opencv源码分析：icvGetTrainingDataCallback简介

/**函数icvGetTrainingDataCallback介绍*功能：对所有样本计算特征编号从first开始的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。保存每个样本的num个特征值。*First：特征类型编号的开始处*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。保存每个样本的num个特征值。*/staticvoid 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_ARRANGEMENTassert( mat->rows >= num );#elseassert( mat->cols >= num );#endif//userdata = cvUserdata( data, haarFeatures )//userdata包含了参与训练的积分图和特征，其指针应该是用于回调的用户参数training_data = ((CvUserdata*) userdata)->trainingData;haar_features = ((CvUserdata*) userdata)->haarFeatures;if( sampleIdx == NULL ){int num_samples;#ifdef CV_COL_ARRANGEMENTnum_samples = mat->cols;#elsenum_samples = mat->rows;#endiffor( i = 0; i < num_samples; i++ )//样本数量{for( j = 0; j < num; j++ )//每个样本的第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_ARRANGEMENTCV_MAT_ELEM( *mat, float, j, i ) = val;#elseCV_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_ARRANGEMENTCV_MAT_ELEM( *mat, float, j, idx ) = val;#elseCV_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 */}

那些曾经以为念念不忘的事情就在我们念念不忘的过程里，被我们遗忘了。