[opengm] 161/386: new example

[Ghislain Vaillant]

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ghisvail-guest pushed a commit to branch debian/master
in repository opengm.

commit 4044885329d223f7c7e82b5977c189dc2a1cbd79
Author: DerThorsten <thorsten.beier at iwr.uni-heidelberg.de>
Date:   Fri Dec 19 13:57:10 2014 +0100

    new example
---
 fubar/real_example_2.py | 128 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 128 insertions(+)

diff --git a/fubar/real_example_2.py b/fubar/real_example_2.py
new file mode 100644
index 0000000..a2974d8
--- /dev/null
+++ b/fubar/real_example_2.py
@@ -0,0 +1,128 @@
+import opengm
+import opengm.learning as learning
+from opengm import numpy
+import vigra
+import pylab as plt
+
+nModels =1
+nLables = 2 
+shape = [10, 10]
+numVar = shape[0]*shape[1]
+
+sSmooth = [1.0, 1.5]
+sGrad = [1.0, 1.5]
+
+nUWeights = len(sSmooth) + 1
+nBWeights = len(sGrad) + 1
+nWeights = nUWeights + nBWeights
+
+def makeGt(shape):
+    gt=numpy.ones(shape,dtype='uint8')
+    gt[0:shape[0]/2,:] = 0
+    return gt
+
+
+
+weightVals = numpy.ones(nWeights)
+weights = opengm.learning.Weights(weightVals)
+
+uWeightIds = numpy.arange(nUWeights ,dtype='uint64')
+bWeightIds = numpy.arange(start=nUWeights,stop=nWeights,dtype='uint64')
+
+
+dataset = learning.createDataset(numWeights=nWeights, loss='h')
+weights = dataset.getWeights()
+
+def makeFeatures(gt):
+    random  = numpy.random.rand(*gt.shape)-0.5
+    randGt = random + gt
+
+    #vigra.imshow(randGt)
+    #plt.colorbar()
+    #vigra.show()
+
+
+    feat = []
+    for sigma in sSmooth:
+        feat.append(vigra.filters.gaussianSmoothing(randGt.astype('float32'),sigma) )
+
+        #vigra.imshow(feat[-1])
+        #plt.colorbar()
+        #vigra.show()
+
+
+    featB = []
+    for sigma in sGrad:
+        featB.append(vigra.filters.gaussianGradientMagnitude(randGt.astype('float32'),sigma) )
+
+    a=None
+    b=None
+    if len(feat)>0:    
+        a =  numpy.rollaxis(numpy.array(feat), axis=0, start=3)
+    if len(featB)>0:
+        b =  numpy.rollaxis(numpy.array(featB), axis=0, start=3)
+    return a,b
+
+for mi in range(nModels):
+
+
+    gm = opengm.gm(numpy.ones(numVar)*nLables)
+    gt = makeGt(shape) 
+    gtFlat = gt.reshape([-1])
+
+    unaries, binaries = makeFeatures(gt)
+
+    # print unaries, binaries
+
+
+    for x in range(shape[0]):
+        for y in range(shape[1]):
+            uFeat = numpy.append(unaries[x,y,:],[1])
+
+            #print uFeat
+            #print uWeightIds
+            #print(unaries[x,y,:])
+            #print(unaries.shape)
+            #print(uFeat)
+            #print(uFeat.shape)
+
+            lu = learning.lUnaryFunction(weights=weights,numberOfLabels=nLables, 
+                                         features=uFeat, weightIds=uWeightIds)
+            fid = gm.addFunction(lu)
+            facIndex = gm.addFactor(fid, y+x*shape[1])
+            #facIndex = gm.addFactor(fid, x+y*shape[0])
+
+    if True:
+        for x in range(shape[0]):
+            for y in range(shape[1]):
+
+                if x+1 < shape[0]:
+                    bFeat = numpy.append(binaries[x,y,:], [1])+numpy.append(binaries[x+1,y,:], [1])
+                    pf = opengm.LPottsFunction(weights=weights,numberOfLabels=nLables, features=bFeat, weightIds=bWeightIds)
+                    fid= gm.addFunction(pf)
+                    gm.addFactor(fid, [y+x*shape[1], y+(x+1)*shape[1]])
+                if y+1 < shape[1]:
+                    bFeat = numpy.append(binaries[x,y,:], [1]).astype(opengm.value_type) + numpy.append(binaries[x,y+1,:], [1]).astype(opengm.value_type)
+                    pf = opengm.LPottsFunction(weights=weights,numberOfLabels=nLables, features=bFeat, weightIds=bWeightIds)
+                    fid= gm.addFunction(pf)
+                    gm.addFactor(fid, [y+x*shape[1], y+1+x*shape[1]])
+
+    dataset.pushBackInstance(gm,gtFlat.astype(opengm.label_type))
+    backGt = dataset.getGT(0)
+
+    #print "back",backGt
+    #sys.exit()
+
+# for grid search learner
+lowerBounds = numpy.ones(nWeights)*-2.0
+upperBounds = numpy.ones(nWeights)*2.0
+nTestPoints  =numpy.ones(nWeights).astype('uint64')*5
+
+learner = learning.gridSearchLearner(dataset=dataset,lowerBounds=lowerBounds, upperBounds=upperBounds,nTestPoints=nTestPoints)
+#learner = learning.structMaxMarginLearner(dataset, 1.0, 0.001, 0)
+
+learner.learn(infCls=opengm.inference.Icm, 
+              parameter=opengm.InfParam())
+
+for w in range(nWeights):
+    print weights[w]

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