bug in SimpleBlobDetector?

asked 2017-06-16 18:16:09 -0500

ARYL518 gravatar image

updated 2020-09-17 09:07:40 -0500

Hi, I am looking the implementation of SimpleBlobDetector. It looks the insertions sort is not correct as shown below. I also copy entire SimpleBlobDetector codes at the end.

For the insertion sort, we should compare with the key or switch values, right? The following OpenCV code looks weird.

size_t k = centers[j].size() - 1;
while( k > 0 && centers[j][k].radius < centers[j][k-1].radius )//compare with key
{
    centers[j][k] = centers[j][k-1];//or switch values here
    k--;
}
centers[j][k] = curCenters[i];

Entire blob detection codes:

void SimpleBlobDetectorImpl::detect(InputArray image, std::vector<cv::KeyPoint>& keypoints, InputArray)
{
    //TODO: support mask
    keypoints.clear();
    Mat grayscaleImage;
    if (image.channels() == 3)
        cvtColor(image, grayscaleImage, COLOR_BGR2GRAY);
    else
        grayscaleImage = image.getMat();

    std::vector < std::vector<Center> > centers;
    for (double thresh = params.minThreshold; thresh < params.maxThreshold; thresh += params.thresholdStep)
    {
        Mat binarizedImage;
        threshold(grayscaleImage, binarizedImage, thresh, 255, THRESH_BINARY);

        std::vector < Center > curCenters;
        findBlobs(grayscaleImage, binarizedImage, curCenters);
        std::vector < std::vector<Center> > newCenters;
        for (size_t i = 0; i < curCenters.size(); i++)
        {
            bool isNew = true;
            for (size_t j = 0; j < centers.size(); j++)
            {
                double dist = norm(centers[j][ centers[j].size() / 2 ].location - curCenters[i].location);
                isNew = dist >= params.minDistBetweenBlobs && dist >= centers[j][ centers[j].size() / 2 ].radius && dist >= curCenters[i].radius;
                if (!isNew)
                {
                    centers[j].push_back(curCenters[i]);

                    size_t k = centers[j].size() - 1;
                    while( k > 0 && centers[j][k].radius < centers[j][k-1].radius )
                    {
                        centers[j][k] = centers[j][k-1];//should switch here?
                        k--;
                    }
                    centers[j][k] = curCenters[i];

                    break;
                }
            }
            if (isNew)
                newCenters.push_back(std::vector<Center> (1, curCenters[i]));
        }
        std::copy(newCenters.begin(), newCenters.end(), std::back_inserter(centers));
    }

    for (size_t i = 0; i < centers.size(); i++)
    {
        if (centers[i].size() < params.minRepeatability)
            continue;
        Point2d sumPoint(0, 0);
        double normalizer = 0;
        for (size_t j = 0; j < centers[i].size(); j++)
        {
            sumPoint += centers[i][j].confidence * centers[i][j].location;
            normalizer += centers[i][j].confidence;
        }
        sumPoint *= (1. / normalizer);
        KeyPoint kpt(sumPoint, (float)(centers[i][centers[i].size() / 2].radius) * 2.0f);
        keypoints.push_back(kpt);
    }
}
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