Apply to KMeansIndex KMeanspp the same modification as in HierarchicalClusteringIndex

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

@@ -211,6 +211,7 @@ public:
 
         for (int i = 0; i < n; i++) {
             closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
+            closestDistSq[i] *= closestDistSq[i];
             currentPot += closestDistSq[i];
         }
 
@@ -236,7 +237,10 @@ public:
 
                 // Compute the new potential
                 double newPot = 0;
-                for (int i = 0; i < n; i++) newPot += std::min( distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols), closestDistSq[i] );
+                for (int i = 0; i < n; i++) {
+                    DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
+                    newPot += std::min( dist*dist, closestDistSq[i] );
+                }
 
                 // Store the best result
                 if ((bestNewPot < 0)||(newPot < bestNewPot)) {
@@ -248,7 +252,10 @@ public:
             // Add the appropriate center
             centers[centerCount] = indices[bestNewIndex];
             currentPot = bestNewPot;
-            for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols), closestDistSq[i] );
+            for (int i = 0; i < n; i++) {
+                DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
+                closestDistSq[i] = std::min( dist*dist, closestDistSq[i] );
+            }
         }
 
         centers_length = centerCount;