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/* Copyright (c) 2014 Donovan Baarda <abo@minkirri.apana.org.au>
*
* Permission to use, copy, modify, and/or distribute this software for any
* purpose with or without fee is hereby granted, provided that the above
* copyright notice and this permission notice appear in all copies.
*/
#include "ftnoir_filter_ewma2.h"
#include <cmath>
#include <QDebug>
#include <QWidget>
#include "api/plugin-api.hpp"
#include <algorithm>
#include <QMutexLocker>
// Exponentially Weighted Moving Average (EWMA) Filter with dynamic smoothing.
//
// This filter tries to adjust the amount of filtering to minimize lag when
// moving, and minimize noise when still. It uses the delta filtered over the
// last 1/60sec (16ms) compared to the delta's average noise variance over
// the last 60sec to try and detect movement vs noise. As the delta increases
// from 0 to 3 stdevs of the noise, the filtering scales down from maxSmooth
// to minSmooth at a rate controlled by the powCurve setting.
ewma::ewma() : first_run(true)
{
}
void ewma::filter(const double *input, double *output)
{
// Start the timer and initialise filter state if it's not running.
if (first_run)
{
first_run = false;
timer.start();
for (int i=0;i<6;i++)
{
last_output[i] = input[i];
last_delta[i] = 0;
last_noise[i] = 0;
}
}
// Get the time in seconds since last run and restart the timer.
const double dt = timer.elapsed_seconds();
timer.start();
// Calculate delta_alpha and noise_alpha from dt.
double delta_alpha = dt/(dt + delta_RC);
double noise_alpha = dt/(dt + noise_RC);
// scale curve .01->1 where 1.0 is sqrt(norm_noise).
const double smoothing_scale_curve = static_cast<slider_value>(s.kSmoothingScaleCurve);
// min/max smoothing .01->1
const double min_smoothing = static_cast<slider_value>(s.kMinSmoothing);
const double max_smoothing = std::fmax(min_smoothing, static_cast<slider_value>(s.kMaxSmoothing));
// Calculate the new camera position.
for (int i=0;i<6;i++)
{
using std::pow;
// Calculate the current and smoothed delta.
double delta = input[i] - last_output[i];
last_delta[i] = delta_alpha*delta + (1.0-delta_alpha)*last_delta[i];
// Calculate the current and smoothed noise variance.
double noise = last_delta[i]*last_delta[i];
last_noise[i] = noise_alpha*noise + (1.0-noise_alpha)*last_noise[i];
// Normalise the noise between 0->1 for 0->9 variances (0->3 stddevs).
double norm_noise = last_noise[i] < 1e-10 ? 0 : std::fmin(noise/(9.0*last_noise[i]), 1.0);
// Calculate the smoothing 0.0->1.0 from the normalized noise.
double smoothing = 1.0 - pow(norm_noise, smoothing_scale_curve);
double RC = (min_smoothing + smoothing*(max_smoothing - min_smoothing));
// Calculate the dynamic alpha.
double alpha = dt/(dt + RC);
// Calculate the new output position.
output[i] = last_output[i] = alpha*input[i] + (1.0-alpha)*last_output[i];
}
}
OPENTRACK_DECLARE_FILTER(ewma, dialog_ewma, ewmaDll)
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