In [1]:

    

import vigra
from vigra import graphs
import numpy
import opengm
import matplotlib
import pylab
#try:
#from sklearn.cluster import MiniBatchKMeans, KMeans
import sklearn.mixture as mixture
#from sklearn import mixture
#except:
#    raise RuntimeError("this examples needs sklearn")


    

In [2]:

    

# parameter:
filepath = '124084.jpg'   # input image path
# 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)

vigra.imshow(img)
vigra.show()


    

In [3]:

    

superpixelDiameter = 15   # super-pixel size
slicWeight = 15.0        # SLIC color - spatial weight
labels, nseg = vigra.analysis.slicSuperpixels(imgLab, slicWeight,
                                              superpixelDiameter)
labels = vigra.analysis.labelImage(labels)-1

# get 2D grid graph and RAG
gridGraph = graphs.gridGraph(img.shape[0:2])
rag = graphs.regionAdjacencyGraph(gridGraph, labels)

# plot superpixels
cmap = matplotlib.colors.ListedColormap ( numpy.random.rand ( nseg,3))
pylab.imshow ( labels.swapaxes(0,1).squeeze(), cmap = cmap)
pylab.show()


    

In [4]:

    

# accumulate node features from grid graph node map
# which is just a plain image (with channels)
nodeFeatures = rag.accumulateNodeFeatures(imgLab)
nodeFeaturesImg = rag.projectNodeFeaturesToGridGraph(nodeFeatures)
nodeFeaturesImg = vigra.taggedView(nodeFeaturesImg, "xyc")
nodeFeaturesImgRgb = vigra.colors.transform_Lab2RGB(nodeFeaturesImg)
vigra.imshow(nodeFeaturesImgRgb)


    

    













    
Out[4]:





<matplotlib.image.AxesImage at 0xab8d3d0c>

In [5]:

    

nCluster   = 3

g = mixture.GMM(n_components=nCluster)
g.fit(nodeFeatures[:,:])
clusterProb = g.predict_proba(nodeFeatures)



clusterProbImg = rag.projectNodeFeaturesToGridGraph(clusterProb.astype(numpy.float32))
clusterProbImg = vigra.taggedView(clusterProbImg, "xyc")
vigra.imshow(clusterProbImg)
vigra.show()

for c in range(nCluster):
    clusterProbImg = rag.projectNodeFeaturesToGridGraph(clusterProb[:,c].astype(numpy.float32))
    clusterProbImg = vigra.taggedView(clusterProbImg, "xy")
    vigra.imshow(clusterProbImg)
    vigra.show()


    

In [11]:

    

# strength of potts regularizer
beta = 4000.0

# graphical model with as many variables
# as superpixels, each has 3 states
gm = opengm.gm(numpy.ones(rag.nodeNum,dtype=opengm.label_type)*nCluster)

# convert probabilites to energies
probs = numpy.clip(clusterProb, 0.00001, 0.99999)
costs = -1.0*numpy.log(probs)

# add ALL unaries AT ONCE
fids = gm.addFunctions(costs)
gm.addFactors(fids,numpy.arange(rag.nodeNum))

# add a potts function
regularizer = opengm.pottsFunction([nCluster]*2,0.0,beta)
fid = gm.addFunction(regularizer)

# get variable indices of adjacent superpixels
# - or "u" and "v" node id's for edges
uvIds = rag.uvIds()
print uvIds
uvIds = numpy.sort(uvIds,axis=1)

# add all second order factors at once
gm.addFactors(fid,uvIds)


    

    




[[  1   0]
 [  2   1]
 [  3   2]
 ..., 
 [841 840]
 [836 837]
 [826 836]]






    
Out[11]:





3184L

In [12]:

    

Inf  = opengm.inference.BeliefPropagation
parameter = opengm.InfParam(steps=10,damping=0.5,convergenceBound=0.001)
inf = Inf(gm,parameter=parameter)


class PyCallback(object):
    def __init__(self,):
        self.labels=[]
    def begin(self,inference):
        print "begin of inference"
    def end(self,inference):
        self.labels.append(inference.arg())
    def visit(self,inference):
        gm=inference.gm()
        labelVector=inference.arg()
        print "energy  ",gm.evaluate(labelVector)
        self.labels.append(labelVector)


callback=PyCallback()
visitor=inf.pythonVisitor(callback,visitNth=1)


inf.infer(visitor)

cmap = matplotlib.colors.ListedColormap ( numpy.random.rand ( nCluster,3))
for arg in callback.labels:
    arg = vigra.taggedView(arg, "n")
    argImg = rag.projectNodeFeaturesToGridGraph(arg.astype(numpy.uint32))
    argImg = vigra.taggedView(argImg, "xy")
    # plot superpixels
    pylab.imshow ( argImg.swapaxes(0,1).squeeze(), cmap = cmap)
    pylab.show()


    

    




begin of inference
energy   1196078.16202
energy   1004252.78641
energy   904345.420588
energy   832447.770497
energy   772505.207237
energy   748553.799194
energy   736640.155039
energy   700758.493456
energy   628918.15882
energy   609048.22784






    













    













    













    













    













    













    













    













    













    













    

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