Fixed float.h issue. OpenCV with built libraries working for linux x64

1 files changed, 420 insertions, 0 deletions

diff --git a/thirdparty/includes/OpenCV/opencv2/flann/lsh_index.h b/thirdparty/includes/OpenCV/opencv2/flann/lsh_index.h
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+/***********************************************************************
+ * Software License Agreement (BSD License)
+ *
+ * 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.
+ *
+ * THE BSD LICENSE
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+ * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
+ * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+ * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
+ * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
+ * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
+ * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *************************************************************************/
+
+/***********************************************************************
+ * Author: Vincent Rabaud
+ *************************************************************************/
+
+#ifndef OPENCV_FLANN_LSH_INDEX_H_
+#define OPENCV_FLANN_LSH_INDEX_H_
+
+#include <algorithm>
+#include <cassert>
+#include <cstring>
+#include <map>
+#include <vector>
+
+#include "general.h"
+#include "nn_index.h"
+#include "matrix.h"
+#include "result_set.h"
+#include "heap.h"
+#include "lsh_table.h"
+#include "allocator.h"
+#include "random.h"
+#include "saving.h"
+
+namespace cvflann
+{
+
+struct LshIndexParams : public IndexParams
+{
+    LshIndexParams(unsigned int table_number = 12, unsigned int key_size = 20, unsigned int multi_probe_level = 2)
+    {
+        (* this)["algorithm"] = FLANN_INDEX_LSH;
+        // The number of hash tables to use
+        (*this)["table_number"] = table_number;
+        // The length of the key in the hash tables
+        (*this)["key_size"] = key_size;
+        // Number of levels to use in multi-probe (0 for standard LSH)
+        (*this)["multi_probe_level"] = multi_probe_level;
+    }
+};
+
+/**
+ * Randomized kd-tree index
+ *
+ * Contains the k-d trees and other information for indexing a set of points
+ * for nearest-neighbor matching.
+ */
+template<typename Distance>
+class LshIndex : public NNIndex<Distance>
+{
+public:
+    typedef typename Distance::ElementType ElementType;
+    typedef typename Distance::ResultType DistanceType;
+
+    /** Constructor
+     * @param input_data dataset with the input features
+     * @param params parameters passed to the LSH algorithm
+     * @param d the distance used
+     */
+    LshIndex(const Matrix<ElementType>& input_data, const IndexParams& params = LshIndexParams(),
+             Distance d = Distance()) :
+        dataset_(input_data), index_params_(params), distance_(d)
+    {
+        // cv::flann::IndexParams sets integer params as 'int', so it is used with get_param
+        // in place of 'unsigned int'
+        table_number_ = (unsigned int)get_param<int>(index_params_,"table_number",12);
+        key_size_ = (unsigned int)get_param<int>(index_params_,"key_size",20);
+        multi_probe_level_ = (unsigned int)get_param<int>(index_params_,"multi_probe_level",2);
+
+        feature_size_ = (unsigned)dataset_.cols;
+        fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_);
+    }
+
+
+    LshIndex(const LshIndex&);
+    LshIndex& operator=(const LshIndex&);
+
+    /**
+    * Implementation for the LSH addable indexes after that.
+    * @param wholeData whole dataset with the input features
+    * @param additionalData additional dataset with the input features
+    */
+    void addIndex(const Matrix<ElementType>& wholeData, const Matrix<ElementType>& additionalData)
+    {
+        tables_.resize(table_number_);
+        for (unsigned int i = 0; i < table_number_; ++i) {
+            lsh::LshTable<ElementType>& table = tables_[i];
+            // Add the features to the table with indexed offset
+            table.add((int)(wholeData.rows - additionalData.rows), additionalData);
+        }
+        dataset_ = wholeData;
+    }
+
+    /**
+     * Builds the index
+     */
+    void buildIndex()
+    {
+        std::vector<size_t> indices(feature_size_ * CHAR_BIT);
+
+        tables_.resize(table_number_);
+        for (unsigned int i = 0; i < table_number_; ++i) {
+
+            //re-initialize the random indices table that the LshTable will use to pick its sub-dimensions
+            if( (indices.size() == feature_size_ * CHAR_BIT) || (indices.size() < key_size_) )
+            {
+              indices.resize( feature_size_ * CHAR_BIT );
+              for (size_t j = 0; j < feature_size_ * CHAR_BIT; ++j)
+                  indices[j] = j;
+              std::random_shuffle(indices.begin(), indices.end());
+            }
+
+            lsh::LshTable<ElementType>& table = tables_[i];
+            table = lsh::LshTable<ElementType>(feature_size_, key_size_, indices);
+
+            // Add the features to the table with offset 0
+            table.add(0, dataset_);
+        }
+    }
+
+    flann_algorithm_t getType() const
+    {
+        return FLANN_INDEX_LSH;
+    }
+
+
+    void saveIndex(FILE* stream)
+    {
+        save_value(stream,table_number_);
+        save_value(stream,key_size_);
+        save_value(stream,multi_probe_level_);
+        save_value(stream, dataset_);
+    }
+
+    void loadIndex(FILE* stream)
+    {
+        load_value(stream, table_number_);
+        load_value(stream, key_size_);
+        load_value(stream, multi_probe_level_);
+        load_value(stream, dataset_);
+        // Building the index is so fast we can afford not storing it
+        buildIndex();
+
+        index_params_["algorithm"] = getType();
+        index_params_["table_number"] = table_number_;
+        index_params_["key_size"] = key_size_;
+        index_params_["multi_probe_level"] = multi_probe_level_;
+    }
+
+    /**
+     *  Returns size of index.
+     */
+    size_t size() const
+    {
+        return dataset_.rows;
+    }
+
+    /**
+     * Returns the length of an index feature.
+     */
+    size_t veclen() const
+    {
+        return feature_size_;
+    }
+
+    /**
+     * Computes the index memory usage
+     * Returns: memory used by the index
+     */
+    int usedMemory() const
+    {
+        return (int)(dataset_.rows * sizeof(int));
+    }
+
+
+    IndexParams getParameters() const
+    {
+        return index_params_;
+    }
+
+    /**
+     * \brief Perform k-nearest neighbor search
+     * \param[in] queries The query points for which to find the nearest neighbors
+     * \param[out] indices The indices of the nearest neighbors found
+     * \param[out] dists Distances to the nearest neighbors found
+     * \param[in] knn Number of nearest neighbors to return
+     * \param[in] params Search parameters
+     */
+    virtual void knnSearch(const Matrix<ElementType>& queries, Matrix<int>& indices, Matrix<DistanceType>& dists, int knn, const SearchParams& params)
+    {
+        assert(queries.cols == veclen());
+        assert(indices.rows >= queries.rows);
+        assert(dists.rows >= queries.rows);
+        assert(int(indices.cols) >= knn);
+        assert(int(dists.cols) >= knn);
+
+
+        KNNUniqueResultSet<DistanceType> resultSet(knn);
+        for (size_t i = 0; i < queries.rows; i++) {
+            resultSet.clear();
+            std::fill_n(indices[i], knn, -1);
+            std::fill_n(dists[i], knn, std::numeric_limits<DistanceType>::max());
+            findNeighbors(resultSet, queries[i], params);
+            if (get_param(params,"sorted",true)) resultSet.sortAndCopy(indices[i], dists[i], knn);
+            else resultSet.copy(indices[i], dists[i], knn);
+        }
+    }
+
+
+    /**
+     * Find set of nearest neighbors to vec. Their indices are stored inside
+     * the result object.
+     *
+     * Params:
+     *     result = the result object in which the indices of the nearest-neighbors are stored
+     *     vec = the vector for which to search the nearest neighbors
+     *     maxCheck = the maximum number of restarts (in a best-bin-first manner)
+     */
+    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& /*searchParams*/)
+    {
+        getNeighbors(vec, result);
+    }
+
+private:
+    /** Defines the comparator on score and index
+     */
+    typedef std::pair<float, unsigned int> ScoreIndexPair;
+    struct SortScoreIndexPairOnSecond
+    {
+        bool operator()(const ScoreIndexPair& left, const ScoreIndexPair& right) const
+        {
+            return left.second < right.second;
+        }
+    };
+
+    /** Fills the different xor masks to use when getting the neighbors in multi-probe LSH
+     * @param key the key we build neighbors from
+     * @param lowest_index the lowest index of the bit set
+     * @param level the multi-probe level we are at
+     * @param xor_masks all the xor mask
+     */
+    void fill_xor_mask(lsh::BucketKey key, int lowest_index, unsigned int level,
+                       std::vector<lsh::BucketKey>& xor_masks)
+    {
+        xor_masks.push_back(key);
+        if (level == 0) return;
+        for (int index = lowest_index - 1; index >= 0; --index) {
+            // Create a new key
+            lsh::BucketKey new_key = key | (1 << index);
+            fill_xor_mask(new_key, index, level - 1, xor_masks);
+        }
+    }
+
+    /** Performs the approximate nearest-neighbor search.
+     * @param vec the feature to analyze
+     * @param do_radius flag indicating if we check the radius too
+     * @param radius the radius if it is a radius search
+     * @param do_k flag indicating if we limit the number of nn
+     * @param k_nn the number of nearest neighbors
+     * @param checked_average used for debugging
+     */
+    void getNeighbors(const ElementType* vec, bool /*do_radius*/, float radius, bool do_k, unsigned int k_nn,
+                      float& /*checked_average*/)
+    {
+        static std::vector<ScoreIndexPair> score_index_heap;
+
+        if (do_k) {
+            unsigned int worst_score = std::numeric_limits<unsigned int>::max();
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
+            for (; table != table_end; ++table) {
+                size_t key = table->getKey(vec);
+                std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
+                std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
+                for (; xor_mask != xor_mask_end; ++xor_mask) {
+                    size_t sub_key = key ^ (*xor_mask);
+                    const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
+                    if (bucket == 0) continue;
+
+                    // Go over each descriptor index
+                    std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
+                    std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
+                    DistanceType hamming_distance;
+
+                    // Process the rest of the candidates
+                    for (; training_index < last_training_index; ++training_index) {
+                        hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
+
+                        if (hamming_distance < worst_score) {
+                            // Insert the new element
+                            score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
+                            std::push_heap(score_index_heap.begin(), score_index_heap.end());
+
+                            if (score_index_heap.size() > (unsigned int)k_nn) {
+                                // Remove the highest distance value as we have too many elements
+                                std::pop_heap(score_index_heap.begin(), score_index_heap.end());
+                                score_index_heap.pop_back();
+                                // Keep track of the worst score
+                                worst_score = score_index_heap.front().first;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        else {
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
+            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
+            for (; table != table_end; ++table) {
+                size_t key = table->getKey(vec);
+                std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
+                std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
+                for (; xor_mask != xor_mask_end; ++xor_mask) {
+                    size_t sub_key = key ^ (*xor_mask);
+                    const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
+                    if (bucket == 0) continue;
+
+                    // Go over each descriptor index
+                    std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
+                    std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
+                    DistanceType hamming_distance;
+
+                    // Process the rest of the candidates
+                    for (; training_index < last_training_index; ++training_index) {
+                        // Compute the Hamming distance
+                        hamming_distance = distance_(vec, dataset_[*training_index], dataset_.cols);
+                        if (hamming_distance < radius) score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
+                    }
+                }
+            }
+        }
+    }
+
+    /** Performs the approximate nearest-neighbor search.
+     * This is a slower version than the above as it uses the ResultSet
+     * @param vec the feature to analyze
+     */
+    void getNeighbors(const ElementType* vec, ResultSet<DistanceType>& result)
+    {
+        typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
+        typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
+        for (; table != table_end; ++table) {
+            size_t key = table->getKey(vec);
+            std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
+            std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
+            for (; xor_mask != xor_mask_end; ++xor_mask) {
+                size_t sub_key = key ^ (*xor_mask);
+                const lsh::Bucket* bucket = table->getBucketFromKey((lsh::BucketKey)sub_key);
+                if (bucket == 0) continue;
+
+                // Go over each descriptor index
+                std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
+                std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
+                DistanceType hamming_distance;
+
+                // Process the rest of the candidates
+                for (; training_index < last_training_index; ++training_index) {
+                    // Compute the Hamming distance
+                    hamming_distance = distance_(vec, dataset_[*training_index], (int)dataset_.cols);
+                    result.addPoint(hamming_distance, *training_index);
+                }
+            }
+        }
+    }
+
+    /** The different hash tables */
+    std::vector<lsh::LshTable<ElementType> > tables_;
+
+    /** The data the LSH tables where built from */
+    Matrix<ElementType> dataset_;
+
+    /** The size of the features (as ElementType[]) */
+    unsigned int feature_size_;
+
+    IndexParams index_params_;
+
+    /** table number */
+    unsigned int table_number_;
+    /** key size */
+    unsigned int key_size_;
+    /** How far should we look for neighbors in multi-probe LSH */
+    unsigned int multi_probe_level_;
+
+    /** The XOR masks to apply to a key to get the neighboring buckets */
+    std::vector<lsh::BucketKey> xor_masks_;
+
+    Distance distance_;
+};
+}
+
+#endif //OPENCV_FLANN_LSH_INDEX_H_