def generate_mc_grid(dimx, dimy, operator="adder"):
labels=dimx*dimy
nos = numpy.ones(labels, dtype=numpy.uint64) * labels
gm = opengm.gm(nos, operator, 0)
for y in range(dimy):
for x in range(dimx):
if x + 1 < dimx:
vis = [x + y * dimx, x + 1 + y * dimx]
assert vis.sort is not None
vis.sort
l=random.random()*2.0 - 1.0
fr=opengm.pottsFunction([labels,labels],0.0,l)
fid2=gm.addFunction(fr)
gm.addFactor(fid2, vis)
if y + 1 < dimy:
vis = [x + y * dimx, x + (y + 1) * dimx]
vis.sort()
l=random.random()*2.0 - 1.0
fr=opengm.pottsFunction([labels,labels],0.0,l)
fid2=gm.addFunction(fr)
gm.addFactor(fid2, vis)
return gm
def generate_mc_grid(dimx, dimy, operator="adder"):
labels=dimx*dimy
nos = numpy.ones(labels, dtype=numpy.uint64) * labels
gm = opengm.gm(nos, operator, 0)
for y in range(dimy):
for x in range(dimx):
if x + 1 < dimx:
vis = [x + y * dimx, x + 1 + y * dimx]
assert vis.sort is not None
vis.sort
l=random.random()*2.0 - 1.0
fr=opengm.pottsFunction([labels,labels],0.0,l)
fid2=gm.addFunction(fr)
gm.addFactor(fid2, vis)
if y + 1 < dimy:
vis = [x + y * dimx, x + (y + 1) * dimx]
vis.sort()
l=random.random()*2.0 - 1.0
fr=opengm.pottsFunction([labels,labels],0.0,l)
fid2=gm.addFunction(fr)
gm.addFactor(fid2, vis)
return gm
def buildGM(img,rag,dataImage,numLabels,boundaryPixels,regionPixels,beta,sigma,verbose=False):
print "get region clustering"
regionFeatures=numpy.ones([rag.numberOfRegions(),3],dtype=numpy.float64)
print "lab type in gm" ,type(img)
print "lab type in gm shape" ,img.shape
npimg=numpy.ones(img.shape)
npimg[:,:,:]=img[:,:,:]
print "npimg type in gm shape" ,npimg.shape
for r in range(rag.numberOfRegions()):
for c in range(3):
regionFeatures[r,c]=numpy.mean(npimg[regionPixels[r][:,0],regionPixels[r][:,1]][c])
print "do clustering"
code,dists=doClustering(regionFeatures,k=numLabels,steps=100)
dists=(dists-dists.min())/(dists.max()-dists.min())
if verbose==True : print "get boundary evidence"
boundaryEvidence=numpy.ones(rag.numberOfBoundaries(),dtype=numpy.float64)
be=numpy.ones([rag.numberOfBoundaries(),2],dtype=numpy.float64)
energy=numpy.ones([rag.numberOfBoundaries(),2],dtype=numpy.float64)
for b in range(rag.numberOfBoundaries()):
boundaryEvidence[b]=numpy.mean(dataImage[boundaryPixels[b][:,0],boundaryPixels[b][:,1]])
r=rag.adjacentRegions(b)
boundaryEvidence=(boundaryEvidence-boundaryEvidence.min())/(boundaryEvidence.max()-boundaryEvidence.min())*(1.0-2.0*epsilon) + epsilon
be[:,1]=numpy.exp(-1.0*boundaryEvidence[:]*sigma)
be[:,0]=1.0-be[:,1]
energy [:,0]= (-1.0*numpy.log( (1)*(1.0-beta) ) ) +be[:,0]
energy [:,1]= (-1.0*numpy.log( (1)*(beta) ) ) +be[:,1]
if verbose==True : print "build gm"
gm=opengm.graphicalModel(numpy.ones(rag.numberOfRegions(),dtype=numpy.uint64)*numLabels)
shapePotts=[numLabels,numLabels]
print "add unaries"
for r in range(rag.numberOfRegions()):
f=dists[r,:]*gamma
vis=[r]
gm.addFactor(gm.addFunction(f),vis)
print "add 2.order"
for b in range(rag.numberOfBoundaries()):
f=opengm.pottsFunction(shapePotts, energy[b,0] ,energy[b,1])
vis=rag.adjacentRegions(b)
gm.addFactor(gm.addFunction(f),vis)
return gm
if img.shape[2] != 3:
print "Image must be RGB"
sys.exit(0)
T = float(args[6])
beta = float(args[7])
imgFlat = img.reshape([-1, 3]).view(numpy.ndarray)
numVar = imgFlat.shape[0]
gm = opengm.gm(numpy.ones(numVar, dtype=opengm.label_type) * 2)
protoColor = numpy.array([args[3], args[4], args[5]],
dtype=opengm.value_type).reshape([3, -1])
protoColor = numpy.repeat(protoColor, numVar, axis=1).swapaxes(0, 1)
diffArray = numpy.sum(numpy.abs(imgFlat - protoColor), axis=1)
unaries = numpy.ones([numVar, 2], dtype=opengm.value_type)
unaries[:, 0] = T
unaries[:, 1] = diffArray
print diffArray
gm.addFactors(gm.addFunctions(unaries), numpy.arange(numVar))
regularizer = opengm.pottsFunction([2, 2], 0.0, beta)
gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
fid = gm.addFunction(regularizer)
gm.addFactors(fid, gridVariableIndices)
opengm.hdf5.saveGraphicalModel(gm, args[2], "gm")
# run some tests on the package
shape = (4, 4)
n_pixels = shape[0] * shape[1]
n_pixel_labels = 2
n_segment_labels = 2
pixels = np.random.random((shape + (n_pixel_labels, )))
segment_map = np.arange(n_pixels).reshape(shape)
segment_values = np.random.random(
(np.max(segment_map) + 1, n_segment_labels))
t0 = time.time()
gm = pixel_lattice_graph(
pixels, opengm.pottsFunction([n_pixel_labels, n_pixel_labels], 0.0,
0.5))
t1 = time.time()
opengm.visualizeGm(gm)
print "graph build in", t1 - t0, "seconds"
# test inference is possible
inference = opengm.inference.GraphCut(gm=gm)
t0 = time.time()
inference.infer()
t1 = time.time()
print "inference completed in", t1 - t0, "seconds"
t0 = time.time()
gm = segment_adjacency_graph(segment_values,
imgFlat = img.reshape([-1,3]).view(numpy.ndarray) numVar = imgFlat.shape[0] gm = opengm.gm(numpy.ones(numVar,dtype=opengm.label_type)*2) protoColor = numpy.array([args[3],args[4],args[5]],dtype=opengm.value_type).reshape([3,-1]) protoColor = numpy.repeat(protoColor,numVar,axis=1).swapaxes(0,1) diffArray = numpy.sum(numpy.abs(imgFlat - protoColor),axis=1) unaries = numpy.ones([numVar,2],dtype=opengm.value_type) unaries[:,0]=T unaries[:,1]=diffArray print diffArray gm.addFactors(gm.addFunctions(unaries),numpy.arange(numVar)) regularizer=opengm.pottsFunction([2,2],0.0,beta) gridVariableIndices=opengm.secondOrderGridVis(img.shape[0],img.shape[1]) fid=gm.addFunction(regularizer) gm.addFactors(fid,gridVariableIndices) print gm inf=opengm.inference.GraphCut(gm) inf.infer() arg=inf.arg().reshape(img.shape[0:2]) vigra.impex.writeImage(arg,args[2])
def segmentation_example():
import vigra
import opengm
import sklearn
import sklearn.mixture
import numpy as np
from vigra import graphs
import matplotlib as mpl
import plottool as pt
pt.ensure_pylab_qt4()
# 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)
# Node Features
nodeFeatures = rag.accumulateNodeFeatures(imgLab)
nodeFeaturesImg = rag.projectNodeFeaturesToGridGraph(nodeFeatures)
nodeFeaturesImg = vigra.taggedView(nodeFeaturesImg, "xyc")
nodeFeaturesImgRgb = vigra.colors.transform_Lab2RGB(nodeFeaturesImg)
nCluster = 5
g = sklearn.mixture.GMM(n_components=nCluster)
g.fit(nodeFeatures[:, :])
clusterProb = g.predict_proba(nodeFeatures)
# 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(np.float32))
clusterProbImg = vigra.taggedView(clusterProbImg, "xyc")
ndim_data = clusterProbImg.reshape((-1, nCluster))
pca = sklearn.decomposition.PCA(n_components=3)
print(ndim_data.shape)
pca.fit(ndim_data)
print(ut.repr2(pca.explained_variance_ratio_, precision=2))
oldshape = (clusterProbImg.shape[0:2] + (-1,))
clusterProgImg3 = pca.transform(ndim_data).reshape(oldshape)
print(clusterProgImg3.shape)
# graphical model with as many variables
# as superpixels, each has 3 states
gm = opengm.gm(np.ones(rag.nodeNum, dtype=opengm.label_type) * nCluster)
# convert probabilites to energies
probs = np.clip(clusterProb, 0.00001, 0.99999)
costs = -1.0 * np.log(probs)
# add ALL unaries AT ONCE
fids = gm.addFunctions(costs)
gm.addFactors(fids, np.arange(rag.nodeNum))
# add a potts function
beta = 40.0 # strength of potts regularizer
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 = np.sort(uvIds, axis=1)
# add all second order factors at once
gm.addFactors(fid, uvIds)
# get super-pixels with slic on LAB image
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 %r" % (gm.evaluate(labelVector),))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
pt.imshow(clusterProgImg3.swapaxes(0, 1))
# plot superpixels
cmap = mpl.colors.ListedColormap(np.random.rand(nseg, 3))
pt.imshow(labels.swapaxes(0, 1).squeeze(), cmap=cmap)
pt.imshow(nodeFeaturesImgRgb)
cmap = mpl.colors.ListedColormap(np.random.rand(nCluster, 3))
for arg in callback.labels:
arg = vigra.taggedView(arg, "n")
argImg = rag.projectNodeFeaturesToGridGraph(arg.astype(np.uint32))
argImg = vigra.taggedView(argImg, "xy")
# plot superpixels
pt.imshow(argImg.swapaxes(0, 1).squeeze(), cmap=cmap)
def segmentation_example():
import vigra
import opengm
import sklearn
import sklearn.mixture
import numpy as np
from vigra import graphs
import matplotlib as mpl
import plottool as pt
pt.ensure_pylab_qt4()
# 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)
# Node Features
nodeFeatures = rag.accumulateNodeFeatures(imgLab)
nodeFeaturesImg = rag.projectNodeFeaturesToGridGraph(nodeFeatures)
nodeFeaturesImg = vigra.taggedView(nodeFeaturesImg, "xyc")
nodeFeaturesImgRgb = vigra.colors.transform_Lab2RGB(nodeFeaturesImg)
nCluster = 5
g = sklearn.mixture.GMM(n_components=nCluster)
g.fit(nodeFeatures[:, :])
clusterProb = g.predict_proba(nodeFeatures)
# 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(np.float32))
clusterProbImg = vigra.taggedView(clusterProbImg, "xyc")
ndim_data = clusterProbImg.reshape((-1, nCluster))
pca = sklearn.decomposition.PCA(n_components=3)
print(ndim_data.shape)
pca.fit(ndim_data)
print(ut.repr2(pca.explained_variance_ratio_, precision=2))
oldshape = (clusterProbImg.shape[0:2] + (-1, ))
clusterProgImg3 = pca.transform(ndim_data).reshape(oldshape)
print(clusterProgImg3.shape)
# graphical model with as many variables
# as superpixels, each has 3 states
gm = opengm.gm(np.ones(rag.nodeNum, dtype=opengm.label_type) * nCluster)
# convert probabilites to energies
probs = np.clip(clusterProb, 0.00001, 0.99999)
costs = -1.0 * np.log(probs)
# add ALL unaries AT ONCE
fids = gm.addFunctions(costs)
gm.addFactors(fids, np.arange(rag.nodeNum))
# add a potts function
beta = 40.0 # strength of potts regularizer
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 = np.sort(uvIds, axis=1)
# add all second order factors at once
gm.addFactors(fid, uvIds)
# get super-pixels with slic on LAB image
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 %r" % (gm.evaluate(labelVector), ))
self.labels.append(labelVector)
callback = PyCallback()
visitor = inf.pythonVisitor(callback, visitNth=1)
inf.infer(visitor)
pt.imshow(clusterProgImg3.swapaxes(0, 1))
# plot superpixels
cmap = mpl.colors.ListedColormap(np.random.rand(nseg, 3))
pt.imshow(labels.swapaxes(0, 1).squeeze(), cmap=cmap)
pt.imshow(nodeFeaturesImgRgb)
cmap = mpl.colors.ListedColormap(np.random.rand(nCluster, 3))
for arg in callback.labels:
arg = vigra.taggedView(arg, "n")
argImg = rag.projectNodeFeaturesToGridGraph(arg.astype(np.uint32))
argImg = vigra.taggedView(argImg, "xy")
# plot superpixels
pt.imshow(argImg.swapaxes(0, 1).squeeze(), cmap=cmap)
