/****************************************************************************************\*Boosting*\****************************************************************************************/typedef struct CvBoostTrainer{CvBoostType type;int count;/* (idx) ? number_of_indices : number_of_samples */int* idx;float* F;} CvBoostTrainer;/* * cvBoostStartTraining, cvBoostNextWeakClassifier, cvBoostEndTraining * * These functions perform training of 2-class boosting classifier * using ANY appropriate weak classifier */staticCvBoostTrainer* icvBoostStartTraining( CvMat* trainClasses,CvMat* weakTrainVals,CvMat* /*weights*/,CvMat* sampleIdx,CvBoostType type ){uchar* ydata;int ystep;int m;uchar* traindata;int trainstep;int trainnum;int i;int idx;size_t datasize;CvBoostTrainer* ptr;int idxnum;int idxstep;uchar* idxdata;assert( trainClasses != NULL );assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );assert( weakTrainVals != NULL );assert( CV_MAT_TYPE( weakTrainVals->type ) == CV_32FC1 );CV_MAT2VEC( *trainClasses, ydata, ystep, m );CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum );CV_Assert( m == trainnum );idxnum = 0;idxstep = 0;idxdata = NULL;if( sampleIdx ){CV_MAT2VEC( *sampleIdx, idxdata, idxstep, idxnum );}datasize = sizeof( *ptr ) + sizeof( *ptr->idx ) * idxnum;ptr = (CvBoostTrainer*) cvAlloc( datasize );memset( ptr, 0, datasize );ptr->F = NULL;ptr->idx = NULL;ptr->count = m;ptr->type = type;if( idxnum > 0 ){CvScalar s;ptr->idx = (int*) (ptr + 1);ptr->count = idxnum;for( i = 0; i < ptr->count; i++ ){cvRawDataToScalar( idxdata + i*idxstep, CV_MAT_TYPE( sampleIdx->type ), &s );ptr->idx[i] = (int) s.val[0];}}for( i = 0; i < ptr->count; i++ ){idx = (ptr->idx) ? ptr->idx[i] : i;*((float*) (traindata + idx * trainstep)) =2.0F * (*((float*) (ydata + idx * ystep))) – 1.0F;}return ptr;}/* * * Discrete AdaBoost functions * */staticfloat icvBoostNextWeakClassifierDAB( CvMat* weakEvalVals,CvMat* trainClasses,CvMat* /*weakTrainVals*/,CvMat* weights,CvBoostTrainer* trainer ){uchar* evaldata;int evalstep;int m;uchar* ydata;int ystep;int ynum;uchar* wdata;int wstep;int wnum;float sumw;float err;int i;int idx;CV_Assert( weakEvalVals != NULL );CV_Assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );CV_Assert( trainClasses != NULL );CV_Assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );CV_Assert( weights != NULL );CV_Assert( CV_MAT_TYPE( weights ->type ) == CV_32FC1 );CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );CV_MAT2VEC( *weights, wdata, wstep, wnum );CV_Assert( m == ynum );CV_Assert( m == wnum );sumw = 0.0F;err = 0.0F;for( i = 0; i < trainer->count; i++ ){idx = (trainer->idx) ? trainer->idx[i] : i;sumw += *((float*) (wdata + idx*wstep));err += (*((float*) (wdata + idx*wstep))) *( (*((float*) (evaldata + idx*evalstep))) !=2.0F * (*((float*) (ydata + idx*ystep))) – 1.0F );}err /= sumw;err = -cvLogRatio( err );for( i = 0; i < trainer->count; i++ ){idx = (trainer->idx) ? trainer->idx[i] : i;*((float*) (wdata + idx*wstep)) *= expf( err *((*((float*) (evaldata + idx*evalstep))) !=2.0F * (*((float*) (ydata + idx*ystep))) – 1.0F) );sumw += *((float*) (wdata + idx*wstep));}for( i = 0; i < trainer->count; i++ ){idx = (trainer->idx) ? trainer->idx[i] : i;*((float*) (wdata + idx * wstep)) /= sumw;}return err;}/* * * Real AdaBoost functions * */staticfloat icvBoostNextWeakClassifierRAB( CvMat* weakEvalVals,CvMat* trainClasses,CvMat* /*weakTrainVals*/,CvMat* weights,CvBoostTrainer* trainer ){uchar* evaldata;int evalstep;int m;uchar* ydata;int ystep;int ynum;uchar* wdata;int wstep;int wnum;float sumw;int i, idx;CV_Assert( weakEvalVals != NULL );CV_Assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );CV_Assert( trainClasses != NULL );CV_Assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );CV_Assert( weights != NULL );CV_Assert( CV_MAT_TYPE( weights ->type ) == CV_32FC1 );CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );CV_MAT2VEC( *weights, wdata, wstep, wnum );CV_Assert( m == ynum );CV_Assert( m == wnum );sumw = 0.0F;for( i = 0; i < trainer->count; i++ ){idx = (trainer->idx) ? trainer->idx[i] : i;*((float*) (wdata + idx*wstep)) *= expf( (-(*((float*) (ydata + idx*ystep))) + 0.5F)* cvLogRatio( *((float*) (evaldata + idx*evalstep)) ) );sumw += *((float*) (wdata + idx*wstep));}for( i = 0; i < trainer->count; i++ ){idx = (trainer->idx) ? trainer->idx[i] : i;*((float*) (wdata + idx*wstep)) /= sumw;}return 1.0F;}/* * * LogitBoost functions * */#define CV_LB_PROB_THRESH0.01F#define CV_LB_WEIGHT_THRESHOLD 0.0001Fstaticvoid icvResponsesAndWeightsLB( int num, uchar* wdata, int wstep,uchar* ydata, int ystep,uchar* fdata, int fstep,uchar* traindata, int trainstep,int* indices ){int i, idx;float p;for( i = 0; i < num; i++ ){idx = (indices) ? indices[i] : i;p = 1.0F / (1.0F + expf( -(*((float*) (fdata + idx*fstep)))) );*((float*) (wdata + idx*wstep)) = MAX( p * (1.0F – p), CV_LB_WEIGHT_THRESHOLD );if( *((float*) (ydata + idx*ystep)) == 1.0F ){*((float*) (traindata + idx*trainstep)) =1.0F / (MAX( p, CV_LB_PROB_THRESH ));}else{*((float*) (traindata + idx*trainstep)) =-1.0F / (MAX( 1.0F – p, CV_LB_PROB_THRESH ));}}}staticCvBoostTrainer* icvBoostStartTrainingLB( CvMat* trainClasses,CvMat* weakTrainVals,CvMat* weights,CvMat* sampleIdx,CvBoostType type ){size_t datasize;CvBoostTrainer* ptr;uchar* ydata;int ystep;int m;uchar* traindata;int trainstep;int trainnum;uchar* wdata;int wstep;int wnum;int i;int idxnum;int idxstep;uchar* idxdata;assert( trainClasses != NULL );assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );assert( weakTrainVals != NULL );assert( CV_MAT_TYPE( weakTrainVals->type ) == CV_32FC1 );assert( weights != NULL );assert( CV_MAT_TYPE( weights->type ) == CV_32FC1 );CV_MAT2VEC( *trainClasses, ydata, ystep, m );CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum );CV_MAT2VEC( *weights, wdata, wstep, wnum );CV_Assert( m == trainnum );CV_Assert( m == wnum );idxnum = 0;idxstep = 0;idxdata = NULL;if( sampleIdx ){CV_MAT2VEC( *sampleIdx, idxdata, idxstep, idxnum );}datasize = sizeof( *ptr ) + sizeof( *ptr->F ) * m + sizeof( *ptr->idx ) * idxnum;ptr = (CvBoostTrainer*) cvAlloc( datasize );memset( ptr, 0, datasize );ptr->F = (float*) (ptr + 1);ptr->idx = NULL;ptr->count = m;ptr->type = type;if( idxnum > 0 ){CvScalar s;ptr->idx = (int*) (ptr->F + m);ptr->count = idxnum;for( i = 0; i < ptr->count; i++ ){cvRawDataToScalar( idxdata + i*idxstep, CV_MAT_TYPE( sampleIdx->type ), &s );ptr->idx[i] = (int) s.val[0];}}for( i = 0; i < m; i++ ){ptr->F[i] = 0.0F;}icvResponsesAndWeightsLB( ptr->count, wdata, wstep, ydata, ystep,(uchar*) ptr->F, sizeof( *ptr->F ),traindata, trainstep, ptr->idx );return ptr;}staticfloat icvBoostNextWeakClassifierLB( CvMat* weakEvalVals,CvMat* trainClasses,CvMat* weakTrainVals,CvMat* weights,CvBoostTrainer* trainer ){uchar* evaldata;int evalstep;int m;uchar* ydata;int ystep;int ynum;uchar* traindata;int trainstep;int trainnum;uchar* wdata;int wstep;int wnum;int i, idx;assert( weakEvalVals != NULL );assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );assert( trainClasses != NULL );assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );assert( weakTrainVals != NULL );assert( CV_MAT_TYPE( weakTrainVals->type ) == CV_32FC1 );assert( weights != NULL );assert( CV_MAT_TYPE( weights ->type ) == CV_32FC1 );CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );CV_MAT2VEC( *weakTrainVals, traindata, trainstep, trainnum );CV_MAT2VEC( *weights, wdata, wstep, wnum );CV_Assert( m == ynum );CV_Assert( m == wnum );CV_Assert( m == trainnum );//assert( m == trainer->count );for( i = 0; i < trainer->count; i++ ){idx = (trainer->idx) ? trainer->idx[i] : i;trainer->F[idx] += *((float*) (evaldata + idx * evalstep));}icvResponsesAndWeightsLB( trainer->count, wdata, wstep, ydata, ystep,(uchar*) trainer->F, sizeof( *trainer->F ),traindata, trainstep, trainer->idx );return 1.0F;}/* * * Gentle AdaBoost * */staticfloat icvBoostNextWeakClassifierGAB( CvMat* weakEvalVals,CvMat* trainClasses,CvMat* /*weakTrainVals*/,CvMat* weights,CvBoostTrainer* trainer ){uchar* evaldata;int evalstep;int m;uchar* ydata;int ystep;int ynum;uchar* wdata;int wstep;int wnum;int i, idx;float sumw;CV_Assert( weakEvalVals != NULL );CV_Assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );CV_Assert( trainClasses != NULL );CV_Assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );CV_Assert( weights != NULL );CV_Assert( CV_MAT_TYPE( weights->type ) == CV_32FC1 );CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );CV_MAT2VEC( *weights, wdata, wstep, wnum );CV_Assert( m == ynum );CV_Assert( m == wnum );sumw = 0.0F;for( i = 0; i < trainer->count; i++ ){idx = (trainer->idx) ? trainer->idx[i] : i;*((float*) (wdata + idx*wstep)) *=expf( -(*((float*) (evaldata + idx*evalstep)))* ( 2.0F * (*((float*) (ydata + idx*ystep))) – 1.0F ) );sumw += *((float*) (wdata + idx*wstep));}for( i = 0; i < trainer->count; i++ ){idx = (trainer->idx) ? trainer->idx[i] : i;*((float*) (wdata + idx*wstep)) /= sumw;}return 1.0F;}typedef CvBoostTrainer* (*CvBoostStartTraining)( CvMat* trainClasses,CvMat* weakTrainVals,CvMat* weights,CvMat* sampleIdx,CvBoostType type );typedef float (*CvBoostNextWeakClassifier)( CvMat* weakEvalVals,CvMat* trainClasses,CvMat* weakTrainVals,CvMat* weights,CvBoostTrainer* data );CvBoostStartTraining startTraining[4] = {icvBoostStartTraining,icvBoostStartTraining,icvBoostStartTrainingLB,icvBoostStartTraining};CvBoostNextWeakClassifier nextWeakClassifier[4] = {icvBoostNextWeakClassifierDAB,icvBoostNextWeakClassifierRAB,icvBoostNextWeakClassifierLB,icvBoostNextWeakClassifierGAB};/* * * Dispatchers * */CV_BOOST_IMPLCvBoostTrainer* cvBoostStartTraining( CvMat* trainClasses,CvMat* weakTrainVals,CvMat* weights,CvMat* sampleIdx,CvBoostType type ){return startTraining[type]( trainClasses, weakTrainVals, weights, sampleIdx, type );}CV_BOOST_IMPLvoid cvBoostEndTraining( CvBoostTrainer** trainer ){cvFree( trainer );*trainer = NULL;}CV_BOOST_IMPLfloat cvBoostNextWeakClassifier( CvMat* weakEvalVals,CvMat* trainClasses,CvMat* weakTrainVals,CvMat* weights,CvBoostTrainer* trainer ){return nextWeakClassifier[trainer->type]( weakEvalVals, trainClasses,weakTrainVals, weights, trainer );}
，怀着淡定从容的心态去面对，也就没有了真正意义上的寂寞了。
