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//! \example tutorial-klt-tracker-with-reinit.cpp
#include <iostream>
#include <visp3/core/vpConfig.h>
#if defined(VISP_HAVE_OPENCV) && defined(HAVE_OPENCV_HIGHGUI) && defined(HAVE_OPENCV_IMGPROC) && defined(HAVE_OPENCV_VIDEO) && defined(HAVE_OPENCV_VIDEOIO)
#include <visp3/core/vpImageConvert.h>
#include <visp3/gui/vpDisplayOpenCV.h>
#include <visp3/io/vpVideoReader.h>
#include <visp3/klt/vpKltOpencv.h>
int main()
{
#ifdef ENABLE_VISP_NAMESPACE
using namespace VISP_NAMESPACE_NAME;
#endif
try {
vpVideoReader reader;
reader.setFileName("video-postcard.mp4");
vpImage<unsigned char> I;
reader.acquire(I);
cv::Mat cvI;
vpImageConvert::convert(I, cvI);
// Display initialisation
vpDisplayOpenCV d(I, 0, 0, "Klt tracking");
vpDisplay::display(I);
vpDisplay::flush(I);
vpKltOpencv tracker;
// Set tracker parameters
tracker.setMaxFeatures(200);
tracker.setWindowSize(10);
tracker.setQuality(0.01);
tracker.setMinDistance(15);
tracker.setHarrisFreeParameter(0.04);
tracker.setBlockSize(9);
tracker.setUseHarris(1);
tracker.setPyramidLevels(3);
// Initialise the tracking
tracker.initTracking(cvI);
while (!reader.end()) {
reader.acquire(I);
std::cout << "Process image " << reader.getFrameIndex() << std::endl;
vpDisplay::display(I);
vpImageConvert::convert(I, cvI);
//! [Re-init tracker]
// Restart the initialization to detect new keypoints
if (reader.getFrameIndex() == 25) {
std::cout << "Re initialize the tracker" << std::endl;
// Save of previous features
std::vector<cv::Point2f> prev_features = tracker.getFeatures();
// Start a new feature detection
tracker.initTracking(cvI);
std::vector<cv::Point2f> new_features = tracker.getFeatures();
// Add previous features if they are not to close to detected one
double distance, minDistance_ = tracker.getMinDistance();
for (size_t i = 0; i < prev_features.size(); i++) {
// Test if a previous feature is not redundant with one of the newly
// detected
bool is_redundant = false;
for (size_t j = 0; j < new_features.size(); j++) {
distance = sqrt(vpMath::sqr(new_features[j].x - prev_features[i].x) +
vpMath::sqr(new_features[j].y - prev_features[i].y));
if (distance < minDistance_) {
is_redundant = true;
break;
}
}
if (is_redundant) {
continue;
}
// std::cout << "Add previous feature with index " << i <<
// std::endl;
tracker.addFeature(prev_features[i]);
}
}
// Track the features
tracker.track(cvI);
//! [Re-init tracker]
std::cout << "tracking of " << tracker.getNbFeatures() << " features" << std::endl;
tracker.display(I, vpColor::red);
vpDisplay::flush(I);
}
vpDisplay::getClick(I);
}
catch (const vpException &e) {
std::cout << "Catch an exception: " << e << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
#else
int main()
{
#if !defined(HAVE_OPENCV_HIGHGUI)
std::cout << "This tutorial needs OpenCV highgui module that is missing." << std::endl;
#endif
#if !defined(HAVE_OPENCV_IMGPROC)
std::cout << "This tutorial needs OpenCV imgproc module that is missing." << std::endl;
#endif
#if !defined(HAVE_OPENCV_VIDEO)
std::cout << "This tutorial needs OpenCV video module that is missing." << std::endl;
#endif
#if !defined(HAVE_OPENCV_VIDEOIO)
std::cout << "This tutorial needs OpenCV videoio module that is missing." << std::endl;
#endif
}
#endif
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