Adding an export for HOGDetector compatibility.
An SVM train with HOG could export a single vector for HOGDetector.
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@ -518,6 +518,9 @@ public:
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virtual CvSVMParams get_params() const { return params; };
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CV_WRAP virtual void clear();
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// return a single vector for HOG detector.
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virtual void get_svm_detector( std::vector< float > & detector ) const;
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static CvParamGrid get_default_grid( int param_id );
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virtual void write( CvFileStorage* storage, const char* name ) const;
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@ -1245,6 +1245,37 @@ const float* CvSVM::get_support_vector(int i) const
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return sv && (unsigned)i < (unsigned)sv_total ? sv[i] : 0;
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}
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void CvSVM::get_svm_detector( std::vector< float > & detector ) const
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{
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CV_Assert( var_all > 0 &&
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sv_total > 0 &&
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sv != 0 &&
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decision_func != 0 &&
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decision_func->alpha != 0 &&
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decision_func->sv_count == sv_total );
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float svi = 0.f;
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detector.clear(); //clear stuff in vector.
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detector.reserve( var_all + 1 ); //reserve place for memory efficiency.
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/**
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* detector^i = \sum_j support_vector_j^i * \alpha_j
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* detector^dim = -\rho
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*/
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for( int i = 0 ; i < var_all ; ++i )
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{
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svi = 0.f;
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for( int j = 0 ; j < sv_total ; ++j )
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{
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if( decision_func->sv_index != NULL ) // sometime the sv_index isn't store on YML/XML.
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svi += (float)( sv[decision_func->sv_index[j]][i] * decision_func->alpha[ j ] );
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else
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svi += (float)( sv[j][i] * decision_func->alpha[ j ] );
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}
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detector.push_back( svi );
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}
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detector.push_back( -decision_func->rho );
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}
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bool CvSVM::set_params( const CvSVMParams& _params )
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{
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