/**
* 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
}
}())