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 restrictedLocalMinima(image, minAllowed, neighborhood=26):
""" discard any min where minAllowed[x,y,y]==0
"""
localMin = vigra.analysis.extendedLocalMinima3D(numpy.ensure(image,dtype=numpy.float32),neighborhood=neighborhood)
minNotAllowed = numpy.where(minAllowed==0)
localMin[minNotAllowed]=0
return localMin
def restrictedLocalMinima(image, minAllowed, neighborhood=26):
""" discard any min where minAllowed[x,y,y]==0
"""
localMin = vigra.analysis.extendedLocalMinima3D(numpy.ensure(
image, dtype=numpy.float32),
neighborhood=neighborhood)
minNotAllowed = numpy.where(minAllowed == 0)
localMin[minNotAllowed] = 0
return localMin
def pmapSizeFilter(neuroCCBinaryPmap, minSize):
"""
keywords:
neuroCCBinaryPmap: zero where membranes are
one where neurons are
"""
shape = neuroCCBinaryPmap.shape
# get the connected components
neuroCCMap = vigra.analysis.labelVolume(neuroCCBinaryPmap)
print "min max",neuroCCMap.min(), neuroCCMap.max()
assert neuroCCMap.min() == 1
neuroCCMap -= 1
# get region adjacency graph from super-pixel labels
gridGraph = graphs.gridGraph(shape[0:3])
rag = graphs.regionAdjacencyGraph(gridGraph, neuroCCMap)
# get all sizes
nodeSizes = rag.nodeSize()
# nodeColoring
print "neuroCCBinaryPmap",neuroCCBinaryPmap.dtype,neuroCCBinaryPmap.shape
nodeColor = rag.accumulateNodeFeatures(neuroCCBinaryPmap.astype('float32'))
# find to small nodes
toSmallNodesIds = numpy.where(nodeSizes<minSize)[0]
toSmallNodesSizes = nodeSizes[toSmallNodesIds]
toSmallNodesColor = nodeColor[toSmallNodesIds]
sortedIndices = numpy.argsort(toSmallNodesSizes)
sortedIndices.shape,sortedIndices
toSmallNodesIds = toSmallNodesIds[sortedIndices]
# todo (impl degree map as numpy array)
for nid in toSmallNodesIds:
neigbours = []
for n in rag.neighbourNodeIter(rag.nodeFromId(nid)):
neigbours.append(n)
if len(neigbours)==1:
# flip color
nodeColor[nid] = int(not bool(nodeColor[nid]))
pixelColor = rag.projectNodeFeaturesToGridGraph(nodeColor)
return pixelColor,neuroCCBinaryPmap
def getValidRegionCentersAndTheirIDs(featureDict,
countFeatureName='Count',
regionCenterName='RegionCenter'):
"""
From the feature dictionary of a certain frame,
find all objects with pixel count > 0, and return their
region centers and ids.
"""
validObjectMask = featureDict[countFeatureName] > 0
validObjectMask[0] = False
regionCenters = featureDict[regionCenterName][validObjectMask, :]
objectIds = list(np.where(validObjectMask)[0])
return regionCenters, objectIds
def getValidRegionCentersAndTheirIDs(featureDict,
countFeatureName='Count',
regionCenterName='RegionCenter'):
"""
From the feature dictionary of a certain frame,
find all objects with pixel count > 0, and return their
region centers and ids.
"""
validObjectMask = featureDict[countFeatureName] > 0
validObjectMask[0] = False
regionCenters = featureDict[regionCenterName][validObjectMask, :]
objectIds = list(np.where(validObjectMask)[0])
return regionCenters, objectIds
def pmapSizeFilter(neuroCCBinaryPmap, minSize):
"""
keywords:
neuroCCBinaryPmap: zero where membranes are
one where neurons are
"""
shape = neuroCCBinaryPmap.shape
# get the connected components
neuroCCMap = vigra.analysis.labelVolume(neuroCCBinaryPmap)
print "min max", neuroCCMap.min(), neuroCCMap.max()
assert neuroCCMap.min() == 1
neuroCCMap -= 1
# get region adjacency graph from super-pixel labels
gridGraph = graphs.gridGraph(shape[0:3])
rag = graphs.regionAdjacencyGraph(gridGraph, neuroCCMap)
# get all sizes
nodeSizes = rag.nodeSize()
# nodeColoring
print "neuroCCBinaryPmap", neuroCCBinaryPmap.dtype, neuroCCBinaryPmap.shape
nodeColor = rag.accumulateNodeFeatures(neuroCCBinaryPmap.astype('float32'))
# find to small nodes
toSmallNodesIds = numpy.where(nodeSizes < minSize)[0]
toSmallNodesSizes = nodeSizes[toSmallNodesIds]
toSmallNodesColor = nodeColor[toSmallNodesIds]
sortedIndices = numpy.argsort(toSmallNodesSizes)
sortedIndices.shape, sortedIndices
toSmallNodesIds = toSmallNodesIds[sortedIndices]
# todo (impl degree map as numpy array)
for nid in toSmallNodesIds:
neigbours = []
for n in rag.neighbourNodeIter(rag.nodeFromId(nid)):
neigbours.append(n)
if len(neigbours) == 1:
# flip color
nodeColor[nid] = int(not bool(nodeColor[nid]))
pixelColor = rag.projectNodeFeaturesToGridGraph(nodeColor)
return pixelColor, neuroCCBinaryPmap
def agglomerative_clustering_2d(img, labels, use_gradient = False): labels = vigra.analysis.labelImage(labels) imgBig = vigra.resize(img, [img.shape[0]*2-1, img.shape[1]*2-1]) if use_gradient: # compute the gradient on interpolated image sigmaGradMag = 2.0 gradMag = vigra.filters.gaussianGradientMagnitude(imgBig, sigmaGradMag) # get 2D grid graph and edgeMap for grid graph # from gradMag of interpolated image or from plain image (depending on use_gradient) gridGraph = vigra.graphs.gridGraph(img.shape[0:2]) gridGraphEdgeIndicator = [] if use_gradient: gridGraphEdgeIndicator = vigra.graphs.edgeFeaturesFromInterpolatedImage(gridGraph, gradMag) else: gridGraphEdgeIndicator = vigra.graphs.edgeFeaturesFromInterpolatedImage(gridGraph, imgBig) # get region adjacency graph from super-pixel labels rag = vigra.graphs.regionAdjacencyGraph(gridGraph, labels) # accumalate edge weights from gradient magintude edgeWeights = rag.accumulateEdgeFeatures(gridGraphEdgeIndicator) # agglomerative clustering beta = 0.5 nodeNumStop = 20 clustering = vigra.graphs.agglomerativeClustering(graph = rag, edgeWeights = edgeWeights, beta = beta, nodeNumStop = nodeNumStop) res = np.zeros( labels.shape ) for c in range(len(clustering)): res[ np.where( labels== (c-1) ) ] = clustering[c] res = vigra.Image(res) res = vigra.analysis.labelImage(res) return res
def volumeToListOfPoints(seedsVolume, threshold=0.):
return numpy.array(numpy.where(seedsVolume > threshold)).transpose()
def volumeToListOfPoints(seedsVolume, threshold=0.):
return numpy.array(numpy.where(seedsVolume > threshold)).transpose()