diff options
| -rw-r--r-- | src/Python/ccpi/fista/FISTAReconstructor.py | 609 | ||||
| -rw-r--r-- | src/Python/ccpi/fista/Reconstructor.py | 425 | ||||
| -rw-r--r-- | src/Python/ccpi/fista/__init__.py | 0 |
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 |