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# -*- coding: utf-8 -*-
cimport pcl_defs as cpp
cimport pcl_kdtree as pclkdt
cdef class KdTreeFLANN:
"""
Finds k nearest neighbours from points in another pointcloud to points in
a reference pointcloud.
Must be constructed from the reference point cloud, which is copied, so
changed to pc are not reflected in KdTreeFLANN(pc).
"""
cdef pclkdt.KdTreeFLANN_t *me
def __cinit__(self, PointCloud pc not None):
self.me = new pclkdt.KdTreeFLANN_t()
self.me.setInputCloud(pc.thisptr_shared)
def __dealloc__(self):
del self.me
def nearest_k_search_for_cloud(self, PointCloud pc not None, int k=1):
"""
Find the k nearest neighbours and squared distances for all points
in the pointcloud. Results are in ndarrays, size (pc.size, k)
Returns: (k_indices, k_sqr_distances)
"""
cdef cnp.npy_intp n_points = pc.size
cdef cnp.ndarray[float, ndim=2] sqdist = np.zeros((n_points, k),
dtype=np.float32)
cdef cnp.ndarray[int, ndim=2] ind = np.zeros((n_points, k),
dtype=np.int32)
for i in range(n_points):
self._nearest_k(pc, i, k, ind[i], sqdist[i])
return ind, sqdist
def nearest_k_search_for_point(self, PointCloud pc not None, int index,
int k=1):
"""
Find the k nearest neighbours and squared distances for the point
at pc[index]. Results are in ndarrays, size (k)
Returns: (k_indices, k_sqr_distances)
"""
cdef cnp.ndarray[float] sqdist = np.zeros(k, dtype=np.float32)
cdef cnp.ndarray[int] ind = np.zeros(k, dtype=np.int32)
self._nearest_k(pc, index, k, ind, sqdist)
return ind, sqdist
@cython.boundscheck(False)
cdef void _nearest_k(self, PointCloud pc, int index, int k,
cnp.ndarray[ndim=1, dtype=int, mode='c'] ind,
cnp.ndarray[ndim=1, dtype=float, mode='c'] sqdist
) except +:
# k nearest neighbors query for a single point.
cdef vector[int] k_indices
cdef vector[float] k_sqr_distances
k_indices.resize(k)
k_sqr_distances.resize(k)
self.me.nearestKSearch(pc.thisptr()[0], index, k, k_indices,
k_sqr_distances)
for i in range(k):
sqdist[i] = k_sqr_distances[i]
ind[i] = k_indices[i]
def radius_search_for_cloud(self, PointCloud pc not None, double radius, unsigned int max_nn = 0):
"""
Find the radius and squared distances for all points
in the pointcloud. Results are in ndarrays, size (pc.size, k)
Returns: (radius_indices, radius_distances)
"""
k = max_nn
cdef cnp.npy_intp n_points = pc.size
cdef cnp.ndarray[float, ndim=2] sqdist = np.zeros((n_points, k),
dtype=np.float32)
cdef cnp.ndarray[int, ndim=2] ind = np.zeros((n_points, k),
dtype=np.int32)
for i in range(n_points):
self._search_radius(pc, i, k, radius, ind[i], sqdist[i])
return ind, sqdist
@cython.boundscheck(False)
cdef void _search_radius(self, PointCloud pc, int index, int k, double radius,
cnp.ndarray[ndim=1, dtype=int, mode='c'] ind,
cnp.ndarray[ndim=1, dtype=float, mode='c'] sqdist
) except +:
# radius query for a single point.
cdef vector[int] radius_indices
cdef vector[float] radius_distances
radius_indices.resize(k)
radius_distances.resize(k)
self.me.radiusSearch(pc.thisptr()[0], index, radius, radius_indices, radius_distances)
for i in range(k):
sqdist[i] = radius_distances[i]
ind[i] = radius_indices[i]
cdef class KdTreeFLANN_PointXYZI:
"""
Finds k nearest neighbours from points in another pointcloud to points in
a reference pointcloud.
Must be constructed from the reference point cloud, which is copied, so
changed to pc are not reflected in KdTreeFLANN(pc).
"""
cdef pclkdt.KdTreeFLANN_PointXYZI_t *me
def __cinit__(self, PointCloud_PointXYZI pc not None):
self.me = new pclkdt.KdTreeFLANN_PointXYZI_t()
self.me.setInputCloud(pc.thisptr_shared)
def __dealloc__(self):
del self.me
def nearest_k_search_for_cloud(self, PointCloud_PointXYZI pc not None, int k=1):
"""
Find the k nearest neighbours and squared distances for all points
in the pointcloud. Results are in ndarrays, size (pc.size, k)
Returns: (k_indices, k_sqr_distances)
"""
cdef cnp.npy_intp n_points = pc.size
cdef cnp.ndarray[float, ndim=2] sqdist = np.zeros((n_points, k),
dtype=np.float32)
cdef cnp.ndarray[int, ndim=2] ind = np.zeros((n_points, k),
dtype=np.int32)
for i in range(n_points):
self._nearest_k(pc, i, k, ind[i], sqdist[i])
return ind, sqdist
def nearest_k_search_for_point(self, PointCloud_PointXYZI pc not None, int index,
int k=1):
"""
Find the k nearest neighbours and squared distances for the point
at pc[index]. Results are in ndarrays, size (k)
Returns: (k_indices, k_sqr_distances)
"""
cdef cnp.ndarray[float] sqdist = np.zeros(k, dtype=np.float32)
cdef cnp.ndarray[int] ind = np.zeros(k, dtype=np.int32)
self._nearest_k(pc, index, k, ind, sqdist)
return ind, sqdist
@cython.boundscheck(False)
cdef void _nearest_k(self, PointCloud_PointXYZI pc, int index, int k,
cnp.ndarray[ndim=1, dtype=int, mode='c'] ind,
cnp.ndarray[ndim=1, dtype=float, mode='c'] sqdist
) except +:
# k nearest neighbors query for a single point.
cdef vector[int] k_indices
cdef vector[float] k_sqr_distances
k_indices.resize(k)
k_sqr_distances.resize(k)
self.me.nearestKSearch(pc.thisptr()[0], index, k, k_indices,
k_sqr_distances)
for i in range(k):
sqdist[i] = k_sqr_distances[i]
ind[i] = k_indices[i]
cdef class KdTreeFLANN_PointXYZRGB:
"""
Finds k nearest neighbours from points in another pointcloud to points in
a reference pointcloud.
Must be constructed from the reference point cloud, which is copied, so
changed to pc are not reflected in KdTreeFLANN(pc).
"""
cdef pclkdt.KdTreeFLANN_PointXYZRGB_t *me
def __cinit__(self, PointCloud_PointXYZRGB pc not None):
self.me = new pclkdt.KdTreeFLANN_PointXYZRGB_t()
self.me.setInputCloud(pc.thisptr_shared)
def __dealloc__(self):
del self.me
def nearest_k_search_for_cloud(self, PointCloud_PointXYZRGB pc not None, int k=1):
"""
Find the k nearest neighbours and squared distances for all points
in the pointcloud. Results are in ndarrays, size (pc.size, k)
Returns: (k_indices, k_sqr_distances)
"""
cdef cnp.npy_intp n_points = pc.size
cdef cnp.ndarray[float, ndim=2] sqdist = np.zeros((n_points, k),
dtype=np.float32)
cdef cnp.ndarray[int, ndim=2] ind = np.zeros((n_points, k),
dtype=np.int32)
for i in range(n_points):
self._nearest_k(pc, i, k, ind[i], sqdist[i])
return ind, sqdist
def nearest_k_search_for_point(self, PointCloud_PointXYZRGB pc not None, int index,
int k=1):
"""
Find the k nearest neighbours and squared distances for the point
at pc[index]. Results are in ndarrays, size (k)
Returns: (k_indices, k_sqr_distances)
"""
cdef cnp.ndarray[float] sqdist = np.zeros(k, dtype=np.float32)
cdef cnp.ndarray[int] ind = np.zeros(k, dtype=np.int32)
self._nearest_k(pc, index, k, ind, sqdist)
return ind, sqdist
@cython.boundscheck(False)
cdef void _nearest_k(self, PointCloud_PointXYZRGB pc, int index, int k,
cnp.ndarray[ndim=1, dtype=int, mode='c'] ind,
cnp.ndarray[ndim=1, dtype=float, mode='c'] sqdist
) except +:
# k nearest neighbors query for a single point.
cdef vector[int] k_indices
cdef vector[float] k_sqr_distances
k_indices.resize(k)
k_sqr_distances.resize(k)
self.me.nearestKSearch(pc.thisptr()[0], index, k, k_indices,
k_sqr_distances)
for i in range(k):
sqdist[i] = k_sqr_distances[i]
ind[i] = k_indices[i]
cdef class KdTreeFLANN_PointXYZRGBA:
"""
Finds k nearest neighbours from points in another pointcloud to points in
a reference pointcloud.
Must be constructed from the reference point cloud, which is copied, so
changed to pc are not reflected in KdTreeFLANN(pc).
"""
cdef pclkdt.KdTreeFLANN_PointXYZRGBA_t *me
def __cinit__(self, PointCloud_PointXYZRGBA pc not None):
self.me = new pclkdt.KdTreeFLANN_PointXYZRGBA_t()
self.me.setInputCloud(pc.thisptr_shared)
def __dealloc__(self):
del self.me
def nearest_k_search_for_cloud(self, PointCloud_PointXYZRGBA pc not None, int k=1):
"""
Find the k nearest neighbours and squared distances for all points
in the pointcloud. Results are in ndarrays, size (pc.size, k)
Returns: (k_indices, k_sqr_distances)
"""
cdef cnp.npy_intp n_points = pc.size
cdef cnp.ndarray[float, ndim=2] sqdist = np.zeros((n_points, k),
dtype=np.float32)
cdef cnp.ndarray[int, ndim=2] ind = np.zeros((n_points, k),
dtype=np.int32)
for i in range(n_points):
self._nearest_k(pc, i, k, ind[i], sqdist[i])
return ind, sqdist
def nearest_k_search_for_point(self, PointCloud_PointXYZRGBA pc not None, int index,
int k=1):
"""
Find the k nearest neighbours and squared distances for the point
at pc[index]. Results are in ndarrays, size (k)
Returns: (k_indices, k_sqr_distances)
"""
cdef cnp.ndarray[float] sqdist = np.zeros(k, dtype=np.float32)
cdef cnp.ndarray[int] ind = np.zeros(k, dtype=np.int32)
self._nearest_k(pc, index, k, ind, sqdist)
return ind, sqdist
@cython.boundscheck(False)
cdef void _nearest_k(self, PointCloud_PointXYZRGBA pc, int index, int k,
cnp.ndarray[ndim=1, dtype=int, mode='c'] ind,
cnp.ndarray[ndim=1, dtype=float, mode='c'] sqdist
) except +:
# k nearest neighbors query for a single point.
cdef vector[int] k_indices
cdef vector[float] k_sqr_distances
k_indices.resize(k)
k_sqr_distances.resize(k)
self.me.nearestKSearch(pc.thisptr()[0], index, k, k_indices,
k_sqr_distances)
for i in range(k):
sqdist[i] = k_sqr_distances[i]
ind[i] = k_indices[i]
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