icvPrecalculate

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/* *icvPrecalculate *作用：计算特征值，并排序 *具体来说也就是根据训练样本信息和haar特征信息，在函数内部引用icvGetTrainingDataCallback来 *分批计算正负样本的前numprecalated个haar特征值，并把计算好的特征值进行排序，最后结果存储在data->valcache之中 */staticvoid icvPrecalculate( CvHaarTrainingData* data,          //训练样本信息                      CvIntHaarFeatures* haarFeatures,   //haar特征信息                      int numprecalculated )             //预计算特征个数{    CV_FUNCNAME( "icvPrecalculate" );    __BEGIN__;    icvReleaseHaarTrainingDataCache( &data );    numprecalculated -= numprecalculated % CV_STUMP_TRAIN_PORTION;    numprecalculated = MIN( numprecalculated, haarFeatures->count );    if( numprecalculated > 0 )    {        //size_t datasize;        int m;        CvUserdata userdata;        /* private variables */        #ifdef CV_OPENMP        CvMat t_data;        CvMat t_idx;        int first;        int t_portion;        int portion = CV_STUMP_TRAIN_PORTION;      //每批计算特征的数量        #endif /* CV_OPENMP */        m = data->sum.rows;                        //确定样本总数量，正负样本数之和#ifdef CV_COL_ARRANGEMENT        CV_CALL( data->valcache = cvCreateMat( numprecalculated, m, CV_32FC1 ) );       //下面这三组代码是给data->valcache和data->idxcache分配内存#else        CV_CALL( data->valcache = cvCreateMat( m, numprecalculated, CV_32FC1 ) );#endif        CV_CALL( data->idxcache = cvCreateMat( numprecalculated, m, CV_IDX_MAT_TYPE ) );        userdata = cvUserdata( data, haarFeatures );        #ifdef CV_OPENMP        #pragma omp parallel for private(t_data, t_idx, first, t_portion)        for( first = 0; first < numprecalculated; first += portion )        {            t_data = *data->valcache;            t_idx = *data->idxcache;            t_portion = MIN( portion, (numprecalculated - first) );            /* indices */            t_idx.rows = t_portion;             //每批计算特征的个数            t_idx.data.ptr = data->idxcache->data.ptr + first * ((size_t)t_idx.step);            /* feature values */#ifdef CV_COL_ARRANGEMENT            t_data.rows = t_portion;            t_data.data.ptr = data->valcache->data.ptr +                first * ((size_t) t_data.step );#else            t_data.cols = t_portion;            t_data.data.ptr = data->valcache->data.ptr +                first * ((size_t) CV_ELEM_SIZE( t_data.type ));#endif            icvGetTrainingDataCallback( &t_data, NULL, NULL, first, t_portion,                                        &userdata );#ifdef CV_COL_ARRANGEMENT            cvGetSortedIndices( &t_data, &t_idx, 0 );#else            cvGetSortedIndices( &t_data, &t_idx, 1 );#endif#ifdef CV_VERBOSE            putc( '.', stderr );            fflush( stderr );#endif /* CV_VERBOSE */        }#ifdef CV_VERBOSE        fprintf( stderr, "\n" );        fflush( stderr );#endif /* CV_VERBOSE */        #else        icvGetTrainingDataCallback( data->valcache, NULL, NULL, 0, numprecalculated,                                    &userdata );                   //调用icvGetTrainingDataCallback函数计算特征值，把计算好的特征值存储在data->valcache中#ifdef CV_COL_ARRANGEMENT        cvGetSortedIndices( data->valcache, data->idxcache, 0 );   //对计算好的特征值data->valcache进行排序，并存储在data->idxcache中#else        cvGetSortedIndices( data->valcache, data->idxcache, 1 );#endif        #endif /* CV_OPENMP */    }    __END__;}
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