bool StrongClassifier::decide(const FeatureVector &featureVector) const {
std::vector<float> features(featureVector.size());
for (int i=0; i<featureVector.size(); i++) {
features[i] = featureVector.at(i);
}
return decide(features);
}
double Slic::_ComputeDistance(const FeatureVector &vec1, const FeatureVector &vec2)
{
assert(vec1.size() == vec2.size());
double dis = 0;
for (unsigned int i=0;i<vec1.size();++i)
{
dis += (vec1[i]-vec2[i])*(vec1[i]-vec2[i]);
}
return dis;
}
inline
Tp1 dot_product(const FeatureVector<Tp1, Alloc1>& x, const WeightVector<Tp, Alloc>& w, const FeatureVector<Tp2, Alloc2>& y)
{
typedef FeatureVector<Tp1, Alloc1> feature_vector1_type;
typedef WeightVector<Tp, Alloc> weight_vector_type;
typedef FeatureVector<Tp2, Alloc2> feature_vector2_type;
if (x.empty() || y.empty()) return Tp1();
if (static_cast<const void*>(&x) == static_cast<const void*>(&y))
return details::__inner_product(x.begin(), x.end(), w, Tp1());
if (x.size() < y.size())
return dot_product(x.begin(), x.end(), w, y, Tp1());
else
return dot_product(x, w, y.begin(), y.end(), Tp1());
}
inline
Tp1 dot_product(const FeatureVector<Tp1, Alloc1>& x, const FeatureVector<Tp2, Alloc2>& y)
{
typedef FeatureVector<Tp1, Alloc1> feature_vector1_type;
typedef FeatureVector<Tp2, Alloc2> feature_vector2_type;
if (x.empty() || y.empty()) return Tp1();
// if the same address, we are identical!
if (static_cast<const void*>(&x) == static_cast<const void*>(&y))
return details::__inner_product(x.begin(), x.end(), Tp1());
if (x.size() < y.size())
return dot_product(x.begin(), x.end(), y, Tp1());
else
return dot_product(x, y.begin(), y.end(), Tp1());
}
FeatureSet FeatureSet::clone() const
{
FeatureSet other;
for (FeatureSet::const_iterator i = begin(); i != end(); ++i) {
FeatureVector cv = i->clone();
// check that all data is the same:
if (cv.size() != i->size())
throw std::runtime_error("ssssssssss");
for (unsigned int idx=0; idx<cv.size(); ++idx) {
if (cv[idx] != (*i)[idx]) {
cout << "(" << cv[idx] << "!=" << (*i)[idx] << ")" << flush;
throw std::runtime_error("??????????");
}
}
if (cv.getTag<TagTrueClassLabel>().label != i->getTag<TagTrueClassLabel>().label) {
cout << "(" << cv.getTag<TagTrueClassLabel>().label << "!=" << i->getTag<TagTrueClassLabel>().label << ")" << flush;
throw std::runtime_error("!!!!!!!!!!!");
}
other.push_back(cv);
}
return other;
}
PERIPHERAL_ERROR GetFeatures(const JOYSTICK_INFO* joystick, const char* controller_id,
unsigned int* feature_count, JOYSTICK_FEATURE** features)
{
if (!joystick || !controller_id || !feature_count || !features)
return PERIPHERAL_ERROR_INVALID_PARAMETERS;
FeatureVector featureVector;
if (CStorageManager::Get().GetFeatures(ADDON::Joystick(*joystick), controller_id, featureVector))
{
*feature_count = featureVector.size();
ADDON::JoystickFeatures::ToStructs(featureVector, features);
return PERIPHERAL_NO_ERROR;
}
return PERIPHERAL_ERROR_FAILED;
}
int main(int argc, char** argv)
{
static const size_t K = 3;
typedef double Scalar;
typedef Eigen::Matrix<Scalar, 1, K> Feature;
typedef std::vector<Feature, Eigen::aligned_allocator<Feature> > FeatureVector;
FeatureVector features(1000*K);
FeatureVector centers;
std::vector<unsigned int> membership;
FILE* data = fopen(argv[1], "r");
for (size_t i = 0; i < features.size(); ++i) {
Feature& f = features[i];
fscanf(data, "%lf %lf %lf", &f[0], &f[1], &f[2]);
}
vt::SimpleKmeans<Feature> kmeans(Feature::Zero());
double sse = kmeans.cluster(features, K, centers, membership);
for (size_t i = 0; i < centers.size(); ++i) {
printf("%f %f %f\n", centers[i][0], centers[i][1], centers[i][2]);
}
printf("sse = %f\n", sse);
unsigned int pattern[K];
pattern[0] = membership[0];
pattern[1] = membership[1];
pattern[2] = membership[2];
for (size_t i = 0; i < membership.size(); i+=K) {
if (membership[i] != pattern[0] ||
membership[i+1] != pattern[1] ||
membership[i+2] != pattern[2]) {
printf("Misassignment!\n");
return -1;
}
}
}
void ClassifySvmSharedCommand::processSharedAndDesignData(vector<SharedRAbundVector*> lookup) {
try {
OutputFilter outputFilter(verbosity);
LabeledObservationVector labeledObservationVector;
FeatureVector featureVector;
readSharedRAbundVectors(lookup, designMap, labeledObservationVector, featureVector);
// optionally remove features with low standard deviation
if ( stdthreshold > 0.0 ) {
FeatureVector removedFeatureVector = applyStdThreshold(stdthreshold, labeledObservationVector, featureVector);
if (removedFeatureVector.size() > 0) {
std::cout << removedFeatureVector.size() << " OTUs were below the stdthreshold of " << stdthreshold << " and were removed" << std::endl;
if ( outputFilter.debug() ) {
std::cout << "the following OTUs were below the standard deviation threshold of " << stdthreshold << std::endl;
for (FeatureVector::iterator i = removedFeatureVector.begin(); i != removedFeatureVector.end(); i++) {
std::cout << " " << i->getFeatureLabel() << std::endl;
}
}
}
}
// apply [0,1] standardization
if ( transformName == "zeroone") {
std::cout << "transforming data to lie within range [0,1]" << std::endl;
transformZeroOne(labeledObservationVector);
}
else {
std::cout << "transforming data to have zero mean and unit variance" << std::endl;
transformZeroMeanUnitVariance(labeledObservationVector);
}
SvmDataset svmDataset(labeledObservationVector, featureVector);
OneVsOneMultiClassSvmTrainer trainer(svmDataset, evaluationFoldCount, trainingFoldCount, *this, outputFilter);
if ( mode == "rfe" ) {
SvmRfe svmRfe;
ParameterRange& linearKernelConstantRange = kernelParameterRangeMap["linear"]["constant"];
ParameterRange& linearKernelSmoCRange = kernelParameterRangeMap["linear"]["smoc"];
RankedFeatureList rankedFeatureList = svmRfe.getOrderedFeatureList(svmDataset, trainer, linearKernelConstantRange, linearKernelSmoCRange);
map<string, string> variables;
variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(sharedfile));
variables["[distance]"] = lookup[0]->getLabel();
string filename = getOutputFileName("summary", variables);
outputNames.push_back(filename);
outputTypes["summary"].push_back(filename);
m->mothurOutEndLine();
std::ofstream outputFile(filename.c_str());
int n = 0;
int rfeRoundCount = rankedFeatureList.front().getRank();
std::cout << "ordered features:" << std::endl;
std::cout << setw(5) << "index"
<< setw(12) << "OTU"
<< setw(5) << "rank"
<< std::endl;
outputFile << setw(5) << "index"
<< setw(12) << "OTU"
<< setw(5) << "rank"
<< std::endl;
for (RankedFeatureList::iterator i = rankedFeatureList.begin(); i != rankedFeatureList.end(); i++) {
n++;
int rank = rfeRoundCount - i->getRank() + 1;
outputFile << setw(5) << n
<< setw(12) << i->getFeature().getFeatureLabel()
<< setw(5) << rank
<< std::endl;
if ( n <= 20 ) {
std::cout << setw(5) << n
<< setw(12) << i->getFeature().getFeatureLabel()
<< setw(5) << rank
<< std::endl;
}
}
outputFile.close();
}
else {
MultiClassSVM* mcsvm = trainer.train(kernelParameterRangeMap);
map<string, string> variables;
variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(sharedfile));
variables["[distance]"] = lookup[0]->getLabel();
string filename = getOutputFileName("summary", variables);
outputNames.push_back(filename);
outputTypes["summary"].push_back(filename);
m->mothurOutEndLine();
std::ofstream outputFile(filename.c_str());
printPerformanceSummary(mcsvm, std::cout);
printPerformanceSummary(mcsvm, outputFile);
outputFile << "actual predicted" << std::endl;
for ( LabeledObservationVector::const_iterator i = labeledObservationVector.begin(); i != labeledObservationVector.end(); i++ ) {
Label actualLabel = i->getLabel();
outputFile << i->getDatasetIndex() << " " << actualLabel << " ";
try {
Label predictedLabel = mcsvm->classify(*(i->getObservation()));
outputFile << predictedLabel << std::endl;
}
catch ( MultiClassSvmClassificationTie& m ) {
outputFile << "tie" << std::endl;
std::cout << "classification tie for observation " << i->datasetIndex << " with label " << i->first << std::endl;
}
}
outputFile.close();
delete mcsvm;
}
}
catch (exception& e) {
m->errorOut(e, "ClassifySvmSharedCommand", "processSharedAndDesignData");
exit(1);
}
}
bool CButtonMapXml::Load(void)
{
TiXmlDocument xmlFile;
if (!xmlFile.LoadFile(m_strResourcePath))
{
esyslog("Error opening %s: %s", m_strResourcePath.c_str(), xmlFile.ErrorDesc());
return false;
}
TiXmlElement* pRootElement = xmlFile.RootElement();
if (!pRootElement || pRootElement->NoChildren() || pRootElement->ValueStr() != BUTTONMAP_XML_ROOT)
{
esyslog("Can't find root <%s> tag", BUTTONMAP_XML_ROOT);
return false;
}
const TiXmlElement* pDevice = pRootElement->FirstChildElement(DEVICES_XML_ELEM_DEVICE);
if (!pDevice)
{
esyslog("Can't find <%s> tag", DEVICES_XML_ELEM_DEVICE);
return false;
}
if (!CDeviceXml::Deserialize(pDevice, m_device))
return false;
const TiXmlElement* pController = pDevice->FirstChildElement(BUTTONMAP_XML_ELEM_CONTROLLER);
if (!pController)
{
esyslog("Device \"%s\": can't find <%s> tag", m_device.Name().c_str(), BUTTONMAP_XML_ELEM_CONTROLLER);
return false;
}
// For logging purposes
unsigned int totalFeatureCount = 0;
while (pController)
{
const char* id = pController->Attribute(BUTTONMAP_XML_ATTR_CONTROLLER_ID);
if (!id)
{
esyslog("Device \"%s\": <%s> tag has no attribute \"%s\"", m_device.Name().c_str(),
BUTTONMAP_XML_ELEM_CONTROLLER, BUTTONMAP_XML_ATTR_CONTROLLER_ID);
return false;
}
FeatureVector features;
if (!Deserialize(pController, features))
return false;
if (features.empty())
{
esyslog("Device \"%s\" has no features for controller %s", m_device.Name().c_str(), id);
}
else
{
totalFeatureCount += features.size();
m_buttonMap[id] = std::move(features);
}
pController = pController->NextSiblingElement(BUTTONMAP_XML_ELEM_CONTROLLER);
}
dsyslog("Loaded device \"%s\" with %u controller profiles and %u total features", m_device.Name().c_str(), m_buttonMap.size(), totalFeatureCount);
return true;
}
void TrackingInfo::addFromFeatureCandidates( const FeatureVector &eyesCandidates, const FeatureVector &noseCandidates, const FeatureVector &mouthCandidates, double time, double posAffWeight, double sizeAffWeight )
{
typedef std::multimap<double, const _2Real::Space2D*> PriorityMap;
if( eyesCandidates.size() )
{
PriorityMap mapLeft;
PriorityMap mapRight;
for( FeatureVector::const_iterator it = eyesCandidates.begin(); it != eyesCandidates.end(); ++it )
{
_2Real::Vec2 center( ( it->getP0() + it->getP1() ) * 0.5f );
if( center[0] > 0.5f )
{
if( m_eyeLeftTrajectory.canCalcAffinity() )
mapLeft.insert( std::make_pair( m_eyeLeftTrajectory.calcAffinity( *it, posAffWeight, sizeAffWeight, 1.0, time ), &( *it ) ) );
else
mapLeft.insert( std::make_pair( 0.0, &( *it ) ) );
}
else
{
if( m_eyeRightTrajectory.canCalcAffinity() )
mapRight.insert( std::make_pair( m_eyeRightTrajectory.calcAffinity( *it, posAffWeight, sizeAffWeight, 1.0, time ), &( *it ) ) );
else
mapRight.insert( std::make_pair( 0.0, &( *it ) ) );
}
}
if( mapLeft.size() )
m_eyeLeftTrajectory.add( *( mapLeft.begin()->second ), 0.0, time );
else
{
//NOTE: there is no measure (yet?) to decide whether or not it would make sense to still extrapolate face feature, so this is not done at all,
// instead trajectories are pruned so entries don't last forever. (actually *duplicating* last entry would make more sense in context of
// face feature regions)
m_eyeLeftTrajectory.prune();
}
if( mapRight.size() )
m_eyeRightTrajectory.add( *( mapRight.begin()->second ), 0.0, time );
else
{
//NOTE: there is no measure (yet?) to decide whether or not it would make sense to still extrapolate face feature, so this is not done at all,
// instead trajectories are pruned so entries don't last forever. (actually *duplicating* last entry would make more sense in context of
// face feature regions)
m_eyeRightTrajectory.prune();
}
}
else
{
//NOTE: there is no measure (yet?) to decide whether or not it would make sense to still extrapolate face feature, so this is not done at all,
// instead trajectories are pruned so entries don't last forever. (actually *duplicating* last entry would make more sense in context of
// face feature regions)
m_eyeLeftTrajectory.prune();
m_eyeRightTrajectory.prune();
}
if( noseCandidates.size() )
{
double minAffinity = 0.0;
const _2Real::Space2D *bestCandidate = NULL;
if( !m_noseTrajectory.canCalcAffinity() )
bestCandidate = &( noseCandidates.front() ); //not enough regions in trajectory history in order to judge, just take first in candidate list
else
{
for( FeatureVector::const_iterator it = noseCandidates.begin(); it != noseCandidates.end(); ++it )
{
if( !bestCandidate )
{
bestCandidate = &( *it );
minAffinity = m_noseTrajectory.calcAffinity( *it, posAffWeight, sizeAffWeight, 1.0, time );
}
else
{
double affinity = m_noseTrajectory.calcAffinity( *it, posAffWeight, sizeAffWeight, 1.0, time );
if( affinity < minAffinity )
{
bestCandidate = &( *it );
minAffinity = affinity;
}
}
}
}
m_noseTrajectory.add( *bestCandidate, 0.0, time );
}
else
{
//NOTE: there is no measure (yet?) to decide whether or not it would make sense to still extrapolate face feature, so this is not done at all,
// instead trajectories are pruned so entries don't last forever. (actually *duplicating* last entry would make more sense in context of
// face feature regions)
m_noseTrajectory.prune();
}
if( mouthCandidates.size() )
{
double minAffinity = 0.0;
const _2Real::Space2D *bestCandidate = NULL;
if( !m_mouthTrajectory.canCalcAffinity() )
bestCandidate = &( mouthCandidates.front() ); //not enough regions in trajectory history in order to judge, just take first in candidate list
else
{
for( FeatureVector::const_iterator it = mouthCandidates.begin(); it != mouthCandidates.end(); ++it )
{
if( !bestCandidate )
{
bestCandidate = &( *it );
minAffinity = m_mouthTrajectory.calcAffinity( *it, posAffWeight, sizeAffWeight, 1.0, time );
}
else
{
double affinity = m_mouthTrajectory.calcAffinity( *it, posAffWeight, sizeAffWeight, 1.0, time );
if( affinity < minAffinity )
{
bestCandidate = &( *it );
minAffinity = affinity;
}
}
}
}
m_mouthTrajectory.add( *bestCandidate, 0.0, time );
}
else
{
//NOTE: there is no measure (yet?) to decide whether or not it would make sense to still extrapolate face feature, so this is not done at all,
// instead trajectories are pruned so entries don't last forever. (actually *duplicating* last entry would make more sense in context of
// face feature regions)
m_mouthTrajectory.prune();
}
}
int main(int argc, char** argv )
{
if ( argc >= 2 )
{
std::cout << "ESC to quit, any other key to continue to next image." << std::endl;
std::vector<cv::String> filenames;
cv::String folder = cv::String(argv[1]);
cv::glob(folder, filenames);
std::string configFolder = argv[1];
configFolder.append( "config" );
std::string carName = "Goffin";
if( argc >= 3 )
{
carName = argv[2];
}
std::string mConfigPath( Config::setConfigPath( configFolder, carName ) );
std::string mBirdViewCalFile( ( boost::filesystem::path(configFolder) / carName / "BirdviewCal.yml").string() );
CoordinateConverter converter( mConfigPath );
HaarFilter filter( &converter );
BirdViewConverter mBirdViewConverter;
mBirdViewConverter.loadConfig( mBirdViewCalFile );
cvflann::StartStopTimer timer;
for(size_t i = 0; i < filenames.size(); ++i)
{
cv::Mat image = cv::imread( filenames[i], CV_LOAD_IMAGE_GRAYSCALE );
std::cout << filenames[i] << std::endl;
cv::Mat mBirdViewImageGray = mBirdViewConverter.transform( image );
cv::Mat mBirdViewImage;
cv::cvtColor(mBirdViewImageGray, mBirdViewImage, CV_GRAY2BGR);
timer.start();
filter.setImage( mBirdViewImageGray );
filter.calculateFeaturesPerLine(
true,
true);
FeatureVector* featureVec = filter.getFeatures();
std::cout << featureVec->size() << std::endl;
timer.stop();
std::cout << "Time taken for image: " << timer.value << std::endl;
//cv::Mat result = filter.generateDebugImage();
cv::Mat channels[4];
//cv::cvtColor( mLastBirdViewImage, output, CV_GRAY2BGR );
cv::Mat features = filter.generateDebugImage();
cv::split( features, channels );
cv::Mat sum;
cv::bitwise_or( mBirdViewImage, 0, sum, 255 - channels[3] );
cv::Mat featuresRGB;
cv::cvtColor( features, featuresRGB, CV_BGRA2BGR );
sum = sum + featuresRGB;
imshow( "input", mBirdViewImageGray );
imshow( "result", sum );
int code = cv::waitKey(0);
if ( code == 27 ) // ESC to abort
return 1;
}
}
else
{
std::cout << "Usage: test_oadrive_haarfeatures <path> [CarName]" << std::endl;
std::cout << "\tWhere <path> is a folder containing a test case and" << std::endl;
std::cout << "\tCarName is an optional car name (default: Goffin)" << std::endl;
}
return 0;
}
