def pmapWatershed(pmap, raw, visu=True, seedThreshold=0.6): viewGrayData = [(pmap, "pmap") ] viewLabelData= [] print "densoise" pmapD = denoise.tvBregman(pmap, weight=4.5, isotropic=True).astype(numpy.float32) pmapG = vigra.filters.gaussianSmoothing(pmap, 1.0) viewGrayData.append((pmapG, "pmapGauss")) viewGrayData.append((pmapD, "pmapTotalVariation")) viewGrayData.append((raw, "raw")) #addHocViewer(viewGrayData, viewLabelData, visu=visu) print "compute local minima " localMin = vigra.analysis.extendedLocalMinima3D(pmapD,neighborhood=26) localMin2 = localMin.astype(numpy.float32) print "tweak min" localMin2 *= pmap whereZero = numpy.where(localMin == 0) localMin2[whereZero] = 100.0 whereMin = numpy.where(localMin2 <= seedThreshold) filteredLocalMin = numpy.zeros(localMin.shape, dtype=numpy.uint8) filteredLocalMin[whereMin] = 1 viewGrayData.append([localMin,"localMin"]) viewGrayData.append([filteredLocalMin,"filteredLocalMin"]) # compute connected components seeds = vigra.analysis.labelVolumeWithBackground(filteredLocalMin, neighborhood=26) viewLabelData.append([seeds, "seeds"]) print "watersheds" seg, nseg = vigra.analysis.watersheds(pmapG.astype(numpy.float32), seeds=seeds.astype(numpy.uint32)) print "nseg",nseg viewLabelData.append([seg, "seg"]) addHocViewer(viewGrayData, viewLabelData, visu=visu) return se
def pmapWatershed(pmap, raw, visu=True, seedThreshold=0.6): viewGrayData = [(pmap, "pmap")] viewLabelData = [] print "densoise" pmapD = denoise.tvBregman(pmap, weight=4.5, isotropic=True).astype(numpy.float32) pmapG = vigra.filters.gaussianSmoothing(pmap, 1.0) viewGrayData.append((pmapG, "pmapGauss")) viewGrayData.append((pmapD, "pmapTotalVariation")) viewGrayData.append((raw, "raw")) #addHocViewer(viewGrayData, viewLabelData, visu=visu) print "compute local minima " localMin = vigra.analysis.extendedLocalMinima3D(pmapD, neighborhood=26) localMin2 = localMin.astype(numpy.float32) print "tweak min" localMin2 *= pmap whereZero = numpy.where(localMin == 0) localMin2[whereZero] = 100.0 whereMin = numpy.where(localMin2 <= seedThreshold) filteredLocalMin = numpy.zeros(localMin.shape, dtype=numpy.uint8) filteredLocalMin[whereMin] = 1 viewGrayData.append([localMin, "localMin"]) viewGrayData.append([filteredLocalMin, "filteredLocalMin"]) # compute connected components seeds = vigra.analysis.labelVolumeWithBackground(filteredLocalMin, neighborhood=26) viewLabelData.append([seeds, "seeds"]) print "watersheds" seg, nseg = vigra.analysis.watersheds(pmapG.astype(numpy.float32), seeds=seeds.astype(numpy.uint32)) print "nseg", nseg viewLabelData.append([seg, "seg"]) addHocViewer(viewGrayData, viewLabelData, visu=visu) return se
def prepareMinMap(raw, pmap, sPre=0.8, sInt=5.0, mapInterval=0.5, scaleEw=4.0, ewBeta=0.01, tvWeightSoft=None, isotropicTvSoft=True, tvWeightHard=None, isotropicTvHard=True, sPost=0.6, visu=False ): """ """ print "prepare stuff" if tvWeightSoft is None and isotropicTvSoft: tvWeightSoft=5.0 elif tvWeightSoft is None and isotropicTvSoft==False: tvWeightSoft=25.0 if tvWeightHard is None and isotropicTvHard: tvWeightHard=0.7 elif tvWeightHard is None and isotropicTvHard==False: tvWeightHard=15.0 grayData = [] labelsData = [] # do minimalistic raw map presmoothing to remove artifacts if sPre>0.0001: rawG = vigra.filters.gaussianSmoothing(numpy.require(raw ,dtype=numpy.float32), sigma=sPre) else : rawG = numpy.require(image,dtype=numpy.float32) print "pmap integral" # get pmap integral pmapIntegral = vigra.filters.gaussianSmoothing(numpy.require(pmap, dtype=numpy.float32), sigma=sInt ) pmapIntegral = numpy.array(pmapIntegral) grayData.append([rawG,'rawG']) grayData.append([pmapIntegral,'pmapIntegral']) if visu: addHocViewer(grayData, labelsData, visu=visu) # remap integral pmapIntegral[pmapIntegral>mapInterval]=mapInterval pmapIntegral*=1.0/mapInterval print "soft tv" # do soft TV smoothing pmapTVSoft = denoise.tvBregman(pmap, weight=tvWeightSoft, isotropic=isotropicTvSoft).astype(numpy.float32) print "hard tv" # do hard heavy TV smoothing pmapTVHard = denoise.tvBregman(pmap, weight=tvWeightHard, isotropic=isotropicTvHard).astype(numpy.float32) grayData.append([pmapTVSoft,'pmapTVSoft']) grayData.append([pmapTVHard,'pmapTVHard']) if visu: addHocViewer(grayData, labelsData, visu=visu) # mix hard and soft according to pmap probability mixedPmap = numpy.empty(raw.shape) mixedPmap = (1.0 - pmapIntegral)*pmapTVHard + pmapIntegral*pmapTVSoft print "le min le max",mixedPmap.min(), mixedPmap.max() #grayData.append([mixedPmap,'mixedPmap']) #addHocViewer(grayData, labelsData, visu=visu) # add a tiny portion of eigenvalues of hessian give flat wide boundaries the min at the right position # but we only add this at places where the boundary is strong (in a hard fashion) aew = vigra.filters.hessianOfGaussianEigenvalues(numpy.require(raw, dtype=numpy.float32), scale=scaleEw).squeeze() sew = numpy.sort(aew,axis=3) ew = sew[:, :, :, 2] ew *= pmap**2 ew -= ew.min() ew /= ew.max() ew *= ewBeta mixedPmap+=ew grayData.append([mixedPmap,'mixedPmapWITHEW']) if visu: addHocViewer(grayData, labelsData, visu=visu) # do minimalistic final smoothing to remove artefacts if sPre>0.0001: mixedPmapG = vigra.filters.gaussianSmoothing(numpy.require(mixedPmap,dtype=numpy.float32), sigma=sPost) else : mixedPmapG = numpy.require(mixedPmap,dtype=numpy.float32) grayData.append([mixedPmapG,'finalSeedingMap']) if visu: addHocViewer(grayData, labelsData, visu=visu) return mixedPmapG
sigmaPresmoothing=1.0, stepSize=2, iterations=1, verbose=True) vigra.impex.writeHDF5(res, pnlm, 'data') if True: print "non local mean" policy = denoising.RatioPolicy(sigma=2.0, meanRatio=0.90, varRatio=0.80) res = denoising.nonLocalMean(image=data, policy=policy, patchRadius=2, searchRadius=14, sigmaSpatial=1.5, sigmaPresmoothing=1.0, stepSize=2, iterations=1, verbose=True) vigra.impex.writeHDF5(res, pnlm2, 'data') ###################### # compute tv bregman # ###################### if False: print "tvBregman isotropic" res = denoising.tvBregman(data,weight=2.0, isotropic=True) vigra.impex.writeHDF5(res, ptvbi2, 'data') print "tvBregman anisotropic" res = denoising.tvBregman(data,weight=20.0, isotropic=False) vigra.impex.writeHDF5(res, ptvbai20, 'data') ###################### # compute tv chambolle ###################### if False: print "tvChambolle isotropic" res = denoising.tvChambolle(data,weight=0.1) vigra.impex.writeHDF5(res, ptvc5, 'data')
def prepareMinMap(raw, pmap, sPre=0.8, sInt=5.0, mapInterval=0.5, scaleEw=4.0, ewBeta=0.01, tvWeightSoft=None, isotropicTvSoft=True, tvWeightHard=None, isotropicTvHard=True, sPost=0.6, visu=False): """ """ print "prepare stuff" if tvWeightSoft is None and isotropicTvSoft: tvWeightSoft = 5.0 elif tvWeightSoft is None and isotropicTvSoft == False: tvWeightSoft = 25.0 if tvWeightHard is None and isotropicTvHard: tvWeightHard = 0.7 elif tvWeightHard is None and isotropicTvHard == False: tvWeightHard = 15.0 grayData = [] labelsData = [] # do minimalistic raw map presmoothing to remove artifacts if sPre > 0.0001: rawG = vigra.filters.gaussianSmoothing(numpy.require( raw, dtype=numpy.float32), sigma=sPre) else: rawG = numpy.require(image, dtype=numpy.float32) print "pmap integral" # get pmap integral pmapIntegral = vigra.filters.gaussianSmoothing(numpy.require( pmap, dtype=numpy.float32), sigma=sInt) pmapIntegral = numpy.array(pmapIntegral) grayData.append([rawG, 'rawG']) grayData.append([pmapIntegral, 'pmapIntegral']) if visu: addHocViewer(grayData, labelsData, visu=visu) # remap integral pmapIntegral[pmapIntegral > mapInterval] = mapInterval pmapIntegral *= 1.0 / mapInterval print "soft tv" # do soft TV smoothing pmapTVSoft = denoise.tvBregman(pmap, weight=tvWeightSoft, isotropic=isotropicTvSoft).astype( numpy.float32) print "hard tv" # do hard heavy TV smoothing pmapTVHard = denoise.tvBregman(pmap, weight=tvWeightHard, isotropic=isotropicTvHard).astype( numpy.float32) grayData.append([pmapTVSoft, 'pmapTVSoft']) grayData.append([pmapTVHard, 'pmapTVHard']) if visu: addHocViewer(grayData, labelsData, visu=visu) # mix hard and soft according to pmap probability mixedPmap = numpy.empty(raw.shape) mixedPmap = (1.0 - pmapIntegral) * pmapTVHard + pmapIntegral * pmapTVSoft print "le min le max", mixedPmap.min(), mixedPmap.max() #grayData.append([mixedPmap,'mixedPmap']) #addHocViewer(grayData, labelsData, visu=visu) # add a tiny portion of eigenvalues of hessian give flat wide boundaries the min at the right position # but we only add this at places where the boundary is strong (in a hard fashion) aew = vigra.filters.hessianOfGaussianEigenvalues(numpy.require( raw, dtype=numpy.float32), scale=scaleEw).squeeze() sew = numpy.sort(aew, axis=3) ew = sew[:, :, :, 2] ew *= pmap**2 ew -= ew.min() ew /= ew.max() ew *= ewBeta mixedPmap += ew grayData.append([mixedPmap, 'mixedPmapWITHEW']) if visu: addHocViewer(grayData, labelsData, visu=visu) # do minimalistic final smoothing to remove artefacts if sPre > 0.0001: mixedPmapG = vigra.filters.gaussianSmoothing(numpy.require( mixedPmap, dtype=numpy.float32), sigma=sPost) else: mixedPmapG = numpy.require(mixedPmap, dtype=numpy.float32) grayData.append([mixedPmapG, 'finalSeedingMap']) if visu: addHocViewer(grayData, labelsData, visu=visu) return mixedPmapG