diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/Python/ccpi/fista/FISTAReconstructor.pyc | bin | 3804 -> 0 bytes | |||
| -rw-r--r-- | src/Python/ccpi/fista/FISTAReconstructor.py~ | 349 | ||||
| -rw-r--r-- | src/Python/ccpi/fista/Reconstructor.py~ | 598 | ||||
| -rw-r--r-- | src/Python/ccpi/fista/__init__.pyc | bin | 189 -> 0 bytes | |||
| -rw-r--r-- | src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc | bin | 3641 -> 0 bytes | |||
| -rw-r--r-- | src/Python/ccpi/fista/__pycache__/__init__.cpython-35.pyc | bin | 185 -> 0 bytes | |||
| -rw-r--r-- | src/Python/test_reconstructor.py | 2 |
7 files changed, 1 insertions, 948 deletions
diff --git a/src/Python/ccpi/fista/FISTAReconstructor.pyc b/src/Python/ccpi/fista/FISTAReconstructor.pyc Binary files differdeleted file mode 100644 index ecc4d7d..0000000 --- a/src/Python/ccpi/fista/FISTAReconstructor.pyc +++ /dev/null diff --git a/src/Python/ccpi/fista/FISTAReconstructor.py~ b/src/Python/ccpi/fista/FISTAReconstructor.py~ deleted file mode 100644 index 6c7024d..0000000 --- a/src/Python/ccpi/fista/FISTAReconstructor.py~ +++ /dev/null @@ -1,349 +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) - - -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() - - diff --git a/src/Python/ccpi/fista/Reconstructor.py~ b/src/Python/ccpi/fista/Reconstructor.py~ deleted file mode 100644 index ba67327..0000000 --- a/src/Python/ccpi/fista/Reconstructor.py~ +++ /dev/null @@ -1,598 +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 Reconstructor: - - class Algorithm(Enum): - CGLS = alg.cgls - CGLS_CONV = alg.cgls_conv - SIRT = alg.sirt - MLEM = alg.mlem - CGLS_TICHONOV = alg.cgls_tikhonov - CGLS_TVREG = alg.cgls_TVreg - FISTA = 'fista' - - def __init__(self, algorithm = None, projection_data = None, - angles = None, center_of_rotation = None , - flat_field = None, dark_field = None, - iterations = None, resolution = None, isLogScale = False, threads = None, - normalized_projection = None): - - self.pars = dict() - self.pars['algorithm'] = algorithm - self.pars['projection_data'] = projection_data - self.pars['normalized_projection'] = normalized_projection - self.pars['angles'] = angles - self.pars['center_of_rotation'] = numpy.double(center_of_rotation) - self.pars['flat_field'] = flat_field - self.pars['iterations'] = iterations - self.pars['dark_field'] = dark_field - self.pars['resolution'] = resolution - self.pars['isLogScale'] = isLogScale - self.pars['threads'] = threads - if (iterations != None): - self.pars['iterationValues'] = numpy.zeros((iterations)) - - if projection_data != None and dark_field != None and flat_field != None: - norm = self.normalize(projection_data, dark_field, flat_field, 0.1) - self.pars['normalized_projection'] = norm - - - def setPars(self, parameters): - keys = ['algorithm','projection_data' ,'normalized_projection', \ - 'angles' , 'center_of_rotation' , 'flat_field', \ - 'iterations','dark_field' , 'resolution', 'isLogScale' , \ - 'threads' , 'iterationValues', 'regularize'] - - for k in keys: - if k not in parameters.keys(): - self.pars[k] = None - else: - self.pars[k] = parameters[k] - - - def sanityCheck(self): - projection_data = self.pars['projection_data'] - dark_field = self.pars['dark_field'] - flat_field = self.pars['flat_field'] - angles = self.pars['angles'] - - if projection_data != None and dark_field != None and \ - angles != None and flat_field != None: - data_shape = numpy.shape(projection_data) - angle_shape = numpy.shape(angles) - - if angle_shape[0] != data_shape[0]: - #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \ - # (angle_shape[0] , data_shape[0]) ) - return (False , 'Projections and angles dimensions do not match: %d vs %d' % \ - (angle_shape[0] , data_shape[0]) ) - - if data_shape[1:] != numpy.shape(flat_field): - #raise Exception('Projection and flat field dimensions do not match') - return (False , 'Projection and flat field dimensions do not match') - if data_shape[1:] != numpy.shape(dark_field): - #raise Exception('Projection and dark field dimensions do not match') - return (False , 'Projection and dark field dimensions do not match') - - return (True , '' ) - elif self.pars['normalized_projection'] != None: - data_shape = numpy.shape(self.pars['normalized_projection']) - angle_shape = numpy.shape(angles) - - if angle_shape[0] != data_shape[0]: - #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \ - # (angle_shape[0] , data_shape[0]) ) - return (False , 'Projections and angles dimensions do not match: %d vs %d' % \ - (angle_shape[0] , data_shape[0]) ) - else: - return (True , '' ) - else: - return (False , 'Not enough data') - - def reconstruct(self, parameters = None): - if parameters != None: - self.setPars(parameters) - - go , reason = self.sanityCheck() - if go: - return self._reconstruct() - else: - raise Exception(reason) - - - def _reconstruct(self, parameters=None): - if parameters!=None: - self.setPars(parameters) - parameters = self.pars - - if parameters['algorithm'] != None and \ - parameters['normalized_projection'] != None and \ - parameters['angles'] != None and \ - parameters['center_of_rotation'] != None and \ - parameters['iterations'] != None and \ - parameters['resolution'] != None and\ - parameters['threads'] != None and\ - parameters['isLogScale'] != None: - - - if parameters['algorithm'] in (Reconstructor.Algorithm.CGLS, - Reconstructor.Algorithm.MLEM, Reconstructor.Algorithm.SIRT): - #store parameters - self.pars = parameters - result = parameters['algorithm']( - parameters['normalized_projection'] , - parameters['angles'], - parameters['center_of_rotation'], - parameters['resolution'], - parameters['iterations'], - parameters['threads'] , - parameters['isLogScale'] - ) - return result - elif parameters['algorithm'] in (Reconstructor.Algorithm.CGLS_CONV, - Reconstructor.Algorithm.CGLS_TICHONOV, - Reconstructor.Algorithm.CGLS_TVREG) : - self.pars = parameters - result = parameters['algorithm']( - parameters['normalized_projection'] , - parameters['angles'], - parameters['center_of_rotation'], - parameters['resolution'], - parameters['iterations'], - parameters['threads'] , - parameters['regularize'], - numpy.zeros((parameters['iterations'])), - parameters['isLogScale'] - ) - - elif parameters['algorithm'] == Reconstructor.Algorithm.FISTA: - pass - - else: - if parameters['projection_data'] != None and \ - parameters['dark_field'] != None and \ - parameters['flat_field'] != None: - norm = self.normalize(parameters['projection_data'], - parameters['dark_field'], - parameters['flat_field'], 0.1) - self.pars['normalized_projection'] = norm - return self._reconstruct(parameters) - - - - def _normalize(self, projection, dark, flat, def_val=0): - 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 - - def normalize(self, projections, dark, flat, def_val=0): - norm = [self._normalize(projection, dark, flat, def_val) for projection in projections] - return numpy.asarray (norm, dtype=numpy.float32) - - - -class FISTA(): - '''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__.pyc b/src/Python/ccpi/fista/__init__.pyc Binary files differdeleted file mode 100644 index 719e264..0000000 --- a/src/Python/ccpi/fista/__init__.pyc +++ /dev/null diff --git a/src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc b/src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc Binary files differdeleted file mode 100644 index 84f16e2..0000000 --- a/src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc +++ /dev/null diff --git a/src/Python/ccpi/fista/__pycache__/__init__.cpython-35.pyc b/src/Python/ccpi/fista/__pycache__/__init__.cpython-35.pyc Binary files differdeleted file mode 100644 index 90c23ff..0000000 --- a/src/Python/ccpi/fista/__pycache__/__init__.cpython-35.pyc +++ /dev/null diff --git a/src/Python/test_reconstructor.py b/src/Python/test_reconstructor.py index 6f46e96..a4a622b 100644 --- a/src/Python/test_reconstructor.py +++ b/src/Python/test_reconstructor.py @@ -9,7 +9,7 @@ Based on DemoRD2.m import h5py import numpy -from ccpi.reconstruction_dev.FISTAReconstructor import FISTAReconstructor +from ccpi.fista.FISTAReconstructor import FISTAReconstructor import astra ##def getEntry(nx, location): |