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+/*M///////////////////////////////////////////////////////////////////////////////////////
+ //
+ //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+ //
+ //  By downloading, copying, installing or using the software you agree to this license.
+ //  If you do not agree to this license, do not download, install,
+ //  copy or use the software.
+ //
+ //
+ //                           License Agreement
+ //                For Open Source Computer Vision Library
+ //
+ // Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+ // Third party copyrights are property of their respective owners.
+ //
+ // Redistribution and use in source and binary forms, with or without modification,
+ // are permitted provided that the following conditions are met:
+ //
+ //   * Redistribution's of source code must retain the above copyright notice,
+ //     this list of conditions and the following disclaimer.
+ //
+ //   * Redistribution's in binary form must reproduce the above copyright notice,
+ //     this list of conditions and the following disclaimer in the documentation
+ //     and/or other materials provided with the distribution.
+ //
+ //   * The name of the copyright holders may not be used to endorse or promote products
+ //     derived from this software without specific prior written permission.
+ //
+ // This software is provided by the copyright holders and contributors "as is" and
+ // any express or implied warranties, including, but not limited to, the implied
+ // warranties of merchantability and fitness for a particular purpose are disclaimed.
+ // In no event shall the Intel Corporation or contributors be liable for any direct,
+ // indirect, incidental, special, exemplary, or consequential damages
+ // (including, but not limited to, procurement of substitute goods or services;
+ // loss of use, data, or profits; or business interruption) however caused
+ // and on any theory of liability, whether in contract, strict liability,
+ // or tort (including negligence or otherwise) arising in any way out of
+ // the use of this software, even if advised of the possibility of such damage.
+ //
+ //M*/
+
+#ifndef __OPENCV_ONLINEBOOSTING_HPP__
+#define __OPENCV_ONLINEBOOSTING_HPP__
+
+#include "opencv2/core.hpp"
+
+namespace cv
+{
+
+//! @addtogroup tracking
+//! @{
+
+//TODO based on the original implementation
+//http://vision.ucsd.edu/~bbabenko/project_miltrack.shtml
+
+class BaseClassifier;
+class WeakClassifierHaarFeature;
+class EstimatedGaussDistribution;
+class ClassifierThreshold;
+class Detector;
+
+class StrongClassifierDirectSelection
+{
+ public:
+
+  StrongClassifierDirectSelection( int numBaseClf, int numWeakClf, Size patchSz, const Rect& sampleROI, bool useFeatureEx = false, int iterationInit =
+                                       0 );
+  virtual ~StrongClassifierDirectSelection();
+
+  void initBaseClassifier();
+
+  bool update( const Mat& image, int target, float importance = 1.0 );
+  float eval( const Mat& response );
+  std::vector<int> getSelectedWeakClassifier();
+  float classifySmooth( const std::vector<Mat>& images, const Rect& sampleROI, int& idx );
+  int getNumBaseClassifier();
+  Size getPatchSize() const;
+  Rect getROI() const;
+  bool getUseFeatureExchange() const;
+  int getReplacedClassifier() const;
+
+  void replaceWeakClassifier( int idx );
+  int getSwappedClassifier() const;
+ private:
+
+  //StrongClassifier
+  int numBaseClassifier;
+  int numAllWeakClassifier;
+  int numWeakClassifier;
+  int iterInit;
+  BaseClassifier** baseClassifier;
+  std::vector<float> alpha;
+  cv::Size patchSize;
+
+  bool useFeatureExchange;
+
+  //StrongClassifierDirectSelection
+  std::vector<bool> m_errorMask;
+  std::vector<float> m_errors;
+  std::vector<float> m_sumErrors;
+
+  Detector* detector;
+  Rect ROI;
+
+  int replacedClassifier;
+  int swappedClassifier;
+};
+
+class BaseClassifier
+{
+ public:
+
+  BaseClassifier( int numWeakClassifier, int iterationInit );
+  BaseClassifier( int numWeakClassifier, int iterationInit, WeakClassifierHaarFeature** weakCls );
+
+  WeakClassifierHaarFeature** getReferenceWeakClassifier()
+  {
+    return weakClassifier;
+  }
+  ;
+  void trainClassifier( const Mat& image, int target, float importance, std::vector<bool>& errorMask );
+  int selectBestClassifier( std::vector<bool>& errorMask, float importance, std::vector<float> & errors );
+  int computeReplaceWeakestClassifier( const std::vector<float> & errors );
+  void replaceClassifierStatistic( int sourceIndex, int targetIndex );
+  int getIdxOfNewWeakClassifier()
+  {
+    return m_idxOfNewWeakClassifier;
+  }
+  ;
+  int eval( const Mat& image );
+  virtual ~BaseClassifier();
+  float getError( int curWeakClassifier );
+  void getErrors( float* errors );
+  int getSelectedClassifier() const;
+  void replaceWeakClassifier( int index );
+
+ protected:
+
+  void generateRandomClassifier();
+  WeakClassifierHaarFeature** weakClassifier;
+  bool m_referenceWeakClassifier;
+  int m_numWeakClassifier;
+  int m_selectedClassifier;
+  int m_idxOfNewWeakClassifier;
+  std::vector<float> m_wCorrect;
+  std::vector<float> m_wWrong;
+  int m_iterationInit;
+
+};
+
+class EstimatedGaussDistribution
+{
+ public:
+
+  EstimatedGaussDistribution();
+  EstimatedGaussDistribution( float P_mean, float R_mean, float P_sigma, float R_sigma );
+  virtual ~EstimatedGaussDistribution();
+  void update( float value );  //, float timeConstant = -1.0);
+  float getMean();
+  float getSigma();
+  void setValues( float mean, float sigma );
+
+ private:
+
+  float m_mean;
+  float m_sigma;
+  float m_P_mean;
+  float m_P_sigma;
+  float m_R_mean;
+  float m_R_sigma;
+};
+
+class WeakClassifierHaarFeature
+{
+
+ public:
+
+  WeakClassifierHaarFeature();
+  virtual ~WeakClassifierHaarFeature();
+
+  bool update( float value, int target );
+  int eval( float value );
+
+ private:
+
+  float sigma;
+  float mean;
+  ClassifierThreshold* m_classifier;
+
+  void getInitialDistribution( EstimatedGaussDistribution *distribution );
+  void generateRandomClassifier( EstimatedGaussDistribution* m_posSamples, EstimatedGaussDistribution* m_negSamples );
+
+};
+
+class Detector
+{
+ public:
+
+  Detector( StrongClassifierDirectSelection* classifier );
+  virtual
+  ~Detector( void );
+
+  void
+  classifySmooth( const std::vector<Mat>& image, float minMargin = 0 );
+
+  int
+  getNumDetections();
+  float
+  getConfidence( int patchIdx );
+  float
+  getConfidenceOfDetection( int detectionIdx );
+
+  float getConfidenceOfBestDetection()
+  {
+    return m_maxConfidence;
+  }
+  ;
+  int
+  getPatchIdxOfBestDetection();
+
+  int
+  getPatchIdxOfDetection( int detectionIdx );
+
+  const std::vector<int> &
+  getIdxDetections() const
+  {
+    return m_idxDetections;
+  }
+  ;
+  const std::vector<float> &
+  getConfidences() const
+  {
+    return m_confidences;
+  }
+  ;
+
+  const cv::Mat &
+  getConfImageDisplay() const
+  {
+    return m_confImageDisplay;
+  }
+
+ private:
+
+  void
+  prepareConfidencesMemory( int numPatches );
+  void
+  prepareDetectionsMemory( int numDetections );
+
+  StrongClassifierDirectSelection* m_classifier;
+  std::vector<float> m_confidences;
+  int m_sizeConfidences;
+  int m_numDetections;
+  std::vector<int> m_idxDetections;
+  int m_sizeDetections;
+  int m_idxBestDetection;
+  float m_maxConfidence;
+  cv::Mat_<float> m_confMatrix;
+  cv::Mat_<float> m_confMatrixSmooth;
+  cv::Mat_<unsigned char> m_confImageDisplay;
+};
+
+class ClassifierThreshold
+{
+ public:
+
+  ClassifierThreshold( EstimatedGaussDistribution* posSamples, EstimatedGaussDistribution* negSamples );
+  virtual ~ClassifierThreshold();
+
+  void update( float value, int target );
+  int eval( float value );
+
+  void* getDistribution( int target );
+
+ private:
+
+  EstimatedGaussDistribution* m_posSamples;
+  EstimatedGaussDistribution* m_negSamples;
+
+  float m_threshold;
+  int m_parity;
+};
+
+//! @}
+
+} /* namespace cv */
+
+#endif