CascadeClassifier refactored. Most of the members and methods are private now.

modules/objdetect/include/opencv2/objdetect/objdetect.hpp

@@ -278,6 +278,7 @@ class CV_EXPORTS FeatureEvaluator
 public:    
     enum { HAAR = 0, LBP = 1 };
     virtual ~FeatureEvaluator();
+
     virtual bool read(const FileNode& node);
     virtual Ptr<FeatureEvaluator> clone() const;
     virtual int getFeatureType() const;
@@ -296,65 +297,96 @@ template<> CV_EXPORTS void Ptr<CvHaarClassifierCascade>::delete_obj();
 class CV_EXPORTS_W CascadeClassifier
 {
 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;
-    };
-    
-    enum { BOOST = 0 };
-    enum { DO_CANNY_PRUNING = 1, SCALE_IMAGE = 2,
-           FIND_BIGGEST_OBJECT = 4, DO_ROUGH_SEARCH = 8 };
-
     CV_WRAP CascadeClassifier();
-    CV_WRAP CascadeClassifier(const string& filename);
-    ~CascadeClassifier();
+    CV_WRAP CascadeClassifier( const string& filename );
+    virtual ~CascadeClassifier();
     
-    CV_WRAP bool empty() const;
-    CV_WRAP bool load(const string& filename);
-    bool read(const FileNode& node);
+    CV_WRAP virtual bool empty() const;
+    CV_WRAP bool load( const string& filename );
+    bool read( const FileNode& node );
     CV_WRAP void detectMultiScale( const Mat& image,
                                    CV_OUT vector<Rect>& objects,
                                    double scaleFactor=1.1,
                                    int minNeighbors=3, int flags=0,
                                    Size minSize=Size(),
-                                   Size maxSize=Size());
- 
+                                   Size maxSize=Size() );
+
+
+    bool isOldFormatCascade() const;
+    virtual Size getOriginalWindowSize() const;
+    int getFeatureType() const;
+    bool setImage(const Mat&);
+
+protected:
+    virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,
+                                    int stripSize, int yStep, double factor, vector<Rect>& candidates );
+
+private:
+    enum { BOOST = 0 };
+    enum { DO_CANNY_PRUNING = 1, SCALE_IMAGE = 2,
+           FIND_BIGGEST_OBJECT = 4, DO_ROUGH_SEARCH = 8 };
+
+    friend class CascadeClassifierInvoker;
+
+    template<class FEval>
+    friend int predictOrdered( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
+
+    template<class FEval>
+    friend int predictCategorical( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
+
+    template<class FEval>
+    friend int predictOrderedStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
+
+    template<class FEval>
+    friend int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvaluator> &featureEvaluator);
+
     bool setImage( Ptr<FeatureEvaluator>&, const Mat& );
     int runAt( Ptr<FeatureEvaluator>&, Point );
 
-    bool isStumpBased;
-
-    int stageType;
-    int featureType;
-    int ncategories;
-    Size origWinSize;
-    
-    vector<Stage> stages;
-    vector<DTree> classifiers;
-    vector<DTreeNode> nodes;
-    vector<float> leaves;
-    vector<int> subsets;
+    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;
+
+        vector<Stage> stages;
+        vector<DTree> classifiers;
+        vector<DTreeNode> nodes;
+        vector<float> leaves;
+        vector<int> subsets;
+    };
 
-    Ptr<FeatureEvaluator> feval;
+    Data data;
+    Ptr<FeatureEvaluator> featureEvaluator;
     Ptr<CvHaarClassifierCascade> oldCascade;
 };
 
-
 //////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
 
 struct CV_EXPORTS_W HOGDescriptor

modules/traincascade/boost.cpp

@@ -474,9 +474,9 @@ float CvCascadeBoostTrainData::getVarValue( int vi, int si )
 
 struct FeatureIdxOnlyPrecalc
 {
-    FeatureIdxOnlyPrecalc( const CvFeatureEvaluator* _feval, CvMat* _buf, int _sample_count, bool _is_buf_16u )
+    FeatureIdxOnlyPrecalc( const CvFeatureEvaluator* _featureEvaluator, CvMat* _buf, int _sample_count, bool _is_buf_16u )
     {
-        feval = _feval;
+        featureEvaluator = _featureEvaluator;
         sample_count = _sample_count;
         udst = (unsigned short*)_buf->data.s;
         idst = _buf->data.i;
@@ -490,7 +490,7 @@ struct FeatureIdxOnlyPrecalc
         {
             for( int si = 0; si < sample_count; si++ )
             {
-                valCachePtr[si] = (*feval)( fi, si );
+                valCachePtr[si] = (*featureEvaluator)( fi, si );
                 if ( is_buf_16u )
                     *(udst + fi*sample_count + si) = (unsigned short)si;
                 else
@@ -502,7 +502,7 @@ struct FeatureIdxOnlyPrecalc
                 icvSortIntAux( idst + fi*sample_count, sample_count, valCachePtr );
         }
     }
-    const CvFeatureEvaluator* feval;
+    const CvFeatureEvaluator* featureEvaluator;
     int sample_count;
     int* idst;
     unsigned short* udst;
@@ -511,9 +511,9 @@ struct FeatureIdxOnlyPrecalc
 
 struct FeatureValAndIdxPrecalc
 {
-    FeatureValAndIdxPrecalc( const CvFeatureEvaluator* _feval, CvMat* _buf, Mat* _valCache, int _sample_count, bool _is_buf_16u )
+    FeatureValAndIdxPrecalc( const CvFeatureEvaluator* _featureEvaluator, CvMat* _buf, Mat* _valCache, int _sample_count, bool _is_buf_16u )
     {
-        feval = _feval;
+        featureEvaluator = _featureEvaluator;
         valCache = _valCache;
         sample_count = _sample_count;
         udst = (unsigned short*)_buf->data.s;
@@ -526,7 +526,7 @@ struct FeatureValAndIdxPrecalc
         {
             for( int si = 0; si < sample_count; si++ )
             {
-                valCache->at<float>(fi,si) = (*feval)( fi, si );
+                valCache->at<float>(fi,si) = (*featureEvaluator)( fi, si );
                 if ( is_buf_16u )
                     *(udst + fi*sample_count + si) = (unsigned short)si;
                 else
@@ -538,7 +538,7 @@ struct FeatureValAndIdxPrecalc
                 icvSortIntAux( idst + fi*sample_count, sample_count, valCache->ptr<float>(fi) );
         }
     }
-    const CvFeatureEvaluator* feval;
+    const CvFeatureEvaluator* featureEvaluator;
     Mat* valCache;
     int sample_count;
     int* idst;
@@ -548,9 +548,9 @@ struct FeatureValAndIdxPrecalc
 
 struct FeatureValOnlyPrecalc
 {
-    FeatureValOnlyPrecalc( const CvFeatureEvaluator* _feval, Mat* _valCache, int _sample_count )
+    FeatureValOnlyPrecalc( const CvFeatureEvaluator* _featureEvaluator, Mat* _valCache, int _sample_count )
     {
-        feval = _feval;
+        featureEvaluator = _featureEvaluator;
         valCache = _valCache;
         sample_count = _sample_count;
     }
@@ -558,9 +558,9 @@ struct FeatureValOnlyPrecalc
     {
         for ( int fi = range.begin(); fi < range.end(); fi++)
             for( int si = 0; si < sample_count; si++ )
-                valCache->at<float>(fi,si) = (*feval)( fi, si );
+                valCache->at<float>(fi,si) = (*featureEvaluator)( fi, si );
     }
-    const CvFeatureEvaluator* feval;
+    const CvFeatureEvaluator* featureEvaluator;
     Mat* valCache;
     int sample_count;
 };