dopt = opt['train']
print "load cues"
eX = vigra.impex.readHDF5(dopt['ragFeatures'], 'edgeFeatures')
nX = vigra.impex.readHDF5(dopt['ragFeatures'], 'nodeFeatures')
print "load sizes"
eSize = vigra.impex.readHDF5(dopt['ragL1EdgeSize'], 'data')
nSize = vigra.impex.readHDF5(dopt['ragL1NodeSize'], 'data')
print "load label file"
eY = vigra.impex.readHDF5(dopt['ragL1EdgeGt'], 'data')
print "load rag"
rag = graphs.loadGridRagHDF5(dopt['ragL1'], 'data')
graphData = learning.GraphData(rag=rag, eX=eX, nX=nX, eSize=eSize, nSize=nSize)
rfFolder = dopt['activeRfDir'].encode('ASCII')
treeCount = 1000
noise = 0.010
if False:
print "do active initial learning"
learner = learning.ActiveGraphLearning(treeCount=treeCount, noise=noise)
rf0Path = rfFolder + 'rf0.h5'
rf0Path = rf0Path.encode('ASCII')
learner.initialTraining(graphData=graphData, eY=eY, rfPath=rf0Path)
segData.append([seg, "seg"])
gridGraph = graphs.gridGraph(seg.shape[0:3])
print "make rag"
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, seg, isDense=False)
print "save rag to file"
rag.writeHDF5(opt['ragL0'], 'data')
if False:
print "accumulate edge weights and save them"
print "load rag"
# get region adjacency graph from super-pixel labels
rag = graphs.loadGridRagHDF5(opt['ragL0'], 'data')
gridGraph = rag.baseGraph
print "read dmap"
tmap = vigra.impex.readHDF5(opt['thinnedDistTransformPMap1'], 'data')
tmap = numpy.require(tmap, dtype=numpy.float32)
grayData.append([tmap, "tmap"])
print "extract"
ggTmap = graphs.implicitMeanEdgeMap(gridGraph, tmap)
ewDmap = rag.accumulateEdgeFeatures(ggTmap)
vigra.impex.writeHDF5(ewDmap, opt['ragEdgeDmap'], "data")
print "read pmap"
pmap = boundaryP1 = vigra.impex.readHDF5(
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, seg, isDense=False)
print "save rag to file"
rag.writeHDF5(opt['ragL0'],'data')
if False:
print "accumulate edge weights and save them"
print "load rag"
# get region adjacency graph from super-pixel labels
rag = graphs.loadGridRagHDF5(opt['ragL0'],'data')
gridGraph = rag.baseGraph
print "read dmap"
tmap = vigra.impex.readHDF5(opt['thinnedDistTransformPMap1'], 'data')
tmap = numpy.require(tmap, dtype=numpy.float32)
grayData.append([tmap, "tmap"])
print "extract"
ggTmap = graphs.implicitMeanEdgeMap(gridGraph, tmap)
ewDmap = rag.accumulateEdgeFeatures(ggTmap)
vigra.impex.writeHDF5(ewDmap, opt['ragEdgeDmap'], "data")
print "read pmap"
pmap = boundaryP1 = vigra.impex.readHDF5(opt['boundaryP1'],'exported_data').view(numpy.ndarray)[:, :, :, 0]
data = numpy.random.rand(*shape).astype(numpy.float32)
data = vigra.taggedView(data, "xyz")
if False:
labels = numpy.random.randint(5, size=shape[0] * shape[1] * shape[2])
labels = labels.reshape(shape).astype(numpy.uint32)
labels = vigra.analysis.labelVolume(labels)
adjListGraph = graphs.listGraph()
gridGraph = graphs.gridGraph(shape)
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
rag.writeHDF5("bla.h5", "dset")
else:
# load the region adjacency graph
rag = graphs.loadGridRagHDF5("bla.h5", "dset")
print rag.labels.shape, rag.labels.dtype, type(rag.labels)
print "accumulate edge and node features"
edgeCuesMean = rag.accumulateEdgeFeatures(iEdgeMap(rag.baseGraph, data))
edgeCuesMean = numpy.array([edgeCuesMean, edgeCuesMean]).T
nodeCuesMean = rag.accumulateNodeFeatures(data)
nodeCuesMean = numpy.array([nodeCuesMean, nodeCuesMean]).T
mergeGraph = graphs.mergeGraph(rag)
featureManager = graphs.NeuroDynamicFeatures(rag, mergeGraph)
data = numpy.random.rand(*shape).astype(numpy.float32)
data = vigra.taggedView(data,"xyz")
if False:
labels = numpy.random.randint(5, size=shape[0]*shape[1]*shape[2])
labels = labels.reshape(shape).astype(numpy.uint32)
labels = vigra.analysis.labelVolume(labels)
adjListGraph = graphs.listGraph()
gridGraph = graphs.gridGraph(shape)
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
rag.writeHDF5("bla.h5", "dset")
else :
# load the region adjacency graph
rag = graphs.loadGridRagHDF5("bla.h5","dset")
print rag.labels.shape, rag.labels.dtype ,type(rag.labels)
print "accumulate edge and node features"
edgeCuesMean = rag.accumulateEdgeFeatures( iEdgeMap(rag.baseGraph, data) )
edgeCuesMean = numpy.array([edgeCuesMean, edgeCuesMean]).T
nodeCuesMean = rag.accumulateNodeFeatures(data)
nodeCuesMean = numpy.array([nodeCuesMean, nodeCuesMean]).T
print "load cues"
eX = vigra.impex.readHDF5(dopt['ragFeatures'], 'edgeFeatures')
nX = vigra.impex.readHDF5(dopt['ragFeatures'], 'nodeFeatures')
print "load sizes"
eSize = vigra.impex.readHDF5(dopt['ragL1EdgeSize'], 'data')
nSize = vigra.impex.readHDF5(dopt['ragL1NodeSize'], 'data')
print "load label file"
eY = vigra.impex.readHDF5(dopt['ragL1EdgeGt'], 'data')
print "load rag"
rag = graphs.loadGridRagHDF5(dopt['ragL1'],'data')
graphData = learning.GraphData(rag=rag, eX=eX, nX=nX,
eSize=eSize, nSize=nSize)
rfFolder = dopt['activeRfDir'].encode('ASCII')
treeCount = 1000
noise = 0.010
if False :
print "do active initial learning"
print "load cues" edgeCues = vigra.impex.readHDF5(dopt["ragFeatures"], "edgeFeatures") nodeCues = vigra.impex.readHDF5(dopt["ragFeatures"], "nodeFeatures") print "load sizes" edgeSize = vigra.impex.readHDF5(dopt["ragL1EdgeSize"], "data") nodeSize = vigra.impex.readHDF5(dopt["ragL1NodeSize"], "data") print "load label file" edgeLabels = vigra.impex.readHDF5(dopt["ragL1EdgeGt"], "data") print "load rag" rag = graphs.loadGridRagHDF5(dopt["ragL1"], "data") gridGraph = rag.baseGraph print "get merge graph" mg = graphs.mergeGraph(rag) df = graphs.NeuroDynamicFeatures(rag, mg) # assign features df.assignEdgeCues(edgeCues) df.assignNodeCues(nodeCues) df.assignEdgeSizes(edgeSize) df.assignNodeSizes(nodeSize)
