You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
274 lines
9.5 KiB
274 lines
9.5 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
|
// |
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
|
// |
|
// By downloading, copying, installing or using the software you agree to this license. |
|
// If you do not agree to this license, do not download, install, |
|
// copy or use the software. |
|
// |
|
// |
|
// License Agreement |
|
// For Open Source Computer Vision Library |
|
// |
|
// Copyright (C) 2013, OpenCV Foundation, all rights reserved. |
|
// Third party copyrights are property of their respective owners. |
|
// |
|
// Redistribution and use in source and binary forms, with or without modification, |
|
// are permitted provided that the following conditions are met: |
|
// |
|
// * Redistribution's of source code must retain the above copyright notice, |
|
// this list of conditions and the following disclaimer. |
|
// |
|
// * Redistribution's in binary form must reproduce the above copyright notice, |
|
// this list of conditions and the following disclaimer in the documentation |
|
// and/or other materials provided with the distribution. |
|
// |
|
// * The name of the copyright holders may not be used to endorse or promote products |
|
// derived from this software without specific prior written permission. |
|
// |
|
// This software is provided by the copyright holders and contributors "as is" and |
|
// any express or implied warranties, including, but not limited to, the implied |
|
// warranties of merchantability and fitness for a particular purpose are disclaimed. |
|
// In no event shall the Intel Corporation or contributors be liable for any direct, |
|
// indirect, incidental, special, exemplary, or consequential damages |
|
// (including, but not limited to, procurement of substitute goods or services; |
|
// loss of use, data, or profits; or business interruption) however caused |
|
// and on any theory of liability, whether in contract, strict liability, |
|
// or tort (including negligence or otherwise) arising in any way out of |
|
// the use of this software, even if advised of the possibility of such damage. |
|
// |
|
//M*/ |
|
|
|
#include "precomp.hpp" |
|
#include "trackerMILModel.hpp" |
|
|
|
namespace cv |
|
{ |
|
|
|
/* |
|
* TrackerMIL |
|
*/ |
|
|
|
/* |
|
* Parameters |
|
*/ |
|
TrackerMIL::Params::Params() |
|
{ |
|
samplerInitInRadius = 3; |
|
samplerTrackInRadius = 4; |
|
samplerSearchWinSize = 25; |
|
samplerInitMaxNegNum = 65; |
|
samplerTrackMaxPosNum = 100000; |
|
samplerTrackMaxNegNum = 65; |
|
featureSetNumFeatures = 250; |
|
} |
|
|
|
void TrackerMIL::Params::read( const cv::FileNode& fn ) |
|
{ |
|
samplerInitInRadius = fn["samplerInitInRadius"]; |
|
samplerSearchWinSize = fn["samplerSearchWinSize"]; |
|
samplerInitInRadius = fn["samplerInitInRadius"]; |
|
samplerTrackInRadius = fn["samplerTrackInRadius"]; |
|
samplerTrackMaxPosNum = fn["samplerTrackMaxPosNum"]; |
|
samplerTrackMaxNegNum = fn["samplerTrackMaxNegNum"]; |
|
featureSetNumFeatures = fn["featureSetNumFeatures"]; |
|
} |
|
|
|
void TrackerMIL::Params::write( cv::FileStorage& fs ) const |
|
{ |
|
fs << "samplerInitInRadius" << samplerInitInRadius; |
|
fs << "samplerSearchWinSize" << samplerSearchWinSize; |
|
fs << "samplerInitInRadius" << samplerInitInRadius; |
|
fs << "samplerTrackInRadius" << samplerTrackInRadius; |
|
fs << "samplerTrackMaxPosNum" << samplerTrackMaxPosNum; |
|
fs << "samplerTrackMaxNegNum" << samplerTrackMaxNegNum; |
|
fs << "featureSetNumFeatures" << featureSetNumFeatures; |
|
|
|
} |
|
|
|
/* |
|
* Constructor |
|
*/ |
|
TrackerMIL::TrackerMIL( const TrackerMIL::Params ¶meters ) : |
|
params( parameters ) |
|
{ |
|
isInit = false; |
|
} |
|
|
|
/* |
|
* Destructor |
|
*/ |
|
TrackerMIL::~TrackerMIL() |
|
{ |
|
|
|
} |
|
|
|
void TrackerMIL::read( const cv::FileNode& fn ) |
|
{ |
|
params.read( fn ); |
|
} |
|
|
|
void TrackerMIL::write( cv::FileStorage& fs ) const |
|
{ |
|
params.write( fs ); |
|
} |
|
|
|
void TrackerMIL::compute_integral( const Mat & img, Mat & ii_img ) |
|
{ |
|
Mat ii; |
|
std::vector<Mat> ii_imgs; |
|
integral( img, ii, CV_32F ); |
|
split( ii, ii_imgs ); |
|
ii_img = ii_imgs[0]; |
|
} |
|
|
|
bool TrackerMIL::initImpl( const Mat& image, const Rect2d& boundingBox ) |
|
{ |
|
srand (1); |
|
Mat intImage; |
|
compute_integral( image, intImage ); |
|
TrackerSamplerCSC::Params CSCparameters; |
|
CSCparameters.initInRad = params.samplerInitInRadius; |
|
CSCparameters.searchWinSize = params.samplerSearchWinSize; |
|
CSCparameters.initMaxNegNum = params.samplerInitMaxNegNum; |
|
CSCparameters.trackInPosRad = params.samplerTrackInRadius; |
|
CSCparameters.trackMaxPosNum = params.samplerTrackMaxPosNum; |
|
CSCparameters.trackMaxNegNum = params.samplerTrackMaxNegNum; |
|
|
|
Ptr<TrackerSamplerAlgorithm> CSCSampler = Ptr<TrackerSamplerCSC>( new TrackerSamplerCSC( CSCparameters ) ); |
|
if( !sampler->addTrackerSamplerAlgorithm( CSCSampler ) ) |
|
return false; |
|
|
|
//or add CSC sampler with default parameters |
|
//sampler->addTrackerSamplerAlgorithm( "CSC" ); |
|
|
|
//Positive sampling |
|
CSCSampler.staticCast<TrackerSamplerCSC>()->setMode( TrackerSamplerCSC::MODE_INIT_POS ); |
|
sampler->sampling( intImage, boundingBox ); |
|
std::vector<Mat> posSamples = sampler->getSamples(); |
|
|
|
//Negative sampling |
|
CSCSampler.staticCast<TrackerSamplerCSC>()->setMode( TrackerSamplerCSC::MODE_INIT_NEG ); |
|
sampler->sampling( intImage, boundingBox ); |
|
std::vector<Mat> negSamples = sampler->getSamples(); |
|
|
|
if( posSamples.empty() || negSamples.empty() ) |
|
return false; |
|
|
|
//compute HAAR features |
|
TrackerFeatureHAAR::Params HAARparameters; |
|
HAARparameters.numFeatures = params.featureSetNumFeatures; |
|
HAARparameters.rectSize = Size( boundingBox.width, boundingBox.height ); |
|
HAARparameters.isIntegral = true; |
|
Ptr<TrackerFeature> trackerFeature = Ptr<TrackerFeatureHAAR>( new TrackerFeatureHAAR( HAARparameters ) ); |
|
featureSet->addTrackerFeature( trackerFeature ); |
|
|
|
featureSet->extraction( posSamples ); |
|
const std::vector<Mat> posResponse = featureSet->getResponses(); |
|
|
|
featureSet->extraction( negSamples ); |
|
const std::vector<Mat> negResponse = featureSet->getResponses(); |
|
|
|
model = Ptr<TrackerMILModel>( new TrackerMILModel( boundingBox ) ); |
|
Ptr<TrackerStateEstimatorMILBoosting> stateEstimator = Ptr<TrackerStateEstimatorMILBoosting>( |
|
new TrackerStateEstimatorMILBoosting( params.featureSetNumFeatures ) ); |
|
model->setTrackerStateEstimator( stateEstimator ); |
|
|
|
//Run model estimation and update |
|
model.staticCast<TrackerMILModel>()->setMode( TrackerMILModel::MODE_POSITIVE, posSamples ); |
|
model->modelEstimation( posResponse ); |
|
model.staticCast<TrackerMILModel>()->setMode( TrackerMILModel::MODE_NEGATIVE, negSamples ); |
|
model->modelEstimation( negResponse ); |
|
model->modelUpdate(); |
|
|
|
return true; |
|
} |
|
|
|
bool TrackerMIL::updateImpl( const Mat& image, Rect2d& boundingBox ) |
|
{ |
|
Mat intImage; |
|
compute_integral( image, intImage ); |
|
|
|
//get the last location [AAM] X(k-1) |
|
Ptr<TrackerTargetState> lastLocation = model->getLastTargetState(); |
|
Rect lastBoundingBox( lastLocation->getTargetPosition().x, lastLocation->getTargetPosition().y, lastLocation->getTargetWidth(), |
|
lastLocation->getTargetHeight() ); |
|
|
|
//sampling new frame based on last location |
|
( sampler->getSamplers().at( 0 ).second ).staticCast<TrackerSamplerCSC>()->setMode( TrackerSamplerCSC::MODE_DETECT ); |
|
sampler->sampling( intImage, lastBoundingBox ); |
|
std::vector<Mat> detectSamples = sampler->getSamples(); |
|
if( detectSamples.empty() ) |
|
return false; |
|
|
|
/*//TODO debug samples |
|
Mat f; |
|
image.copyTo(f); |
|
|
|
for( size_t i = 0; i < detectSamples.size(); i=i+10 ) |
|
{ |
|
Size sz; |
|
Point off; |
|
detectSamples.at(i).locateROI(sz, off); |
|
rectangle(f, Rect(off.x,off.y,detectSamples.at(i).cols,detectSamples.at(i).rows), Scalar(255,0,0), 1); |
|
}*/ |
|
|
|
//extract features from new samples |
|
featureSet->extraction( detectSamples ); |
|
std::vector<Mat> response = featureSet->getResponses(); |
|
|
|
//predict new location |
|
ConfidenceMap cmap; |
|
model.staticCast<TrackerMILModel>()->setMode( TrackerMILModel::MODE_ESTIMATON, detectSamples ); |
|
model.staticCast<TrackerMILModel>()->responseToConfidenceMap( response, cmap ); |
|
model->getTrackerStateEstimator().staticCast<TrackerStateEstimatorMILBoosting>()->setCurrentConfidenceMap( cmap ); |
|
|
|
if( !model->runStateEstimator() ) |
|
{ |
|
return false; |
|
} |
|
|
|
Ptr<TrackerTargetState> currentState = model->getLastTargetState(); |
|
boundingBox = Rect( currentState->getTargetPosition().x, currentState->getTargetPosition().y, currentState->getTargetWidth(), |
|
currentState->getTargetHeight() ); |
|
|
|
/*//TODO debug |
|
rectangle(f, lastBoundingBox, Scalar(0,255,0), 1); |
|
rectangle(f, boundingBox, Scalar(0,0,255), 1); |
|
imshow("f", f); |
|
//waitKey( 0 );*/ |
|
|
|
//sampling new frame based on new location |
|
//Positive sampling |
|
( sampler->getSamplers().at( 0 ).second ).staticCast<TrackerSamplerCSC>()->setMode( TrackerSamplerCSC::MODE_INIT_POS ); |
|
sampler->sampling( intImage, boundingBox ); |
|
std::vector<Mat> posSamples = sampler->getSamples(); |
|
|
|
//Negative sampling |
|
( sampler->getSamplers().at( 0 ).second ).staticCast<TrackerSamplerCSC>()->setMode( TrackerSamplerCSC::MODE_INIT_NEG ); |
|
sampler->sampling( intImage, boundingBox ); |
|
std::vector<Mat> negSamples = sampler->getSamples(); |
|
|
|
if( posSamples.empty() || negSamples.empty() ) |
|
return false; |
|
|
|
//extract features |
|
featureSet->extraction( posSamples ); |
|
std::vector<Mat> posResponse = featureSet->getResponses(); |
|
|
|
featureSet->extraction( negSamples ); |
|
std::vector<Mat> negResponse = featureSet->getResponses(); |
|
|
|
//model estimate |
|
model.staticCast<TrackerMILModel>()->setMode( TrackerMILModel::MODE_POSITIVE, posSamples ); |
|
model->modelEstimation( posResponse ); |
|
model.staticCast<TrackerMILModel>()->setMode( TrackerMILModel::MODE_NEGATIVE, negSamples ); |
|
model->modelEstimation( negResponse ); |
|
|
|
//model update |
|
model->modelUpdate(); |
|
|
|
return true; |
|
} |
|
|
|
} /* namespace cv */
|
|
|