From 73fed4964d81f1f47a0b6ecbe66517f569327b27 Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Wed, 23 Aug 2017 14:31:16 +0100
Subject: initial commit of Reconstructor.py
---
src/Python/ccpi/reconstruction/Reconstructor.py | 598 ++++++++++++++++++++++++
1 file changed, 598 insertions(+)
create mode 100644 src/Python/ccpi/reconstruction/Reconstructor.py
(limited to 'src/Python/ccpi')
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()
--
cgit v1.2.3
From 105b57a1e98c2bb7b3bf94c43b6c669925ebb1b9 Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Wed, 23 Aug 2017 15:01:28 +0100
Subject: added viewer for testing
---
src/Python/ccpi/viewer/CILViewer.py | 361 +++++++
src/Python/ccpi/viewer/CILViewer2D.py | 1126 ++++++++++++++++++++
src/Python/ccpi/viewer/QVTKWidget.py | 340 ++++++
src/Python/ccpi/viewer/QVTKWidget2.py | 84 ++
src/Python/ccpi/viewer/__init__.py | 1 +
.../viewer/__pycache__/CILViewer.cpython-35.pyc | Bin 0 -> 10542 bytes
.../viewer/__pycache__/CILViewer2D.cpython-35.pyc | Bin 0 -> 35633 bytes
.../viewer/__pycache__/QVTKWidget.cpython-35.pyc | Bin 0 -> 10099 bytes
.../viewer/__pycache__/QVTKWidget2.cpython-35.pyc | Bin 0 -> 1316 bytes
.../viewer/__pycache__/__init__.cpython-35.pyc | Bin 0 -> 210 bytes
src/Python/ccpi/viewer/embedvtk.py | 75 ++
11 files changed, 1987 insertions(+)
create mode 100644 src/Python/ccpi/viewer/CILViewer.py
create mode 100644 src/Python/ccpi/viewer/CILViewer2D.py
create mode 100644 src/Python/ccpi/viewer/QVTKWidget.py
create mode 100644 src/Python/ccpi/viewer/QVTKWidget2.py
create mode 100644 src/Python/ccpi/viewer/__init__.py
create mode 100644 src/Python/ccpi/viewer/__pycache__/CILViewer.cpython-35.pyc
create mode 100644 src/Python/ccpi/viewer/__pycache__/CILViewer2D.cpython-35.pyc
create mode 100644 src/Python/ccpi/viewer/__pycache__/QVTKWidget.cpython-35.pyc
create mode 100644 src/Python/ccpi/viewer/__pycache__/QVTKWidget2.cpython-35.pyc
create mode 100644 src/Python/ccpi/viewer/__pycache__/__init__.cpython-35.pyc
create mode 100644 src/Python/ccpi/viewer/embedvtk.py
(limited to 'src/Python/ccpi')
diff --git a/src/Python/ccpi/viewer/CILViewer.py b/src/Python/ccpi/viewer/CILViewer.py
new file mode 100644
index 0000000..efcf8be
--- /dev/null
+++ b/src/Python/ccpi/viewer/CILViewer.py
@@ -0,0 +1,361 @@
+# -*- coding: utf-8 -*-
+# Copyright 2017 Edoardo Pasca
+#
+# 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 vtk
+import numpy
+import math
+from vtk.util import numpy_support
+
+SLICE_ORIENTATION_XY = 2 # Z
+SLICE_ORIENTATION_XZ = 1 # Y
+SLICE_ORIENTATION_YZ = 0 # X
+
+
+
+class CILViewer():
+ '''Simple 3D Viewer based on VTK classes'''
+
+ def __init__(self, dimx=600,dimy=600):
+ '''creates the rendering pipeline'''
+
+ # create a rendering window and renderer
+ self.ren = vtk.vtkRenderer()
+ self.renWin = vtk.vtkRenderWindow()
+ self.renWin.SetSize(dimx,dimy)
+ self.renWin.AddRenderer(self.ren)
+
+ # img 3D as slice
+ self.img3D = None
+ self.sliceno = 0
+ self.sliceOrientation = SLICE_ORIENTATION_XY
+ self.sliceActor = None
+ self.voi = None
+ self.wl = None
+ self.ia = None
+ self.sliceActorNo = 0
+ # create a renderwindowinteractor
+ self.iren = vtk.vtkRenderWindowInteractor()
+ self.iren.SetRenderWindow(self.renWin)
+
+ self.style = vtk.vtkInteractorStyleTrackballCamera()
+ self.iren.SetInteractorStyle(self.style)
+
+ self.ren.SetBackground(.1, .2, .4)
+
+ self.actors = {}
+ self.iren.RemoveObservers('MouseWheelForwardEvent')
+ self.iren.RemoveObservers('MouseWheelBackwardEvent')
+
+ self.iren.AddObserver('MouseWheelForwardEvent', self.mouseInteraction, 1.0)
+ self.iren.AddObserver('MouseWheelBackwardEvent', self.mouseInteraction, 1.0)
+
+ self.iren.RemoveObservers('KeyPressEvent')
+ self.iren.AddObserver('KeyPressEvent', self.keyPress, 1.0)
+
+
+ self.iren.Initialize()
+
+
+
+ def getRenderer(self):
+ '''returns the renderer'''
+ return self.ren
+
+ def getRenderWindow(self):
+ '''returns the render window'''
+ return self.renWin
+
+ def getInteractor(self):
+ '''returns the render window interactor'''
+ return self.iren
+
+ def getCamera(self):
+ '''returns the active camera'''
+ return self.ren.GetActiveCamera()
+
+ def createPolyDataActor(self, polydata):
+ '''returns an actor for a given polydata'''
+ mapper = vtk.vtkPolyDataMapper()
+ if vtk.VTK_MAJOR_VERSION <= 5:
+ mapper.SetInput(polydata)
+ else:
+ mapper.SetInputData(polydata)
+
+ # actor
+ actor = vtk.vtkActor()
+ actor.SetMapper(mapper)
+ #actor.GetProperty().SetOpacity(0.8)
+ return actor
+
+ def setPolyDataActor(self, actor):
+ '''displays the given polydata'''
+
+ self.ren.AddActor(actor)
+
+ self.actors[len(self.actors)+1] = [actor, True]
+ self.iren.Initialize()
+ self.renWin.Render()
+
+ def displayPolyData(self, polydata):
+ self.setPolyDataActor(self.createPolyDataActor(polydata))
+
+ def hideActor(self, actorno):
+ '''Hides an actor identified by its number in the list of actors'''
+ try:
+ if self.actors[actorno][1]:
+ self.ren.RemoveActor(self.actors[actorno][0])
+ self.actors[actorno][1] = False
+ except KeyError as ke:
+ print ("Warning Actor not present")
+
+ def showActor(self, actorno, actor = None):
+ '''Shows hidden actor identified by its number in the list of actors'''
+ try:
+ if not self.actors[actorno][1]:
+ self.ren.AddActor(self.actors[actorno][0])
+ self.actors[actorno][1] = True
+ return actorno
+ except KeyError as ke:
+ # adds it to the actors if not there already
+ if actor != None:
+ self.ren.AddActor(actor)
+ self.actors[len(self.actors)+1] = [actor, True]
+ return len(self.actors)
+
+ def addActor(self, actor):
+ '''Adds an actor to the render'''
+ return self.showActor(0, actor)
+
+
+ def saveRender(self, filename, renWin=None):
+ '''Save the render window to PNG file'''
+ # screenshot code:
+ w2if = vtk.vtkWindowToImageFilter()
+ if renWin == None:
+ renWin = self.renWin
+ w2if.SetInput(renWin)
+ w2if.Update()
+
+ writer = vtk.vtkPNGWriter()
+ writer.SetFileName("%s.png" % (filename))
+ writer.SetInputConnection(w2if.GetOutputPort())
+ writer.Write()
+
+
+ def startRenderLoop(self):
+ self.iren.Start()
+
+
+ def setupObservers(self, interactor):
+ interactor.RemoveObservers('LeftButtonPressEvent')
+ interactor.AddObserver('LeftButtonPressEvent', self.mouseInteraction)
+ interactor.Initialize()
+
+
+ def mouseInteraction(self, interactor, event):
+ if event == 'MouseWheelForwardEvent':
+ maxSlice = self.img3D.GetDimensions()[self.sliceOrientation]
+ if (self.sliceno + 1 < maxSlice):
+ self.hideActor(self.sliceActorNo)
+ self.sliceno = self.sliceno + 1
+ self.displaySliceActor(self.sliceno)
+ else:
+ minSlice = 0
+ if (self.sliceno - 1 > minSlice):
+ self.hideActor(self.sliceActorNo)
+ self.sliceno = self.sliceno - 1
+ self.displaySliceActor(self.sliceno)
+
+
+ def keyPress(self, interactor, event):
+ #print ("Pressed key %s" % interactor.GetKeyCode())
+ # Slice Orientation
+ if interactor.GetKeyCode() == "x":
+ # slice on the other orientation
+ self.sliceOrientation = SLICE_ORIENTATION_YZ
+ self.sliceno = int(self.img3D.GetDimensions()[1] / 2)
+ self.hideActor(self.sliceActorNo)
+ self.displaySliceActor(self.sliceno)
+ elif interactor.GetKeyCode() == "y":
+ # slice on the other orientation
+ self.sliceOrientation = SLICE_ORIENTATION_XZ
+ self.sliceno = int(self.img3D.GetDimensions()[1] / 2)
+ self.hideActor(self.sliceActorNo)
+ self.displaySliceActor(self.sliceno)
+ elif interactor.GetKeyCode() == "z":
+ # slice on the other orientation
+ self.sliceOrientation = SLICE_ORIENTATION_XY
+ self.sliceno = int(self.img3D.GetDimensions()[2] / 2)
+ self.hideActor(self.sliceActorNo)
+ self.displaySliceActor(self.sliceno)
+ if interactor.GetKeyCode() == "X":
+ # Change the camera view point
+ camera = vtk.vtkCamera()
+ camera.SetFocalPoint(self.ren.GetActiveCamera().GetFocalPoint())
+ camera.SetViewUp(self.ren.GetActiveCamera().GetViewUp())
+ newposition = [i for i in self.ren.GetActiveCamera().GetFocalPoint()]
+ newposition[SLICE_ORIENTATION_YZ] = math.sqrt(newposition[SLICE_ORIENTATION_XY] ** 2 + newposition[SLICE_ORIENTATION_XZ] ** 2)
+ camera.SetPosition(newposition)
+ camera.SetViewUp(0,0,-1)
+ self.ren.SetActiveCamera(camera)
+ self.ren.ResetCamera()
+ self.ren.Render()
+ interactor.SetKeyCode("x")
+ self.keyPress(interactor, event)
+ elif interactor.GetKeyCode() == "Y":
+ # Change the camera view point
+ camera = vtk.vtkCamera()
+ camera.SetFocalPoint(self.ren.GetActiveCamera().GetFocalPoint())
+ camera.SetViewUp(self.ren.GetActiveCamera().GetViewUp())
+ newposition = [i for i in self.ren.GetActiveCamera().GetFocalPoint()]
+ newposition[SLICE_ORIENTATION_XZ] = math.sqrt(newposition[SLICE_ORIENTATION_XY] ** 2 + newposition[SLICE_ORIENTATION_YZ] ** 2)
+ camera.SetPosition(newposition)
+ camera.SetViewUp(0,0,-1)
+ self.ren.SetActiveCamera(camera)
+ self.ren.ResetCamera()
+ self.ren.Render()
+ interactor.SetKeyCode("y")
+ self.keyPress(interactor, event)
+ elif interactor.GetKeyCode() == "Z":
+ # Change the camera view point
+ camera = vtk.vtkCamera()
+ camera.SetFocalPoint(self.ren.GetActiveCamera().GetFocalPoint())
+ camera.SetViewUp(self.ren.GetActiveCamera().GetViewUp())
+ newposition = [i for i in self.ren.GetActiveCamera().GetFocalPoint()]
+ newposition[SLICE_ORIENTATION_XY] = math.sqrt(newposition[SLICE_ORIENTATION_YZ] ** 2 + newposition[SLICE_ORIENTATION_XZ] ** 2)
+ camera.SetPosition(newposition)
+ camera.SetViewUp(0,0,-1)
+ self.ren.SetActiveCamera(camera)
+ self.ren.ResetCamera()
+ self.ren.Render()
+ interactor.SetKeyCode("z")
+ self.keyPress(interactor, event)
+ else :
+ print ("Unhandled event %s" % interactor.GetKeyCode())
+
+
+
+ def setInput3DData(self, imageData):
+ self.img3D = imageData
+
+ def setInputAsNumpy(self, 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()
+ self.img3D = shiftScaler.GetOutput()
+
+ def displaySliceActor(self, sliceno = 0):
+ self.sliceno = sliceno
+ first = False
+
+ self.sliceActor , self.voi, self.wl , self.ia = \
+ self.getSliceActor(self.img3D,
+ sliceno,
+ self.sliceActor,
+ self.voi,
+ self.wl,
+ self.ia)
+ no = self.showActor(self.sliceActorNo, self.sliceActor)
+ self.sliceActorNo = no
+
+ self.iren.Initialize()
+ self.renWin.Render()
+
+ return self.sliceActorNo
+
+
+ def getSliceActor(self,
+ imageData ,
+ sliceno=0,
+ imageActor=None ,
+ voi=None,
+ windowLevel=None,
+ imageAccumulate=None):
+ '''Slices a 3D volume and then creates an actor to be rendered'''
+ if (voi==None):
+ voi = vtk.vtkExtractVOI()
+ #voi = vtk.vtkImageClip()
+ voi.SetInputData(imageData)
+ #select one slice in Z
+ extent = [ i for i in self.img3D.GetExtent()]
+ extent[self.sliceOrientation * 2] = sliceno
+ extent[self.sliceOrientation * 2 + 1] = sliceno
+ voi.SetVOI(extent[0], extent[1],
+ extent[2], extent[3],
+ extent[4], extent[5])
+
+ voi.Update()
+ # set window/level for all slices
+ if imageAccumulate == None:
+ imageAccumulate = vtk.vtkImageAccumulate()
+
+ if (windowLevel == None):
+ windowLevel = vtk.vtkImageMapToWindowLevelColors()
+ imageAccumulate.SetInputData(imageData)
+ imageAccumulate.Update()
+ cmax = imageAccumulate.GetMax()[0]
+ cmin = imageAccumulate.GetMin()[0]
+ windowLevel.SetLevel((cmax+cmin)/2)
+ windowLevel.SetWindow(cmax-cmin)
+
+ windowLevel.SetInputData(voi.GetOutput())
+ windowLevel.Update()
+
+ if imageActor == None:
+ imageActor = vtk.vtkImageActor()
+ imageActor.SetInputData(windowLevel.GetOutput())
+ imageActor.SetDisplayExtent(extent[0], extent[1],
+ extent[2], extent[3],
+ extent[4], extent[5])
+ imageActor.Update()
+ return (imageActor , voi, windowLevel, imageAccumulate)
+
+
+ # Set interpolation on
+ def setInterpolateOn(self):
+ self.sliceActor.SetInterpolate(True)
+ self.renWin.Render()
+
+ # Set interpolation off
+ def setInterpolateOff(self):
+ self.sliceActor.SetInterpolate(False)
+ self.renWin.Render()
\ No newline at end of file
diff --git a/src/Python/ccpi/viewer/CILViewer2D.py b/src/Python/ccpi/viewer/CILViewer2D.py
new file mode 100644
index 0000000..c1629af
--- /dev/null
+++ b/src/Python/ccpi/viewer/CILViewer2D.py
@@ -0,0 +1,1126 @@
+# -*- coding: utf-8 -*-
+# Copyright 2017 Edoardo Pasca
+#
+# 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 vtk
+import numpy
+from vtk.util import numpy_support , vtkImageImportFromArray
+from enum import Enum
+
+SLICE_ORIENTATION_XY = 2 # Z
+SLICE_ORIENTATION_XZ = 1 # Y
+SLICE_ORIENTATION_YZ = 0 # X
+
+CONTROL_KEY = 8
+SHIFT_KEY = 4
+ALT_KEY = -128
+
+
+# Converter class
+class Converter():
+
+ # Utility functions to transform numpy arrays to vtkImageData and viceversa
+ @staticmethod
+ def numpy2vtkImporter(nparray, spacing=(1.,1.,1.), origin=(0,0,0)):
+ '''Creates a vtkImageImportFromArray object and returns it.
+
+ It handles the different axis order from numpy to VTK'''
+ importer = vtkImageImportFromArray.vtkImageImportFromArray()
+ importer.SetArray(numpy.transpose(nparray).copy())
+ importer.SetDataSpacing(spacing)
+ importer.SetDataOrigin(origin)
+ return importer
+
+ @staticmethod
+ def numpy2vtk(nparray, spacing=(1.,1.,1.), origin=(0,0,0)):
+ '''Converts a 3D numpy array to a vtkImageData'''
+ importer = Converter.numpy2vtkImporter(nparray, spacing, origin)
+ importer.Update()
+ return importer.GetOutput()
+
+ @staticmethod
+ def vtk2numpy(imgdata):
+ '''Converts the VTK data to 3D numpy array'''
+ img_data = numpy_support.vtk_to_numpy(
+ imgdata.GetPointData().GetScalars())
+
+ dims = imgdata.GetDimensions()
+ dims = (dims[2],dims[1],dims[0])
+ data3d = numpy.reshape(img_data, dims)
+
+ return numpy.transpose(data3d).copy()
+
+ @staticmethod
+ def tiffStack2numpy(filename, indices,
+ extent = None , sampleRate = None ,\
+ flatField = None, darkField = None):
+ '''Converts a stack of TIFF files to numpy array.
+
+ filename must contain the whole path. The filename is supposed to be named and
+ have a suffix with the ordinal file number, i.e. /path/to/projection_%03d.tif
+
+ indices are the suffix, generally an increasing number
+
+ Optionally extracts only a selection of the 2D images and (optionally)
+ normalizes.
+ '''
+
+ stack = vtk.vtkImageData()
+ reader = vtk.vtkTIFFReader()
+ voi = vtk.vtkExtractVOI()
+
+ #directory = "C:\\Users\\ofn77899\\Documents\\CCPi\\IMAT\\20170419_crabtomo\\crabtomo\\"
+
+ stack_image = numpy.asarray([])
+ nreduced = len(indices)
+
+ for num in range(len(indices)):
+ fn = filename % indices[num]
+ print ("resampling %s" % ( fn ) )
+ reader.SetFileName(fn)
+ reader.Update()
+ print (reader.GetOutput().GetScalarTypeAsString())
+ if num == 0:
+ if (extent == None):
+ sliced = reader.GetOutput().GetExtent()
+ stack.SetExtent(sliced[0],sliced[1], sliced[2],sliced[3], 0, nreduced-1)
+ else:
+ sliced = extent
+ voi.SetVOI(extent)
+
+ if sampleRate is not None:
+ voi.SetSampleRate(sampleRate)
+ ext = numpy.asarray([(sliced[2*i+1] - sliced[2*i])/sampleRate[i] for i in range(3)], dtype=int)
+ print ("ext {0}".format(ext))
+ stack.SetExtent(0, ext[0] , 0, ext[1], 0, nreduced-1)
+ else:
+ stack.SetExtent(0, sliced[1] - sliced[0] , 0, sliced[3]-sliced[2], 0, nreduced-1)
+ if (flatField != None and darkField != None):
+ stack.AllocateScalars(vtk.VTK_FLOAT, 1)
+ else:
+ stack.AllocateScalars(reader.GetOutput().GetScalarType(), 1)
+ print ("Image Size: %d" % ((sliced[1]+1)*(sliced[3]+1) ))
+ stack_image = Converter.vtk2numpy(stack)
+ print ("Stack shape %s" % str(numpy.shape(stack_image)))
+
+ if extent!=None:
+ voi.SetInputData(reader.GetOutput())
+ voi.Update()
+ img = voi.GetOutput()
+ else:
+ img = reader.GetOutput()
+
+ theSlice = Converter.vtk2numpy(img).T[0]
+ if darkField != None and flatField != None:
+ print("Try to normalize")
+ #if numpy.shape(darkField) == numpy.shape(flatField) and numpy.shape(flatField) == numpy.shape(theSlice):
+ theSlice = Converter.normalize(theSlice, darkField, flatField, 0.01)
+ print (theSlice.dtype)
+
+
+ print ("Slice shape %s" % str(numpy.shape(theSlice)))
+ stack_image.T[num] = theSlice.copy()
+
+ return stack_image
+
+ @staticmethod
+ def normalize(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
+
+
+
+## Utility functions to transform numpy arrays to vtkImageData and viceversa
+#def numpy2vtkImporter(nparray, spacing=(1.,1.,1.), origin=(0,0,0)):
+# return Converter.numpy2vtkImporter(nparray, spacing, origin)
+#
+#def numpy2vtk(nparray, spacing=(1.,1.,1.), origin=(0,0,0)):
+# return Converter.numpy2vtk(nparray, spacing, origin)
+#
+#def vtk2numpy(imgdata):
+# return Converter.vtk2numpy(imgdata)
+#
+#def tiffStack2numpy(filename, indices):
+# return Converter.tiffStack2numpy(filename, indices)
+
+class ViewerEvent(Enum):
+ # left button
+ PICK_EVENT = 0
+ # alt + right button + move
+ WINDOW_LEVEL_EVENT = 1
+ # shift + right button
+ ZOOM_EVENT = 2
+ # control + right button
+ PAN_EVENT = 3
+ # control + left button
+ CREATE_ROI_EVENT = 4
+ # alt + left button
+ DELETE_ROI_EVENT = 5
+ # release button
+ NO_EVENT = -1
+
+
+#class CILInteractorStyle(vtk.vtkInteractorStyleTrackballCamera):
+class CILInteractorStyle(vtk.vtkInteractorStyleImage):
+
+ def __init__(self, callback):
+ vtk.vtkInteractorStyleImage.__init__(self)
+ self.callback = callback
+ self._viewer = callback
+ priority = 1.0
+
+# self.AddObserver("MouseWheelForwardEvent" , callback.OnMouseWheelForward , priority)
+# self.AddObserver("MouseWheelBackwardEvent" , callback.OnMouseWheelBackward, priority)
+# self.AddObserver('KeyPressEvent', callback.OnKeyPress, priority)
+# self.AddObserver('LeftButtonPressEvent', callback.OnLeftButtonPressEvent, priority)
+# self.AddObserver('RightButtonPressEvent', callback.OnRightButtonPressEvent, priority)
+# self.AddObserver('LeftButtonReleaseEvent', callback.OnLeftButtonReleaseEvent, priority)
+# self.AddObserver('RightButtonReleaseEvent', callback.OnRightButtonReleaseEvent, priority)
+# self.AddObserver('MouseMoveEvent', callback.OnMouseMoveEvent, priority)
+
+ self.AddObserver("MouseWheelForwardEvent" , self.OnMouseWheelForward , priority)
+ self.AddObserver("MouseWheelBackwardEvent" , self.OnMouseWheelBackward, priority)
+ self.AddObserver('KeyPressEvent', self.OnKeyPress, priority)
+ self.AddObserver('LeftButtonPressEvent', self.OnLeftButtonPressEvent, priority)
+ self.AddObserver('RightButtonPressEvent', self.OnRightButtonPressEvent, priority)
+ self.AddObserver('LeftButtonReleaseEvent', self.OnLeftButtonReleaseEvent, priority)
+ self.AddObserver('RightButtonReleaseEvent', self.OnRightButtonReleaseEvent, priority)
+ self.AddObserver('MouseMoveEvent', self.OnMouseMoveEvent, priority)
+
+ self.InitialEventPosition = (0,0)
+
+
+ def SetInitialEventPosition(self, xy):
+ self.InitialEventPosition = xy
+
+ def GetInitialEventPosition(self):
+ return self.InitialEventPosition
+
+ def GetKeyCode(self):
+ return self.GetInteractor().GetKeyCode()
+
+ def SetKeyCode(self, keycode):
+ self.GetInteractor().SetKeyCode(keycode)
+
+ def GetControlKey(self):
+ return self.GetInteractor().GetControlKey() == CONTROL_KEY
+
+ def GetShiftKey(self):
+ return self.GetInteractor().GetShiftKey() == SHIFT_KEY
+
+ def GetAltKey(self):
+ return self.GetInteractor().GetAltKey() == ALT_KEY
+
+ def GetEventPosition(self):
+ return self.GetInteractor().GetEventPosition()
+
+ def GetEventPositionInWorldCoordinates(self):
+ pass
+
+ def GetDeltaEventPosition(self):
+ x,y = self.GetInteractor().GetEventPosition()
+ return (x - self.InitialEventPosition[0] , y - self.InitialEventPosition[1])
+
+ def Dolly(self, factor):
+ self.callback.camera.Dolly(factor)
+ self.callback.ren.ResetCameraClippingRange()
+
+ def GetDimensions(self):
+ return self._viewer.img3D.GetDimensions()
+
+ def GetInputData(self):
+ return self._viewer.img3D
+
+ def GetSliceOrientation(self):
+ return self._viewer.sliceOrientation
+
+ def SetSliceOrientation(self, orientation):
+ self._viewer.sliceOrientation = orientation
+
+ def GetActiveSlice(self):
+ return self._viewer.sliceno
+
+ def SetActiveSlice(self, sliceno):
+ self._viewer.sliceno = sliceno
+
+ def UpdatePipeline(self, reset = False):
+ self._viewer.updatePipeline(reset)
+
+ def GetActiveCamera(self):
+ return self._viewer.ren.GetActiveCamera()
+
+ def SetActiveCamera(self, camera):
+ self._viewer.ren.SetActiveCamera(camera)
+
+ def ResetCamera(self):
+ self._viewer.ren.ResetCamera()
+
+ def Render(self):
+ self._viewer.renWin.Render()
+
+ def UpdateSliceActor(self):
+ self._viewer.sliceActor.Update()
+
+ def AdjustCamera(self):
+ self._viewer.AdjustCamera()
+
+ def SaveRender(self, filename):
+ self._viewer.SaveRender(filename)
+
+ def GetRenderWindow(self):
+ return self._viewer.renWin
+
+ def GetRenderer(self):
+ return self._viewer.ren
+
+ def GetROIWidget(self):
+ return self._viewer.ROIWidget
+
+ def SetViewerEvent(self, event):
+ self._viewer.event = event
+
+ def GetViewerEvent(self):
+ return self._viewer.event
+
+ def SetInitialCameraPosition(self, position):
+ self._viewer.InitialCameraPosition = position
+
+ def GetInitialCameraPosition(self):
+ return self._viewer.InitialCameraPosition
+
+ def SetInitialLevel(self, level):
+ self._viewer.InitialLevel = level
+
+ def GetInitialLevel(self):
+ return self._viewer.InitialLevel
+
+ def SetInitialWindow(self, window):
+ self._viewer.InitialWindow = window
+
+ def GetInitialWindow(self):
+ return self._viewer.InitialWindow
+
+ def GetWindowLevel(self):
+ return self._viewer.wl
+
+ def SetROI(self, roi):
+ self._viewer.ROI = roi
+
+ def GetROI(self):
+ return self._viewer.ROI
+
+ def UpdateCornerAnnotation(self, text, corner):
+ self._viewer.updateCornerAnnotation(text, corner)
+
+ def GetPicker(self):
+ return self._viewer.picker
+
+ def GetCornerAnnotation(self):
+ return self._viewer.cornerAnnotation
+
+ def UpdateROIHistogram(self):
+ self._viewer.updateROIHistogram()
+
+
+ ############### Handle events
+ def OnMouseWheelForward(self, interactor, event):
+ maxSlice = self.GetDimensions()[self.GetSliceOrientation()]
+ shift = interactor.GetShiftKey()
+ advance = 1
+ if shift:
+ advance = 10
+
+ if (self.GetActiveSlice() + advance < maxSlice):
+ self.SetActiveSlice(self.GetActiveSlice() + advance)
+
+ self.UpdatePipeline()
+ else:
+ print ("maxSlice %d request %d" % (maxSlice, self.GetActiveSlice() + 1 ))
+
+ def OnMouseWheelBackward(self, interactor, event):
+ minSlice = 0
+ shift = interactor.GetShiftKey()
+ advance = 1
+ if shift:
+ advance = 10
+ if (self.GetActiveSlice() - advance >= minSlice):
+ self.SetActiveSlice( self.GetActiveSlice() - advance)
+ self.UpdatePipeline()
+ else:
+ print ("minSlice %d request %d" % (minSlice, self.GetActiveSlice() + 1 ))
+
+ def OnKeyPress(self, interactor, event):
+ #print ("Pressed key %s" % interactor.GetKeyCode())
+ # Slice Orientation
+ if interactor.GetKeyCode() == "X":
+ # slice on the other orientation
+ self.SetSliceOrientation ( SLICE_ORIENTATION_YZ )
+ self.SetActiveSlice( int(self.GetDimensions()[1] / 2) )
+ self.UpdatePipeline(True)
+ elif interactor.GetKeyCode() == "Y":
+ # slice on the other orientation
+ self.SetSliceOrientation ( SLICE_ORIENTATION_XZ )
+ self.SetActiveSlice ( int(self.GetInputData().GetDimensions()[1] / 2) )
+ self.UpdatePipeline(True)
+ elif interactor.GetKeyCode() == "Z":
+ # slice on the other orientation
+ self.SetSliceOrientation ( SLICE_ORIENTATION_XY )
+ self.SetActiveSlice ( int(self.GetInputData().GetDimensions()[2] / 2) )
+ self.UpdatePipeline(True)
+ if interactor.GetKeyCode() == "x":
+ # Change the camera view point
+ camera = vtk.vtkCamera()
+ camera.SetFocalPoint(self.GetActiveCamera().GetFocalPoint())
+ camera.SetViewUp(self.GetActiveCamera().GetViewUp())
+ newposition = [i for i in self.GetActiveCamera().GetFocalPoint()]
+ newposition[SLICE_ORIENTATION_YZ] = numpy.sqrt(newposition[SLICE_ORIENTATION_XY] ** 2 + newposition[SLICE_ORIENTATION_XZ] ** 2)
+ camera.SetPosition(newposition)
+ camera.SetViewUp(0,0,-1)
+ self.SetActiveCamera(camera)
+ self.Render()
+ interactor.SetKeyCode("X")
+ self.OnKeyPress(interactor, event)
+ elif interactor.GetKeyCode() == "y":
+ # Change the camera view point
+ camera = vtk.vtkCamera()
+ camera.SetFocalPoint(self.GetActiveCamera().GetFocalPoint())
+ camera.SetViewUp(self.GetActiveCamera().GetViewUp())
+ newposition = [i for i in self.GetActiveCamera().GetFocalPoint()]
+ newposition[SLICE_ORIENTATION_XZ] = numpy.sqrt(newposition[SLICE_ORIENTATION_XY] ** 2 + newposition[SLICE_ORIENTATION_YZ] ** 2)
+ camera.SetPosition(newposition)
+ camera.SetViewUp(0,0,-1)
+ self.SetActiveCamera(camera)
+ self.Render()
+ interactor.SetKeyCode("Y")
+ self.OnKeyPress(interactor, event)
+ elif interactor.GetKeyCode() == "z":
+ # Change the camera view point
+ camera = vtk.vtkCamera()
+ camera.SetFocalPoint(self.GetActiveCamera().GetFocalPoint())
+ camera.SetViewUp(self.GetActiveCamera().GetViewUp())
+ newposition = [i for i in self.GetActiveCamera().GetFocalPoint()]
+ newposition[SLICE_ORIENTATION_XY] = numpy.sqrt(newposition[SLICE_ORIENTATION_YZ] ** 2 + newposition[SLICE_ORIENTATION_XZ] ** 2)
+ camera.SetPosition(newposition)
+ camera.SetViewUp(0,1,0)
+ self.SetActiveCamera(camera)
+ self.ResetCamera()
+ self.Render()
+ interactor.SetKeyCode("Z")
+ self.OnKeyPress(interactor, event)
+ elif interactor.GetKeyCode() == "a":
+ # reset color/window
+ cmax = self._viewer.ia.GetMax()[0]
+ cmin = self._viewer.ia.GetMin()[0]
+
+ self.SetInitialLevel( (cmax+cmin)/2 )
+ self.SetInitialWindow( cmax-cmin )
+
+ self.GetWindowLevel().SetLevel(self.GetInitialLevel())
+ self.GetWindowLevel().SetWindow(self.GetInitialWindow())
+
+ self.GetWindowLevel().Update()
+
+ self.UpdateSliceActor()
+ self.AdjustCamera()
+ self.Render()
+
+ elif interactor.GetKeyCode() == "s":
+ filename = "current_render"
+ self.SaveRender(filename)
+ elif interactor.GetKeyCode() == "q":
+ print ("Terminating by pressing q %s" % (interactor.GetKeyCode(), ))
+ interactor.SetKeyCode("e")
+ self.OnKeyPress(interactor, event)
+ else :
+ #print ("Unhandled event %s" % (interactor.GetKeyCode(), )))
+ pass
+
+ def OnLeftButtonPressEvent(self, interactor, event):
+ alt = interactor.GetAltKey()
+ shift = interactor.GetShiftKey()
+ ctrl = interactor.GetControlKey()
+# print ("alt pressed " + (lambda x : "Yes" if x else "No")(alt))
+# print ("shift pressed " + (lambda x : "Yes" if x else "No")(shift))
+# print ("ctrl pressed " + (lambda x : "Yes" if x else "No")(ctrl))
+
+ interactor.SetInitialEventPosition(interactor.GetEventPosition())
+
+ if ctrl and not (alt and shift):
+ self.SetViewerEvent( ViewerEvent.CREATE_ROI_EVENT )
+ wsize = self.GetRenderWindow().GetSize()
+ position = interactor.GetEventPosition()
+ self.GetROIWidget().GetBorderRepresentation().SetPosition((position[0]/wsize[0] - 0.05) , (position[1]/wsize[1] - 0.05))
+ self.GetROIWidget().GetBorderRepresentation().SetPosition2( (0.1) , (0.1))
+
+ self.GetROIWidget().On()
+ self.SetDisplayHistogram(True)
+ self.Render()
+ print ("Event %s is CREATE_ROI_EVENT" % (event))
+ elif alt and not (shift and ctrl):
+ self.SetViewerEvent( ViewerEvent.DELETE_ROI_EVENT )
+ self.GetROIWidget().Off()
+ self._viewer.updateCornerAnnotation("", 1, False)
+ self.SetDisplayHistogram(False)
+ self.Render()
+ print ("Event %s is DELETE_ROI_EVENT" % (event))
+ elif not (ctrl and alt and shift):
+ self.SetViewerEvent ( ViewerEvent.PICK_EVENT )
+ self.HandlePickEvent(interactor, event)
+ print ("Event %s is PICK_EVENT" % (event))
+
+
+ def SetDisplayHistogram(self, display):
+ if display:
+ if (self._viewer.displayHistogram == 0):
+ self.GetRenderer().AddActor(self._viewer.histogramPlotActor)
+ self.firstHistogram = 1
+ self.Render()
+
+ self._viewer.histogramPlotActor.VisibilityOn()
+ self._viewer.displayHistogram = True
+ else:
+ self._viewer.histogramPlotActor.VisibilityOff()
+ self._viewer.displayHistogram = False
+
+
+ def OnLeftButtonReleaseEvent(self, interactor, event):
+ if self.GetViewerEvent() == ViewerEvent.CREATE_ROI_EVENT:
+ #bc = self.ROIWidget.GetBorderRepresentation().GetPositionCoordinate()
+ #print (bc.GetValue())
+ self.OnROIModifiedEvent(interactor, event)
+
+ elif self.GetViewerEvent() == ViewerEvent.PICK_EVENT:
+ self.HandlePickEvent(interactor, event)
+
+ self.SetViewerEvent( ViewerEvent.NO_EVENT )
+
+ def OnRightButtonPressEvent(self, interactor, event):
+ alt = interactor.GetAltKey()
+ shift = interactor.GetShiftKey()
+ ctrl = interactor.GetControlKey()
+# print ("alt pressed " + (lambda x : "Yes" if x else "No")(alt))
+# print ("shift pressed " + (lambda x : "Yes" if x else "No")(shift))
+# print ("ctrl pressed " + (lambda x : "Yes" if x else "No")(ctrl))
+
+ interactor.SetInitialEventPosition(interactor.GetEventPosition())
+
+
+ if alt and not (ctrl and shift):
+ self.SetViewerEvent( ViewerEvent.WINDOW_LEVEL_EVENT )
+ print ("Event %s is WINDOW_LEVEL_EVENT" % (event))
+ self.HandleWindowLevel(interactor, event)
+ elif shift and not (ctrl and alt):
+ self.SetViewerEvent( ViewerEvent.ZOOM_EVENT )
+ self.SetInitialCameraPosition( self.GetActiveCamera().GetPosition())
+ print ("Event %s is ZOOM_EVENT" % (event))
+ elif ctrl and not (shift and alt):
+ self.SetViewerEvent (ViewerEvent.PAN_EVENT )
+ self.SetInitialCameraPosition ( self.GetActiveCamera().GetPosition() )
+ print ("Event %s is PAN_EVENT" % (event))
+
+ def OnRightButtonReleaseEvent(self, interactor, event):
+ print (event)
+ if self.GetViewerEvent() == ViewerEvent.WINDOW_LEVEL_EVENT:
+ self.SetInitialLevel( self.GetWindowLevel().GetLevel() )
+ self.SetInitialWindow ( self.GetWindowLevel().GetWindow() )
+ elif self.GetViewerEvent() == ViewerEvent.ZOOM_EVENT or \
+ self.GetViewerEvent() == ViewerEvent.PAN_EVENT:
+ self.SetInitialCameraPosition( () )
+
+ self.SetViewerEvent( ViewerEvent.NO_EVENT )
+
+
+ def OnROIModifiedEvent(self, interactor, event):
+
+ #print ("ROI EVENT " + event)
+ p1 = self.GetROIWidget().GetBorderRepresentation().GetPositionCoordinate()
+ p2 = self.GetROIWidget().GetBorderRepresentation().GetPosition2Coordinate()
+ wsize = self.GetRenderWindow().GetSize()
+
+ #print (p1.GetValue())
+ #print (p2.GetValue())
+ pp1 = [p1.GetValue()[0] * wsize[0] , p1.GetValue()[1] * wsize[1] , 0.0]
+ pp2 = [p2.GetValue()[0] * wsize[0] + pp1[0] , p2.GetValue()[1] * wsize[1] + pp1[1] , 0.0]
+ vox1 = self.viewport2imageCoordinate(pp1)
+ vox2 = self.viewport2imageCoordinate(pp2)
+
+ self.SetROI( (vox1 , vox2) )
+ roi = self.GetROI()
+ print ("Pixel1 %d,%d,%d Value %f" % vox1 )
+ print ("Pixel2 %d,%d,%d Value %f" % vox2 )
+ if self.GetSliceOrientation() == SLICE_ORIENTATION_XY:
+ print ("slice orientation : XY")
+ x = abs(roi[1][0] - roi[0][0])
+ y = abs(roi[1][1] - roi[0][1])
+ elif self.GetSliceOrientation() == SLICE_ORIENTATION_XZ:
+ print ("slice orientation : XY")
+ x = abs(roi[1][0] - roi[0][0])
+ y = abs(roi[1][2] - roi[0][2])
+ elif self.GetSliceOrientation() == SLICE_ORIENTATION_YZ:
+ print ("slice orientation : XY")
+ x = abs(roi[1][1] - roi[0][1])
+ y = abs(roi[1][2] - roi[0][2])
+
+ text = "ROI: %d x %d, %.2f kp" % (x,y,float(x*y)/1024.)
+ print (text)
+ self.UpdateCornerAnnotation(text, 1)
+ self.UpdateROIHistogram()
+ self.SetViewerEvent( ViewerEvent.NO_EVENT )
+
+ def viewport2imageCoordinate(self, viewerposition):
+ #Determine point index
+
+ self.GetPicker().Pick(viewerposition[0], viewerposition[1], 0.0, self.GetRenderer())
+ pickPosition = list(self.GetPicker().GetPickPosition())
+ pickPosition[self.GetSliceOrientation()] = \
+ self.GetInputData().GetSpacing()[self.GetSliceOrientation()] * self.GetActiveSlice() + \
+ self.GetInputData().GetOrigin()[self.GetSliceOrientation()]
+ print ("Pick Position " + str (pickPosition))
+
+ if (pickPosition != [0,0,0]):
+ dims = self.GetInputData().GetDimensions()
+ print (dims)
+ spac = self.GetInputData().GetSpacing()
+ orig = self.GetInputData().GetOrigin()
+ imagePosition = [int(pickPosition[i] / spac[i] + orig[i]) for i in range(3) ]
+
+ pixelValue = self.GetInputData().GetScalarComponentAsDouble(imagePosition[0], imagePosition[1], imagePosition[2], 0)
+ return (imagePosition[0], imagePosition[1], imagePosition[2] , pixelValue)
+ else:
+ return (0,0,0,0)
+
+
+
+
+ def OnMouseMoveEvent(self, interactor, event):
+ if self.GetViewerEvent() == ViewerEvent.WINDOW_LEVEL_EVENT:
+ print ("Event %s is WINDOW_LEVEL_EVENT" % (event))
+ self.HandleWindowLevel(interactor, event)
+ elif self.GetViewerEvent() == ViewerEvent.PICK_EVENT:
+ self.HandlePickEvent(interactor, event)
+ elif self.GetViewerEvent() == ViewerEvent.ZOOM_EVENT:
+ self.HandleZoomEvent(interactor, event)
+ elif self.GetViewerEvent() == ViewerEvent.PAN_EVENT:
+ self.HandlePanEvent(interactor, event)
+
+
+ def HandleZoomEvent(self, interactor, event):
+ dx,dy = interactor.GetDeltaEventPosition()
+ size = self.GetRenderWindow().GetSize()
+ dy = - 4 * dy / size[1]
+
+ print ("distance: " + str(self.GetActiveCamera().GetDistance()))
+
+ print ("\ndy: %f\ncamera dolly %f\n" % (dy, 1 + dy))
+
+ camera = vtk.vtkCamera()
+ camera.SetFocalPoint(self.GetActiveCamera().GetFocalPoint())
+ #print ("current position " + str(self.InitialCameraPosition))
+ camera.SetViewUp(self.GetActiveCamera().GetViewUp())
+ camera.SetPosition(self.GetInitialCameraPosition())
+ newposition = [i for i in self.GetInitialCameraPosition()]
+ if self.GetSliceOrientation() == SLICE_ORIENTATION_XY:
+ dist = newposition[SLICE_ORIENTATION_XY] * ( 1 + dy )
+ newposition[SLICE_ORIENTATION_XY] *= ( 1 + dy )
+ elif self.GetSliceOrientation() == SLICE_ORIENTATION_XZ:
+ newposition[SLICE_ORIENTATION_XZ] *= ( 1 + dy )
+ elif self.GetSliceOrientation() == SLICE_ORIENTATION_YZ:
+ newposition[SLICE_ORIENTATION_YZ] *= ( 1 + dy )
+ #print ("new position " + str(newposition))
+ camera.SetPosition(newposition)
+ self.SetActiveCamera(camera)
+
+ self.Render()
+
+ print ("distance after: " + str(self.GetActiveCamera().GetDistance()))
+
+ def HandlePanEvent(self, interactor, event):
+ x,y = interactor.GetEventPosition()
+ x0,y0 = interactor.GetInitialEventPosition()
+
+ ic = self.viewport2imageCoordinate((x,y))
+ ic0 = self.viewport2imageCoordinate((x0,y0))
+
+ dx = 4 *( ic[0] - ic0[0])
+ dy = 4* (ic[1] - ic0[1])
+
+ camera = vtk.vtkCamera()
+ #print ("current position " + str(self.InitialCameraPosition))
+ camera.SetViewUp(self.GetActiveCamera().GetViewUp())
+ camera.SetPosition(self.GetInitialCameraPosition())
+ newposition = [i for i in self.GetInitialCameraPosition()]
+ newfocalpoint = [i for i in self.GetActiveCamera().GetFocalPoint()]
+ if self.GetSliceOrientation() == SLICE_ORIENTATION_XY:
+ newposition[0] -= dx
+ newposition[1] -= dy
+ newfocalpoint[0] = newposition[0]
+ newfocalpoint[1] = newposition[1]
+ elif self.GetSliceOrientation() == SLICE_ORIENTATION_XZ:
+ newposition[0] -= dx
+ newposition[2] -= dy
+ newfocalpoint[0] = newposition[0]
+ newfocalpoint[2] = newposition[2]
+ elif self.GetSliceOrientation() == SLICE_ORIENTATION_YZ:
+ newposition[1] -= dx
+ newposition[2] -= dy
+ newfocalpoint[2] = newposition[2]
+ newfocalpoint[1] = newposition[1]
+ #print ("new position " + str(newposition))
+ camera.SetFocalPoint(newfocalpoint)
+ camera.SetPosition(newposition)
+ self.SetActiveCamera(camera)
+
+ self.Render()
+
+ def HandleWindowLevel(self, interactor, event):
+ dx,dy = interactor.GetDeltaEventPosition()
+ print ("Event delta %d %d" % (dx,dy))
+ size = self.GetRenderWindow().GetSize()
+
+ dx = 4 * dx / size[0]
+ dy = 4 * dy / size[1]
+ window = self.GetInitialWindow()
+ level = self.GetInitialLevel()
+
+ if abs(window) > 0.01:
+ dx = dx * window
+ else:
+ dx = dx * (lambda x: -0.01 if x <0 else 0.01)(window);
+
+ if abs(level) > 0.01:
+ dy = dy * level
+ else:
+ dy = dy * (lambda x: -0.01 if x <0 else 0.01)(level)
+
+
+ # Abs so that direction does not flip
+
+ if window < 0.0:
+ dx = -1*dx
+ if level < 0.0:
+ dy = -1*dy
+
+ # Compute new window level
+
+ newWindow = dx + window
+ newLevel = level - dy
+
+ # Stay away from zero and really
+
+ if abs(newWindow) < 0.01:
+ newWindow = 0.01 * (lambda x: -1 if x <0 else 1)(newWindow)
+
+ if abs(newLevel) < 0.01:
+ newLevel = 0.01 * (lambda x: -1 if x <0 else 1)(newLevel)
+
+ self.GetWindowLevel().SetWindow(newWindow)
+ self.GetWindowLevel().SetLevel(newLevel)
+
+ self.GetWindowLevel().Update()
+ self.UpdateSliceActor()
+ self.AdjustCamera()
+
+ self.Render()
+
+ def HandlePickEvent(self, interactor, event):
+ position = interactor.GetEventPosition()
+ #print ("PICK " + str(position))
+ vox = self.viewport2imageCoordinate(position)
+ #print ("Pixel %d,%d,%d Value %f" % vox )
+ self._viewer.cornerAnnotation.VisibilityOn()
+ self.UpdateCornerAnnotation("[%d,%d,%d] : %.2f" % vox , 0)
+ self.Render()
+
+###############################################################################
+
+
+
+class CILViewer2D():
+ '''Simple Interactive Viewer based on VTK classes'''
+
+ def __init__(self, dimx=600,dimy=600, ren=None, renWin=None,iren=None):
+ '''creates the rendering pipeline'''
+ # create a rendering window and renderer
+ if ren == None:
+ self.ren = vtk.vtkRenderer()
+ else:
+ self.ren = ren
+ if renWin == None:
+ self.renWin = vtk.vtkRenderWindow()
+ else:
+ self.renWin = renWin
+ if iren == None:
+ self.iren = vtk.vtkRenderWindowInteractor()
+ else:
+ self.iren = iren
+
+ self.renWin.SetSize(dimx,dimy)
+ self.renWin.AddRenderer(self.ren)
+
+ self.style = CILInteractorStyle(self)
+
+ self.iren.SetInteractorStyle(self.style)
+ self.iren.SetRenderWindow(self.renWin)
+ self.iren.Initialize()
+ self.ren.SetBackground(.1, .2, .4)
+
+ self.camera = vtk.vtkCamera()
+ self.camera.ParallelProjectionOn()
+ self.ren.SetActiveCamera(self.camera)
+
+ # data
+ self.img3D = None
+ self.sliceno = 0
+ self.sliceOrientation = SLICE_ORIENTATION_XY
+
+ #Actors
+ self.sliceActor = vtk.vtkImageActor()
+ self.voi = vtk.vtkExtractVOI()
+ self.wl = vtk.vtkImageMapToWindowLevelColors()
+ self.ia = vtk.vtkImageAccumulate()
+ self.sliceActorNo = 0
+
+ #initial Window/Level
+ self.InitialLevel = 0
+ self.InitialWindow = 0
+
+ #ViewerEvent
+ self.event = ViewerEvent.NO_EVENT
+
+ # ROI Widget
+ self.ROIWidget = vtk.vtkBorderWidget()
+ self.ROIWidget.SetInteractor(self.iren)
+ self.ROIWidget.CreateDefaultRepresentation()
+ self.ROIWidget.GetBorderRepresentation().GetBorderProperty().SetColor(0,1,0)
+ self.ROIWidget.AddObserver(vtk.vtkWidgetEvent.Select, self.style.OnROIModifiedEvent, 1.0)
+
+ # edge points of the ROI
+ self.ROI = ()
+
+ #picker
+ self.picker = vtk.vtkPropPicker()
+ self.picker.PickFromListOn()
+ self.picker.AddPickList(self.sliceActor)
+
+ self.iren.SetPicker(self.picker)
+
+ # corner annotation
+ self.cornerAnnotation = vtk.vtkCornerAnnotation()
+ self.cornerAnnotation.SetMaximumFontSize(12);
+ self.cornerAnnotation.PickableOff();
+ self.cornerAnnotation.VisibilityOff();
+ self.cornerAnnotation.GetTextProperty().ShadowOn();
+ self.cornerAnnotation.SetLayerNumber(1);
+
+
+
+ # cursor doesn't show up
+ self.cursor = vtk.vtkCursor2D()
+ self.cursorMapper = vtk.vtkPolyDataMapper2D()
+ self.cursorActor = vtk.vtkActor2D()
+ self.cursor.SetModelBounds(-10, 10, -10, 10, 0, 0)
+ self.cursor.SetFocalPoint(0, 0, 0)
+ self.cursor.AllOff()
+ self.cursor.AxesOn()
+ self.cursorActor.PickableOff()
+ self.cursorActor.VisibilityOn()
+ self.cursorActor.GetProperty().SetColor(1, 1, 1)
+ self.cursorActor.SetLayerNumber(1)
+ self.cursorMapper.SetInputData(self.cursor.GetOutput())
+ self.cursorActor.SetMapper(self.cursorMapper)
+
+ # Zoom
+ self.InitialCameraPosition = ()
+
+ # XY Plot actor for histogram
+ self.displayHistogram = False
+ self.firstHistogram = 0
+ self.roiIA = vtk.vtkImageAccumulate()
+ self.roiVOI = vtk.vtkExtractVOI()
+ self.histogramPlotActor = vtk.vtkXYPlotActor()
+ self.histogramPlotActor.ExchangeAxesOff();
+ self.histogramPlotActor.SetXLabelFormat( "%g" )
+ self.histogramPlotActor.SetXLabelFormat( "%g" )
+ self.histogramPlotActor.SetAdjustXLabels(3)
+ self.histogramPlotActor.SetXTitle( "Level" )
+ self.histogramPlotActor.SetYTitle( "N" )
+ self.histogramPlotActor.SetXValuesToValue()
+ self.histogramPlotActor.SetPlotColor(0, (0,1,1) )
+ self.histogramPlotActor.SetPosition(0.6,0.6)
+ self.histogramPlotActor.SetPosition2(0.4,0.4)
+
+
+
+ def GetInteractor(self):
+ return self.iren
+
+ def GetRenderer(self):
+ return self.ren
+
+ def setInput3DData(self, imageData):
+ self.img3D = imageData
+ self.installPipeline()
+
+ def setInputAsNumpy(self, numpyarray, origin=(0,0,0), spacing=(1.,1.,1.),
+ rescale=True, dtype=vtk.VTK_UNSIGNED_SHORT):
+ importer = Converter.numpy2vtkImporter(numpyarray, spacing, origin)
+ importer.Update()
+
+ if rescale:
+ # rescale to appropriate VTK_UNSIGNED_SHORT
+ stats = vtk.vtkImageAccumulate()
+ stats.SetInputData(importer.GetOutput())
+ stats.Update()
+ iMin = stats.GetMin()[0]
+ iMax = stats.GetMax()[0]
+ if (iMax - iMin == 0):
+ scale = 1
+ else:
+ if dtype == vtk.VTK_UNSIGNED_SHORT:
+ scale = vtk.VTK_UNSIGNED_SHORT_MAX / (iMax - iMin)
+ elif dtype == vtk.VTK_UNSIGNED_INT:
+ scale = vtk.VTK_UNSIGNED_INT_MAX / (iMax - iMin)
+
+ shiftScaler = vtk.vtkImageShiftScale ()
+ shiftScaler.SetInputData(importer.GetOutput())
+ shiftScaler.SetScale(scale)
+ shiftScaler.SetShift(-iMin)
+ shiftScaler.SetOutputScalarType(dtype)
+ shiftScaler.Update()
+ self.img3D = shiftScaler.GetOutput()
+ else:
+ self.img3D = importer.GetOutput()
+
+ self.installPipeline()
+
+ def displaySlice(self, sliceno = 0):
+ self.sliceno = sliceno
+
+ self.updatePipeline()
+
+ self.renWin.Render()
+
+ return self.sliceActorNo
+
+ def updatePipeline(self, resetcamera = False):
+ extent = [ i for i in self.img3D.GetExtent()]
+ extent[self.sliceOrientation * 2] = self.sliceno
+ extent[self.sliceOrientation * 2 + 1] = self.sliceno
+ self.voi.SetVOI(extent[0], extent[1],
+ extent[2], extent[3],
+ extent[4], extent[5])
+
+ self.voi.Update()
+ self.ia.Update()
+ self.wl.Update()
+ self.sliceActor.SetDisplayExtent(extent[0], extent[1],
+ extent[2], extent[3],
+ extent[4], extent[5])
+ self.sliceActor.Update()
+
+ self.updateCornerAnnotation("Slice %d/%d" % (self.sliceno + 1 , self.img3D.GetDimensions()[self.sliceOrientation]))
+
+ if self.displayHistogram:
+ self.updateROIHistogram()
+
+ self.AdjustCamera(resetcamera)
+
+ self.renWin.Render()
+
+
+ def installPipeline(self):
+ '''Slices a 3D volume and then creates an actor to be rendered'''
+
+ self.ren.AddViewProp(self.cornerAnnotation)
+
+ self.voi.SetInputData(self.img3D)
+ #select one slice in Z
+ extent = [ i for i in self.img3D.GetExtent()]
+ extent[self.sliceOrientation * 2] = self.sliceno
+ extent[self.sliceOrientation * 2 + 1] = self.sliceno
+ self.voi.SetVOI(extent[0], extent[1],
+ extent[2], extent[3],
+ extent[4], extent[5])
+
+ self.voi.Update()
+ # set window/level for current slices
+
+
+ self.wl = vtk.vtkImageMapToWindowLevelColors()
+ self.ia.SetInputData(self.voi.GetOutput())
+ self.ia.Update()
+ cmax = self.ia.GetMax()[0]
+ cmin = self.ia.GetMin()[0]
+
+ self.InitialLevel = (cmax+cmin)/2
+ self.InitialWindow = cmax-cmin
+
+
+ self.wl.SetLevel(self.InitialLevel)
+ self.wl.SetWindow(self.InitialWindow)
+
+ self.wl.SetInputData(self.voi.GetOutput())
+ self.wl.Update()
+
+ self.sliceActor.SetInputData(self.wl.GetOutput())
+ self.sliceActor.SetDisplayExtent(extent[0], extent[1],
+ extent[2], extent[3],
+ extent[4], extent[5])
+ self.sliceActor.Update()
+ self.sliceActor.SetInterpolate(False)
+ self.ren.AddActor(self.sliceActor)
+ self.ren.ResetCamera()
+ self.ren.Render()
+
+ self.AdjustCamera()
+
+ self.ren.AddViewProp(self.cursorActor)
+ self.cursorActor.VisibilityOn()
+
+ self.iren.Initialize()
+ self.renWin.Render()
+ #self.iren.Start()
+
+ def AdjustCamera(self, resetcamera = False):
+ self.ren.ResetCameraClippingRange()
+ if resetcamera:
+ self.ren.ResetCamera()
+
+
+ def getROI(self):
+ return self.ROI
+
+ def getROIExtent(self):
+ p0 = self.ROI[0]
+ p1 = self.ROI[1]
+ return (p0[0], p1[0],p0[1],p1[1],p0[2],p1[2])
+
+ ############### Handle events are moved to the interactor style
+
+
+ def viewport2imageCoordinate(self, viewerposition):
+ #Determine point index
+
+ self.picker.Pick(viewerposition[0], viewerposition[1], 0.0, self.GetRenderer())
+ pickPosition = list(self.picker.GetPickPosition())
+ pickPosition[self.sliceOrientation] = \
+ self.img3D.GetSpacing()[self.sliceOrientation] * self.sliceno + \
+ self.img3D.GetOrigin()[self.sliceOrientation]
+ print ("Pick Position " + str (pickPosition))
+
+ if (pickPosition != [0,0,0]):
+ dims = self.img3D.GetDimensions()
+ print (dims)
+ spac = self.img3D.GetSpacing()
+ orig = self.img3D.GetOrigin()
+ imagePosition = [int(pickPosition[i] / spac[i] + orig[i]) for i in range(3) ]
+
+ pixelValue = self.img3D.GetScalarComponentAsDouble(imagePosition[0], imagePosition[1], imagePosition[2], 0)
+ return (imagePosition[0], imagePosition[1], imagePosition[2] , pixelValue)
+ else:
+ return (0,0,0,0)
+
+
+
+ def GetRenderWindow(self):
+ return self.renWin
+
+
+ def startRenderLoop(self):
+ self.iren.Start()
+
+ def GetSliceOrientation(self):
+ return self.sliceOrientation
+
+ def GetActiveSlice(self):
+ return self.sliceno
+
+ def updateCornerAnnotation(self, text , idx=0, visibility=True):
+ if visibility:
+ self.cornerAnnotation.VisibilityOn()
+ else:
+ self.cornerAnnotation.VisibilityOff()
+
+ self.cornerAnnotation.SetText(idx, text)
+ self.iren.Render()
+
+ def saveRender(self, filename, renWin=None):
+ '''Save the render window to PNG file'''
+ # screenshot code:
+ w2if = vtk.vtkWindowToImageFilter()
+ if renWin == None:
+ renWin = self.renWin
+ w2if.SetInput(renWin)
+ w2if.Update()
+
+ writer = vtk.vtkPNGWriter()
+ writer.SetFileName("%s.png" % (filename))
+ writer.SetInputConnection(w2if.GetOutputPort())
+ writer.Write()
+
+ def updateROIHistogram(self):
+
+ extent = [0 for i in range(6)]
+ if self.GetSliceOrientation() == SLICE_ORIENTATION_XY:
+ print ("slice orientation : XY")
+ extent[0] = self.ROI[0][0]
+ extent[1] = self.ROI[1][0]
+ extent[2] = self.ROI[0][1]
+ extent[3] = self.ROI[1][1]
+ extent[4] = self.GetActiveSlice()
+ extent[5] = self.GetActiveSlice()+1
+ #y = abs(roi[1][1] - roi[0][1])
+ elif self.GetSliceOrientation() == SLICE_ORIENTATION_XZ:
+ print ("slice orientation : XY")
+ extent[0] = self.ROI[0][0]
+ extent[1] = self.ROI[1][0]
+ #x = abs(roi[1][0] - roi[0][0])
+ extent[4] = self.ROI[0][2]
+ extent[5] = self.ROI[1][2]
+ #y = abs(roi[1][2] - roi[0][2])
+ extent[2] = self.GetActiveSlice()
+ extent[3] = self.GetActiveSlice()+1
+ elif self.GetSliceOrientation() == SLICE_ORIENTATION_YZ:
+ print ("slice orientation : XY")
+ extent[2] = self.ROI[0][1]
+ extent[3] = self.ROI[1][1]
+ #x = abs(roi[1][1] - roi[0][1])
+ extent[4] = self.ROI[0][2]
+ extent[5] = self.ROI[1][2]
+ #y = abs(roi[1][2] - roi[0][2])
+ extent[0] = self.GetActiveSlice()
+ extent[1] = self.GetActiveSlice()+1
+
+ self.roiVOI.SetVOI(extent)
+ self.roiVOI.SetInputData(self.img3D)
+ self.roiVOI.Update()
+ irange = self.roiVOI.GetOutput().GetScalarRange()
+
+ self.roiIA.SetInputData(self.roiVOI.GetOutput())
+ self.roiIA.IgnoreZeroOff()
+ self.roiIA.SetComponentExtent(0,int(irange[1]-irange[0]-1),0,0,0,0 )
+ self.roiIA.SetComponentOrigin( int(irange[0]),0,0 );
+ self.roiIA.SetComponentSpacing( 1,0,0 );
+ self.roiIA.Update()
+
+ self.histogramPlotActor.AddDataSetInputConnection(self.roiIA.GetOutputPort())
+ self.histogramPlotActor.SetXRange(irange[0],irange[1])
+
+ self.histogramPlotActor.SetYRange( self.roiIA.GetOutput().GetScalarRange() )
+
+
\ No newline at end of file
diff --git a/src/Python/ccpi/viewer/QVTKWidget.py b/src/Python/ccpi/viewer/QVTKWidget.py
new file mode 100644
index 0000000..906786b
--- /dev/null
+++ b/src/Python/ccpi/viewer/QVTKWidget.py
@@ -0,0 +1,340 @@
+################################################################################
+# File: QVTKWidget.py
+# Author: Edoardo Pasca
+# Description: PyVE Viewer Qt widget
+#
+# License:
+# This file is part of PyVE. PyVE is an open-source image
+# analysis and visualization environment focused on medical
+# imaging. More info at http://pyve.sourceforge.net
+#
+# Copyright (c) 2011-2012 Edoardo Pasca, Lukas Batteau
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or
+# without modification, are permitted provided that the following
+# conditions are met:
+#
+# Redistributions of source code must retain the above copyright
+# notice, this list of conditions and the following disclaimer.
+# Redistributions in binary form must reproduce the above
+# copyright notice, this list of conditions and the following
+# disclaimer in the documentation and/or other materials provided
+# with the distribution. Neither name of Edoardo Pasca or Lukas
+# Batteau nor the names of any contributors may be used to endorse
+# or promote products derived from this software without specific
+# prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
+# CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES,
+# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE AUTHORS OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
+# OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
+# OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
+# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
+# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY
+# OF SUCH DAMAGE.
+#
+# CHANGE HISTORY
+#
+# 20120118 Edoardo Pasca Initial version
+#
+###############################################################################
+
+import os
+from PyQt5 import QtCore, QtGui, QtWidgets
+#import itk
+import vtk
+#from viewer import PyveViewer
+from ccpi.viewer.CILViewer2D import CILViewer2D , Converter
+
+class QVTKWidget(QtWidgets.QWidget):
+
+ """ A QVTKWidget for Python and Qt."""
+
+ # Map between VTK and Qt cursors.
+ _CURSOR_MAP = {
+ 0: QtCore.Qt.ArrowCursor, # VTK_CURSOR_DEFAULT
+ 1: QtCore.Qt.ArrowCursor, # VTK_CURSOR_ARROW
+ 2: QtCore.Qt.SizeBDiagCursor, # VTK_CURSOR_SIZENE
+ 3: QtCore.Qt.SizeFDiagCursor, # VTK_CURSOR_SIZENWSE
+ 4: QtCore.Qt.SizeBDiagCursor, # VTK_CURSOR_SIZESW
+ 5: QtCore.Qt.SizeFDiagCursor, # VTK_CURSOR_SIZESE
+ 6: QtCore.Qt.SizeVerCursor, # VTK_CURSOR_SIZENS
+ 7: QtCore.Qt.SizeHorCursor, # VTK_CURSOR_SIZEWE
+ 8: QtCore.Qt.SizeAllCursor, # VTK_CURSOR_SIZEALL
+ 9: QtCore.Qt.PointingHandCursor, # VTK_CURSOR_HAND
+ 10: QtCore.Qt.CrossCursor, # VTK_CURSOR_CROSSHAIR
+ }
+
+ def __init__(self, parent=None, wflags=QtCore.Qt.WindowFlags(), **kw):
+ # the current button
+ self._ActiveButton = QtCore.Qt.NoButton
+
+ # private attributes
+ self.__oldFocus = None
+ self.__saveX = 0
+ self.__saveY = 0
+ self.__saveModifiers = QtCore.Qt.NoModifier
+ self.__saveButtons = QtCore.Qt.NoButton
+ self.__timeframe = 0
+
+ # create qt-level widget
+ QtWidgets.QWidget.__init__(self, parent, wflags|QtCore.Qt.MSWindowsOwnDC)
+
+ # Link to PyVE Viewer
+ self._PyveViewer = CILViewer2D()
+ #self._Viewer = self._PyveViewer._vtkPyveViewer
+
+ self._Iren = self._PyveViewer.GetInteractor()
+ #self._Iren = self._Viewer.GetRenderWindow().GetInteractor()
+ self._RenderWindow = self._PyveViewer.GetRenderWindow()
+ #self._RenderWindow = self._Viewer.GetRenderWindow()
+
+ self._Iren.Register(self._RenderWindow)
+ self._Iren.SetRenderWindow(self._RenderWindow)
+ self._RenderWindow.SetWindowInfo(str(int(self.winId())))
+
+ # do all the necessary qt setup
+ self.setAttribute(QtCore.Qt.WA_OpaquePaintEvent)
+ self.setAttribute(QtCore.Qt.WA_PaintOnScreen)
+ self.setMouseTracking(True) # get all mouse events
+ self.setFocusPolicy(QtCore.Qt.WheelFocus)
+ self.setSizePolicy(QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding))
+
+ self._Timer = QtCore.QTimer(self)
+ #self.connect(self._Timer, QtCore.pyqtSignal('timeout()'), self.TimerEvent)
+
+ self._Iren.AddObserver('CreateTimerEvent', self.CreateTimer)
+ self._Iren.AddObserver('DestroyTimerEvent', self.DestroyTimer)
+ self._Iren.GetRenderWindow().AddObserver('CursorChangedEvent',
+ self.CursorChangedEvent)
+
+ # Destructor
+ def __del__(self):
+ self._Iren.UnRegister(self._RenderWindow)
+ #QtWidgets.QWidget.__del__(self)
+
+ # Display image data
+ def SetInput(self, imageData):
+ self._PyveViewer.setInput3DData(imageData)
+
+ # GetInteractor
+ def GetInteractor(self):
+ return self._Iren
+
+ # Display image data
+ def GetPyveViewer(self):
+ return self._PyveViewer
+
+ def __getattr__(self, attr):
+ """Makes the object behave like a vtkGenericRenderWindowInteractor"""
+ print (attr)
+ if attr == '__vtk__':
+ return lambda t=self._Iren: t
+ elif hasattr(self._Iren, attr):
+ return getattr(self._Iren, attr)
+# else:
+# raise AttributeError( self.__class__.__name__ + \
+# " has no attribute named " + attr )
+
+ def CreateTimer(self, obj, evt):
+ self._Timer.start(10)
+
+ def DestroyTimer(self, obj, evt):
+ self._Timer.stop()
+ return 1
+
+ def TimerEvent(self):
+ self._Iren.InvokeEvent("TimerEvent")
+
+ def CursorChangedEvent(self, obj, evt):
+ """Called when the CursorChangedEvent fires on the render window."""
+ # This indirection is needed since when the event fires, the current
+ # cursor is not yet set so we defer this by which time the current
+ # cursor should have been set.
+ QtCore.QTimer.singleShot(0, self.ShowCursor)
+
+ def HideCursor(self):
+ """Hides the cursor."""
+ self.setCursor(QtCore.Qt.BlankCursor)
+
+ def ShowCursor(self):
+ """Shows the cursor."""
+ vtk_cursor = self._Iren.GetRenderWindow().GetCurrentCursor()
+ qt_cursor = self._CURSOR_MAP.get(vtk_cursor, QtCore.Qt.ArrowCursor)
+ self.setCursor(qt_cursor)
+
+ def sizeHint(self):
+ return QtCore.QSize(400, 400)
+
+ def paintEngine(self):
+ return None
+
+ def paintEvent(self, ev):
+ self._RenderWindow.Render()
+
+ def resizeEvent(self, ev):
+ self._RenderWindow.Render()
+ w = self.width()
+ h = self.height()
+
+ self._RenderWindow.SetSize(w, h)
+ self._Iren.SetSize(w, h)
+
+ def _GetCtrlShiftAlt(self, ev):
+ ctrl = shift = alt = False
+
+ if hasattr(ev, 'modifiers'):
+ if ev.modifiers() & QtCore.Qt.ShiftModifier:
+ shift = True
+ if ev.modifiers() & QtCore.Qt.ControlModifier:
+ ctrl = True
+ if ev.modifiers() & QtCore.Qt.AltModifier:
+ alt = True
+ else:
+ if self.__saveModifiers & QtCore.Qt.ShiftModifier:
+ shift = True
+ if self.__saveModifiers & QtCore.Qt.ControlModifier:
+ ctrl = True
+ if self.__saveModifiers & QtCore.Qt.AltModifier:
+ alt = True
+
+ return ctrl, shift, alt
+
+ def enterEvent(self, ev):
+ if not self.hasFocus():
+ self.__oldFocus = self.focusWidget()
+ self.setFocus()
+
+ ctrl, shift, alt = self._GetCtrlShiftAlt(ev)
+ self._Iren.SetEventInformationFlipY(self.__saveX, self.__saveY,
+ ctrl, shift, chr(0), 0, None)
+ self._Iren.SetAltKey(alt)
+ self._Iren.InvokeEvent("EnterEvent")
+
+ def leaveEvent(self, ev):
+ if self.__saveButtons == QtCore.Qt.NoButton and self.__oldFocus:
+ self.__oldFocus.setFocus()
+ self.__oldFocus = None
+
+ ctrl, shift, alt = self._GetCtrlShiftAlt(ev)
+ self._Iren.SetEventInformationFlipY(self.__saveX, self.__saveY,
+ ctrl, shift, chr(0), 0, None)
+ self._Iren.SetAltKey(alt)
+ self._Iren.InvokeEvent("LeaveEvent")
+
+ def mousePressEvent(self, ev):
+ ctrl, shift, alt = self._GetCtrlShiftAlt(ev)
+ repeat = 0
+ if ev.type() == QtCore.QEvent.MouseButtonDblClick:
+ repeat = 1
+ self._Iren.SetEventInformationFlipY(ev.x(), ev.y(),
+ ctrl, shift, chr(0), repeat, None)
+
+ self._Iren.SetAltKey(alt)
+ self._ActiveButton = ev.button()
+
+ if self._ActiveButton == QtCore.Qt.LeftButton:
+ self._Iren.InvokeEvent("LeftButtonPressEvent")
+ elif self._ActiveButton == QtCore.Qt.RightButton:
+ self._Iren.InvokeEvent("RightButtonPressEvent")
+ elif self._ActiveButton == QtCore.Qt.MidButton:
+ self._Iren.InvokeEvent("MiddleButtonPressEvent")
+
+ def mouseReleaseEvent(self, ev):
+ ctrl, shift, alt = self._GetCtrlShiftAlt(ev)
+ self._Iren.SetEventInformationFlipY(ev.x(), ev.y(),
+ ctrl, shift, chr(0), 0, None)
+ self._Iren.SetAltKey(alt)
+
+ if self._ActiveButton == QtCore.Qt.LeftButton:
+ self._Iren.InvokeEvent("LeftButtonReleaseEvent")
+ elif self._ActiveButton == QtCore.Qt.RightButton:
+ self._Iren.InvokeEvent("RightButtonReleaseEvent")
+ elif self._ActiveButton == QtCore.Qt.MidButton:
+ self._Iren.InvokeEvent("MiddleButtonReleaseEvent")
+
+ def mouseMoveEvent(self, ev):
+ self.__saveModifiers = ev.modifiers()
+ self.__saveButtons = ev.buttons()
+ self.__saveX = ev.x()
+ self.__saveY = ev.y()
+
+ ctrl, shift, alt = self._GetCtrlShiftAlt(ev)
+ self._Iren.SetEventInformationFlipY(ev.x(), ev.y(),
+ ctrl, shift, chr(0), 0, None)
+ self._Iren.SetAltKey(alt)
+ self._Iren.InvokeEvent("MouseMoveEvent")
+
+ def keyPressEvent(self, ev):
+ ctrl, shift, alt = self._GetCtrlShiftAlt(ev)
+ if ev.key() < 256:
+ key = str(ev.text())
+ else:
+ key = chr(0)
+
+ self._Iren.SetEventInformationFlipY(self.__saveX, self.__saveY,
+ ctrl, shift, key, 0, None)
+ self._Iren.SetAltKey(alt)
+ self._Iren.InvokeEvent("KeyPressEvent")
+ self._Iren.InvokeEvent("CharEvent")
+
+ def keyReleaseEvent(self, ev):
+ ctrl, shift, alt = self._GetCtrlShiftAlt(ev)
+ if ev.key() < 256:
+ key = chr(ev.key())
+ else:
+ key = chr(0)
+
+ self._Iren.SetEventInformationFlipY(self.__saveX, self.__saveY,
+ ctrl, shift, key, 0, None)
+ self._Iren.SetAltKey(alt)
+ self._Iren.InvokeEvent("KeyReleaseEvent")
+
+ def wheelEvent(self, ev):
+ print ("angleDeltaX %d" % ev.angleDelta().x())
+ print ("angleDeltaY %d" % ev.angleDelta().y())
+ if ev.angleDelta().y() >= 0:
+ self._Iren.InvokeEvent("MouseWheelForwardEvent")
+ else:
+ self._Iren.InvokeEvent("MouseWheelBackwardEvent")
+
+ def GetRenderWindow(self):
+ return self._RenderWindow
+
+ def Render(self):
+ self.update()
+
+
+def QVTKExample():
+ """A simple example that uses the QVTKWidget class."""
+
+ # every QT app needs an app
+ app = QtWidgets.QApplication(['PyVE QVTKWidget Example'])
+ page_VTK = QtWidgets.QWidget()
+ page_VTK.resize(500,500)
+ layout = QtWidgets.QVBoxLayout(page_VTK)
+ # create the widget
+ widget = QVTKWidget(parent=None)
+ layout.addWidget(widget)
+
+ #reader = vtk.vtkPNGReader()
+ #reader.SetFileName("F:\Diagnostics\Images\PyVE\VTKData\Data\camscene.png")
+ reader = vtk.vtkMetaImageReader()
+ reader.SetFileName("C:\\Users\\ofn77899\\Documents\\GitHub\\CCPi-Simpleflex\\data\\head.mha")
+ reader.Update()
+
+ widget.SetInput(reader.GetOutput())
+
+ # show the widget
+ page_VTK.show()
+ # start event processing
+ app.exec_()
+
+if __name__ == "__main__":
+ QVTKExample()
diff --git a/src/Python/ccpi/viewer/QVTKWidget2.py b/src/Python/ccpi/viewer/QVTKWidget2.py
new file mode 100644
index 0000000..e32e1c2
--- /dev/null
+++ b/src/Python/ccpi/viewer/QVTKWidget2.py
@@ -0,0 +1,84 @@
+################################################################################
+# File: QVTKWidget.py
+# Author: Edoardo Pasca
+# Description: PyVE Viewer Qt widget
+#
+# License:
+# This file is part of PyVE. PyVE is an open-source image
+# analysis and visualization environment focused on medical
+# imaging. More info at http://pyve.sourceforge.net
+#
+# Copyright (c) 2011-2012 Edoardo Pasca, Lukas Batteau
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or
+# without modification, are permitted provided that the following
+# conditions are met:
+#
+# Redistributions of source code must retain the above copyright
+# notice, this list of conditions and the following disclaimer.
+# Redistributions in binary form must reproduce the above
+# copyright notice, this list of conditions and the following
+# disclaimer in the documentation and/or other materials provided
+# with the distribution. Neither name of Edoardo Pasca or Lukas
+# Batteau nor the names of any contributors may be used to endorse
+# or promote products derived from this software without specific
+# prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
+# CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES,
+# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE AUTHORS OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
+# OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
+# OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
+# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
+# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY
+# OF SUCH DAMAGE.
+#
+# CHANGE HISTORY
+#
+# 20120118 Edoardo Pasca Initial version
+#
+###############################################################################
+
+import os
+from PyQt5 import QtCore, QtGui, QtWidgets
+#import itk
+import vtk
+#from viewer import PyveViewer
+from ccpi.viewer.CILViewer2D import CILViewer2D , Converter
+from vtk.qt.QVTKRenderWindowInteractor import QVTKRenderWindowInteractor
+
+class QVTKWidget(QVTKRenderWindowInteractor):
+
+
+ def __init__(self, parent=None, wflags=QtCore.Qt.WindowFlags(), **kw):
+ kw = dict()
+ super().__init__(parent, **kw)
+
+
+ # Link to PyVE Viewer
+ self._PyveViewer = CILViewer2D(400,400)
+ #self._Viewer = self._PyveViewer._vtkPyveViewer
+
+ self._Iren = self._PyveViewer.GetInteractor()
+ kw['iren'] = self._Iren
+ #self._Iren = self._Viewer.GetRenderWindow().GetInteractor()
+ self._RenderWindow = self._PyveViewer.GetRenderWindow()
+ #self._RenderWindow = self._Viewer.GetRenderWindow()
+ kw['rw'] = self._RenderWindow
+
+
+
+
+ def GetInteractor(self):
+ return self._Iren
+
+ # Display image data
+ def SetInput(self, imageData):
+ self._PyveViewer.setInput3DData(imageData)
+
\ No newline at end of file
diff --git a/src/Python/ccpi/viewer/__init__.py b/src/Python/ccpi/viewer/__init__.py
new file mode 100644
index 0000000..946188b
--- /dev/null
+++ b/src/Python/ccpi/viewer/__init__.py
@@ -0,0 +1 @@
+from ccpi.viewer.CILViewer import CILViewer
\ No newline at end of file
diff --git a/src/Python/ccpi/viewer/__pycache__/CILViewer.cpython-35.pyc b/src/Python/ccpi/viewer/__pycache__/CILViewer.cpython-35.pyc
new file mode 100644
index 0000000..711f77a
Binary files /dev/null and b/src/Python/ccpi/viewer/__pycache__/CILViewer.cpython-35.pyc differ
diff --git a/src/Python/ccpi/viewer/__pycache__/CILViewer2D.cpython-35.pyc b/src/Python/ccpi/viewer/__pycache__/CILViewer2D.cpython-35.pyc
new file mode 100644
index 0000000..77c2ca8
Binary files /dev/null and b/src/Python/ccpi/viewer/__pycache__/CILViewer2D.cpython-35.pyc differ
diff --git a/src/Python/ccpi/viewer/__pycache__/QVTKWidget.cpython-35.pyc b/src/Python/ccpi/viewer/__pycache__/QVTKWidget.cpython-35.pyc
new file mode 100644
index 0000000..3d11b87
Binary files /dev/null and b/src/Python/ccpi/viewer/__pycache__/QVTKWidget.cpython-35.pyc differ
diff --git a/src/Python/ccpi/viewer/__pycache__/QVTKWidget2.cpython-35.pyc b/src/Python/ccpi/viewer/__pycache__/QVTKWidget2.cpython-35.pyc
new file mode 100644
index 0000000..2fa2eaf
Binary files /dev/null and b/src/Python/ccpi/viewer/__pycache__/QVTKWidget2.cpython-35.pyc differ
diff --git a/src/Python/ccpi/viewer/__pycache__/__init__.cpython-35.pyc b/src/Python/ccpi/viewer/__pycache__/__init__.cpython-35.pyc
new file mode 100644
index 0000000..fcea537
Binary files /dev/null and b/src/Python/ccpi/viewer/__pycache__/__init__.cpython-35.pyc differ
diff --git a/src/Python/ccpi/viewer/embedvtk.py b/src/Python/ccpi/viewer/embedvtk.py
new file mode 100644
index 0000000..b5eb0a7
--- /dev/null
+++ b/src/Python/ccpi/viewer/embedvtk.py
@@ -0,0 +1,75 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jul 27 12:18:58 2017
+
+@author: ofn77899
+"""
+
+#!/usr/bin/env python
+
+import sys
+import vtk
+from PyQt5 import QtCore, QtWidgets
+from vtk.qt.QVTKRenderWindowInteractor import QVTKRenderWindowInteractor
+import QVTKWidget2
+
+class MainWindow(QtWidgets.QMainWindow):
+
+ def __init__(self, parent = None):
+ QtWidgets.QMainWindow.__init__(self, parent)
+
+ self.frame = QtWidgets.QFrame()
+
+ self.vl = QtWidgets.QVBoxLayout()
+# self.vtkWidget = QVTKRenderWindowInteractor(self.frame)
+
+ self.vtkWidget = QVTKWidget2.QVTKWidget(self.frame)
+ self.iren = self.vtkWidget.GetInteractor()
+ self.vl.addWidget(self.vtkWidget)
+
+
+
+
+ self.ren = vtk.vtkRenderer()
+ self.vtkWidget.GetRenderWindow().AddRenderer(self.ren)
+# self.iren = self.vtkWidget.GetRenderWindow().GetInteractor()
+#
+# # Create source
+# source = vtk.vtkSphereSource()
+# source.SetCenter(0, 0, 0)
+# source.SetRadius(5.0)
+#
+# # Create a mapper
+# mapper = vtk.vtkPolyDataMapper()
+# mapper.SetInputConnection(source.GetOutputPort())
+#
+# # Create an actor
+# actor = vtk.vtkActor()
+# actor.SetMapper(mapper)
+#
+# self.ren.AddActor(actor)
+#
+# self.ren.ResetCamera()
+#
+ self.frame.setLayout(self.vl)
+ self.setCentralWidget(self.frame)
+ reader = vtk.vtkMetaImageReader()
+ reader.SetFileName("C:\\Users\\ofn77899\\Documents\\GitHub\\CCPi-Simpleflex\\data\\head.mha")
+ reader.Update()
+
+ self.vtkWidget.SetInput(reader.GetOutput())
+
+ #self.vktWidget.Initialize()
+ #self.vktWidget.Start()
+
+ self.show()
+ #self.iren.Initialize()
+
+
+if __name__ == "__main__":
+
+ app = QtWidgets.QApplication(sys.argv)
+
+ window = MainWindow()
+
+ sys.exit(app.exec_())
\ No newline at end of file
--
cgit v1.2.3
From ad62962697509d977087c25d24a3ff083d9c4308 Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Wed, 23 Aug 2017 15:08:32 +0100
Subject: initial revision
---
src/Python/ccpi/imaging/Regularizer.py | 322 +++++++++++++++++++++++++++++++++
1 file changed, 322 insertions(+)
create mode 100644 src/Python/ccpi/imaging/Regularizer.py
(limited to 'src/Python/ccpi')
diff --git a/src/Python/ccpi/imaging/Regularizer.py b/src/Python/ccpi/imaging/Regularizer.py
new file mode 100644
index 0000000..fb9ae08
--- /dev/null
+++ b/src/Python/ccpi/imaging/Regularizer.py
@@ -0,0 +1,322 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug 8 14:26:00 2017
+
+@author: ofn77899
+"""
+
+from ccpi.imaging import cpu_regularizers
+import numpy as np
+from enum import Enum
+import timeit
+
+class Regularizer():
+ '''Class to handle regularizer algorithms to be used during reconstruction
+
+ Currently 5 CPU (OMP) regularization algorithms are available:
+
+ 1) SplitBregman_TV
+ 2) FGP_TV
+ 3) LLT_model
+ 4) PatchBased_Regul
+ 5) TGV_PD
+
+ Usage:
+ the regularizer can be invoked as object or as static method
+ Depending on the actual regularizer the input parameter may vary, and
+ a different default setting is defined.
+ reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+
+ out = reg(input=u0, regularization_parameter=10., number_of_iterations=30,
+ tolerance_constant=1e-4,
+ TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+
+ out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10.,
+ number_of_iterations=30, tolerance_constant=1e-4,
+ TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+
+ A number of optional parameters can be passed or skipped
+ out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
+
+ '''
+ class Algorithm(Enum):
+ SplitBregman_TV = cpu_regularizers.SplitBregman_TV
+ FGP_TV = cpu_regularizers.FGP_TV
+ LLT_model = cpu_regularizers.LLT_model
+ PatchBased_Regul = cpu_regularizers.PatchBased_Regul
+ TGV_PD = cpu_regularizers.TGV_PD
+ # Algorithm
+
+ class TotalVariationPenalty(Enum):
+ isotropic = 0
+ l1 = 1
+ # TotalVariationPenalty
+
+ def __init__(self , algorithm, debug = True):
+ self.setAlgorithm ( algorithm )
+ self.debug = debug
+ # __init__
+
+ def setAlgorithm(self, algorithm):
+ self.algorithm = algorithm
+ self.pars = self.getDefaultParsForAlgorithm(algorithm)
+ # setAlgorithm
+
+ def getDefaultParsForAlgorithm(self, algorithm):
+ pars = dict()
+
+ if algorithm == Regularizer.Algorithm.SplitBregman_TV :
+ pars['algorithm'] = algorithm
+ pars['input'] = None
+ pars['regularization_parameter'] = None
+ pars['number_of_iterations'] = 35
+ pars['tolerance_constant'] = 0.0001
+ pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
+
+ elif algorithm == Regularizer.Algorithm.FGP_TV :
+ pars['algorithm'] = algorithm
+ pars['input'] = None
+ pars['regularization_parameter'] = None
+ pars['number_of_iterations'] = 50
+ pars['tolerance_constant'] = 0.001
+ pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic
+
+ elif algorithm == Regularizer.Algorithm.LLT_model:
+ pars['algorithm'] = algorithm
+ pars['input'] = None
+ pars['regularization_parameter'] = None
+ pars['time_step'] = None
+ pars['number_of_iterations'] = None
+ pars['tolerance_constant'] = None
+ pars['restrictive_Z_smoothing'] = 0
+
+ elif algorithm == Regularizer.Algorithm.PatchBased_Regul:
+ pars['algorithm'] = algorithm
+ pars['input'] = None
+ pars['searching_window_ratio'] = None
+ pars['similarity_window_ratio'] = None
+ pars['PB_filtering_parameter'] = None
+ pars['regularization_parameter'] = None
+
+ elif algorithm == Regularizer.Algorithm.TGV_PD:
+ pars['algorithm'] = algorithm
+ pars['input'] = None
+ pars['first_order_term'] = None
+ pars['second_order_term'] = None
+ pars['number_of_iterations'] = None
+ pars['regularization_parameter'] = None
+
+ else:
+ raise Exception('Unknown regularizer algorithm')
+
+ return pars
+ # parsForAlgorithm
+
+ def setParameter(self, **kwargs):
+ '''set named parameter for the regularization engine
+
+ raises Exception if the named parameter is not recognized
+ Typical usage is:
+
+ reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+ reg.setParameter(input=u0)
+ reg.setParameter(regularization_parameter=10.)
+
+ it can be also used as
+ reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+ reg.setParameter(input=u0 , regularization_parameter=10.)
+ '''
+
+ for key , value in kwargs.items():
+ if key in self.pars.keys():
+ self.pars[key] = value
+ else:
+ raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
+ # setParameter
+
+ def getParameter(self, **kwargs):
+ ret = {}
+ for key , value in kwargs.items():
+ if key in self.pars.keys():
+ ret[key] = self.pars[key]
+ else:
+ raise Exception('Wrong parameter {0} for regularizer algorithm'.format(key))
+ # setParameter
+
+
+ def __call__(self, input = None, regularization_parameter = None, **kwargs):
+ '''Actual call for the regularizer.
+
+ One can either set the regularization parameters first and then call the
+ algorithm or set the regularization parameter during the call (as
+ is done in the static methods).
+ '''
+
+ if kwargs is not None:
+ for key, value in kwargs.items():
+ #print("{0} = {1}".format(key, value))
+ self.pars[key] = value
+
+ if input is not None:
+ self.pars['input'] = input
+ if regularization_parameter is not None:
+ self.pars['regularization_parameter'] = regularization_parameter
+
+ if self.debug:
+ print ("--------------------------------------------------")
+ for key, value in self.pars.items():
+ if key== 'algorithm' :
+ print("{0} = {1}".format(key, value.__name__))
+ elif key == 'input':
+ print("{0} = {1}".format(key, np.shape(value)))
+ else:
+ print("{0} = {1}".format(key, value))
+
+
+ if None in self.pars:
+ raise Exception("Not all parameters have been provided")
+
+ input = self.pars['input']
+ regularization_parameter = self.pars['regularization_parameter']
+ if self.algorithm == Regularizer.Algorithm.SplitBregman_TV :
+ return self.algorithm(input, regularization_parameter,
+ self.pars['number_of_iterations'],
+ self.pars['tolerance_constant'],
+ self.pars['TV_penalty'].value )
+ elif self.algorithm == Regularizer.Algorithm.FGP_TV :
+ return self.algorithm(input, regularization_parameter,
+ self.pars['number_of_iterations'],
+ self.pars['tolerance_constant'],
+ self.pars['TV_penalty'].value )
+ elif self.algorithm == Regularizer.Algorithm.LLT_model :
+ #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
+ # no default
+ return self.algorithm(input,
+ regularization_parameter,
+ self.pars['time_step'] ,
+ self.pars['number_of_iterations'],
+ self.pars['tolerance_constant'],
+ self.pars['restrictive_Z_smoothing'] )
+ elif self.algorithm == Regularizer.Algorithm.PatchBased_Regul :
+ #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
+ # no default
+ return self.algorithm(input, regularization_parameter,
+ self.pars['searching_window_ratio'] ,
+ self.pars['similarity_window_ratio'] ,
+ self.pars['PB_filtering_parameter'])
+ elif self.algorithm == Regularizer.Algorithm.TGV_PD :
+ #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher)
+ # no default
+ if len(np.shape(input)) == 2:
+ return self.algorithm(input, regularization_parameter,
+ self.pars['first_order_term'] ,
+ self.pars['second_order_term'] ,
+ self.pars['number_of_iterations'])
+ elif len(np.shape(input)) == 3:
+ #assuming it's 3D
+ # run independent calls on each slice
+ out3d = input.copy()
+ for i in range(np.shape(input)[2]):
+ out = self.algorithm(input, regularization_parameter,
+ self.pars['first_order_term'] ,
+ self.pars['second_order_term'] ,
+ self.pars['number_of_iterations'])
+ # copy the result in the 3D image
+ out3d.T[i] = out[0].copy()
+ # append the rest of the info that the algorithm returns
+ output = [out3d]
+ for i in range(1,len(out)):
+ output.append(out[i])
+ return output
+
+
+
+
+
+ # __call__
+
+ @staticmethod
+ def SplitBregman_TV(input, regularization_parameter , **kwargs):
+ start_time = timeit.default_timer()
+ reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+ out = list( reg(input, regularization_parameter, **kwargs) )
+ out.append(reg.pars)
+ txt = reg.printParametersToString()
+ txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ out.append(txt)
+ return out
+
+ @staticmethod
+ def FGP_TV(input, regularization_parameter , **kwargs):
+ start_time = timeit.default_timer()
+ reg = Regularizer(Regularizer.Algorithm.FGP_TV)
+ out = list( reg(input, regularization_parameter, **kwargs) )
+ out.append(reg.pars)
+ txt = reg.printParametersToString()
+ txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ out.append(txt)
+ return out
+
+ @staticmethod
+ def LLT_model(input, regularization_parameter , time_step, number_of_iterations,
+ tolerance_constant, restrictive_Z_smoothing=0):
+ start_time = timeit.default_timer()
+ reg = Regularizer(Regularizer.Algorithm.LLT_model)
+ out = list( reg(input, regularization_parameter, time_step=time_step,
+ number_of_iterations=number_of_iterations,
+ tolerance_constant=tolerance_constant,
+ restrictive_Z_smoothing=restrictive_Z_smoothing) )
+ out.append(reg.pars)
+ txt = reg.printParametersToString()
+ txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ out.append(txt)
+ return out
+
+ @staticmethod
+ def PatchBased_Regul(input, regularization_parameter,
+ searching_window_ratio,
+ similarity_window_ratio,
+ PB_filtering_parameter):
+ start_time = timeit.default_timer()
+ reg = Regularizer(Regularizer.Algorithm.PatchBased_Regul)
+ out = list( reg(input,
+ regularization_parameter,
+ searching_window_ratio=searching_window_ratio,
+ similarity_window_ratio=similarity_window_ratio,
+ PB_filtering_parameter=PB_filtering_parameter )
+ )
+ out.append(reg.pars)
+ txt = reg.printParametersToString()
+ txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ out.append(txt)
+ return out
+
+ @staticmethod
+ def TGV_PD(input, regularization_parameter , first_order_term,
+ second_order_term, number_of_iterations):
+ start_time = timeit.default_timer()
+
+ reg = Regularizer(Regularizer.Algorithm.TGV_PD)
+ out = list( reg(input, regularization_parameter,
+ first_order_term=first_order_term,
+ second_order_term=second_order_term,
+ number_of_iterations=number_of_iterations) )
+ out.append(reg.pars)
+ txt = reg.printParametersToString()
+ txt += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+ out.append(txt)
+
+ return out
+
+ def printParametersToString(self):
+ txt = r''
+ for key, value in self.pars.items():
+ if key== 'algorithm' :
+ txt += "{0} = {1}".format(key, value.__name__)
+ elif key == 'input':
+ txt += "{0} = {1}".format(key, np.shape(value))
+ else:
+ txt += "{0} = {1}".format(key, value)
+ txt += '\n'
+ return txt
+
--
cgit v1.2.3
From 56915cc00ded38d24c23b9ab1a0717d52d430ddd Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Wed, 23 Aug 2017 16:54:59 +0100
Subject: initial revision for testing
---
.../ccpi/reconstruction/FISTAReconstructor.py | 354 +++++++++++++++++++++
1 file changed, 354 insertions(+)
create mode 100644 src/Python/ccpi/reconstruction/FISTAReconstructor.py
(limited to 'src/Python/ccpi')
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()
+##
+
--
cgit v1.2.3
From 7111d98258becca09e4c93e3c66edb7d524d6463 Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Thu, 24 Aug 2017 16:42:05 +0100
Subject: initial revision
---
src/Python/ccpi/imaging/__init__.py | 0
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 src/Python/ccpi/imaging/__init__.py
(limited to 'src/Python/ccpi')
diff --git a/src/Python/ccpi/imaging/__init__.py b/src/Python/ccpi/imaging/__init__.py
new file mode 100644
index 0000000..e69de29
--
cgit v1.2.3
From 3f26b1d8ab3a632ceca97bdf04225008f9163684 Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Thu, 24 Aug 2017 16:42:27 +0100
Subject: initial revision
---
src/Python/ccpi/__init__.py | 0
1 file changed, 0 insertions(+), 0 deletions(-)
create mode 100644 src/Python/ccpi/__init__.py
(limited to 'src/Python/ccpi')
diff --git a/src/Python/ccpi/__init__.py b/src/Python/ccpi/__init__.py
new file mode 100644
index 0000000..e69de29
--
cgit v1.2.3
From 391473269674bc98697eabac0b4fb2bd89f5d85e Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Fri, 25 Aug 2017 16:55:48 +0100
Subject: Reorganized code with new fista package name
---
src/Python/ccpi/fista/FISTAReconstructor.py | 389 ++++++++++++++
src/Python/ccpi/fista/FISTAReconstructor.pyc | Bin 0 -> 3804 bytes
src/Python/ccpi/fista/FISTAReconstructor.py~ | 349 ++++++++++++
src/Python/ccpi/fista/Reconstructor.py | 425 +++++++++++++++
src/Python/ccpi/fista/Reconstructor.py~ | 598 +++++++++++++++++++++
src/Python/ccpi/fista/__init__.py | 0
src/Python/ccpi/fista/__init__.pyc | Bin 0 -> 189 bytes
.../__pycache__/FISTAReconstructor.cpython-35.pyc | Bin 0 -> 3641 bytes
.../ccpi/fista/__pycache__/__init__.cpython-35.pyc | Bin 0 -> 185 bytes
9 files changed, 1761 insertions(+)
create mode 100644 src/Python/ccpi/fista/FISTAReconstructor.py
create mode 100644 src/Python/ccpi/fista/FISTAReconstructor.pyc
create mode 100644 src/Python/ccpi/fista/FISTAReconstructor.py~
create mode 100644 src/Python/ccpi/fista/Reconstructor.py
create mode 100644 src/Python/ccpi/fista/Reconstructor.py~
create mode 100644 src/Python/ccpi/fista/__init__.py
create mode 100644 src/Python/ccpi/fista/__init__.pyc
create mode 100644 src/Python/ccpi/fista/__pycache__/FISTAReconstructor.cpython-35.pyc
create mode 100644 src/Python/ccpi/fista/__pycache__/__init__.cpython-35.pyc
(limited to 'src/Python/ccpi')
diff --git a/src/Python/ccpi/fista/FISTAReconstructor.py b/src/Python/ccpi/fista/FISTAReconstructor.py
new file mode 100644
index 0000000..1e76815
--- /dev/null
+++ b/src/Python/ccpi/fista/FISTAReconstructor.py
@@ -0,0 +1,389 @@
+# -*- 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
+ self.pars['output_geometry'] = output_geometry
+ self.pars['input_sinogram'] = input_sinogram
+ detectors, nangles, sliceZ = numpy.shape(input_sinogram)
+ self.pars['detectors'] = detectors
+ self.pars['number_og_angles'] = nangles
+ self.pars['SlicesZ'] = sliceZ
+
+ print (self.pars)
+ # handle optional input parameters (at instantiation)
+
+ # 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.pars['input_sinogram']))
+ if 'Lipschitz_constant' in kwargs.keys():
+ self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
+ else:
+ self.pars['Lipschitz_constant'] = self.calculateLipschitzConstantWithPowerMethod()
+
+ 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:
+ pass
+ else:
+ self.pars['region_of_interest'] = numpy.nonzero(self.pars['ideal_image']>0.0)
+
+ if not 'regularizer' in kwargs.keys() :
+ self.pars['regularizer'] = None
+ else:
+ # the regularizer must be a correctly instantiated object
+ 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
+
+
+
+
+ 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 getEntry(location, nx):
+ 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()
+##
+
diff --git a/src/Python/ccpi/fista/FISTAReconstructor.pyc b/src/Python/ccpi/fista/FISTAReconstructor.pyc
new file mode 100644
index 0000000..ecc4d7d
Binary files /dev/null and b/src/Python/ccpi/fista/FISTAReconstructor.pyc differ
diff --git a/src/Python/ccpi/fista/FISTAReconstructor.py~ b/src/Python/ccpi/fista/FISTAReconstructor.py~
new file mode 100644
index 0000000..6c7024d
--- /dev/null
+++ b/src/Python/ccpi/fista/FISTAReconstructor.py~
@@ -0,0 +1,349 @@
+# -*- 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
new file mode 100644
index 0000000..d29ac0d
--- /dev/null
+++ b/src/Python/ccpi/fista/Reconstructor.py
@@ -0,0 +1,425 @@
+# -*- 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/Reconstructor.py~ b/src/Python/ccpi/fista/Reconstructor.py~
new file mode 100644
index 0000000..ba67327
--- /dev/null
+++ b/src/Python/ccpi/fista/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()
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--
cgit v1.2.3
From 64c0b9e7a1bcfc54e6ed8b57274d53c3ed9bb950 Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Fri, 25 Aug 2017 17:03:17 +0100
Subject: use refactore code
---
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.../__pycache__/FISTAReconstructor.cpython-35.pyc | Bin 3641 -> 0 bytes
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--- a/src/Python/ccpi/fista/FISTAReconstructor.py~
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@@ -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()
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