├── .gitignore ├── LICENSE ├── README.MD ├── git_media ├── conver_tfjs.PNG ├── objects_demo.gif └── spotlight_banner.png ├── package-lock.json ├── package.json ├── public ├── images │ └── voccmap.png └── index.html ├── src ├── index.js └── styles.css ├── weights ├── group1-shard1of4 ├── group1-shard2of4 ├── group1-shard3of4 ├── group1-shard4of4 └── model.json └── yarn.lock /.gitignore: -------------------------------------------------------------------------------- 1 | node_modules 2 | .cache -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | 2 | Apache License 3 | Version 2.0, January 2004 4 | https://www.apache.org/licenses/ 5 | 6 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 7 | 8 | 1. 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However, in accepting such obligations, You may act only 171 | on Your own behalf and on Your sole responsibility, not on behalf 172 | of any other Contributor, and only if You agree to indemnify, 173 | defend, and hold each Contributor harmless for any liability 174 | incurred by, or claims asserted against, such Contributor by reason 175 | of your accepting any such warranty or additional liability. 176 | 177 | END OF TERMS AND CONDITIONS 178 | 179 | Copyright 2013-2018 Docker, Inc. 180 | 181 | Licensed under the Apache License, Version 2.0 (the "License"); 182 | you may not use this file except in compliance with the License. 183 | You may obtain a copy of the License at 184 | 185 | https://www.apache.org/licenses/LICENSE-2.0 186 | 187 | Unless required by applicable law or agreed to in writing, software 188 | distributed under the License is distributed on an "AS IS" BASIS, 189 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 190 | See the License for the specific language governing permissions and 191 | limitations under the License. -------------------------------------------------------------------------------- /README.MD: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | # Realtime Semantic Segmentation in the browser 5 | 6 | To a more detailed description about this project, [check my article](https://lnkd.in/dFm95MJ) at Towards Data Science. 7 | 8 | This project uses TensorFlow.js to perform realtime instance segmentation in the browser using [RefineNet](https://arxiv.org/pdf/1611.06612.pdf). Many thanks to [Bernardo de Oliveira](https://github.com/bernieOllie), that help me to fix some bugs in the code, and [Vladimir Nekrasov](https://drsleep.github.io/) that provided the *.js weights* of the [Light-Weight RefineNet](http://bmvc2018.org/contents/papers/0494.pdf) - a more compact implementation of RefineNet, suitable for tasks requiring real-time performance. 9 | 10 | 11 | ![enter image description here](./git_media/objects_demo.gif) 12 | 13 | 14 | 15 | ### Getting started 16 | Tensorflow.js models have to be served through an URL to be loaded in javascript. The Refine Net model is based on a main JSON file and shared weights files in a binary format. The idea is to create an HTTP server that will make the model available in a URL allowing requests and be treated as REST API. When loading the model, the TensorFlow.js will do the following requests: 17 | 18 | GET /model.json 19 | GET /group1-shard1of4 20 | GET /group1-shard2of4 21 | GET /group1-shard3of4 22 | GET /group1-shard4of4 23 | 24 | The [http-server](https://www.npmjs.com/package/http-server) can be installed using the following command: 25 | 26 | `npm install http-server -g` 27 | 28 | Once you have the program installed, go to the weights folder, run the command below and the model you be available at http://127.0.0.1:8080. 29 | 30 | `http-server -c1 --cors .` 31 | 32 | To install the required packages, it can be used `npm` or `yarn`. 33 | 34 | If you are using `yarn`: 35 | 36 | yarn 37 | yarn start 38 | 39 | If you are using `npm`: 40 | 41 | npm install 42 | npm start 43 | 44 | To test the project just go to http://localhost:3000/ and the application will be running there. 45 | 46 | ### References 47 | [1] Romera-Paredes, Bernardino, and Philip Hilaire Sean Torr. “Recurrent instance segmentation.” _European conference on computer vision_. Springer, Cham, 2016. 48 | 49 | [2] Lin, Guosheng, et al. “Refinenet: Multi-path refinement networks for high-resolution semantic segmentation.” _Proceedings of the IEEE conference on computer vision and pattern recognition_. 2017. 50 | 51 | [3] Nekrasov, Vladimir, Chunhua Shen, and Ian Reid. “Light-weight refinenet for real-time semantic segmentation.” _arXiv preprint arXiv:1810.03272_ (2018). 52 | 53 | [4] Medium. 2020. _Introducing Tensorflow.Js: Machine Learning In Javascript_. [online] Available at: [Accessed 3 June 2020]. 54 | 55 | [5] [https://www.tensorflow.org/js/tutorials/conversion/import_keras](https://www.tensorflow.org/js/tutorials/conversion/import_keras) 56 | 57 | [6] [https://github.com/DrSleep/light-weight-refinenet](https://github.com/DrSleep/light-weight-refinenet) 58 | 59 | [7] [https://www.tensorflow.org/js/guide/conversion](https://www.tensorflow.org/js/guide/conversion) 60 | -------------------------------------------------------------------------------- /git_media/conver_tfjs.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hugozanini/realtime-semantic-segmentation/55c5e920c1215052468ae3667e4c7ec5da8bdaa0/git_media/conver_tfjs.PNG -------------------------------------------------------------------------------- /git_media/objects_demo.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hugozanini/realtime-semantic-segmentation/55c5e920c1215052468ae3667e4c7ec5da8bdaa0/git_media/objects_demo.gif -------------------------------------------------------------------------------- /git_media/spotlight_banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hugozanini/realtime-semantic-segmentation/55c5e920c1215052468ae3667e4c7ec5da8bdaa0/git_media/spotlight_banner.png -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "tensorflowjs-real-time-object-detection", 3 | "version": "1.0.0", 4 | "description": "", 5 | "keywords": [], 6 | "main": "src/index.js", 7 | "dependencies": { 8 | "@tensorflow-models/coco-ssd": "^0.1.1", 9 | "@tensorflow/tfjs": "^1.7.4", 10 | "@tensorflow/tfjs-node": "^1.7.4", 11 | "array-to-image": "^1.0.0", 12 | "aws-sdk": "^2.678.0", 13 | "node-gyp": "^6.1.0", 14 | "node-pre-gyp": "^0.14.0", 15 | "react": "16.5.2", 16 | "react-dom": "16.5.2", 17 | "react-magic-dropzone": "1.0.1", 18 | "react-scripts": "2.0.3", 19 | "webgl": "0.0.7" 20 | }, 21 | "devDependencies": {}, 22 | "scripts": { 23 | "start": "react-scripts start", 24 | "build": "react-scripts build", 25 | "test": "react-scripts test --env=jsdom", 26 | "eject": "react-scripts eject" 27 | }, 28 | "browserslist": [ 29 | ">0.2%", 30 | "not dead", 31 | "not ie <= 11", 32 | "not op_mini all" 33 | ] 34 | } 35 | -------------------------------------------------------------------------------- /public/images/voccmap.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hugozanini/realtime-semantic-segmentation/55c5e920c1215052468ae3667e4c7ec5da8bdaa0/public/images/voccmap.png -------------------------------------------------------------------------------- /public/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 7 | 8 | 9 | 10 | 11 | 20 | Semantic Segmentation 21 | 22 | 23 | 24 | 27 |
28 |
29 | Instance Segmentation 31 |
32 | 42 | 43 | 44 | -------------------------------------------------------------------------------- /src/index.js: -------------------------------------------------------------------------------- 1 | import React from "react"; 2 | import ReactDOM from "react-dom"; 3 | import * as tf from '@tensorflow/tfjs'; 4 | import "./styles.css"; 5 | tf.setBackend('webgl'); 6 | 7 | const pascalvoc = [[ 0,0,0 ],[ 128,0,0 ],[ 0,128,0 ], 8 | [ 128,128,0 ],[ 0,0,128 ],[ 128,0,128 ], 9 | [ 0,128,128 ],[ 128,128,128 ],[ 64,0,0 ], 10 | [ 192,0,0 ],[ 64,128,0 ],[ 192,128,0 ], 11 | [ 64,0,128 ],[ 192,0,128 ],[ 64,128,128 ], 12 | [ 192,128,128 ],[ 0,64,0 ],[ 128,64,0 ], 13 | [ 0,192,0 ],[ 128,192,0 ],[ 0,64,128 ], 14 | [ 128,64,128 ],[ 0,192,128 ],[ 128,192,128 ], 15 | [ 64,64,0 ],[ 192,64,0 ],[ 64,192,0 ], 16 | [ 192,192,0 ],[ 64,64,128 ],[ 192,64,128 ], 17 | [ 64,192,128 ],[ 192,192,128 ],[ 0,0,64 ], 18 | [ 128,0,64 ],[ 0,128,64 ],[ 128,128,64 ], 19 | [ 0,0,192 ],[ 128,0,192 ],[ 0,128,192 ], 20 | [ 128,128,192 ],[ 64,0,64 ]]; 21 | 22 | 23 | async function load_model() { 24 | const model = await tf.loadLayersModel("http://127.0.0.1:8080/model.json"); 25 | return model; 26 | } 27 | 28 | const modelPromise = load_model(); 29 | 30 | class App extends React.Component { 31 | videoRef = React.createRef(); 32 | canvasRef = React.createRef(); 33 | 34 | componentDidMount() { 35 | if (navigator.mediaDevices && navigator.mediaDevices.getUserMedia) { 36 | const webCamPromise = navigator.mediaDevices 37 | .getUserMedia({ 38 | audio: false, 39 | video: { 40 | facingMode: "user" 41 | } 42 | }) 43 | .then(stream => { 44 | window.stream = stream; 45 | this.videoRef.current.srcObject = stream; 46 | return new Promise((resolve, reject) => { 47 | this.videoRef.current.onloadedmetadata = () => { 48 | resolve(); 49 | }; 50 | }); 51 | }); 52 | Promise.all([modelPromise, webCamPromise]) 53 | .then(values => { 54 | this.detectFrame(this.videoRef.current, values[0]); 55 | }) 56 | .catch(error => { 57 | console.error(error); 58 | }); 59 | } 60 | } 61 | 62 | detectFrame = (video, model) => { 63 | tf.engine().startScope(); 64 | const predictions = model.predict(this.process_input(video)); 65 | this.renderPredictions(predictions); 66 | requestAnimationFrame(() => { 67 | this.detectFrame(video, model); 68 | }); 69 | tf.engine().endScope(); 70 | }; 71 | 72 | process_input(video_frame){ 73 | const img = tf.browser.fromPixels(video_frame).toFloat(); 74 | const scale = tf.scalar(255.); 75 | const mean = tf.tensor3d([0.485, 0.456, 0.406], [1,1,3]); 76 | const std = tf.tensor3d([0.229, 0.224, 0.225], [1,1,3]); 77 | const normalised = img.div(scale).sub(mean).div(std); 78 | const batched = normalised.transpose([2,0,1]).expandDims(); 79 | return batched; 80 | }; 81 | renderPredictions = async (predictions) => { 82 | const img_shape = [480, 480] 83 | const offset = 0; 84 | const segmPred = tf.image.resizeBilinear(predictions.transpose([0,2,3,1]), 85 | img_shape); 86 | const segmMask = segmPred.argMax(3).reshape(img_shape); 87 | const width = segmMask.shape.slice(0, 1); 88 | const height = segmMask.shape.slice(1, 2); 89 | const data = await segmMask.data(); 90 | const bytes = new Uint8ClampedArray(width * height * 4); 91 | for (let i = 0; i < height * width; ++i) { 92 | const partId = data[i]; 93 | const j = i * 4; 94 | if (partId === -1) { 95 | bytes[j + 0] = 255; 96 | bytes[j + 1] = 255; 97 | bytes[j + 2] = 255; 98 | bytes[j + 3] = 255; 99 | } else { 100 | const color = pascalvoc[partId + offset]; 101 | 102 | if (!color) { 103 | throw new Error(`No color could be found for part id ${partId}`); 104 | } 105 | bytes[j + 0] = color[0]; 106 | bytes[j + 1] = color[1]; 107 | bytes[j + 2] = color[2]; 108 | bytes[j + 3] = 255; 109 | } 110 | } 111 | const out = new ImageData(bytes, width, height); 112 | const ctx = this.canvasRef.current.getContext("2d"); 113 | ctx.scale(1.5, 1.5); 114 | ctx.putImageData(out, 520, 60); 115 | }; 116 | 117 | render() { 118 | return ( 119 |
120 |

Real-Time Semantic Segmentation

121 |

Refine Net

122 |
139 | ); 140 | } 141 | 142 | } 143 | const rootElement = document.getElementById("root"); 144 | ReactDOM.render(, rootElement); 145 | -------------------------------------------------------------------------------- /src/styles.css: -------------------------------------------------------------------------------- 1 | .size { 2 | position: fixed; 3 | top: 0; 4 | left: 0; 5 | } 6 | 7 | section { 8 | float: right; 9 | } 10 | 11 | -------------------------------------------------------------------------------- /weights/group1-shard1of4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hugozanini/realtime-semantic-segmentation/55c5e920c1215052468ae3667e4c7ec5da8bdaa0/weights/group1-shard1of4 -------------------------------------------------------------------------------- /weights/group1-shard2of4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hugozanini/realtime-semantic-segmentation/55c5e920c1215052468ae3667e4c7ec5da8bdaa0/weights/group1-shard2of4 -------------------------------------------------------------------------------- /weights/group1-shard3of4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hugozanini/realtime-semantic-segmentation/55c5e920c1215052468ae3667e4c7ec5da8bdaa0/weights/group1-shard3of4 -------------------------------------------------------------------------------- /weights/group1-shard4of4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hugozanini/realtime-semantic-segmentation/55c5e920c1215052468ae3667e4c7ec5da8bdaa0/weights/group1-shard4of4 -------------------------------------------------------------------------------- /weights/model.json: -------------------------------------------------------------------------------- 1 | {"modelTopology": {"keras_version": "2.2.4", "backend": "tensorflow", "model_config": {"class_name": "Model", "config": {"name": "model_1", "layers": [{"name": "input0", "class_name": "InputLayer", "config": {"batch_input_shape": [null, 3, null, null], "dtype": "float32", "sparse": false, "name": "input0"}, "inbound_nodes": []}, {"name": "CoihONsZ_pad", "class_name": "ZeroPadding2D", "config": {"name": "CoihONsZ_pad", "trainable": true, "padding": [[1, 1], [1, 1]], "data_format": "channels_first"}, "inbound_nodes": [[["input0", 0, 0, {}]]]}, {"name": "CoihONsZ", "class_name": "Conv2D", "config": {"name": "CoihONsZ", "trainable": true, "filters": 32, "kernel_size": [3, 3], "strides": [2, 2], "padding": "valid", "data_format": "channels_first", "dilation_rate": [1, 1], "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "Zeros", "config": {}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["CoihONsZ_pad", 0, 0, {}]]]}, {"name": "BNsTVmnq", "class_name": "BatchNormalization", "config": {"name": "BNsTVmnq", "trainable": true, "axis": 1, "momentum": 1.0, "epsilon": 1e-05, "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": [[["CoihONsZ", 0, 0, {}]]]}, {"name": "re_lu_1", "class_name": "ReLU", "config": {"name": "re_lu_1", "trainable": true, "max_value": 6.0, "negative_slope": 0.0, "threshold": 0.0}, "inbound_nodes": [[["BNsTVmnq", 0, 0, {}]]]}, {"name": "CRKUnsfy", "class_name": "Conv2D", "config": {"name": "CRKUnsfy", "trainable": true, "filters": 32, "kernel_size": [1, 1], "strides": [1, 1], "padding": "valid", "data_format": "channels_first", "dilation_rate": [1, 1], "activation": "linear", "use_bias": false, "kernel_initializer": {"class_name": "Zeros", "config": {}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["re_lu_1", 0, 0, {}]]]}, {"name": "BNzcAhKx", "class_name": "BatchNormalization", "config": {"name": "BNzcAhKx", "trainable": true, "axis": 1, "momentum": 1.0, "epsilon": 1e-05, "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, 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