merged the trunk r8704:8716
This commit is contained in:
Marina Kolpakova
@@ -46,20 +46,20 @@ using namespace cv;
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using namespace std;
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CvHybridTrackerParams::CvHybridTrackerParams(float _ft_tracker_weight, float _ms_tracker_weight,
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CvFeatureTrackerParams _ft_params,
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CvMeanShiftTrackerParams _ms_params,
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CvMotionModel)
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CvFeatureTrackerParams _ft_params,
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CvMeanShiftTrackerParams _ms_params,
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CvMotionModel)
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{
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ft_tracker_weight = _ft_tracker_weight;
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ms_tracker_weight = _ms_tracker_weight;
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ft_params = _ft_params;
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ms_params = _ms_params;
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ft_tracker_weight = _ft_tracker_weight;
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ms_tracker_weight = _ms_tracker_weight;
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ft_params = _ft_params;
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ms_params = _ms_params;
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}
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CvMeanShiftTrackerParams::CvMeanShiftTrackerParams(int _tracking_type, CvTermCriteria _term_crit)
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{
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tracking_type = _tracking_type;
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term_crit = _term_crit;
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tracking_type = _tracking_type;
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term_crit = _term_crit;
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}
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CvHybridTracker::CvHybridTracker() {
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@@ -67,171 +67,171 @@ CvHybridTracker::CvHybridTracker() {
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}
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CvHybridTracker::CvHybridTracker(HybridTrackerParams _params) :
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params(_params) {
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params.ft_params.feature_type = CvFeatureTrackerParams::SIFT;
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mstracker = new CvMeanShiftTracker(params.ms_params);
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fttracker = new CvFeatureTracker(params.ft_params);
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params(_params) {
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params.ft_params.feature_type = CvFeatureTrackerParams::SIFT;
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mstracker = new CvMeanShiftTracker(params.ms_params);
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fttracker = new CvFeatureTracker(params.ft_params);
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}
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CvHybridTracker::~CvHybridTracker() {
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if (mstracker != NULL)
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delete mstracker;
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if (fttracker != NULL)
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delete fttracker;
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if (mstracker != NULL)
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delete mstracker;
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if (fttracker != NULL)
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delete fttracker;
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}
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inline float CvHybridTracker::getL2Norm(Point2f p1, Point2f p2) {
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float distance = (p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y
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- p2.y);
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return sqrt(distance);
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float distance = (p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y
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- p2.y);
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return sqrt(distance);
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}
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Mat CvHybridTracker::getDistanceProjection(Mat image, Point2f center) {
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Mat hist(image.size(), CV_64F);
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Mat hist(image.size(), CV_64F);
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double lu = getL2Norm(Point(0, 0), center);
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double ru = getL2Norm(Point(0, image.size().width), center);
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double rd = getL2Norm(Point(image.size().height, image.size().width),
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center);
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double ld = getL2Norm(Point(image.size().height, 0), center);
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double lu = getL2Norm(Point(0, 0), center);
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double ru = getL2Norm(Point(0, image.size().width), center);
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double rd = getL2Norm(Point(image.size().height, image.size().width),
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center);
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double ld = getL2Norm(Point(image.size().height, 0), center);
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double max = (lu < ru) ? lu : ru;
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max = (max < rd) ? max : rd;
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max = (max < ld) ? max : ld;
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double max = (lu < ru) ? lu : ru;
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max = (max < rd) ? max : rd;
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max = (max < ld) ? max : ld;
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for (int i = 0; i < hist.rows; i++)
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for (int j = 0; j < hist.cols; j++)
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hist.at<double> (i, j) = 1.0 - (getL2Norm(Point(i, j), center)
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/ max);
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for (int i = 0; i < hist.rows; i++)
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for (int j = 0; j < hist.cols; j++)
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hist.at<double> (i, j) = 1.0 - (getL2Norm(Point(i, j), center)
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/ max);
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return hist;
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return hist;
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}
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Mat CvHybridTracker::getGaussianProjection(Mat image, int ksize, double sigma,
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Point2f center) {
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Mat kernel = getGaussianKernel(ksize, sigma, CV_64F);
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double max = kernel.at<double> (ksize / 2);
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Point2f center) {
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Mat kernel = getGaussianKernel(ksize, sigma, CV_64F);
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double max = kernel.at<double> (ksize / 2);
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Mat hist(image.size(), CV_64F);
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for (int i = 0; i < hist.rows; i++)
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for (int j = 0; j < hist.cols; j++) {
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int pos = cvRound(getL2Norm(Point(i, j), center));
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if (pos < ksize / 2.0)
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hist.at<double> (i, j) = 1.0 - (kernel.at<double> (pos) / max);
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}
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Mat hist(image.size(), CV_64F);
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for (int i = 0; i < hist.rows; i++)
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for (int j = 0; j < hist.cols; j++) {
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int pos = cvRound(getL2Norm(Point(i, j), center));
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if (pos < ksize / 2.0)
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hist.at<double> (i, j) = 1.0 - (kernel.at<double> (pos) / max);
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}
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return hist;
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return hist;
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}
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void CvHybridTracker::newTracker(Mat image, Rect selection) {
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prev_proj = Mat::zeros(image.size(), CV_64FC1);
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prev_center = Point2f(selection.x + selection.width / 2.0f, selection.y
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+ selection.height / 2.0f);
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prev_window = selection;
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prev_proj = Mat::zeros(image.size(), CV_64FC1);
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prev_center = Point2f(selection.x + selection.width / 2.0f, selection.y
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+ selection.height / 2.0f);
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prev_window = selection;
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mstracker->newTrackingWindow(image, selection);
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fttracker->newTrackingWindow(image, selection);
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mstracker->newTrackingWindow(image, selection);
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fttracker->newTrackingWindow(image, selection);
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samples = cvCreateMat(2, 1, CV_32FC1);
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labels = cvCreateMat(2, 1, CV_32SC1);
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samples = cvCreateMat(2, 1, CV_32FC1);
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labels = cvCreateMat(2, 1, CV_32SC1);
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ittr = 0;
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ittr = 0;
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}
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void CvHybridTracker::updateTracker(Mat image) {
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ittr++;
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ittr++;
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//copy over clean images: TODO
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mstracker->updateTrackingWindow(image);
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fttracker->updateTrackingWindowWithFlow(image);
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//copy over clean images: TODO
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mstracker->updateTrackingWindow(image);
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fttracker->updateTrackingWindowWithFlow(image);
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if (params.motion_model == CvMotionModel::EM)
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updateTrackerWithEM(image);
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else
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updateTrackerWithLowPassFilter(image);
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if (params.motion_model == CvMotionModel::EM)
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updateTrackerWithEM(image);
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else
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updateTrackerWithLowPassFilter(image);
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// Regression to find new weights
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Point2f ms_center = mstracker->getTrackingEllipse().center;
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Point2f ft_center = fttracker->getTrackingCenter();
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// Regression to find new weights
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Point2f ms_center = mstracker->getTrackingEllipse().center;
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Point2f ft_center = fttracker->getTrackingCenter();
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#ifdef DEBUG_HYTRACKER
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circle(image, ms_center, 3, Scalar(0, 0, 255), -1, 8);
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circle(image, ft_center, 3, Scalar(255, 0, 0), -1, 8);
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putText(image, "ms", Point(ms_center.x+2, ms_center.y), FONT_HERSHEY_PLAIN, 0.75, Scalar(255, 255, 255));
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putText(image, "ft", Point(ft_center.x+2, ft_center.y), FONT_HERSHEY_PLAIN, 0.75, Scalar(255, 255, 255));
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circle(image, ms_center, 3, Scalar(0, 0, 255), -1, 8);
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circle(image, ft_center, 3, Scalar(255, 0, 0), -1, 8);
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putText(image, "ms", Point(ms_center.x+2, ms_center.y), FONT_HERSHEY_PLAIN, 0.75, Scalar(255, 255, 255));
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putText(image, "ft", Point(ft_center.x+2, ft_center.y), FONT_HERSHEY_PLAIN, 0.75, Scalar(255, 255, 255));
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#endif
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double ms_len = getL2Norm(ms_center, curr_center);
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double ft_len = getL2Norm(ft_center, curr_center);
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double total_len = ms_len + ft_len;
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double ms_len = getL2Norm(ms_center, curr_center);
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double ft_len = getL2Norm(ft_center, curr_center);
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double total_len = ms_len + ft_len;
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params.ms_tracker_weight *= (ittr - 1);
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params.ms_tracker_weight += (float)((ms_len / total_len));
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params.ms_tracker_weight /= ittr;
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params.ft_tracker_weight *= (ittr - 1);
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params.ft_tracker_weight += (float)((ft_len / total_len));
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params.ft_tracker_weight /= ittr;
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params.ms_tracker_weight *= (ittr - 1);
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params.ms_tracker_weight += (float)((ms_len / total_len));
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params.ms_tracker_weight /= ittr;
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params.ft_tracker_weight *= (ittr - 1);
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params.ft_tracker_weight += (float)((ft_len / total_len));
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params.ft_tracker_weight /= ittr;
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circle(image, prev_center, 3, Scalar(0, 0, 0), -1, 8);
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circle(image, curr_center, 3, Scalar(255, 255, 255), -1, 8);
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circle(image, prev_center, 3, Scalar(0, 0, 0), -1, 8);
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circle(image, curr_center, 3, Scalar(255, 255, 255), -1, 8);
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prev_center = curr_center;
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prev_window.x = (int)(curr_center.x-prev_window.width/2.0);
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prev_window.y = (int)(curr_center.y-prev_window.height/2.0);
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prev_center = curr_center;
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prev_window.x = (int)(curr_center.x-prev_window.width/2.0);
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prev_window.y = (int)(curr_center.y-prev_window.height/2.0);
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mstracker->setTrackingWindow(prev_window);
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fttracker->setTrackingWindow(prev_window);
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mstracker->setTrackingWindow(prev_window);
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fttracker->setTrackingWindow(prev_window);
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}
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void CvHybridTracker::updateTrackerWithEM(Mat image) {
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Mat ms_backproj = mstracker->getHistogramProjection(CV_64F);
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Mat ms_distproj = getDistanceProjection(image, mstracker->getTrackingCenter());
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Mat ms_proj = ms_backproj.mul(ms_distproj);
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Mat ms_backproj = mstracker->getHistogramProjection(CV_64F);
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Mat ms_distproj = getDistanceProjection(image, mstracker->getTrackingCenter());
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Mat ms_proj = ms_backproj.mul(ms_distproj);
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float dist_err = getL2Norm(mstracker->getTrackingCenter(), fttracker->getTrackingCenter());
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Mat ft_gaussproj = getGaussianProjection(image, cvRound(dist_err), -1, fttracker->getTrackingCenter());
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Mat ft_distproj = getDistanceProjection(image, fttracker->getTrackingCenter());
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Mat ft_proj = ft_gaussproj.mul(ft_distproj);
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float dist_err = getL2Norm(mstracker->getTrackingCenter(), fttracker->getTrackingCenter());
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Mat ft_gaussproj = getGaussianProjection(image, cvRound(dist_err), -1, fttracker->getTrackingCenter());
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Mat ft_distproj = getDistanceProjection(image, fttracker->getTrackingCenter());
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Mat ft_proj = ft_gaussproj.mul(ft_distproj);
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Mat proj = params.ms_tracker_weight * ms_proj + params.ft_tracker_weight * ft_proj + prev_proj;
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Mat proj = params.ms_tracker_weight * ms_proj + params.ft_tracker_weight * ft_proj + prev_proj;
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int sample_count = countNonZero(proj);
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cvReleaseMat(&samples);
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cvReleaseMat(&labels);
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samples = cvCreateMat(sample_count, 2, CV_32FC1);
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labels = cvCreateMat(sample_count, 1, CV_32SC1);
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int sample_count = countNonZero(proj);
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cvReleaseMat(&samples);
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cvReleaseMat(&labels);
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samples = cvCreateMat(sample_count, 2, CV_32FC1);
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labels = cvCreateMat(sample_count, 1, CV_32SC1);
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int count = 0;
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for (int i = 0; i < proj.rows; i++)
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for (int j = 0; j < proj.cols; j++)
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if (proj.at<double> (i, j) > 0) {
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samples->data.fl[count * 2] = (float)i;
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samples->data.fl[count * 2 + 1] = (float)j;
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count++;
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}
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int count = 0;
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for (int i = 0; i < proj.rows; i++)
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for (int j = 0; j < proj.cols; j++)
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if (proj.at<double> (i, j) > 0) {
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samples->data.fl[count * 2] = (float)i;
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samples->data.fl[count * 2 + 1] = (float)j;
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count++;
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}
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cv::Mat lbls;
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EM em_model(1, EM::COV_MAT_SPHERICAL, TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 10000, 0.001));
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em_model.train(cvarrToMat(samples), noArray(), lbls);
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if(labels)
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lbls.copyTo(cvarrToMat(labels));
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Mat em_means = em_model.get<Mat>("means");
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curr_center.x = (float)em_means.at<float>(0, 0);
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curr_center.y = (float)em_means.at<float>(0, 1);
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curr_center.x = (float)em_means.at<float>(0, 0);
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curr_center.y = (float)em_means.at<float>(0, 1);
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}
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void CvHybridTracker::updateTrackerWithLowPassFilter(Mat image) {
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RotatedRect ms_track = mstracker->getTrackingEllipse();
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Point2f ft_center = fttracker->getTrackingCenter();
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void CvHybridTracker::updateTrackerWithLowPassFilter(Mat) {
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RotatedRect ms_track = mstracker->getTrackingEllipse();
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Point2f ft_center = fttracker->getTrackingCenter();
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float a = params.low_pass_gain;
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curr_center.x = (1 - a) * prev_center.x + a * (params.ms_tracker_weight * ms_track.center.x + params.ft_tracker_weight * ft_center.x);
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curr_center.y = (1 - a) * prev_center.y + a * (params.ms_tracker_weight * ms_track.center.y + params.ft_tracker_weight * ft_center.y);
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float a = params.low_pass_gain;
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curr_center.x = (1 - a) * prev_center.x + a * (params.ms_tracker_weight * ms_track.center.x + params.ft_tracker_weight * ft_center.x);
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curr_center.y = (1 - a) * prev_center.y + a * (params.ms_tracker_weight * ms_track.center.y + params.ft_tracker_weight * ft_center.y);
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}
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Rect CvHybridTracker::getTrackingWindow() {
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return prev_window;
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return prev_window;
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}
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