void *DetectDescribe::pthreadParallelTracking(int part2Process) {
std::vector<uchar> status;
std::vector<float> err;
cv::TermCriteria termcrit(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 15, 0.05);
std::vector<cv::Point2f> points2Track, temp = std::vector<cv::Point2f>();
cv::KeyPoint::convert(keypoints[part2Process], points2Track);
// Calculating movement of features
if ( points2Track.size() > 0)
{
cv::calcOpticalFlowPyrLK(img[part2Process], img2[part2Process],
points2Track, temp, status, err, cvSize(5, 5), 3, termcrit);
}
}
void trackKlt(
FramePtr frame_ref,
FramePtr frame_cur,
vector<cv::Point2f>& px_ref,
vector<cv::Point2f>& px_cur,
vector<Vector3d>& f_ref,
vector<Vector3d>& f_cur,
vector<double>& disparities)
{
const double klt_win_size = 30.0;
const int klt_max_iter = 30;
const double klt_eps = 0.001;
vector<uchar> status;
vector<float> error;
vector<float> min_eig_vec;
cv::TermCriteria termcrit(cv::TermCriteria::COUNT+cv::TermCriteria::EPS, klt_max_iter, klt_eps);
cv::calcOpticalFlowPyrLK(frame_ref->img_pyr_[0], frame_cur->img_pyr_[0],
px_ref, px_cur,
status, error,
cv::Size2i(klt_win_size, klt_win_size),
4, termcrit, cv::OPTFLOW_USE_INITIAL_FLOW);
vector<cv::Point2f>::iterator px_ref_it = px_ref.begin();
vector<cv::Point2f>::iterator px_cur_it = px_cur.begin();
vector<Vector3d>::iterator f_ref_it = f_ref.begin();
f_cur.clear(); f_cur.reserve(px_cur.size());
disparities.clear(); disparities.reserve(px_cur.size());
for(size_t i=0; px_ref_it != px_ref.end(); ++i)
{
if(!status[i])
{
px_ref_it = px_ref.erase(px_ref_it);
px_cur_it = px_cur.erase(px_cur_it);
f_ref_it = f_ref.erase(f_ref_it);
continue;
}
f_cur.push_back(frame_cur->c2f(px_cur_it->x, px_cur_it->y));
disparities.push_back(Vector2d(px_ref_it->x - px_cur_it->x, px_ref_it->y - px_cur_it->y).norm());
++px_ref_it;
++px_cur_it;
++f_ref_it;
}
}
vector<Point2f> track_dots(IplImage* gray, IplImage* prevGray, vector<Point2f> points, CvRect* faceBox)
{
//expands region to search for dots
faceBox->x -= faceBox->width/2;
if(faceBox->x < 0)
{
faceBox->x = 0;
}
faceBox->width *= 2;
if(faceBox->x + faceBox->width > gray->width)
{
faceBox->width = gray->width - faceBox->x;
}
faceBox->y -= faceBox->height/2;
if(faceBox->y < 0)
{
faceBox->y = 0;
}
faceBox->height *= 2;
if(faceBox->y + faceBox->height > gray->height)
{
faceBox->height = gray->height - faceBox->y;
}
cvSetImageROI(gray,*faceBox);
cvSetImageROI(prevGray,*faceBox);
vector<Point2f> tempPoints;
vector<uchar> status;
vector<float> err;
TermCriteria termcrit(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS,1,0.3);
Size winSize(20,20);
calcOpticalFlowPyrLK(prevGray, gray, points, tempPoints, status, err, winSize, 0, termcrit, 0);
cvResetImageROI(gray);
cvResetImageROI(prevGray);
return tempPoints;
}
//--------------------------------------------------------------
void testApp::update(){
bool bNewFrame = false;
ofBackground(0,0,0);
#ifdef _USE_LIVE_VIDEO
vidGrabber.update();
bNewFrame = vidGrabber.isFrameNew();
#else
vidPlayer.update();
bNewFrame = vidPlayer.isFrameNew();
#endif
//do we have a new frame?
if (bNewFrame){
#ifdef _USE_LIVE_VIDEO
colorImg.setFromPixels(vidGrabber.getPixels().getData(), 320,240);
#else
colorImg.setFromPixels(vidPlayer.getPixels().getData(), 320,240);
#endif
grayImage = colorImg; // convert our color image to a grayscale image
if (bLearnBakground == true) {
grayBg = grayImage; // update the background image
bLearnBakground = false;
}
cv::Mat mat_curr(CAM_HEIGHT, CAM_WIDTH, CV_8UC1, grayImage.getPixels().getData(), cv::Mat::AUTO_STEP);
cv::Mat mat_prev(CAM_HEIGHT, CAM_WIDTH, CV_8UC1, grayBg.getPixels().getData(), cv::Mat::AUTO_STEP);
if(useFAST){
vector<cv::KeyPoint> keyPoints;
cv::FAST(mat_prev, keyPoints,30,true);
cv::KeyPoint::convert(keyPoints, prev_good_points);
}
else {
cv::goodFeaturesToTrack(mat_prev, // input, the image from which we want to know good features to track
prev_good_points, // output, the points will be stored in this output vector
100, // max points, maximum number of good features to track
0.05, // quality level, "minimal accepted quality of corners", the lower the more points we will get
10, // minDistance, minimum distance between points
cv::Mat(), // mask
4, // block size
false, // useHarrisDetector, makes tracking a bit better when set to true
0.04 // free parameter for harris detector
);
}
cv::TermCriteria termcrit(cv::TermCriteria::COUNT|cv::TermCriteria::EPS,prev_good_points.size(),0.03);
cv::calcOpticalFlowPyrLK(mat_prev, // prev image
mat_curr, // curr image
prev_good_points, // find these points in the new image
curr_good_points, // result of found points
status, // output status vector, found points are set to 1
error, // each point gets an error value (see flag)
cv::Size(21, 21), // size of the window at each pyramid level
0, // maxLevel - 0 = no pyramids, > 0 use this level of pyramids
termcrit, // termination criteria
0, // flags OPTFLOW_USE_INITIAL_FLOW or OPTFLOW_LK_GET_MIN_EIGENVALS
0.1 // minEigThreshold
);
grayBg = grayImage;
}
}
void main()
{
bool patternfound = false;
bool reset = false;
bool resetAuto = false;
int nbImages = 0;
double moyFinale = 0;
cv::TermCriteria termcrit(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03);
cv::Size winSize(31, 31);
cv::Mat cameraMatrix, distCoeffs;
cv::Mat imCalib;
cv::Mat imCalibColor;
cv::Mat imCalibNext;
cv::Mat rvecs, tvecs;
std::vector<cv::Point2f> imagePoints;
std::vector<cv::Point3f> objectPoints;
std::vector<cv::Point3f> cubeObjectPoints;
std::vector<std::vector<cv::Point2f>> chessCornersInit(2);
std::vector<cv::Point3f> chessCorners3D;
std::vector<double> distances;
double moyDistances;
// Creation des points a projeter
for(int x = 0; x < COLCHESSBOARD; x++)
for(int y = 0; y < ROWCHESSBOARD; y++)
objectPoints.push_back(cv::Point3f(x * 26.0f, y * 26.0f, 0.0f));
// Creation des points a projeter
cubeObjectPoints.push_back(cv::Point3f(52, 26, 0));
cubeObjectPoints.push_back(cv::Point3f(156, 26, 0));
cubeObjectPoints.push_back(cv::Point3f(156, 128, 0));
cubeObjectPoints.push_back(cv::Point3f(52, 128, 0));
cubeObjectPoints.push_back(cv::Point3f(52, 26, 104));
cubeObjectPoints.push_back(cv::Point3f(156, 26, 104));
cubeObjectPoints.push_back(cv::Point3f(156, 128, 104));
cubeObjectPoints.push_back(cv::Point3f(52, 128, 104));
// Creation des coins de la mire
for(int x = 0; x < COLCHESSBOARD; x++)
for(int y = 0; y < ROWCHESSBOARD; y++)
chessCorners3D.push_back(cv::Point3f(x * 26.0f, y * 26.0f, 0.0f));
cv::FileStorage fs("../rsc/intrinsicMatrix.yml", cv::FileStorage::READ);
fs["cameraMatrix"] >> cameraMatrix;
fs["distCoeffs"] >> distCoeffs;
fs.release();
cv::VideoCapture vcap(0);
if(!vcap.isOpened()){
std::cout << "FAIL!" << std::endl;
return;
}
cv::Mat *frame = new cv::Mat(cv::Mat::zeros(vcap.get(CV_CAP_PROP_FRAME_HEIGHT), vcap.get(CV_CAP_PROP_FRAME_WIDTH), CV_8UC3));
do
{
vcap >> *frame;
}while(frame->empty());
osg::ref_ptr<osg::Image> backgroundImage = new osg::Image;
backgroundImage->setImage(frame->cols, frame->rows, 3,
GL_RGB, GL_BGR, GL_UNSIGNED_BYTE,
(uchar*)(frame->data),
osg::Image::AllocationMode::NO_DELETE, 1);
// read the scene from the list of file specified commandline args.
osg::ref_ptr<osg::Group> group = new osg::Group;
osg::ref_ptr<osg::Node> objet3D = osgDB::readNodeFile("dumptruck.osgt");
osg::ref_ptr<osg::Camera> cam = createHUD(backgroundImage);
osg::ref_ptr<osg::PositionAttitudeTransform> pat = new osg::PositionAttitudeTransform;
osg::ref_ptr<osg::PositionAttitudeTransform> pat2 = new osg::PositionAttitudeTransform;
// construct the viewer.
osgViewer::Viewer viewer;
// add the HUD subgraph.
group->addChild(cam);
pat->addChild(pat2);
pat2->addChild(objet3D);
group->addChild(pat);
// set the scene to render
viewer.setSceneData(group.get());
viewer.realize(); // set up windows and associated threads.
char key = 0;
bool detectionMire = false;
cv::Mat Rc, C = cv::Mat(3, 1, CV_64F), rotVecInv;
pat->setScale(osg::Vec3d(0.08, 0.08, 0.08));
pat->setAttitude(osg::Quat(osg::DegreesToRadians(180.0), osg::Vec3d(1.0, 0.0, 0.0)));
pat2->setPosition(osg::Vec3d(15.0, 4.0, 5.0));
/*
// projection
double fx = cameraMatrix.at<double>(0, 0);
double fy = cameraMatrix.at<double>(1, 1);
double cx = cameraMatrix.at<double>(1, 2);
double cy = cameraMatrix.at<double>(1, 0);
double W = (double)frame->cols;
double H = (double)frame->rows;
double near = .1;
double far = 100.0;
osg::Matrixd projectionMatrix;
projectionMatrix.set(
2 * fx / W, 0, 0, 0,
0, 2 * fy / H, 0, 0,
2 * (cx / W) - 1, 2 * (cy - H) - 1, (far + near) / (far - near), 1,
0, 0, 2 * far * near / (near - far), 0);
projectionMatrix.set(
2 * fx / W, 0, 0, 0,
0, 2 * fy / H, 0, 0,
2 * (cx / W) - 1, 2 * (cy / H) - 1, (far + near) / (far - near), 1,
0, 0, 2 * far * near / (near - far), 0);
viewer.getCamera()->setProjectionMatrix(projectionMatrix);*/
do
{
patternfound = false;
resetAuto = false;
detectionMire = false;
imagePoints.clear();
chessCornersInit[0].clear();
chessCornersInit[1].clear();
moyDistances = 0;
distances.clear();
imCalibNext.release();
group->removeChild(pat);
std::cout << "recherche de mire" << std::endl;
do
{
vcap >> *frame;
backgroundImage->dirty();
detectionMire = detecterMire(frame, &chessCornersInit[1], &imCalibNext);
viewer.frame();
}while(!detectionMire && !viewer.done());
if(viewer.done())
break;
std::cout << "mire detectee" << std::endl << std::endl;
group->addChild(pat);
do
{
vcap >> *frame;
cv::Mat rotVec = trackingMire(frame, &imCalibNext, &chessCornersInit, &chessCorners3D, &cameraMatrix, &distCoeffs, &tvecs);
imagePoints = dessinerPoints(frame, objectPoints, rotVec, tvecs, cameraMatrix, distCoeffs);
cv::transpose(rotVec, Rc);
cv::invert(rotVec, rotVecInv);
for(int i = 0; i < 3; i++)
C.at<double>(i, 0) = -1 * (
rotVecInv.at<double>(i, 0) * tvecs.at<double>(0, 0) +
rotVecInv.at<double>(i, 1) * tvecs.at<double>(1, 0) +
rotVecInv.at<double>(i, 2) * tvecs.at<double>(2, 0));
osg::Matrixd viewMatrixR, viewMatrixT, viewMatrix90;
viewMatrixT.makeTranslate(
-C.at<double>(0, 0) / 100,
C.at<double>(1, 0) / 100,
C.at<double>(2, 0) / 100);
double r11 = rotVec.at<double>(0, 0);
double r21 = rotVec.at<double>(1, 0);
double r31 = rotVec.at<double>(2, 0);
double r32 = rotVec.at<double>(2, 1);
double r33 = rotVec.at<double>(2, 2);
viewMatrixR.makeRotate(
atan2(r32, r33), osg::Vec3d(1.0, 0.0, 0.0),
-atan2(-r31, sqrt((r32 * r32) + (r33 * r33))), osg::Vec3d(0.0, 1.0, 0.0),
-atan2(r21, r11), osg::Vec3d(0.0, 0.0, 1.0));
viewMatrix90.makeRotate(osg::DegreesToRadians(-90.0), osg::Vec3d(1.0, 0.0, 0.0));
viewer.getCamera()->setViewMatrix(viewMatrixT * viewMatrixR);
// Calcul d'erreur de reprojection
double moy = 0;
for(int j = 0; j < COLCHESSBOARD * ROWCHESSBOARD; j++)
{
double d = sqrt(pow(chessCornersInit[0][j].y - imagePoints[j].y, 2) + pow(chessCornersInit[0][j].x - imagePoints[j].x, 2));
distances.push_back(d);
moy += d;
}
moyDistances = moy / (COLCHESSBOARD * ROWCHESSBOARD);
if(moyDistances > 2) // si l'ecart de reproj est trop grand, reset
resetAuto = true;
key = cv::waitKey(33);
backgroundImage->dirty();
viewer.frame();
}while(!viewer.done() && !resetAuto && key != 32);
}while(!viewer.done());
}
string optical_flow::get_final_direction(Mat m1, Mat m2,
Rect old_hand_boundary, Rect new_hand_boundary) {
left_count = 0;
up_count = 0;
down_count = 0;
right_count = 0;
non_count = 0;
TermCriteria termcrit(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03);
cvtColor(m1, old_gray_img, COLOR_BGR2GRAY);//convert to gray
cvtColor(m2, new_gray_img, COLOR_BGR2GRAY);
// extract features
goodFeaturesToTrack(old_gray_img, old_frame_points, max_count, .01,
min_distance, Mat(), block_size, 0, .04);
cornerSubPix(old_gray_img, old_frame_points, Size(10, 10), Size(-1, -1),
termcrit);
// track features in next frame
vector<uchar> status;
vector<float> err;
calcOpticalFlowPyrLK(old_gray_img, new_gray_img, old_frame_points,
new_frame_points, status, err, Size(10, 10), 3, termcrit, 0, 0.001);
for (unsigned int i = 0; i < new_frame_points.size(); i++) {
Point2f old_point = old_frame_points.at(i);
Point2f new_point = new_frame_points.at(i);
if (!(old_hand_boundary.contains(old_point)
&& new_hand_boundary.contains(new_point)))
continue;
float dist = get_distance(old_point, new_point);
// cout<<dist<<endl;
if (dist < threshold) {
directions.push_back(non_direction);
non_count++;
} else {
float dx = new_point.x - old_point.x;
float dy = new_point.y - old_point.y;
if (abs(dx) > abs(dy)) {//horizontal
if (abs(dx) <= thresh_in_on_dir)
non_count++;
else {
if (dx < 0) {
directions.push_back(left_direction);
left_count++;
} else {
directions.push_back(right_direction);
right_count++;
}
}
} else { //vertical
if (abs(dy) <= thresh_in_on_dir)
non_count++;
else {
if (dy < 0) {
directions.push_back(up_direction);
up_count++;
} else {
directions.push_back(down_direction);
down_count++;
}
}
}
}
}
int dirs_counts[] = { up_count, down_count, left_count, right_count,
non_count };
int max_elem = *max_element(dirs_counts, dirs_counts + 5);
// cout<<up_count << " "<<down_count<<" "<<left_count<<" " <<right_count<<" "<<non_count<<endl;
final_direction = "";
if (up_count == max_elem)
final_direction = "up";
else if (down_count == max_elem)
final_direction = "down";
else if (left_count == max_elem)
final_direction = "left";
else if (right_count == max_elem)
final_direction = "right";
else
final_direction = "none";
return final_direction;
}
// Lucas-Kanade optical flow
void QOpenCvImageBox::lucasKanadeOF( IplImage* img ) {
Point2f pt;
TermCriteria termcrit(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS,20,0.03); // ERROR
bool addRemovePt = false;
Size winSize(10,10);
cvtColor(image, gray, CV_BGR2GRAY);
if( nightMode )
image = Scalar::all(0);
if( needToInit )
{
// automatic initialization
goodFeaturesToTrack(gray, points[1], MAX_COUNT, 0.01, 10, Mat(), 3, 0, 0.04);
cornerSubPix(gray, points[1], winSize, Size(-1,-1), termcrit);
addRemovePt = false;
}
else if( !points[0].empty() )
{
vector<uchar> status;
vector<float> err;
if(prevGray.empty())
gray.copyTo(prevGray);
calcOpticalFlowPyrLK(prevGray, gray, points[0], points[1], status, err, winSize, 3, termcrit, 0);
size_t i, k;
for( i = k = 0; i < points[1].size(); i++ )
{
if( addRemovePt )
{
if( norm(pt - points[1][i]) <= 5 )
{
addRemovePt = false;
continue;
}
}
if( !status[i] )
continue;
points[1][k++] = points[1][i];
circle( image, points[1][i], 3, Scalar(0,255,0), -1, 8);
}
points[1].resize(k);
}
if( addRemovePt && points[1].size() < (size_t)MAX_COUNT )
{
vector<Point2f> tmp;
tmp.push_back(pt);
cornerSubPix( gray, tmp, winSize, cvSize(-1,-1), termcrit);
points[1].push_back(tmp[0]);
addRemovePt = false;
}
needToInit = false;
/*
imshow("LK Demo", image);
char c = (char)waitKey(10);
if( c == 27 )
break;
switch( c )
{
case 'r':
needToInit = true;
break;
case 'c':
points[1].clear();
break;
case 'n':
nightMode = !nightMode;
break;
default:
;
}
*/
std::swap(points[1], points[0]);
swap(prevGray, gray);
}
int Counter::startCount(std::string file, char frontRear/* = 'F'*/)
{
cv::VideoCapture cap(file.c_str());
if (!cap.isOpened()) {
std::cout << "Could not open file" << std::endl;
return 1;
}
fps = 1000/cap.get(CV_CAP_PROP_FPS);
//int frate = 1000/fps;
int frate = 20;
int dumy = 13700; // @debug 13700 15840 18246 18890 21900
// Location recognition
DigitRecognizer dr(1,10,5,7, "./origImages");
dr.learnFromImages();
dr.setClassifier();
// set parameters
if ('F'==frontRear) {
setFrontDoor();
} else {
setRearDoor();
}
std::vector<cv::Point2f> tripWire; // points on the tripwire
std::list<std::vector<cv::Point2f> > trajectories; // a list of trajectories being tracked
std::vector<std::list<int> > on_models; // each model is a list of start times
std::vector<std::list<int> > off_models;
float mean_x=0.0f, mean_y=0.0f, var_x=0.0f, var_y=0.0f, length=0.0f; // trajectory stats
cv::Mat capframe, frame, image, gray, prevGray, location;
cv::Mat doorHistBG, door, doorHist;
cv::Size winSize(31,31); // window size for optical flow computation
cv::TermCriteria termcrit(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 20, 0.03);
int onPassengers = 0;
int offPassengers = 0;
int missPassengers = 0;
int histSize = 2; // size of background histogram
float range[] = { 0, 256 }; // range of pixel values for histogram calculation
const float* histRange = { range }; //
std::string prevGPS, currGPS, speed;
generateTrackingPoints(tripWire);
while (true) {
int fno = cap.get(CV_CAP_PROP_POS_FRAMES);
if (fno>=dumy) {
std::cout << "";
}
cap >> capframe;
if (capframe.empty()) break;
frame = capframe(cv::Rect(0,0,580,450));
//cv::warpPerspective(frame, frame, M, frame.size() );
frame.copyTo(image);
cv::cvtColor(image, gray, CV_BGR2GRAY);
//gammaCorrection(gray); // note: it becomes worse with Gamma (anti-) correction
if (prevGray.empty()) {
gray.copyTo(prevGray);
}
// check gps location
location = capframe(cv::Rect(810, 90, 90, 30));
currGPS = dr.analyseLocationImage(location, false);
/*int gpsDistance = 0;
if (!prevGPS.empty()) {
std::inner_product(prevGPS.begin(), prevGPS.end(), currGPS.begin(), gpsDistance,
std::plus<int>(), std::not_equal_to<char>());
}
// add points to trajectories /// NEED TO KNOW THAT GPS DOESN'T CHANGE FOR SEVERAL FRAMES
if(trajectories.size()<tripWire.size()-10 && gpsDistance<3) { //160 0.8
addPoints(trajectories, tripWire, fno);
}*/
// check if door is closed
door = gray(rectDoor);
if (fno<5) {
cv::Mat tmpDoorHistBG;
//cv::calcHist(&door, 1, 0, cv::Mat(), tmpDoorHistBG, 1, &histSize, &histRange, true, false);
tmpDoorHistBG = Utility::HogComp(door);
cv::normalize(tmpDoorHistBG, tmpDoorHistBG, 1, 0, cv::NORM_L2, -1, cv::Mat());
if (doorHistBG.empty()) {
doorHistBG = tmpDoorHistBG;
} else {
cv::addWeighted(doorHistBG, 0.7, tmpDoorHistBG, 0.3, 0, doorHistBG, -1);
}
}
//cv::calcHist(&door, 1, 0, cv::Mat(), doorHist, 1, &histSize, &histRange, true, false);
doorHist = Utility::HogComp(door);
cv::normalize(doorHist, doorHist, 1, 0, cv::NORM_L2, -1, cv::Mat());
//float similarityDoor = doorHistBG.dot(doorHist);
float similarityDoor = cv::compareHist(doorHistBG, doorHist, CV_COMP_CORREL);
bool bDoorOpen = similarityDoor<0.9;
// add points to trajectories
if(trajectories.size()<tripWire.size()-10 && bDoorOpen) { //160 0.8
addPoints(trajectories, tripWire, fno);
}
std::vector<uchar> status;
std::vector<float> err;
std::vector<cv::Point2f> nextPoints;
std::vector<cv::Point2f> prevPoints = lastPoints(trajectories);
if (prevPoints.empty()==false) {
cv::calcOpticalFlowPyrLK(prevGray, gray, prevPoints, nextPoints, status, err, winSize, 3, termcrit, 0, 0.001);
}
int i=0;
std::list<std::vector<cv::Point2f> >::iterator iTrack = trajectories.begin();
for (; iTrack!=trajectories.end(); i++) {
int szTrack = iTrack->size();
isValidTrack(*iTrack, mean_x, mean_y, var_x, var_y, length);
if ((szTrack>3) && (var_x<1.0f) && (var_y<1.0f)) { // stationary points
iTrack = trajectories.erase(iTrack);
} else if ((!status[i] || err[i]>13.0) && (szTrack>10)) { // lost of tracking
iTrack->at(0).y = 1.0;
iTrack++;
} else if (szTrack>80) { // too long, remove 120
iTrack = trajectories.erase(iTrack);
} else if (szTrack>30) { // long trajectory, try to check 80
iTrack->at(0).y = 2.0;
iTrack->push_back(nextPoints[i]);
iTrack++;
} else {
iTrack->push_back(nextPoints[i]);
iTrack++;
}
}
// update models according to the direction of trajectories
std::vector<int> startTimes;
getStartTimes(trajectories, startTimes, fno);
std::vector<int>::iterator iTime = startTimes.begin();
for (; iTime!=startTimes.end(); iTime++) {
int overall_direction = getMajorityDirection(trajectories, *iTime);
for (i=0, iTrack=trajectories.begin(); iTrack!=trajectories.end(); i++) {
drawtrajectory(*iTrack, image);
if (((int)(iTrack->at(0).x) == *iTime) && (iTrack->at(0).y>0.0f)) { // only use trajectories long enough
bool validTrack = isValidTrack(*iTrack, mean_x, mean_y, var_x, var_y, length);
int onoff = onOroff(*iTrack);
if (validTrack && (onoff==overall_direction)) {
switch(onoff) {
case 0: {offPassengers = updateModel(off_models, *iTrack, onoff);
/*std::vector<cv::Point2f>::iterator iit = iTrack->begin();
while (iit!=iTrack->end()) {
std::cout << iit->x << " " << iit->y << " ";
++iit;
}
std::cout << std::endl;*/
iTrack = trajectories.erase(iTrack);
continue;}
case 1: {onPassengers = updateModel(on_models, *iTrack, onoff);
iTrack = trajectories.erase(iTrack);
continue;}
case 2: {missPassengers++;
iTrack = trajectories.erase(iTrack);
continue;}
default: std::cout << "Error: Wrong branch!" << std::endl;
}
}
if ((int)(iTrack->at(0).y) == 1) { // lost tracking
iTrack = trajectories.erase(iTrack);
}
}
iTrack++;
}
}
//cv::rectangle(image, rectDoor, cv::Scalar(0,255,0));
showResultImage(image, onPassengers, offPassengers, currGPS, speed);
if ((char)cv::waitKey(frate/speedratio)==27) break;
cv::swap(prevGray, gray);
std::swap(currGPS, prevGPS);
}
return 0;
}
void main()
{
bool patternfound = false;
bool reset = false;
bool resetAuto = false;
cv::TermCriteria termcrit(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03);
cv::Size winSize(31, 31);
cv::Mat cameraMatrix, distCoeffs;
cv::Mat imCalib;
cv::Mat imCalibColor;
cv::Mat imCalibNext;
cv::Mat rvecs, tvecs;
std::vector<cv::Point2f> imagePoints;
std::vector<cv::Point3f> objectPoints;
std::vector<cv::Point3f> cubeObjectPoints;
std::vector<std::vector<cv::Point2f>> chessCornersInit(2);
std::vector<cv::Point3f> chessCorners3D;
std::vector<double> distances;
double moyDistances;
// Creation des points a projeter
for(int x = 0; x < COLCHESSBOARD; x++)
for(int y = 0; y < ROWCHESSBOARD; y++)
objectPoints.push_back(cv::Point3f(x * 26.0f, y * 26.0f, 0.0f));
// Creation des coins de la mire
for(int x = 0; x < COLCHESSBOARD; x++)
for(int y = 0; y < ROWCHESSBOARD; y++)
chessCorners3D.push_back(cv::Point3f(x * 26.0f, y * 26.0f, 0.0f));
cv::FileStorage fs("../rsc/intrinsicMatrix.yml", cv::FileStorage::READ);
fs["cameraMatrix"] >> cameraMatrix;
fs["distCoeffs"] >> distCoeffs;
fs.release();
cv::VideoCapture vcap(0);
if(!vcap.isOpened()){
std::cout << "FAIL!" << std::endl;
return;
}
cv::Mat *frame = new cv::Mat(cv::Mat::zeros(vcap.get(CV_CAP_PROP_FRAME_HEIGHT), vcap.get(CV_CAP_PROP_FRAME_WIDTH), CV_8UC3));
do
{
vcap >> *frame;
}while(frame->empty());
osg::ref_ptr<osg::Image> backgroundImage = new osg::Image;
backgroundImage->setImage(frame->cols, frame->rows, 3,
GL_RGB, GL_BGR, GL_UNSIGNED_BYTE,
(uchar*)(frame->data),
osg::Image::AllocationMode::NO_DELETE, 1);
// read the scene from the list of file specified commandline args.
osg::ref_ptr<osg::Group> group = new osg::Group;
osg::ref_ptr<osg::Node> objet3D;
objet3D = osgDB::readNodeFile("dumptruck.osgt");
osg::ref_ptr<osg::Camera> cam = createHUD(backgroundImage);
osgViewer::Viewer viewer;
group->addChild(cam);
group->addChild(objet3D);
// set the scene to render
viewer.setSceneData(group.get());
// projection
viewer.getCamera()->setProjectionMatrixAsPerspective( 40., 1., 1., 100. );
// Create a matrix to specify a distance from the viewpoint.
osg::Matrix trans;
trans.makeTranslate( 7, 0., -50. );
// Rotation angle (in radians)
double angle( 0. );
char key = 0;
bool detectionMire = false;
do
{
patternfound = false;
resetAuto = false;
detectionMire = false;
imagePoints.clear();
chessCornersInit[0].clear();
chessCornersInit[1].clear();
moyDistances = 0;
distances.clear();
imCalibNext.release();
group->removeChild(objet3D);
std::cout << "recherche de mire" << std::endl;
do
{
vcap >> *frame;
backgroundImage->dirty();
detectionMire = detecterMire(frame, &chessCornersInit[1], &imCalibNext);
viewer.frame();
}while(!detectionMire && !viewer.done());
if(viewer.done())
break;
std::cout << "mire detectee" << std::endl << std::endl;
group->addChild(objet3D);
do
{
vcap >> *frame;
cv::Mat rotVec = trackingMire(frame, &imCalibNext, &chessCornersInit, &chessCorners3D, &cameraMatrix, &distCoeffs, &tvecs);
imagePoints = dessinerPoints(frame, objectPoints, rotVec, tvecs, cameraMatrix, distCoeffs);
// Create the rotation matrix.
osg::Matrix rot;
rot.makeRotate( angle, osg::Vec3( 1., 0., 0. ) );
angle += 0.01;
// Set the view matrix (the concatenation of the rotation and
// translation matrices).
viewer.getCamera()->setViewMatrix( rot * trans );
double moy = 0;
for(int j = 0; j < COLCHESSBOARD * ROWCHESSBOARD; j++)
{
double d = sqrt(pow(chessCornersInit[0][j].y - imagePoints[j].y, 2) + pow(chessCornersInit[0][j].x - imagePoints[j].x, 2));
distances.push_back(d);
moy += d;
}
moyDistances = moy / (COLCHESSBOARD * ROWCHESSBOARD);
if(moyDistances > 2) // si l'ecart de reproj est trop grand, reset
resetAuto = true;
key = cv::waitKey(33);
// Draw the next frame.
backgroundImage->dirty();
viewer.frame();
}while(!viewer.done() && !resetAuto && key != 32);
} while(!viewer.done());
}
int main()
{
time_t timer = 0;
time_t start = clock();
time_t startImage = 0;
std::cout << "Debut projection\t" << std::endl;
bool patternfound = false;
int i = 0;
cv::TermCriteria termcrit(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 20, 0.03);
cv::Size winSize(31,31);
cv::Mat cameraMatrix, distCoeffs;
cv::Mat imCalib;
cv::Mat imCalibColor;
cv::Mat imCalibNext;
cv::Mat rvecs, tvecs;
std::vector<cv::Point2f> imagePoints;
std::vector<cv::Point3f> objectPoints;
std::vector<cv::Point2f> chessCornersInit[2];
std::vector<cv::Point3f> chessCorners3D;
// Creation des points a projeter
objectPoints.push_back(cv::Point3f(50,25,0));
objectPoints.push_back(cv::Point3f(150,25,0));
objectPoints.push_back(cv::Point3f(150,125,0));
objectPoints.push_back(cv::Point3f(50,125,0));
objectPoints.push_back(cv::Point3f(50,25,100));
objectPoints.push_back(cv::Point3f(150,25,100));
objectPoints.push_back(cv::Point3f(150,125,100));
objectPoints.push_back(cv::Point3f(50,125,100));
// Creation des coins de la mire
for(int x=0 ; x<COLCHESSBOARD ; x++)
for(int y=0 ; y<ROWCHESSBOARD ; y++)
chessCorners3D.push_back(cv::Point3f(x*26.0f,y*26.0f,0.0f));
cv::FileStorage fs("../rsc/intrinsicMatrix.yml", cv::FileStorage::READ);
fs["cameraMatrix"] >> cameraMatrix;
fs["distCoeffs"] >> distCoeffs;
fs.release();
cv::VideoCapture vcap("../rsc/capture.avi");
if(!vcap.isOpened()){
std::cout << "FAIL!" << std::endl;
return -1;
}
do{
vcap >> imCalibColor;
cv::imshow("Projection", imCalibColor);
cv::cvtColor(imCalibColor, imCalib, CV_BGR2GRAY);
cv::waitKey();
timer = clock();
startImage = clock();
patternfound = cv::findChessboardCorners(imCalib, cv::Size(ROWCHESSBOARD, COLCHESSBOARD), chessCornersInit[0], cv::CALIB_CB_FAST_CHECK);
std::cout << "findChessboardCorners\t" << float(clock()-timer)/CLOCKS_PER_SEC << " sec" << std::endl;
timer = clock();
} while(!patternfound);
for(;;)
{
vcap >> imCalibColor;
if(!imCalibNext.empty())
{
cv::swap(imCalib, imCalibNext); // copie de l'ancienne image pour le flot optique
for(size_t c = 0; c < chessCornersInit[0].size(); c++)
chessCornersInit[0][c] = chessCornersInit[1][c];
chessCornersInit[1].clear();
}
else
cv::cornerSubPix(imCalib, chessCornersInit[0], cv::Size(5, 5), cv::Size(-1, -1), cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
cv::cvtColor(imCalibColor, imCalibNext, CV_BGR2GRAY);
std::vector<uchar> status;
std::vector<float> err;
cv::calcOpticalFlowPyrLK(imCalib, imCalibNext, chessCornersInit[0], chessCornersInit[1], status, err, winSize, 3, termcrit, 0, 0.0001);
cv::solvePnP(chessCorners3D, chessCornersInit[0], cameraMatrix, distCoeffs, rvecs, tvecs);
cv::Mat rotVec(3, 3, CV_64F);
cv::Rodrigues(rvecs, rotVec);
//Projection
cv::projectPoints(objectPoints, rotVec, tvecs, cameraMatrix, distCoeffs, imagePoints);
// Dessin des points projetes
cv::line(imCalibColor, imagePoints[0], imagePoints[4], cv::Scalar(255,255,0), 2, 8);
cv::line(imCalibColor, imagePoints[1], imagePoints[5], cv::Scalar(255,255,0), 2, 8);
cv::line(imCalibColor, imagePoints[2], imagePoints[6], cv::Scalar(255,255,0), 2, 8);
cv::line(imCalibColor, imagePoints[3], imagePoints[7], cv::Scalar(255,255,0), 2, 8);
cv::line(imCalibColor, imagePoints[0], imagePoints[1], cv::Scalar(255,0,255), 2, 8);
cv::line(imCalibColor, imagePoints[1], imagePoints[2], cv::Scalar(255,0,255), 2, 8);
cv::line(imCalibColor, imagePoints[2], imagePoints[3], cv::Scalar(255,0,255), 2, 8);
cv::line(imCalibColor, imagePoints[3], imagePoints[0], cv::Scalar(255,0,255), 2, 8);
cv::line(imCalibColor, imagePoints[4], imagePoints[5], cv::Scalar(0,255,255), 2, 8);
cv::line(imCalibColor, imagePoints[5], imagePoints[6], cv::Scalar(0,255,255), 2, 8);
cv::line(imCalibColor, imagePoints[6], imagePoints[7], cv::Scalar(0,255,255), 2, 8);
cv::line(imCalibColor, imagePoints[7], imagePoints[4], cv::Scalar(0,255,255), 2, 8);
cv::imshow("Projection", imCalibColor);
cv::waitKey(67);
}
return 0;
}
// ----------------------------------------------------------------------------------
void QualityMatcher::doTheMagic(cv::Mat imageSrc, cv::Mat imageDst, cv::Mat priorH, MatchingResultCallback cb)
{
// keypoints
std::vector<cv::KeyPoint> featuresSrc;
std::vector<cv::KeyPoint> featuresDst;
// TODO - use the provided prior
// prefilter slightly
cv::Mat imgSrc = imageSrc, imgDst = imageDst;
//cv::GaussianBlur(imageSrc, imgSrc, cv::Size(3,3), 5.0);
//cv::GaussianBlur(imageDst, imgDst, cv::Size(3,3), 5.0);
//cv::medianBlur(imageSrc, imgSrc, 3);
//cv::medianBlur(imageDst, imgDst, 3);
cv::Mat descriptorsSrc, descriptorsDst;
// detect
//cv::Ptr<cv::FeatureDetector> detector = cv::FeatureDetector::create("SURF");
//detector->detect(imgSrc, featuresSrc);
//detector->detect(imgDst, featuresDst);
// features
cv::FAST(imgSrc, featuresSrc, 50, cv::FastFeatureDetector::TYPE_9_16);
cv::FAST(imgDst, featuresDst, 50, cv::FastFeatureDetector::TYPE_9_16);
printf("input %d vs %d\n", (int)featuresSrc.size(), (int)featuresDst.size());
cv::Ptr<cv::DescriptorExtractor> descriptor = cv::DescriptorExtractor::create("ORB" );
descriptor->compute(imgSrc, featuresSrc, descriptorsSrc);
descriptor->compute(imgDst, featuresDst, descriptorsDst);
// descriptors
//cv::BriefDescriptorExtractor descriptor;
//descriptor.compute(imgSrc, featuresSrc, descriptorsSrc);
//descriptor.compute(imgDst, featuresDst, descriptorsDst);
if (featuresDst.size() < 10 || featuresSrc.size() < 10
|| descriptorsSrc.rows != featuresSrc.size()
|| descriptorsDst.rows != featuresDst.size())
{
cb(false, priorH);
return;
}
// matching (simple nearest neighbours)
cv::BFMatcher matcher(cv::NORM_HAMMING);
std::vector<cv::DMatch> matches;
matcher.match( descriptorsSrc, descriptorsDst, matches );
std::vector<cv::DMatch> goodMatches;
std::vector<cv::Point2f> ptsSrc, ptsDst;
for( int i = 0; i < matches.size(); i++ )
{
if( matches[i].distance <= 20)//std::max(4. * min_dist, 0.02) )
{
goodMatches.push_back(matches[i]);
}
}
for( int i = 0; i < goodMatches.size(); i++ )
{
ptsSrc.push_back( featuresSrc[ goodMatches[i].queryIdx ].pt );
ptsDst.push_back( featuresDst[ goodMatches[i].trainIdx ].pt );
}
if (goodMatches.size() < 10)
{
printf("MATCH FAILED\n");
cb(false, priorH);
return;
}
/*cv::namedWindow( "Display window", cv::WINDOW_AUTOSIZE );
cv::Mat img;
cv::drawMatches(imgSrc, featuresSrc, imgDst, featuresDst, goodMatches, img);
cv::imshow("imgae1", img);
//cv::imshow("imgae1", imgSrc);
//cv::imshow("imgae2", imgDst);
cv::waitKey(0); */
// ----------------------------
// KLT tracker to further improve the result
// ----------------------------
cv::TermCriteria termcrit(cv::TermCriteria::COUNT | cv::TermCriteria::EPS, 30, 0.03);
cv::cornerSubPix(imgSrc, ptsSrc, cv::Size(3,3), cv::Size(-1,-1), termcrit);
cv::cornerSubPix(imgDst, ptsDst, cv::Size(3,3), cv::Size(-1,-1), termcrit);
if(1)
{
std::vector<uchar> status;
std::vector<float> err;
cv::Size winSize(7,7);
std::vector<cv::Point2f> ptsDstKlt = ptsDst;
std::vector<cv::Point2f> ptsSrcOld = ptsSrc;
std::vector<cv::Mat> pyrSrc, pyrDst;
cv::buildOpticalFlowPyramid(imgSrc, pyrSrc, winSize, 4);
cv::buildOpticalFlowPyramid(imgDst, pyrDst, winSize, 4);
/*cv::namedWindow( "Display window", cv::WINDOW_AUTOSIZE );
cv::Mat img;
cv::drawMatches(imgSrc, featuresSrc, imgDst, featuresDst, goodMatches, img);
cv::imshow("imgae1", img);
cv::waitKey(0);*/
cv::calcOpticalFlowPyrLK(pyrSrc, pyrDst, ptsSrc, ptsDstKlt, status, err, winSize, 4, termcrit, cv::OPTFLOW_USE_INITIAL_FLOW);
// remove bad points
ptsSrc.clear();
ptsDst.clear();
for (size_t i=0; i < status.size(); i++)
{
if (!status[i]) continue;
ptsSrc.push_back(ptsSrcOld[i]);
ptsDst.push_back(ptsDstKlt[i]);
}
}
printf("klt tracked %d\n", (int)ptsDst.size());
if (ptsDst.size() < 10)
{
printf("MATCH FAILED\n");
cb(false, priorH);
return;
}
cv::Mat H = cv::findHomography(ptsSrc, ptsDst, CV_RANSAC, 10.);
H.convertTo(H, CV_32FC1);
if (!niceHomography(H))
{
printf("MATCH FAILED\n");
cb(false, priorH);
return;
}
// DEBUG
printf("H:\n");
for (int i=0; i < 3; i++)
printf("%f %f %f\n", H.at<float>(i,0), H.at<float>(i,1), H.at<float>(i,2));
printf("prior H:\n");
for (int i=0; i < 3; i++)
printf("%f %f %f\n", priorH.at<float>(i,0), priorH.at<float>(i,1), priorH.at<float>(i,2));
float nrm = cv::norm(priorH);
if (nrm > 2)
{
nrm = cv::norm(priorH, H);
printf("(H-prior).norm() = %f\n", nrm);
if (nrm > 10.0)
{
printf("MATCH FAILED - bad H\n");
cb(false, priorH);
return;
}
}
cb(true, H);
printf("matched %d features\n", (int)featuresSrc.size());
}
void main()
{
bool patternfound = false;
bool reset = false;
bool resetAuto = false;
int nbImages = 0;
double moyFinale = 0;
char key = 0;
bool detectionMire = false;
bool detectionVisage = false;
int cpt = 0, moyCpt = 0, i = 0;
std::cout << "initialisation de Chehra..." << std::endl;
Chehra chehra;
std::cout << "done" << std::endl;
cv::TermCriteria termcrit(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03);
cv::Size winSize(31, 31);
cv::Mat cameraMatrix, distCoeffs;
cv::Mat imCalib;
cv::Mat imCalibColor;
cv::Mat imCalibNext;
cv::Mat rvecs, tvecs;
cv::Mat Rc, C = cv::Mat(3, 1, CV_64F), rotVecInv;
std::vector<cv::Point2f> imagePoints;
std::vector<cv::Point3f> objectPoints;
std::vector<cv::Point3f> cubeObjectPoints;
std::vector<cv::Point3f> dessinPointsVisage;
std::vector<std::vector<cv::Point2f>> chessCornersInit(2);
std::vector<std::vector<cv::Point2f>> pointsVisageInit(2);
std::vector<cv::Point3f> chessCorners3D;
std::vector<cv::Point3f> pointsVisage3D;
std::vector<cv::Point3f> visage;
std::vector<double> distances;
double moyDistances;
// Creation des coins de la mire
for(int x = 0; x < COLCHESSBOARD; x++)
for(int y = 0; y < ROWCHESSBOARD; y++)
chessCorners3D.push_back(cv::Point3f(x * SIZEMIRE, y * SIZEMIRE, 0.0f));
// Creation des points a projeter
for(int x = 0; x < COLCHESSBOARD; x++)
for(int y = 0; y < ROWCHESSBOARD; y++)
objectPoints.push_back(cv::Point3f(x * SIZEMIRE, y * SIZEMIRE, 0.0f));
cv::FileStorage fs("../rsc/intrinsicMatrix.yml", cv::FileStorage::READ);
fs["cameraMatrix"] >> cameraMatrix;
fs["distCoeffs"] >> distCoeffs;
double f = (cameraMatrix.at<double>(0, 0) + cameraMatrix.at<double>(1, 1)) / 2; // NEAR = distance focale ; si pixels carrés, fx = fy -> np
//mais est généralement différent de fy donc on prend (pour l'instant) par défaut la valeur médiane
double g = 2000 * f; // je sais pas pourquoi. au pif.
fs.release();
cv::VideoCapture vcap(0);
if(!vcap.isOpened()){
std::cout << "FAIL!" << std::endl;
return;
}
cv::Mat *frame = new cv::Mat(cv::Mat::zeros(vcap.get(CV_CAP_PROP_FRAME_HEIGHT), vcap.get(CV_CAP_PROP_FRAME_WIDTH), CV_8UC3));
do
{
vcap >> *frame;
}while(frame->empty());
osg::ref_ptr<osg::Image> backgroundImage = new osg::Image;
backgroundImage->setImage(frame->cols, frame->rows, 3,
GL_RGB, GL_BGR, GL_UNSIGNED_BYTE,
(uchar*)(frame->data),
osg::Image::AllocationMode::NO_DELETE, 1);
// read the scene from the list of file specified commandline args.
osg::ref_ptr<osg::Group> group = new osg::Group;
osg::ref_ptr<osg::Geode> cam = createHUD(backgroundImage, vcap.get(CV_CAP_PROP_FRAME_WIDTH), vcap.get(CV_CAP_PROP_FRAME_HEIGHT), cameraMatrix.at<double>(0, 2), cameraMatrix.at<double>(1, 2), f);
std::cout << "initialisation de l'objet 3D..." << std::endl;
osg::ref_ptr<osg::Node> objet3D = osgDB::readNodeFile("../rsc/objets3D/Creature.obj");
std::cout << "done" << std::endl;
osg::StateSet* obectStateset = objet3D->getOrCreateStateSet();
obectStateset->setMode(GL_DEPTH_TEST,osg::StateAttribute::OFF);
osg::ref_ptr<osg::MatrixTransform> mat = new osg::MatrixTransform();
osg::ref_ptr<osg::PositionAttitudeTransform> pat = new osg::PositionAttitudeTransform();
// construct the viewer.
osgViewer::CompositeViewer compositeViewer;
osgViewer::View* viewer = new osgViewer::View;
osgViewer::View* viewer2 = new osgViewer::View;
// add the HUD subgraph.
group->addChild(cam);
mat->addChild(objet3D);
pat->addChild(mat);
group->addChild(pat);
pat->setScale(osg::Vec3d(3, 3, 3));
osg::Matrixd projectionMatrix;
projectionMatrix.makeFrustum(
-cameraMatrix.at<double>(0, 2), vcap.get(CV_CAP_PROP_FRAME_WIDTH) - cameraMatrix.at<double>(0, 2),
-cameraMatrix.at<double>(1, 2), vcap.get(CV_CAP_PROP_FRAME_HEIGHT) - cameraMatrix.at<double>(1, 2),
f, g);
osg::Vec3d eye(0.0f, 0.0f, 0.0f), target(0.0f, g, 0.0f), normal(0.0f, 0.0f, 1.0f);
// set the scene to render
viewer->setSceneData(group.get());
viewer->setUpViewInWindow(0, 0, 1920 / 2, 1080 / 2);
viewer->getCamera()->setProjectionMatrix(projectionMatrix);
viewer->getCamera()->setViewMatrixAsLookAt(eye, target, normal);
viewer2->setSceneData(group.get());
viewer2->setUpViewInWindow(1920 / 2, 0, 1920 / 2, 1080 / 2);
viewer2->getCamera()->setProjectionMatrix(projectionMatrix);
osg::Vec3d eye2(4 * f, 3 * f / 2, 0.0f), target2(0.0f, f, 0.0f), normal2(0.0f, 0.0f, 1.0f);
viewer2->getCamera()->setViewMatrixAsLookAt(eye2, target2, normal2);
compositeViewer.addView(viewer);
compositeViewer.addView(viewer2);
compositeViewer.realize(); // set up windows and associated threads.
do
{
group->removeChild(pat);
patternfound = false;
resetAuto = false;
detectionMire = false;
detectionVisage = false;
imagePoints.clear();
chessCornersInit[0].clear();
chessCornersInit[1].clear();
pointsVisageInit[0].clear();
pointsVisageInit[1].clear();
pointsVisage3D.clear();
dessinPointsVisage.clear();
visage.clear();
moyDistances = 0;
distances.clear();
imCalibNext.release();
std::cout << "recherche de pattern" << std::endl;
time_t start = clock();
double timer = 0;
do
{
start = clock();
vcap >> *frame;
backgroundImage->dirty();
//detectionMire = detecterMire(frame, &chessCornersInit[1], &imCalibNext);
detectionVisage = detecterVisage(frame, &chehra, &pointsVisageInit[1], &visage, &pointsVisage3D, &imCalibNext);
cpt++;
double duree = (clock() - start)/(double) CLOCKS_PER_SEC;
timer += duree;
if(timer >= 1){
std::cout << cpt << " fps" << std::endl;
moyCpt += cpt;
timer = 0;
duree = 0;
i++;
cpt = 0;
start = clock();
}
compositeViewer.frame();
}while(!detectionMire && !detectionVisage && !compositeViewer.done());
if(compositeViewer.done())
break;
std::cout << "pattern detectee" << std::endl << std::endl;
group->addChild(pat);
do
{
start = clock();
vcap >> *frame;
cv::Mat rotVec = trackingMire(frame, &imCalibNext, &pointsVisageInit, &pointsVisage3D, &cameraMatrix, &distCoeffs, &tvecs);
//cv::Mat rotVec = trackingMire(frame, &imCalibNext, &chessCornersInit, &chessCorners3D, &cameraMatrix, &distCoeffs, &tvecs);
//imagePoints = dessinerPoints(frame, objectPoints, rotVec, tvecs, cameraMatrix, distCoeffs);
imagePoints = dessinerPoints(frame, pointsVisage3D, rotVec, tvecs, cameraMatrix, distCoeffs);
double r11 = rotVec.at<double>(0, 0);
double r21 = rotVec.at<double>(1, 0);
double r31 = rotVec.at<double>(2, 0);
double r32 = rotVec.at<double>(2, 1);
double r33 = rotVec.at<double>(2, 2);
osg::Matrixd matrixR;
matrixR.makeRotate(
atan2(r32, r33), osg::Vec3d(1.0, 0.0, 0.0),
-atan2(-r31, sqrt((r32 * r32) + (r33 * r33))), osg::Vec3d(0.0, 0.0, 1.0),
atan2(r21, r11), osg::Vec3d(0.0, 1.0, 0.0));
mat->setMatrix(matrixR);
pat->setPosition(osg::Vec3d(tvecs.at<double>(0, 0), tvecs.at<double>(2, 0), -tvecs.at<double>(1, 0)));
//std::cout << "x = " << tvecs.at<double>(0, 0) << " - y = " << tvecs.at<double>(1, 0) << " - z = " << tvecs.at<double>(2, 0) << std::endl;
// Calcul d'erreur de reprojection
double moy = 0;
for(int j = 0; j < pointsVisageInit[1].size() ; j++)
{
double d = sqrt(pow(pointsVisageInit[0][j].y - imagePoints[j].y, 2) + pow(pointsVisageInit[0][j].x - imagePoints[j].x, 2));
distances.push_back(d);
moy += d;
}
moyDistances = moy / pointsVisageInit[1].size();
if(moyDistances > 1) // si l'ecart de reproj est trop grand, reset
resetAuto = true;
double duree = (clock() - start)/(double) CLOCKS_PER_SEC;
std::cout << (int)(1/duree) << " fps" << std::endl;
moyCpt += (int)(1/duree);
duree = 0;
i++;
backgroundImage->dirty();
compositeViewer.frame();
}while(!compositeViewer.done() && !resetAuto);
}while(!compositeViewer.done());
std::cout << std::endl << "Moyenne des fps : " << moyCpt/i << std::endl;
std::system("PAUSE");
}
int main()
{
time_t timer = 0;
time_t start = clock();
time_t startImage = 0;
std::cout << "Debut projection\t" << std::endl;
bool patternfound = false;
bool reset = false;
int i = 0;
cv::TermCriteria termcrit(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03);
cv::Size winSize(31, 31);
cv::Mat cameraMatrix, distCoeffs;
cv::Mat imCalib;
cv::Mat imCalibColor;
cv::Mat imCalibNext;
cv::Mat rvecs, tvecs;
std::vector<cv::Point2f> imagePoints;
std::vector<cv::Point3f> objectPoints;
std::vector<cv::Point3f> cubeObjectPoints;
std::vector<std::vector<cv::Point2f>> chessCornersInit(2);
std::vector<cv::Point3f> chessCorners3D;
// Creation des points a projeter
for(int x = 0; x < COLCHESSBOARD; x++)
for(int y = 0; y < ROWCHESSBOARD; y++)
objectPoints.push_back(cv::Point3f(x * 26.0f, y * 26.0f, 0.0f));
// Creation des points a projeter
cubeObjectPoints.push_back(cv::Point3f(52, 26, 0));
cubeObjectPoints.push_back(cv::Point3f(156, 26, 0));
cubeObjectPoints.push_back(cv::Point3f(156, 128, 0));
cubeObjectPoints.push_back(cv::Point3f(52, 128, 0));
cubeObjectPoints.push_back(cv::Point3f(52, 26, 104));
cubeObjectPoints.push_back(cv::Point3f(156, 26, 104));
cubeObjectPoints.push_back(cv::Point3f(156, 128, 104));
cubeObjectPoints.push_back(cv::Point3f(52, 128, 104));
// Creation des coins de la mire
for(int x = 0; x < COLCHESSBOARD; x++)
for(int y = 0; y < ROWCHESSBOARD; y++)
chessCorners3D.push_back(cv::Point3f(x * 26.0f, y * 26.0f, 0.0f));
cv::FileStorage fs("../rsc/intrinsicMatrix.yml", cv::FileStorage::READ);
fs["cameraMatrix"] >> cameraMatrix;
fs["distCoeffs"] >> distCoeffs;
fs.release();
cv::VideoCapture vcap(0);
if(!vcap.isOpened())
{
std::cout << "FAIL!" << std::endl;
return -1;
}
char key = 0;
do
{
std::cout << "recherche de mire" << std::endl;
bool detectionMire = detecterMire(vcap, &chessCornersInit[1], &imCalibNext);
std::cout << "mire detectee" << std::endl << std::endl;
if(!detectionMire)
break;
do
{
vcap >> imCalibColor;
cv::Mat rotVec = trackingMire(&imCalibColor, &imCalibNext, &chessCornersInit, &chessCorners3D, &cameraMatrix, &distCoeffs, &tvecs);
dessinerCube(&imCalibColor, cubeObjectPoints, rotVec, tvecs, cameraMatrix, distCoeffs);
dessinerPoints(&imCalibColor, objectPoints, rotVec, tvecs, cameraMatrix, distCoeffs);
cv::imshow("Projection", imCalibColor);
key = (char)cv::waitKey(30);
}while(key != 27 && key != 32);
if(key == 32)
{
patternfound = false;
imagePoints.clear();
chessCornersInit[0].clear();
chessCornersInit[1].clear();
imCalibNext.release();
}
}while(key != 27);
return 0;
}
int main()
{
time_t timer = 0;
time_t start = clock();
time_t startImage = 0;
std::cout << "Debut projection\t" << std::endl;
bool patternfound = false;
bool reset = false;
bool endVideo = false;
bool resetAuto = false;
int i = 0;
int nbImages = 0;
double moyFinale = 0;
cv::TermCriteria termcrit(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03);
cv::Size winSize(31, 31);
cv::Mat cameraMatrix, distCoeffs;
cv::Mat imCalib;
cv::Mat imCalibColor;
cv::Mat imCalibNext;
cv::Mat rvecs, tvecs;
std::vector<cv::Point2f> imagePoints;
std::vector<cv::Point3f> objectPoints;
std::vector<cv::Point3f> QRpointObject3D;
std::vector<std::vector<cv::Point2f>> chessCornersInit(2);
std::vector<std::vector<cv::Point2f>> QRpointinit(2);
std::vector<cv::Point3f> QRpoint3D;
std::vector<cv::Point3f> tabuseless;
std::vector<cv::Point3f> chessCorners3D;
std::vector<double> distances;
std::vector<double> moyDistances;
cv::FileStorage fs("../rsc/intrinsicMatrix.yml", cv::FileStorage::READ);
fs["cameraMatrix"] >> cameraMatrix;
fs["distCoeffs"] >> distCoeffs;
fs.release();
std::ofstream file;
file.open ("../rsc/error.txt");
cv::VideoCapture vcap(0);
if(!vcap.isOpened())
{
std::cout << "FAIL!" << std::endl;
return -1;
}
char key = 0;
do
{
/*std::cout << "recherche de mire" << std::endl;
bool detectionMire = detecterMire(vcap, &chessCornersInit[1], &imCalibNext);
std::cout << "mire detectee" << std::endl << std::endl;*/
bool detectionQR = detecterQR(vcap , &QRpointinit[1], &QRpoint3D, &tabuseless, &QRpointObject3D, &imCalibNext);
if(!detectionQR)
break;
do
{
vcap >> imCalibColor;
if(imCalibColor.empty()){
endVideo = true;
break;
}
cv::Mat rotVec = trackingMire(&imCalibColor, &imCalibNext, &QRpointinit, &QRpoint3D, &cameraMatrix, &distCoeffs, &tvecs);
dessinerPyra(&imCalibColor, QRpointObject3D, rotVec, tvecs, cameraMatrix, distCoeffs);
imagePoints = dessinerPoints(&imCalibColor, tabuseless, rotVec, tvecs, cameraMatrix, distCoeffs);
//Calcul d'erreur de reprojection
double moy = 0;
for(int j = 0; j < QRpointinit[1].size(); j++)
{
double d = sqrt(pow(QRpointinit[0][j].y - tabuseless[j].y, 2) + pow(QRpointinit[0][j].x - tabuseless[j].x, 2));
distances.push_back(d);
moy += d;
/*std::cout << "distance point numero " << j << " : " << std::endl
<< " subpix : x = " << chessCornersInit[0][j].x << " y = " << chessCornersInit[0][j].y << std::endl
<< " projec : x = " << imagePoints[j].x << " y = " << imagePoints[j].y << std::endl
<< " distance : " << d << std::endl << std::endl;*/
}
moyDistances.push_back(moy / QRpointinit[1].size());
////std::cout << std::endl << std::endl << "moyenne ecart points image " << i << " : " << moyDistances[i] << std::endl << std::endl;
//file << "moyenne ecart points image " << i << " : " << moyDistances[i] << " px" << std::endl;
if(moyDistances[i] > 10){ // si l'ecart de reproj est trop grand, reset
resetAuto = true;
std::cout << "RESET" << std::endl;
break;
}
//moyFinale += moyDistances[i];
i++;
nbImages++;
cv::imshow("Projection", imCalibColor);
key = (char)cv::waitKey(67);
}while(key != 27 && key != 32 && resetAuto != true);
if(key == 32 || resetAuto == true)
{
patternfound = false;
resetAuto = false;
i = 0;
imagePoints.clear();
chessCornersInit[0].clear();
chessCornersInit[1].clear();
QRpointinit[0].clear();
QRpointinit[1].clear();
QRpoint3D.clear();
QRpointObject3D.clear();
tabuseless.clear();
moyDistances.clear();
distances.clear();
imCalibNext.release();
}
}while(key != 27 && endVideo != true);
return 0;
}