Method update_weights

openbr/core/boost.cpp:1120–1359

}

void CascadeBoost::update_weights(CvBoostTree* tree)
{
    int n = data->sample_count;
    double sumW = 0.;
    int step = 0;
    float* fdata = 0;
    int *sampleIdxBuf;
    const int* sampleIdx = 0;
    int inn_buf_size = ((params.boost_type == LOGIT) || (params.boost_type == GENTLE) ? n*sizeof(int) : 0) +
                       ( !tree ? n*sizeof(int) : 0 );
    cv::AutoBuffer<uchar> inn_buf(inn_buf_size);
    uchar* cur_inn_buf_pos = (uchar*)inn_buf;
    if ( (params.boost_type == LOGIT) || (params.boost_type == GENTLE) )
    {
        step = CV_IS_MAT_CONT(data->responses_copy->type) ?
            1 : data->responses_copy->step / CV_ELEM_SIZE(data->responses_copy->type);
        fdata = data->responses_copy->data.fl;
        sampleIdxBuf = (int*)cur_inn_buf_pos; cur_inn_buf_pos = (uchar*)(sampleIdxBuf + n);
        sampleIdx = data->get_sample_indices( data->data_root, sampleIdxBuf );
    }
    CvMat* buf = data->buf;
    size_t length_buf_row = data->get_length_subbuf();
    if( !tree ) // before training the first tree, initialize weights and other parameters
    {
        int* classLabelsBuf = (int*)cur_inn_buf_pos; cur_inn_buf_pos = (uchar*)(classLabelsBuf + n);
        const int* classLabels = data->get_class_labels(data->data_root, classLabelsBuf);
        // in case of logitboost and gentle adaboost each weak tree is a regression tree,
        // so we need to convert class labels to floating-point values
        double w0 = 1./n;
        double p[2] = { 1, 1 };

        cvReleaseMat( &orig_response );
        cvReleaseMat( &sum_response );
        cvReleaseMat( &weak_eval );
        cvReleaseMat( &subsample_mask );
        cvReleaseMat( &weights );

        orig_response = cvCreateMat( 1, n, CV_32S );
        weak_eval = cvCreateMat( 1, n, CV_64F );
        subsample_mask = cvCreateMat( 1, n, CV_8U );
        weights = cvCreateMat( 1, n, CV_64F );
        subtree_weights = cvCreateMat( 1, n + 2, CV_64F );

        if (data->is_buf_16u)
        {
            unsigned short* labels = (unsigned short*)(buf->data.s + data->data_root->buf_idx*length_buf_row +
                data->data_root->offset + (data->work_var_count-1)*data->sample_count);
            for( int i = 0; i < n; i++ )
            {
                // save original categorical responses {0,1}, convert them to {-1,1}
                orig_response->data.i[i] = classLabels[i]*2 - 1;
                // make all the samples active at start.
                // later, in trim_weights() deactivate/reactive again some, if need
                subsample_mask->data.ptr[i] = (uchar)1;
                // make all the initial weights the same.
                weights->data.db[i] = w0*p[classLabels[i]];
                // set the labels to find (from within weak tree learning proc)
                // the particular sample weight, and where to store the response.