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 largeSeedWatershed(raw, pmap, seeds, membraneWidth = 7.0, visu=False):
blockShape = (100, )*3
cOpts = vbw.convOpts
pmap = numpy.require(pmap, dtype='float32')
with vigra.Timer("add noise"):
mx = pmap.max()
sshape = pmap.squeeze().shape
noise = numpy.random.rand(*sshape)*(0.05*mx)
noise = vigra.taggedView(noise.astype('float32'),'xyz')
opts = vbw.convOpts(blockShape=blockShape, sigma=4.0)
noise = vbw.gaussianSmooth(noise, options=opts)
pmap += noise
with vigra.Timer("smoothed tie breaker"):
# compute a smoothed map as tie breaker
opts = vbw.convOpts(blockShape=blockShape, sigma=membraneWidth/2.0)
gaussianSmoothedPmap = vbw.gaussianSmooth(pmap, options=opts)
addEps = 0.3
growingMap = gaussianSmoothedPmap
growingMap *= addEps
#
opts = vbw.convOpts(blockShape=blockShape, sigma=membraneWidth/7.50)
notSoMuch = vbw.gaussianSmooth(pmap, options=opts)
# get the actual growing mapz
growingMap += notSoMuch
growingMap /= 1.0 + addEps
with vigra.Timer("watershedsNew"):
# do the actual watershed
growingMap = vigra.taggedView(growingMap, 'xyz')
seeds = numpy.require(seeds, dtype='uint32')
seeds = vigra.taggedView(seeds, 'xyz')
seg,nSeg = vigra.analysis.watershedsNew(image=growingMap, seeds=seeds)
if visu:
grayData = [
(raw, "raw"),
(pmap,"pmap"),
(growingMap,"growingMap")
]
segData = [
(seeds, "seeds"),
(seg, "seg")
]
skneuro.addHocViewer(grayData, segData)
return seg,nSeg
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 largeSeedWatershed(raw, pmap, seeds, membraneWidth=7.0, visu=False):
blockShape = (100, ) * 3
cOpts = vbw.convOpts
pmap = numpy.require(pmap, dtype='float32')
with vigra.Timer("add noise"):
mx = pmap.max()
sshape = pmap.squeeze().shape
noise = numpy.random.rand(*sshape) * (0.05 * mx)
noise = vigra.taggedView(noise.astype('float32'), 'xyz')
opts = vbw.convOpts(blockShape=blockShape, sigma=4.0)
noise = vbw.gaussianSmooth(noise, options=opts)
pmap += noise
with vigra.Timer("smoothed tie breaker"):
# compute a smoothed map as tie breaker
opts = vbw.convOpts(blockShape=blockShape, sigma=membraneWidth / 2.0)
gaussianSmoothedPmap = vbw.gaussianSmooth(pmap, options=opts)
addEps = 0.3
growingMap = gaussianSmoothedPmap
growingMap *= addEps
#
opts = vbw.convOpts(blockShape=blockShape, sigma=membraneWidth / 7.50)
notSoMuch = vbw.gaussianSmooth(pmap, options=opts)
# get the actual growing mapz
growingMap += notSoMuch
growingMap /= 1.0 + addEps
with vigra.Timer("watershedsNew"):
# do the actual watershed
growingMap = vigra.taggedView(growingMap, 'xyz')
seeds = numpy.require(seeds, dtype='uint32')
seeds = vigra.taggedView(seeds, 'xyz')
seg, nSeg = vigra.analysis.watershedsNew(image=growingMap, seeds=seeds)
if visu:
grayData = [(raw, "raw"), (pmap, "pmap"), (growingMap, "growingMap")]
segData = [(seeds, "seeds"), (seg, "seg")]
skneuro.addHocViewer(grayData, segData)
return seg, nSeg
def computeGlobalFeatures(pmap,
mitoIndex,
membraneIndex,
visu=True,
dsetName='data'):
f = h5py.File(pmap, 'r')
raw = f[dsetName][0:300, 0:300, 300:400, 0]
pMito = f[dsetName][0:300, 0:300, 300:400, membraneIndex].astype('float32')
pMito -= pMito.min()
pMito /= pMito.max()
bMito = numpy.zeros(pMito.shape, dtype='uint8')
bMito[pMito > 0.9] = 1
sdt = vigra.filters.signedDistanceTransform(bMito)
grayData = [(raw, "raw"), (pMito, "pMito"), (bMito, "bMito"), (sdt, "sdt")]
segData = [
#(labels, "labels")
]
skneuro.addHocViewer(grayData, segData, visu=visu)
def computeGlobalFeatures(pmap, mitoIndex, membraneIndex, visu=True, dsetName='data'):
f = h5py.File(pmap,'r')
raw = f[dsetName][0:300,0:300,300:400,0]
pMito = f[dsetName][0:300,0:300,300:400,membraneIndex].astype('float32')
pMito-=pMito.min()
pMito/=pMito.max()
bMito = numpy.zeros(pMito.shape, dtype='uint8')
bMito[pMito>0.9] = 1
sdt = vigra.filters.signedDistanceTransform(bMito)
grayData = [
(raw,"raw"),
(pMito,"pMito"),
(bMito,"bMito"),
(sdt,"sdt")
]
segData = [
#(labels, "labels")
]
skneuro.addHocViewer(grayData, segData,visu=visu)
# ew /= ew.max()
# grayData.append([ew,"hessian ew"])
# skneuro.addHocViewer(grayData, segData)
# vigra.impex.writeHDF5(ew, hessianPath, "data")
# make thinned map
if False:
print "read pmap"
pmap = boundaryP1 = vigra.impex.readHDF5(
opt['boundaryP1'], 'exported_data').view(numpy.ndarray)[:, :, :, 0]
print "done"
pmap = numpy.require(pmap, dtype=numpy.float32)
grayData.append([pmap, "pmap"])
skneuro.addHocViewer(grayData, segData)
bpmap = numpy.zeros(pmap.shape, dtype=numpy.float32)
bpmap[pmap > 0.5] = 1
dist = vigra.filters.distanceTransform3D(bpmap, background=False)
print "write distance transform"
vigra.impex.writeHDF5(dist, opt['distTransformPMap1'], "data")
res = 1.0 - (1.0 / (0.05 * dist**2 + 1))
grayData.append([dist, "dist"])
grayData.append([res, "thinned"])
print "write thinned pmap transform"
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 getLargeSeeds(raw, pmap, membraneWidth , threshold , rank, visu=False):
with vigra.Timer("ballRankOrderFilter"):
# make mebrane wider with ballRankOrderFilter
r = int(membraneWidth*0.35 + 0.5)
r = max(r, 1)
widerPmap1 = denoise.ballRankOrderFilter(pmap, radius=r, rank=rank)
widerPmap = denoise.ballRankOrderFilter(widerPmap1, radius=r, rank=rank)
widerPmap1 = None
with vigra.Timer("normalize"):
# renormalize
widerPmap -= widerPmap.min()
widerPmap /= widerPmap.max()
with vigra.Timer("binarize"):
# binarize
binaryPmap = numpy.zeros(widerPmap.shape, dtype='uint8')
binaryPmap[pmap>threshold] = 1
#widerPmap = None # save mem
if visu == False:
widerPmap = None
with vigra.Timer("multiBinaryDilation"):
# morphology
# 1) make membrane wider by r
r = int(membraneWidth*1.2 + 0.5)
r = max(r, 2)
mBinaryPmapA = vigra.filters.multiBinaryDilation(binaryPmap,r)
#binaryPmap = None # save mem
if visu == False:
binaryPmap = None
#with vigra.Timer("multiBinaryErosion"):
# # morphology
# # 1) make membrane smaller by r
# r = int(membraneWidth*0.1 + 0.5)
# r = max(r, 1)
# mBinaryPmapB = vigra.filters.multiBinaryErosion(mBinaryPmapA,r)
# if visu == False:
# mBinaryPmapA = None
with vigra.Timer("labelVolumeWithBackground"):
# get seeds
invertedBinaryPmap = 1- mBinaryPmapA
if visu == False:
mBinaryPmapB = None
invertedBinaryPmap = numpy.require(invertedBinaryPmap, dtype='uint32')
ccImg = vigra.analysis.labelVolumeWithBackground(invertedBinaryPmap)
if visu == False:
invertedBinaryPmap = None
if visu:
grayData = [
(raw, "raw"),
(pmap,"pmap"),
(widerPmap,"widerPmap"),
(binaryPmap,"binaryPmap"),
(mBinaryPmapA,"mBinaryPmapA"),
#(mBinaryPmapB,"mBinaryPmapB"),
]
segData = [
(ccImg, "seeds"),
#(seg, "seg")
]
skneuro.addHocViewer(grayData, segData)
return ccImg
# vigra.impex.writeHDF5(ew, hessianPath, "data")
# make thinned map
if False:
print "read pmap"
pmap = boundaryP1 = vigra.impex.readHDF5(opt['boundaryP1'],'exported_data').view(numpy.ndarray)[:, :, :, 0]
print "done"
pmap = numpy.require(pmap, dtype=numpy.float32)
grayData.append([pmap, "pmap"])
skneuro.addHocViewer(grayData, segData)
bpmap = numpy.zeros(pmap.shape, dtype=numpy.float32)
bpmap[pmap > 0.5] = 1
dist = vigra.filters.distanceTransform3D(bpmap, background=False)
print "write distance transform"
vigra.impex.writeHDF5(dist, opt['distTransformPMap1'], "data")
res = 1.0 - (1.0 / (0.05*dist**2+1))
grayData.append([dist, "dist"])
grayData.append([res, "thinned"])
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 getLargeSeeds(raw, pmap, membraneWidth, threshold, rank, visu=False):
with vigra.Timer("ballRankOrderFilter"):
# make mebrane wider with ballRankOrderFilter
r = int(membraneWidth * 0.35 + 0.5)
r = max(r, 1)
widerPmap1 = denoise.ballRankOrderFilter(pmap, radius=r, rank=rank)
widerPmap = denoise.ballRankOrderFilter(widerPmap1,
radius=r,
rank=rank)
widerPmap1 = None
with vigra.Timer("normalize"):
# renormalize
widerPmap -= widerPmap.min()
widerPmap /= widerPmap.max()
with vigra.Timer("binarize"):
# binarize
binaryPmap = numpy.zeros(widerPmap.shape, dtype='uint8')
binaryPmap[pmap > threshold] = 1
#widerPmap = None # save mem
if visu == False:
widerPmap = None
with vigra.Timer("multiBinaryDilation"):
# morphology
# 1) make membrane wider by r
r = int(membraneWidth * 1.2 + 0.5)
r = max(r, 2)
mBinaryPmapA = vigra.filters.multiBinaryDilation(binaryPmap, r)
#binaryPmap = None # save mem
if visu == False:
binaryPmap = None
#with vigra.Timer("multiBinaryErosion"):
# # morphology
# # 1) make membrane smaller by r
# r = int(membraneWidth*0.1 + 0.5)
# r = max(r, 1)
# mBinaryPmapB = vigra.filters.multiBinaryErosion(mBinaryPmapA,r)
# if visu == False:
# mBinaryPmapA = None
with vigra.Timer("labelVolumeWithBackground"):
# get seeds
invertedBinaryPmap = 1 - mBinaryPmapA
if visu == False:
mBinaryPmapB = None
invertedBinaryPmap = numpy.require(invertedBinaryPmap, dtype='uint32')
ccImg = vigra.analysis.labelVolumeWithBackground(invertedBinaryPmap)
if visu == False:
invertedBinaryPmap = None
if visu:
grayData = [
(raw, "raw"),
(pmap, "pmap"),
(widerPmap, "widerPmap"),
(binaryPmap, "binaryPmap"),
(mBinaryPmapA, "mBinaryPmapA"),
#(mBinaryPmapB,"mBinaryPmapB"),
]
segData = [
(ccImg, "seeds"),
#(seg, "seg")
]
skneuro.addHocViewer(grayData, segData)
return ccImg
