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@@ -1,3111 +0,0 @@
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-/**
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- * tracking - A modern approach for Computer Vision on the web.
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- * @author Eduardo Lundgren <edu@rdo.io>
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- * @version v1.1.3
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- * @link http://trackingjs.com
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- * @license BSD
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- */
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-(function(window, undefined) {
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- window.tracking = window.tracking || {};
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-
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- /**
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- * Inherit the prototype methods from one constructor into another.
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- *
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- * Usage:
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- * <pre>
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- * function ParentClass(a, b) { }
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- * ParentClass.prototype.foo = function(a) { }
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- *
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- * function ChildClass(a, b, c) {
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- * tracking.base(this, a, b);
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- * }
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- * tracking.inherits(ChildClass, ParentClass);
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- *
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- * var child = new ChildClass('a', 'b', 'c');
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- * child.foo();
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- * </pre>
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- *
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- * @param {Function} childCtor Child class.
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- * @param {Function} parentCtor Parent class.
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- */
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- tracking.inherits = function(childCtor, parentCtor) {
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- function TempCtor() {
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- }
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- TempCtor.prototype = parentCtor.prototype;
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- childCtor.superClass_ = parentCtor.prototype;
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- childCtor.prototype = new TempCtor();
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- childCtor.prototype.constructor = childCtor;
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-
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- /**
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- * Calls superclass constructor/method.
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- *
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- * This function is only available if you use tracking.inherits to express
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- * inheritance relationships between classes.
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- *
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- * @param {!object} me Should always be "this".
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- * @param {string} methodName The method name to call. Calling superclass
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- * constructor can be done with the special string 'constructor'.
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- * @param {...*} var_args The arguments to pass to superclass
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- * method/constructor.
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- * @return {*} The return value of the superclass method/constructor.
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- */
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- childCtor.base = function(me, methodName) {
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- var args = Array.prototype.slice.call(arguments, 2);
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- return parentCtor.prototype[methodName].apply(me, args);
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- };
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- };
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-
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- /**
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- * Captures the user camera when tracking a video element and set its source
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- * to the camera stream.
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- * @param {HTMLVideoElement} element Canvas element to track.
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- * @param {object} opt_options Optional configuration to the tracker.
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- */
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- tracking.initUserMedia_ = function(element, opt_options) {
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- window.navigator.mediaDevices.getUserMedia({
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- video: true,
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- audio: (opt_options && opt_options.audio) ? true : false,
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- }).then(function(stream) {
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- element.srcObject = stream;
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- }).catch(function(err) {
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- throw Error('Cannot capture user camera.');
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- });
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- };
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-
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- /**
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- * Tests whether the object is a dom node.
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- * @param {object} o Object to be tested.
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- * @return {boolean} True if the object is a dom node.
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- */
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- tracking.isNode = function(o) {
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- return o.nodeType || this.isWindow(o);
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- };
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-
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- /**
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- * Tests whether the object is the `window` object.
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- * @param {object} o Object to be tested.
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- * @return {boolean} True if the object is the `window` object.
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- */
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- tracking.isWindow = function(o) {
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- return !!(o && o.alert && o.document);
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- };
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-
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- /**
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- * Selects a dom node from a CSS3 selector using `document.querySelector`.
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- * @param {string} selector
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- * @param {object} opt_element The root element for the query. When not
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- * specified `document` is used as root element.
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- * @return {HTMLElement} The first dom element that matches to the selector.
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- * If not found, returns `null`.
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- */
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- tracking.one = function(selector, opt_element) {
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- if (this.isNode(selector)) {
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- return selector;
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- }
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- return (opt_element || document).querySelector(selector);
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- };
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-
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- /**
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- * Tracks a canvas, image or video element based on the specified `tracker`
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- * instance. This method extract the pixel information of the input element
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- * to pass to the `tracker` instance. When tracking a video, the
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- * `tracker.track(pixels, width, height)` will be in a
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- * `requestAnimationFrame` loop in order to track all video frames.
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- *
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- * Example:
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- * var tracker = new tracking.ColorTracker();
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- *
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- * tracking.track('#video', tracker);
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- * or
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- * tracking.track('#video', tracker, { camera: true });
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- *
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- * tracker.on('track', function(event) {
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- * // console.log(event.data[0].x, event.data[0].y)
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- * });
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- *
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- * @param {HTMLElement} element The element to track, canvas, image or
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- * video.
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- * @param {tracking.Tracker} tracker The tracker instance used to track the
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- * element.
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- * @param {object} opt_options Optional configuration to the tracker.
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- */
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- tracking.track = function(element, tracker, opt_options) {
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- element = tracking.one(element);
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- if (!element) {
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- throw new Error('Element not found, try a different element or selector.');
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- }
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- if (!tracker) {
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- throw new Error('Tracker not specified, try `tracking.track(element, new tracking.FaceTracker())`.');
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- }
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-
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- switch (element.nodeName.toLowerCase()) {
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- case 'canvas':
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- return this.trackCanvas_(element, tracker, opt_options);
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- case 'img':
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- return this.trackImg_(element, tracker, opt_options);
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- case 'video':
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- if (opt_options) {
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- if (opt_options.camera) {
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- this.initUserMedia_(element, opt_options);
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- }
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- }
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- return this.trackVideo_(element, tracker, opt_options);
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- default:
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- throw new Error('Element not supported, try in a canvas, img, or video.');
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- }
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- };
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-
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- /**
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- * Tracks a canvas element based on the specified `tracker` instance and
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- * returns a `TrackerTask` for this track.
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- * @param {HTMLCanvasElement} element Canvas element to track.
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- * @param {tracking.Tracker} tracker The tracker instance used to track the
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- * element.
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- * @param {object} opt_options Optional configuration to the tracker.
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- * @return {tracking.TrackerTask}
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- * @private
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- */
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- tracking.trackCanvas_ = function(element, tracker) {
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- var self = this;
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- var task = new tracking.TrackerTask(tracker);
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- task.on('run', function() {
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- self.trackCanvasInternal_(element, tracker);
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- });
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- return task.run();
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- };
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-
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- /**
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- * Tracks a canvas element based on the specified `tracker` instance. This
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- * method extract the pixel information of the input element to pass to the
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- * `tracker` instance.
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- * @param {HTMLCanvasElement} element Canvas element to track.
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- * @param {tracking.Tracker} tracker The tracker instance used to track the
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- * element.
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- * @param {object} opt_options Optional configuration to the tracker.
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- * @private
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- */
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- tracking.trackCanvasInternal_ = function(element, tracker) {
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- var width = element.width;
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- var height = element.height;
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- var context = element.getContext('2d');
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- var imageData = context.getImageData(0, 0, width, height);
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- tracker.track(imageData.data, width, height);
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- };
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-
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- /**
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- * Tracks a image element based on the specified `tracker` instance. This
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- * method extract the pixel information of the input element to pass to the
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- * `tracker` instance.
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- * @param {HTMLImageElement} element Canvas element to track.
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- * @param {tracking.Tracker} tracker The tracker instance used to track the
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- * element.
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- * @param {object} opt_options Optional configuration to the tracker.
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- * @private
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- */
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- tracking.trackImg_ = function(element, tracker) {
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- var width = element.width;
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- var height = element.height;
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- var canvas = document.createElement('canvas');
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-
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- canvas.width = width;
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- canvas.height = height;
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-
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- var task = new tracking.TrackerTask(tracker);
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- task.on('run', function() {
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- tracking.Canvas.loadImage(canvas, element.src, 0, 0, width, height, function() {
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- tracking.trackCanvasInternal_(canvas, tracker);
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- });
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- });
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- return task.run();
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- };
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-
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- /**
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- * Tracks a video element based on the specified `tracker` instance. This
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- * method extract the pixel information of the input element to pass to the
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- * `tracker` instance. The `tracker.track(pixels, width, height)` will be in
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- * a `requestAnimationFrame` loop in order to track all video frames.
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- * @param {HTMLVideoElement} element Canvas element to track.
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- * @param {tracking.Tracker} tracker The tracker instance used to track the
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- * element.
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- * @param {object} opt_options Optional configuration to the tracker.
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- * @private
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- */
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- tracking.trackVideo_ = function(element, tracker) {
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- var canvas = document.createElement('canvas');
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- var context = canvas.getContext('2d');
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- var width;
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- var height;
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-
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- var resizeCanvas_ = function() {
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- width = element.offsetWidth;
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- height = element.offsetHeight;
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- canvas.width = width;
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- canvas.height = height;
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- };
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- resizeCanvas_();
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- element.addEventListener('resize', resizeCanvas_);
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-
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- var requestId;
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- var requestAnimationFrame_ = function() {
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- requestId = window.requestAnimationFrame(function() {
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- if (element.readyState === element.HAVE_ENOUGH_DATA) {
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- try {
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- // Firefox v~30.0 gets confused with the video readyState firing an
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- // erroneous HAVE_ENOUGH_DATA just before HAVE_CURRENT_DATA state,
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- // hence keep trying to read it until resolved.
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- context.drawImage(element, 0, 0, width, height);
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- } catch (err) {}
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- tracking.trackCanvasInternal_(canvas, tracker);
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- }
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- requestAnimationFrame_();
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- });
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- };
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-
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- var task = new tracking.TrackerTask(tracker);
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- task.on('stop', function() {
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- window.cancelAnimationFrame(requestId);
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- });
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- task.on('run', function() {
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- requestAnimationFrame_();
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- });
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- return task.run();
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- };
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-
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- // Browser polyfills
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- //===================
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-
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- if (!window.URL) {
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- window.URL = window.URL || window.webkitURL || window.msURL || window.oURL;
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- }
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-
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- if (!navigator.getUserMedia) {
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- navigator.getUserMedia = navigator.getUserMedia || navigator.webkitGetUserMedia ||
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- navigator.mozGetUserMedia || navigator.msGetUserMedia;
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- }
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-}(window));
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-
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-(function() {
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- /**
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- * EventEmitter utility.
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- * @constructor
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- */
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- tracking.EventEmitter = function() {};
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-
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- /**
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- * Holds event listeners scoped by event type.
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- * @type {object}
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- * @private
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- */
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- tracking.EventEmitter.prototype.events_ = null;
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-
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- /**
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- * Adds a listener to the end of the listeners array for the specified event.
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- * @param {string} event
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- * @param {function} listener
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- * @return {object} Returns emitter, so calls can be chained.
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- */
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- tracking.EventEmitter.prototype.addListener = function(event, listener) {
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- if (typeof listener !== 'function') {
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- throw new TypeError('Listener must be a function');
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- }
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- if (!this.events_) {
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- this.events_ = {};
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- }
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-
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- this.emit('newListener', event, listener);
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-
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- if (!this.events_[event]) {
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- this.events_[event] = [];
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- }
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-
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- this.events_[event].push(listener);
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-
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- return this;
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- };
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-
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- /**
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- * Returns an array of listeners for the specified event.
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- * @param {string} event
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- * @return {array} Array of listeners.
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- */
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- tracking.EventEmitter.prototype.listeners = function(event) {
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- return this.events_ && this.events_[event];
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- };
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-
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- /**
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- * Execute each of the listeners in order with the supplied arguments.
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- * @param {string} event
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- * @param {*} opt_args [arg1], [arg2], [...]
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- * @return {boolean} Returns true if event had listeners, false otherwise.
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- */
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- tracking.EventEmitter.prototype.emit = function(event) {
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- var listeners = this.listeners(event);
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- if (listeners) {
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- var args = Array.prototype.slice.call(arguments, 1);
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- for (var i = 0; i < listeners.length; i++) {
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- if (listeners[i]) {
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- listeners[i].apply(this, args);
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- }
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- }
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- return true;
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- }
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- return false;
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- };
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-
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- /**
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- * Adds a listener to the end of the listeners array for the specified event.
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- * @param {string} event
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- * @param {function} listener
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- * @return {object} Returns emitter, so calls can be chained.
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- */
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- tracking.EventEmitter.prototype.on = tracking.EventEmitter.prototype.addListener;
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-
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- /**
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- * Adds a one time listener for the event. This listener is invoked only the
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- * next time the event is fired, after which it is removed.
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- * @param {string} event
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- * @param {function} listener
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- * @return {object} Returns emitter, so calls can be chained.
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- */
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- tracking.EventEmitter.prototype.once = function(event, listener) {
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- var self = this;
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- self.on(event, function handlerInternal() {
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- self.removeListener(event, handlerInternal);
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- listener.apply(this, arguments);
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- });
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- };
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-
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- /**
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- * Removes all listeners, or those of the specified event. It's not a good
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- * idea to remove listeners that were added elsewhere in the code,
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- * especially when it's on an emitter that you didn't create.
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- * @param {string} event
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- * @return {object} Returns emitter, so calls can be chained.
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- */
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- tracking.EventEmitter.prototype.removeAllListeners = function(opt_event) {
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- if (!this.events_) {
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- return this;
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- }
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- if (opt_event) {
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- delete this.events_[opt_event];
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- } else {
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- delete this.events_;
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- }
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- return this;
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- };
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-
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- /**
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- * Remove a listener from the listener array for the specified event.
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- * Caution: changes array indices in the listener array behind the listener.
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- * @param {string} event
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- * @param {function} listener
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- * @return {object} Returns emitter, so calls can be chained.
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- */
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- tracking.EventEmitter.prototype.removeListener = function(event, listener) {
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- if (typeof listener !== 'function') {
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- throw new TypeError('Listener must be a function');
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- }
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- if (!this.events_) {
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- return this;
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- }
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-
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- var listeners = this.listeners(event);
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- if (Array.isArray(listeners)) {
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- var i = listeners.indexOf(listener);
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- if (i < 0) {
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- return this;
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- }
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- listeners.splice(i, 1);
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- }
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-
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|
- 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;
|
|
|
- };
|
|
|
-
|
|
|
- /**
|
|
|
- * Equalizes the histogram of a grayscale image, normalizing the
|
|
|
- * brightness and increasing the contrast of the image.
|
|
|
- * @param {pixels} pixels The grayscale pixels in a linear array.
|
|
|
- * @param {number} width The image width.
|
|
|
- * @param {number} height The image height.
|
|
|
- * @return {array} The equalized grayscale pixels in a linear array.
|
|
|
- */
|
|
|
- tracking.Image.equalizeHist = function(pixels, width, height){
|
|
|
- var equalized = new Uint8ClampedArray(pixels.length);
|
|
|
-
|
|
|
- var histogram = new Array(256);
|
|
|
- for(var i=0; i < 256; i++) histogram[i] = 0;
|
|
|
-
|
|
|
- for(var i=0; i < pixels.length; i++){
|
|
|
- equalized[i] = pixels[i];
|
|
|
- histogram[pixels[i]]++;
|
|
|
- }
|
|
|
-
|
|
|
- var prev = histogram[0];
|
|
|
- for(var i=0; i < 256; i++){
|
|
|
- histogram[i] += prev;
|
|
|
- prev = histogram[i];
|
|
|
- }
|
|
|
-
|
|
|
- var norm = 255 / pixels.length;
|
|
|
- for(var i=0; i < pixels.length; i++)
|
|
|
- equalized[i] = (histogram[pixels[i]] * norm + 0.5) | 0;
|
|
|
-
|
|
|
- return equalized;
|
|
|
- }
|
|
|
-
|
|
|
-}());
|
|
|
-
|
|
|
-(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);
|
|
|
- }
|
|
|
- }
|
|
|
- };
|
|
|
-
|
|
|
- /**
|
|
|
- * Calculates the per-element subtraction of two NxM matrices and returns a
|
|
|
- * new NxM matrix as the result.
|
|
|
- * @param {matrix} a The first matrix.
|
|
|
- * @param {matrix} a The second matrix.
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.Matrix.sub = function(a, b){
|
|
|
- var res = tracking.Matrix.clone(a);
|
|
|
- for(var i=0; i < res.length; i++){
|
|
|
- for(var j=0; j < res[i].length; j++){
|
|
|
- res[i][j] -= b[i][j];
|
|
|
- }
|
|
|
- }
|
|
|
- return res;
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Calculates the per-element sum of two NxM matrices and returns a new NxM
|
|
|
- * NxM matrix as the result.
|
|
|
- * @param {matrix} a The first matrix.
|
|
|
- * @param {matrix} a The second matrix.
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.Matrix.add = function(a, b){
|
|
|
- var res = tracking.Matrix.clone(a);
|
|
|
- for(var i=0; i < res.length; i++){
|
|
|
- for(var j=0; j < res[i].length; j++){
|
|
|
- res[i][j] += b[i][j];
|
|
|
- }
|
|
|
- }
|
|
|
- return res;
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Clones a matrix (or part of it) and returns a new matrix as the result.
|
|
|
- * @param {matrix} src The matrix to be cloned.
|
|
|
- * @param {number} width The second matrix.
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.Matrix.clone = function(src, width, height){
|
|
|
- width = width || src[0].length;
|
|
|
- height = height || src.length;
|
|
|
- var temp = new Array(height);
|
|
|
- var i = height;
|
|
|
- while(i--){
|
|
|
- temp[i] = new Array(width);
|
|
|
- var j = width;
|
|
|
- while(j--) temp[i][j] = src[i][j];
|
|
|
- }
|
|
|
- return temp;
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Multiply a matrix by a scalar and returns a new matrix as the result.
|
|
|
- * @param {number} scalar The scalar to multiply the matrix by.
|
|
|
- * @param {matrix} src The matrix to be multiplied.
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.Matrix.mulScalar = function(scalar, src){
|
|
|
- var res = tracking.Matrix.clone(src);
|
|
|
- for(var i=0; i < src.length; i++){
|
|
|
- for(var j=0; j < src[i].length; j++){
|
|
|
- res[i][j] *= scalar;
|
|
|
- }
|
|
|
- }
|
|
|
- return res;
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Transpose a matrix and returns a new matrix as the result.
|
|
|
- * @param {matrix} src The matrix to be transposed.
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.Matrix.transpose = function(src){
|
|
|
- var transpose = new Array(src[0].length);
|
|
|
- for(var i=0; i < src[0].length; i++){
|
|
|
- transpose[i] = new Array(src.length);
|
|
|
- for(var j=0; j < src.length; j++){
|
|
|
- transpose[i][j] = src[j][i];
|
|
|
- }
|
|
|
- }
|
|
|
- return transpose;
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Multiply an MxN matrix with an NxP matrix and returns a new MxP matrix
|
|
|
- * as the result.
|
|
|
- * @param {matrix} a The first matrix.
|
|
|
- * @param {matrix} b The second matrix.
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.Matrix.mul = function(a, b) {
|
|
|
- var res = new Array(a.length);
|
|
|
- for (var i = 0; i < a.length; i++) {
|
|
|
- res[i] = new Array(b[0].length);
|
|
|
- for (var j = 0; j < b[0].length; j++) {
|
|
|
- res[i][j] = 0;
|
|
|
- for (var k = 0; k < a[0].length; k++) {
|
|
|
- res[i][j] += a[i][k] * b[k][j];
|
|
|
- }
|
|
|
- }
|
|
|
- }
|
|
|
- return res;
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Calculates the absolute norm of a matrix.
|
|
|
- * @param {matrix} src The matrix which norm will be calculated.
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.Matrix.norm = function(src){
|
|
|
- var res = 0;
|
|
|
- for(var i=0; i < src.length; i++){
|
|
|
- for(var j=0; j < src[i].length; j++){
|
|
|
- res += src[i][j]*src[i][j];
|
|
|
- }
|
|
|
- }
|
|
|
- return Math.sqrt(res);
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Calculates and returns the covariance matrix of a set of vectors as well
|
|
|
- * as the mean of the matrix.
|
|
|
- * @param {matrix} src The matrix which covariance matrix will be calculated.
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.Matrix.calcCovarMatrix = function(src){
|
|
|
-
|
|
|
- var mean = new Array(src.length);
|
|
|
- for(var i=0; i < src.length; i++){
|
|
|
- mean[i] = [0.0];
|
|
|
- for(var j=0; j < src[i].length; j++){
|
|
|
- mean[i][0] += src[i][j]/src[i].length;
|
|
|
- }
|
|
|
- }
|
|
|
-
|
|
|
- var deltaFull = tracking.Matrix.clone(mean);
|
|
|
- for(var i=0; i < deltaFull.length; i++){
|
|
|
- for(var j=0; j < src[0].length - 1; j++){
|
|
|
- deltaFull[i].push(deltaFull[i][0]);
|
|
|
- }
|
|
|
- }
|
|
|
-
|
|
|
- var a = tracking.Matrix.sub(src, deltaFull);
|
|
|
- var b = tracking.Matrix.transpose(a);
|
|
|
- var covar = tracking.Matrix.mul(b,a);
|
|
|
- return [covar, mean];
|
|
|
-
|
|
|
- }
|
|
|
-
|
|
|
-}());
|
|
|
-(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;
|
|
|
- };
|
|
|
-
|
|
|
-}());
|
|
|
-
|
|
|
-(function() {
|
|
|
-
|
|
|
-
|
|
|
- tracking.LandmarksTracker = function() {
|
|
|
- tracking.LandmarksTracker.base(this, 'constructor');
|
|
|
- }
|
|
|
-
|
|
|
- tracking.inherits(tracking.LandmarksTracker, tracking.ObjectTracker);
|
|
|
-
|
|
|
- tracking.LandmarksTracker.prototype.track = function(pixels, width, height) {
|
|
|
-
|
|
|
- var image = {
|
|
|
- 'data': pixels,
|
|
|
- 'width': width,
|
|
|
- 'height': height
|
|
|
- };
|
|
|
-
|
|
|
- var classifier = tracking.ViolaJones.classifiers['face'];
|
|
|
-
|
|
|
- var faces = tracking.ViolaJones.detect(pixels, width, height,
|
|
|
- this.getInitialScale(), this.getScaleFactor(), this.getStepSize(),
|
|
|
- this.getEdgesDensity(), classifier);
|
|
|
-
|
|
|
- var landmarks = tracking.LBF.align(pixels, width, height, faces);
|
|
|
-
|
|
|
- this.emit('track', {
|
|
|
- 'data': {
|
|
|
- 'faces' : faces,
|
|
|
- 'landmarks' : landmarks
|
|
|
- }
|
|
|
- });
|
|
|
-
|
|
|
- }
|
|
|
-
|
|
|
-}());
|
|
|
-
|
|
|
-(function() {
|
|
|
-
|
|
|
- tracking.LBF = {};
|
|
|
-
|
|
|
- /**
|
|
|
- * LBF Regressor utility.
|
|
|
- * @constructor
|
|
|
- */
|
|
|
- tracking.LBF.Regressor = function(maxNumStages){
|
|
|
- this.maxNumStages = maxNumStages;
|
|
|
-
|
|
|
- this.rfs = new Array(maxNumStages);
|
|
|
- this.models = new Array(maxNumStages);
|
|
|
- for(var i=0; i < maxNumStages; i++){
|
|
|
- this.rfs[i] = new tracking.LBF.RandomForest(i);
|
|
|
- this.models[i] = tracking.LBF.RegressorData[i].models;
|
|
|
- }
|
|
|
-
|
|
|
- this.meanShape = tracking.LBF.LandmarksData;
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Predicts the position of the landmarks based on the bounding box of the face.
|
|
|
- * @param {pixels} pixels The grayscale pixels in a linear array.
|
|
|
- * @param {number} width Width of the image.
|
|
|
- * @param {number} height Height of the image.
|
|
|
- * @param {object} boudingBox Bounding box of the face to be aligned.
|
|
|
- * @return {matrix} A matrix with each landmark position in a row [x,y].
|
|
|
- */
|
|
|
- tracking.LBF.Regressor.prototype.predict = function(pixels, width, height, boundingBox) {
|
|
|
-
|
|
|
- var images = [];
|
|
|
- var currentShapes = [];
|
|
|
- var boundingBoxes = [];
|
|
|
-
|
|
|
- var meanShapeClone = tracking.Matrix.clone(this.meanShape);
|
|
|
-
|
|
|
- images.push({
|
|
|
- 'data': pixels,
|
|
|
- 'width': width,
|
|
|
- 'height': height
|
|
|
- });
|
|
|
- boundingBoxes.push(boundingBox);
|
|
|
-
|
|
|
- currentShapes.push(tracking.LBF.projectShapeToBoundingBox_(meanShapeClone, boundingBox));
|
|
|
-
|
|
|
- for(var stage = 0; stage < this.maxNumStages; stage++){
|
|
|
- var binaryFeatures = tracking.LBF.Regressor.deriveBinaryFeat(this.rfs[stage], images, currentShapes, boundingBoxes, meanShapeClone);
|
|
|
- this.applyGlobalPrediction(binaryFeatures, this.models[stage], currentShapes, boundingBoxes);
|
|
|
- }
|
|
|
-
|
|
|
- return currentShapes[0];
|
|
|
- };
|
|
|
-
|
|
|
- /**
|
|
|
- * Multiplies the binary features of the landmarks with the regression matrix
|
|
|
- * to obtain the displacement for each landmark. Then applies this displacement
|
|
|
- * into the landmarks shape.
|
|
|
- * @param {object} binaryFeatures The binary features for the landmarks.
|
|
|
- * @param {object} models The regressor models.
|
|
|
- * @param {matrix} currentShapes The landmarks shapes.
|
|
|
- * @param {array} boudingBoxes The bounding boxes of the faces.
|
|
|
- */
|
|
|
- tracking.LBF.Regressor.prototype.applyGlobalPrediction = function(binaryFeatures, models, currentShapes,
|
|
|
- boundingBoxes){
|
|
|
-
|
|
|
- var residual = currentShapes[0].length * 2;
|
|
|
-
|
|
|
- var rotation = [];
|
|
|
- var deltashape = new Array(residual/2);
|
|
|
- for(var i=0; i < residual/2; i++){
|
|
|
- deltashape[i] = [0.0, 0.0];
|
|
|
- }
|
|
|
-
|
|
|
- for(var i=0; i < currentShapes.length; i++){
|
|
|
- for(var j=0; j < residual; j++){
|
|
|
- var tmp = 0;
|
|
|
- for(var lx=0, idx=0; (idx = binaryFeatures[i][lx].index) != -1; lx++){
|
|
|
- if(idx <= models[j].nr_feature){
|
|
|
- tmp += models[j].data[(idx - 1)] * binaryFeatures[i][lx].value;
|
|
|
- }
|
|
|
- }
|
|
|
- if(j < residual/2){
|
|
|
- deltashape[j][0] = tmp;
|
|
|
- }else{
|
|
|
- deltashape[j - residual/2][1] = tmp;
|
|
|
- }
|
|
|
- }
|
|
|
-
|
|
|
- var res = tracking.LBF.similarityTransform_(tracking.LBF.unprojectShapeToBoundingBox_(currentShapes[i], boundingBoxes[i]), this.meanShape);
|
|
|
- var rotation = tracking.Matrix.transpose(res[0]);
|
|
|
-
|
|
|
- var s = tracking.LBF.unprojectShapeToBoundingBox_(currentShapes[i], boundingBoxes[i]);
|
|
|
- s = tracking.Matrix.add(s, deltashape);
|
|
|
-
|
|
|
- currentShapes[i] = tracking.LBF.projectShapeToBoundingBox_(s, boundingBoxes[i]);
|
|
|
-
|
|
|
- }
|
|
|
- };
|
|
|
-
|
|
|
- /**
|
|
|
- * Derives the binary features from the image for each landmark.
|
|
|
- * @param {object} forest The random forest to search for the best binary feature match.
|
|
|
- * @param {array} images The images with pixels in a grayscale linear array.
|
|
|
- * @param {array} currentShapes The current landmarks shape.
|
|
|
- * @param {array} boudingBoxes The bounding boxes of the faces.
|
|
|
- * @param {matrix} meanShape The mean shape of the current landmarks set.
|
|
|
- * @return {array} The binary features extracted from the image and matched with the
|
|
|
- * training data.
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.LBF.Regressor.deriveBinaryFeat = function(forest, images, currentShapes, boundingBoxes, meanShape){
|
|
|
-
|
|
|
- var binaryFeatures = new Array(images.length);
|
|
|
- for(var i=0; i < images.length; i++){
|
|
|
- var t = forest.maxNumTrees * forest.landmarkNum + 1;
|
|
|
- binaryFeatures[i] = new Array(t);
|
|
|
- for(var j=0; j < t; j++){
|
|
|
- binaryFeatures[i][j] = {};
|
|
|
- }
|
|
|
- }
|
|
|
-
|
|
|
- var leafnodesPerTree = 1 << (forest.maxDepth - 1);
|
|
|
-
|
|
|
- for(var i=0; i < images.length; i++){
|
|
|
-
|
|
|
- var projectedShape = tracking.LBF.unprojectShapeToBoundingBox_(currentShapes[i], boundingBoxes[i]);
|
|
|
- var transform = tracking.LBF.similarityTransform_(projectedShape, meanShape);
|
|
|
-
|
|
|
- for(var j=0; j < forest.landmarkNum; j++){
|
|
|
- for(var k=0; k < forest.maxNumTrees; k++){
|
|
|
-
|
|
|
- var binaryCode = tracking.LBF.Regressor.getCodeFromTree(forest.rfs[j][k], images[i],
|
|
|
- currentShapes[i], boundingBoxes[i], transform[0], transform[1]);
|
|
|
-
|
|
|
- var index = j*forest.maxNumTrees + k;
|
|
|
- binaryFeatures[i][index].index = leafnodesPerTree * index + binaryCode;
|
|
|
- binaryFeatures[i][index].value = 1;
|
|
|
-
|
|
|
- }
|
|
|
- }
|
|
|
- binaryFeatures[i][forest.landmarkNum * forest.maxNumTrees].index = -1;
|
|
|
- binaryFeatures[i][forest.landmarkNum * forest.maxNumTrees].value = -1;
|
|
|
- }
|
|
|
- return binaryFeatures;
|
|
|
-
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Gets the binary code for a specific tree in a random forest. For each landmark,
|
|
|
- * the position from two pre-defined points are recovered from the training data
|
|
|
- * and then the intensity of the pixels corresponding to these points is extracted
|
|
|
- * from the image and used to traverse the trees in the random forest. At the end,
|
|
|
- * the ending nodes will be represented by 1, and the remaining nodes by 0.
|
|
|
- *
|
|
|
- * +--------------------------- Random Forest -----------------------------+
|
|
|
- * | Ø = Ending leaf |
|
|
|
- * | |
|
|
|
- * | O O O O O |
|
|
|
- * | / \ / \ / \ / \ / \ |
|
|
|
- * | O O O O O O O O O O |
|
|
|
- * | / \ / \ / \ / \ / \ / \ / \ / \ / \ / \ |
|
|
|
- * | Ø O O O O O Ø O O Ø O O O O Ø O O O O Ø |
|
|
|
- * | 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 |
|
|
|
- * +-----------------------------------------------------------------------+
|
|
|
- * Final binary code for this landmark: 10000010010000100001
|
|
|
- *
|
|
|
- * @param {object} forest The tree to be analyzed.
|
|
|
- * @param {array} image The image with pixels in a grayscale linear array.
|
|
|
- * @param {matrix} shape The current landmarks shape.
|
|
|
- * @param {object} boudingBoxes The bounding box of the face.
|
|
|
- * @param {matrix} rotation The rotation matrix used to transform the projected landmarks
|
|
|
- * into the mean shape.
|
|
|
- * @param {number} scale The scale factor used to transform the projected landmarks
|
|
|
- * into the mean shape.
|
|
|
- * @return {number} The binary code extracted from the tree.
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.LBF.Regressor.getCodeFromTree = function(tree, image, shape, boundingBox, rotation, scale){
|
|
|
- var current = 0;
|
|
|
- var bincode = 0;
|
|
|
-
|
|
|
- while(true){
|
|
|
-
|
|
|
- var x1 = Math.cos(tree.nodes[current].feats[0]) * tree.nodes[current].feats[2] * tree.maxRadioRadius * boundingBox.width;
|
|
|
- var y1 = Math.sin(tree.nodes[current].feats[0]) * tree.nodes[current].feats[2] * tree.maxRadioRadius * boundingBox.height;
|
|
|
- var x2 = Math.cos(tree.nodes[current].feats[1]) * tree.nodes[current].feats[3] * tree.maxRadioRadius * boundingBox.width;
|
|
|
- var y2 = Math.sin(tree.nodes[current].feats[1]) * tree.nodes[current].feats[3] * tree.maxRadioRadius * boundingBox.height;
|
|
|
-
|
|
|
- var project_x1 = rotation[0][0] * x1 + rotation[0][1] * y1;
|
|
|
- var project_y1 = rotation[1][0] * x1 + rotation[1][1] * y1;
|
|
|
-
|
|
|
- var real_x1 = Math.floor(project_x1 + shape[tree.landmarkID][0]);
|
|
|
- var real_y1 = Math.floor(project_y1 + shape[tree.landmarkID][1]);
|
|
|
- real_x1 = Math.max(0.0, Math.min(real_x1, image.height - 1.0));
|
|
|
- real_y1 = Math.max(0.0, Math.min(real_y1, image.width - 1.0));
|
|
|
-
|
|
|
- var project_x2 = rotation[0][0] * x2 + rotation[0][1] * y2;
|
|
|
- var project_y2 = rotation[1][0] * x2 + rotation[1][1] * y2;
|
|
|
-
|
|
|
- var real_x2 = Math.floor(project_x2 + shape[tree.landmarkID][0]);
|
|
|
- var real_y2 = Math.floor(project_y2 + shape[tree.landmarkID][1]);
|
|
|
- real_x2 = Math.max(0.0, Math.min(real_x2, image.height - 1.0));
|
|
|
- real_y2 = Math.max(0.0, Math.min(real_y2, image.width - 1.0));
|
|
|
- var pdf = Math.floor(image.data[real_y1*image.width + real_x1]) -
|
|
|
- Math.floor(image.data[real_y2 * image.width +real_x2]);
|
|
|
-
|
|
|
- if(pdf < tree.nodes[current].thresh){
|
|
|
- current = tree.nodes[current].cnodes[0];
|
|
|
- }else{
|
|
|
- current = tree.nodes[current].cnodes[1];
|
|
|
- }
|
|
|
-
|
|
|
- if (tree.nodes[current].is_leafnode == 1) {
|
|
|
- bincode = 1;
|
|
|
- for (var i=0; i < tree.leafnodes.length; i++) {
|
|
|
- if (tree.leafnodes[i] == current) {
|
|
|
- return bincode;
|
|
|
- }
|
|
|
- bincode++;
|
|
|
- }
|
|
|
- return bincode;
|
|
|
- }
|
|
|
-
|
|
|
- }
|
|
|
-
|
|
|
- return bincode;
|
|
|
- }
|
|
|
-
|
|
|
-}());
|
|
|
-(function() {
|
|
|
- /**
|
|
|
- * Face Alignment via Regressing Local Binary Features (LBF)
|
|
|
- * This approach has two components: a set of local binary features and
|
|
|
- * a locality principle for learning those features.
|
|
|
- * The locality principle is used to guide the learning of a set of highly
|
|
|
- * discriminative local binary features for each landmark independently.
|
|
|
- * The obtained local binary features are used to learn a linear regression
|
|
|
- * that later will be used to guide the landmarks in the alignment phase.
|
|
|
- *
|
|
|
- * @authors: VoxarLabs Team (http://cin.ufpe.br/~voxarlabs)
|
|
|
- * Lucas Figueiredo <lsf@cin.ufpe.br>, Thiago Menezes <tmc2@cin.ufpe.br>,
|
|
|
- * Thiago Domingues <tald@cin.ufpe.br>, Rafael Roberto <rar3@cin.ufpe.br>,
|
|
|
- * Thulio Araujo <tlsa@cin.ufpe.br>, Joao Victor <jvfl@cin.ufpe.br>,
|
|
|
- * Tomer Simis <tls@cin.ufpe.br>)
|
|
|
- */
|
|
|
-
|
|
|
- /**
|
|
|
- * Holds the maximum number of stages that will be used in the alignment algorithm.
|
|
|
- * Each stage contains a different set of random forests and retrieves the binary
|
|
|
- * code from a more "specialized" (i.e. smaller) region around the landmarks.
|
|
|
- * @type {number}
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.LBF.maxNumStages = 4;
|
|
|
-
|
|
|
- /**
|
|
|
- * Holds the regressor that will be responsible for extracting the local features from
|
|
|
- * the image and guide the landmarks using the training data.
|
|
|
- * @type {object}
|
|
|
- * @protected
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.LBF.regressor_ = null;
|
|
|
-
|
|
|
- /**
|
|
|
- * Generates a set of landmarks for a set of faces
|
|
|
- * @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} faces The list of faces detected in the image
|
|
|
- * @return {array} The aligned landmarks, each set of landmarks corresponding
|
|
|
- * to a specific face.
|
|
|
- * @static
|
|
|
- */
|
|
|
- tracking.LBF.align = function(pixels, width, height, faces){
|
|
|
-
|
|
|
- if(tracking.LBF.regressor_ == null){
|
|
|
- tracking.LBF.regressor_ = new tracking.LBF.Regressor(
|
|
|
- tracking.LBF.maxNumStages
|
|
|
- );
|
|
|
- }
|
|
|
-
|
|
|
- pixels = tracking.Image.grayscale(pixels, width, height, false);
|
|
|
-
|
|
|
- pixels = tracking.Image.equalizeHist(pixels, width, height);
|
|
|
-
|
|
|
- var shapes = new Array(faces.length);
|
|
|
-
|
|
|
- for(var i in faces){
|
|
|
-
|
|
|
- faces[i].height = faces[i].width;
|
|
|
-
|
|
|
- var boundingBox = {};
|
|
|
- boundingBox.startX = faces[i].x;
|
|
|
- boundingBox.startY = faces[i].y;
|
|
|
- boundingBox.width = faces[i].width;
|
|
|
- boundingBox.height = faces[i].height;
|
|
|
-
|
|
|
- shapes[i] = tracking.LBF.regressor_.predict(pixels, width, height, boundingBox);
|
|
|
- }
|
|
|
-
|
|
|
- return shapes;
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Unprojects the landmarks shape from the bounding box.
|
|
|
- * @param {matrix} shape The landmarks shape.
|
|
|
- * @param {matrix} boudingBox The bounding box.
|
|
|
- * @return {matrix} The landmarks shape projected into the bounding box.
|
|
|
- * @static
|
|
|
- * @protected
|
|
|
- */
|
|
|
- tracking.LBF.unprojectShapeToBoundingBox_ = function(shape, boundingBox){
|
|
|
- var temp = new Array(shape.length);
|
|
|
- for(var i=0; i < shape.length; i++){
|
|
|
- temp[i] = [
|
|
|
- (shape[i][0] - boundingBox.startX) / boundingBox.width,
|
|
|
- (shape[i][1] - boundingBox.startY) / boundingBox.height
|
|
|
- ];
|
|
|
- }
|
|
|
- return temp;
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Projects the landmarks shape into the bounding box. The landmarks shape has
|
|
|
- * normalized coordinates, so it is necessary to map these coordinates into
|
|
|
- * the bounding box coordinates.
|
|
|
- * @param {matrix} shape The landmarks shape.
|
|
|
- * @param {matrix} boudingBox The bounding box.
|
|
|
- * @return {matrix} The landmarks shape.
|
|
|
- * @static
|
|
|
- * @protected
|
|
|
- */
|
|
|
- tracking.LBF.projectShapeToBoundingBox_ = function(shape, boundingBox){
|
|
|
- var temp = new Array(shape.length);
|
|
|
- for(var i=0; i < shape.length; i++){
|
|
|
- temp[i] = [
|
|
|
- shape[i][0] * boundingBox.width + boundingBox.startX,
|
|
|
- shape[i][1] * boundingBox.height + boundingBox.startY
|
|
|
- ];
|
|
|
- }
|
|
|
- return temp;
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * Calculates the rotation and scale necessary to transform shape1 into shape2.
|
|
|
- * @param {matrix} shape1 The shape to be transformed.
|
|
|
- * @param {matrix} shape2 The shape to be transformed in.
|
|
|
- * @return {[matrix, scalar]} The rotation matrix and scale that applied to shape1
|
|
|
- * results in shape2.
|
|
|
- * @static
|
|
|
- * @protected
|
|
|
- */
|
|
|
- tracking.LBF.similarityTransform_ = function(shape1, shape2){
|
|
|
-
|
|
|
- var center1 = [0,0];
|
|
|
- var center2 = [0,0];
|
|
|
- for (var i = 0; i < shape1.length; i++) {
|
|
|
- center1[0] += shape1[i][0];
|
|
|
- center1[1] += shape1[i][1];
|
|
|
- center2[0] += shape2[i][0];
|
|
|
- center2[1] += shape2[i][1];
|
|
|
- }
|
|
|
- center1[0] /= shape1.length;
|
|
|
- center1[1] /= shape1.length;
|
|
|
- center2[0] /= shape2.length;
|
|
|
- center2[1] /= shape2.length;
|
|
|
-
|
|
|
- var temp1 = tracking.Matrix.clone(shape1);
|
|
|
- var temp2 = tracking.Matrix.clone(shape2);
|
|
|
- for(var i=0; i < shape1.length; i++){
|
|
|
- temp1[i][0] -= center1[0];
|
|
|
- temp1[i][1] -= center1[1];
|
|
|
- temp2[i][0] -= center2[0];
|
|
|
- temp2[i][1] -= center2[1];
|
|
|
- }
|
|
|
-
|
|
|
- var covariance1, covariance2;
|
|
|
- var mean1, mean2;
|
|
|
-
|
|
|
- var t = tracking.Matrix.calcCovarMatrix(temp1);
|
|
|
- covariance1 = t[0];
|
|
|
- mean1 = t[1];
|
|
|
-
|
|
|
- t = tracking.Matrix.calcCovarMatrix(temp2);
|
|
|
- covariance2 = t[0];
|
|
|
- mean2 = t[1];
|
|
|
-
|
|
|
- var s1 = Math.sqrt(tracking.Matrix.norm(covariance1));
|
|
|
- var s2 = Math.sqrt(tracking.Matrix.norm(covariance2));
|
|
|
-
|
|
|
- var scale = s1/s2;
|
|
|
- temp1 = tracking.Matrix.mulScalar(1.0/s1, temp1);
|
|
|
- temp2 = tracking.Matrix.mulScalar(1.0/s2, temp2);
|
|
|
-
|
|
|
- var num = 0, den = 0;
|
|
|
- for (var i = 0; i < shape1.length; i++) {
|
|
|
- num = num + temp1[i][1] * temp2[i][0] - temp1[i][0] * temp2[i][1];
|
|
|
- den = den + temp1[i][0] * temp2[i][0] + temp1[i][1] * temp2[i][1];
|
|
|
- }
|
|
|
-
|
|
|
- var norm = Math.sqrt(num*num + den*den);
|
|
|
- var sin_theta = num/norm;
|
|
|
- var cos_theta = den/norm;
|
|
|
- var rotation = [
|
|
|
- [cos_theta, -sin_theta],
|
|
|
- [sin_theta, cos_theta]
|
|
|
- ];
|
|
|
-
|
|
|
- return [rotation, scale];
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * LBF Random Forest data structure.
|
|
|
- * @static
|
|
|
- * @constructor
|
|
|
- */
|
|
|
- tracking.LBF.RandomForest = function(forestIndex){
|
|
|
- this.maxNumTrees = tracking.LBF.RegressorData[forestIndex].max_numtrees;
|
|
|
- this.landmarkNum = tracking.LBF.RegressorData[forestIndex].num_landmark;
|
|
|
- this.maxDepth = tracking.LBF.RegressorData[forestIndex].max_depth;
|
|
|
- this.stages = tracking.LBF.RegressorData[forestIndex].stages;
|
|
|
-
|
|
|
- this.rfs = new Array(this.landmarkNum);
|
|
|
- for(var i=0; i < this.landmarkNum; i++){
|
|
|
- this.rfs[i] = new Array(this.maxNumTrees);
|
|
|
- for(var j=0; j < this.maxNumTrees; j++){
|
|
|
- this.rfs[i][j] = new tracking.LBF.Tree(forestIndex, i, j);
|
|
|
- }
|
|
|
- }
|
|
|
- }
|
|
|
-
|
|
|
- /**
|
|
|
- * LBF Tree data structure.
|
|
|
- * @static
|
|
|
- * @constructor
|
|
|
- */
|
|
|
- tracking.LBF.Tree = function(forestIndex, landmarkIndex, treeIndex){
|
|
|
- var data = tracking.LBF.RegressorData[forestIndex].landmarks[landmarkIndex][treeIndex];
|
|
|
- this.maxDepth = data.max_depth;
|
|
|
- this.maxNumNodes = data.max_numnodes;
|
|
|
- this.nodes = data.nodes;
|
|
|
- this.landmarkID = data.landmark_id;
|
|
|
- this.numLeafnodes = data.num_leafnodes;
|
|
|
- this.numNodes = data.num_nodes;
|
|
|
- this.maxNumFeats = data.max_numfeats;
|
|
|
- this.maxRadioRadius = data.max_radio_radius;
|
|
|
- this.leafnodes = data.id_leafnodes;
|
|
|
- }
|
|
|
-
|
|
|
-}());
|
fanghuisheng
