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