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m.overtheil 2014-12-15 08:55:15 +01:00
parent eb55c71236
commit 1e8ca0eb79
4 changed files with 258 additions and 42 deletions
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244
Spatial_searching/benchmark/Spatial_searching/include/nanoflann.hpp
View File

@ -3,7 +3,7 @@
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
* Copyright 2011-2013 Jose Luis Blanco (joseluisblancoc@gmail.com).
* Copyright 2011-2014 Jose Luis Blanco (joseluisblancoc@gmail.com).
* All rights reserved.
*
* THE BSD LICENSE
@ -56,7 +56,7 @@ namespace nanoflann
* @{ */
/** Library version: 0xMmP (M=Major,m=minor,P=path) */
#define NANOFLANN_VERSION 0x114
#define NANOFLANN_VERSION 0x118
/** @addtogroup result_sets_grp Result set classes
* @{ */
@ -150,7 +150,7 @@ namespace nanoflann
inline void addPoint(DistanceType dist, IndexType index)
{
if (dist<radius)
m_indices_dists.push_back(std::pair<IndexType,DistanceType>(index,dist));
m_indices_dists.push_back(std::make_pair(index,dist));
}
inline DistanceType worstDist() const { return radius; }
@ -282,9 +282,9 @@ namespace nanoflann
}
template <typename U, typename V>
inline DistanceType accum_dist(const U a, const V b, int dim) const
inline DistanceType accum_dist(const U a, const V b, int ) const
{
return (a-b)*(a-b);
return nanoflann::abs(a-b);
}
};
@ -331,7 +331,7 @@ namespace nanoflann
}
template <typename U, typename V>
inline DistanceType accum_dist(const U a, const V b, int dim) const
inline DistanceType accum_dist(const U a, const V b, int ) const
{
return (a-b)*(a-b);
}
@ -357,7 +357,7 @@ namespace nanoflann
}
template <typename U, typename V>
inline DistanceType accum_dist(const U a, const V b, int dim) const
inline DistanceType accum_dist(const U a, const V b, int ) const
{
return (a-b)*(a-b);
}
@ -409,7 +409,7 @@ namespace nanoflann
{
/** Note: The first argument (checks_IGNORED_) is ignored, but kept for compatibility with the FLANN interface */
SearchParams(int checks_IGNORED_ = 32, float eps_ = 0, bool sorted_ = true ) :
eps(eps_), sorted(sorted_) {}
checks(checks_IGNORED_), eps(eps_), sorted(sorted_) {}
int checks; //!< Ignored parameter (Kept for compatibility with the FLANN interface).
float eps; //!< search for eps-approximate neighbours (default: 0)
@ -467,6 +467,13 @@ namespace nanoflann
void* loc; /* Current location in block to next allocate memory. */
size_t blocksize;
void internal_init()
{
remaining = 0;
base = NULL;
usedMemory = 0;
wastedMemory = 0;
}
public:
size_t usedMemory;
@ -475,26 +482,26 @@ namespace nanoflann
/**
Default constructor. Initializes a new pool.
*/
PooledAllocator(const size_t blocksize = BLOCKSIZE)
{
this->blocksize = blocksize;
remaining = 0;
base = NULL;
usedMemory = 0;
wastedMemory = 0;
PooledAllocator(const size_t blocksize_ = BLOCKSIZE) : blocksize(blocksize_) {
internal_init();
}
/**
* Destructor. Frees all the memory allocated in this pool.
*/
~PooledAllocator()
~PooledAllocator() {
free_all();
}
/** Frees all allocated memory chunks */
void free_all()
{
while (base != NULL) {
void *prev = *((void**) base); /* Get pointer to prev block. */
::free(base);
base = prev;
}
internal_init();
}
/**
@ -563,6 +570,128 @@ namespace nanoflann
};
/** @} */
/** @addtogroup nanoflann_metaprog_grp Auxiliary metaprogramming stuff
* @{ */
// ---------------- CArray -------------------------
/** A STL container (as wrapper) for arrays of constant size defined at compile time (class imported from the MRPT project)
* This code is an adapted version from Boost, modifed for its integration
* within MRPT (JLBC, Dec/2009) (Renamed array -> CArray to avoid possible potential conflicts).
* See
* http://www.josuttis.com/cppcode
* for details and the latest version.
* See
* http://www.boost.org/libs/array for Documentation.
* for documentation.
*
* (C) Copyright Nicolai M. Josuttis 2001.
* Permission to copy, use, modify, sell and distribute this software
* is granted provided this copyright notice appears in all copies.
* This software is provided "as is" without express or implied
* warranty, and with no claim as to its suitability for any purpose.
*
* 29 Jan 2004 - minor fixes (Nico Josuttis)
* 04 Dec 2003 - update to synch with library TR1 (Alisdair Meredith)
* 23 Aug 2002 - fix for Non-MSVC compilers combined with MSVC libraries.
* 05 Aug 2001 - minor update (Nico Josuttis)
* 20 Jan 2001 - STLport fix (Beman Dawes)
* 29 Sep 2000 - Initial Revision (Nico Josuttis)
*
* Jan 30, 2004
*/
template <typename T, std::size_t N>
class CArray {
public:
T elems[N]; // fixed-size array of elements of type T
public:
// type definitions
typedef T value_type;
typedef T* iterator;
typedef const T* const_iterator;
typedef T& reference;
typedef const T& const_reference;
typedef std::size_t size_type;
typedef std::ptrdiff_t difference_type;
// iterator support
inline iterator begin() { return elems; }
inline const_iterator begin() const { return elems; }
inline iterator end() { return elems+N; }
inline const_iterator end() const { return elems+N; }
// reverse iterator support
#if !defined(BOOST_NO_TEMPLATE_PARTIAL_SPECIALIZATION) && !defined(BOOST_MSVC_STD_ITERATOR) && !defined(BOOST_NO_STD_ITERATOR_TRAITS)
typedef std::reverse_iterator<iterator> reverse_iterator;
typedef std::reverse_iterator<const_iterator> const_reverse_iterator;
#elif defined(_MSC_VER) && (_MSC_VER == 1300) && defined(BOOST_DINKUMWARE_STDLIB) && (BOOST_DINKUMWARE_STDLIB == 310)
// workaround for broken reverse_iterator in VC7
typedef std::reverse_iterator<std::_Ptrit<value_type, difference_type, iterator,
reference, iterator, reference> > reverse_iterator;
typedef std::reverse_iterator<std::_Ptrit<value_type, difference_type, const_iterator,
const_reference, iterator, reference> > const_reverse_iterator;
#else
// workaround for broken reverse_iterator implementations
typedef std::reverse_iterator<iterator,T> reverse_iterator;
typedef std::reverse_iterator<const_iterator,T> const_reverse_iterator;
#endif
reverse_iterator rbegin() { return reverse_iterator(end()); }
const_reverse_iterator rbegin() const { return const_reverse_iterator(end()); }
reverse_iterator rend() { return reverse_iterator(begin()); }
const_reverse_iterator rend() const { return const_reverse_iterator(begin()); }
// operator[]
inline reference operator[](size_type i) { return elems[i]; }
inline const_reference operator[](size_type i) const { return elems[i]; }
// at() with range check
reference at(size_type i) { rangecheck(i); return elems[i]; }
const_reference at(size_type i) const { rangecheck(i); return elems[i]; }
// front() and back()
reference front() { return elems[0]; }
const_reference front() const { return elems[0]; }
reference back() { return elems[N-1]; }
const_reference back() const { return elems[N-1]; }
// size is constant
static inline size_type size() { return N; }
static bool empty() { return false; }
static size_type max_size() { return N; }
enum { static_size = N };
/** This method has no effects in this class, but raises an exception if the expected size does not match */
inline void resize(const size_t nElements) { if (nElements!=N) throw std::logic_error("Try to change the size of a CArray."); }
// swap (note: linear complexity in N, constant for given instantiation)
void swap (CArray<T,N>& y) { std::swap_ranges(begin(),end(),y.begin()); }
// direct access to data (read-only)
const T* data() const { return elems; }
// use array as C array (direct read/write access to data)
T* data() { return elems; }
// assignment with type conversion
template <typename T2> CArray<T,N>& operator= (const CArray<T2,N>& rhs) {
std::copy(rhs.begin(),rhs.end(), begin());
return *this;
}
// assign one value to all elements
inline void assign (const T& value) { for (size_t i=0;i<N;i++) elems[i]=value; }
// assign (compatible with std::vector's one) (by JLBC for MRPT)
void assign (const size_t n, const T& value) { assert(N==n); for (size_t i=0;i<N;i++) elems[i]=value; }
private:
// check range (may be private because it is static)
static void rangecheck (size_type i) { if (i >= size()) { throw std::out_of_range("CArray<>: index out of range"); } }
}; // end of CArray
/** Used to declare fixed-size arrays when DIM>0, dynamically-allocated vectors when DIM=-1.
* Fixed size version for a generic DIM:
*/
template <int DIM, typename T>
struct array_or_vector_selector
{
typedef CArray<T,DIM> container_t;
};
/** Dynamic size version */
template <typename T>
struct array_or_vector_selector<-1,T> {
typedef std::vector<T> container_t;
};
/** @} */
/** @addtogroup kdtrees_grp KD-tree classes and adaptors
* @{ */
@ -603,6 +732,9 @@ namespace nanoflann
template <typename Distance, class DatasetAdaptor,int DIM = -1, typename IndexType = size_t>
class KDTreeSingleIndexAdaptor
{
private:
/** Hidden copy constructor, to disallow copying indices (Not implemented) */
KDTreeSingleIndexAdaptor(const KDTreeSingleIndexAdaptor<Distance,DatasetAdaptor,DIM,IndexType>&);
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::DistanceType DistanceType;
@ -663,8 +795,11 @@ namespace nanoflann
ElementType low, high;
};
typedef std::vector<Interval> BoundingBox;
/** Define "BoundingBox" as a fixed-size or variable-size container depending on "DIM" */
typedef typename array_or_vector_selector<DIM,Interval>::container_t BoundingBox;
/** Define "distance_vector_t" as a fixed-size or variable-size container depending on "DIM" */
typedef typename array_or_vector_selector<DIM,DistanceType>::container_t distance_vector_t;
/** This record represents a branch point when finding neighbors in
the tree. It contains a record of the minimum distance to the query
@ -706,6 +841,8 @@ namespace nanoflann
public:
Distance distance;
int items,leafs,internals;
mutable int count_items,count_leafs,count_internals;
/**
* KDTree constructor
@ -719,6 +856,8 @@ namespace nanoflann
{
m_size = dataset.kdtree_get_point_count();
dim = dimensionality;
items=0,leafs=0,internals=0;
count_items=0,count_leafs=0,count_internals=0;
if (DIM>0) dim=DIM;
else {
if (params.dim>0) dim = params.dim;
@ -736,14 +875,33 @@ namespace nanoflann
{
}
//memory usage
void print_mem_usage()
{
CGAL::Memory_sizer mem;
std::cerr<< "Virtual memory: "<<mem.virtual_size()<<"\n Resident memory: "<<mem.resident_size()<<std::endl;
}
/** Frees the previously-built index. Automatically called within buildIndex(). */
void freeIndex()
{
pool.free_all();
root_node=NULL;
}
/**
* Builds the index
*/
void buildIndex()
{
init_vind();
freeIndex();
if(m_size == 0) return;
computeBoundingBox(root_bbox);
root_node = divideTree(0, m_size, root_bbox ); // construct the tree
root_node = divideTree(0, m_size, root_bbox );
std::cerr << "Size of node : " << sizeof(*root_node) << std::endl;
std::cerr << "Size of container : " << sizeof(pool) + (pool.usedMemory + pool.wastedMemory) << std::endl;
std::cerr << "Size of vector pts : " << sizeof(vind) + sizeof(IndexType) * vind.size() << std::endl;// construct the tree
}
/**
@ -789,10 +947,11 @@ namespace nanoflann
void findNeighbors(RESULTSET& result, const ElementType* vec, const SearchParams& searchParams) const
{
assert(vec);
if (!root_node) throw std::runtime_error("[nanoflann] findNeighbors() called before building the index.");
if (!root_node) throw std::runtime_error("[nanoflann] findNeighbors() called before building the index or no data points.");
float epsError = 1+searchParams.eps;
std::vector<DistanceType> dists( (DIM>0 ? DIM : dim) ,0);
distance_vector_t dists; // fixed or variable-sized container (depending on DIM)
dists.assign((DIM>0 ? DIM : dim) ,0); // Fill it with zeros.
DistanceType distsq = computeInitialDistances(vec, dists);
searchLevel(result, vec, root_node, distsq, dists, epsError); // "count_leaf" parameter removed since was neither used nor returned to the user.
}
@ -803,7 +962,7 @@ namespace nanoflann
* \sa radiusSearch, findNeighbors
* \note nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.
*/
inline void knnSearch(const ElementType *query_point, const size_t num_closest, IndexType *out_indices, DistanceType *out_distances_sq, const int nChecks_IGNORED = 10) const
inline void knnSearch(const ElementType *query_point, const size_t num_closest, IndexType *out_indices, DistanceType *out_distances_sq,const int nChecks_IGNORED = 10) const
{
nanoflann::KNNResultSet<DistanceType,IndexType> resultSet(num_closest);
resultSet.init(out_indices, out_distances_sq);
@ -833,6 +992,18 @@ namespace nanoflann
return resultSet.size();
}
inline int depth(){
return depth(root_node);
}
inline int depth(NodePtr node){
if(node->child1 == NULL && node->child2 == NULL)
return 1;
else{
return std::max(depth(node->child1)+1,depth(node->child2)+1);
}
}
/** @} */
private:
@ -841,12 +1012,9 @@ namespace nanoflann
{
// Create a permutable array of indices to the input vectors.
m_size = dataset.kdtree_get_point_count();
if (vind.size()!=m_size)
{
vind.resize(m_size);
if (vind.size()!=m_size) vind.resize(m_size);
for (size_t i = 0; i < m_size; i++) vind[i] = i;
}
}
/// Helper accessor to the dataset points:
inline ElementType dataset_get(size_t idx, int component) const {
@ -888,11 +1056,12 @@ namespace nanoflann
}
else
{
const size_t N = dataset.kdtree_get_point_count();
if (!N) throw std::runtime_error("[nanoflann] computeBoundingBox() called but no data points found.");
for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
bbox[i].low =
bbox[i].high = dataset_get(0,i);
}
const size_t N = dataset.kdtree_get_point_count();
for (size_t k=1; k<N; ++k) {
for (int i=0; i<(DIM>0 ? DIM : dim); ++i) {
if (dataset_get(k,i)<bbox[i].low) bbox[i].low = dataset_get(k,i);
@ -918,6 +1087,9 @@ namespace nanoflann
/* If too few exemplars remain, then make this a leaf node. */
if ( (right-left) <= m_leaf_max_size) {
leafs++;
items+=right-left;
node->child1 = node->child2 = NULL; /* Mark as leaf node. */
node->lr.left = left;
node->lr.right = right;
@ -935,6 +1107,8 @@ namespace nanoflann
}
}
else {
internals++;
IndexType idx;
int cutfeat;
DistanceType cutval;
@ -1098,7 +1272,7 @@ namespace nanoflann
lim2 = left;
}
DistanceType computeInitialDistances(const ElementType* vec, std::vector<DistanceType>& dists) const
DistanceType computeInitialDistances(const ElementType* vec, distance_vector_t& dists) const
{
assert(vec);
DistanceType distsq = 0.0;
@ -1121,16 +1295,20 @@ namespace nanoflann
* Performs an exact search in the tree starting from a node.
* \tparam RESULTSET Should be any ResultSet<DistanceType>
*/
template <class RESULTSET>
void searchLevel(RESULTSET& result_set, const ElementType* vec, const NodePtr node, DistanceType mindistsq,
std::vector<DistanceType>& dists, const float epsError) const
distance_vector_t& dists, const float epsError) const
{
/* If this is a leaf node, then do check and return. */
if ((node->child1 == NULL)&&(node->child2 == NULL)) {
count_leafs++;
//count_leaf += (node->lr.right-node->lr.left); // Removed since was neither used nor returned to the user.
DistanceType worst_dist = result_set.worstDist();
for (IndexType i=node->lr.left; i<node->lr.right; ++i) {
const IndexType index = vind[i];// reorder... : i;
count_items++;
DistanceType dist = distance(vec, index, (DIM>0 ? DIM : dim));
if (dist<worst_dist) {
result_set.addPoint(dist,vind[i]);
@ -1138,7 +1316,7 @@ namespace nanoflann
}
return;
}
count_internals++;
/* Which child branch should be taken first? */
int idx = node->sub.divfeat;
ElementType val = vec[idx];
@ -1225,7 +1403,7 @@ namespace nanoflann
template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>
struct KDTreeEigenMatrixAdaptor
{
typedef KDTreeEigenMatrixAdaptor<MatrixType,DIM,Distance> self_t;
typedef KDTreeEigenMatrixAdaptor<MatrixType,DIM,Distance,IndexType> self_t;
typedef typename MatrixType::Scalar num_t;
typedef typename Distance::template traits<num_t,self_t>::distance_t metric_t;
typedef KDTreeSingleIndexAdaptor< metric_t,self_t,DIM,IndexType> index_t;
@ -1241,6 +1419,10 @@ namespace nanoflann
index = new index_t( dims, *this /* adaptor */, nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size, dims ) );
index->buildIndex();
}
private:
/** Hidden copy constructor, to disallow copying this class (Not implemented) */
KDTreeEigenMatrixAdaptor(const self_t&);
public:
~KDTreeEigenMatrixAdaptor() {
delete index;

17
Spatial_searching/benchmark/Spatial_searching/nn3cgal.cpp
View File

@ -85,12 +85,15 @@ int main(int argc,char *argv[])
double sum=0;
bool dump = true;
for(int i = 0 ; i<runs; ++i){
t.reset();t.start();
for(Points::iterator it = query_points_3.begin(); it != query_points_3.end(); ++it) {
// Initialize the search structure, and search all NN_number neighbors
t.reset();t.start();
Neighbor_search_3 search(tree, *it, NN_number);
t.stop();
int i=0;
for (Neighbor_search_3::iterator it = search.begin(); it != search.end();it++, i++) {
result[i] = it->first;
@ -99,10 +102,18 @@ int main(int argc,char *argv[])
}
}
dump = false;
}
t.stop();
sum += t.time();
items+=search.items_visited();
leafs+=search.leafs_visited();
internals+=search.internals_visited();
}
}
std::cerr << items <<" items\n";
std::cerr << leafs <<" leaf\n";
std::cerr << internals <<" internals visited\n";
std::cerr<<std::endl << "total: " << sum << " sec\n";
if(runs>1){
std::cerr << "average: " << sum/runs << " sec\n";

22
Spatial_searching/benchmark/Spatial_searching/nn3nanoflan.cpp
View File

@ -132,7 +132,12 @@ void kdtree_demo(int argc, char** argv)
timer.stop();
std::cout << "construction time: " << timer.time() << " sec" << std::endl;
std::cerr << "Tree statistics:" << std::endl;
std::cerr << "Number of items stored: "
<< index.items << std::endl;
std::cerr << "Number of nodes: "
<< index.internals << std::endl;
std::cerr << " Tree depth: " << index.depth() << std::endl;
// do a knn search
const size_t num_results = 10;
size_t ret_index[num_results];
@ -144,8 +149,7 @@ void kdtree_demo(int argc, char** argv)
double sum = 0;
for(int i=0;i<runs;++i){
timer.reset();
timer.start();
nanoflann::KNNResultSet<num_t> resultSet(num_results);
for(int i = 0 ; i < size; i++){
@ -153,17 +157,25 @@ void kdtree_demo(int argc, char** argv)
query_pt[1] = queries[i].y;
query_pt[2] = queries[i].z;
resultSet.init(ret_index, out_dist_sqr );
timer.reset();
timer.start();
index.findNeighbors(resultSet, &query_pt[0], nanoflann::SearchParams(10,0));
timer.stop();
for (int k=0; k<num_results; ++k){
if(dump)
std::cerr <<cloud.pts[ret_index[k]] << std::endl;
}
dump=false;
}
timer.stop();
sum += timer.time();
}
}
std::cerr << index.count_items <<" items\n";
std::cerr << index.count_leafs <<" leaf\n";
std::cerr << index.count_internals <<" internals visited\n";
std::cerr<<std::endl << "total: " << sum << " sec\n";
if(runs>1){
std::cerr << "average: " << sum/runs << " sec\n";

11
Spatial_searching/include/CGAL/internal/K_neighbor_search.h
View File

@ -165,6 +165,17 @@ public:
return s;
}
int internals_visited(){
return number_of_internal_nodes_visited;
}
int leafs_visited(){
return number_of_leaf_nodes_visited;
}
int items_visited(){
return number_of_items_visited;
}
}; // class
}} // namespace CGAL::internal