int main() {
#if VIDEO
FollowVisibleFace video;
#else
JPSequence video;
#endif
#if HSV
HSVFrameTransformation trans;
#else
GrayFrameTransformation trans;
#endif
switch (PART) {
case 1: {
ParticleFilter filter(video, trans);
filter.track();
}
break;
case 2: {
MeanShift meanShift(video, trans);
meanShift.track();
}
break;
default: {
PFMS pfms(video, trans);
pfms.track();
}
}
return 0;
}
QGraphicsScene * ClusteredArranger::arrange(SegmentList const & segments) const {
QGraphicsScene * arrangement = new QGraphicsScene();
QTime time;
time.start();
// determine background
Segment * background = determineBackground(segments);
SegmentList segmentsWOBack = removeBackground(segments, background);
arrangement->setBackgroundBrush(QBrush(QColor(background->color().toQRgb())));
segmentsWOBack.calculateFeatureVariances();
// initialize layout
//initializeLayout(segmentsWOBack, segmentsWOBack.featX(), segmentsWOBack.featY());
initializeLayout(segmentsWOBack, xAxisBox->currentIndex(), yAxisBox->currentIndex());
// find clusters
time.restart();
QList<SegmentList> clusters = meanShift(segmentsWOBack);
qDebug("Segments clustered in %f seconds", time.restart()/1000.0);
qDebug(" %d clusters found", clusters.size());
// refine clusters
//int counter = 0;
foreach (SegmentList cluster, clusters) {
if (clusterBox->currentIndex() == 0) {
refineLayoutCircles(cluster);
}
else if (clusterBox->currentIndex() == 1) {
refineLayoutPiles(cluster);
}
// debug output
/*QGraphicsScene scene;
scene.setBackgroundBrush(QBrush(QColor(255, 255, 255)));
foreach(Segment * const segment, cluster) {
scene.addItem(segment->toQGraphicsItem());
// without the following line QPainter tends to crash
scene.width();
}
++counter;
saveScene(&scene, QString("Test%1.png").arg(counter, 2));*/
}
// refine layout
if (clusterBox->currentIndex() == 0) {
refineLayoutByPlace(clusters);
}
else if (clusterBox->currentIndex() == 1) {
refineLayoutBySize(clusters);
}
// convert the segments to QGraphicsItems and add to QGraphicsScene
foreach(Segment const * const segment, segmentsWOBack) {
arrangement->addItem(segment->toQGraphicsItem());
// without the following line QPainter tends to crash
arrangement->width();
}
/* see meanShift.m for usage info */
void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[] ) {
double radius, rate, *data, *labels, *means; int p, n, maxIter; bool blur;
/* Check inputs */
if(nrhs < 4) mexErrMsgTxt("At least four input arguments required.");
if(nlhs > 2) mexErrMsgTxt("Too many output arguments.");
if(nrhs==5) blur = mxGetScalar(prhs[4])!=0;
/* Get inputs */
data = mxGetPr(prhs[0]);
radius = mxGetScalar(prhs[1]);
rate = mxGetScalar(prhs[2]);
maxIter = (int) mxGetScalar(prhs[3]);
p=mxGetM(prhs[0]); n=mxGetN(prhs[0]);
/* Create outputs */
plhs[0] = mxCreateNumericMatrix(n, 1, mxDOUBLE_CLASS, mxREAL);
plhs[1] = mxCreateNumericMatrix(p, n, mxDOUBLE_CLASS, mxREAL);
labels=mxGetPr(plhs[0]); means=mxGetPr(plhs[1]);
/* Do the actual computations in a subroutine */
meanShift( data, p, n, radius, rate, maxIter, blur, labels, means );
}
int main(int argc, char** argv)
{
Point2f cp;
cv::initModule_nonfree();
// Read the VIDEO
VideoCapture cap("video1.avi");
if( !cap.isOpened() )
{ cout << "Could not initialize capturing...\n"; return 0;}
//Initialize Video Writer
//writeOut.open("MStrack_3.avi", CV_FOURCC('M', 'J', 'P', 'G'), 15, Size(640,480), 1 );
cv::SURF mySURF; mySURF.extended = 0;
Ptr<FeatureDetector> detector = FeatureDetector::create( "SURF"); // SURF,SIFT ,MSER
Ptr<DescriptorExtractor> descriptorExtractor = DescriptorExtractor::create( "SURF" );
Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create( "FlannBased" ); // FlannBased , BruteForce
int matcherFilterType = getMatcherFilterType( "CrossCheckFilter" );
// Get the first frame and select the ROI to be tracked in the subsequent frames
Mat frame, img1, img2;
cap >> frame;
if( frame.empty() )
return -1;
else
img1 = frame.clone() ;
Mat temp = img1.clone() ;
if(img1.empty())
{
cout << "Exiting as the input image is empty" << endl;
exit(-1);
}
const char* name = "Initiate_ROI";
box = cvRect(-1,-1,0,0);
cvNamedWindow( name );
// Set up the callback
cvSetMouseCallback( name, my_mouse_callback);
// Wait until ROI is selected by the user
while( 1 )
{
img1.copyTo(temp);
if( drawing_box )
draw_box( temp, box );
cv::imshow(name,temp) ;
cvWaitKey( 15 );
if(rect_drawn)
break;
}
// storing the initial selected Box, as "box" variable changes in consecutive matching
boxOrg = box;
Mat img1ROI, labels1, clusters1, descriptors1, descriptors2;
vector<int> reprojections; // number of reprojections per keypoint, size same as keypoint (increasing)
vector<KeyPoint> keypoints1, keypoints2;
//human aspect ratio (not used)
double aspectRatio = (double)box.width / box.height;
// Compute SURF features within the *selected* ROI
img1ROI = img1(boxOrg);
mySURF.detect(img1ROI, keypoints1 );
mySURF.compute(img1ROI, keypoints1, descriptors1 );
// Create a Template Pool that contains both descriptors as well as Keypoints (local & Global)
Mat tpDescriptors;
vector<KeyPoint> tpKeypoints;
vector<float> tpWeights;
int tpMaxSize = 1000;
//Initially copy of the descriptor of Ist image ROI into it.
descriptors1.copyTo(tpDescriptors);
tpWeights.resize(tpDescriptors.rows,2.0); // Initial values of all weights is 2.0
for(uint i = 0; i < keypoints1.size(); ++i)
tpKeypoints.push_back(keypoints1.at(i));
//==========================================
// Main Iteration Loop starts here : Tracking
//============================================
int MP, count;
struct timeval t1, t2;
//Rect msBox; // Box obtained from mean-shift tracker
// Loop over all images
for(int k=1;;k++) //int i=2;i<1002;i+=1)
{
gettimeofday(&t1, NULL);
//create clusters in the SURF descriptor space
// clusters are created in the template pool
cv::kmeans(tpDescriptors, NOC, labels1,
TermCriteria( CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 50, 1.0 ), 1,
/*KMEANS_PP_CENTERS*/KMEANS_RANDOM_CENTERS, clusters1);
// img1 - source image
// img2 - Destination image
// Mean-shift algorithm returns the window on Destination image given the source ROI in boxOrg.
//Capture a New frame
cap >> frame;
if( frame.empty() )
return -1;
else
img2 = frame.clone() ;
temp = img2.clone();
if(img2.empty() )
{
cout<< "Could not open image: " << img2 << endl ;
//continue ;
exit(-1);
}
int flag=1; // what is this flag ??
MP=0; count=0; // ??
//Call the mean-shift tracker
vector<int> queryIdxs, trainIdxs;
meanShift(img1, img2, descriptorMatcher, matcherFilterType, keypoints1, descriptors1,
keypoints2, descriptors2, clusters1, reprojections, cp, flag, count, MP,
temp, queryIdxs, trainIdxs);
DivideAndComputeScaling(img1, img2);
// box.height = (int)(scaleValue * box.height);
// box.width = (int)(aspectRatio * box.height);
// box.x = cp.x - box.width/2.0;
// box.y = cp.y - box.height/2.0;
//cout << "Scale Value = " << scaleValue << endl;
// Add the target ROI descriptors into the template pool.
for(int i=0;i< descriptors2.rows;i++)
{
tpDescriptors.push_back(descriptors2.row(i));
tpKeypoints.push_back(keypoints2.at(i));
}
// If the size of template pool exceeds max size, remove that many number of points from top
Mat tempMat;
if(tpDescriptors.rows > tpMaxSize)
{
//cout << "Time to Truncate Template Pool" << endl;
uint dLength = tpDescriptors.rows - tpMaxSize;
tempMat = tpDescriptors.rowRange(Range(dLength, tpDescriptors.rows));
tpKeypoints.erase(tpKeypoints.begin(), tpKeypoints.begin()+dLength);
//tpDescriptors.release(); tpDescriptors = tempMat;
tpDescriptors = tempMat;
}
tempMat.release();
//cout << "Template Pool size =" << tpDescriptors.rows << endl;
// Current target image becomes the source image for the next iteration
img1=img2.clone();
boxOrg = box;
// source descriptors and keypoints are taken from the template pool
keypoints1 = tpKeypoints;
descriptors1 = tpDescriptors;
gettimeofday(&t2, NULL);
double diff = (float)((t2.tv_sec * 1000000 + t2.tv_usec) - (t1.tv_sec * 1000000 + t1.tv_usec));
diff = diff/1000;
cout << k << "\tTime taken in mili sec \t" << diff<< endl;
//f1 << k << "\t" << MP << "\t" << count << "\t" << diff << "\n";
cv::circle(temp, cp, 2, Scalar(0,255,255), 2);
//=======================================
imshow("main", temp);
//imshow("img2", img2);
char c = (char)waitKey(10);
if( c == '\x1b' ) // esc
{
cout << "Exiting ..." << endl;
break;
}
waitKey(5);
}
return 0;
}
cv::RotatedRect cv::CamShift( InputArray _probImage, Rect& window,
TermCriteria criteria )
{
CV_INSTRUMENT_REGION()
const int TOLERANCE = 10;
Size size;
Mat mat;
UMat umat;
bool isUMat = _probImage.isUMat();
if (isUMat)
umat = _probImage.getUMat(), size = umat.size();
else
mat = _probImage.getMat(), size = mat.size();
meanShift( _probImage, window, criteria );
window.x -= TOLERANCE;
if( window.x < 0 )
window.x = 0;
window.y -= TOLERANCE;
if( window.y < 0 )
window.y = 0;
window.width += 2 * TOLERANCE;
if( window.x + window.width > size.width )
window.width = size.width - window.x;
window.height += 2 * TOLERANCE;
if( window.y + window.height > size.height )
window.height = size.height - window.y;
// Calculating moments in new center mass
Moments m = isUMat ? moments(umat(window)) : moments(mat(window));
double m00 = m.m00, m10 = m.m10, m01 = m.m01;
double mu11 = m.mu11, mu20 = m.mu20, mu02 = m.mu02;
if( fabs(m00) < DBL_EPSILON )
return RotatedRect();
double inv_m00 = 1. / m00;
int xc = cvRound( m10 * inv_m00 + window.x );
int yc = cvRound( m01 * inv_m00 + window.y );
double a = mu20 * inv_m00, b = mu11 * inv_m00, c = mu02 * inv_m00;
// Calculating width & height
double square = std::sqrt( 4 * b * b + (a - c) * (a - c) );
// Calculating orientation
double theta = atan2( 2 * b, a - c + square );
// Calculating width & length of figure
double cs = cos( theta );
double sn = sin( theta );
double rotate_a = cs * cs * mu20 + 2 * cs * sn * mu11 + sn * sn * mu02;
double rotate_c = sn * sn * mu20 - 2 * cs * sn * mu11 + cs * cs * mu02;
double length = std::sqrt( rotate_a * inv_m00 ) * 4;
double width = std::sqrt( rotate_c * inv_m00 ) * 4;
// In case, when tetta is 0 or 1.57... the Length & Width may be exchanged
if( length < width )
{
std::swap( length, width );
std::swap( cs, sn );
theta = CV_PI*0.5 - theta;
}
// Saving results
int _xc = cvRound( xc );
int _yc = cvRound( yc );
int t0 = cvRound( fabs( length * cs ));
int t1 = cvRound( fabs( width * sn ));
t0 = MAX( t0, t1 ) + 2;
window.width = MIN( t0, (size.width - _xc) * 2 );
t0 = cvRound( fabs( length * sn ));
t1 = cvRound( fabs( width * cs ));
t0 = MAX( t0, t1 ) + 2;
window.height = MIN( t0, (size.height - _yc) * 2 );
window.x = MAX( 0, _xc - window.width / 2 );
window.y = MAX( 0, _yc - window.height / 2 );
window.width = MIN( size.width - window.x, window.width );
window.height = MIN( size.height - window.y, window.height );
RotatedRect box;
box.size.height = (float)length;
box.size.width = (float)width;
box.angle = (float)((CV_PI*0.5+theta)*180./CV_PI);
while(box.angle < 0)
box.angle += 360;
while(box.angle >= 360)
box.angle -= 360;
if(box.angle >= 180)
box.angle -= 180;
box.center = Point2f( window.x + window.width*0.5f, window.y + window.height*0.5f);
return box;
}
AppTemplate::AppTemplate(const Mat* frame_set, const Rect iniWin,int ID)
:ID(ID)//bgr,hsv,lab
{
//get roi out of frame set
Rect body_win=scaleWin(iniWin,1/TRACKING_TO_BODYSIZE_RATIO);
Rect roi_win(body_win.x-body_win.width,body_win.y-body_win.width,3*body_win.width,2*body_win.width+body_win.height);
body_win= body_win&Rect(0,0,frame_set[0].cols,frame_set[0].rows);
roi_win=roi_win&Rect(0,0,frame_set[0].cols,frame_set[0].rows);
Mat roi_set[]={Mat(frame_set[0],roi_win),Mat(frame_set[1],roi_win),Mat(frame_set[2],roi_win)};
Rect iniWin_roi=iniWin-Point(roi_win.x,roi_win.y);
//scores for each channel
list<ChannelScore> channel_score;
Mat mask_roi(roi_set[0].rows,roi_set[0].cols,CV_8UC1,Scalar(0));
rectangle(mask_roi,iniWin_roi,Scalar(255),-1);
Mat inv_mask_roi(roi_set[0].rows,roi_set[0].cols,CV_8UC1,Scalar(255));
rectangle(inv_mask_roi,body_win-Point(roi_win.x,roi_win.y),Scalar(0),-1);
//calculate score for each channel
Mat temp_hist;
Mat temp_bp;
int hist_size[]={BIN_NUMBER};
for (int i=0;i<9;i++)
{
float range1[]={0,255};
if (i==3)
{
range1[1]=179;
}
const float* hist_range[]={range1};
calcHist(roi_set,3,&i,inv_mask_roi,temp_hist,1,hist_size,hist_range);
normalize(temp_hist,temp_hist,255,0.0,NORM_L1);//scale to 255 for display
calcBackProject(roi_set,3,&i,temp_hist,temp_bp,hist_range);
int c[]={0};
int hs[]={BIN_NUMBER};
float hr[]={0,255};
const float* hrr[]={hr};
Mat hist_fore;
Mat hist_back;
calcHist(&temp_bp,1,c,mask_roi,hist_fore,1,hs,hrr);
calcHist(&temp_bp,1,c,inv_mask_roi,hist_back,1,hs,hrr);
normalize(hist_fore,hist_fore,1.0,0.0,NORM_L1);
normalize(hist_back,hist_back,1.0,0.0,NORM_L1);
//deal with gray image to get rid of #IND
double score=getVR(hist_back,hist_fore);
score=score==score ? score:0;
channel_score.push_back(ChannelScore(i,score));
}
//choose the 2 highest scored channels
channel_score.sort(compareChannel);
channels[0]=channel_score.back().idx;
channel_score.pop_back();
channels[1]=channel_score.back().idx;
//using 2 best channel to calculate histogram
for (int i=0;i<2;++i)
{
_hRang[i][0]=0;
if (channels[i]==3)
_hRang[i][1]=179;
else
_hRang[i][1]=255;
hRange[i]=_hRang[i];
}
calcHist(roi_set,3,channels,inv_mask_roi,temp_hist,2,hSize,hRange);
normalize(temp_hist,temp_hist,255,0,NORM_L1);
Mat final_mask;//mask for sampling
calcBackProject(roi_set,3,channels,temp_hist,final_mask,hRange);
threshold(final_mask,final_mask,5,255,CV_THRESH_BINARY_INV);
final_mask=min(final_mask,mask_roi);
//choose the best two feature space for foreground****************
Mat hist_fore,hist_back;
channel_score.clear();
double sum_score=0;
for (int i=0;i<9;i++)
{
float range1[]={0,255};
if (i==3)
{
range1[1]=179;
}
const float* hist_range[]={range1};
Mat temp_hist_neg;
calcHist(roi_set,3,&i,final_mask,temp_hist,1,hist_size,hist_range);
normalize(temp_hist,temp_hist,255,0,NORM_L1);
calcHist(roi_set,3,&i,inv_mask_roi,temp_hist_neg,1,hist_size,hist_range);
normalize(temp_hist_neg,temp_hist_neg,255,0,NORM_L1);
log(temp_hist,temp_hist);
log(temp_hist_neg,temp_hist_neg);
temp_hist=temp_hist-temp_hist_neg;
threshold(temp_hist,temp_hist,0,255,CV_THRESH_TOZERO);
normalize(temp_hist,temp_hist,255,0.0,NORM_L1);//scale to 255 for display
calcBackProject(roi_set,3,&i,temp_hist,temp_bp,hist_range);
int c[]={0};
int hs[]={BIN_NUMBER};
float hr[]={0,255};
const float* hrr[]={hr};
calcHist(&temp_bp,1,c,final_mask,hist_fore,1,hs,hrr);
calcHist(&temp_bp,1,c,inv_mask_roi,hist_back,1,hs,hrr);
normalize(hist_fore,hist_fore,1.0,0.0,NORM_L1);
normalize(hist_back,hist_back,1.0,0.0,NORM_L1);
double score=getVR(hist_back,hist_fore);
score=score==score ? score:0;
channel_score.push_back(ChannelScore(i,score));
sum_score+=exp(score);
}
channel_score.sort(compareChannel);
channels[0]=channel_score.back().idx;
channel_score.pop_back();
channels[1]=channel_score.back().idx;
for (int i=0;i<2;++i)
{
_hRang[i][0]=0;
if (channels[i]==3)
_hRang[i][1]=179;
else
_hRang[i][1]=255;
hRange[i]=_hRang[i];
}
calcHist(roi_set,3,channels,final_mask,hist,2,hSize,hRange);///////////////////
normalize(hist,hist,255,0,NORM_L1);
//recover the shift_vector
Mat backPro;
calcBackProject(roi_set,3,channels,hist,backPro,hRange);
iniWin_roi=iniWin-Point(roi_win.x,roi_win.y);
Point2f origin_point_roi((float)(iniWin_roi.x+0.5*iniWin_roi.width),(float)(iniWin_roi.y+0.5*iniWin_roi.height));
meanShift(backPro,iniWin_roi,TermCriteria( CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1 ));
Point2f shift_point_roi((float)(iniWin_roi.x+0.5*iniWin_roi.width),(float)(iniWin_roi.y+0.5*iniWin_roi.height));
shift_vector=(shift_point_roi-origin_point_roi)*(1/(float)iniWin.width);
}
void MeanShiftDemo( VideoCapture& video, Rect& starting_position, int starting_frame_number, int end_frame)
{
bool half_size = true;
video.set(CV_CAP_PROP_POS_FRAMES,starting_frame_number);
Mat current_frame, hls_image;
std::vector<cv::Mat> hls_planes(3);
video >> current_frame;
Rect current_position(starting_position);
if (half_size)
{
resize(current_frame, current_frame, Size( current_frame.cols/2, current_frame.rows/2 ));
current_position.height /= 2;
current_position.width /= 2;
current_position.x /= 2;
current_position.y /= 2;
}
cvtColor(current_frame, hls_image, CV_BGR2HLS);
split(hls_image,hls_planes);
int chosen_channel = 0; // Hue channel
Mat image1ROI = hls_planes[chosen_channel](current_position);
float channel_range[2] = { 0.0, 255.0 };
int channel_numbers[1] = { 0 };
int number_bins[1] = { 32 };
MatND histogram[1];
const float* channel_ranges = channel_range;
calcHist(&(image1ROI), 1, channel_numbers, Mat(), histogram[0], 1 , number_bins, &channel_ranges);
normalize(histogram[0],histogram[0],1.0);
rectangle(current_frame,current_position,Scalar(0,255,0),2);
Mat starting_frame = current_frame.clone();
int frame_number = starting_frame_number;
while (!current_frame.empty() && (frame_number < end_frame))
{
// Calculate back projection
Mat back_projection_probabilities;
calcBackProject(&(hls_planes[chosen_channel]),1,channel_numbers,*histogram,back_projection_probabilities,&channel_ranges,255.0);
// Remove low saturation points from consideration
Mat saturation_mask;
inRange( hls_image, Scalar(0,10,50,0),Scalar(180,256,256,0), saturation_mask );
bitwise_and( back_projection_probabilities, back_projection_probabilities,back_projection_probabilities, saturation_mask );
// Mean shift
TermCriteria criteria(cv::TermCriteria::MAX_ITER,5,0.01);
meanShift(back_projection_probabilities,current_position,criteria);
// Output to screen
rectangle(current_frame,current_position,Scalar(0,255,0),2);
Mat chosen_channel_image, back_projection_image;
cvtColor(hls_planes[chosen_channel], chosen_channel_image, CV_GRAY2BGR);
cvtColor(back_projection_probabilities, back_projection_image, CV_GRAY2BGR);
Mat row1_output = JoinImagesHorizontally( starting_frame, "Starting position", chosen_channel_image, "Chosen channel (Hue)", 4 );
Mat row2_output = JoinImagesHorizontally( back_projection_image, "Back projection", current_frame, "Current position", 4 );
Mat mean_shift_output = JoinImagesVertically(row1_output,"",row2_output,"", 4);
imshow("Mean Shift Tracking", mean_shift_output );
// Advance to next frame
video >> current_frame;
if (half_size)
resize(current_frame, current_frame, Size( current_frame.cols/2, current_frame.rows/2 ));
cvtColor(current_frame, hls_image, CV_BGR2HLS);
split(hls_image,hls_planes);
frame_number++;
cvWaitKey(1000);
}
char c = cvWaitKey();
cvDestroyAllWindows();
}
int main(int argc, char** argv)
{
ofstream f1;
f1.open("result.txt");
size_t i,j;
Point2f cp;
cv::initModule_nonfree();
vector<Point2f> MP1,MP2;
vector<int> trainIdxs, queryIdxs;
//Read Video File
VideoCapture cap("video1.avi");
if( !cap.isOpened() )
{ cout << "Could not initialize capturing...\n"; return 0;}
VideoWriter writer("ms_tracking.avi",CV_FOURCC('D','I','V','3'),
10,cvSize(640,480),1);
cv::SURF mySURF; mySURF.extended = 0;
Ptr<DescriptorMatcher> descriptorMatcher = DescriptorMatcher::create( "FlannBased" );
int mactherFilterType = getMatcherFilterType( "CrossCheckFilter" );
Mat frame,img1,img2;
cap >> frame;
if( frame.empty() )
return -1;
img1 = frame.clone() ;
Mat temp,temp1;
if(img1.empty())
cout << "Exiting as the input image is empty" << endl;
const char* name = "Initiate_ROI";
box = cvRect(-1,-1,0,0);
cvNamedWindow( name,1);
cvSetMouseCallback( name, my_mouse_callback2);
// Main loop
while( 1 )
{
img1.copyTo(temp);
if( drawing_poly)
{
for ( i=0; i < polyPoints.size(); i++)
circle(temp, polyPoints[i], 2, Scalar(0,255,0), -1,8);
}
cv::imshow(name,temp) ;
char c = (char)waitKey(10);
if( c == '\x1b' ) // esc
break;
if(poly_drawn)
break;
}
//Read the polygon points from a text file
FILE *f11;
polyPoints.clear();
IpolyPoints.clear();
f11 = fopen("points.txt","r");
Point a;
for(int j=0;j<37;j++)
{
fscanf(f11,"%d",&(a.x));
fscanf(f11,"%d",&(a.y));
polyPoints.push_back(a);
IpolyPoints.push_back(a);
}
fclose(f11);
// Drawing Polygon
Point pointArr[polyPoints.size()];
for (i=0; i< polyPoints.size(); i++)
pointArr[i] = polyPoints[i];
const Point* pointsArray[1] = {pointArr};
int nCurvePts[1] = { polyPoints.size() };
polylines(temp, pointsArray, nCurvePts, 1, 1, Scalar(0,255,0), 1);
cout << polyPoints.size() << endl;
box= boundingRect(polyPoints);
//boxOrg = Rect(box.x-15, box.y-15, box.width+30, box.height+30);
boxOuter = Rect(box.x-30, box.y-30, box.width+60, box.height+60);
//box =boxOrg; // storing the initial selected Box, as "box" variable changes in consecutive matching
boxP=box;
Mat img1ROI, labels1, clusters1, descriptors,roidescriptors, descriptors1,bdescriptors, bmdescriptors;
vector<int> reprojections; // number of reprojections per KP, size same as KP(incresing)
vector<Point2f> points,points1,points2, Mpoints1,Mpoints2,bpoints,npoints1,npoints2; //bmpoints,tpoints;
vector<KeyPoint> roikeypoints, bkeypoints,keypoints,keypoints1, keypoints2;
draw_box(temp, box ); //Show InnerBox - This is used by the Mean-Shift Tracker
draw_box(temp,boxOuter); //Show OuterBox - This is used for removing background points
bpoints.clear();
//calculating keypoints and descriptors of the selected polygon in image roi
//==============================================================================================//
for(i=0;i<polyPoints.size();i++)
{
// cout << polyPoints[i] << endl; //
polyPoints[i].x = polyPoints[i].x -boxOuter.x;
polyPoints[i].y = polyPoints[i].y- boxOuter.y;
}
img1ROI = img1(boxOuter);
points1.clear();
mySURF.detect(img1ROI, roikeypoints);
KeyPoint::convert(roikeypoints, points);
mySURF.compute(img1ROI, roikeypoints, roidescriptors);
bdescriptors.release();bkeypoints.clear();
bcategorizePoints( points, bpoints,polyPoints, roikeypoints, roidescriptors, bkeypoints, bdescriptors);
shiftPoints(bpoints,boxOuter);
for(i=0;i<bpoints.size();i++)
circle(temp, bpoints[i], 2, Scalar(0,255,0),2);
vector<KeyPoint> tpkeypoints; Mat tpdescriptors;
categorizePoints( points, points1,polyPoints, roikeypoints, roidescriptors, tpkeypoints, tpdescriptors);
shiftPoints(points1, boxOuter);
for(i=0;i<points1.size();i++)
circle(temp, points1[i], 2, Scalar(0,0,255),2);
//====================================================================================================//
points1.clear();
Mat img2ROI;
// tpkeypoints = keypoints1; tpdescriptors = descriptors1;
cv::imshow(name,temp) ;
imwrite("a.jpg",temp);
cout << "BD_SIZE \t" << bdescriptors.rows << "\t" << "FD_SIZE \t" << tpdescriptors.rows << endl;
// Mat newimg = img1ROI.clone();
// KeyPoint::convert(tpkeypoints, points1);
// for(size_t i=0;i<points1.size();i++)
// circle(newimg, points1[i], 2, Scalar(255,0,255),2);
// imshow( "newimg", newimg );
// points1.clear();
waitKey(0);
cvDestroyWindow( name );
int FG_mp, FG, BG_mp, BG, FG_BG, msI ; //Foreground matching points
struct timeval t1, t2;
for(int l=0;;l++)
{
gettimeofday(&t1, NULL);
cv::kmeans(tpdescriptors, NOC, labels1, TermCriteria( CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 50, 1.0 ), 1,
KMEANS_RANDOM_CENTERS, clusters1);
cap >> frame;
img2 = frame.clone() ;
temp1 =frame.clone() ;
if(img2.empty() )
{
cout<< "Could not open image: " << endl ;
break;}
int flag=1;
Mpoints1.clear();
Mat descriptors2;
msI=0;
meanShift(img1, img2, descriptorMatcher, mactherFilterType, tpkeypoints, tpdescriptors,keypoints2,descriptors2,
clusters1, cp, flag, MP1,img2ROI,bkeypoints, bdescriptors, temp1,FG_mp, FG, BG_mp, BG, FG_BG,msI);
//==========scaling=================
float scale=1;
// cout <<"MP1size \t" << MP1.size() <<endl;
if(APPLY_SCALING)
{
vector<DMatch> filteredMatches;
if(descriptors1.rows > 4 && descriptors2.rows > 4)
{
crossCheckMatching( descriptorMatcher, descriptors1, descriptors2, filteredMatches, 1 );
trainIdxs.clear(); queryIdxs.clear();
for( i = 0; i < filteredMatches.size(); i++ )
{
queryIdxs.push_back(filteredMatches[i].queryIdx);
trainIdxs.push_back(filteredMatches[i].trainIdx);
}
points1.clear(); points2.clear();
KeyPoint::convert(keypoints1, points1, queryIdxs);
KeyPoint::convert(keypoints2, points2, trainIdxs);
// cout << "point2size" << points2.size() << endl;
//homography
npoints1.clear();npoints2.clear();
Mpoints1.clear();Mpoints2.clear();
Mat H12, points1t;
double ransacReprojThreshold = 10;
if( ransacReprojThreshold >= 0 && points1.size() > 4)
H12 = findHomography( Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold );
vector<char> matchesMask( filteredMatches.size(), 0 );// NONmatchesMask( filteredMatches.size(), 0 );
if( !H12.empty() )
{
perspectiveTransform(Mat(points1), points1t, H12);
double maxInlierDist = 10;//ransacReprojThreshold < 0 ? 3 : ransacReprojThreshold;
for(i = 0; i < points1.size(); i++ )
{
if( norm(points2[i] - points1t.at<Point2f>((int)i,0)) <= 5)// maxInlierDist ) // inlier
{
matchesMask[i] = 1;
npoints2.push_back(points2[i]);
npoints1.push_back(points1[i]);
}
}
for(i=0; i<npoints2.size();i++)
{
for(j=0;j<MP1.size();j++)
{
double dist = norm(npoints2[i]-MP1[j]);
// cout <<"dist \t" <<dist << endl;
// waitKey(0);
if(dist < 0.1)
{
Mpoints2.push_back(npoints2[i]);
Mpoints1.push_back(npoints1[i]);
break;
}
}
}
}
Mat drawImg;
drawMatches( img1ROI, keypoints1, img2ROI, keypoints2, filteredMatches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask
#if DRAW_RICH_KEYPOINTS_MODE
, DrawMatchesFlags::DRAW_RICH_KEYPOINTS
#endif
);
imshow( "correspondance", drawImg );
cout << "npoints1.size \t" << Mpoints1.size() << "\t" << Mpoints2.size() << endl;
if(Mpoints1.size() > 8)
weightScalingAspect(Mpoints1,Mpoints2,&scale);
}
}
img1=img2;
img1ROI = img2ROI;
boxOrg =box;
keypoints1 = keypoints2;
descriptors1 =descriptors2;
box.x += box.width/2;
box.y += box.height/2;
box.height = round(boxOrg.height *scale);
box.width = round(( float(boxOrg.width)/float(boxOrg.height) ) * box.height);
box.x -= box.width/2;
box.y -= box.height/2;
boundaryCheckRect(box);
cout <<"SCALE \t" << scale << endl;
gettimeofday(&t2, NULL);
double diff = (float)((t2.tv_sec * 1000000 + t2.tv_usec) - (t1.tv_sec * 1000000 + t1.tv_usec));
diff = diff/1000;
cout <<"Time taken in mili sec \t" << diff<< endl;
// cout << tpdescriptors.rows << endl;
//cout <<"BD \t" << bdescriptors.rows << endl;
f1 << l << "\t" << FG_mp << "\t" << BG_mp << "\t" << FG << "\t"<< msI << "\n";
cout << "l \t" << l << "\t" <<" msI \t"<< msI << endl;
imshow("img2",temp1);
writer << temp1;
waitKey(0);
// boxOrg = eBox;
char c = (char)waitKey(10);
if( c == '\x1b' ) // esc
{
cout << "Exiting ..." << endl;
break;
}
}
trajectory.close();
return 0;
}
