OpenCV的softcascade代码解读(6)

if( can_split && data->is_classifier )
    {
        // check if we have a "pure" node,
        // we assume that cls_count is filled by calc_node_value()
        int* cls_count = data->counts->data.i;
        int nz = 0, m = data->get_num_classes();
        for( i = 0; i < m; i++ )
            nz += cls_count[i] != 0;
        if( nz == 1 ) // there is only one class
            can_split = false;
    }
    else if( can_split )
    {
        if( sqrt(node->node_risk)/n < data->params.regression_accuracy )
            can_split = false;
    }

if( can_split )
    {
        best_split = find_best_split(node);
        // TODO: check the split quality ...
        node->split = best_split;
    }
    if( !can_split || !best_split )
    {
        data->free_node_data(node);
        return;
    }

quality_scale = calc_node_dir( node );
    if( data->params.use_surrogates )
    {
        // find all the surrogate splits
        // and sort them by their similarity to the primary one
        for( vi = 0; vi < data->var_count; vi++ )
        {
            CvDTreeSplit* split;
            int ci = data->get_var_type(vi);

if( vi == best_split->var_idx )
                continue;

if( ci >= 0 )
                split = find_surrogate_split_cat( node, vi );
            else
                split = find_surrogate_split_ord( node, vi );

if( split )
            {
                // insert the split
                CvDTreeSplit* prev_split = node->split;
                split->quality = (float)(split->quality*quality_scale);

while( prev_split->next &&
                      prev_split->next->quality > split->quality )
                    prev_split = prev_split->next;
                split->next = prev_split->next;
                prev_split->next = split;
            }
        }
    }
    split_node_data( node );
    try_split_node( node->left );
    try_split_node( node->right );
}

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