723 lines
15 KiB
C++
723 lines
15 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install, copy or use the software.
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//
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// Copyright (C) 2009, Farhad Dadgostar
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// Intel Corporation and third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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CvFuzzyPoint::CvFuzzyPoint(double _x, double _y)
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{
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x = _x;
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y = _y;
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};
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bool CvFuzzyCurve::between(double x, double x1, double x2)
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{
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if ((x >= x1) && (x <= x2))
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return true;
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else if ((x >= x2) && (x <= x1))
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return true;
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return false;
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};
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CvFuzzyCurve::CvFuzzyCurve()
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{
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value = 0;
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};
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CvFuzzyCurve::~CvFuzzyCurve()
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{
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// nothing to do
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};
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void CvFuzzyCurve::setCentre(double _centre)
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{
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centre = _centre;
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};
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double CvFuzzyCurve::getCentre()
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{
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return centre;
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};
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void CvFuzzyCurve::clear()
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{
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points.clear();
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};
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void CvFuzzyCurve::addPoint(double x, double y)
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{
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CvFuzzyPoint *point;
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point = new CvFuzzyPoint(x, y);
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points.push_back(*point);
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};
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double CvFuzzyCurve::calcValue(double param)
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{
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int size = (int)points.size();
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double x1, y1, x2, y2, m, y;
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for (int i = 1; i < size; i++)
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{
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x1 = points[i-1].x;
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x2 = points[i].x;
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if (between(param, x1, x2)) {
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y1 = points[i-1].y;
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y2 = points[i].y;
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if (x2 == x1)
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return y2;
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m = (y2-y1)/(x2-x1);
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y = m*(param-x1)+y1;
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return y;
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}
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}
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return 0;
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};
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double CvFuzzyCurve::getValue()
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{
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return value;
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};
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void CvFuzzyCurve::setValue(double _value)
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{
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value = _value;
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};
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CvFuzzyFunction::CvFuzzyFunction()
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{
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// nothing to do
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};
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CvFuzzyFunction::~CvFuzzyFunction()
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{
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curves.clear();
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};
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void CvFuzzyFunction::addCurve(CvFuzzyCurve *curve, double value)
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{
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curves.push_back(*curve);
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curve->setValue(value);
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};
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void CvFuzzyFunction::resetValues()
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{
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int numCurves = (int)curves.size();
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for (int i = 0; i < numCurves; i++)
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curves[i].setValue(0);
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};
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double CvFuzzyFunction::calcValue()
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{
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double s1 = 0, s2 = 0, v;
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int numCurves = (int)curves.size();
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for (int i = 0; i < numCurves; i++)
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{
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v = curves[i].getValue();
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s1 += curves[i].getCentre() * v;
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s2 += v;
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}
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if (s2 != 0)
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return s1/s2;
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else
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return 0;
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};
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CvFuzzyCurve *CvFuzzyFunction::newCurve()
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{
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CvFuzzyCurve *c;
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c = new CvFuzzyCurve();
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addCurve(c);
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return c;
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};
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CvFuzzyRule::CvFuzzyRule()
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{
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fuzzyInput1 = NULL;
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fuzzyInput2 = NULL;
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fuzzyOutput = NULL;
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};
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CvFuzzyRule::~CvFuzzyRule()
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{
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if (fuzzyInput1 != NULL)
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delete fuzzyInput1;
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if (fuzzyInput2 != NULL)
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delete fuzzyInput2;
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if (fuzzyOutput != NULL)
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delete fuzzyOutput;
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};
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void CvFuzzyRule::setRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1)
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{
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fuzzyInput1 = c1;
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fuzzyInput2 = c2;
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fuzzyOutput = o1;
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};
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double CvFuzzyRule::calcValue(double param1, double param2)
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{
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double v1, v2;
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v1 = fuzzyInput1->calcValue(param1);
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if (fuzzyInput2 != NULL)
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{
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v2 = fuzzyInput2->calcValue(param2);
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if (v1 < v2)
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return v1;
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else
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return v2;
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}
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else
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return v1;
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};
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CvFuzzyCurve *CvFuzzyRule::getOutputCurve()
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{
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return fuzzyOutput;
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};
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CvFuzzyController::CvFuzzyController()
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{
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// nothing to do
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};
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CvFuzzyController::~CvFuzzyController()
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{
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int size = (int)rules.size();
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for(int i = 0; i < size; i++)
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delete rules[i];
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};
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void CvFuzzyController::addRule(CvFuzzyCurve *c1, CvFuzzyCurve *c2, CvFuzzyCurve *o1)
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{
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CvFuzzyRule *f = new CvFuzzyRule();
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rules.push_back(f);
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f->setRule(c1, c2, o1);
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};
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double CvFuzzyController::calcOutput(double param1, double param2)
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{
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double v;
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CvFuzzyFunction list;
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int size = (int)rules.size();
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for(int i = 0; i < size; i++)
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{
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v = rules[i]->calcValue(param1, param2);
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if (v != 0)
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list.addCurve(rules[i]->getOutputCurve(), v);
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}
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v = list.calcValue();
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return v;
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};
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CvFuzzyMeanShiftTracker::FuzzyResizer::FuzzyResizer()
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{
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CvFuzzyCurve *i1L, *i1M, *i1H;
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CvFuzzyCurve *oS, *oZE, *oE;
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CvFuzzyCurve *c;
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double MedStart = 0.1, MedWidth = 0.15;
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c = iInput.newCurve();
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c->addPoint(0, 1);
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c->addPoint(0.1, 0);
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c->setCentre(0);
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i1L = c;
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c = iInput.newCurve();
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c->addPoint(0.05, 0);
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c->addPoint(MedStart, 1);
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c->addPoint(MedStart+MedWidth, 1);
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c->addPoint(MedStart+MedWidth+0.05, 0);
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c->setCentre(MedStart+(MedWidth/2));
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i1M = c;
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c = iInput.newCurve();
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c->addPoint(MedStart+MedWidth, 0);
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c->addPoint(1, 1);
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c->addPoint(1000, 1);
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c->setCentre(1);
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i1H = c;
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c = iOutput.newCurve();
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c->addPoint(-10000, 1);
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c->addPoint(-5, 1);
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c->addPoint(-0.5, 0);
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c->setCentre(-5);
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oS = c;
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c = iOutput.newCurve();
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c->addPoint(-1, 0);
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c->addPoint(-0.05, 1);
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c->addPoint(0.05, 1);
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c->addPoint(1, 0);
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c->setCentre(0);
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oZE = c;
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c = iOutput.newCurve();
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c->addPoint(-0.5, 0);
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c->addPoint(5, 1);
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c->addPoint(1000, 1);
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c->setCentre(5);
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oE = c;
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fuzzyController.addRule(i1L, NULL, oS);
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fuzzyController.addRule(i1M, NULL, oZE);
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fuzzyController.addRule(i1H, NULL, oE);
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};
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int CvFuzzyMeanShiftTracker::FuzzyResizer::calcOutput(double edgeDensity, double density)
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{
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return (int)fuzzyController.calcOutput(edgeDensity, density);
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};
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CvFuzzyMeanShiftTracker::SearchWindow::SearchWindow()
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{
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x = 0;
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y = 0;
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width = 0;
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height = 0;
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maxWidth = 0;
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maxHeight = 0;
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xGc = 0;
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yGc = 0;
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m00 = 0;
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m01 = 0;
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m10 = 0;
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m11 = 0;
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m02 = 0;
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m20 = 0;
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ellipseHeight = 0;
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ellipseWidth = 0;
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ellipseAngle = 0;
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density = 0;
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depthLow = 0;
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depthHigh = 0;
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fuzzyResizer = NULL;
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};
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CvFuzzyMeanShiftTracker::SearchWindow::~SearchWindow()
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{
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if (fuzzyResizer != NULL)
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delete fuzzyResizer;
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}
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void CvFuzzyMeanShiftTracker::SearchWindow::setSize(int _x, int _y, int _width, int _height)
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{
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x = _x;
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y = _y;
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width = _width;
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height = _height;
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if (x < 0)
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x = 0;
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if (y < 0)
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y = 0;
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if (x + width > maxWidth)
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width = maxWidth - x;
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if (y + height > maxHeight)
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height = maxHeight - y;
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};
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void CvFuzzyMeanShiftTracker::SearchWindow::initDepthValues(IplImage *maskImage, IplImage *depthMap)
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{
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unsigned int d=0, mind = 0xFFFF, maxd = 0, m0 = 0, m1 = 0, mc, dd;
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unsigned char *data = NULL;
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unsigned short *depthData = NULL;
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for (int j = 0; j < height; j++)
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{
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data = (unsigned char *)(maskImage->imageData + (maskImage->widthStep * (j + y)) + x);
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if (depthMap)
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depthData = (unsigned short *)(depthMap->imageData + (depthMap->widthStep * (j + y)) + x);
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for (int i = 0; i < width; i++)
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{
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if (*data)
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{
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m0 += 1;
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if (depthData)
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{
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if (*depthData)
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{
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m1 += d;
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if (d < mind)
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mind = d;
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if (d > maxd)
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maxd = d;
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}
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depthData++;
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}
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}
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data++;
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}
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}
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if (m0 > 0)
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{
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mc = m1/m0;
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if ((mc - mind) > (maxd - mc))
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dd = maxd - mc;
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else
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dd = mc - mind;
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dd = dd - dd/10;
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depthHigh = mc + dd;
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depthLow = mc - dd;
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}
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else
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{
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depthHigh = 32000;
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depthLow = 0;
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}
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};
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bool CvFuzzyMeanShiftTracker::SearchWindow::shift()
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{
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if ((xGc != (width/2)) || (yGc != (height/2)))
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{
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setSize(x + (xGc-(width/2)), y + (yGc-(height/2)), width, height);
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return true;
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}
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else
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{
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return false;
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}
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};
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void CvFuzzyMeanShiftTracker::SearchWindow::extractInfo(IplImage *maskImage, IplImage *depthMap, bool initDepth)
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{
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m00 = 0;
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m10 = 0;
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m01 = 0;
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m11 = 0;
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density = 0;
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m02 = 0;
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m20 = 0;
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ellipseHeight = 0;
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ellipseWidth = 0;
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maxWidth = maskImage->width;
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maxHeight = maskImage->height;
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if (initDepth)
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initDepthValues(maskImage, depthMap);
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unsigned char *maskData = NULL;
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unsigned short *depthData = NULL, depth;
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bool isOk;
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unsigned long count;
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verticalEdgeLeft = 0;
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verticalEdgeRight = 0;
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horizontalEdgeTop = 0;
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horizontalEdgeBottom = 0;
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for (int j = 0; j < height; j++)
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{
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maskData = (unsigned char *)(maskImage->imageData + (maskImage->widthStep * (j + y)) + x);
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if (depthMap)
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depthData = (unsigned short *)(depthMap->imageData + (depthMap->widthStep * (j + y)) + x);
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count = 0;
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for (int i = 0; i < width; i++)
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{
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if (*maskData)
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{
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isOk = true;
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if (depthData)
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{
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depth = (*depthData);
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if ((depth > depthHigh) || (depth < depthLow))
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isOk = false;
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depthData++;
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}
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if (isOk)
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{
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m00++;
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m01 += j;
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m10 += i;
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m02 += (j * j);
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m20 += (i * i);
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m11 += (j * i);
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if (i == 0)
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verticalEdgeLeft++;
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else if (i == width-1)
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verticalEdgeRight++;
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else if (j == 0)
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horizontalEdgeTop++;
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else if (j == height-1)
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horizontalEdgeBottom++;
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count++;
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}
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}
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maskData++;
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}
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}
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if (m00 > 0)
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{
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xGc = (m10 / m00);
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yGc = (m01 / m00);
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double a, b, c, e1, e2, e3;
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a = ((double)m20/(double)m00)-(xGc * xGc);
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b = 2*(((double)m11/(double)m00)-(xGc * yGc));
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c = ((double)m02/(double)m00)-(yGc * yGc);
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e1 = a+c;
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e3 = a-c;
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e2 = sqrt((b*b)+(e3*e3));
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ellipseHeight = int(sqrt(0.5*(e1+e2)));
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ellipseWidth = int(sqrt(0.5*(e1-e2)));
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if (e3 == 0)
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ellipseAngle = 0;
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else
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ellipseAngle = 0.5*atan(b/e3);
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density = (double)m00/(double)(width * height);
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}
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else
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{
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xGc = width / 2;
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yGc = height / 2;
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ellipseHeight = 0;
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ellipseWidth = 0;
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ellipseAngle = 0;
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density = 0;
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}
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};
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void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityLinear(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh) {
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int x1 = horizontalEdgeTop;
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int x2 = horizontalEdgeBottom;
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int y1 = verticalEdgeLeft;
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int y2 = verticalEdgeRight;
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int gx = (width*2)/5;
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int gy = (height*2)/5;
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int lx = width/10;
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int ly = height/10;
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resizeDy = 0;
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resizeDh = 0;
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resizeDx = 0;
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resizeDw = 0;
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if (x1 > gx) {
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resizeDy = -1;
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} else if (x1 < lx) {
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resizeDy = +1;
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}
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if (x2 > gx) {
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resizeDh = resizeDy + 1;
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} else if (x2 < lx) {
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resizeDh = - (resizeDy + 1);
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} else {
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resizeDh = - resizeDy;
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}
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if (y1 > gy) {
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resizeDx = -1;
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} else if (y1 < ly) {
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resizeDx = +1;
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}
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if (y2 > gy) {
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resizeDw = resizeDx + 1;
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} else if (y2 < ly) {
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resizeDw = - (resizeDx + 1);
|
|
} else {
|
|
resizeDw = - resizeDx;
|
|
}
|
|
};
|
|
|
|
void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsInnerDensity(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh)
|
|
{
|
|
int newWidth, newHeight, dx, dy;
|
|
double px, py;
|
|
newWidth = int(sqrt(double(m00)*1.3));
|
|
newHeight = int(newWidth*1.2);
|
|
dx = (newWidth - width);
|
|
dy = (newHeight - height);
|
|
px = (double)xGc/(double)width;
|
|
py = (double)yGc/(double)height;
|
|
resizeDx = (int)(px*dx);
|
|
resizeDy = (int)(py*dy);
|
|
resizeDw = (int)((1-px)*dx);
|
|
resizeDh = (int)((1-py)*dy);
|
|
};
|
|
|
|
void CvFuzzyMeanShiftTracker::SearchWindow::getResizeAttribsEdgeDensityFuzzy(int &resizeDx, int &resizeDy, int &resizeDw, int &resizeDh)
|
|
{
|
|
double dx1=0, dx2, dy1, dy2;
|
|
|
|
resizeDy = 0;
|
|
resizeDh = 0;
|
|
resizeDx = 0;
|
|
resizeDw = 0;
|
|
|
|
if (fuzzyResizer == NULL)
|
|
fuzzyResizer = new FuzzyResizer();
|
|
|
|
dx2 = fuzzyResizer->calcOutput(double(verticalEdgeRight)/double(height), density);
|
|
if (dx1 == dx2)
|
|
{
|
|
resizeDx = int(-dx1);
|
|
resizeDw = int(dx1+dx2);
|
|
}
|
|
|
|
dy1 = fuzzyResizer->calcOutput(double(horizontalEdgeTop)/double(width), density);
|
|
dy2 = fuzzyResizer->calcOutput(double(horizontalEdgeBottom)/double(width), density);
|
|
|
|
dx1 = fuzzyResizer->calcOutput(double(verticalEdgeLeft)/double(height), density);
|
|
dx2 = fuzzyResizer->calcOutput(double(verticalEdgeRight)/double(height), density);
|
|
//if (dx1 == dx2)
|
|
{
|
|
resizeDx = int(-dx1);
|
|
resizeDw = int(dx1+dx2);
|
|
}
|
|
|
|
dy1 = fuzzyResizer->calcOutput(double(horizontalEdgeTop)/double(width), density);
|
|
dy2 = fuzzyResizer->calcOutput(double(horizontalEdgeBottom)/double(width), density);
|
|
//if (dy1 == dy2)
|
|
{
|
|
resizeDy = int(-dy1);
|
|
resizeDh = int(dy1+dy2);
|
|
}
|
|
};
|
|
|
|
bool CvFuzzyMeanShiftTracker::SearchWindow::meanShift(IplImage *maskImage, IplImage *depthMap, int maxIteration, bool initDepth)
|
|
{
|
|
numShifts = 0;
|
|
do
|
|
{
|
|
extractInfo(maskImage, depthMap, initDepth);
|
|
if (! shift())
|
|
return true;
|
|
} while (++numShifts < maxIteration);
|
|
|
|
return false;
|
|
};
|
|
|
|
void CvFuzzyMeanShiftTracker::findOptimumSearchWindow(SearchWindow &searchWindow, IplImage *maskImage, IplImage *depthMap, int maxIteration, int resizeMethod, bool initDepth)
|
|
{
|
|
int resizeDx, resizeDy, resizeDw, resizeDh;
|
|
resizeDx = 0;
|
|
resizeDy = 0;
|
|
resizeDw = 0;
|
|
resizeDh = 0;
|
|
searchWindow.numIters = 0;
|
|
for (int i = 0; i < maxIteration; i++)
|
|
{
|
|
searchWindow.numIters++;
|
|
searchWindow.meanShift(maskImage, depthMap, MaxMeanShiftIteration, initDepth);
|
|
switch (resizeMethod)
|
|
{
|
|
case rmEdgeDensityLinear :
|
|
searchWindow.getResizeAttribsEdgeDensityLinear(resizeDx, resizeDy, resizeDw, resizeDh);
|
|
break;
|
|
case rmEdgeDensityFuzzy :
|
|
//searchWindow.getResizeAttribsEdgeDensityLinear(resizeDx, resizeDy, resizeDw, resizeDh);
|
|
searchWindow.getResizeAttribsEdgeDensityFuzzy(resizeDx, resizeDy, resizeDw, resizeDh);
|
|
break;
|
|
case rmInnerDensity :
|
|
searchWindow.getResizeAttribsInnerDensity(resizeDx, resizeDy, resizeDw, resizeDh);
|
|
break;
|
|
default:
|
|
searchWindow.getResizeAttribsEdgeDensityLinear(resizeDx, resizeDy, resizeDw, resizeDh);
|
|
}
|
|
|
|
searchWindow.ldx = resizeDx;
|
|
searchWindow.ldy = resizeDy;
|
|
searchWindow.ldw = resizeDw;
|
|
searchWindow.ldh = resizeDh;
|
|
|
|
if ((resizeDx == 0) && (resizeDy == 0) && (resizeDw == 0) && (resizeDh == 0))
|
|
break;
|
|
|
|
searchWindow.setSize(searchWindow.x + resizeDx, searchWindow.y + resizeDy, searchWindow.width + resizeDw, searchWindow.height + resizeDh);
|
|
}
|
|
};
|
|
|
|
CvFuzzyMeanShiftTracker::CvFuzzyMeanShiftTracker()
|
|
{
|
|
searchMode = tsSetWindow;
|
|
};
|
|
|
|
CvFuzzyMeanShiftTracker::~CvFuzzyMeanShiftTracker()
|
|
{
|
|
// nothing to do
|
|
};
|
|
|
|
void CvFuzzyMeanShiftTracker::track(IplImage *maskImage, IplImage *depthMap, int resizeMethod, bool resetSearch, int minKernelMass)
|
|
{
|
|
bool initDepth = false;
|
|
|
|
if (resetSearch)
|
|
searchMode = tsSetWindow;
|
|
|
|
switch (searchMode)
|
|
{
|
|
case tsDisabled:
|
|
return;
|
|
case tsSearching:
|
|
return;
|
|
case tsSetWindow:
|
|
kernel.maxWidth = maskImage->width;
|
|
kernel.maxHeight = maskImage->height;
|
|
kernel.setSize(0, 0, maskImage->width, maskImage->height);
|
|
initDepth = true;
|
|
case tsTracking:
|
|
searchMode = tsSearching;
|
|
findOptimumSearchWindow(kernel, maskImage, depthMap, MaxSetSizeIteration, resizeMethod, initDepth);
|
|
if ((kernel.density == 0) || (kernel.m00 < minKernelMass))
|
|
searchMode = tsSetWindow;
|
|
else
|
|
searchMode = tsTracking;
|
|
}
|
|
};
|
|
|