/****************************************************************************************
* Boosting *
****************************************************************************************/
typedef struct CvBoostTrainer
{
CvBoostType type; //一共四类例如以下
/* CV_DABCLASS = 0, // 2 class Discrete AdaBoost
CV_RABCLASS = 1, // 2 class Real AdaBoost
CV_LBCLASS = 2, // 2 class LogitBoost
CV_GABCLASS = 3, //2 class Gentle AdaBoost */
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
*/
static
CvBoostTrainer* icvBoostStartTraining( CvMat* trainClasses, //训练样本的类别为0,1
CvMat* weakTrainVals, //训练的弱分类器的输出值，-1和1
CvMat* /*weights*/, //样本权重向量
CvMat* sampleIdx, //正负样本索引
CvBoostType type ) //类型如上
{
uchar* ydata;
int ystep;
int m;
uchar* traindata;
int trainstep;
int trainnum;
int i;
int idx;
size_t datasize;
CvBoostTrainer* ptr; //该函数中这个最为重要
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 );
}
/*******************************ptr的初始化*********************************************/
datasize = sizeof( *ptr ) + sizeof( *ptr->idx ) * idxnum;
ptr = (CvBoostTrainer*) cvAlloc( datasize ); //为ptr分配内存
memset( ptr, 0, datasize ); //初始化ptr，所有为0
ptr->F = NULL;
ptr->idx = NULL;
ptr->count = m;
ptr->type = type;
if( idxnum > 0 )
{
CvScalar s; //s内部是四个double型的val，分别为val[0],val[1],val[2]val[3]
ptr->idx = (int*) (ptr + 1);
ptr->count = idxnum;
for( i = 0; i < ptr->count; i++ )
{
//将原始数据转化为cvScale类型的数据
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;////y*=2y-1,类别标签由{0，1}变为{-1，1}
}
return ptr;
}
/*
*
* Discrete AdaBoost functions
*依据训练出来的结果与标签进行比較，更新所有样本权重
*/
static
float 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)); //所有训练样本权重和
err += (*((float*) (wdata + idx*wstep))) *
( (*((float*) (evaldata + idx*evalstep))) !=
2.0F * (*((float*) (ydata + idx*ystep))) - 1.0F ); //训练错误样本的权重和
}
err /= sumw; //错误率比值
err = -cvLogRatio( err ); //取对数后，再取相反数，目的是把把err变成正值
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) );//依据训练的结果正确与否，用指数函数更新权重。
sumw += *((float*) (wdata + idx*wstep)); //更新权重后再又一次计算所有样本的权重和
}
for( i = 0; i < trainer->count; i++ )
{
idx = (trainer->idx) ?
trainer->idx[i] : i;
*((float*) (wdata + idx * wstep)) /= sumw; //把更新后的训练样本权重归一化
}
return err; //返回err。注意这个err是取对数后，再取相反数的那个err，也就是上文程序中最后那个err
}
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_IMPL
CvBoostTrainer* cvBoostStartTraining( CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvMat* sampleIdx,
CvBoostType type )
{
return startTraining[type]( trainClasses, weakTrainVals, weights, sampleIdx, type );
}
CV_BOOST_IMPL
void cvBoostEndTraining( CvBoostTrainer** trainer )
{
cvFree( trainer );
*trainer = NULL;
}
CV_BOOST_IMPL
float cvBoostNextWeakClassifier( CvMat* weakEvalVals,
CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvBoostTrainer* trainer )
{
return nextWeakClassifier[trainer->type]( weakEvalVals, trainClasses,
weakTrainVals, weights, trainer );
}