opencv源代码分析：icvGetTrainingDataCallback简单介绍

/*
*函数icvGetTrainingDataCallback介绍
*功能：对全部样本计算特征编号从first開始的num个特征，并保存到mat里。

*输入：
*CvMat* mat矩阵样本总数个行，num个列。保存每一个样本的num个特征值。

*First：特征类型编号的開始处
*Num：要计算的特征类型个数。

*Userdata：积分矩阵和权重、特征模板等信息。
*输出：
*CvMat* mat矩阵样本总数个行。num个列。保存每一个样本的num个特征值。
*/
static
void icvGetTrainingDataCallback( CvMat* mat, CvMat* sampleIdx, CvMat*,
int first, int num, void* userdata )
{
int i = 0;
int j = 0;
float val = 0.0F;
float normfactor = 0.0F;

CvHaarTrainingData* training_data;
CvIntHaarFeatures* haar_features;

#ifdef CV_COL_ARRANGEMENT
assert( mat->rows >= num );
#else
assert( mat->cols >= num );
#endif
//userdata = cvUserdata( data, haarFeatures )
//userdata包括了參与训练的积分图和特征。其指针应该是用于回调的用户參数
training_data = ((CvUserdata*) userdata)->trainingData;
haar_features = ((CvUserdata*) userdata)->haarFeatures;
if( sampleIdx == NULL )
{
int num_samples;

#ifdef CV_COL_ARRANGEMENT
num_samples = mat->cols;
#else
num_samples = mat->rows;
#endif
for( i = 0; i < num_samples; i++ )//样本数量
{
for( j = 0; j < num; j++ )//每一个样本的第j个特征
{   //计算一个样本（积分图为sum和tilted）的一个HaarFeature，并返回该值
val = cvEvalFastHaarFeature(
( haar_features->fastfeature
+ first + j ),
(sum_type*) (training_data->sum.data.ptr
+ i * training_data->sum.step),
(sum_type*) (training_data->tilted.data.ptr
+ i * training_data->tilted.step) );
normfactor = training_data->normfactor.data.fl[i];
val = ( normfactor == 0.0F ) ? 0.0F : (val / normfactor);

#ifdef CV_COL_ARRANGEMENT
CV_MAT_ELEM( *mat, float, j, i ) = val;
#else
CV_MAT_ELEM( *mat, float, i, j ) = val;
#endif
}
}
}
else
{
uchar* idxdata = NULL;
size_t step    = 0;
int    numidx  = 0;
int    idx     = 0;

assert( CV_MAT_TYPE( sampleIdx->type ) == CV_32FC1 );

idxdata = sampleIdx->data.ptr;
if( sampleIdx->rows == 1 )
{
step = sizeof( float );
numidx = sampleIdx->cols;
}
else
{
step = sampleIdx->step;
numidx = sampleIdx->rows;
}

for( i = 0; i < numidx; i++ )
{
for( j = 0; j < num; j++ )
{
idx = (int)( *((float*) (idxdata + i * step)) );
val = cvEvalFastHaarFeature(
( haar_features->fastfeature
+ first + j ),
(sum_type*) (training_data->sum.data.ptr
+ idx * training_data->sum.step),
(sum_type*) (training_data->tilted.data.ptr
+ idx * training_data->tilted.step) );
normfactor = training_data->normfactor.data.fl[idx];
val = ( normfactor == 0.0F ) ? 0.0F : (val / normfactor);

#ifdef CV_COL_ARRANGEMENT
CV_MAT_ELEM( *mat, float, j, idx ) = val;
#else
CV_MAT_ELEM( *mat, float, idx, j ) = val;
#endif

}
}
}
#if 0 /*def CV_VERBOSE*/
if( first % 5000 == 0 )
{
fprintf( stderr, "%3d%%\r", (int) (100.0 * first /
haar_features->count) );
fflush( stderr );
}
#endif /* CV_VERBOSE */
}