f4334277
 Hu Chunming
提交3rdparty
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#ifndef _OPENCV_BOOST_H_
#define _OPENCV_BOOST_H_
#include "traincascade_features.h"
#include "old_ml.hpp"
struct CvCascadeBoostParams : CvBoostParams
{
float minHitRate;
float maxFalseAlarm;
CvCascadeBoostParams();
CvCascadeBoostParams( int _boostType, float _minHitRate, float _maxFalseAlarm,
double _weightTrimRate, int _maxDepth, int _maxWeakCount );
virtual ~CvCascadeBoostParams() {}
void write( cv::FileStorage &fs ) const;
bool read( const cv::FileNode &node );
virtual void printDefaults() const;
virtual void printAttrs() const;
virtual bool scanAttr( const std::string prmName, const std::string val);
};
struct CvCascadeBoostTrainData : CvDTreeTrainData
{
CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator,
const CvDTreeParams& _params );
CvCascadeBoostTrainData( const CvFeatureEvaluator* _featureEvaluator,
int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
const CvDTreeParams& _params = CvDTreeParams() );
virtual void setData( const CvFeatureEvaluator* _featureEvaluator,
int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
const CvDTreeParams& _params=CvDTreeParams() );
void precalculate();
virtual CvDTreeNode* subsample_data( const CvMat* _subsample_idx );
virtual const int* get_class_labels( CvDTreeNode* n, int* labelsBuf );
virtual const int* get_cv_labels( CvDTreeNode* n, int* labelsBuf);
virtual const int* get_sample_indices( CvDTreeNode* n, int* indicesBuf );
virtual void get_ord_var_data( CvDTreeNode* n, int vi, float* ordValuesBuf, int* sortedIndicesBuf,
const float** ordValues, const int** sortedIndices, int* sampleIndicesBuf );
virtual const int* get_cat_var_data( CvDTreeNode* n, int vi, int* catValuesBuf );
virtual float getVarValue( int vi, int si );
virtual void free_train_data();
const CvFeatureEvaluator* featureEvaluator;
cv::Mat valCache; // precalculated feature values (CV_32FC1)
CvMat _resp; // for casting
int numPrecalcVal, numPrecalcIdx;
};
class CvCascadeBoostTree : public CvBoostTree
{
public:
virtual CvDTreeNode* predict( int sampleIdx ) const;
void write( cv::FileStorage &fs, const cv::Mat& featureMap );
void read( const cv::FileNode &node, CvBoost* _ensemble, CvDTreeTrainData* _data );
void markFeaturesInMap( cv::Mat& featureMap );
protected:
virtual void split_node_data( CvDTreeNode* n );
};
class CvCascadeBoost : public CvBoost
{
public:
virtual bool train( const CvFeatureEvaluator* _featureEvaluator,
int _numSamples, int _precalcValBufSize, int _precalcIdxBufSize,
const CvCascadeBoostParams& _params=CvCascadeBoostParams() );
virtual float predict( int sampleIdx, bool returnSum = false ) const;
float getThreshold() const { return threshold; }
void write( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
bool read( const cv::FileNode &node, const CvFeatureEvaluator* _featureEvaluator,
const CvCascadeBoostParams& _params );
void markUsedFeaturesInMap( cv::Mat& featureMap );
protected:
virtual bool set_params( const CvBoostParams& _params );
virtual void update_weights( CvBoostTree* tree );
virtual bool isErrDesired();
float threshold;
float minHitRate, maxFalseAlarm;
};
#endif
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