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asked 2016-12-02 14:34:25 -0600

sarmad gravatar image

eye landmark points

Hi

I'm using facial landmark detector (dlib) to detect eye blinks . How the eye landmarks can be imported to a file ?

I need to use eye landmarks to calculate the ration between height and width of eye and to use SVM to classify blinks

Thanks

eye landmark points

Hi

I'm using facial landmark detector (dlib) to detect eye blinks . How the eye landmarks can be imported to a file ?

I need to use eye landmarks to calculate the ration between height and width of eye and to use SVM to classify blinks

Update : when I try to write landmark point to a file , different valuses are saved than the displayed landmarks in terminal windows , how to fix ?

Thanks

#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/image_processing/render_face_detections.h>
#include <dlib/image_processing.h>
#include <dlib/gui_widgets.h>
#include <dlib/image_io.h>
#include <iostream>
#include <fstream>


  using namespace dlib;
  using namespace std;

 int main(int argc, char** argv)
{  
try
{

    if (argc == 1)
    {
        cout << "Call this program like this:" << endl;
        cout << "./face_landmark_detection_ex shape_predictor_68_face_landmarks.dat faces/*.jpg" << endl;
        cout << "\nYou can get the shape_predictor_68_face_landmarks.dat file from:\n";
        cout << "http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;
        return 0;
    }

    frontal_face_detector detector = get_frontal_face_detector();

    shape_predictor sp;
    deserialize(argv[1]) >> sp;


    image_window win, win_faces;
    // Loop over all the images provided on the command line.
    for (int i = 2; i < argc; ++i)
    {
        cout << "processing image " << argv[i] << endl;
        array2d<rgb_pixel> img;
        load_image(img, argv[i]);
        pyramid_up(img);

        std::vector<rectangle> dets = detector(img);
        cout << "Number of faces detected: " << dets.size() << endl;


        std::vector<full_object_detection> shapes;
        for (unsigned long j = 0; j < dets.size(); ++j)
        {
            full_object_detection shape = sp(img, dets[j]);
               cout << "number of parts: "<< shape.num_parts() << endl;


        cout << "Eye Landmark points for right eye : "<< endl;
        cout << "pixel position of 36 part:  " << shape.part(36) << endl;
        cout << "pixel position of 37 part: " << shape.part(37) << endl;
        cout << "pixel position of 38 part:  " << shape.part(38) << endl;
        cout << "pixel position of 39 part: " << shape.part(39) << endl;
        cout << "pixel position of 40 part: " << shape.part(40) << endl;
        cout << "pixel position of 41 part: " << shape.part(41) << endl;

        cout << endl;

       cout << "Eye Landmark points for left eye : "<< endl;

        cout << "pixel position of 42 part:  " << shape.part(42) << endl;
        cout << "pixel position of 43 part: " << shape.part(43) << endl;
        cout << "pixel position of 44 part:  " << shape.part(44) << endl;
        cout << "pixel position of 45 part: " << shape.part(45) << endl;
        cout << "pixel position of 46 part: " << shape.part(46) << endl;
        cout << "pixel position of 47 part: " << shape.part(47) << endl;

            shapes.push_back(shape);



           const full_object_detection& d = shapes[0];
           ofstream outputfile;
           outputfile.open("data.txt");

            for (unsigned long k = 0; k < shape.num_parts(); ++k)
            {
                outputfile<< shape.part(k).x() << " " << shape.part(k).y() << endl;

            }

        }
        win.clear_overlay();
        win.set_image(img);
        win.add_overlay(render_face_detections(shapes));

        dlib::array<array2d<rgb_pixel> > face_chips;
        extract_image_chips(img, get_face_chip_details(shapes), face_chips);
        win_faces.set_image(tile_images(face_chips));

        cout << "Hit enter to process the next image..." << endl;
        cin.get();
    }
}
catch (exception& e)
{
    cout << "\nexception thrown!" << endl;
    cout << e.what() << endl;
}
}

eye landmark points

Hi

I'm using facial landmark detector (dlib) to detect eye blinks . How the eye landmarks can be imported to a file ?

I need to use eye landmarks to calculate the ration between height and width of eye and to use SVM to classify blinks

Update : when I try to write landmark point to a file , different valuses are saved than the displayed landmarks in terminal windows , how to fix ?

Thanks

 #include <dlib/opencv.h>
 #include <opencv2/highgui/highgui.hpp>
 #include <dlib/image_processing/frontal_face_detector.h>
 #include <dlib/image_processing/render_face_detections.h>
 #include <dlib/image_processing.h>
 #include <dlib/gui_widgets.h>
#include <dlib/image_io.h>
#include <iostream>
#include <fstream>


   using namespace dlib;
 using namespace std;

 int main(int argc, char** argv)
{  
main()
{
try
{
     cv::VideoCapture cap(0);
    if (argc == 1)
(!cap.isOpened())
    {
        cout << "Call this program like this:" << endl;
        cout << "./face_landmark_detection_ex shape_predictor_68_face_landmarks.dat faces/*.jpg" << endl;
        cout << "\nYou can get the shape_predictor_68_face_landmarks.dat file from:\n";
        cout << "http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" cerr << "Unable to connect to camera" << endl;
        return 0;
1;
    }

    image_window win;
    frontal_face_detector detector = get_frontal_face_detector();
     shape_predictor sp;
    deserialize(argv[1]) pose_model;
    deserialize("shape_predictor_68_face_landmarks.dat") >> sp;


    image_window win, win_faces;
pose_model;

    while(!win.is_closed())
    {
        cv::Mat temp;
        cap >> temp;

        cv_image<bgr_pixel> cimg(temp);

        // Loop over all Detect faces 
        std::vector<rectangle> faces = detector(cimg);
        // Find the images provided on the command line.
    for (int i = 2; i < argc; ++i)
    {
        cout << "processing image " << argv[i] << endl;
        array2d<rgb_pixel> img;
        load_image(img, argv[i]);
        pyramid_up(img);

        std::vector<rectangle> dets = detector(img);
        cout << "Number of faces detected: " << dets.size() << endl;


pose of each face.
        std::vector<full_object_detection> shapes;
           ofstream outputfile;
           outputfile.open("data1.csv");

        for (unsigned long j i = 0; j i < dets.size(); ++j)
        {
faces.size(); ++i)
      {  

               full_object_detection shape = sp(img, dets[j]);
pose_model(cimg, faces[i]);
               cout << "number of parts: "<< shape.num_parts() << endl;
 
        cout << "Eye Landmark points for right eye : "<< endl;
        cout << "pixel position of 36 part:  " << shape.part(36) << endl;
        cout << "pixel position of 37 part: " << shape.part(37) << endl;
        cout << "pixel position of 38 part:  " << shape.part(38) << endl;
        cout << "pixel position of 39 part: " << shape.part(39) << endl;
        cout << "pixel position of 40 part: " << shape.part(40) << endl;
        cout << "pixel position of 41 part: " << shape.part(41) << endl;

        cout << endl;

        cout << "Eye Landmark points for left eye : "<< endl;

        cout << "pixel position of 42 part:  " << shape.part(42) << endl;
        cout << "pixel position of 43 part: " << shape.part(43) << endl;
        cout << "pixel position of 44 part:  " << shape.part(44) << endl;
        cout << "pixel position of 45 part: " << shape.part(45) << endl;
        cout << "pixel position of 46 part: " << shape.part(46) << endl;
        cout << "pixel position of 47 part: " << shape.part(47) << endl;

            shapes.push_back(shape);



        double P37_41_x = shape.part(37).x() - shape.part(41).x();
        double P37_41_y=  shape.part(37).y() -shape.part(41).y() ;

        double p37_41_sqrt=sqrt((P37_41_x * P37_41_x) + (P37_41_y * P37_41_y));


       double P38_40_x = shape.part(38).x() - shape.part(40).x();
       double P38_40_y = shape.part(38).y() - shape.part(40).y();

       double p38_40_sqrt=sqrt((P38_40_x * P38_40_x) + (P38_40_y * P38_40_y));



      double P36_39_x = shape.part(36).x() - shape.part(39).x();  
      double P36_39_y = shape.part(36).y() - shape.part(39).y();

      double p36_39_sqrt=sqrt((P36_39_x * P36_39_x) + (P36_39_y * P36_39_y));



     double EAR= p37_41_sqrt +  p38_40_sqrt/2* p36_39_sqrt;


    cout << "EAR value =  " << EAR << endl;


  shapes.push_back(pose_model(cimg, faces[i]));


   const full_object_detection& d = shapes[0];
           ofstream outputfile;
           outputfile.open("data.txt");

            for (unsigned long k = 0; k < shape.num_parts(); ++k)
            {
                outputfile<< shape.part(k).x() << " " << shape.part(k).y() << endl;

             }

        }
        win.clear_overlay();
        win.set_image(img);
win.set_image(cimg);
        win.add_overlay(render_face_detections(shapes));

        dlib::array<array2d<rgb_pixel> > face_chips;
        extract_image_chips(img, get_face_chip_details(shapes), face_chips);
        win_faces.set_image(tile_images(face_chips));

        cout << "Hit enter to process the next image..." << endl;
        cin.get();
    }
}
catch (exception& catch(serialization_error& e)
{
    cout << "\nexception thrown!" << endl;
    cout "You need dlib's default face landmarking model file to run this example." << endl;
    cout << "You can get it from the following URL: " << endl;
    cout << "   http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;
    cout << endl << e.what() << endl;
}
catch(exception& e)
{
    cout << e.what() << endl;
}
 }
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