import opengm import numpy chainLength=5 numLabels=100 numberOfStates=numpy.ones(chainLength,dtype=opengm.label_type)*numLabels gm=opengm.gm(numberOfStates,operator='adder') #add some random unaries for vi in range(chainLength): unaryFuction=numpy.random.random(numLabels) gm.addFactor(gm.addFunction(unaryFuction),[vi]) #add one 2.order function f=opengm.differenceFunction(shape=[numLabels]*2,weight=0.1) print type(f),f fid=gm.addFunction(f) #add factors on a chain for vi in range(chainLength-1): gm.addFactor(fid,[vi,vi+1]) inf = opengm.inference.BeliefPropagation(gm,parameter=opengm.InfParam(steps=40,convergenceBound=0 ,damping=0.9)) inf.infer(inf.verboseVisitor()) print inf.arg()
def add_a_function(w):
return gm.addFunction(opengm.differenceFunction(shape=[2, 2],
weight=w))
def denoiseModel(
img,
norm = 2,
weight = 1.0,
truncate = None,
numLabels = 256,
neighbourhood = 4,
inpaintPixels = None,
randInpaitStartingPoint = False
):
"""
this function is used to set up a graphical model similar to
**Denoising and inpainting problems:** from `Mrf- Benchmark <http://vision.middlebury.edu/MRF/results/ >`_
Args :
img : a grayscale image in the range [0,256)
norm : used norm for unaries and 2-order functions (default : 2)
weight : weight of 2-order functions (default : 1.0)
truncate : Truncate second order function at an given value (defaut : None)
numLabels : number of labels for each variable in the graphical model,
set this to a lower number to speed up inference (default : 255)
neighbourhood : neighbourhood for the second order functions, so far only 4 is allowed (default : 4)
inpaintPixels : a tuple of x and y coordinates where no unaries are added
randInpaitStartingPoint : use a random starting point for all pixels without unaries (default : False)
"""
shape = img.shape
if(img.ndim!=2):
raise RuntimeError("image must be gray")
if neighbourhood != 4 :
raise RuntimeError("A neighbourhood other than 4 is not yet implemented")
# normalize and flatten image
iMin = numpy.min(img)
iMax = numpy.max(img)
imgNorm = ((img[:,:]-iMin)/(iMax-iMin))*float(numLabels)
imgFlat = imgNorm.reshape(-1).astype(numpy.uint64)
# Set up Grapical Model:
numVar = int(img.size)
gm = opengm.gm([numLabels]*numVar,operator='adder')
gm.reserveFunctions(numLabels,'explicit')
numberOfPairwiseFactors=shape[0]*(shape[1]-1) + shape[1]*(shape[0]-1)
gm.reserveFactors(numVar-len(inpaintPixels[0]) + numberOfPairwiseFactors )
# Set up unaries:
# - create a range of all possible labels
allPossiblePixelValues=numpy.arange(numLabels)
pixelValueRep = numpy.repeat(allPossiblePixelValues[:,numpy.newaxis],numLabels,1)
# - repeat [0,1,2,3,...,253,254,255] numVar times
labelRange = numpy.arange(numLabels,dtype=opengm.value_type)
labelRange = numpy.repeat(labelRange[numpy.newaxis,:], numLabels, 0)
unaries = numpy.abs(pixelValueRep - labelRange)**norm
# - add unaries to the graphical model
fids=gm.addFunctions(unaries.astype(opengm.value_type))
# add unary factors to graphical model
if(inpaintPixels is None):
for l in xrange(numLabels):
whereL=numpy.where(imgFlat==l)
gm.addFactors(fids[l],whereL[0].astype(opengm.index_type))
else:
# get vis of inpaint pixels
ipX = inpaintPixels[0]
ipY = inpaintPixels[1]
ipVi = ipX*shape[1] + ipY
for l in xrange(numLabels):
whereL=numpy.where(imgFlat==l)
notInInpaint=numpy.setdiff1d(whereL[0],ipVi)
gm.addFactors(fids[l],notInInpaint.astype(opengm.index_type))
# add ONE second order function
f=opengm.differenceFunction(shape=[numLabels,numLabels],norm=2,weight=weight)
fid=gm.addFunction(f)
vis2Order=opengm.secondOrderGridVis(shape[0],shape[1],True)
# add all second order factors
gm.addFactors(fid,vis2Order)
# create a starting point
startingPoint = imgFlat.copy()
if randInpaitStartingPoint :
startingPointRandom = numpy.random.randint(0,numLabels,size=numVar).astype(opengm.index_type)
ipVi = inpaintPixels[0]*shape[1] + inpaintPixels[1]
for x in ipVi:
startingPoint[x]=startingPointRandom[x]
startingPoint[startingPoint==numLabels]=numLabels-1
return gm,startingPoint.astype(opengm.index_type)
def denoiseModel(img,
norm=2,
weight=1.0,
truncate=None,
numLabels=256,
neighbourhood=4,
inpaintPixels=None,
randInpaitStartingPoint=False):
"""
this function is used to set up a graphical model similar to
**Denoising and inpainting problems:** from `Mrf- Benchmark <http://vision.middlebury.edu/MRF/results/ >`_
Args :
img : a grayscale image in the range [0,256)
norm : used norm for unaries and 2-order functions (default : 2)
weight : weight of 2-order functions (default : 1.0)
truncate : Truncate second order function at an given value (defaut : None)
numLabels : number of labels for each variable in the graphical model,
set this to a lower number to speed up inference (default : 255)
neighbourhood : neighbourhood for the second order functions, so far only 4 is allowed (default : 4)
inpaintPixels : a tuple of x and y coordinates where no unaries are added
randInpaitStartingPoint : use a random starting point for all pixels without unaries (default : False)
"""
shape = img.shape
if (img.ndim != 2):
raise RuntimeError("image must be gray")
if neighbourhood != 4:
raise RuntimeError(
"A neighbourhood other than 4 is not yet implemented")
# normalize and flatten image
iMin = numpy.min(img)
iMax = numpy.max(img)
imgNorm = ((img[:, :] - iMin) / (iMax - iMin)) * float(numLabels)
imgFlat = imgNorm.reshape(-1).astype(numpy.uint64)
# Set up Grapical Model:
numVar = int(img.size)
gm = opengm.gm([numLabels] * numVar, operator='adder')
gm.reserveFunctions(numLabels, 'explicit')
numberOfPairwiseFactors = shape[0] * (shape[1] -
1) + shape[1] * (shape[0] - 1)
gm.reserveFactors(numVar - len(inpaintPixels[0]) + numberOfPairwiseFactors)
# Set up unaries:
# - create a range of all possible labels
allPossiblePixelValues = numpy.arange(numLabels)
pixelValueRep = numpy.repeat(allPossiblePixelValues[:, numpy.newaxis],
numLabels, 1)
# - repeat [0,1,2,3,...,253,254,255] numVar times
labelRange = numpy.arange(numLabels, dtype=opengm.value_type)
labelRange = numpy.repeat(labelRange[numpy.newaxis, :], numLabels, 0)
unaries = numpy.abs(pixelValueRep - labelRange)**norm
# - add unaries to the graphical model
fids = gm.addFunctions(unaries.astype(opengm.value_type))
# add unary factors to graphical model
if (inpaintPixels is None):
for l in xrange(numLabels):
whereL = numpy.where(imgFlat == l)
gm.addFactors(fids[l], whereL[0].astype(opengm.index_type))
else:
# get vis of inpaint pixels
ipX = inpaintPixels[0]
ipY = inpaintPixels[1]
ipVi = ipX * shape[1] + ipY
for l in xrange(numLabels):
whereL = numpy.where(imgFlat == l)
notInInpaint = numpy.setdiff1d(whereL[0], ipVi)
gm.addFactors(fids[l], notInInpaint.astype(opengm.index_type))
# add ONE second order function
f = opengm.differenceFunction(shape=[numLabels, numLabels],
norm=2,
weight=weight)
fid = gm.addFunction(f)
vis2Order = opengm.secondOrderGridVis(shape[0], shape[1], True)
# add all second order factors
gm.addFactors(fid, vis2Order)
# create a starting point
startingPoint = imgFlat.copy()
if randInpaitStartingPoint:
startingPointRandom = numpy.random.randint(
0, numLabels, size=numVar).astype(opengm.index_type)
ipVi = inpaintPixels[0] * shape[1] + inpaintPixels[1]
for x in ipVi:
startingPoint[x] = startingPointRandom[x]
startingPoint[startingPoint == numLabels] = numLabels - 1
return gm, startingPoint.astype(opengm.index_type)
import opengm
import numpy
chainLength = 5
numLabels = 100
numberOfStates = numpy.ones(chainLength, dtype=opengm.label_type) * numLabels
gm = opengm.gm(numberOfStates, operator='adder')
#add some random unaries
for vi in range(chainLength):
unaryFuction = numpy.random.random(numLabels)
gm.addFactor(gm.addFunction(unaryFuction), [vi])
#add one 2.order function
f = opengm.differenceFunction(shape=[numLabels] * 2, weight=0.1)
print type(f), f
fid = gm.addFunction(f)
#add factors on a chain
for vi in range(chainLength - 1):
gm.addFactor(fid, [vi, vi + 1])
inf = opengm.inference.BeliefPropagation(gm,
parameter=opengm.InfParam(
steps=40,
convergenceBound=0,
damping=0.9))
inf.infer(inf.verboseVisitor())
print inf.arg()
