Merge pull request opencv#17639 from pemmanuelviel:pev--binary-kmeans

Pev binary kmeans

* Ongoing work transposing kmeans clustering method for bitfields: the computeClustering method

Ongoing work transposing kmeans clustering method for bitfields: interface computeBitfieldClustering

Fix genericity of computeNodeStatistics

Ongoing work transposing kmeans clustering method for bitfields: adapt computeNodeStatistics()

Ongoing work transposing kmeans clustering method for bitfields: adapt findNN() method

Ongoing work transposing kmeans clustering method for bitfields: allow kmeans with Hamming distance

Ongoing work transposing kmeans clustering method for bitfields: adapt distances code

Ongoing work transposing kmeans clustering method for bitfields: adapt load/save code

Ongoing work transposing kmeans clustering method for bitfields: adapt kmeans hierarchicalClustring()

PivotType -> CentersType Renaming

Fix type casting for ARM SIMD implementation of Hamming

Fix warnings with Win32 compilation

Fix warnings with Win64 compilation

Fix wrong parenthesis position on rounding

* Ensure proper rounding when CentersType is integral

Author: pemmanuelviel

modules/flann/include/opencv2/flann.hpp

@@ -536,7 +536,7 @@ class Index_
 @param features The points to be clustered. The matrix must have elements of type
 Distance::ElementType.
 @param centers The centers of the clusters obtained. The matrix must have type
-Distance::ResultType. The number of rows in this matrix represents the number of clusters desired,
+Distance::CentersType. The number of rows in this matrix represents the number of clusters desired,
 however, because of the way the cut in the hierarchical tree is chosen, the number of clusters
 computed will be the highest number of the form (branching-1)\*k+1 that's lower than the number of
 clusters desired, where branching is the tree's branching factor (see description of the
@@ -553,15 +553,15 @@ int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::K
                            Distance d = Distance())
 {
     typedef typename Distance::ElementType ElementType;
-    typedef typename Distance::ResultType DistanceType;
+    typedef typename Distance::CentersType CentersType;
 
     CV_Assert(features.type() == CvType<ElementType>::type());
     CV_Assert(features.isContinuous());
     ::cvflann::Matrix<ElementType> m_features((ElementType*)features.ptr<ElementType>(0), features.rows, features.cols);
 
-    CV_Assert(centers.type() == CvType<DistanceType>::type());
+    CV_Assert(centers.type() == CvType<CentersType>::type());
     CV_Assert(centers.isContinuous());
-    ::cvflann::Matrix<DistanceType> m_centers((DistanceType*)centers.ptr<DistanceType>(0), centers.rows, centers.cols);
+    ::cvflann::Matrix<CentersType> m_centers((CentersType*)centers.ptr<CentersType>(0), centers.rows, centers.cols);
 
     return ::cvflann::hierarchicalClustering<Distance>(m_features, m_centers, params, d);
 }

modules/flann/include/opencv2/flann/all_indices.h

@@ -130,6 +130,9 @@ struct index_creator<False,False,Distance>
         case FLANN_INDEX_LINEAR:
             nnIndex = new LinearIndex<Distance>(dataset, params, distance);
             break;
+        case FLANN_INDEX_KMEANS:
+            nnIndex = new KMeansIndex<Distance>(dataset, params, distance);
+            break;
         case FLANN_INDEX_HIERARCHICAL:
             nnIndex = new HierarchicalClusteringIndex<Distance>(dataset, params, distance);
             break;