mirror of https://github.com/opencv/opencv.git
Alexey Kazakov
14 years ago
parent
fb0b25692e
commit
1e69bd5118
3 changed files with 148 additions and 208 deletions
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/*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,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// 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 the copyright holders 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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using namespace cv; |
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MeanshiftGrouping::MeanshiftGrouping(const Point3d& densKer, const vector<Point3d>& posV,
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const vector<double>& wV, double modeEps, int maxIter) |
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{ |
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densityKernel = densKer; |
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weightsV = wV; |
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positionsV = posV; |
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positionsCount = posV.size(); |
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meanshiftV.resize(positionsCount); |
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distanceV.resize(positionsCount); |
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modeEps = modeEps; |
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iterMax = maxIter; |
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for (unsigned i=0; i<positionsV.size(); i++) |
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{ |
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meanshiftV[i] = getNewValue(positionsV[i]); |
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distanceV[i] = moveToMode(meanshiftV[i]); |
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meanshiftV[i] -= positionsV[i]; |
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} |
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} |
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void MeanshiftGrouping::getModes(vector<Point3d>& modesV, vector<double>& resWeightsV, double eps) |
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{ |
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for (size_t i=0; i <distanceV.size(); i++) |
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{ |
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bool is_found = false; |
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for(size_t j=0; j<modesV.size(); j++) |
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{ |
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if ( getDistance(distanceV[i], modesV[j]) < eps) |
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{ |
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is_found=true; |
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break; |
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} |
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} |
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if (!is_found) |
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{ |
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modesV.push_back(distanceV[i]); |
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} |
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} |
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resWeightsV.resize(modesV.size()); |
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for (size_t i=0; i<modesV.size(); i++) |
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{ |
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resWeightsV[i] = getResultWeight(modesV[i]); |
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} |
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} |
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Point3d MeanshiftGrouping::moveToMode(Point3d aPt) const |
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{ |
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Point3d bPt; |
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for (int i = 0; i<iterMax; i++) |
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{ |
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bPt = aPt; |
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aPt = getNewValue(bPt); |
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if ( getDistance(aPt, bPt) <= modeEps ) |
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{ |
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break; |
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} |
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} |
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return aPt; |
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} |
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Point3d MeanshiftGrouping::getNewValue(const Point3d& inPt) const |
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{ |
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Point3d resPoint(.0); |
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Point3d ratPoint(.0); |
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for (size_t i=0; i<positionsV.size(); i++) |
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{ |
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Point3d aPt= positionsV[i]; |
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Point3d bPt = inPt; |
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Point3d sPt = densityKernel; |
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sPt.x *= exp(aPt.z); |
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sPt.y *= exp(aPt.z); |
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aPt.x /= sPt.x; |
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aPt.y /= sPt.y; |
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aPt.z /= sPt.z; |
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bPt.x /= sPt.x; |
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bPt.y /= sPt.y; |
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bPt.z /= sPt.z; |
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double w = (weightsV[i])*std::exp(-((aPt-bPt).dot(aPt-bPt))/2)/std::sqrt(sPt.dot(Point3d(1,1,1))); |
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resPoint += w*aPt; |
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ratPoint.x += w/sPt.x; |
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ratPoint.y += w/sPt.y; |
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ratPoint.z += w/sPt.z; |
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} |
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resPoint.x /= ratPoint.x; |
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resPoint.y /= ratPoint.y; |
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resPoint.z /= ratPoint.z; |
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return resPoint; |
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}
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double MeanshiftGrouping::getResultWeight(const Point3d& inPt) const |
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{ |
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double sumW=0; |
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for (size_t i=0; i<positionsV.size(); i++) |
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{ |
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Point3d aPt = positionsV[i]; |
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Point3d sPt = densityKernel; |
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sPt.x *= exp(aPt.z); |
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sPt.y *= exp(aPt.z); |
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aPt -= inPt; |
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aPt.x /= sPt.x; |
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aPt.y /= sPt.y; |
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aPt.z /= sPt.z; |
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sumW+=(weightsV[i])*std::exp(-(aPt.dot(aPt))/2)/std::sqrt(sPt.dot(Point3d(1,1,1))); |
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} |
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return sumW; |
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} |
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double MeanshiftGrouping::getDistance(Point3d p1, Point3d p2) const
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{ |
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Point3d ns = densityKernel; |
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ns.x *= exp(p2.z); |
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ns.y *= exp(p2.z); |
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p2 -= p1; |
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p2.x /= ns.x; |
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p2.y /= ns.y; |
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p2.z /= ns.z; |
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return p2.dot(p2); |
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} |
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