void UIMachineSettingsDisplay::sltHandleVideoCaptureBitRateEditorChange()
{
/* Calculate/apply proposed quality: */
m_pSliderVideoCaptureQuality->blockSignals(true);
m_pSliderVideoCaptureQuality->setValue(calculateQuality(m_pEditorVideoCaptureWidth->value(),
m_pEditorVideoCaptureHeight->value(),
m_pEditorVideoCaptureFrameRate->value(),
m_pEditorVideoCaptureBitRate->value()));
m_pSliderVideoCaptureQuality->blockSignals(false);
}
// update filter correction
bool TrackerKalman::updateCorrection(const tf::Vector3& meas, const MatrixWrapper::SymmetricMatrix& cov)
{
assert(cov.columns() == 3);
// copy measurement
ColumnVector meas_vec(3);
for (unsigned int i=0; i<3; i++)
meas_vec(i+1) = meas[i];
// set covariance
((LinearAnalyticConditionalGaussian*)(meas_model_->MeasurementPdfGet()))->AdditiveNoiseSigmaSet(cov);
// update filter
bool res = filter_->Update(meas_model_, meas_vec);
if (!res) quality_ = 0;
else quality_ = calculateQuality();
return res;
};
// update filter prediction
bool TrackerKalman::updatePrediction(const double time)
{
bool res = true;
if (time > filter_time_){
// set dt in sys model
for (unsigned int i=1; i<=3; i++)
sys_matrix_(i, i+3) = time - filter_time_;
sys_pdf_->MatrixSet(0, sys_matrix_);
// scale system noise for dt
sys_pdf_->AdditiveNoiseSigmaSet(sys_sigma_ * pow(time - filter_time_,2));
filter_time_ = time;
// update filter
res = filter_->Update(sys_model_);
if (!res) quality_ = 0;
else quality_ = calculateQuality();
}
return res;
};
