Exemplo n.º 1
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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
Exemplo n.º 2
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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()
Exemplo n.º 4
0
                                                                  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)
Exemplo n.º 5
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# 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()
Exemplo n.º 6
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# 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