How to detect and track the motion of eyeball in OpenCV using C++?

Here, we will learn how to detect and track the motion of eyeball in OpenCV.

The following program demonstrates to detect the eyeball and track the location.

Example

#include<iostream>
#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/objdetect/objdetect.hpp>
#include<string>
using namespace cv;
using namespace std;
Vec3f eyeBallDetection(Mat& eye, vector<Vec3f>& circles) {
   vector<int>sums(circles.size(), 0);
   for (int y = 0; y < eye.rows; y++) {
      uchar* data = eye.ptr<uchar>(y);
      for (int x = 0; x < eye.cols; x++) {
         int pixel_value = static_cast<int>(*data);
         for (int i = 0; i < circles.size(); i++) {
            Point center((int)round(circles[i][0]), (int)round(circles[i][1]));
            int radius = (int)round(circles[i][2]);
            if (pow(x - center.x, 2) + pow(y - center.y, 2) < pow(radius, 2)) {
               sums[i] = sums[i] + pixel_value;
            }
         }
         ++data;
      }
   }
   int smallestSum = 9999999;
   int smallestSumIndex = -1;
   for (int i = 0; i < circles.size(); i++) {
      if (sums[i] < smallestSum) {
         smallestSum = sums[i];
         smallestSumIndex = i;
      }
   }
   return circles[smallestSumIndex];
}
Rect detectLeftEye(vector& eyes) {
   int leftEye = 99999999;
   int index;
   for (int i = 0; i < eyes.size(); i++) {
      if (eyes[i].tl().x < leftEye) {
         leftEye = eyes[i].tl().x;
         index = i;
      }
   }
   return eyes[index];
}
vector<Point>centers;
Point track_Eyeball;
Point makeStable(vector<Point>& points, int iteration) {
   float sum_of_X = 0;
   float sum_of_Y = 0;
   int count = 0;
   int j = max(0, (int)(points.size() - iteration));
   int number_of_points = points.size();
   for (j; j < number_of_points; j++) {
      sum_of_X = sum_of_X + points[j].x;
      sum_of_Y = sum_of_Y + points[j].y;
      ++count;
   }
   if (count > 0) {
      sum_of_X /= count;
      sum_of_Y /= count;
   }
   return Point(sum_of_X, sum_of_Y);
}
void eyeDetection(Mat& frame, CascadeClassifier& faceCascade, CascadeClassifier& eyeCascade) {
   Mat grayImage;
   cvtColor(frame, grayImage, COLOR_BGR2GRAY);
   equalizeHist(grayImage, grayImage);
   //Detecting the face//
   Mat inputImage = grayImage;
   vector<Rect>storedFaces;
   float scaleFactor = 1.1;
   int minimumNeighbour = 2;
   Size minImageSize = Size(150, 150);
   faceCascade.detectMultiScale(
      inputImage, storedFaces, scaleFactor, minimumNeighbour, 0 | CASCADE_SCALE_IMAGE, minImageSize);
   if (storedFaces.size() == 0)return;
   Mat face = grayImage(storedFaces[0]);
   //Drawing rectangle around the detected face//
   int x = storedFaces[0].x;
   int y = storedFaces[0].y;
   int h = y + storedFaces[0].height;
   int w = x + storedFaces[0].width;
   rectangle(frame, Point(x, y), Point(w, h), Scalar(255, 0, 255), 2, 8, 0);
   //Detecting the eyes//
   Mat faceRegion = face;
   vector<Rect>eyes;
   float eyeScaleFactor = 1.1;
   int eyeMinimumNeighbour = 2;
   Size eyeMinImageSize = Size(30, 30);
   eyeCascade.detectMultiScale(faceRegion, eyes, eyeScaleFactor, eyeMinimumNeighbour, 0 | CASCADE_SCALE_IMAGE, eyeMinImageSize);
   if (eyes.size() != 2)return;
   //Drawing the rectangle around the eyes//
   for (Rect& eye : eyes) {
      rectangle(frame, storedFaces[0].tl() + eye.tl(), storedFaces[0].tl() + eye.br(), Scalar(0, 255, 0), 2);
   }
   //Getting the left eye//
   Rect eyeRect = detectLeftEye(eyes);
   Mat eye = face(eyeRect);
   equalizeHist(eye, eye);
   //Applying Hough Circles to detect the circles in eye region//
   Mat hough_Circle_Input = eye;
   vector<Vec3f>circles;
   int method = 3;
   int detect_Pixel = 1;
   int minimum_Distance = eye.cols / 8;
   int threshold = 250;
   int minimum_Area = 15;
   int minimum_Radius = eye.rows / 8;
   int maximum_Radius = eye.rows / 3;
   HoughCircles(hough_Circle_Input, circles,
      HOUGH_GRADIENT, detect_Pixel, minimum_Distance, threshold, minimum_Area, minimum_Radius, maximum_Radius);
   //Detecting the drawing circle that encloses the eyeball//
   if (circles.size() > 0) {
      Vec3f eyeball = eyeBallDetection(eye, circles);
      Point center(eyeball[0], eyeball[1]);
      centers.push_back(center);
      center = makeStable(centers, 5);
      track_Eyeball = center;
      int radius = (int)eyeball[2];
      circle(frame, storedFaces[0].tl() + eyeRect.tl() + center, radius, Scalar(0, 0, 255), 2);
      circle(eye, center, radius, Scalar(255, 255, 255), 2);
   }
   cout << "The location of the eyeball is" << track_Eyeball << endl;
   imshow("Eye", eye);
}
int main() {
   //loading the cascade classifier//
   CascadeClassifier faceCascade;
   faceCascade.load("C:/opencv/sources/data/haarcascades/haarcascade_frontalface_alt.xml");
   CascadeClassifier eyeCascade;
   eyeCascade.load("C:/opencv/sources/data/haarcascades/haarcascade_eye.xml");
   //Capturing camera feed and calling eyeDetection function//
   VideoCapture cap(0);
   Mat frame;
   while (1) {
      cap >> frame;
      eyeDetection(frame, faceCascade, eyeCascade);
      imshow("Webcam", frame);
      if (waitKey(30) >= 0) break;
   }
   return 0;
}

Output