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knn.pyx
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# distutils: language = c++
# distutils: sources = knn.cxx
import numpy as np
cimport numpy as np
import cython
cdef extern from "knn_.h":
void cpp_knn(const float* points, const size_t npts, const size_t dim,
const float* queries, const size_t nqueries,
const size_t K, long* indices)
void cpp_knn_omp(const float* points, const size_t npts, const size_t dim,
const float* queries, const size_t nqueries,
const size_t K, long* indices)
void cpp_knn_batch(const float* batch_data, const size_t batch_size, const size_t npts, const size_t dim,
const float* queries, const size_t nqueries,
const size_t K, long* batch_indices)
void cpp_knn_batch_omp(const float* batch_data, const size_t batch_size, const size_t npts, const size_t dim,
const float* queries, const size_t nqueries,
const size_t K, long* batch_indices)
void cpp_knn_batch_distance_pick(const float* batch_data, const size_t batch_size, const size_t npts, const size_t dim,
float* queries, const size_t nqueries,
const size_t K, long* batch_indices)
void cpp_knn_batch_distance_pick_omp(const float* batch_data, const size_t batch_size, const size_t npts, const size_t dim,
float* batch_queries, const size_t nqueries,
const size_t K, long* batch_indices)
def knn(pts, queries, K, omp=False):
# define shape parameters
cdef int npts
cdef int dim
cdef int K_cpp
cdef int nqueries
# define tables
cdef np.ndarray[np.float32_t, ndim=2] pts_cpp
cdef np.ndarray[np.float32_t, ndim=2] queries_cpp
cdef np.ndarray[np.int64_t, ndim=2] indices_cpp
# set shape values
npts = pts.shape[0]
nqueries = queries.shape[0]
dim = pts.shape[1]
K_cpp = K
# create indices tensor
indices = np.zeros((queries.shape[0], K), dtype=np.int64)
pts_cpp = np.ascontiguousarray(pts, dtype=np.float32)
queries_cpp = np.ascontiguousarray(queries, dtype=np.float32)
indices_cpp = indices
# normal estimation
if omp:
cpp_knn_omp(<float*> pts_cpp.data, npts, dim,
<float*> queries_cpp.data, nqueries,
K_cpp, <long*> indices_cpp.data)
else:
cpp_knn(<float*> pts_cpp.data, npts, dim,
<float*> queries_cpp.data, nqueries,
K_cpp, <long*> indices_cpp.data)
return indices
def knn_batch(pts, queries, K, omp=False):
# define shape parameters
cdef int batch_size
cdef int npts
cdef int nqueries
cdef int K_cpp
cdef int dim
# define tables
cdef np.ndarray[np.float32_t, ndim=3] pts_cpp
cdef np.ndarray[np.float32_t, ndim=3] queries_cpp
cdef np.ndarray[np.int64_t, ndim=3] indices_cpp
# set shape values
batch_size = pts.shape[0]
npts = pts.shape[1]
dim = pts.shape[2]
nqueries = queries.shape[1]
K_cpp = K
# create indices tensor
indices = np.zeros((pts.shape[0], queries.shape[1], K), dtype=np.int64)
pts_cpp = np.ascontiguousarray(pts, dtype=np.float32)
queries_cpp = np.ascontiguousarray(queries, dtype=np.float32)
indices_cpp = indices
# normal estimation
if omp:
cpp_knn_batch_omp(<float*> pts_cpp.data, batch_size, npts, dim,
<float*> queries_cpp.data, nqueries,
K_cpp, <long*> indices_cpp.data)
else:
cpp_knn_batch(<float*> pts_cpp.data, batch_size, npts, dim,
<float*> queries_cpp.data, nqueries,
K_cpp, <long*> indices_cpp.data)
return indices
def knn_batch_distance_pick(pts, nqueries, K, omp=False):
# define shape parameters
cdef int batch_size
cdef int npts
cdef int nqueries_cpp
cdef int K_cpp
cdef int dim
# define tables
cdef np.ndarray[np.float32_t, ndim=3] pts_cpp
cdef np.ndarray[np.float32_t, ndim=3] queries_cpp
cdef np.ndarray[np.int64_t, ndim=3] indices_cpp
# set shape values
batch_size = pts.shape[0]
npts = pts.shape[1]
dim = pts.shape[2]
nqueries_cpp = nqueries
K_cpp = K
# create indices tensor
indices = np.zeros((pts.shape[0], nqueries, K), dtype=np.long)
queries = np.zeros((pts.shape[0], nqueries, dim), dtype=np.float32)
pts_cpp = np.ascontiguousarray(pts, dtype=np.float32)
queries_cpp = np.ascontiguousarray(queries, dtype=np.float32)
indices_cpp = indices
if omp:
cpp_knn_batch_distance_pick_omp(<float*> pts_cpp.data, batch_size, npts, dim,
<float*> queries_cpp.data, nqueries,
K_cpp, <long*> indices_cpp.data)
else:
cpp_knn_batch_distance_pick(<float*> pts_cpp.data, batch_size, npts, dim,
<float*> queries_cpp.data, nqueries,
K_cpp, <long*> indices_cpp.data)
return indices, queries