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import vigra
import vigra.graphs as graphs
# parameter:
filepath = '12003.jpg' # input image path
sigmaGradMag = 2.0 # sigma Gaussian gradient
superpixelDiameter = 10 # super-pixel size
slicWeight = 10.0 # SLIC color - spatial weight
k = 10 # free parameter in felzenszwalbs method
nodeNumStop = 500 # desired num. nodes in result
# load image and convert to LAB
img = vigra.impex.readImage(filepath)
# 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()
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