def MRF_modelling(superpixels, labels, unaries, data, p=0.2):
edges, surfaces = adjGraph(superpixels, data, False)
# p : smoothing parameter
# Labels : is supposed to be an array: length = #SP, width= #Labels with the probaility for each label and each SP
nLabels = labels.shape[1] # The width of the labels array is the number of different labels
nVariables = superpixels.max() # The max number of the sp ID is the total number of SPs
nEdges = edges.shape[0] # Number of edges = lenght of edges-array
edgeVis = edges[:,0:2] # Vertice-Tuples in Edges list
unaryValues= labels
sameLabel = np.zeros((nVariables,1))
diffLabel = np.ones((nVariables,1)) * p
edgeValues = np.concatenate((sameLabel, diffLabel), axis=1)
gm = getGraphicalModel(
nLabels = nLabels,
nVariables = nVariables,
nEdges = nEdges,
edgeVis = edgeVis,
unaryValues = unaryValues,
edgeValues = edgeValues,
gmOperator = 'adder')
gm
return gm
def MRF_modelling(superpixels, labels, edges, data, p=0.2):
# p : smoothing parameter
# Labels : is supposed to be an array: length = #SP, width= #Labels with the probaility for each label and each SP
labels = l["labels"].value
import pdb
pdb.set_trace()
labels = labels/labels.sum(axis=1)
nVariables = superpixels.max()
sameLabel = np.zeros((nVariables,1))
diffLabel = np.ones((nVariables,1)) * p
edgeValues = np.concatenate((sameLabel, diffLabel), axis=1)
gm = getGraphicalModel(
nLabels = labels.shape[1], # The width of the labels array is the number of different labels
nVariables = superpixels.max(), # The max number of the sp ID is the total number of SPs
nEdges = edges.shape[0], # Number of edges = lenght of edges-array
edgeVis = edges[:,0:2], # Vertice-Tuples in Edges list
unaryValues = labels,
edgeValues = edgeValues,
gmOperator = 'adder')
infer = opengm.inference.AlphaBetaSwap(gm)
infer.infer()
result = infer.arg()
return result
def MRF_modelling(superpixels, labels, unaries, data):
edges, surfaces = adjGraph(superpixels, data)
nLabels = labels.shape[0]#################################################
nVariables = superpixels.max()###############################################
nEdges = edges.shape[0]
edgeVis = edges[:,0:2]
unaryValues= unaries#########################################################
edgeValues = binaryClassifier(edges)#########################################
gm = getGraphicalModel(
nLabels = nLabels,
nVariables = nVariables,
nEdges = nEdges,
edgeVis = edgeVis,
unaryValues = unaryValues,
edgeValues = edgeValues,
gmOperator = 'adder')
gm
return gm