=0) - -pixh = sinogram_wb.shape[1] # detectors -pixv = sinogram_wb.shape[1] # detectors - -# WhiteBeam Geometry -igWB = ImageGeometry(voxel_num_x = pixh, voxel_num_y = pixv) - -# Load Golden angles -with open("golden_angles.txt") as f: - angles_string = [line.rstrip() for line in f] - angles = numpy.array(angles_string).astype(float) -agWB = AcquisitionGeometry('parallel', '2D', angles * numpy.pi / 180, pixh) -op_WB = AstraProjectorSimple(igWB, agWB, 'gpu') -sinogram_aqdata = AcquisitionData(sinogram_wb, agWB) - -# BackProjection -result_bp = op_WB.adjoint(sinogram_aqdata) - -plt.imshow(result_bp.subset(channel=50).array) -plt.title('BackProjection') -plt.show() - - - -#%% - -# Regularisation Parameter -alpha = 2000 - -# Create operators -op1 = Gradient(igWB) -op2 = op_WB - -# Create BlockOperator -operator = BlockOperator(op1, op2, shape=(2,1) ) - -# Create functions - -f1 = alpha * MixedL21Norm() -f2 = KullbackLeibler(sinogram_aqdata) -#f2 = L2NormSquared(b = sinogram_aqdata) -f = BlockFunction(f1, f2) - -g = ZeroFunction() - -diag_precon = False - -if diag_precon: - - def tau_sigma_precond(operator): - - tau = 1/operator.sum_abs_row() - sigma = 1/ operator.sum_abs_col() - - return tau, sigma - - tau, sigma = tau_sigma_precond(operator) - -else: - # Compute operator Norm - normK = operator.norm() - print ("normK", normK) - # Primal & dual stepsizes - sigma = 0.1 - tau = 1/(sigma*normK**2) - -#%% - - -## Primal & dual stepsizes -#sigma = 0.1 -#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 = 10000 -pdhg.update_objective_interval = 500 - -def circ_mask(h, w, center=None, radius=None): - - if center is None: # use the middle of the image - center = [int(w/2), int(h/2)] - if radius is None: # use the smallest distance between the center and image walls - radius = min(center[0], center[1], w-center[0], h-center[1]) - - Y, X = numpy.ogrid[:h, :w] - dist_from_center = numpy.sqrt((X - center[0])**2 + (Y-center[1])**2) - - mask = dist_from_center <= radius - return mask - -def show_result(niter, objective, solution): - - mask = circ_mask(pixh, pixv, center=None, radius = 220) # 55 with 141, - plt.imshow(solution.as_array() * mask) - plt.colorbar() - plt.title("Iter: {}".format(niter)) - plt.show() - - - print( "{:04}/{:04} {:<5} {:.4f} {:<5} {:.4f} {:<5} {:.4f}".\ - format(niter, pdhg.max_iteration,'', \ - objective[0],'',\ - objective[1],'',\ - objective[2])) - -pdhg.run(10000, callback = show_result) - -#%% - -mask = circ_mask(pixh, pixv, center=None, radius = 210) # 55 with 141, -plt.figure(figsize=(15,15)) -plt.subplot(3,1,1) -plt.imshow(pdhg.get_output().as_array() * mask) -plt.title('Ground Truth') -plt.colorbar() -plt.show() diff --git a/Wrappers/Python/wip/Demos/IMAT_Reconstruction/golden_angles.txt b/Wrappers/Python/wip/Demos/IMAT_Reconstruction/golden_angles.txt deleted file mode 100644 index 95ce73a..0000000 --- a/Wrappers/Python/wip/Demos/IMAT_Reconstruction/golden_angles.txt +++ /dev/null @@ -1,186 +0,0 @@ -0 -0.9045 -1.809 -2.368 -3.2725 -4.736 -5.6405 -6.1995 -7.104 -8.5675 -9.472 -10.9356 -11.8401 -12.3991 -13.3036 -14.7671 -15.6716 -16.2306 -17.1351 -18.0396 -18.5986 -19.5031 -20.9666 -21.8711 -22.4301 -23.3346 -24.7981 -25.7026 -27.1661 -28.0706 -28.6297 -29.5342 -30.9977 -31.9022 -32.4612 -33.3657 -34.8292 -35.7337 -37.1972 -38.1017 -38.6607 -39.5652 -41.0287 -41.9332 -42.4922 -43.3967 -44.3012 -44.8602 -45.7647 -47.2283 -48.1328 -48.6918 -49.5963 -51.0598 -51.9643 -53.4278 -54.3323 -54.8913 -55.7958 -57.2593 -58.1638 -58.7228 -59.6273 -60.5318 -61.0908 -61.9953 -63.4588 -64.3633 -64.9224 -65.8269 -67.2904 -68.1949 -69.6584 -70.5629 -71.1219 -72.0264 -73.4899 -74.3944 -74.9534 -75.8579 -77.3214 -78.2259 -79.6894 -80.5939 -81.1529 -82.0574 -83.521 -84.4255 -84.9845 -85.889 -86.7935 -87.3525 -88.257 -89.7205 -90.625 -91.184 -92.0885 -93.552 -94.4565 -95.92 -96.8245 -97.3835 -98.288 -99.7516 -100.656 -101.215 -102.12 -103.583 -104.488 -105.951 -106.856 -107.415 -108.319 -109.783 -110.687 -111.246 -112.151 -113.055 -113.614 -114.519 -115.982 -116.887 -117.446 -118.35 -119.814 -120.718 -122.182 -123.086 -123.645 -124.55 -126.013 -126.918 -127.477 -128.381 -129.286 -129.845 -130.749 -132.213 -133.117 -133.676 -134.581 -136.044 -136.949 -138.412 -139.317 -139.876 -140.78 -142.244 -143.148 -143.707 -144.612 -146.075 -146.98 -148.443 -149.348 -149.907 -150.811 -152.275 -153.179 -153.738 -154.643 -155.547 -156.106 -157.011 -158.474 -159.379 -159.938 -160.842 -162.306 -163.21 -164.674 -165.578 -166.137 -167.042 -168.505 -169.41 -169.969 -170.873 -172.337 -173.242 -174.705 -175.609 -176.168 -177.073 -178.536 -179.441 diff --git a/Wrappers/Python/wip/Demos/LeastSq_CGLS_FISTA_PDHG.py b/Wrappers/Python/wip/Demos/LeastSq_CGLS_FISTA_PDHG.py deleted file mode 100644 index 97c71ba..0000000 --- a/Wrappers/Python/wip/Demos/LeastSq_CGLS_FISTA_PDHG.py +++ /dev/null @@ -1,154 +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 - -import numpy as np -import numpy -import matplotlib.pyplot as plt - -from ccpi.optimisation.algorithms import PDHG, CGLS, FISTA - -from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, FunctionOperatorComposition -from ccpi.astra.ops import AstraProjectorSimple - -#%% - -N = 68 -x = np.zeros((N,N)) -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -data = ImageData(x) -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, 'cpu') -sin = Aop.direct(data) - -noisy_data = sin - - -#%% -############################################################################### -## Setup and run the CGLS algorithm - -x_init = ig.allocate() -cgls = CGLS(x_init=x_init, operator=Aop, data=noisy_data) -cgls.max_iteration = 500 -cgls.update_objective_interval = 50 -cgls.run(500, verbose=True) - -#%% -plt.imshow(cgls.get_output().as_array()) -#%% -############################################################################### -## Setup and run the PDHG algorithm - -operator = Aop -f = L2NormSquared(b = noisy_data) -g = ZeroFunction() - -## Compute operator Norm -normK = operator.norm() - -## Primal & dual stepsizes -sigma = 0.1 -tau = 1/(sigma*normK**2) - -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) - - -#%% -############################################################################### -## Setup and run the FISTA algorithm - -fidelity = FunctionOperatorComposition(L2NormSquared(b=noisy_data), Aop) -regularizer = ZeroFunction() - -## Setup and run the FISTA algorithm -opt = {'memopt':True} -fista = FISTA(x_init=x_init , f=fidelity, g=regularizer, opt=opt) -fista.max_iteration = 2000 -fista.update_objective_interval = 200 -fista.run(2000, verbose=True) - -#%% Show results - -diff1 = pdhg.get_output() - cgls.get_output() -diff2 = fista.get_output() - cgls.get_output() - -print( diff1.norm()) -print( diff2.norm()) - -plt.figure(figsize=(10,10)) -plt.subplot(2,3,1) -plt.imshow(cgls.get_output().as_array()) -plt.title('CGLS reconstruction') -plt.subplot(2,3,2) -plt.imshow(pdhg.get_output().as_array()) -plt.title('PDHG reconstruction') -plt.subplot(2,3,3) -plt.imshow(fista.get_output().as_array()) -plt.title('FISTA reconstruction') -plt.subplot(2,3,4) -plt.imshow(diff1.abs().as_array()) -plt.title('Diff PDHG vs CGLS') -plt.colorbar() -plt.subplot(2,3,5) -plt.imshow(diff2.abs().as_array()) -plt.title('Diff FISTA vs CGLS') -plt.colorbar() -plt.show() - - - - - - - - - - - - - - - - - - - - - - -# -# -# -# -# -# -# -# diff --git a/Wrappers/Python/wip/Demos/PDHG_TGV_Denoising_SaltPepper.py b/Wrappers/Python/wip/Demos/PDHG_TGV_Denoising_SaltPepper.py deleted file mode 100644 index 7b65c31..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_TGV_Denoising_SaltPepper.py +++ /dev/null @@ -1,194 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri Feb 22 14:53:03 2019 - -@author: evangelos -""" - -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, SymmetrizedGradient, ZeroOperator -from ccpi.optimisation.functions import ZeroFunction, L1Norm, \ - MixedL21Norm, BlockFunction - -from skimage.util import random_noise - -# Create phantom for TGV SaltPepper denoising - -N = 100 - -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) -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 Parameters -alpha = 0.8 -beta = numpy.sqrt(2)* alpha - -method = '1' - -if method == '0': - - # Create operators - op11 = Gradient(ig) - op12 = Identity(op11.range_geometry()) - - op22 = SymmetrizedGradient(op11.domain_geometry()) - op21 = ZeroOperator(ig, op22.range_geometry()) - - op31 = Identity(ig, ag) - 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 = L1Norm(b=noisy_data) - f = BlockFunction(f1, f2, f3) - g = ZeroFunction() - -else: - - # Create operators - op11 = Gradient(ig) - op12 = Identity(op11.range_geometry()) - op22 = SymmetrizedGradient(op11.domain_geometry()) - op21 = ZeroOperator(ig, op22.range_geometry()) - - operator = BlockOperator(op11, -1*op12, op21, op22, shape=(2,2) ) - - f1 = alpha * MixedL21Norm() - f2 = beta * MixedL21Norm() - - f = BlockFunction(f1, f2) - g = BlockFunction(L1Norm(b=noisy_data), 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 = '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: - - u = Variable(ig.shape) - w1 = Variable((N, N)) - w2 = Variable((N, N)) - - # create TGV regulariser - DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') - DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') - - regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u) - vec(w1), \ - DY.matrix() * vec(u) - vec(w2)]), 2, axis = 0)) + \ - beta * sum(norm(vstack([ DX.matrix().transpose() * vec(w1), DY.matrix().transpose() * vec(w2), \ - 0.5 * ( DX.matrix().transpose() * vec(w2) + DY.matrix().transpose() * vec(w1) ), \ - 0.5 * ( DX.matrix().transpose() * vec(w2) + DY.matrix().transpose() * vec(w1) ) ]), 2, axis = 0 ) ) - - constraints = [] - fidelity = pnorm(u - noisy_data.as_array(),1) - solver = MOSEK - - # 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()[0].as_array() - u.value ) - - plt.figure(figsize=(15,15)) - plt.subplot(3,1,1) - plt.imshow(pdhg.get_output()[0].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()[0].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_TV_Denoising_Gaussian_DiagPrecond.py b/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian_DiagPrecond.py deleted file mode 100644 index d65478c..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_TV_Denoising_Gaussian_DiagPrecond.py +++ /dev/null @@ -1,208 +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 - -Problem: min_x \alpha * ||\nabla x||_{1} + || x - g ||_{2}^{2} - - \nabla: Gradient operator - g: Noisy Data with Gaussian Noise - \alpha: Regularization parameter - -""" - -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 - - -# Create phantom for TV Gaussian denoising -N = 400 - -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 -np.random.seed(10) -noisy_data = ImageData( data.as_array() + np.random.normal(0, 0.05, size=ig.shape) ) - -# 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 = 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) - - -diag_precon = False - - -if diag_precon: - - def tau_sigma_precond(operator): - - tau = 1/operator.sum_abs_col() - sigma = 1/operator.sum_abs_row() - - sigma[0].as_array()[sigma[0].as_array()==np.inf]=0 - sigma[1].as_array()[sigma[1].as_array()==np.inf]=0 - - return tau, sigma - - tau, sigma = tau_sigma_precond(operator) - -else: - # 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 = 3000 -pdhg.update_objective_interval = 200 -pdhg.run(3000, verbose=False) - -#%% -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_Tomo2D.py b/Wrappers/Python/wip/Demos/PDHG_TV_Tomo2D.py deleted file mode 100644 index 87d5328..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_TV_Tomo2D.py +++ /dev/null @@ -1,245 +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, Identity, Gradient -from ccpi.optimisation.functions import ZeroFunction, KullbackLeibler, \ - MixedL21Norm, BlockFunction - -from ccpi.astra.ops import AstraProjectorSimple - -""" - -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 A x -g log(Ax + \eta) - - \nabla: Gradient operator - - A: Projection Matrix - g: Noisy sinogram corrupted with Poisson Noise - - \eta: Background Noise - \alpha: Regularization parameter - -""" - -# Create phantom for TV 2D tomography -N = 75 -x = np.zeros((N,N)) -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -data = ImageData(x) -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, 'cpu') -sin = Aop.direct(data) - -# Create noisy data. Apply Poisson noise -scale = 2 -n1 = scale * np.random.poisson(sin.as_array()/scale) -noisy_data = AcquisitionData(n1, ag) - -# Regularisation Parameter -alpha = 5 - -# Create operators -op1 = Gradient(ig) -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() - -diag_precon = True - -if diag_precon: - - def tau_sigma_precond(operator): - - tau = 1/operator.sum_abs_row() - sigma = 1/ operator.sum_abs_col() - - return tau, sigma - - tau, sigma = tau_sigma_precond(operator) - -else: - # Compute operator Norm - normK = operator.norm() - # Primal & dual stepsizes - sigma = 10 - 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().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 -import astra -import numpy - -try: - from cvxpy import * - cvx_not_installable = True -except ImportError: - cvx_not_installable = False - - -if cvx_not_installable: - - - ##Construct problem - u = Variable(N*N) - #q = Variable() - - DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') - DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') - - regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u), DY.matrix() * vec(u)]), 2, axis = 0)) - - # create matrix representation for Astra operator - - vol_geom = astra.create_vol_geom(N, N) - proj_geom = astra.create_proj_geom('parallel', 1.0, detectors, angles) - - proj_id = astra.create_projector('strip', proj_geom, vol_geom) - - matrix_id = astra.projector.matrix(proj_id) - - ProjMat = astra.matrix.get(matrix_id) - - fidelity = sum( ProjMat * u - noisy_data.as_array().ravel() * log(ProjMat * u)) - #constraints = [q>= fidelity, u>=0] - constraints = [u>=0] - - solver = SCS - obj = Minimize( regulariser + fidelity) - prob = Problem(obj, constraints) - result = prob.solve(verbose = True, solver = solver) - - -##%% 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) - - - 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(np.reshape(u.value, (N, N))) - 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]))
\ No newline at end of file diff --git a/Wrappers/Python/wip/Demos/PDHG_TV_Tomo2D_time.py b/Wrappers/Python/wip/Demos/PDHG_TV_Tomo2D_time.py deleted file mode 100644 index 045458a..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_TV_Tomo2D_time.py +++ /dev/null @@ -1,169 +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 -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/Demos/PDHG_Tikhonov_Tomo2D.py b/Wrappers/Python/wip/Demos/PDHG_Tikhonov_Tomo2D.py deleted file mode 100644 index f17c4fe..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_Tikhonov_Tomo2D.py +++ /dev/null @@ -1,108 +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 -from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, BlockFunction -from skimage.util import random_noise -from ccpi.astra.ops import AstraProjectorSimple - -# Create phantom for TV 2D tomography -N = 75 -x = np.zeros((N,N)) -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -data = ImageData(x) -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 Gaussian noise - -np.random.seed(10) -noisy_data = sin + AcquisitionData(np.random.normal(0, 3, sin.shape)) - -# Regularisation Parameter -alpha = 500 - -# Create operators -op1 = Gradient(ig) -op2 = Aop - -# 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() - -# 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 = 5000 -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() - - diff --git a/Wrappers/Python/wip/Demos/PDHG_vs_CGLS.py b/Wrappers/Python/wip/Demos/PDHG_vs_CGLS.py deleted file mode 100644 index 3155654..0000000 --- a/Wrappers/Python/wip/Demos/PDHG_vs_CGLS.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, AcquisitionGeometry, AcquisitionData - -import numpy as np -import numpy -import matplotlib.pyplot as plt - -from ccpi.optimisation.algorithms import PDHG, CGLS - -from ccpi.optimisation.operators import Gradient -from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, FunctionOperatorComposition -from skimage.util import random_noise -from ccpi.astra.ops import AstraProjectorSimple - -#%% - -N = 128 -x = np.zeros((N,N)) -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -data = ImageData(x) -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, 'cpu') -sin = Aop.direct(data) - -noisy_data = sin - -x_init = ig.allocate() - -## Setup and run the CGLS algorithm -cgls = CGLS(x_init=x_init, operator=Aop, data=noisy_data) -cgls.max_iteration = 500 -cgls.update_objective_interval = 50 -cgls.run(500, verbose=True) - -# Create BlockOperator -operator = Aop -f = 0.5 * L2NormSquared(b = noisy_data) -g = ZeroFunction() - -## Compute operator Norm -normK = operator.norm() - -## Primal & dual stepsizes -sigma = 0.1 -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) - -#%% - -diff = pdhg.get_output() - cgls.get_output() -print( diff.norm()) -# -plt.figure(figsize=(15,15)) -plt.subplot(3,1,1) -plt.imshow(pdhg.get_output().as_array()) -plt.title('PDHG reconstruction') -plt.colorbar() -plt.subplot(3,1,2) -plt.imshow(cgls.get_output().as_array()) -plt.title('CGLS reconstruction') -plt.colorbar() -plt.subplot(3,1,3) -plt.imshow(diff.abs().as_array()) -plt.title('Difference reconstruction') -plt.colorbar() -plt.show() - - - - - - - - - - - - - - - - - - - - - - -# -# -# -# -# -# -# -# diff --git a/Wrappers/Python/wip/Demos/check_blockOperator_sum_row_cols.py b/Wrappers/Python/wip/Demos/check_blockOperator_sum_row_cols.py deleted file mode 100644 index bdb2c38..0000000 --- a/Wrappers/Python/wip/Demos/check_blockOperator_sum_row_cols.py +++ /dev/null @@ -1,89 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri May 3 13:10:09 2019 - -@author: evangelos -""" - -from ccpi.optimisation.operators import FiniteDiff, SparseFiniteDiff, BlockOperator, Gradient -from ccpi.framework import ImageGeometry, AcquisitionGeometry, BlockDataContainer, ImageData -from ccpi.astra.ops import AstraProjectorSimple - -from scipy import sparse -import numpy as np - -N = 3 -ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) -u = ig.allocate('random_int') - -# Compare FiniteDiff with SparseFiniteDiff - -DY = FiniteDiff(ig, direction = 0, bnd_cond = 'Neumann') -DX = FiniteDiff(ig, direction = 1, bnd_cond = 'Neumann') - -DXu = DX.direct(u) -DYu = DY.direct(u) - -DX_sparse = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') -DY_sparse = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') - -DXu_sparse = DX_sparse.direct(u) -DYu_sparse = DY_sparse.direct(u) - -#np.testing.assert_array_almost_equal(DYu.as_array(), DYu_sparse.as_array(), decimal=4) -#np.testing.assert_array_almost_equal(DXu.as_array(), DXu_sparse.as_array(), decimal=4) - -#%% Tau/ Sigma - -A1 = DY_sparse.matrix() -A2 = DX_sparse.matrix() -A3 = sparse.eye(np.prod(ig.shape)) - -sum_rows1 = np.array(np.sum(abs(A1), axis=1)) -sum_rows2 = np.array(np.sum(abs(A2), axis=1)) -sum_rows3 = np.array(np.sum(abs(A3), axis=1)) - -sum_cols1 = np.array(np.sum(abs(A1), axis=0)) -sum_cols2 = np.array(np.sum(abs(A2), axis=0)) -sum_cols3 = np.array(np.sum(abs(A2), axis=0)) - -# Check if Grad sum row/cols is OK -Grad = Gradient(ig) - -Sum_Block_row = Grad.sum_abs_row() -Sum_Block_col = Grad.sum_abs_col() - -tmp1 = BlockDataContainer( ImageData(np.reshape(sum_rows1, ig.shape, order='F')),\ - ImageData(np.reshape(sum_rows2, ig.shape, order='F'))) - - -#np.testing.assert_array_almost_equal(tmp1[0].as_array(), Sum_Block_row[0].as_array(), decimal=4) -#np.testing.assert_array_almost_equal(tmp1[1].as_array(), Sum_Block_row[1].as_array(), decimal=4) - -tmp2 = ImageData(np.reshape(sum_cols1 + sum_cols2, ig.shape, order='F')) - -#np.testing.assert_array_almost_equal(tmp2.as_array(), Sum_Block_col.as_array(), decimal=4) - - -#%% BlockOperator with Gradient, Identity - -Id = Identity(ig) -Block_GrId = BlockOperator(Grad, Id, shape=(2,1)) - - -Sum_Block_GrId_row = Block_GrId.sum_abs_row() - - - - - - - - - - - - - - diff --git a/Wrappers/Python/wip/Demos/check_precond.py b/Wrappers/Python/wip/Demos/check_precond.py deleted file mode 100644 index 8cf95fa..0000000 --- a/Wrappers/Python/wip/Demos/check_precond.py +++ /dev/null @@ -1,182 +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 - -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 - -from ccpi.astra.ops import AstraProjectorSimple - -# Create phantom for TV 2D tomography -N = 75 -x = np.zeros((N,N)) -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -data = ImageData(x) -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, 'cpu') -sin = Aop.direct(data) - -# Create noisy data -np.random.seed(10) -n1 = np.random.random(sin.shape) -noisy_data = sin + ImageData(5*n1) - -#%% - -# Regularisation Parameter -alpha = 50 - -# Create operators -op1 = Gradient(ig) -op2 = Aop - -# Create BlockOperator -operator = BlockOperator(op1, op2, shape=(2,1) ) - - - -# Create functions - -f1 = alpha * MixedL21Norm() -f2 = L2NormSquared(b=noisy_data) -f = BlockFunction(f1, f2) - -g = ZeroFunction() - -diag_precon = True - -if diag_precon: - - def tau_sigma_precond(operator): - - tau = 1/operator.sum_abs_row() - sigma = 1/ operator.sum_abs_col() - - return tau, sigma - - tau, sigma = tau_sigma_precond(operator) - -else: - # Compute operator Norm - normK = operator.norm() - # Primal & dual stepsizes - sigma = 10 - 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 = 1000 -pdhg.update_objective_interval = 200 -pdhg.run(1000) - -#%% Check with CVX solution - -from ccpi.optimisation.operators import SparseFiniteDiff -import astra -import numpy - -try: - from cvxpy import * - cvx_not_installable = True -except ImportError: - cvx_not_installable = False - - -if cvx_not_installable: - - ##Construct problem - u = Variable(N*N) - - DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann') - DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann') - - regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u), DY.matrix() * vec(u)]), 2, axis = 0)) - - # create matrix representation for Astra operator - - vol_geom = astra.create_vol_geom(N, N) - proj_geom = astra.create_proj_geom('parallel', 1.0, detectors, angles) - - proj_id = astra.create_projector('line', proj_geom, vol_geom) - - matrix_id = astra.projector.matrix(proj_id) - - ProjMat = astra.matrix.get(matrix_id) - - fidelity = sum_squares( ProjMat * u - noisy_data.as_array().ravel()) - #constraints = [q>=fidelity] -# constraints = [u>=0] - - solver = MOSEK - obj = Minimize( regulariser + fidelity) - prob = Problem(obj) - result = prob.solve(verbose = True, solver = solver) - - -#%% - -plt.figure(figsize=(15,15)) -plt.subplot(2,2,1) -plt.imshow(data.as_array()) -plt.title('Ground Truth') - -plt.subplot(2,2,2) -plt.imshow(noisy_data.as_array()) -plt.title('Noisy Data') - -plt.subplot(2,2,3) -plt.imshow(pdhg.get_output().as_array()) -plt.title('PDHG Reconstruction') - -plt.subplot(2,2,4) -plt.imshow(np.reshape(u.value, ig.shape)) -plt.title('CVX Reconstruction') - -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), np.reshape(u.value, ig.shape)[int(N/2),:], label = 'CVX') -plt.legend() -plt.title('Middle Line Profiles') -plt.show() - - - - - - - - diff --git a/Wrappers/Python/wip/compare_CGLS_algos.py b/Wrappers/Python/wip/compare_CGLS_algos.py deleted file mode 100644 index 52f3f31..0000000 --- a/Wrappers/Python/wip/compare_CGLS_algos.py +++ /dev/null @@ -1,133 +0,0 @@ -# This demo illustrates how to use the SIRT algorithm without and with -# nonnegativity and box constraints. The ASTRA 2D projectors are used. - -# First make all imports -from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, \ - AcquisitionData -from ccpi.optimisation.algs import FISTA, FBPD, CGLS, SIRT -from ccpi.astra.operators import AstraProjectorSimple - -from ccpi.optimisation.algorithms import CGLS as CGLSalg - -import numpy as np -import matplotlib.pyplot as plt - -from ccpi.optimisation.functions import Norm2Sq - -# Choose either a parallel-beam (1=parallel2D) or fan-beam (2=cone2D) test case -test_case = 1 - -# Set up phantom size NxN by creating ImageGeometry, initialising the -# ImageData object with this geometry and empty array and finally put some -# data into its array, and display as image. -N = 128 -ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N) -Phantom = ImageData(geometry=ig) - -x = Phantom.as_array() -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -#plt.figure() -#plt.imshow(x) -#plt.title('Phantom image') -#plt.show() - -# Set up AcquisitionGeometry object to hold the parameters of the measurement -# setup geometry: # Number of angles, the actual angles from 0 to -# pi for parallel beam and 0 to 2pi for fanbeam, set the width of a detector -# pixel relative to an object pixel, the number of detector pixels, and the -# source-origin and origin-detector distance (here the origin-detector distance -# set to 0 to simulate a "virtual detector" with same detector pixel size as -# object pixel size). -angles_num = 20 -det_w = 1.0 -det_num = N -SourceOrig = 200 -OrigDetec = 0 - -if test_case==1: - angles = np.linspace(0,np.pi,angles_num,endpoint=False) - ag = AcquisitionGeometry('parallel', - '2D', - angles, - det_num,det_w) -elif test_case==2: - angles = np.linspace(0,2*np.pi,angles_num,endpoint=False) - ag = AcquisitionGeometry('cone', - '2D', - angles, - det_num, - det_w, - dist_source_center=SourceOrig, - dist_center_detector=OrigDetec) -else: - NotImplemented - -# Set up Operator object combining the ImageGeometry and AcquisitionGeometry -# wrapping calls to ASTRA as well as specifying whether to use CPU or GPU. -Aop = AstraProjectorSimple(ig, ag, 'cpu') - -# Forward and backprojection are available as methods direct and adjoint. Here -# generate test data b and do simple backprojection to obtain z. -b = Aop.direct(Phantom) -z = Aop.adjoint(b) - -#plt.figure() -#plt.imshow(b.array) -#plt.title('Simulated data') -#plt.show() - -#plt.figure() -#plt.imshow(z.array) -#plt.title('Backprojected data') -#plt.show() - -# Using the test data b, different reconstruction methods can now be set up as -# demonstrated in the rest of this file. In general all methods need an initial -# guess and some algorithm options to be set: -x_init = ImageData(np.zeros(x.shape),geometry=ig) -opt = {'tol': 1e-4, 'iter': 7} - -# First a CGLS reconstruction using the function version of CGLS can be done: -x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, b, opt) - -#plt.figure() -#plt.imshow(x_CGLS.array) -#plt.title('CGLS') -#plt.colorbar() -#plt.show() - -#plt.figure() -#plt.semilogy(criter_CGLS) -#plt.title('CGLS criterion') -#plt.show() - -f = Norm2Sq(Aop, b, c=1.) - -def callback(it, objective, solution): - print (objective, f(solution)) - -# Now CLGS using the algorithm class -CGLS_alg = CGLSalg() -CGLS_alg.set_up(x_init, Aop, b ) -CGLS_alg.max_iteration = 500 -CGLS_alg.update_objective_interval = 10 -CGLS_alg.run(300, callback=callback) -x_CGLS_alg = CGLS_alg.get_output() - -plt.figure() -plt.imshow(x_CGLS_alg.as_array()) -plt.title('CGLS ALG') -plt.colorbar() -plt.show() - -plt.figure() -plt.semilogy(CGLS_alg.objective) -plt.title('CGLS criterion') -plt.show() - -print(criter_CGLS) -print(CGLS_alg.objective) - -print((x_CGLS - x_CGLS_alg).norm())
\ No newline at end of file diff --git a/Wrappers/Python/wip/demo_SIRT.py b/Wrappers/Python/wip/demo_SIRT.py deleted file mode 100644 index 5a85d41..0000000 --- a/Wrappers/Python/wip/demo_SIRT.py +++ /dev/null @@ -1,205 +0,0 @@ -# This demo illustrates how to use the SIRT algorithm without and with -# nonnegativity and box constraints. The ASTRA 2D projectors are used. - -# First make all imports -from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, \ - AcquisitionData -from ccpi.optimisation.functions import IndicatorBox -from ccpi.astra.ops import AstraProjectorSimple -from ccpi.optimisation.algorithms import SIRT, CGLS - -import numpy as np -import matplotlib.pyplot as plt - -# Choose either a parallel-beam (1=parallel2D) or fan-beam (2=cone2D) test case -test_case = 1 - -# Set up phantom size NxN by creating ImageGeometry, initialising the -# ImageData object with this geometry and empty array and finally put some -# data into its array, and display as image. -N = 128 -ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N) -Phantom = ImageData(geometry=ig) - -x = Phantom.as_array() -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -plt.figure() -plt.imshow(x) -plt.title('Phantom image') -plt.show() - -# Set up AcquisitionGeometry object to hold the parameters of the measurement -# setup geometry: # Number of angles, the actual angles from 0 to -# pi for parallel beam and 0 to 2pi for fanbeam, set the width of a detector -# pixel relative to an object pixel, the number of detector pixels, and the -# source-origin and origin-detector distance (here the origin-detector distance -# set to 0 to simulate a "virtual detector" with same detector pixel size as -# object pixel size). -angles_num = 20 -det_w = 1.0 -det_num = N -SourceOrig = 200 -OrigDetec = 0 - -if test_case==1: - angles = np.linspace(0,np.pi,angles_num,endpoint=False) - ag = AcquisitionGeometry('parallel', - '2D', - angles, - det_num,det_w) -elif test_case==2: - angles = np.linspace(0,2*np.pi,angles_num,endpoint=False) - ag = AcquisitionGeometry('cone', - '2D', - angles, - det_num, - det_w, - dist_source_center=SourceOrig, - dist_center_detector=OrigDetec) -else: - NotImplemented - -# Set up Operator object combining the ImageGeometry and AcquisitionGeometry -# wrapping calls to ASTRA as well as specifying whether to use CPU or GPU. -Aop = AstraProjectorSimple(ig, ag, 'gpu') - -# Forward and backprojection are available as methods direct and adjoint. Here -# generate test data b and do simple backprojection to obtain z. -b = Aop.direct(Phantom) -z = Aop.adjoint(b) - -plt.figure() -plt.imshow(b.as_array()) -plt.title('Simulated data') -plt.show() - -plt.figure() -plt.imshow(z.as_array()) -plt.title('Backprojected data') -plt.show() - -# Using the test data b, different reconstruction methods can now be set up as -# demonstrated in the rest of this file. In general all methods need an initial -# guess and some algorithm options to be set: -x_init = ImageData(np.zeros(x.shape),geometry=ig) -opt = {'tol': 1e-4, 'iter': 100} - - -# First run a simple CGLS reconstruction: -CGLS_alg = CGLS() -CGLS_alg.set_up(x_init, Aop, b ) -CGLS_alg.max_iteration = 2000 -CGLS_alg.run(opt['iter']) -x_CGLS_alg = CGLS_alg.get_output() - -plt.figure() -plt.imshow(x_CGLS_alg.as_array()) -plt.title('CGLS ALG') -plt.colorbar() -plt.show() - -plt.figure() -plt.semilogy(CGLS_alg.objective) -plt.title('CGLS criterion') -plt.show() - - -# A SIRT reconstruction can be done simply by replacing CGLS by SIRT. -# In the first instance, no constraints are enforced. -SIRT_alg = SIRT() -SIRT_alg.set_up(x_init, Aop, b ) -SIRT_alg.max_iteration = 2000 -SIRT_alg.run(opt['iter']) -x_SIRT_alg = SIRT_alg.get_output() - -plt.figure() -plt.imshow(x_SIRT_alg.as_array()) -plt.title('SIRT unconstrained') -plt.colorbar() -plt.show() - -plt.figure() -plt.semilogy(SIRT_alg.objective) -plt.title('SIRT unconstrained criterion') -plt.show() - -# The SIRT algorithm is stopped after the specified number of iterations has -# been run. It can be resumed by calling the run command again, which will run -# it for the specificed number of iterations -SIRT_alg.run(opt['iter']) -x_SIRT_alg2 = SIRT_alg.get_output() - -plt.figure() -plt.imshow(x_SIRT_alg2.as_array()) -plt.title('SIRT unconstrained, extra iterations') -plt.colorbar() -plt.show() - -plt.figure() -plt.semilogy(SIRT_alg.objective) -plt.title('SIRT unconstrained criterion, extra iterations') -plt.show() - - -# A SIRT nonnegativity constrained reconstruction can be done using the -# additional input "constraint" set to a box indicator function with 0 as the -# lower bound and the default upper bound of infinity. First setup a new -# instance of the SIRT algorithm. -SIRT_alg0 = SIRT() -SIRT_alg0.set_up(x_init, Aop, b, constraint=IndicatorBox(lower=0) ) -SIRT_alg0.max_iteration = 2000 -SIRT_alg0.run(opt['iter']) -x_SIRT_alg0 = SIRT_alg0.get_output() - -plt.figure() -plt.imshow(x_SIRT_alg0.as_array()) -plt.title('SIRT nonnegativity constrained') -plt.colorbar() -plt.show() - -plt.figure() -plt.semilogy(SIRT_alg0.objective) -plt.title('SIRT nonnegativity criterion') -plt.show() - - -# A SIRT reconstruction with box constraints on [0,1] can also be done. -SIRT_alg01 = SIRT() -SIRT_alg01.set_up(x_init, Aop, b, constraint=IndicatorBox(lower=0,upper=1) ) -SIRT_alg01.max_iteration = 2000 -SIRT_alg01.run(opt['iter']) -x_SIRT_alg01 = SIRT_alg01.get_output() - -plt.figure() -plt.imshow(x_SIRT_alg01.as_array()) -plt.title('SIRT boc(0,1)') -plt.colorbar() -plt.show() - -plt.figure() -plt.semilogy(SIRT_alg01.objective) -plt.title('SIRT box(0,1) criterion') -plt.show() - -# The test image has values in the range [0,1], so enforcing values in the -# reconstruction to be within this interval improves a lot. Just for fun -# we can also easily see what happens if we choose a narrower interval as -# constrint in the reconstruction, lower bound 0.2, upper bound 0.8. -SIRT_alg0208 = SIRT() -SIRT_alg0208.set_up(x_init,Aop,b,constraint=IndicatorBox(lower=0.2,upper=0.8)) -SIRT_alg0208.max_iteration = 2000 -SIRT_alg0208.run(opt['iter']) -x_SIRT_alg0208 = SIRT_alg0208.get_output() - -plt.figure() -plt.imshow(x_SIRT_alg0208.as_array()) -plt.title('SIRT boc(0.2,0.8)') -plt.colorbar() -plt.show() - -plt.figure() -plt.semilogy(SIRT_alg0208.objective) -plt.title('SIRT box(0.2,0.8) criterion') -plt.show()
\ No newline at end of file diff --git a/Wrappers/Python/wip/demo_box_constraints_FISTA.py b/Wrappers/Python/wip/demo_box_constraints_FISTA.py deleted file mode 100644 index b15dd45..0000000 --- a/Wrappers/Python/wip/demo_box_constraints_FISTA.py +++ /dev/null @@ -1,158 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed Apr 17 14:46:21 2019 - -@author: jakob - -Demonstrate the use of box constraints in FISTA -""" - -# First make all imports -from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, \ - AcquisitionData -from ccpi.optimisation.algorithms import FISTA -from ccpi.optimisation.functions import Norm2sq, IndicatorBox -from ccpi.astra.ops import AstraProjectorSimple - -from ccpi.optimisation.operators import Identity - -import numpy as np -import matplotlib.pyplot as plt - - -# Set up phantom size NxN by creating ImageGeometry, initialising the -# ImageData object with this geometry and empty array and finally put some -# data into its array, and display as image. -N = 128 -ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N) -Phantom = ImageData(geometry=ig) - -x = Phantom.as_array() -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -plt.figure() -plt.imshow(x) -plt.title('Phantom image') -plt.show() - -# Set up AcquisitionGeometry object to hold the parameters of the measurement -# setup geometry: # Number of angles, the actual angles from 0 to -# pi for parallel beam and 0 to 2pi for fanbeam, set the width of a detector -# pixel relative to an object pixel, the number of detector pixels, and the -# source-origin and origin-detector distance (here the origin-detector distance -# set to 0 to simulate a "virtual detector" with same detector pixel size as -# object pixel size). -angles_num = 20 -det_w = 1.0 -det_num = N -SourceOrig = 200 -OrigDetec = 0 - -test_case = 1 - -if test_case==1: - angles = np.linspace(0,np.pi,angles_num,endpoint=False) - ag = AcquisitionGeometry('parallel', - '2D', - angles, - det_num,det_w) -elif test_case==2: - angles = np.linspace(0,2*np.pi,angles_num,endpoint=False) - ag = AcquisitionGeometry('cone', - '2D', - angles, - det_num, - det_w, - dist_source_center=SourceOrig, - dist_center_detector=OrigDetec) -else: - NotImplemented - -# Set up Operator object combining the ImageGeometry and AcquisitionGeometry -# wrapping calls to ASTRA as well as specifying whether to use CPU or GPU. -Aop = AstraProjectorSimple(ig, ag, 'cpu') - -Aop = Identity(ig,ig) - -# Forward and backprojection are available as methods direct and adjoint. Here -# generate test data b and do simple backprojection to obtain z. -b = Aop.direct(Phantom) -z = Aop.adjoint(b) - -plt.figure() -plt.imshow(b.array) -plt.title('Simulated data') -plt.show() - -plt.figure() -plt.imshow(z.array) -plt.title('Backprojected data') -plt.show() - -# Using the test data b, different reconstruction methods can now be set up as -# demonstrated in the rest of this file. In general all methods need an initial -# guess and some algorithm options to be set: -x_init = ImageData(np.zeros(x.shape),geometry=ig) -opt = {'tol': 1e-4, 'iter': 100} - - - -# Create least squares object instance with projector, test data and a constant -# coefficient of 0.5: -f = Norm2sq(Aop,b,c=0.5) - -# Run FISTA for least squares without constraints -FISTA_alg = FISTA() -FISTA_alg.set_up(x_init=x_init, f=f, opt=opt) -FISTA_alg.max_iteration = 2000 -FISTA_alg.run(opt['iter']) -x_FISTA = FISTA_alg.get_output() - -plt.figure() -plt.imshow(x_FISTA.array) -plt.title('FISTA unconstrained') -plt.colorbar() -plt.show() - -plt.figure() -plt.semilogy(FISTA_alg.objective) -plt.title('FISTA unconstrained criterion') -plt.show() - -# Run FISTA for least squares with lower bound 0.1 -FISTA_alg0 = FISTA() -FISTA_alg0.set_up(x_init=x_init, f=f, g=IndicatorBox(lower=0.1), opt=opt) -FISTA_alg0.max_iteration = 2000 -FISTA_alg0.run(opt['iter']) -x_FISTA0 = FISTA_alg0.get_output() - -plt.figure() -plt.imshow(x_FISTA0.array) -plt.title('FISTA lower bound 0.1') -plt.colorbar() -plt.show() - -plt.figure() -plt.semilogy(FISTA_alg0.objective) -plt.title('FISTA criterion, lower bound 0.1') -plt.show() - -# Run FISTA for least squares with box constraint [0.1,0.8] -FISTA_alg0 = FISTA() -FISTA_alg0.set_up(x_init=x_init, f=f, g=IndicatorBox(lower=0.1,upper=0.8), opt=opt) -FISTA_alg0.max_iteration = 2000 -FISTA_alg0.run(opt['iter']) -x_FISTA0 = FISTA_alg0.get_output() - -plt.figure() -plt.imshow(x_FISTA0.array) -plt.title('FISTA box(0.1,0.8) constrained') -plt.colorbar() -plt.show() - -plt.figure() -plt.semilogy(FISTA_alg0.objective) -plt.title('FISTA criterion, box(0.1,0.8) constrained criterion') -plt.show()
\ No newline at end of file diff --git a/Wrappers/Python/wip/demo_colourbay.py b/Wrappers/Python/wip/demo_colourbay.py deleted file mode 100644 index 0536b07..0000000 --- a/Wrappers/Python/wip/demo_colourbay.py +++ /dev/null @@ -1,137 +0,0 @@ -# This script demonstrates how to load a mat-file with UoM colour-bay data -# into the CIL optimisation framework and run (simple) multichannel -# reconstruction methods. - -# All third-party imports. -import numpy -from scipy.io import loadmat -import matplotlib.pyplot as plt - -# All own imports. -from ccpi.framework import AcquisitionData, AcquisitionGeometry, ImageGeometry, ImageData -from ccpi.astra.ops import AstraProjectorMC -from ccpi.optimisation.algs import CGLS, FISTA -from ccpi.optimisation.funcs import Norm2sq, Norm1 - -# Load full data and permute to expected ordering. Change path as necessary. -# The loaded X has dims 80x60x80x150, which is pix x angle x pix x channel. -# Permute (numpy.transpose) puts into our default ordering which is -# (channel, angle, vertical, horizontal). - -pathname = '/media/newhd/shared/Data/ColourBay/spectral_data_sets/CarbonPd/' -filename = 'carbonPd_full_sinogram_stripes_removed.mat' - -X = loadmat(pathname + filename) -X = numpy.transpose(X['SS'],(3,1,2,0)) - -# Store geometric variables for reuse -num_channels = X.shape[0] -num_pixels_h = X.shape[3] -num_pixels_v = X.shape[2] -num_angles = X.shape[1] - -# Display a single projection in a single channel -plt.imshow(X[100,5,:,:]) -plt.title('Example of a projection image in one channel' ) -plt.show() - -# Set angles to use -angles = numpy.linspace(-numpy.pi,numpy.pi,num_angles,endpoint=False) - -# Define full 3D acquisition geometry and data container. -# Geometric info is taken from the txt-file in the same dir as the mat-file -ag = AcquisitionGeometry('cone', - '3D', - angles, - pixel_num_h=num_pixels_h, - pixel_size_h=0.25, - pixel_num_v=num_pixels_v, - pixel_size_v=0.25, - dist_source_center=233.0, - dist_center_detector=245.0, - channels=num_channels) -data = AcquisitionData(X, geometry=ag) - -# Reduce to central slice by extracting relevant parameters from data and its -# geometry. Perhaps create function to extract central slice automatically? -data2d = data.subset(vertical=40) -ag2d = AcquisitionGeometry('cone', - '2D', - ag.angles, - pixel_num_h=ag.pixel_num_h, - pixel_size_h=ag.pixel_size_h, - pixel_num_v=1, - pixel_size_v=ag.pixel_size_h, - dist_source_center=ag.dist_source_center, - dist_center_detector=ag.dist_center_detector, - channels=ag.channels) -data2d.geometry = ag2d - -# Set up 2D Image Geometry. -# First need the geometric magnification to scale the voxel size relative -# to the detector pixel size. -mag = (ag.dist_source_center + ag.dist_center_detector)/ag.dist_source_center -ig2d = ImageGeometry(voxel_num_x=ag2d.pixel_num_h, - voxel_num_y=ag2d.pixel_num_h, - voxel_size_x=ag2d.pixel_size_h/mag, - voxel_size_y=ag2d.pixel_size_h/mag, - channels=X.shape[0]) - -# Create GPU multichannel projector/backprojector operator with ASTRA. -Aall = AstraProjectorMC(ig2d,ag2d,'gpu') - -# Compute and simple backprojction and display one channel as image. -Xbp = Aall.adjoint(data2d) -plt.imshow(Xbp.subset(channel=100).array) -plt.show() - -# Set initial guess ImageData with zeros for algorithms, and algorithm options. -x_init = ImageData(numpy.zeros((num_channels,num_pixels_v,num_pixels_h)), - geometry=ig2d, - dimension_labels=['channel','horizontal_y','horizontal_x']) -opt_CGLS = {'tol': 1e-4, 'iter': 5} - -# Run CGLS algorithm and display one channel. -x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aall, data2d, opt_CGLS) - -plt.imshow(x_CGLS.subset(channel=100).array) -plt.title('CGLS') -plt.show() - -plt.semilogy(criter_CGLS) -plt.title('CGLS Criterion vs iterations') -plt.show() - -# Create least squares object instance with projector, test data and a constant -# coefficient of 0.5. Note it is least squares over all channels. -f = Norm2sq(Aall,data2d,c=0.5) - -# Options for FISTA algorithm. -opt = {'tol': 1e-4, 'iter': 100} - -# Run FISTA for least squares without regularization and display one channel -# reconstruction as image. -x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt) - -plt.imshow(x_fista0.subset(channel=100).array) -plt.title('FISTA LS') -plt.show() - -plt.semilogy(criter0) -plt.title('FISTA LS Criterion vs iterations') -plt.show() - -# Set up 1-norm regularisation (over all channels), solve with FISTA, and -# display one channel of reconstruction. -lam = 0.1 -g0 = Norm1(lam) - -x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g0, opt) - -plt.imshow(x_fista1.subset(channel=100).array) -plt.title('FISTA LS+1') -plt.show() - -plt.semilogy(criter1) -plt.title('FISTA LS+1 Criterion vs iterations') -plt.show()
\ No newline at end of file diff --git a/Wrappers/Python/wip/demo_compare_cvx.py b/Wrappers/Python/wip/demo_compare_cvx.py deleted file mode 100644 index 27b1c97..0000000 --- a/Wrappers/Python/wip/demo_compare_cvx.py +++ /dev/null @@ -1,306 +0,0 @@ - -from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, DataContainer -from ccpi.optimisation.algs import FISTA, FBPD, CGLS -from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D, Norm2 - -from ccpi.optimisation.ops import LinearOperatorMatrix, TomoIdentity -from ccpi.optimisation.ops import Identity -from ccpi.optimisation.ops import FiniteDiff2D - -# Requires CVXPY, see http://www.cvxpy.org/ -# CVXPY can be installed in anaconda using -# conda install -c cvxgrp cvxpy libgcc - -# Whether to use or omit CVXPY -use_cvxpy = True -if use_cvxpy: - from cvxpy import * - -import numpy as np -import matplotlib.pyplot as plt - -# Problem data. -m = 30 -n = 20 -np.random.seed(1) -Amat = np.random.randn(m, n) -A = LinearOperatorMatrix(Amat) -bmat = np.random.randn(m) -bmat.shape = (bmat.shape[0],1) - -# A = Identity() -# Change n to equal to m. - -b = DataContainer(bmat) - -# Regularization parameter -lam = 10 -opt = {'memopt':True} -# Create object instances with the test data A and b. -f = Norm2sq(A,b,c=0.5, memopt=True) -g0 = ZeroFun() - -# Initial guess -x_init = DataContainer(np.zeros((n,1))) - -f.grad(x_init) - -# Run FISTA for least squares plus zero function. -x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0 , opt=opt) - -# Print solution and final objective/criterion value for comparison -print("FISTA least squares plus zero function solution and objective value:") -print(x_fista0.array) -print(criter0[-1]) - -if use_cvxpy: - # Compare to CVXPY - - # Construct the problem. - x0 = Variable(n) - objective0 = Minimize(0.5*sum_squares(Amat*x0 - bmat.T[0]) ) - prob0 = Problem(objective0) - - # The optimal objective is returned by prob.solve(). - result0 = prob0.solve(verbose=False,solver=SCS,eps=1e-9) - - # The optimal solution for x is stored in x.value and optimal objective value - # is in result as well as in objective.value - print("CVXPY least squares plus zero function solution and objective value:") - print(x0.value) - print(objective0.value) - -# Plot criterion curve to see FISTA converge to same value as CVX. -iternum = np.arange(1,1001) -plt.figure() -plt.loglog(iternum[[0,-1]],[objective0.value, objective0.value], label='CVX LS') -plt.loglog(iternum,criter0,label='FISTA LS') -plt.legend() -plt.show() - -# Create 1-norm object instance -g1 = Norm1(lam) - -g1(x_init) -x_rand = DataContainer(np.reshape(np.random.rand(n),(n,1))) -x_rand2 = DataContainer(np.reshape(np.random.rand(n-1),(n-1,1))) -v = g1.prox(x_rand,0.02) -#vv = g1.prox(x_rand2,0.02) -vv = v.copy() -vv *= 0 -print (">>>>>>>>>>vv" , vv.as_array()) -vv.fill(v) -print (">>>>>>>>>>fill" , vv.as_array()) -g1.proximal(x_rand, 0.02, out=vv) -print (">>>>>>>>>>v" , v.as_array()) -print (">>>>>>>>>>gradient" , vv.as_array()) - -print (">>>>>>>>>>" , (v-vv).as_array()) -import sys -#sys.exit(0) -# Combine with least squares and solve using generic FISTA implementation -x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1,opt=opt) - -# Print for comparison -print("FISTA least squares plus 1-norm solution and objective value:") -print(x_fista1) -print(criter1[-1]) - -if use_cvxpy: - # Compare to CVXPY - - # Construct the problem. - x1 = Variable(n) - objective1 = Minimize(0.5*sum_squares(Amat*x1 - bmat.T[0]) + lam*norm(x1,1) ) - prob1 = Problem(objective1) - - # The optimal objective is returned by prob.solve(). - result1 = prob1.solve(verbose=False,solver=SCS,eps=1e-9) - - # The optimal solution for x is stored in x.value and optimal objective value - # is in result as well as in objective.value - print("CVXPY least squares plus 1-norm solution and objective value:") - print(x1.value) - print(objective1.value) - -# Now try another algorithm FBPD for same problem: -x_fbpd1, itfbpd1, timingfbpd1, criterfbpd1 = FBPD(x_init,Identity(), None, f, g1) -print(x_fbpd1) -print(criterfbpd1[-1]) - -# Plot criterion curve to see both FISTA and FBPD converge to same value. -# Note that FISTA is very efficient for 1-norm minimization so it beats -# FBPD in this test by a lot. But FBPD can handle a larger class of problems -# than FISTA can. -plt.figure() -plt.loglog(iternum[[0,-1]],[objective1.value, objective1.value], label='CVX LS+1') -plt.loglog(iternum,criter1,label='FISTA LS+1') -plt.legend() -plt.show() - -plt.figure() -plt.loglog(iternum[[0,-1]],[objective1.value, objective1.value], label='CVX LS+1') -plt.loglog(iternum,criter1,label='FISTA LS+1') -plt.loglog(iternum,criterfbpd1,label='FBPD LS+1') -plt.legend() -plt.show() - -# Now try 1-norm and TV denoising with FBPD, first 1-norm. - -# Set up phantom size NxN by creating ImageGeometry, initialising the -# ImageData object with this geometry and empty array and finally put some -# data into its array, and display as image. -N = 64 -ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N) -Phantom = ImageData(geometry=ig) - -x = Phantom.as_array() -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -plt.imshow(x) -plt.title('Phantom image') -plt.show() - -# Identity operator for denoising -I = TomoIdentity(ig) - -# Data and add noise -y = I.direct(Phantom) -y.array = y.array + 0.1*np.random.randn(N, N) - -plt.imshow(y.array) -plt.title('Noisy image') -plt.show() - - -################### -# Data fidelity term -f_denoise = Norm2sq(I,y,c=0.5,memopt=True) - -# 1-norm regulariser -lam1_denoise = 1.0 -g1_denoise = Norm1(lam1_denoise) - -# Initial guess -x_init_denoise = ImageData(np.zeros((N,N))) - -# Combine with least squares and solve using generic FISTA implementation -x_fista1_denoise, it1_denoise, timing1_denoise, criter1_denoise = FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt) - -print(x_fista1_denoise) -print(criter1_denoise[-1]) - -#plt.imshow(x_fista1_denoise.as_array()) -#plt.title('FISTA LS+1') -#plt.show() - -# Now denoise LS + 1-norm with FBPD -x_fbpd1_denoise, itfbpd1_denoise, timingfbpd1_denoise, \ - criterfbpd1_denoise = FBPD(x_init_denoise, I, None, f_denoise, g1_denoise) -print(x_fbpd1_denoise) -print(criterfbpd1_denoise[-1]) - -#plt.imshow(x_fbpd1_denoise.as_array()) -#plt.title('FBPD LS+1') -#plt.show() - -if use_cvxpy: - # Compare to CVXPY - - # Construct the problem. - x1_denoise = Variable(N**2,1) - objective1_denoise = Minimize(0.5*sum_squares(x1_denoise - y.array.flatten()) + lam1_denoise*norm(x1_denoise,1) ) - prob1_denoise = Problem(objective1_denoise) - - # The optimal objective is returned by prob.solve(). - result1_denoise = prob1_denoise.solve(verbose=False,solver=SCS,eps=1e-12) - - # The optimal solution for x is stored in x.value and optimal objective value - # is in result as well as in objective.value - print("CVXPY least squares plus 1-norm solution and objective value:") - print(x1_denoise.value) - print(objective1_denoise.value) - -x1_cvx = x1_denoise.value -x1_cvx.shape = (N,N) - - - -#plt.imshow(x1_cvx) -#plt.title('CVX LS+1') -#plt.show() - -fig = plt.figure() -plt.subplot(1,4,1) -plt.imshow(y.array) -plt.title("LS+1") -plt.subplot(1,4,2) -plt.imshow(x_fista1_denoise.as_array()) -plt.title("fista") -plt.subplot(1,4,3) -plt.imshow(x_fbpd1_denoise.as_array()) -plt.title("fbpd") -plt.subplot(1,4,4) -plt.imshow(x1_cvx) -plt.title("cvx") -plt.show() - -############################################################## -# Now TV with FBPD and Norm2 -lam_tv = 0.1 -gtv = TV2D(lam_tv) -norm2 = Norm2(lam_tv) -op = FiniteDiff2D() -#gtv(gtv.op.direct(x_init_denoise)) - -opt_tv = {'tol': 1e-4, 'iter': 10000} - -x_fbpdtv_denoise, itfbpdtv_denoise, timingfbpdtv_denoise, \ - criterfbpdtv_denoise = FBPD(x_init_denoise, op, None, \ - f_denoise, norm2 ,opt=opt_tv) -print(x_fbpdtv_denoise) -print(criterfbpdtv_denoise[-1]) - -plt.imshow(x_fbpdtv_denoise.as_array()) -plt.title('FBPD TV') -#plt.show() - -if use_cvxpy: - # Compare to CVXPY - - # Construct the problem. - xtv_denoise = Variable((N,N)) - #print (xtv_denoise.value.shape) - objectivetv_denoise = Minimize(0.5*sum_squares(xtv_denoise - y.array) + lam_tv*tv(xtv_denoise) ) - probtv_denoise = Problem(objectivetv_denoise) - - # The optimal objective is returned by prob.solve(). - resulttv_denoise = probtv_denoise.solve(verbose=False,solver=SCS,eps=1e-12) - - # The optimal solution for x is stored in x.value and optimal objective value - # is in result as well as in objective.value - print("CVXPY least squares plus 1-norm solution and objective value:") - print(xtv_denoise.value) - print(objectivetv_denoise.value) - -plt.imshow(xtv_denoise.value) -plt.title('CVX TV') -#plt.show() - -fig = plt.figure() -plt.subplot(1,3,1) -plt.imshow(y.array) -plt.title("TV2D") -plt.subplot(1,3,2) -plt.imshow(x_fbpdtv_denoise.as_array()) -plt.title("fbpd tv denoise") -plt.subplot(1,3,3) -plt.imshow(xtv_denoise.value) -plt.title("CVX tv") -plt.show() - - - -plt.loglog([0,opt_tv['iter']], [objectivetv_denoise.value,objectivetv_denoise.value], label='CVX TV') -plt.loglog(criterfbpdtv_denoise, label='FBPD TV') diff --git a/Wrappers/Python/wip/demo_gradient_descent.py b/Wrappers/Python/wip/demo_gradient_descent.py deleted file mode 100755 index 4d6647e..0000000 --- a/Wrappers/Python/wip/demo_gradient_descent.py +++ /dev/null @@ -1,295 +0,0 @@ - -from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, DataContainer -from ccpi.optimisation.algs import FISTA, FBPD, CGLS -from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D, Norm2 - -from ccpi.optimisation.ops import LinearOperatorMatrix, TomoIdentity -from ccpi.optimisation.ops import Identity -from ccpi.optimisation.ops import FiniteDiff2D - -# Requires CVXPY, see http://www.cvxpy.org/ -# CVXPY can be installed in anaconda using -# conda install -c cvxgrp cvxpy libgcc - -# Whether to use or omit CVXPY - -import numpy as np -import matplotlib.pyplot as plt - -class Algorithm(object): - def __init__(self, *args, **kwargs): - pass - def set_up(self, *args, **kwargs): - raise NotImplementedError() - def update(self): - raise NotImplementedError() - - def should_stop(self): - raise NotImplementedError() - - def __iter__(self): - return self - - def __next__(self): - if self.should_stop(): - raise StopIteration() - else: - self.update() - -class GradientDescent(Algorithm): - x = None - rate = 0 - objective_function = None - regulariser = None - iteration = 0 - stop_cryterion = 'max_iter' - __max_iteration = 0 - __loss = [] - def __init__(self, **kwargs): - args = ['x_init', 'objective_function', 'rate'] - present = True - for k,v in kwargs.items(): - if k in args: - args.pop(args.index(k)) - if len(args) == 0: - return self.set_up(x_init=kwargs['x_init'], - objective_function=kwargs['objective_function'], - rate=kwargs['rate']) - - def should_stop(self): - return self.iteration >= self.max_iteration - - def set_up(self, x_init, objective_function, rate): - self.x = x_init.copy() - self.x_update = x_init.copy() - self.objective_function = objective_function - self.rate = rate - self.__loss.append(objective_function(x_init)) - - def update(self): - - self.objective_function.gradient(self.x, out=self.x_update) - self.x_update *= -self.rate - self.x += self.x_update - self.__loss.append(self.objective_function(self.x)) - self.iteration += 1 - - def get_output(self): - return self.x - def get_current_loss(self): - return self.__loss[-1] - @property - def loss(self): - return self.__loss - @property - def max_iteration(self): - return self.__max_iteration - @max_iteration.setter - def max_iteration(self, value): - assert isinstance(value, int) - self.__max_iteration = value - - - - - -# Problem data. -m = 30 -n = 20 -np.random.seed(1) -Amat = np.random.randn(m, n) -A = LinearOperatorMatrix(Amat) -bmat = np.random.randn(m) -bmat.shape = (bmat.shape[0],1) - -# A = Identity() -# Change n to equal to m. - -b = DataContainer(bmat) - -# Regularization parameter -lam = 10 -opt = {'memopt':True} -# Create object instances with the test data A and b. -f = Norm2sq(A,b,c=0.5, memopt=True) -g0 = ZeroFun() - -# Initial guess -x_init = DataContainer(np.zeros((n,1))) - -f.grad(x_init) - -# Run FISTA for least squares plus zero function. -x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0 , opt=opt) - -# Print solution and final objective/criterion value for comparison -print("FISTA least squares plus zero function solution and objective value:") -print(x_fista0.array) -print(criter0[-1]) - -gd = GradientDescent(x_init=x_init, objective_function=f, rate=0.001) -gd.max_iteration = 5000 - -for i,el in enumerate(gd): - if i%100 == 0: - print ("\rIteration {} Loss: {}".format(gd.iteration, - gd.get_current_loss())) - - -#%% - - -# -#if use_cvxpy: -# # Compare to CVXPY -# -# # Construct the problem. -# x0 = Variable(n) -# objective0 = Minimize(0.5*sum_squares(Amat*x0 - bmat.T[0]) ) -# prob0 = Problem(objective0) -# -# # The optimal objective is returned by prob.solve(). -# result0 = prob0.solve(verbose=False,solver=SCS,eps=1e-9) -# -# # The optimal solution for x is stored in x.value and optimal objective value -# # is in result as well as in objective.value -# print("CVXPY least squares plus zero function solution and objective value:") -# print(x0.value) -# print(objective0.value) -# -## Plot criterion curve to see FISTA converge to same value as CVX. -#iternum = np.arange(1,1001) -#plt.figure() -#plt.loglog(iternum[[0,-1]],[objective0.value, objective0.value], label='CVX LS') -#plt.loglog(iternum,criter0,label='FISTA LS') -#plt.legend() -#plt.show() -# -## Create 1-norm object instance -#g1 = Norm1(lam) -# -#g1(x_init) -#x_rand = DataContainer(np.reshape(np.random.rand(n),(n,1))) -#x_rand2 = DataContainer(np.reshape(np.random.rand(n-1),(n-1,1))) -#v = g1.prox(x_rand,0.02) -##vv = g1.prox(x_rand2,0.02) -#vv = v.copy() -#vv *= 0 -#print (">>>>>>>>>>vv" , vv.as_array()) -#vv.fill(v) -#print (">>>>>>>>>>fill" , vv.as_array()) -#g1.proximal(x_rand, 0.02, out=vv) -#print (">>>>>>>>>>v" , v.as_array()) -#print (">>>>>>>>>>gradient" , vv.as_array()) -# -#print (">>>>>>>>>>" , (v-vv).as_array()) -#import sys -##sys.exit(0) -## Combine with least squares and solve using generic FISTA implementation -#x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1,opt=opt) -# -## Print for comparison -#print("FISTA least squares plus 1-norm solution and objective value:") -#print(x_fista1) -#print(criter1[-1]) -# -#if use_cvxpy: -# # Compare to CVXPY -# -# # Construct the problem. -# x1 = Variable(n) -# objective1 = Minimize(0.5*sum_squares(Amat*x1 - bmat.T[0]) + lam*norm(x1,1) ) -# prob1 = Problem(objective1) -# -# # The optimal objective is returned by prob.solve(). -# result1 = prob1.solve(verbose=False,solver=SCS,eps=1e-9) -# -# # The optimal solution for x is stored in x.value and optimal objective value -# # is in result as well as in objective.value -# print("CVXPY least squares plus 1-norm solution and objective value:") -# print(x1.value) -# print(objective1.value) -# -## Now try another algorithm FBPD for same problem: -#x_fbpd1, itfbpd1, timingfbpd1, criterfbpd1 = FBPD(x_init,Identity(), None, f, g1) -#print(x_fbpd1) -#print(criterfbpd1[-1]) -# -## Plot criterion curve to see both FISTA and FBPD converge to same value. -## Note that FISTA is very efficient for 1-norm minimization so it beats -## FBPD in this test by a lot. But FBPD can handle a larger class of problems -## than FISTA can. -#plt.figure() -#plt.loglog(iternum[[0,-1]],[objective1.value, objective1.value], label='CVX LS+1') -#plt.loglog(iternum,criter1,label='FISTA LS+1') -#plt.legend() -#plt.show() -# -#plt.figure() -#plt.loglog(iternum[[0,-1]],[objective1.value, objective1.value], label='CVX LS+1') -#plt.loglog(iternum,criter1,label='FISTA LS+1') -#plt.loglog(iternum,criterfbpd1,label='FBPD LS+1') -#plt.legend() -#plt.show() - -# Now try 1-norm and TV denoising with FBPD, first 1-norm. - -# Set up phantom size NxN by creating ImageGeometry, initialising the -# ImageData object with this geometry and empty array and finally put some -# data into its array, and display as image. -N = 64 -ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N) -Phantom = ImageData(geometry=ig) - -x = Phantom.as_array() -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -plt.imshow(x) -plt.title('Phantom image') -plt.show() - -# Identity operator for denoising -I = TomoIdentity(ig) - -# Data and add noise -y = I.direct(Phantom) -y.array = y.array + 0.1*np.random.randn(N, N) - -plt.imshow(y.array) -plt.title('Noisy image') -plt.show() - - -################### -# Data fidelity term -f_denoise = Norm2sq(I,y,c=0.5,memopt=True) - -# 1-norm regulariser -lam1_denoise = 1.0 -g1_denoise = Norm1(lam1_denoise) - -# Initial guess -x_init_denoise = ImageData(np.zeros((N,N))) - -# Combine with least squares and solve using generic FISTA implementation -x_fista1_denoise, it1_denoise, timing1_denoise, criter1_denoise = \ - FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt) - -print(x_fista1_denoise) -print(criter1_denoise[-1]) - -f_2 = -gd = GradientDescent(x_init=x_init_denoise, - objective_function=f, rate=0.001) -gd.max_iteration = 5000 - -for i,el in enumerate(gd): - if i%100 == 0: - print ("\rIteration {} Loss: {}".format(gd.iteration, - gd.get_current_loss())) - -plt.imshow(gd.get_output().as_array()) -plt.title('GD image') -plt.show() - diff --git a/Wrappers/Python/wip/demo_imat_multichan_RGLTK.py b/Wrappers/Python/wip/demo_imat_multichan_RGLTK.py deleted file mode 100644 index 8370c78..0000000 --- a/Wrappers/Python/wip/demo_imat_multichan_RGLTK.py +++ /dev/null @@ -1,151 +0,0 @@ -# This script demonstrates how to load IMAT fits data -# into the CIL optimisation framework and run reconstruction methods. -# -# Demo to reconstruct energy-discretized channels of IMAT data - -# needs dxchange: conda install -c conda-forge dxchange -# needs astropy: conda install astropy - - -# All third-party imports. -import numpy as np -import matplotlib.pyplot as plt -from dxchange.reader import read_fits -from astropy.io import fits - -# All own imports. -from ccpi.framework import AcquisitionData, AcquisitionGeometry, ImageGeometry, ImageData, DataContainer -from ccpi.astra.ops import AstraProjectorSimple, AstraProjector3DSimple -from ccpi.optimisation.algs import CGLS, FISTA -from ccpi.optimisation.funcs import Norm2sq, Norm1 -from ccpi.plugins.regularisers import FGP_TV - -# set main parameters here -n = 512 -totalAngles = 250 # total number of projection angles -# spectral discretization parameter -num_average = 145 # channel discretization frequency (total number of averaged channels) -numChannels = 2970 # 2970 -totChannels = round(numChannels/num_average) # the resulting number of channels -Projections_stack = np.zeros((num_average,n,n),dtype='uint16') -ProjAngleChannels = np.zeros((totalAngles,totChannels,n,n),dtype='float32') - -######################################################################### -print ("Loading the data...") -MainPath = '/media/jakob/050d8d45-fab3-4285-935f-260e6c5f162c1/Data/neutrondata/' # path to data -pathname0 = '{!s}{!s}'.format(MainPath,'PSI_phantom_IMAT/DATA/Sample/') -counterFileName = 4675 -# A main loop over all available angles -for ll in range(0,totalAngles,1): - pathnameData = '{!s}{!s}{!s}/'.format(pathname0,'angle',str(ll)) - filenameCurr = '{!s}{!s}{!s}'.format('IMAT0000',str(counterFileName),'_Tomo_test_000_') - counterT = 0 - # loop over reduced channels (discretized) - for i in range(0,totChannels,1): - sumCount = 0 - # loop over actual channels to obtain averaged one - for j in range(0,num_average,1): - if counterT < 10: - outfile = '{!s}{!s}{!s}{!s}.fits'.format(pathnameData,filenameCurr,'0000',str(counterT)) - if ((counterT >= 10) & (counterT < 100)): - outfile = '{!s}{!s}{!s}{!s}.fits'.format(pathnameData,filenameCurr,'000',str(counterT)) - if ((counterT >= 100) & (counterT < 1000)): - outfile = '{!s}{!s}{!s}{!s}.fits'.format(pathnameData,filenameCurr,'00',str(counterT)) - if ((counterT >= 1000) & (counterT < 10000)): - outfile = '{!s}{!s}{!s}{!s}.fits'.format(pathnameData,filenameCurr,'0',str(counterT)) - try: - Projections_stack[j,:,:] = read_fits(outfile) - except: - print("Fits is corrupted, skipping no.", counterT) - sumCount -= 1 - counterT += 1 - sumCount += 1 - AverageProj=np.sum(Projections_stack,axis=0)/sumCount # averaged projection over "num_average" channels - ProjAngleChannels[ll,i,:,:] = AverageProj - print("Angle is processed", ll) - counterFileName += 1 -######################################################################### - -flat1 = read_fits('{!s}{!s}{!s}'.format(MainPath,'PSI_phantom_IMAT/DATA/','OpenBeam_aft1/IMAT00004932_Tomo_test_000_SummedImg.fits')) -nonzero = flat1 > 0 -# Apply flat field and take negative log -for ll in range(0,totalAngles,1): - for i in range(0,totChannels,1): - ProjAngleChannels[ll,i,nonzero] = ProjAngleChannels[ll,i,nonzero]/flat1[nonzero] - -eqzero = ProjAngleChannels == 0 -ProjAngleChannels[eqzero] = 1 -ProjAngleChannels_NormLog = -np.log(ProjAngleChannels) # normalised and neg-log data - -# extact sinogram over energy channels -selectedVertical_slice = 256 -sino_all_channels = ProjAngleChannels_NormLog[:,:,:,selectedVertical_slice] -# Set angles to use -angles = np.linspace(-np.pi,np.pi,totalAngles,endpoint=False) - -# set the geometry -ig = ImageGeometry(n,n) -ag = AcquisitionGeometry('parallel', - '2D', - angles, - n,1) -Aop = AstraProjectorSimple(ig, ag, 'gpu') - - -# loop to reconstruct energy channels -REC_chan = np.zeros((totChannels, n, n), 'float32') -for i in range(0,totChannels,1): - sino_channel = sino_all_channels[:,i,:] # extract a sinogram for i-th channel - - print ("Initial guess") - x_init = ImageData(geometry=ig) - - # Create least squares object instance with projector and data. - print ("Create least squares object instance with projector and data.") - f = Norm2sq(Aop,DataContainer(sino_channel),c=0.5) - - print ("Run FISTA-TV for least squares") - lamtv = 5 - opt = {'tol': 1e-4, 'iter': 200} - g_fgp = FGP_TV(lambdaReg = lamtv, - iterationsTV=50, - tolerance=1e-6, - methodTV=0, - nonnegativity=0, - printing=0, - device='gpu') - - x_fista_fgp, it1, timing1, criter_fgp = FISTA(x_init, f, g_fgp, opt) - REC_chan[i,:,:] = x_fista_fgp.array - """ - plt.figure() - plt.subplot(121) - plt.imshow(x_fista_fgp.array, vmin=0, vmax=0.05) - plt.title('FISTA FGP TV') - plt.subplot(122) - plt.semilogy(criter_fgp) - plt.show() - """ - """ - print ("Run CGLS for least squares") - opt = {'tol': 1e-4, 'iter': 20} - x_init = ImageData(geometry=ig) - x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, DataContainer(sino_channel), opt=opt) - - plt.figure() - plt.imshow(x_CGLS.array,vmin=0, vmax=0.05) - plt.title('CGLS') - plt.show() - """ -# Saving images into fits using astrapy if required -counter = 0 -filename = 'FISTA_TV_imat_slice' -for i in range(totChannels): - im = REC_chan[i,:,:] - add_val = np.min(im[:]) - im += abs(add_val) - im = im/np.max(im[:])*65535 - outfile = '{!s}_{!s}_{!s}.fits'.format(filename,str(selectedVertical_slice),str(counter)) - hdu = fits.PrimaryHDU(np.uint16(im)) - hdu.writeto(outfile, overwrite=True) - counter += 1
\ No newline at end of file diff --git a/Wrappers/Python/wip/demo_imat_whitebeam.py b/Wrappers/Python/wip/demo_imat_whitebeam.py deleted file mode 100644 index e0d213e..0000000 --- a/Wrappers/Python/wip/demo_imat_whitebeam.py +++ /dev/null @@ -1,138 +0,0 @@ -# This script demonstrates how to load IMAT fits data -# into the CIL optimisation framework and run reconstruction methods. -# -# This demo loads the summedImg files which are the non-spectral images -# resulting from summing projections over all spectral channels. - -# needs dxchange: conda install -c conda-forge dxchange -# needs astropy: conda install astropy - - -# All third-party imports. -import numpy -from scipy.io import loadmat -import matplotlib.pyplot as plt -from dxchange.reader import read_fits - -# All own imports. -from ccpi.framework import AcquisitionData, AcquisitionGeometry, ImageGeometry, ImageData -from ccpi.astra.ops import AstraProjectorSimple, AstraProjector3DSimple -from ccpi.optimisation.algs import CGLS, FISTA -from ccpi.optimisation.funcs import Norm2sq, Norm1 - -# Load and display a couple of summed projection as examples -pathname0 = '/media/newhd/shared/Data/neutrondata/PSI_phantom_IMAT/DATA/Sample/angle0/' -filename0 = 'IMAT00004675_Tomo_test_000_SummedImg.fits' - -data0 = read_fits(pathname0 + filename0) - -pathname10 = '/media/newhd/shared/Data/neutrondata/PSI_phantom_IMAT/DATA/Sample/angle10/' -filename10 = 'IMAT00004685_Tomo_test_000_SummedImg.fits' - -data10 = read_fits(pathname10 + filename10) - -# Load a flat field (more are available, should we average over them?) -flat1 = read_fits('/media/newhd/shared/Data/neutrondata/PSI_phantom_IMAT/DATA/OpenBeam_aft1/IMAT00004932_Tomo_test_000_SummedImg.fits') - -# Apply flat field and display after flat-field correction and negative log -data0_rel = numpy.zeros(numpy.shape(flat1), dtype = float) -nonzero = flat1 > 0 -data0_rel[nonzero] = data0[nonzero] / flat1[nonzero] -data10_rel = numpy.zeros(numpy.shape(flat1), dtype = float) -data10_rel[nonzero] = data10[nonzero] / flat1[nonzero] - -plt.imshow(data0_rel) -plt.colorbar() -plt.show() - -plt.imshow(-numpy.log(data0_rel)) -plt.colorbar() -plt.show() - -plt.imshow(data10_rel) -plt.colorbar() -plt.show() - -plt.imshow(-numpy.log(data10_rel)) -plt.colorbar() -plt.show() - -# Set up for loading all summed images at 250 angles. -pathname = '/media/newhd/shared/Data/neutrondata/PSI_phantom_IMAT/DATA/Sample/angle{}/' -filename = 'IMAT0000{}_Tomo_test_000_SummedImg.fits' - -# Dimensions -num_angles = 250 -imsize = 512 - -# Initialise array -data = numpy.zeros((num_angles,imsize,imsize)) - -# Load only 0-249, as 250 is at repetition of zero degrees just like image 0 -for i in range(0,250): - curimfile = (pathname + filename).format(i, i+4675) - data[i,:,:] = read_fits(curimfile) - -# Apply flat field and take negative log -nonzero = flat1 > 0 -for i in range(0,250): - data[i,nonzero] = data[i,nonzero]/flat1[nonzero] - -eqzero = data == 0 -data[eqzero] = 1 - -data_rel = -numpy.log(data) - -# Permute order to get: angles, vertical, horizontal, as default in framework. -data_rel = numpy.transpose(data_rel,(0,2,1)) - -# Set angles to use -angles = numpy.linspace(-numpy.pi,numpy.pi,num_angles,endpoint=False) - -# Create 3D acquisition geometry and acquisition data -ag = AcquisitionGeometry('parallel', - '3D', - angles, - pixel_num_h=imsize, - pixel_num_v=imsize) -b = AcquisitionData(data_rel, geometry=ag) - -# Reduce to single (noncentral) slice by extracting relevant parameters from data and its -# geometry. Perhaps create function to extract central slice automatically? -b2d = b.subset(vertical=128) -ag2d = AcquisitionGeometry('parallel', - '2D', - ag.angles, - pixel_num_h=ag.pixel_num_h) -b2d.geometry = ag2d - -# Create 2D image geometry -ig2d = ImageGeometry(voxel_num_x=ag2d.pixel_num_h, - voxel_num_y=ag2d.pixel_num_h) - -# Create GPU projector/backprojector operator with ASTRA. -Aop = AstraProjectorSimple(ig2d,ag2d,'gpu') - -# Demonstrate operator is working by applying simple backprojection. -z = Aop.adjoint(b2d) -plt.imshow(z.array) -plt.title('Simple backprojection') -plt.colorbar() -plt.show() - -# Set initial guess ImageData with zeros for algorithms, and algorithm options. -x_init = ImageData(numpy.zeros((imsize,imsize)), - geometry=ig2d) -opt_CGLS = {'tol': 1e-4, 'iter': 20} - -# Run CGLS algorithm and display reconstruction. -x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, b2d, opt_CGLS) - -plt.imshow(x_CGLS.array) -plt.title('CGLS') -plt.colorbar() -plt.show() - -plt.semilogy(criter_CGLS) -plt.title('CGLS Criterion vs iterations') -plt.show()
\ No newline at end of file diff --git a/Wrappers/Python/wip/demo_memhandle.py b/Wrappers/Python/wip/demo_memhandle.py deleted file mode 100755 index db48d73..0000000 --- a/Wrappers/Python/wip/demo_memhandle.py +++ /dev/null @@ -1,193 +0,0 @@ - -from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, DataContainer -from ccpi.optimisation.algs import FISTA, FBPD, CGLS -from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D - -from ccpi.optimisation.ops import LinearOperatorMatrix, Identity -from ccpi.optimisation.ops import TomoIdentity - -# Requires CVXPY, see http://www.cvxpy.org/ -# CVXPY can be installed in anaconda using -# conda install -c cvxgrp cvxpy libgcc - - -import numpy as np -import matplotlib.pyplot as plt - -# Problem data. -m = 30 -n = 20 -np.random.seed(1) -Amat = np.random.randn(m, n) -A = LinearOperatorMatrix(Amat) -bmat = np.random.randn(m) -bmat.shape = (bmat.shape[0],1) - - - -# A = Identity() -# Change n to equal to m. - -b = DataContainer(bmat) - -# Regularization parameter -lam = 10 - -# Create object instances with the test data A and b. -f = Norm2sq(A,b,c=0.5, memopt=True) -g0 = ZeroFun() - -# Initial guess -x_init = DataContainer(np.zeros((n,1))) - -f.grad(x_init) -opt = {'memopt': True} -# Run FISTA for least squares plus zero function. -x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0) -x_fista0_m, it0_m, timing0_m, criter0_m = FISTA(x_init, f, g0, opt=opt) - -iternum = [i for i in range(len(criter0))] -# Print solution and final objective/criterion value for comparison -print("FISTA least squares plus zero function solution and objective value:") -print(x_fista0.array) -print(criter0[-1]) - - -# Plot criterion curve to see FISTA converge to same value as CVX. -#iternum = np.arange(1,1001) -plt.figure() -plt.loglog(iternum,criter0,label='FISTA LS') -plt.loglog(iternum,criter0_m,label='FISTA LS memopt') -plt.legend() -plt.show() -#%% -# Create 1-norm object instance -g1 = Norm1(lam) - -g1(x_init) -g1.prox(x_init,0.02) - -# Combine with least squares and solve using generic FISTA implementation -x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1) -x_fista1_m, it1_m, timing1_m, criter1_m = FISTA(x_init, f, g1, opt=opt) -iternum = [i for i in range(len(criter1))] -# Print for comparison -print("FISTA least squares plus 1-norm solution and objective value:") -print(x_fista1) -print(criter1[-1]) - - -# Now try another algorithm FBPD for same problem: -x_fbpd1, itfbpd1, timingfbpd1, criterfbpd1 = FBPD(x_init, None, f, g1) -iternum = [i for i in range(len(criterfbpd1))] -print(x_fbpd1) -print(criterfbpd1[-1]) - -# Plot criterion curve to see both FISTA and FBPD converge to same value. -# Note that FISTA is very efficient for 1-norm minimization so it beats -# FBPD in this test by a lot. But FBPD can handle a larger class of problems -# than FISTA can. -plt.figure() -plt.loglog(iternum,criter1,label='FISTA LS+1') -plt.loglog(iternum,criter1_m,label='FISTA LS+1 memopt') -plt.legend() -plt.show() - -plt.figure() -plt.loglog(iternum,criter1,label='FISTA LS+1') -plt.loglog(iternum,criterfbpd1,label='FBPD LS+1') -plt.legend() -plt.show() -#%% -# Now try 1-norm and TV denoising with FBPD, first 1-norm. - -# Set up phantom size NxN by creating ImageGeometry, initialising the -# ImageData object with this geometry and empty array and finally put some -# data into its array, and display as image. -N = 1000 -ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N) -Phantom = ImageData(geometry=ig) - -x = Phantom.as_array() -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -plt.imshow(x) -plt.title('Phantom image') -plt.show() - -# Identity operator for denoising -I = TomoIdentity(ig) - -# Data and add noise -y = I.direct(Phantom) -y.array += 0.1*np.random.randn(N, N) - -plt.figure() -plt.imshow(y.array) -plt.title('Noisy image') -plt.show() - -# Data fidelity term -f_denoise = Norm2sq(I,y,c=0.5, memopt=True) - -# 1-norm regulariser -lam1_denoise = 1.0 -g1_denoise = Norm1(lam1_denoise) - -# Initial guess -x_init_denoise = ImageData(np.zeros((N,N))) -opt = {'memopt': False, 'iter' : 50} -# Combine with least squares and solve using generic FISTA implementation -print ("no memopt") -x_fista1_denoise, it1_denoise, timing1_denoise, \ - criter1_denoise = FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt) -opt = {'memopt': True, 'iter' : 50} -print ("yes memopt") -x_fista1_denoise_m, it1_denoise_m, timing1_denoise_m, \ - criter1_denoise_m = FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt) - -print(x_fista1_denoise) -print(criter1_denoise[-1]) - -plt.figure() -plt.imshow(x_fista1_denoise.as_array()) -plt.title('FISTA LS+1') -plt.show() - -plt.figure() -plt.imshow(x_fista1_denoise_m.as_array()) -plt.title('FISTA LS+1 memopt') -plt.show() - -plt.figure() -plt.loglog(iternum,criter1_denoise,label='FISTA LS+1') -plt.loglog(iternum,criter1_denoise_m,label='FISTA LS+1 memopt') -plt.legend() -plt.show() -#%% -# Now denoise LS + 1-norm with FBPD -x_fbpd1_denoise, itfbpd1_denoise, timingfbpd1_denoise, criterfbpd1_denoise = FBPD(x_init_denoise, None, f_denoise, g1_denoise) -print(x_fbpd1_denoise) -print(criterfbpd1_denoise[-1]) - -plt.figure() -plt.imshow(x_fbpd1_denoise.as_array()) -plt.title('FBPD LS+1') -plt.show() - - -# Now TV with FBPD -lam_tv = 0.1 -gtv = TV2D(lam_tv) -gtv(gtv.op.direct(x_init_denoise)) - -opt_tv = {'tol': 1e-4, 'iter': 10000} - -x_fbpdtv_denoise, itfbpdtv_denoise, timingfbpdtv_denoise, criterfbpdtv_denoise = FBPD(x_init_denoise, None, f_denoise, gtv,opt=opt_tv) -print(x_fbpdtv_denoise) -print(criterfbpdtv_denoise[-1]) - -plt.imshow(x_fbpdtv_denoise.as_array()) -plt.title('FBPD TV') -plt.show() diff --git a/Wrappers/Python/wip/fix_test.py b/Wrappers/Python/wip/fix_test.py deleted file mode 100755 index b1006c0..0000000 --- a/Wrappers/Python/wip/fix_test.py +++ /dev/null @@ -1,208 +0,0 @@ -import numpy as np -import numpy -from ccpi.optimisation.operators import * -from ccpi.optimisation.algorithms import * -from ccpi.optimisation.functions import * -from ccpi.framework import * - -def isSizeCorrect(data1 ,data2): - if issubclass(type(data1), DataContainer) and \ - issubclass(type(data2), DataContainer): - # check dimensionality - if data1.check_dimensions(data2): - return True - elif issubclass(type(data1) , numpy.ndarray) and \ - issubclass(type(data2) , numpy.ndarray): - return data1.shape == data2.shape - else: - raise ValueError("{0}: getting two incompatible types: {1} {2}"\ - .format('Function', type(data1), type(data2))) - return False - -class Norm1(Function): - - def __init__(self,gamma): - super(Norm1, self).__init__() - self.gamma = gamma - self.L = 1 - self.sign_x = None - - def __call__(self,x,out=None): - if out is None: - return self.gamma*(x.abs().sum()) - else: - if not x.shape == out.shape: - raise ValueError('Norm1 Incompatible size:', - x.shape, out.shape) - x.abs(out=out) - return out.sum() * self.gamma - - def prox(self,x,tau): - return (x.abs() - tau*self.gamma).maximum(0) * x.sign() - - def proximal(self, x, tau, out=None): - if out is None: - return self.prox(x, tau) - else: - if isSizeCorrect(x,out): - # check dimensionality - if issubclass(type(out), DataContainer): - v = (x.abs() - tau*self.gamma).maximum(0) - x.sign(out=out) - out *= v - #out.fill(self.prox(x,tau)) - elif issubclass(type(out) , numpy.ndarray): - v = (x.abs() - tau*self.gamma).maximum(0) - out[:] = x.sign() - out *= v - #out[:] = self.prox(x,tau) - else: - raise ValueError ('Wrong size: x{0} out{1}'.format(x.shape,out.shape) ) - -opt = {'memopt': True} -# Problem data. -m = 500 -n = 200 - -# if m < n then the problem is under-determined and algorithms will struggle to find a solution. -# One approach is to add regularisation - -#np.random.seed(1) -Amat = np.asarray( np.random.randn(m, n), dtype=numpy.float32) -Amat = np.asarray( np.random.random_integers(1,10, (m, n)), dtype=numpy.float32) -#Amat = np.asarray(np.eye(m), dtype=np.float32) * 2 -A = LinearOperatorMatrix(Amat) -bmat = np.asarray( np.random.randn(m), dtype=numpy.float32) -#bmat *= 0 -#bmat += 2 -print ("bmat", bmat.shape) -print ("A", A.A) -#bmat.shape = (bmat.shape[0], 1) - -# A = Identity() -# Change n to equal to m. -vgb = VectorGeometry(m) -vgx = VectorGeometry(n) -b = vgb.allocate(VectorGeometry.RANDOM_INT, dtype=numpy.float32) -# b.fill(bmat) -#b = DataContainer(bmat) - -# Regularization parameter -lam = 10 -opt = {'memopt': True} -# Create object instances with the test data A and b. -f = Norm2Sq(A, b, c=1., memopt=True) -#f = FunctionOperatorComposition(A, L2NormSquared(b=bmat)) -g0 = ZeroFunction() - -#f.L = 30.003 -x_init = vgx.allocate(VectorGeometry.RANDOM, dtype=numpy.float32) -x_initcgls = x_init.copy() - -a = VectorData(x_init.as_array(), deep_copy=True) - -assert id(x_init.as_array()) != id(a.as_array()) - - -#f.L = LinearOperator.PowerMethod(A, 25, x_init)[0] -#print ('f.L', f.L) -rate = (1 / f.L) / 6 -#f.L *= 12 - -# Initial guess -#x_init = DataContainer(np.zeros((n, 1))) -print ('x_init', x_init.as_array()) -print ('b', b.as_array()) -# Create 1-norm object instance -g1_new = lam * L1Norm() -g1 = Norm1(lam) - -g1 = ZeroFunction() -#g1(x_init) -x = g1.prox(x_init, 1/f.L ) -print ("g1.proximal ", x.as_array()) - -x = g1.prox(x_init, 0.03 ) -print ("g1.proximal ", x.as_array()) -x = g1_new.proximal(x_init, 0.03 ) -print ("g1.proximal ", x.as_array()) - -x1 = vgx.allocate(VectorGeometry.RANDOM, dtype=numpy.float32) -pippo = vgx.allocate() - -print ("x_init", x_init.as_array()) -print ("x1", x1.as_array()) -a = x_init.subtract(x1, out=pippo) - -print ("pippo", pippo.as_array()) -print ("x_init", x_init.as_array()) -print ("x1", x1.as_array()) - -y = A.direct(x_init) -y *= 0 -A.direct(x_init, out=y) - -# Combine with least squares and solve using generic FISTA implementation -#x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1, opt=opt) -def callback(it, objective, solution): - print ("callback " , it , objective, f(solution)) - -fa = FISTA(x_init=x_init, f=f, g=g1) -fa.max_iteration = 1000 -fa.update_objective_interval = int( fa.max_iteration / 10 ) -fa.run(fa.max_iteration, callback = None, verbose=True) - -gd = GradientDescent(x_init=x_init, objective_function=f, rate = rate ) -gd.max_iteration = 5000 -gd.update_objective_interval = int( gd.max_iteration / 10 ) -gd.run(gd.max_iteration, callback = None, verbose=True) - - - -cgls = CGLS(x_init= x_initcgls, operator=A, data=b) -cgls.max_iteration = 1000 -cgls.update_objective_interval = int( cgls.max_iteration / 10 ) - -#cgls.should_stop = stop_criterion(cgls) -cgls.run(cgls.max_iteration, callback = callback, verbose=True) - - - -# Print for comparison -print("FISTA least squares plus 1-norm solution and objective value:") -print(fa.get_output().as_array()) -print(fa.get_last_objective()) - -print ("data ", b.as_array()) -print ('FISTA ', A.direct(fa.get_output()).as_array()) -print ('GradientDescent', A.direct(gd.get_output()).as_array()) -print ('CGLS ', A.direct(cgls.get_output()).as_array()) - -cond = numpy.linalg.cond(A.A) - -print ("cond" , cond) - -#%% -try: - import cvxpy as cp - # Construct the problem. - x = cp.Variable(n) - objective = cp.Minimize(cp.sum_squares(A.A*x - bmat)) - prob = cp.Problem(objective) - # The optimal objective is returned by prob.solve(). - result = prob.solve(solver = cp.MOSEK) - - print ('CGLS ', cgls.get_output().as_array()) - print ('CVX ', x.value) - - print ('FISTA ', fa.get_output().as_array()) - print ('GD ', gd.get_output().as_array()) -except ImportError as ir: - pass - - #%% - - - - - diff --git a/Wrappers/Python/wip/multifile_nexus.py b/Wrappers/Python/wip/multifile_nexus.py deleted file mode 100755 index d1ad438..0000000 --- a/Wrappers/Python/wip/multifile_nexus.py +++ /dev/null @@ -1,307 +0,0 @@ -# -*- coding: utf-8 -*-
-"""
-Created on Wed Aug 15 16:00:53 2018
-
-@author: ofn77899
-"""
-
-import os
-from ccpi.io.reader import NexusReader
-
-from sys import getsizeof
-
-import matplotlib.pyplot as plt
-
-from ccpi.framework import DataProcessor, DataContainer
-from ccpi.processors import Normalizer
-from ccpi.processors import CenterOfRotationFinder
-import numpy
-
-
-class averager(object):
- def __init__(self):
- self.reset()
-
- def reset(self):
- self.N = 0
- self.avg = 0
- self.min = 0
- self.max = 0
- self.var = 0
- self.ske = 0
- self.kur = 0
-
- def add_reading(self, val):
-
- if (self.N == 0):
- self.avg = val;
- self.min = val;
- self.max = val;
- elif (self.N == 1):
- #//set min/max
- self.max = val if val > self.max else self.max
- self.min = val if val < self.min else self.min
-
-
- thisavg = (self.avg + val)/2
- #// initial value is in avg
- self.var = (self.avg - thisavg)*(self.avg-thisavg) + (val - thisavg) * (val-thisavg)
- self.ske = self.var * (self.avg - thisavg)
- self.kur = self.var * self.var
- self.avg = thisavg
- else:
- self.max = val if val > self.max else self.max
- self.min = val if val < self.min else self.min
-
- M = self.N
-
- #// b-factor =(<v>_N + v_(N+1)) / (N+1)
- #float b = (val -avg) / (M+1);
- b = (val -self.avg) / (M+1)
-
- self.kur = self.kur + (M *(b*b*b*b) * (1+M*M*M))- (4* b * self.ske) + (6 * b *b * self.var * (M-1))
-
- self.ske = self.ske + (M * b*b*b *(M-1)*(M+1)) - (3 * b * self.var * (M-1))
-
- #//var = var * ((M-1)/M) + ((val - avg)*(val - avg)/(M+1)) ;
- self.var = self.var * ((M-1)/M) + (b * b * (M+1))
-
- self.avg = self.avg * (M/(M+1)) + val / (M+1)
-
- self.N += 1
-
- def stats(self, vector):
- i = 0
- while i < vector.size:
- self.add_reading(vector[i])
- i+=1
-
-avg = averager()
-a = numpy.linspace(0,39,40)
-avg.stats(a)
-print ("average" , avg.avg, a.mean())
-print ("variance" , avg.var, a.var())
-b = a - a.mean()
-b *= b
-b = numpy.sqrt(sum(b)/(a.size-1))
-print ("std" , numpy.sqrt(avg.var), b)
-#%%
-
-class DataStatMoments(DataProcessor):
- '''Normalization based on flat and dark
-
- This processor read in a AcquisitionData and normalises it based on
- the instrument reading with and without incident photons or neutrons.
-
- Input: AcquisitionData
- Parameter: 2D projection with flat field (or stack)
- 2D projection with dark field (or stack)
- Output: AcquisitionDataSetn
- '''
-
- def __init__(self, axis, skewness=False, kurtosis=False, offset=0):
- kwargs = {
- 'axis' : axis,
- 'skewness' : skewness,
- 'kurtosis' : kurtosis,
- 'offset' : offset,
- }
- #DataProcessor.__init__(self, **kwargs)
- super(DataStatMoments, self).__init__(**kwargs)
-
-
- def check_input(self, dataset):
- #self.N = dataset.get_dimension_size(self.axis)
- return True
-
- @staticmethod
- def add_sample(dataset, N, axis, stats=None, offset=0):
- #dataset = self.get_input()
- if (N == 0):
- # get the axis number along to calculate the stats
-
-
- #axs = dataset.get_dimension_size(self.axis)
- # create a placeholder for the output
- if stats is None:
- ax = dataset.get_dimension_axis(axis)
- shape = [dataset.shape[i] for i in range(len(dataset.shape)) if i != ax]
- # output has 4 components avg, std, skewness and kurtosis + last avg+ (val-thisavg)
- shape.insert(0, 4+2)
- stats = numpy.zeros(shape)
-
- stats[0] = dataset.subset(**{axis:N-offset}).array[:]
-
- #avg = val
- elif (N == 1):
- # val
- stats[5] = dataset.subset(**{axis:N-offset}).array
- stats[4] = stats[0] + stats[5]
- stats[4] /= 2 # thisavg
- stats[5] -= stats[4] # (val - thisavg)
-
- #float thisavg = (avg + val)/2;
-
- #// initial value is in avg
- #var = (avg - thisavg)*(avg-thisavg) + (val - thisavg) * (val-thisavg);
- stats[1] = stats[5] * stats[5] + stats[5] * stats[5]
- #skewness = var * (avg - thisavg);
- stats[2] = stats[1] * stats[5]
- #kurtosis = var * var;
- stats[3] = stats[1] * stats[1]
- #avg = thisavg;
- stats[0] = stats[4]
- else:
-
- #float M = (float)N;
- M = N
- #val
- stats[4] = dataset.subset(**{axis:N-offset}).array
- #// b-factor =(<v>_N + v_(N+1)) / (N+1)
- stats[5] = stats[4] - stats[0]
- stats[5] /= (M+1) # b factor
- #float b = (val -avg) / (M+1);
-
- #kurtosis = kurtosis + (M *(b*b*b*b) * (1+M*M*M))- (4* b * skewness) + (6 * b *b * var * (M-1));
- #if self.kurtosis:
- # stats[3] += (M * stats[5] * stats[5] * stats[5] * stats[5]) - \
- # (4 * stats[5] * stats[2]) + \
- # (6 * stats[5] * stats[5] * stats[1] * (M-1))
-
- #skewness = skewness + (M * b*b*b *(M-1)*(M+1)) - (3 * b * var * (M-1));
- #if self.skewness:
- # stats[2] = stats[2] + (M * stats[5]* stats[5] * stats[5] * (M-1)*(M-1) ) -\
- # 3 * stats[5] * stats[1] * (M-1)
- #//var = var * ((M-1)/M) + ((val - avg)*(val - avg)/(M+1)) ;
- #var = var * ((M-1)/M) + (b * b * (M+1));
- stats[1] = ((M-1)/M) * stats[1] + (stats[5] * stats[5] * (M+1))
-
- #avg = avg * (M/(M+1)) + val / (M+1)
- stats[0] = stats[0] * (M/(M+1)) + stats[4] / (M+1)
-
- N += 1
- return stats , N
-
-
- def process(self):
-
- data = self.get_input()
-
- #stats, i = add_sample(0)
- N = data.get_dimension_size(self.axis)
- ax = data.get_dimension_axis(self.axis)
- stats = None
- i = 0
- while i < N:
- stats , i = DataStatMoments.add_sample(data, i, self.axis, stats, offset=self.offset)
- self.offset += N
- labels = ['StatisticalMoments']
-
- labels += [data.dimension_labels[i] \
- for i in range(len(data.dimension_labels)) if i != ax]
- y = DataContainer( stats[:4] , False,
- dimension_labels=labels)
- return y
-
-directory = r'E:\Documents\Dataset\CCPi\Nexus_test'
-data_path="entry1/instrument/pco1_hw_hdf_nochunking/data"
-
-reader = NexusReader(os.path.join( os.path.abspath(directory) , '74331.nxs'))
-
-print ("read flat")
-read_flat = NexusReader(os.path.join( os.path.abspath(directory) , '74240.nxs'))
-read_flat.data_path = data_path
-flatsslice = read_flat.get_acquisition_data_whole()
-avg = DataStatMoments('angle')
-
-avg.set_input(flatsslice)
-flats = avg.get_output()
-
-ave = averager()
-ave.stats(flatsslice.array[:,0,0])
-
-print ("avg" , ave.avg, flats.array[0][0][0])
-print ("var" , ave.var, flats.array[1][0][0])
-
-print ("read dark")
-read_dark = NexusReader(os.path.join( os.path.abspath(directory) , '74243.nxs'))
-read_dark.data_path = data_path
-
-## darks are very many so we proceed in batches
-total_size = read_dark.get_projection_dimensions()[0]
-print ("total_size", total_size)
-
-batchsize = 40
-if batchsize > total_size:
- batchlimits = [batchsize * (i+1) for i in range(int(total_size/batchsize))] + [total_size-1]
-else:
- batchlimits = [total_size]
-#avg.N = 0
-avg.offset = 0
-N = 0
-for batch in range(len(batchlimits)):
- print ("running batch " , batch)
- bmax = batchlimits[batch]
- bmin = bmax-batchsize
-
- darksslice = read_dark.get_acquisition_data_batch(bmin,bmax)
- if batch == 0:
- #create stats
- ax = darksslice.get_dimension_axis('angle')
- shape = [darksslice.shape[i] for i in range(len(darksslice.shape)) if i != ax]
- # output has 4 components avg, std, skewness and kurtosis + last avg+ (val-thisavg)
- shape.insert(0, 4+2)
- print ("create stats shape ", shape)
- stats = numpy.zeros(shape)
- print ("N" , N)
- #avg.set_input(darksslice)
- i = bmin
- while i < bmax:
- stats , i = DataStatMoments.add_sample(darksslice, i, 'angle', stats, bmin)
- print ("{0}-{1}-{2}".format(bmin, i , bmax ) )
-
-darks = stats
-#%%
-
-fig = plt.subplot(2,2,1)
-fig.imshow(flats.subset(StatisticalMoments=0).array)
-fig = plt.subplot(2,2,2)
-fig.imshow(numpy.sqrt(flats.subset(StatisticalMoments=1).array))
-fig = plt.subplot(2,2,3)
-fig.imshow(darks[0])
-fig = plt.subplot(2,2,4)
-fig.imshow(numpy.sqrt(darks[1]))
-plt.show()
-
-
-#%%
-norm = Normalizer(flat_field=flats.array[0,200,:], dark_field=darks[0,200,:])
-#norm.set_flat_field(flats.array[0,200,:])
-#norm.set_dark_field(darks.array[0,200,:])
-norm.set_input(reader.get_acquisition_data_slice(200))
-
-n = Normalizer.normalize_projection(norm.get_input().as_array(), flats.array[0,200,:], darks[0,200,:], 1e-5)
-#dn_n= Normalizer.estimate_normalised_error(norm.get_input().as_array(), flats.array[0,200,:], darks[0,200,:],
-# numpy.sqrt(flats.array[1,200,:]), numpy.sqrt(darks[1,200,:]))
-#%%
-
-
-#%%
-fig = plt.subplot(2,1,1)
-
-
-fig.imshow(norm.get_input().as_array())
-fig = plt.subplot(2,1,2)
-fig.imshow(n)
-
-#fig = plt.subplot(3,1,3)
-#fig.imshow(dn_n)
-
-
-plt.show()
-
-
-
-
-
-
diff --git a/Wrappers/Python/wip/old_demos/demo_colourbay.py b/Wrappers/Python/wip/old_demos/demo_colourbay.py deleted file mode 100644 index 5dbf2e1..0000000 --- a/Wrappers/Python/wip/old_demos/demo_colourbay.py +++ /dev/null @@ -1,137 +0,0 @@ -# This script demonstrates how to load a mat-file with UoM colour-bay data -# into the CIL optimisation framework and run (simple) multichannel -# reconstruction methods. - -# All third-party imports. -import numpy -from scipy.io import loadmat -import matplotlib.pyplot as plt - -# All own imports. -from ccpi.framework import AcquisitionData, AcquisitionGeometry, ImageGeometry, ImageData -from ccpi.astra.ops import AstraProjectorMC -from ccpi.optimisation.algs import CGLS, FISTA -from ccpi.optimisation.funcs import Norm2sq, Norm1 - -# Load full data and permute to expected ordering. Change path as necessary. -# The loaded X has dims 80x60x80x150, which is pix x angle x pix x channel. -# Permute (numpy.transpose) puts into our default ordering which is -# (channel, angle, vertical, horizontal). - -pathname = '/media/jakob/050d8d45-fab3-4285-935f-260e6c5f162c1/Data/ColourBay/spectral_data_sets/CarbonPd/' -filename = 'carbonPd_full_sinogram_stripes_removed.mat' - -X = loadmat(pathname + filename) -X = numpy.transpose(X['SS'],(3,1,2,0)) - -# Store geometric variables for reuse -num_channels = X.shape[0] -num_pixels_h = X.shape[3] -num_pixels_v = X.shape[2] -num_angles = X.shape[1] - -# Display a single projection in a single channel -plt.imshow(X[100,5,:,:]) -plt.title('Example of a projection image in one channel' ) -plt.show() - -# Set angles to use -angles = numpy.linspace(-numpy.pi,numpy.pi,num_angles,endpoint=False) - -# Define full 3D acquisition geometry and data container. -# Geometric info is taken from the txt-file in the same dir as the mat-file -ag = AcquisitionGeometry('cone', - '3D', - angles, - pixel_num_h=num_pixels_h, - pixel_size_h=0.25, - pixel_num_v=num_pixels_v, - pixel_size_v=0.25, - dist_source_center=233.0, - dist_center_detector=245.0, - channels=num_channels) -data = AcquisitionData(X, geometry=ag) - -# Reduce to central slice by extracting relevant parameters from data and its -# geometry. Perhaps create function to extract central slice automatically? -data2d = data.subset(vertical=40) -ag2d = AcquisitionGeometry('cone', - '2D', - ag.angles, - pixel_num_h=ag.pixel_num_h, - pixel_size_h=ag.pixel_size_h, - pixel_num_v=1, - pixel_size_v=ag.pixel_size_h, - dist_source_center=ag.dist_source_center, - dist_center_detector=ag.dist_center_detector, - channels=ag.channels) -data2d.geometry = ag2d - -# Set up 2D Image Geometry. -# First need the geometric magnification to scale the voxel size relative -# to the detector pixel size. -mag = (ag.dist_source_center + ag.dist_center_detector)/ag.dist_source_center -ig2d = ImageGeometry(voxel_num_x=ag2d.pixel_num_h, - voxel_num_y=ag2d.pixel_num_h, - voxel_size_x=ag2d.pixel_size_h/mag, - voxel_size_y=ag2d.pixel_size_h/mag, - channels=X.shape[0]) - -# Create GPU multichannel projector/backprojector operator with ASTRA. -Aall = AstraProjectorMC(ig2d,ag2d,'gpu') - -# Compute and simple backprojction and display one channel as image. -Xbp = Aall.adjoint(data2d) -plt.imshow(Xbp.subset(channel=100).array) -plt.show() - -# Set initial guess ImageData with zeros for algorithms, and algorithm options. -x_init = ImageData(numpy.zeros((num_channels,num_pixels_v,num_pixels_h)), - geometry=ig2d, - dimension_labels=['channel','horizontal_y','horizontal_x']) -opt_CGLS = {'tol': 1e-4, 'iter': 5} - -# Run CGLS algorithm and display one channel. -x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aall, data2d, opt_CGLS) - -plt.imshow(x_CGLS.subset(channel=100).array) -plt.title('CGLS') -plt.show() - -plt.semilogy(criter_CGLS) -plt.title('CGLS Criterion vs iterations') -plt.show() - -# Create least squares object instance with projector, test data and a constant -# coefficient of 0.5. Note it is least squares over all channels. -f = Norm2sq(Aall,data2d,c=0.5) - -# Options for FISTA algorithm. -opt = {'tol': 1e-4, 'iter': 100} - -# Run FISTA for least squares without regularization and display one channel -# reconstruction as image. -x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt) - -plt.imshow(x_fista0.subset(channel=100).array) -plt.title('FISTA LS') -plt.show() - -plt.semilogy(criter0) -plt.title('FISTA LS Criterion vs iterations') -plt.show() - -# Set up 1-norm regularisation (over all channels), solve with FISTA, and -# display one channel of reconstruction. -lam = 0.1 -g0 = Norm1(lam) - -x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g0, opt) - -plt.imshow(x_fista1.subset(channel=100).array) -plt.title('FISTA LS+1') -plt.show() - -plt.semilogy(criter1) -plt.title('FISTA LS+1 Criterion vs iterations') -plt.show()
\ No newline at end of file diff --git a/Wrappers/Python/wip/old_demos/demo_compare_cvx.py b/Wrappers/Python/wip/old_demos/demo_compare_cvx.py deleted file mode 100644 index 27b1c97..0000000 --- a/Wrappers/Python/wip/old_demos/demo_compare_cvx.py +++ /dev/null @@ -1,306 +0,0 @@ - -from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, DataContainer -from ccpi.optimisation.algs import FISTA, FBPD, CGLS -from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D, Norm2 - -from ccpi.optimisation.ops import LinearOperatorMatrix, TomoIdentity -from ccpi.optimisation.ops import Identity -from ccpi.optimisation.ops import FiniteDiff2D - -# Requires CVXPY, see http://www.cvxpy.org/ -# CVXPY can be installed in anaconda using -# conda install -c cvxgrp cvxpy libgcc - -# Whether to use or omit CVXPY -use_cvxpy = True -if use_cvxpy: - from cvxpy import * - -import numpy as np -import matplotlib.pyplot as plt - -# Problem data. -m = 30 -n = 20 -np.random.seed(1) -Amat = np.random.randn(m, n) -A = LinearOperatorMatrix(Amat) -bmat = np.random.randn(m) -bmat.shape = (bmat.shape[0],1) - -# A = Identity() -# Change n to equal to m. - -b = DataContainer(bmat) - -# Regularization parameter -lam = 10 -opt = {'memopt':True} -# Create object instances with the test data A and b. -f = Norm2sq(A,b,c=0.5, memopt=True) -g0 = ZeroFun() - -# Initial guess -x_init = DataContainer(np.zeros((n,1))) - -f.grad(x_init) - -# Run FISTA for least squares plus zero function. -x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0 , opt=opt) - -# Print solution and final objective/criterion value for comparison -print("FISTA least squares plus zero function solution and objective value:") -print(x_fista0.array) -print(criter0[-1]) - -if use_cvxpy: - # Compare to CVXPY - - # Construct the problem. - x0 = Variable(n) - objective0 = Minimize(0.5*sum_squares(Amat*x0 - bmat.T[0]) ) - prob0 = Problem(objective0) - - # The optimal objective is returned by prob.solve(). - result0 = prob0.solve(verbose=False,solver=SCS,eps=1e-9) - - # The optimal solution for x is stored in x.value and optimal objective value - # is in result as well as in objective.value - print("CVXPY least squares plus zero function solution and objective value:") - print(x0.value) - print(objective0.value) - -# Plot criterion curve to see FISTA converge to same value as CVX. -iternum = np.arange(1,1001) -plt.figure() -plt.loglog(iternum[[0,-1]],[objective0.value, objective0.value], label='CVX LS') -plt.loglog(iternum,criter0,label='FISTA LS') -plt.legend() -plt.show() - -# Create 1-norm object instance -g1 = Norm1(lam) - -g1(x_init) -x_rand = DataContainer(np.reshape(np.random.rand(n),(n,1))) -x_rand2 = DataContainer(np.reshape(np.random.rand(n-1),(n-1,1))) -v = g1.prox(x_rand,0.02) -#vv = g1.prox(x_rand2,0.02) -vv = v.copy() -vv *= 0 -print (">>>>>>>>>>vv" , vv.as_array()) -vv.fill(v) -print (">>>>>>>>>>fill" , vv.as_array()) -g1.proximal(x_rand, 0.02, out=vv) -print (">>>>>>>>>>v" , v.as_array()) -print (">>>>>>>>>>gradient" , vv.as_array()) - -print (">>>>>>>>>>" , (v-vv).as_array()) -import sys -#sys.exit(0) -# Combine with least squares and solve using generic FISTA implementation -x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1,opt=opt) - -# Print for comparison -print("FISTA least squares plus 1-norm solution and objective value:") -print(x_fista1) -print(criter1[-1]) - -if use_cvxpy: - # Compare to CVXPY - - # Construct the problem. - x1 = Variable(n) - objective1 = Minimize(0.5*sum_squares(Amat*x1 - bmat.T[0]) + lam*norm(x1,1) ) - prob1 = Problem(objective1) - - # The optimal objective is returned by prob.solve(). - result1 = prob1.solve(verbose=False,solver=SCS,eps=1e-9) - - # The optimal solution for x is stored in x.value and optimal objective value - # is in result as well as in objective.value - print("CVXPY least squares plus 1-norm solution and objective value:") - print(x1.value) - print(objective1.value) - -# Now try another algorithm FBPD for same problem: -x_fbpd1, itfbpd1, timingfbpd1, criterfbpd1 = FBPD(x_init,Identity(), None, f, g1) -print(x_fbpd1) -print(criterfbpd1[-1]) - -# Plot criterion curve to see both FISTA and FBPD converge to same value. -# Note that FISTA is very efficient for 1-norm minimization so it beats -# FBPD in this test by a lot. But FBPD can handle a larger class of problems -# than FISTA can. -plt.figure() -plt.loglog(iternum[[0,-1]],[objective1.value, objective1.value], label='CVX LS+1') -plt.loglog(iternum,criter1,label='FISTA LS+1') -plt.legend() -plt.show() - -plt.figure() -plt.loglog(iternum[[0,-1]],[objective1.value, objective1.value], label='CVX LS+1') -plt.loglog(iternum,criter1,label='FISTA LS+1') -plt.loglog(iternum,criterfbpd1,label='FBPD LS+1') -plt.legend() -plt.show() - -# Now try 1-norm and TV denoising with FBPD, first 1-norm. - -# Set up phantom size NxN by creating ImageGeometry, initialising the -# ImageData object with this geometry and empty array and finally put some -# data into its array, and display as image. -N = 64 -ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N) -Phantom = ImageData(geometry=ig) - -x = Phantom.as_array() -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -plt.imshow(x) -plt.title('Phantom image') -plt.show() - -# Identity operator for denoising -I = TomoIdentity(ig) - -# Data and add noise -y = I.direct(Phantom) -y.array = y.array + 0.1*np.random.randn(N, N) - -plt.imshow(y.array) -plt.title('Noisy image') -plt.show() - - -################### -# Data fidelity term -f_denoise = Norm2sq(I,y,c=0.5,memopt=True) - -# 1-norm regulariser -lam1_denoise = 1.0 -g1_denoise = Norm1(lam1_denoise) - -# Initial guess -x_init_denoise = ImageData(np.zeros((N,N))) - -# Combine with least squares and solve using generic FISTA implementation -x_fista1_denoise, it1_denoise, timing1_denoise, criter1_denoise = FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt) - -print(x_fista1_denoise) -print(criter1_denoise[-1]) - -#plt.imshow(x_fista1_denoise.as_array()) -#plt.title('FISTA LS+1') -#plt.show() - -# Now denoise LS + 1-norm with FBPD -x_fbpd1_denoise, itfbpd1_denoise, timingfbpd1_denoise, \ - criterfbpd1_denoise = FBPD(x_init_denoise, I, None, f_denoise, g1_denoise) -print(x_fbpd1_denoise) -print(criterfbpd1_denoise[-1]) - -#plt.imshow(x_fbpd1_denoise.as_array()) -#plt.title('FBPD LS+1') -#plt.show() - -if use_cvxpy: - # Compare to CVXPY - - # Construct the problem. - x1_denoise = Variable(N**2,1) - objective1_denoise = Minimize(0.5*sum_squares(x1_denoise - y.array.flatten()) + lam1_denoise*norm(x1_denoise,1) ) - prob1_denoise = Problem(objective1_denoise) - - # The optimal objective is returned by prob.solve(). - result1_denoise = prob1_denoise.solve(verbose=False,solver=SCS,eps=1e-12) - - # The optimal solution for x is stored in x.value and optimal objective value - # is in result as well as in objective.value - print("CVXPY least squares plus 1-norm solution and objective value:") - print(x1_denoise.value) - print(objective1_denoise.value) - -x1_cvx = x1_denoise.value -x1_cvx.shape = (N,N) - - - -#plt.imshow(x1_cvx) -#plt.title('CVX LS+1') -#plt.show() - -fig = plt.figure() -plt.subplot(1,4,1) -plt.imshow(y.array) -plt.title("LS+1") -plt.subplot(1,4,2) -plt.imshow(x_fista1_denoise.as_array()) -plt.title("fista") -plt.subplot(1,4,3) -plt.imshow(x_fbpd1_denoise.as_array()) -plt.title("fbpd") -plt.subplot(1,4,4) -plt.imshow(x1_cvx) -plt.title("cvx") -plt.show() - -############################################################## -# Now TV with FBPD and Norm2 -lam_tv = 0.1 -gtv = TV2D(lam_tv) -norm2 = Norm2(lam_tv) -op = FiniteDiff2D() -#gtv(gtv.op.direct(x_init_denoise)) - -opt_tv = {'tol': 1e-4, 'iter': 10000} - -x_fbpdtv_denoise, itfbpdtv_denoise, timingfbpdtv_denoise, \ - criterfbpdtv_denoise = FBPD(x_init_denoise, op, None, \ - f_denoise, norm2 ,opt=opt_tv) -print(x_fbpdtv_denoise) -print(criterfbpdtv_denoise[-1]) - -plt.imshow(x_fbpdtv_denoise.as_array()) -plt.title('FBPD TV') -#plt.show() - -if use_cvxpy: - # Compare to CVXPY - - # Construct the problem. - xtv_denoise = Variable((N,N)) - #print (xtv_denoise.value.shape) - objectivetv_denoise = Minimize(0.5*sum_squares(xtv_denoise - y.array) + lam_tv*tv(xtv_denoise) ) - probtv_denoise = Problem(objectivetv_denoise) - - # The optimal objective is returned by prob.solve(). - resulttv_denoise = probtv_denoise.solve(verbose=False,solver=SCS,eps=1e-12) - - # The optimal solution for x is stored in x.value and optimal objective value - # is in result as well as in objective.value - print("CVXPY least squares plus 1-norm solution and objective value:") - print(xtv_denoise.value) - print(objectivetv_denoise.value) - -plt.imshow(xtv_denoise.value) -plt.title('CVX TV') -#plt.show() - -fig = plt.figure() -plt.subplot(1,3,1) -plt.imshow(y.array) -plt.title("TV2D") -plt.subplot(1,3,2) -plt.imshow(x_fbpdtv_denoise.as_array()) -plt.title("fbpd tv denoise") -plt.subplot(1,3,3) -plt.imshow(xtv_denoise.value) -plt.title("CVX tv") -plt.show() - - - -plt.loglog([0,opt_tv['iter']], [objectivetv_denoise.value,objectivetv_denoise.value], label='CVX TV') -plt.loglog(criterfbpdtv_denoise, label='FBPD TV') diff --git a/Wrappers/Python/wip/old_demos/demo_gradient_descent.py b/Wrappers/Python/wip/old_demos/demo_gradient_descent.py deleted file mode 100755 index 4d6647e..0000000 --- a/Wrappers/Python/wip/old_demos/demo_gradient_descent.py +++ /dev/null @@ -1,295 +0,0 @@ - -from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, DataContainer -from ccpi.optimisation.algs import FISTA, FBPD, CGLS -from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D, Norm2 - -from ccpi.optimisation.ops import LinearOperatorMatrix, TomoIdentity -from ccpi.optimisation.ops import Identity -from ccpi.optimisation.ops import FiniteDiff2D - -# Requires CVXPY, see http://www.cvxpy.org/ -# CVXPY can be installed in anaconda using -# conda install -c cvxgrp cvxpy libgcc - -# Whether to use or omit CVXPY - -import numpy as np -import matplotlib.pyplot as plt - -class Algorithm(object): - def __init__(self, *args, **kwargs): - pass - def set_up(self, *args, **kwargs): - raise NotImplementedError() - def update(self): - raise NotImplementedError() - - def should_stop(self): - raise NotImplementedError() - - def __iter__(self): - return self - - def __next__(self): - if self.should_stop(): - raise StopIteration() - else: - self.update() - -class GradientDescent(Algorithm): - x = None - rate = 0 - objective_function = None - regulariser = None - iteration = 0 - stop_cryterion = 'max_iter' - __max_iteration = 0 - __loss = [] - def __init__(self, **kwargs): - args = ['x_init', 'objective_function', 'rate'] - present = True - for k,v in kwargs.items(): - if k in args: - args.pop(args.index(k)) - if len(args) == 0: - return self.set_up(x_init=kwargs['x_init'], - objective_function=kwargs['objective_function'], - rate=kwargs['rate']) - - def should_stop(self): - return self.iteration >= self.max_iteration - - def set_up(self, x_init, objective_function, rate): - self.x = x_init.copy() - self.x_update = x_init.copy() - self.objective_function = objective_function - self.rate = rate - self.__loss.append(objective_function(x_init)) - - def update(self): - - self.objective_function.gradient(self.x, out=self.x_update) - self.x_update *= -self.rate - self.x += self.x_update - self.__loss.append(self.objective_function(self.x)) - self.iteration += 1 - - def get_output(self): - return self.x - def get_current_loss(self): - return self.__loss[-1] - @property - def loss(self): - return self.__loss - @property - def max_iteration(self): - return self.__max_iteration - @max_iteration.setter - def max_iteration(self, value): - assert isinstance(value, int) - self.__max_iteration = value - - - - - -# Problem data. -m = 30 -n = 20 -np.random.seed(1) -Amat = np.random.randn(m, n) -A = LinearOperatorMatrix(Amat) -bmat = np.random.randn(m) -bmat.shape = (bmat.shape[0],1) - -# A = Identity() -# Change n to equal to m. - -b = DataContainer(bmat) - -# Regularization parameter -lam = 10 -opt = {'memopt':True} -# Create object instances with the test data A and b. -f = Norm2sq(A,b,c=0.5, memopt=True) -g0 = ZeroFun() - -# Initial guess -x_init = DataContainer(np.zeros((n,1))) - -f.grad(x_init) - -# Run FISTA for least squares plus zero function. -x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0 , opt=opt) - -# Print solution and final objective/criterion value for comparison -print("FISTA least squares plus zero function solution and objective value:") -print(x_fista0.array) -print(criter0[-1]) - -gd = GradientDescent(x_init=x_init, objective_function=f, rate=0.001) -gd.max_iteration = 5000 - -for i,el in enumerate(gd): - if i%100 == 0: - print ("\rIteration {} Loss: {}".format(gd.iteration, - gd.get_current_loss())) - - -#%% - - -# -#if use_cvxpy: -# # Compare to CVXPY -# -# # Construct the problem. -# x0 = Variable(n) -# objective0 = Minimize(0.5*sum_squares(Amat*x0 - bmat.T[0]) ) -# prob0 = Problem(objective0) -# -# # The optimal objective is returned by prob.solve(). -# result0 = prob0.solve(verbose=False,solver=SCS,eps=1e-9) -# -# # The optimal solution for x is stored in x.value and optimal objective value -# # is in result as well as in objective.value -# print("CVXPY least squares plus zero function solution and objective value:") -# print(x0.value) -# print(objective0.value) -# -## Plot criterion curve to see FISTA converge to same value as CVX. -#iternum = np.arange(1,1001) -#plt.figure() -#plt.loglog(iternum[[0,-1]],[objective0.value, objective0.value], label='CVX LS') -#plt.loglog(iternum,criter0,label='FISTA LS') -#plt.legend() -#plt.show() -# -## Create 1-norm object instance -#g1 = Norm1(lam) -# -#g1(x_init) -#x_rand = DataContainer(np.reshape(np.random.rand(n),(n,1))) -#x_rand2 = DataContainer(np.reshape(np.random.rand(n-1),(n-1,1))) -#v = g1.prox(x_rand,0.02) -##vv = g1.prox(x_rand2,0.02) -#vv = v.copy() -#vv *= 0 -#print (">>>>>>>>>>vv" , vv.as_array()) -#vv.fill(v) -#print (">>>>>>>>>>fill" , vv.as_array()) -#g1.proximal(x_rand, 0.02, out=vv) -#print (">>>>>>>>>>v" , v.as_array()) -#print (">>>>>>>>>>gradient" , vv.as_array()) -# -#print (">>>>>>>>>>" , (v-vv).as_array()) -#import sys -##sys.exit(0) -## Combine with least squares and solve using generic FISTA implementation -#x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1,opt=opt) -# -## Print for comparison -#print("FISTA least squares plus 1-norm solution and objective value:") -#print(x_fista1) -#print(criter1[-1]) -# -#if use_cvxpy: -# # Compare to CVXPY -# -# # Construct the problem. -# x1 = Variable(n) -# objective1 = Minimize(0.5*sum_squares(Amat*x1 - bmat.T[0]) + lam*norm(x1,1) ) -# prob1 = Problem(objective1) -# -# # The optimal objective is returned by prob.solve(). -# result1 = prob1.solve(verbose=False,solver=SCS,eps=1e-9) -# -# # The optimal solution for x is stored in x.value and optimal objective value -# # is in result as well as in objective.value -# print("CVXPY least squares plus 1-norm solution and objective value:") -# print(x1.value) -# print(objective1.value) -# -## Now try another algorithm FBPD for same problem: -#x_fbpd1, itfbpd1, timingfbpd1, criterfbpd1 = FBPD(x_init,Identity(), None, f, g1) -#print(x_fbpd1) -#print(criterfbpd1[-1]) -# -## Plot criterion curve to see both FISTA and FBPD converge to same value. -## Note that FISTA is very efficient for 1-norm minimization so it beats -## FBPD in this test by a lot. But FBPD can handle a larger class of problems -## than FISTA can. -#plt.figure() -#plt.loglog(iternum[[0,-1]],[objective1.value, objective1.value], label='CVX LS+1') -#plt.loglog(iternum,criter1,label='FISTA LS+1') -#plt.legend() -#plt.show() -# -#plt.figure() -#plt.loglog(iternum[[0,-1]],[objective1.value, objective1.value], label='CVX LS+1') -#plt.loglog(iternum,criter1,label='FISTA LS+1') -#plt.loglog(iternum,criterfbpd1,label='FBPD LS+1') -#plt.legend() -#plt.show() - -# Now try 1-norm and TV denoising with FBPD, first 1-norm. - -# Set up phantom size NxN by creating ImageGeometry, initialising the -# ImageData object with this geometry and empty array and finally put some -# data into its array, and display as image. -N = 64 -ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N) -Phantom = ImageData(geometry=ig) - -x = Phantom.as_array() -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -plt.imshow(x) -plt.title('Phantom image') -plt.show() - -# Identity operator for denoising -I = TomoIdentity(ig) - -# Data and add noise -y = I.direct(Phantom) -y.array = y.array + 0.1*np.random.randn(N, N) - -plt.imshow(y.array) -plt.title('Noisy image') -plt.show() - - -################### -# Data fidelity term -f_denoise = Norm2sq(I,y,c=0.5,memopt=True) - -# 1-norm regulariser -lam1_denoise = 1.0 -g1_denoise = Norm1(lam1_denoise) - -# Initial guess -x_init_denoise = ImageData(np.zeros((N,N))) - -# Combine with least squares and solve using generic FISTA implementation -x_fista1_denoise, it1_denoise, timing1_denoise, criter1_denoise = \ - FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt) - -print(x_fista1_denoise) -print(criter1_denoise[-1]) - -f_2 = -gd = GradientDescent(x_init=x_init_denoise, - objective_function=f, rate=0.001) -gd.max_iteration = 5000 - -for i,el in enumerate(gd): - if i%100 == 0: - print ("\rIteration {} Loss: {}".format(gd.iteration, - gd.get_current_loss())) - -plt.imshow(gd.get_output().as_array()) -plt.title('GD image') -plt.show() - diff --git a/Wrappers/Python/wip/old_demos/demo_imat_multichan_RGLTK.py b/Wrappers/Python/wip/old_demos/demo_imat_multichan_RGLTK.py deleted file mode 100644 index 8370c78..0000000 --- a/Wrappers/Python/wip/old_demos/demo_imat_multichan_RGLTK.py +++ /dev/null @@ -1,151 +0,0 @@ -# This script demonstrates how to load IMAT fits data -# into the CIL optimisation framework and run reconstruction methods. -# -# Demo to reconstruct energy-discretized channels of IMAT data - -# needs dxchange: conda install -c conda-forge dxchange -# needs astropy: conda install astropy - - -# All third-party imports. -import numpy as np -import matplotlib.pyplot as plt -from dxchange.reader import read_fits -from astropy.io import fits - -# All own imports. -from ccpi.framework import AcquisitionData, AcquisitionGeometry, ImageGeometry, ImageData, DataContainer -from ccpi.astra.ops import AstraProjectorSimple, AstraProjector3DSimple -from ccpi.optimisation.algs import CGLS, FISTA -from ccpi.optimisation.funcs import Norm2sq, Norm1 -from ccpi.plugins.regularisers import FGP_TV - -# set main parameters here -n = 512 -totalAngles = 250 # total number of projection angles -# spectral discretization parameter -num_average = 145 # channel discretization frequency (total number of averaged channels) -numChannels = 2970 # 2970 -totChannels = round(numChannels/num_average) # the resulting number of channels -Projections_stack = np.zeros((num_average,n,n),dtype='uint16') -ProjAngleChannels = np.zeros((totalAngles,totChannels,n,n),dtype='float32') - -######################################################################### -print ("Loading the data...") -MainPath = '/media/jakob/050d8d45-fab3-4285-935f-260e6c5f162c1/Data/neutrondata/' # path to data -pathname0 = '{!s}{!s}'.format(MainPath,'PSI_phantom_IMAT/DATA/Sample/') -counterFileName = 4675 -# A main loop over all available angles -for ll in range(0,totalAngles,1): - pathnameData = '{!s}{!s}{!s}/'.format(pathname0,'angle',str(ll)) - filenameCurr = '{!s}{!s}{!s}'.format('IMAT0000',str(counterFileName),'_Tomo_test_000_') - counterT = 0 - # loop over reduced channels (discretized) - for i in range(0,totChannels,1): - sumCount = 0 - # loop over actual channels to obtain averaged one - for j in range(0,num_average,1): - if counterT < 10: - outfile = '{!s}{!s}{!s}{!s}.fits'.format(pathnameData,filenameCurr,'0000',str(counterT)) - if ((counterT >= 10) & (counterT < 100)): - outfile = '{!s}{!s}{!s}{!s}.fits'.format(pathnameData,filenameCurr,'000',str(counterT)) - if ((counterT >= 100) & (counterT < 1000)): - outfile = '{!s}{!s}{!s}{!s}.fits'.format(pathnameData,filenameCurr,'00',str(counterT)) - if ((counterT >= 1000) & (counterT < 10000)): - outfile = '{!s}{!s}{!s}{!s}.fits'.format(pathnameData,filenameCurr,'0',str(counterT)) - try: - Projections_stack[j,:,:] = read_fits(outfile) - except: - print("Fits is corrupted, skipping no.", counterT) - sumCount -= 1 - counterT += 1 - sumCount += 1 - AverageProj=np.sum(Projections_stack,axis=0)/sumCount # averaged projection over "num_average" channels - ProjAngleChannels[ll,i,:,:] = AverageProj - print("Angle is processed", ll) - counterFileName += 1 -######################################################################### - -flat1 = read_fits('{!s}{!s}{!s}'.format(MainPath,'PSI_phantom_IMAT/DATA/','OpenBeam_aft1/IMAT00004932_Tomo_test_000_SummedImg.fits')) -nonzero = flat1 > 0 -# Apply flat field and take negative log -for ll in range(0,totalAngles,1): - for i in range(0,totChannels,1): - ProjAngleChannels[ll,i,nonzero] = ProjAngleChannels[ll,i,nonzero]/flat1[nonzero] - -eqzero = ProjAngleChannels == 0 -ProjAngleChannels[eqzero] = 1 -ProjAngleChannels_NormLog = -np.log(ProjAngleChannels) # normalised and neg-log data - -# extact sinogram over energy channels -selectedVertical_slice = 256 -sino_all_channels = ProjAngleChannels_NormLog[:,:,:,selectedVertical_slice] -# Set angles to use -angles = np.linspace(-np.pi,np.pi,totalAngles,endpoint=False) - -# set the geometry -ig = ImageGeometry(n,n) -ag = AcquisitionGeometry('parallel', - '2D', - angles, - n,1) -Aop = AstraProjectorSimple(ig, ag, 'gpu') - - -# loop to reconstruct energy channels -REC_chan = np.zeros((totChannels, n, n), 'float32') -for i in range(0,totChannels,1): - sino_channel = sino_all_channels[:,i,:] # extract a sinogram for i-th channel - - print ("Initial guess") - x_init = ImageData(geometry=ig) - - # Create least squares object instance with projector and data. - print ("Create least squares object instance with projector and data.") - f = Norm2sq(Aop,DataContainer(sino_channel),c=0.5) - - print ("Run FISTA-TV for least squares") - lamtv = 5 - opt = {'tol': 1e-4, 'iter': 200} - g_fgp = FGP_TV(lambdaReg = lamtv, - iterationsTV=50, - tolerance=1e-6, - methodTV=0, - nonnegativity=0, - printing=0, - device='gpu') - - x_fista_fgp, it1, timing1, criter_fgp = FISTA(x_init, f, g_fgp, opt) - REC_chan[i,:,:] = x_fista_fgp.array - """ - plt.figure() - plt.subplot(121) - plt.imshow(x_fista_fgp.array, vmin=0, vmax=0.05) - plt.title('FISTA FGP TV') - plt.subplot(122) - plt.semilogy(criter_fgp) - plt.show() - """ - """ - print ("Run CGLS for least squares") - opt = {'tol': 1e-4, 'iter': 20} - x_init = ImageData(geometry=ig) - x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, DataContainer(sino_channel), opt=opt) - - plt.figure() - plt.imshow(x_CGLS.array,vmin=0, vmax=0.05) - plt.title('CGLS') - plt.show() - """ -# Saving images into fits using astrapy if required -counter = 0 -filename = 'FISTA_TV_imat_slice' -for i in range(totChannels): - im = REC_chan[i,:,:] - add_val = np.min(im[:]) - im += abs(add_val) - im = im/np.max(im[:])*65535 - outfile = '{!s}_{!s}_{!s}.fits'.format(filename,str(selectedVertical_slice),str(counter)) - hdu = fits.PrimaryHDU(np.uint16(im)) - hdu.writeto(outfile, overwrite=True) - counter += 1
\ No newline at end of file diff --git a/Wrappers/Python/wip/old_demos/demo_imat_whitebeam.py b/Wrappers/Python/wip/old_demos/demo_imat_whitebeam.py deleted file mode 100644 index e0d213e..0000000 --- a/Wrappers/Python/wip/old_demos/demo_imat_whitebeam.py +++ /dev/null @@ -1,138 +0,0 @@ -# This script demonstrates how to load IMAT fits data -# into the CIL optimisation framework and run reconstruction methods. -# -# This demo loads the summedImg files which are the non-spectral images -# resulting from summing projections over all spectral channels. - -# needs dxchange: conda install -c conda-forge dxchange -# needs astropy: conda install astropy - - -# All third-party imports. -import numpy -from scipy.io import loadmat -import matplotlib.pyplot as plt -from dxchange.reader import read_fits - -# All own imports. -from ccpi.framework import AcquisitionData, AcquisitionGeometry, ImageGeometry, ImageData -from ccpi.astra.ops import AstraProjectorSimple, AstraProjector3DSimple -from ccpi.optimisation.algs import CGLS, FISTA -from ccpi.optimisation.funcs import Norm2sq, Norm1 - -# Load and display a couple of summed projection as examples -pathname0 = '/media/newhd/shared/Data/neutrondata/PSI_phantom_IMAT/DATA/Sample/angle0/' -filename0 = 'IMAT00004675_Tomo_test_000_SummedImg.fits' - -data0 = read_fits(pathname0 + filename0) - -pathname10 = '/media/newhd/shared/Data/neutrondata/PSI_phantom_IMAT/DATA/Sample/angle10/' -filename10 = 'IMAT00004685_Tomo_test_000_SummedImg.fits' - -data10 = read_fits(pathname10 + filename10) - -# Load a flat field (more are available, should we average over them?) -flat1 = read_fits('/media/newhd/shared/Data/neutrondata/PSI_phantom_IMAT/DATA/OpenBeam_aft1/IMAT00004932_Tomo_test_000_SummedImg.fits') - -# Apply flat field and display after flat-field correction and negative log -data0_rel = numpy.zeros(numpy.shape(flat1), dtype = float) -nonzero = flat1 > 0 -data0_rel[nonzero] = data0[nonzero] / flat1[nonzero] -data10_rel = numpy.zeros(numpy.shape(flat1), dtype = float) -data10_rel[nonzero] = data10[nonzero] / flat1[nonzero] - -plt.imshow(data0_rel) -plt.colorbar() -plt.show() - -plt.imshow(-numpy.log(data0_rel)) -plt.colorbar() -plt.show() - -plt.imshow(data10_rel) -plt.colorbar() -plt.show() - -plt.imshow(-numpy.log(data10_rel)) -plt.colorbar() -plt.show() - -# Set up for loading all summed images at 250 angles. -pathname = '/media/newhd/shared/Data/neutrondata/PSI_phantom_IMAT/DATA/Sample/angle{}/' -filename = 'IMAT0000{}_Tomo_test_000_SummedImg.fits' - -# Dimensions -num_angles = 250 -imsize = 512 - -# Initialise array -data = numpy.zeros((num_angles,imsize,imsize)) - -# Load only 0-249, as 250 is at repetition of zero degrees just like image 0 -for i in range(0,250): - curimfile = (pathname + filename).format(i, i+4675) - data[i,:,:] = read_fits(curimfile) - -# Apply flat field and take negative log -nonzero = flat1 > 0 -for i in range(0,250): - data[i,nonzero] = data[i,nonzero]/flat1[nonzero] - -eqzero = data == 0 -data[eqzero] = 1 - -data_rel = -numpy.log(data) - -# Permute order to get: angles, vertical, horizontal, as default in framework. -data_rel = numpy.transpose(data_rel,(0,2,1)) - -# Set angles to use -angles = numpy.linspace(-numpy.pi,numpy.pi,num_angles,endpoint=False) - -# Create 3D acquisition geometry and acquisition data -ag = AcquisitionGeometry('parallel', - '3D', - angles, - pixel_num_h=imsize, - pixel_num_v=imsize) -b = AcquisitionData(data_rel, geometry=ag) - -# Reduce to single (noncentral) slice by extracting relevant parameters from data and its -# geometry. Perhaps create function to extract central slice automatically? -b2d = b.subset(vertical=128) -ag2d = AcquisitionGeometry('parallel', - '2D', - ag.angles, - pixel_num_h=ag.pixel_num_h) -b2d.geometry = ag2d - -# Create 2D image geometry -ig2d = ImageGeometry(voxel_num_x=ag2d.pixel_num_h, - voxel_num_y=ag2d.pixel_num_h) - -# Create GPU projector/backprojector operator with ASTRA. -Aop = AstraProjectorSimple(ig2d,ag2d,'gpu') - -# Demonstrate operator is working by applying simple backprojection. -z = Aop.adjoint(b2d) -plt.imshow(z.array) -plt.title('Simple backprojection') -plt.colorbar() -plt.show() - -# Set initial guess ImageData with zeros for algorithms, and algorithm options. -x_init = ImageData(numpy.zeros((imsize,imsize)), - geometry=ig2d) -opt_CGLS = {'tol': 1e-4, 'iter': 20} - -# Run CGLS algorithm and display reconstruction. -x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, b2d, opt_CGLS) - -plt.imshow(x_CGLS.array) -plt.title('CGLS') -plt.colorbar() -plt.show() - -plt.semilogy(criter_CGLS) -plt.title('CGLS Criterion vs iterations') -plt.show()
\ No newline at end of file diff --git a/Wrappers/Python/wip/old_demos/demo_memhandle.py b/Wrappers/Python/wip/old_demos/demo_memhandle.py deleted file mode 100755 index db48d73..0000000 --- a/Wrappers/Python/wip/old_demos/demo_memhandle.py +++ /dev/null @@ -1,193 +0,0 @@ - -from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, DataContainer -from ccpi.optimisation.algs import FISTA, FBPD, CGLS -from ccpi.optimisation.funcs import Norm2sq, ZeroFun, Norm1, TV2D - -from ccpi.optimisation.ops import LinearOperatorMatrix, Identity -from ccpi.optimisation.ops import TomoIdentity - -# Requires CVXPY, see http://www.cvxpy.org/ -# CVXPY can be installed in anaconda using -# conda install -c cvxgrp cvxpy libgcc - - -import numpy as np -import matplotlib.pyplot as plt - -# Problem data. -m = 30 -n = 20 -np.random.seed(1) -Amat = np.random.randn(m, n) -A = LinearOperatorMatrix(Amat) -bmat = np.random.randn(m) -bmat.shape = (bmat.shape[0],1) - - - -# A = Identity() -# Change n to equal to m. - -b = DataContainer(bmat) - -# Regularization parameter -lam = 10 - -# Create object instances with the test data A and b. -f = Norm2sq(A,b,c=0.5, memopt=True) -g0 = ZeroFun() - -# Initial guess -x_init = DataContainer(np.zeros((n,1))) - -f.grad(x_init) -opt = {'memopt': True} -# Run FISTA for least squares plus zero function. -x_fista0, it0, timing0, criter0 = FISTA(x_init, f, g0) -x_fista0_m, it0_m, timing0_m, criter0_m = FISTA(x_init, f, g0, opt=opt) - -iternum = [i for i in range(len(criter0))] -# Print solution and final objective/criterion value for comparison -print("FISTA least squares plus zero function solution and objective value:") -print(x_fista0.array) -print(criter0[-1]) - - -# Plot criterion curve to see FISTA converge to same value as CVX. -#iternum = np.arange(1,1001) -plt.figure() -plt.loglog(iternum,criter0,label='FISTA LS') -plt.loglog(iternum,criter0_m,label='FISTA LS memopt') -plt.legend() -plt.show() -#%% -# Create 1-norm object instance -g1 = Norm1(lam) - -g1(x_init) -g1.prox(x_init,0.02) - -# Combine with least squares and solve using generic FISTA implementation -x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1) -x_fista1_m, it1_m, timing1_m, criter1_m = FISTA(x_init, f, g1, opt=opt) -iternum = [i for i in range(len(criter1))] -# Print for comparison -print("FISTA least squares plus 1-norm solution and objective value:") -print(x_fista1) -print(criter1[-1]) - - -# Now try another algorithm FBPD for same problem: -x_fbpd1, itfbpd1, timingfbpd1, criterfbpd1 = FBPD(x_init, None, f, g1) -iternum = [i for i in range(len(criterfbpd1))] -print(x_fbpd1) -print(criterfbpd1[-1]) - -# Plot criterion curve to see both FISTA and FBPD converge to same value. -# Note that FISTA is very efficient for 1-norm minimization so it beats -# FBPD in this test by a lot. But FBPD can handle a larger class of problems -# than FISTA can. -plt.figure() -plt.loglog(iternum,criter1,label='FISTA LS+1') -plt.loglog(iternum,criter1_m,label='FISTA LS+1 memopt') -plt.legend() -plt.show() - -plt.figure() -plt.loglog(iternum,criter1,label='FISTA LS+1') -plt.loglog(iternum,criterfbpd1,label='FBPD LS+1') -plt.legend() -plt.show() -#%% -# Now try 1-norm and TV denoising with FBPD, first 1-norm. - -# Set up phantom size NxN by creating ImageGeometry, initialising the -# ImageData object with this geometry and empty array and finally put some -# data into its array, and display as image. -N = 1000 -ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N) -Phantom = ImageData(geometry=ig) - -x = Phantom.as_array() -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -plt.imshow(x) -plt.title('Phantom image') -plt.show() - -# Identity operator for denoising -I = TomoIdentity(ig) - -# Data and add noise -y = I.direct(Phantom) -y.array += 0.1*np.random.randn(N, N) - -plt.figure() -plt.imshow(y.array) -plt.title('Noisy image') -plt.show() - -# Data fidelity term -f_denoise = Norm2sq(I,y,c=0.5, memopt=True) - -# 1-norm regulariser -lam1_denoise = 1.0 -g1_denoise = Norm1(lam1_denoise) - -# Initial guess -x_init_denoise = ImageData(np.zeros((N,N))) -opt = {'memopt': False, 'iter' : 50} -# Combine with least squares and solve using generic FISTA implementation -print ("no memopt") -x_fista1_denoise, it1_denoise, timing1_denoise, \ - criter1_denoise = FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt) -opt = {'memopt': True, 'iter' : 50} -print ("yes memopt") -x_fista1_denoise_m, it1_denoise_m, timing1_denoise_m, \ - criter1_denoise_m = FISTA(x_init_denoise, f_denoise, g1_denoise, opt=opt) - -print(x_fista1_denoise) -print(criter1_denoise[-1]) - -plt.figure() -plt.imshow(x_fista1_denoise.as_array()) -plt.title('FISTA LS+1') -plt.show() - -plt.figure() -plt.imshow(x_fista1_denoise_m.as_array()) -plt.title('FISTA LS+1 memopt') -plt.show() - -plt.figure() -plt.loglog(iternum,criter1_denoise,label='FISTA LS+1') -plt.loglog(iternum,criter1_denoise_m,label='FISTA LS+1 memopt') -plt.legend() -plt.show() -#%% -# Now denoise LS + 1-norm with FBPD -x_fbpd1_denoise, itfbpd1_denoise, timingfbpd1_denoise, criterfbpd1_denoise = FBPD(x_init_denoise, None, f_denoise, g1_denoise) -print(x_fbpd1_denoise) -print(criterfbpd1_denoise[-1]) - -plt.figure() -plt.imshow(x_fbpd1_denoise.as_array()) -plt.title('FBPD LS+1') -plt.show() - - -# Now TV with FBPD -lam_tv = 0.1 -gtv = TV2D(lam_tv) -gtv(gtv.op.direct(x_init_denoise)) - -opt_tv = {'tol': 1e-4, 'iter': 10000} - -x_fbpdtv_denoise, itfbpdtv_denoise, timingfbpdtv_denoise, criterfbpdtv_denoise = FBPD(x_init_denoise, None, f_denoise, gtv,opt=opt_tv) -print(x_fbpdtv_denoise) -print(criterfbpdtv_denoise[-1]) - -plt.imshow(x_fbpdtv_denoise.as_array()) -plt.title('FBPD TV') -plt.show() diff --git a/Wrappers/Python/wip/old_demos/demo_test_sirt.py b/Wrappers/Python/wip/old_demos/demo_test_sirt.py deleted file mode 100644 index 6f5a44d..0000000 --- a/Wrappers/Python/wip/old_demos/demo_test_sirt.py +++ /dev/null @@ -1,176 +0,0 @@ -# This demo illustrates how to use the SIRT algorithm without and with -# nonnegativity and box constraints. The ASTRA 2D projectors are used. - -# First make all imports -from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, \ - AcquisitionData -from ccpi.optimisation.algs import FISTA, FBPD, CGLS, SIRT -from ccpi.optimisation.funcs import Norm2sq, Norm1, TV2D, IndicatorBox -from ccpi.astra.ops import AstraProjectorSimple - -import numpy as np -import matplotlib.pyplot as plt - -# Choose either a parallel-beam (1=parallel2D) or fan-beam (2=cone2D) test case -test_case = 1 - -# Set up phantom size NxN by creating ImageGeometry, initialising the -# ImageData object with this geometry and empty array and finally put some -# data into its array, and display as image. -N = 128 -ig = ImageGeometry(voxel_num_x=N,voxel_num_y=N) -Phantom = ImageData(geometry=ig) - -x = Phantom.as_array() -x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 -x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1 - -plt.imshow(x) -plt.title('Phantom image') -plt.show() - -# Set up AcquisitionGeometry object to hold the parameters of the measurement -# setup geometry: # Number of angles, the actual angles from 0 to -# pi for parallel beam and 0 to 2pi for fanbeam, set the width of a detector -# pixel relative to an object pixel, the number of detector pixels, and the -# source-origin and origin-detector distance (here the origin-detector distance -# set to 0 to simulate a "virtual detector" with same detector pixel size as -# object pixel size). -angles_num = 20 -det_w = 1.0 -det_num = N -SourceOrig = 200 -OrigDetec = 0 - -if test_case==1: - angles = np.linspace(0,np.pi,angles_num,endpoint=False) - ag = AcquisitionGeometry('parallel', - '2D', - angles, - det_num,det_w) -elif test_case==2: - angles = np.linspace(0,2*np.pi,angles_num,endpoint=False) - ag = AcquisitionGeometry('cone', - '2D', - angles, - det_num, - det_w, - dist_source_center=SourceOrig, - dist_center_detector=OrigDetec) -else: - NotImplemented - -# Set up Operator object combining the ImageGeometry and AcquisitionGeometry -# wrapping calls to ASTRA as well as specifying whether to use CPU or GPU. -Aop = AstraProjectorSimple(ig, ag, 'gpu') - -# Forward and backprojection are available as methods direct and adjoint. Here -# generate test data b and do simple backprojection to obtain z. -b = Aop.direct(Phantom) -z = Aop.adjoint(b) - -plt.imshow(b.array) -plt.title('Simulated data') -plt.show() - -plt.imshow(z.array) -plt.title('Backprojected data') -plt.show() - -# Using the test data b, different reconstruction methods can now be set up as -# demonstrated in the rest of this file. In general all methods need an initial -# guess and some algorithm options to be set: -x_init = ImageData(np.zeros(x.shape),geometry=ig) -opt = {'tol': 1e-4, 'iter': 1000} - -# First a CGLS reconstruction can be done: -x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, b, opt) - -plt.imshow(x_CGLS.array) -plt.title('CGLS') -plt.colorbar() -plt.show() - -plt.semilogy(criter_CGLS) -plt.title('CGLS criterion') -plt.show() - -# A SIRT unconstrained reconstruction can be done: similarly: -x_SIRT, it_SIRT, timing_SIRT, criter_SIRT = SIRT(x_init, Aop, b, opt) - -plt.imshow(x_SIRT.array) -plt.title('SIRT unconstrained') -plt.colorbar() -plt.show() - -plt.semilogy(criter_SIRT) -plt.title('SIRT unconstrained criterion') -plt.show() - -# A SIRT nonnegativity constrained reconstruction can be done using the -# additional input "constraint" set to a box indicator function with 0 as the -# lower bound and the default upper bound of infinity: -x_SIRT0, it_SIRT0, timing_SIRT0, criter_SIRT0 = SIRT(x_init, Aop, b, opt, - constraint=IndicatorBox(lower=0)) - -plt.imshow(x_SIRT0.array) -plt.title('SIRT nonneg') -plt.colorbar() -plt.show() - -plt.semilogy(criter_SIRT0) -plt.title('SIRT nonneg criterion') -plt.show() - -# A SIRT reconstruction with box constraints on [0,1] can also be done: -x_SIRT01, it_SIRT01, timing_SIRT01, criter_SIRT01 = SIRT(x_init, Aop, b, opt, - constraint=IndicatorBox(lower=0,upper=1)) - -plt.imshow(x_SIRT01.array) -plt.title('SIRT box(0,1)') -plt.colorbar() -plt.show() - -plt.semilogy(criter_SIRT01) -plt.title('SIRT box(0,1) criterion') -plt.show() - -# The indicator function can also be used with the FISTA algorithm to do -# least squares with nonnegativity constraint. - -# Create least squares object instance with projector, test data and a constant -# coefficient of 0.5: -f = Norm2sq(Aop,b,c=0.5) - -# Run FISTA for least squares without constraints -x_fista, it, timing, criter = FISTA(x_init, f, None,opt) - -plt.imshow(x_fista.array) -plt.title('FISTA Least squares') -plt.show() - -plt.semilogy(criter) -plt.title('FISTA Least squares criterion') -plt.show() - -# Run FISTA for least squares with nonnegativity constraint -x_fista0, it0, timing0, criter0 = FISTA(x_init, f, IndicatorBox(lower=0),opt) - -plt.imshow(x_fista0.array) -plt.title('FISTA Least squares nonneg') -plt.show() - -plt.semilogy(criter0) -plt.title('FISTA Least squares nonneg criterion') -plt.show() - -# Run FISTA for least squares with box constraint [0,1] -x_fista01, it01, timing01, criter01 = FISTA(x_init, f, IndicatorBox(lower=0,upper=1),opt) - -plt.imshow(x_fista01.array) -plt.title('FISTA Least squares box(0,1)') -plt.show() - -plt.semilogy(criter01) -plt.title('FISTA Least squares box(0,1) criterion') -plt.show()
\ No newline at end of file diff --git a/Wrappers/Python/wip/old_demos/multifile_nexus.py b/Wrappers/Python/wip/old_demos/multifile_nexus.py deleted file mode 100755 index d1ad438..0000000 --- a/Wrappers/Python/wip/old_demos/multifile_nexus.py +++ /dev/null @@ -1,307 +0,0 @@ -# -*- coding: utf-8 -*-
-"""
-Created on Wed Aug 15 16:00:53 2018
-
-@author: ofn77899
-"""
-
-import os
-from ccpi.io.reader import NexusReader
-
-from sys import getsizeof
-
-import matplotlib.pyplot as plt
-
-from ccpi.framework import DataProcessor, DataContainer
-from ccpi.processors import Normalizer
-from ccpi.processors import CenterOfRotationFinder
-import numpy
-
-
-class averager(object):
- def __init__(self):
- self.reset()
-
- def reset(self):
- self.N = 0
- self.avg = 0
- self.min = 0
- self.max = 0
- self.var = 0
- self.ske = 0
- self.kur = 0
-
- def add_reading(self, val):
-
- if (self.N == 0):
- self.avg = val;
- self.min = val;
- self.max = val;
- elif (self.N == 1):
- #//set min/max
- self.max = val if val > self.max else self.max
- self.min = val if val < self.min else self.min
-
-
- thisavg = (self.avg + val)/2
- #// initial value is in avg
- self.var = (self.avg - thisavg)*(self.avg-thisavg) + (val - thisavg) * (val-thisavg)
- self.ske = self.var * (self.avg - thisavg)
- self.kur = self.var * self.var
- self.avg = thisavg
- else:
- self.max = val if val > self.max else self.max
- self.min = val if val < self.min else self.min
-
- M = self.N
-
- #// b-factor =(<v>_N + v_(N+1)) / (N+1)
- #float b = (val -avg) / (M+1);
- b = (val -self.avg) / (M+1)
-
- self.kur = self.kur + (M *(b*b*b*b) * (1+M*M*M))- (4* b * self.ske) + (6 * b *b * self.var * (M-1))
-
- self.ske = self.ske + (M * b*b*b *(M-1)*(M+1)) - (3 * b * self.var * (M-1))
-
- #//var = var * ((M-1)/M) + ((val - avg)*(val - avg)/(M+1)) ;
- self.var = self.var * ((M-1)/M) + (b * b * (M+1))
-
- self.avg = self.avg * (M/(M+1)) + val / (M+1)
-
- self.N += 1
-
- def stats(self, vector):
- i = 0
- while i < vector.size:
- self.add_reading(vector[i])
- i+=1
-
-avg = averager()
-a = numpy.linspace(0,39,40)
-avg.stats(a)
-print ("average" , avg.avg, a.mean())
-print ("variance" , avg.var, a.var())
-b = a - a.mean()
-b *= b
-b = numpy.sqrt(sum(b)/(a.size-1))
-print ("std" , numpy.sqrt(avg.var), b)
-#%%
-
-class DataStatMoments(DataProcessor):
- '''Normalization based on flat and dark
-
- This processor read in a AcquisitionData and normalises it based on
- the instrument reading with and without incident photons or neutrons.
-
- Input: AcquisitionData
- Parameter: 2D projection with flat field (or stack)
- 2D projection with dark field (or stack)
- Output: AcquisitionDataSetn
- '''
-
- def __init__(self, axis, skewness=False, kurtosis=False, offset=0):
- kwargs = {
- 'axis' : axis,
- 'skewness' : skewness,
- 'kurtosis' : kurtosis,
- 'offset' : offset,
- }
- #DataProcessor.__init__(self, **kwargs)
- super(DataStatMoments, self).__init__(**kwargs)
-
-
- def check_input(self, dataset):
- #self.N = dataset.get_dimension_size(self.axis)
- return True
-
- @staticmethod
- def add_sample(dataset, N, axis, stats=None, offset=0):
- #dataset = self.get_input()
- if (N == 0):
- # get the axis number along to calculate the stats
-
-
- #axs = dataset.get_dimension_size(self.axis)
- # create a placeholder for the output
- if stats is None:
- ax = dataset.get_dimension_axis(axis)
- shape = [dataset.shape[i] for i in range(len(dataset.shape)) if i != ax]
- # output has 4 components avg, std, skewness and kurtosis + last avg+ (val-thisavg)
- shape.insert(0, 4+2)
- stats = numpy.zeros(shape)
-
- stats[0] = dataset.subset(**{axis:N-offset}).array[:]
-
- #avg = val
- elif (N == 1):
- # val
- stats[5] = dataset.subset(**{axis:N-offset}).array
- stats[4] = stats[0] + stats[5]
- stats[4] /= 2 # thisavg
- stats[5] -= stats[4] # (val - thisavg)
-
- #float thisavg = (avg + val)/2;
-
- #// initial value is in avg
- #var = (avg - thisavg)*(avg-thisavg) + (val - thisavg) * (val-thisavg);
- stats[1] = stats[5] * stats[5] + stats[5] * stats[5]
- #skewness = var * (avg - thisavg);
- stats[2] = stats[1] * stats[5]
- #kurtosis = var * var;
- stats[3] = stats[1] * stats[1]
- #avg = thisavg;
- stats[0] = stats[4]
- else:
-
- #float M = (float)N;
- M = N
- #val
- stats[4] = dataset.subset(**{axis:N-offset}).array
- #// b-factor =(<v>_N + v_(N+1)) / (N+1)
- stats[5] = stats[4] - stats[0]
- stats[5] /= (M+1) # b factor
- #float b = (val -avg) / (M+1);
-
- #kurtosis = kurtosis + (M *(b*b*b*b) * (1+M*M*M))- (4* b * skewness) + (6 * b *b * var * (M-1));
- #if self.kurtosis:
- # stats[3] += (M * stats[5] * stats[5] * stats[5] * stats[5]) - \
- # (4 * stats[5] * stats[2]) + \
- # (6 * stats[5] * stats[5] * stats[1] * (M-1))
-
- #skewness = skewness + (M * b*b*b *(M-1)*(M+1)) - (3 * b * var * (M-1));
- #if self.skewness:
- # stats[2] = stats[2] + (M * stats[5]* stats[5] * stats[5] * (M-1)*(M-1) ) -\
- # 3 * stats[5] * stats[1] * (M-1)
- #//var = var * ((M-1)/M) + ((val - avg)*(val - avg)/(M+1)) ;
- #var = var * ((M-1)/M) + (b * b * (M+1));
- stats[1] = ((M-1)/M) * stats[1] + (stats[5] * stats[5] * (M+1))
-
- #avg = avg * (M/(M+1)) + val / (M+1)
- stats[0] = stats[0] * (M/(M+1)) + stats[4] / (M+1)
-
- N += 1
- return stats , N
-
-
- def process(self):
-
- data = self.get_input()
-
- #stats, i = add_sample(0)
- N = data.get_dimension_size(self.axis)
- ax = data.get_dimension_axis(self.axis)
- stats = None
- i = 0
- while i < N:
- stats , i = DataStatMoments.add_sample(data, i, self.axis, stats, offset=self.offset)
- self.offset += N
- labels = ['StatisticalMoments']
-
- labels += [data.dimension_labels[i] \
- for i in range(len(data.dimension_labels)) if i != ax]
- y = DataContainer( stats[:4] , False,
- dimension_labels=labels)
- return y
-
-directory = r'E:\Documents\Dataset\CCPi\Nexus_test'
-data_path="entry1/instrument/pco1_hw_hdf_nochunking/data"
-
-reader = NexusReader(os.path.join( os.path.abspath(directory) , '74331.nxs'))
-
-print ("read flat")
-read_flat = NexusReader(os.path.join( os.path.abspath(directory) , '74240.nxs'))
-read_flat.data_path = data_path
-flatsslice = read_flat.get_acquisition_data_whole()
-avg = DataStatMoments('angle')
-
-avg.set_input(flatsslice)
-flats = avg.get_output()
-
-ave = averager()
-ave.stats(flatsslice.array[:,0,0])
-
-print ("avg" , ave.avg, flats.array[0][0][0])
-print ("var" , ave.var, flats.array[1][0][0])
-
-print ("read dark")
-read_dark = NexusReader(os.path.join( os.path.abspath(directory) , '74243.nxs'))
-read_dark.data_path = data_path
-
-## darks are very many so we proceed in batches
-total_size = read_dark.get_projection_dimensions()[0]
-print ("total_size", total_size)
-
-batchsize = 40
-if batchsize > total_size:
- batchlimits = [batchsize * (i+1) for i in range(int(total_size/batchsize))] + [total_size-1]
-else:
- batchlimits = [total_size]
-#avg.N = 0
-avg.offset = 0
-N = 0
-for batch in range(len(batchlimits)):
- print ("running batch " , batch)
- bmax = batchlimits[batch]
- bmin = bmax-batchsize
-
- darksslice = read_dark.get_acquisition_data_batch(bmin,bmax)
- if batch == 0:
- #create stats
- ax = darksslice.get_dimension_axis('angle')
- shape = [darksslice.shape[i] for i in range(len(darksslice.shape)) if i != ax]
- # output has 4 components avg, std, skewness and kurtosis + last avg+ (val-thisavg)
- shape.insert(0, 4+2)
- print ("create stats shape ", shape)
- stats = numpy.zeros(shape)
- print ("N" , N)
- #avg.set_input(darksslice)
- i = bmin
- while i < bmax:
- stats , i = DataStatMoments.add_sample(darksslice, i, 'angle', stats, bmin)
- print ("{0}-{1}-{2}".format(bmin, i , bmax ) )
-
-darks = stats
-#%%
-
-fig = plt.subplot(2,2,1)
-fig.imshow(flats.subset(StatisticalMoments=0).array)
-fig = plt.subplot(2,2,2)
-fig.imshow(numpy.sqrt(flats.subset(StatisticalMoments=1).array))
-fig = plt.subplot(2,2,3)
-fig.imshow(darks[0])
-fig = plt.subplot(2,2,4)
-fig.imshow(numpy.sqrt(darks[1]))
-plt.show()
-
-
-#%%
-norm = Normalizer(flat_field=flats.array[0,200,:], dark_field=darks[0,200,:])
-#norm.set_flat_field(flats.array[0,200,:])
-#norm.set_dark_field(darks.array[0,200,:])
-norm.set_input(reader.get_acquisition_data_slice(200))
-
-n = Normalizer.normalize_projection(norm.get_input().as_array(), flats.array[0,200,:], darks[0,200,:], 1e-5)
-#dn_n= Normalizer.estimate_normalised_error(norm.get_input().as_array(), flats.array[0,200,:], darks[0,200,:],
-# numpy.sqrt(flats.array[1,200,:]), numpy.sqrt(darks[1,200,:]))
-#%%
-
-
-#%%
-fig = plt.subplot(2,1,1)
-
-
-fig.imshow(norm.get_input().as_array())
-fig = plt.subplot(2,1,2)
-fig.imshow(n)
-
-#fig = plt.subplot(3,1,3)
-#fig.imshow(dn_n)
-
-
-plt.show()
-
-
-
-
-
-
diff --git a/Wrappers/Python/wip/pdhg_TV_denoising_precond.py b/Wrappers/Python/wip/pdhg_TV_denoising_precond.py deleted file mode 100644 index 3fc9320..0000000 --- a/Wrappers/Python/wip/pdhg_TV_denoising_precond.py +++ /dev/null @@ -1,156 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Fri Feb 22 14:53:03 2019 - -@author: evangelos -""" - -from ccpi.framework import ImageData, ImageGeometry, BlockDataContainer - -import numpy as np -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, \ - MixedL21Norm, FunctionOperatorComposition, BlockFunction, ScaledFunction - -from skimage.util import random_noise - - - -# ############################################################################ -# Create phantom for TV 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 - -ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N) -ag = ig - -# Create noisy data. Add Gaussian noise -n1 = random_noise(data, mode='gaussian', seed=10) -noisy_data = ImageData(n1) - - -#%% - -# Regularisation Parameter -alpha = 2 - -#method = input("Enter structure of PDHG (0=Composite or 1=NotComposite): ") -method = '0' -if method == '0': - - # Create operators - op1 = Gradient(ig) - op2 = Identity(ig, ag) - - # Form Composite Operator - operator = BlockOperator(op1, op2, shape=(2,1) ) - - #### Create functions -# f = FunctionComposition_new(operator, mixed_L12Norm(alpha), \ -# L2NormSq(0.5, b = noisy_data) ) - - f1 = alpha * MixedL21Norm() - f2 = 0.5 * L2NormSquared(b = noisy_data) - - f = BlockFunction(f1, f2 ) - g = ZeroFun() - -else: - - ########################################################################### - # No Composite # - ########################################################################### - operator = Gradient(ig) - f = alpha * FunctionOperatorComposition(operator, MixedL21Norm()) - g = 0.5 * L2NormSquared(b = noisy_data) - ########################################################################### -#%% - -diag_precon = True - -if diag_precon: - - def tau_sigma_precond(operator): - - tau = 1/operator.sum_abs_row() - sigma = 1/ operator.sum_abs_col() - - return tau, sigma - - tau, sigma = tau_sigma_precond(operator) - -else: - # Compute operator Norm - normK = operator.norm() - print ("normK", normK) - # Primal & dual stepsizes - sigma = 1/normK - tau = 1/normK -# tau = 1/(sigma*normK**2) - -#%% - -opt = {'niter':2000} - -res, time, primal, dual, pdgap = PDHG_old(f, g, operator, tau = tau, sigma = sigma, opt = opt) - -plt.figure(figsize=(5,5)) -plt.imshow(res.as_array()) -plt.colorbar() -plt.show() - -#aaa = res[0].as_array() -# -#plt.imshow(aaa) -#plt.colorbar() -#plt.show() -#c2 = aaa -#del aaa -#%% - -#c2 = aaa -##%% -#%% -#z = c1 - c2 -#plt.imshow(np.abs(z[0:95,0:95])) -#plt.colorbar() - -#%% -#pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma) -#pdhg.max_iteration = 2000 -#pdhg.update_objective_interval = 10 -# -#pdhg.run(2000) -# -# -# -#sol = pdhg.get_output().as_array() -##sol = result.as_array() -## -#fig = plt.figure() -#plt.subplot(1,2,1) -#plt.imshow(noisy_data.as_array()) -##plt.colorbar() -#plt.subplot(1,2,2) -#plt.imshow(sol) -##plt.colorbar() -#plt.show() -## -# -### -#plt.plot(np.linspace(0,N,N), data[int(N/2),:], label = 'GTruth') -#plt.plot(np.linspace(0,N,N), sol[int(N/2),:], label = 'Recon') -#plt.legend() -#plt.show() -# - -#%% -# |