void extractBall()
{
imgTransform(BALL_HUE_U, BALL_HUE_L, BALL_SAT_U, BALL_SAT_L, VAL_U, VAL_L);
blobRes = CBlobResult(dst, NULL, 0);
blobRes.Filter( blobRes, B_EXCLUDE, CBlobGetArea(), B_LESS, BLOB_SIZE_MIN );// keep blobs larger than BLOB_SIZE_MIN
numOfBlobs = blobRes.GetNumBlobs(); cout << numOfBlobs << endl;
blobRes.Filter( blobRes, B_EXCLUDE, CBlobGetArea(), B_GREATER, BALL_SIZE_MAX );// keep blobs smaller than BALL_SIZE_MAX
numOfBlobs = blobRes.GetNumBlobs(); cout << numOfBlobs << endl;
blobRes.Filter( blobRes, B_INCLUDE, CBlobGetCompactness(), B_GREATER, BALL_COMPACTNESS );// keep blobs greater than BALL_COMPACTNESS
numOfBlobs = blobRes.GetNumBlobs(); cout << numOfBlobs << endl;
for(int i=0; i<numOfBlobs; i++)
blobs[i] = blobRes.GetBlob(i);
};
void ForegroundDetector::nextIteration(const Mat &img)
{
if(bgImg.empty())
{
return;
}
Mat absImg = Mat(img.cols, img.rows, img.type());
Mat threshImg = Mat(img.cols, img.rows, img.type());
absdiff(bgImg, img, absImg);
threshold(absImg, threshImg, fgThreshold, 255, CV_THRESH_BINARY);
IplImage im = (IplImage)threshImg;
CBlobResult blobs = CBlobResult(&im, NULL, 0);
blobs.Filter(blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, minBlobSize);
vector<Rect>* fgList = detectionResult->fgList;
fgList->clear();
for(int i = 0; i < blobs.GetNumBlobs(); i++)
{
CBlob *blob = blobs.GetBlob(i);
CvRect rect = blob->GetBoundingBox();
fgList->push_back(rect);
}
}
void ScheinrieseApp::findBlobs() {
CBlobResult blobs;
int i;
CBlob *currentBlob;
IplImage *original, *originalThr;
// load an image and threshold it
original = cvLoadImage("pic1.png", 0);
cvThreshold( original, originalThr, 100, 0, 255, CV_THRESH_BINARY );
// find non-white blobs in thresholded image
blobs = CBlobResult( originalThr, NULL, 255 );
// exclude the ones smaller than param2 value
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, param2 );
// get mean gray color of biggest blob
CBlob biggestBlob;
CBlobGetMean getMeanColor( original );
double meanGray;
blobs.GetNth( CBlobGetArea(), 0, biggestBlob );
meanGray = getMeanColor( biggestBlob );
// display filtered blobs
cvMerge( originalThr, originalThr, originalThr, NULL, displayedImage );
for (i = 0; i < blobs.GetNumBlobs(); i++ )
{
currentBlob = blobs.GetBlob(i);
currentBlob->FillBlob( displayedImage, CV_RGB(255,0,0));
}
}
CBlob getNearestBlob(CBlobResult blobs, coord coordinate){
int tot = blobs.GetNumBlobs();
CBlob Blob;
float distance[10]; // 10 è il numero massimo di blob trovabile in un video
float minimum;
coord tempCoord;
//Questo ciclo for fa la distanza manhattan tra le coordinate passate e tutti i blob catturati e crea il vettore con tutte le distanze.
for (int i=0; i<tot; i++){
Blob = blobs.GetBlob(i);
tempCoord.set( (int) Blob.MaxX(), (int) Blob.MinX(), (int) Blob.MaxY(), (int) Blob.MinY());
distance[i] = sqrt((double)(tempCoord.cX - coordinate.cX)*(tempCoord.cX - coordinate.cX) + (tempCoord.cY - coordinate.cY)*(tempCoord.cY - coordinate.cY));
}
int minDistanceId=0;
//Questo ciclo for becca la minima distanza fra tutte quelle calcolate
for (int j=0; j<tot; j++){
minimum = min( distance[j], distance[minDistanceId]);
if ( distance[j] == minimum ) minDistanceId = j;
}
//Ottenuta la minima distanza si va a ritornare il Blob corrispondente
Blob = blobs.GetBlob( minDistanceId );
//delete[] distance;
return Blob;
}
CBlobResult computeWhiteMaskOtsu(Mat& imgRGBin, Mat& imgHSVIn, CBlobResult& blobs, int limitRGB, int limitHSV, double RGBratio, double HSVratio, int bmin, int bmax, int i){
waitKey(30);
Mat BGRbands[3];
split(imgRGBin,BGRbands);
Mat imgHSV;
cvtColor(imgHSVIn,imgHSV,CV_BGR2HSV);
Mat HSVbands[3];
split(imgHSV,HSVbands);
Mat maskHSV, maskRGB, maskT;
int otsuTRGB = getThreshVal_Otsu_8u(BGRbands[2]);
do{
threshold(BGRbands[2],maskRGB,otsuTRGB,255,THRESH_BINARY);
otsuTRGB++;
}while(countNonZero(maskRGB)>(RGBratio*limitRGB) & otsuTRGB<=255);
int otsuTHSV = getThreshVal_Otsu_8u(HSVbands[1]);
do{
threshold(HSVbands[1],maskHSV,otsuTHSV,255,THRESH_BINARY_INV);
otsuTHSV--;
}while(countNonZero(maskHSV)>(HSVratio*limitHSV) & otsuTHSV>=0); // 0.1
bitwise_or(maskHSV,maskRGB,maskT);
int blobSizeBefore = blobs.GetNumBlobs();
blobs = blobs + CBlobResult( maskT ,Mat(),8);
blobs.Filter( blobs, B_EXCLUDE, CBlobGetLength(), B_GREATER, bmax );
blobs.Filter( blobs, B_EXCLUDE, CBlobGetLength(), B_LESS, bmin );
int blobSizeAfter = blobs.GetNumBlobs();
Mat newMask(maskT.size(),maskT.type());
newMask.setTo(0);
for(;i<blobs.GetNumBlobs();i++){
double area = blobs.GetBlob(i)->Area();
if(area < 5000 && area > 400)
blobs.GetBlob(i)->FillBlob(newMask,CV_RGB(255,255,255),0,0,true);
}
if(countNonZero(maskRGB)>400 && countNonZero(maskHSV)>400 && blobSizeBefore!=blobSizeAfter){
vector<Mat> BGRbands; split(imgRGBin,BGRbands);
Mat maskedRGB = applyMaskBandByBand(newMask,BGRbands);
bitwise_not(newMask,newMask);
split(imgHSVIn,BGRbands);
Mat maskedHSV = applyMaskBandByBand(newMask,BGRbands);
blobs = computeWhiteMaskOtsu(maskedRGB, maskedHSV, blobs, countNonZero(maskRGB),countNonZero(maskHSV),RGBratio, HSVratio, bmin, bmax, i-1);
}
return blobs;
}
/**
- FUNCTION: CBlobResult
- FUNCTIONALITY: Copy constructor
- PARAMETERS:
- source: object to copy
- RESULT:
- RESTRICTIONS:
- AUTHOR: Ricard Borràs
- CREATION DATE: 25-05-2005.
- MODIFICATION: Date. Author. Description.
*/
CBlobResult::CBlobResult( const CBlobResult &source )
{
m_blobs = blob_vector( source.GetNumBlobs() );
// creem el nou a partir del passat com a paràmetre
m_blobs = blob_vector( source.GetNumBlobs() );
// copiem els blobs de l'origen a l'actual
blob_vector::const_iterator pBlobsSrc = source.m_blobs.begin();
blob_vector::iterator pBlobsDst = m_blobs.begin();
while( pBlobsSrc != source.m_blobs.end() )
{
// no podem cridar a l'operador = ja que blob_vector és un
// vector de CBlob*. Per tant, creem un blob nou a partir del
// blob original
*pBlobsDst = new CBlob(**pBlobsSrc);
pBlobsSrc++;
pBlobsDst++;
}
}
void drawInitialBlobs(IplImage * tmp_frame, CBlobResult blobs){
coord drawCoord;
for (int i=0; i<blobs.GetNumBlobs();i++){
//!Creating the coordinate struct
drawCoord.set( (int) blobs.GetBlob(i).MaxX(), (int) blobs.GetBlob(i).MinX(), (int) blobs.GetBlob(i).MaxY(), (int) blobs.GetBlob(i).MinY());
drawBlob(tmp_frame, drawCoord, 255, 255, 0);
}
}
/* Fetch a frame (if available) and process it, calling appropriate
callbacks when data becomes available. */
void MarkerCapture::tick(){
IplImage *thresh_frame = NULL;
CBlobResult blobs;
// Acquire the lock, update the current frame.
pthread_mutex_lock(&frame_mutex);
current_frame = cvCloneImage(cvQueryFrame(camera));
if(color_acquired && current_frame){
thresh_frame = apply_threshold(current_frame, target_color);
}else{
// create a suplicant.
thresh_frame = cvCreateImage(cvGetSize(current_frame),IPL_DEPTH_8U,1);
}
pthread_mutex_unlock(&frame_mutex);
// Lock released. Done messing with buffers.
if(frame_update_callback){
(*frame_update_callback)(this, current_frame, thresh_frame);
}
if(color_acquired){
blobs = detect_blobs(thresh_frame, CV_BLOB_SIZE_MIN);
if(blobs.GetNumBlobs() >= 2){ // need 2 or more blobs for positional fix.
MarkerPositionEstimate position;
// fetch the two largest blobs, by area.
CBlob blob0, blob1;
blobs.GetNthBlob(CBlobGetArea(), 0, blob0);
blobs.GetNthBlob(CBlobGetArea(), 1, blob1);
// perform positional calculations
position.distance = distance(blob0, blob1);
position.angle = angle(blob0, blob1);
position.blob0_center = blob_center(blob0);
position.blob1_center = blob_center(blob1);
// call the update handler.
if(position_update_callback){
(*position_update_callback)(this, position);
}
}
blobs.ClearBlobs();
}
pthread_mutex_lock(&frame_mutex);
cvReleaseImage(¤t_frame);
cvReleaseImage(&thresh_frame);
pthread_mutex_unlock(&frame_mutex);
int curr_time = clock();
fps = CLOCKS_PER_SEC/(double)(curr_time - time);
time = curr_time;
}
vector<Bubble> OMRSheet::getBubbles(int xi1, int yi1, int xi2, int yi2){
vector <Bubble> bubbles;
cout<<"Bubble area "<<bubbleArea;
int minArea = bubbleArea/2, maxArea = bubbleArea*1.5;
CBlobResult blobs = ImageUtils::findBlobs(rawSheet, minArea, maxArea, cvRect(xi1, yi1, xi2-xi1, yi2-yi1));
for (int i = 0; i < blobs.GetNumBlobs(); i++ )
{
CvRect rect = blobs.GetBlob(i)->GetBoundingBox();
Point centroid = ImageUtils::findCentroid(rawSheet, &rect);
Point centroidMM((centroid.x() - x1)/15, (centroid.y() - y1)/15);
Bubble bubble(blobs.GetBlob(i), ¢roidMM, ¢roid);
bubbles.push_back(bubble);
}
return bubbles;
}
/**
- FUNCTION: CBlobResult
- FUNCTIONALITY: Copy constructor
- PARAMETERS:
- source: object to copy
- RESULT:
- RESTRICTIONS:
- AUTHOR: Ricard Borr�s
- CREATION DATE: 25-05-2005.
- MODIFICATION: Date. Author. Description.
*/
CBlobResult::CBlobResult( const CBlobResult &source )
{
// creem el nou a partir del passat com a par�metre
//m_blobs = Blob_vector( source.GetNumBlobs() );
m_blobs.reserve(source.GetNumBlobs());
// copiem els blobs de l'origen a l'actual
Blob_vector::const_iterator pBlobsSrc = source.m_blobs.begin();
Blob_vector::iterator pBlobsDst = m_blobs.begin();
while( pBlobsSrc != source.m_blobs.end() )
{
// no podem cridar a l'operador = ja que Blob_vector �s un
// vector de CBlob*. Per tant, creem un blob nou a partir del
// blob original
m_blobs.push_back(new CBlob(**pBlobsSrc));
pBlobsSrc++;
}
}
void extractBots()
{
//RED TEAM
imgTransform(TEAM_R_HUE_U, TEAM_R_HUE_L, TEAM_R_SAT_U, TEAM_R_SAT_L, VAL_U, VAL_L);
blobRes = CBlobResult(dst, NULL, 0);
blobRes.Filter( blobRes, B_EXCLUDE, CBlobGetArea(), B_LESS, BLOB_SIZE_MIN );// keep blobs larger than BLOB_SIZE_MIN
numOfBlobs = blobRes.GetNumBlobs(); cout << numOfBlobs << endl;
if(numOfBlobs == 2)
{
for (int i=0; i<2; i++)
blobRes.GetBlob(i)
for(int i=0; i<numOfBlobs; i++)
blobs[i] = blobRes.GetBlob(i);
};
void printBlobs()
{
CBlobGetXCenter getXC;
CBlobGetYCenter getYC;
CBlobGetArea getArea;
CBlobGetCompactness getCompactness;
printf("-----Printng Blobs------\n");
for(int i=0; i<numOfBlobs; i++)
{
printf("%d\t(%3.2f,%3.2f),%3.2f %3.2f\n", i, getXC(blobs[i]), getYC(blobs[i]), getArea(blobs[i]), getCompactness(blobs[i]));
}
printf("\n");
cvNamedWindow("old", 1);
cvNamedWindow("new", 1);
cvMoveWindow("old", 0,0);
cvMoveWindow("new", 0,400);
cvShowImage("old", img);
cvShowImage("new", dst);
cvWaitKey();
};
coord extractBlob(CBlobResult blobs, coord selectedCoord){
coord coordinate;
CBlob Blob;
if ( blobs.GetNumBlobs()==0 ) {
coordinate.flag=false;
return coordinate;
}
else {
//!Get the blob info
Blob = getNearestBlob( blobs, selectedCoord);
//!Creating the coordinate struct
coordinate.set( (int) Blob.MaxX(), (int) Blob.MinX(), (int) Blob.MaxY(), (int) Blob.MinY());
return coordinate;
}
}
/**
- FUNCTION: + operator
- FUNCTIONALITY: Joins the blobs in source with the current ones
- PARAMETERS:
- source: object to copy the blobs
- RESULT:
- object with the actual blobs and the source blobs
- RESTRICTIONS:
- AUTHOR: Ricard Borràs
- CREATION DATE: 25-05-2005.
- MODIFICATION: Date. Author. Description.
*/
CBlobResult CBlobResult::operator+( const CBlobResult& source )
{
//creem el resultat a partir dels blobs actuals
CBlobResult resultat( *this );
// reservem memòria per als nous blobs
resultat.m_blobs.resize( resultat.GetNumBlobs() + source.GetNumBlobs() );
// declarem els iterador per recòrrer els blobs d'origen i desti
blob_vector::const_iterator pBlobsSrc = source.m_blobs.begin();
blob_vector::iterator pBlobsDst = resultat.m_blobs.end();
// insertem els blobs de l'origen a l'actual
while( pBlobsSrc != source.m_blobs.end() )
{
pBlobsDst--;
*pBlobsDst = new CBlob(**pBlobsSrc);
pBlobsSrc++;
}
return resultat;
}
// threshold trackbar callback
void on_trackbar( int dummy )
{
if(!originalThr)
{
originalThr = cvCreateImage(cvGetSize(original), IPL_DEPTH_8U,1);
}
if(!displayedImage)
{
displayedImage = cvCreateImage(cvGetSize(original), IPL_DEPTH_8U,3);
}
// threshold input image
cvThreshold( original, originalThr, param1, 255, CV_THRESH_BINARY );
// get blobs and filter them using its area
CBlobResult blobs;
int i;
CBlob *currentBlob;
// find blobs in image
blobs = CBlobResult( originalThr, NULL, 255 );
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, param2 );
// display filtered blobs
cvMerge( originalThr, originalThr, originalThr, NULL, displayedImage );
for (i = 0; i < blobs.GetNumBlobs(); i++ )
{
currentBlob = blobs.GetBlob(i);
currentBlob->FillBlob( displayedImage, CV_RGB(255,0,0));
}
cvShowImage( wndname, displayedImage );
}
int main(int argc, char * argv[])
{
vector <string> imgNames;
vector <string> imgNamesMask;
char strFrame[20];
readImageSequenceFiles(imgNames, imgNamesMask);
list<TrackLine> trackLineArr;
// read org frame and forground for process
// you can modify it to read video by add a segment alg
for(unsigned int i = 40; i < imgNames.size() - 1; i++)
{
Mat frame = imread(imgNames[i]);
Mat grayImg;
cvtColor(frame, grayImg, CV_RGB2GRAY);
Mat maskImage = imread(imgNamesMask[i], 0);
// get blobs and filter them using its area
// use 'cvblobslib' to get the object blobs
threshold( maskImage, maskImage, 81, 255, CV_THRESH_BINARY );
medianBlur(maskImage, maskImage, 3);
IplImage ipl_maskImage = maskImage;
CBlobResult blobs = CBlobResult( &ipl_maskImage, NULL, 0 );
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, 30 ); // filter blobs that area smaller than a certern num
list<CBlob *> remBlob;
for (int k = 0; k < blobs.GetNumBlobs(); k++)
{
remBlob.push_back(blobs.GetBlob(k));
}
printf("%d\n", trackLineArr.size());
for (list<TrackLine>::iterator trackIter = trackLineArr.begin(); trackIter != trackLineArr.end(); )
{
//kf predicition, get kfRect
Mat kfPrediction = (trackIter->kf).predict();
Point kfPrePt((int)(kfPrediction.at<float>(0)), (int)(kfPrediction.at<float>(1)));
Rect kfRect(kfPrePt.x - (trackIter->box).width / 2, kfPrePt.y - (trackIter->box).height / 2, (trackIter->box).width, (trackIter->box).height);
//ct predicition, get ctRect
int ctError = 0;
Rect ctRect(trackIter->box);
float score = (trackIter->ct).predicition(grayImg, ctRect);
rectangle(frame, kfRect, Scalar(0, 200, 0)); //green, kf predicition box
rectangle(frame, ctRect, Scalar(0, 0, 200)); //red, ct predicition box
//union predicit rectangle
//if they have no same area, we consider ct is wrong, because kalman is physical movement
float areaScale = (float)(sqrt((kfRect & ctRect).area() *1.0 / kfRect.area()));
Point movePoint((int)((ctRect.x - kfRect.x) * areaScale), (int)((ctRect.y - kfRect.y) * areaScale));
Rect unionPreRect = kfRect + movePoint;
//calc object box
Rect objRect;
int j = 0;
for (list<CBlob *>::iterator blobIter = remBlob.begin(); blobIter != remBlob.end(); )
{
Rect detRect((*blobIter)->GetBoundingBox());
float detArea = (float)((*blobIter)->Area());
if ((unionPreRect & detRect).area() > 0)
{
if (j++ == 0) objRect = detRect;
else objRect = objRect | detRect;
blobIter = remBlob.erase(blobIter);
}
else blobIter++;
}
// let box's area equal
float objArea = (float)(objRect.area());
objRect = Rect((int)(objRect.x + objRect.width / 2.0 - unionPreRect.width / 2.0),
(int)(objRect.y + objRect.height / 2.0 - unionPreRect.height / 2.0),
unionPreRect.width, unionPreRect.height);
float detAreaScale = (float)(sqrt(objArea * 1.0 / unionPreRect.area()));
if (detAreaScale > 1.0) detAreaScale = 1.0;
Point detMovePoint((int)((objRect.x - unionPreRect.x) * detAreaScale), (int)((objRect.y - unionPreRect.y) * detAreaScale));
Rect unionCorrRect = unionPreRect + detMovePoint;
// if detect area > 0
if (objArea > 0)
{
trackIter->box = unionCorrRect;
rectangle(frame, unionCorrRect, Scalar(200,0,0), 1);
//kf correct
Mat_<float> measurement(2,1);
measurement(0) = (float)((trackIter->box).x + (trackIter->box).width / 2.0);
measurement(1) = (float)((trackIter->box).y + (trackIter->box).height / 2.0);
(trackIter->kf).correct(measurement);
//ct update
(trackIter->ct).update(grayImg, trackIter->box);
trackIter++;
}
// else we beleve tracking miss
else
{
if ((trackIter->miss)++ == 5) trackIter = trackLineArr.erase(trackIter);
else trackIter++;
}
}
// !!!
// use a sample way to get a new track init object box, i just add all others box toghter and expand it bigger
// it's not a good idea when two object appear at the same time will lead only one init box
// and, this sample is reasonless. so, i suggest you to use another method to get the init box
// here, i just give a tracking alg, with a bad method to get init box, all -_-!
// !!!
Rect tmprect;
int u = 0;
for (list<CBlob *>::iterator blobIter = remBlob.begin(); blobIter != remBlob.end(); blobIter++)
{
if (u++ == 0) tmprect = Rect((*blobIter)->GetBoundingBox());
else tmprect = tmprect | Rect((*blobIter)->GetBoundingBox());
}
if (tmprect.area() > 0) tmprect = Rect(tmprect.x - 5, tmprect.y - 8, tmprect.width + 10, tmprect.height + 16);
if (tmprect.area() > 0 && tmprect.x != 0 && tmprect.y != 0 && (tmprect.x + tmprect.width) != 319 && (tmprect.y + tmprect.height) != 239)
{
TrackLine track;
track.kf.transitionMatrix = *(Mat_<float>(4, 4) << 1,0,1,0, 0,1,0,1, 0,0,1,0, 0,0,0,1);
track.kf.measurementMatrix = *(Mat_<float>(2, 4) << 1,0,0,0, 0,1,0,0);
setIdentity(track.kf.processNoiseCov, Scalar::all(1e-4));
setIdentity(track.kf.measurementNoiseCov, Scalar::all(1e-1));
setIdentity(track.kf.errorCovPost, Scalar::all(.1));
// kf init
track.kf.statePre.at<float>(0) = (float)(tmprect.x + tmprect.width / 2.0);
track.kf.statePre.at<float>(1) = (float)(tmprect.y + tmprect.height / 2.0);
track.kf.statePre.at<float>(2) = 0;
track.kf.statePre.at<float>(3) = 0;
track.kf.statePost.at<float>(0) = (float)(tmprect.x + tmprect.width / 2.0);
track.kf.statePost.at<float>(1) = (float)(tmprect.y + tmprect.height / 2.0);
track.kf.statePost.at<float>(2) = 0;
track.kf.statePost.at<float>(3) = 0;
// ct init
track.ct.init(grayImg, tmprect);
rectangle(frame, tmprect, Scalar(255, 0, 0), 2, 7);
track.box = tmprect;
trackLineArr.push_back(track);
}
sprintf(strFrame, "#%d ",i) ;
putText(frame,strFrame,cvPoint(0,20),2,1,CV_RGB(25,200,25));
char outstr[20];
//if (0)
//if ((i >= 450 && i <= 600) || (i >= 930 && i <= 960) || (i >= 1420 && i <= 1450))
{
sprintf(outstr, "output\\%d.png", i);
string outstring(outstr);
imwrite(outstring, frame);
sprintf(outstr, "output\\mask_%d.png", i);
string outstring2(outstr);
imwrite(outstring2, maskImage);
}
//imshow("ORG", frame);
//imshow("mask", maskImage);
//waitKey(1);
}
return 0;
}
float thresholdSegmentation(Rect r, ntk::RGBDImage* current_frame, Mat& dst){
Mat depth = current_frame->depth();
Rect& rr = r;
Mat depthROI = depth(rr), maskROI;
Mat& rDepthROI = depthROI, &rMaskROI = maskROI;
double var = 0.3;
// maskROI for nonZero values in the Face Region
inRange(depthROI, Scalar::all(0.001), Scalar::all(255), maskROI);
// Mean depth of Face Region
Scalar mFace = cv::mean(rDepthROI, rMaskROI);
//mFace[0] = mFace[0] - mFace[0] * var;
inRange(depthROI, Scalar::all(0.001), mFace, maskROI);
mFace = cv::mean(rDepthROI, rMaskROI);
//inRange(depthROI, Scalar::all(0.001), mFace, maskROI);
//mFace = cv::mean(rDepthROI, rMaskROI);
// Mask for nearer than the mean of face.
inRange(depth, Scalar::all(0.001), mFace, dst);
Mat rgbImage = current_frame->rgb();
Mat outFrame = cvCreateMat(rgbImage.rows, rgbImage.cols, CV_32FC3);
rgbImage.copyTo(outFrame, dst);
Mat outFrameROI;
outFrameROI = outFrame(rr);
//cvCopy(&rgbImage, &outFrame, &dst);
//rgbImageROI = rgbImageROI(rr);
imshow("ROI", outFrameROI);
//imshow("thresholdSeg", dst);
// For debug of cvblobslib
// Display the color image
//imshow("faceRIO", maskROI);
imshow("faceRIO", outFrameROI);
bool iswrite;
const int nchannel = 1;
vector<Rect> faces;
//iswrite = imwrite("faceROI.png", maskROI);
iswrite = imwrite("faceROI.png", outFrameROI);
//iswrite = cvSaveImage("faceROI.jpeg", pOutFrame, &nchannel);
// ---- blob segmentation on maskROI by using cvblobslib ----
// --- Third Trial ---
//visualizeBlobs("faceROI.png", "faceRIO");
// --- First Trial Not Successful ---
//Mat maskROIThr=cvCreateMat(maskROI.rows, maskROI.cols, CV_8UC1);
//maskROIThr = maskROI;
//IplImage imgMaskROIThr = maskROIThr;
//IplImage* pImgMaskROIThr = &imgMaskROIThr;
//cvThreshold(pImgMaskROIThr, pImgMaskROIThr, 0.1, 255, CV_THRESH_BINARY_INV);
// --- Second Trial ---
IplImage* original = cvLoadImage("faceROI.png", 0);
IplImage* originalThr = cvCreateImage(cvGetSize(original), IPL_DEPTH_8U, 1);
IplImage* displayBiggestBlob = cvCreateImage(cvGetSize(original), IPL_DEPTH_8U, 3);
CBlobResult blobs;
CBlob biggestBlob;
//IplImage source = maskROIThr; IplImage* pSource = &source;
//blobs = CBlobResult(
cvThreshold(original, originalThr, 0.1, 255, CV_THRESH_BINARY_INV);
blobs = CBlobResult( originalThr, NULL, 255);
printf("%d blobs \n", blobs.GetNumBlobs());
blobs.GetNthBlob(CBlobGetArea(), 0, biggestBlob);
biggestBlob.FillBlob(displayBiggestBlob, CV_RGB(255, 0, 0));
// Drawing the eclipse and Rect on the blob
Mat mat(displayBiggestBlob);
cv::RotatedRect blobEllipseContour;
cv::Rect blobRectContour;
//RotatedRect blobEllipseContour;
blobEllipseContour = biggestBlob.GetEllipse();
blobRectContour = biggestBlob.GetBoundingBox();
//cv::ellipse(
cv::ellipse(mat, blobEllipseContour, cv::Scalar(0,255, 0), 3, CV_AA);
cv::rectangle(mat, blobRectContour, cv::Scalar(255, 0, 0), 3, CV_AA);
//cv::ellipse(mat, blobEllipseContour);
float headOritation = blobEllipseContour.angle;
if (headOritation <= 180)
headOritation = headOritation - 90;
else
headOritation = headOritation - 270;
cv::putText(mat,
cv::format("%f degree", headOritation),
Point(10,20), 0, 0.5, Scalar(255,0,0,255));
cv::imshow("faceRIO", mat);
return(headOritation);
}
// A Simple Camera Capture Framework
int main() {
CvCapture* capture = cvCaptureFromCAM( 0 );
if( !capture ) {
fprintf( stderr, "ERROR: capture is NULL \n" );
return -1;
}
#ifdef HALF_SIZE_CAPTURE
cvSetCaptureProperty(capture, CV_CAP_PROP_FRAME_WIDTH, 352/2);
cvSetCaptureProperty(capture, CV_CAP_PROP_FRAME_HEIGHT, 288/2);
#endif
// Create a window in which the captured images will be presented
cvNamedWindow( "Source Image Window", CV_WINDOW_AUTOSIZE );
cvNamedWindow( "Back Projected Image", CV_WINDOW_AUTOSIZE );
cvNamedWindow( "Brightness and Contrast Window", CV_WINDOW_AUTOSIZE );
cvNamedWindow( "Blob Output Window", CV_WINDOW_AUTOSIZE );
cvNamedWindow( "Histogram Window", 0);
cvNamedWindow( "Rainbow Window", CV_WINDOW_AUTOSIZE );
// Capture one frame to get image attributes:
source_frame = cvQueryFrame( capture );
if( !source_frame ) {
fprintf( stderr, "ERROR: frame is null...\n" );
return -1;
}
cvCreateTrackbar("histogram\nnormalization", "Back Projected Image", &normalization_sum, 6000, NULL);
cvCreateTrackbar("brightness", "Brightness and Contrast Window", &_brightness, 200, NULL);
cvCreateTrackbar("contrast", "Brightness and Contrast Window", &_contrast, 200, NULL);
cvCreateTrackbar("threshold", "Blob Output Window", &blob_extraction_threshold, 255, NULL);
cvCreateTrackbar("min blob size", "Blob Output Window", &min_blob_size, 2000, NULL);
cvCreateTrackbar("max blob size", "Blob Output Window", &max_blob_size, source_frame->width*source_frame->height/4, NULL);
inputImage = cvCreateImage(cvGetSize(source_frame), IPL_DEPTH_8U, 1);
histAdjustedImage = cvCreateImage(cvGetSize(source_frame), IPL_DEPTH_8U, 1);
outputImage = cvCreateImage(cvGetSize(source_frame), IPL_DEPTH_8U, 3 );
hist_image = cvCreateImage(cvSize(320,200), 8, 1);
rainbowImage = cvCreateImage(cvGetSize(source_frame), IPL_DEPTH_8U, 3 );
// object that will contain blobs of inputImage
CBlobResult blobs;
CBlob my_enumerated_blob;
cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX|CV_FONT_ITALIC, hScale, vScale, 0, lineWidth);
// Some brightness/contrast stuff:
bright_cont_image = cvCloneImage(inputImage);
lut_mat = cvCreateMatHeader( 1, 256, CV_8UC1 );
cvSetData( lut_mat, lut, 0 );
while( 1 ) {
// Get one frame
source_frame = cvQueryFrame( capture );
if( !source_frame ) {
fprintf( stderr, "ERROR: frame is null...\n" );
getchar();
break;
}
cvShowImage( "Source Image Window", source_frame );
// Do not release the frame!
cvCvtColor(source_frame, inputImage, CV_RGB2GRAY);
// Histogram Stuff!
my_hist = cvCreateHist(1, hist_size_array, CV_HIST_ARRAY, ranges, 1);
cvCalcHist( &inputImage, my_hist, 0, NULL );
cvNormalizeHist(my_hist, normalization_sum);
// NOTE: First argument MUST have an ampersand, or a segmentation fault will result
cvCalcBackProject(&inputImage, histAdjustedImage, my_hist);
// Histogram Picture
int bin_w;
float max_value = 0;
cvGetMinMaxHistValue( my_hist, 0, &max_value, 0, 0 );
cvScale( my_hist->bins, my_hist->bins, ((double)hist_image->height)/max_value, 0 );
cvSet( hist_image, cvScalarAll(255), 0 );
bin_w = cvRound((double)hist_image->width/hist_size);
for(int i = 0; i < hist_size; i++ )
cvRectangle( hist_image, cvPoint(i*bin_w, hist_image->height), cvPoint((i+1)*bin_w, hist_image->height - cvRound(cvGetReal1D(my_hist->bins,i))), cvScalarAll(0), -1, 8, 0 );
cvShowImage( "Histogram Window", hist_image );
cvShowImage("Back Projected Image", histAdjustedImage);
// Brightness/contrast loop stuff:
int brightness = _brightness - 100;
int contrast = _contrast - 100;
/*
* The algorithm is by Werner D. Streidt
* (http://visca.com/ffactory/archives/5-99/msg00021.html)
*/
if( contrast > 0 ) {
double delta = 127.*contrast/100;
double a = 255./(255. - delta*2);
double b = a*(brightness - delta);
for(int i = 0; i < 256; i++ )
{
int v = cvRound(a*i + b);
if( v < 0 ) v = 0;
if( v > 255 ) v = 255;
lut[i] = (uchar)v;
}
}
else {
double delta = -128.*contrast/100;
double a = (256.-delta*2)/255.;
double b = a*brightness + delta;
for(int i = 0; i < 256; i++ ) {
int v = cvRound(a*i + b);
if( v < 0 )
v = 0;
if( v > 255 )
v = 255;
lut[i] = (uchar)v;
}
}
cvLUT( inputImage, bright_cont_image, lut_mat );
cvShowImage( "Brightness and Contrast Window", bright_cont_image);
// ---------------
// Blob Manipulation Code begins here:
// Extract the blobs using a threshold of 100 in the image
blobs = CBlobResult( bright_cont_image, NULL, blob_extraction_threshold, true );
// discard the blobs with less area than 5000 pixels
// ( the criteria to filter can be any class derived from COperadorBlob )
blobs.Filter( blobs, B_INCLUDE, CBlobGetArea(), B_GREATER_OR_EQUAL, min_blob_size);
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_GREATER, max_blob_size);
// build an output image equal to the input but with 3 channels (to draw the coloured blobs)
cvMerge( bright_cont_image, bright_cont_image, bright_cont_image, NULL, outputImage );
// plot the selected blobs in a output image
for (int i=0; i < blobs.GetNumBlobs(); i++) {
blobs.GetNthBlob( CBlobGetArea(), i, my_enumerated_blob );
// Color 5/6 of the color wheel (300 degrees)
my_enumerated_blob.FillBlob( outputImage, cv_hsv2rgb((float)i/blobs.GetNumBlobs() * 300, 1, 1));
}
// END Blob Manipulation Code
// ---------------
sprintf(str, "Count: %d", blobs.GetNumBlobs());
cvPutText(outputImage, str, cvPoint(50, 25), &font, cvScalar(255,0,255));
cvShowImage("Blob Output Window", outputImage);
/*
// Rainbow manipulation:
for (int i=0; i < CV_CAP_PROP_FRAME_WIDTH; i++) {
for (int j=0; j < CV_CAP_PROP_FRAME_HEIGHT; j++) {
// This line is not figure out yet...
// pixel_color_set = ((uchar*)(rainbowImage->imageData + rainbowImage->widthStep * j))[i * 3]
((uchar*)(rainbowImage->imageData + rainbowImage->widthStep * j))[i * 3] = 30;
((uchar*)(rainbowImage->imageData + rainbowImage->widthStep * j))[i * 3 + 1] = 30;
((uchar*)(rainbowImage->imageData + rainbowImage->widthStep * j))[i * 3 + 2] = 30;
}
}
cvShowImage("Rainbow Window", rainbowImage);
*/
//If ESC key pressed, Key=0x10001B under OpenCV 0.9.7(linux version),
//remove higher bits using AND operator
if( (cvWaitKey(10) & 255) == 27 ) break;
}
cvReleaseImage(&inputImage);
cvReleaseImage(&histAdjustedImage);
cvReleaseImage(&hist_image);
cvReleaseImage(&bright_cont_image);
cvReleaseImage(&outputImage);
cvReleaseImage(&rainbowImage);
// Release the capture device housekeeping
cvReleaseCapture( &capture );
cvDestroyAllWindows();
return 0;
}
void detect2(Mat img, vector<Mat>& regionsOfInterest,vector<Blob>& blobs){
/* Mat blurred;
GaussianBlur(img, blurred, Size(), _SharpSigma, _SharpSigma);
Mat lowContrastMask = abs(img - blurred) < _SharpThreshold;
Mat sharpened = img*(1+_SharpAmount) + blurred*(-_SharpAmount);
img.copyTo(sharpened, lowContrastMask);
sharpened.copyTo(img);*/
/*************INIZIALIZZAZIONI**********/
Mat gray;
Mat out = Mat::zeros(Size(WIDTH,HEIGH), CV_8U);
Mat masked = Mat::zeros(Size(WIDTH,HEIGH), CV_8U);
Mat morph = Mat::zeros(Size(WIDTH,HEIGH), CV_8U);
Mat bwmorph = Mat::zeros(Size(WIDTH,HEIGH), CV_8U);
Mat cont = Mat::zeros(Size(WIDTH,HEIGH), CV_8U);
Mat maskHSV = Mat::zeros(Size(WIDTH,HEIGH), CV_8U);
Mat whiteMaskMasked = Mat::zeros(Size(WIDTH,HEIGH), CV_8U);
Mat whiteMaskOrig = Mat::zeros(Size(WIDTH,HEIGH), CV_8U);
Mat Bands[3];
Mat noBackMask = Mat::zeros(Size(WIDTH,HEIGH), CV_8U);
Mat kernelEr = getStructuringElement(MORPH_ELLIPSE,Size(5,5));
Mat thMasked; Mat thOrig; Mat bwOrig; Mat bwNoBackMask;
Mat kernelOp = getStructuringElement(MORPH_ELLIPSE,Size(13,13));
vector<Mat> BGRbands; split(img,BGRbands);
vector< vector<Point> > contours;
/***************************************/
/*cvtColor(img,gray,CV_BGR2GRAY);
gray = (gray!=0);
imshow("gray",gray);*/
/*Rimozione Ombre e Background*/
// masked = applyMaskBandByBand(maskHSV,BGRbands); split(masked,BGRbands);
/*Rimozione sfondo e sogliatura per videnziare esclusivamente ciò che è bianco*/
noBackMask = backgroundRemoval(img);
masked = applyMaskBandByBand(noBackMask,BGRbands);
/*
whiteMaskOrig = computeWhiteMaskLight(img);
whiteMaskOrig = whiteMaskOrig + computeWhiteMaskShadow(img);
whiteMaskMasked = computeWhiteMaskLight(masked);
whiteMaskMasked = whiteMaskMasked + computeWhiteMaskShadow(masked);
*/
CBlobResult blobsRs;
blobsRs = computeWhiteMaskOtsu(img, img, blobsRs, img.rows*img.cols, img.rows*img.cols, 0.8, 0.8, 30, 200, 0);
//Mat newimg(img.size(),img.type());
whiteMaskOrig.setTo(0);
for(int i=0;i<blobsRs.GetNumBlobs();i++){
blobsRs.GetBlob(i)->FillBlob(whiteMaskOrig,CV_RGB(255,255,255),0,0,true);
}
threshold(masked,whiteMaskMasked,0,255,THRESH_BINARY);
cvtColor(whiteMaskMasked,whiteMaskMasked,CV_BGR2GRAY);
cout << whiteMaskMasked.type() << " " << whiteMaskOrig.type() << endl;
bitwise_or(whiteMaskMasked,whiteMaskOrig,thOrig);
masked = applyMaskBandByBand(thOrig,BGRbands);
#if DO_MORPH
/*Operazioni morfologiche per poter riempire i buchi e rimuovere i bordi frastagliati*/
dilate(masked,morph,kernelEr);
erode(morph,morph,kernelEr);
erode(morph,morph,kernelOp);
dilate(morph,morph,kernelOp);
#else
morph = masked;
#endif
/*Ricerca componenti connesse e rimozione in base all'area*/
cvtColor(morph,bwmorph,CV_BGR2GRAY);
findContours(bwmorph, contours, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
vector<double> areas = computeArea(contours);
for(int j = areas.size()-1; j>=0; j--){
if(areas.at(j)>MAX_AREA || areas.at(j)<MIN_AREA )
contours.erase(contours.begin()+j);
}
/*Calcolo Bounding Rectangle a partire dall'immagine con componenti connesse di interesse*/
vector<Rect> boundRect( contours.size() );
vector<vector<Point> > contours_poly( contours.size() );
vector<Point2f>center( contours.size() );
vector<float>radius( contours.size() );
/*Costruzione immagine finale ed estrazione regioni di interesse*/
for (int idx = 0; idx < contours.size(); idx++){
Blob b; b.originalImage = &img;
Scalar color(255);
approxPolyDP( Mat(contours[idx]), contours_poly[idx], 3, true );
boundRect[idx] = boundingRect( Mat(contours_poly[idx]) );
minEnclosingCircle( (Mat)contours_poly[idx], center[idx], radius[idx] );
// Rect tmpRect(center[idx].x-boundRect[idx].width/2,center[idx].y-boundRect[idx].height/2,boundRect[idx].width,boundRect[idx].height);
Rect tmpRect(center[idx].x-radius[idx],center[idx].y-radius[idx],radius[idx]*2,radius[idx]*2);
//Rect tmpRect = boundRect[idx];
Rect toPrint;
tmpRect += Size(tmpRect.width*RECT_AUGMENT ,tmpRect.height*RECT_AUGMENT); // Aumenta area di RECT_ARGUMENT
tmpRect -= Point((tmpRect.width*RECT_AUGMENT)/2 , (tmpRect.height*RECT_AUGMENT)/2 ); // Ricentra il rettangolo
drawContours(cont, contours, idx, color, CV_FILLED, 8);
if(tmpRect.x>0 && tmpRect.y>0 && tmpRect.x+tmpRect.width < morph.cols && tmpRect.y+tmpRect.height < morph.rows){ //Se il nuovo rettangolo allargato
// NON esce fuori dall'immagine, accettalo
regionsOfInterest.push_back(masked(tmpRect));
b.cuttedWithBack = img(tmpRect);
b.cuttedImages = masked(tmpRect);
b.blobsImage = cont(tmpRect);
b.rectangles = tmpRect;
toPrint = tmpRect;
}
else{
toPrint = boundRect[idx];
regionsOfInterest.push_back(masked(boundRect[idx]));
b.cuttedImages = masked(boundRect[idx]);
b.cuttedWithBack = img(boundRect[idx]);
b.rectangles = boundRect[idx];
b.blobsImage = cont(boundRect[idx]);
}
Point centroid = computeCentroid(contours[idx]);
b.centroid = centroid;
b.area = contourArea(contours[idx]);
b.distance = HEIGH - centroid.y;
/*rectangle( cont, toPrint.tl(), toPrint.br(), color, 2, 8, 0 );
circle( cont, center[idx], (int)radius[idx], color, 2, 8, 0 );*/
blobs.push_back(b);
}
//out = out+cont;
bitwise_xor(out,cont,out);
/*imshow("img",img);
imshow("out",out);
waitKey(0);*/
}
void iptask::markerDetect(void)
{
IplImage * frame,*img_hsv,*img_proc,* new1;
CvMemStorage * storage = cvCreateMemStorage(0);
ros::NodeHandle n;
ros::Publisher marker = n.advertise<ikat_ip_data::ip_marker_data>("marker_data",3);
ros::Rate looprate(5);
int count = 0;
CvSeq * contours,*final_contour;
int total_con;
double maxarea;
marker_data * Data =(marker_data *)malloc(sizeof(marker_data));
CBlobResult blobs;
CBlob * currentblob;
CvPoint2D32f vertices[4];
//CvCapture * img_video=cvCaptureFromAVI("downward-pipe-15_56_17.avi");
frame=cvQueryFrame(img);
cvNamedWindow("Image Actual");
cvNamedWindow("final Image");
img_hsv=cvCreateImage(cvGetSize(frame),8,3);
img_proc=cvCreateImage(cvGetSize(frame),8,1);
new1=cvCreateImage(cvGetSize(frame),8,1);
while(ros::ok())
{
ikat_ip_data::ip_marker_data msg;
IplImage * img_con=cvCreateImage(cvGetSize(frame),8,1);
frame=cvQueryFrame(img);
if(!frame)
break;
cvShowImage("Image Actual",frame);
cvCvtColor(frame,img_hsv,CV_RGB2HSV);
cvInRangeS(img_hsv,cvScalar(100,100,100),cvScalar(120,170,255),img_proc);
cvSmooth(img_proc,img_proc,CV_GAUSSIAN,11,11);
cvErode(img_proc,img_proc);
blobs=CBlobResult(img_proc,NULL,0);
blobs.Filter(blobs,B_EXCLUDE,CBlobGetArea(),B_LESS,75);
for (int i = 0; i < blobs.GetNumBlobs(); i++ )
{
currentblob = blobs.GetBlob(i);
currentblob->FillBlob(img_proc,cvScalar(255));
}
cvCanny(img_proc,img_proc,10,200);
total_con=cvFindContours(img_proc,storage,&contours,sizeof(CvContour),CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
if(contours->total==0)
continue;
final_contour=cvApproxPoly(contours,sizeof(CvContour),storage,CV_POLY_APPROX_DP,1,1);
maxarea=0;
cvZero(img_con);
CvBox2D rect;
while(final_contour)
{
rect=cvMinAreaRect2(final_contour, storage);
if(rect.size.height*rect.size.width>maxarea)
{
Data->center.x=rect.center.x;
Data->center.y=rect.center.y;
Data->size.x=rect.size.width;
Data->size.y=rect.size.height;
Data->angle=rect.angle;
maxarea=rect.size.height*rect.size.width;
msg.Marker_data[0]=Data->center.x;
msg.Marker_data[1]=Data->center.y;
msg.Marker_data[2]=Data->angle;
}
final_contour=final_contour->h_next;
}
cvBoxPoints(rect,vertices);
cvLine(frame,cvPointFrom32f(vertices[0]),cvPointFrom32f(vertices[1]),cvScalarAll(255),2);
cvLine(frame,cvPointFrom32f(vertices[1]),cvPointFrom32f(vertices[2]),cvScalarAll(255),2);
cvLine(frame,cvPointFrom32f(vertices[2]),cvPointFrom32f(vertices[3]),cvScalarAll(255),2);
cvLine(frame,cvPointFrom32f(vertices[3]),cvPointFrom32f(vertices[0]),cvScalarAll(255),2);
ROS_INFO("center x :[%f]",msg.Marker_data[0]);
ROS_INFO("center y :[%f]",msg.Marker_data[1]);
ROS_INFO("angle : [%f]",msg.Marker_data[2]);
marker.publish(msg);
cvShowImage("final Image",frame);
char c=cvWaitKey(33);
if (c==27)
break;
ros::spinOnce();
++count;
looprate.sleep();
}
cvDestroyWindow("Image Actual");
cvDestroyWindow("final Image");
free(Data);
}
void App::Update(Image &camera)
{
/*camera=camera.Scale(camera.m_Image->width/2,
camera.m_Image->height/2);
*/
//cvFlip(camera.m_Image, NULL, 0);
///////////////////////////////////
// dispatch from input
int key=cvWaitKey(10);
// usleep(500);
static int t=150;
static bool viewthresh=false;
static bool off=false;
static int spirit=0;
static int crop_x=0;
static int crop_y=0;
static int crop_w=camera.m_Image->width;
static int crop_h=camera.m_Image->height;
switch (key)
{
case 't': viewthresh=!viewthresh; break;
case 'q': t--; break;
case 'w': t++; break;
case 'e': t-=20; break;
case 'r': t+=20; break;
case 'o': off=!off; break;
case 'p': spirit++; break;
case 'z': crop_x+=10; break;
case 'x': crop_x-=10; break;
case 'c': crop_y+=10; break;
case 'v': crop_y-=10; break;
case 'b': crop_w+=10; break;
case 'n': crop_w-=10; break;
case 'm': crop_h+=10; break;
case ',': crop_h-=10; break;
}
if (crop_x<0) crop_x=0;
if (crop_x>=camera.m_Image->width) crop_x=camera.m_Image->width-1;
if (crop_y<0) crop_x=0;
if (crop_y>=camera.m_Image->height) crop_y=camera.m_Image->height-1;
if (crop_w+crop_x>camera.m_Image->width)
{
crop_w=camera.m_Image->width-crop_x;
}
if (crop_h+crop_y>camera.m_Image->height)
{
crop_h=camera.m_Image->height-crop_y;
}
if (off)
{
sleep(1);
cerr<<"off..."<<endl;
return;
}
Image thresh=camera.RGB2GRAY().SubImage(crop_x,crop_y,crop_w,crop_h);
cvThreshold(thresh.m_Image,thresh.m_Image,t,255,CV_THRESH_BINARY);
// copy the threshold into a colour image
Image tofill=thresh.GRAY2RGB();
cvFloodFill(tofill.m_Image,cvPoint(camera.m_Image->width/2,
camera.m_Image->height/2),
CV_RGB(0,255,0),cvScalar(0),cvScalar(255));
CBlobResult blobs;
blobs = CBlobResult( thresh.m_Image, NULL, 255 );
// exclude the ones smaller than param2 value
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, 10);
CBlob *currentBlob;
Image *out=NULL;
if (key=='s')
{
// add the alpha channel
Image src=camera.SubImage(crop_x,crop_y,crop_w,crop_h);
out = new Image(src.m_Image->width,
src.m_Image->height, 8, 4);
for(int y=0; y<src.m_Image->height; y++)
{
for(int x=0; x<src.m_Image->width; x++)
{
CvScalar col = cvGet2D(src.m_Image,y,x);
CvScalar alpha = cvGet2D(tofill.m_Image,y,x);
if (alpha.val[0]==0 &&
alpha.val[1]==255 &&
alpha.val[2]==0)
col.val[3]=0;
else
col.val[3]=255;
cvSet2D(out->m_Image,y,x,col);
}
}
}
if (key=='s')
{
cerr<<"deleting old images in islands/"<<endl;
int r=system("rm islands/*");
}
list<CvRect> allrects;
for (int i = 0; i < blobs.GetNumBlobs(); i++ )
{
currentBlob = blobs.GetBlob(i);
allrects.push_back(currentBlob->GetBoundingBox());
}
list<CvRect> filteredrects=allrects;
/* for (list<CvRect>::iterator i=allrects.begin();
i!=allrects.end(); ++i)
{
bool in=false;
for (list<CvRect>::iterator j=allrects.begin();
j!=allrects.end(); ++j)
{
if (Inside(*i,*j)) in=true;
}
if (!in) filteredrects.push_back(*i);
}*/
unsigned int instance = rand();
unsigned int count=0;
for (list<CvRect>::iterator i=filteredrects.begin();
i!=filteredrects.end(); ++i)
{
CvRect rect = *i;
if (key=='s')
{
Image island = out->SubImage(rect.x,rect.y,
rect.width,rect.height);
char buf[256];
sprintf(buf,"islands/island-%d-%d-%d.png",count,
rect.x+rect.width/2,
rect.y+rect.height/2);
cerr<<"saving "<<buf<<endl;
island.Save(buf);
sprintf(buf,"dump/island-%d-%d-%d-%d.png",
instance,
count,
rect.x+rect.width/2,
rect.y+rect.height/2);
cerr<<"saving "<<buf<<endl;
island.Save(buf);
}
else
{
cvRectangle(camera.m_Image,
cvPoint(crop_x+rect.x,crop_y+rect.y),
cvPoint(crop_x+rect.x+rect.width,
crop_y+rect.y+rect.height),
colors[1]);
}
count++;
}
if (key=='s')
{
cerr<<"copying images to server"<<endl;
//int r=system("scp -r islands [email protected]:/home/garden/GerminationX/oak/");
string path("/home/dave/code/lirec/scenarios/GerminationX/oak/public/");
path+=string(spirits[spirit%3]);
string command=string("rm ")+path+string("/*.*");
int r=system(command.c_str());
string command2=string("cp islands/* ")+path;
r=system(command2.c_str());
//cerr<<"finished copying...("<<r<<")"<<endl;
}
if (viewthresh) camera=tofill;
char buf[256];
sprintf(buf,"spirit: %s thresh: %d", spirits[spirit%3], t);
cvPutText(camera.m_Image, buf, cvPoint(10,20),
&m_Font, colors[0]);
cvRectangle(camera.m_Image,
cvPoint(crop_x,crop_y),
cvPoint(crop_x+crop_w,crop_y+crop_h),
colors[2]);
if (out!=NULL) delete out;
}
// starts the auto targeting sequence
void MainWindow::on_startstopbutton_clicked()
{
shootingstopped=false;
QImage* currimage=getQImage();
n=currimage->width();
k=currimage->height();
IplImage* curriplimage=Qimage2IplImage(&currimage->convertToFormat(QImage::Format_RGB32));
IplImage* threshedimage=threshimage(curriplimage);
CBlobResult blobs;
CBlob* currentblob;
blobs=CBlobResult(threshedimage,NULL,0);
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, 150 );
int j=blobs.GetNumBlobs();
if(j==0)
{
QMessageBox::information(this,"No Targets","No Targets Found!");
cvReleaseImage(&threshedimage);
cvReleaseImage(&curriplimage);
return;
}
CBlobGetXCenter XCenter;
CBlobGetYCenter YCenter;
for(int i=0;i<blobs.GetNumBlobs();i++)
{
tmptargetcenter=new targetcenter;
currentblob=blobs.GetBlob(i);
tmptargetcenter->x=XCenter(*currentblob);
tmptargetcenter->y=YCenter(*currentblob);
getangles(tmptargetcenter);
targets.append(tmptargetcenter);
}
checkformissiles();
ui->targetcountdisplay->display(targets.size());
setupautobuttons();
qApp->processEvents();
ui->timeNumber->display(0);
timeshooting.start(100);
turr->initAngle();
if(shootingstopped)
{
timeshooting.stop();
targets.clear();
return;
}
foreach(targetcenter* target,targets)
{
checkformissiles();
qApp->processEvents();
turr->setAngle(target->beta,target->betav);
ui->shotcountdisplay->display(turr->currentmissilecount());
if(shootingstopped)
{
timeshooting.stop();
targets.clear();
delete target;
return;
}
ui->targetcountdisplay->display(ui->targetcountdisplay->value()-1);
qApp->processEvents();
delete target;
}
int main(int argc, char *argv[])
{
CvCapture* capture = cvCreateFileCapture( "recording_01.avi");
handOrientation rightOrientationLast = NONE, leftOrientationLast = NONE;
handOrientation rightOrientationCur = NONE, leftOrientationCur = NONE;
//cvNamedWindow("Input Image", CV_WINDOW_AUTOSIZE);
//cvNamedWindow("Skin Pixels", CV_WINDOW_AUTOSIZE);
cvNamedWindow("Skin Blobs", CV_WINDOW_AUTOSIZE);
while(1){
Mat imageBGR = cvQueryFrame(capture);
if(imageBGR.empty())break;
//imshow("Input Image", imageBGR);
// Convert the image to HSV colors.
Mat imageHSV = Mat(imageBGR.size(), CV_8UC3); // Full HSV color image.
cvtColor(imageBGR, imageHSV, CV_BGR2HSV); // Convert from a BGR to an HSV image.
std::vector<Mat> channels(3);
split(imageHSV, channels);
Mat planeH = channels[0];
Mat planeS = channels[1];
Mat planeV = channels[2];
// Detect which pixels in each of the H, S and V channels are probably skin pixels.
threshold(channels[0], channels[0], 150, UCHAR_MAX, CV_THRESH_BINARY_INV);//18
threshold(channels[1], channels[1], 60, UCHAR_MAX, CV_THRESH_BINARY);//50
threshold(channels[2], channels[2], 170, UCHAR_MAX, CV_THRESH_BINARY);//80
// Combine all 3 thresholded color components, so that an output pixel will only
// be white if the H, S and V pixels were also white.
Mat imageSkinPixels = Mat( imageBGR.size(), CV_8UC3); // Greyscale output image.
bitwise_and(channels[0], channels[1], imageSkinPixels); // imageSkin = H {BITWISE_AND} S.
bitwise_and(imageSkinPixels, channels[2], imageSkinPixels); // imageSkin = H {BITWISE_AND} S {BITWISE_AND} V.
// Show the output image on the screen.
//imshow("Skin Pixels", imageSkinPixels);
IplImage ipl_imageSkinPixels = imageSkinPixels;
// Find blobs in the image.
CBlobResult blobs;
blobs = CBlobResult(&ipl_imageSkinPixels, NULL, 0); // Use a black background color.
// Ignore the blobs whose area is less than minArea.
blobs.Filter(blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, minBlobArea);
srand (time(NULL));
// Show the large blobs.
IplImage* imageSkinBlobs = cvCreateImage(imageBGR.size(), 8, 3); //Colored Output//,1); Greyscale output image.
for (int i = 0; i < blobs.GetNumBlobs(); i++) {
CBlob *currentBlob = blobs.GetBlob(i);
currentBlob->FillBlob(imageSkinBlobs, CV_RGB(rand()%255,rand()%255,rand()%255)); // Draw the large blobs as white.
cvDrawRect(imageSkinBlobs,
cvPoint(currentBlob->GetBoundingBox().x,currentBlob->GetBoundingBox().y),
cvPoint(currentBlob->GetBoundingBox().x + currentBlob->GetBoundingBox().width,currentBlob->GetBoundingBox().y + currentBlob->GetBoundingBox().height),
cvScalar(0,0,255),
2);//Draw Bounding Boxes
}
cvShowImage("Skin Blobs", imageSkinBlobs);
//Gestures
//std::cout << "Number of Blobs: "<< blobs.GetNumBlobs() <<endl;
if(blobs.GetNumBlobs() == 0){
//picture empty
}else if(blobs.GetNumBlobs() == 1) {
//head detected
}else if(blobs.GetNumBlobs() == 2 || blobs.GetNumBlobs() == 3){
//head + one hand || head + two hands
CvRect rect[3];
int indexHead = -1, indexHandLeft = -1, indexHandRight = -1;
//Get Bounding Boxes
for(int i = 0; i< blobs.GetNumBlobs(); i++){
rect[i] = blobs.GetBlob(i)->GetBoundingBox();
}
//Detect Head and Hand indexes
if(blobs.GetNumBlobs() == 2){
int indexHand = -1;
if(getCenterPoint(rect[0]).y < getCenterPoint(rect[1]).y){
indexHead = 0;
indexHand = 1;
}else{
indexHead = 1;
indexHand = 0;
}
if(getHandside(rect[indexHead], rect[indexHand]) == LEFT){
indexHandLeft = 1;
indexHandRight = -1;
}else{
// right hand
indexHandLeft = -1;
indexHandRight = 1;
}
}else{
//two hands
int indexHand1 = -1;
int indexHand2 = -1;
if(getCenterPoint(rect[0]).y < getCenterPoint(rect[1]).y && getCenterPoint(rect[0]).y < getCenterPoint(rect[2]).y){
indexHead = 0;
indexHand1 = 1;
indexHand2 = 2;
}else if(getCenterPoint(rect[1]).y < getCenterPoint(rect[0]).y && getCenterPoint(rect[1]).y < getCenterPoint(rect[2]).y){
indexHead = 1;
indexHand1 = 0;
indexHand2 = 2;
}else{
indexHead = 2;
indexHand1 = 0;
indexHand2 = 1;
}
if(getHandside(rect[indexHead], rect[indexHand1]) == LEFT){
indexHandLeft = indexHand1;
indexHandRight = indexHand2;
}else{
indexHandLeft = indexHand2;
indexHandRight = indexHand1;
}
}
// follow the right hand
if(indexHandRight > 0) {
//std::cout << "right hand deteced" <<endl;
if(isMoving(handRight)) {
std::cout << "hand moving" <<endl;
handRight.centerPrev = handRight.centerCurr;
handRight.centerCurr = getCenterPoint(rect[indexHandRight]);
} else {
std::cout << "hand not moving" <<endl;
if(handRight.centerInit.y != 0 && abs(handRight.centerInit.y - handRight.centerCurr.y) > 20) {
if(handRight.centerInit.y < handRight.centerCurr.y) {
// hand moved down
std::cout << " hand moved down" <<endl;
} else {
// hand moved up
std::cout << " hand moved up" <<endl;
}
}
handRight.centerInit = getCenterPoint(rect[indexHandRight]);
handRight.centerPrev = handRight.centerCurr;
handRight.centerCurr = getCenterPoint(rect[indexHandRight]);
}
}
//Get Orientations from Hand rects
leftOrientationCur = (indexHandLeft != -1)?getOrientationOfRect(rect[indexHandLeft]):NONE;
rightOrientationCur = (indexHandRight != -1)?getOrientationOfRect(rect[indexHandRight]):NONE;
//Check Change of Left hand
/*switch(detectHandStateChange(leftOrientationLast, leftOrientationCur)){
case PORTRAIT_TO_LANDSCAPE:
handleGestures(LEFT_FLIP_DOWN);
break;
case LANDSCAPE_TO_PORTRAIT:
handleGestures(LEFT_FLIP_UP);
break;
case NOCHANGE:
default:
break;
}
//Check Change of Right hand
switch(detectHandStateChange(rightOrientationLast, rightOrientationCur)){
case PORTRAIT_TO_LANDSCAPE:
handleGestures(RIGHT_FLIP_DOWN);
break;
case LANDSCAPE_TO_PORTRAIT:
handleGestures(RIGHT_FLIP_UP);
break;
case NOCHANGE:
default:
break;
}*/
}else if(blobs.GetNumBlobs() > 3){
//too much information
cout<<"too much information"<<endl;
}
leftOrientationLast = leftOrientationCur;
rightOrientationLast = rightOrientationCur;
// Free all the resources.
/*cvReleaseImage( &imageBGR );
cvReleaseImage( &imageHSV );
cvReleaseImage( &planeH );
cvReleaseImage( &planeS );
cvReleaseImage( &planeV );
cvReleaseImage( &imageSkinPixels );
cvReleaseImage( &imageSkinBlobs );*/
//if ESC is pressed then exit loop
cvWaitKey(33);
}
cvWaitKey(0);
return 0;
}
//==============================================================================
void PanTiltCameraClass::blobTracking(IplImage* hsv_mask,
IplImage* pFour,
IplImage* pImg)
{
//--- Get blobs and filter them using the blob area
CBlobResult blobs;
CBlob *currentBlob;
//--- Create a thresholded image and display image --------------------
//--- Creates binary image
IplImage* originalThr = cvCreateImage(cvGetSize(hsv_mask), IPL_DEPTH_8U,1);
//--- Create 3-channel image
IplImage* display = cvCreateImage(cvGetSize(hsv_mask),IPL_DEPTH_8U,3);
//--- Copies the original
cvMerge( hsv_mask, hsv_mask, hsv_mask, NULL, display );
//--- Makes a copy for processing
cvCopy(hsv_mask,originalThr);
//--- Find blobs in image ---------------------------------------------
int blobThreshold = 0;
bool blobFindMoments = true;
blobs = CBlobResult( originalThr, originalThr, blobThreshold, blobFindMoments);
//--- filters blobs according to size and radius constraints
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, this->minBlobSize );
//--- display filtered blobs ------------------------------------------
//--- copies the original in (for background)
cvMerge( originalThr, originalThr, originalThr, NULL, display );
CvPoint pts[this->NUMBER_OF_CIRCLES];
//--- This sequence marks all the blobs
for (int i = 0; i < blobs.GetNumBlobs(); i++ )
{
currentBlob = blobs.GetBlob(i);
currentBlob->FillBlob( display, CV_RGB(0,0,255));
//--- Get blobs centerpoint
CvPoint bcg;
bcg.x = (int)(currentBlob->MinX()+((currentBlob->MaxX()-currentBlob->MinX())/2));
bcg.y = (int)(currentBlob->MinY()+((currentBlob->MaxY()-currentBlob->MinY())/2));
//--- Print the CG on the picture
char blobtext[40];
for(int k=0;k<this->NUMBER_OF_CIRCLES;k++)
{
sprintf(blobtext,"%d",k+1);
TargetReticle(display,&pts[k],blobtext,6,CV_RGB(255,0,0));
}//for
}//for each blob
//--- Set the ROI in the pFour image
cvSetImageROI(pFour,cvRect(pImg->width,pImg->height+80,pImg->width,pImg->height));
cvCopy(display,pFour);
//Reset region of interest
cvResetImageROI(display);
//Clean up
cvReleaseImage( &originalThr );
cvReleaseImage( &display);
}
int main(){
Scalar robotColor = CV_RGB(255, 0, 0);
Scalar rightColor = CV_RGB(0, 255, 0);
Scalar leftColor = CV_RGB(0, 0, 255);
Scalar robotColor_2 = CV_RGB(0, 255, 255);
Scalar rightColor_2 = CV_RGB(255, 0, 255);
Scalar leftColor_2 = CV_RGB(255, 255, 0);
int lowH = 0;
int highH = 14;
int top_cut = 120;
int bot_cut = 70;
int lowV = 200;
int type = 0;
int ticks = 0;
int nb_errors = 0;
int len = 150;
int trace = 25;
int sensitivity = 100;
int area = 3000;
int flip = 0; //set to 0 if no flips are needed, 1 for y axis, 2 for x axis and 3 for both
namedWindow("My Window", 1);
createTrackbar("lowH", "My Window", &lowH, 180);
createTrackbar("highH", "My Window", &highH, 180);
createTrackbar("top_cut", "My Window", &top_cut, 255);
createTrackbar("bot_cut", "My Window", &bot_cut, 255);
createTrackbar("lowV", "My Window", &lowV, 255);
createTrackbar("LEN", "My Window", &len, 300);
createTrackbar("TRACE", "My Window", &trace, 100);
createTrackbar("SENSITIVITY", "My Window", &sensitivity, 200);
createTrackbar("AREA", "My Window", &area, 7000);
createTrackbar("FLIP", "My Window", &flip, 3);
moveWindow("My Window", 0, 0);
namedWindow("kalman", 1);
moveWindow("kalman", 500, 0);
namedWindow("Blobs Image", 1);
moveWindow("Blobs Image", 500, 300);
namedWindow("frame", 1);
moveWindow("frame", 0, 500);
namedWindow("test", WINDOW_AUTOSIZE);
moveWindow("test", 0, 500);
namedWindow("white", WINDOW_AUTOSIZE);
moveWindow("white", 0, 500);
//file of video input
string filename = "testVideo_5.webm";
ofstream logs;
ofstream stats;
stats.open("stats.txt");
logs.open("logs.csv");
logs << "Left_x,Left_y,Left_holds,Right_x,Right_y,Right_holds,confirmed" << endl;
Point center_window = Point(WIDTH/2, (HEIGHT - top_cut - bot_cut)/2);
Point center_left = Point(WIDTH/4, .5*max(10, HEIGHT - top_cut - bot_cut));
Point center_right = Point(3*WIDTH/4, .5*max(10, HEIGHT - top_cut - bot_cut));
// initialize the kalman filters
KalmanFilter KF_left(4, 2, 0);
KalmanFilter KF_right(4, 2, 0);
Mat_<float> measurement_left(2,1); measurement_left.setTo(Scalar(0));
Mat_<float> measurement_right(2,1); measurement_right.setTo(Scalar(0));
initialize_kalman(&KF_left, center_left);
initialize_kalman(&KF_right, center_right);
VideoCapture cap(0);
// VideoCapture cap(filename);
Mat kf_img(HEIGHT - top_cut - bot_cut, WIDTH, CV_8UC3);
vector<Point> mousev_left,kalmanv_left;
mousev_left.clear();
kalmanv_left.clear();
vector<Point> mousev_right,kalmanv_right;
mousev_right.clear();
kalmanv_right.clear();
int counter = 0;
int nb_confirmed = 0;
int nb_total = 0;
double average_left = 0;
double average_right = 0;
double error_left = 0;
double error_right = 0;
double prev_dist = norm(center_left - center_right);
double new_dist = prev_dist;
bool left_valid = false;
bool right_valid = true;
Mat temp = Mat::zeros(100,400, CV_8UC3);
putText(temp, "Press any key to start", Point(50,50), FONT_HERSHEY_SIMPLEX, .5, Scalar(255,255,255));
putText(temp, "and ESC to end", Point(50, 75), FONT_HERSHEY_SIMPLEX, .5, Scalar(255,255,255));
imshow("Blobs Image", temp);
waitKey(-1);
int key;
bool eof = false;
for(;;){
Mat frame;
Mat prediction_left = KF_left.predict();
Point new_left(prediction_left.at<float>(0), prediction_left.at<float>(1));
measurement_left(0) = center_left.x;
measurement_left(1) = center_left.y;
Mat estimated_left = KF_left.correct(measurement_left);
Point statePt_left(estimated_left.at<float>(0),estimated_left.at<float>(1));
Point measPt_left(measurement_left(0),measurement_left(1));
Mat prediction_right = KF_right.predict();
Point new_right(prediction_right.at<float>(0), prediction_right.at<float>(1));
measurement_right(0) = center_right.x;
measurement_right(1) = center_right.y;
Mat estimated_right = KF_right.correct(measurement_right);
Point statePt_right(estimated_right.at<float>(0),estimated_right.at<float>(1));
Point measPt_right(measurement_right(0),measurement_right(1));
ticks ++;
error_left = norm(statePt_left - measPt_left);
average_left = ((average_left * (ticks - 1)) + error_left) / ticks;
error_right = norm(statePt_right - measPt_right);
average_right = ((average_right * (ticks - 1)) + error_right) / ticks;
imshow("kalman", kf_img);
// waitKey(-1);
kf_img = Scalar::all(0);
mousev_left.push_back(measPt_left);
kalmanv_left.push_back(statePt_left);
circle(kf_img, statePt_left, 1, Scalar(255,255,255), -1);
circle(kf_img, measPt_left, 1, Scalar(0,0,255), -1);
int nb_mousev_left = mousev_left.size() - 1;
int nb_kalmanv_left = mousev_left.size() - 1;
int nb_mousev_right = mousev_left.size() - 1;
int nb_kalmanv_right = mousev_left.size() - 1;
for(int i = max(0, nb_mousev_left - trace); i< nb_mousev_left; i++){
line(kf_img, mousev_left[i], mousev_left[i+1], Scalar(255,255,0), 1);
}
for(int i = max(0, nb_kalmanv_left - trace); i< nb_kalmanv_left; i++){
line(kf_img, kalmanv_left[i], kalmanv_left[i+1], Scalar(0,0,255), 1);
}
mousev_right.push_back(measPt_right);
kalmanv_right.push_back(statePt_right);
circle(kf_img, statePt_right, 1, Scalar(255,255,255), -1);
circle(kf_img, measPt_right, 1, Scalar(0,0,255), -1);
for(int i = max(0, nb_mousev_right - trace); i< nb_mousev_right; i++){
line(kf_img, mousev_right[i], mousev_right[i+1], Scalar(0,255,0), 1);
}
for(int i = max(0, nb_kalmanv_right - trace); i< nb_kalmanv_right; i++){
line(kf_img, kalmanv_right[i], kalmanv_right[i+1], Scalar(255,0,255), 1);
}
Rect border(0, top_cut, WIDTH, max(10, HEIGHT - top_cut - bot_cut));
cap >> frame;
if(!frame.empty()){
Mat image;
int flip_type = 1;
switch (flip) {
case 0: break;
case 1: break;
case 2: flip_type = 0;
break;
case 3: flip_type = -1;
break;
}
if(flip) cv::flip(frame, frame, flip_type);
resize(frame, frame, Size(WIDTH, HEIGHT));
image = frame(border);
imshow("frame", image);
//performs the skin detection
Mat converted_skin;
cvtColor(image, converted_skin, CV_BGR2HSV);
Mat skin_masked;
inRange(converted_skin, Scalar(min(lowH, highH), 48, 80),Scalar(max(lowH, highH), 255, 255), skin_masked);
imshow("test", skin_masked);
//performs the robot detection
Mat converted_white, white_masked, lights_masked;
cvtColor(image, converted_white, CV_BGR2GRAY);
inRange(converted_skin, Scalar(0, 0, 245), Scalar(180, 255, 255), lights_masked);
threshold(converted_white, white_masked, lowV, 255, type);
bitwise_or(white_masked, lights_masked, white_masked);
imshow("white", white_masked);
Mat copy(converted_skin.size(), converted_skin.type());// = converted.clone();
//detects hands as blobs
CBlobResult blobs;
IplImage temp = (IplImage)skin_masked;
blobs = CBlobResult(&temp,NULL,1);
blobs = CBlobResult(skin_masked,Mat(),NUMCORES);
int numBlobs = blobs.GetNumBlobs();
if(0 == numBlobs){
cout << "can't find blobs!" << endl;
continue;
}
// detects robot as a blob
CBlobResult robot_blobs;
IplImage robot_temp = (IplImage) white_masked;
robot_blobs = CBlobResult(&robot_temp, NULL, 1);
robot_blobs = CBlobResult(white_masked, Mat(), NUMCORES);
if(0 == robot_blobs.GetNumBlobs()){
cout << "can't find robot_blobs!" << endl;
continue;
}
CBlob *curblob;
CBlob* blob_1;
CBlob* blob_2;
CBlob* leftBlob;
CBlob* rightBlob;
CBlob* robotBlob;
copy.setTo(Vec3b(0,0,0));
// chooses the two largest blobs for the hands
Point center_1, center_2;
int max_1 = 0;
int max_2 = 0;
int maxArea_1 = 0;
int maxArea_2 = 0;
for(int i=0;i<numBlobs;i++){
int area = blobs.GetBlob(i)->Area();
if(area > maxArea_1){
maxArea_2 = maxArea_1;
maxArea_1 = area;
max_2 = max_1;
max_1 = i;
} else if(area > maxArea_2){
maxArea_2 = area;
max_2 = i;
}
}
int i_1 = max_1;
int i_2 = max_2;
double area_left, area_right;
Rect rect_1;
Rect rect_2;
//determines which hand is left/right
blob_1 = blobs.GetBlob(i_1);
blob_2 = blobs.GetBlob(i_2);
center_1 = blob_1->getCenter();
center_2 = blob_2->getCenter();
bool left_is_1 = (center_1.x < center_2.x)? true : false;
leftBlob = (left_is_1)? blob_1 : blob_2;
rightBlob = (left_is_1)? blob_2 : blob_1;
center_left = leftBlob->getCenter();
center_right = rightBlob->getCenter();
//determine the number of valid hands
//validity is decided by whether or not the hand followed a logical movement,
//and if the area of the blob is large enough to be accepted
int valids = 0;
rect_1 = leftBlob->GetBoundingBox();
rectangle(copy, rect_1.tl(), rect_1.br(), leftColor_2, 5);
error_left = norm(statePt_left - center_left);
area_left = leftBlob->Area();
left_valid = error_left < sensitivity && area_left > area;
if(left_valid){
leftBlob->FillBlob(copy,leftColor, true);
valids ++;
}
circle(copy, center_left, 5, leftColor_2, -1);
rect_2 = rightBlob->GetBoundingBox();
rectangle(copy, rect_2.tl(), rect_2.br(), rightColor_2, 5);
error_right = norm(statePt_right - center_right);
area_right = rightBlob->Area();
right_valid = error_right < sensitivity && area_right > area;
if(right_valid){
rightBlob->FillBlob(copy,rightColor, true);
valids ++;
}
circle(copy, center_right, 5, rightColor_2, -1);
//finds the blob representing the robot
//we could add a restriction to only choose a blob between the two hands
//in terms of x-coordinate
//a Kalman check can easily be done for the robot
Point robot_center;
maxArea_1 = 0;
max_1 = 0;
numBlobs = robot_blobs.GetNumBlobs();
if(0 < numBlobs){
for(int i=0;i<numBlobs;i++){
curblob = robot_blobs.GetBlob(i);
robot_center = curblob->getCenter();
double dist_1 = norm(center_1 - robot_center);
double dist_2 = norm(center_2 - robot_center);
if(dist_1 < len || dist_2 < len){
double area = robot_blobs.GetBlob(i)->Area();
if(area > maxArea_1){
max_1 = i;
maxArea_1 = area;
}
}
}
int i_3 = max_1;
curblob = robot_blobs.GetBlob(i_3);
curblob->FillBlob(copy,robotColor, true);
robot_center = curblob->getCenter();
circle(copy, robot_center, 5, robotColor_2, -1);
Rect rect_3 = curblob->GetBoundingBox();
rectangle(copy, rect_3.tl(), rect_3.br(), robotColor_2, 5);
// determines which hand is controlling the robot
// by cheching the position of the 3 blobs
// an additional check could be done by verifying if
//the center of the robot is moving in the same direction
//as the center of the hand moving it
bool is_left = false;
bool is_right = false;
bool confirmed = false;
double dist_left = norm(center_left - robot_center);
double dist_right = norm(center_right - robot_center);
double dist_both = norm(center_left - center_right);
Point robot_tl = rect_3.tl();
Point robot_br = rect_3.br();
int left_count = 0;
int right_count = 0;
if(rect_1.contains(robot_tl)) left_count++;
if(rect_1.contains(robot_br)) left_count++;
if(rect_1.contains(robot_center)) left_count++;
if(rect_2.contains(robot_tl)) right_count++;
if(rect_2.contains(robot_br)) right_count++;
if(rect_2.contains(robot_center)) right_count++;
switch(valids){
case 0: break;
case 1:{
int area_sum = area_left + area_right;
if(dist_left > 2* dist_right || dist_right > 2*dist_left){
if(area_sum > 2 * area && (area_left > 2*area_right || area_right > 2*area_left) &&
((left_valid && left_count > 0)||(right_valid && right_count > 0))){
is_left = true;
is_right = true;
if(left_count > 2 || right_count > 2) confirmed = true;
}
}
if(left_valid && left_count > 1) {
is_left = true;
if(left_count > 2) confirmed = true;
}
if(right_valid && right_count > 1) {
is_right = true;
if(right_count > 2) confirmed = true;
}
//if just one hand is on screen
if(area_right < area/2){
if(center_left.x > robot_center.x){
is_left = true;
} else{
is_right = true;
}
} else if (area_left < area/2){
if(center_right.x < robot_center.x){
is_right = true;
} else{
is_left = true;
}
}
break;}
case 2:{
int moreLeft = left_count - right_count;
int moreRight = right_count - left_count;
int countSum = left_count + right_count;
switch (countSum) {
case 3:{
switch (left_count) {
case 3: is_left = true;
confirmed = true;
break;
case 2:
case 1: is_left = true;
is_right = true;
confirmed= true;
break;
case 0: is_right = true;
confirmed = true;
break;
}
}
case 2:{
switch (left_count) {
case 2: is_left = true;
confirmed = true;
break;
case 1: is_left = true;
is_right = true;
break;
case 0: is_right = true;
confirmed = true;
break;
}
}
case 1:{
switch (left_count) {
case 1: is_left = true;
break;
case 0: is_right = true;
break;
}
}
case 0:{
break;
}
}
break;}
}
bool found = false;
for(size_t i = robot_tl.x; i<= robot_br.x && !found; i++){
for(size_t j = robot_tl.y; j<= robot_br.y && !found; j++){
int color1 = 0; int color2 = 255;
Vec3b colour = copy.at<Vec3b>(Point(i, j));
if(colour[1] == color1 && colour[0] == color2){
found = true;
is_left = true;
}
if(colour[1] == color2 && colour[0] == color1){
found = true;
is_right = true;
}
}
}
if (found) confirmed = true;
if(!is_left && !is_right){
cout << "-- none!";
if(left_count == 0 && right_count == 0) confirmed = true;
} else if(is_left && is_right){
cout << "-- both!";
} else {
if (is_left){
cout << " -- left!";
} else {
cout << " -- right!";
}
}
imshow("kalman", kf_img);
// up till here
if(confirmed){
nb_confirmed ++;
cout << " -- confirmed" << endl;
} else {
cout << endl;
}
csv(&logs, center_left.x, center_left.y, is_left, center_right.x, center_right.y, is_right, confirmed);
}
nb_total ++;
//
// //displayOverlay("Blobs Image","Multi Thread");
new_dist = norm(center_left - center_right);
// don't throw errors in the first 10 frames
if(ticks > 10){
if(error_left > 20 && error_right > 20 /*&& new_dist < prev_dist*/){
circle(copy, Point(WIDTH/2, HEIGHT/2), 100, Scalar(0, 0, 255), 30);
nb_errors ++;
}
}
prev_dist = new_dist;
imshow("Blobs Image",copy);
key = waitKey(10);
} else{
eof = true;
}
if(27 == key || 1048603 == key || eof){
double kalman_error_percentage = (nb_errors*100.0)/ticks;
double confirm_percentage = (nb_confirmed*100.0/nb_total);
stats << "kalman error frequency: " << kalman_error_percentage << "\%" << endl;
stats << "confirmed: " << confirm_percentage << "\%" << endl;
logs.close();
stats.close();
return 0;
}
}
}
bool findBiggestBlobImage(IplImage* img, int color, IplImage* &output)
{
CBlobResult blobs;
CBlob *currentBlob;
blobs = CBlobResult( img, NULL, 0 );
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, m_minBlobSize );
double biggestArea = m_minBlobSize;
int biggestBlob = -1;
for (int i = 0; i < blobs.GetNumBlobs(); i++ )
{
currentBlob = blobs.GetBlob(i);
double blobArea = currentBlob->Area();
if(blobArea > biggestArea)
{
biggestBlob = i;
biggestArea = blobArea;
}
}
if(biggestBlob >= 0)
{
int x = (int) blobs.GetBlob(biggestBlob)->MinX();
int y = (int) blobs.GetBlob(biggestBlob)->MinY();
int width= (int) blobs.GetBlob(biggestBlob)->MaxX()-x;
int height= (int) blobs.GetBlob(biggestBlob)->MaxY()-y;
IplImage* temp = cvCreateImage(cvGetSize(img),IPL_DEPTH_8U, 1);
IplImage* temp2 = cvCreateImage(cvSize(width, height),IPL_DEPTH_8U, 1);
IplImage* result = cvCreateImage(cvSize(width, height),IPL_DEPTH_8U, 1);
if(biggestBlob>=0) blobs.GetBlob(biggestBlob)->FillBlob(temp,cvScalar(255),x,y);
cvSetImageROI(temp, cvRect(x, y, width, height));
cvCopy(temp,temp2);
uchar* tempData;
uchar* resultData;
tempData = (uchar *)(temp2->imageData);
resultData = (uchar *) (result->imageData);
for (int j = 0; j < width*height; j++)
{
if (tempData[j]==255) resultData[j] = color;
else resultData[j] = 0;
}
cvResize(result, output);
cvReleaseImage(&temp);
cvReleaseImage(&temp2);
cvReleaseImage(&result);
return true;
}
else
return false;
}
/*
arg1: Width of each frame
arg2: Height of each frame
arg3: Target frames per second of the program
arg4: Maximum number of blobs to track. Each blob MAY corresspond to a person in front of the camera
*/
int main(int argc, char* argv[])
{
if (argc < 5)
{
cout << "Too few arguments to the program. Exiting...\n";
return 0;
}
int width, height, fps, numberOfBlobs;
try
{
//Read the arguments
width = atoi(argv[1]);
height = atoi(argv[2]);
fps = atoi(argv[3]);
numberOfBlobs = atoi(argv[4]);
//Done reading arguments
}
catch(...)
{
cout << "One or more arguments are invalid!. Exiting...\n";
return 0;
}
/*
int width = 320;
int height = 240;
int fps = 10;
int numberOfBlobs = 2;
*/
tempImageV4L = cvCreateImage(cvSize(width, height), 8, 3);
frameNumber = 0;
//Beginning initialising cameras
rightCamera = new Camera("/dev/video0", width, height, fps);
leftCamera = new Camera("/dev/video1", width, height, fps);
//leftCamera = rightCamera; //If only one camera is available, uncomment this line and comment the line above this.
//Done initialising cameras
//Waste some frames so as to get the cameras running in full flow
WasteNFrames(10);
//Beginning capturing background
backImageRight = GetNextCameraShot(rightCamera);
backImageLeft = GetNextCameraShot(leftCamera);
frameNumber++;
cvtColor(backImageRight, backImageRight, CV_BGR2HSV);
cvtColor(backImageLeft, backImageLeft, CV_BGR2HSV);
//Done capturing background
//General Stuff
Mat motionImageRight(backImageRight.rows, backImageRight.cols, CV_8UC1);
Mat motionImageLeft(backImageLeft.rows, backImageLeft.cols, CV_8UC1);
Mat HSVImageRight, HSVImageLeft;
Mat displayImageRight, displayImageLeft;
//End of General Stuff
while (1) //The infinite loop
{
//Beginning getting camera shots
rightImage = GetNextCameraShot(rightCamera);
leftImage = GetNextCameraShot(leftCamera);
frameNumber++;
//Done getting camera shots
//Beginning getting motion images
HSVImageRight = rightImage.clone();
cvtColor(HSVImageRight, HSVImageRight, CV_BGR2HSV);
CompareWithBackground(HSVImageRight, backImageRight, motionImageRight);
medianBlur(motionImageRight, motionImageRight, 3);
HSVImageLeft = leftImage.clone();
cvtColor(HSVImageLeft, HSVImageLeft, CV_BGR2HSV);
CompareWithBackground(HSVImageLeft, backImageLeft, motionImageLeft);
medianBlur(motionImageLeft, motionImageLeft, 3);
//Ended getting motion images
cout << "\nFor frame #" << frameNumber << " :\n";
//Beginning Getting Blobs
IplImage imageblobPixels = motionImageRight;
CBlobResult blobs;
blobs = CBlobResult(&imageblobPixels, NULL, 0); // Use a black background color.
int minArea = 100 / ((640 / width) * (640 / width));
blobs.Filter(blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, minArea);
int foundBlobs = blobs.GetNumBlobs();
//Ended Getting Blobs
cout << "Found " << foundBlobs << " motion blobs\n";
//Creating copies of original images for modifying and displaying
displayImageRight = rightImage.clone();
displayImageLeft = leftImage.clone();
//Done creating copies
//Cycling through the blobs
for (int blobIndex = 0; blobIndex < blobs.GetNumBlobs() && blobIndex < numberOfBlobs; blobIndex++)
{
cout << "Blob #" << blobIndex << " : ";
//Getting blob details
CBlob * blob = blobs.GetBlob(blobIndex);
int x = blob->GetBoundingBox().x;
int y = blob->GetBoundingBox().y;
int w = blob->GetBoundingBox().width;
int h = blob->GetBoundingBox().height;
//Done getting blob details
int sep = 0;
//The point for which we want to find depth
PixPoint inP = {x + w/2, y + h/2}, oP = {0, 0};
cout << "inPoint = {" << inP.x << ", " << inP.y << "} ";
//Initialing the rectangle in which the corressponding point is likely in
Rectangle rect;
rect.location.x = -1;
rect.location.y = inP.y - 5;
rect.size.x = rightImage.cols;
rect.size.y = 11;
//Done initialising the target rectangle
//Find the corressponding point and calculate the sepertion
oP = PointCorresponder::correspondPoint(rightImage, leftImage, inP, rect, motionImageLeft);
sep = inP.x - oP.x;
cout << "foundPoint = {" << oP.x << ", " << oP.y << "} ";
//Just for visual presentation
DrawRect(displayImageRight, x, y, w, h);
cv::circle(displayImageRight, Point(inP.x, inP.y), 10, Scalar(0), 3);
cv::circle(displayImageLeft, Point(oP.x, oP.y), 10, Scalar(0), 3);
//Done decoration
//The thing we were looking for... how can we forget to print this? :P
cout << "seperation = " << sep << "\n";
}
//Show the windows
cv::namedWindow("RIGHT");
cv::namedWindow("thresh");
cv::namedWindow("LEFT");
imshow("LEFT", displayImageLeft);
imshow("RIGHT", displayImageRight);
imshow("thresh", motionImageRight);
//End of code for showing windows
//The loop terminating condition
if (waitKey(27) >= 0) break;
}
//Mission Successful!! :D :)
return 0;
}
void
Auvsi_Recognize::extractShape( void )
{
typedef cv::Vec<T, 1> VT;
// Reduce input to two colors
cv::Mat reducedColors = doClustering<T>( _image, 2 );
cv::Mat grayScaled, binary;
// Make output grayscale
grayScaled = convertToGray( reducedColors );
//cv::cvtColor( reducedColors, grayScaled, CV_RGB2GRAY );
// Make binary
double min, max;
cv::minMaxLoc( grayScaled, &min, &max );
cv::threshold( grayScaled, binary, min, 1.0, cv::THRESH_BINARY );
// ensure that background is black, image white
if( binary.at<VT>(0, 0)[0] > 0.0f )
cv::threshold( grayScaled, binary, min, 1.0, cv::THRESH_BINARY_INV );
binary.convertTo( binary, CV_8U, 255.0f );
// Fill in all black regions smaller than largest black region with white
CBlobResult blobs;
CBlob * currentBlob;
IplImage binaryIpl = binary;
blobs = CBlobResult( &binaryIpl, NULL, 255 );
// Get area of biggest blob
CBlob biggestBlob;
blobs.GetNthBlob( CBlobGetArea(), 0, biggestBlob );
// Remove all blobs of smaller area
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_GREATER_OR_EQUAL, biggestBlob.Area() );
for (int i = 0; i < blobs.GetNumBlobs(); i++ )
{
currentBlob = blobs.GetBlob(i);
currentBlob->FillBlob( &binaryIpl, cvScalar(255));
}
// Fill in all small white regions black
blobs = CBlobResult( &binaryIpl, NULL, 0 );
blobs.GetNthBlob( CBlobGetArea(), 0, biggestBlob );
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_GREATER_OR_EQUAL, biggestBlob.Area() );
for (int i = 0; i < blobs.GetNumBlobs(); i++ )
{
currentBlob = blobs.GetBlob(i);
currentBlob->FillBlob( &binaryIpl, cvScalar(0));
}
binary = cv::Scalar(0);
biggestBlob.FillBlob( &binaryIpl, cvScalar(255));
_shape = binary;
}
SHModel* ShapeModel( CvCapture* g_capture,StaticBGModel* BGModel , BGModelParams* BGParams){
int num_frames = 0;
int total_blobs=0;
float Sumatorio = 0;
float SumatorioDes = 0;
IplImage* frame = NULL;
STFrame* frameData = NULL;
SHModel* Shape = NULL;
CBlobResult blobs;
CBlob *currentBlob;
IplImage* ImGris = cvCreateImage(cvGetSize( BGModel->Imed ), 8, 1 );
IplImage* Imblob = cvCreateImage(cvGetSize( BGModel->Imed ), 8, 3 );
IplImage* lastBG = cvCreateImage( cvGetSize( BGModel->Imed ),8, 1 );
IplImage* lastIdes = cvCreateImage( cvGetSize( BGModel->Imed ), IPL_DEPTH_32F, 1);
cvZero(Imblob);
// Iniciar estructura para modelo de forma
Shape = ( SHModel *) malloc( sizeof( SHModel));
if ( !Shape ) {error(4);return 0;}
Shape->FlyAreaDes = 0;
Shape->FlyAreaMedia=0;
//Pone a 0 los valores del vector areas
//EXTRACCION DE LOS BLOBS Y CALCULO DE MEDIANA/MEDIA Y DESVIACION TIPICA PARA TODOS LOS FRAMES
cvSetCaptureProperty( g_capture,1,BGParams->initDelay ); // establecemos la posición
while( num_frames < ShParams->FramesTraining ){
frame = cvQueryFrame( g_capture );
if ( !frame ) {
error(2);
break;
}
if ( (cvWaitKey(10) & 255) == 27 ) break;
ImPreProcess( frame, ImGris, BGModel->ImFMask, 0, BGModel->DataFROI);
// Cargamos datos del fondo
if(!frameData ) { //en la primera iteración iniciamos el modelo dinamico al estático
// Iniciar estructura para datos del nuevo frame
frameData = InitNewFrameData( frame );
cvCopy( BGModel->Imed,frameData->BGModel);
cvSet(frameData->IDesvf, cvScalar(1));
cvCopy( BGModel->Imed,lastBG);
}
else{ // cargamos los últimos parámetros del fondo.
cvCopy( lastBG, frameData->BGModel);
cvCopy( lastIdes,frameData->IDesvf );
}
// obtener la mascara del FG y la lista con los datos de sus blobs.
//// BACKGROUND UPDATE
// Actualización del fondo
// establecer parametros
UpdateBGModel( ImGris,frameData->BGModel,frameData->IDesvf, BGParams, BGModel->DataFROI, BGModel->ImFMask );
/////// BACKGROUND DIFERENCE. Obtención de la máscara del foreground
BackgroundDifference( ImGris, frameData->BGModel,frameData->IDesvf, frameData->FG ,BGParams, BGModel->DataFROI);
// guardamos las imagenes para iniciar el siguiente frame
cvCopy( frameData->BGModel, lastBG);
cvCopy( frameData->IDesvf,lastIdes);
//Obtener los Blobs y excluir aquellos que no interesan por su tamaño
// cvSetImageROI( frameData->FG , BGModel->DataFROI);
blobs = CBlobResult( frameData->FG, NULL, 100, true );
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(),B_GREATER,100);
blobs.Filter( blobs, B_EXCLUDE, CBlobGetPerimeter(),B_GREATER,1000);
int j = blobs.GetNumBlobs();//numero de blos encontrados en el frame
total_blobs=total_blobs+j; // Contabiliza los blobs encontrados para todos los frames
//Recorrer Blob a blob y obtener las caracteristicas del AREA de cada uno de ellos
for (int i = 0; i < blobs.GetNumBlobs(); i++ ){ //for 1
currentBlob = blobs.GetBlob(i);
CBlobGetArea();
if(ShParams->SHOW_DATA_AREAS) {
//printf("Area blob %d = %f ",i,currentBlob->area);
}
//Estimar la media de las Areas
Sumatorio = Sumatorio + currentBlob->area;
SumatorioDes = SumatorioDes + currentBlob->area*currentBlob->area;
muestrearAreas( currentBlob->area);
currentBlob->FillBlob( Imblob, CV_RGB(255,0,0));
}//Fin del For 1
Shape->FlyAreaMedia = Sumatorio / total_blobs;
Shape->FlyAreaDes = (SumatorioDes / total_blobs) - Shape->FlyAreaMedia*Shape->FlyAreaMedia;
num_frames += 1;
// cvResetImageROI(frameData->FG);
DraWWindow(Imblob, frameData, BGModel, SHOW_SHAPE_MODELING, COMPLETO);
DraWWindow(Imblob, frameData, BGModel, SHAPE,SIMPLE );
}
desvanecer( NULL, 20);
Shape->FlyAreaDes = sqrt(abs(Shape->FlyAreaDes) ) ;
if( Shape->FlyAreaDes == 0){
printf("hola");
}
//Mostrar mediana y media para todos los frames
if(ShParams->SHOW_DATA_AREAS )
printf("\n MEDIA AREAS: %f \t DESVIACION AREAS: %f",Shape->FlyAreaMedia,Shape->FlyAreaDes);
free( ShParams);
liberarSTFrame( frameData );
cvReleaseImage( &ImGris);
cvReleaseImage( &Imblob);
cvReleaseImage( &lastIdes);
cvReleaseImage( &lastBG);
return Shape;
}//Fin de la función ShapeModel2
void
Auvsi_Recognize::extractLetter( void )
{
typedef cv::Vec<unsigned char, 1> VT_binary;
#ifdef TWO_CHANNEL
typedef cv::Vec<T, 2> VT;
#else
typedef cv::Vec<T, 3> VT;
#endif
typedef cv::Vec<int, 1> IT;
// Erode input slightly
cv::Mat input;
cv::erode( _shape, input, cv::Mat() );
// Remove any small white blobs left over
CBlobResult blobs;
CBlob * currentBlob;
CBlob biggestBlob;
IplImage binaryIpl = input;
blobs = CBlobResult( &binaryIpl, NULL, 0 );
blobs.GetNthBlob( CBlobGetArea(), 0, biggestBlob );
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_GREATER_OR_EQUAL, biggestBlob.Area() );
for (int i = 0; i < blobs.GetNumBlobs(); i++ )
{
currentBlob = blobs.GetBlob(i);
currentBlob->FillBlob( &binaryIpl, cvScalar(0));
}
// Perform k-means on this region only
int areaLetter = (int)biggestBlob.Area();
cv::Mat kMeansInput = cv::Mat( areaLetter, 1, _image.type() );
// Discard if we couldn't extract a letter
if( areaLetter <= 0 )
{
_letter = cv::Mat( _shape );
_letter = cv::Scalar(0);
return;
}
cv::MatIterator_<VT_binary> binaryIterator = input.begin<VT_binary>();
cv::MatIterator_<VT_binary> binaryEnd = input.end<VT_binary>();
cv::MatIterator_<VT> kMeansIterator = kMeansInput.begin<VT>();
for( ; binaryIterator != binaryEnd; ++binaryIterator )
{
if( (*binaryIterator)[0] > 0 )
{
(*kMeansIterator) = _image.at<VT>( binaryIterator.pos() );
++kMeansIterator;
}
}
// Get k-means labels
cv::Mat labels = doClustering<T>( kMeansInput, 2, false );
int numZeros = areaLetter - cv::countNonZero( labels );
bool useZeros = numZeros < cv::countNonZero( labels );
// Reshape into original form
_letter = cv::Mat( _shape.size(), _shape.type() );
_letter = cv::Scalar(0);
binaryIterator = input.begin<VT_binary>();
binaryEnd = input.end<VT_binary>();
cv::MatIterator_<IT> labelsIterator = labels.begin<IT>();
for( int index = 0; binaryIterator != binaryEnd; ++binaryIterator )
{
if( (*binaryIterator)[0] > 0 )
{
// Whichever label was the minority, we make that value white and all other values black
unsigned char value = (*labelsIterator)[0];
if( useZeros )
if( value )
value = 0;
else
value = 255;
else
if( value )
value = 255;
else
value = 0;
_letter.at<VT_binary>( binaryIterator.pos() ) = VT_binary( value );
++labelsIterator;
}
}
}
void BlobDetection::init()
{
/** Init is called just after construction. */
try
{
initStatusMask();
// Create a proxy to ALVideoDevice on the robot.
ALVideoDeviceProxy* camProxy = new ALVideoDeviceProxy(getParentBroker());
behavoirProxy = new ALBehaviorManagerProxy(getParentBroker());
ledProxy = new ALLedsProxy(getParentBroker());
motionProxy = new ALMotionProxy(getParentBroker());
initLeds();
// Subscribe a client image requiring 640*480px and RGB colorspace.
const std::string cameraID = camProxy->subscribeCamera("camera_01", 0, AL::kVGA, AL::kRGBColorSpace , 10);
// Create a proxy to ALMemoryProxy on the robot.
ALMemoryProxy fMemoryProxy = ALMemoryProxy(getParentBroker());
fMemoryProxy.subscribeToEvent("FrontTactilTouched", "BlobDetection","onFrontTactilTouched");
fMemoryProxy.subscribeToEvent("MiddleTactilTouched", "BlobDetection","onMiddleTactilTouched");
HandOrientation rightOrientationLast = NONE;
HandOrientation leftOrientationLast = NONE;
HandOrientation rightOrientationCur = NONE, leftOrientationCur = NONE;
// stand up
behavoirProxy->runBehavior(STAND);
// RECODING: prepare vido recording
/*
int size;
std::string arvFile = std::string("/home/nao/video");
streamHeader tmpStreamHeader;
std::vector<streamHeader> streamHeaderVector;
ALVideo videoFile;
tmpStreamHeader.width = 640;
tmpStreamHeader.height = 480;
tmpStreamHeader.colorSpace = AL::kRGBColorSpace; // this is not really necessary, coz in pyuv u decide in which colorspace the vid is shown
tmpStreamHeader.pixelDepth = 8;
streamHeaderVector.push_back(tmpStreamHeader);
std::cout<<"Output arv file properties: "<< streamHeaderVector[0].width <<"x"<< streamHeaderVector[0].height
<<" Colorspace id:"<< streamHeaderVector[0].colorSpace <<" Pixel depth:"<< streamHeaderVector[0].pixelDepth
<<std::endl;
if( !videoFile.recordVideo( arvFile, 0, streamHeaderVector ) ) {
std::cout<<"Error writing "<< arvFile <<" file."<<std::endl;
return;
}
*/
int j = 0;
while(1)
{
if(touched)
{
//j++;
//Switch LEDs RED OFF, BLUE ON
if(red_on == 1)
{
ledProxy->off(FACE_LED_RED);
red_on = 0;
}
if(blue_on == 0)
{
ledProxy->on(FACE_LED_BLUE);
blue_on = 1;
}
// Fetch the image from the nao camera, we subscribed on. Its in RGB colorspace
ALImage *img_cam = (ALImage*)camProxy->getImageLocal(cameraID);
// Create an openCv Mat header to convert the aldebaran AlImage image.
// To convert the aldebaran image only the data are of it are assigned to the openCv image.
Mat img_hsv = Mat(Size(img_cam->getWidth(), img_cam->getHeight()), CV_8UC3);
img_hsv.data = (uchar*) img_cam->getData();
// Convert the RGB image from the camera to an HSV image */
cvtColor(img_hsv, img_hsv, CV_RGB2HSV);
// RECORDING: record converted to hsv video
//videoFile.write((char*) img_hsv.data, size); //video ging hier
// Get the separate HSV color components of the color input image.
std::vector<Mat> channels(3);
split(img_hsv, channels);
Mat planeH = channels[0];
Mat planeS = channels[1];
Mat planeV = channels[2];
// Detect which pixels in each of the H, S and V channels are probably skin pixels.
threshold(planeH, planeH, 150, UCHAR_MAX, CV_THRESH_BINARY_INV);//18
threshold(planeS, planeS, 60, UCHAR_MAX, CV_THRESH_BINARY);//50
threshold(planeV, planeV, 170, UCHAR_MAX, CV_THRESH_BINARY);//80
// Combine all 3 thresholded color components, so that an output pixel will only
// be white if the H, S and V pixels were also white.
Mat imageSkinPixels = Mat(img_hsv.size(), CV_8UC3); // Greyscale output image.
bitwise_and(planeH, planeS, imageSkinPixels); // imageSkin = H {BITWISE_AND} S.
bitwise_and(imageSkinPixels, planeV, imageSkinPixels); // imageSkin = H {BITWISE_AND} S {BITWISE_AND} V.
// Assing the Mat (C++) to an IplImage (C), this is necessary because the blob detection is writtn in old opnCv C version
IplImage ipl_imageSkinPixels = imageSkinPixels;
// RECODING: record the video using the C container variable
// RECODING: store the size (in memory meaning) of the image for recording purpouse
//size = img_cam->getSize();
//videoFile.write((char*) ipl_imageSkinPixels.imageData, size/3);
// Set up the blob detection.
CBlobResult blobs;
blobs.ClearBlobs();
blobs = CBlobResult(&ipl_imageSkinPixels, NULL, 0); // Use a black background color.
// Ignore the blobs whose area is less than minArea.
blobs.Filter(blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, minBlobArea);
// ##### Gestures #####
std::cout << "Number of Blobs: " << blobs.GetNumBlobs() <<endl;
if(blobs.GetNumBlobs() == 0)
{
//picture empty
}
else if(blobs.GetNumBlobs() == 1)
{
//head detected
trackHead(getCenterPoint(blobs.GetBlob(0)->GetBoundingBox()).x, getCenterPoint(blobs.GetBlob(0)->GetBoundingBox()).y);
}
else if(blobs.GetNumBlobs() == 2 || blobs.GetNumBlobs() == 3)
{
//head + one hand || head + two hands
Rect rect[3];
int indexHead = -1, indexHandLeft = -1, indexHandRight = -1;
//Get Bounding Boxes
for(int i = 0; i< blobs.GetNumBlobs(); i++)
{
rect[i] = blobs.GetBlob(i)->GetBoundingBox();
}
//Detect Head and Hand indexes
if(blobs.GetNumBlobs() == 2)
{
// head and one hand
int indexHand = -1;
if(getCenterPoint(rect[0]).y < getCenterPoint(rect[1]).y)
{
// rect[0] is head
indexHead = 0;
indexHand = 1;
}
else
{
// rect[1] is head
indexHead = 1;
indexHand = 0;
}
if(getHandside(rect[indexHead], rect[indexHand]) == LEFT)
{
// hand is left
indexHandLeft = 1;
indexHandRight = -1;
}
else
{
// hand ist right
indexHandLeft = -1;
indexHandRight = 1;
}
}
else
{
//two hands
int indexHand1 = -1;
int indexHand2 = -1;
if(getCenterPoint(rect[0]).y < getCenterPoint(rect[1]).y && getCenterPoint(rect[0]).y < getCenterPoint(rect[2]).y)
{
// rect[0] is head
indexHead = 0;
indexHand1 = 1;
indexHand2 = 2;
}
else if(getCenterPoint(rect[1]).y < getCenterPoint(rect[0]).y && getCenterPoint(rect[1]).y < getCenterPoint(rect[2]).y)
{
// rect[1] is head
indexHead = 1;
indexHand1 = 0;
indexHand2 = 2;
}
else
{
// rect[2] is head
indexHead = 2;
indexHand1 = 0;
indexHand2 = 1;
}
if(getHandside(rect[indexHead], rect[indexHand1]) == LEFT)
{
indexHandLeft = indexHand1;
indexHandRight = indexHand2;
}
else
{
indexHandLeft = indexHand2;
indexHandRight = indexHand1;
}
}
// bobs are detected.
// adjuste naos head to detected head-bolb
trackHead(getCenterPoint(rect[indexHead]).x, getCenterPoint(rect[indexHead]).y);
//Get Orientations from Hand rects
leftOrientationCur = (indexHandLeft != -1)?getOrientationOfRect(rect[indexHandLeft]):NONE;
rightOrientationCur = (indexHandRight != -1)?getOrientationOfRect(rect[indexHandRight]):NONE;
//Check Change of Left hand
switch(detectHandStateChange(leftOrientationLast, leftOrientationCur))
{
case PORTRAIT_TO_LANDSCAPE:
handleGestures(LEFT_FLIP_DOWN);
break;
case LANDSCAPE_TO_PORTRAIT:
handleGestures(LEFT_FLIP_UP);
break;
case NOCHANGE:
// TODO
default:
break;
}
//Check Change of Right hand
switch(detectHandStateChange(rightOrientationLast, rightOrientationCur))
{
case PORTRAIT_TO_LANDSCAPE:
handleGestures(RIGHT_FLIP_DOWN);
break;
case LANDSCAPE_TO_PORTRAIT:
handleGestures(RIGHT_FLIP_UP);
break;
case NOCHANGE:
//TODO
default:
break;
}
}
else if(blobs.GetNumBlobs() > 3)
{
//too much information
cout<<"too much information"<<endl;
}
leftOrientationLast = leftOrientationCur;
rightOrientationLast = rightOrientationCur;
// RECODING: close the video recorder
//videoFile.closeVideo();
// Free all the resources.
camProxy->releaseImage(cameraID);
//IplImage* p_iplImage = &ipl_imageSkinPixels;
//cvReleaseImage(&p_iplImage);
qi::os::sleep(0.5f);
//sleep(1);
}
else
{
//Switch LEDs RED ON, BLUE OFF
if(red_on == 0)
{
ledProxy->on(FACE_LED_RED);
red_on = 1;
behavoirProxy->runBehavior(STAND);
}
if(blue_on == 1)
{
ledProxy->off(FACE_LED_BLUE);
blue_on = 0;
}
}
}
camProxy->unsubscribe(cameraID);
}
catch (const AL::ALError& e)
{
std::cerr << "Caught exception: " << e.what() << std::endl;
return;
}
return;
}
