class CV_EXPORTS_W CascadeClassifier
{
public:CV_WRAP CascadeClassifier();CV_WRAP CascadeClassifier( const string& filename );virtual ~CascadeClassifier();CV_WRAP virtual bool empty() const;CV_WRAP bool load( const string& filename );virtual bool read( const FileNode& node );CV_WRAP virtual 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() );CV_WRAP virtual void detectMultiScale( const Mat& image,CV_OUT vector<Rect>& objects,vector<int>& rejectLevels,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;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 );virtual bool detectSingleScale( const Mat& image, int stripCount, Size processingRectSize,int stripSize, int yStep, double factor, vector<Rect>& candidates,vector<int>& rejectLevels, vector<double>& levelWeights, bool outputRejectLevels=false);protected: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, 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 onlyint 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;};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;
};class LBPEvaluator : public FeatureEvaluator
{
public:struct Feature{Feature();Feature( int x, int y, int _block_w, int _block_h  ) :rect(x, y, _block_w, _block_h) {}int calc( int offset ) const;void updatePtrs( const Mat& sum );bool read(const FileNode& node );        Rect rect; // weight and height for blockconst int* p[16]; // fast
    };LBPEvaluator();virtual ~LBPEvaluator();virtual bool read( const FileNode& node );virtual Ptr<FeatureEvaluator> clone() const;virtual int getFeatureType() const { return FeatureEvaluator::LBP; }virtual bool setImage(const Mat& image, Size _origWinSize);virtual bool setWindow(Point pt);int operator()(int featureIdx) const{ return featuresPtr[featureIdx].calc(offset); }virtual int calcCat(int featureIdx) const{ return (*this)(featureIdx); }
protected:Size origWinSize;Ptr<vector<Feature> > features;Feature* featuresPtr; // optimization
    Mat sum0, sum;Rect normrect;int offset;
};

#ifndef    _CP_ADABOOST_
#define    _CP_ADABOOST_
#ifdef   __cplusplus
extern "C"{
#endif
typedef struct tagCpSize
{int iWidth;int iHeight;
}CP_SIZE_S;typedef struct  tagCPDTreeNode
{int featureIdx;float threshold; // for ordered features onlyint left;int right;
}CP_DTREE_NODE_S;typedef struct  tagCpDTree
{int nodeCount;
}CP_DTREE_S;typedef struct  tagCpStage
{int first;int ntrees;float threshold;
}CP_STAGE_S;typedef struct tagCPRect
{int x;int y;int width;int height;
}CP_RECT_S;typedef struct tagLBPFeature
{CP_RECT_S rect;/*特征位置*/int* p[16];/* 特征在积分图中的地址 */
}CP_LBP_FEATURE_S;typedef struct tagCpClassifier
{bool isStumpBased;int stageType;int featureType;int ncategories;CP_SIZE_S origWinSize;CP_STAGE_S stages[50]; /*强分类器级数*/int stagerNum;        CP_DTREE_S classifiers[500];    int classfierNum;CP_DTREE_NODE_S nodes[500];    int nodeNum;CP_LBP_FEATURE_S feature[500];    int featureNum;float leaves[1000];    int leaveNum;int subsets[5000];    int subsetNum;
}CP_CLASSIFIER_S;#ifdef   __cplusplus
}
#endif
#endif /* _CP_ADABOOST_ */