Ejemplo n.º 1
0
float SVMClassifier::predict( const cv::Mat_<float>& z) const
{
    if ( svmp.isLinear())
        return (z.dot(linx) - b)/linx.total();  // Normalise by the vector length

    double result = -b;

    KernelThreadFunc ktf( as, kernel, xs);
    ktf.calcResult( 0, numSVs, &z);
    result += ktf.getResult();
    return result / z.total();

    /*
    static const int nthreads = boost::thread::hardware_concurrency();   // CPU threads available
    const int segSz = numSVs / nthreads;
    int rem = numSVs % nthreads;

    boost::thread_group tgroup;
    vector<KernelThreadFunc*> tobjs(nthreads);
    int segOffset = 0;
    for ( int i = 0; i < nthreads; ++i)
    {
        int ssz = segSz;
        if ( rem > 0)
        {
            ssz++;
            rem--;
        }   // end if

        tobjs[i] = new KernelThreadFunc( as, kernel, xs);
        tgroup.create_thread( boost::bind( &KernelThreadFunc::calcResult, tobjs[i], segOffset, ssz, &z));  // Start thread
        segOffset += ssz; // Offset for next thread
    }   // end for
    tgroup.join_all();    // All processing done

    // Collect the result for return
    for ( int i = 0; i < nthreads; ++i)
    {
        result += tobjs[i]->getResult();
        delete tobjs[i];
    }   // end for

    return result / z.total();   // Normalise by the vector length
    */
}   // end predict
Ejemplo n.º 2
0
/**
 * @brief       Gabor filtering
 * @ref         http://en.wikipedia.org/wiki/Gabor_filter
 */
float VO_Gabor::VO_GaborFiltering(const cv::Mat_<float>& iImg)
{
    return iImg.dot(this->m_VOWindowFunc->m_MatWindowedKernel);
}