Pev transpose opencv commits

src/cpp/flann/algorithms/autotuned_index.h

@@ -54,7 +54,7 @@ inline NNIndex<Distance>*
 
 struct AutotunedIndexParams : public IndexParams
 {
-    AutotunedIndexParams(float target_precision = 0.8, float build_weight = 0.01, float memory_weight = 0, float sample_fraction = 0.1)
+    AutotunedIndexParams(float target_precision = 0.8f, float build_weight = 0.01f, float memory_weight = 0.f, float sample_fraction = 0.1f)
     {
         (*this)["algorithm"] = FLANN_INDEX_AUTOTUNED;
         // precision desired (used for autotuning, -1 otherwise)
@@ -161,7 +161,7 @@ class AutotunedIndex : public NNIndex<Distance>
     }
 
 
-    void addPoints(const Matrix<ElementType>& points, float rebuild_threshold = 2)
+    void addPoints(const Matrix<ElementType>& points, float rebuild_threshold = 2.f)
     {
         if (bestIndex_) {
             bestIndex_->addPoints(points, rebuild_threshold);

src/cpp/flann/algorithms/composite_index.h

@@ -45,7 +45,7 @@ namespace flann
 struct CompositeIndexParams : public IndexParams
 {
     CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11,
-                         flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
+                         flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2f )
     {
         (*this)["algorithm"] = FLANN_INDEX_KMEANS;
         // number of randomized trees to use (for kdtree)
@@ -166,7 +166,7 @@ class CompositeIndex : public NNIndex<Distance>
         kdtree_index_->buildIndex();
     }
 
-    void addPoints(const Matrix<ElementType>& points, float rebuild_threshold = 2)
+    void addPoints(const Matrix<ElementType>& points, float rebuild_threshold = 2.f)
     {
         kmeans_index_->addPoints(points, rebuild_threshold);
         kdtree_index_->addPoints(points, rebuild_threshold);

src/cpp/flann/algorithms/kmeans_index.h

@@ -58,7 +58,7 @@ namespace flann
 struct KMeansIndexParams : public IndexParams
 {
     KMeansIndexParams(int branching = 32, int iterations = 11,
-                      flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2 )
+                      flann_centers_init_t centers_init = FLANN_CENTERS_RANDOM, float cb_index = 0.2f )
     {
         (*this)["algorithm"] = FLANN_INDEX_KMEANS;
         // branching factor
@@ -212,7 +212,7 @@ class KMeansIndex : public NNIndex<Distance>
 
     using BaseClass::buildIndex;
 
-    void addPoints(const Matrix<ElementType>& points, float rebuild_threshold = 2)
+    void addPoints(const Matrix<ElementType>& points, float rebuild_threshold = 2.f)
     {
         assert(points.cols==veclen_);
         size_t old_size = size_;

src/cpp/flann/flann.hpp

@@ -376,6 +376,7 @@ class Index
         }
         IndexHeader header = load_header(fin);
         if (header.h.data_type != flann_datatype_value<ElementType>::value) {
+            fclose(fin);
             throw FLANNException("Datatype of saved index is different than of the one to be loaded.");
         }
 

src/cpp/flann/util/dynamic_bitset.h

@@ -52,13 +52,13 @@ namespace flann {
 class DynamicBitset
 {
 public:
-    /** @param default constructor
+    /** default constructor
      */
     DynamicBitset() : size_(0)
     {
     }
 
-    /** @param only constructor we use in our code
+    /** only constructor we use in our code
      * @param the size of the bitset (in bits)
      */
     DynamicBitset(size_t size)
@@ -82,15 +82,15 @@ class DynamicBitset
         return bitset_.empty();
     }
 
-    /** @param set all the bits to 0
+    /** set all the bits to 0
      */
     void reset()
     {
         std::fill(bitset_.begin(), bitset_.end(), 0);
     }
 
     /** @brief set one bit to 0
-     * @param
+     * @param index
      */
     void reset(size_t index)
     {
@@ -101,7 +101,7 @@ class DynamicBitset
      * This function is useful when resetting a given set of bits so that the
      * whole bitset ends up being 0: if that's the case, we don't care about setting
      * other bits to 0
-     * @param
+     * @param index
      */
     void reset_block(size_t index)
     {
@@ -117,22 +117,22 @@ class DynamicBitset
         bitset_.resize(size / cell_bit_size_ + 1);
     }
 
-    /** @param set a bit to true
+    /** set a bit to true
      * @param index the index of the bit to set to 1
      */
     void set(size_t index)
     {
         bitset_[index / cell_bit_size_] |= size_t(1) << (index % cell_bit_size_);
     }
 
-    /** @param gives the number of contained bits
+    /** gives the number of contained bits
      */
     size_t size() const
     {
         return size_;
     }
 
-    /** @param check if a bit is set
+    /** check if a bit is set
      * @param index the index of the bit to check
      * @return true if the bit is set
      */

src/cpp/flann/util/lsh_table.h

@@ -149,8 +149,10 @@ class LshTable
      * @param feature_size is the size of the feature (considered as a ElementType[])
      * @param key_size is the number of bits that are turned on in the feature
      */
-    LshTable(unsigned int /*feature_size*/, unsigned int /*key_size*/)
+    LshTable(unsigned int feature_size, unsigned int key_size)
     {
+        (void)feature_size;
+        (void)key_size;
         std::cerr << "LSH is not implemented for that type" << std::endl;
         throw;
     }

src/cpp/flann/util/result_set.h

@@ -845,7 +845,7 @@ class RadiusUniqueResultSet : public UniqueResultSet<DistanceType>
 {
 public:
     /** Constructor
-     * @param capacity the number of neighbors to store at max
+     * @param radius the maximum distance of a neighbor
      */
     RadiusUniqueResultSet(DistanceType radius) :
         radius_(radius)
@@ -905,6 +905,7 @@ class KNNRadiusUniqueResultSet : public KNNUniqueResultSet<DistanceType>
 public:
     /** Constructor
      * @param capacity the number of neighbors to store at max
+     * @param radius the maximum distance of a neighbor
      */
     KNNRadiusUniqueResultSet(DistanceType radius, size_t capacity) : KNNUniqueResultSet<DistanceType>(capacity)
     {