webgazer/src/ridgeReg.mjs

WebGazer.js is an eye tracking library that uses common webcams to infer the eye-gaze locations of web visitors on a page in real time. The eye tracking model it contains self-calibrates by watching web visitors interact with the web page and trains a map

import util from './util.mjs';
import util_regression from './util_regression.mjs';
import params from './params.mjs';

const reg = {};

/**
 * Constructor of RidgeReg object,
 * this object allow to perform ridge regression
 * @constructor
 */
reg.RidgeReg = function() {
  this.init();
};

/**
 * Initialize new arrays and initialize Kalman filter.
 */
reg.RidgeReg.prototype.init = util_regression.InitRegression

/**
 * Add given data from eyes
 * @param {Object} eyes - eyes where extract data to add
 * @param {Object} screenPos - The current screen point
 * @param {Object} type - The type of performed action
 */
reg.RidgeReg.prototype.addData = util_regression.addData

/**
 * Try to predict coordinates from pupil data
 * after apply linear regression on data set
 * @param {Object} eyesObj - The current user eyes object
 * @returns {Object}
 */
reg.RidgeReg.prototype.predict = function(eyesObj) {
  if (!eyesObj || this.eyeFeaturesClicks.length === 0) {
    return null;
  }
  var acceptTime = performance.now() - this.trailTime;
  var trailX = [];
  var trailY = [];
  var trailFeat = [];
  for (var i = 0; i < this.trailDataWindow; i++) {
    if (this.trailTimes.get(i) > acceptTime) {
      trailX.push(this.screenXTrailArray.get(i));
      trailY.push(this.screenYTrailArray.get(i));
      trailFeat.push(this.eyeFeaturesTrail.get(i));
    }
  }

  var screenXArray = this.screenXClicksArray.data.concat(trailX);
  var screenYArray = this.screenYClicksArray.data.concat(trailY);
  var eyeFeatures = this.eyeFeaturesClicks.data.concat(trailFeat);

  var coefficientsX = util_regression.ridge(screenXArray, eyeFeatures, this.ridgeParameter);
  var coefficientsY = util_regression.ridge(screenYArray, eyeFeatures, this.ridgeParameter);

  var eyeFeats = util.getEyeFeats(eyesObj);
  var predictedX = 0;
  for(var i=0; i< eyeFeats.length; i++){
    predictedX += eyeFeats[i] * coefficientsX[i];
  }
  var predictedY = 0;
  for(var i=0; i< eyeFeats.length; i++){
    predictedY += eyeFeats[i] * coefficientsY[i];
  }

  predictedX = Math.floor(predictedX);
  predictedY = Math.floor(predictedY);

  if (params.applyKalmanFilter) {
    // Update Kalman model, and get prediction
    var newGaze = [predictedX, predictedY]; // [20200607 xk] Should we use a 1x4 vector?
    newGaze = this.kalman.update(newGaze);

    return {
      x: newGaze[0],
      y: newGaze[1]
    };
  } else {
    return {
      x: predictedX,
      y: predictedY
    };
  }
};

reg.RidgeReg.prototype.setData = util_regression.setData;

/**
 * Return the data
 * @returns {Array.<Object>|*}
 */
reg.RidgeReg.prototype.getData = function() {
  return this.dataClicks.data;
}

/**
 * The RidgeReg object name
 * @type {string}
 */
reg.RidgeReg.prototype.name = 'ridge';

export default reg;