refactored CascadeClassifier to hide implementation details (take 2)

modules/objdetect/include/opencv2/objdetect.hpp

@@ -149,16 +149,58 @@ enum { CASCADE_DO_CANNY_PRUNING    = 1,
        CASCADE_DO_ROUGH_SEARCH     = 8
      };
 
-class CV_EXPORTS_W CascadeClassifier
+class CV_EXPORTS_W BaseCascadeClassifier : public Algorithm
+{
+public:
+    virtual ~BaseCascadeClassifier();
+    virtual bool empty() const = 0;
+    virtual bool load( const String& filename ) = 0;
+    virtual void detectMultiScale( InputArray image,
+                           CV_OUT std::vector<Rect>& objects,
+                           double scaleFactor,
+                           int minNeighbors, int flags,
+                           Size minSize, Size maxSize ) = 0;
+
+    virtual void detectMultiScale( InputArray image,
+                           CV_OUT std::vector<Rect>& objects,
+                           CV_OUT std::vector<int>& numDetections,
+                           double scaleFactor,
+                           int minNeighbors, int flags,
+                           Size minSize, Size maxSize ) = 0;
+
+    virtual void detectMultiScale( InputArray image,
+                                   CV_OUT std::vector<Rect>& objects,
+                                   CV_OUT std::vector<int>& rejectLevels,
+                                   CV_OUT std::vector<double>& levelWeights,
+                                   double scaleFactor,
+                                   int minNeighbors, int flags,
+                                   Size minSize, Size maxSize,
+                                   bool outputRejectLevels ) = 0;
+
+    virtual bool isOldFormatCascade() const = 0;
+    virtual Size getOriginalWindowSize() const = 0;
+    virtual int getFeatureType() const = 0;
+    virtual void* getOldCascade() = 0;
+
+    class CV_EXPORTS MaskGenerator
+    {
+    public:
+        virtual ~MaskGenerator() {}
+        virtual Mat generateMask(const Mat& src)=0;
+        virtual void initializeMask(const Mat& /*src*/) {};
+    };
+    virtual void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator) = 0;
+    virtual Ptr<MaskGenerator> getMaskGenerator() = 0;
+};
+
+class CV_EXPORTS_W CascadeClassifier : public BaseCascadeClassifier
 {
 public:
     CV_WRAP CascadeClassifier();
-    CV_WRAP CascadeClassifier( const String& filename );
+    CV_WRAP explicit CascadeClassifier(const String& filename);
     virtual ~CascadeClassifier();
-
     CV_WRAP virtual bool empty() const;
-    CV_WRAP bool load( const String& filename );
+    CV_WRAP virtual bool load( const String& filename );
-    virtual bool read( const FileNode& node );
     CV_WRAP virtual void detectMultiScale( InputArray image,
                                   CV_OUT std::vector<Rect>& objects,
                                   double scaleFactor = 1.1,
@@ -184,108 +226,19 @@ public:
                                   Size maxSize = Size(),
                                   bool outputRejectLevels = false );
 
+    CV_WRAP virtual bool isOldFormatCascade() const;
+    CV_WRAP virtual Size getOriginalWindowSize() const;
+    CV_WRAP virtual int getFeatureType() const;
+    virtual void* getOldCascade();
 
-    bool isOldFormatCascade() const;
+    virtual void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator);
-    virtual Size getOriginalWindowSize() const;
+    virtual Ptr<MaskGenerator> getMaskGenerator();
-    int getFeatureType() const;
-    bool setImage( const Mat& );
-
 protected:
-    virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
+    Ptr<BaseCascadeClassifier> cc;
-                                    int stripSize, int yStep, double factor, std::vector<Rect>& candidates,
-                                    std::vector<int>& rejectLevels, std::vector<double>& levelWeights, bool outputRejectLevels = false );
-
-    virtual void detectMultiScaleNoGrouping( const Mat& image, std::vector<Rect>& candidates,
-                                             std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
-                                             double scaleFactor, Size minObjectSize, Size maxObjectSize,
-                                             bool outputRejectLevels = false );
-
-protected:
-    enum { BOOST = 0
-         };
-    enum { DO_CANNY_PRUNING    = CASCADE_DO_CANNY_PRUNING,
-           SCALE_IMAGE         = CASCADE_SCALE_IMAGE,
-           FIND_BIGGEST_OBJECT = CASCADE_FIND_BIGGEST_OBJECT,
-           DO_ROUGH_SEARCH     = CASCADE_DO_ROUGH_SEARCH
-         };
-
-    friend class CascadeClassifierInvoker;
-
-    template<class FEval>
-    friend int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
-
-    template<class FEval>
-    friend int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
-
-    template<class FEval>
-    friend int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
-
-    template<class FEval>
-    friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
-
-    bool setImage( Ptr<FeatureEvaluator>& feval, const Mat& image);
-    virtual int runAt( Ptr<FeatureEvaluator>& feval, Point pt, double& weight );
-
-    class Data
-    {
-    public:
-        struct CV_EXPORTS DTreeNode
-        {
-            int featureIdx;
-            float threshold; // for ordered features only
-            int left;
-            int right;
-        };
-
-        struct CV_EXPORTS DTree
-        {
-            int nodeCount;
-        };
-
-        struct CV_EXPORTS Stage
-        {
-            int first;
-            int ntrees;
-            float threshold;
-        };
-
-        bool read(const FileNode &node);
-
-        bool isStumpBased;
-
-        int stageType;
-        int featureType;
-        int ncategories;
-        Size origWinSize;
-
-        std::vector<Stage> stages;
-        std::vector<DTree> classifiers;
-        std::vector<DTreeNode> nodes;
-        std::vector<float> leaves;
-        std::vector<int> subsets;
-    };
-
-    Data data;
-    Ptr<FeatureEvaluator> featureEvaluator;
-    Ptr<CvHaarClassifierCascade> oldCascade;
-
-public:
-    class CV_EXPORTS MaskGenerator
-    {
-    public:
-        virtual ~MaskGenerator() {}
-        virtual cv::Mat generateMask(const cv::Mat& src)=0;
-        virtual void initializeMask(const cv::Mat& /*src*/) {};
-    };
-    void setMaskGenerator(Ptr<MaskGenerator> maskGenerator);
-    Ptr<MaskGenerator> getMaskGenerator();
-
-    void setFaceDetectionMaskGenerator();
-
-protected:
-    Ptr<MaskGenerator> maskGenerator;
 };
 
+CV_EXPORTS Ptr<CascadeClassifier::MaskGenerator> createFaceDetectionMaskGenerator();
+
 //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
 
 // struct for detection region of interest (ROI)

modules/objdetect/src/cascadedetect.cpp

@@ -430,7 +430,6 @@ void groupRectangles_meanshift(std::vector<Rect>& rectList, std::vector<double>&
 }
 
 
-
 FeatureEvaluator::~FeatureEvaluator() {}
 bool FeatureEvaluator::read(const FileNode&) {return true;}
 Ptr<FeatureEvaluator> FeatureEvaluator::clone() const { return Ptr<FeatureEvaluator>(); }
@@ -834,25 +833,20 @@ Ptr<FeatureEvaluator> FeatureEvaluator::create( int featureType )
 
 //---------------------------------------- Classifier Cascade --------------------------------------------
 
-CascadeClassifier::CascadeClassifier()
+CascadeClassifierImpl::CascadeClassifierImpl()
 {
 }
 
-CascadeClassifier::CascadeClassifier(const String& filename)
+CascadeClassifierImpl::~CascadeClassifierImpl()
-{
-    load(filename);
-}
-
-CascadeClassifier::~CascadeClassifier()
 {
 }
 
-bool CascadeClassifier::empty() const
+bool CascadeClassifierImpl::empty() const
 {
     return !oldCascade && data.stages.empty();
 }
 
-bool CascadeClassifier::load(const String& filename)
+bool CascadeClassifierImpl::load(const String& filename)
 {
     oldCascade.release();
     data = Data();
@@ -862,7 +856,7 @@ bool CascadeClassifier::load(const String& filename)
     if( !fs.isOpened() )
         return false;
 
-    if( read(fs.getFirstTopLevelNode()) )
+    if( read_(fs.getFirstTopLevelNode()) )
         return true;
 
     fs.release();
@@ -871,7 +865,12 @@ bool CascadeClassifier::load(const String& filename)
     return !oldCascade.empty();
 }
 
-int CascadeClassifier::runAt( Ptr<FeatureEvaluator>& evaluator, Point pt, double& weight )
+void CascadeClassifierImpl::read(const FileNode& node)
+{
+    read_(node);
+}
+
+int CascadeClassifierImpl::runAt( Ptr<FeatureEvaluator>& evaluator, Point pt, double& weight )
 {
     CV_Assert( !oldCascade );
 
@@ -905,33 +904,33 @@ int CascadeClassifier::runAt( Ptr<FeatureEvaluator>& evaluator, Point pt, double
     }
 }
 
-bool CascadeClassifier::setImage( Ptr<FeatureEvaluator>& evaluator, const Mat& image )
+bool CascadeClassifierImpl::setImage( Ptr<FeatureEvaluator>& evaluator, const Mat& image )
 {
     return empty() ? false : evaluator->setImage(image, data.origWinSize);
 }
 
-void CascadeClassifier::setMaskGenerator(Ptr<MaskGenerator> _maskGenerator)
+void CascadeClassifierImpl::setMaskGenerator(const Ptr<MaskGenerator>& _maskGenerator)
 {
     maskGenerator=_maskGenerator;
 }
-Ptr<CascadeClassifier::MaskGenerator> CascadeClassifier::getMaskGenerator()
+Ptr<CascadeClassifierImpl::MaskGenerator> CascadeClassifierImpl::getMaskGenerator()
 {
     return maskGenerator;
 }
 
-void CascadeClassifier::setFaceDetectionMaskGenerator()
+Ptr<CascadeClassifier::MaskGenerator> createFaceDetectionMaskGenerator()
 {
 #ifdef HAVE_TEGRA_OPTIMIZATION
-    setMaskGenerator(tegra::getCascadeClassifierMaskGenerator(*this));
+    return tegra::getCascadeClassifierMaskGenerator(*this);
 #else
-    setMaskGenerator(Ptr<CascadeClassifier::MaskGenerator>());
+    return Ptr<CascadeClassifierImpl::MaskGenerator>();
 #endif
 }
 
 class CascadeClassifierInvoker : public ParallelLoopBody
 {
 public:
-    CascadeClassifierInvoker( CascadeClassifier& _cc, Size _sz1, int _stripSize, int _yStep, double _factor,
+    CascadeClassifierInvoker( CascadeClassifierImpl& _cc, Size _sz1, int _stripSize, int _yStep, double _factor,
         std::vector<Rect>& _vec, std::vector<int>& _levels, std::vector<double>& _weights, bool outputLevels, const Mat& _mask, Mutex* _mtx)
     {
         classifier = &_cc;
@@ -950,7 +949,8 @@ public:
     {
         Ptr<FeatureEvaluator> evaluator = classifier->featureEvaluator->clone();
 
-        Size winSize(cvRound(classifier->data.origWinSize.width * scalingFactor), cvRound(classifier->data.origWinSize.height * scalingFactor));
+        Size winSize(cvRound(classifier->data.origWinSize.width * scalingFactor),
+                     cvRound(classifier->data.origWinSize.height * scalingFactor));
 
         int y1 = range.start * stripSize;
         int y2 = std::min(range.end * stripSize, processingRectSize.height);
@@ -995,7 +995,7 @@ public:
         }
     }
 
-    CascadeClassifier* classifier;
+    CascadeClassifierImpl* classifier;
     std::vector<Rect>* rectangles;
     Size processingRectSize;
     int stripSize, yStep;
@@ -1010,7 +1010,7 @@ struct getRect { Rect operator ()(const CvAvgComp& e) const { return e.rect; } }
 struct getNeighbors { int operator ()(const CvAvgComp& e) const { return e.neighbors; } };
 
 
-bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
+bool CascadeClassifierImpl::detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
                                            int stripSize, int yStep, double factor, std::vector<Rect>& candidates,
                                            std::vector<int>& levels, std::vector<double>& weights, bool outputRejectLevels )
 {
@@ -1051,27 +1051,33 @@ bool CascadeClassifier::detectSingleScale( const Mat& image, int stripCount, Siz
     return true;
 }
 
-bool CascadeClassifier::isOldFormatCascade() const
+bool CascadeClassifierImpl::isOldFormatCascade() const
 {
     return !oldCascade.empty();
 }
 
 
-int CascadeClassifier::getFeatureType() const
+int CascadeClassifierImpl::getFeatureType() const
 {
     return featureEvaluator->getFeatureType();
 }
 
-Size CascadeClassifier::getOriginalWindowSize() const
+Size CascadeClassifierImpl::getOriginalWindowSize() const
 {
     return data.origWinSize;
 }
 
-bool CascadeClassifier::setImage(const Mat& image)
+bool CascadeClassifierImpl::setImage(InputArray _image)
 {
+    Mat image = _image.getMat();
     return featureEvaluator->setImage(image, data.origWinSize);
 }
 
+void* CascadeClassifierImpl::getOldCascade()
+{
+    return oldCascade;
+}
+
 static void detectMultiScaleOldFormat( const Mat& image, Ptr<CvHaarClassifierCascade> oldCascade,
                                        std::vector<Rect>& objects,
                                        std::vector<int>& rejectLevels,
@@ -1090,7 +1096,7 @@ static void detectMultiScaleOldFormat( const Mat& image, Ptr<CvHaarClassifierCas
     std::transform(vecAvgComp.begin(), vecAvgComp.end(), objects.begin(), getRect());
 }
 
-void CascadeClassifier::detectMultiScaleNoGrouping( const Mat& image, std::vector<Rect>& candidates,
+void CascadeClassifierImpl::detectMultiScaleNoGrouping( const Mat& image, std::vector<Rect>& candidates,
                                                     std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
                                                     double scaleFactor, Size minObjectSize, Size maxObjectSize,
                                                     bool outputRejectLevels )
@@ -1154,7 +1160,7 @@ void CascadeClassifier::detectMultiScaleNoGrouping( const Mat& image, std::vecto
     }
 }
 
-void CascadeClassifier::detectMultiScale( InputArray _image, std::vector<Rect>& objects,
+void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rect>& objects,
                                           std::vector<int>& rejectLevels,
                                           std::vector<double>& levelWeights,
                                           double scaleFactor, int minNeighbors,
@@ -1189,7 +1195,7 @@ void CascadeClassifier::detectMultiScale( InputArray _image, std::vector<Rect>&
     }
 }
 
-void CascadeClassifier::detectMultiScale( InputArray _image, std::vector<Rect>& objects,
+void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rect>& objects,
                                           double scaleFactor, int minNeighbors,
                                           int flags, Size minObjectSize, Size maxObjectSize)
 {
@@ -1200,7 +1206,7 @@ void CascadeClassifier::detectMultiScale( InputArray _image, std::vector<Rect>&
         minNeighbors, flags, minObjectSize, maxObjectSize );
 }
 
-void CascadeClassifier::detectMultiScale( InputArray _image, std::vector<Rect>& objects,
+void CascadeClassifierImpl::detectMultiScale( InputArray _image, std::vector<Rect>& objects,
                                           std::vector<int>& numDetections, double scaleFactor,
                                           int minNeighbors, int flags, Size minObjectSize,
                                           Size maxObjectSize )
@@ -1229,7 +1235,7 @@ void CascadeClassifier::detectMultiScale( InputArray _image, std::vector<Rect>&
     }
 }
 
-bool CascadeClassifier::Data::read(const FileNode &root)
+bool CascadeClassifierImpl::Data::read(const FileNode &root)
 {
     static const float THRESHOLD_EPS = 1e-5f;
 
@@ -1339,7 +1345,7 @@ bool CascadeClassifier::Data::read(const FileNode &root)
     return true;
 }
 
-bool CascadeClassifier::read(const FileNode& root)
+bool CascadeClassifierImpl::read_(const FileNode& root)
 {
     if( !data.read(root) )
         return false;
@@ -1356,4 +1362,106 @@ bool CascadeClassifier::read(const FileNode& root)
 template<> void DefaultDeleter<CvHaarClassifierCascade>::operator ()(CvHaarClassifierCascade* obj) const
 { cvReleaseHaarClassifierCascade(&obj); }
 
+
+BaseCascadeClassifier::~BaseCascadeClassifier()
+{
+}
+
+CascadeClassifier::CascadeClassifier() {}
+CascadeClassifier::CascadeClassifier(const String& filename)
+{
+    load(filename);
+}
+
+CascadeClassifier::~CascadeClassifier()
+{
+}
+
+bool CascadeClassifier::empty() const
+{
+    return cc.empty() || cc->empty();
+}
+
+bool CascadeClassifier::load( const String& filename )
+{
+    cc = makePtr<CascadeClassifierImpl>();
+    if(!cc->load(filename))
+        cc.release();
+    return !empty();
+}
+
+void CascadeClassifier::detectMultiScale( InputArray image,
+                      CV_OUT std::vector<Rect>& objects,
+                      double scaleFactor,
+                      int minNeighbors, int flags,
+                      Size minSize,
+                      Size maxSize )
+{
+    CV_Assert(!empty());
+    cc->detectMultiScale(image, objects, scaleFactor, minNeighbors, flags, minSize, maxSize);
+}
+
+void CascadeClassifier::detectMultiScale( InputArray image,
+                      CV_OUT std::vector<Rect>& objects,
+                      CV_OUT std::vector<int>& numDetections,
+                      double scaleFactor,
+                      int minNeighbors, int flags,
+                      Size minSize, Size maxSize )
+{
+    CV_Assert(!empty());
+    cc->detectMultiScale(image, objects, numDetections,
+                         scaleFactor, minNeighbors, flags, minSize, maxSize);
+}
+
+void CascadeClassifier::detectMultiScale( InputArray image,
+                      CV_OUT std::vector<Rect>& objects,
+                      CV_OUT std::vector<int>& rejectLevels,
+                      CV_OUT std::vector<double>& levelWeights,
+                      double scaleFactor,
+                      int minNeighbors, int flags,
+                      Size minSize, Size maxSize,
+                      bool outputRejectLevels )
+{
+    CV_Assert(!empty());
+    cc->detectMultiScale(image, objects, rejectLevels, levelWeights,
+                         scaleFactor, minNeighbors, flags,
+                         minSize, maxSize, outputRejectLevels);
+}
+
+bool CascadeClassifier::isOldFormatCascade() const
+{
+    CV_Assert(!empty());
+    return cc->isOldFormatCascade();
+}
+
+Size CascadeClassifier::getOriginalWindowSize() const
+{
+    CV_Assert(!empty());
+    return cc->getOriginalWindowSize();
+}
+
+int CascadeClassifier::getFeatureType() const
+{
+    CV_Assert(!empty());
+    return cc->getFeatureType();
+}
+
+void* CascadeClassifier::getOldCascade()
+{
+    CV_Assert(!empty());
+    return cc->getOldCascade();
+}
+
+void CascadeClassifier::setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator)
+{
+    CV_Assert(!empty());
+    cc->setMaskGenerator(maskGenerator);
+}
+
+Ptr<BaseCascadeClassifier::MaskGenerator> CascadeClassifier::getMaskGenerator()
+{
+    CV_Assert(!empty());
+    return cc->getMaskGenerator();
+}
+
 } // namespace cv

modules/objdetect/src/cascadedetect.hpp

@@ -3,6 +3,132 @@
 namespace cv
 {
 
+class CascadeClassifierImpl : public BaseCascadeClassifier
+{
+public:
+    CascadeClassifierImpl();
+    virtual ~CascadeClassifierImpl();
+
+    bool empty() const;
+    bool load( const String& filename );
+    void read( const FileNode& node );
+    bool read_( const FileNode& node );
+    void detectMultiScale( InputArray image,
+                          CV_OUT std::vector<Rect>& objects,
+                          double scaleFactor = 1.1,
+                          int minNeighbors = 3, int flags = 0,
+                          Size minSize = Size(),
+                          Size maxSize = Size() );
+
+    void detectMultiScale( InputArray image,
+                          CV_OUT std::vector<Rect>& objects,
+                          CV_OUT std::vector<int>& numDetections,
+                          double scaleFactor=1.1,
+                          int minNeighbors=3, int flags=0,
+                          Size minSize=Size(),
+                          Size maxSize=Size() );
+
+    void detectMultiScale( InputArray image,
+                          CV_OUT std::vector<Rect>& objects,
+                          CV_OUT std::vector<int>& rejectLevels,
+                          CV_OUT std::vector<double>& levelWeights,
+                          double scaleFactor = 1.1,
+                          int minNeighbors = 3, int flags = 0,
+                          Size minSize = Size(),
+                          Size maxSize = Size(),
+                          bool outputRejectLevels = false );
+
+
+    bool isOldFormatCascade() const;
+    Size getOriginalWindowSize() const;
+    int getFeatureType() const;
+    bool setImage( InputArray );
+    void* getOldCascade();
+
+    void setMaskGenerator(const Ptr<MaskGenerator>& maskGenerator);
+    Ptr<MaskGenerator> getMaskGenerator();
+
+protected:
+    bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
+                           int stripSize, int yStep, double factor, std::vector<Rect>& candidates,
+                           std::vector<int>& rejectLevels, std::vector<double>& levelWeights, bool outputRejectLevels = false );
+
+    void detectMultiScaleNoGrouping( const Mat& image, std::vector<Rect>& candidates,
+                                    std::vector<int>& rejectLevels, std::vector<double>& levelWeights,
+                                    double scaleFactor, Size minObjectSize, Size maxObjectSize,
+                                    bool outputRejectLevels = false );
+
+    enum { BOOST = 0
+    };
+    enum { DO_CANNY_PRUNING    = CASCADE_DO_CANNY_PRUNING,
+        SCALE_IMAGE         = CASCADE_SCALE_IMAGE,
+        FIND_BIGGEST_OBJECT = CASCADE_FIND_BIGGEST_OBJECT,
+        DO_ROUGH_SEARCH     = CASCADE_DO_ROUGH_SEARCH
+    };
+
+    friend class CascadeClassifierInvoker;
+
+    template<class FEval>
+    friend int predictOrdered( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
+
+    template<class FEval>
+    friend int predictCategorical( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
+
+    template<class FEval>
+    friend int predictOrderedStump( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
+
+    template<class FEval>
+    friend int predictCategoricalStump( CascadeClassifierImpl& cascade, Ptr<FeatureEvaluator> &featureEvaluator, double& weight);
+
+    bool setImage( Ptr<FeatureEvaluator>& feval, const Mat& image);
+    int runAt( Ptr<FeatureEvaluator>& feval, Point pt, double& weight );
+
+    class Data
+    {
+    public:
+        struct CV_EXPORTS DTreeNode
+        {
+            int featureIdx;
+            float threshold; // for ordered features only
+            int left;
+            int right;
+        };
+
+        struct CV_EXPORTS DTree
+        {
+            int nodeCount;
+        };
+
+        struct CV_EXPORTS Stage
+        {
+            int first;
+            int ntrees;
+            float threshold;
+        };
+
+        bool read(const FileNode &node);
+
+        bool isStumpBased;
+
+        int stageType;
+        int featureType;
+        int ncategories;
+        Size origWinSize;
+
+        std::vector<Stage> stages;
+        std::vector<DTree> classifiers;
+        std::vector<DTreeNode> nodes;
+        std::vector<float> leaves;
+        std::vector<int> subsets;
+    };
+
+    Data data;
+    Ptr<FeatureEvaluator> featureEvaluator;
+    Ptr<CvHaarClassifierCascade> oldCascade;
+
+    Ptr<MaskGenerator> maskGenerator;
+};
+
 #define CC_CASCADE_PARAMS "cascadeParams"
 #define CC_STAGE_TYPE     "stageType"
 #define CC_FEATURE_TYPE   "featureType"
@@ -322,30 +448,31 @@ inline void HOGEvaluator::Feature :: updatePtrs( const std::vector<Mat> &_hist,
 //----------------------------------------------  predictor functions -------------------------------------
 
 template<class FEval>
-inline int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
+inline int predictOrdered( CascadeClassifierImpl& cascade,
+                           Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
 {
     int nstages = (int)cascade.data.stages.size();
     int nodeOfs = 0, leafOfs = 0;
     FEval& featureEvaluator = (FEval&)*_featureEvaluator;
     float* cascadeLeaves = &cascade.data.leaves[0];
-    CascadeClassifier::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
+    CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
-    CascadeClassifier::Data::DTree* cascadeWeaks = &cascade.data.classifiers[0];
+    CascadeClassifierImpl::Data::DTree* cascadeWeaks = &cascade.data.classifiers[0];
-    CascadeClassifier::Data::Stage* cascadeStages = &cascade.data.stages[0];
+    CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
 
     for( int si = 0; si < nstages; si++ )
     {
-        CascadeClassifier::Data::Stage& stage = cascadeStages[si];
+        CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
         int wi, ntrees = stage.ntrees;
         sum = 0;
 
         for( wi = 0; wi < ntrees; wi++ )
         {
-            CascadeClassifier::Data::DTree& weak = cascadeWeaks[stage.first + wi];
+            CascadeClassifierImpl::Data::DTree& weak = cascadeWeaks[stage.first + wi];
             int idx = 0, root = nodeOfs;
 
             do
             {
-                CascadeClassifier::Data::DTreeNode& node = cascadeNodes[root + idx];
+                CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[root + idx];
                 double val = featureEvaluator(node.featureIdx);
                 idx = val < node.threshold ? node.left : node.right;
             }
@@ -361,7 +488,8 @@ inline int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_f
 }
 
 template<class FEval>
-inline int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
+inline int predictCategorical( CascadeClassifierImpl& cascade,
+                               Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
 {
     int nstages = (int)cascade.data.stages.size();
     int nodeOfs = 0, leafOfs = 0;
@@ -369,23 +497,23 @@ inline int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator>
     size_t subsetSize = (cascade.data.ncategories + 31)/32;
     int* cascadeSubsets = &cascade.data.subsets[0];
     float* cascadeLeaves = &cascade.data.leaves[0];
-    CascadeClassifier::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
+    CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
-    CascadeClassifier::Data::DTree* cascadeWeaks = &cascade.data.classifiers[0];
+    CascadeClassifierImpl::Data::DTree* cascadeWeaks = &cascade.data.classifiers[0];
-    CascadeClassifier::Data::Stage* cascadeStages = &cascade.data.stages[0];
+    CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
 
     for(int si = 0; si < nstages; si++ )
     {
-        CascadeClassifier::Data::Stage& stage = cascadeStages[si];
+        CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
         int wi, ntrees = stage.ntrees;
         sum = 0;
 
         for( wi = 0; wi < ntrees; wi++ )
         {
-            CascadeClassifier::Data::DTree& weak = cascadeWeaks[stage.first + wi];
+            CascadeClassifierImpl::Data::DTree& weak = cascadeWeaks[stage.first + wi];
             int idx = 0, root = nodeOfs;
             do
             {
-                CascadeClassifier::Data::DTreeNode& node = cascadeNodes[root + idx];
+                CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[root + idx];
                 int c = featureEvaluator(node.featureIdx);
                 const int* subset = &cascadeSubsets[(root + idx)*subsetSize];
                 idx = (subset[c>>5] & (1 << (c & 31))) ? node.left : node.right;
@@ -402,24 +530,25 @@ inline int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator>
 }
 
 template<class FEval>
-inline int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
+inline int predictOrderedStump( CascadeClassifierImpl& cascade,
+                                Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
 {
     int nodeOfs = 0, leafOfs = 0;
     FEval& featureEvaluator = (FEval&)*_featureEvaluator;
     float* cascadeLeaves = &cascade.data.leaves[0];
-    CascadeClassifier::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
+    CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
-    CascadeClassifier::Data::Stage* cascadeStages = &cascade.data.stages[0];
+    CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
 
     int nstages = (int)cascade.data.stages.size();
     for( int stageIdx = 0; stageIdx < nstages; stageIdx++ )
     {
-        CascadeClassifier::Data::Stage& stage = cascadeStages[stageIdx];
+        CascadeClassifierImpl::Data::Stage& stage = cascadeStages[stageIdx];
         sum = 0.0;
 
         int ntrees = stage.ntrees;
         for( int i = 0; i < ntrees; i++, nodeOfs++, leafOfs+= 2 )
         {
-            CascadeClassifier::Data::DTreeNode& node = cascadeNodes[nodeOfs];
+            CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[nodeOfs];
             double value = featureEvaluator(node.featureIdx);
             sum += cascadeLeaves[ value < node.threshold ? leafOfs : leafOfs + 1 ];
         }
@@ -432,7 +561,8 @@ inline int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator
 }
 
 template<class FEval>
-inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
+inline int predictCategoricalStump( CascadeClassifierImpl& cascade,
+                                    Ptr<FeatureEvaluator> &_featureEvaluator, double& sum )
 {
     int nstages = (int)cascade.data.stages.size();
     int nodeOfs = 0, leafOfs = 0;
@@ -440,15 +570,15 @@ inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvalu
     size_t subsetSize = (cascade.data.ncategories + 31)/32;
     int* cascadeSubsets = &cascade.data.subsets[0];
     float* cascadeLeaves = &cascade.data.leaves[0];
-    CascadeClassifier::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
+    CascadeClassifierImpl::Data::DTreeNode* cascadeNodes = &cascade.data.nodes[0];
-    CascadeClassifier::Data::Stage* cascadeStages = &cascade.data.stages[0];
+    CascadeClassifierImpl::Data::Stage* cascadeStages = &cascade.data.stages[0];
 
 #ifdef HAVE_TEGRA_OPTIMIZATION
     float tmp = 0; // float accumulator -- float operations are quicker
 #endif
     for( int si = 0; si < nstages; si++ )
     {
-        CascadeClassifier::Data::Stage& stage = cascadeStages[si];
+        CascadeClassifierImpl::Data::Stage& stage = cascadeStages[si];
         int wi, ntrees = stage.ntrees;
 #ifdef HAVE_TEGRA_OPTIMIZATION
         tmp = 0;
@@ -458,7 +588,7 @@ inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvalu
 
         for( wi = 0; wi < ntrees; wi++ )
         {
-            CascadeClassifier::Data::DTreeNode& node = cascadeNodes[nodeOfs];
+            CascadeClassifierImpl::Data::DTreeNode& node = cascadeNodes[nodeOfs];
             int c = featureEvaluator(node.featureIdx);
             const int* subset = &cascadeSubsets[nodeOfs*subsetSize];
 #ifdef HAVE_TEGRA_OPTIMIZATION

modules/ocl/src/haar.cpp

@@ -655,7 +655,7 @@ void OclCascadeClassifier::detectMultiScale(oclMat &gimg, CV_OUT std::vector<cv:
 //CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemStorage *storage, double scaleFactor,
 //        int minNeighbors, int flags, CvSize minSize, CvSize maxSize)
 {
-    CvHaarClassifierCascade *cascade = oldCascade;
+    CvHaarClassifierCascade *cascade = (CvHaarClassifierCascade*)getOldCascade();
 
     const double GROUP_EPS = 0.2;