Updated FLANN to version 1.5
This commit is contained in:
Marius Muja
@@ -3985,6 +3985,5 @@ public:
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#include "opencv2/core/operations.hpp"
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#include "opencv2/core/mat.hpp"
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#include "opencv2/core/flann.hpp" // FLANN (Fast Library for Approximate Nearest Neighbors)
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#endif /*__OPENCV_CORE_HPP__*/
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@@ -1,220 +0,0 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_CORE_FLANN_HPP__
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#define __OPENCV_CORE_FLANN_HPP__
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#ifdef __cplusplus
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namespace cvflann
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{
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class Index;
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}
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namespace cv {
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namespace flann {
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/* Nearest neighbor index algorithms */
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enum flann_algorithm_t {
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LINEAR = 0,
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KDTREE = 1,
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KMEANS = 2,
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COMPOSITE = 3,
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SAVED = 254,
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AUTOTUNED = 255
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};
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enum flann_centers_init_t {
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CENTERS_RANDOM = 0,
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CENTERS_GONZALES = 1,
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CENTERS_KMEANSPP = 2
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};
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enum flann_log_level_t {
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LOG_NONE = 0,
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LOG_FATAL = 1,
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LOG_ERROR = 2,
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LOG_WARN = 3,
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LOG_INFO = 4
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};
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enum flann_distance_t {
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EUCLIDEAN = 1,
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MANHATTAN = 2,
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MINKOWSKI = 3
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};
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class CV_EXPORTS IndexFactory
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{
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public:
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virtual ~IndexFactory() {}
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virtual ::cvflann::Index* createIndex(const Mat& dataset) const = 0;
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};
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struct CV_EXPORTS IndexParams : public IndexFactory {
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protected:
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IndexParams() {};
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};
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struct CV_EXPORTS LinearIndexParams : public IndexParams {
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LinearIndexParams() {};
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::cvflann::Index* createIndex(const Mat& dataset) const;
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};
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struct CV_EXPORTS KDTreeIndexParams : public IndexParams {
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KDTreeIndexParams(int trees_ = 4) : trees(trees_) {};
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int trees; // number of randomized trees to use (for kdtree)
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::cvflann::Index* createIndex(const Mat& dataset) const;
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};
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struct CV_EXPORTS KMeansIndexParams : public IndexParams {
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KMeansIndexParams(int branching_ = 32, int iterations_ = 11,
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flann_centers_init_t centers_init_ = CENTERS_RANDOM, float cb_index_ = 0.2 ) :
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branching(branching_),
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iterations(iterations_),
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centers_init(centers_init_),
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cb_index(cb_index_) {};
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int branching; // branching factor (for kmeans tree)
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int iterations; // max iterations to perform in one kmeans clustering (kmeans tree)
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flann_centers_init_t centers_init; // algorithm used for picking the initial cluster centers for kmeans tree
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float cb_index; // cluster boundary index. Used when searching the kmeans tree
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::cvflann::Index* createIndex(const Mat& dataset) const;
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};
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struct CV_EXPORTS CompositeIndexParams : public IndexParams {
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CompositeIndexParams(int trees_ = 4, int branching_ = 32, int iterations_ = 11,
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flann_centers_init_t centers_init_ = CENTERS_RANDOM, float cb_index_ = 0.2 ) :
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trees(trees_),
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branching(branching_),
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iterations(iterations_),
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centers_init(centers_init_),
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cb_index(cb_index_) {};
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int trees; // number of randomized trees to use (for kdtree)
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int branching; // branching factor (for kmeans tree)
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int iterations; // max iterations to perform in one kmeans clustering (kmeans tree)
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flann_centers_init_t centers_init; // algorithm used for picking the initial cluster centers for kmeans tree
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float cb_index; // cluster boundary index. Used when searching the kmeans tree
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::cvflann::Index* createIndex(const Mat& dataset) const;
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};
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struct CV_EXPORTS AutotunedIndexParams : public IndexParams {
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AutotunedIndexParams( float target_precision_ = 0.9, float build_weight_ = 0.01,
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float memory_weight_ = 0, float sample_fraction_ = 0.1) :
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target_precision(target_precision_),
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build_weight(build_weight_),
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memory_weight(memory_weight_),
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sample_fraction(sample_fraction_) {};
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float target_precision; // precision desired (used for autotuning, -1 otherwise)
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float build_weight; // build tree time weighting factor
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float memory_weight; // index memory weighting factor
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float sample_fraction; // what fraction of the dataset to use for autotuning
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::cvflann::Index* createIndex(const Mat& dataset) const;
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};
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struct CV_EXPORTS SavedIndexParams : public IndexParams {
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SavedIndexParams() {}
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SavedIndexParams(std::string filename_) : filename(filename_) {}
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std::string filename; // filename of the stored index
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::cvflann::Index* createIndex(const Mat& dataset) const;
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};
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struct CV_EXPORTS SearchParams {
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SearchParams(int checks_ = 32) :
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checks(checks_) {};
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int checks;
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};
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class CV_EXPORTS Index {
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::cvflann::Index* nnIndex;
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public:
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Index(const Mat& features, const IndexParams& params);
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~Index();
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void knnSearch(const vector<float>& queries, vector<int>& indices, vector<float>& dists, int knn, const SearchParams& params);
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void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const SearchParams& params);
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int radiusSearch(const vector<float>& query, vector<int>& indices, vector<float>& dists, float radius, const SearchParams& params);
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int radiusSearch(const Mat& query, Mat& indices, Mat& dists, float radius, const SearchParams& params);
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void save(std::string filename);
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int veclen() const;
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int size() const;
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};
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CV_EXPORTS int hierarchicalClustering(const Mat& features, Mat& centers,
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const KMeansIndexParams& params);
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}
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}
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#endif // __cplusplus
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#endif
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