Release 1.6

CMakeLists.txt

@@ -8,7 +8,7 @@ project(flann)
 string(TOLOWER ${PROJECT_NAME} PROJECT_NAME_LOWER)
 
 include(${PROJECT_SOURCE_DIR}/cmake/flann_utils.cmake)
-set(FLANN_VERSION 1.5.0)
+set(FLANN_VERSION 1.6.0)
 DISSECT_VERSION()
 GET_OS_INFO()
 

doc/manual.tex

@@ -16,7 +16,7 @@ Manual\\[1cm]}
 
 \author{Marius Muja, mariusm@cs.ubc.ca\\David Lowe, lowe@cs.ubc.ca}
 
-\date{September 26, 2009}
+\date{November 26, 2009}
 
 \begin{document}
 
@@ -60,8 +60,7 @@ through the C, MATLAB and Python bindings provided with the library.
 
 
 This section contains small examples of how to use the FLANN library from
-different programming languages (C++, C, MATLAB and Python) and from the
-command line.
+different programming languages (C++, C, MATLAB and Python).
 
 \begin{itemize}
 
@@ -88,7 +87,7 @@ int main(int argc, char** argv)
     flann::Matrix<float> dists(new float[query.rows*nn], query.rows, nn);
 
     // construct an randomized kd-tree index using 4 kd-trees
-    flann::Index<float> index(dataset, flann::KDTreeIndexParams(4));
+    flann::Index<flann::L2<float> > index(dataset, flann::KDTreeIndexParams(4));
     index.buildIndex();                                                                                               
 
     // do a knn search, using 128 checks
@@ -189,22 +188,6 @@ result,dists = flann.nn(dataset,testset,5,algorithm="kmeans",
                branching=32, iterations=7, checks=16);
 \end{Verbatim}
 
-\item \textbf{Command line application}
-
-
-
-\begin{Verbatim}[fontsize=\scriptsize,frame=single]
-$ flann compute_nn --input-file=dataset.dat --test-file=testset.dat 
---algorithm=kdtree --trees=8 --checks=64 --nn=5 --output-file=nn.dat
-Reading input dataset from dataset.dat
-Building index
-Building index took: 0.76
-Reading test dataset from testset.dat
-Searching for nearest neighbors
-Searching took 0.06 seconds
-Writing matches to nn.dat
-\end{Verbatim}
-
 \end{itemize}
 
 
@@ -270,25 +253,27 @@ types of nearest neighbor search indexes.
 \begin{Verbatim}[fontsize=\footnotesize,frame=single]
 namespace flann
 {
-    template<typename T>
+    template<typename Distance>
     class Index 
     {
+	typedef typename Distance::ElementType ElementType;
+	typedef typename Distance::ResultType DistanceType;
     public:
-	Index(const Matrix<T>& features, const IndexParams& params);
+	Index(const Matrix<ElementType>& features, const IndexParams& params);
 
 	~Index();
 
 	void buildIndex();
 
-	void knnSearch(const Matrix<T>& queries, 
+	void knnSearch(const Matrix<ElementType>& queries, 
 		       Matrix<int>& indices, 
-		       Matrix<float>& dists, 
+		       Matrix<DistanceType>& dists, 
 		       int knn, 
 		       const SearchParams& params);
 
-	int radiusSearch(const Matrix<T>& query, 
+	int radiusSearch(const Matrix<ElementType>& query, 
 			 Matrix<int>& indices, 
-			 Matrix<float>& dists, 
+			 Matrix<DistanceType>& dists, 
 			 float radius, 
 			 const SearchParams& params);
 
@@ -306,7 +291,7 @@ namespace flann
 \textbf{flann::Index::Index}
 Constructs a nearest neighbor search index for a given dataset.
 \begin{Verbatim}[fontsize=\footnotesize,frame=single]
-Index(const Matrix<T>& features, const IndexParams& params);
+Index(const Matrix<ElementType>& features, const IndexParams& params);
 \end{Verbatim}
 \begin{description}
 \item[features] Matrix containing the features(points) to index. The size of the matrix is $num\_features \times dimensionality$.
@@ -437,9 +422,9 @@ exception of saved index type).
 \subsubsection{flann::Index::knnSearch}
 Performs a K-nearest neighbor search for a given query point using the index.
 \begin{Verbatim}[fontsize=\footnotesize,frame=single]
-void Index::knnSearch(const Matrix<T>& queries,
+void Index::knnSearch(const Matrix<ElementType>& queries,
 		Matrix<int>& indices, 
-		Matrix<float>& dists,
+		Matrix<DistanceType>& dists,
 		int knn,
 		const SearchParams& params);
 \end{Verbatim}
@@ -459,9 +444,9 @@ precision was also computed, in which case this parameter is ignored.}
 \subsubsection{flann::Index::radiusSearch}
 Performs a radius nearest neighbor search for a given query point.
 \begin{Verbatim}[fontsize=\footnotesize,frame=single]
-int Index::radiusSearch(const Matrix<T>& query,
+int Index::radiusSearch(const Matrix<ElementType>& query,
 		  Matrix<int>& indices,
-		  Matrix<float>& dists,
+		  Matrix<DistanceType>& dists,
 		  float radius,
 		  const SearchParams& params); 
 \end{Verbatim}
@@ -485,39 +470,16 @@ Saves the index to a file.
 \item[filename]{The file to save the index to}
 \end{description}
 
-\subsubsection{flann::set\_distance\_type}
-\label{flann::setDistanceType}
-This function chooses the distance function to use when computing distances between
-data points.
-\begin{Verbatim}[fontsize=\footnotesize,frame=single]
-void set_distance_type(flann_distance_t distance_type, int order);
-\end{Verbatim}
-\begin{description}
-\item[distance\_type] The distance type to use. Possible values are
-\begin{Verbatim}[fontsize=\footnotesize]
-enum flann_distance_t {
-	EUCLIDEAN = 1,  // squared euclidean distance
-	MANHATTAN = 2,
-	MINKOWSKI = 3,
-	MAX_DIST   = 4,  // L_infinity - not valid for kd-tree  index type
-	HIK       = 5,
-	HELLINGER = 6,
-	CS        = 7,   // chi-square
-	KL        = 8,   // kullback-leibler divergence
-};
-\end{Verbatim}
-\item[order] Used in for the \texttt{MINKOWSKI} distance type, to choose the order of the Minkowski distance.
-\end{description}
-
-
 \subsubsection{flann::hierarchicalClustering}
 \label{flann::hierarchicalClustering}
 Clusters the given points by constructing a hierarchical k-means tree and choosing a cut in the tree that minimizes the clusters' variance.
 
 \begin{Verbatim}[fontsize=\footnotesize,frame=single]
-template <typename ELEM_TYPE, typename DIST_TYPE>
-int hierarchicalClustering(const Matrix<ELEM_TYPE>& features, 
-	  Matrix<DIST_TYPE>& centers, const KMeansIndexParams& params)
+template <typename Distance>
+int hierarchicalClustering(const Matrix<typename Distance::ElementType>& features, 
+			Matrix<typename Distance::ResultType>& centers,
+			const KMeansIndexParams& params, 
+		        Distance d = Distance())
 \end{Verbatim}
 \begin{description}
 \item[features]{The points to be clustered}
@@ -751,11 +713,29 @@ This function deletes a previously constructed index and frees all the memory
 used by it.
 
 \subsubsection{flann\_set\_distance\_type}
+\label{flann::setDistanceType}
 This function chooses the distance function to use when computing distances between
-data points (see \ref{flann::setDistanceType}).
+data points.
 \begin{Verbatim}[fontsize=\footnotesize,frame=single]
 void flann_set_distance_type(enum flann_distance_t distance_type, int order);
 \end{Verbatim}
+\begin{description}
+\item[distance\_type] The distance type to use. Possible values are
+\begin{Verbatim}[fontsize=\footnotesize]
+enum flann_distance_t {
+	EUCLIDEAN = 1,  // squared euclidean distance
+	MANHATTAN = 2,
+	MINKOWSKI = 3,
+	HIK       = 5,
+	HELLINGER = 6,
+	CS        = 7,   // chi-square
+	KL        = 8,   // kullback-leibler divergence
+};
+\end{Verbatim}
+\item[order] Used in for the \texttt{MINKOWSKI} distance type, to choose the order of the Minkowski distance.
+\end{description}
+
+
 
 
 \subsubsection{flann\_compute\_cluster\_centers()}
@@ -1179,76 +1159,6 @@ See section \ref{sec:quickstart} for an example of how to use the Python
 bindings.
 
 
-\subsection{Using the \texttt{flann} command line application}
-
-The FLANN distribution also contains a command line application that can be
-used to perform nearest-neighbor searches using datasets stored in files. The
-application can read datasets stored in CSV format, space-separated values or
-raw binary format.
-
-The command line application takes a command name as the first argument and
-then the arguments for that command:
-\begin{Verbatim}[fontsize=\footnotesize,frame=single]
-$ flann
-Usage: flann.py [command commans_args]
-
-Comamnds:
-      generate_random
-      compute_gt
-      compute_nn
-      autotune
-      sample_dataset
-      cluster
-      run_test
-
-For command specific help type: flann.py <command> -h
-\end{Verbatim}
-
-To see the possible arguments for each command, use \texttt{flann help
-<command>}. For example:
-
-\begin{Verbatim}[fontsize=\footnotesize,frame=single]
-$ flann run_test -h
-Usage: flann.py [command command_args]
-
-Options:
-  -h, --help            show this help message and exit
-  -i FILE, --input-file=FILE
-                        Name of file with input dataset
-  -a ALGORITHM, --algorithm=ALGORITHM
-                        The algorithm to use when constructing the index
-                        (kdtree, kmeans...)
-  -r TREES, --trees=TREES
-                        Number of parallel trees to use (where available, for
-                        example kdtree)
-  -b BRANCHING, --branching=BRANCHING
-                        Branching factor (where applicable, for example
-                        kmeans) (default: 2)
-  -C CENTERS_INIT, --centers-init=CENTERS_INIT
-                        How to choose the initial cluster centers for kmeans
-                        (random, gonzales) (default: random)
-  -M MAX_ITERATIONS, --max-iterations=MAX_ITERATIONS
-                        Max iterations to perform for kmeans (default: until
-                        convergence)
-  -l LOG_LEVEL, --log-level=LOG_LEVEL
-                        Log level (none < fatal < error < warning < info)
-                        (Default: info)
-  -t FILE, --test-file=FILE
-                        Name of file with test dataset
-  -m FILE, --match-file=FILE
-                        File with ground truth matches
-  -n NN, --nn=NN        Number of nearest neighbors to search for
-  -c CHECKS, --checks=CHECKS
-                        Number of times to restart search (in best-bin-first
-                        manner)
-  -P PRECISION, --precision=PRECISION
-                        Run the test until reaching this precision
-  -K NUM, --skip-matches=NUM
-                        Skip the first NUM matches at test phase
-
-\end{Verbatim}
-
-
 \bibliographystyle{alpha}
 \bibliography{references}
 

src/cpp/flann/flann.cpp

@@ -95,7 +95,7 @@ flann_index_t __flann_build_index(typename Distance::ElementType* dataset, int r
 		index_params->toParameters(*flann_params);
 
         if (index->getIndex()->getType()==AUTOTUNED) {
-            AutotunedIndex<T>* autotuned_index = (AutotunedIndex<T>*)index->getIndex(); 
+            AutotunedIndex<Distance>* autotuned_index = (AutotunedIndex<Distance>*)index->getIndex(); 
             flann_params->checks = autotuned_index->getSearchParameters()->checks;
             *speedup = autotuned_index->getSpeedup();
         }
@@ -762,4 +762,3 @@ EXPORTED int flann_compute_cluster_centers_int(int* dataset, int rows, int cols,
 	return _flann_compute_cluster_centers(dataset, rows, cols, clusters, result, flann_params);
 }
 
-}

src/cpp/flann/flann.hpp

@@ -259,7 +259,7 @@ int Index<Distance>::veclen() const
 
 template <typename Distance>
 int hierarchicalClustering(const Matrix<typename Distance::ElementType>& features, Matrix<typename Distance::ResultType>& centers,
-				const KMeansIndexParams& params, Distance d)
+				const KMeansIndexParams& params, Distance d = Distance())
 {
     KMeansIndex<Distance> kmeans(features, params, d);
 	kmeans.buildIndex();

src/cpp/flann/util/saving.cpp

@@ -34,7 +34,7 @@ namespace flann
 {
 
 const char FLANN_SIGNATURE[] = "FLANN_INDEX";
-const char FLANN_VERSION[] = "1.5.0";
+const char FLANN_VERSION[] = "1.6.0";
 
 
 IndexHeader load_header(FILE* stream)

src/python/pyflann/bindings/flann_ctypes.py

@@ -353,58 +353,6 @@ flannlib.flann_free_index_%(C)s.argtypes = [
 flann.free_index[%(numpy)s] = flannlib.flann_free_index_%(C)s
 """)
 
-flannlib.compute_ground_truth_float.restype = None
-flannlib.compute_ground_truth_float.argtypes = [ 
-        ndpointer(float32, ndim = 2, flags='aligned, c_contiguous'), # dataset
-        c_int, # dshape
-        c_int,
-        ndpointer(float32, ndim = 2, flags='aligned, c_contiguous'), # testset
-        c_int, # tshape
-        c_int,
-        ndpointer(int32, ndim = 2, flags='aligned, c_contiguous, writeable'), # matches
-        c_int, # mshape
-        c_int,
-        c_int # skip
-]
-
-flannlib.test_with_precision.restype = c_float
-flannlib.test_with_precision.argtypes = [
-        FLANN_INDEX, 
-        ndpointer(float32, ndim = 2, flags='aligned, c_contiguous'), # dataset
-        c_int, # dshape
-        c_int,
-        ndpointer(float32, ndim = 2, flags='aligned, c_contiguous'), # testset
-        c_int, # tshape
-        c_int,
-        ndpointer(int32, ndim = 2, flags='aligned, c_contiguous'), # matches
-        c_int, # mshape
-        c_int,
-        c_int, # nn
-        c_float, #precision
-        POINTER(c_int), # checks
-        c_int # skip
-]
-
-
-flannlib.test_with_checks.restype = c_float
-flannlib.test_with_checks.argtypes = [
-        FLANN_INDEX, 
-        ndpointer(float32, ndim = 2, flags='aligned, c_contiguous'), # dataset
-        c_int, # dshape
-        c_int,
-        ndpointer(float32, ndim = 2, flags='aligned, c_contiguous'), # testset
-        c_int, # tshape
-        c_int,
-        ndpointer(int32, ndim = 2, flags='aligned, c_contiguous'), # matches
-        c_int, # mshape
-        c_int,
-        c_int, # nn
-        c_int, # checks
-        POINTER(c_float), #precision
-        c_int # skip
-]
-
-
 
 def ensure_2d_array(array, flags, **kwargs):
     array = require(array, requirements = flags, **kwargs) 

src/python/setup.py

@@ -8,13 +8,14 @@ setup_path = os.path.dirname(os.path.abspath(sys.argv[0]))
 lib_path = os.path.abspath(os.path.join(setup_path,'../../../lib'))
 
 setup(name='flann',
-      version='1.5.0',
+      version='1.6.0',
       description='Fast Library for Approximate Nearest Neighbors',
       author='Marius Muja',
       author_email='mariusm@cs.ubc.ca',
       license='BSD',
       url='http://www.cs.ubc.ca/~mariusm/flann/',
-      packages=['pyflann', 'pyflann.io', 'pyflann.bindings', 'pyflann.util', 'pyflann.lib'],
+      packages=['pyflann','pyflann.command', 'pyflann.io', 'pyflann.bindings', 'pyflann.util', 'pyflann.lib'],
+      scripts=['flann'],
       package_dir={'pyflann.lib':lib_path},
       package_data={'pyflann.lib': ['libflann.so','flann.dll', 'libflann.dylib']}, 
 )