#/*########################################################################## # Copyright (C) 2013-2014 European Synchrotron Radiation Facility # # This file is part of the PyMca X-ray Fluorescence Toolkit developed at # the ESRF by the Software group. # # This toolkit is free software; you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by the Free # Software Foundation; either version 2 of the License, or (at your option) # any later version. # # PyMca is distributed in the hope that it will be useful, but WITHOUT ANY # WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the GNU General Public License for more # details. # # You should have received a copy of the GNU General Public License along with # PyMca; if not, write to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. # # PyMca follows the dual licensing model of Riverbank's PyQt and cannot be # used as a free plugin for a non-free program. # # Please contact the ESRF industrial unit (industry@esrf.fr) if this license # is a problem for you. #############################################################################*/ __author__ = "V.A. Sole - ESRF Data Analysis" """ A Stack plugin is a module that will be automatically added to the PyMca stack windows in order to perform user defined operations on the data stack. These plugins will be compatible with any stack window that provides the functions: #data related getStackDataObject getStackData getStackInfo setStack #images related addImage removeImage replaceImage #mask related setSelectionMask getSelectionMask #displayed curves getActiveCurve getGraphXLimits getGraphYLimits #information method stackUpdated selectionMaskUpdated """ import sys import os import numpy from PyMca import StackPluginBase from PyMca import PyMcaQt as qt from PyMca import FFTAlignmentWindow from PyMca import ImageRegistration from PyMca import SpecfitFuns from PyMca import CalculationThread from PyMca.PyMcaIO import ArraySave from PyMca import PyMcaFileDialogs from PyMca.PyMcaIO import specfilewrapper from PyMca import HDF5Widget try: from PyMca import SIFTAlignmentWindow sift = SIFTAlignmentWindow.sift SIFT = True except: SIFT = False try: import h5py HDF5 = True except: HDF5 = False DEBUG = 0 class ImageAlignmentStackPlugin(StackPluginBase.StackPluginBase): def __init__(self, stackWindow, **kw): StackPluginBase.DEBUG = DEBUG StackPluginBase.StackPluginBase.__init__(self, stackWindow, **kw) self.methodDict = {'FFT Alignment':[self._fftAlignment, "Align using FFT", None]} self.__methodKeys = ['FFT Alignment'] if SIFT: key = 'SIFT Alignment' self.methodDict[key] = [self._siftAlignment, "Align using SIFT Algorithm", None] self.__methodKeys.append(key) key = 'From File Alignment' self.methodDict[key] = [self._shiftFromFile, "Align using shifts from file", None] self.__methodKeys.append(key) self.widget = None def stackUpdated(self): self.widget = None #Methods implemented by the plugin def getMethods(self): return self.__methodKeys def getMethodToolTip(self, name): return self.methodDict[name][1] def getMethodPixmap(self, name): return self.methodDict[name][2] def applyMethod(self, name): return self.methodDict[name][0]() def _fftAlignment(self): stack = self.getStackDataObject() if stack is None: return mcaIndex = stack.info.get('McaIndex') if not (mcaIndex in [0, -1, 2]): raise IndexError("1D index must be 0, 2, or -1") if self.widget is None: self.widget = FFTAlignmentWindow.FFTAlignmentDialog() self.widget.setStack(stack) ret = self.widget.exec_() if ret: ddict = self.widget.getParameters() self.widget.setDummyStack() offsets = [ddict['Dim 0']['offset'], ddict['Dim 1']['offset']] widths = [ddict['Dim 0']['width'], ddict['Dim 1']['width']] if mcaIndex == 0: reference = stack.data[ddict['reference_index']] else: reference = ddict['reference_image'] crop = False if ddict['file_use']: filename = ddict['file_name'] else: filename = None if filename is not None: self.__hdf5 = self.initializeHDF5File(filename) if DEBUG: shifts = self.calculateShiftsFFT(stack, reference, offsets=offsets, widths=widths, crop=crop) result = self.shiftStack(stack, shifts, crop=crop, filename=filename) else: result = self.__calculateShiftsFFT(stack, reference, offsets=offsets, widths=widths, crop=crop) if result[0] == 'Exception': # exception occurred raise Exception(result[1], result[2], result[3]) else: shifts = result result = self.__shiftStack(stack, shifts, crop=crop, filename=filename) if result is not None: # exception occurred raise Exception(result[1], result[2], result[3]) if filename is not None: hdf = self.__hdf5 alignmentGroup = hdf['/entry_000/Alignment'] outputShifts = self.getHDF5BufferIntoGroup(alignmentGroup, shape=(stack.data.shape[mcaIndex], 2), name="shifts", dtype=numpy.float32) outputShifts[:,:] = shifts attributes={'interpretation':'image'} referenceFrame = self.getHDF5BufferIntoGroup(alignmentGroup, shape=reference.shape, name="reference_frame", dtype=numpy.float32, attributes=attributes) referenceFrame[:,:] = reference[:,:] maskFrame = self.getHDF5BufferIntoGroup(alignmentGroup, shape=reference.shape, name="reference_mask", dtype=numpy.uint8, attributes=attributes) maskData = numpy.zeros(reference.shape, dtype=numpy.uint8) maskData[offsets[0]:offsets[0] + widths[0], offsets[1] : offsets[1] + widths[1]] = 1 maskFrame[:,:] = maskData[:,:] # fill the axes information dataGroup = hdf['/entry_000/Data'] try: activeCurve = self.getActiveCurve() if activeCurve is None: activeCurve = self.getAllCurves()[0] x, y, legend, info = activeCurve dataGroup[info['xlabel']] = numpy.array(x, dtype=numpy.float32) dataGroup[info['xlabel']].attrs['axis'] = numpy.int32(1) axesAttribute = '%s:dim_1:dim_2' % info['xlabel'] except: if DEBUG: raise dataGroup['dim_0'] = numpy.arange(stack.data.shape[mcaIndex]).astype(numpy.float32) dataGroup['dim_0'].attrs['axis'] = numpy.int32(1) axesAttribute = 'dim_0:dim_1:dim_2' dataGroup['dim_1'] = numpy.arange(reference.shape[0]).astype(numpy.float32) dataGroup['dim_1'].attrs['axis'] = numpy.int32(2) dataGroup['dim_2'] = numpy.arange(reference.shape[1]).astype(numpy.float32) dataGroup['dim_2'].attrs['axis'] = numpy.int32(3) dim2 = numpy.arange(reference.shape[1]).astype(numpy.float32) dataGroup['data'].attrs['axes'] = axesAttribute self.finishHDF5File(hdf) else: self.setStack(stack) def __calculateShiftsFFT(self, *var, **kw): self._progress = 0.0 thread = CalculationThread.CalculationThread(\ calculation_method=self.calculateShiftsFFT, calculation_vars=var, calculation_kw=kw) thread.start() CalculationThread.waitingMessageDialog(thread, message="Please wait. Calculation going on.", parent=self.widget, modal=True, update_callback=self._waitingCallback) return thread.result def __shiftStack(self, *var, **kw): self._progress = 0.0 thread = CalculationThread.CalculationThread(\ calculation_method=self.shiftStack, calculation_vars=var, calculation_kw=kw) thread.start() CalculationThread.waitingMessageDialog(thread, message="Please wait. Calculation going on.", parent=self.widget, modal=True, update_callback=self._waitingCallback) return thread.result def __calculateShiftsSIFT(self, *var, **kw): self._progress = 0.0 thread = CalculationThread.CalculationThread(\ calculation_method=self.calculateShiftsSIFT, calculation_vars=var, calculation_kw=kw) thread.start() CalculationThread.waitingMessageDialog(thread, message="Please wait. Calculation going on.", parent=self.widget, modal=True, update_callback=self._waitingCallback) return thread.result def _waitingCallback(self): ddict = {} ddict['message'] = "Calculation Progress = %d %%" % self._progress return ddict def _siftAlignment(self): if not SIFT: try: import pyopencl except: raise ImportError("PyOpenCL does not seem to be installed on your system") if sift.opencl.ocl is None: raise ImportError("PyOpenCL does not seem to be installed on your system") stack = self.getStackDataObject() if stack is None: return mcaIndex = stack.info.get('McaIndex') if not (mcaIndex in [0, 2, -1]): raise IndexError("Unsupported 1D index %d" % mcaIndex) widget = SIFTAlignmentWindow.SIFTAlignmentDialog() widget.setStack(stack) mask = self.getStackSelectionMask() widget.setSelectionMask(mask) ret = widget.exec_() if ret: ddict = widget.getParameters() widget.setDummyStack() reference = ddict['reference_image'] mask = ddict['mask'] if ddict['file_use']: filename = ddict['file_name'] else: filename = None if filename is not None: self.__hdf5 = self.initializeHDF5File(filename) crop = False device = ddict['opencl_device'] if DEBUG: result = self.calculateShiftsSIFT(stack, reference, mask=mask, device=device, crop=crop, filename=filename) else: result = self.__calculateShiftsSIFT(stack, reference, mask=mask, device=device, crop=crop, filename=filename) if result is not None: if len(result): if result[0] == 'Exception': # exception occurred raise Exception(result[1], result[2], result[3]) if filename is None: self.setStack(stack) def calculateShiftsSIFT(self, stack, reference, mask=None, device=None, crop=None, sigma=None, filename=None): mask = self.getStackSelectionMask() if mask is not None: if mask.sum() == 0: mask = None if device is None: if sys.platform == 'darwin': max_workgroup_size = 1 siftInstance = sift.LinearAlign(reference.astype(numpy.float32), max_workgroup_size=max_workgroup_size, devicetype="cpu", init_sigma=sigma) else: siftInstance = sift.LinearAlign(reference.astype(numpy.float32), devicetype="cpu", init_sigma=sigma) else: deviceType = sift.opencl.ocl.platforms[device[0]].devices[device[1]].type if deviceType.lower() == "cpu" and sys.platform == 'darwin': max_workgroup_size = 1 siftInstance = sift.LinearAlign(reference.astype(numpy.float32), max_workgroup_size=max_workgroup_size, device=device, init_sigma=sigma) else: siftInstance = sift.LinearAlign(reference.astype(numpy.float32), device=device, init_sigma=sigma) data = stack.data mcaIndex = stack.info['McaIndex'] if not (mcaIndex in [0, 2, -1]): raise IndexError("Unsupported 1D index %d" % mcaIndex) total = float(data.shape[mcaIndex]) if filename is not None: hdf = self.__hdf5 dataGroup = hdf['/entry_000/Data'] attributes = {} attributes['interpretation'] = "image" attributes['signal'] = numpy.int32(1) outputStack = self.getHDF5BufferIntoGroup(dataGroup, shape=(data.shape[mcaIndex], reference.shape[0], reference.shape[1]), name="data", dtype=numpy.float32, attributes=attributes) shifts = numpy.zeros((data.shape[mcaIndex], 2), dtype=numpy.float32) if mcaIndex == 0: for i in range(data.shape[mcaIndex]): if DEBUG: print("SIFT Shifting image %d" % i) result = siftInstance.align(data[i].astype(numpy.float32), shift_only=True, return_all=True) if DEBUG: print("Index = %d shift = %.4f, %.4f" % (i, result['offset'][0], result['offset'][1])) if filename is None: stack.data[i] = result['result'] else: outputStack[i] = result['result'] shifts[i, 0] = result['offset'][0] shifts[i, 1] = result['offset'][1] self._progress = (100 * i) / total else: image2 = numpy.zeros(reference.shape, dtype=numpy.float32) for i in range(data.shape[mcaIndex]): if DEBUG: print("SIFT Shifting image %d" % i) image2[:, :] = data[:, :, i] result = siftInstance.align(image2, shift_only=True, return_all=True) if DEBUG: print("Index = %d shift = %.4f, %.4f" % (i, result['offset'][0], result['offset'][1])) if filename is None: stack.data[:, :, i] = result['result'] else: outputStack[i] = result['result'] shifts[i, 0] = result['offset'][0] shifts[i, 1] = result['offset'][1] self._progress = (100 * i) / total if filename is not None: hdf = self.__hdf5 alignmentGroup = hdf['/entry_000/Alignment'] outputShifts = self.getHDF5BufferIntoGroup(alignmentGroup, shape=(stack.data.shape[mcaIndex], 2), name="shifts", dtype=numpy.float32) outputShifts[:,:] = shifts attributes={'interpretation':'image'} referenceFrame = self.getHDF5BufferIntoGroup(alignmentGroup, shape=reference.shape, name="reference_frame", dtype=numpy.float32, attributes=attributes) referenceFrame[:,:] = reference[:,:] maskFrame = self.getHDF5BufferIntoGroup(alignmentGroup, shape=reference.shape, name="reference_mask", dtype=numpy.uint8, attributes=attributes) if mask is None: maskData = numpy.ones(reference.shape, dtype=numpy.uint8) else: maskData = mask maskFrame[:,:] = maskData[:,:] # fill the axes information dataGroup = hdf['/entry_000/Data'] try: activeCurve = self.getActiveCurve() if activeCurve is None: activeCurve = self.getAllCurves()[0] x, y, legend, info = activeCurve dataGroup[info['xlabel']] = numpy.array(x, dtype=numpy.float32) dataGroup[info['xlabel']].attrs['axis'] = numpy.int32(1) axesAttribute = '%s:dim_1:dim_2' % info['xlabel'] except: if DEBUG: raise dataGroup['dim_0'] = numpy.arange(stack.data.shape[mcaIndex]).astype(numpy.float32) dataGroup['dim_0'].attrs['axis'] = numpy.int32(1) axesAttribute = 'dim_0:dim_1:dim_2' dataGroup['dim_1'] = numpy.arange(reference.shape[0]).astype(numpy.float32) dataGroup['dim_1'].attrs['axis'] = numpy.int32(2) dataGroup['dim_2'] = numpy.arange(reference.shape[1]).astype(numpy.float32) dataGroup['dim_2'].attrs['axis'] = numpy.int32(3) dim2 = numpy.arange(reference.shape[1]).astype(numpy.float32) dataGroup['data'].attrs['axes'] = axesAttribute self.finishHDF5File(hdf) def calculateShiftsFFT(self, stack, reference, offsets=None, widths=None, crop=False): if DEBUG: print("Offsets = ", offsets) print("Widths = ", widths) data = stack.data if offsets is None: offsets = [0.0, 0.0] if widths is None: widths = [reference.shape[0], reference.shape[1]] fft2Function = numpy.fft.fft2 if 1: DTYPE = numpy.float32 else: DTYPE = numpy.float64 image2 = numpy.zeros((widths[0], widths[1]), dtype=DTYPE) shape = image2.shape USE_APODIZATION_WINDOW = False apo = [10, 10] if USE_APODIZATION_WINDOW: # use apodization window window = numpy.outer(SpecfitFuns.slit([0.5, shape[0] * 0.5, shape[0] - 4 * apo[0], apo[0]], numpy.arange(float(shape[0]))), SpecfitFuns.slit([0.5, shape[1] * 0.5, shape[1] - 4 * apo[1], apo[1]], numpy.arange(float(shape[1])))).astype(DTYPE) else: window = numpy.zeros((shape[0], shape[1]), dtype=DTYPE) window[apo[0]:shape[0] - apo[0], apo[1]:shape[1] - apo[1]] = 1 image2[:,:] = window * reference[offsets[0]:offsets[0]+widths[0], offsets[1]:offsets[1]+widths[1]] image2fft2 = fft2Function(image2) mcaIndex = stack.info.get('McaIndex') shifts = numpy.zeros((data.shape[mcaIndex], 2), numpy.float) image1 = numpy.zeros(image2.shape, dtype=DTYPE) total = float(data.shape[mcaIndex]) if mcaIndex == 0: for i in range(data.shape[mcaIndex]): image1[:,:] = window * data[i][offsets[0]:offsets[0]+widths[0], offsets[1]:offsets[1]+widths[1]] image1fft2 = fft2Function(image1) shifts[i] = ImageRegistration.measure_offset_from_ffts(image2fft2, image1fft2) if DEBUG: print("Index = %d shift = %.4f, %.4f" % (i, shifts[i][0], shifts[i][1])) self._progress = (100 * i) / total elif mcaIndex in [2, -1]: for i in range(data.shape[mcaIndex]): image1[:,:] = window * data[:,:,i][offsets[0]:offsets[0]+widths[0], offsets[1]:offsets[1]+widths[1]] image1fft2 = fft2Function(image1) shifts[i] = ImageRegistration.measure_offset_from_ffts(image2fft2, image1fft2) if DEBUG: print("Index = %d shift = %.4f, %.4f" % (i, shifts[i][0], shifts[i][1])) self._progress = (100 * i) / total else: raise IndexError("Only stacks of images or spectra supported. 1D index should be 0 or 2") return shifts def _shiftFromFile(self): stack = self.getStackDataObject() if stack is None: return data = stack.data mcaIndex = stack.info.get('McaIndex') if not (mcaIndex in [0, -1, 2]): raise IndexError("1D index must be 0, 2, or -1") filefilter = ['HDF5 Files (*.h5 *.nxs *.hdf)', 'CSV 2-column (*.csv)', 'ASCII 2-column (*)'] filename, ffilter = PyMcaFileDialogs.\ getFileList(parent=None, filetypelist=filefilter, message='Load', mode='OPEN', single=True, getfilter=True, currentfilter=filefilter[0]) if len(filename): if DEBUG: print("file name = %s file filter = %s" % (filename, ffilter)) else: if DEBUG: print("nothing selected") return filename = filename[0] if ffilter.startswith('HDF5'): # browse self.__hdf5Dialog = qt.QDialog() self.__hdf5Dialog.setWindowTitle('Select your data set by a double click') self.__hdf5Dialog.mainLayout = qt.QVBoxLayout(self.__hdf5Dialog) self.__hdf5Dialog.mainLayout.setMargin(0) self.__hdf5Dialog.mainLayout.setSpacing(0) fileModel = HDF5Widget.FileModel() fileView = HDF5Widget.HDF5Widget(fileModel) hdf5File = fileModel.openFile(filename) shiftsDataset = None qt.QObject.connect(fileView, qt.SIGNAL("HDF5WidgetSignal"), self._hdf5WidgetSlot) self.__hdf5Dialog.mainLayout.addWidget(fileView) self.__hdf5Dialog.resize(400, 200) ret = self.__hdf5Dialog.exec_() if not ret: return shifts = hdf5File[self.__shitfsDataset].value hdf5File.close() else: sf = specfilewrapper.Specfile(filename) nScans = len(sf) targetScan = None for scan in sf: if scan.lines() == data.shape[stack.info['McaIndex']]: targetScan = scan break if targetScan is None: scan = None sf = None raise IOError("Number of read lines does not match stack shape") shifts = targetScan.data() targetScan = None scan = None sf = None if shifts.shape[0] == 3 and\ shifts.shape[1] == data.shape[stack.info['McaIndex']]: # one column was added (point number) shifts = shifts[1:].T filename = None if not isinstance(data, numpy.ndarray): filefilter = ['HDF5 Files (*.h5)'] filename = PyMcaFileDialogs.\ getFileList(parent=None, filetypelist=filefilter, message='Select output file', mode='SAVE', single=True, getfilter=False, currentfilter=filefilter[0]) if len(filename): filename = filename[0] if DEBUG: print("file name = %s" % filename) else: raise IOError("No output file selected") if filename is not None: self.__hdf5 = self.initializeHDF5File(filename) crop = False if DEBUG: result = self.shiftStack(stack, shifts, crop=crop, filename=filename) else: result = self.__shiftStack(stack, shifts, crop=crop, filename=filename) if result is not None: # exception occurred raise Exception(result[1], result[2], result[3]) if filename is not None: hdf = self.__hdf5 alignmentGroup = hdf['/entry_000/Alignment'] outputShifts = self.getHDF5BufferIntoGroup(alignmentGroup, shape=(stack.data.shape[mcaIndex], 2), name="shifts", dtype=numpy.float32) outputShifts[:,:] = shifts attributes={'interpretation':'image'} # fill the axes information dataGroup = hdf['/entry_000/Data'] if mcaIndex == 0: reference_shape = data[0].shape else: reference_shape = data.shape[0], data.shape[1] try: activeCurve = self.getActiveCurve() if activeCurve is None: activeCurve = self.getAllCurves()[0] x, y, legend, info = activeCurve dataGroup[info['xlabel']] = numpy.array(x, dtype=numpy.float32) dataGroup[info['xlabel']].attrs['axis'] = numpy.int32(1) axesAttribute = '%s:dim_1:dim_2' % info['xlabel'] except: if DEBUG: raise dataGroup['dim_0'] = numpy.arange(stack.data.shape[mcaIndex]).astype(numpy.float32) dataGroup['dim_0'].attrs['axis'] = numpy.int32(1) axesAttribute = 'dim_0:dim_1:dim_2' dataGroup['dim_1'] = numpy.arange(reference_shape[0]).astype(numpy.float32) dataGroup['dim_1'].attrs['axis'] = numpy.int32(2) dataGroup['dim_2'] = numpy.arange(reference_shape[1]).astype(numpy.float32) dataGroup['dim_2'].attrs['axis'] = numpy.int32(3) dim2 = numpy.arange(reference_shape[1]).astype(numpy.float32) dataGroup['data'].attrs['axes'] = axesAttribute self.finishHDF5File(hdf) else: self.setStack(stack) def _hdf5WidgetSlot(self, ddict): if ddict['event'] == "itemDoubleClicked": if ddict['type'].lower() in ['dataset']: self.__shitfsDataset = ddict['name'] self.__hdf5Dialog.accept() def shiftStack(self, stack, shifts, crop=False, filename=None): """ """ data = stack.data mcaIndex = stack.info['McaIndex'] if mcaIndex not in [0, 2, -1]: raise IndexError("Only stacks of images or spectra supported. 1D index should be 0 or 2") if mcaIndex == 0: shape = data[mcaIndex].shape else: shape = data.shape[0], data.shape[1] d0_start, d0_end, d1_start, d1_end = \ ImageRegistration.get_crop_indices(shape, shifts[:, 0], shifts[:, 1]) window = numpy.zeros(shape, numpy.float32) window[d0_start:d0_end, d1_start:d1_end] = 1.0 self._progress = 0.0 total = float(data.shape[mcaIndex]) if filename is not None: hdf = self.__hdf5 dataGroup = hdf['/entry_000/Data'] attributes = {} attributes['interpretation'] = "image" attributes['signal'] = numpy.int32(1) outputStack = self.getHDF5BufferIntoGroup(dataGroup, shape=(data.shape[mcaIndex], shape[0], shape[1]), name="data", dtype=numpy.float32, attributes=attributes) for i in range(data.shape[mcaIndex]): #tmpImage = ImageRegistration.shiftFFT(data[i], shifts[i]) if mcaIndex == 0: tmpImage = ImageRegistration.shiftBilinear(data[i], shifts[i]) #tmpImage = ImageRegistration.shiftImage(data[i], -shifts[i], method="fft") if filename is None: stack.data[i] = tmpImage * window else: outputStack[i] = tmpImage * window else: tmpImage = ImageRegistration.shiftBilinear(data[:,:,i], shifts[i]) if filename is None: stack.data[:, :, i] = tmpImage * window else: outputStack[i] = tmpImage * window if DEBUG: print("Index %d bilinear shifted" % i) self._progress = (100 * i) / total def initializeHDF5File(self, fname): #for the time being overwriting if os.path.exists(fname): os.remove(fname) hdf = h5py.File(fname, 'w') entryName = "entry_000" nxEntry = hdf.require_group(entryName) if 'NX_class' not in nxEntry.attrs: nxEntry.attrs['NX_class'] = 'NXentry'.encode('utf-8') nxEntry['title'] = numpy.string_("PyMca saved 3D Array".encode('utf-8')) nxEntry['start_time'] = numpy.string_(ArraySave.getDate().encode('utf-8')) alignmentGroup = nxEntry.require_group('Alignment') dataGroup = nxEntry.require_group('Data') dataGroup.attrs['NX_class'] = 'NXdata'.encode('utf-8') return hdf def finishHDF5File(self, hdf): #add final date toplevelEntry = hdf["entry_000"] toplevelEntry['end_time'] = numpy.string_(ArraySave.getDate().encode('utf-8')) hdf.flush() hdf.close() def getHDF5BufferIntoGroup(self, h5Group, shape, name="data", dtype=numpy.float32, attributes=None, compression=None, shuffle=False, chunks=None, chunk_cache=None): dataset = h5Group.require_dataset(name, shape=shape, dtype=dtype, chunks=chunks, shuffle=shuffle, compression=compression) if attributes is None: attributes = {} for attribute in attributes: dataset.attrs[attribute] = attributes[attribute] return dataset MENU_TEXT = "Image Alignment Tool" def getStackPluginInstance(stackWindow, **kw): ob = ImageAlignmentStackPlugin(stackWindow) return ob