1 利用icvGetIdxAt直接从矩阵中提取
for( j = 0; j < numtrimmed; j++ )
{
// 获取训练样本
idx = icvGetIdxAt( trimmedIdx, j );
// 对每个训练样本计算Haar特征
eval.data.fl[idx] = cvEvalFastHaarFeature( &classifier->fastfeature[i],
(sum_type*) (data->sum.data.ptr + idx * data->sum.step),
(sum_type*) (data->tilted.data.ptr + idx * data->tilted.step) );
// 归一化因子
normfactor = data->normfactor.data.fl[idx];
// 对Haar特征归一化
eval.data.fl[idx] = ( normfactor == 0.0F )
? 0.0F : (eval.data.fl[idx] / normfactor);
}
2 利用宏CV_MAT2VEC把矩阵转化为向量
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 );