libnabo 1.0.7
nabo_private.h
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1/*
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3Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland
4You can contact the author at <stephane at magnenat dot net>
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30*/
31
32#ifndef __NABO_PRIVATE_H
33#define __NABO_PRIVATE_H
34
35#include "nabo.h"
36
37#include <cstdint>
38using std::uint32_t;
39
40// OpenCL
41#ifdef HAVE_OPENCL
42 #define __CL_ENABLE_EXCEPTIONS
43 #include "CL/cl.hpp"
44#endif // HAVE_OPENCL
45
46// Unused macro, add support for your favorite compiler
47#if defined(__GNUC__)
48 #define _UNUSED __attribute__ ((unused))
49#else
50 #define _UNUSED
51#endif
52
58namespace Nabo
59{
61
62
64 template<typename T, typename A, typename B>
65 inline T dist2(const A& v0, const B& v1)
66 {
67 return (v0 - v1).squaredNorm();
68 }
69
71 template<typename T, typename CloudType = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> >
72 struct BruteForceSearch : public NearestNeighbourSearch<T, CloudType>
73 {
77 typedef typename NearestNeighbourSearch<T, CloudType>::IndexVector IndexVector;
78 typedef typename NearestNeighbourSearch<T, CloudType>::IndexMatrix IndexMatrix;
79
85
87 BruteForceSearch(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags);
88 virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const;
89 virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Vector& maxRadii, const Index k = 1, const T epsilon = 0, const unsigned optionFlags = 0) const;
90 };
91
93 template<typename T, typename Heap, typename CloudType = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> >
95 {
99 typedef typename NearestNeighbourSearch<T, CloudType>::IndexVector IndexVector;
100 typedef typename NearestNeighbourSearch<T, CloudType>::IndexMatrix IndexMatrix;
101
108
109 protected:
111 typedef std::vector<Index> BuildPoints;
113 typedef typename BuildPoints::iterator BuildPointsIt;
115 typedef typename BuildPoints::const_iterator BuildPointsCstIt;
116
118 const unsigned bucketSize;
119
121 const uint32_t dimBitCount;
123 const uint32_t dimMask;
124
126 inline uint32_t createDimChildBucketSize(const uint32_t dim, const uint32_t childIndex) const
127 { return dim | (childIndex << dimBitCount); }
129 inline uint32_t getDim(const uint32_t dimChildBucketSize) const
130 { return dimChildBucketSize & dimMask; }
132 inline uint32_t getChildBucketSize(const uint32_t dimChildBucketSize) const
133 { return dimChildBucketSize >> dimBitCount; }
134
135 struct BucketEntry;
136
138 struct Node
139 {
141 union
142 {
144 uint32_t bucketIndex;
145 };
146
148 Node(const uint32_t dimChild, const T cutVal):
149 dimChildBucketSize(dimChild), cutVal(cutVal) {}
151 Node(const uint32_t bucketSize, const uint32_t bucketIndex):
153 };
155 typedef std::vector<Node> Nodes;
156
159 {
160 const T* pt;
161 Index index;
162
164
167 BucketEntry(const T* pt = 0, const Index index = 0): pt(pt), index(index) {}
168 };
169
171 typedef std::vector<BucketEntry> Buckets;
172
175
178
180 std::pair<T,T> getBounds(const BuildPointsIt first, const BuildPointsIt last, const unsigned dim);
182 unsigned buildNodes(const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues);
183
185
197 unsigned long onePointKnn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, int i, Heap& heap, std::vector<T>& off, const T maxError, const T maxRadius2, const bool allowSelfMatch, const bool collectStatistics, const bool sortResults) const;
198
200
208 template<bool allowSelfMatch, bool collectStatistics>
209 unsigned long recurseKnn(const T* query, const unsigned n, T rd, Heap& heap, std::vector<T>& off, const T maxError, const T maxRadius2) const;
210
211 public:
213 KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const Parameters& additionalParameters);
214 virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const;
215 virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Vector& maxRadii, const Index k = 1, const T epsilon = 0, const unsigned optionFlags = 0) const;
216 };
217
218 #ifdef HAVE_OPENCL
219
221 template<typename T, typename CloudType = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> >
222 struct OpenCLSearch: public NearestNeighbourSearch<T, CloudType>
223 {
224 typedef typename NearestNeighbourSearch<T, CloudType>::Vector Vector;
225 typedef typename NearestNeighbourSearch<T, CloudType>::Matrix Matrix;
226 typedef typename NearestNeighbourSearch<T, CloudType>::Index Index;
227 typedef typename NearestNeighbourSearch<T, CloudType>::IndexVector IndexVector;
228 typedef typename NearestNeighbourSearch<T, CloudType>::IndexMatrix IndexMatrix;
229
234
235 protected:
236 const cl_device_type deviceType;
237 cl::Context& context;
238 mutable cl::Kernel knnKernel;
239 cl::CommandQueue queue;
240 cl::Buffer cloudCL;
241
243 OpenCLSearch(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType);
245
249 void initOpenCL(const char* clFileName, const char* kernelName, const std::string& additionalDefines = "");
250
251 public:
252 virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const;
253 virtual unsigned long knn(const Matrix& query, IndexMatrix& indices, Matrix& dists2, const Vector& maxRadii, const Index k = 1, const T epsilon = 0, const unsigned optionFlags = 0) const;
254 };
255
257 template<typename T, typename CloudType = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> >
258 struct BruteForceSearchOpenCL: public OpenCLSearch<T, CloudType>
259 {
260 typedef typename NearestNeighbourSearch<T, CloudType>::Vector Vector;
261 typedef typename NearestNeighbourSearch<T, CloudType>::Matrix Matrix;
262 typedef typename NearestNeighbourSearch<T, CloudType>::Index Index;
263
265
267 BruteForceSearchOpenCL(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType);
268 };
269
271 template<typename T, typename CloudType = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> >
273 {
274 typedef typename NearestNeighbourSearch<T, CloudType>::Vector Vector;
275 typedef typename NearestNeighbourSearch<T, CloudType>::Matrix Matrix;
276 typedef typename NearestNeighbourSearch<T, CloudType>::Index Index;
277 typedef typename NearestNeighbourSearch<T, CloudType>::IndexVector IndexVector;
278 typedef typename NearestNeighbourSearch<T, CloudType>::IndexMatrix IndexMatrix;
279
285
288
290
291 protected:
294 {
295 Vector pos;
296 size_t index;
298 BuildPoint(const Vector& pos = Vector(), const size_t index = 0): pos(pos), index(index) {}
299 };
301 typedef std::vector<BuildPoint> BuildPoints;
303 typedef typename BuildPoints::iterator BuildPointsIt;
305 typedef typename BuildPoints::const_iterator BuildPointsCstIt;
306
309 {
310 size_t dim;
312 CompareDim(const size_t dim):dim(dim){}
314 bool operator() (const BuildPoint& p0, const BuildPoint& p1) { return p0.pos(dim) < p1.pos(dim); }
315 };
316
318 struct Node
319 {
320 int dim;
323 Node(const int dim = -1, const T cutVal = 0):dim(dim), cutVal(cutVal) {}
324 };
326 typedef std::vector<Node> Nodes;
327
329 cl::Buffer nodesCL;
330
331
332 inline size_t childLeft(size_t pos) const { return 2*pos + 1; }
333 inline size_t childRight(size_t pos) const { return 2*pos + 2; }
334 inline size_t parent(size_t pos) const { return (pos-1)/2; }
335 size_t getTreeDepth(size_t size) const;
336 size_t getTreeSize(size_t size) const;
337
339 void buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues);
340
341 public:
343 KDTreeBalancedPtInLeavesStackOpenCL(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType);
344 };
345
347 template<typename T, typename CloudType = Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic> >
349 {
350 typedef typename NearestNeighbourSearch<T, CloudType>::Vector Vector;
351 typedef typename NearestNeighbourSearch<T, CloudType>::Matrix Matrix;
352 typedef typename NearestNeighbourSearch<T, CloudType>::Index Index;
353 typedef typename NearestNeighbourSearch<T, CloudType>::IndexVector IndexVector;
354 typedef typename NearestNeighbourSearch<T, CloudType>::IndexMatrix IndexMatrix;
355
361
364
366
367 protected:
369 typedef Index BuildPoint;
371 typedef std::vector<BuildPoint> BuildPoints;
373 typedef typename BuildPoints::iterator BuildPointsIt;
375 typedef typename BuildPoints::const_iterator BuildPointsCstIt;
376
379 {
381 size_t dim;
383 CompareDim(const CloudType& cloud, const size_t dim): cloud(cloud), dim(dim){}
385 bool operator() (const BuildPoint& p0, const BuildPoint& p1) { return cloud.coeff(dim, p0) < cloud.coeff(dim, p1); }
386 };
387
389 struct Node
390 {
391 int dim;
392 Index index;
394 Node(const int dim = -2, const Index index = 0):dim(dim), index(index) {}
395 };
397 typedef std::vector<Node> Nodes;
398
400 cl::Buffer nodesCL;
401
402
403 inline size_t childLeft(size_t pos) const { return 2*pos + 1; }
404 inline size_t childRight(size_t pos) const { return 2*pos + 2; }
405 inline size_t parent(size_t pos) const { return (pos-1)/2; }
406 size_t getTreeDepth(size_t size) const;
407 size_t getTreeSize(size_t size) const;
408
410 void buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues);
411
412 public:
414 KDTreeBalancedPtInNodesStackOpenCL(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const cl_device_type deviceType);
415 };
416
417 #endif // HAVE_OPENCL
418
420}
421
422#endif // __NABO_H
public interface
Namespace for Nabo.
Definition: brute_force_cpu.cpp:41
T dist2(const A &v0, const B &v1)
Euclidean distance.
Definition: nabo_private.h:65
KDTree, balanced, points in leaves, stack, implicit bounds, balance aspect ratio.
Definition: nabo_private.h:259
Brute-force nearest neighbour.
Definition: nabo_private.h:73
Point during kd-tree construction.
Definition: nabo_private.h:294
Vector pos
point
Definition: nabo_private.h:295
BuildPoint(const Vector &pos=Vector(), const size_t index=0)
Construct a build point, at a given pos with a specific index.
Definition: nabo_private.h:298
size_t index
index of point in cloud
Definition: nabo_private.h:296
Functor to compare point values on a given dimension.
Definition: nabo_private.h:309
bool operator()(const BuildPoint &p0, const BuildPoint &p1)
Compare the values of p0 and p1 on dim, and return whether p0[dim] < p1[dim].
Definition: nabo_private.h:314
CompareDim(const size_t dim)
Build the functor for a specific dimension.
Definition: nabo_private.h:312
size_t dim
dimension on which to compare
Definition: nabo_private.h:310
Tree node for CL.
Definition: nabo_private.h:319
T cutVal
value of the cut
Definition: nabo_private.h:321
Node(const int dim=-1, const T cutVal=0)
Build a tree node, with a given dimension and value to cut on, or, if leaf and dim <= -2,...
Definition: nabo_private.h:323
int dim
dimension of the cut, or, if negative, index of the point: -1 == invalid, <= -2 = index of pt
Definition: nabo_private.h:320
KDTree, balanced, points in leaves, stack, implicit bounds, balance aspect ratio.
Definition: nabo_private.h:273
size_t getTreeDepth(size_t size) const
Return the max depth of a tree of a given size.
Definition: kdtree_opencl.cpp:460
Nodes nodes
search nodes
Definition: nabo_private.h:328
size_t childLeft(size_t pos) const
Return the left child of pos.
Definition: nabo_private.h:332
std::vector< Node > Nodes
dense vector of search nodes
Definition: nabo_private.h:326
void buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues)
Recurse to build nodes.
Definition: kdtree_opencl.cpp:475
BuildPoints::iterator BuildPointsIt
iterator to points during kd-tree construction
Definition: nabo_private.h:303
size_t parent(size_t pos) const
Return the parent of pos.
Definition: nabo_private.h:334
std::vector< BuildPoint > BuildPoints
points during kd-tree construction
Definition: nabo_private.h:301
cl::Buffer nodesCL
CL buffer for search nodes.
Definition: nabo_private.h:329
size_t childRight(size_t pos) const
Return the right child of pos.
Definition: nabo_private.h:333
BuildPoints::const_iterator BuildPointsCstIt
const-iterator to points during kd-tree construction
Definition: nabo_private.h:305
size_t getTreeSize(size_t size) const
Return the storage size of tree of a given size.
Definition: kdtree_opencl.cpp:440
Functor to compare point values on a given dimension.
Definition: nabo_private.h:379
CompareDim(const CloudType &cloud, const size_t dim)
Build the functor for a specific dimension on a specific cloud.
Definition: nabo_private.h:383
size_t dim
dimension on which to compare
Definition: nabo_private.h:381
const CloudType & cloud
reference to data points used to compare
Definition: nabo_private.h:380
bool operator()(const BuildPoint &p0, const BuildPoint &p1)
Compare the values of p0 and p1 on dim, and return whether p0[dim] < p1[dim].
Definition: nabo_private.h:385
Tree node for CL.
Definition: nabo_private.h:390
int dim
dimension of the cut, or, if -1 == leaf, -2 == invalid
Definition: nabo_private.h:391
Index index
index of the point to cut
Definition: nabo_private.h:392
Node(const int dim=-2, const Index index=0)
Build a tree node, with a given index and a given dimension to cut on.
Definition: nabo_private.h:394
KDTree, balanced, points in nodes, stack, implicit bounds, balance aspect ratio.
Definition: nabo_private.h:349
BuildPoints::iterator BuildPointsIt
iterator to points during kd-tree construction
Definition: nabo_private.h:373
size_t getTreeDepth(size_t size) const
Return the max depth of a tree of a given size.
Definition: kdtree_opencl.cpp:581
void buildNodes(const BuildPointsIt first, const BuildPointsIt last, const size_t pos, const Vector minValues, const Vector maxValues)
Recurse to build nodes.
Definition: kdtree_opencl.cpp:594
size_t parent(size_t pos) const
Return the parent of pos.
Definition: nabo_private.h:405
cl::Buffer nodesCL
CL buffer for search nodes.
Definition: nabo_private.h:400
std::vector< BuildPoint > BuildPoints
points during kd-tree construction
Definition: nabo_private.h:371
size_t childLeft(size_t pos) const
Return the left child of pos.
Definition: nabo_private.h:403
Nodes nodes
search nodes
Definition: nabo_private.h:399
Index BuildPoint
a point during kd-tree construction is just its index
Definition: nabo_private.h:369
size_t getTreeSize(size_t size) const
Return the storage size of tree of a given size.
Definition: kdtree_opencl.cpp:564
std::vector< Node > Nodes
dense vector of search nodes
Definition: nabo_private.h:397
size_t childRight(size_t pos) const
Return the right child of pos.
Definition: nabo_private.h:404
BuildPoints::const_iterator BuildPointsCstIt
const-iterator to points during kd-tree construction
Definition: nabo_private.h:375
entry in a bucket
Definition: nabo_private.h:159
Index index
index of point
Definition: nabo_private.h:161
BucketEntry(const T *pt=0, const Index index=0)
create a new bucket entry for a point in the data
Definition: nabo_private.h:167
const T * pt
pointer to first value of point data, 0 if end of bucket
Definition: nabo_private.h:160
search node
Definition: nabo_private.h:139
T cutVal
for split node, split value
Definition: nabo_private.h:143
uint32_t bucketIndex
for leaf node, pointer to bucket
Definition: nabo_private.h:144
Node(const uint32_t dimChild, const T cutVal)
construct a split node
Definition: nabo_private.h:148
Node(const uint32_t bucketSize, const uint32_t bucketIndex)
construct a leaf node
Definition: nabo_private.h:151
uint32_t dimChildBucketSize
cut dimension for split nodes (dimBitCount lsb), index of right node or number of bucket(rest)....
Definition: nabo_private.h:140
KDTree, unbalanced, points in leaves, stack, implicit bounds, ANN_KD_SL_MIDPT, optimised implementati...
Definition: nabo_private.h:95
uint32_t getChildBucketSize(const uint32_t dimChildBucketSize) const
get the child index or the bucket size out of the coumpount index
Definition: nabo_private.h:132
const uint32_t dimMask
mask to access dim
Definition: nabo_private.h:123
BuildPoints::const_iterator BuildPointsCstIt
const-iterator to indices of points during kd-tree construction
Definition: nabo_private.h:115
BuildPoints::iterator BuildPointsIt
iterator to indices of points during kd-tree construction
Definition: nabo_private.h:113
std::vector< Node > Nodes
dense vector of search nodes, provides better memory performances than many small objects
Definition: nabo_private.h:155
uint32_t getDim(const uint32_t dimChildBucketSize) const
get the dimension out of the compound index
Definition: nabo_private.h:129
std::pair< T, T > getBounds(const BuildPointsIt first, const BuildPointsIt last, const unsigned dim)
return the bounds of points from [first..last[ on dimension dim
Definition: kdtree_cpu.cpp:94
unsigned long onePointKnn(const Matrix &query, IndexMatrix &indices, Matrix &dists2, int i, Heap &heap, std::vector< T > &off, const T maxError, const T maxRadius2, const bool allowSelfMatch, const bool collectStatistics, const bool sortResults) const
search one point, call recurseKnn with the correct template parameters
Definition: kdtree_cpu.cpp:339
Buckets buckets
buckets
Definition: nabo_private.h:177
std::vector< BucketEntry > Buckets
bucket data
Definition: nabo_private.h:171
uint32_t createDimChildBucketSize(const uint32_t dim, const uint32_t childIndex) const
create the compound index containing the dimension and the index of the child or the bucket size
Definition: nabo_private.h:126
std::vector< Index > BuildPoints
indices of points during kd-tree construction
Definition: nabo_private.h:111
Nodes nodes
search nodes
Definition: nabo_private.h:174
const uint32_t dimBitCount
number of bits required to store dimension index + number of dimensions
Definition: nabo_private.h:121
unsigned buildNodes(const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues)
construct nodes for points [first..last[ inside the hyperrectangle [minValues..maxValues]
Definition: kdtree_cpu.cpp:110
const unsigned bucketSize
size of bucket
Definition: nabo_private.h:118
unsigned long recurseKnn(const T *query, const unsigned n, T rd, Heap &heap, std::vector< T > &off, const T maxError, const T maxRadius2) const
recursive search, strongly inspired by ANN and [Arya & Mount, Algorithms for fast vector quantization...
Definition: kdtree_cpu.cpp:368
Nearest neighbour search interface, templatized on scalar type.
Definition: nabo.h:259
void checkSizesKnn(const Matrix &query, const IndexMatrix &indices, const Matrix &dists2, const Index k, const unsigned optionFlags, const Vector *maxRadii=0) const
Make sure that the output matrices have the right sizes. Throw an exception otherwise.
Definition: nabo.cpp:103
int Index
an index to a Vector or a Matrix, for refering to data points
Definition: nabo.h:267
Cloud_T CloudType
a column-major Eigen matrix in which each column is a point; this matrix has dim rows
Definition: nabo.h:265
const Vector maxBound
the high bound of the search space (axis-aligned bounding box)
Definition: nabo.h:282
const CloudType & cloud
the reference to the data-point cloud, which must remain valid during the lifetime of the NearestNeig...
Definition: nabo.h:274
const unsigned creationOptionFlags
creation options
Definition: nabo.h:278
Eigen::Matrix< Index, Eigen::Dynamic, 1 > IndexVector
a vector of indices to data points
Definition: nabo.h:269
const Index dim
the dimensionality of the data-point cloud
Definition: nabo.h:276
Eigen::Matrix< Index, Eigen::Dynamic, Eigen::Dynamic > IndexMatrix
a matrix of indices to data points
Definition: nabo.h:271
const Vector minBound
the low bound of the search space (axis-aligned bounding box)
Definition: nabo.h:280
Eigen::Matrix< T, Eigen::Dynamic, 1 > Vector
an Eigen vector of type T, to hold the coordinates of a point
Definition: nabo.h:261
Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > Matrix
a column-major Eigen matrix in which each column is a point; this matrix has dim rows
Definition: nabo.h:263
OpenCL support for nearest neighbour search
Definition: nabo_private.h:223
cl::Kernel knnKernel
the kernel to perform knnSearch, mutable because it is stateful, but conceptually const
Definition: nabo_private.h:238
void initOpenCL(const char *clFileName, const char *kernelName, const std::string &additionalDefines="")
Initialize CL support.
Definition: kdtree_opencl.cpp:239
cl::Buffer cloudCL
the buffer for the input data
Definition: nabo_private.h:240
cl::CommandQueue queue
the command queue
Definition: nabo_private.h:239
cl::Context & context
the CL context
Definition: nabo_private.h:237
const cl_device_type deviceType
the type of device to run CL code on (CL_DEVICE_TYPE_CPU or CL_DEVICE_TYPE_GPU)
Definition: nabo_private.h:236
Parameter vector.
Definition: nabo.h:232