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
