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()