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| -rw-r--r-- | src/Python/ccpi/reconstruction/Reconstructor.py | 598 |
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diff --git a/src/Python/ccpi/reconstruction/Reconstructor.py b/src/Python/ccpi/reconstruction/Reconstructor.py new file mode 100644 index 0000000..ba67327 --- /dev/null +++ b/src/Python/ccpi/reconstruction/Reconstructor.py @@ -0,0 +1,598 @@ +# -*- 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() |