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);
}
}
