/*
* ShowGLMult.cpp
*
* Created on: 28/01/2015
* Author: pedro
*/
#include "ShowGLMult.h"
DetectorMult ShowGLMult::detector = DetectorMult();
Util ShowGLMult::util = Util();
int ShowGLMult::MODOS = 0;
vector<ObjetoMult> ShowGLMult::objetos = vector<ObjetoMult>();
vector<ObjetoMult> ShowGLMult::ultimasPosicoes = ShowGLMult::objetos;
Kalman ShowGLMult::kalman = Kalman();
ObjetoMult ShowGLMult::endGame = ObjetoMult();
bool ShowGLMult::gameOver = false;
ShowGLMult::ShowGLMult(DetectorMult& detector,vector<ObjetoMult> objetos, ObjetoMult& endGame){
if(objetos.size() == detector.getBoards().size()){
ShowGLMult::detector = detector;
ShowGLMult::objetos = objetos;
ShowGLMult::endGame = endGame;
for(unsigned int i=0;i<objetos.size();i++)
ShowGLMult::objetos.at(i).setConfig(ShowGLMult::detector.getBoards().at(i));
ShowGLMult::ultimasPosicoes = ShowGLMult::objetos ;
}
}
void ShowGLMult::ativarIluminacao(){
float luzAmbiente[]={0.2,0.2,0.2,1.0};
float luzDifusa[]={0.9,0.7,0.7,1.0};
int main(int argc, char *argv[])
{
//test pour savoir si l'utilisateur a renseigne un parametre
if(argc <= 3)
{
std::cout<<"---------------------------------------"<<std::endl<<
"Veuillez rentrer la methode choisie : "<<std::endl<<
"- template_tracking" <<std::endl<<
"- LK_tracking" <<std::endl<<
"- farneback" <<std::endl<<
"- camshift_kalman" <<std::endl<<
"- background_lk" <<std::endl<<
"- background_kalman" <<std::endl<<std::endl<<
"Le type de format : " <<std::endl<<
"- video" <<std::endl<<
"- image" <<std::endl<<std::endl<<
"Le nom du fichier d'input" <<std::endl<<
"---------------------------------------"<<std::endl;
std::exit(EXIT_FAILURE);
}
//------------------VARIABLES----------------------------------------------//
//Variables communes
choiceAlgo algo;
formatVideo format;
std::string inputFileName(argv[3]);
int nbTrames = 501;
double fps = 0;
std::vector<cv::Mat> sequence;
cv::Mat previousSequence;
int nbPedestrians = 0;
cv::HOGDescriptor hog;
hog.setSVMDetector(cv::HOGDescriptor::getDefaultPeopleDetector());
std::vector<cv::Rect> detectedPedestrian;
// HOG + Good feature to track + LK
std::vector<std::vector<cv::Point2f>> featuresDetected;
std::vector<std::vector<cv::Point2f>> previousFeaturesDetected;
// HOG + Template tracking
std::vector<cv::Rect> boxes;
std::vector<cv::Rect> previousBoxes;
// Optical flow farneback
cv::Mat flow;
cv::Mat imGray;
cv::Mat imGrayPrev;
//camshift and kalman filter
std::vector<cv::MatND> backProj;
std::vector<cv::Rect> roiHogDetected;
std::vector<cv::Rect> roiCamShift;
std::vector<bool> detected;
std::vector<cv::Mat> hist;
std::vector<cv::RotatedRect> rectCamShift;
cv::Point2f rect_points[4];
//--------------------------------------------------------------------------------------//
//Background substraction, pour le tracking LK et goodfeaturestotrack regarder au dessus
trackingOption tracking;
cv::Ptr<cv::BackgroundSubtractor> pMOG2;
std::vector<cv::Rect> detectedPedestrianFiltered;
cv::KalmanFilter KF(4,2,0,CV_32F);
cv::Mat_<float> measurement(2,1);
cv::Mat prediction;
cv::Mat estimated;
pMOG2 = cv::createBackgroundSubtractorMOG2();
//Avec ajout du Haar cascade classifier
std::vector<std::vector<cv::Rect>> rect_upper_body;
cv::CascadeClassifier classifier;
std::string upper_body_cascade_name = "haarcascade_fullbody.xml";
if(!classifier.load(upper_body_cascade_name))
{
std::cout<<"le fichier "<<upper_body_cascade_name<<" ne peut etre charge"<<std::endl;
return -1;
}
//--------------------------------------------------------------------------------------//
//Background substraction and Kalman
bool initKalman = true;
cv::KalmanFilter Kalman(4,2,0,CV_32F);
cv::Mat_<float> measurmt(2,1);
cv::Mat predict;
cv::Mat estim;
Kalman.transitionMatrix.at<float>(0,0) = 1;
Kalman.transitionMatrix.at<float>(0,1) = 0;
Kalman.transitionMatrix.at<float>(0,2) = 1;
Kalman.transitionMatrix.at<float>(0,3) = 0;
Kalman.transitionMatrix.at<float>(1,0) = 0;
Kalman.transitionMatrix.at<float>(1,1) = 1;
Kalman.transitionMatrix.at<float>(1,2) = 0;
Kalman.transitionMatrix.at<float>(1,3) = 1;
Kalman.transitionMatrix.at<float>(2,0) = 0;
Kalman.transitionMatrix.at<float>(2,1) = 0;
Kalman.transitionMatrix.at<float>(2,2) = 1;
Kalman.transitionMatrix.at<float>(2,3) = 0;
Kalman.transitionMatrix.at<float>(3,0) = 0;
Kalman.transitionMatrix.at<float>(3,1) = 0;
Kalman.transitionMatrix.at<float>(3,2) = 0;
Kalman.transitionMatrix.at<float>(3,3) = 1;
measurmt.setTo(cv::Scalar(0));
cv::setIdentity(Kalman.measurementMatrix);
cv::setIdentity(Kalman.processNoiseCov, cv::Scalar::all(1e-4));
cv::setIdentity(Kalman.measurementNoiseCov, cv::Scalar::all(1e-1));
cv::setIdentity(Kalman.errorCovPost, cv::Scalar::all(.1));
cv::Rect rectK;
cv::Rect prevRectK;
//acquisition de la video
algo = detectAlgo(std::string(argv[1]));
format = detectFormat(std::string(argv[2]));
//------------------VIDEO--------------------------------------------------//
if(format == SEQUENCE_IMAGE)
sequence.resize(nbTrames);
else if(format == VIDEO)
std::cout<<"video"<<std::endl;
extractVideoData(sequence, format, inputFileName, nbTrames, fps);
cv::namedWindow("Video", cv::WINDOW_AUTOSIZE);
//------------------TRAITEMENT-VIDEO---------------------------------------//
for(int i=0;i<nbTrames;i++)
{
if(i>0)
previousSequence = sequence[i-1];
else
previousSequence = sequence[i];
///------------------HOG + Good Features to track + LK-----------------//
if(algo == HOG_GOODFEATURESTOTRACK_LK)
{
if(i%20 == 0)
{
detectedPedestrian = hogDetection(sequence[i], hog);
nbPedestrians = detectedPedestrian.size();
if(nbPedestrians != 0)
{
featuresDetected = featuresDetection(sequence[i], detectedPedestrian);
previousFeaturesDetected.resize(featuresDetected.size());
previousFeaturesDetected = featuresDetected;
}
}
else if(previousFeaturesDetected.size() != 0)
{
featuresDetected = lucasKanadeTracking(previousSequence, sequence[i], previousFeaturesDetected);
previousFeaturesDetected.clear();
previousFeaturesDetected.resize(featuresDetected.size());
previousFeaturesDetected = featuresDetected;
}
//--------Representation--------------------
/*
cv::Scalar myColor;
for(size_t j=0;j<featuresDetected.size();j++)
{
if(j%3 == 0)
myColor = cv::Scalar(0,0,cv::RNG().uniform(200,255));
else if(j%2 == 0)
myColor = cv::Scalar(0,cv::RNG().uniform(200,255),0);
else
myColor = cv::Scalar(cv::RNG().uniform(200,255),0,0);
for(size_t k=0;k<featuresDetected[j].size();k++)
{
cv::circle(sequence[i], featuresDetected[j][k], 1, myColor,-1);
}
}
*/
for(size_t j=0;j<featuresDetected.size();j++)
{
cv::rectangle(sequence[i], cv::boundingRect(featuresDetected[j]), cv::Scalar( 0, 0, 255), 2, 8, 0 );
}
//affichage de la video
cv::imshow("Video", sequence[i]);
}
///------------------HOG + Template Tracking---------------------------//
else if(algo == HOG_TEMPLATE_TRACKING)
{
if(i%20 == 0)
{
detectedPedestrian = hogDetection(sequence[i], hog);
nbPedestrians = detectedPedestrian.size();
if(nbPedestrians != 0)
{
boxes = templateTracking(sequence[i], detectedPedestrian, CV_TM_CCORR_NORMED);
previousBoxes.resize(boxes.size());
previousBoxes = boxes;
}
}
else if(previousBoxes.size() != 0)
{
boxes = templateTracking(sequence[i], previousBoxes, CV_TM_CCORR_NORMED);
previousBoxes.clear();
previousBoxes.resize(boxes.size());
previousBoxes = boxes;
}
//--------Representation--------------------
for(size_t j=0;j<boxes.size();j++)
{
cv::rectangle(sequence[i], boxes[j], cv::Scalar( 0, 0, 255), 2, 8, 0 );
}
//affichage de la video
cv::imshow("Video", sequence[i]);
}
///------------------HOG + Optical Flow Farneback----------------------//
else if(algo == OPT_FLOW_FARNEBACK)
{
if(i!=0)
{
flow = cv::Mat::zeros(sequence[i].size(), CV_32FC2);
cv::cvtColor(sequence[i], imGray, CV_BGR2GRAY);
cv::cvtColor(sequence[i-1], imGrayPrev, CV_BGR2GRAY);
cv::calcOpticalFlowFarneback(imGrayPrev, imGray, flow, 0.5, 3, 15, 3, 5, 1.2, 0);
//-----------------Representation------------------------------//
drawOptFlowMap(flow, imGrayPrev, 16, CV_RGB(0, 255, 0)); //dessin test
//affichage de la video
cv::imshow("Video", imGrayPrev);
}
}
///--------------HOG+Camshift + Kalman Filter--------------------------//
else if(algo == CAMSHIFT_KALMAN_FILTER)
{
//camshift
if(i%20 == 0 && roiCamShift.size() == 0)
{
roiHogDetected = hogDetection(sequence[i], hog);
refineROI(roiCamShift, detected, roiHogDetected);
//test
if(roiCamShift.size() != 0)
{
/*
roiCamShift[0].x += 30;
roiCamShift[0].width -= 60;
roiCamShift[0].y += 40;
roiCamShift[0].height -= 100;
*/
roiCamShift[0].x += roiCamShift[0].width/2;
roiCamShift[0].width = roiCamShift[0].width/3;
//roiCamShift[0].y += roiCamShift[0].height/2;
roiCamShift[0].height = roiCamShift[0].height/3;
}
//
}
backProj = computeProbImage(sequence[i], roiCamShift, hist, detected);
if (roiCamShift.size() != 0)
cv::imshow("temp", backProj[0]);
///-------Test-Camshift--------------------///
rectCamShift.resize(roiCamShift.size());
for(size_t j=0;j<roiCamShift.size();j++)
{
/*
std::cout<<roiCamShift[j]<<std::endl;
cv::rectangle(backProj[j], roiCamShift[j], cv::Scalar( 255, 0, 0), 2, 8, 0 ); //DEBUG
cv::imshow("before camshift", backProj[j]);
cv::waitKey(0);
*/
cv::Rect rectMeanShift;
rectMeanShift = roiCamShift[j];
cv::meanShift(backProj[j], rectMeanShift, cv::TermCriteria(cv::TermCriteria::EPS | cv::TermCriteria::COUNT, 10, 1));
cv::rectangle(sequence[i], rectMeanShift, cv::Scalar( 0, 255, 0), 2, 8, 0 );
rectCamShift[j] = cv::CamShift(backProj[j], roiCamShift[j], cv::TermCriteria(cv::TermCriteria::EPS | cv::TermCriteria::COUNT, 10, 1));
rectCamShift[j].points(rect_points);
for(int k = 0; k < 4; k++)
cv::line(sequence[i], rect_points[k], rect_points[(k+1)%4], cv::Scalar( 0, 0, 255), 2, 8);
}
///----------------------------------------///
//-----------------Representation----------------------------------//
//dessin du rectangle
for(size_t j=0;j<roiCamShift.size();j++)
cv::rectangle(sequence[i], roiCamShift[j], cv::Scalar( 255, 0, 0), 2, 8, 0 );
//affichage de la video
cv::imshow("Video", sequence[i]);
}
///------------------BACKGROUND-SUBSTRACTION---------------------------//
else if(algo == BACKGROUND_LK)
{
if(i%10 == 0) //égal 0 pour le test
{
backgroundSubstractionDetection(sequence[i], detectedPedestrianFiltered, pMOG2, tracking);
}
if(tracking == GOOD_FEATURES_TO_TRACK)
{
featuresDetected.resize(detectedPedestrianFiltered.size());
featuresDetected = featuresDetection(sequence[i], detectedPedestrianFiltered);
previousFeaturesDetected.resize(featuresDetected.size());
previousFeaturesDetected = featuresDetected;
tracking = LUCAS_KANADE;
KF.transitionMatrix.at<float>(0,0) = 1;
KF.transitionMatrix.at<float>(0,1) = 0;
KF.transitionMatrix.at<float>(0,2) = 1;
KF.transitionMatrix.at<float>(0,3) = 0;
KF.transitionMatrix.at<float>(1,0) = 0;
KF.transitionMatrix.at<float>(1,1) = 1;
KF.transitionMatrix.at<float>(1,2) = 0;
KF.transitionMatrix.at<float>(1,3) = 1;
KF.transitionMatrix.at<float>(2,0) = 0;
KF.transitionMatrix.at<float>(2,1) = 0;
KF.transitionMatrix.at<float>(2,2) = 1;
KF.transitionMatrix.at<float>(2,3) = 0;
KF.transitionMatrix.at<float>(3,0) = 0;
KF.transitionMatrix.at<float>(3,1) = 0;
KF.transitionMatrix.at<float>(3,2) = 0;
KF.transitionMatrix.at<float>(3,3) = 1;
measurement.setTo(cv::Scalar(0));
for(size_t j=0;j<featuresDetected.size();j++)
{
detectedPedestrianFiltered[j] = cv::boundingRect(featuresDetected[j]);
}
KF.statePre.at<float>(0) = rectCenter(detectedPedestrianFiltered[0]).x;
KF.statePre.at<float>(1) = rectCenter(detectedPedestrianFiltered[0]).y;
KF.statePre.at<float>(2) = 0;
KF.statePre.at<float>(3) = 0;
cv::setIdentity(KF.measurementMatrix);
cv::setIdentity(KF.processNoiseCov, cv::Scalar::all(1e-4));
cv::setIdentity(KF.measurementNoiseCov, cv::Scalar::all(1e-1));
cv::setIdentity(KF.errorCovPost, cv::Scalar::all(.1));
}
else if(tracking == LUCAS_KANADE)
{
for(size_t j=0;j<featuresDetected.size();j++)
{
detectedPedestrianFiltered[j] = cv::boundingRect(featuresDetected[j]);
}
featuresDetected = lucasKanadeTracking(previousSequence, sequence[i], previousFeaturesDetected);
previousFeaturesDetected.clear();
previousFeaturesDetected.resize(featuresDetected.size());
previousFeaturesDetected = featuresDetected;
prediction = KF.predict();
cv::Point predictPt(prediction.at<float>(0),prediction.at<float>(1));
// Get mouse point
measurement(0) = rectCenter(detectedPedestrianFiltered[0]).x;
measurement(1) = rectCenter(detectedPedestrianFiltered[0]).y;
cv::Point measPt(measurement(0),measurement(1));
// The "correct" phase that is going to use the predicted value and our measurement
cv::Mat estimated = KF.correct(measurement);
cv::Point statePt(estimated.at<float>(0),estimated.at<float>(1));
//cv::circle(sequence[i], measPt, 1, cv::Scalar(0,255,0), 7, 24);
//cv::circle(sequence[i], predictPt, 1, cv::Scalar(0,255,255), 7, 24);
}
//--------Representation--------------------
if(tracking != NOTHING_TO_TRACK)
findUpperBody(classifier, sequence[i], detectedPedestrianFiltered, rect_upper_body);
for(size_t j=0;j<featuresDetected.size();j++)
{
for(size_t k=0;k<rect_upper_body[j].size();k++)
{
cv::rectangle(sequence[i], rect_upper_body[j][k], cv::Scalar( 0, 255, 0), 2, 8, 0 );
}
//detectedPedestrianFiltered[j] = cv::boundingRect(featuresDetected[j]);
cv::rectangle(sequence[i], cv::boundingRect(featuresDetected[j]), cv::Scalar( 0, 0, 255), 2, 8, 0 );
}
//affichage de la video
cv::imshow("Video", sequence[i]);
}
else if(algo == BACKGROUND_KALMAN)
{
int refresh = 6;
if(i%refresh == 0)
{
backgroundSubstractionDetection(sequence[i], detectedPedestrianFiltered, pMOG2, tracking);
}
if(initKalman && detectedPedestrianFiltered.size() != 0)
{
Kalman.statePre.at<float>(0) = rectCenter(detectedPedestrianFiltered[0]).x;
Kalman.statePre.at<float>(1) = rectCenter(detectedPedestrianFiltered[0]).y;
Kalman.statePre.at<float>(2) = 0;
Kalman.statePre.at<float>(3) = 0;
initKalman = false;
}
if(detectedPedestrianFiltered.size() != 0)
{
predict = Kalman.predict();
cv::Point predictPt(predict.at<float>(0),predict.at<float>(1));
// The "correct" phase that is going to use the predicted value and our measurement
if(i%refresh == 0)
{
rectK = findROI(predictPt, detectedPedestrianFiltered);
if(!fusionRects(prevRectK, rectK))
{
cv::Point refPoint = rectCenter(rectK);
// Get center point
measurmt(0) = refPoint.x;
measurmt(1) = refPoint.y;
cv::Point measPt(measurmt(0),measurmt(1));
cv::Mat estim = Kalman.correct(measurmt);
cv::Point statePt(estim.at<float>(0),estim.at<float>(1));
}
prevRectK = rectK;
}
std::string str = std::to_string(sqrt(pow(Kalman.statePre.at<float>(2), 2)+pow(Kalman.statePre.at<float>(3), 2)));
//cv::circle(sequence[i], measPt, 1, cv::Scalar(0,255,0), 7, 24);
cv::circle(sequence[i], predictPt, 1, cv::Scalar(0,255,255), 7, 24);
cv::putText(sequence[i], str, predictPt, cv::FONT_HERSHEY_PLAIN, 1.0, CV_RGB(255,0,0), 2.0);
}
cv::imshow("Video", sequence[i]);
}
//------------------CLEAR-VARIABLES------------------------------------//
detectedPedestrian.clear();
featuresDetected.clear();
boxes.clear();
previousSequence.release();
flow.release();
imGray.release();
imGrayPrev.release();
roiHogDetected.clear();
backProj.clear();
//------------------CONDITIONS-ARRET-----------------------------------//
if (cv::waitKey((int)(1000/fps)) == 27) //wait for 'esc' key press for 30ms. If 'esc' key is pressed, break loop
{
std::cout << "esc key is pressed by user" << std::endl;
return 0;
}
}
cv::waitKey(0);
return 0;
}
STFrame* Tracking( STFrame* frameDataIn, int MaxTracks,StaticBGModel* BGModel, int FPS ){
STFrame* frameDataOut = NULL; // frame de salida
#ifdef MEDIR_TIEMPOS
struct timeval ti,tif; // iniciamos la estructura
float tiempoParcial;
gettimeofday(&tif, NULL);
printf("\n2)Tracking:\n");
gettimeofday(&ti, NULL);
printf("\t1)Asignación de identidades\n");
#endif
////////////// AÑADIR AL BUFFER /////////////
anyadirAlFinal( frameDataIn, framesBuf);
// MotionTemplate( framesBuf,Identities );
////////////// ASIGNACIÓN DE IDENTIDADES ///////////
//APLICAR EL METODO DE OPTIMIZACION HUNGARO A LA MATRIZ DE PESOS
// Asignar identidades.
// resolver las asociaciones usando las predicciones de kalman mediante el algoritmo Hungaro
// Si varias dan a la misma etiquetarla como 0. Enlazar flies.
// Se trabaja en las posiciones frame MAX_BUFFER - 1 y MAX_BUFFER -2.
asignarIdentidades( lsTracks,frameDataIn->Flies);
ordenarTracks( lsTracks );
#ifdef MEDIR_TIEMPOS
tiempoParcial= obtenerTiempo( ti, 0);
printf("\t\t-Tiempo: %5.4g ms\n", tiempoParcial);
gettimeofday(&ti, NULL);
fprintf(stderr,"\t2)Filtro de Kalman\n");
#endif
/////////////// ELIMIRAR FALSOS TRACKS. APLICACIÓN DE HEURíSTICAS AL FINAL DEL BUFFER ///
frameDataIn->numTracks = validarTracks( framesBuf, lsTracks,Identities, trackParams->MaxBlobs, frameDataIn->num_frame );
/////////////// FILTRO DE KALMAN //////////////
// El filtro de kalman trabaja en la posicion MAX_BUFFER -1. Ultimo elemento anyadido.
Kalman( frameDataIn , Identities, lsTracks, trackParams);
frameDataIn->Tracks = lsTracks;
#ifdef MEDIR_TIEMPOS
tiempoParcial= obtenerTiempo( ti, 0);
printf("\t\t-Tiempo: %5.4g ms\n", tiempoParcial);
#endif
/////// FASE DE CORRECCIÓN. APLICACION DE HEURISTICAS AL FRAME DE SALIDA /////////
despertarTrack(framesBuf, lsTracks, Identities );
ordenarTracks( lsTracks );
// SI BUFFER LLENO
if( framesBuf->numeroDeElementos == trackParams->MaxBuffer ){
corregirTracks(framesBuf, lsTracks, Identities );
////////// LIBERAR MEMORIA ////////////
frameDataOut = (STFrame*)liberarPrimero( framesBuf ) ;
if(!frameDataOut){error(7); exit(1);}
VisualizarEl( framesBuf, PENULTIMO, BGModel );
#ifdef MEDIR_TIEMPOS
tiempoParcial = obtenerTiempo( tif , NULL);
printf("Tracking correcto.Tiempo total %5.4g ms\n", tiempoParcial);
#endif
return frameDataOut;
}
else {
VerEstadoBuffer( frameDataIn->Frame, framesBuf->numeroDeElementos, trackParams->MaxBuffer);
VisualizarEl( framesBuf, PENULTIMO, BGModel );
#ifdef MEDIR_TIEMPOS
tiempoParcial = obtenerTiempo( tif , NULL);
printf("Tracking correcto.Tiempo total %5.4g ms\n", tiempoParcial);
#endif
return 0;
}
}
