新版新增和修改人员信息

App.vue

@@ -5,6 +5,13 @@
 	} from '@/common/auth.js'
 	export default {
 		onLaunch: function() {
+
+			uni.getSystemInfo({
+				success: function(res) {
+					console.log("系统信息：", res)
+				}
+			});
+
 			if (getToken()) {
 				console.log('存在');
 				uni.reLaunch({
@@ -32,7 +39,7 @@
 <style>
 	/*每个页面公共css */
 	@font-face {
-	/* 	font-family: 'puhui';
+		/* 	font-family: 'puhui';
 		src: url('./static/fonts/AlibabaPuHuiTi-3-65-Medium.ttf'); */
 	}
 

api/dict.js

@@ -2,17 +2,27 @@ import request from '@/common/request.js'
 
 // 根据字典类型查询字典数据信息
 export const getDicts = (dictType) => {
-  return request({
-    url: '/api/system/dict/data/common/type/' + dictType,
-    method: 'get',
-  })
+	return request({
+		url: '/api/system/dict/data/common/type/' + dictType,
+		method: 'get',
+	})
 }
 
 // 业务字典查询
 export const ObjdictList = (query) => {
-  return request({
-    url: '/api/system/dict/type/objdict',
-    method: 'get',
-    data: query
-  })
+	return request({
+		url: '/api/system/dict/type/objdict',
+		method: 'get',
+		data: query
+	})
+}
+
+
+// 地区查询
+export const GetChildListByCode = (query) => {
+	return request({
+		url: '/api/system/jlDept/getChildListByCode',
+		method: 'get',
+		data: query
+	})
 }

api/kh.js

@@ -3,7 +3,7 @@ import request from '@/common/request.js'
 // 老人信息修改
 export function updateKhjbxx(data) {
 	return request({
-		url: '/api/business/lrjbxx',
+		url: '/api/lnst/lrxx',
 		method: 'put',
 		data: data
 	})
@@ -14,7 +14,7 @@ export function updateKhjbxx(data) {
 export function SaveKhjbxx(data) {
 	data.source = '1'
 	return request({
-		url: '/api/business/lrjbxx',
+		url: '/api/lnst/lrxx',
 		method: 'post',
 		data: data
 	})
@@ -23,7 +23,7 @@ export function SaveKhjbxx(data) {
 // 老人信息详情
 export function infoKhjbxx(id) {
 	return request({
-		url: '/api/business/lrjbxx/' + id,
+		url: '/api/lnst/lrxx/' + id,
 		method: 'get',
 		data: {}
 	})
@@ -33,7 +33,7 @@ export function infoKhjbxx(id) {
 // 老人信息列表
 export function ListKhjbxx(data) {
 	return request({
-		url: '/api/business/lrjbxx/list',
+		url: '/api/lnst/lrxx/list',
 		method: 'get',
 		data: data
 	})
@@ -43,7 +43,7 @@ export function ListKhjbxx(data) {
 // 老人信息详情
 export function GetLrByZjhm(data) {
 	return request({
-		url: '/api/business/lrjbxx/getLrByZjhm',
+		url: '/api/lnst/lrxx/getLrByZjhm',
 		method: 'get',
 		data: data
 	})
@@ -53,7 +53,7 @@ export function GetLrByZjhm(data) {
 // 核实老人信息
 export function CheckZjhm(data) {
 	return request({
-		url: '/api/business/lrjbxx/checkZjhm',
+		url: '/api/lnst/lrxx/checkZjhm',
 		method: 'get',
 		data: data
 	})
@@ -62,7 +62,7 @@ export function CheckZjhm(data) {
 // 套餐列表
 export function TcList(data) {
 	return request({
-		url: '/api/business/tc/list',
+		url: '/api/lnst/tc/list',
 		method: 'get',
 		data: data
 	})
@@ -72,7 +72,7 @@ export function TcList(data) {
 // 查询价格
 export function GetJg(data) {
 	return request({
-		url: '/api/business/lrjbxx/getJg',
+		url: '/api/lnst/lrjbxx/getJg',
 		method: 'get',
 		data: data
 	})
@@ -82,7 +82,7 @@ export function GetJg(data) {
 // 保存用餐信息
 export function SaveYcxx(data) {
 	return request({
-		url: '/api/business/ycxx',
+		url: '/api/lnst/ycxx',
 		method: 'post',
 		data: data
 	})

api/upload.js

@@ -52,4 +52,16 @@ export function UploadOne(file, formData = {}) {
 		}
 
 	})
+}
+
+
+
+
+
+export function UploadSome(data) {
+	return request({
+		url: '/api/file/uploadMul',
+		method: 'post',
+		data: data
+	})
 }

common/baiduUtil.js

@@ -0,0 +1,146 @@
+import config from '@/config.js'
+// 获取AccessToken
+export function getAccessToken(callback) {
+	uni.request({
+		url: '/baiduApi/oauth/2.0/token',
+		data: {
+			grant_type: 'client_credentials',
+			client_id: config.face_client_id,
+			client_secret: config.face_client_secret
+		},
+		method: 'POST',
+		header: {
+			'Content-Type': 'application/x-www-form-urlencoded'
+		},
+		success: (res) => {
+			// res.data.access_token
+			callback(res.data.access_token)
+		}
+	})
+}
+
+// 身份证识别
+export function idcard(path, token, type, callback) {
+	if (!type) type = 'front'
+	uni.request({
+		url: '/baiduApi/rest/2.0/ocr/v1/idcard',
+		data: {
+			image: path,
+			access_token: token,
+			id_card_side: type,
+			detect_photo: true,
+			detect_risk: true,
+			detect_card: true,
+		},
+		timeout: 30000,
+		method: 'POST',
+		header: {
+			'Content-Type': 'application/x-www-form-urlencoded'
+		},
+		success: (res) => {
+			callback(res.data)
+		}
+	})
+}
+
+// 对比
+export function match(token, face1, face2, callback) {
+	let data = [{
+		image: face1,
+		image_type: 'BASE64',
+		liveness_control: 'NORMAL',
+	}, {
+		image: face2,
+		image_type: 'BASE64'
+	}]
+	console.log("人脸对比:", data)
+	uni.request({
+		url: '/baiduApi/rest/2.0/face/v3/match?access_token=' + token,
+		data: data,
+		method: 'POST',
+		header: {
+			'Content-Type': 'application/json'
+		},
+		success: (res) => {
+			callback(res)
+		},
+		error: (err) => {
+			console.log("对比失败,", err);
+		}
+	})
+}
+
+// 创建组
+export function createGroup(token, groupId, callback) {
+	uni.request({
+		url: '/baiduApi/rest/2.0/face/v3/faceset/group/add?access_token=' + token,
+		data: {
+			group_id: groupId,
+		},
+		method: 'POST',
+		header: {
+			'Content-Type': 'application/x-www-form-urlencoded'
+		},
+		success: (res) => {
+			callback(res)
+		},
+		error: (err) => {
+			console.log("创建组失败,", err);
+		}
+	})
+}
+
+
+// 人脸注册
+export function faceAdd(token, face, groupId, user_id, callback) {
+	// https://cloud.baidu.com/doc/FACE/s/Gk37c1uzc#%E4%BA%BA%E8%84%B8%E6%B3%A8%E5%86%8C
+	let data = {
+		image: face,
+		image_type: 'BASE64',
+		group_id: groupId,
+		user_id: user_id,
+		action_type: 'REPLACE', // 操作方式 APPEND: 当user_id在库中已经存在时，对此user_id重复注册时，新注册的图片默认会追加到该user_id下 REPLACE : 当对此user_id重复注册时,则会用新图替换库中该user_id下所有图片 默认使用APPEND
+	}
+	console.log("人脸注册:", data)
+	uni.request({
+		url: '/baiduApi/rest/2.0/face/v3/faceset/user/add?access_token=' + token,
+		data: data,
+		method: 'POST',
+		header: {
+			'Content-Type': 'application/json'
+		},
+		success: (res) => {
+			callback(res)
+		},
+		error: (err) => {
+			console.log("人脸注册失败,", err);
+		}
+	})
+}
+
+
+// 人脸搜索
+export function faceSearch(token, face, groupId, callback) {
+	let data = {
+		image: face,
+		image_type: 'BASE64',
+		group_id_list: groupId,
+		match_threshold: config.score,
+		max_user_num: 50
+	}
+	console.log("人脸搜索:", data)
+	uni.request({
+		url: '/baiduApi/rest/2.0/face/v3/search?access_token=' + token,
+		data: data,
+		method: 'POST',
+		header: {
+			'Content-Type': 'application/json'
+		},
+		success: (res) => {
+			callback(res)
+		},
+		error: (err) => {
+			console.log("人脸搜索失败,", err);
+		}
+	})
+}

common/image.js

@@ -77,6 +77,84 @@ export function translate(imgSrc, scale, type, callback) {
 		}
 	}
 }
+
+
+export function translateAll(imgSrc, callback) {
+	translate1(imgSrc, 0.6, 'blob', callback)
+}
+
+
+
+export function translate1(imgSrc, scale, type, callback) {
+	var img = new Image();
+	img.src = imgSrc;
+	img.onload = function() {
+		var that = this;
+		let yasuobi = 700 / that.width
+		let h = that.height * yasuobi;
+		let w = that.width * yasuobi;
+		console.log("宽高", w, h, yasuobi);
+		var canvas = document.createElement('canvas');
+		var ctx = canvas.getContext('2d');
+		var width = document.createAttribute("width");
+		width.nodeValue = w;
+		var height = document.createAttribute("height");
+		height.nodeValue = h;
+		canvas.setAttributeNode(width);
+		canvas.setAttributeNode(height);
+		ctx.drawImage(that, 0, 0, w, h);
+		var base64 = canvas.toDataURL('image/jpeg', scale); //压缩比例
+		canvas = null;
+		if (type == 'base64') {
+			var blob = base64ToBlob(base64);
+			var blobUrl = window.URL.createObjectURL(blob); //blob地址
+			// callback(base64, blobUrl, callback1);
+			uni.getFileInfo({
+				filePath: blobUrl,
+				success: (res) => {
+					console.log(res);
+					let size = res.size
+					let scale = 1
+					if (size / 1024 / 1024 > 0.12) {
+						scale = 0.6
+						translate1(blobUrl, scale, 'blob', callback)
+					} else {
+						callback(base64, blobUrl)
+					}
+				},
+				fail: (err) => {
+					console.log("err:", err);
+				}
+			})
+
+
+		} else {
+			var blob = base64ToBlob(base64);
+			var blobUrl = window.URL.createObjectURL(blob); //blob地址
+			// callback(base64, blobUrl, callback1);
+
+			uni.getFileInfo({
+				filePath: blobUrl,
+				success: (res) => {
+					console.log(res);
+					let size = res.size
+					let scale = 1
+					if (size / 1024 / 1024 > 0.12) {
+						scale = 0.6
+						translate1(blobUrl, scale, 'blob', callback)
+					} else {
+						callback(base64, blobUrl)
+					}
+				},
+				fail: (err) => {
+					console.log("err:", err);
+				}
+			})
+		}
+	}
+}
+
+
 // base转Blob
 export function base64ToBlob(base64) {
 	var arr = base64.split(','),
@@ -95,7 +173,7 @@ export function base64ToBlob(base64) {
 // base转url
 export function base64ToUrl(base64) {
 	var arr = base64.split(',')
-		let blob = {}
+	let blob = {}
 	if (arr.length < 2) {
 		const arrayBuffer = uni.base64ToArrayBuffer(base64);
 		// 创建Blob对象
@@ -115,4 +193,14 @@ export function base64ToUrl(base64) {
 		});
 	}
 	return URL.createObjectURL(blob);
+}
+
+
+// blob 转 base64
+export function blobToDataURI(blob, callback) {
+	var reader = new FileReader();
+	reader.readAsDataURL(blob);
+	reader.onload = function(e) {
+		callback(e.target.result);
+	};
 }

common/tracking.js/.bowerrc

@@ -0,0 +1,3 @@
+{
+  "directory": "../"
+}

common/tracking.js/.editorconfig

@@ -0,0 +1,14 @@
+# editorconfig.org
+root = true
+
+[*]
+indent_style = tab
+end_of_line = lf
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = true
+indent_style = space
+indent_size = 2
+
+[*.md]
+trim_trailing_whitespace = false

common/tracking.js/.gitignore

@@ -0,0 +1,3 @@
+.DS_Store
+node_modules
+test/assets/benchmark.json

common/tracking.js/.jshintrc

@@ -0,0 +1,30 @@
+{
+  "asi": false,
+  "bitwise": false,
+  "curly": true,
+  "eqeqeq": true,
+  "esnext": true,
+  "evil": false,
+  "forin": false,
+  "globals": {
+    "document": true,
+    "navigator": true,
+    "tracking": true,
+    "window": true
+  },
+  "immed": true,
+  "indent": 2,
+  "lastsemic": false,
+  "maxdepth": false,
+  "multistr": false,
+  "newcap": true,
+  "noarg": true,
+  "node": true,
+  "onevar": false,
+  "quotmark": "single",
+  "regexp": true,
+  "smarttabs": true,
+  "trailing": true,
+  "undef": true,
+  "unused": true
+}

common/tracking.js/.travis.yml

@@ -0,0 +1,4 @@
+language: node_js
+node_js:
+  - "0.11"
+  - "0.10"

common/tracking.js/LICENSE.md

@@ -0,0 +1,30 @@
+Software License Agreement (BSD License)
+
+Copyright (c) 2014, Eduardo A. Lundgren Melo.
+All rights reserved.
+
+Redistribution and use of this software in source and binary forms, with or without modification, are
+permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above
+  copyright notice, this list of conditions and the
+  following disclaimer.
+
+* Redistributions in binary form must reproduce the above
+  copyright notice, this list of conditions and the
+  following disclaimer in the documentation and/or other
+  materials provided with the distribution.
+
+* The name of Eduardo A. Lundgren Melo may not be used to endorse or promote products
+  derived from this software without specific prior
+  written permission of Eduardo A. Lundgren Melo.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
+WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
+TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
+ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+

common/tracking.js/TODO.md

@@ -0,0 +1,37 @@
+### Face tracking
+- DONE display line with the face
+- impressive speed and accuracy from @clmtrackr - https://github.com/auduno/clmtrackr
+- http://blog.dlib.net/2014/08/real-time-face-pose-estimation.html
+  - dlib implementation
+- PerspectiveCamera.setViewOffset - todo the https://www.youtube.com/watch?v=LEPvUfC7wh8
+- support for webworker ?
+  - it consume a lot of cpu
+- try blur in source image
+- get a video on the internet to use as example
+- DONE do lerp on output 
+
+---
+
+### Misc
+- fix image source
+- handle a proper versioning
+  - master is last stable
+  - stable is tagged in github repo
+  - dev is 'next-stable'
+
+- Tracking.Image without destination buffer - force reallocation
+  - allow to provide destination, if not present, 
+- three.js is r67 in the examples - current three.js is r86
+  - TODO port on current three.js
+- some examples are not running well - list which one
+  - webcam one ?
+- some examples are unclear - no instructions 
+  - provide info in color tracking on how to run it
+  - TODO list which one
+- add more interactive examples - stuff i can try with a webcam
+- merge lots of good PR
+  - https://github.com/eduardolundgren/tracking.js/pull/229 - Add support for Safari 11
+  - https://github.com/eduardolundgren/tracking.js/pull/144 Regressing Local Binary Features more details on face detection
+  - https://github.com/eduardolundgren/tracking.js/pull/164 - Creating conversor from haarcascade to tracking.js array
+  - https://github.com/eduardolundgren/tracking.js/pull/131 <- merge or close
+- ```gulp test``` fails in the benchmarks

common/tracking.js/assets/opencv_haarcascade_converter.html

@@ -0,0 +1,101 @@
+<!DOCTYPE HTML>
+<html lang="en-US">
+<head>
+	<meta charset="UTF-8">
+	<title></title>
+
+	<script type="text/javascript" src="opencv_haarcascade_frontalface_alt.js"></script>
+	<script type="text/javascript" src="opencv_haarcascade_eye.js"></script>
+	<script type="text/javascript" src="opencv_haarcascade_upper_body.js"></script>
+	<script type="text/javascript" src="opencv_haarcascade_mouth.js"></script>
+</head>
+<body>
+	<script>
+		// [
+		// 	[
+		// 		-1, // index
+		// 		0.8226894140243530, // stage threshold
+		//		// tree
+		// 		[
+		//			// node 1
+		// 			[
+		// 				3, 7, 14, 4, -1, // rect1
+		// 				3, 9, 14, 2, 2, // rect 2
+		// 				4.0141958743333817e-003, // node threshold
+		// 				0.0337941907346249, // left
+		// 				0.8378106951713562 // right
+		// 			],
+		//			// node 2
+		// 			[
+		// 				3, 7, 14, 4, -1,
+		// 				3, 9, 14, 2, 2,
+		// 				4.0141958743333817e-003,
+		// 				0.0337941907346249,
+		// 				0.8378106951713562
+		// 			]
+		// 		]
+		// 	]
+		// ];
+
+		var toFloat = function(v) {
+			// return parseFloat(parseFloat(v).toFixed(1));
+			return parseFloat(v);
+		},
+
+		toInt = function(v) {
+			return parseInt(v, 10);
+		},
+
+		convert = function(haarcascade) {
+			var stages = [],
+				hstages = haarcascade.stages,
+				i,
+				j;
+
+			for (i = 0; i < hstages.length; i++) {
+				var stage = [],
+					trees = [],
+					hstage = hstages[i],
+					htrees = hstage.trees,
+					parent = toInt(hstage.parent),
+					stageThreshold = toFloat(hstage.stage_threshold);
+
+				for (j = 0; j < htrees.length; j++) {
+					var node = [],
+						hnode = htrees[j][0],
+						hnodeThreshold = toFloat(hnode.threshold),
+						hnodeLeft = toFloat(hnode.left_val),
+						hnodeRight = toFloat(hnode.right_val),
+						hnodeRects = hnode.feature.rects,
+						hr,
+						r;
+
+						for (r = 0; r < hnodeRects.length; r++) {
+							hr = hnodeRects[r].split(" ").map(toFloat),
+							node = node.concat(hr);
+						}
+
+						node.push(hnodeThreshold, hnodeLeft, hnodeRight);
+						trees.push(node);
+				}
+
+				stage.push(parent, stageThreshold, trees);
+				stages.push(stage);
+			}
+
+			console.log(stages);
+
+			return JSON.stringify(stages);
+		};
+
+		// output
+
+		// var json = convert(opencv_haarcascade_frontalface_alt);
+		// var json = convert(opencv_haarcascade_eye);
+		// var json = convert(opencv_haarcascade_upper_body);
+		var json = convert(opencv_haarcascade_mouth);
+
+		console.log(json);
+	</script>
+</body>
+</html>

common/tracking.js/bower.json

@@ -0,0 +1,22 @@
+{
+  "name": "tracking",
+  "homepage": "http://trackingjs.com",
+  "authors": [
+    "Eduardo Lundgren <edu@rdo.io>"
+  ],
+  "description": "Augmented Reality JavaScript Framework.",
+  "main": "build/tracking.js",
+  "keywords": [
+    "tracking",
+    "webrtc"
+  ],
+  "license": "BSD",
+  "ignore": [
+    "**/.*",
+    "node_modules"
+  ],
+  "dependencies": {
+    "dat-gui": "0.5.0",
+    "threejs": "r67"
+  }
+}

common/tracking.js/examples/assets/color_camera_gui.js

@@ -0,0 +1,94 @@
+function initGUIControllers(tracker) {
+  // GUI Controllers
+
+  var gui = new dat.GUI();
+
+  var trackedColors = {
+    custom: false
+  };
+
+  Object.keys(tracking.ColorTracker.knownColors_).forEach(function(color) {
+    trackedColors[color] = true;
+  });
+
+  tracker.customColor = '#000000';
+
+  function createCustomColor(value) {
+    var components = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(value);
+    var customColorR = parseInt(components[1], 16);
+    var customColorG = parseInt(components[2], 16);
+    var customColorB = parseInt(components[3], 16);
+
+    var colorTotal = customColorR + customColorG + customColorB;
+
+    if (colorTotal === 0) {
+      tracking.ColorTracker.registerColor('custom', function(r, g, b) {
+        return r + g + b < 10;
+      });
+    } else {
+      var rRatio = customColorR / colorTotal;
+      var gRatio = customColorG / colorTotal;
+
+      tracking.ColorTracker.registerColor('custom', function(r, g, b) {
+        var colorTotal2 = r + g + b;
+
+        if (colorTotal2 === 0) {
+          if (colorTotal < 10) {
+            return true;
+          }
+          return false;
+        }
+
+        var rRatio2 = r / colorTotal2,
+          gRatio2 = g / colorTotal2,
+          deltaColorTotal = colorTotal / colorTotal2,
+          deltaR = rRatio / rRatio2,
+          deltaG = gRatio / gRatio2;
+
+        return deltaColorTotal > 0.9 && deltaColorTotal < 1.1 &&
+          deltaR > 0.9 && deltaR < 1.1 &&
+          deltaG > 0.9 && deltaG < 1.1;
+      });
+    }
+
+    updateColors();
+  }
+
+  function updateColors() {
+    var colors = [];
+
+    for (var color in trackedColors) {
+      if (trackedColors[color]) {
+        colors.push(color);
+      }
+    }
+
+    tracker.setColors(colors);
+  }
+
+  var colorsFolder = gui.addFolder('Colors');
+
+  Object.keys(trackedColors).forEach(function(color) {
+    if (color !== 'custom') {
+      colorsFolder.add(trackedColors, color).onFinishChange(updateColors);
+    }
+  });
+
+  colorsFolder.add(trackedColors, 'custom').onFinishChange(function(value) {
+    if (value) {
+      this.customColorElement = colorsFolder.addColor(tracker, 'customColor').onChange(createCustomColor);
+    } else {
+      colorsFolder.remove(this.customColorElement);
+    }
+  });
+
+  var parametersFolder = gui.addFolder('Parameters');
+
+  parametersFolder.add(tracker, 'minDimension', 1, 100);
+  parametersFolder.add(tracker, 'minGroupSize', 1, 100);
+
+  colorsFolder.open();
+  parametersFolder.open();
+
+  updateColors();
+}

common/tracking.js/examples/assets/demo.css

@@ -0,0 +1,57 @@
+* {
+  margin: 0;
+  padding: 0;
+  font-family: Helvetica, Arial, sans-serif;
+}
+
+.demo-title {
+  position: absolute;
+  width: 100%;
+  background: #2e2f33;
+  z-index: 2;
+  padding: .7em 0;
+}
+
+.demo-title a {
+  color: #fff;
+  border-bottom: 1px dotted #a64ceb;
+  text-decoration: none;
+}
+
+.demo-title p {
+  color: #fff;
+  text-align: center;
+  text-transform: lowercase;
+  font-size: 15px;
+}
+
+.demo-frame {
+  background: url(frame.png) no-repeat;
+  width: 854px;
+  height: 658px;
+  position: fixed;
+  top: 50%;
+  left: 50%;
+  margin: -329px 0 0 -429px;
+  padding: 95px 20px 45px 34px;
+  overflow: hidden;
+  -webkit-box-sizing: border-box;
+  -moz-box-sizing: border-box;
+  -ms-box-sizing: border-box;
+  box-sizing: border-box;
+}
+
+.demo-container {
+  width: 100%;
+  height: 530px;
+  position: relative;
+  background: #eee;
+  overflow: hidden;
+  border-bottom-right-radius: 10px;
+  border-bottom-left-radius: 10px;
+}
+
+.dg.ac {
+  z-index: 100 !important;
+  top: 50px !important;
+}

common/tracking.js/examples/assets/fish_tank/FishTankRenderer.js

@@ -0,0 +1,111 @@
+(function() {
+
+  var FishTankRenderer = function() {};
+
+  FishTankRenderer.prototype.init = function(container) {
+    if (!FishTankRenderer.isWebGLEnabled()) {
+      throw new Error('WebGL is not enabled in your browser.');
+    }
+
+    var mesh, geometry;
+
+    this.spheres = [];
+
+    this.camera = new THREE.PerspectiveCamera(60, window.innerWidth / window.innerHeight, 1, 100000);
+    this.camera.position.z = 3200;
+
+    this.scene = new THREE.Scene();
+
+    var geometry = new THREE.SphereGeometry(100, 32, 16);
+
+    var path = 'assets/fish_tank/';
+    var format = '.png';
+    var urls = [
+      path + 'px' + format, path + 'nx' + format,
+      path + 'py' + format, path + 'ny' + format,
+      path + 'pz' + format, path + 'nz' + format
+    ];
+
+    var textureCube = THREE.ImageUtils.loadTextureCube(urls);
+    var material = new THREE.MeshBasicMaterial({
+      color: 0xffffff,
+      envMap: textureCube
+    });
+
+    for (var i = 0; i < 500; i++) {
+
+      var mesh = new THREE.Mesh(geometry, material);
+
+      mesh.position.x = Math.random() * 100000 - 50000;
+      mesh.position.y = Math.random() * 100000 - 50000;
+      mesh.position.z = Math.random() * 100000 - 50000;
+
+      mesh.scale.x = mesh.scale.y = mesh.scale.z = Math.random() * 3 + 1;
+
+      this.scene.add(mesh);
+
+      this.spheres.push(mesh);
+
+    }
+
+    // Skybox
+
+    var shader = THREE.ShaderLib["cube"];
+    shader.uniforms["tCube"].value = textureCube;
+
+    var material = new THREE.ShaderMaterial({
+
+        fragmentShader: shader.fragmentShader,
+        vertexShader: shader.vertexShader,
+        uniforms: shader.uniforms,
+        side: THREE.BackSide
+
+      }),
+
+      mesh = new THREE.Mesh(new THREE.BoxGeometry(100000, 100000, 100000), material);
+    this.scene.add(mesh);
+
+    var _params = {
+      minFilter: THREE.LinearFilter,
+      magFilter: THREE.NearestFilter,
+      format: THREE.RGBAFormat
+    };
+
+    var width = window.innerWidth || 2;
+    var height = window.innerHeight || 2;
+
+    this.renderer = new THREE.WebGLRenderer(width, height, _params);
+    container.appendChild(this.renderer.domElement);
+    this.renderer.setSize(width, height);
+  };
+
+  FishTankRenderer.prototype.render = function(controlX, controlY) {
+    var timer = 0.0001 * Date.now();
+
+    this.camera.position.x += (-controlX - this.camera.position.x) * 0.05;
+    this.camera.position.y += (-controlY - this.camera.position.y) * 0.05;
+
+    this.camera.lookAt(this.scene.position);
+
+    for (var i = 0, il = this.spheres.length; i < il; i++) {
+      var sphere = this.spheres[i];
+      sphere.position.x += 50 * Math.cos(timer + i);
+      sphere.position.y += 50 * Math.sin(timer + i * 1.1);
+    }
+
+    this.renderer.render(this.scene, this.camera);
+  };
+
+  FishTankRenderer.isWebGLEnabled = function() {
+    try {
+      var canvas = document.createElement('canvas');
+      return !!window.WebGLRenderingContext &&
+        (canvas.getContext('webgl') || canvas.getContext('experimental-webgl'));
+    } catch (e) {
+      return false;
+    }
+  };
+
+  window.FishTankRenderer = FishTankRenderer;
+
+})();

common/tracking.js/examples/brief.html

@@ -0,0 +1,105 @@
+<!doctype html>
+
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - feature matching</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+  <script src="../node_modules/dat.gui/build/dat.gui.min.js"></script>
+
+  <style>
+  .demo-container {
+    background-color: black;
+  }
+  #image1, #image2 {
+    position: absolute;
+    left: -1000px;
+    top: -1000px;
+  }
+  #canvas {
+    position: absolute;
+    left: 50%;
+    top: 50%;
+    margin-left: -393px;
+    margin-top: -147px;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － match similar feature points in two images</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <img id="image1" src="assets/brief1.png" />
+      <img id="image2" src="assets/brief2.png" />
+      <canvas id="canvas" width="786" height="295"></canvas>
+    </div>
+  </div>
+
+  <script>
+  window.onload = function() {
+    var width = 393;
+    var height = 295;
+    var canvas = document.getElementById('canvas');
+    var context = canvas.getContext('2d');
+
+    var image1 = document.getElementById('image1');
+    var image2 = document.getElementById('image2');
+
+    window.descriptorLength = 256;
+    window.matchesShown = 30;
+    window.blurRadius = 3;
+
+    var doMatch = function() {
+      tracking.Brief.N = window.descriptorLength;
+
+      context.drawImage(image1, 0, 0, width, height);
+      context.drawImage(image2, width, 0, width, height);
+  
+      var imageData1 = context.getImageData(0, 0, width, height);
+      var imageData2 = context.getImageData(width, 0, width, height);
+  
+      var gray1 = tracking.Image.grayscale(tracking.Image.blur(imageData1.data, width, height, blurRadius), width, height);
+      var gray2 = tracking.Image.grayscale(tracking.Image.blur(imageData2.data, width, height, blurRadius), width, height);
+  
+      var corners1 = tracking.Fast.findCorners(gray1, width, height);
+      var corners2 = tracking.Fast.findCorners(gray2, width, height);
+  
+      var descriptors1 = tracking.Brief.getDescriptors(gray1, width, corners1);
+      var descriptors2 = tracking.Brief.getDescriptors(gray2, width, corners2);
+  
+      var matches = tracking.Brief.reciprocalMatch(corners1, descriptors1, corners2, descriptors2);
+
+      matches.sort(function(a, b) {
+        return b.confidence - a.confidence;
+      });
+  
+      for (var i = 0; i < Math.min(window.matchesShown, matches.length); i++) {
+        var color = '#' + Math.floor(Math.random()*16777215).toString(16);
+        context.fillStyle = color;
+        context.strokeStyle = color;
+        context.fillRect(matches[i].keypoint1[0], matches[i].keypoint1[1], 4, 4);
+        context.fillRect(matches[i].keypoint2[0] + width, matches[i].keypoint2[1], 4, 4);
+
+        context.beginPath();
+        context.moveTo(matches[i].keypoint1[0], matches[i].keypoint1[1]);
+        context.lineTo(matches[i].keypoint2[0] + width, matches[i].keypoint2[1]);
+        context.stroke();
+
+      }
+    };
+
+    doMatch();
+
+    var gui = new dat.GUI();
+    gui.add(window, 'descriptorLength', 128, 512).step(32).onChange(doMatch);
+    gui.add(window, 'matchesShown', 1, 100).onChange(doMatch);
+    gui.add(window, 'blurRadius', 1.1, 5).onChange(doMatch);
+  }
+  </script>
+</body>
+</html>

common/tracking.js/examples/brief_camera.html

@@ -0,0 +1,185 @@
+<!doctype html>
+<html>
+<head>
+
+  <title>tracking.js - bounding box with camera</title>
+
+  <meta charset="utf-8">
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+   <script src="../node_modules/dat.gui/build/dat.gui.min.js"></script>
+  <script src="assets/stats.min.js"></script>
+
+  <style>
+  #boundingBox {
+    display: none;
+    position: absolute;
+    background: white;
+    border: 1px dashed;
+    opacity: .5;
+    z-index: 1;
+  }
+  #video {
+    position: absolute;
+    top: -1000px;
+    cursor: crosshair;
+  }
+  body {
+    -webkit-touch-callout: none;
+    -webkit-user-select: none;
+    -khtml-user-select: none;
+    -moz-user-select: none;
+    -ms-user-select: none;
+    user-select: none;
+  }
+  </style>
+</head>
+<body>
+
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － Click and drag to select the area to be tracked</p>
+  </div>
+
+  <div id="boundingBox"></div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <video id="video" width="393" height="295" preload autoplay loop muted controls></video>
+      <canvas id="canvas" width="800" height="530"></canvas>
+  </div>
+</div>
+
+  <script>
+    (function() {
+      // BoundingBoxTracker ======================================================
+      var BoundingBoxTracker = function() {
+        BoundingBoxTracker.base(this, 'constructor');
+      };
+      tracking.inherits(BoundingBoxTracker, tracking.Tracker);
+
+      BoundingBoxTracker.prototype.templateDescriptors_ = null;
+      BoundingBoxTracker.prototype.templateKeypoints_ = null;
+      BoundingBoxTracker.prototype.fastThreshold = 60;
+      BoundingBoxTracker.prototype.blur = 3;
+
+      BoundingBoxTracker.prototype.setTemplate = function(pixels, width, height) {
+        var blur = tracking.Image.blur(pixels, width, height, 3);
+        var grayscale = tracking.Image.grayscale(blur, width, height);
+        this.templateKeypoints_ = tracking.Fast.findCorners(grayscale, width, height);
+        this.templateDescriptors_ = tracking.Brief.getDescriptors(grayscale, width, this.templateKeypoints_);
+      };
+
+      BoundingBoxTracker.prototype.track = function(pixels, width, height) {
+        var blur = tracking.Image.blur(pixels, width, height, this.blur);
+        var grayscale = tracking.Image.grayscale(blur, width, height);
+        var keypoints = tracking.Fast.findCorners(grayscale, width, height, this.fastThreshold);
+        var descriptors = tracking.Brief.getDescriptors(grayscale, width, keypoints);
+        this.emit('track', {
+          data: tracking.Brief.reciprocalMatch(this.templateKeypoints_, this.templateDescriptors_, keypoints, descriptors)
+        });
+      };
+
+      // Track ===================================================================
+      var boundingBox = document.getElementById('boundingBox');
+      var boxLeft = 403;
+      var video = document.getElementById('video');
+      var canvas = document.getElementById('canvas');
+      var canvasRect = canvas.getBoundingClientRect();
+      var context = canvas.getContext('2d');
+      var templateImageData;
+      var capturing = false;
+      var videoHeight = 295;
+      var videoWidth = 393;
+
+      var tracker = new BoundingBoxTracker();
+
+      tracker.on('track', function(event) {
+        stats.end();
+
+        if (capturing) {
+          return;
+        }
+        // Sorts best matches by confidence.
+        event.data.sort(function(a, b) {
+          return b.confidence - a.confidence;
+        });
+        // Re-draws template on canvas.
+        context.putImageData(templateImageData, boxLeft, 0);
+
+        // Plots lines connecting matches.
+        for (var i = 0; i < Math.min(10, event.data.length); i++) {
+          var template = event.data[i].keypoint1;
+          var frame = event.data[i].keypoint2;
+          context.beginPath();
+          context.strokeStyle = 'magenta';
+          context.moveTo(frame[0], frame[1]);
+          context.lineTo(boxLeft + template[0], template[1]);
+          context.stroke();
+        }
+      });
+
+      var trackerTask = tracking.track(video, tracker, { camera: true });
+      // Waits for the user to accept the camera.
+      trackerTask.stop();
+
+      // Sync video ============================================================
+      function requestFrame() {
+        window.requestAnimationFrame(function() {
+          context.clearRect(0, 0, canvas.width, canvas.height);
+          if (video.readyState === video.HAVE_ENOUGH_DATA) {
+            try {
+              context.drawImage(video, 0, 0, videoWidth, videoHeight);
+            } catch (err) {}
+          }
+          requestFrame();
+        });
+      }
+      requestFrame();
+
+      // Bounding box drag =====================================================
+      var initialPoint;
+      var left;
+      var top;
+      var width;
+      var height;
+      canvas.addEventListener('mousedown', function(event) {
+        initialPoint = [event.pageX, event.pageY];
+        capturing = true;
+      });
+      canvas.addEventListener('mousemove', function(event) {
+        if (capturing) {
+          left = Math.min(initialPoint[0], event.pageX);
+          top = Math.min(initialPoint[1], event.pageY);
+          width = Math.max(initialPoint[0], event.pageX) - left;
+          height = Math.max(initialPoint[1], event.pageY) - top;
+          boundingBox.style.display = 'block';
+          boundingBox.style.left = left + 'px';
+          boundingBox.style.top = top + 'px';
+          boundingBox.style.width = width + 'px';
+          boundingBox.style.height = height + 'px';
+        }
+      });
+      document.addEventListener('mouseup', function() {
+        boundingBox.style.display = 'none';
+        setTackerTemplate(left, top, width, height);
+        capturing = false;
+      });
+      function setTackerTemplate(left, top, width, height) {
+        templateImageData = context.getImageData(left - canvasRect.left, top - canvasRect.top, width, height);
+        canvas.width = boxLeft + width;
+        context.putImageData(templateImageData, boxLeft, 0);
+        trackerTask.stop();
+        tracker.setTemplate(templateImageData.data, width, height);
+        trackerTask.run();
+      }
+
+      // GUI Controllers
+      var gui = new dat.GUI();
+      gui.add(tracker, 'fastThreshold', 20, 100).step(5);
+      gui.add(tracker, 'blur', 1.1, 5.0).step(0.1);
+    }());
+  </script>
+
+</body>
+</html>

common/tracking.js/examples/color_camera.html

@@ -0,0 +1,65 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - color with camera</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+  <script src="../node_modules/dat.gui/build/dat.gui.min.js"></script>
+  <script src="assets/stats.min.js"></script>
+  <script src="assets/color_camera_gui.js"></script>
+
+  <style>
+  video, canvas {
+    margin-left: 100px;
+    margin-top: 35px;
+    position: absolute;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － choose the colors you want to detect through the controls on the right</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <video id="video" width="600" height="450" preload autoplay loop muted controls></video>
+      <canvas id="canvas" width="600" height="450"></canvas>
+    </div>
+  </div>
+
+  <script>
+    window.onload = function() {
+      var video = document.getElementById('video');
+      var canvas = document.getElementById('canvas');
+      var context = canvas.getContext('2d');
+
+      var tracker = new tracking.ColorTracker();
+
+      tracking.track('#video', tracker, { camera: true });
+
+      tracker.on('track', function(event) {
+        context.clearRect(0, 0, canvas.width, canvas.height);
+
+        event.data.forEach(function(rect) {
+          if (rect.color === 'custom') {
+            rect.color = tracker.customColor;
+          }
+
+          context.strokeStyle = rect.color;
+          context.strokeRect(rect.x, rect.y, rect.width, rect.height);
+          context.font = '11px Helvetica';
+          context.fillStyle = "#fff";
+          context.fillText('x: ' + rect.x + 'px', rect.x + rect.width + 5, rect.y + 11);
+          context.fillText('y: ' + rect.y + 'px', rect.x + rect.width + 5, rect.y + 22);
+        });
+      });
+
+     initGUIControllers(tracker);
+    };
+  </script>
+
+</body>
+</html>

common/tracking.js/examples/color_draw_something.html

@@ -0,0 +1,114 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - draw something</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking.js"></script>
+  <script src="assets/splines.min.js"></script>
+  <script src="assets/stats.min.js"></script>
+
+  <style>
+    #canvas,
+    #video {
+      height: 300px;
+      position: absolute;
+      width: 400px;
+      padding-top: 66px;
+    }
+    .draw-frame {
+      background: url(assets/draw_frame.png);
+      width: 400px;
+      height: 414px;
+      border: 1px solid #ccc;
+      top: 50%;
+      left: 50%;
+      position: absolute;
+      margin: -207px 0 0 -200px;
+    }
+
+    canvas, video {
+      -moz-transform: scale(-1, 1);
+      -o-transform: scale(-1, 1);
+      -webkit-transform: scale(-1, 1);
+      filter: FlipH;
+      transform: scale(-1, 1);
+    }
+  </style>
+</head>
+<body>
+
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － use magenta color to draw and cyan to erase</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <div class="draw-frame">
+        <video id="video" width="400" height="300" preload autoplay loop muted></video>
+        <canvas id="canvas" width="400" height="300"></canvas>
+      </div>
+    </div>
+  </div>
+
+  <script>
+    window.onload = function() {
+      var video = document.getElementById('video');
+      var canvas = document.getElementById('canvas');
+      var context = canvas.getContext('2d');
+
+      var drawSegments = [[]];
+      var segment = 0;
+
+      var tracker = new tracking.ColorTracker(['magenta', 'cyan']);
+
+      tracking.track('#video', tracker, { camera: true });
+
+      tracker.on('track', function(event) {
+        if (event.data.length === 0 && drawSegments[segment].length > 0) {
+          segment++;
+
+          if (!drawSegments[segment]) {
+            drawSegments[segment] = [];
+          }
+        }
+
+        event.data.forEach(function(rect) {
+          if (rect.color === 'magenta') {
+            draw(rect);
+          }
+          else if (rect.color === 'cyan') {
+            erase(rect);
+          }
+        });
+      });
+
+      function draw(rect) {
+        drawSegments[segment].push(rect.x + rect.width / 2, rect.y + rect.height / 2);
+      }
+
+      function erase(rect) {
+        context.clearRect(rect.x, rect.y, rect.width, rect.height);
+      }
+
+      function isInsideRect(x, y, rect) {
+        return rect.x <= x && x <= rect.x + rect.width &&
+            rect.y <= y && y <= rect.y + rect.height;
+      }
+
+      (function loop() {
+          for (var i = 0, len = drawSegments.length; i < len; i++) {
+              drawSpline(context, drawSegments[i], 0.5, false);
+          }
+
+          drawSegments = [drawSegments[drawSegments.length - 1]];
+          segment = 0;
+
+          requestAnimationFrame(loop);
+      }());
+    };
+  </script>
+
+</body>
+</html>

common/tracking.js/examples/color_fish_tank.html

@@ -0,0 +1,150 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - color tracking fish tank</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+  <script src="../../threejs/build/three.min.js"></script>
+
+  <style>
+  body {
+    overflow: hidden;
+  }
+
+  #video, #canvas {
+    bottom: 0;
+    position: absolute;
+    z-index: 100;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － use a magenta colored object to control the scene</p>
+  </div>
+
+  <video id="video" width="320" height="240" preload autoplay loop muted></video>
+  <canvas id="canvas" width="320" height="240"></canvas>
+
+  <script>
+    var container;
+    var camera, scene, renderer, group, particle;
+    var mouseX = 0, mouseY = 0;
+    var video = document.getElementById('video');
+    var canvas = document.getElementById('canvas');
+    var context = canvas.getContext('2d');
+
+    var windowHalfX = window.innerWidth / 2;
+    var windowHalfY = window.innerHeight / 2;
+
+    init();
+    animate();
+
+    window.onload = function() {
+      var tracker = new tracking.ColorTracker();
+      tracker.setMinDimension(5);
+      tracker.setMinGroupSize(10);
+
+      tracking.track('#video', tracker, { camera: true });
+
+      tracker.on('track', onColorMove);
+    };
+
+    function init() {
+      container = document.createElement('div');
+      document.body.appendChild(container);
+
+      camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 1, 3000);
+      camera.position.z = 1000;
+
+      scene = new THREE.Scene();
+
+      var PI2 = Math.PI * 2;
+      var program = function (context) {
+        context.beginPath();
+        context.arc(0, 0, 0.5, 0, PI2, true);
+        context.fill();
+      }
+
+      group = new THREE.Object3D();
+      scene.add(group);
+
+      for (var i = 0; i < 1000; i++) {
+        var material = new THREE.SpriteCanvasMaterial({
+          color: Math.random() * 0x808008 + 0x808080,
+          program: program
+        });
+
+        particle = new THREE.Sprite(material);
+        particle.position.x = Math.random() * 2000 - 1000;
+        particle.position.y = Math.random() * 2000 - 1000;
+        particle.position.z = Math.random() * 2000 - 1000;
+        particle.scale.x = particle.scale.y = Math.random() * 20 + 10;
+
+        group.add(particle);
+      }
+
+      renderer = new THREE.CanvasRenderer();
+      renderer.setSize(window.innerWidth, window.innerHeight);
+      container.appendChild(renderer.domElement);
+
+      window.addEventListener('resize', onWindowResize, false);
+    }
+
+    function onWindowResize() {
+      windowHalfX = window.innerWidth / 2;
+      windowHalfY = window.innerHeight / 2;
+
+      camera.aspect = window.innerWidth / window.innerHeight;
+      camera.updateProjectionMatrix();
+
+      renderer.setSize(window.innerWidth, window.innerHeight);
+    }
+
+    function onColorMove(event) {
+      if (event.data.length === 0) {
+        return;
+      }
+
+      var maxRect;
+      var maxRectArea = 0;
+
+      event.data.forEach(function(rect) {
+        if (rect.width * rect.height > maxRectArea){
+          maxRectArea = rect.width * rect.height;
+          maxRect = rect;
+        }
+      });
+
+      if (maxRectArea > 0) {
+        var rectCenterX = maxRect.x + (maxRect.width/2);
+        var rectCenterY = maxRect.y + (maxRect.height/2);
+        mouseX = (rectCenterX - 160) * (window.innerWidth/320) * 10;
+        mouseY = (rectCenterY - 120) * (window.innerHeight/240) * 10;
+
+        context.clearRect(0, 0, canvas.width, canvas.height);
+        context.strokeStyle = maxRect.color;
+        context.strokeRect(maxRect.x, maxRect.y, maxRect.width, maxRect.height);
+        context.font = '11px Helvetica';
+        context.fillStyle = "#fff";
+        context.fillText('x: ' + maxRect.x + 'px', maxRect.x + maxRect.width + 5, maxRect.y + 11);
+        context.fillText('y: ' + maxRect.y + 'px', maxRect.x + maxRect.width + 5, maxRect.y + 22);
+      }
+    }
+
+    function animate() {
+      window.requestAnimationFrame(animate);
+      render();
+    }
+
+    function render() {
+      camera.position.x += (mouseX - camera.position.x) * 0.05;
+      camera.position.y += (- mouseY - camera.position.y) * 0.05;
+      camera.lookAt(scene.position);
+      renderer.render(scene, camera);
+    }
+  </script>
+</body>
+</html>

common/tracking.js/examples/color_hello_world.html

@@ -0,0 +1,60 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - color hello world</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+
+  <style>
+  .rect {
+    width: 80px;
+    height: 80px;
+    position: absolute;
+    left: -1000px;
+    top: -1000px;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － detect certain colors in a image</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <img id="img" src="assets/psmove.png" />
+    </div>
+  </div>
+
+  <script>
+    window.onload = function() {
+      var img = document.getElementById('img');
+      var demoContainer = document.querySelector('.demo-container');
+
+      var tracker = new tracking.ColorTracker(['magenta', 'cyan', 'yellow']);
+
+      tracker.on('track', function(event) {
+        event.data.forEach(function(rect) {
+          window.plot(rect.x, rect.y, rect.width, rect.height, rect.color);
+        });
+      });
+
+      tracking.track('#img', tracker);
+
+      window.plot = function(x, y, w, h, color) {
+        var rect = document.createElement('div');
+        document.querySelector('.demo-container').appendChild(rect);
+        rect.classList.add('rect');
+        rect.style.border = '2px solid ' + color;
+        rect.style.width = w + 'px';
+        rect.style.height = h + 'px';
+        rect.style.left = (img.offsetLeft + x) + 'px';
+        rect.style.top = (img.offsetTop + y) + 'px';
+      };
+    };
+  </script>
+
+</body>
+</html>

common/tracking.js/examples/color_video.html

@@ -0,0 +1,82 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - color with video</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+   <script src="../node_modules/dat.gui/build/dat.gui.min.js"></script>
+  <script src="assets/stats.min.js"></script>
+  <script src="assets/color_camera_gui.js"></script>
+
+  <style>
+  .demo-container {
+    background-color: black;
+  }
+
+  video, canvas {
+    position: absolute;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － detect certain colors in a video</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <div id="rectangle"></div>
+      <video id="video" width="800" height="530" preload autoplay loop muted controls>
+        <source src="assets/minions.mp4" type="video/mp4">
+        <source src="assets/minions.ogv" type="video/ogg">
+      </video>
+      <canvas id="canvas" width="800" height="500"></canvas>
+    </div>
+  </div>
+
+  <script>
+    window.onload = function() {
+      var canvas = document.getElementById('canvas');
+      var context = canvas.getContext('2d');
+
+      tracking.ColorTracker.registerColor('purple', function(r, g, b) {
+        var dx = r - 120;
+        var dy = g - 60;
+        var dz = b - 210;
+
+        if ((b - g) >= 100 && (r - g) >= 60) {
+          return true;
+        }
+        return dx * dx + dy * dy + dz * dz < 3500;
+      });
+
+      var tracker = new tracking.ColorTracker(['yellow', 'purple']);
+      tracker.setMinDimension(5);
+
+      tracking.track('#video', tracker);
+
+      tracker.on('track', function(event) {
+        context.clearRect(0, 0, canvas.width, canvas.height);
+
+        event.data.forEach(function(rect) {
+          if (rect.color === 'custom') {
+            rect.color = tracker.customColor;
+          }
+
+          context.strokeStyle = rect.color;
+          context.strokeRect(rect.x, rect.y, rect.width, rect.height);
+          context.font = '11px Helvetica';
+          context.fillStyle = "#fff";
+          context.fillText('x: ' + rect.x + 'px', rect.x + rect.width + 5, rect.y + 11);
+          context.fillText('y: ' + rect.y + 'px', rect.x + rect.width + 5, rect.y + 22);
+        });
+      });
+
+      initGUIControllers(tracker);
+    };
+  </script>
+
+</body>
+</html>

common/tracking.js/examples/face_alignment_image.html

@@ -0,0 +1,100 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - face alignment with images</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking.js"></script>
+  <script src="../build/data/face-min.js"></script>
+  <script src="../src/alignment/training/Landmarks.js"></script>
+  <script src="../src/alignment/training/Regressor.js"></script>
+  
+  <script src="../node_modules/dat.gui/build/dat.gui.min.js"></script>
+  <script src="assets/stats.min.js"></script>
+
+  <style>
+  .rect, .circle {
+    left: -1000px;
+    position: absolute;
+    top: -1000px;
+  }
+  .rect{
+    border: 2px solid #a64ceb;
+  }
+  .circle {
+    border-radius: 50%;
+    box-shadow: 0px 0px 3px rgba(0,0,0,0.3);
+  }
+  #img {
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    margin: -200px 0 0 -200px;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － align face landmarks to images</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <img id="img" src="assets/emilia.jpg" />
+    </div>
+  </div>
+
+  <script>
+    window.onload = function() {
+      var img = document.getElementById('img');
+
+      var tracker = new tracking.LandmarksTracker();
+      tracker.setInitialScale(4);
+      tracker.setStepSize(2);
+      tracker.setEdgesDensity(0.1);
+
+      tracking.track('#img', tracker);
+
+      tracker.on('track', function(event) {
+
+        if(!event.data) return;
+
+        event.data.faces.forEach(function(rect) {
+          window.plot(rect.x, rect.y, rect.width, rect.height);
+        });
+
+        event.data.landmarks.forEach(function(landmarks) {
+          for(var i=0; i < landmarks.length; i++){
+            window.plotLandmark(landmarks[i][0], landmarks[i][1], 2, '#44ABDA');
+          }
+        });
+
+      });
+
+      window.plot = function(x, y, w, h) {
+        var rect = document.createElement('div');
+        document.querySelector('.demo-container').appendChild(rect);
+        rect.classList.add('rect');
+        rect.style.width = w + 'px';
+        rect.style.height = h + 'px';
+        rect.style.left = (img.offsetLeft + x) + 'px';
+        rect.style.top = (img.offsetTop + y) + 'px';
+      };
+
+      window.plotLandmark = function(x,y, radius, color){
+        var circle = document.createElement('div');
+        document.querySelector('.demo-container').appendChild(circle);
+        circle.classList.add('circle');
+        circle.style.backgroundColor = color;
+        circle.style.width = (radius*2) + 'px';
+        circle.style.height = (radius*2) + 'px';
+        circle.style.left = (img.offsetLeft + x) + 'px';
+        circle.style.top = (img.offsetTop + y) + 'px';
+      }
+
+    };
+  </script>
+
+</body>
+</html>

common/tracking.js/examples/face_alignment_video.html

@@ -0,0 +1,84 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - face alignment with camera</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking.js"></script>
+  <script src="../build/data/face-min.js"></script>
+  <script src="../src/alignment/training/Landmarks.js"></script>
+  <script src="../src/alignment/training/Regressor.js"></script>
+
+  <script src="../node_modules/dat.gui/build/dat.gui.min.js"></script>
+  <script src="assets/stats.min.js"></script>
+
+  <style>
+  video, canvas {
+    margin-left: 230px;
+    margin-top: 120px;
+    position: absolute;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － get user's webcam and align face landmarks to detected faces</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <video id="video" width="320" height="240" src="assets/franck.mp4" preload autoplay loop muted></video>
+      <canvas id="canvas" width="320" height="240"></canvas>
+    </div>
+  </div>
+
+  <script>
+    window.onload = function() {
+      var video = document.getElementById('video');
+      var canvas = document.getElementById('canvas');
+      var context = canvas.getContext('2d');
+
+      var tracker = new tracking.LandmarksTracker();
+      tracker.setInitialScale(4);
+      tracker.setStepSize(2);
+      tracker.setEdgesDensity(0.1);
+
+      tracking.track('#video', tracker);
+
+      tracker.on('track', function(event) {
+
+        context.clearRect(0,0,canvas.width, canvas.height);
+
+        if(!event.data) return;
+
+          event.data.faces.forEach(function(rect) {
+            context.strokeStyle = '#a64ceb';
+            context.strokeRect(rect.x, rect.y, rect.width, rect.height);
+            context.font = '11px Helvetica';
+            context.fillStyle = "#fff";
+            context.fillText('x: ' + rect.x + 'px', rect.x + rect.width + 5, rect.y + 11);
+            context.fillText('y: ' + rect.y + 'px', rect.x + rect.width + 5, rect.y + 22);
+          });
+
+          event.data.landmarks.forEach(function(landmarks) {
+            for(var l in landmarks){
+              context.beginPath();
+              context.fillStyle = "#fff";
+              context.arc(landmarks[l][0],landmarks[l][1],1,0,2*Math.PI);
+              context.fill();
+            }
+          });
+
+      });
+
+      var gui = new dat.GUI();
+      gui.add(tracker, 'edgesDensity', 0.1, 0.5).step(0.01).listen();
+      gui.add(tracker, 'initialScale', 1.0, 10.0).step(0.1).listen();
+      gui.add(tracker, 'stepSize', 1, 5).step(0.1).listen();
+
+    };
+  </script>
+
+</body>
+</html>

common/tracking.js/examples/face_alignment_webcam.html

@@ -0,0 +1,229 @@
+<!doctype html>
+<html>
+<head>
+        <meta charset="utf-8">
+        <title>tracking.js - face alignment with camera</title>
+        <!-- here is the frame around each example - to be removed - to a fullscreen video - working on mobile too -->
+        <!-- <link rel="stylesheet" href="assets/demo.css"> -->
+        
+        <script src="../build/tracking.js"></script>
+        <script src="../build/data/face-min.js"></script>
+        <script src="../src/alignment/training/Landmarks.js"></script>
+        <script src="../src/alignment/training/Regressor.js"></script>
+        
+        <script src="../node_modules/dat.gui/build/dat.gui.min.js"></script>
+</head>
+<body>
+        <style>
+                #videoWebcam {
+                        position: absolute;
+                        top: 0px;
+                        left: 0px;
+                        width : 320px;
+                        height: auto;
+                        zoom: 3;
+                }
+                #canvasDetection {
+                        position: absolute;
+                        top: 0px;
+                        left: 0px;
+                        width : 320px;
+                        height: auto;
+                        zoom: 3;
+                }
+        </style>
+	<video id="videoWebcam" width="368" height="288" autoplay loop>
+		<source src="./assets/franck.mp4" type="video/mp4"/>
+		<source src="./assets/franck.ogv" type="video/ogg"/>
+	</video>
+        <!-- <video id="videoWebcam" preload autoplay loop muted></video> -->
+        <canvas id="canvasDetection"></canvas>
+        
+<script>
+        var canvasDetection = document.querySelector('#canvasDetection');
+        canvasDetection.width = 320
+        canvasDetection.height = 240
+        var context = canvasDetection.getContext('2d');
+
+        // tracking.LBF.maxNumStages = 10
+        var tracker = new tracking.LandmarksTracker();
+        tracker.setEdgesDensity(0.1);
+        tracker.setInitialScale(4);
+        tracker.setStepSize(2);
+
+        tracker.setInitialScale(2);
+        tracker.setStepSize(1);
+
+        
+        var gui = new dat.GUI();
+        gui.add(tracker, 'edgesDensity', 0.1, 0.5).step(0.01).listen();
+        gui.add(tracker, 'initialScale', 1.0, 10.0).step(0.1).listen();
+        gui.add(tracker, 'stepSize', 0.5, 5).step(0.1).listen();
+        
+
+        var videoElement = document.querySelector('#videoWebcam')
+        tracking.track(videoElement, tracker);
+        // tracking.track(videoElement, tracker, { camera: true });
+        
+        var landmarksPerFace = 30
+        var landmarkFeatures = {
+                jaw : {
+                        first: 0,
+                        last: 8,
+                        fillStyle: 'white',
+                        closed: false,
+                },
+                nose : {
+                        first:15,
+                        last: 18,
+                        fillStyle: 'green',
+                        closed: true,
+                },
+                mouth : {
+                        first:27,
+                        last: 30,
+                        fillStyle: 'red',
+                        closed: true,
+                },
+                eyeL : {
+                        first:19,
+                        last: 22,
+                        fillStyle: 'purple',
+                        closed: false,
+                },
+                eyeR : {
+                        first:23,
+                        last: 26,
+                        fillStyle: 'purple',
+                        closed: false,
+                },
+                eyeBrowL : {
+                        first: 9,
+                        last: 11,
+                        fillStyle: 'yellow',
+                        closed: false,
+                },
+                eyeBrowR : {
+                        first:12,
+                        last: 14,
+                        fillStyle: 'yellow',
+                        closed: false,
+                },
+        }
+
+        //////////////////////////////////////////////////////////////////////////////
+        //                Code Separator
+        //////////////////////////////////////////////////////////////////////////////
+        var parameters = {
+                landmarkLerpFactor : 0.7,
+                boundinBoxVisible : true,
+                jawVisible : true,
+                eyeBrowLVisible : true,
+                eyeBrowRVisible : true,
+                noseVisible : true,
+                eyeLVisible : true,
+                eyeRVisible : true,
+                mouthVisible : true,
+        }
+        gui.add(parameters, 'landmarkLerpFactor', 0.0, 1).listen().name('Landmarks Lerp');
+        gui.add(parameters, 'boundinBoxVisible', 0.0, 1).listen().name('bounding box');
+        Object.keys(landmarkFeatures).forEach(function(featureLabel){
+                gui.add(parameters, featureLabel + 'Visible').listen().name(featureLabel);
+        })
+
+        var lerpedFacesLandmarks = []
+        
+        tracker.on('track', function(event) {
+                // clear debug canvasDetection
+                context.clearRect(0,0,canvasDetection.width, canvasDetection.height);
+
+                if( event.data === undefined ) return;
+                
+                event.data.faces.forEach(function(boundingBox, faceIndex) {
+                        var faceLandmarks = event.data.landmarks[faceIndex]
+
+                        if( parameters.boundinBoxVisible === true ) displayFaceLandmarksBoundingBox(boundingBox, faceIndex)
+
+                        // lerpFacesLandmarks
+                        lerpFacesLandmarks(faceLandmarks)
+                        
+                        // display each faceLandmarks
+                        displayFaceLandmarksDot(lerpedFacesLandmarks)
+                });
+        })
+
+        function lerpFacesLandmarks(newFaceLandmarks){
+                // init lerpFacesLandmarks if needed
+                for(var i = 0; i < newFaceLandmarks.length; i++){
+                        if( lerpedFacesLandmarks[i] !== undefined ) continue
+                        lerpedFacesLandmarks[i] = [
+                                newFaceLandmarks[i][0],
+                                newFaceLandmarks[i][1],
+                        ]                        
+                }
+
+                // init lerpFacesLandmarks if needed
+                for(var i = 0; i < newFaceLandmarks.length; i++){
+                        var lerpFactor = parameters.landmarkLerpFactor
+                        lerpedFacesLandmarks[i][0] = newFaceLandmarks[i][0] * lerpFactor  + lerpedFacesLandmarks[i][0] * (1-lerpFactor)
+                        lerpedFacesLandmarks[i][1] = newFaceLandmarks[i][1] * lerpFactor  + lerpedFacesLandmarks[i][1] * (1-lerpFactor)
+                }
+        }
+
+        //////////////////////////////////////////////////////////////////////////////
+        //                Code Separator
+        //////////////////////////////////////////////////////////////////////////////
+        function displayFaceLandmarksBoundingBox(boundingBox, faceIndex){
+                // display the box
+                context.strokeStyle = '#a64ceb';
+                context.strokeRect(boundingBox.x, boundingBox.y, boundingBox.width, boundingBox.height);
+
+                // display the size of the box
+                context.font = '11px Helvetica';
+                context.fillStyle = "#fff";
+                context.fillText('idx: '+faceIndex, boundingBox.x + boundingBox.width + 5, boundingBox.y + 11);
+                context.fillText('x: ' + boundingBox.x + 'px', boundingBox.x + boundingBox.width + 5, boundingBox.y + 22);
+                context.fillText('y: ' + boundingBox.y + 'px', boundingBox.x + boundingBox.width + 5, boundingBox.y + 33);
+        }
+        
+        function displayFaceLandmarksDot(faceLandmarks){
+                Object.keys(landmarkFeatures).forEach(function(featureLabel){
+                        if( parameters[featureLabel+'Visible'] === false )      return
+                        displayFaceLandmarksFeature(faceLandmarks, featureLabel)
+                })
+        }
+        function displayFaceLandmarksFeature(faceLandmarks, featureLabel){
+                var feature = landmarkFeatures[featureLabel]
+                
+                // draw dots
+                context.fillStyle = feature.fillStyle
+                for(var i = feature.first; i <= feature.last; i++){
+                        var xy = faceLandmarks[i]
+                        context.beginPath();
+                        context.arc(xy[0],xy[1],1,0,2*Math.PI);
+                        context.fill();                                
+                }                
+                
+                // draw lines
+                var feature = landmarkFeatures[featureLabel]
+                context.strokeStyle = feature.fillStyle
+                context.beginPath();
+                for(var i = feature.first; i <= feature.last; i++){
+                        var x = faceLandmarks[i][0]
+                        var y = faceLandmarks[i][1]
+                        if( i === 0 ){
+                                context.moveTo(x, y)
+                        }else{
+                                context.lineTo(x, y)
+                        }
+                }                
+                if( feature.closed === true ){
+                        var x = faceLandmarks[feature.first][0]
+                        var y = faceLandmarks[feature.first][1]
+                        context.lineTo(x, y)
+                }
+
+                context.stroke();
+
+        }
+</script></body>

common/tracking.js/examples/face_camera.html

@@ -0,0 +1,67 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - face with camera</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+  <script src="../build/data/face-min.js"></script>
+   <script src="../node_modules/dat.gui/build/dat.gui.min.js"></script>
+  <script src="assets/stats.min.js"></script>
+
+  <style>
+  video, canvas {
+    margin-left: 230px;
+    margin-top: 120px;
+    position: absolute;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － get user's webcam and detect faces</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <video id="video" width="320" height="240" preload autoplay loop muted></video>
+      <canvas id="canvas" width="320" height="240"></canvas>
+    </div>
+  </div>
+
+  <script>
+    window.onload = function() {
+      var video = document.getElementById('video');
+      var canvas = document.getElementById('canvas');
+      var context = canvas.getContext('2d');
+
+      var tracker = new tracking.ObjectTracker('face');
+      tracker.setInitialScale(4);
+      tracker.setStepSize(2);
+      tracker.setEdgesDensity(0.1);
+
+      tracking.track('#video', tracker, { camera: true });
+
+      tracker.on('track', function(event) {
+        context.clearRect(0, 0, canvas.width, canvas.height);
+
+        event.data.forEach(function(rect) {
+          context.strokeStyle = '#a64ceb';
+          context.strokeRect(rect.x, rect.y, rect.width, rect.height);
+          context.font = '11px Helvetica';
+          context.fillStyle = "#fff";
+          context.fillText('x: ' + rect.x + 'px', rect.x + rect.width + 5, rect.y + 11);
+          context.fillText('y: ' + rect.y + 'px', rect.x + rect.width + 5, rect.y + 22);
+        });
+      });
+
+      var gui = new dat.GUI();
+      gui.add(tracker, 'edgesDensity', 0.1, 0.5).step(0.01);
+      gui.add(tracker, 'initialScale', 1.0, 10.0).step(0.1);
+      gui.add(tracker, 'stepSize', 1, 5).step(0.1);
+    };
+  </script>
+
+</body>
+</html>

common/tracking.js/examples/face_fish_tank.html

@@ -0,0 +1,89 @@
+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width, user-scalable=no, minimum-scale=1.0, maximum-scale=1.0">
+  <title>tracking.js - face tracking fish tank</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+  <script src="../build/data/face-min.js"></script>
+  <script src="../../threejs/build/three.min.js"></script>
+  <script src="assets/fish_tank/FishTankRenderer.js"></script>
+
+  <style>
+  body {
+    overflow: hidden;
+  }
+
+  #video, #canvas {
+    bottom: 0;
+    position: absolute;
+    z-index: 100;
+  }
+
+  #viewport {
+    padding-top: 40px;
+  }
+  </style>
+</head>
+
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － get user's webcam and detect faces to control the scene</p>
+  </div>
+
+  <div id="viewport">
+    <video id="video" width="320" height="240" preload autoplay loop muted></video>
+    <canvas id="canvas" width="320" height="240"></canvas>
+  </div>
+
+  <script>
+    var viewport = document.getElementById('viewport');
+    var canvas = document.getElementById('canvas');
+    var context = canvas.getContext('2d');
+
+    var fishTankRenderer = new FishTankRenderer();
+    fishTankRenderer.init(viewport);
+
+    var faceX = 0;
+    var faceY = 0;
+
+    var tracker = new tracking.ObjectTracker('face');
+    tracker.setInitialScale(4);
+    tracker.setStepSize(2);
+
+    tracking.track('#video', tracker, { camera: true });
+
+    tracker.on('track', function(event) {
+      var maxRectArea = 0;
+      var maxRect;
+
+      event.data.forEach(function(rect) {
+        if (rect.width * rect.height > maxRectArea){
+          maxRectArea = rect.width * rect.height;
+          maxRect = rect;
+        }
+
+        context.clearRect(0, 0, canvas.width, canvas.height);
+        context.strokeStyle = 'magenta';
+        context.strokeRect(rect.x, rect.y, rect.width, rect.height);
+        context.font = '11px Helvetica';
+        context.fillStyle = "#fff";
+        context.fillText('x: ' + rect.x + 'px', rect.x + rect.width + 5, rect.y + 11);
+        context.fillText('y: ' + rect.y + 'px', rect.x + rect.width + 5, rect.y + 22);
+      });
+
+      if(maxRectArea > 0) {
+        var rectCenterX = maxRect.x + (maxRect.width/2);
+        var rectCenterY = maxRect.y + (maxRect.height/2);
+        faceX = (rectCenterX - 160) * (window.innerWidth/320) * 50;
+        faceY = (rectCenterY - 120) * (window.innerHeight/240) * 50;
+      }
+
+      fishTankRenderer.render(faceX, faceY);
+    });
+  </script>
+
+</body>
+</html>

common/tracking.js/examples/face_hello_world.html

@@ -0,0 +1,68 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - face hello world</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+  <script src="../build/data/face-min.js"></script>
+  <script src="../build/data/eye-min.js"></script>
+  <script src="../build/data/mouth-min.js"></script>
+
+  <style>
+  .rect {
+    border: 2px solid #a64ceb;
+    left: -1000px;
+    position: absolute;
+    top: -1000px;
+  }
+
+  #img {
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    margin: -173px 0 0 -300px;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － detect faces, eyes and mouths in a image</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <img id="img" src="assets/faces.jpg" />
+    </div>
+  </div>
+
+  <script>
+    window.onload = function() {
+      var img = document.getElementById('img');
+
+      var tracker = new tracking.ObjectTracker(['face', 'eye', 'mouth']);
+      tracker.setStepSize(1.7);
+
+      tracking.track('#img', tracker);
+
+      tracker.on('track', function(event) {
+        event.data.forEach(function(rect) {
+          window.plot(rect.x, rect.y, rect.width, rect.height);
+        });
+      });
+
+      window.plot = function(x, y, w, h) {
+        var rect = document.createElement('div');
+        document.querySelector('.demo-container').appendChild(rect);
+        rect.classList.add('rect');
+        rect.style.width = w + 'px';
+        rect.style.height = h + 'px';
+        rect.style.left = (img.offsetLeft + x) + 'px';
+        rect.style.top = (img.offsetTop + y) + 'px';
+      };
+    };
+  </script>
+
+</body>
+</html>

common/tracking.js/examples/face_tag_friends.html

@@ -0,0 +1,123 @@
+<!doctype html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - tag friends</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+  <script src="../build/data/face-min.js"></script>
+
+  <style>
+  #photo:hover .rect {
+    opacity: .75;
+    transition: opacity .75s ease-out;
+  }
+
+  .rect:hover * {
+    opacity: 1;
+  }
+
+  .rect {
+    border-radius: 2px;
+    border: 3px solid white;
+    box-shadow: 0 16px 28px 0 rgba(0, 0, 0, 0.3);
+    cursor: pointer;
+    left: -1000px;
+    opacity: 0;
+    position: absolute;
+    top: -1000px;
+  }
+
+  .arrow {
+    border-bottom: 10px solid white;
+    border-left: 10px solid transparent;
+    border-right: 10px solid transparent;
+    height: 0;
+    width: 0;
+    position: absolute;
+    left: 50%;
+    margin-left: -5px;
+    bottom: -12px;
+    opacity: 0;
+  }
+
+  input {
+    border: 0px;
+    bottom: -42px;
+    color: #a64ceb;
+    font-size: 15px;
+    height: 30px;
+    left: 50%;
+    margin-left: -90px;
+    opacity: 0;
+    outline: none;
+    position: absolute;
+    text-align: center;
+    width: 180px;
+    transition: opacity .35s ease-out;
+  }
+
+  #img {
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    margin: -173px 0 0 -300px;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － hover image to see all faces detected</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <span id="photo"><img id="img" src="assets/faces.jpg" /></span>
+    </div>
+  </div>
+
+  <script>
+    window.onload = function() {
+      var img = document.getElementById('img');
+
+      var tracker = new tracking.ObjectTracker('face');
+
+      tracking.track(img, tracker);
+
+      tracker.on('track', function(event) {
+        event.data.forEach(function(rect) {
+          plotRectangle(rect.x, rect.y, rect.width, rect.height);
+        });
+      });
+
+      var friends = [ 'Thomas Middleditch', 'Martin Starr', 'Zach Woods' ];
+
+      var plotRectangle = function(x, y, w, h) {
+        var rect = document.createElement('div');
+        var arrow = document.createElement('div');
+        var input = document.createElement('input');
+
+        input.value = friends.pop();
+
+        rect.onclick = function name() {
+          input.select();
+        };
+
+        arrow.classList.add('arrow');
+        rect.classList.add('rect');
+
+        rect.appendChild(input);
+        rect.appendChild(arrow);
+        document.getElementById('photo').appendChild(rect);
+
+        rect.style.width = w + 'px';
+        rect.style.height = h + 'px';
+        rect.style.left = (img.offsetLeft + x) + 'px';
+        rect.style.top = (img.offsetTop + y) + 'px';
+      };
+    };
+  </script>
+
+</body>
+</html>

common/tracking.js/examples/fast.html

@@ -0,0 +1,73 @@
+<!doctype html>
+
+<html>
+<head>
+  <meta charset="utf-8">
+  <title>tracking.js - feature detection</title>
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+   <script src="../node_modules/dat.gui/build/dat.gui.min.js"></script>
+
+  <style>
+  .demo-container {
+    background: #131112;
+  }
+  #image {
+    position: absolute;
+    left: -1000px;
+    top: -1000px;
+  }
+  #canvas {
+    position: absolute;
+    left: 50%;
+    top: 50%;
+    margin: -200px 0 0 -200px;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － detect feature points on a image</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <img id="image" src="assets/fast.png" />
+      <canvas id="canvas" width="400" height="400"></canvas>
+    </div>
+  </div>
+
+  <script>
+    window.onload = function() {
+      var width = 400;
+      var height = 400;
+      var canvas = document.getElementById('canvas');
+      var context = canvas.getContext('2d');
+
+      var image = document.getElementById('image');
+
+      window.fastThreshold = 10;
+
+      var doFindFeatures = function() {
+        tracking.Fast.THRESHOLD = window.fastThreshold;
+        context.drawImage(image, 0, 0, width, height);
+
+        var imageData = context.getImageData(0, 0, width, height);
+        var gray = tracking.Image.grayscale(imageData.data, width, height);
+        var corners = tracking.Fast.findCorners(gray, width, height);
+
+        for (var i = 0; i < corners.length; i += 2) {
+          context.fillStyle = '#f00';
+          context.fillRect(corners[i], corners[i + 1], 3, 3);
+        }
+      };
+
+      doFindFeatures();
+
+      var gui = new dat.GUI();
+      gui.add(window, 'fastThreshold', 0, 100).onChange(doFindFeatures);
+    }
+  </script>
+</body>
+</html>

common/tracking.js/examples/fast_camera.html

@@ -0,0 +1,85 @@
+<!doctype html>
+
+<html>
+<head>
+  <title>tracking.js -  feature detector with camera</title>
+
+  <meta charset="utf-8">
+  <link rel="stylesheet" href="assets/demo.css">
+
+  <script src="../build/tracking-min.js"></script>
+  <script src="../node_modules/dat.gui/build/dat.gui.min.js"></script>
+  <script src="assets/stats.min.js"></script>
+
+  <style>
+  #video {
+    position: absolute;
+    top: -1000px;
+  }
+
+  #canvas {
+    left: 50%;
+    top: 50%;
+    margin-left: -200px;
+    margin-top: -150px;
+    position: absolute;
+  }
+  </style>
+</head>
+<body>
+  <div class="demo-title">
+    <p><a href="http://trackingjs.com" target="_parent">tracking.js</a> － display detected features</p>
+  </div>
+
+  <div class="demo-frame">
+    <div class="demo-container">
+      <video id="video" width="400" height="300" preload autoplay loop muted></video>
+      <canvas id="canvas" width="400" height="300"></canvas>
+    </div>
+  </div>
+
+  <script>
+    var canvas = document.getElementById('canvas');
+    var context = canvas.getContext('2d');
+
+    var FastTracker = function() {
+      FastTracker.base(this, 'constructor');
+    };
+    tracking.inherits(FastTracker, tracking.Tracker);
+
+    tracking.Fast.THRESHOLD = 2;
+    FastTracker.prototype.threshold = tracking.Fast.THRESHOLD;
+
+    FastTracker.prototype.track = function(pixels, width, height) {
+      stats.begin();
+      var gray = tracking.Image.grayscale(pixels, width, height);
+      var corners = tracking.Fast.findCorners(gray, width, height);
+      stats.end();
+
+      this.emit('track', {
+        data: corners
+      });
+    };
+
+    var tracker = new FastTracker();
+
+    tracker.on('track', function(event) {
+      context.clearRect(0, 0, canvas.width, canvas.height);
+      var corners = event.data;
+      for (var i = 0; i < corners.length; i += 2) {
+        context.fillStyle = '#f00';
+        context.fillRect(corners[i], corners[i + 1], 2, 2);
+      }
+    });
+
+    tracking.track('#video', tracker, { camera: true });
+
+    // GUI Controllers
+    var gui = new dat.GUI();
+
+    gui.add(tracker, 'threshold', 1, 100).onChange(function(value) {
+      tracking.Fast.THRESHOLD = value;
+    });
+  </script>
+</body>
+</html>

common/tracking.js/gulpfile.js

@@ -0,0 +1,118 @@
+'use strict';
+var gulp = require('gulp');
+var concat = require('gulp-concat');
+var header = require('gulp-header');
+var jsdoc = require('gulp-jsdoc');
+var jshint = require('gulp-jshint');
+var nodeunit = require('gulp-nodeunit');
+var pkg = require('./package.json');
+var rename = require('gulp-rename');
+var rimraf = require('gulp-rimraf');
+var stylish = require('jshint-stylish');
+var uglify = require('gulp-uglify');
+var esformatter = require('gulp-esformatter');
+var runSequence = require('run-sequence');
+
+gulp.task('all', ['clean'], function() {
+  return runSequence(['build', 'build-data']);
+});
+
+gulp.task('clean', function() {
+  return gulp.src('build').pipe(rimraf());
+});
+
+gulp.task('build', function() {
+  var files = [
+    'src/tracking.js',
+    'src/utils/EventEmitter.js',
+    'src/utils/Canvas.js',
+    'src/utils/DisjointSet.js',
+    'src/utils/Image.js',
+    'src/detection/ViolaJones.js',
+    'src/features/Brief.js',
+    'src/features/Fast.js',
+    'src/math/Math.js',
+    'src/math/Matrix.js',
+    'src/pose/EPnP.js',
+    'src/trackers/Tracker.js',
+    'src/trackers/TrackerTask.js',
+    'src/trackers/ColorTracker.js',
+    'src/trackers/ObjectTracker.js',
+    'src/trackers/LandmarksTracker.js',
+    'src/alignment/Regressor.js',
+    'src/alignment/LBF.js'
+  ];
+
+  return gulp.src(files)
+    .pipe(concat('tracking.js'))
+    .pipe(banner())
+    .pipe(gulp.dest('build'))
+    .pipe(uglify())
+    .pipe(rename({
+      suffix: '-min'
+    }))
+    .pipe(banner())
+    .pipe(gulp.dest('build'));
+});
+
+gulp.task('build-data', function() {
+  return gulp.src('src/detection/training/haar/**.js')
+    .pipe(banner())
+    .pipe(gulp.dest('build/data'))
+    .pipe(rename({
+      suffix: '-min'
+    }))
+    .pipe(uglify())
+    .pipe(banner())
+    .pipe(gulp.dest('build/data'));
+});
+
+gulp.task('docs', function() {
+  return gulp.src(['src/**/*.js', 'README.md'])
+    .pipe(jsdoc('docs'));
+});
+
+gulp.task('format', function() {
+  return gulp.src(['src/**/*.js', '!src/detection/training/**/*.js'])
+    .pipe(esformatter())
+    .pipe(gulp.dest('src'));
+});
+
+gulp.task('lint', function() {
+  return gulp.src('src/**/**.js')
+    .pipe(jshint())
+    .pipe(jshint.reporter(stylish));
+});
+
+gulp.task('test', function(cb) {
+  gulp.src('test/*.js')
+    .pipe(nodeunit())
+    .on('end', cb);
+});
+
+gulp.task('test-watch', function() {
+  return gulp.watch(['src/**/*.js', 'test/**/*.js'], ['test']);
+});
+
+gulp.task('watch', function() {
+  gulp.watch('src/**/*.js', ['build']);
+  gulp.watch('src/data/*.js', ['build-data']);
+});
+
+// Private helpers
+// ===============
+
+function banner() {
+  var stamp = [
+    '/**',
+    ' * <%= pkg.name %> - <%= pkg.description %>',
+    ' * @author <%= pkg.author.name %> <<%= pkg.author.email %>>',
+    ' * @version v<%= pkg.version %>',
+    ' * @link <%= pkg.homepage %>',
+    ' * @license <%= pkg.license %>',
+    ' */',
+    ''
+  ].join('\n');
+
+  return header(stamp, { pkg: pkg });
+}

common/tracking.js/package.json

@@ -0,0 +1,45 @@
+{
+  "name": "tracking",
+  "version": "1.1.3",
+  "main": "build/tracking.js",
+  "description": "A modern approach for Computer Vision on the web.",
+  "homepage": "http://trackingjs.com",
+  "keywords": [
+    "tracking",
+    "trackingjs",
+    "webrtc"
+  ],
+  "author": {
+    "name": "Eduardo Lundgren",
+    "email": "edu@rdo.io",
+    "web": "http://eduardo.io",
+    "twitter": "eduardolundgren"
+  },
+  "repository": {
+    "type": "git",
+    "url": "git@github.com:eduardolundgren/tracking.js.git"
+  },
+  "scripts": {
+    "test": "gulp test",
+    "build": "gulp build"
+  },
+  "license": "BSD",
+  "devDependencies": {
+    "gulp": "^3.9.0",
+    "gulp-concat": "^2.6.0",
+    "gulp-esformatter": "^5.0.0",
+    "gulp-header": "^1.7.1",
+    "gulp-jsdoc": "^0.1.5",
+    "gulp-jshint": "^2.0.0",
+    "gulp-nodeunit": "0.0.5",
+    "gulp-rename": "^1.2.2",
+    "gulp-rimraf": "^0.2.0",
+    "gulp-uglify": "^1.5.1",
+    "jshint": "^2.8.0",
+    "jshint-stylish": "^2.1.0",
+    "nodeunit": "^0.9.1",
+    "png-js": "^0.1.1",
+    "run-sequence": "^1.1.5",
+    "dat.gui": "^0.6.1"
+  }
+}

common/tracking.js/src/alignment/LBF.js

@@ -0,0 +1,222 @@
+(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
+      );
+    }
+// NOTE: is this thesholding suitable ? if it is on image, why no skin-color filter ? and a adaptative threshold
+    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;
+  }
+
+}());

common/tracking.js/src/alignment/Regressor.js

@@ -0,0 +1,230 @@
+(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;
+  }
+
+}());

common/tracking.js/src/detection/ViolaJones.js

@@ -0,0 +1,280 @@
+(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;
+  };
+
+}());

common/tracking.js/src/features/Brief.js

@@ -0,0 +1,198 @@
+(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];
+  };
+}());

common/tracking.js/src/features/Fast.js

@@ -0,0 +1,250 @@
+(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;
+  };
+}());

common/tracking.js/src/math/Math.js

@@ -0,0 +1,82 @@
+(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);
+  };
+
+}());

common/tracking.js/src/math/Matrix.js

@@ -0,0 +1,185 @@
+(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];
+
+  }
+
+}());

common/tracking.js/src/pose/EPnP.js

@@ -0,0 +1,10 @@
+(function() {
+  /**
+   * EPnp utility.
+   * @static
+   * @constructor
+   */
+  tracking.EPnP = {};
+
+  tracking.EPnP.solve = function(objectPoints, imagePoints, cameraMatrix) {};
+}());

common/tracking.js/src/trackers/ColorTracker.js

@@ -0,0 +1,425 @@
+(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]);
+}());

common/tracking.js/src/trackers/LandmarksTracker.js

@@ -0,0 +1,35 @@
+(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
+      }
+    });
+
+  }
+
+}());

common/tracking.js/src/trackers/ObjectTracker.js

@@ -0,0 +1,169 @@
+(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;
+  };
+
+}());

common/tracking.js/src/trackers/Tracker.js

@@ -0,0 +1,21 @@
+(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() {};
+}());

common/tracking.js/src/trackers/TrackerTask.js

@@ -0,0 +1,103 @@
+(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;
+  };
+}());

common/tracking.js/src/tracking.js

@@ -0,0 +1,285 @@
+(function(window, undefined) {
+  window.tracking = window.tracking || {};
+
+  /**
+   * Inherit the prototype methods from one constructor into another.
+   *
+   * Usage:
+   * <pre>
+   * function ParentClass(a, b) { }
+   * ParentClass.prototype.foo = function(a) { }
+   *
+   * function ChildClass(a, b, c) {
+   *   tracking.base(this, a, b);
+   * }
+   * tracking.inherits(ChildClass, ParentClass);
+   *
+   * var child = new ChildClass('a', 'b', 'c');
+   * child.foo();
+   * </pre>
+   *
+   * @param {Function} childCtor Child class.
+   * @param {Function} parentCtor Parent class.
+   */
+  tracking.inherits = function(childCtor, parentCtor) {
+    function TempCtor() {
+    }
+    TempCtor.prototype = parentCtor.prototype;
+    childCtor.superClass_ = parentCtor.prototype;
+    childCtor.prototype = new TempCtor();
+    childCtor.prototype.constructor = childCtor;
+
+    /**
+     * Calls superclass constructor/method.
+     *
+     * This function is only available if you use tracking.inherits to express
+     * inheritance relationships between classes.
+     *
+     * @param {!object} me Should always be "this".
+     * @param {string} methodName The method name to call. Calling superclass
+     *     constructor can be done with the special string 'constructor'.
+     * @param {...*} var_args The arguments to pass to superclass
+     *     method/constructor.
+     * @return {*} The return value of the superclass method/constructor.
+     */
+    childCtor.base = function(me, methodName) {
+      var args = Array.prototype.slice.call(arguments, 2);
+      return parentCtor.prototype[methodName].apply(me, args);
+    };
+  };
+
+  /**
+   * Captures the user camera when tracking a video element and set its source
+   * to the camera stream.
+   * @param {HTMLVideoElement} element Canvas element to track.
+   * @param {object} opt_options Optional configuration to the tracker.
+   */
+  tracking.initUserMedia_ = function(element, opt_options) {
+    window.navigator.mediaDevices.getUserMedia({
+      video: true,
+      audio: (opt_options && opt_options.audio) ? true : false,
+    }).then(function(stream) {
+      element.srcObject = stream;
+    }).catch(function(err) {
+      throw Error('Cannot capture user camera.');
+    });
+  };
+
+  /**
+   * Tests whether the object is a dom node.
+   * @param {object} o Object to be tested.
+   * @return {boolean} True if the object is a dom node.
+   */
+  tracking.isNode = function(o) {
+    return o.nodeType || this.isWindow(o);
+  };
+
+  /**
+   * Tests whether the object is the `window` object.
+   * @param {object} o Object to be tested.
+   * @return {boolean} True if the object is the `window` object.
+   */
+  tracking.isWindow = function(o) {
+    return !!(o && o.alert && o.document);
+  };
+
+  /**
+   * Selects a dom node from a CSS3 selector using `document.querySelector`.
+   * @param {string} selector
+   * @param {object} opt_element The root element for the query. When not
+   *     specified `document` is used as root element.
+   * @return {HTMLElement} The first dom element that matches to the selector.
+   *     If not found, returns `null`.
+   */
+  tracking.one = function(selector, opt_element) {
+    if (this.isNode(selector)) {
+      return selector;
+    }
+    return (opt_element || document).querySelector(selector);
+  };
+
+  /**
+   * Tracks a canvas, image or video element based on the specified `tracker`
+   * instance. This method extract the pixel information of the input element
+   * to pass to the `tracker` instance. When tracking a video, the
+   * `tracker.track(pixels, width, height)` will be in a
+   * `requestAnimationFrame` loop in order to track all video frames.
+   *
+   * Example:
+   * var tracker = new tracking.ColorTracker();
+   *
+   * tracking.track('#video', tracker);
+   * or
+   * tracking.track('#video', tracker, { camera: true });
+   *
+   * tracker.on('track', function(event) {
+   *   // console.log(event.data[0].x, event.data[0].y)
+   * });
+   *
+   * @param {HTMLElement} element The element to track, canvas, image or
+   *     video.
+   * @param {tracking.Tracker} tracker The tracker instance used to track the
+   *     element.
+   * @param {object} opt_options Optional configuration to the tracker.
+   */
+  tracking.track = function(element, tracker, opt_options) {
+    element = tracking.one(element);
+    if (!element) {
+      throw new Error('Element not found, try a different element or selector.');
+    }
+    if (!tracker) {
+      throw new Error('Tracker not specified, try `tracking.track(element, new tracking.FaceTracker())`.');
+    }
+
+    switch (element.nodeName.toLowerCase()) {
+      case 'canvas':
+        return this.trackCanvas_(element, tracker, opt_options);
+      case 'img':
+        return this.trackImg_(element, tracker, opt_options);
+      case 'video':
+        if (opt_options) {
+          if (opt_options.camera) {
+            this.initUserMedia_(element, opt_options);
+          }
+        }
+        return this.trackVideo_(element, tracker, opt_options);
+      default:
+        throw new Error('Element not supported, try in a canvas, img, or video.');
+    }
+  };
+
+  /**
+   * Tracks a canvas element based on the specified `tracker` instance and
+   * returns a `TrackerTask` for this track.
+   * @param {HTMLCanvasElement} element Canvas element to track.
+   * @param {tracking.Tracker} tracker The tracker instance used to track the
+   *     element.
+   * @param {object} opt_options Optional configuration to the tracker.
+   * @return {tracking.TrackerTask}
+   * @private
+   */
+  tracking.trackCanvas_ = function(element, tracker) {
+    var self = this;
+    var task = new tracking.TrackerTask(tracker);
+    task.on('run', function() {
+      self.trackCanvasInternal_(element, tracker);
+    });
+    return task.run();
+  };
+
+  /**
+   * Tracks a canvas element based on the specified `tracker` instance. This
+   * method extract the pixel information of the input element to pass to the
+   * `tracker` instance.
+   * @param {HTMLCanvasElement} element Canvas element to track.
+   * @param {tracking.Tracker} tracker The tracker instance used to track the
+   *     element.
+   * @param {object} opt_options Optional configuration to the tracker.
+   * @private
+   */
+  tracking.trackCanvasInternal_ = function(element, tracker) {
+    var width = element.width;
+    var height = element.height;
+    var context = element.getContext('2d');
+    var imageData = context.getImageData(0, 0, width, height);
+    tracker.track(imageData.data, width, height);
+  };
+
+  /**
+   * Tracks a image element based on the specified `tracker` instance. This
+   * method extract the pixel information of the input element to pass to the
+   * `tracker` instance.
+   * @param {HTMLImageElement} element Canvas element to track.
+   * @param {tracking.Tracker} tracker The tracker instance used to track the
+   *     element.
+   * @param {object} opt_options Optional configuration to the tracker.
+   * @private
+   */
+  tracking.trackImg_ = function(element, tracker) {
+    var width = element.naturalWidth;
+    var height = element.naturalHeight;
+    var canvas = document.createElement('canvas');
+
+    canvas.width = width;
+    canvas.height = height;
+
+    var task = new tracking.TrackerTask(tracker);
+    task.on('run', function() {
+      tracking.Canvas.loadImage(canvas, element.src, 0, 0, width, height, function() {
+        tracking.trackCanvasInternal_(canvas, tracker);
+      });
+    });
+    return task.run();
+  };
+
+  /**
+   * Tracks a video element based on the specified `tracker` instance. This
+   * method extract the pixel information of the input element to pass to the
+   * `tracker` instance. The `tracker.track(pixels, width, height)` will be in
+   * a `requestAnimationFrame` loop in order to track all video frames.
+   * @param {HTMLVideoElement} element Canvas element to track.
+   * @param {tracking.Tracker} tracker The tracker instance used to track the
+   *     element.
+   * @param {object} opt_options Optional configuration to the tracker.
+   * @private
+   */
+  tracking.trackVideo_ = function(element, tracker) {
+    var canvas = document.createElement('canvas');
+    var context = canvas.getContext('2d');
+    var width;
+    var height;
+
+
+// FIXME here the video display size of the analysed size
+    var resizeCanvas_ = function() {
+      width = element.offsetWidth;
+      height = element.offsetHeight;
+      canvas.width = width;
+      canvas.height = height;
+    };
+    resizeCanvas_();
+    element.addEventListener('resize', resizeCanvas_);
+
+
+// FIXME: do a process function - it is up to the caller to handle the frequency of detection
+// it seems all handled in the tracking.TrackerTask..
+// so in short, remove the tracking.TrackerTask from here
+// if the user want to use it, it can create it himself
+    var requestId;
+    var requestAnimationFrame_ = function() {
+      requestId = window.requestAnimationFrame(function() {
+        if (element.readyState === element.HAVE_ENOUGH_DATA) {
+          try {
+            // Firefox v~30.0 gets confused with the video readyState firing an
+            // erroneous HAVE_ENOUGH_DATA just before HAVE_CURRENT_DATA state,
+            // hence keep trying to read it until resolved.
+            context.drawImage(element, 0, 0, width, height);
+          } catch (err) {}
+          tracking.trackCanvasInternal_(canvas, tracker);
+        }
+        requestAnimationFrame_();
+      });
+    };
+
+    var task = new tracking.TrackerTask(tracker);
+    task.on('stop', function() {
+      window.cancelAnimationFrame(requestId);
+    });
+    task.on('run', function() {
+      requestAnimationFrame_();
+    });
+    return task.run();
+  };
+
+  // Browser polyfills
+  //===================
+
+  if (!window.URL) {
+    window.URL = window.URL || window.webkitURL || window.msURL || window.oURL;
+  }
+
+  if (!navigator.getUserMedia) {
+    navigator.getUserMedia = navigator.getUserMedia || navigator.webkitGetUserMedia ||
+    navigator.mozGetUserMedia || navigator.msGetUserMedia;
+  }
+}(window));

common/tracking.js/src/utils/Canvas.js

@@ -0,0 +1,37 @@
+(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;
+  };
+}());

common/tracking.js/src/utils/DisjointSet.js

@@ -0,0 +1,60 @@
+(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;
+  };
+
+}());

common/tracking.js/src/utils/EventEmitter.js

@@ -0,0 +1,149 @@
+(function() {
+  /**
+   * EventEmitter utility.
+   * @constructor
+   */
+  tracking.EventEmitter = function() {};
+
+  /**
+   * Holds event listeners scoped by event type.
+   * @type {object}
+   * @private
+   */
+  tracking.EventEmitter.prototype.events_ = null;
+
+  /**
+   * Adds a listener to the end of the listeners array for the specified event.
+   * @param {string} event
+   * @param {function} listener
+   * @return {object} Returns emitter, so calls can be chained.
+   */
+  tracking.EventEmitter.prototype.addListener = function(event, listener) {
+    if (typeof listener !== 'function') {
+      throw new TypeError('Listener must be a function');
+    }
+    if (!this.events_) {
+      this.events_ = {};
+    }
+
+    this.emit('newListener', event, listener);
+
+    if (!this.events_[event]) {
+      this.events_[event] = [];
+    }
+
+    this.events_[event].push(listener);
+
+    return this;
+  };
+
+  /**
+   * Returns an array of listeners for the specified event.
+   * @param {string} event
+   * @return {array} Array of listeners.
+   */
+  tracking.EventEmitter.prototype.listeners = function(event) {
+    return this.events_ && this.events_[event];
+  };
+
+  /**
+   * Execute each of the listeners in order with the supplied arguments.
+   * @param {string} event
+   * @param {*} opt_args [arg1], [arg2], [...]
+   * @return {boolean} Returns true if event had listeners, false otherwise.
+   */
+  tracking.EventEmitter.prototype.emit = function(event) {
+    var listeners = this.listeners(event);
+    if (listeners) {
+      var args = Array.prototype.slice.call(arguments, 1);
+      for (var i = 0; i < listeners.length; i++) {
+        if (listeners[i]) {
+          listeners[i].apply(this, args);
+        }
+      }
+      return true;
+    }
+    return false;
+  };
+
+  /**
+   * Adds a listener to the end of the listeners array for the specified event.
+   * @param {string} event
+   * @param {function} listener
+   * @return {object} Returns emitter, so calls can be chained.
+   */
+  tracking.EventEmitter.prototype.on = tracking.EventEmitter.prototype.addListener;
+
+  /**
+   * Adds a one time listener for the event. This listener is invoked only the
+   * next time the event is fired, after which it is removed.
+   * @param {string} event
+   * @param {function} listener
+   * @return {object} Returns emitter, so calls can be chained.
+   */
+  tracking.EventEmitter.prototype.once = function(event, listener) {
+    var self = this;
+    self.on(event, function handlerInternal() {
+      self.removeListener(event, handlerInternal);
+      listener.apply(this, arguments);
+    });
+  };
+
+  /**
+   * Removes all listeners, or those of the specified event. It's not a good
+   * idea to remove listeners that were added elsewhere in the code,
+   * especially when it's on an emitter that you didn't create.
+   * @param {string} event
+   * @return {object} Returns emitter, so calls can be chained.
+   */
+  tracking.EventEmitter.prototype.removeAllListeners = function(opt_event) {
+    if (!this.events_) {
+      return this;
+    }
+    if (opt_event) {
+      delete this.events_[opt_event];
+    } else {
+      delete this.events_;
+    }
+    return this;
+  };
+
+  /**
+   * Remove a listener from the listener array for the specified event.
+   * Caution: changes array indices in the listener array behind the listener.
+   * @param {string} event
+   * @param {function} listener
+   * @return {object} Returns emitter, so calls can be chained.
+   */
+  tracking.EventEmitter.prototype.removeListener = function(event, listener) {
+    if (typeof listener !== 'function') {
+      throw new TypeError('Listener must be a function');
+    }
+    if (!this.events_) {
+      return this;
+    }
+
+    var listeners = this.listeners(event);
+    if (Array.isArray(listeners)) {
+      var i = listeners.indexOf(listener);
+      if (i < 0) {
+        return this;
+      }
+      listeners.splice(i, 1);
+    }
+
+    return this;
+  };
+
+  /**
+   * By default EventEmitters will print a warning if more than 10 listeners
+   * are added for a particular event. This is a useful default which helps
+   * finding memory leaks. Obviously not all Emitters should be limited to 10.
+   * This function allows that to be increased. Set to zero for unlimited.
+   * @param {number} n The maximum number of listeners.
+   */
+  tracking.EventEmitter.prototype.setMaxListeners = function() {
+    throw new Error('Not implemented');
+  };
+
+}());

common/tracking.js/src/utils/Image.js

@@ -0,0 +1,392 @@
+(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 color-space 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 {Uint8Array|Uint8ClampedArray|Array} 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.
+   * @return {Uint8Array} 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) {
+
+    /*
+      Performance result (rough EST. - image size, CPU arch. will affect):
+      https://jsperf.com/tracking-new-image-to-grayscale
+
+      Firefox v.60b:
+            fillRGBA  Gray only
+      Old      11       551     OPs/sec
+      New    3548      6487     OPs/sec
+      ---------------------------------
+              322.5x     11.8x  faster
+
+      Chrome v.67b:
+            fillRGBA  Gray only
+      Old     291       489     OPs/sec
+      New    6975      6635     OPs/sec
+      ---------------------------------
+              24.0x      13.6x  faster
+
+      - Ken Nilsen / epistemex
+     */
+
+    var len = pixels.length>>2;
+    var gray = fillRGBA ? new Uint32Array(len) : new Uint8Array(len);
+    var data32 = new Uint32Array(pixels.buffer || new Uint8Array(pixels).buffer);
+    var i = 0;
+    var c = 0;
+    var luma = 0;
+
+    // unrolled loops to not have to check fillRGBA each iteration
+    if (fillRGBA) {
+      while(i < len) {
+        // Entire pixel in little-endian order (ABGR)
+        c = data32[i];
+
+        // Using the more up-to-date REC/BT.709 approx. weights for luma instead: [0.2126, 0.7152, 0.0722].
+        //   luma = ((c>>>16 & 0xff) * 0.2126 + (c>>>8 & 0xff) * 0.7152 + (c & 0xff) * 0.0722 + 0.5)|0;
+        // But I'm using scaled integers here for speed (x 0xffff). This can be improved more using 2^n
+        //   close to the factors allowing for shift-ops (i.e. 4732 -> 4096 => .. (c&0xff) << 12 .. etc.)
+        //   if "accuracy" is not important (luma is anyway an visual approx.):
+        luma = ((c>>>16&0xff) * 13933 + (c>>>8&0xff) * 46871 + (c&0xff) * 4732)>>>16;
+        gray[i++] = luma * 0x10101 | c & 0xff000000;
+      }
+    }
+    else {
+      while(i < len) {
+        c = data32[i];
+        luma = ((c>>>16&0xff) * 13933 + (c>>>8&0xff) * 46871 + (c&0xff) * 4732)>>>16;
+        // ideally, alpha should affect value here: value * (alpha/255) or with shift-ops for the above version
+        gray[i++] = luma;
+      }
+    }
+
+    // Consolidate array view to byte component format independent of source view
+    return new Uint8Array(gray.buffer);
+  };
+
+  /**
+   * 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;
+  }
+
+}());

common/tracking.js/test/Benchmark.js

@@ -0,0 +1,14 @@
+var Benchmark = require('./utils/benchmark.js');
+
+module.exports = {
+  setUp: function(done) {
+    Benchmark.setUpAll(done);
+  },
+
+  testBenchmark: function(test) {
+    Benchmark.runAll(function(results) {
+      test.ok(results.passed, Benchmark.createFailureMessage(results.resultDetails));
+      test.done();
+    });
+  }
+};