/**
 * tracking - A modern approach for Computer Vision on the web.
 * @author Eduardo Lundgren <edu@rdo.io>
 * @version v1.1.2
 * @link http://trackingjs.com
 * @license BSD
 */
(function(window, undefined) {
  window.tracking = window.tracking || {}

  /**
   * Inherit the prototype methods from one constructor into another.
   *
   * Usage:
   * <pre>
   * function ParentClass(a, b) { }
   * ParentClass.prototype.foo = function(a) { }
   *
   * function ChildClass(a, b, c) {
   *   tracking.base(this, a, b);
   * }
   * tracking.inherits(ChildClass, ParentClass);
   *
   * var child = new ChildClass('a', 'b', 'c');
   * child.foo();
   * </pre>
   *
   * @param {Function} childCtor Child class.
   * @param {Function} parentCtor Parent class.
   */
  tracking.inherits = function(childCtor, parentCtor) {
    function TempCtor() {
    }
    TempCtor.prototype = parentCtor.prototype
    childCtor.superClass_ = parentCtor.prototype
    childCtor.prototype = new TempCtor()
    childCtor.prototype.constructor = childCtor

    /**
     * Calls superclass constructor/method.
     *
     * This function is only available if you use tracking.inherits to express
     * inheritance relationships between classes.
     *
     * @param {!object} me Should always be "this".
     * @param {string} methodName The method name to call. Calling superclass
     *     constructor can be done with the special string 'constructor'.
     * @param {...*} var_args The arguments to pass to superclass
     *     method/constructor.
     * @return {*} The return value of the superclass method/constructor.
     */
    childCtor.base = function(me, methodName) {
      var args = Array.prototype.slice.call(arguments, 2)
      return parentCtor.prototype[methodName].apply(me, args)
    }
  }

  /**
   * Captures the user camera when tracking a video element and set its source
   * to the camera stream.
   * @param {HTMLVideoElement} element Canvas element to track.
   * @param {object} opt_options Optional configuration to the tracker.
   */
  tracking.initUserMedia_ = function(element, opt_options) {
    window.navigator.getUserMedia({
      video: true,
      audio: !!(opt_options && opt_options.audio)
    }, function(stream) {
      try {
        element.src = window.URL.createObjectURL(stream)
      } catch (err) {
        element.src = stream
      }
    }, function() {
      throw Error('Cannot capture user camera.')
    }
    )
  }

  /**
   * Tests whether the object is a dom node.
   * @param {object} o Object to be tested.
   * @return {boolean} True if the object is a dom node.
   */
  tracking.isNode = function(o) {
    return o.nodeType || this.isWindow(o)
  }

  /**
   * Tests whether the object is the `window` object.
   * @param {object} o Object to be tested.
   * @return {boolean} True if the object is the `window` object.
   */
  tracking.isWindow = function(o) {
    return !!(o && o.alert && o.document)
  }

  /**
   * Selects a dom node from a CSS3 selector using `document.querySelector`.
   * @param {string} selector
   * @param {object} opt_element The root element for the query. When not
   *     specified `document` is used as root element.
   * @return {HTMLElement} The first dom element that matches to the selector.
   *     If not found, returns `null`.
   */
  tracking.one = function(selector, opt_element) {
    if (this.isNode(selector)) {
      return selector
    }
    return (opt_element || document).querySelector(selector)
  }

  /**
   * Tracks a canvas, image or video element based on the specified `tracker`
   * instance. This method extract the pixel information of the input element
   * to pass to the `tracker` instance. When tracking a video, the
   * `tracker.track(pixels, width, height)` will be in a
   * `requestAnimationFrame` loop in order to track all video frames.
   *
   * Example:
   * var tracker = new tracking.ColorTracker();
   *
   * tracking.track('#video', tracker);
   * or
   * tracking.track('#video', tracker, { camera: true });
   *
   * tracker.on('track', function(event) {
   *   // console.log(event.data[0].x, event.data[0].y)
   * });
   *
   * @param {HTMLElement} element The element to track, canvas, image or
   *     video.
   * @param {tracking.Tracker} tracker The tracker instance used to track the
   *     element.
   * @param {object} opt_options Optional configuration to the tracker.
   */
  tracking.track = function(element, tracker, opt_options) {
    element = tracking.one(element)
    if (!element) {
      throw new Error('Element not found, try a different element or selector.')
    }
    if (!tracker) {
      throw new Error('Tracker not specified, try `tracking.track(element, new tracking.FaceTracker())`.')
    }

    switch (element.nodeName.toLowerCase()) {
      case 'canvas':
        return this.trackCanvas_(element, tracker, opt_options)
      case 'img':
        return this.trackImg_(element, tracker, opt_options)
      case 'video':
        if (opt_options) {
          if (opt_options.camera) {
            this.initUserMedia_(element, opt_options)
          }
        }
        return this.trackVideo_(element, tracker, opt_options)
      default:
        throw new Error('Element not supported, try in a canvas, img, or video.')
    }
  }

  /**
   * Tracks a canvas element based on the specified `tracker` instance and
   * returns a `TrackerTask` for this track.
   * @param {HTMLCanvasElement} element Canvas element to track.
   * @param {tracking.Tracker} tracker The tracker instance used to track the
   *     element.
   * @param {object} opt_options Optional configuration to the tracker.
   * @return {tracking.TrackerTask}
   * @private
   */
  tracking.trackCanvas_ = function(element, tracker) {
    var self = this
    var task = new tracking.TrackerTask(tracker)
    task.on('run', function() {
      self.trackCanvasInternal_(element, tracker)
    })
    return task.run()
  }

  /**
   * Tracks a canvas element based on the specified `tracker` instance. This
   * method extract the pixel information of the input element to pass to the
   * `tracker` instance.
   * @param {HTMLCanvasElement} element Canvas element to track.
   * @param {tracking.Tracker} tracker The tracker instance used to track the
   *     element.
   * @param {object} opt_options Optional configuration to the tracker.
   * @private
   */
  tracking.trackCanvasInternal_ = function(element, tracker) {
    var width = element.width
    var height = element.height
    var context = element.getContext('2d')
    var imageData = context.getImageData(0, 0, width, height)
    tracker.track(imageData.data, width, height)
  }

  /**
   * Tracks a image element based on the specified `tracker` instance. This
   * method extract the pixel information of the input element to pass to the
   * `tracker` instance.
   * @param {HTMLImageElement} element Canvas element to track.
   * @param {tracking.Tracker} tracker The tracker instance used to track the
   *     element.
   * @param {object} opt_options Optional configuration to the tracker.
   * @private
   */
  tracking.trackImg_ = function(element, tracker) {
    var width = element.width
    var height = element.height
    var canvas = document.createElement('canvas')

    canvas.width = width
    canvas.height = height

    var task = new tracking.TrackerTask(tracker)
    task.on('run', function() {
      tracking.Canvas.loadImage(canvas, element.src, 0, 0, width, height, function() {
        tracking.trackCanvasInternal_(canvas, tracker)
      })
    })
    return task.run()
  }

  /**
   * Tracks a video element based on the specified `tracker` instance. This
   * method extract the pixel information of the input element to pass to the
   * `tracker` instance. The `tracker.track(pixels, width, height)` will be in
   * a `requestAnimationFrame` loop in order to track all video frames.
   * @param {HTMLVideoElement} element Canvas element to track.
   * @param {tracking.Tracker} tracker The tracker instance used to track the
   *     element.
   * @param {object} opt_options Optional configuration to the tracker.
   * @private
   */
  tracking.trackVideo_ = function(element, tracker) {
    var canvas = document.createElement('canvas')
    var context = canvas.getContext('2d')
    var width
    var height

    var resizeCanvas_ = function() {
      width = element.offsetWidth
      height = element.offsetHeight
      canvas.width = width
      canvas.height = height
    }
    resizeCanvas_()
    element.addEventListener('resize', resizeCanvas_)

    var requestId
    var requestAnimationFrame_ = function() {
      requestId = window.requestAnimationFrame(function() {
        if (element.readyState === element.HAVE_ENOUGH_DATA) {
          try {
            // Firefox v~30.0 gets confused with the video readyState firing an
            // erroneous HAVE_ENOUGH_DATA just before HAVE_CURRENT_DATA state,
            // hence keep trying to read it until resolved.
            context.drawImage(element, 0, 0, width, height)
          } catch (err) {}
          tracking.trackCanvasInternal_(canvas, tracker)
        }
        requestAnimationFrame_()
      })
    }

    var task = new tracking.TrackerTask(tracker)
    task.on('stop', function() {
      window.cancelAnimationFrame(requestId)
    })
    task.on('run', function() {
      requestAnimationFrame_()
    })
    return task.run()
  }

  // Browser polyfills
  // ===================

  if (!window.URL) {
    window.URL = window.URL || window.webkitURL || window.msURL || window.oURL
  }

  if (!navigator.getUserMedia) {
    navigator.getUserMedia = navigator.getUserMedia || navigator.webkitGetUserMedia ||
    navigator.mozGetUserMedia || navigator.msGetUserMedia
  }
}(window));

(function() {
  /**
   * EventEmitter utility.
   * @constructor
   */
  tracking.EventEmitter = function() {}

  /**
   * Holds event listeners scoped by event type.
   * @type {object}
   * @private
   */
  tracking.EventEmitter.prototype.events_ = null

  /**
   * Adds a listener to the end of the listeners array for the specified event.
   * @param {string} event
   * @param {function} listener
   * @return {object} Returns emitter, so calls can be chained.
   */
  tracking.EventEmitter.prototype.addListener = function(event, listener) {
    if (typeof listener !== 'function') {
      throw new TypeError('Listener must be a function')
    }
    if (!this.events_) {
      this.events_ = {}
    }

    this.emit('newListener', event, listener)

    if (!this.events_[event]) {
      this.events_[event] = []
    }

    this.events_[event].push(listener)

    return this
  }

  /**
   * Returns an array of listeners for the specified event.
   * @param {string} event
   * @return {array} Array of listeners.
   */
  tracking.EventEmitter.prototype.listeners = function(event) {
    return this.events_ && this.events_[event]
  }

  /**
   * Execute each of the listeners in order with the supplied arguments.
   * @param {string} event
   * @param {*} opt_args [arg1], [arg2], [...]
   * @return {boolean} Returns true if event had listeners, false otherwise.
   */
  tracking.EventEmitter.prototype.emit = function(event) {
    var listeners = this.listeners(event)
    if (listeners) {
      var args = Array.prototype.slice.call(arguments, 1)
      for (var i = 0; i < listeners.length; i++) {
        if (listeners[i]) {
          listeners[i].apply(this, args)
        }
      }
      return true
    }
    return false
  }

  /**
   * Adds a listener to the end of the listeners array for the specified event.
   * @param {string} event
   * @param {function} listener
   * @return {object} Returns emitter, so calls can be chained.
   */
  tracking.EventEmitter.prototype.on = tracking.EventEmitter.prototype.addListener

  /**
   * Adds a one time listener for the event. This listener is invoked only the
   * next time the event is fired, after which it is removed.
   * @param {string} event
   * @param {function} listener
   * @return {object} Returns emitter, so calls can be chained.
   */
  tracking.EventEmitter.prototype.once = function(event, listener) {
    var self = this
    self.on(event, function handlerInternal() {
      self.removeListener(event, handlerInternal)
      listener.apply(this, arguments)
    })
  }

  /**
   * Removes all listeners, or those of the specified event. It's not a good
   * idea to remove listeners that were added elsewhere in the code,
   * especially when it's on an emitter that you didn't create.
   * @param {string} event
   * @return {object} Returns emitter, so calls can be chained.
   */
  tracking.EventEmitter.prototype.removeAllListeners = function(opt_event) {
    if (!this.events_) {
      return this
    }
    if (opt_event) {
      delete this.events_[opt_event]
    } else {
      delete this.events_
    }
    return this
  }

  /**
   * Remove a listener from the listener array for the specified event.
   * Caution: changes array indices in the listener array behind the listener.
   * @param {string} event
   * @param {function} listener
   * @return {object} Returns emitter, so calls can be chained.
   */
  tracking.EventEmitter.prototype.removeListener = function(event, listener) {
    if (typeof listener !== 'function') {
      throw new TypeError('Listener must be a function')
    }
    if (!this.events_) {
      return this
    }

    var listeners = this.listeners(event)
    if (Array.isArray(listeners)) {
      var i = listeners.indexOf(listener)
      if (i < 0) {
        return this
      }
      listeners.splice(i, 1)
    }

    return this
  }

  /**
   * By default EventEmitters will print a warning if more than 10 listeners
   * are added for a particular event. This is a useful default which helps
   * finding memory leaks. Obviously not all Emitters should be limited to 10.
   * This function allows that to be increased. Set to zero for unlimited.
   * @param {number} n The maximum number of listeners.
   */
  tracking.EventEmitter.prototype.setMaxListeners = function() {
    throw new Error('Not implemented')
  }
}());

(function() {
  /**
   * Canvas utility.
   * @static
   * @constructor
   */
  tracking.Canvas = {}

  /**
   * Loads an image source into the canvas.
   * @param {HTMLCanvasElement} canvas The canvas dom element.
   * @param {string} src The image source.
   * @param {number} x The canvas horizontal coordinate to load the image.
   * @param {number} y The canvas vertical coordinate to load the image.
   * @param {number} width The image width.
   * @param {number} height The image height.
   * @param {function} opt_callback Callback that fires when the image is loaded
   *     into the canvas.
   * @static
   */
  tracking.Canvas.loadImage = function(canvas, src, x, y, width, height, opt_callback) {
    var instance = this
    var img = new window.Image()
    img.crossOrigin = '*'
    img.onload = function() {
      var context = canvas.getContext('2d')
      canvas.width = width
      canvas.height = height
      context.drawImage(img, x, y, width, height)
      if (opt_callback) {
        opt_callback.call(instance)
      }
      img = null
    }
    img.src = src
  }
}());

(function() {
  /**
   * DisjointSet utility with path compression. Some applications involve
   * grouping n distinct objects into a collection of disjoint sets. Two
   * important operations are then finding which set a given object belongs to
   * and uniting the two sets. A disjoint set data structure maintains a
   * collection S={ S1 , S2 ,..., Sk } of disjoint dynamic sets. Each set is
   * identified by a representative, which usually is a member in the set.
   * @static
   * @constructor
   */
  tracking.DisjointSet = function(length) {
    if (length === undefined) {
      throw new Error('DisjointSet length not specified.')
    }
    this.length = length
    this.parent = new Uint32Array(length)
    for (var i = 0; i < length; i++) {
      this.parent[i] = i
    }
  }

  /**
   * Holds the length of the internal set.
   * @type {number}
   */
  tracking.DisjointSet.prototype.length = null

  /**
   * Holds the set containing the representative values.
   * @type {Array.<number>}
   */
  tracking.DisjointSet.prototype.parent = null

  /**
   * Finds a pointer to the representative of the set containing i.
   * @param {number} i
   * @return {number} The representative set of i.
   */
  tracking.DisjointSet.prototype.find = function(i) {
    if (this.parent[i] === i) {
      return i
    } else {
      return (this.parent[i] = this.find(this.parent[i]))
    }
  }

  /**
   * Unites two dynamic sets containing objects i and j, say Si and Sj, into
   * a new set that Si ∪ Sj, assuming that Si ∩ Sj = ∅;
   * @param {number} i
   * @param {number} j
   */
  tracking.DisjointSet.prototype.union = function(i, j) {
    var iRepresentative = this.find(i)
    var jRepresentative = this.find(j)
    this.parent[iRepresentative] = jRepresentative
  }
}());

(function() {
  /**
   * Image utility.
   * @static
   * @constructor
   */
  tracking.Image = {}

  /**
   * Computes gaussian blur. Adapted from
   * https://github.com/kig/canvasfilters.
   * @param {pixels} pixels The pixels in a linear [r,g,b,a,...] array.
   * @param {number} width The image width.
   * @param {number} height The image height.
   * @param {number} diameter Gaussian blur diameter, must be greater than 1.
   * @return {array} The edge pixels in a linear [r,g,b,a,...] array.
   */
  tracking.Image.blur = function(pixels, width, height, diameter) {
    diameter = Math.abs(diameter)
    if (diameter <= 1) {
      throw new Error('Diameter should be greater than 1.')
    }
    var radius = diameter / 2
    var len = Math.ceil(diameter) + (1 - (Math.ceil(diameter) % 2))
    var weights = new Float32Array(len)
    var rho = (radius + 0.5) / 3
    var rhoSq = rho * rho
    var gaussianFactor = 1 / Math.sqrt(2 * Math.PI * rhoSq)
    var rhoFactor = -1 / (2 * rho * rho)
    var wsum = 0
    var middle = Math.floor(len / 2)
    for (var i = 0; i < len; i++) {
      var x = i - middle
      var gx = gaussianFactor * Math.exp(x * x * rhoFactor)
      weights[i] = gx
      wsum += gx
    }
    for (var j = 0; j < weights.length; j++) {
      weights[j] /= wsum
    }
    return this.separableConvolve(pixels, width, height, weights, weights, false)
  }

  /**
   * Computes the integral image for summed, squared, rotated and sobel pixels.
   * @param {array} pixels The pixels in a linear [r,g,b,a,...] array to loop
   *     through.
   * @param {number} width The image width.
   * @param {number} height The image height.
   * @param {array} opt_integralImage Empty array of size `width * height` to
   *     be filled with the integral image values. If not specified compute sum
   *     values will be skipped.
   * @param {array} opt_integralImageSquare Empty array of size `width *
   *     height` to be filled with the integral image squared values. If not
   *     specified compute squared values will be skipped.
   * @param {array} opt_tiltedIntegralImage Empty array of size `width *
   *     height` to be filled with the rotated integral image values. If not
   *     specified compute sum values will be skipped.
   * @param {array} opt_integralImageSobel Empty array of size `width *
   *     height` to be filled with the integral image of sobel values. If not
   *     specified compute sobel filtering will be skipped.
   * @static
   */
  tracking.Image.computeIntegralImage = function(pixels, width, height, opt_integralImage, opt_integralImageSquare, opt_tiltedIntegralImage, opt_integralImageSobel) {
    if (arguments.length < 4) {
      throw new Error('You should specify at least one output array in the order: sum, square, tilted, sobel.')
    }
    var pixelsSobel
    if (opt_integralImageSobel) {
      pixelsSobel = tracking.Image.sobel(pixels, width, height)
    }
    for (var i = 0; i < height; i++) {
      for (var j = 0; j < width; j++) {
        var w = i * width * 4 + j * 4
        var pixel = ~~(pixels[w] * 0.299 + pixels[w + 1] * 0.587 + pixels[w + 2] * 0.114)
        if (opt_integralImage) {
          this.computePixelValueSAT_(opt_integralImage, width, i, j, pixel)
        }
        if (opt_integralImageSquare) {
          this.computePixelValueSAT_(opt_integralImageSquare, width, i, j, pixel * pixel)
        }
        if (opt_tiltedIntegralImage) {
          var w1 = w - width * 4
          var pixelAbove = ~~(pixels[w1] * 0.299 + pixels[w1 + 1] * 0.587 + pixels[w1 + 2] * 0.114)
          this.computePixelValueRSAT_(opt_tiltedIntegralImage, width, i, j, pixel, pixelAbove || 0)
        }
        if (opt_integralImageSobel) {
          this.computePixelValueSAT_(opt_integralImageSobel, width, i, j, pixelsSobel[w])
        }
      }
    }
  }

  /**
   * Helper method to compute the rotated summed area table (RSAT) by the
   * formula:
   *
   * RSAT(x, y) = RSAT(x-1, y-1) + RSAT(x+1, y-1) - RSAT(x, y-2) + I(x, y) + I(x, y-1)
   *
   * @param {number} width The image width.
   * @param {array} RSAT Empty array of size `width * height` to be filled with
   *     the integral image values. If not specified compute sum values will be
   *     skipped.
   * @param {number} i Vertical position of the pixel to be evaluated.
   * @param {number} j Horizontal position of the pixel to be evaluated.
   * @param {number} pixel Pixel value to be added to the integral image.
   * @static
   * @private
   */
  tracking.Image.computePixelValueRSAT_ = function(RSAT, width, i, j, pixel, pixelAbove) {
    var w = i * width + j
    RSAT[w] = (RSAT[w - width - 1] || 0) + (RSAT[w - width + 1] || 0) - (RSAT[w - width - width] || 0) + pixel + pixelAbove
  }

  /**
   * Helper method to compute the summed area table (SAT) by the formula:
   *
   * SAT(x, y) = SAT(x, y-1) + SAT(x-1, y) + I(x, y) - SAT(x-1, y-1)
   *
   * @param {number} width The image width.
   * @param {array} SAT Empty array of size `width * height` to be filled with
   *     the integral image values. If not specified compute sum values will be
   *     skipped.
   * @param {number} i Vertical position of the pixel to be evaluated.
   * @param {number} j Horizontal position of the pixel to be evaluated.
   * @param {number} pixel Pixel value to be added to the integral image.
   * @static
   * @private
   */
  tracking.Image.computePixelValueSAT_ = function(SAT, width, i, j, pixel) {
    var w = i * width + j
    SAT[w] = (SAT[w - width] || 0) + (SAT[w - 1] || 0) + pixel - (SAT[w - width - 1] || 0)
  }

  /**
   * Converts a color from a colorspace based on an RGB color model to a
   * grayscale representation of its luminance. The coefficients represent the
   * measured intensity perception of typical trichromat humans, in
   * particular, human vision is most sensitive to green and least sensitive
   * to blue.
   * @param {pixels} pixels The pixels in a linear [r,g,b,a,...] array.
   * @param {number} width The image width.
   * @param {number} height The image height.
   * @param {boolean} fillRGBA If the result should fill all RGBA values with the gray scale
   *  values, instead of returning a single value per pixel.
   * @param {Uint8ClampedArray} The grayscale pixels in a linear array ([p,p,p,a,...] if fillRGBA
   *  is true and [p1, p2, p3, ...] if fillRGBA is false).
   * @static
   */
  tracking.Image.grayscale = function(pixels, width, height, fillRGBA) {
    var gray = new Uint8ClampedArray(fillRGBA ? pixels.length : pixels.length >> 2)
    var p = 0
    var w = 0
    for (var i = 0; i < height; i++) {
      for (var j = 0; j < width; j++) {
        var value = pixels[w] * 0.299 + pixels[w + 1] * 0.587 + pixels[w + 2] * 0.114
        gray[p++] = value

        if (fillRGBA) {
          gray[p++] = value
          gray[p++] = value
          gray[p++] = pixels[w + 3]
        }

        w += 4
      }
    }
    return gray
  }

  /**
   * Fast horizontal separable convolution. A point spread function (PSF) is
   * said to be separable if it can be broken into two one-dimensional
   * signals: a vertical and a horizontal projection. The convolution is
   * performed by sliding the kernel over the image, generally starting at the
   * top left corner, so as to move the kernel through all the positions where
   * the kernel fits entirely within the boundaries of the image. Adapted from
   * https://github.com/kig/canvasfilters.
   * @param {pixels} pixels The pixels in a linear [r,g,b,a,...] array.
   * @param {number} width The image width.
   * @param {number} height The image height.
   * @param {array} weightsVector The weighting vector, e.g [-1,0,1].
   * @param {number} opaque
   * @return {array} The convoluted pixels in a linear [r,g,b,a,...] array.
   */
  tracking.Image.horizontalConvolve = function(pixels, width, height, weightsVector, opaque) {
    var side = weightsVector.length
    var halfSide = Math.floor(side / 2)
    var output = new Float32Array(width * height * 4)
    var alphaFac = opaque ? 1 : 0

    for (var y = 0; y < height; y++) {
      for (var x = 0; x < width; x++) {
        var sy = y
        var sx = x
        var offset = (y * width + x) * 4
        var r = 0
        var g = 0
        var b = 0
        var a = 0
        for (var cx = 0; cx < side; cx++) {
          var scy = sy
          var scx = Math.min(width - 1, Math.max(0, sx + cx - halfSide))
          var poffset = (scy * width + scx) * 4
          var wt = weightsVector[cx]
          r += pixels[poffset] * wt
          g += pixels[poffset + 1] * wt
          b += pixels[poffset + 2] * wt
          a += pixels[poffset + 3] * wt
        }
        output[offset] = r
        output[offset + 1] = g
        output[offset + 2] = b
        output[offset + 3] = a + alphaFac * (255 - a)
      }
    }
    return output
  }

  /**
   * Fast vertical separable convolution. A point spread function (PSF) is
   * said to be separable if it can be broken into two one-dimensional
   * signals: a vertical and a horizontal projection. The convolution is
   * performed by sliding the kernel over the image, generally starting at the
   * top left corner, so as to move the kernel through all the positions where
   * the kernel fits entirely within the boundaries of the image. Adapted from
   * https://github.com/kig/canvasfilters.
   * @param {pixels} pixels The pixels in a linear [r,g,b,a,...] array.
   * @param {number} width The image width.
   * @param {number} height The image height.
   * @param {array} weightsVector The weighting vector, e.g [-1,0,1].
   * @param {number} opaque
   * @return {array} The convoluted pixels in a linear [r,g,b,a,...] array.
   */
  tracking.Image.verticalConvolve = function(pixels, width, height, weightsVector, opaque) {
    var side = weightsVector.length
    var halfSide = Math.floor(side / 2)
    var output = new Float32Array(width * height * 4)
    var alphaFac = opaque ? 1 : 0

    for (var y = 0; y < height; y++) {
      for (var x = 0; x < width; x++) {
        var sy = y
        var sx = x
        var offset = (y * width + x) * 4
        var r = 0
        var g = 0
        var b = 0
        var a = 0
        for (var cy = 0; cy < side; cy++) {
          var scy = Math.min(height - 1, Math.max(0, sy + cy - halfSide))
          var scx = sx
          var poffset = (scy * width + scx) * 4
          var wt = weightsVector[cy]
          r += pixels[poffset] * wt
          g += pixels[poffset + 1] * wt
          b += pixels[poffset + 2] * wt
          a += pixels[poffset + 3] * wt
        }
        output[offset] = r
        output[offset + 1] = g
        output[offset + 2] = b
        output[offset + 3] = a + alphaFac * (255 - a)
      }
    }
    return output
  }

  /**
   * Fast separable convolution. A point spread function (PSF) is said to be
   * separable if it can be broken into two one-dimensional signals: a
   * vertical and a horizontal projection. The convolution is performed by
   * sliding the kernel over the image, generally starting at the top left
   * corner, so as to move the kernel through all the positions where the
   * kernel fits entirely within the boundaries of the image. Adapted from
   * https://github.com/kig/canvasfilters.
   * @param {pixels} pixels The pixels in a linear [r,g,b,a,...] array.
   * @param {number} width The image width.
   * @param {number} height The image height.
   * @param {array} horizWeights The horizontal weighting vector, e.g [-1,0,1].
   * @param {array} vertWeights The vertical vector, e.g [-1,0,1].
   * @param {number} opaque
   * @return {array} The convoluted pixels in a linear [r,g,b,a,...] array.
   */
  tracking.Image.separableConvolve = function(pixels, width, height, horizWeights, vertWeights, opaque) {
    var vertical = this.verticalConvolve(pixels, width, height, vertWeights, opaque)
    return this.horizontalConvolve(vertical, width, height, horizWeights, opaque)
  }

  /**
   * Compute image edges using Sobel operator. Computes the vertical and
   * horizontal gradients of the image and combines the computed images to
   * find edges in the image. The way we implement the Sobel filter here is by
   * first grayscaling the image, then taking the horizontal and vertical
   * gradients and finally combining the gradient images to make up the final
   * image. Adapted from https://github.com/kig/canvasfilters.
   * @param {pixels} pixels The pixels in a linear [r,g,b,a,...] array.
   * @param {number} width The image width.
   * @param {number} height The image height.
   * @return {array} The edge pixels in a linear [r,g,b,a,...] array.
   */
  tracking.Image.sobel = function(pixels, width, height) {
    pixels = this.grayscale(pixels, width, height, true)
    var output = new Float32Array(width * height * 4)
    var sobelSignVector = new Float32Array([-1, 0, 1])
    var sobelScaleVector = new Float32Array([1, 2, 1])
    var vertical = this.separableConvolve(pixels, width, height, sobelSignVector, sobelScaleVector)
    var horizontal = this.separableConvolve(pixels, width, height, sobelScaleVector, sobelSignVector)

    for (var i = 0; i < output.length; i += 4) {
      var v = vertical[i]
      var h = horizontal[i]
      var p = Math.sqrt(h * h + v * v)
      output[i] = p
      output[i + 1] = p
      output[i + 2] = p
      output[i + 3] = 255
    }

    return output
  }
}());

(function() {
  /**
   * ViolaJones utility.
   * @static
   * @constructor
   */
  tracking.ViolaJones = {}

  /**
   * Holds the minimum area of intersection that defines when a rectangle is
   * from the same group. Often when a face is matched multiple rectangles are
   * classified as possible rectangles to represent the face, when they
   * intersects they are grouped as one face.
   * @type {number}
   * @default 0.5
   * @static
   */
  tracking.ViolaJones.REGIONS_OVERLAP = 0.5

  /**
   * Holds the HAAR cascade classifiers converted from OpenCV training.
   * @type {array}
   * @static
   */
  tracking.ViolaJones.classifiers = {}

  /**
   * Detects through the HAAR cascade data rectangles matches.
   * @param {pixels} pixels The pixels in a linear [r,g,b,a,...] array.
   * @param {number} width The image width.
   * @param {number} height The image height.
   * @param {number} initialScale The initial scale to start the block
   *     scaling.
   * @param {number} scaleFactor The scale factor to scale the feature block.
   * @param {number} stepSize The block step size.
   * @param {number} edgesDensity Percentage density edges inside the
   *     classifier block. Value from [0.0, 1.0], defaults to 0.2. If specified
   *     edge detection will be applied to the image to prune dead areas of the
   *     image, this can improve significantly performance.
   * @param {number} data The HAAR cascade data.
   * @return {array} Found rectangles.
   * @static
   */
  tracking.ViolaJones.detect = function(pixels, width, height, initialScale, scaleFactor, stepSize, edgesDensity, data) {
    var total = 0
    var rects = []
    var integralImage = new Int32Array(width * height)
    var integralImageSquare = new Int32Array(width * height)
    var tiltedIntegralImage = new Int32Array(width * height)

    var integralImageSobel
    if (edgesDensity > 0) {
      integralImageSobel = new Int32Array(width * height)
    }

    tracking.Image.computeIntegralImage(pixels, width, height, integralImage, integralImageSquare, tiltedIntegralImage, integralImageSobel)

    var minWidth = data[0]
    var minHeight = data[1]
    var scale = initialScale * scaleFactor
    var blockWidth = (scale * minWidth) | 0
    var blockHeight = (scale * minHeight) | 0

    while (blockWidth < width && blockHeight < height) {
      var step = (scale * stepSize + 0.5) | 0
      for (var i = 0; i < (height - blockHeight); i += step) {
        for (var j = 0; j < (width - blockWidth); j += step) {
          if (edgesDensity > 0) {
            if (this.isTriviallyExcluded(edgesDensity, integralImageSobel, i, j, width, blockWidth, blockHeight)) {
              continue
            }
          }

          if (this.evalStages_(data, integralImage, integralImageSquare, tiltedIntegralImage, i, j, width, blockWidth, blockHeight, scale)) {
            rects[total++] = {
              width: blockWidth,
              height: blockHeight,
              x: j,
              y: i
            }
          }
        }
      }

      scale *= scaleFactor
      blockWidth = (scale * minWidth) | 0
      blockHeight = (scale * minHeight) | 0
    }
    return this.mergeRectangles_(rects)
  }

  /**
   * Fast check to test whether the edges density inside the block is greater
   * than a threshold, if true it tests the stages. This can improve
   * significantly performance.
   * @param {number} edgesDensity Percentage density edges inside the
   *     classifier block.
   * @param {array} integralImageSobel The integral image of a sobel image.
   * @param {number} i Vertical position of the pixel to be evaluated.
   * @param {number} j Horizontal position of the pixel to be evaluated.
   * @param {number} width The image width.
   * @return {boolean} True whether the block at position i,j can be skipped,
   *     false otherwise.
   * @static
   * @protected
   */
  tracking.ViolaJones.isTriviallyExcluded = function(edgesDensity, integralImageSobel, i, j, width, blockWidth, blockHeight) {
    var wbA = i * width + j
    var wbB = wbA + blockWidth
    var wbD = wbA + blockHeight * width
    var wbC = wbD + blockWidth
    var blockEdgesDensity = (integralImageSobel[wbA] - integralImageSobel[wbB] - integralImageSobel[wbD] + integralImageSobel[wbC]) / (blockWidth * blockHeight * 255)
    if (blockEdgesDensity < edgesDensity) {
      return true
    }
    return false
  }

  /**
   * Evaluates if the block size on i,j position is a valid HAAR cascade
   * stage.
   * @param {number} data The HAAR cascade data.
   * @param {number} i Vertical position of the pixel to be evaluated.
   * @param {number} j Horizontal position of the pixel to be evaluated.
   * @param {number} width The image width.
   * @param {number} blockSize The block size.
   * @param {number} scale The scale factor of the block size and its original
   *     size.
   * @param {number} inverseArea The inverse area of the block size.
   * @return {boolean} Whether the region passes all the stage tests.
   * @private
   * @static
   */
  tracking.ViolaJones.evalStages_ = function(data, integralImage, integralImageSquare, tiltedIntegralImage, i, j, width, blockWidth, blockHeight, scale) {
    var inverseArea = 1.0 / (blockWidth * blockHeight)
    var wbA = i * width + j
    var wbB = wbA + blockWidth
    var wbD = wbA + blockHeight * width
    var wbC = wbD + blockWidth
    var mean = (integralImage[wbA] - integralImage[wbB] - integralImage[wbD] + integralImage[wbC]) * inverseArea
    var variance = (integralImageSquare[wbA] - integralImageSquare[wbB] - integralImageSquare[wbD] + integralImageSquare[wbC]) * inverseArea - mean * mean

    var standardDeviation = 1
    if (variance > 0) {
      standardDeviation = Math.sqrt(variance)
    }

    var length = data.length

    for (var w = 2; w < length;) {
      var stageSum = 0
      var stageThreshold = data[w++]
      var nodeLength = data[w++]

      while (nodeLength--) {
        var rectsSum = 0
        var tilted = data[w++]
        var rectsLength = data[w++]

        for (var r = 0; r < rectsLength; r++) {
          var rectLeft = (j + data[w++] * scale + 0.5) | 0
          var rectTop = (i + data[w++] * scale + 0.5) | 0
          var rectWidth = (data[w++] * scale + 0.5) | 0
          var rectHeight = (data[w++] * scale + 0.5) | 0
          var rectWeight = data[w++]

          var w1
          var w2
          var w3
          var w4
          if (tilted) {
            // RectSum(r) = RSAT(x-h+w, y+w+h-1) + RSAT(x, y-1) - RSAT(x-h, y+h-1) - RSAT(x+w, y+w-1)
            w1 = (rectLeft - rectHeight + rectWidth) + (rectTop + rectWidth + rectHeight - 1) * width
            w2 = rectLeft + (rectTop - 1) * width
            w3 = (rectLeft - rectHeight) + (rectTop + rectHeight - 1) * width
            w4 = (rectLeft + rectWidth) + (rectTop + rectWidth - 1) * width
            rectsSum += (tiltedIntegralImage[w1] + tiltedIntegralImage[w2] - tiltedIntegralImage[w3] - tiltedIntegralImage[w4]) * rectWeight
          } else {
            // RectSum(r) = SAT(x-1, y-1) + SAT(x+w-1, y+h-1) - SAT(x-1, y+h-1) - SAT(x+w-1, y-1)
            w1 = rectTop * width + rectLeft
            w2 = w1 + rectWidth
            w3 = w1 + rectHeight * width
            w4 = w3 + rectWidth
            rectsSum += (integralImage[w1] - integralImage[w2] - integralImage[w3] + integralImage[w4]) * rectWeight
            // TODO: Review the code below to analyze performance when using it instead.
            // w1 = (rectLeft - 1) + (rectTop - 1) * width;
            // w2 = (rectLeft + rectWidth - 1) + (rectTop + rectHeight - 1) * width;
            // w3 = (rectLeft - 1) + (rectTop + rectHeight - 1) * width;
            // w4 = (rectLeft + rectWidth - 1) + (rectTop - 1) * width;
            // rectsSum += (integralImage[w1] + integralImage[w2] - integralImage[w3] - integralImage[w4]) * rectWeight;
          }
        }

        var nodeThreshold = data[w++]
        var nodeLeft = data[w++]
        var nodeRight = data[w++]

        if (rectsSum * inverseArea < nodeThreshold * standardDeviation) {
          stageSum += nodeLeft
        } else {
          stageSum += nodeRight
        }
      }

      if (stageSum < stageThreshold) {
        return false
      }
    }
    return true
  }

  /**
   * Postprocess the detected sub-windows in order to combine overlapping
   * detections into a single detection.
   * @param {array} rects
   * @return {array}
   * @private
   * @static
   */
  tracking.ViolaJones.mergeRectangles_ = function(rects) {
    var disjointSet = new tracking.DisjointSet(rects.length)

    for (var i = 0; i < rects.length; i++) {
      var r1 = rects[i]
      for (var j = 0; j < rects.length; j++) {
        var r2 = rects[j]
        if (tracking.Math.intersectRect(r1.x, r1.y, r1.x + r1.width, r1.y + r1.height, r2.x, r2.y, r2.x + r2.width, r2.y + r2.height)) {
          var x1 = Math.max(r1.x, r2.x)
          var y1 = Math.max(r1.y, r2.y)
          var x2 = Math.min(r1.x + r1.width, r2.x + r2.width)
          var y2 = Math.min(r1.y + r1.height, r2.y + r2.height)
          var overlap = (x1 - x2) * (y1 - y2)
          var area1 = (r1.width * r1.height)
          var area2 = (r2.width * r2.height)

          if ((overlap / (area1 * (area1 / area2)) >= this.REGIONS_OVERLAP) &&
            (overlap / (area2 * (area1 / area2)) >= this.REGIONS_OVERLAP)) {
            disjointSet.union(i, j)
          }
        }
      }
    }

    var map = {}
    for (var k = 0; k < disjointSet.length; k++) {
      var rep = disjointSet.find(k)
      if (!map[rep]) {
        map[rep] = {
          total: 1,
          width: rects[k].width,
          height: rects[k].height,
          x: rects[k].x,
          y: rects[k].y
        }
        continue
      }
      map[rep].total++
      map[rep].width += rects[k].width
      map[rep].height += rects[k].height
      map[rep].x += rects[k].x
      map[rep].y += rects[k].y
    }

    var result = []
    Object.keys(map).forEach(function(key) {
      var rect = map[key]
      result.push({
        total: rect.total,
        width: (rect.width / rect.total + 0.5) | 0,
        height: (rect.height / rect.total + 0.5) | 0,
        x: (rect.x / rect.total + 0.5) | 0,
        y: (rect.y / rect.total + 0.5) | 0
      })
    })

    return result
  }
}());

(function() {
  /**
   * Brief intends for "Binary Robust Independent Elementary Features".This
   * method generates a binary string for each keypoint found by an extractor
   * method.
   * @static
   * @constructor
   */
  tracking.Brief = {}

  /**
   * The set of binary tests is defined by the nd (x,y)-location pairs
   * uniquely chosen during the initialization. Values could vary between N =
   * 128,256,512. N=128 yield good compromises between speed, storage
   * efficiency, and recognition rate.
   * @type {number}
   */
  tracking.Brief.N = 512

  /**
   * Caches coordinates values of (x,y)-location pairs uniquely chosen during
   * the initialization.
   * @type {Object.<number, Int32Array>}
   * @private
   * @static
   */
  tracking.Brief.randomImageOffsets_ = {}

  /**
   * Caches delta values of (x,y)-location pairs uniquely chosen during
   * the initialization.
   * @type {Int32Array}
   * @private
   * @static
   */
  tracking.Brief.randomWindowOffsets_ = null

  /**
   * Generates a binary string for each found keypoints extracted using an
   * extractor method.
   * @param {array} The grayscale pixels in a linear [p1,p2,...] array.
   * @param {number} width The image width.
   * @param {array} keypoints
   * @return {Int32Array} Returns an array where for each four sequence int
   *     values represent the descriptor binary string (128 bits) necessary
   *     to describe the corner, e.g. [0,0,0,0, 0,0,0,0, ...].
   * @static
   */
  tracking.Brief.getDescriptors = function(pixels, width, keypoints) {
    // Optimizing divide by 32 operation using binary shift
    // (this.N >> 5) === this.N/32.
    var descriptors = new Int32Array((keypoints.length >> 1) * (this.N >> 5))
    var descriptorWord = 0
    var offsets = this.getRandomOffsets_(width)
    var position = 0

    for (var i = 0; i < keypoints.length; i += 2) {
      var w = width * keypoints[i + 1] + keypoints[i]

      var offsetsPosition = 0
      for (var j = 0, n = this.N; j < n; j++) {
        if (pixels[offsets[offsetsPosition++] + w] < pixels[offsets[offsetsPosition++] + w]) {
          // The bit in the position `j % 32` of descriptorWord should be set to 1. We do
          // this by making an OR operation with a binary number that only has the bit
          // in that position set to 1. That binary number is obtained by shifting 1 left by
          // `j % 32` (which is the same as `j & 31` left) positions.
          descriptorWord |= 1 << (j & 31)
        }

        // If the next j is a multiple of 32, we will need to use a new descriptor word to hold
        // the next results.
        if (!((j + 1) & 31)) {
          descriptors[position++] = descriptorWord
          descriptorWord = 0
        }
      }
    }

    return descriptors
  }

  /**
   * Matches sets of features {mi} and {m′j} extracted from two images taken
   * from similar, and often successive, viewpoints. A classical procedure
   * runs as follows. For each point {mi} in the first image, search in a
   * region of the second image around location {mi} for point {m′j}. The
   * search is based on the similarity of the local image windows, also known
   * as kernel windows, centered on the points, which strongly characterizes
   * the points when the images are sufficiently close. Once each keypoint is
   * described with its binary string, they need to be compared with the
   * closest matching point. Distance metric is critical to the performance of
   * in- trusion detection systems. Thus using binary strings reduces the size
   * of the descriptor and provides an interesting data structure that is fast
   * to operate whose similarity can be measured by the Hamming distance.
   * @param {array} keypoints1
   * @param {array} descriptors1
   * @param {array} keypoints2
   * @param {array} descriptors2
   * @return {Int32Array} Returns an array where the index is the corner1
   *     index coordinate, and the value is the corresponding match index of
   *     corner2, e.g. keypoints1=[x0,y0,x1,y1,...] and
   *     keypoints2=[x'0,y'0,x'1,y'1,...], if x0 matches x'1 and x1 matches x'0,
   *     the return array would be [3,0].
   * @static
   */
  tracking.Brief.match = function(keypoints1, descriptors1, keypoints2, descriptors2) {
    var len1 = keypoints1.length >> 1
    var len2 = keypoints2.length >> 1
    var matches = new Array(len1)

    for (var i = 0; i < len1; i++) {
      var min = Infinity
      var minj = 0
      for (var j = 0; j < len2; j++) {
        var dist = 0
        // Optimizing divide by 32 operation using binary shift
        // (this.N >> 5) === this.N/32.
        for (var k = 0, n = this.N >> 5; k < n; k++) {
          dist += tracking.Math.hammingWeight(descriptors1[i * n + k] ^ descriptors2[j * n + k])
        }
        if (dist < min) {
          min = dist
          minj = j
        }
      }
      matches[i] = {
        index1: i,
        index2: minj,
        keypoint1: [keypoints1[2 * i], keypoints1[2 * i + 1]],
        keypoint2: [keypoints2[2 * minj], keypoints2[2 * minj + 1]],
        confidence: 1 - min / this.N
      }
    }

    return matches
  }

  /**
   * Removes matches outliers by testing matches on both directions.
   * @param {array} keypoints1
   * @param {array} descriptors1
   * @param {array} keypoints2
   * @param {array} descriptors2
   * @return {Int32Array} Returns an array where the index is the corner1
   *     index coordinate, and the value is the corresponding match index of
   *     corner2, e.g. keypoints1=[x0,y0,x1,y1,...] and
   *     keypoints2=[x'0,y'0,x'1,y'1,...], if x0 matches x'1 and x1 matches x'0,
   *     the return array would be [3,0].
   * @static
   */
  tracking.Brief.reciprocalMatch = function(keypoints1, descriptors1, keypoints2, descriptors2) {
    var matches = []
    if (keypoints1.length === 0 || keypoints2.length === 0) {
      return matches
    }

    var matches1 = tracking.Brief.match(keypoints1, descriptors1, keypoints2, descriptors2)
    var matches2 = tracking.Brief.match(keypoints2, descriptors2, keypoints1, descriptors1)
    for (var i = 0; i < matches1.length; i++) {
      if (matches2[matches1[i].index2].index2 === i) {
        matches.push(matches1[i])
      }
    }
    return matches
  }

  /**
   * Gets the coordinates values of (x,y)-location pairs uniquely chosen
   * during the initialization.
   * @return {array} Array with the random offset values.
   * @private
   */
  tracking.Brief.getRandomOffsets_ = function(width) {
    if (!this.randomWindowOffsets_) {
      var windowPosition = 0
      var windowOffsets = new Int32Array(4 * this.N)
      for (var i = 0; i < this.N; i++) {
        windowOffsets[windowPosition++] = Math.round(tracking.Math.uniformRandom(-15, 16))
        windowOffsets[windowPosition++] = Math.round(tracking.Math.uniformRandom(-15, 16))
        windowOffsets[windowPosition++] = Math.round(tracking.Math.uniformRandom(-15, 16))
        windowOffsets[windowPosition++] = Math.round(tracking.Math.uniformRandom(-15, 16))
      }
      this.randomWindowOffsets_ = windowOffsets
    }

    if (!this.randomImageOffsets_[width]) {
      var imagePosition = 0
      var imageOffsets = new Int32Array(2 * this.N)
      for (var j = 0; j < this.N; j++) {
        imageOffsets[imagePosition++] = this.randomWindowOffsets_[4 * j] * width + this.randomWindowOffsets_[4 * j + 1]
        imageOffsets[imagePosition++] = this.randomWindowOffsets_[4 * j + 2] * width + this.randomWindowOffsets_[4 * j + 3]
      }
      this.randomImageOffsets_[width] = imageOffsets
    }

    return this.randomImageOffsets_[width]
  }
}());

(function() {
  /**
   * FAST intends for "Features from Accelerated Segment Test". This method
   * performs a point segment test corner detection. The segment test
   * criterion operates by considering a circle of sixteen pixels around the
   * corner candidate p. The detector classifies p as a corner if there exists
   * a set of n contiguous pixelsin the circle which are all brighter than the
   * intensity of the candidate pixel Ip plus a threshold t, or all darker
   * than Ip − t.
   *
   *       15 00 01
   *    14          02
   * 13                03
   * 12       []       04
   * 11                05
   *    10          06
   *       09 08 07
   *
   * For more reference:
   * http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.60.3991&rep=rep1&type=pdf
   * @static
   * @constructor
   */
  tracking.Fast = {}

  /**
   * Holds the threshold to determine whether the tested pixel is brighter or
   * darker than the corner candidate p.
   * @type {number}
   * @default 40
   * @static
   */
  tracking.Fast.THRESHOLD = 40

  /**
   * Caches coordinates values of the circle surrounding the pixel candidate p.
   * @type {Object.<number, Int32Array>}
   * @private
   * @static
   */
  tracking.Fast.circles_ = {}

  /**
   * Finds corners coordinates on the graysacaled image.
   * @param {array} The grayscale pixels in a linear [p1,p2,...] array.
   * @param {number} width The image width.
   * @param {number} height The image height.
   * @param {number} threshold to determine whether the tested pixel is brighter or
   *     darker than the corner candidate p. Default value is 40.
   * @return {array} Array containing the coordinates of all found corners,
   *     e.g. [x0,y0,x1,y1,...], where P(x0,y0) represents a corner coordinate.
   * @static
   */
  tracking.Fast.findCorners = function(pixels, width, height, opt_threshold) {
    var circleOffsets = this.getCircleOffsets_(width)
    var circlePixels = new Int32Array(16)
    var corners = []

    if (opt_threshold === undefined) {
      opt_threshold = this.THRESHOLD
    }

    // When looping through the image pixels, skips the first three lines from
    // the image boundaries to constrain the surrounding circle inside the image
    // area.
    for (var i = 3; i < height - 3; i++) {
      for (var j = 3; j < width - 3; j++) {
        var w = i * width + j
        var p = pixels[w]

        // Loops the circle offsets to read the pixel value for the sixteen
        // surrounding pixels.
        for (var k = 0; k < 16; k++) {
          circlePixels[k] = pixels[w + circleOffsets[k]]
        }

        if (this.isCorner(p, circlePixels, opt_threshold)) {
          // The pixel p is classified as a corner, as optimization increment j
          // by the circle radius 3 to skip the neighbor pixels inside the
          // surrounding circle. This can be removed without compromising the
          // result.
          corners.push(j, i)
          j += 3
        }
      }
    }

    return corners
  }

  /**
   * Checks if the circle pixel is brighter than the candidate pixel p by
   * a threshold.
   * @param {number} circlePixel The circle pixel value.
   * @param {number} p The value of the candidate pixel p.
   * @param {number} threshold
   * @return {Boolean}
   * @static
   */
  tracking.Fast.isBrighter = function(circlePixel, p, threshold) {
    return circlePixel - p > threshold
  }

  /**
   * Checks if the circle pixel is within the corner of the candidate pixel p
   * by a threshold.
   * @param {number} p The value of the candidate pixel p.
   * @param {number} circlePixel The circle pixel value.
   * @param {number} threshold
   * @return {Boolean}
   * @static
   */
  tracking.Fast.isCorner = function(p, circlePixels, threshold) {
    if (this.isTriviallyExcluded(circlePixels, p, threshold)) {
      return false
    }

    for (var x = 0; x < 16; x++) {
      var darker = true
      var brighter = true

      for (var y = 0; y < 9; y++) {
        var circlePixel = circlePixels[(x + y) & 15]

        if (!this.isBrighter(p, circlePixel, threshold)) {
          brighter = false
          if (darker === false) {
            break
          }
        }

        if (!this.isDarker(p, circlePixel, threshold)) {
          darker = false
          if (brighter === false) {
            break
          }
        }
      }

      if (brighter || darker) {
        return true
      }
    }

    return false
  }

  /**
   * Checks if the circle pixel is darker than the candidate pixel p by
   * a threshold.
   * @param {number} circlePixel The circle pixel value.
   * @param {number} p The value of the candidate pixel p.
   * @param {number} threshold
   * @return {Boolean}
   * @static
   */
  tracking.Fast.isDarker = function(circlePixel, p, threshold) {
    return p - circlePixel > threshold
  }

  /**
   * Fast check to test if the candidate pixel is a trivially excluded value.
   * In order to be a corner, the candidate pixel value should be darker or
   * brighter than 9-12 surrounding pixels, when at least three of the top,
   * bottom, left and right pixels are brighter or darker it can be
   * automatically excluded improving the performance.
   * @param {number} circlePixel The circle pixel value.
   * @param {number} p The value of the candidate pixel p.
   * @param {number} threshold
   * @return {Boolean}
   * @static
   * @protected
   */
  tracking.Fast.isTriviallyExcluded = function(circlePixels, p, threshold) {
    var count = 0
    var circleBottom = circlePixels[8]
    var circleLeft = circlePixels[12]
    var circleRight = circlePixels[4]
    var circleTop = circlePixels[0]

    if (this.isBrighter(circleTop, p, threshold)) {
      count++
    }
    if (this.isBrighter(circleRight, p, threshold)) {
      count++
    }
    if (this.isBrighter(circleBottom, p, threshold)) {
      count++
    }
    if (this.isBrighter(circleLeft, p, threshold)) {
      count++
    }

    if (count < 3) {
      count = 0
      if (this.isDarker(circleTop, p, threshold)) {
        count++
      }
      if (this.isDarker(circleRight, p, threshold)) {
        count++
      }
      if (this.isDarker(circleBottom, p, threshold)) {
        count++
      }
      if (this.isDarker(circleLeft, p, threshold)) {
        count++
      }
      if (count < 3) {
        return true
      }
    }

    return false
  }

  /**
   * Gets the sixteen offset values of the circle surrounding pixel.
   * @param {number} width The image width.
   * @return {array} Array with the sixteen offset values of the circle
   *     surrounding pixel.
   * @private
   */
  tracking.Fast.getCircleOffsets_ = function(width) {
    if (this.circles_[width]) {
      return this.circles_[width]
    }

    var circle = new Int32Array(16)

    circle[0] = -width - width - width
    circle[1] = circle[0] + 1
    circle[2] = circle[1] + width + 1
    circle[3] = circle[2] + width + 1
    circle[4] = circle[3] + width
    circle[5] = circle[4] + width
    circle[6] = circle[5] + width - 1
    circle[7] = circle[6] + width - 1
    circle[8] = circle[7] - 1
    circle[9] = circle[8] - 1
    circle[10] = circle[9] - width - 1
    circle[11] = circle[10] - width - 1
    circle[12] = circle[11] - width
    circle[13] = circle[12] - width
    circle[14] = circle[13] - width + 1
    circle[15] = circle[14] - width + 1

    this.circles_[width] = circle
    return circle
  }
}());

(function() {
  /**
   * Math utility.
   * @static
   * @constructor
   */
  tracking.Math = {}

  /**
   * Euclidean distance between two points P(x0, y0) and P(x1, y1).
   * @param {number} x0 Horizontal coordinate of P0.
   * @param {number} y0 Vertical coordinate of P0.
   * @param {number} x1 Horizontal coordinate of P1.
   * @param {number} y1 Vertical coordinate of P1.
   * @return {number} The euclidean distance.
   */
  tracking.Math.distance = function(x0, y0, x1, y1) {
    var dx = x1 - x0
    var dy = y1 - y0

    return Math.sqrt(dx * dx + dy * dy)
  }

  /**
   * Calculates the Hamming weight of a string, which is the number of symbols that are
   * different from the zero-symbol of the alphabet used. It is thus
   * equivalent to the Hamming distance from the all-zero string of the same
   * length. For the most typical case, a string of bits, this is the number
   * of 1's in the string.
   *
   * Example:
   *
   * <pre>
   *  Binary string     Hamming weight
   *   11101                 4
   *   11101010              5
   * </pre>
   *
   * @param {number} i Number that holds the binary string to extract the hamming weight.
   * @return {number} The hamming weight.
   */
  tracking.Math.hammingWeight = function(i) {
    i = i - ((i >> 1) & 0x55555555)
    i = (i & 0x33333333) + ((i >> 2) & 0x33333333)

    return ((i + (i >> 4) & 0xF0F0F0F) * 0x1010101) >> 24
  }

  /**
   * Generates a random number between [a, b] interval.
   * @param {number} a
   * @param {number} b
   * @return {number}
   */
  tracking.Math.uniformRandom = function(a, b) {
    return a + Math.random() * (b - a)
  }

  /**
   * Tests if a rectangle intersects with another.
   *
   *  <pre>
   *  x0y0 --------       x2y2 --------
   *      |       |           |       |
   *      -------- x1y1       -------- x3y3
   * </pre>
   *
   * @param {number} x0 Horizontal coordinate of P0.
   * @param {number} y0 Vertical coordinate of P0.
   * @param {number} x1 Horizontal coordinate of P1.
   * @param {number} y1 Vertical coordinate of P1.
   * @param {number} x2 Horizontal coordinate of P2.
   * @param {number} y2 Vertical coordinate of P2.
   * @param {number} x3 Horizontal coordinate of P3.
   * @param {number} y3 Vertical coordinate of P3.
   * @return {boolean}
   */
  tracking.Math.intersectRect = function(x0, y0, x1, y1, x2, y2, x3, y3) {
    return !(x2 > x1 || x3 < x0 || y2 > y1 || y3 < y0)
  }
}());

(function() {
  /**
   * Matrix utility.
   * @static
   * @constructor
   */
  tracking.Matrix = {}

  /**
   * Loops the array organized as major-row order and executes `fn` callback
   * for each iteration. The `fn` callback receives the following parameters:
   * `(r,g,b,a,index,i,j)`, where `r,g,b,a` represents the pixel color with
   * alpha channel, `index` represents the position in the major-row order
   * array and `i,j` the respective indexes positions in two dimensions.
   * @param {array} pixels The pixels in a linear [r,g,b,a,...] array to loop
   *     through.
   * @param {number} width The image width.
   * @param {number} height The image height.
   * @param {function} fn The callback function for each pixel.
   * @param {number} opt_jump Optional jump for the iteration, by default it
   *     is 1, hence loops all the pixels of the array.
   * @static
   */
  tracking.Matrix.forEach = function(pixels, width, height, fn, opt_jump) {
    opt_jump = opt_jump || 1
    for (var i = 0; i < height; i += opt_jump) {
      for (var j = 0; j < width; j += opt_jump) {
        var w = i * width * 4 + j * 4
        fn.call(this, pixels[w], pixels[w + 1], pixels[w + 2], pixels[w + 3], w, i, j)
      }
    }
  }
}());

(function() {
  /**
   * EPnp utility.
   * @static
   * @constructor
   */
  tracking.EPnP = {}

  tracking.EPnP.solve = function(objectPoints, imagePoints, cameraMatrix) {}
}());

(function() {
  /**
   * Tracker utility.
   * @constructor
   * @extends {tracking.EventEmitter}
   */
  tracking.Tracker = function() {
    tracking.Tracker.base(this, 'constructor')
  }

  tracking.inherits(tracking.Tracker, tracking.EventEmitter)

  /**
   * Tracks the pixels on the array. This method is called for each video
   * frame in order to emit `track` event.
   * @param {Uint8ClampedArray} pixels The pixels data to track.
   * @param {number} width The pixels canvas width.
   * @param {number} height The pixels canvas height.
   */
  tracking.Tracker.prototype.track = function() {}
}());

(function() {
  /**
   * TrackerTask utility.
   * @constructor
   * @extends {tracking.EventEmitter}
   */
  tracking.TrackerTask = function(tracker) {
    tracking.TrackerTask.base(this, 'constructor')

    if (!tracker) {
      throw new Error('Tracker instance not specified.')
    }

    this.setTracker(tracker)
  }

  tracking.inherits(tracking.TrackerTask, tracking.EventEmitter)

  /**
   * Holds the tracker instance managed by this task.
   * @type {tracking.Tracker}
   * @private
   */
  tracking.TrackerTask.prototype.tracker_ = null

  /**
   * Holds if the tracker task is in running.
   * @type {boolean}
   * @private
   */
  tracking.TrackerTask.prototype.running_ = false

  /**
   * Gets the tracker instance managed by this task.
   * @return {tracking.Tracker}
   */
  tracking.TrackerTask.prototype.getTracker = function() {
    return this.tracker_
  }

  /**
   * Returns true if the tracker task is in running, false otherwise.
   * @return {boolean}
   * @private
   */
  tracking.TrackerTask.prototype.inRunning = function() {
    return this.running_
  }

  /**
   * Sets if the tracker task is in running.
   * @param {boolean} running
   * @private
   */
  tracking.TrackerTask.prototype.setRunning = function(running) {
    this.running_ = running
  }

  /**
   * Sets the tracker instance managed by this task.
   * @return {tracking.Tracker}
   */
  tracking.TrackerTask.prototype.setTracker = function(tracker) {
    this.tracker_ = tracker
  }

  /**
   * Emits a `run` event on the tracker task for the implementers to run any
   * child action, e.g. `requestAnimationFrame`.
   * @return {object} Returns itself, so calls can be chained.
   */
  tracking.TrackerTask.prototype.run = function() {
    var self = this

    if (this.inRunning()) {
      return
    }

    this.setRunning(true)
    this.reemitTrackEvent_ = function(event) {
      self.emit('track', event)
    }
    this.tracker_.on('track', this.reemitTrackEvent_)
    this.emit('run')
    return this
  }

  /**
   * Emits a `stop` event on the tracker task for the implementers to stop any
   * child action being done, e.g. `requestAnimationFrame`.
   * @return {object} Returns itself, so calls can be chained.
   */
  tracking.TrackerTask.prototype.stop = function() {
    if (!this.inRunning()) {
      return
    }

    this.setRunning(false)
    this.emit('stop')
    this.tracker_.removeListener('track', this.reemitTrackEvent_)
    return this
  }
}());

(function() {
  /**
   * ColorTracker utility to track colored blobs in a frame using color
   * difference evaluation.
   * @constructor
   * @param {string|Array.<string>} opt_colors Optional colors to track.
   * @extends {tracking.Tracker}
   */
  tracking.ColorTracker = function(opt_colors) {
    tracking.ColorTracker.base(this, 'constructor')

    if (typeof opt_colors === 'string') {
      opt_colors = [opt_colors]
    }

    if (opt_colors) {
      opt_colors.forEach(function(color) {
        if (!tracking.ColorTracker.getColor(color)) {
          throw new Error('Color not valid, try `new tracking.ColorTracker("magenta")`.')
        }
      })
      this.setColors(opt_colors)
    }
  }

  tracking.inherits(tracking.ColorTracker, tracking.Tracker)

  /**
   * Holds the known colors.
   * @type {Object.<string, function>}
   * @private
   * @static
   */
  tracking.ColorTracker.knownColors_ = {}

  /**
   * Caches coordinates values of the neighbours surrounding a pixel.
   * @type {Object.<number, Int32Array>}
   * @private
   * @static
   */
  tracking.ColorTracker.neighbours_ = {}

  /**
   * Registers a color as known color.
   * @param {string} name The color name.
   * @param {function} fn The color function to test if the passed (r,g,b) is
   *     the desired color.
   * @static
   */
  tracking.ColorTracker.registerColor = function(name, fn) {
    tracking.ColorTracker.knownColors_[name] = fn
  }

  /**
   * Gets the known color function that is able to test whether an (r,g,b) is
   * the desired color.
   * @param {string} name The color name.
   * @return {function} The known color test function.
   * @static
   */
  tracking.ColorTracker.getColor = function(name) {
    return tracking.ColorTracker.knownColors_[name]
  }

  /**
   * Holds the colors to be tracked by the `ColorTracker` instance.
   * @default ['magenta']
   * @type {Array.<string>}
   */
  tracking.ColorTracker.prototype.colors = ['magenta']

  /**
   * Holds the minimum dimension to classify a rectangle.
   * @default 20
   * @type {number}
   */
  tracking.ColorTracker.prototype.minDimension = 20

  /**
   * Holds the maximum dimension to classify a rectangle.
   * @default Infinity
   * @type {number}
   */
  tracking.ColorTracker.prototype.maxDimension = Infinity

  /**
   * Holds the minimum group size to be classified as a rectangle.
   * @default 30
   * @type {number}
   */
  tracking.ColorTracker.prototype.minGroupSize = 30

  /**
   * Calculates the central coordinate from the cloud points. The cloud points
   * are all points that matches the desired color.
   * @param {Array.<number>} cloud Major row order array containing all the
   *     points from the desired color, e.g. [x1, y1, c2, y2, ...].
   * @param {number} total Total numbers of pixels of the desired color.
   * @return {object} Object containing the x, y and estimated z coordinate of
   *     the blog extracted from the cloud points.
   * @private
   */
  tracking.ColorTracker.prototype.calculateDimensions_ = function(cloud, total) {
    var maxx = -1
    var maxy = -1
    var minx = Infinity
    var miny = Infinity

    for (var c = 0; c < total; c += 2) {
      var x = cloud[c]
      var y = cloud[c + 1]

      if (x < minx) {
        minx = x
      }
      if (x > maxx) {
        maxx = x
      }
      if (y < miny) {
        miny = y
      }
      if (y > maxy) {
        maxy = y
      }
    }

    return {
      width: maxx - minx,
      height: maxy - miny,
      x: minx,
      y: miny
    }
  }

  /**
   * Gets the colors being tracked by the `ColorTracker` instance.
   * @return {Array.<string>}
   */
  tracking.ColorTracker.prototype.getColors = function() {
    return this.colors
  }

  /**
   * Gets the minimum dimension to classify a rectangle.
   * @return {number}
   */
  tracking.ColorTracker.prototype.getMinDimension = function() {
    return this.minDimension
  }

  /**
   * Gets the maximum dimension to classify a rectangle.
   * @return {number}
   */
  tracking.ColorTracker.prototype.getMaxDimension = function() {
    return this.maxDimension
  }

  /**
   * Gets the minimum group size to be classified as a rectangle.
   * @return {number}
   */
  tracking.ColorTracker.prototype.getMinGroupSize = function() {
    return this.minGroupSize
  }

  /**
   * Gets the eight offset values of the neighbours surrounding a pixel.
   * @param {number} width The image width.
   * @return {array} Array with the eight offset values of the neighbours
   *     surrounding a pixel.
   * @private
   */
  tracking.ColorTracker.prototype.getNeighboursForWidth_ = function(width) {
    if (tracking.ColorTracker.neighbours_[width]) {
      return tracking.ColorTracker.neighbours_[width]
    }

    var neighbours = new Int32Array(8)

    neighbours[0] = -width * 4
    neighbours[1] = -width * 4 + 4
    neighbours[2] = 4
    neighbours[3] = width * 4 + 4
    neighbours[4] = width * 4
    neighbours[5] = width * 4 - 4
    neighbours[6] = -4
    neighbours[7] = -width * 4 - 4

    tracking.ColorTracker.neighbours_[width] = neighbours

    return neighbours
  }

  /**
   * Unites groups whose bounding box intersect with each other.
   * @param {Array.<Object>} rects
   * @private
   */
  tracking.ColorTracker.prototype.mergeRectangles_ = function(rects) {
    var intersects
    var results = []
    var minDimension = this.getMinDimension()
    var maxDimension = this.getMaxDimension()

    for (var r = 0; r < rects.length; r++) {
      var r1 = rects[r]
      intersects = true
      for (var s = r + 1; s < rects.length; s++) {
        var r2 = rects[s]
        if (tracking.Math.intersectRect(r1.x, r1.y, r1.x + r1.width, r1.y + r1.height, r2.x, r2.y, r2.x + r2.width, r2.y + r2.height)) {
          intersects = false
          var x1 = Math.min(r1.x, r2.x)
          var y1 = Math.min(r1.y, r2.y)
          var x2 = Math.max(r1.x + r1.width, r2.x + r2.width)
          var y2 = Math.max(r1.y + r1.height, r2.y + r2.height)
          r2.height = y2 - y1
          r2.width = x2 - x1
          r2.x = x1
          r2.y = y1
          break
        }
      }

      if (intersects) {
        if (r1.width >= minDimension && r1.height >= minDimension) {
          if (r1.width <= maxDimension && r1.height <= maxDimension) {
            results.push(r1)
          }
        }
      }
    }

    return results
  }

  /**
   * Sets the colors to be tracked by the `ColorTracker` instance.
   * @param {Array.<string>} colors
   */
  tracking.ColorTracker.prototype.setColors = function(colors) {
    this.colors = colors
  }

  /**
   * Sets the minimum dimension to classify a rectangle.
   * @param {number} minDimension
   */
  tracking.ColorTracker.prototype.setMinDimension = function(minDimension) {
    this.minDimension = minDimension
  }

  /**
   * Sets the maximum dimension to classify a rectangle.
   * @param {number} maxDimension
   */
  tracking.ColorTracker.prototype.setMaxDimension = function(maxDimension) {
    this.maxDimension = maxDimension
  }

  /**
   * Sets the minimum group size to be classified as a rectangle.
   * @param {number} minGroupSize
   */
  tracking.ColorTracker.prototype.setMinGroupSize = function(minGroupSize) {
    this.minGroupSize = minGroupSize
  }

  /**
   * Tracks the `Video` frames. This method is called for each video frame in
   * order to emit `track` event.
   * @param {Uint8ClampedArray} pixels The pixels data to track.
   * @param {number} width The pixels canvas width.
   * @param {number} height The pixels canvas height.
   */
  tracking.ColorTracker.prototype.track = function(pixels, width, height) {
    var self = this
    var colors = this.getColors()

    if (!colors) {
      throw new Error('Colors not specified, try `new tracking.ColorTracker("magenta")`.')
    }

    var results = []

    colors.forEach(function(color) {
      results = results.concat(self.trackColor_(pixels, width, height, color))
    })

    this.emit('track', {
      data: results
    })
  }

  /**
   * Find the given color in the given matrix of pixels using Flood fill
   * algorithm to determines the area connected to a given node in a
   * multi-dimensional array.
   * @param {Uint8ClampedArray} pixels The pixels data to track.
   * @param {number} width The pixels canvas width.
   * @param {number} height The pixels canvas height.
   * @param {string} color The color to be found
   * @private
   */
  tracking.ColorTracker.prototype.trackColor_ = function(pixels, width, height, color) {
    var colorFn = tracking.ColorTracker.knownColors_[color]
    var currGroup = new Int32Array(pixels.length >> 2)
    var currGroupSize
    var currI
    var currJ
    var currW
    var marked = new Int8Array(pixels.length)
    var minGroupSize = this.getMinGroupSize()
    var neighboursW = this.getNeighboursForWidth_(width)
    var queue = new Int32Array(pixels.length)
    var queuePosition
    var results = []
    var w = -4

    if (!colorFn) {
      return results
    }

    for (var i = 0; i < height; i++) {
      for (var j = 0; j < width; j++) {
        w += 4

        if (marked[w]) {
          continue
        }

        currGroupSize = 0

        queuePosition = -1
        queue[++queuePosition] = w
        queue[++queuePosition] = i
        queue[++queuePosition] = j

        marked[w] = 1

        while (queuePosition >= 0) {
          currJ = queue[queuePosition--]
          currI = queue[queuePosition--]
          currW = queue[queuePosition--]

          if (colorFn(pixels[currW], pixels[currW + 1], pixels[currW + 2], pixels[currW + 3], currW, currI, currJ)) {
            currGroup[currGroupSize++] = currJ
            currGroup[currGroupSize++] = currI

            for (var k = 0; k < neighboursW.length; k++) {
              var otherW = currW + neighboursW[k]
              var otherI = currI + neighboursI[k]
              var otherJ = currJ + neighboursJ[k]
              if (!marked[otherW] && otherI >= 0 && otherI < height && otherJ >= 0 && otherJ < width) {
                queue[++queuePosition] = otherW
                queue[++queuePosition] = otherI
                queue[++queuePosition] = otherJ

                marked[otherW] = 1
              }
            }
          }
        }

        if (currGroupSize >= minGroupSize) {
          var data = this.calculateDimensions_(currGroup, currGroupSize)
          if (data) {
            data.color = color
            results.push(data)
          }
        }
      }
    }

    return this.mergeRectangles_(results)
  }

  // Default colors
  // ===================

  tracking.ColorTracker.registerColor('cyan', function(r, g, b) {
    var thresholdGreen = 50,
      thresholdBlue = 70,
      dx = r - 0,
      dy = g - 255,
      dz = b - 255

    if ((g - r) >= thresholdGreen && (b - r) >= thresholdBlue) {
      return true
    }
    return dx * dx + dy * dy + dz * dz < 6400
  })

  tracking.ColorTracker.registerColor('magenta', function(r, g, b) {
    var threshold = 50,
      dx = r - 255,
      dy = g - 0,
      dz = b - 255

    if ((r - g) >= threshold && (b - g) >= threshold) {
      return true
    }
    return dx * dx + dy * dy + dz * dz < 19600
  })

  tracking.ColorTracker.registerColor('yellow', function(r, g, b) {
    var threshold = 50,
      dx = r - 255,
      dy = g - 255,
      dz = b - 0

    if ((r - b) >= threshold && (g - b) >= threshold) {
      return true
    }
    return dx * dx + dy * dy + dz * dz < 10000
  })

  // Caching neighbour i/j offset values.
  // =====================================
  var neighboursI = new Int32Array([-1, -1, 0, 1, 1, 1, 0, -1])
  var neighboursJ = new Int32Array([0, 1, 1, 1, 0, -1, -1, -1])
}());

(function() {
  /**
   * ObjectTracker utility.
   * @constructor
   * @param {string|Array.<string|Array.<number>>} opt_classifiers Optional
   *     object classifiers to track.
   * @extends {tracking.Tracker}
   */
  tracking.ObjectTracker = function(opt_classifiers) {
    tracking.ObjectTracker.base(this, 'constructor')

    if (opt_classifiers) {
      if (!Array.isArray(opt_classifiers)) {
        opt_classifiers = [opt_classifiers]
      }

      if (Array.isArray(opt_classifiers)) {
        opt_classifiers.forEach(function(classifier, i) {
          if (typeof classifier === 'string') {
            opt_classifiers[i] = tracking.ViolaJones.classifiers[classifier]
          }
          if (!opt_classifiers[i]) {
            throw new Error('Object classifier not valid, try `new tracking.ObjectTracker("face")`.')
          }
        })
      }
    }

    this.setClassifiers(opt_classifiers)
  }

  tracking.inherits(tracking.ObjectTracker, tracking.Tracker)

  /**
   * Specifies the edges density of a block in order to decide whether to skip
   * it or not.
   * @default 0.2
   * @type {number}
   */
  tracking.ObjectTracker.prototype.edgesDensity = 0.2

  /**
   * Specifies the initial scale to start the feature block scaling.
   * @default 1.0
   * @type {number}
   */
  tracking.ObjectTracker.prototype.initialScale = 1.0

  /**
   * Specifies the scale factor to scale the feature block.
   * @default 1.25
   * @type {number}
   */
  tracking.ObjectTracker.prototype.scaleFactor = 1.25

  /**
   * Specifies the block step size.
   * @default 1.5
   * @type {number}
   */
  tracking.ObjectTracker.prototype.stepSize = 1.5

  /**
   * Gets the tracker HAAR classifiers.
   * @return {TypedArray.<number>}
   */
  tracking.ObjectTracker.prototype.getClassifiers = function() {
    return this.classifiers
  }

  /**
   * Gets the edges density value.
   * @return {number}
   */
  tracking.ObjectTracker.prototype.getEdgesDensity = function() {
    return this.edgesDensity
  }

  /**
   * Gets the initial scale to start the feature block scaling.
   * @return {number}
   */
  tracking.ObjectTracker.prototype.getInitialScale = function() {
    return this.initialScale
  }

  /**
   * Gets the scale factor to scale the feature block.
   * @return {number}
   */
  tracking.ObjectTracker.prototype.getScaleFactor = function() {
    return this.scaleFactor
  }

  /**
   * Gets the block step size.
   * @return {number}
   */
  tracking.ObjectTracker.prototype.getStepSize = function() {
    return this.stepSize
  }

  /**
   * Tracks the `Video` frames. This method is called for each video frame in
   * order to emit `track` event.
   * @param {Uint8ClampedArray} pixels The pixels data to track.
   * @param {number} width The pixels canvas width.
   * @param {number} height The pixels canvas height.
   */
  tracking.ObjectTracker.prototype.track = function(pixels, width, height) {
    var self = this
    var classifiers = this.getClassifiers()

    if (!classifiers) {
      throw new Error('Object classifier not specified, try `new tracking.ObjectTracker("face")`.')
    }

    var results = []

    classifiers.forEach(function(classifier) {
      results = results.concat(tracking.ViolaJones.detect(pixels, width, height, self.getInitialScale(), self.getScaleFactor(), self.getStepSize(), self.getEdgesDensity(), classifier))
    })

    this.emit('track', {
      data: results
    })
  }

  /**
   * Sets the tracker HAAR classifiers.
   * @param {TypedArray.<number>} classifiers
   */
  tracking.ObjectTracker.prototype.setClassifiers = function(classifiers) {
    this.classifiers = classifiers
  }

  /**
   * Sets the edges density.
   * @param {number} edgesDensity
   */
  tracking.ObjectTracker.prototype.setEdgesDensity = function(edgesDensity) {
    this.edgesDensity = edgesDensity
  }

  /**
   * Sets the initial scale to start the block scaling.
   * @param {number} initialScale
   */
  tracking.ObjectTracker.prototype.setInitialScale = function(initialScale) {
    this.initialScale = initialScale
  }

  /**
   * Sets the scale factor to scale the feature block.
   * @param {number} scaleFactor
   */
  tracking.ObjectTracker.prototype.setScaleFactor = function(scaleFactor) {
    this.scaleFactor = scaleFactor
  }

  /**
   * Sets the block step size.
   * @param {number} stepSize
   */
  tracking.ObjectTracker.prototype.setStepSize = function(stepSize) {
    this.stepSize = stepSize
  }
}())