def inference_qpbo(unary_potentials, pairwise_potentials, edges, **kwargs):
"""Inference with PyQPBO backend.
Used QPBO-I based move-making for undergenerating inference.
Parameters
----------
unary_potentials : nd-array
Unary potentials of energy function.
pairwise_potentials : nd-array
Pairwise potentials of energy function.
edges : nd-array
Edges of energy function.
Returns
-------
labels : nd-array
Approximate (usually) MAP variable assignment.
"""
from pyqpbo import alpha_expansion_general_graph
shape_org = unary_potentials.shape[:-1]
n_states, pairwise_potentials = \
_validate_params(unary_potentials, pairwise_potentials, edges)
unary_potentials = (-1000 * unary_potentials).copy().astype(np.int32)
unary_potentials = unary_potentials.reshape(-1, n_states)
pairwise_potentials = (-1000 * pairwise_potentials).copy().astype(np.int32)
edges = edges.astype(np.int32).copy()
y = alpha_expansion_general_graph(edges, unary_potentials,
pairwise_potentials, random_seed=1)
return y.reshape(shape_org)
def inference_qpbo(unary_potentials, pairwise_potentials, edges):
from pyqpbo import alpha_expansion_general_graph
shape_org = unary_potentials.shape[:-1]
n_states, pairwise_potentials = \
_validate_params(unary_potentials, pairwise_potentials, edges)
unary_potentials = (-1000 * unary_potentials).copy().astype(np.int32)
unary_potentials = unary_potentials.reshape(-1, n_states)
pairwise_potentials = (-1000 * pairwise_potentials).copy().astype(np.int32)
edges = edges.astype(np.int32)
y = alpha_expansion_general_graph(edges, unary_potentials,
pairwise_potentials, random_seed=1)
return y.reshape(shape_org)
def inference_qpbo(unary_potentials, pairwise_potentials, edges):
from pyqpbo import alpha_expansion_general_graph
shape_org = unary_potentials.shape[:-1]
n_states, pairwise_potentials = \
_validate_params(unary_potentials, pairwise_potentials, edges)
unary_potentials = (-1000 * unary_potentials).copy().astype(np.int32)
unary_potentials = unary_potentials.reshape(-1, n_states)
pairwise_potentials = (-1000 * pairwise_potentials).copy().astype(np.int32)
edges = edges.astype(np.int32)
y = alpha_expansion_general_graph(edges,
unary_potentials,
pairwise_potentials,
random_seed=1)
return y.reshape(shape_org)
def graphCut(img, centroids, unary):
link = linkWeight(img)
unary = (alpha * unary).astype('int32')
pairwise = pairwiseCost(centroids)
numIter = -1
edge, edgeCost = edgeAndWeight(img, pairwise)
# unary2D = np.zeros((img.H,img.W,K)).astype('int32')
# for i in range(0,img.H):
# for j in range(0,img.W):
# unary2D[i,j,:] = unary[i*img.W+j,:]
img.labels = pyqpbo.alpha_expansion_general_graph(edge, unary, edgeCost)
# img.labels = pyqpbo.alpha_expansion_grid(unary2D, pairwise)
# img.labels = pygco.cut_simple(unary2D, pairwise, n_iter=1)
# img.labels = pygco.cut_simple_vh(unary2D, pairwise, costV, costH)
# img.labels = pygco.cut_from_graph(link, unary, pairwise, n_iter=numIter, algorithm='swap')
def inference_qpbo(unary_potentials, pairwise_potentials, edges, **kwargs):
"""Inference with PyQPBO backend.
Used QPBO-I based move-making for undergenerating inference.
Parameters
----------
unary_potentials : nd-array, shape (n_nodes, n_states)
Unary potentials of energy function.
pairwise_potentials : nd-array, shape (n_states, n_states)
or (n_states, n_states, n_edges).
Pairwise potentials of energy function.
If the first case, edge potentials are assumed to be the same for all
edges.
In the second case, the sequence needs to correspond to the edges.
edges : nd-array, shape (n_edges, 2)
Graph edges for pairwise potentials, given as pair of node indices. As
pairwise potentials are not assumed to be symmetric, the direction of
the edge matters.
Returns
-------
labels : nd-array
Approximate (usually) MAP variable assignment.
"""
from pyqpbo import alpha_expansion_general_graph
shape_org = unary_potentials.shape[:-1]
n_states, pairwise_potentials = \
_validate_params(unary_potentials, pairwise_potentials, edges)
unary_potentials = (-1000 * unary_potentials).copy().astype(np.int32)
unary_potentials = unary_potentials.reshape(-1, n_states)
pairwise_potentials = (-1000 * pairwise_potentials).copy().astype(np.int32)
edges = edges.astype(np.int32).copy()
y = alpha_expansion_general_graph(edges,
unary_potentials,
pairwise_potentials,
random_seed=1)
return y.reshape(shape_org)
def inference_qpbo(unary_potentials, pairwise_potentials, edges, **kwargs):
"""Inference with PyQPBO backend.
Used QPBO-I based move-making for undergenerating inference.
Parameters
----------
unary_potentials : nd-array, shape (n_nodes, n_states)
Unary potentials of energy function.
pairwise_potentials : nd-array, shape (n_states, n_states)
or (n_states, n_states, n_edges).
Pairwise potentials of energy function.
If the first case, edge potentials are assumed to be the same for all
edges.
In the second case, the sequence needs to correspond to the edges.
edges : nd-array, shape (n_edges, 2)
Graph edges for pairwise potentials, given as pair of node indices. As
pairwise potentials are not assumed to be symmetric, the direction of
the edge matters.
Returns
-------
labels : nd-array
Approximate (usually) MAP variable assignment.
"""
from pyqpbo import alpha_expansion_general_graph
shape_org = unary_potentials.shape[:-1]
n_states, pairwise_potentials = \
_validate_params(unary_potentials, pairwise_potentials, edges)
unary_potentials = (-1000 * unary_potentials).copy().astype(np.int32)
unary_potentials = unary_potentials.reshape(-1, n_states)
pairwise_potentials = (-1000 * pairwise_potentials).copy().astype(np.int32)
edges = edges.astype(np.int32).copy()
y = alpha_expansion_general_graph(edges, unary_potentials,
pairwise_potentials, random_seed=1)
return y.reshape(shape_org)
def inference_qpbo(unary_potentials, pairwise_potentials, edges, **kwargs):
"""Inference with PyQPBO backend.
Used QPBO-I based move-making for undergenerating inference.
Parameters
----------
unary_potentials : nd-array
Unary potentials of energy function.
pairwise_potentials : nd-array
Pairwise potentials of energy function.
edges : nd-array
Edges of energy function.
Returns
-------
labels : nd-array
Approximate (usually) MAP variable assignment.
"""
from pyqpbo import alpha_expansion_general_graph
shape_org = unary_potentials.shape[:-1]
n_states, pairwise_potentials = \
_validate_params(unary_potentials, pairwise_potentials, edges)
unary_potentials = (-1000 * unary_potentials).copy().astype(np.int32)
unary_potentials = unary_potentials.reshape(-1, n_states)
pairwise_potentials = (-1000 * pairwise_potentials).copy().astype(np.int32)
edges = edges.astype(np.int32).copy()
y = alpha_expansion_general_graph(edges,
unary_potentials,
pairwise_potentials,
random_seed=1)
return y.reshape(shape_org)
