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FindObject.cpp
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FindObject.cpp
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/*
Copyright (c) 2011-2014, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions 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.
* Neither the name of the Universite de Sherbrooke nor the
names of its contributors may 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 COPYRIGHT HOLDER 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.
*/
#include "find_object/FindObject.h"
#include "find_object/Settings.h"
#include "find_object/utilite/ULogger.h"
#include "utilite/UConversion.h"
#include "ObjSignature.h"
#include "utilite/UDirectory.h"
#include "Vocabulary.h"
#include <QtCore/QThread>
#include <QtCore/QFileInfo>
#include <QtCore/QStringList>
#include <QtCore/QTime>
#include <QtCore/QDir>
#include <QGraphicsRectItem>
#include <stdio.h>
#if CV_MAJOR_VERSION > 3
#include <opencv2/core/types_c.h>
#endif
namespace find_object {
FindObject::FindObject(bool keepImagesInRAM, QObject * parent) :
QObject(parent),
vocabulary_(new Vocabulary()),
detector_(Settings::createKeypointDetector()),
extractor_(Settings::createDescriptorExtractor()),
sessionModified_(false),
keepImagesInRAM_(keepImagesInRAM)
{
qRegisterMetaType<find_object::DetectionInfo>("find_object::DetectionInfo");
UASSERT(detector_ != 0 && extractor_ != 0);
if(Settings::getGeneral_debug())
{
ULogger::setPrintWhere(true);
ULogger::setLevel(ULogger::kDebug);
}
else
{
ULogger::setPrintWhere(false);
ULogger::setLevel(ULogger::kInfo);
}
}
FindObject::~FindObject() {
delete detector_;
delete extractor_;
delete vocabulary_;
objectsDescriptors_.clear();
}
bool FindObject::loadSession(const QString & path, const ParametersMap & customParameters)
{
if(QFile::exists(path) && !path.isEmpty() && QFileInfo(path).suffix().compare("bin") == 0)
{
QFile file(path);
file.open(QIODevice::ReadOnly);
QDataStream in(&file);
ParametersMap parameters;
// load parameters
in >> parameters;
for(QMap<QString, QVariant>::iterator iter=parameters.begin(); iter!=parameters.end(); ++iter)
{
QMap<QString, QVariant>::const_iterator cter = customParameters.find(iter.key());
if(cter != customParameters.constEnd())
{
Settings::setParameter(cter.key(), cter.value());
}
else
{
Settings::setParameter(iter.key(), iter.value());
}
}
updateDetectorExtractor();
// load vocabulary
vocabulary_->load(in);
// load objects
while(!in.atEnd())
{
ObjSignature * obj = new ObjSignature();
obj->load(in, !keepImagesInRAM_);
if(obj->id() >= 0)
{
objects_.insert(obj->id(), obj);
}
else
{
UERROR("Failed to load and object!");
delete obj;
}
}
file.close();
if(!Settings::getGeneral_invertedSearch())
{
// this will fill objectsDescriptors_ matrix
updateVocabulary();
}
sessionModified_ = false;
return true;
}
else
{
UERROR("Invalid session file (should be *.bin): \"%s\"", path.toStdString().c_str());
}
return false;
}
bool FindObject::saveSession(const QString & path)
{
if(!path.isEmpty() && QFileInfo(path).suffix().compare("bin") == 0)
{
QFile file(path);
file.open(QIODevice::WriteOnly);
QDataStream out(&file);
// save parameters
out << Settings::getParameters();
// save vocabulary
vocabulary_->save(out);
// save objects
for(QMultiMap<int, ObjSignature*>::const_iterator iter=objects_.constBegin(); iter!=objects_.constEnd(); ++iter)
{
iter.value()->save(out);
}
file.close();
sessionModified_ = false;
return true;
}
UERROR("Path \"%s\" not valid (should be *.bin)", path.toStdString().c_str());
return false;
}
bool FindObject::saveVocabulary(const QString & filePath) const
{
if(!filePath.isEmpty() && QFileInfo(filePath).suffix().compare("bin") == 0)
{
QFile file(filePath);
file.open(QIODevice::WriteOnly);
QDataStream out(&file);
// ignore parameters
out << ParametersMap();
// save vocabulary
vocabulary_->save(out, true);
file.close();
return true;
}
else
{
return vocabulary_->save(filePath);
}
return false;
}
bool FindObject::loadVocabulary(const QString & filePath)
{
if(!Settings::getGeneral_vocabularyFixed() || !Settings::getGeneral_invertedSearch())
{
UWARN("Doesn't make sense to load a vocabulary if \"General/vocabularyFixed\" and \"General/invertedSearch\" are not enabled! It will "
"be cleared at the time the objects are updated.");
}
if(QFile::exists(filePath) && !filePath.isEmpty() && QFileInfo(filePath).suffix().compare("bin") == 0)
{
//binary format (from session format)
QFile file(filePath);
file.open(QIODevice::ReadOnly);
QDataStream in(&file);
ParametersMap parameters;
// ignore parameters
in >> parameters;
// load vocabulary
vocabulary_->load(in, true);
file.close();
return true;
}
else
{
//yaml/xml format
if(vocabulary_->load(filePath))
{
if(objects_.size())
{
updateVocabulary();
}
return true;
}
}
return false;
}
int FindObject::loadObjects(const QString & dirPath, bool recursive)
{
QString formats = Settings::getGeneral_imageFormats().remove('*').remove('.');
QStringList paths;
paths.append(dirPath);
QList<int> idsLoaded;
while(paths.size())
{
QString currentDir = paths.front();
UDirectory dir(currentDir.toStdString(), formats.toStdString());
if(dir.isValid())
{
const std::list<std::string> & names = dir.getFileNames(); // sorted in natural order
for(std::list<std::string>::const_iterator iter=names.begin(); iter!=names.end(); ++iter)
{
const ObjSignature * s = this->addObject((currentDir.toStdString()+dir.separator()+*iter).c_str());
if(s)
{
idsLoaded.push_back(s->id());
}
}
}
paths.pop_front();
if(recursive)
{
QDir d(currentDir);
QStringList subDirs = d.entryList(QDir::AllDirs|QDir::NoDotAndDotDot, QDir::Name);
for(int i=subDirs.size()-1; i>=0; --i)
{
paths.prepend(currentDir+QDir::separator()+subDirs[i]);
}
}
}
if(idsLoaded.size())
{
this->updateObjects(idsLoaded);
this->updateVocabulary(idsLoaded);
}
return idsLoaded.size();
}
const ObjSignature * FindObject::addObject(const QString & filePath)
{
if(!filePath.isNull())
{
cv::Mat img = cv::imread(filePath.toStdString().c_str(), cv::IMREAD_GRAYSCALE);
if(!img.empty())
{
int id = 0;
QFileInfo file(filePath);
QStringList list = file.fileName().split('.');
if(list.size())
{
bool ok = false;
id = list.front().toInt(&ok);
if(ok && id>0)
{
if(objects_.contains(id))
{
UWARN("Object %d already added, a new ID will be generated (new id=%d).", id, Settings::getGeneral_nextObjID());
id = 0;
}
}
else
{
id = 0;
}
}
else
{
UERROR("File name doesn't contain \".\" (\"%s\")", filePath.toStdString().c_str());
}
const ObjSignature * s = this->addObject(img, id, filePath);
if(s)
{
UINFO("Added object %d (%s)", s->id(), filePath.toStdString().c_str());
return s;
}
}
else
{
UERROR("Could not read image \"%s\"", filePath.toStdString().c_str());
}
}
else
{
UERROR("File path is null!?");
}
return 0;
}
const ObjSignature * FindObject::addObject(const cv::Mat & image, int id, const QString & filePath)
{
UASSERT(id >= 0);
ObjSignature * s = new ObjSignature(id, image, filePath);
if(!this->addObject(s))
{
delete s;
return 0;
}
return s;
}
bool FindObject::addObject(ObjSignature * obj)
{
UASSERT(obj != 0 && obj->id() >= 0);
if(obj->id() && objects_.contains(obj->id()))
{
UERROR("object with id %d already added!", obj->id());
return false;
}
else if(obj->id() == 0)
{
obj->setId(Settings::getGeneral_nextObjID());
}
Settings::setGeneral_nextObjID(obj->id()+1);
objects_.insert(obj->id(), obj);
return true;
}
void FindObject::removeObject(int id)
{
if(objects_.contains(id))
{
delete objects_.value(id);
objects_.remove(id);
clearVocabulary();
}
}
void FindObject::removeAllObjects()
{
qDeleteAll(objects_);
objects_.clear();
clearVocabulary();
}
void FindObject::addObjectAndUpdate(const cv::Mat & image, int id, const QString & filePath)
{
const ObjSignature * s = this->addObject(image, id, filePath);
if(s)
{
QList<int> ids;
ids.push_back(s->id());
updateObjects(ids);
updateVocabulary(ids);
}
}
void FindObject::removeObjectAndUpdate(int id)
{
if(objects_.contains(id))
{
delete objects_.value(id);
objects_.remove(id);
}
updateVocabulary();
}
void FindObject::updateDetectorExtractor()
{
delete detector_;
delete extractor_;
detector_ = Settings::createKeypointDetector();
extractor_ = Settings::createDescriptorExtractor();
UASSERT(detector_ != 0 && extractor_ != 0);
}
std::vector<cv::KeyPoint> limitKeypoints(const std::vector<cv::KeyPoint> & keypoints, int maxKeypoints)
{
std::vector<cv::KeyPoint> kptsKept;
if(maxKeypoints > 0 && (int)keypoints.size() > maxKeypoints)
{
// Sort words by response
std::multimap<float, int> reponseMap; // <response,id>
for(unsigned int i = 0; i <keypoints.size(); ++i)
{
//Keep track of the data, to be easier to manage the data in the next step
reponseMap.insert(std::pair<float, int>(fabs(keypoints[i].response), i));
}
// Remove them
std::multimap<float, int>::reverse_iterator iter = reponseMap.rbegin();
kptsKept.resize(maxKeypoints);
for(unsigned int k=0; k < kptsKept.size() && iter!=reponseMap.rend(); ++k, ++iter)
{
kptsKept[k] = keypoints[iter->second];
}
}
else
{
kptsKept = keypoints;
}
return kptsKept;
}
void limitKeypoints(std::vector<cv::KeyPoint> & keypoints, cv::Mat & descriptors, int maxKeypoints)
{
UASSERT((int)keypoints.size() == descriptors.rows);
std::vector<cv::KeyPoint> kptsKept;
cv::Mat descriptorsKept;
if(maxKeypoints > 0 && (int)keypoints.size() > maxKeypoints)
{
descriptorsKept = cv::Mat(0, descriptors.cols, descriptors.type());
// Sort words by response
std::multimap<float, int> reponseMap; // <response,id>
for(unsigned int i = 0; i <keypoints.size(); ++i)
{
//Keep track of the data, to be easier to manage the data in the next step
reponseMap.insert(std::pair<float, int>(fabs(keypoints[i].response), i));
}
// Remove them
std::multimap<float, int>::reverse_iterator iter = reponseMap.rbegin();
kptsKept.resize(maxKeypoints);
descriptorsKept.reserve(maxKeypoints);
for(unsigned int k=0; k < kptsKept.size() && iter!=reponseMap.rend(); ++k, ++iter)
{
kptsKept[k] = keypoints[iter->second];
descriptorsKept.push_back(descriptors.row(iter->second));
}
}
keypoints = kptsKept;
descriptors = descriptorsKept;
UASSERT_MSG((int)keypoints.size() == descriptors.rows, uFormat("%d vs %d", (int)keypoints.size(), descriptors.rows).c_str());
}
void computeFeatures(
Feature2D * detector,
Feature2D * extractor,
const cv::Mat & image,
const cv::Mat & mask,
std::vector<cv::KeyPoint> & keypoints,
cv::Mat & descriptors,
int & timeDetection,
int & timeExtraction)
{
QTime timeStep;
timeStep.start();
keypoints.clear();
descriptors = cv::Mat();
int maxFeatures = Settings::getFeature2D_3MaxFeatures();
if(Settings::currentDetectorType() == Settings::currentDescriptorType())
{
detector->detectAndCompute(image, keypoints, descriptors, mask);
UASSERT_MSG((int)keypoints.size() == descriptors.rows, uFormat("%d vs %d", (int)keypoints.size(), descriptors.rows).c_str());
if(maxFeatures > 0 && (int)keypoints.size() > maxFeatures)
{
limitKeypoints(keypoints, descriptors, maxFeatures);
}
timeDetection=timeStep.restart();
timeExtraction = 0;
}
else
{
detector->detect(image, keypoints, mask);
if(maxFeatures > 0 && (int)keypoints.size() > maxFeatures)
{
keypoints = limitKeypoints(keypoints, maxFeatures);
}
timeDetection=timeStep.restart();
//Extract descriptors
try
{
extractor->compute(image, keypoints, descriptors);
UASSERT_MSG((int)keypoints.size() == descriptors.rows, uFormat("%d vs %d", (int)keypoints.size(), descriptors.rows).c_str());
}
catch(cv::Exception & e)
{
UERROR("Descriptor exception: %s. Maybe some keypoints are invalid "
"for the selected descriptor extractor.", e.what());
descriptors = cv::Mat();
keypoints.clear();
}
catch ( const std::exception& e )
{
// standard exceptions
UERROR("Descriptor exception: %s. Maybe some keypoints are invalid "
"for the selected descriptor extractor.", e.what());
descriptors = cv::Mat();
keypoints.clear();
}
timeExtraction+=timeStep.restart();
}
if( Settings::getFeature2D_SIFT_rootSIFT() &&
Settings::currentDescriptorType() == "SIFT" &&
!descriptors.empty())
{
UINFO("Performing RootSIFT...");
// see http://www.pyimagesearch.com/2015/04/13/implementing-rootsift-in-python-and-opencv/
// apply the Hellinger kernel by first L1-normalizing and taking the
// square-root
for(int i=0; i<descriptors.rows; ++i)
{
descriptors.row(i) = descriptors.row(i) / cv::sum(descriptors.row(i))[0];
cv::sqrt(descriptors.row(i), descriptors.row(i));
// By taking the L1 norm, followed by the square-root, we have
// already L2 normalized the feature vector and further normalization
// is not needed.
//descs /= (np.linalg.norm(descs, axis=1, ord=2) + eps);
}
}
}
// taken from ASIFT example https://github.com/Itseez/opencv/blob/master/samples/python2/asift.py
// affine - is an affine transform matrix from skew_img to img
void FindObject::affineSkew(
float tilt,
float phi,
const cv::Mat & image,
cv::Mat & skewImage,
cv::Mat & skewMask,
cv::Mat & Ai)
{
float h = image.rows;
float w = image.cols;
cv::Mat A = cv::Mat::zeros(2,3,CV_32FC1);
A.at<float>(0,0) = A.at<float>(1,1) = 1;
skewMask = cv::Mat::ones(h, w, CV_8U) * 255;
if(phi != 0.0)
{
phi = phi*CV_PI/180.0f; // deg2rad
float s = std::sin(phi);
float c = std::cos(phi);
cv::Mat A22 = (cv::Mat_<float>(2, 2) <<
c, -s,
s, c);
cv::Mat cornersIn = (cv::Mat_<float>(4, 2) <<
0,0,
w,0,
w,h,
0,h);
cv::Mat cornersOut = cornersIn * A22.t();
cv::Rect rect = cv::boundingRect(cornersOut.reshape(2,4));
A = (cv::Mat_<float>(2, 3) <<
c, -s, -rect.x,
s, c, -rect.y);
cv::warpAffine(image, skewImage, A, cv::Size(rect.width, rect.height), cv::INTER_LINEAR, cv::BORDER_REPLICATE);
}
else
{
skewImage = image;
}
if(tilt != 1.0)
{
float s = 0.8*std::sqrt(tilt*tilt-1);
cv::Mat out, out2;
cv::GaussianBlur(skewImage, out, cv::Size(0, 0), s, 0.01);
cv::resize(out, out2, cv::Size(0, 0), 1.0/tilt, 1.0, cv::INTER_NEAREST);
skewImage = out2;
A.row(0) /= tilt;
}
if(phi != 0.0 || tilt != 1.0)
{
cv::Mat mask = skewMask;
cv::warpAffine(mask, skewMask, A, skewImage.size(), cv::INTER_NEAREST);
}
cv::invertAffineTransform(A, Ai);
}
class AffineExtractionThread : public QThread
{
public:
AffineExtractionThread(
Feature2D * detector,
Feature2D * extractor,
const cv::Mat & image,
float tilt,
float phi) :
detector_(detector),
extractor_(extractor),
image_(image),
tilt_(tilt),
phi_(phi),
timeSkewAffine_(0),
timeDetection_(0),
timeExtraction_(0),
timeSubPix_(0)
{
UASSERT(detector && extractor);
}
const cv::Mat & image() const {return image_;}
const std::vector<cv::KeyPoint> & keypoints() const {return keypoints_;}
const cv::Mat & descriptors() const {return descriptors_;}
int timeSkewAffine() const {return timeSkewAffine_;}
int timeDetection() const {return timeDetection_;}
int timeExtraction() const {return timeExtraction_;}
int timeSubPix() const {return timeSubPix_;}
protected:
virtual void run()
{
QTime timeStep;
timeStep.start();
cv::Mat skewImage, skewMask, Ai;
FindObject::affineSkew(tilt_, phi_, image_, skewImage, skewMask, Ai);
timeSkewAffine_=timeStep.restart();
//Detect features
computeFeatures(
detector_,
extractor_,
skewImage,
skewMask,
keypoints_,
descriptors_,
timeDetection_,
timeExtraction_);
timeStep.start();
// Transform points to original image coordinates
for(unsigned int i=0; i<keypoints_.size(); ++i)
{
cv::Mat p = (cv::Mat_<float>(3, 1) << keypoints_[i].pt.x, keypoints_[i].pt.y, 1);
cv::Mat pa = Ai * p;
keypoints_[i].pt.x = pa.at<float>(0,0);
keypoints_[i].pt.y = pa.at<float>(1,0);
}
if(keypoints_.size() && Settings::getFeature2D_6SubPix())
{
// Sub pixel should be done after descriptors extraction
std::vector<cv::Point2f> corners;
cv::KeyPoint::convert(keypoints_, corners);
cv::cornerSubPix(image_,
corners,
cv::Size(Settings::getFeature2D_7SubPixWinSize(), Settings::getFeature2D_7SubPixWinSize()),
cv::Size(-1,-1),
cv::TermCriteria( CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, Settings::getFeature2D_8SubPixIterations(), Settings::getFeature2D_9SubPixEps() ));
UASSERT(corners.size() == keypoints_.size());
for(unsigned int i=0; i<corners.size(); ++i)
{
keypoints_[i].pt = corners[i];
}
timeSubPix_ +=timeStep.restart();
}
}
private:
Feature2D * detector_;
Feature2D * extractor_;
cv::Mat image_;
float tilt_;
float phi_;
std::vector<cv::KeyPoint> keypoints_;
cv::Mat descriptors_;
int timeSkewAffine_;
int timeDetection_;
int timeExtraction_;
int timeSubPix_;
};
class ExtractFeaturesThread : public QThread
{
public:
ExtractFeaturesThread(
Feature2D * detector,
Feature2D * extractor,
int objectId,
const cv::Mat & image) :
detector_(detector),
extractor_(extractor),
objectId_(objectId),
image_(image),
timeSkewAffine_(0),
timeDetection_(0),
timeExtraction_(0),
timeSubPix_(0)
{
UASSERT(detector && extractor);
UASSERT_MSG(!image.empty() && image.type() == CV_8UC1,
uFormat("Image of object %d is null or not type CV_8UC1!?!? (cols=%d, rows=%d, type=%d)",
objectId, image.cols, image.rows, image.type()).c_str());
}
virtual ~ExtractFeaturesThread() {}
int objectId() const {return objectId_;}
const cv::Mat & image() const {return image_;}
const std::vector<cv::KeyPoint> & keypoints() const {return keypoints_;}
const cv::Mat & descriptors() const {return descriptors_;}
int timeSkewAffine() const {return timeSkewAffine_;}
int timeDetection() const {return timeDetection_;}
int timeExtraction() const {return timeExtraction_;}
int timeSubPix() const {return timeSubPix_;}
protected:
virtual void run()
{
QTime time;
time.start();
UDEBUG("Extracting descriptors from object %d...", objectId_);
QTime timeStep;
timeStep.start();
if(!Settings::getFeature2D_4Affine())
{
computeFeatures(
detector_,
extractor_,
image_,
cv::Mat(),
keypoints_,
descriptors_,
timeDetection_,
timeExtraction_);
timeStep.start();
if(keypoints_.size())
{
UDEBUG("Detected %d features from object %d...", (int)keypoints_.size(), objectId_);
if(Settings::getFeature2D_6SubPix())
{
// Sub pixel should be done after descriptors extraction
std::vector<cv::Point2f> corners;
cv::KeyPoint::convert(keypoints_, corners);
cv::cornerSubPix(image_,
corners,
cv::Size(Settings::getFeature2D_7SubPixWinSize(), Settings::getFeature2D_7SubPixWinSize()),
cv::Size(-1,-1),
cv::TermCriteria( CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, Settings::getFeature2D_8SubPixIterations(), Settings::getFeature2D_9SubPixEps() ));
UASSERT(corners.size() == keypoints_.size());
for(unsigned int i=0; i<corners.size(); ++i)
{
keypoints_[i].pt = corners[i];
}
timeSubPix_ +=timeStep.restart();
}
}
else
{
UWARN("no features detected in object %d !?!", objectId_);
}
}
else
{
//ASIFT
std::vector<float> tilts;
std::vector<float> phis;
tilts.push_back(1.0f);
phis.push_back(0.0f);
int nTilt = Settings::getFeature2D_5AffineCount();
for(int t=1; t<nTilt; ++t)
{
float tilt = std::pow(2.0f, 0.5f*float(t));
float inc = 72.0f / float(tilt);
for(float phi=0.0f; phi<180.0f; phi+=inc)
{
tilts.push_back(tilt);
phis.push_back(phi);
}
}
//multi-threaded
unsigned int threadCounts = Settings::getGeneral_threads();
if(threadCounts == 0)
{
threadCounts = (unsigned int)tilts.size();
}
for(unsigned int i=0; i<tilts.size(); i+=threadCounts)
{
QVector<AffineExtractionThread*> threads;
for(unsigned int k=i; k<i+threadCounts && k<tilts.size(); ++k)
{
threads.push_back(new AffineExtractionThread(detector_, extractor_, image_, tilts[k], phis[k]));
threads.back()->start();
}
for(int k=0; k<threads.size(); ++k)
{
threads[k]->wait();
keypoints_.insert(keypoints_.end(), threads[k]->keypoints().begin(), threads[k]->keypoints().end());
descriptors_.push_back(threads[k]->descriptors());
timeSkewAffine_ += threads[k]->timeSkewAffine();
timeDetection_ += threads[k]->timeDetection();
timeExtraction_ += threads[k]->timeExtraction();
timeSubPix_ += threads[k]->timeSubPix();
delete threads[k];
}
}
}
UINFO("%d descriptors extracted from object %d (in %d ms)", descriptors_.rows, objectId_, time.elapsed());
}
private:
Feature2D * detector_;
Feature2D * extractor_;
int objectId_;
cv::Mat image_;
std::vector<cv::KeyPoint> keypoints_;
cv::Mat descriptors_;
int timeSkewAffine_;
int timeDetection_;
int timeExtraction_;
int timeSubPix_;
};
void FindObject::updateObjects(const QList<int> & ids)
{
UINFO("Update %d objects...", ids.size());
QList<ObjSignature*> objectsList;
if(ids.size())
{
for(int i=0; i<ids.size(); ++i)
{
if(objects_.contains(ids[i]))
{
objectsList.push_back(objects_[ids[i]]);
}
else
{
UERROR("Not found object %d!", ids[i]);
}
}
}
else
{
objectsList = objects_.values();
}
if(objectsList.size())
{
sessionModified_ = true;
int threadCounts = Settings::getGeneral_threads();
if(threadCounts == 0)
{
threadCounts = objectsList.size();
}
QTime time;
time.start();
if(objectsList.size())
{
UINFO("Features extraction from %d objects... (threads=%d)", objectsList.size(), threadCounts);
for(int i=0; i<objectsList.size(); i+=threadCounts)
{
QVector<ExtractFeaturesThread*> threads;
for(int k=i; k<i+threadCounts && k<objectsList.size(); ++k)
{
if(!objectsList.at(k)->image().empty())
{
threads.push_back(new ExtractFeaturesThread(detector_, extractor_, objectsList.at(k)->id(), objectsList.at(k)->image()));
threads.back()->start();
}
else
{
objects_.value(objectsList.at(k)->id())->setData(std::vector<cv::KeyPoint>(), cv::Mat());
if(keepImagesInRAM_)
{
UERROR("Empty image detected for object %d!? No features can be detected.", objectsList.at(k)->id());
}
else
{
UWARN("Empty image detected for object %d! No features can be detected. Note that images are in not kept in RAM.", objectsList.at(k)->id());
}
}
}
for(int j=0; j<threads.size(); ++j)
{
threads[j]->wait();
int id = threads[j]->objectId();
objects_.value(id)->setData(threads[j]->keypoints(), threads[j]->descriptors());
if(!keepImagesInRAM_)
{
objects_.value(id)->removeImage();
}
delete threads[j];
}
}
UINFO("Features extraction from %d objects... done! (%d ms)", objectsList.size(), time.elapsed());
}
}
else
{
UINFO("No objects to update...");
}
}
void FindObject::clearVocabulary()
{
objectsDescriptors_.clear();
dataRange_.clear();
vocabulary_->clear();
}
void FindObject::updateVocabulary(const QList<int> & ids)
{
int count = 0;
int dim = -1;
int type = -1;
QList<ObjSignature*> objectsList;
if(ids.size())
{
for(int i=0; i<ids.size(); ++i)
{
if(objects_.contains(ids[i]))
{
objectsList.push_back(objects_[ids[i]]);
}
else
{
UERROR("Not found object %d!", ids[i]);
}
}
if(vocabulary_->size())
{
dim = vocabulary_->dim();
type = vocabulary_->type();
}
}
else
{
clearVocabulary();
objectsList = objects_.values();
}
// Get the total size and verify descriptors
for(int i=0; i<objectsList.size(); ++i)
{
if(!objectsList.at(i)->descriptors().empty())
{
if(dim >= 0 && objectsList.at(i)->descriptors().cols != dim)
{
UERROR("Descriptors of the objects are not all the same size! Objects "
"opened must have all the same size (and from the same descriptor extractor).");
return;
}
dim = objectsList.at(i)->descriptors().cols;
if(type >= 0 && objectsList.at(i)->descriptors().type() != type)
{
UERROR("Descriptors of the objects are not all the same type! Objects opened "
"must have been processed by the same descriptor extractor.");
return;
}
type = objectsList.at(i)->descriptors().type();
count += objectsList.at(i)->descriptors().rows;
}