def testGridGraphSegmentationFelzenszwalbSegmentation():
dataRGB = numpy.random.random([3, 3, 3]).astype(numpy.float32)
dataRGB = taggedView(dataRGB, 'xyc')
data = numpy.random.random([3, 3]).astype(numpy.float32)
edata = numpy.random.random([3 * 2 - 1, 3 * 2 - 1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(g0, edata)
labels = graphs.felzenszwalbSegmentation(graph=g0,
edgeWeights=ew,
k=1.0,
nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0, labels=labels)
assert g1.nodeNum == 5
data = numpy.random.random([3, 3, 3]).astype(numpy.float32)
edata = numpy.random.random([3 * 2 - 1, 3 * 2 - 1,
3 * 2 - 1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(g0, edata)
labels = graphs.felzenszwalbSegmentation(graph=g0,
edgeWeights=ew,
k=1.0,
nodeNumStop=15)
g1 = graphs.regionAdjacencyGraph(graph=g0, labels=labels)
assert g1.nodeNum == 15
def testGridGraphSegmentationFelzenszwalbSegmentation():
dataRGB = numpy.random.random([3,3,3]).astype(numpy.float32)
dataRGB = taggedView(dataRGB,'xyc')
data = numpy.random.random([3,3]).astype(numpy.float32)
edata = numpy.random.random([3*2-1,3*2-1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(g0,edata)
labels = graphs.felzenszwalbSegmentation(graph=g0,edgeWeights=ew,k=1.0,nodeNumStop=5)
g1 = graphs.regionAdjacencyGraph(graph=g0,labels=labels)
assert g1.nodeNum == 5
data = numpy.random.random([3,3,3]).astype(numpy.float32)
edata = numpy.random.random([3*2-1,3*2-1,3*2-1]).astype(numpy.float32)
g0 = graphs.gridGraph(data.shape)
ew = graphs.edgeFeaturesFromInterpolatedImage(g0,edata)
labels = graphs.felzenszwalbSegmentation(graph=g0,edgeWeights=ew,k=1.0,nodeNumStop=15)
g1 = graphs.regionAdjacencyGraph(graph=g0,labels=labels)
assert g1.nodeNum == 15
imgLab = vigra.colors.transform_RGB2Lab(img)
labels, nseg = vigra.analysis.slicSuperpixels(imgLab, slicWeight,
superpixelDiameter)
labels = vigra.analysis.labelImage(labels)
# compute gradient on interpolated image
imgLabBig = vigra.resize(imgLab,
[imgLab.shape[0] * 2 - 1, imgLab.shape[1] * 2 - 1])
gradMag = vigra.filters.gaussianGradientMagnitude(imgLabBig, sigmaGradMag)
# get 2D grid graph and edgeMap for grid graph
# from gradMag of interpolated image
gridGraph = graphs.gridGraph(img.shape[0:2])
gridGraphEdgeIndicator = graphs.edgeFeaturesFromInterpolatedImage(
gridGraph, gradMag)
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# accumulate edge weights from gradient magnitude
edgeWeights = rag.accumulateEdgeFeatures(gridGraphEdgeIndicator)
# do the segmentation with felzenszwalbs method
labels = graphs.felzenszwalbSegmentation(rag,
edgeWeights,
k=50,
nodeNumStop=nodeNumStop)
rag.show(img, labels)
vigra.show()
gradMag)
# get 2D grid graph and edgeMap for grid graph
# from gradMag of interpolated image
gridGraph = graphs.gridGraph(img.shape[0:2])
gridGraphEdgeIndicator = graphs.edgeFeaturesFromInterpolatedImage(gridGraph,
gradMag)
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# accumulate edge weights from gradient magnitude
ragEdgeIndicator = rag.accumulateEdgeFeatures(gridGraphEdgeIndicator)
# get labels/segmentation for rag
ragLabels = graphs.felzenszwalbSegmentation(rag, ragEdgeIndicator,
k=10, nodeNumStop=1000)
# get more corsair graph from labeled rag
rag2 = graphs.regionAdjacencyGraph(graph=rag, labels=ragLabels)
# accumulate new edge weights
rag2EdgeIndicator = rag2.accumulateEdgeFeatures(ragEdgeIndicator,
acc='mean')
# get labels/segmentation for rag2
rag2Labels = graphs.felzenszwalbSegmentation(rag2, ragEdgeIndicator,
k=20, nodeNumStop=100)
# get more corsair graph from labeled rag2
rag3 = graphs.regionAdjacencyGraph(graph=rag2, labels=rag2Labels)
# get super-pixels with slic on LAB image
imgLab = vigra.colors.transform_RGB2Lab(img)
labels, nseg = vigra.analysis.slicSuperpixels(imgLab, slicWeight,
superpixelDiameter)
labels = vigra.analysis.labelImage(labels)
# compute gradient on interpolated image
imgLabBig = vigra.resize(imgLab, [imgLab.shape[0]*2-1, imgLab.shape[1]*2-1])
gradMag = vigra.filters.gaussianGradientMagnitude(imgLabBig, sigmaGradMag)
# get 2D grid graph and edgeMap for grid graph
# from gradMag of interpolated image
gridGraph = graphs.gridGraph(img.shape[0:2])
gridGraphEdgeIndicator = graphs.edgeFeaturesFromInterpolatedImage(gridGraph,
gradMag)
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# accumulate edge weights from gradient magnitude
edgeWeights = rag.accumulateEdgeFeatures(gridGraphEdgeIndicator)
# do the segmentation with felzenszwalbs method
labels = graphs.felzenszwalbSegmentation(rag, edgeWeights,
k=50,nodeNumStop=nodeNumStop)
rag.show(img, labels)
vigra.show()
# get 2D grid graph and edgeMap for grid graph
# from gradMag of interpolated image
gridGraph = graphs.gridGraph(img.shape[0:2])
gridGraphEdgeIndicator = graphs.edgeFeaturesFromInterpolatedImage(
gridGraph, gradMag)
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
# accumulate edge weights from gradient magnitude
ragEdgeIndicator = rag.accumulateEdgeFeatures(gridGraphEdgeIndicator)
# get labels/segmentation for rag
ragLabels = graphs.felzenszwalbSegmentation(rag,
ragEdgeIndicator,
k=10,
nodeNumStop=1000)
# get more corsair graph from labeled rag
rag2 = graphs.regionAdjacencyGraph(graph=rag, labels=ragLabels)
# accumulate new edge weights
rag2EdgeIndicator = rag2.accumulateEdgeFeatures(ragEdgeIndicator, acc='mean')
# get labels/segmentation for rag2
rag2Labels = graphs.felzenszwalbSegmentation(rag2,
ragEdgeIndicator,
k=20,
nodeNumStop=100)
# get more corsair graph from labeled rag2
