def intra_encounter_matching():
import numpy as np
from scipy.sparse import coo_matrix, csgraph
qreq_, cm_list = testdata_workflow()
# qaids = [cm.qaid for cm in cm_list]
# top_aids = [cm.get_top_aids(5) for cm in cm_list]
aid_pairs = np.array([(cm.qaid, daid)
for cm in cm_list for daid in cm.get_top_aids(5)])
top_scores = ut.flatten([cm.get_top_scores(5) for cm in cm_list])
N = aid_pairs.max() + 1
mat = coo_matrix((top_scores, aid_pairs.T), shape=(N, N))
csgraph.connected_components(mat)
tree = csgraph.minimum_spanning_tree(mat) # NOQA
import plottool as pt
dense = mat.todense()
pt.imshow(dense / dense.max() * 255)
pt.show_if_requested()
# baseline jobid
import opengm
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/OpenGM%20tutorial.ipynb
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(
workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'),
)
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg() # NOQA
"""
def intra_encounter_matching():
import numpy as np
from scipy.sparse import coo_matrix, csgraph
qreq_, cm_list = testdata_workflow()
# qaids = [cm.qaid for cm in cm_list]
# top_aids = [cm.get_top_aids(5) for cm in cm_list]
aid_pairs = np.array([(cm.qaid, daid) for cm in cm_list
for daid in cm.get_top_aids(5)])
top_scores = ut.flatten([cm.get_top_scores(5) for cm in cm_list])
N = aid_pairs.max() + 1
mat = coo_matrix((top_scores, aid_pairs.T), shape=(N, N))
csgraph.connected_components(mat)
tree = csgraph.minimum_spanning_tree(mat) # NOQA
import plottool as pt
dense = mat.todense()
pt.imshow(dense / dense.max() * 255)
pt.show_if_requested()
# baseline jobid
import opengm
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/OpenGM%20tutorial.ipynb
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'), )
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg() # NOQA
"""
import opengm
import numpy
#---------------------------------------------------------------
# MinSum with SelfFusion
#---------------------------------------------------------------
numpy.random.seed(42)
n=100
nl=100
unaries=numpy.random.rand(n , n, nl)
potts=opengm.PottsFunction([nl,nl],0.0,0.5)
gm=opengm.grid2d2Order(unaries=unaries,regularizer=potts)
#---------------------------------------------------------------
# Minimize
#---------------------------------------------------------------
#get an instance of the optimizer / inference-algorithm
infParam = opengm.InfParam(
generator='trws'
)
inf=opengm.inference.SelfFusion(gm,parameter=infParam)
# start inference (in this case verbose infernce)
visitor=inf.verboseVisitor(printNth=1,multiline=True)
inf.infer(visitor)
# get the result states
argmin=inf.arg()
# print the argmin (on the grid)
print argmin.reshape(n,n)
def crftest():
"""
pip install pyqpbo
pip install pystruct
http://taku910.github.io/crfpp/#install
cd ~/tmp
#wget https://drive.google.com/folderview?id=0B4y35FiV1wh7fngteFhHQUN2Y1B5eUJBNHZUemJYQV9VWlBUb3JlX0xBdWVZTWtSbVBneU0&usp=drive_web#list
7z x CRF++-0.58.tar.gz
7z x CRF++-0.58.tar
cd CRF++-0.58
chmod +x configure
./configure
make
"""
import pystruct
import pystruct.models
inference_method_options = ['lp', 'max-product']
inference_method = inference_method_options[1]
# graph = pystruct.models.GraphCRF(
# n_states=None,
# n_features=None,
# inference_method=inference_method,
# class_weight=None,
# directed=False,
# )
num_annots = 5
num_names = num_annots
aids = np.arange(5)
rng = np.random.RandomState(0)
hidden_nids = rng.randint(0, num_names, num_annots)
unique_nids, groupxs = ut.group_indices(hidden_nids)
# Indicator vector indicating the name
node_features = np.zeros((num_annots, num_names))
node_features[(aids, hidden_nids)] = 1
toy_params = {True: {'mu': 1.0, 'sigma': 2.2}, False: {'mu': 7.0, 'sigma': 0.9}}
if False:
import vtool as vt
import wbia.plottool as pt
pt.ensureqt()
xdata = np.linspace(0, 100, 1000)
tp_pdf = vt.gauss_func1d(xdata, **toy_params[True])
fp_pdf = vt.gauss_func1d(xdata, **toy_params[False])
pt.plot_probabilities([tp_pdf, fp_pdf], ['TP', 'TF'], xdata=xdata)
def metric(aidx1, aidx2, hidden_nids=hidden_nids, toy_params=toy_params):
if aidx1 == aidx2:
return 0
rng = np.random.RandomState(int(aidx1 + aidx2))
same = hidden_nids[int(aidx1)] == hidden_nids[int(aidx2)]
mu, sigma = ut.dict_take(toy_params[same], ['mu', 'sigma'])
return np.clip(rng.normal(mu, sigma), 0, np.inf)
pairwise_aidxs = list(ut.iprod(range(num_annots), range(num_annots)))
pairwise_labels = np.array( # NOQA
[hidden_nids[a1] == hidden_nids[a2] for a1, a2 in pairwise_aidxs]
)
pairwise_scores = np.array([metric(*zz) for zz in pairwise_aidxs])
pairwise_scores_mat = pairwise_scores.reshape(num_annots, num_annots) # NOQA
graph = pystruct.models.EdgeFeatureGraphCRF( # NOQA
n_states=num_annots,
n_features=num_names,
n_edge_features=1,
inference_method=inference_method,
)
import opengm
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'))
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg() # NOQA
img=vigra.impex.readImage('118035.jpg')
shape=img.shape
imgLab=vigra.colors.transform_RGB2Lab(img)
shape=(shape[0]*resizeFactor,shape[1]*resizeFactor)
imgLab=vigra.sampling.resize(imgLab, shape,order=3)
gradMag=vigra.filters.gaussianGradientMagnitude(imgLab,gradScale)
unaries=numpy.zeros([shape[0],shape[1],2])
unaries[:,:,1]=numpy.exp(-1.0*gradMag[:,:,0]*sigma)
unaries[:,:,0]=1.0-unaries[:,:,1]
regularizer=opengm.PottsFunction(2,2,0.0,energyNotEqual)
gm=opengm.grid2d2Order(unaries=unaries,regularizer=regularizer,order='numpy',operator='adder')
inf=opengm.Inference(gm,alg='gc',impl='k')
inf.infer()
argmin=inf.arg().reshape(shape[0:2])
plt.figure(1)
ax=plt.subplot(2,1,1)
plt.imshow(unaries[:,:,1].T, interpolation="nearest")
plt.set_cmap(cm.copper)
plt.colorbar()
ax.set_title('costs / unaries label=1')
ax=plt.subplot(2,1,2)
plt.imshow(argmin.T, interpolation="nearest")
import opengm import numpy #--------------------------------------------------------------- # MinSum with ICM #--------------------------------------------------------------- n = 10 nl = 10 unaries = numpy.random.rand(n, n, nl) potts = opengm.PottsFunction([nl, nl], 0.0, 0.05) gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts) #--------------------------------------------------------------- # Minimize #--------------------------------------------------------------- #get an instance of the optimizer / inference-algorithm inf = opengm.inference.Icm(gm) # start inference (in this case verbose infernce) visitor = inf.verboseVisitor(printNth=10000, multiline=True) inf.infer(visitor) # get the result states argmin = inf.arg() # print the argmin (on the grid) print argmin.reshape(n, n)
shape=[3,3] numVar=shape[0]*shape[1] img=numpy.random.rand(shape[0],shape[1])*255.0 # unaries img1d=img.reshape(numVar) lrange=numpy.arange(0,256,1) unaries=numpy.repeat(lrange[:,numpy.newaxis], numVar, 1).T for l in xrange(256): unaries[:,l]=numpy.abs(img1d-l) unaries=unaries.reshape(shape+[256]) # higher order function def regularizer(labels): val=abs(float(labels[0])-float(labels[1])) return val*0.2 print "generate 2d grid gm" regularizer=opengm.PythonFunction(function=regularizer,shape=[256,256]) print "unaries shape ",unaries.shape print "reg shape ",regularizer.shape gm=opengm.grid2d2Order(unaries,regularizer=regularizer) icm=opengm.inference.Icm(gm) #icm.infer() arg=icm.arg() arg=arg.reshape(shape) print arg
img = vigra.impex.readImage('118035.jpg')
shape = img.shape
imgLab = vigra.colors.transform_RGB2Lab(img)
shape = (shape[0] * resizeFactor, shape[1] * resizeFactor)
imgLab = vigra.sampling.resize(imgLab, shape, order=3)
gradMag = vigra.filters.gaussianGradientMagnitude(imgLab, gradScale)
unaries = numpy.zeros([shape[0], shape[1], 2])
unaries[:, :, 1] = numpy.exp(-1.0 * gradMag[:, :, 0] * sigma)
unaries[:, :, 0] = 1.0 - unaries[:, :, 1]
regularizer = opengm.PottsFunction(2, 2, 0.0, energyNotEqual)
gm = opengm.grid2d2Order(unaries=unaries,
regularizer=regularizer,
order='numpy',
operator='adder')
inf = opengm.inference.GraphCut(gm)
inf.infer()
argmin = inf.arg().reshape(shape[0:2])
plt.figure(1)
ax = plt.subplot(2, 1, 1)
plt.imshow(unaries[:, :, 1].T, interpolation="nearest")
plt.set_cmap(cm.copper)
plt.colorbar()
ax.set_title('costs / unaries label=1')
ax = plt.subplot(2, 1, 2)
plt.imshow(argmin.T, interpolation="nearest")
def crftest():
"""
pip install pyqpbo
pip install pystruct
http://taku910.github.io/crfpp/#install
cd ~/tmp
#wget https://drive.google.com/folderview?id=0B4y35FiV1wh7fngteFhHQUN2Y1B5eUJBNHZUemJYQV9VWlBUb3JlX0xBdWVZTWtSbVBneU0&usp=drive_web#list
7z x CRF++-0.58.tar.gz
7z x CRF++-0.58.tar
cd CRF++-0.58
chmod +x configure
./configure
make
"""
import pystruct
import pystruct.models
inference_method_options = ['lp', 'max-product']
inference_method = inference_method_options[1]
#graph = pystruct.models.GraphCRF(
# n_states=None,
# n_features=None,
# inference_method=inference_method,
# class_weight=None,
# directed=False,
#)
num_annots = 5
num_names = num_annots
aids = np.arange(5)
rng = np.random.RandomState(0)
hidden_nids = rng.randint(0, num_names, num_annots)
unique_nids, groupxs = ut.group_indices(hidden_nids)
# Indicator vector indicating the name
node_features = np.zeros((num_annots, num_names))
node_features[(aids, hidden_nids)] = 1
toy_params = {
True: {'mu': 1.0, 'sigma': 2.2},
False: {'mu': 7.0, 'sigma': .9}
}
if False:
import vtool as vt
import plottool as pt
pt.ensure_pylab_qt4()
xdata = np.linspace(0, 100, 1000)
tp_pdf = vt.gauss_func1d(xdata, **toy_params[True])
fp_pdf = vt.gauss_func1d(xdata, **toy_params[False])
pt.plot_probabilities([tp_pdf, fp_pdf], ['TP', 'TF'], xdata=xdata)
def metric(aidx1, aidx2, hidden_nids=hidden_nids, toy_params=toy_params):
if aidx1 == aidx2:
return 0
rng = np.random.RandomState(int(aidx1 + aidx2))
same = hidden_nids[int(aidx1)] == hidden_nids[int(aidx2)]
mu, sigma = ut.dict_take(toy_params[same], ['mu', 'sigma'])
return np.clip(rng.normal(mu, sigma), 0, np.inf)
pairwise_aidxs = list(ut.iprod(range(num_annots), range(num_annots)))
pairwise_labels = np.array([hidden_nids[a1] == hidden_nids[a2] for a1, a2 in pairwise_aidxs])
pairwise_scores = np.array([metric(*zz) for zz in pairwise_aidxs])
pairwise_scores_mat = pairwise_scores.reshape(num_annots, num_annots)
graph = pystruct.models.EdgeFeatureGraphCRF(
n_states=num_annots,
n_features=num_names,
n_edge_features=1,
inference_method=inference_method,
)
import opengm
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(
workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'),
)
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg()
def intra_encounter_matching():
qreq_, cm_list = testdata_workflow()
# qaids = [cm.qaid for cm in cm_list]
# top_aids = [cm.get_top_aids(5) for cm in cm_list]
import numpy as np
from scipy.sparse import coo_matrix, csgraph
aid_pairs = np.array([(cm.qaid, daid) for cm in cm_list for daid in cm.get_top_aids(5)])
top_scores = ut.flatten([cm.get_top_scores(5) for cm in cm_list])
N = aid_pairs.max() + 1
mat = coo_matrix((top_scores, aid_pairs.T), shape=(N, N))
csgraph.connected_components(mat)
tree = csgraph.minimum_spanning_tree(mat) # NOQA
import plottool as pt
dense = mat.todense()
pt.imshow(dense / dense.max() * 255)
pt.show_if_requested()
# load image and convert to LAB
img_fpath = str(ut.grab_test_imgpath(str('lena.png')))
img = vigra.impex.readImage(img_fpath)
imgLab = vigra.colors.transform_RGB2Lab(img)
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)
nodeFeatures = rag.accumulateNodeFeatures(imgLab)
nodeFeaturesImg = rag.projectNodeFeaturesToGridGraph(nodeFeatures)
nodeFeaturesImg = vigra.taggedView(nodeFeaturesImg, "xyc")
nodeFeaturesImgRgb = vigra.colors.transform_Lab2RGB(nodeFeaturesImg)
#from sklearn.cluster import MiniBatchKMeans, KMeans
from sklearn import mixture
nCluster = 3
g = mixture.GMM(n_components=nCluster)
g.fit(nodeFeatures[:,:])
clusterProb = g.predict_proba(nodeFeatures)
import numpy
#https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Irregular%20Factor%20Graphs.ipynb
#https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Hard%20and%20Soft%20Constraints.ipynb
clusterProbImg = rag.projectNodeFeaturesToGridGraph(clusterProb.astype(numpy.float32))
clusterProbImg = vigra.taggedView(clusterProbImg, "xyc")
# strength of potts regularizer
beta = 40.0
# 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()
uvIds = numpy.sort(uvIds,axis=1)
# add all second order factors at once
gm.addFactors(fid,uvIds)
# get super-pixels with slic on LAB image
import opengm
# Matching Graph
cost_matrix = np.array([
[0.5, 0.6, 0.2, 0.4, 0.1],
[0.0, 0.5, 0.2, 0.9, 0.2],
[0.0, 0.0, 0.5, 0.1, 0.1],
[0.0, 0.0, 0.0, 0.5, 0.1],
[0.0, 0.0, 0.0, 0.0, 0.5],
])
cost_matrix += cost_matrix.T
number_of_labels = 5
num_annots = 5
cost_matrix = (cost_matrix * 2) - 1
#gm = opengm.gm(number_of_labels)
gm = opengm.gm(np.ones(num_annots) * number_of_labels)
aids = np.arange(num_annots)
aid_pairs = np.array([(a1, a2) for a1, a2 in ut.iprod(aids, aids) if a1 != a2], dtype=np.uint32)
aid_pairs.sort(axis=1)
# 2nd order function
fid = gm.addFunction(cost_matrix)
gm.addFactors(fid, aid_pairs)
Inf = opengm.inference.BeliefPropagation
#Inf = opengm.inference.Multicut
parameter = opengm.InfParam(steps=10, damping=0.5, convergenceBound=0.001)
parameter = opengm.InfParam()
inf = Inf(gm, parameter=parameter)
class PyCallback(object):
def __init__(self,):
self.labels=[]
pass
def begin(self,inference):
print("begin of inference")
pass
def end(self,inference):
self.labels.append(inference.arg())
pass
def visit(self,inference):
gm=inference.gm()
labelVector=inference.arg()
print("energy %r" % (gm.evaluate(labelVector),))
self.labels.append(labelVector)
pass
callback=PyCallback()
visitor=inf.pythonVisitor(callback,visitNth=1)
inf.infer(visitor)
print(callback.labels)
# baseline jobid
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/OpenGM%20tutorial.ipynb
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(
workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'),
)
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg() # NOQA
"""
# unaries
img1d = img.reshape(numVar)
lrange = numpy.arange(0, 256, 1)
unaries = numpy.repeat(lrange[:, numpy.newaxis], numVar, 1).T
for l in xrange(256):
unaries[:, l] -= img1d
unaries = numpy.abs(unaries)
unaries = unaries.reshape(shape + [256])
# higher order function
def regularizer(labels):
val = abs(float(labels[0]) - float(labels[1]))
return val * 0.4
print "generate 2d grid gm"
regularizer = opengm.PythonFunction(function=regularizer, shape=[256, 256])
gm = opengm.grid2d2Order(unaries, regularizer=regularizer)
icm = opengm.inference.Icm(gm)
icm.infer()
arg = icm.arg()
arg = arg.reshape(shape)
print numpy.round(img)
print arg
