├── LICENSE ├── README.md ├── dist ├── face-api.js ├── face-api.js.map └── face-api.min.js ├── images ├── tfjs_upload.gif ├── tfjs_webcam.gif └── yolo_webcam.gif ├── models ├── mobilenetv1_models │ ├── group1-shard1of5 │ ├── group1-shard2of5 │ ├── group1-shard3of5 │ ├── group1-shard4of5 │ ├── group1-shard5of5 │ └── model.json ├── mobilenetv2_models │ ├── group1-shard1of4 │ ├── group1-shard2of4 │ ├── group1-shard3of4 │ ├── group1-shard4of4 │ └── model.json └── tiny_face_detector │ ├── tiny_face_detector_model-shard1 │ └── tiny_face_detector_model-weights_manifest.json └── src ├── MobileNetImage.html ├── MobileNetWebcam.html ├── TinyFaceDetectWebcam.html └── public ├── commons.js └── styles.css /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Kevin.Hsiao 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FrontEnd-EmotionDetection 2 | ### This is a real-time Emotion detection using [TensorFlow.js](https://js.tensorflow.org/) to load a pretrained model into the browser . 3 | [![Packagist](https://img.shields.io/badge/TensorFlow-1.10.1-orange.svg)]() 4 | [![Packagist](https://img.shields.io/badge/Keras-2.2.2-blue.svg)]() 5 | [![Packagist](https://img.shields.io/badge/Python-3.5.0-blue.svg)]() 6 | [![Packagist](https://img.shields.io/badge/TensorFlow.js-0.12.6-orange.svg)]() 7 | ## Abstract 8 | ### Detect faces 9 | * We use two methods to detect faces: 10 | 11 | 1. [Chrome Shape Detection API](https://www.chromestatus.com/feature/4757990523535360) 12 | 2. [face-api.js](https://github.com/justadudewhohacks/face-api.js/) 13 | 14 | ### Dataset 15 | * We combine two datasets: 16 | 1. Microsoft [FERPlus](https://github.com/Microsoft/FERPlus) as one of the dataset to train the emotion-detect model. 17 | 2. Real-world Affective Faces Database [(RAF-DB)](http://www.whdeng.cn/RAF/model1.html) as another dataset. 18 | 19 | ### Convert Model 20 | * Use [TensorFlow.js converter](https://github.com/tensorflow/tfjs-converter) convert Keras model to .json file for loading and running Javascript inference. 21 | 22 | ## Requirements 23 | * For **MobileNetImage.html** and **MobileNetWebcam.html**: 24 | 25 | [Chrome Shape Detection API](https://www.chromestatus.com/feature/4757990523535360) : 26 | FaceDetector: Chrome on Android, macOS, Windows 10 platfrom. 27 | Go to chrome browser ```chrome://flags/#enable-experimental-web-platform-features``` and enable the feature 28 | 29 | 30 | * For **TinyFaceDetectWebcam.html** you can run on Chrome, Safari and Firefox. 31 | 32 | ## Demo 33 | Note, that wearing glasses might decrease the accuracy of the prediction results. 34 | 35 | **All demos are on our [LAB](http://mirlab.org/index.asp) server** 36 | 37 | * [MobileNetImage.html](https://mirlab.org:444/demo/FrontEnd-EmotionDetection/src/MobileNetImage.html) ( face detection via Chrome Shape Detection API ) 38 | 39 | ![](https://github.com/kevinisbest/FrontEnd-EmotionDetection/blob/master/images/tfjs_upload.gif) 40 | 41 | * [TinyFaceDetectWebcam.html](https://mirlab.org:444/demo/FrontEnd-EmotionDetection/src/TinyFaceDetectWebcam.html) ( face detection via [face-api tiny-face-detector](https://github.com/justadudewhohacks/face-api.js#tiny-face-detector) ) 42 | 43 | ![](https://github.com/kevinisbest/FrontEnd-EmotionDetection/blob/master/images/yolo_webcam.gif) 44 | 45 | * [MobileNetWebcam.html](https://mirlab.org:444/demo/FrontEnd-EmotionDetection/src/MobileNetWebcam.html) ( face detection via Chrome Shape Detection API ) 46 | 47 | ![](https://github.com/kevinisbest/FrontEnd-EmotionDetection/blob/master/images/tfjs_webcam.gif) 48 | -------------------------------------------------------------------------------- /images/tfjs_upload.gif: 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"model_config": {"class_name": "Model", "config": {"name": "model_1", "layers": [{"name": "image_input", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 100, 100, 3], "dtype": "float32", "sparse": false, "name": "image_input"}, "inbound_nodes": []}, {"name": "mobilenet_1.00_224", "class_name": "Model", "config": {"name": "mobilenet_1.00_224", "layers": [{"name": "input_1", "class_name": "InputLayer", "config": {"batch_input_shape": [null, null, null, 3], "dtype": "float32", "sparse": false, "name": "input_1"}, "inbound_nodes": []}, {"name": "conv1_pad", "class_name": "ZeroPadding2D", "config": {"name": "conv1_pad", "trainable": true, "padding": [[0, 1], [0, 1]], "data_format": "channels_last"}, "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"name": "conv1", "class_name": "Conv2D", "config": {"name": "conv1", "trainable": true, "filters": 32, "kernel_size": [3, 3], "strides": [2, 2], "padding": "valid", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["conv1_pad", 0, 0, {}]]]}, {"name": "conv1_bn", "class_name": "BatchNormalization", "config": {"name": "conv1_bn", "trainable": true, "axis": -1, "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv1", 0, 0, {}]]]}, {"name": "conv1_relu", "class_name": "ReLU", "config": {"name": "conv1_relu", "trainable": true, "max_value": 6.0}, "inbound_nodes": [[["conv1_bn", 0, 0, {}]]]}, {"name": "conv_dw_1", "class_name": "DepthwiseConv2D", "config": {"name": "conv_dw_1", "trainable": true, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": false, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": null, "activity_regularizer": null, "bias_constraint": null, "depth_multiplier": 1, "depthwise_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "depthwise_regularizer": null, "depthwise_constraint": null}, "inbound_nodes": [[["conv1_relu", 0, 0, {}]]]}, {"name": "conv_dw_1_bn", "class_name": "BatchNormalization", "config": {"name": "conv_dw_1_bn", "trainable": true, "axis": -1, "momentum": 0.99, "epsilon": 0.001, "center": true, "scale": true, "beta_initializer": {"class_name": "Zeros", "config": {}}, "gamma_initializer": {"class_name": "Ones", "config": {}}, "moving_mean_initializer": {"class_name": "Zeros", "config": {}}, "moving_variance_initializer": {"class_name": "Ones", "config": {}}, "beta_regularizer": null, "gamma_regularizer": null, "beta_constraint": null, "gamma_constraint": null}, "inbound_nodes": [[["conv_dw_1", 0, 0, {}]]]}, {"name": "conv_dw_1_relu", "class_name": "ReLU", "config": {"name": "conv_dw_1_relu", "trainable": true, "max_value": 6.0}, "inbound_nodes": [[["conv_dw_1_bn", 0, 0, {}]]]}, {"name": "conv_pw_1", "class_name": "Conv2D", "config": {"name": "conv_pw_1", "trainable": true, "filters": 64, "kernel_size": [1, 1], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, 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"inbound_nodes": [[["conv_pw_2", 0, 0, {}]]]}, {"name": "conv_pw_2_relu", "class_name": "ReLU", "config": {"name": "conv_pw_2_relu", "trainable": true, "max_value": 6.0}, "inbound_nodes": [[["conv_pw_2_bn", 0, 0, {}]]]}, {"name": "conv_dw_3", "class_name": "DepthwiseConv2D", "config": {"name": "conv_dw_3", "trainable": true, "kernel_size": [3, 3], "strides": [1, 1], "padding": "same", "data_format": "channels_last", "dilation_rate": [1, 1], "activation": "linear", "use_bias": false, "bias_initializer": {"class_name": "Zeros", "config": {}}, "bias_regularizer": null, "activity_regularizer": null, "bias_constraint": null, "depth_multiplier": 1, "depthwise_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "depthwise_regularizer": null, "depthwise_constraint": null}, "inbound_nodes": [[["conv_pw_2_relu", 0, 0, {}]]]}, {"name": "conv_dw_3_bn", "class_name": "BatchNormalization", "config": {"name": 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81 | 90 | 275 | 276 | 277 | -------------------------------------------------------------------------------- /src/public/commons.js: -------------------------------------------------------------------------------- 1 | const classes = ['amy', 'bernadette', 'howard', 'leonard', 'penny', 'raj', 'sheldon', 'stuart'] 2 | 3 | function getImageUri(imageName) { 4 | return `../public/images/${imageName}` 5 | } 6 | 7 | function getFaceImageUri(className, idx) { 8 | return `images/${className}/${className}${idx}.png` 9 | } 10 | 11 | async function fetchImage(uri) { 12 | 13 | return (await fetch(uri)).blob() 14 | } 15 | 16 | async function requestExternalImage(imageUrl) { 17 | const res = await fetch('fetch_external_image', { 18 | method: 'post', 19 | headers: { 20 | 'content-type': 'application/json' 21 | }, 22 | body: JSON.stringify({ imageUrl }) 23 | }) 24 | if (!(res.status < 400)) { 25 | console.error(res.status + ' : ' + await res.text()) 26 | throw new Error('failed to fetch image from url: ' + imageUrl) 27 | } 28 | 29 | let blob 30 | try { 31 | blob = await res.blob() 32 | return await faceapi.bufferToImage(blob) 33 | } catch (e) { 34 | console.error('received blob:', blob) 35 | console.error('error:', e) 36 | throw new Error('failed to load image from url: ' + imageUrl) 37 | } 38 | } 39 | 40 | // fetch first image of each class and compute their descriptors 41 | async function initTrainDescriptorsByClass(net, numImagesForTraining = 1) { 42 | const maxAvailableImagesPerClass = 5 43 | numImagesForTraining = Math.min(numImagesForTraining, maxAvailableImagesPerClass) 44 | return Promise.all(classes.map( 45 | async className => { 46 | const descriptors = [] 47 | for (let i = 1; i < (numImagesForTraining + 1); i++) { 48 | const img = await faceapi.bufferToImage( 49 | await fetchImage(getFaceImageUri(className, i)) 50 | ) 51 | descriptors.push(await net.computeFaceDescriptor(img)) 52 | } 53 | return { 54 | descriptors, 55 | className 56 | } 57 | } 58 | )) 59 | } 60 | 61 | function getBestMatch(descriptorsByClass, queryDescriptor) { 62 | function computeMeanDistance(descriptorsOfClass) { 63 | return faceapi.round( 64 | descriptorsOfClass 65 | .map(d => faceapi.euclideanDistance(d, queryDescriptor)) 66 | .reduce((d1, d2) => d1 + d2, 0) 67 | / (descriptorsOfClass.length || 1) 68 | ) 69 | } 70 | return descriptorsByClass 71 | .map( 72 | ({ descriptors, className }) => ({ 73 | distance: computeMeanDistance(descriptors), 74 | className 75 | }) 76 | ) 77 | .reduce((best, curr) => best.distance < curr.distance ? best : curr) 78 | } 79 | 80 | function renderNavBar(navbarId, exampleUri) { 81 | const examples = [ 82 | { 83 | uri: 'face_detection', 84 | name: 'Face Detection' 85 | }, 86 | { 87 | uri: 'face_detection_video', 88 | name: 'Face Detection Video' 89 | }, 90 | { 91 | uri: 'face_recognition', 92 | name: 'Face Recognition' 93 | }, 94 | { 95 | uri: 'face_similarity', 96 | name: 'Face Similarity' 97 | }, 98 | { 99 | uri: 'face_landmarks', 100 | name: 'Face Landmarks' 101 | }, 102 | { 103 | uri: 'detect_and_draw_landmarks', 104 | name: 'Detect and Draw Landmarks' 105 | }, 106 | { 107 | uri: 'detect_and_draw_faces', 108 | name: 'Detect and Draw Faces' 109 | }, 110 | { 111 | uri: 'face_alignment', 112 | name: 'Face Alignment' 113 | }, 114 | { 115 | uri: 'detect_and_recognize_faces', 116 | name: 'Detect and Recognize Faces' 117 | }, 118 | { 119 | uri: 'mtcnn_face_detection', 120 | name: 'MTCNN Face Detection' 121 | }, 122 | { 123 | uri: 'mtcnn_face_detection_video', 124 | name: 'MTCNN Face Detection Video' 125 | }, 126 | { 127 | uri: 'mtcnn_face_detection_webcam', 128 | name: 'MTCNN Face Detection Webcam' 129 | }, 130 | { 131 | uri: 'mtcnn_face_recognition', 132 | name: 'MTCNN Face Recognition' 133 | }, 134 | { 135 | uri: 'mtcnn_face_recognition_webcam', 136 | name: 'MTCNN Face Recognition Webcam' 137 | }, 138 | { 139 | uri: 'tiny_yolov2_face_detection', 140 | name: 'Tiny Yolov2 Face Detection' 141 | }, 142 | { 143 | uri: 'tiny_yolov2_face_detection_video', 144 | name: 'Tiny Yolov2 Face Detection Video' 145 | }, 146 | { 147 | uri: 'tiny_yolov2_face_detection_webcam', 148 | name: 'Tiny Yolov2 Face Detection Webcam' 149 | }, 150 | { 151 | uri: 'tiny_yolov2_face_recognition', 152 | name: 'Tiny Yolov2 Face Recognition' 153 | }, 154 | { 155 | uri: 'batch_face_landmarks', 156 | name: 'Batch Face Landmarks' 157 | }, 158 | { 159 | uri: 'batch_face_recognition', 160 | name: 'Batch Face Recognition' 161 | } 162 | ] 163 | 164 | const navbar = $(navbarId).get(0) 165 | const pageContainer = $('.page-container').get(0) 166 | 167 | const header = document.createElement('h3') 168 | header.innerHTML = examples.find(ex => ex.uri === exampleUri).name 169 | pageContainer.insertBefore(header, pageContainer.children[0]) 170 | 171 | const menuContent = document.createElement('ul') 172 | menuContent.id = 'slide-out' 173 | menuContent.classList.add('side-nav', 'fixed') 174 | navbar.appendChild(menuContent) 175 | 176 | const menuButton = document.createElement('a') 177 | menuButton.href='#' 178 | menuButton.classList.add('button-collapse', 'show-on-large') 179 | menuButton.setAttribute('data-activates', 'slide-out') 180 | const menuButtonIcon = document.createElement('img') 181 | menuButtonIcon.src = 'menu_icon.png' 182 | menuButton.appendChild(menuButtonIcon) 183 | navbar.appendChild(menuButton) 184 | 185 | const li = document.createElement('li') 186 | const githubLink = document.createElement('a') 187 | githubLink.classList.add('waves-effect', 'waves-light', 'side-by-side') 188 | githubLink.id = 'github-link' 189 | githubLink.href = 'https://github.com/justadudewhohacks/face-api.js' 190 | const h5 = document.createElement('h5') 191 | h5.innerHTML = 'face-api.js' 192 | githubLink.appendChild(h5) 193 | const githubLinkIcon = document.createElement('img') 194 | githubLinkIcon.src = 'github_link_icon.png' 195 | githubLink.appendChild(githubLinkIcon) 196 | li.appendChild(githubLink) 197 | menuContent.appendChild(li) 198 | 199 | examples 200 | .forEach(ex => { 201 | const li = document.createElement('li') 202 | if (ex.uri === exampleUri) { 203 | li.style.background='#b0b0b0' 204 | } 205 | const a = document.createElement('a') 206 | a.classList.add('waves-effect', 'waves-light') 207 | a.href = ex.uri 208 | const span = document.createElement('span') 209 | span.innerHTML = ex.name 210 | span.style.whiteSpace = 'nowrap' 211 | a.appendChild(span) 212 | li.appendChild(a) 213 | menuContent.appendChild(li) 214 | }) 215 | 216 | $('.button-collapse').sideNav({ 217 | menuWidth: 280 218 | }) 219 | } 220 | 221 | function renderSelectList(selectListId, onChange, initialValue, renderChildren) { 222 | const select = document.createElement('select') 223 | $(selectListId).get(0).appendChild(select) 224 | renderChildren(select) 225 | $(select).val(initialValue) 226 | $(select).on('change', (e) => onChange(e.target.value)) 227 | $(select).material_select() 228 | } 229 | 230 | function renderOption(parent, text, value) { 231 | const option = document.createElement('option') 232 | option.innerHTML = text 233 | option.value = value 234 | parent.appendChild(option) 235 | } 236 | 237 | function renderFaceImageSelectList(selectListId, onChange, initialValue) { 238 | const indices = [1, 2, 3, 4, 5] 239 | function renderChildren(select) { 240 | classes.forEach(className => { 241 | const optgroup = document.createElement('optgroup') 242 | optgroup.label = className 243 | select.appendChild(optgroup) 244 | indices.forEach(imageIdx => 245 | renderOption( 246 | optgroup, 247 | `${className} ${imageIdx}`, 248 | getFaceImageUri(className, imageIdx) 249 | ) 250 | ) 251 | }) 252 | } 253 | 254 | renderSelectList( 255 | selectListId, 256 | onChange, 257 | getFaceImageUri(initialValue.className, initialValue.imageIdx), 258 | renderChildren 259 | ) 260 | } 261 | 262 | function renderImageSelectList(selectListId, onChange, initialValue) { 263 | const images = [1, 2, 3, 4, 5].map(idx => `bbt${idx}.jpg`) 264 | function renderChildren(select) { 265 | images.forEach(imageName => 266 | renderOption( 267 | select, 268 | imageName, 269 | getImageUri(imageName) 270 | ) 271 | ) 272 | } 273 | 274 | renderSelectList( 275 | selectListId, 276 | onChange, 277 | getImageUri(initialValue), 278 | renderChildren 279 | ) 280 | } -------------------------------------------------------------------------------- /src/public/styles.css: -------------------------------------------------------------------------------- 1 | .page-container { 2 | left: 0; 3 | right: 0; 4 | margin: auto; 5 | margin-top: 20px; 6 | padding-left: 300px; 7 | display: inline-flex !important; 8 | } 9 | 10 | @media only screen and (max-width : 992px) { 11 | .page-container { 12 | padding-left: 0; 13 | display: flex !important; 14 | } 15 | } 16 | 17 | #navbar { 18 | position: absolute; 19 | top: 20px; 20 | left: 20px; 21 | } 22 | 23 | .center-content { 24 | display: flex; 25 | flex-direction: column; 26 | justify-content: center; 27 | align-items: center; 28 | flex-wrap: wrap; 29 | } 30 | 31 | .side-by-side { 32 | display: flex; 33 | justify-content: center; 34 | align-items: center; 35 | } 36 | .side-by-side >* { 37 | margin: 0 5px; 38 | } 39 | 40 | .bold { 41 | font-weight: bold; 42 | } 43 | 44 | .margin-sm { 45 | margin: 5px; 46 | } 47 | 48 | .margin { 49 | margin: 20px; 50 | } 51 | 52 | .button-sm { 53 | padding: 0 10px !important; 54 | } 55 | 56 | #github-link { 57 | display: flex !important; 58 | justify-content: center; 59 | align-items: center; 60 | border-bottom: 1px solid; 61 | margin-bottom: 10px; 62 | } 63 | 64 | #overlay { 65 | position: absolute; 66 | top: 0; 67 | left: 0; 68 | } 69 | 70 | #facesContainer canvas { 71 | margin: 10px; 72 | } --------------------------------------------------------------------------------