Fixing for tbb search

Author: mariusmuja

src/cpp/flann/algorithms/nn_index.h

@@ -469,7 +469,7 @@ class NNIndex : public IndexBase
             atomic_count = 0;
 
             // Use auto partitioner to choose the optimal grainsize for dividing the query points
-            flann::parallel_knnSearch2<Distance> parallel_knn(queries, indices, dists, knn, params, static_cast<Index*>(this), atomic_count);
+            flann::parallel_knnSearch2<Distance> parallel_knn(queries, indices, dists, knn, params, this, atomic_count);
             tbb::parallel_for(tbb::blocked_range<size_t>(0,queries.rows),
                               parallel_knn,
                               tbb::auto_partitioner());
@@ -587,7 +587,7 @@ class NNIndex : public IndexBase
             atomic_count = 0;
 
             // Use auto partitioner to choose the optimal grainsize for dividing the query points
-            flann::parallel_radiusSearch<Distance> parallel_radius(queries, indices, dists, radius, params, static_cast<Index*>(this), atomic_count);
+            flann::parallel_radiusSearch<Distance> parallel_radius(queries, indices, dists, radius, params, this, atomic_count);
             tbb::parallel_for(tbb::blocked_range<size_t>(0,queries.rows),
                               parallel_radius,
                               tbb::auto_partitioner());
@@ -703,7 +703,7 @@ class NNIndex : public IndexBase
           atomic_count = 0;
 
           // Use auto partitioner to choose the optimal grainsize for dividing the query points
-          flann::parallel_radiusSearch2<Distance> parallel_radius(queries, indices, dists, radius, params, static_cast<Index*>(this), atomic_count);
+          flann::parallel_radiusSearch2<Distance> parallel_radius(queries, indices, dists, radius, params, this, atomic_count);
           tbb::parallel_for(tbb::blocked_range<size_t>(0,queries.rows),
                             parallel_radius,
                             tbb::auto_partitioner());

src/cpp/flann/tbb/bodies.hpp

@@ -51,7 +51,7 @@ class parallel_knnSearch
 	typedef typename Distance::ResultType DistanceType;
 
 	parallel_knnSearch(const Matrix<ElementType>& queries,
-			Matrix<int>& indices,
+			Matrix<size_t>& indices,
 			Matrix<DistanceType>& distances,
 			size_t knn,
 			const SearchParams& params,
@@ -111,7 +111,7 @@ class parallel_knnSearch
 	//! Matrix for storing the indices of the nearest neighbors
 	//! \note no need for this to be a parallel container, each worker thread
 	//!       solely operates on its specified range!
-	Matrix<int>& indices_;
+	Matrix<size_t>& indices_;
 
 	//! Matrix for storing the distances to the nearest neighbors
 	//! \note no need for this to be a parallel container, each worker thread
@@ -141,7 +141,7 @@ class parallel_knnSearch2
 	typedef typename Distance::ResultType DistanceType;
 
 	parallel_knnSearch2(const Matrix<ElementType>& queries,
-			std::vector< std::vector<int> >& indices,
+			std::vector< std::vector<size_t> >& indices,
 			std::vector<std::vector<DistanceType> >& distances,
 			size_t knn,
 			const SearchParams& params,
@@ -209,7 +209,7 @@ class parallel_knnSearch2
 	//! Vector for storing the indices of the nearest neighbors
 	//! \note no need for this to be a parallel container, each worker thread
 	//!       solely operates on its specified range!
-	std::vector< std::vector<int> >& indices_;
+	std::vector< std::vector<size_t> >& indices_;
 
 	//! Vector for storing the distances to the nearest neighbors
 	//! \note no need for this to be a parallel container, each worker thread
@@ -249,7 +249,7 @@ class parallel_radiusSearch
 	 * (specified by the blocked_range parameter)
 	 */
 	parallel_radiusSearch(const Matrix<ElementType>& queries,
-			Matrix<int>& indices,
+			Matrix<size_t>& indices,
 			Matrix<DistanceType>& distances,
 			float radius,
 			const SearchParams& params,
@@ -329,7 +329,7 @@ class parallel_radiusSearch
 	//! Matrix for storing the indices of the nearest neighbors
 	//! \note no need for this to be a parallel container, each worker thread
 	//!       solely operates on its specified range!
-	Matrix<int>& indices_;
+	Matrix<size_t>& indices_;
 
 	//! Matrix for storing the distances to the nearest neighbors
 	//! \note no need for this to be a parallel container, each worker thread
@@ -369,7 +369,7 @@ class parallel_radiusSearch2
 	 * (specified by the blocked_range parameter)
 	 */
 	parallel_radiusSearch2(const Matrix<ElementType>& queries,
-			std::vector< std::vector<int> >& indices,
+			std::vector< std::vector<size_t> >& indices,
 			std::vector<std::vector<DistanceType> >& distances,
 			float radius,
 			const SearchParams& params,
@@ -446,7 +446,7 @@ class parallel_radiusSearch2
 	//! Vector for storing the indices of the nearest neighbors
 	//! \note no need for this to be a parallel container, each worker thread
 	//!       solely operates on its specified range!
-	std::vector< std::vector<int> >& indices_;
+	std::vector< std::vector<size_t> >& indices_;
 
 	//! Vector for storing the distances to the nearest neighbors
 	//! \note no need for this to be a parallel container, each worker thread

test/CMakeLists.txt

@@ -36,7 +36,6 @@ if (GTEST_FOUND AND HDF5_FOUND)
         add_definitions(-DTBB)
         flann_add_gtest(flann_multithreaded_test flann_multithreaded_test.cpp)
         target_link_libraries(flann_multithreaded_test ${TBB_LIBRARIES})
-        target_link_libraries(flann_simple_test ${TBB_LIBRARIES})
     endif()
 	    target_link_libraries(flann_linear_test flann_cpp ${HDF5_LIBRARIES})
 	    target_link_libraries(flann_kdtree_test flann_cpp ${HDF5_LIBRARIES})

test/flann_multithreaded_test.cpp

@@ -67,7 +67,7 @@ class FlannTest : public FLANNTestFixture {
         fflush(stdout);
         flann::load_from_file(data, "cloud.h5","dataset");
         flann::load_from_file(query,"cloud.h5","query");
-        flann::load_from_file(match,"cloud.h5","indices");
+        flann::load_from_file(match,"cloud.h5","match");
 
         dists = flann::Matrix<float>(new float[query.rows*nn], query.rows, nn);
         indices = flann::Matrix<int>(new int[query.rows*nn], query.rows, nn);