void
RoboyVision::recognizeFaces()
{
// Resizing the face is necessary for Eigenfaces and Fisherfaces. You can easily
// verify this, by reading through the face recognition tutorial coming with OpenCV.
// Resizing IS NOT NEEDED for Local Binary Patterns Histograms, so preparing the
// input data really depends on the algorithm used.
//
// I strongly encourage you to play around with the algorithms. See which work best
// in your scenario, LBPH should always be a contender for robust face recognition.
//
// Since I am showing the Fisherfaces algorithm here, I also show how to resize the
// face you have just found:
faceRecognitionPredictions.clear();
faceRecognitionConfidences.clear();
for(int i = 0; i < faces.size(); i++) {
cv::Rect face_i = faces[i];
// Crop the face from the image. So simple with OpenCV C++:
cv::Mat face = grayFrame(face_i);
cv::Mat face_resized;
cv::resize(face, face_resized, cv::Size(RECOGNITION_FACE_WIDTH, RECOGNITION_FACE_HEIGHT), 1.0, 1.0, cv::INTER_CUBIC);
int prediction = -1;
double confidence = 0.0;
recognitionModel->predict(face_resized, prediction, confidence);
faceRecognitionPredictions.push_back(prediction);
faceRecognitionConfidences.push_back(confidence);
}
}
void GTVideo::snakeTracking2()
{
if(foregroundMask.isEmpty() || abnormallist.isEmpty())
{
QMessageBox msgBox;
msgBox.setText("Video source and initial abnormal range must be set before tracking");
msgBox.exec();
return;
}
int totalAbnormalCount = getAbnormalFrameCount();
int currCount = 0;
QProgressDialog progress("Generating Groundtruth Images...", "Abort", 0, totalAbnormalCount-1);
progress.setWindowModality(Qt::WindowModal);
progress.setValue(currCount);
// Initialize the MATLAB Compiler Runtime global state
if (!mclInitializeApplication(NULL,0))
{
std::cerr << "Could not initialize the application properly."
<< std::endl;
}
// Initialize the segmentation library
if (!libsegInitialize())
{
std::cerr << "Could not initialize the library properly."
<< std::endl;
}
//initialize the groundtruth
cv::Mat eye = foregroundMask.at(0).clone();
eye.setTo(cv::Scalar(0)); //cv::Scalar(0,0,0)
grdtruth.fill(eye);
for (int iAb=0; iAb<abnormallist.size(); iAb++)
{
uint start = abnormallist[iAb].getStart();
uint end = abnormallist[iAb].getEnd();
const QVector<cv::Point>& boundaryPoints = abnormallist[iAb].getBoundaryPoints();
const cv::Point *pAddBoundary = boundaryPoints.data();
const cv::Point **pBoundaryPoints = &pAddBoundary;
// initialize the segmentation mask
cv::Mat initmask = abnormallist[iAb].getROI();
// set tracked object as abnormal ROI
for (uint iFrame=start; iFrame<=end; iFrame++)
{
currCount++;
progress.setValue(currCount);
if(progress.wasCanceled())
{
return;
}
else
{
cv::Mat grayFrame(foregroundMask.at(iFrame));
// generate groundtruth and update mask using that in previous frame
cv::Mat resultImage = segmentByActiveContour(grayFrame, initmask, 200, false);
if (NULL == resultImage.data)
{
qDebug() << QString("No groudtruth generated for frame %1").arg(iFrame);
continue;
}
initmask = resultImage.clone();
// set groudtruth result
setGroundtruth(resultImage, iFrame);
}
}
}
progress.setValue(totalAbnormalCount-1);
mclTerminateApplication();
libsegTerminate();
}
void GTVideo::snakeTracking()
{
if(foregroundMask.isEmpty() || abnormallist.isEmpty())
{
qDebug() << "Video source and initial abnormal range must be set before tracking\n";
return;
}
//initialize the groundtruth
cv::Mat eye = foregroundMask.at(0);
eye.setTo(cv::Scalar(0)); //cv::Scalar(0,0,0)
grdtruth.fill(eye);
for (int iAb=0; iAb<abnormallist.size(); iAb++)
{
uint start = abnormallist[iAb].getStart();
uint end = abnormallist[iAb].getEnd();
int length = end-start+1;
const QVector<cv::Point>& boundaryPoints = abnormallist[iAb].getBoundaryPoints();
// consctruct a new array of type CvPoint because it will be modified for each frame
const int npts = boundaryPoints.size();
CvPoint pts_snake[npts];
for (int i=0; i<npts; i++)
{
pts_snake[i] = boundaryPoints[i];
}
// set parameters for cvSnakeImage()
float alpha = 0.5f;
float beta = 0.5f;
float gamma = 0.5f;
int coeff_usage = CV_VALUE;
CvSize win = cvSize(21,21);
CvTermCriteria criteria = cvTermCriteria(CV_TERMCRIT_ITER, 100, 0.5);
// set tracked object as abnormal ROI
for (uint iFrame=start; iFrame<=end; iFrame++)
{
// update boundary using that in previous frame
cv::Mat grayFrame(foregroundMask[iFrame]);
//cv::cvtColor(foregroundMask[iFrame], grayFrame, CV_RGB2GRAY);
IplImage *ipFrame = new IplImage(grayFrame);
cvSnakeImage(ipFrame, pts_snake, npts, &alpha, &beta, &gamma, coeff_usage, win, criteria, 1);
cvSaveImage("frame.tif", ipFrame);
// convert boundary points from CvPoint[] to vector<Point>
std::vector<cv::Point> stdBoundPoints;
for (int i=0; i<npts; i++)
{
cv::Point p(pts_snake[i].x, pts_snake[i].y);
stdBoundPoints.push_back(p);
}
// fill the empty grayFrame using popygon to get roi
cv::Mat roi(foregroundMask[iFrame]);
//cv::cvtColor(foregroundMask[iFrame], roi, CV_RGB2GRAY);
roi.setTo(cv::Scalar(0)); //cv::Scalar(0,0,0)
//cv::fillPoly(roi, stdBoundPoints, cv::Scalar(0)); //cv::Scalar(255,255,255)
const cv::Point *pAddBoundary = stdBoundPoints.data();
const cv::Point **pBoundaryPoints = &pAddBoundary;
cv::fillPoly(roi, pBoundaryPoints, &npts, 1, cv::Scalar(255)); //cv::Scalar(255,255,255)
setGroundtruth(roi, iFrame);
delete ipFrame;
cv::imwrite("output.tif", roi);
}
}
}
