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319 lines
13 KiB
319 lines
13 KiB
/*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) 2013, OpenCV Foundation, 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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#include "trackerBoostingModel.hpp" |
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namespace cv |
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{ |
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class TrackerBoostingImpl : public TrackerBoosting |
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{ |
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public: |
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TrackerBoostingImpl( const TrackerBoosting::Params ¶meters = TrackerBoosting::Params() ); |
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void read( const FileNode& fn ); |
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void write( FileStorage& fs ) const; |
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protected: |
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bool initImpl( const Mat& image, const Rect2d& boundingBox ); |
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bool updateImpl( const Mat& image, Rect2d& boundingBox ); |
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TrackerBoosting::Params params; |
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}; |
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/* |
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* TrackerBoosting |
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*/ |
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/* |
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* Parameters |
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*/ |
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TrackerBoosting::Params::Params() |
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{ |
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numClassifiers = 100; |
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samplerOverlap = 0.99f; |
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samplerSearchFactor = 1.8f; |
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iterationInit = 50; |
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featureSetNumFeatures = ( numClassifiers * 10 ) + iterationInit; |
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} |
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void TrackerBoosting::Params::read( const cv::FileNode& fn ) |
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{ |
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numClassifiers = fn["numClassifiers"]; |
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samplerOverlap = fn["overlap"]; |
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samplerSearchFactor = fn["samplerSearchFactor"]; |
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iterationInit = fn["iterationInit"]; |
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samplerSearchFactor = fn["searchFactor"]; |
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} |
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void TrackerBoosting::Params::write( cv::FileStorage& fs ) const |
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{ |
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fs << "numClassifiers" << numClassifiers; |
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fs << "overlap" << samplerOverlap; |
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fs << "searchFactor" << samplerSearchFactor; |
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fs << "iterationInit" << iterationInit; |
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fs << "samplerSearchFactor" << samplerSearchFactor; |
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} |
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/* |
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* Constructor |
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*/ |
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Ptr<TrackerBoosting> TrackerBoosting::createTracker(const TrackerBoosting::Params ¶meters){ |
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return Ptr<TrackerBoostingImpl>(new TrackerBoostingImpl(parameters)); |
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} |
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TrackerBoostingImpl::TrackerBoostingImpl( const TrackerBoostingImpl::Params ¶meters ) : |
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params( parameters ) |
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{ |
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isInit = false; |
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} |
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void TrackerBoostingImpl::read( const cv::FileNode& fn ) |
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{ |
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params.read( fn ); |
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} |
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void TrackerBoostingImpl::write( cv::FileStorage& fs ) const |
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{ |
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params.write( fs ); |
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} |
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bool TrackerBoostingImpl::initImpl( const Mat& image, const Rect2d& boundingBox ) |
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{ |
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srand (1); |
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//sampling |
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Mat_<int> intImage; |
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Mat_<double> intSqImage; |
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Mat image_; |
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cvtColor( image, image_, CV_RGB2GRAY ); |
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integral( image_, intImage, intSqImage, CV_32S ); |
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TrackerSamplerCS::Params CSparameters; |
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CSparameters.overlap = params.samplerOverlap; |
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CSparameters.searchFactor = params.samplerSearchFactor; |
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Ptr<TrackerSamplerAlgorithm> CSSampler = Ptr<TrackerSamplerCS>( new TrackerSamplerCS( CSparameters ) ); |
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if( !sampler->addTrackerSamplerAlgorithm( CSSampler ) ) |
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return false; |
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CSSampler.staticCast<TrackerSamplerCS>()->setMode( TrackerSamplerCS::MODE_POSITIVE ); |
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sampler->sampling( intImage, boundingBox ); |
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const std::vector<Mat> posSamples = sampler->getSamples(); |
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CSSampler.staticCast<TrackerSamplerCS>()->setMode( TrackerSamplerCS::MODE_NEGATIVE ); |
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sampler->sampling( intImage, boundingBox ); |
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const std::vector<Mat> negSamples = sampler->getSamples(); |
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if( posSamples.empty() || negSamples.empty() ) |
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return false; |
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Rect ROI = CSSampler.staticCast<TrackerSamplerCS>()->getROI(); |
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//compute HAAR features |
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TrackerFeatureHAAR::Params HAARparameters; |
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HAARparameters.numFeatures = params.featureSetNumFeatures; |
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HAARparameters.isIntegral = true; |
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HAARparameters.rectSize = Size( static_cast<int>(boundingBox.width), static_cast<int>(boundingBox.height) ); |
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Ptr<TrackerFeature> trackerFeature = Ptr<TrackerFeatureHAAR>( new TrackerFeatureHAAR( HAARparameters ) ); |
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if( !featureSet->addTrackerFeature( trackerFeature ) ) |
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return false; |
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featureSet->extraction( posSamples ); |
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const std::vector<Mat> posResponse = featureSet->getResponses(); |
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featureSet->extraction( negSamples ); |
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const std::vector<Mat> negResponse = featureSet->getResponses(); |
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//Model |
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model = Ptr<TrackerBoostingModel>( new TrackerBoostingModel( boundingBox ) ); |
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Ptr<TrackerStateEstimatorAdaBoosting> stateEstimator = Ptr<TrackerStateEstimatorAdaBoosting>( |
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new TrackerStateEstimatorAdaBoosting( params.numClassifiers, params.iterationInit, params.featureSetNumFeatures, |
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Size( static_cast<int>(boundingBox.width), static_cast<int>(boundingBox.height) ), ROI ) ); |
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model->setTrackerStateEstimator( stateEstimator ); |
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//Run model estimation and update for iterationInit iterations |
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for ( int i = 0; i < params.iterationInit; i++ ) |
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{ |
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//compute temp features |
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TrackerFeatureHAAR::Params HAARparameters2; |
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HAARparameters2.numFeatures = static_cast<int>( posSamples.size() + negSamples.size() ); |
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HAARparameters2.isIntegral = true; |
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HAARparameters2.rectSize = Size( static_cast<int>(boundingBox.width), static_cast<int>(boundingBox.height) ); |
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Ptr<TrackerFeatureHAAR> trackerFeature2 = Ptr<TrackerFeatureHAAR>( new TrackerFeatureHAAR( HAARparameters2 ) ); |
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model.staticCast<TrackerBoostingModel>()->setMode( TrackerBoostingModel::MODE_NEGATIVE, negSamples ); |
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model->modelEstimation( negResponse ); |
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model.staticCast<TrackerBoostingModel>()->setMode( TrackerBoostingModel::MODE_POSITIVE, posSamples ); |
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model->modelEstimation( posResponse ); |
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model->modelUpdate(); |
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//get replaced classifier and change the features |
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std::vector<int> replacedClassifier = stateEstimator->computeReplacedClassifier(); |
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std::vector<int> swappedClassified = stateEstimator->computeSwappedClassifier(); |
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for ( size_t j = 0; j < replacedClassifier.size(); j++ ) |
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{ |
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if( replacedClassifier[j] != -1 && swappedClassified[j] != -1 ) |
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{ |
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trackerFeature.staticCast<TrackerFeatureHAAR>()->swapFeature( replacedClassifier[j], swappedClassified[j] ); |
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trackerFeature.staticCast<TrackerFeatureHAAR>()->swapFeature( swappedClassified[j], trackerFeature2->getFeatureAt( (int)j ) ); |
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} |
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} |
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} |
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return true; |
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} |
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bool TrackerBoostingImpl::updateImpl( const Mat& image, Rect2d& boundingBox ) |
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{ |
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Mat_<int> intImage; |
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Mat_<double> intSqImage; |
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Mat image_; |
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cvtColor( image, image_, CV_RGB2GRAY ); |
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integral( image_, intImage, intSqImage, CV_32S ); |
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//get the last location [AAM] X(k-1) |
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Ptr<TrackerTargetState> lastLocation = model->getLastTargetState(); |
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Rect lastBoundingBox( (int)lastLocation->getTargetPosition().x, (int)lastLocation->getTargetPosition().y, lastLocation->getTargetWidth(), |
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lastLocation->getTargetHeight() ); |
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//sampling new frame based on last location |
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( sampler->getSamplers().at( 0 ).second ).staticCast<TrackerSamplerCS>()->setMode( TrackerSamplerCS::MODE_CLASSIFY ); |
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sampler->sampling( intImage, lastBoundingBox ); |
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const std::vector<Mat> detectSamples = sampler->getSamples(); |
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Rect ROI = ( sampler->getSamplers().at( 0 ).second ).staticCast<TrackerSamplerCS>()->getROI(); |
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if( detectSamples.empty() ) |
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return false; |
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/*//TODO debug samples |
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Mat f; |
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image.copyTo( f ); |
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for ( size_t i = 0; i < detectSamples.size(); i = i + 10 ) |
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{ |
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Size sz; |
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Point off; |
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detectSamples.at( i ).locateROI( sz, off ); |
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rectangle( f, Rect( off.x, off.y, detectSamples.at( i ).cols, detectSamples.at( i ).rows ), Scalar( 255, 0, 0 ), 1 ); |
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}*/ |
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std::vector<Mat> responses; |
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Mat response; |
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std::vector<int> classifiers = model->getTrackerStateEstimator().staticCast<TrackerStateEstimatorAdaBoosting>()->computeSelectedWeakClassifier(); |
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Ptr<TrackerFeatureHAAR> extractor = featureSet->getTrackerFeature()[0].second.staticCast<TrackerFeatureHAAR>(); |
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extractor->extractSelected( classifiers, detectSamples, response ); |
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responses.push_back( response ); |
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//predict new location |
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ConfidenceMap cmap; |
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model.staticCast<TrackerBoostingModel>()->setMode( TrackerBoostingModel::MODE_CLASSIFY, detectSamples ); |
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model.staticCast<TrackerBoostingModel>()->responseToConfidenceMap( responses, cmap ); |
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model->getTrackerStateEstimator().staticCast<TrackerStateEstimatorAdaBoosting>()->setCurrentConfidenceMap( cmap ); |
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model->getTrackerStateEstimator().staticCast<TrackerStateEstimatorAdaBoosting>()->setSampleROI( ROI ); |
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if( !model->runStateEstimator() ) |
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{ |
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return false; |
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} |
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Ptr<TrackerTargetState> currentState = model->getLastTargetState(); |
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boundingBox = Rect( (int)currentState->getTargetPosition().x, (int)currentState->getTargetPosition().y, currentState->getTargetWidth(), |
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currentState->getTargetHeight() ); |
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/*//TODO debug |
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rectangle( f, lastBoundingBox, Scalar( 0, 255, 0 ), 1 ); |
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rectangle( f, boundingBox, Scalar( 0, 0, 255 ), 1 ); |
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imshow( "f", f ); |
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//waitKey( 0 );*/ |
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//sampling new frame based on new location |
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//Positive sampling |
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( sampler->getSamplers().at( 0 ).second ).staticCast<TrackerSamplerCS>()->setMode( TrackerSamplerCS::MODE_POSITIVE ); |
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sampler->sampling( intImage, boundingBox ); |
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const std::vector<Mat> posSamples = sampler->getSamples(); |
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//Negative sampling |
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( sampler->getSamplers().at( 0 ).second ).staticCast<TrackerSamplerCS>()->setMode( TrackerSamplerCS::MODE_NEGATIVE ); |
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sampler->sampling( intImage, boundingBox ); |
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const std::vector<Mat> negSamples = sampler->getSamples(); |
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if( posSamples.empty() || negSamples.empty() ) |
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return false; |
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//extract features |
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featureSet->extraction( posSamples ); |
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const std::vector<Mat> posResponse = featureSet->getResponses(); |
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featureSet->extraction( negSamples ); |
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const std::vector<Mat> negResponse = featureSet->getResponses(); |
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//compute temp features |
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TrackerFeatureHAAR::Params HAARparameters2; |
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HAARparameters2.numFeatures = static_cast<int>( posSamples.size() + negSamples.size() ); |
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HAARparameters2.isIntegral = true; |
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HAARparameters2.rectSize = Size( static_cast<int>(boundingBox.width), static_cast<int>(boundingBox.height) ); |
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Ptr<TrackerFeatureHAAR> trackerFeature2 = Ptr<TrackerFeatureHAAR>( new TrackerFeatureHAAR( HAARparameters2 ) ); |
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//model estimate |
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model.staticCast<TrackerBoostingModel>()->setMode( TrackerBoostingModel::MODE_NEGATIVE, negSamples ); |
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model->modelEstimation( negResponse ); |
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model.staticCast<TrackerBoostingModel>()->setMode( TrackerBoostingModel::MODE_POSITIVE, posSamples ); |
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model->modelEstimation( posResponse ); |
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//model update |
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model->modelUpdate(); |
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//get replaced classifier and change the features |
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std::vector<int> replacedClassifier = model->getTrackerStateEstimator().staticCast<TrackerStateEstimatorAdaBoosting>()->computeReplacedClassifier(); |
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std::vector<int> swappedClassified = model->getTrackerStateEstimator().staticCast<TrackerStateEstimatorAdaBoosting>()->computeSwappedClassifier(); |
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for ( size_t j = 0; j < replacedClassifier.size(); j++ ) |
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{ |
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if( replacedClassifier[j] != -1 && swappedClassified[j] != -1 ) |
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{ |
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featureSet->getTrackerFeature().at( 0 ).second.staticCast<TrackerFeatureHAAR>()->swapFeature( replacedClassifier[j], swappedClassified[j] ); |
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featureSet->getTrackerFeature().at( 0 ).second.staticCast<TrackerFeatureHAAR>()->swapFeature( swappedClassified[j], |
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trackerFeature2->getFeatureAt( (int)j ) ); |
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} |
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} |
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return true; |
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} |
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} /* namespace cv */
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