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Merge pull request #217 from jlblancoc/feature/rknn2
Add RKNN searches, and more code coverage and tests
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -3,7 +3,7 @@ | |
| * | ||
| * Copyright 2008-2009 Marius Muja ([email protected]). All rights reserved. | ||
| * Copyright 2008-2009 David G. Lowe ([email protected]). All rights reserved. | ||
| * Copyright 2011-2022 Jose Luis Blanco ([email protected]). | ||
| * Copyright 2011-2023 Jose Luis Blanco ([email protected]). | ||
| * All rights reserved. | ||
| * | ||
| * THE BSD LICENSE | ||
|
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@@ -60,7 +60,7 @@ | |
| #include <vector> | ||
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| /** Library version: 0xMmP (M=Major,m=minor,P=patch) */ | ||
| #define NANOFLANN_VERSION 0x150 | ||
| #define NANOFLANN_VERSION 0x151 | ||
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| // Avoid conflicting declaration of min/max macros in Windows headers | ||
| #if !defined(NOMINMAX) && \ | ||
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@@ -158,6 +158,8 @@ inline typename std::enable_if<!has_assign<Container>::value, void>::type | |
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| /** @addtogroup result_sets_grp Result set classes | ||
| * @{ */ | ||
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| /** Result set for KNN searches (N-closest neighbors) */ | ||
| template < | ||
| typename _DistanceType, typename _IndexType = size_t, | ||
| typename _CountType = size_t> | ||
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@@ -191,8 +193,92 @@ class KNNResultSet | |
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| CountType size() const { return count; } | ||
| bool empty() const { return count == 0; } | ||
| bool full() const { return count == capacity; } | ||
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| /** | ||
| * Called during search to add an element matching the criteria. | ||
| * @return true if the search should be continued, false if the results are | ||
| * sufficient | ||
| */ | ||
| bool addPoint(DistanceType dist, IndexType index) | ||
| { | ||
| CountType i; | ||
| for (i = count; i > 0; --i) | ||
| { | ||
| /** If defined and two points have the same distance, the one with | ||
| * the lowest-index will be returned first. */ | ||
| #ifdef NANOFLANN_FIRST_MATCH | ||
| if ((dists[i - 1] > dist) || | ||
| ((dist == dists[i - 1]) && (indices[i - 1] > index))) | ||
| { | ||
| #else | ||
| if (dists[i - 1] > dist) | ||
| { | ||
| #endif | ||
| if (i < capacity) | ||
| { | ||
| dists[i] = dists[i - 1]; | ||
| indices[i] = indices[i - 1]; | ||
| } | ||
| } | ||
| else | ||
| break; | ||
| } | ||
| if (i < capacity) | ||
| { | ||
| dists[i] = dist; | ||
| indices[i] = index; | ||
| } | ||
| if (count < capacity) count++; | ||
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| // tell caller that the search shall continue | ||
| return true; | ||
| } | ||
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| DistanceType worstDist() const { return dists[capacity - 1]; } | ||
| }; | ||
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| /** Result set for RKNN searches (N-closest neighbors with a maximum radius) */ | ||
| template < | ||
| typename _DistanceType, typename _IndexType = size_t, | ||
| typename _CountType = size_t> | ||
| class RKNNResultSet | ||
| { | ||
| public: | ||
| using DistanceType = _DistanceType; | ||
| using IndexType = _IndexType; | ||
| using CountType = _CountType; | ||
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| bool full() const { return count == capacity; } | ||
| private: | ||
| IndexType* indices; | ||
| DistanceType* dists; | ||
| CountType capacity; | ||
| CountType count; | ||
| DistanceType maximumSearchDistanceSquared; | ||
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| public: | ||
| explicit RKNNResultSet( | ||
| CountType capacity_, DistanceType maximumSearchDistanceSquared_) | ||
| : indices(nullptr), | ||
| dists(nullptr), | ||
| capacity(capacity_), | ||
| count(0), | ||
| maximumSearchDistanceSquared(maximumSearchDistanceSquared_) | ||
| { | ||
| } | ||
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| void init(IndexType* indices_, DistanceType* dists_) | ||
| { | ||
| indices = indices_; | ||
| dists = dists_; | ||
| count = 0; | ||
| if (capacity) | ||
| dists[capacity - 1] = (std::numeric_limits<DistanceType>::max)(); | ||
| } | ||
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| CountType size() const { return count; } | ||
| bool empty() const { return count == 0; } | ||
| bool full() const { return count == capacity; } | ||
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| /** | ||
| * Called during search to add an element matching the criteria. | ||
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@@ -201,6 +287,8 @@ class KNNResultSet | |
| */ | ||
| bool addPoint(DistanceType dist, IndexType index) | ||
| { | ||
| if (dist > maximumSearchDistanceSquared) return true; | ||
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| CountType i; | ||
| for (i = count; i > 0; --i) | ||
| { | ||
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@@ -1681,6 +1769,34 @@ class KDTreeSingleIndexAdaptor | |
| return resultSet.size(); | ||
| } | ||
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| /** | ||
| * Find the first N neighbors to \a query_point[0:dim-1] within a maximum | ||
| * radius. The output is given as a vector of pairs, of which the first | ||
| * element is a point index and the second the corresponding distance. | ||
| * Previous contents of \a IndicesDists are cleared. | ||
| * | ||
| * \sa radiusSearch, findNeighbors | ||
| * \return Number `N` of valid points in the result set. | ||
| * | ||
| * \note If L2 norms are used, all returned distances are actually squared | ||
| * distances. | ||
| * | ||
| * \note Only the first `N` entries in `out_indices` and `out_distances` | ||
| * will be valid. Return is less than `num_closest` only if the | ||
| * number of elements in the tree is less than `num_closest`. | ||
| */ | ||
| Size rknnSearch( | ||
| const ElementType* query_point, const Size num_closest, | ||
| IndexType* out_indices, DistanceType* out_distances, | ||
| const DistanceType& radius) const | ||
| { | ||
| nanoflann::RKNNResultSet<DistanceType, IndexType> resultSet( | ||
| num_closest, radius); | ||
| resultSet.init(out_indices, out_distances); | ||
| findNeighbors(resultSet, query_point); | ||
| return resultSet.size(); | ||
| } | ||
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| /** @} */ | ||
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| public: | ||
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