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; };