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| -rw-r--r-- | src/Python/ccpi/reconstruction/FISTAReconstructor.py | 354 |
1 files changed, 354 insertions, 0 deletions
diff --git a/src/Python/ccpi/reconstruction/FISTAReconstructor.py b/src/Python/ccpi/reconstruction/FISTAReconstructor.py new file mode 100644 index 0000000..ea96b53 --- /dev/null +++ b/src/Python/ccpi/reconstruction/FISTAReconstructor.py @@ -0,0 +1,354 @@ +# -*- 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 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): + 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.creators.create_sino3d_gpu(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 is not None: + 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" +##fname = "/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/dendr.h5" +##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() +## + |