removed fista directory

use the standard package reconstruction directory for the fista code

3 files changed, 0 insertions, 1034 deletions

diff --git a/src/Python/ccpi/fista/FISTAReconstructor.py b/src/Python/ccpi/fista/FISTAReconstructor.py
deleted file mode 100644
index 85bfac5..0000000
--- a/src/Python/ccpi/fista/FISTAReconstructor.py
+++ /dev/null
@@ -1,609 +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 2017 Edoardo Pasca, Srikanth Nagella
-#Copyright 2017 Daniil Kazantsev
-#
-#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.
-###############################################################################
-
-
-
-import numpy
-#from ccpi.reconstruction.parallelbeam import alg
-
-#from ccpi.imaging.Regularizer import Regularizer
-from enum import Enum
-
-import astra
-
-   
-    
-class FISTAReconstructor():
-    '''FISTA-based reconstruction algorithm using ASTRA-toolbox
-    
-    '''
-    # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
-    # ___Input___:
-    # params.[] file:
-    #       - .proj_geom (geometry of the projector) [required]
-    #       - .vol_geom (geometry of the reconstructed object) [required]
-    #       - .sino (vectorized in 2D or 3D sinogram) [required]
-    #       - .iterFISTA (iterations for the main loop, default 40)
-    #       - .L_const (Lipschitz constant, default Power method)                                                                                                    )
-    #       - .X_ideal (ideal image, if given)
-    #       - .weights (statisitcal weights, size of the sinogram)
-    #       - .ROI (Region-of-interest, only if X_ideal is given)
-    #       - .initialize (a 'warm start' using SIRT method from ASTRA)
-    #----------------Regularization choices------------------------
-    #       - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
-    #       - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
-    #       - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
-    #       - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
-    #       - .Regul_Iterations (iterations for the selected penalty, default 25)
-    #       - .Regul_tauLLT (time step parameter for LLT term)
-    #       - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
-    #       - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
-    #----------------Visualization parameters------------------------
-    #       - .show (visualize reconstruction 1/0, (0 default))
-    #       - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
-    #       - .slice (for 3D volumes - slice number to imshow)
-    # ___Output___:
-    # 1. X - reconstructed image/volume
-    # 2. output - a structure with
-    #    - .Resid_error - residual error (if X_ideal is given)
-    #    - .objective: value of the objective function
-    #    - .L_const: Lipshitz constant to avoid recalculations
-    
-    # References:
-    # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
-    # Problems" by A. Beck and M Teboulle
-    # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
-    # 3. "A novel tomographic reconstruction method based on the robust
-    # Student's t function for suppressing data outliers" D. Kazantsev et.al.
-    # D. Kazantsev, 2016-17
-    def __init__(self, projector_geometry, output_geometry, input_sinogram,
-                 **kwargs):
-        # handle parmeters:
-        # obligatory parameters
-        self.pars = dict()
-        self.pars['projector_geometry'] = projector_geometry # proj_geom
-        self.pars['output_geometry'] = output_geometry       # vol_geom
-        self.pars['input_sinogram'] = input_sinogram         # sino
-        sliceZ, nangles, detectors = numpy.shape(input_sinogram)
-        self.pars['detectors'] = detectors
-        self.pars['number_of_angles'] = nangles
-        self.pars['SlicesZ'] = sliceZ
-        self.pars['output_volume'] = None
-
-        print (self.pars)
-        # handle optional input parameters (at instantiation)
-        
-        # Accepted input keywords
-        kw = (
-              # mandatory fields
-              'projector_geometry',
-              'output_geometry',
-              'input_sinogram',
-              'detectors',
-              'number_of_angles',
-              'SlicesZ',
-              # optional fields
-              'number_of_iterations', 
-              'Lipschitz_constant' , 
-              'ideal_image' ,
-              'weights' , 
-              'region_of_interest' , 
-              'initialize' , 
-              'regularizer' , 
-              'ring_lambda_R_L1',
-              'ring_alpha',
-              'subsets',
-              'output_volume',
-              'os_subsets',
-              'os_indices',
-              'os_bins')
-        self.acceptedInputKeywords = list(kw)
-        
-        # handle keyworded parameters
-        if kwargs is not None:
-            for key, value in kwargs.items():
-                if key in kw:
-                    #print("{0} = {1}".format(key, value))                        
-                    self.pars[key] = value
-                    
-        # set the default values for the parameters if not set
-        if 'number_of_iterations' in kwargs.keys():
-            self.pars['number_of_iterations'] = kwargs['number_of_iterations']
-        else:
-            self.pars['number_of_iterations'] = 40
-        if 'weights' in kwargs.keys():
-            self.pars['weights'] = kwargs['weights']
-        else:
-            self.pars['weights'] = \
-                                 numpy.ones(numpy.shape(
-                                     self.pars['input_sinogram']))
-        if 'Lipschitz_constant' in kwargs.keys():
-            self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
-        else:
-            self.pars['Lipschitz_constant'] = None
-        
-        if not 'ideal_image' in kwargs.keys():
-            self.pars['ideal_image'] = None
-        
-        if not 'region_of_interest'in kwargs.keys() :
-            if self.pars['ideal_image'] == None:
-                self.pars['region_of_interest'] = None
-            else:
-                ## nonzero if the image is larger than m
-                fsm = numpy.frompyfunc(lambda x,m: 1 if x>m else 0, 2,1)
-                
-                self.pars['region_of_interest'] = fsm(self.pars['ideal_image'], 0)
-                
-        # the regularizer must be a correctly instantiated object    
-        if not 'regularizer' in kwargs.keys() :
-            self.pars['regularizer'] = None
-
-        #RING REMOVAL
-        if not 'ring_lambda_R_L1' in kwargs.keys():
-            self.pars['ring_lambda_R_L1'] = 0
-        if not 'ring_alpha' in kwargs.keys():
-            self.pars['ring_alpha'] = 1
-
-        # ORDERED SUBSET
-        if not 'subsets' in kwargs.keys():
-            self.pars['subsets'] = 0
-        else:
-            self.createOrderedSubsets()
-
-        if not 'initialize' in kwargs.keys():
-            self.pars['initialize'] = False
-
-        
-            
-            
-    def setParameter(self, **kwargs):
-        '''set named parameter for the reconstructor engine
-        
-        raises Exception if the named parameter is not recognized
-        
-        '''
-        for key , value in kwargs.items():
-            if key in self.acceptedInputKeywords:
-                self.pars[key] = value
-            else:
-                raise Exception('Wrong parameter {0} for '.format(key) +
-                                'reconstructor')
-    # setParameter
-
-    def getParameter(self, key):
-        if type(key) is str:
-            if key in self.acceptedInputKeywords:
-                return self.pars[key]
-            else:
-                raise Exception('Unrecongnised parameter: {0} '.format(key) )
-        elif type(key) is list:
-            outpars = []
-            for k in key:
-                outpars.append(self.getParameter(k))
-            return outpars
-        else:
-            raise Exception('Unhandled input {0}' .format(str(type(key))))
-            
-    
-    def calculateLipschitzConstantWithPowerMethod(self):
-        ''' using Power method (PM) to establish L constant'''
-        
-        N = self.pars['output_geometry']['GridColCount']
-        proj_geom = self.pars['projector_geometry']
-        vol_geom = self.pars['output_geometry']
-        weights = self.pars['weights']
-        SlicesZ = self.pars['SlicesZ']
-        
-            
-                               
-        if (proj_geom['type'] == 'parallel') or \
-           (proj_geom['type'] == 'parallel3d'):
-            #% for parallel geometry we can do just one slice
-            #print('Calculating Lipshitz constant for parallel beam geometry...')
-            niter = 5;# % number of iteration for the PM
-            #N = params.vol_geom.GridColCount;
-            #x1 = rand(N,N,1);
-            x1 = numpy.random.rand(1,N,N)
-            #sqweight = sqrt(weights(:,:,1));
-            sqweight = numpy.sqrt(weights[0])
-            proj_geomT = proj_geom.copy();
-            proj_geomT['DetectorRowCount'] = 1;
-            vol_geomT = vol_geom.copy();
-            vol_geomT['GridSliceCount'] = 1;
-            
-            #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-            
-            
-            for i in range(niter):
-            #        [id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geomT, vol_geomT);
-            #            s = norm(x1(:));
-            #            x1 = x1/s;
-            #            [sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-            #            y = sqweight.*y;
-            #            astra_mex_data3d('delete', sino_id);
-            #            astra_mex_data3d('delete', id);
-                #print ("iteration {0}".format(i))
-                                
-                sino_id, y = astra.creators.create_sino3d_gpu(x1,
-                                                          proj_geomT,
-                                                          vol_geomT)
-                
-                y = (sqweight * y).copy() # element wise multiplication
-                
-                #b=fig.add_subplot(2,1,2)
-                #imgplot = plt.imshow(x1[0])
-                #plt.show()
-                
-                #astra_mex_data3d('delete', sino_id);
-                astra.matlab.data3d('delete', sino_id)
-                del x1
-                    
-                idx,x1 = astra.creators.create_backprojection3d_gpu((sqweight*y).copy(), 
-                                                                    proj_geomT,
-                                                                    vol_geomT)
-                del y
-                
-                                                                    
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = (x1/s).copy();
-                
-            #        ### this line?
-            #        sino_id, y = astra.creators.create_sino3d_gpu(x1, 
-            #                                                      proj_geomT, 
-            #                                                      vol_geomT);
-            #        y = sqweight * y;
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx)
-                print ("iteration {0} s= {1}".format(i,s))
-                
-            #end
-            del proj_geomT
-            del vol_geomT
-            #plt.show()
-        else:
-            #% divergen beam geometry
-            print('Calculating Lipshitz constant for divergen beam geometry...')
-            niter = 8; #% number of iteration for PM
-            x1 = numpy.random.rand(SlicesZ , N , N);
-            #sqweight = sqrt(weights);
-            sqweight = numpy.sqrt(weights[0])
-            
-            sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
-            y = sqweight*y;
-            #astra_mex_data3d('delete', sino_id);
-            astra.matlab.data3d('delete', sino_id);
-            
-            for i in range(niter):
-                #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
-                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, 
-                                                                    proj_geom, 
-                                                                    vol_geom)
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = x1/s;
-                ### this line?
-                #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
-                sino_id, y = astra.creators.create_sino3d_gpu(x1, 
-                                                              proj_geom, 
-                                                              vol_geom);
-                
-                y = sqweight*y;
-                #astra_mex_data3d('delete', sino_id);
-                #astra_mex_data3d('delete', id);
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx);
-            #end
-            #clear x1
-            del x1
-
-        
-        return s
-    
-    
-    def setRegularizer(self, regularizer):
-        if regularizer is not None:
-            self.pars['regularizer'] = regularizer
-        
-
-    def initialize(self):
-        # convenience variable storage
-        proj_geom = self.pars['projector_geometry']
-        vol_geom = self.pars['output_geometry']
-        sino = self.pars['input_sinogram']
-        
-        # a 'warm start' with SIRT method
-        # Create a data object for the reconstruction
-        rec_id = astra.matlab.data3d('create', '-vol',
-                                    vol_geom);
-        
-        #sinogram_id = astra_mex_data3d('create', '-proj3d', proj_geom, sino);
-        sinogram_id = astra.matlab.data3d('create', '-proj3d',
-                                          proj_geom,
-                                          sino)
-
-        sirt_config = astra.astra_dict('SIRT3D_CUDA')
-        sirt_config['ReconstructionDataId' ] = rec_id
-        sirt_config['ProjectionDataId'] = sinogram_id
-
-        sirt = astra.algorithm.create(sirt_config)
-        astra.algorithm.run(sirt, iterations=35)
-        X = astra.matlab.data3d('get', rec_id)
-
-        # clean up memory
-        astra.matlab.data3d('delete', rec_id)
-        astra.matlab.data3d('delete', sinogram_id)
-        astra.algorithm.delete(sirt)
-
-        
-
-        return X
-
-    def createOrderedSubsets(self, subsets=None):
-        if subsets is None:
-            try:
-                subsets = self.getParameter('subsets')
-            except Exception():
-                subsets = 0
-            #return subsets
-
-        angles = self.getParameter('projector_geometry')['ProjectionAngles'] 
-        
-        #binEdges = numpy.linspace(angles.min(),
-        #                          angles.max(),
-        #                          subsets + 1)
-        binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
-        # get rearranged subset indices
-        IndicesReorg = numpy.zeros((numpy.shape(angles)))
-        counterM = 0
-        for ii in range(binsDiscr.max()):
-            counter = 0
-            for jj in range(subsets):
-                curr_index = ii + jj  + counter
-                #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
-                if binsDiscr[jj] > ii:
-                    if (counterM < numpy.size(IndicesReorg)):
-                        IndicesReorg[counterM] = curr_index
-                    counterM = counterM + 1
-                    
-                counter = counter + binsDiscr[jj] - 1    
-                
-        # store the OS in parameters
-        self.setParameter(os_subsets=subsets,
-                          os_bins=binsDiscr,
-                          os_indices=IndicesReorg)
-            
-
-    def prepareForIteration(self):
-        print ("FISTA Reconstructor: prepare for iteration")
-        
-        self.residual_error = numpy.zeros((self.pars['number_of_iterations']))
-        self.objective = numpy.zeros((self.pars['number_of_iterations']))
-
-        #2D array (for 3D data) of sparse "ring" 
-        detectors, nangles, sliceZ  = numpy.shape(self.pars['input_sinogram'])
-        self.r = numpy.zeros((detectors, sliceZ), dtype=numpy.float)
-        # another ring variable
-        self.r_x = self.r.copy()
-
-        self.residual = numpy.zeros(numpy.shape(self.pars['input_sinogram']))
-        
-        if self.getParameter('Lipschitz_constant') is None:
-            self.pars['Lipschitz_constant'] = \
-                            self.calculateLipschitzConstantWithPowerMethod()
-        # errors vector (if the ground truth is given)
-        self.Resid_error = numpy.zeros((self.getParameter('number_of_iterations')));
-        # objective function values vector
-        self.objective = numpy.zeros((self.getParameter('number_of_iterations')));      
-        
-
-    # prepareForIteration
-
-    def iterate(self, Xin=None):
-        print ("FISTA Reconstructor: iterate")
-        
-        if Xin is None:    
-            if self.getParameter('initialize'):
-                X = self.initialize()
-            else:
-                N = vol_geom['GridColCount']
-                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
-        else:
-            # copy by reference
-            X = Xin
-        # store the output volume in the parameters
-        self.setParameter(output_volume=X)
-        X_t = X.copy()
-        # convenience variable storage
-        proj_geom , vol_geom, sino , \
-          SlicesZ  = self.getParameter([ 'projector_geometry' ,
-                                                'output_geometry',
-                                                'input_sinogram',
-                                                'SlicesZ' ])
-                   
-        t = 1
-        
-        for i in range(self.getParameter('number_of_iterations')):
-            X_old = X.copy()
-            t_old = t
-            r_old = self.r.copy()
-            if self.getParameter('projector_geometry')['type'] == 'parallel' or \
-               self.getParameter('projector_geometry')['type'] == 'fanflat' or \
-               self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
-                # if the geometry is parallel use slice-by-slice
-                # projection-backprojection routine
-                #sino_updt = zeros(size(sino),'single');
-                proj_geomT = proj_geom.copy()
-                proj_geomT['DetectorRowCount'] = 1
-                vol_geomT = vol_geom.copy()
-                vol_geomT['GridSliceCount'] = 1;
-                self.sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
-                for kkk in range(SlicesZ):
-                    sino_id, self.sino_updt[kkk] = \
-                             astra.creators.create_sino3d_gpu(
-                                 X_t[kkk:kkk+1], proj_geomT, vol_geomT)
-                    astra.matlab.data3d('delete', sino_id)
-            else:
-                # for divergent 3D geometry (watch the GPU memory overflow in
-                # ASTRA versions < 1.8)
-                #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
-                sino_id, self.sino_updt = astra.creators.create_sino3d_gpu(
-                    X_t, proj_geom, vol_geom)
-
-
-            ## RING REMOVAL
-            self.ringRemoval(i)
-            ## Projection/Backprojection Routine
-            self.projectionBackprojection(X, X_t)
-            astra.matlab.data3d('delete', sino_id)
-            ## REGULARIZATION
-            X = self.regularize(X)
-            ## Update Loop
-            X , X_t, t = self.updateLoop(i, X, X_old, r_old, t, t_old)
-            self.setParameter(output_volume=X)
-        return X
-    ## iterate
-    
-    def ringRemoval(self, i):
-        print ("FISTA Reconstructor: ring removal")
-        residual = self.residual
-        lambdaR_L1 , alpha_ring , weights , L_const , sino= \
-                   self.getParameter(['ring_lambda_R_L1',
-                                      'ring_alpha' , 'weights',
-                                      'Lipschitz_constant',
-                                      'input_sinogram'])
-        r_x = self.r_x
-        sino_updt = self.sino_updt
-        
-        SlicesZ, anglesNumb, Detectors = \
-                    numpy.shape(self.getParameter('input_sinogram'))
-        if lambdaR_L1 > 0 :
-             for kkk in range(anglesNumb):
-                 
-                 residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
-                                       ((sino_updt[:,kkk,:]).squeeze() - \
-                                        (sino[:,kkk,:]).squeeze() -\
-                                        (alpha_ring * r_x)
-                                        )
-             vec = residual.sum(axis = 1)
-             #if SlicesZ > 1:
-             #    vec = vec[:,1,:].squeeze()
-             self.r = (r_x - (1./L_const) * vec).copy()
-             self.objective[i] = (0.5 * (residual ** 2).sum())
-
-    def projectionBackprojection(self, X, X_t):
-        print ("FISTA Reconstructor: projection-backprojection routine")
-        
-        # a few useful variables
-        SlicesZ, anglesNumb, Detectors = \
-                    numpy.shape(self.getParameter('input_sinogram'))
-        residual = self.residual
-        proj_geom , vol_geom , L_const = \
-                  self.getParameter(['projector_geometry' ,
-                                                  'output_geometry',
-                                                  'Lipschitz_constant'])
-        
-        
-        if self.getParameter('projector_geometry')['type'] == 'parallel' or \
-           self.getParameter('projector_geometry')['type'] == 'fanflat' or \
-           self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
-            # if the geometry is parallel use slice-by-slice
-            # projection-backprojection routine
-            #sino_updt = zeros(size(sino),'single');
-            proj_geomT = proj_geom.copy()
-            proj_geomT['DetectorRowCount'] = 1
-            vol_geomT = vol_geom.copy()
-            vol_geomT['GridSliceCount'] = 1;
-            x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
-            
-            for kkk in range(SlicesZ):
-                
-                x_id, x_temp[kkk] = \
-                         astra.creators.create_backprojection3d_gpu(
-                             residual[kkk:kkk+1],
-                             proj_geomT, vol_geomT)
-                astra.matlab.data3d('delete', x_id)
-        else:
-            x_id, x_temp = \
-                  astra.creators.create_backprojection3d_gpu(
-                      residual, proj_geom, vol_geom)            
-
-        X = X_t - (1/L_const) * x_temp
-        #astra.matlab.data3d('delete', sino_id)
-        astra.matlab.data3d('delete', x_id)
-
-    def regularize(self, X):
-        print ("FISTA Reconstructor: regularize")
-        
-        regularizer = self.getParameter('regularizer')
-        if regularizer is not None:
-            return regularizer(input=X)
-        else:
-            return X
-
-    def updateLoop(self, i, X, X_old, r_old, t, t_old):
-        print ("FISTA Reconstructor: update loop")
-        lambdaR_L1 = self.getParameter('ring_lambda_R_L1')
-        if lambdaR_L1 > 0:
-            self.r = numpy.max(
-                numpy.abs(self.r) - lambdaR_L1 , 0) * \
-                numpy.sign(self.r)
-        t = (1 + numpy.sqrt(1 + 4 * t**2))/2
-        X_t = X + (((t_old -1)/t) * (X - X_old))
-
-        if lambdaR_L1 > 0:
-            self.r_x = self.r + \
-                             (((t_old-1)/t) * (self.r - r_old))
-
-        if self.getParameter('region_of_interest') is None:
-            string = 'Iteration Number {0} | Objective {1} \n'
-            print (string.format( i, self.objective[i]))
-        else:
-            ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
-                                                    'ideal_image')
-            
-            Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
-            string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
-            print (string.format(i,Resid_error[i], self.objective[i]))
-        return (X , X_t, t)
-
-    def os_iterate(self, Xin=None):
-        print ("FISTA Reconstructor: iterate")
-        
-        if Xin is None:    
-            if self.getParameter('initialize'):
-                X = self.initialize()
-            else:
-                N = vol_geom['GridColCount']
-                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
-        else:
-            # copy by reference
-            X = Xin
-        # store the output volume in the parameters
-        self.setParameter(output_volume=X)
-        X_t = X.copy()
-
-        # some useful constants
-        proj_geom , vol_geom, sino , \
-          SlicesZ, weights , alpha_ring ,
-          lambdaR_L1 , L_const = self.getParameter(
-            ['projector_geometry' , 'output_geometry',
-             'input_sinogram', 'SlicesZ' ,  'weights', 'ring_alpha' ,
-             'ring_lambda_R_L1', 'Lipschitz_constant'])
diff --git a/src/Python/ccpi/fista/Reconstructor.py b/src/Python/ccpi/fista/Reconstructor.py
deleted file mode 100644
index d29ac0d..0000000
--- a/src/Python/ccpi/fista/Reconstructor.py
+++ /dev/null
@@ -1,425 +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 2017 Edoardo Pasca, Srikanth Nagella
-#Copyright 2017 Daniil Kazantsev
-#
-#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.
-###############################################################################
-
-
-
-import numpy
-import h5py
-from ccpi.reconstruction.parallelbeam import alg
-
-from Regularizer import Regularizer
-from enum import Enum
-
-import astra
-
-   
-    
-class FISTAReconstructor():
-    '''FISTA-based reconstruction algorithm using ASTRA-toolbox
-    
-    '''
-    # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
-    # ___Input___:
-    # params.[] file:
-    #       - .proj_geom (geometry of the projector) [required]
-    #       - .vol_geom (geometry of the reconstructed object) [required]
-    #       - .sino (vectorized in 2D or 3D sinogram) [required]
-    #       - .iterFISTA (iterations for the main loop, default 40)
-    #       - .L_const (Lipschitz constant, default Power method)                                                                                                    )
-    #       - .X_ideal (ideal image, if given)
-    #       - .weights (statisitcal weights, size of the sinogram)
-    #       - .ROI (Region-of-interest, only if X_ideal is given)
-    #       - .initialize (a 'warm start' using SIRT method from ASTRA)
-    #----------------Regularization choices------------------------
-    #       - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
-    #       - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
-    #       - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
-    #       - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
-    #       - .Regul_Iterations (iterations for the selected penalty, default 25)
-    #       - .Regul_tauLLT (time step parameter for LLT term)
-    #       - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
-    #       - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
-    #----------------Visualization parameters------------------------
-    #       - .show (visualize reconstruction 1/0, (0 default))
-    #       - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
-    #       - .slice (for 3D volumes - slice number to imshow)
-    # ___Output___:
-    # 1. X - reconstructed image/volume
-    # 2. output - a structure with
-    #    - .Resid_error - residual error (if X_ideal is given)
-    #    - .objective: value of the objective function
-    #    - .L_const: Lipshitz constant to avoid recalculations
-    
-    # References:
-    # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
-    # Problems" by A. Beck and M Teboulle
-    # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
-    # 3. "A novel tomographic reconstruction method based on the robust
-    # Student's t function for suppressing data outliers" D. Kazantsev et.al.
-    # D. Kazantsev, 2016-17
-    def __init__(self, projector_geometry, output_geometry, input_sinogram, **kwargs):
-        self.params = dict()
-        self.params['projector_geometry'] = projector_geometry
-        self.params['output_geometry'] = output_geometry
-        self.params['input_sinogram'] = input_sinogram
-        detectors, nangles, sliceZ = numpy.shape(input_sinogram)
-        self.params['detectors'] = detectors
-        self.params['number_og_angles'] = nangles
-        self.params['SlicesZ'] = sliceZ
-        
-        # Accepted input keywords
-        kw = ('number_of_iterations', 'Lipschitz_constant' , 'ideal_image' ,
-              'weights' , 'region_of_interest' , 'initialize' , 
-              'regularizer' , 
-              'ring_lambda_R_L1',
-              'ring_alpha')
-        
-        # handle keyworded parameters
-        if kwargs is not None:
-            for key, value in kwargs.items():
-                if key in kw:
-                    #print("{0} = {1}".format(key, value))                        
-                    self.pars[key] = value
-                    
-        # set the default values for the parameters if not set
-        if 'number_of_iterations' in kwargs.keys():
-            self.pars['number_of_iterations'] = kwargs['number_of_iterations']
-        else:
-            self.pars['number_of_iterations'] = 40
-        if 'weights' in kwargs.keys():
-            self.pars['weights'] = kwargs['weights']
-        else:
-            self.pars['weights'] = numpy.ones(numpy.shape(self.params['input_sinogram']))
-        if 'Lipschitz_constant' in kwargs.keys():
-            self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
-        else:
-            self.pars['Lipschitz_constant'] = self.calculateLipschitzConstantWithPowerMethod()
-        
-        if not self.pars['ideal_image'] in kwargs.keys():
-            self.pars['ideal_image'] = None
-        
-        if not self.pars['region_of_interest'] :
-            if self.pars['ideal_image'] == None:
-                pass
-            else:
-                self.pars['region_of_interest'] = numpy.nonzero(self.pars['ideal_image']>0.0)
-            
-        if not self.pars['regularizer'] :
-            self.pars['regularizer'] = None
-        else:
-            # the regularizer must be a correctly instantiated object
-            if not self.pars['ring_lambda_R_L1']:
-                self.pars['ring_lambda_R_L1'] = 0
-            if not self.pars['ring_alpha']:
-                self.pars['ring_alpha'] = 1
-        
-            
-            
-        
-    def calculateLipschitzConstantWithPowerMethod(self):
-        ''' using Power method (PM) to establish L constant'''
-        
-        #N = params.vol_geom.GridColCount
-        N = self.pars['output_geometry'].GridColCount
-        proj_geom = self.params['projector_geometry']
-        vol_geom = self.params['output_geometry']
-        weights = self.pars['weights']
-        SlicesZ = self.pars['SlicesZ']
-        
-        if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
-            #% for parallel geometry we can do just one slice
-            #fprintf('%s \n', 'Calculating Lipshitz constant for parallel beam geometry...');
-            niter = 15;# % number of iteration for the PM
-            #N = params.vol_geom.GridColCount;
-            #x1 = rand(N,N,1);
-            x1 = numpy.random.rand(1,N,N)
-            #sqweight = sqrt(weights(:,:,1));
-            sqweight = numpy.sqrt(weights.T[0])
-            proj_geomT = proj_geom.copy();
-            proj_geomT.DetectorRowCount = 1;
-            vol_geomT = vol_geom.copy();
-            vol_geomT['GridSliceCount'] = 1;
-            
-            
-            for i in range(niter):
-                if i == 0:
-                    #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-                    sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
-                    y = sqweight * y # element wise multiplication
-                    #astra_mex_data3d('delete', sino_id);
-                    astra.matlab.data3d('delete', sino_id)
-                    
-                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, proj_geomT, vol_geomT);
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = x1/s;
-                ### this line?
-                sino_id, y = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-                y = sqweight*y;
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx);
-            #end
-            del proj_geomT
-            del vol_geomT
-        else
-            #% divergen beam geometry
-            #fprintf('%s \n', 'Calculating Lipshitz constant for divergen beam geometry...');
-            niter = 8; #% number of iteration for PM
-            x1 = numpy.random.rand(SlicesZ , N , N);
-            #sqweight = sqrt(weights);
-            sqweight = numpy.sqrt(weights.T[0])
-            
-            sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
-            y = sqweight*y;
-            #astra_mex_data3d('delete', sino_id);
-            astra.matlab.data3d('delete', sino_id);
-            
-            for i in range(niter):
-                #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
-                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, 
-                                                                    proj_geom, 
-                                                                    vol_geom)
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = x1/s;
-                ### this line?
-                #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
-                sino_id, y = astra.creators.create_sino3d_gpu(x1, 
-                                                              proj_geom, 
-                                                              vol_geom);
-                
-                y = sqweight*y;
-                #astra_mex_data3d('delete', sino_id);
-                #astra_mex_data3d('delete', id);
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx);
-            #end
-            #clear x1
-            del x1
-        
-        return s
-    
-    
-    def setRegularizer(self, regularizer):
-        if regularizer
-        self.pars['regularizer'] = regularizer
-        
-    
-    
-
-
-def getEntry(location):
-    for item in nx[location].keys():
-        print (item)
-
-
-print ("Loading Data")
-
-##fname = "D:\\Documents\\Dataset\\IMAT\\20170419_crabtomo\\crabtomo\\Sample\\IMAT00005153_crabstomo_Sample_000.tif"
-####ind = [i * 1049 for i in range(360)]
-#### use only 360 images
-##images = 200
-##ind = [int(i * 1049 / images) for i in range(images)]
-##stack_image = dxchange.reader.read_tiff_stack(fname, ind, digit=None, slc=None)
-
-#fname = "D:\\Documents\\Dataset\\CGLS\\24737_fd.nxs"
-fname = "C:\\Users\\ofn77899\\Documents\\CCPi\\CGLS\\24737_fd_2.nxs"
-nx = h5py.File(fname, "r")
-
-# the data are stored in a particular location in the hdf5
-for item in nx['entry1/tomo_entry/data'].keys():
-    print (item)
-
-data = nx.get('entry1/tomo_entry/data/rotation_angle')
-angles = numpy.zeros(data.shape)
-data.read_direct(angles)
-print (angles)
-# angles should be in degrees
-
-data = nx.get('entry1/tomo_entry/data/data')
-stack = numpy.zeros(data.shape)
-data.read_direct(stack)
-print (data.shape)
-
-print ("Data Loaded")
-
-
-# Normalize
-data = nx.get('entry1/tomo_entry/instrument/detector/image_key')
-itype = numpy.zeros(data.shape)
-data.read_direct(itype)
-# 2 is dark field
-darks = [stack[i] for i in range(len(itype)) if itype[i] == 2 ]
-dark = darks[0]
-for i in range(1, len(darks)):
-    dark += darks[i]
-dark = dark / len(darks)
-#dark[0][0] = dark[0][1]
-
-# 1 is flat field
-flats = [stack[i] for i in range(len(itype)) if itype[i] == 1 ]
-flat = flats[0]
-for i in range(1, len(flats)):
-    flat += flats[i]
-flat = flat / len(flats)
-#flat[0][0] = dark[0][1]
-
-
-# 0 is projection data
-proj = [stack[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = [angles[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = numpy.asarray (angle_proj)
-angle_proj = angle_proj.astype(numpy.float32)
-
-# normalized data are
-# norm = (projection - dark)/(flat-dark)
-
-def normalize(projection, dark, flat, def_val=0.1):
-    a = (projection - dark)
-    b = (flat-dark)
-    with numpy.errstate(divide='ignore', invalid='ignore'):
-        c = numpy.true_divide( a, b )
-        c[ ~ numpy.isfinite( c )] = def_val  # set to not zero if 0/0 
-    return c
-    
-
-norm = [normalize(projection, dark, flat) for projection in proj]
-norm = numpy.asarray (norm)
-norm = norm.astype(numpy.float32)
-
-#recon = Reconstructor(algorithm = Algorithm.CGLS, normalized_projection = norm,
-#                 angles = angle_proj, center_of_rotation = 86.2 , 
-#                 flat_field = flat, dark_field = dark, 
-#                 iterations = 15, resolution = 1, isLogScale = False, threads = 3)
-
-#recon = Reconstructor(algorithm = Reconstructor.Algorithm.CGLS, projection_data = proj,
-#                 angles = angle_proj, center_of_rotation = 86.2 , 
-#                 flat_field = flat, dark_field = dark, 
-#                 iterations = 15, resolution = 1, isLogScale = False, threads = 3)
-#img_cgls = recon.reconstruct()
-#
-#pars = dict()
-#pars['algorithm'] = Reconstructor.Algorithm.SIRT
-#pars['projection_data'] = proj
-#pars['angles'] = angle_proj
-#pars['center_of_rotation'] = numpy.double(86.2)
-#pars['flat_field'] = flat
-#pars['iterations'] = 15
-#pars['dark_field'] = dark
-#pars['resolution'] = 1
-#pars['isLogScale'] = False
-#pars['threads'] = 3
-#
-#img_sirt = recon.reconstruct(pars)
-#
-#recon.pars['algorithm'] = Reconstructor.Algorithm.MLEM
-#img_mlem = recon.reconstruct()
-
-############################################################
-############################################################
-#recon.pars['algorithm'] = Reconstructor.Algorithm.CGLS_CONV
-#recon.pars['regularize'] = numpy.double(0.1)
-#img_cgls_conv = recon.reconstruct()
-
-niterations = 15
-threads = 3
-
-img_cgls = alg.cgls(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_mlem = alg.mlem(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_sirt = alg.sirt(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_conv = alg.cgls_conv(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-                              iteration_values, False)
-print ("iteration values %s" % str(iteration_values))
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_tikhonov = alg.cgls_tikhonov(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-                                      numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-iteration_values = numpy.zeros((niterations,))
-img_cgls_TVreg = alg.cgls_TVreg(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-                                      numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-
-
-##numpy.save("cgls_recon.npy", img_data)
-import matplotlib.pyplot as plt
-fig, ax = plt.subplots(1,6,sharey=True)
-ax[0].imshow(img_cgls[80])
-ax[0].axis('off')  # clear x- and y-axes
-ax[1].imshow(img_sirt[80])
-ax[1].axis('off')  # clear x- and y-axes
-ax[2].imshow(img_mlem[80])
-ax[2].axis('off')  # clear x- and y-axesplt.show()
-ax[3].imshow(img_cgls_conv[80])
-ax[3].axis('off')  # clear x- and y-axesplt.show()
-ax[4].imshow(img_cgls_tikhonov[80])
-ax[4].axis('off')  # clear x- and y-axesplt.show()
-ax[5].imshow(img_cgls_TVreg[80])
-ax[5].axis('off')  # clear x- and y-axesplt.show()
-
-
-plt.show()
-
-#viewer = edo.CILViewer()
-#viewer.setInputAsNumpy(img_cgls2)
-#viewer.displaySliceActor(0)
-#viewer.startRenderLoop()
-
-import vtk
-
-def NumpyToVTKImageData(numpyarray):
-    if (len(numpy.shape(numpyarray)) == 3):
-        doubleImg = vtk.vtkImageData()
-        shape = numpy.shape(numpyarray)
-        doubleImg.SetDimensions(shape[0], shape[1], shape[2])
-        doubleImg.SetOrigin(0,0,0)
-        doubleImg.SetSpacing(1,1,1)
-        doubleImg.SetExtent(0, shape[0]-1, 0, shape[1]-1, 0, shape[2]-1)
-        #self.img3D.SetScalarType(vtk.VTK_UNSIGNED_SHORT, vtk.vtkInformation())
-        doubleImg.AllocateScalars(vtk.VTK_DOUBLE,1)
-        
-        for i in range(shape[0]):
-            for j in range(shape[1]):
-                for k in range(shape[2]):
-                    doubleImg.SetScalarComponentFromDouble(
-                        i,j,k,0, numpyarray[i][j][k])
-    #self.setInput3DData( numpy_support.numpy_to_vtk(numpyarray) )
-        # rescale to appropriate VTK_UNSIGNED_SHORT
-        stats = vtk.vtkImageAccumulate()
-        stats.SetInputData(doubleImg)
-        stats.Update()
-        iMin = stats.GetMin()[0]
-        iMax = stats.GetMax()[0]
-        scale = vtk.VTK_UNSIGNED_SHORT_MAX / (iMax - iMin)
-
-        shiftScaler = vtk.vtkImageShiftScale ()
-        shiftScaler.SetInputData(doubleImg)
-        shiftScaler.SetScale(scale)
-        shiftScaler.SetShift(iMin)
-        shiftScaler.SetOutputScalarType(vtk.VTK_UNSIGNED_SHORT)
-        shiftScaler.Update()
-        return shiftScaler.GetOutput()
-        
-#writer = vtk.vtkMetaImageWriter()
-#writer.SetFileName(alg + "_recon.mha")
-#writer.SetInputData(NumpyToVTKImageData(img_cgls2))
-#writer.Write()
diff --git a/src/Python/ccpi/fista/__init__.py b/src/Python/ccpi/fista/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/src/Python/ccpi/fista/__init__.py
+++ /dev/null