def testGridGraphWatersheds():
data = numpy.random.random([10, 10, 10]).astype(numpy.float32)
edata = numpy.random.random([10 * 2 - 1, 10 * 2 - 1,
10 * 2 - 1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0, image=edata)
# generate seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0, nodeWeights=data)
# node weighted watershed seeds
labelsNodeWeightedA = graphs.nodeWeightedWatersheds(graph=g0,
nodeWeights=data,
seeds=seeds)
# node weighted watershed seeds
labelsNodeWeightedB = graphs.nodeWeightedWatersheds(graph=g0,
nodeWeights=data)
# edge weighted watershed seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0, nodeWeights=data)
labelsEdgeWeighted = graphs.edgeWeightedWatersheds(graph=g0,
edgeWeights=ew,
seeds=seeds)
assert numpy.array_equal(labelsNodeWeightedA, labelsNodeWeightedB)
data = numpy.random.random([10, 10]).astype(numpy.float32)
edata = numpy.random.random([10 * 2 - 1, 10 * 2 - 1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0, image=edata)
# generate seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0, nodeWeights=data)
# node weighted watershed seeds
labelsNodeWeightedA = graphs.nodeWeightedWatersheds(graph=g0,
nodeWeights=data,
seeds=seeds)
# node weighted watershed seeds
labelsNodeWeightedB = graphs.nodeWeightedWatersheds(graph=g0,
nodeWeights=data)
# edge weighted watershed seeds
labelsEdgeWeighted = graphs.edgeWeightedWatersheds(graph=g0,
edgeWeights=ew,
seeds=seeds)
assert numpy.array_equal(labelsNodeWeightedA, labelsNodeWeightedB)
def testGridGraphWatersheds():
data = numpy.random.random([10,10,10]).astype(numpy.float32)
edata = numpy.random.random([10*2-1,10*2-1,10*2-1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0,image=edata)
# generate seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0,nodeWeights=data)
# node weighted watershed seeds
labelsNodeWeightedA = graphs.nodeWeightedWatersheds(graph=g0,nodeWeights=data,seeds=seeds)
# node weighted watershed seeds
labelsNodeWeightedB = graphs.nodeWeightedWatersheds(graph=g0,nodeWeights=data)
# edge weighted watershed seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0,nodeWeights=data)
labelsEdgeWeighted = graphs.edgeWeightedWatersheds(graph=g0,edgeWeights=ew,seeds=seeds)
assert numpy.array_equal(labelsNodeWeightedA,labelsNodeWeightedB)
data = numpy.random.random([10,10]).astype(numpy.float32)
edata = numpy.random.random([10*2-1,10*2-1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(graph=g0,image=edata)
# generate seeds
seeds = graphs.nodeWeightedWatershedsSeeds(graph=g0,nodeWeights=data)
# node weighted watershed seeds
labelsNodeWeightedA = graphs.nodeWeightedWatersheds(graph=g0,nodeWeights=data,seeds=seeds)
# node weighted watershed seeds
labelsNodeWeightedB = graphs.nodeWeightedWatersheds(graph=g0,nodeWeights=data)
# edge weighted watershed seeds
labelsEdgeWeighted = graphs.edgeWeightedWatersheds(graph=g0,edgeWeights=ew,seeds=seeds)
assert numpy.array_equal(labelsNodeWeightedA,labelsNodeWeightedB)
gridGraphEdgeIndicator = graphs.edgeFeaturesFromInterpolatedImage(gridGraph,
gradMagBig)
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# accumulate edge and ndie weights from gradient magnitude
ragEdgeWeights = rag.accumulateEdgeFeatures(gridGraphEdgeIndicator)
ragNodeWeights = rag.accumulateNodeFeatures(gradMag)
# generate seeds
seeds = graphs.nodeWeightedWatershedsSeeds(rag, ragNodeWeights)
# node weighted watersheds
labelsNodeWeighted = graphs.nodeWeightedWatersheds(rag, ragNodeWeights, seeds)
# edge weighted watersheds
labelsEdgeWeighted = graphs.edgeWeightedWatersheds(rag, ragEdgeWeights, seeds)
f = pylab.figure()
ax0 = f.add_subplot(1, 2, 0)
rag.showNested(img, labelsNodeWeighted)
ax0.set_title("node weighted")
ax1 = f.add_subplot(1, 2, 1)
rag.showNested(img, labelsEdgeWeighted)
ax1.set_title("edge weighted")
pylab.show()
gridGraphEdgeIndicator = graphs.edgeFeaturesFromInterpolatedImage(gridGraph,
gradMagBig)
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# accumulate edge and ndie weights from gradient magnitude
ragEdgeWeights = rag.accumulateEdgeFeatures(gridGraphEdgeIndicator)
ragNodeWeights = rag.accumulateNodeFeatures(gradMag)
# generate seeds
seeds = graphs.nodeWeightedWatershedsSeeds(rag, ragNodeWeights)
# node weighted watersheds
labelsNodeWeighted = graphs.nodeWeightedWatersheds(rag, ragNodeWeights, seeds)
# edge weighted watersheds
labelsEdgeWeighted = graphs.edgeWeightedWatersheds(rag, ragEdgeWeights, seeds)
f = pylab.figure()
ax0 = f.add_subplot(1, 2, 0)
rag.showNested(img, labelsNodeWeighted)
ax0.set_title("node weighted")
ax1 = f.add_subplot(1, 2, 1)
rag.showNested(img, labelsEdgeWeighted)
ax1.set_title("edge weighted")
pylab.show()
