Ejemplo n.º 1
0
void ComputeMeanAndVariance(Array2d<double>& data,Array2dC<double>& avg,Array2d<double>& cov,const bool subtractMean)
{
    avg.Create(1,data.ncol); avg.Zero();
    cov.Create(data.ncol,data.ncol); cov.Zero();
    for(int i=0;i<data.nrow;i++)
    {
        for(int j=0;j<data.ncol;j++) avg.buf[j] += data.p[i][j];
        for(int j=0;j<data.ncol;j++)
            for(int k=0;k<data.ncol;k++)
                cov.p[j][k] += data.p[i][j]*data.p[i][k];
    }
    const double r = 1.0/data.nrow;
    for(int i=0;i<data.ncol;i++) avg.buf[i] *= r;
    if(subtractMean)
    {
        for(int i=0;i<data.ncol;i++)
            for(int j=0;j<data.ncol;j++)
                cov.p[i][j] = cov.p[i][j]*r-avg.buf[i]*avg.buf[j];
    }
    else
    {
        for(int i=0;i<data.ncol;i++)
            for(int j=0;j<data.ncol;j++)
                cov.p[i][j] = cov.p[i][j]*r;
    }
}
// The function that does the real detection
int DetectionScanner::FastScan(IntImage<double>& original,std::vector<CRect>& results,const int stepsize)
{
    if(original.nrow<height+5 || original.ncol<width+5) return 0;
    const int hd = height/xdiv;
    const int wd = width/ydiv;
    InitImage(original);
    results.clear();

    hist.Create(1,baseflength*(xdiv-EXT)*(ydiv-EXT));

    NodeDetector* node = cascade->nodes[1];
    double** pc = node->classifier.p;
    int oheight = original.nrow, owidth = original.ncol;
    CRect rect;
    while(image.nrow>=height && image.ncol>=width)
    {
        InitIntegralImages(stepsize);
        for(int i=2; i+height<image.nrow-2; i+=stepsize)
        {
            const double* sp = scores.p[i];
            for(int j=2; j+width<image.ncol-2; j+=stepsize)
            {
                if(sp[j]<=0) continue;
                int* p = hist.buf;
                hist.Zero();
                for(int k=0; k<xdiv-EXT; k++)
                {
                    for(int t=0; t<ydiv-EXT; t++)
                    {
                        for(int x=i+k*hd+1; x<i+(k+1+EXT)*hd-1; x++)
                        {
                            int* ctp = ct.p[x];
                            for(int y=j+t*wd+1; y<j+(t+1+EXT)*wd-1; y++)
                                p[ctp[y]]++;
                        }
                        p += baseflength;
                    }
                }
                double score = node->thresh;
                for(int k=0; k<node->classifier.nrow; k++) score += pc[k][hist.buf[k]];
                if(score>0)
                {
                    rect.top = i*oheight/image.nrow;
                    rect.bottom = (i+height)*oheight/image.nrow;
                    rect.left = j*owidth/image.ncol;
                    rect.right = (j+width)*owidth/image.ncol;
                    results.push_back(rect);
                }
            }
        }
        ResizeImage();
    }
    return 0;
}