added some basic functionality needed by the new face module (moved from the old "contrib")

modules/core/include/opencv2/core.hpp

@@ -690,7 +690,61 @@ public:
     Mat mean; //!< mean value subtracted before the projection and added after the back projection
 };
 
+// Linear Discriminant Analysis
+class CV_EXPORTS LDA
+{
+public:
+    // Initializes a LDA with num_components (default 0) and specifies how
+    // samples are aligned (default dataAsRow=true).
+    explicit LDA(int num_components = 0);
 
+    // Initializes and performs a Discriminant Analysis with Fisher's
+    // Optimization Criterion on given data in src and corresponding labels
+    // in labels. If 0 (or less) number of components are given, they are
+    // automatically determined for given data in computation.
+    LDA(InputArrayOfArrays src, InputArray labels, int num_components = 0);
+
+    // Serializes this object to a given filename.
+    void save(const String& filename) const;
+
+    // Deserializes this object from a given filename.
+    void load(const String& filename);
+
+    // Serializes this object to a given cv::FileStorage.
+    void save(FileStorage& fs) const;
+
+        // Deserializes this object from a given cv::FileStorage.
+    void load(const FileStorage& node);
+
+    // Destructor.
+    ~LDA();
+
+    //! Compute the discriminants for data in src and labels.
+    void compute(InputArrayOfArrays src, InputArray labels);
+
+    // Projects samples into the LDA subspace.
+    Mat project(InputArray src);
+
+    // Reconstructs projections from the LDA subspace.
+    Mat reconstruct(InputArray src);
+
+    // Returns the eigenvectors of this LDA.
+    Mat eigenvectors() const { return _eigenvectors; }
+
+    // Returns the eigenvalues of this LDA.
+    Mat eigenvalues() const { return _eigenvalues; }
+
+    static Mat subspaceProject(InputArray W, InputArray mean, InputArray src);
+    static Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src);
+
+protected:
+    bool _dataAsRow;
+    int _num_components;
+    Mat _eigenvectors;
+    Mat _eigenvalues;
+
+    void lda(InputArrayOfArrays src, InputArray labels);
+};
 
 /*!
     Singular Value Decomposition class