void OptFlowThread::run()
{
if( !cap.isOpened() )
return;
Mat prevgray;
Mat gray;
Mat flow;
Mat cflow;
Mat frame;
while(true)
{
cap >> frame;
cvtColor(frame,gray,COLOR_BGR2GRAY);
if(prevgray.data)
{
//Use Gunnar Farneback algorithm to compute optical flow.
calcOpticalFlowFarneback(prevgray,gray,flow,0.5,3,15,3,5,1.2,0);
cvtColor(prevgray,cflow,COLOR_GRAY2BGR);
//Draw green points.
drawOptFlowMap(flow,cflow,16,3,Scalar(0,255,0));
emit NewOptFlowFrame(&cflow);
}
std::swap(prevgray,gray);
waitKey(30);
}
return;
}
void operator()(const cv::Mat& image,bool record)
{
// no image no fun
if (image.empty()) return;
int cF = currentFrame ? 1 : 0;
int pF = currentFrame ? 0 : 1;
// resize the image
cv::resize(image,frames[cF],cv::Size(image.cols*scale,image.rows*scale),0,0,cv::INTER_LINEAR);
std::cout << "Current: " << cF << " Previous: " << pF << std::endl;
std::cout << "Image " << image.size() << " c:" << image.channels() << " > " << frames[cF].size() << std::endl;
if (record && !output.isOpened())
{
// fourcc
int fourCC = FourCC<'X','V','I','D'>::value;
// set framerate to 1fps - easier to check in a standard video player
if (output.open("flow.avi",fourCC,25,frames[cF].size(),false))
{
std::cout << "capture file opened" << std::endl;
}
}
// make a copy for the initial frame
if (frames[pF].empty())
frames[cF].copyTo(frames[pF]);
if (!flow.empty())
flow = cv::Mat::zeros(flow.size(),flow.type());
// calculate dense optical flow
cv::calcOpticalFlowFarneback(frames[pF],frames[cF],flow,.5,2,8,3,7,1.5,0);
// we can't draw into the frame!
cv::Mat outImg = frames[cF].clone();
drawOptFlowMap(flow,outImg,8,cv::Scalar::all(255));
cv::imshow("Flow",outImg);
// flip the buffers
currentFrame = !currentFrame;
// record the frame
if (record && output.isOpened())
output.write(outImg);
}
Mat DepthEstimator1::estimateDepth(const LightFieldPicture lightfield)
{
// render images
ImageRenderer3 renderer = ImageRenderer3();
renderer.setLightfield(lightfield);
renderer.setAlpha(ALPHA);
const Vec2i leftPosition = Vec2i(-5,0);
renderer.setPinholePosition(leftPosition);
Mat image1 = renderer.renderImage();
cvtColor(image1, image1, CV_RGB2GRAY);
image1.convertTo(image1, CV_8UC1, 255.0);
const Vec2i rightPosition = Vec2i(5,0);
renderer.setPinholePosition(rightPosition);
Mat image2 = renderer.renderImage();
cvtColor(image2, image2, CV_RGB2GRAY);
image2.convertTo(image2, CV_8UC1, 255.0);
// compute optical flow
Mat opticalFlow;
calcOpticalFlowFarneback(image1, image2, opticalFlow, 0.5, 3, 15, 3, 5, 1.2, 0);
Mat flowMap;
image1.copyTo(flowMap);
drawOptFlowMap(opticalFlow, flowMap, 16, 1.5, Scalar(0, 255, 0));
/* start of debugging code */
const int windowFlags = WINDOW_NORMAL;
const string window1 = "image1";
namedWindow(window1, windowFlags);
imshow(window1, image1);
const string window2 = "image2";
namedWindow(window2, windowFlags);
imshow(window2, image2);
const string window3 = "optical flow";
namedWindow(window3, windowFlags);
imshow(window3, flowMap);
//cout << "optical flow = " << opticalFlow << endl;
waitKey(0);
/* end of debugging code */
return opticalFlow;
}
int main( int argc, char** argv )
{
CvCapture* capture = cvCreateCameraCapture(0);
CvMat* prevgray = 0, *gray = 0, *flow = 0, *cflow = 0;
(void)argc; (void)argv;
help();
if( !capture )
return -1;
cvNamedWindow("flow", 1);
for(;;)
{
int firstFrame = gray == 0;
IplImage* frame = cvQueryFrame(capture);
if(!frame)
break;
if(!gray)
{
gray = cvCreateMat(frame->height, frame->width, CV_8UC1);
prevgray = cvCreateMat(gray->rows, gray->cols, gray->type);
flow = cvCreateMat(gray->rows, gray->cols, CV_32FC2);
cflow = cvCreateMat(gray->rows, gray->cols, CV_8UC3);
}
cvCvtColor(frame, gray, CV_BGR2GRAY);
if( !firstFrame )
{
cvCalcOpticalFlowFarneback(prevgray, gray, flow, 0.5, 3, 15, 3, 5, 1.2, 0);
cvCvtColor(prevgray, cflow, CV_GRAY2BGR);
drawOptFlowMap(flow, cflow, 16, 1.5, CV_RGB(0, 255, 0));
cvShowImage("flow", cflow);
}
if(cvWaitKey(30)>=0)
break;
{
CvMat* temp;
CV_SWAP(prevgray, gray, temp);
}
}
cvReleaseCapture(&capture);
return 0;
}
void VisualContactCatadioptric::computeTTC(){
// variables initialized in each processing loop, memory management taken care of
// by itself?
float f1, f2, f;
cv_ptr->image.copyTo(gray);
ttc_matrix_.height = gray.rows;
ttc_matrix_.width = gray.cols;
if( !prevgray.empty() )
{
cv::calcOpticalFlowFarneback(prevgray, gray, uflow, GF_pyr_scale_, GF_levels_, GF_winsize_, GF_iterations_, GF_poly_n_, GF_poly_s_, 0);
cv::cvtColor(prevgray, cflow, cv::COLOR_GRAY2BGR);
uflow.copyTo(flow);
drawOptFlowMap(cv::Scalar(0, 255, 0));
for (int ii=0; ii<uflow.rows; ii++){
for (int jj=0; jj<uflow.cols; jj++){
f1 = -uflow.at<cv::Vec2f>(ii,jj)[0]/(jj-u0);
f2 = uflow.at<cv::Vec2f>(ii,jj)[1]/(ii-v0);
f = 1/(f2+f1);
if (!( isinf(f) | isnan(f) ) ){
ttcMap.at<float>(ii,jj) = abs(f);
}
ttc_matrix_.array.push_back(ttcMap.at<float>(ii,jj));
}
}
cv::Mat adjMap;
ttcMap.convertTo(adjMap, CV_8UC1, 255.0/TTC_disp_scale_);
applyColorMap(adjMap, falseColorsMap, cv::COLORMAP_JET);
}
// why swap, could just copy gray to prevgray, right?
std::swap(prevgray, gray);
}
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;
}
