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.
306 lines
11 KiB
306 lines
11 KiB
/*/////////////////////////////////////////////////////////////////////////////////////// |
|
// |
|
// 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 "tldTracker.hpp" |
|
|
|
|
|
namespace cv |
|
{ |
|
|
|
TrackerTLD::Params::Params(){} |
|
|
|
void TrackerTLD::Params::read(const cv::FileNode& /*fn*/){} |
|
|
|
void TrackerTLD::Params::write(cv::FileStorage& /*fs*/) const {} |
|
|
|
|
|
Ptr<TrackerTLD> TrackerTLD::createTracker(const TrackerTLD::Params ¶meters) |
|
{ |
|
return Ptr<tld::TrackerTLDImpl>(new tld::TrackerTLDImpl(parameters)); |
|
} |
|
|
|
namespace tld |
|
{ |
|
|
|
TrackerTLDImpl::TrackerTLDImpl(const TrackerTLD::Params ¶meters) : |
|
params( parameters ) |
|
{ |
|
isInit = false; |
|
trackerProxy = Ptr<TrackerProxyImpl<TrackerMedianFlow, TrackerMedianFlow::Params> > |
|
(new TrackerProxyImpl<TrackerMedianFlow, TrackerMedianFlow::Params>()); |
|
} |
|
|
|
void TrackerTLDImpl::read(const cv::FileNode& fn) |
|
{ |
|
params.read( fn ); |
|
} |
|
|
|
void TrackerTLDImpl::write(cv::FileStorage& fs) const |
|
{ |
|
params.write( fs ); |
|
} |
|
|
|
bool TrackerTLDImpl::initImpl(const Mat& image, const Rect2d& boundingBox) |
|
{ |
|
Mat image_gray; |
|
trackerProxy->init(image, boundingBox); |
|
cvtColor( image, image_gray, COLOR_BGR2GRAY ); |
|
data = Ptr<Data>(new Data(boundingBox)); |
|
double scale = data->getScale(); |
|
Rect2d myBoundingBox = boundingBox; |
|
if( scale > 1.0 ) |
|
{ |
|
Mat image_proxy; |
|
resize(image_gray, image_proxy, Size(cvRound(image.cols * scale), cvRound(image.rows * scale)), 0, 0, DOWNSCALE_MODE); |
|
image_proxy.copyTo(image_gray); |
|
myBoundingBox.x *= scale; |
|
myBoundingBox.y *= scale; |
|
myBoundingBox.width *= scale; |
|
myBoundingBox.height *= scale; |
|
} |
|
model = Ptr<TrackerTLDModel>(new TrackerTLDModel(params, image_gray, myBoundingBox, data->getMinSize())); |
|
|
|
data->confident = false; |
|
data->failedLastTime = false; |
|
|
|
return true; |
|
} |
|
|
|
bool TrackerTLDImpl::updateImpl(const Mat& image, Rect2d& boundingBox) |
|
{ |
|
Mat image_gray, image_blurred, imageForDetector; |
|
cvtColor( image, image_gray, COLOR_BGR2GRAY ); |
|
double scale = data->getScale(); |
|
if( scale > 1.0 ) |
|
resize(image_gray, imageForDetector, Size(cvRound(image.cols*scale), cvRound(image.rows*scale)), 0, 0, DOWNSCALE_MODE); |
|
else |
|
imageForDetector = image_gray; |
|
GaussianBlur(imageForDetector, image_blurred, GaussBlurKernelSize, 0.0); |
|
TrackerTLDModel* tldModel = ((TrackerTLDModel*)static_cast<TrackerModel*>(model)); |
|
data->frameNum++; |
|
Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE); |
|
std::vector<TLDDetector::LabeledPatch> detectorResults; |
|
//best overlap around 92% |
|
std::vector<Rect2d> candidates; |
|
std::vector<double> candidatesRes; |
|
bool trackerNeedsReInit = false; |
|
bool DETECT_FLG = false; |
|
for( int i = 0; i < 2; i++ ) |
|
{ |
|
Rect2d tmpCandid = boundingBox; |
|
|
|
if (i == 1) |
|
{ |
|
#ifdef HAVE_OPENCL |
|
if (ocl::haveOpenCL()) |
|
DETECT_FLG = tldModel->detector->ocl_detect(imageForDetector, image_blurred, tmpCandid, detectorResults, tldModel->getMinSize()); |
|
else |
|
#endif |
|
DETECT_FLG = tldModel->detector->detect(imageForDetector, image_blurred, tmpCandid, detectorResults, tldModel->getMinSize()); |
|
} |
|
if( ( (i == 0) && !data->failedLastTime && trackerProxy->update(image, tmpCandid) ) || ( DETECT_FLG)) |
|
{ |
|
candidates.push_back(tmpCandid); |
|
if( i == 0 ) |
|
resample(image_gray, tmpCandid, standardPatch); |
|
else |
|
resample(imageForDetector, tmpCandid, standardPatch); |
|
candidatesRes.push_back(tldModel->detector->Sc(standardPatch)); |
|
} |
|
else |
|
{ |
|
if( i == 0 ) |
|
trackerNeedsReInit = true; |
|
} |
|
} |
|
std::vector<double>::iterator it = std::max_element(candidatesRes.begin(), candidatesRes.end()); |
|
|
|
if( it == candidatesRes.end() ) |
|
{ |
|
data->confident = false; |
|
data->failedLastTime = true; |
|
return false; |
|
} |
|
else |
|
{ |
|
boundingBox = candidates[it - candidatesRes.begin()]; |
|
data->failedLastTime = false; |
|
if( trackerNeedsReInit || it != candidatesRes.begin() ) |
|
trackerProxy->init(image, boundingBox); |
|
} |
|
|
|
#if 1 |
|
if( it != candidatesRes.end() ) |
|
resample(imageForDetector, candidates[it - candidatesRes.begin()], standardPatch); |
|
#endif |
|
|
|
if( *it > CORE_THRESHOLD ) |
|
data->confident = true; |
|
|
|
if( data->confident ) |
|
{ |
|
Pexpert pExpert(imageForDetector, image_blurred, boundingBox, tldModel->detector, params, data->getMinSize()); |
|
Nexpert nExpert(imageForDetector, boundingBox, tldModel->detector, params); |
|
std::vector<Mat_<uchar> > examplesForModel, examplesForEnsemble; |
|
examplesForModel.reserve(100); examplesForEnsemble.reserve(100); |
|
int negRelabeled = 0; |
|
for( int i = 0; i < (int)detectorResults.size(); i++ ) |
|
{ |
|
bool expertResult; |
|
if( detectorResults[i].isObject ) |
|
{ |
|
expertResult = nExpert(detectorResults[i].rect); |
|
if( expertResult != detectorResults[i].isObject ) |
|
negRelabeled++; |
|
} |
|
else |
|
{ |
|
expertResult = pExpert(detectorResults[i].rect); |
|
} |
|
|
|
detectorResults[i].shouldBeIntegrated = detectorResults[i].shouldBeIntegrated || (detectorResults[i].isObject != expertResult); |
|
detectorResults[i].isObject = expertResult; |
|
} |
|
tldModel->integrateRelabeled(imageForDetector, image_blurred, detectorResults); |
|
pExpert.additionalExamples(examplesForModel, examplesForEnsemble); |
|
#ifdef HAVE_OPENCL |
|
if (ocl::haveOpenCL()) |
|
tldModel->ocl_integrateAdditional(examplesForModel, examplesForEnsemble, true); |
|
else |
|
#endif |
|
tldModel->integrateAdditional(examplesForModel, examplesForEnsemble, true); |
|
examplesForModel.clear(); examplesForEnsemble.clear(); |
|
nExpert.additionalExamples(examplesForModel, examplesForEnsemble); |
|
|
|
#ifdef HAVE_OPENCL |
|
if (ocl::haveOpenCL()) |
|
tldModel->ocl_integrateAdditional(examplesForModel, examplesForEnsemble, false); |
|
else |
|
#endif |
|
tldModel->integrateAdditional(examplesForModel, examplesForEnsemble, false); |
|
} |
|
else |
|
{ |
|
#ifdef CLOSED_LOOP |
|
tldModel->integrateRelabeled(imageForDetector, image_blurred, detectorResults); |
|
#endif |
|
} |
|
|
|
return true; |
|
} |
|
|
|
|
|
int TrackerTLDImpl::Pexpert::additionalExamples(std::vector<Mat_<uchar> >& examplesForModel, std::vector<Mat_<uchar> >& examplesForEnsemble) |
|
{ |
|
examplesForModel.clear(); examplesForEnsemble.clear(); |
|
examplesForModel.reserve(100); examplesForEnsemble.reserve(100); |
|
|
|
std::vector<Rect2d> closest, scanGrid; |
|
Mat scaledImg, blurredImg; |
|
|
|
double scale = scaleAndBlur(img_, cvRound(log(1.0 * resultBox_.width / (initSize_.width)) / log(SCALE_STEP)), |
|
scaledImg, blurredImg, GaussBlurKernelSize, SCALE_STEP); |
|
TLDDetector::generateScanGrid(img_.rows, img_.cols, initSize_, scanGrid); |
|
getClosestN(scanGrid, Rect2d(resultBox_.x / scale, resultBox_.y / scale, resultBox_.width / scale, resultBox_.height / scale), 10, closest); |
|
|
|
for( int i = 0; i < (int)closest.size(); i++ ) |
|
{ |
|
for( int j = 0; j < 10; j++ ) |
|
{ |
|
Point2f center; |
|
Size2f size; |
|
Mat_<uchar> standardPatch(STANDARD_PATCH_SIZE, STANDARD_PATCH_SIZE), blurredPatch(initSize_); |
|
center.x = (float)(closest[i].x + closest[i].width * (0.5 + rng.uniform(-0.01, 0.01))); |
|
center.y = (float)(closest[i].y + closest[i].height * (0.5 + rng.uniform(-0.01, 0.01))); |
|
size.width = (float)(closest[i].width * rng.uniform((double)0.99, (double)1.01)); |
|
size.height = (float)(closest[i].height * rng.uniform((double)0.99, (double)1.01)); |
|
float angle = (float)rng.uniform(-5.0, 5.0); |
|
|
|
for( int y = 0; y < standardPatch.rows; y++ ) |
|
{ |
|
for( int x = 0; x < standardPatch.cols; x++ ) |
|
{ |
|
standardPatch(x, y) += (uchar)rng.gaussian(5.0); |
|
} |
|
} |
|
#ifdef BLUR_AS_VADIM |
|
GaussianBlur(standardPatch, blurredPatch, GaussBlurKernelSize, 0.0); |
|
resize(blurredPatch, blurredPatch, initSize_); |
|
#else |
|
resample(blurredImg, RotatedRect(center, size, angle), blurredPatch); |
|
#endif |
|
resample(scaledImg, RotatedRect(center, size, angle), standardPatch); |
|
examplesForModel.push_back(standardPatch); |
|
examplesForEnsemble.push_back(blurredPatch); |
|
} |
|
} |
|
return 0; |
|
} |
|
|
|
bool TrackerTLDImpl::Nexpert::operator()(Rect2d box) |
|
{ |
|
if( overlap(resultBox_, box) < NEXPERT_THRESHOLD ) |
|
return false; |
|
else |
|
return true; |
|
} |
|
|
|
Data::Data(Rect2d initBox) |
|
{ |
|
double minDim = std::min(initBox.width, initBox.height); |
|
scale = 20.0 / minDim; |
|
minSize.width = (int)(initBox.width * 20.0 / minDim); |
|
minSize.height = (int)(initBox.height * 20.0 / minDim); |
|
frameNum = 0; |
|
} |
|
|
|
void Data::printme(FILE* port) |
|
{ |
|
dfprintf((port, "Data:\n")); |
|
dfprintf((port, "\tframeNum = %d\n", frameNum)); |
|
dfprintf((port, "\tconfident = %s\n", confident?"true":"false")); |
|
dfprintf((port, "\tfailedLastTime = %s\n", failedLastTime?"true":"false")); |
|
dfprintf((port, "\tminSize = %dx%d\n", minSize.width, minSize.height)); |
|
} |
|
|
|
} |
|
|
|
}
|
|
|