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import numpy as np
cimport numpy as np
np.import_array()
cdef extern from "GCoptimization.h":
cdef cppclass GCoptimizationGridGraph:
GCoptimizationGridGraph(int width, int height, int n_labels) except +
void setDataCost(int *) except +
void setSmoothCost(int *) except +
void setLabelCost(int) except +
void expansion(int n_iterations) except +
void swap(int n_iterations) except +
void setSmoothCostVH(int* pairwise, int* V, int* H) except +
int whatLabel(int node) except +
cdef cppclass GCoptimizationGeneralGraph:
GCoptimizationGeneralGraph(int n_vertices, int n_labels) except +
void setDataCost(int *) except +
void setSmoothCost(int *) except +
void setLabelCost(int) except +
void setNeighbors(int, int) except +
void setNeighbors(int, int, int) except +
void expansion(int n_iterations) except +
void swap(int n_iterations) except +
int whatLabel(int node) except +
def cut_simple(np.ndarray[np.int32_t, ndim=3, mode='c'] unary_cost,
np.ndarray[np.int32_t, ndim=2, mode='c'] pairwise_cost,
label_cost=0, n_iter=5, algorithm='expansion'):
"""
Apply multi-label graphcuts to grid graph.
Parameters
----------
unary_cost: ndarray, int32, shape=(width, height, n_labels)
Unary potentials
pairwise_cost: ndarray, int32, shape=(n_labels, n_labels)
Pairwise potentials for label compatibility
label_cost: int32
Cost per label. Penalizes the number of distinct labels in the solution
n_iter: int, (default=5)
Number of iterations
algorithm: string, `expansion` or `swap`, default=expansion
Whether to perform alpha-expansion or alpha-beta-swaps.
"""
if unary_cost.shape[2] != pairwise_cost.shape[0]:
raise ValueError("unary_cost and pairwise_cost have incompatible shapes.\n"
"unary_cost must be height x width x n_labels, pairwise_cost must be n_labels x n_labels.\n"
"Got: unary_cost: (%d, %d, %d), pairwise_cost: (%d, %d)"
%(unary_cost.shape[0], unary_cost.shape[1], unary_cost.shape[2],
pairwise_cost.shape[0], pairwise_cost.shape[1]))
if pairwise_cost.shape[1] != pairwise_cost.shape[0]:
raise ValueError("pairwise_cost must be a square matrix.")
cdef int h = unary_cost.shape[1]
cdef int w = unary_cost.shape[0]
cdef int n_labels = pairwise_cost.shape[0]
if (pairwise_cost != pairwise_cost.T).any():
raise ValueError("pairwise_cost must be symmetric.")
cdef GCoptimizationGridGraph* gc = new GCoptimizationGridGraph(h, w, n_labels)
gc.setDataCost(<int*>unary_cost.data)
gc.setSmoothCost(<int*>pairwise_cost.data)
if label_cost > 0:
gc.setLabelCost(label_cost)
if algorithm == 'swap':
gc.swap(n_iter)
elif algorithm == 'expansion':
gc.expansion(n_iter)
else:
raise ValueError("algorithm should be either `swap` or `expansion`. Got: %s" % algorithm)
cdef np.npy_intp result_shape[2]
result_shape[0] = w
result_shape[1] = h
cdef np.ndarray[np.int32_t, ndim=2] result = np.PyArray_SimpleNew(2, result_shape, np.NPY_INT32)
cdef int * result_ptr = <int*>result.data
for i in xrange(w * h):
result_ptr[i] = gc.whatLabel(i)
del gc
return result
def cut_simple_vh(np.ndarray[np.int32_t, ndim=3, mode='c'] unary_cost,
np.ndarray[np.int32_t, ndim=2, mode='c'] pairwise_cost,
np.ndarray[np.int32_t, ndim=2, mode='c'] costV,
np.ndarray[np.int32_t, ndim=2, mode='c'] costH,
label_cost=0,
n_iter=5,
algorithm='expansion'):
"""
Apply multi-label graphcuts to grid graph.
Parameters
----------
unary_cost: ndarray, int32, shape=(width, height, n_labels)
Unary potentials
pairwise_cost: ndarray, int32, shape=(n_labels, n_labels)
Pairwise potentials for label compatibility
costV: ndarray, int32, shape=(width, height)
Vertical edge weights
costH: ndarray, int32, shape=(width, height)
Horizontal edge weights
label_cost: int32
Cost per label. Penalizes the number of distinct labels in the solution
n_iter: int, (default=5)
Number of iterations
algorithm: string, `expansion` or `swap`, default=expansion
Whether to perform alpha-expansion or alpha-beta-swaps.
"""
if unary_cost.shape[2] != pairwise_cost.shape[0]:
raise ValueError("unary_cost and pairwise_cost have incompatible shapes.\n"
"unary_cost must be height x width x n_labels, pairwise_cost must be n_labels x n_labels.\n"
"Got: unary_cost: (%d, %d, %d), pairwise_cost: (%d, %d)"
%(unary_cost.shape[0], unary_cost.shape[1], unary_cost.shape[2],
pairwise_cost.shape[0], pairwise_cost.shape[1]))
if pairwise_cost.shape[1] != pairwise_cost.shape[0]:
raise ValueError("pairwise_cost must be a square matrix.")
cdef int h = unary_cost.shape[1]
cdef int w = unary_cost.shape[0]
cdef int n_labels = pairwise_cost.shape[0]
if (pairwise_cost != pairwise_cost.T).any():
raise ValueError("pairwise_cost must be symmetric.")
if costV.shape[0] != w or costH.shape[0] != w or costV.shape[1] != h or costH.shape[1] != h:
raise ValueError("incorrect costV or costH dimensions.")
cdef GCoptimizationGridGraph* gc = new GCoptimizationGridGraph(h, w, n_labels)
gc.setDataCost(<int*>unary_cost.data)
gc.setSmoothCostVH(<int*>pairwise_cost.data, <int*>costV.data, <int*>costH.data)
if label_cost > 0:
gc.setLabelCost(label_cost)
if algorithm == 'swap':
gc.swap(n_iter)
elif algorithm == 'expansion':
gc.expansion(n_iter)
else:
raise ValueError("algorithm should be either `swap` or `expansion`. Got: %s" % algorithm)
cdef np.npy_intp result_shape[2]
result_shape[0] = w
result_shape[1] = h
cdef np.ndarray[np.int32_t, ndim=2] result = np.PyArray_SimpleNew(2, result_shape, np.NPY_INT32)
cdef int * result_ptr = <int*>result.data
for i in xrange(w * h):
result_ptr[i] = gc.whatLabel(i)
del gc
return result
def cut_from_graph(np.ndarray[np.int32_t, ndim=2, mode='c'] edges,
np.ndarray[np.int32_t, ndim=2, mode='c'] unary_cost,
np.ndarray[np.int32_t, ndim=2, mode='c'] pairwise_cost,
label_cost=0, n_iter=5, algorithm='expansion'):
"""
Apply multi-label graphcuts to arbitrary graph given by `edges`.
Parameters
----------
edges: ndarray, int32, shape(n_edges, 2 or 3)
Rows correspond to edges in graph, given as vertex indices.
if edges is n_edges x 3 then third parameter is used as edge weight
unary_cost: ndarray, int32, shape=(n_vertices, n_labels)
Unary potentials
pairwise_cost: ndarray, int32, shape=(n_labels, n_labels)
Pairwise potentials for label compatibility
label_cost: int32
Cost per label. Penalizes the number of distinct labels in the solution
n_iter: int, (default=5)
Number of iterations
algorithm: string, `expansion` or `swap`, default=expansion
Whether to perform alpha-expansion or alpha-beta-swaps.
"""
if (pairwise_cost != pairwise_cost.T).any():
raise ValueError("pairwise_cost must be symmetric.")
if unary_cost.shape[1] != pairwise_cost.shape[0]:
raise ValueError("unary_cost and pairwise_cost have incompatible shapes.\n"
"unary_cost must be height x width x n_labels, pairwise_cost must be n_labels x n_labels.\n"
"Got: unary_cost: (%d, %d), pairwise_cost: (%d, %d)"
%(unary_cost.shape[0], unary_cost.shape[1],
pairwise_cost.shape[0], pairwise_cost.shape[1]))
if pairwise_cost.shape[1] != pairwise_cost.shape[0]:
raise ValueError("pairwise_cost must be a square matrix.")
cdef int n_vertices = unary_cost.shape[0]
cdef int n_labels = pairwise_cost.shape[0]
cdef GCoptimizationGeneralGraph* gc = new GCoptimizationGeneralGraph(n_vertices, n_labels)
for e in edges:
if e.shape[0] == 3:
gc.setNeighbors(e[0], e[1], e[2])
else:
gc.setNeighbors(e[0], e[1])
gc.setDataCost(<int*>unary_cost.data)
gc.setSmoothCost(<int*>pairwise_cost.data)
if label_cost > 0:
gc.setLabelCost(label_cost)
if algorithm == 'swap':
gc.swap(n_iter)
elif algorithm == 'expansion':
gc.expansion(n_iter)
else:
raise ValueError("algorithm should be either `swap` or `expansion`. Got: %s" % algorithm)
cdef np.npy_intp result_shape[1]
result_shape[0] = n_vertices
cdef np.ndarray[np.int32_t, ndim=1] result = np.PyArray_SimpleNew(1, result_shape, np.NPY_INT32)
cdef int * result_ptr = <int*>result.data
for i in xrange(n_vertices):
result_ptr[i] = gc.whatLabel(i)
del gc
return result
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