if False:
print "non truncated"
smoothed = denoising.nonLocalMean(wTruncate=0.0, **nlmp)
vigra.impex.writeHDF5(smoothed, sPath, 'data')
if False:
print "truncated"
smoothedT = denoising.nonLocalMean(wTruncate=0.15, **nlmp)
vigra.impex.writeHDF5(smoothedT, stPath, 'data')
if False:
data = data.astype(numpy.float32)
ew = vigra.filters.hessianOfGaussianEigenvalues(data, 4.0)
ew = numpy.sort(ew, axis=3)[:, :, :, 2]
vigra.impex.writeHDF5(ew, ewPath, 'data')
if False:
smoothedT = vigra.readHDF5(stPath, 'data').astype(numpy.float32)
ews = vigra.filters.hessianOfGaussianEigenvalues(smoothedT, 4.0)
ews = numpy.sort(ews, axis=3)[:, :, :, 2]
vigra.impex.writeHDF5(ews, ewsPath, 'data')
if False:
ews = vigra.readHDF5(ewsPath, 'data')
policy = denoising.RatioPolicy(sigma=1.0, meanRatio=0.95, varRatio=0.8)
nlmp = dict(image=ews.astype(numpy.float64),
if False:
print "truncated"
smoothedT = denoising.nonLocalMean(wTruncate=0.15,**nlmp)
vigra.impex.writeHDF5(smoothedT, stPath, 'data')
if False:
data=data.astype(numpy.float32)
ew = vigra.filters.hessianOfGaussianEigenvalues(data, 4.0)
ew = numpy.sort(ew,axis=3)[:, :, :, 2]
vigra.impex.writeHDF5(ew, ewPath, 'data')
if False:
smoothedT = vigra.readHDF5(stPath, 'data').astype(numpy.float32)
ews = vigra.filters.hessianOfGaussianEigenvalues(smoothedT, 4.0)
ews = numpy.sort(ews,axis=3)[:, :, :, 2]
vigra.impex.writeHDF5(ews, ewsPath, 'data')
if False:
ews = vigra.readHDF5(ewsPath, 'data')
policy = denoising.RatioPolicy(sigma=1.0, meanRatio=0.95, varRatio=0.8)
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
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
