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Hi,

I'm going over the code of haar.cpp to understand the sliding window approach. Here is the code:

for( factor = 1; ; factor *= scaleFactor )
        {
            CvSize winSize = { cvRound(winSize0.width*factor),
                                cvRound(winSize0.height*factor) };
            CvSize sz = { cvRound( img->cols/factor ), cvRound( img->rows/factor ) };
            CvSize sz1 = { sz.width - winSize0.width + 1, sz.height - winSize0.height + 1 };

            CvRect equRect = { icv_object_win_border, icv_object_win_border,
                winSize0.width - icv_object_win_border*2,
                winSize0.height - icv_object_win_border*2 };

            CvMat img1, sum1, sqsum1, norm1, tilted1, mask1;
            CvMat* _tilted = 0;

            if( sz1.width <= 0 || sz1.height <= 0 )
                break;
            if( winSize.width > maxSize.width || winSize.height > maxSize.height )
                break;
            if( winSize.width < minSize.width || winSize.height < minSize.height )
                continue;

            img1 = cvMat( sz.height, sz.width, CV_8UC1, imgSmall->data.ptr );
            sum1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, sum->data.ptr );
            sqsum1 = cvMat( sz.height+1, sz.width+1, CV_64FC1, sqsum->data.ptr );
            if( tilted )
            {
                tilted1 = cvMat( sz.height+1, sz.width+1, CV_32SC1, tilted->data.ptr );
                _tilted = &tilted1;
            }
            norm1 = cvMat( sz1.height, sz1.width, CV_32FC1, normImg ? normImg->data.ptr : 0 );
            mask1 = cvMat( sz1.height, sz1.width, CV_8UC1, temp->data.ptr );

            cvResize( img, &img1, CV_INTER_LINEAR );
            cvIntegral( &img1, &sum1, &sqsum1, _tilted );

            int ystep = factor > 2 ? 1 : 2;
            const int LOCS_PER_THREAD = 1000;
            int stripCount = ((sz1.width/ystep)*(sz1.height + ystep-1)/ystep + LOCS_PER_THREAD/2)/LOCS_PER_THREAD;
            stripCount = std::min(std::max(stripCount, 1), 100);

#ifdef HAVE_IPP
            if( use_ipp )
            {
                cv::Mat fsum(sum1.rows, sum1.cols, CV_32F, sum1.data.ptr, sum1.step);
                cv::Mat(&sum1).convertTo(fsum, CV_32F, 1, -(1<<24));
            }
            else
#endif
                cvSetImagesForHaarClassifierCascade( cascade, &sum1, &sqsum1, _tilted, 1. );

            cv::Mat _norm1(&norm1), _mask1(&mask1);
            cv::parallel_for_(cv::Range(0, stripCount),
                         cv::HaarDetectObjects_ScaleImage_Invoker(cascade,
                                (((sz1.height + stripCount - 1)/stripCount + ystep-1)/ystep)*ystep,
                                factor, cv::Mat(&sum1), cv::Mat(&sqsum1), &_norm1, &_mask1,
                                cv::Rect(equRect), allCandidates, rejectLevels, levelWeights, outputRejectLevels, &mtx));
        }
    }

Now, I want to make sure I got everything right. As I understand, we loop over the scales and in each scale we subsample the image and try to find objects at a fixed size (20X20 for faces), going over all the x and y locations.

The pseudo- code is:

for scale=1:ScaleMax

     for X=1:width

          for Y=1:height 

                Try do detect a face at position (x,y) and of a fixedsize of 20X20.

Is that precise or did I get something wrong?

Thanks,

Gil.