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import vigra
import vigra.graphs as graphs
import pylab
import numpy
import matplotlib
# parameter:
filepath = '100075.jpg' # input image path
sigmaGradMag = 3.0 # sigma Gaussian gradient
superpixelDiameter = 100 # super-pixel size
slicWeight = 50.0 # SLIC color - spatial weight
# 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
imgLabBig = vigra.resize(imgLab, [imgLab.shape[0]*2-1, imgLab.shape[1]*2-1])
gradMag = vigra.filters.gaussianGradientMagnitude(imgLab, sigmaGradMag)
gradMagBig = vigra.filters.gaussianGradientMagnitude(imgLabBig, sigmaGradMag*2.0)
# 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,
gradMagBig)
# get region adjacency graph from super-pixel labels
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
cycles = graphs.find3CyclesEdges(rag)
for c in range(cycles.shape[0]):
cic = cycles[c,:]
f = numpy.zeros(rag.edgeNum)
f[cic] = 1
rag.showEdgeFeature(img, f)
vigra.show()
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