Merge remote-tracking branch 'upstream/3.4' into merge-3.4

modules/flann/include/opencv2/flann/dist.h

@@ -708,7 +708,7 @@ struct KL_Divergence
         Iterator1 last = a + size;
 
         while (a < last) {
-            if (* b != 0) {
+            if ( *a != 0 && *b != 0 ) {
                 ResultType ratio = (ResultType)(*a / *b);
                 if (ratio>0) {
                     result += *a * log(ratio);
@@ -731,7 +731,7 @@ struct KL_Divergence
     inline ResultType accum_dist(const U& a, const V& b, int) const
     {
         ResultType result = ResultType();
-        if( *b != 0 ) {
+        if( a != 0 && b != 0 ) {
             ResultType ratio = (ResultType)(a / b);
             if (ratio>0) {
                 result = a * log(ratio);

modules/flann/include/opencv2/flann/kdtree_single_index.h

@@ -461,7 +461,7 @@ private:
             DistanceType span = bbox[i].high-bbox[i].low;
             if (span>(DistanceType)((1-EPS)*max_span)) {
                 ElementType min_elem, max_elem;
-                computeMinMax(ind, count, cutfeat, min_elem, max_elem);
+                computeMinMax(ind, count, (int)i, min_elem, max_elem);
                 DistanceType spread = (DistanceType)(max_elem-min_elem);
                 if (spread>max_spread) {
                     cutfeat = (int)i;
@@ -548,11 +548,19 @@ private:
         /* If this is a leaf node, then do check and return. */
         if ((node->child1 == NULL)&&(node->child2 == NULL)) {
             DistanceType worst_dist = result_set.worstDist();
-            for (int i=node->left; i<node->right; ++i) {
-                int index = reorder_ ? i : vind_[i];
-                DistanceType dist = distance_(vec, data_[index], dim_, worst_dist);
-                if (dist<worst_dist) {
-                    result_set.addPoint(dist,vind_[i]);
+            if (reorder_) {
+                for (int i=node->left; i<node->right; ++i) {
+                    DistanceType dist = distance_(vec, data_[i], dim_, worst_dist);
+                    if (dist<worst_dist) {
+                        result_set.addPoint(dist,vind_[i]);
+                    }
+                }
+            } else {
+                for (int i=node->left; i<node->right; ++i) {
+                    DistanceType dist = distance_(vec, data_[vind_[i]], dim_, worst_dist);
+                    if (dist<worst_dist) {
+                        result_set.addPoint(dist,vind_[i]);
+                    }
                 }
             }
             return;

modules/flann/include/opencv2/flann/kmeans_index.h

@@ -650,7 +650,8 @@ private:
      *
      * Params:
      *     node = the node to use
-     *     indices = the indices of the points belonging to the node
+     *     indices = array of indices of the points belonging to the node
+     *     indices_length = number of indices in the array
      */
     void computeNodeStatistics(KMeansNodePtr node, int* indices, int indices_length)
     {
@@ -662,7 +663,7 @@ private:
 
         memset(mean,0,veclen_*sizeof(DistanceType));
 
-        for (size_t i=0; i<size_; ++i) {
+        for (size_t i=0; i<(size_t)indices_length; ++i) {
             ElementType* vec = dataset_[indices[i]];
             for (size_t j=0; j<veclen_; ++j) {
                 mean[j] += vec[j];

modules/flann/include/opencv2/flann/lsh_index.h

@@ -60,20 +60,20 @@ 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;
+        (*this)["algorithm"] = FLANN_INDEX_LSH;
         // The number of hash tables to use
-        (*this)["table_number"] = table_number;
+        (*this)["table_number"] = static_cast<int>(table_number);
         // The length of the key in the hash tables
-        (*this)["key_size"] = key_size;
+        (*this)["key_size"] = static_cast<int>(key_size);
         // Number of levels to use in multi-probe (0 for standard LSH)
-        (*this)["multi_probe_level"] = multi_probe_level;
+        (*this)["multi_probe_level"] = static_cast<int>(multi_probe_level);
     }
 };
 
 /**
- * Randomized kd-tree index
+ * Locality-sensitive hashing  index
  *
- * Contains the k-d trees and other information for indexing a set of points
+ * Contains the tables and other information for indexing a set of points
  * for nearest-neighbor matching.
  */
 template<typename Distance>
@@ -94,9 +94,9 @@ public:
     {
         // 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);
+        table_number_ = get_param(index_params_,"table_number",12);
+        key_size_ = get_param(index_params_,"key_size",20);
+        multi_probe_level_ = get_param(index_params_,"multi_probe_level",2);
 
         feature_size_ = (unsigned)dataset_.cols;
         fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_);
@@ -112,7 +112,7 @@ public:
     void buildIndex() CV_OVERRIDE
     {
         tables_.resize(table_number_);
-        for (unsigned int i = 0; i < table_number_; ++i) {
+        for (int i = 0; i < table_number_; ++i) {
             lsh::LshTable<ElementType>& table = tables_[i];
             table = lsh::LshTable<ElementType>(feature_size_, key_size_);
 
@@ -378,11 +378,11 @@ private:
     IndexParams index_params_;
 
     /** table number */
-    unsigned int table_number_;
+    int table_number_;
     /** key size */
-    unsigned int key_size_;
+    int key_size_;
     /** How far should we look for neighbors in multi-probe LSH */
-    unsigned int multi_probe_level_;
+    int multi_probe_level_;
 
     /** The XOR masks to apply to a key to get the neighboring buckets */
     std::vector<lsh::BucketKey> xor_masks_;

modules/flann/include/opencv2/flann/lsh_table.h

@@ -245,7 +245,7 @@ public:
     {
         std::cerr << "LSH is not implemented for that type" << std::endl;
         assert(0);
-        return 1;
+        return 0;
     }
 
     /** Get statistics about the table

modules/flann/include/opencv2/flann/result_set.h

@@ -196,12 +196,10 @@ public:
 #endif
             {
                 // Check for duplicate indices
-                int j = i - 1;
-                while ((j >= 0) && (dists[j] == dist)) {
+                for (int j = i; dists[j] == dist && j--;) {
                     if (indices[j] == index) {
                         return;
                     }
-                    --j;
                 }
                 break;
             }