def estimate_from_landmark_maps(self, from_lm, to_lm):
"""
Estimate the similarity transform between two corresponding landmark
maps.
Landmarks with corresponding frame IDs in the two maps are paired for
transform estimation. Landmarks with no corresponding frame ID in the
other map are ignored. An exception is set if there are no shared frame
IDs between the two provided maps (nothing to pair).
:param from_lm: Map of original landmarks, sharing N frames with the
transformed landmarks, where N > 0.
:type from_lm: LandmarkMap
:param to_lm: Map of transformed landmarks, sharing N frames with the
original landmarks, where N > 0.
:type to_lm: LandmarkMap
:return: An estimated similarity transform mapping landmark centers in
the ``from`` space to camera centers in the ``to`` space.
:rtype: Similarity
"""
cptr = self._call_cfunc(
'vital_algorithm_estimate_similarity_transform_estimate_landmark_map',
[
self.C_TYPE_PTR,
LandmarkMap.c_ptr_type(),
LandmarkMap.c_ptr_type()
], [self, from_lm, to_lm], Similarity.c_ptr_type(ctypes.c_double),
{1: VitalAlgorithmException})
return Similarity(ctype=ctypes.c_double, from_cptr=cptr)
def estimate_from_landmark_maps(self, from_lm, to_lm):
"""
Estimate the similarity transform between two corresponding landmark
maps.
Landmarks with corresponding frame IDs in the two maps are paired for
transform estimation. Landmarks with no corresponding frame ID in the
other map are ignored. An exception is set if there are no shared frame
IDs between the two provided maps (nothing to pair).
:param from_lm: Map of original landmarks, sharing N frames with the
transformed landmarks, where N > 0.
:type from_lm: LandmarkMap
:param to_lm: Map of transformed landmarks, sharing N frames with the
original landmarks, where N > 0.
:type to_lm: LandmarkMap
:return: An estimated similarity transform mapping landmark centers in
the ``from`` space to camera centers in the ``to`` space.
:rtype: Similarity
"""
cptr = self._call_cfunc(
'vital_algorithm_estimate_similarity_transform_estimate_landmark_map',
[self.C_TYPE_PTR, LandmarkMap.c_ptr_type(), LandmarkMap.c_ptr_type()],
[self, from_lm, to_lm],
Similarity.c_ptr_type(ctypes.c_double),
{
1: VitalAlgorithmException
}
)
return Similarity(ctype=ctypes.c_double, from_cptr=cptr)
def estimate(self, cameras, landmarks):
"""
Estimate a canonical similarity transform for cameras and points
:param cameras: The camera map containing all the cameras
:type cameras: CameraMap
:param landmarks: The landmark map containing all the 3D landmarks
:type landmarks: LandmarkMap
:return: New estimated similarity transformation mapping the data to the
canonical space.
:rtype: Similarity
"""
cptr = self._call_cfunc(
'vital_algorithm_estimate_canonical_transform_estimate',
[self.C_TYPE_PTR, CameraMap.c_ptr_type(), LandmarkMap.c_ptr_type()],
[self, cameras, landmarks],
Similarity.c_ptr_type(ctypes.c_double),
{
1: VitalAlgorithmException
}
)
return Similarity(ctype=ctypes.c_double, from_cptr=cptr)
def estimate_from_points(self, from_pts, to_pts):
"""
Estimate the similarity transform between two corresponding point sets
:raises VitalAlgorithmException: from and to point sets are misaligned,
insufficient or degenerate
:param from_pts: Iterable of 3D points in the ``from`` space.
:type from_pts: collections.Iterable[EigenArray | collections.Sequence[float]]
:param to_pts: Iterable of 3D points in the ``to`` space.
:type to_pts: collections.Iterable[EigenArray | collections.Sequence[float]]
:return: New similarity instance
:rtype: Similarity
"""
ea_type = EigenArray.c_ptr_type(3, 1, ctypes.c_double)
# make C arrays from input points
from_list = [
EigenArray.from_iterable(p, target_shape=(3, 1)) for p in from_pts
]
to_list = [
EigenArray.from_iterable(p, target_shape=(3, 1)) for p in to_pts
]
if len(from_list) != len(to_list):
raise VitalAlgorithmException(
"From and to iterables not the same length: %d != %d" %
(len(from_list), len(to_list)))
n = len(from_list)
from_ptr_arr = (ea_type * n)()
for i, e in enumerate(from_list):
from_ptr_arr[i] = e.c_pointer
to_ptr_arr = (ea_type * n)()
for i, e in enumerate(to_list):
to_ptr_arr[i] = e.c_pointer
sim_ptr = self._call_cfunc(
'vital_algorithm_estimate_similarity_transform_estimate_transform_points',
[
self.C_TYPE_PTR, ctypes.c_size_t,
ctypes.POINTER(ea_type),
ctypes.POINTER(ea_type)
], [self, n, from_ptr_arr, to_ptr_arr],
Similarity.c_ptr_type(ctypes.c_double),
{1: VitalAlgorithmException})
return Similarity(ctype=ctypes.c_double, from_cptr=sim_ptr)
def estimate_from_points(self, from_pts, to_pts):
"""
Estimate the similarity transform between two corresponding point sets
:raises VitalAlgorithmException: from and to point sets are misaligned,
insufficient or degenerate
:param from_pts: Iterable of 3D points in the ``from`` space.
:type from_pts: collections.Iterable[EigenArray | collections.Sequence[float]]
:param to_pts: Iterable of 3D points in the ``to`` space.
:type to_pts: collections.Iterable[EigenArray | collections.Sequence[float]]
:return: New similarity instance
:rtype: Similarity
"""
ea_type = EigenArray.c_ptr_type(3, 1, ctypes.c_double)
# make C arrays from input points
from_list = [EigenArray.from_iterable(p, target_shape=(3, 1))
for p in from_pts]
to_list = [EigenArray.from_iterable(p, target_shape=(3, 1))
for p in to_pts]
if len(from_list) != len(to_list):
raise VitalAlgorithmException(
"From and to iterables not the same length: %d != %d"
% (len(from_list), len(to_list))
)
n = len(from_list)
from_ptr_arr = (ea_type * n)()
for i, e in enumerate(from_list):
from_ptr_arr[i] = e.c_pointer
to_ptr_arr = (ea_type * n)()
for i, e in enumerate(to_list):
to_ptr_arr[i] = e.c_pointer
sim_ptr = self._call_cfunc(
'vital_algorithm_estimate_similarity_transform_estimate_transform_points',
[self.C_TYPE_PTR, ctypes.c_size_t, ctypes.POINTER(ea_type),
ctypes.POINTER(ea_type)],
[self, n, from_ptr_arr, to_ptr_arr],
Similarity.c_ptr_type(ctypes.c_double),
{
1: VitalAlgorithmException
}
)
return Similarity(ctype=ctypes.c_double, from_cptr=sim_ptr)
def estimate(self, cameras, landmarks):
"""
Estimate a canonical similarity transform for cameras and points
:param cameras: The camera map containing all the cameras
:type cameras: CameraMap
:param landmarks: The landmark map containing all the 3D landmarks
:type landmarks: LandmarkMap
:return: New estimated similarity transformation mapping the data to the
canonical space.
:rtype: Similarity
"""
cptr = self._call_cfunc(
'vital_algorithm_estimate_canonical_transform_estimate', [
self.C_TYPE_PTR,
CameraMap.c_ptr_type(),
LandmarkMap.c_ptr_type()
], [self, cameras, landmarks],
Similarity.c_ptr_type(ctypes.c_double),
{1: VitalAlgorithmException})
return Similarity(ctype=ctypes.c_double, from_cptr=cptr)
