├── .gitignore ├── .travis.yml ├── CONTRIBUTING.md ├── LICENSE ├── MAINTAINERS.md ├── README.md ├── doc └── source │ └── images │ ├── architecture.png │ ├── flow-browser.png │ └── flow-pi.png ├── flows ├── browser-flow.json └── raspberrypi-flows.json ├── node-red-contrib-tfjs-object-detection ├── README.md ├── node │ ├── icons │ │ └── tf-logo.png │ ├── tfjs-object-detection.html │ └── tfjs-object-detection.js ├── package-lock.json ├── package.json └── test │ └── tfjs-object-detection_spec.js ├── package-lock.json └── package.json /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | .DS_Store 3 | .vscode 4 | node_modules/ 5 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | language: node_js 2 | node_js: 10 3 | cache: 4 | directories: 5 | - node_modules 6 | script: cd node-red-contrib-tfjs-object-detection && npm install && npm test 7 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing 2 | 3 | This is an open source project, and we appreciate your help! 4 | 5 | We use the GitHub issue tracker to discuss new features and non-trivial bugs. 6 | 7 | In addition to the issue tracker, [#journeys on 8 | Slack](https://dwopen.slack.com) is the best way to get into contact with the 9 | project's maintainers. 10 | 11 | To contribute code, documentation, or tests, please submit a pull request to 12 | the GitHub repository. Generally, we expect two maintainers to review your pull 13 | request before it is approved for merging. For more details, see the 14 | [MAINTAINERS](MAINTAINERS.md) page. 15 | 16 | Contributions are subject to the [Developer Certificate of Origin, Version 1.1](https://developercertificate.org/) and the [Apache License, Version 2](https://www.apache.org/licenses/LICENSE-2.0.txt). 17 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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While all work can therefore be focused on the master branch, the 11 | quality of this branch should never be compromised. 12 | 13 | The remainder of this document details how to merge pull requests to the 14 | repositories. 15 | 16 | ## Merge approval 17 | 18 | The project maintainers use LGTM (Looks Good To Me) in comments on the pull 19 | request to indicate acceptance prior to merging. A change requires LGTMs from 20 | two project maintainers. If the code is written by a maintainer, the change 21 | only requires one additional LGTM. 22 | 23 | ## Reviewing Pull Requests 24 | 25 | We recommend reviewing pull requests directly within GitHub. This allows a 26 | public commentary on changes, providing transparency for all users. When 27 | providing feedback be civil, courteous, and kind. Disagreement is fine, so long 28 | as the discourse is carried out politely. If we see a record of uncivil or 29 | abusive comments, we will revoke your commit privileges and invite you to leave 30 | the project. 31 | 32 | During your review, consider the following points: 33 | 34 | ### Does the change have positive impact? 35 | 36 | Some proposed changes may not represent a positive impact to the project. Ask 37 | whether or not the change will make understanding the code easier, or if it 38 | could simply be a personal preference on the part of the author (see 39 | [bikeshedding](https://en.wiktionary.org/wiki/bikeshedding)). 40 | 41 | Pull requests that do not have a clear positive impact should be closed without 42 | merging. 43 | 44 | ### Do the changes make sense? 45 | 46 | If you do not understand what the changes are or what they accomplish, ask the 47 | author for clarification. Ask the author to add comments and/or clarify test 48 | case names to make the intentions clear. 49 | 50 | At times, such clarification will reveal that the author may not be using the 51 | code correctly, or is unaware of features that accommodate their needs. If you 52 | feel this is the case, work up a code sample that would address the pull 53 | request for them, and feel free to close the pull request once they confirm. 54 | 55 | ### Does the change introduce a new feature? 56 | 57 | For any given pull request, ask yourself "is this a new feature?" If so, does 58 | the pull request (or associated issue) contain narrative indicating the need 59 | for the feature? If not, ask them to provide that information. 60 | 61 | Are new unit tests in place that test all new behaviors introduced? If not, do 62 | not merge the feature until they are! Is documentation in place for the new 63 | feature? (See the documentation guidelines). If not do not merge the feature 64 | until it is! Is the feature necessary for general use cases? Try and keep the 65 | scope of any given component narrow. If a proposed feature does not fit that 66 | scope, recommend to the user that they maintain the feature on their own, and 67 | close the request. You may also recommend that they see if the feature gains 68 | traction among other users, and suggest they re-submit when they can show such 69 | support. 70 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![Build Status](https://travis-ci.org/IBM/node-red-tensorflowjs.svg?branch=master)](https://travis-ci.org/IBM/node-red-tensorflowjs) 2 | 3 | # Developing a Machine Learning IoT App with Node-RED and TensorFlow.js 4 | 5 | In most cases, enabling your IoT device with AI capabilities involves sending the data from the device to a server. The machine learning calculations would happen on the server. Then the results sent back to the device for appropriate action. 6 | 7 | When data security or network connectivity is a concern this is not an ideal or feasible approach. 8 | 9 | With this code pattern, you will learn how to build and deploy machine learning apps that can run offline and directly on the device (in this case a Raspberry Pi). Using Node-RED with TensorFlow.js you can incorporate machine learning into your device is an easy, low-code way. 10 | 11 | [Node-RED](https://nodered.org) is an open source visual programming tool that offers a browser-based flow editor for wiring together devices, APIs, and services. Built on Node.js, you can extend its features by creating your own nodes or taking advantage of the JavaScript and NPM ecosystem. 12 | 13 | [TensorFlow.js](https://js.tensorflow.org) is an open source JavaScript library to build, train, and run machine learning models in JavaScript environments such as the browser and Node.js. 14 | 15 | Combining Node-RED with TensorFlow.js developers and IoT enthusiasts can more easily add machine learning functionality onto their devices. 16 | 17 | When you have completed this code pattern, you will understand how to: 18 | 19 | * Create a Node-RED node that includes a TensorFlow.js model 20 | * Build and deploy a Node-RED application that uses a TensorFlow.js node 21 | 22 | 23 | ![architecture](doc/source/images/architecture.png) 24 | 25 | 26 | ## Flow 27 | 28 | 1. The user owns or downloads a machine learning model in TensorFlow.js format. 29 | 1. The user creates a Node-RED node for the TensorFlow.js model and wires the TensorFlow.js node in a Node-RED application. 30 | 1. The user can deploy the Node-RED application locally. 31 | 1. The user can access the Node-RED application from a browser and can trigger inferencing on images captured from a webcam. 32 | 1. Alternatively, the user can deploy the Node-RED application to a Raspberry Pi. 33 | 1. The device runs the Node-RED application and performs inferencing on images from a camera. 34 | 1. The device can output to a connected speaker or take some other action depending on the inference results. 35 | 36 | 37 | ## Included Components 38 | 39 | - [Node-RED](https://nodered.org): A flow-based programming tool for wiring together hardware devices, APIs, and online services 40 | - [TensorFlow.js](https://js.tensorflow.org): A JavaScript library for training and deploying ML models in the browser and on Node.js 41 | 42 | 43 | ## Featured Technologies 44 | 45 | - Machine Learning 46 | - IoT 47 | - JavaScript / Node.js 48 | 49 | 50 | ## Watch the Video 51 | 52 | [![](https://img.youtube.com/vi/6sFrQaDtK5Q/0.jpg)](https://www.youtube.com/watch?v=6sFrQaDtK5Q) 53 | 54 | ## Prerequisites 55 | 56 | - [git](https://git-scm.com/downloads) installed and configured 57 | - [Node.js](https://nodejs.org/en/) installation (with NPM) 58 | - [Python 2.7](https://www.python.org/download/releases/2.7/) environment 59 | - Familiarity with basic [Node-RED](https://nodered.org/docs/) concepts 60 | 61 | ## Steps 62 | 63 | Follow these steps to setup and run this code pattern. The steps are described in detail below. 64 | 65 | 1. [Clone the repo](#1-clone-the-repo) 66 | 1. [Install dependencies](#2-install-dependencies) 67 | 1. [Import the Node-RED flow](#3-import-the-node-red-flow) 68 | 1. [Deploy the Node-RED flow](#4-deploy-the-node-red-flow) 69 | 70 | ### 1. Clone the repo 71 | 72 | First let's get the code. From the terminal of the system you plan on running Node-RED from, 73 | do the following: 74 | 75 | 1. Clone the `node-red-tensorflowjs` repo: 76 | ``` 77 | $ git clone https://github.com/IBM/node-red-tensorflowjs 78 | ``` 79 | 80 | 1. Move into the directory of the cloned repo: 81 | ``` 82 | $ cd node-red-tensorflowjs 83 | ``` 84 | 85 | **Note**: For Raspberry Pi users, details on accessing the command line can be found in the 86 | [remote access documentation](https://www.raspberrypi.org/documentation/remote-access/) if not 87 | connecting with a screen and keyboard. 88 | 89 | ### 2. Install dependencies 90 | 91 | You can install the necessary dependencies by running: 92 | 93 | ``` 94 | $ npm install 95 | ``` 96 | 97 | This will install Node-RED along with any necessary custom node packages for running the browser flow 98 | in the local `node_modules` folder, and you can move on to [starting Node-RED](#start-node-red). 99 | 100 | *Alternatively*, if you already have Node-RED installed on your system, you can just install 101 | the dependencies from your Node-RED user directory (`~/.node-red`). Run the following block of code, being 102 | sure to change the `` placeholder to the path of the cloned repo: 103 | 104 | ``` 105 | cd ~/.node-red 106 | npm install /node-red-contrib-tfjs-object-detection 107 | npm install node-red-contrib-browser-utils node-red-contrib-play-audio node-red-contrib-image-output 108 | ``` 109 | 110 | Be sure to restart Node-RED if it was already running when installing this way. 111 | 112 | **Note**: If you are using a Raspberry Pi, instructions for installing Node-RED can be found 113 | [here](https://nodered.org/docs/getting-started/raspberrypi). However, if you are using the Raspbian 114 | operating system for the Raspberry Pi, Node-RED comes pre-installed, so you can just install 115 | dependencies from the `~/.node-red` directory. 116 | 117 | #### Start Node-RED 118 | 119 | Node-RED can be started from a terminal by running this command from within the directory of the cloned repository: 120 | ``` 121 | $ npm start 122 | ``` 123 | 124 | Alternatively, if you have Node-RED installed globally with dependencies installed under `~./node-red`, 125 | you can start Node-RED from any directory: 126 | ``` 127 | $ node-red 128 | ``` 129 | 130 | #### Stop Node-RED 131 | 132 | You can stop Node-RED by closing the terminal window or using `Ctrl-C` in the terminal. 133 | 134 | #### Node-RED editor 135 | 136 | The Node-RED editor can be accessed from `http://localhost:1880`. 137 | 138 | However, if Node-RED is on the Raspberry Pi, you can connect to it via `http://:1880`. 139 | 140 | ### 3. Import the Node-RED flow 141 | 142 | Once installed the node can be added and used in the flow of your Node-RED application. To import the flows available in this repo: 143 | 144 | 1. Make sure Node-RED is running 145 | 1. Open a browser and go to your Node-RED Editor 146 | 1. Click on the Node-RED Menu 147 | 1. Click on **Import** 148 | 1. Select the **Clipboard** tab 149 | 1. Click on **select a file to import** 150 | 1. Browse to and select one of the flow files in the cloned repo 151 | - If trying things out locally on your browser, then use the `browser-flow.json`. 152 | - If using a Raspberry Pi with peripherals, then use the `raspberrypi-flows.json`. 153 | 1. Select **Import to new flow** 154 | 1. Click **Import** 155 | 156 | ### 4. Deploy the Node-RED flow 157 | 158 | The Node-RED flow can be deployed in multiple ways. Follow the option that best fits your use case: 159 | 160 | - [Running on a Raspberry Pi](#running-on-a-raspberry-pi) 161 | - [Running on a laptop or workstation](#running-on-a-laptop-or-workstation) 162 | 163 | #### Running on a Raspberry Pi 164 | 165 | The Raspberry Pi flows use hardware peripherals and Raspberry Pi specific nodes. This assumes you imported 166 | the `raspberrypi-flows.json` file. 167 | 168 | ##### Pre-requisites 169 | 170 | The following hardware components are needed to fully run this flow: 171 | 172 | - Raspberry Pi (tested with version 4, but older versions should work) 173 | - Camera peripheral (Pi camera module or USB camera) 174 | - Speaker peripheral (USB or 3.5mm jack speaker) 175 | - [Optional] GPIO motion sensor (e.g. HC-SR501 PIR Motion Sensor) 176 | 177 | Additionally, a few custom nodes are needed and can be added through the 178 | [Palette Manager](https://nodered.org/docs/user-guide/runtime/adding-nodes): 179 | 180 | - [`node-red-contrib-image-output`](https://flows.nodered.org/node/node-red-contrib-image-output) 181 | - [`node-red-contrib-camerapi`](https://flows.nodered.org/node/node-red-contrib-camerapi) if using a Raspberry Pi 182 | [Camera Module](https://www.raspberrypi.org/products/camera-module-v2/) 183 | - [`node-red-contrib-usbcamera`](https://flows.nodered.org/node/node-red-contrib-usbcamera) if using a USB camera. 184 | - If using this node, you must also install `fswebcam` on the Raspberry Pi by running `sudo apt install fswebcam`. 185 | 186 | 187 | ##### Deploy and run on a Raspberry Pi 188 | 189 | The imported flows file contains two flows: 190 | 191 | - Basic Raspberry Pi Flow 192 | - A flow where a user can manually trigger the camera attached to the Pi (in our case, a USB camera) to take 193 | a snapshot. The snapshot is sent to the `tfjs object detection` node where objects will be detected. 194 | A function node will use simple JavaScript to check if any of the detected classes is a class of interest 195 | (in this case, a `person`). If so, a `.wav` audio file located on the Pi is played through the connected speaker. 196 | - Motion Sensor Flow 197 | - Same as the basic flow above, but with a node to handle input from the GPIO motion sensor. If the sensor 198 | detects motion, the output will be 1, and this will trigger the camera to take a snapshot and execute the 199 | rest of the flow. 200 | 201 | Make sure all your hardware is connected, then: 202 | 203 | 1. Double click on `Play Audio File` exec node and change the path in the append section to the path of 204 | a `.wav` file of your choosing. Click `Done` when finished. 205 | 1. Click the **Deploy** button. 206 | 1. Trigger the camera. 207 | a. Can manually trigger the snapshot by clicking the `Take Photo` inject node. 208 | b. If using the motion sensor flow, motion near the sensor will trigger the camera. 209 | 210 | **Note**: Feel free to change the detected object by editing the code in the `isObjectDetected` node. 211 | 212 | #### Running on a laptop or workstation 213 | 214 | From the Node-RED editor, do the following: 215 | 216 | 1. Click the **Deploy** button. 217 | 1. Upload or capture an image. 218 | a. Click the `file inject` node and browse to an image. 219 | b. Click the `camera` node and allow the browser to access the webcam. 220 | 221 | The image will be processed by the `tfjs object detection` node and the output will be displayed in the **Debug** panel. 222 | If the browser supports the Web Audio API, the objects detected will be spoken. 223 | 224 | ## Output 225 | 226 | #### Raspberry Pi flow 227 | 228 | ![raspberry pi flow](doc/source/images/flow-pi.png) 229 | 230 | #### Browser flow 231 | 232 | ![browser flow](doc/source/images/flow-browser.png) 233 | 234 | 235 | ## Links 236 | 237 | - [Node-RED](https://nodered.org) 238 | - [TensorFlow.js](https://js.tensorflow.org) 239 | - Bring Machine Learning to the Browser With TensorFlow.js - [Part I](https://medium.com/ibm-watson-data-lab/bring-machine-learning-to-the-browser-with-tensorflow-js-part-i-16924457291c), [Part II](https://medium.com/ibm-watson-data-lab/bring-machine-learning-to-the-browser-with-tensorflow-js-part-ii-7555ed9a999e), [Part III](https://medium.com/ibm-watson-data-lab/bring-machine-learning-to-the-browser-with-tensorflow-js-part-iii-62d2b09b10a3) 240 | - [Optimize your visual recognition classification](https://developer.ibm.com/components/node-red/patterns/optimize-visual-recognition-classification) 241 | - [TensorFlow.js and Node-RED: The Low-Code Approach to ML Apps for IoT](https://ibm.biz/tfjs-nodered) 242 | - [Creating Custom Node-RED Nodes for your API: The Easy Way](https://medium.com/codait/creating-custom-node-red-nodes-for-your-api-the-easy-way-10770ccd8923) 243 | - [Node-RED Nodes for Machine Learning with TensorFlow.js](https://github.com/vabarbosa/tfjs-node-red) 244 | - [Node-RED modules for TensorFlow.js](https://github.com/tonanhngo/nodered-tfjs) 245 | - [Node-Red custom node for DeepSpeech2](https://github.com/yhwang/node-red-contrib-ds2-tfjs) 246 | - [Pretrained models for TensorFlow.js](https://github.com/tensorflow/tfjs-models) 247 | 248 | 249 | ## License 250 | 251 | This code pattern is licensed under the Apache License, Version 2. Separate third-party code objects invoked within this code pattern are licensed by their respective providers pursuant to their own separate licenses. Contributions are subject to the [Developer Certificate of Origin, Version 1.1](https://developercertificate.org/) and the [Apache License, Version 2](https://www.apache.org/licenses/LICENSE-2.0.txt). 252 | 253 | [Apache License FAQ](https://www.apache.org/foundation/license-faq.html#WhatDoesItMEAN) 254 | -------------------------------------------------------------------------------- /doc/source/images/architecture.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IBM/node-red-tensorflowjs/6498c378731a76a27d383af1011359493a795902/doc/source/images/architecture.png -------------------------------------------------------------------------------- /doc/source/images/flow-browser.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IBM/node-red-tensorflowjs/6498c378731a76a27d383af1011359493a795902/doc/source/images/flow-browser.png -------------------------------------------------------------------------------- /doc/source/images/flow-pi.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IBM/node-red-tensorflowjs/6498c378731a76a27d383af1011359493a795902/doc/source/images/flow-pi.png -------------------------------------------------------------------------------- /flows/browser-flow.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "id": "ccb1e78f.c9f478", 4 | "type": "tab", 5 | "label": "Browser Flow", 6 | "disabled": false, 7 | "info": "" 8 | }, 9 | { 10 | "id": "3aaffa94.740bee", 11 | "type": "ui_base", 12 | "theme": { 13 | "name": "theme-light", 14 | "lightTheme": { 15 | "default": "#0094CE", 16 | "baseColor": "#0094CE", 17 | "baseFont": "-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Oxygen-Sans,Ubuntu,Cantarell,Helvetica Neue,sans-serif", 18 | "edited": true, 19 | "reset": false 20 | }, 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personDetected = true;\n break;\n }\n}\nmsg.payload = personDetected;\nconsole.log(msg.payload);\nreturn msg;", 42 | "outputs": 1, 43 | "noerr": 0, 44 | "x": 730, 45 | "y": 260, 46 | "wires": [ 47 | [ 48 | "11a38d00.749e53" 49 | ] 50 | ] 51 | }, 52 | { 53 | "id": "14ad11f2.fd0f4e", 54 | "type": "debug", 55 | "z": "8bfc607d.72ebf", 56 | "name": "", 57 | "active": true, 58 | "tosidebar": true, 59 | "console": false, 60 | "tostatus": false, 61 | "complete": "false", 62 | "x": 710, 63 | "y": 200, 64 | "wires": [] 65 | }, 66 | { 67 | "id": "eb247e31.ca30d", 68 | "type": "tfjs-object-detection", 69 | "z": "8bfc607d.72ebf", 70 | "name": "", 71 | "modelUrl": "", 72 | "x": 500, 73 | "y": 200, 74 | "wires": [ 75 | [ 76 | "14ad11f2.fd0f4e", 77 | "abed2cc2.ed753" 78 | ] 79 | ] 80 | }, 81 | { 82 | "id": "6cf9c0e8.e68eb", 83 | "type": "usbcamera", 84 | "z": "8bfc607d.72ebf", 85 | "filemode": "0", 86 | "filename": "image01.jpg", 87 | "filedefpath": "1", 88 | "filepath": "", 89 | "fileformat": "jpeg", 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146 | [] 147 | ] 148 | }, 149 | { 150 | "id": "873a11dd.3269a", 151 | "type": "debug", 152 | "z": "8bfc607d.72ebf", 153 | "name": "Object not detected", 154 | "active": true, 155 | "tosidebar": true, 156 | "console": false, 157 | "tostatus": false, 158 | "complete": "payload", 159 | "targetType": "msg", 160 | "x": 1140, 161 | "y": 320, 162 | "wires": [] 163 | }, 164 | { 165 | "id": "f5384a80.731668", 166 | "type": "image", 167 | "z": "8bfc607d.72ebf", 168 | "name": "", 169 | "data":"payload", 170 | "dataType":"msg", 171 | "width": "240", 172 | "x": 480, 173 | "y": 420, 174 | "wires": [] 175 | }, 176 | { 177 | "id": "74d44f63.e45a1", 178 | "type": "inject", 179 | "z": "b85e6de8.76be6", 180 | "name": "Take Photo", 181 | "topic": "", 182 | "payload": "", 183 | "payloadType": "date", 184 | "repeat": "", 185 | "crontab": "", 186 | "once": false, 187 | "onceDelay": 0.1, 188 | "x": 100, 189 | "y": 220, 190 | "wires": [ 191 | [ 192 | "550b1151.09f07" 193 | ] 194 | ] 195 | }, 196 | { 197 | "id": "709272f3.514a5c", 198 | "type": "function", 199 | "z": "b85e6de8.76be6", 200 | "name": "isObjectDetected", 201 | "func": "let personDetected = false;\n\nfor (let object of msg.payload) {\n if (object.class === 'person') {\n personDetected = true;\n break;\n }\n}\nmsg.payload = personDetected;\nreturn msg;", 202 | "outputs": 1, 203 | "noerr": 0, 204 | "x": 730, 205 | "y": 140, 206 | "wires": [ 207 | [ 208 | "cd4073c8.29c91" 209 | ] 210 | ] 211 | }, 212 | { 213 | "id": "8694e306.1f247", 214 | "type": "debug", 215 | "z": "b85e6de8.76be6", 216 | "name": "", 217 | "active": true, 218 | "tosidebar": true, 219 | "console": false, 220 | "tostatus": false, 221 | "complete": "false", 222 | "x": 710, 223 | "y": 80, 224 | "wires": [] 225 | }, 226 | { 227 | "id": "7bb0f11b.f086a", 228 | "type": "tfjs-object-detection", 229 | "z": "b85e6de8.76be6", 230 | "name": "", 231 | "modelUrl": "", 232 | "x": 500, 233 | "y": 80, 234 | "wires": [ 235 | [ 236 | "8694e306.1f247", 237 | "709272f3.514a5c" 238 | ] 239 | ] 240 | }, 241 | { 242 | "id": "550b1151.09f07", 243 | "type": "usbcamera", 244 | "z": "b85e6de8.76be6", 245 | "filemode": "0", 246 | "filename": "image01.jpg", 247 | "filedefpath": "1", 248 | "filepath": "", 249 | "fileformat": "jpeg", 250 | "resolution": "2", 251 | "name": "", 252 | "x": 290, 253 | "y": 220, 254 | "wires": [ 255 | [ 256 | "7bb0f11b.f086a", 257 | "f14b1d95.7ce61" 258 | ] 259 | ] 260 | }, 261 | { 262 | "id": "cd4073c8.29c91", 263 | "type": "switch", 264 | "z": "b85e6de8.76be6", 265 | "name": "", 266 | "property": "payload", 267 | "propertyType": "msg", 268 | "rules": [ 269 | { 270 | "t": "true" 271 | }, 272 | { 273 | "t": "else" 274 | } 275 | ], 276 | "checkall": "true", 277 | "repair": false, 278 | "outputs": 2, 279 | "x": 910, 280 | "y": 140, 281 | "wires": [ 282 | [ 283 | "4a4e39a4.bfe008" 284 | ], 285 | [ 286 | "dbbde0b9.8c14b" 287 | ] 288 | ] 289 | }, 290 | { 291 | "id": "4a4e39a4.bfe008", 292 | "type": "exec", 293 | "z": "b85e6de8.76be6", 294 | "command": "aplay", 295 | "addpay": false, 296 | "append": "/home/pi/Downloads/get.wav", 297 | "useSpawn": "false", 298 | "timer": "", 299 | "oldrc": false, 300 | "name": "Play Audio File", 301 | "x": 1120, 302 | "y": 80, 303 | "wires": [ 304 | [], 305 | [], 306 | [] 307 | ] 308 | }, 309 | { 310 | "id": "dbbde0b9.8c14b", 311 | "type": "debug", 312 | "z": "b85e6de8.76be6", 313 | "name": "Object not detected", 314 | "active": true, 315 | "tosidebar": true, 316 | "console": false, 317 | "tostatus": false, 318 | "complete": "payload", 319 | "targetType": "msg", 320 | "x": 1140, 321 | "y": 200, 322 | "wires": [] 323 | }, 324 | { 325 | "id": "f39e73e.85b3e9", 326 | "type": "rpi-gpio in", 327 | "z": "b85e6de8.76be6", 328 | "name": "", 329 | "pin": "8", 330 | "intype": "tri", 331 | "debounce": "25", 332 | "read": false, 333 | "x": 70, 334 | "y": 420, 335 | "wires": [ 336 | [ 337 | "34c5ffc1.4e5e" 338 | ] 339 | ] 340 | }, 341 | { 342 | "id": "34c5ffc1.4e5e", 343 | "type": "switch", 344 | "z": "b85e6de8.76be6", 345 | "name": "", 346 | "property": "payload", 347 | "propertyType": "msg", 348 | "rules": [ 349 | { 350 | "t": "eq", 351 | "v": "1", 352 | "vt": "str" 353 | } 354 | ], 355 | "checkall": "true", 356 | "repair": false, 357 | "outputs": 1, 358 | "x": 210, 359 | "y": 420, 360 | "wires": [ 361 | [ 362 | "550b1151.09f07" 363 | ] 364 | ] 365 | }, 366 | { 367 | "id": "f14b1d95.7ce61", 368 | "type": "image", 369 | "z": "b85e6de8.76be6", 370 | "name": "", 371 | "width": "240", 372 | "x": 540, 373 | "y": 220, 374 | "wires": [] 375 | }, 376 | { 377 | "id": "5bb7b15b.a79f2", 378 | "type": "comment", 379 | "z": "b85e6de8.76be6", 380 | "name": "Trigger camera if motion is detected.", 381 | "info": "If the motion sensor attached to the GPIO pins of the Raspberry Pi detects motion, the output will be 1. The switch node will trigger the camera if its input is 1.", 382 | "x": 160, 383 | "y": 480, 384 | "wires": [] 385 | }, 386 | { 387 | "id": "597b6f4c.51721", 388 | "type": "comment", 389 | "z": "8bfc607d.72ebf", 390 | "name": "Check detected classes for specific object.", 391 | "info": "", 392 | "x": 800, 393 | "y": 320, 394 | "wires": [] 395 | } 396 | ] 397 | -------------------------------------------------------------------------------- /node-red-contrib-tfjs-object-detection/README.md: -------------------------------------------------------------------------------- 1 | # node-red-contrib-tfjs-object-detection 2 | 3 | Node-RED node for a TensorFlow.js Object Detection model 4 | -------------------------------------------------------------------------------- /node-red-contrib-tfjs-object-detection/node/icons/tf-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IBM/node-red-tensorflowjs/6498c378731a76a27d383af1011359493a795902/node-red-contrib-tfjs-object-detection/node/icons/tf-logo.png -------------------------------------------------------------------------------- /node-red-contrib-tfjs-object-detection/node/tfjs-object-detection.html: -------------------------------------------------------------------------------- 1 | 16 | 17 | 20 | 21 | 38 | -------------------------------------------------------------------------------- /node-red-contrib-tfjs-object-detection/node/tfjs-object-detection.js: -------------------------------------------------------------------------------- 1 | // called when the runtime loads the node on startup 2 | module.exports = function (RED) { 3 | let tf = null 4 | 5 | // Initialize the TensorFlow.js library and store it in the Global 6 | // context to make sure we are running only one instance 7 | const initTfjs = function (node) { 8 | const globalContext = node.context().global 9 | if (!tf) { 10 | tf = globalContext.get('tfjs') 11 | } 12 | if (!tf) { 13 | tf = require('@tensorflow/tfjs-node') 14 | globalContext.set('tfjs', tf) 15 | node.log(`Loaded TensorFlow.js v${tf.version.tfjs}`) 16 | } 17 | } 18 | 19 | // called whenever a new instance of the node is created 20 | // the 'config' argument contains node specific properties set in the editor 21 | const TFJSObjectDetection = function (config) { 22 | initTfjs(this) 23 | const fs = require('fs') 24 | const cocoSsd = require('@tensorflow-models/coco-ssd') 25 | 26 | RED.nodes.createNode(this, config) 27 | this.modelUrl = config.modelUrl 28 | const node = this 29 | 30 | node.status({ 31 | fill: 'yellow', 32 | shape: 'dot', 33 | text: 'Loading model...' 34 | }) 35 | 36 | // load the TensorFlow.js COCO-SSD model 37 | cocoSsd.load({ modelUrl: node.modelUrl }).then(model => { 38 | node.model = model 39 | node.status({ 40 | fill: 'green', 41 | shape: 'dot', 42 | text: 'Model is ready' 43 | }) 44 | node.log('Coco SSD Model Loaded.') 45 | }) 46 | 47 | // run inference against the input image and return the prediction 48 | const runPrediction = async function (img, msg) { 49 | const inputTensor = tf.node.decodeImage(img, 3) 50 | msg.payload = await node.model.detect(inputTensor) 51 | msg.shape = inputTensor.shape 52 | inputTensor.dispose() 53 | msg.classes = {} 54 | for (let i = 0; i < msg.payload.length; i++) { 55 | msg.classes[msg.payload[i].class] = (msg.classes[msg.payload[i].class] || 0) + 1 56 | } 57 | node.send(msg) 58 | } 59 | 60 | // register a listener to the 'input' event 61 | // which gets called whenever a message arrives at the node 62 | node.on('input', function (msg) { 63 | try { 64 | node.status({ 65 | fill: 'yellow', 66 | shape: 'dot', 67 | text: 'running inference...' 68 | }) 69 | 70 | const p = (typeof msg.payload === 'string') ? fs.readFileSync(msg.payload) : msg.payload 71 | runPrediction(p, msg) 72 | node.status({}) 73 | } catch (error) { 74 | node.status({ 75 | fill: 'red', 76 | shape: 'dot', 77 | text: '' 78 | }) 79 | node.error(error, msg) 80 | } 81 | }) 82 | 83 | node.on('close', function () { 84 | }) 85 | } 86 | 87 | // register the TensorFlow.js Object Detection node 88 | RED.nodes.registerType('tfjs-object-detection', TFJSObjectDetection) 89 | } 90 | -------------------------------------------------------------------------------- /node-red-contrib-tfjs-object-detection/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "node-red-contrib-tfjs-object-detection", 3 | "version": "0.0.2", 4 | "description": "TensorFlow.js Object Detection model Node-RED node", 5 | "dependencies": { 6 | "@tensorflow/tfjs-node": "^2.0.1", 7 | "@tensorflow-models/coco-ssd": "^2.1.0" 8 | }, 9 | "repository": { 10 | "type": "git", 11 | "url": "https://github.com/IBM/node-red-tensorflowjs" 12 | }, 13 | "license": "Apache-2.0", 14 | "keywords": [ 15 | "tensorflow.js", 16 | "tensorflowjs", 17 | "tensorflow", 18 | "tf.js", 19 | "tfjs", 20 | "node-red", 21 | "nodered", 22 | "machine learning" 23 | ], 24 | "node-red": { 25 | "nodes": { 26 | "tfjs-object-detection": "node/tfjs-object-detection.js" 27 | } 28 | }, 29 | "contributors": [ 30 | "Paul Van Eck (https://github.com/pvaneck)", 31 | "Ted Chang (https://github.com/tedhtchang)", 32 | "Ton Ngo (https://github.com/tonanhngo)", 33 | "va barbosa (https://github.com/vabarbosa/)", 34 | "Yi-Hong Wang (https://github.com/yhwang)" 35 | ], 36 | "devDependencies": { 37 | "mocha": "^8.0.1", 38 | "node-red": "^1.1.2", 39 | "node-red-node-test-helper": "^0.2.5", 40 | "standard": "^14.3.4" 41 | }, 42 | "scripts": { 43 | "test": "standard && mocha \"test/**/*_spec.js\"" 44 | } 45 | } 46 | -------------------------------------------------------------------------------- /node-red-contrib-tfjs-object-detection/test/tfjs-object-detection_spec.js: -------------------------------------------------------------------------------- 1 | /* global before, after, afterEach, describe, it */ 2 | 3 | const helper = require('node-red-node-test-helper') 4 | const objDetect = require('../node/tfjs-object-detection.js') 5 | 6 | describe('tfjs-object-detection Node', function () { 7 | before(function (done) { 8 | helper.startServer(done) 9 | }) 10 | 11 | after(function (done) { 12 | helper.stopServer(done) 13 | }) 14 | 15 | afterEach(function () { 16 | helper.unload() 17 | }) 18 | 19 | it('should be loaded', function (done) { 20 | const flow = [{ id: 'n1', type: 'tfjs-object-detection', name: 'tfjs object detection' }] 21 | 22 | helper.load(objDetect, flow, function () { 23 | const n1 = helper.getNode('n1') 24 | n1.should.have.property('name', 'tfjs object detection') 25 | done() 26 | }) 27 | }) 28 | }) 29 | -------------------------------------------------------------------------------- /package-lock.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "node-red-tensorflowjs", 3 | "version": "1.0.0", 4 | 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"version": "1.0.0", 4 | "description": "[![Build Status](https://travis-ci.org/IBM/node-red-tensorflowjs.svg?branch=master)](https://travis-ci.org/IBM/node-red-tensorflowjs)", 5 | "main": "index.js", 6 | "directories": { 7 | "doc": "doc" 8 | }, 9 | "dependencies": { 10 | "node-red": "^1.0.3", 11 | "node-red-contrib-browser-utils": "0.0.9", 12 | "node-red-contrib-image-output": "^0.5.2", 13 | "node-red-contrib-play-audio": "^2.4.0", 14 | "node-red-contrib-tfjs-object-detection": "file:node-red-contrib-tfjs-object-detection", 15 | "node-red-dashboard": "^2.19.3" 16 | }, 17 | "devDependencies": {}, 18 | "scripts": { 19 | "test": "echo \"Error: no test specified\" && exit 1", 20 | "start": "node-red" 21 | }, 22 | "repository": { 23 | "type": "git", 24 | "url": "git+https://github.com/IBM/node-red-tensorflowjs.git" 25 | }, 26 | "author": "", 27 | "license": "Apache-2.0", 28 | "bugs": { 29 | "url": "https://github.com/IBM/node-red-tensorflowjs/issues" 30 | }, 31 | "homepage": "https://github.com/IBM/node-red-tensorflowjs#readme" 32 | } 33 | --------------------------------------------------------------------------------