close

15 | {label} 16 | {!!confidence ? 17 |

21 | : null} 22 |

23 | ); 24 | } 25 | 26 | export default PredictionEntry; 27 | -------------------------------------------------------------------------------- /src/components/SquareButton.tsx: -------------------------------------------------------------------------------- 1 | import React, {FunctionComponent} from "react"; 2 | import BlurContainer from "./BlurContainer"; 3 | import "./SquareButton.css"; 4 | 5 | type SquareButtonProps = { 6 | onClick?: () => void, 7 | setHover?: (hovering: boolean) => void 8 | }; 9 | 10 | const SquareButton: FunctionComponent = ({ onClick, setHover, children }) => { 11 | // Square button that is inside the blur container 12 | return ( 13 |

onClick() : undefined} 15 | onMouseEnter={setHover ? () => setHover(true) : undefined} 16 | onMouseLeave={setHover ? () => setHover(false) : undefined} 17 | > 18 | 19 | { children } 20 | 21 |

22 | 23 | ); 24 | } 25 | 26 | export default SquareButton; 27 | -------------------------------------------------------------------------------- /src/model/worker.ts: -------------------------------------------------------------------------------- 1 | /* 2 | This file is our web worker to run the TensorFlow.js models asynchronously and not block the UI thread. 3 | */ 4 | import ImageClassificationModel from "./imageClassificationModel"; 5 | 6 | let model: ImageClassificationModel; 7 | 8 | export async function loadModel(signaturePath: string, modelPath: string) { 9 | // loads our exported Lobe model from the signature and model files 10 | disposeModel(); 11 | model = new ImageClassificationModel(signaturePath, modelPath); 12 | await model.load(); 13 | } 14 | 15 | export function disposeModel() { 16 | // frees up memory used by the model 17 | if (model) { 18 | model.dispose(); 19 | } 20 | } 21 | 22 | export async function predict(data: ImageData) { 23 | // run the input data through the model 24 | if (model) { 25 | return model.predict(data); 26 | } else { 27 | console.log('Predict called without model loaded.') 28 | } 29 | } 30 | -------------------------------------------------------------------------------- /src/components/camera/SourceSelectorItem.tsx: -------------------------------------------------------------------------------- 1 | import React from "react"; 2 | import check from "../../Icons/check.svg"; 3 | import noCheck from "../../Icons/no-check.svg"; 4 | 5 | import "./SourceSelectorItem.css"; 6 | 7 | 8 | type SourceSelectorItemProps = { 9 | name: string, 10 | selected: boolean, 11 | onSelect: () => void 12 | } 13 | 14 | // Component for selecting the webcam source and flipping the image horizontally 15 | function SourceSelectorItem({selected, onSelect, name}: SourceSelectorItemProps) { 16 | 17 | return ( 18 |

19 |

20 | {name} 21 |

22 |

onSelect()} className="source-radio-button"> 23 | {'Select

24 |

25 |

26 | ) 27 | } 28 | 29 | export default SourceSelectorItem; 30 | -------------------------------------------------------------------------------- /src/Icons/no-check.svg: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Microsoft 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 | -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "lobe-web-sample", 3 | "version": "0.2.0", 4 | "private": true, 5 | "dependencies": { 6 | "@tensorflow/tfjs": "^3.11.0", 7 | "@testing-library/jest-dom": "^5.15.0", 8 | "@testing-library/react": "^12.1.2", 9 | "@testing-library/user-event": "^13.5.0", 10 | "@types/jest": "^27.0.2", 11 | "@types/node": "^16.11.7", 12 | "@types/react": "^17.0.34", 13 | "@types/react-dom": "^17.0.11", 14 | "react": "^17.0.2", 15 | "react-dom": "^17.0.2", 16 | "react-scripts": "4.0.3", 17 | "react-webcam": "^6.0.0", 18 | "typescript": "^4.4.4", 19 | "web-vitals": "^2.1.2", 20 | "workerize-loader": "^1.3.0" 21 | }, 22 | "scripts": { 23 | "start": "react-scripts start", 24 | "build": "react-scripts build", 25 | "test": "react-scripts test", 26 | "eject": "react-scripts eject", 27 | "svgr": "svgr -d src/Icons/ assets/svg/" 28 | }, 29 | "eslintConfig": { 30 | "extends": [ 31 | "react-app", 32 | "react-app/jest" 33 | ] 34 | }, 35 | "browserslist": { 36 | "production": [ 37 | ">0.2%", 38 | "not dead", 39 | "not op_mini all" 40 | ], 41 | "development": [ 42 | "last 1 chrome version", 43 | "last 1 firefox version", 44 | "last 1 safari version" 45 | ] 46 | }, 47 | "devDependencies": {} 48 | } 49 | -------------------------------------------------------------------------------- /src/components/prediction/Prediction.tsx: -------------------------------------------------------------------------------- 1 | import React from "react"; 2 | import BlurContainer from "../BlurContainer"; 3 | import PredictionEntry from "./PredictionEntry"; 4 | import "./Prediction.css"; 5 | 6 | type PredictionProps = { 7 | predictions?: { [label: string]: number }, 8 | top?: number 9 | } 10 | 11 | function Prediction({predictions, top=3}: PredictionProps) { 12 | // display the top N (default 3) predictions returned from the model 13 | let sortedPredictions: Array<[string, number]> | undefined; 14 | if (!!predictions) { 15 | // sort our predictions by the confidence value and take the top N 16 | sortedPredictions = Object.entries(predictions) 17 | .sort((a, b) => b[1] - a[1]) 18 | .slice(0, top); 19 | } 20 | return ( 21 |

22 | 23 | {!!sortedPredictions ? 24 | sortedPredictions.map(([label, confidence], idx) => ( 25 | 26 | )) 27 | : 28 | } 29 | 30 |

31 | ); 32 | } 33 | 34 | export default Prediction; 35 | -------------------------------------------------------------------------------- /src/components/camera/SourceSelector.css: -------------------------------------------------------------------------------- 1 | #camera-select-button { 2 | position: absolute; 3 | z-index: 1; 4 | top: 20px; 5 | right: 20px; 6 | display: flex; 7 | flex-direction: column; 8 | align-items: flex-end; 9 | } 10 | 11 | #gear-icon { 12 | height: 24px; 13 | width: 24px; 14 | transition: transform 0.3s; 15 | } 16 | 17 | .gear-rotated { 18 | transform: rotate(90deg); 19 | } 20 | 21 | .source-selector { 22 | border-radius: 24px; 23 | transition: all 0.3s; 24 | transform-origin: top right; 25 | transform: scaleY(0) scaleX(0.1); 26 | opacity: 0; 27 | } 28 | 29 | .source-expanded { 30 | transform: scaleY(1) scaleX(1); 31 | opacity: 1; 32 | } 33 | 34 | .toggle-container { 35 | margin-left: -4px; 36 | margin-right: -4px; 37 | padding: 15px 12px 14px; 38 | border-radius: 14px; 39 | background-color: rgba(255,255,255,.1); 40 | } 41 | 42 | .toggle-item-container { 43 | display: flex; 44 | flex-direction: row; 45 | align-items: center; 46 | justify-content: space-between; 47 | margin-bottom: 18px; 48 | } 49 | .toggle-item-container:last-child { 50 | margin-bottom: 0; 51 | } 52 | 53 | .toggle-item { 54 | margin-left: 0!important; 55 | margin-right: 52px!important; 56 | } 57 | 58 | .toggle-radio-button { 59 | height: 24px; 60 | width: 24px; 61 | } -------------------------------------------------------------------------------- /src/Icons/check.svg: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /public/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 12 | 13 | 17 | 18 | 27 | Lobe Web 28 | 29 | 30 | 31 |

32 | 42 | 43 | 44 | -------------------------------------------------------------------------------- /src/components/staticImage/ImageSelectorButton.tsx: -------------------------------------------------------------------------------- 1 | import React, {useEffect, useRef} from "react"; 2 | import SquareButton from "../SquareButton"; 3 | import gallery from "../../Icons/gallery.svg"; 4 | 5 | import "./ImageSelectorButton.css"; 6 | 7 | type ImageSelectorButtonProps = { 8 | setImageFile: (image: File | null) => void; 9 | imageFile: File | null; 10 | }; 11 | 12 | // Component for selecting an image file for prediction 13 | function ImageSelectorButton({setImageFile, imageFile}: ImageSelectorButtonProps) { 14 | // ref to the hidden file input element 15 | const fileInput = useRef(null); 16 | 17 | // if we have a null file (from clearing the image), clear the file input value 18 | useEffect( 19 | () => { 20 | if (!imageFile && fileInput.current) { 21 | fileInput.current.value = ""; 22 | } 23 | }, 24 | [imageFile, fileInput] 25 | ) 26 | 27 | // make an onclick that will open the file dialog 28 | const onClick = () => { 29 | if (fileInput.current) { 30 | fileInput.current.click(); 31 | } 32 | } 33 | 34 | // set our image file from the picker 35 | const onChange = (e: React.ChangeEvent) => { 36 | const files = e.target.files; 37 | if (files && files.length > 0) { 38 | setImageFile(files[0]); 39 | } 40 | } 41 | 42 | return ( 43 |

47 | 48 |

49 | 50 | 51 |

52 | ) 53 | } 54 | 55 | export default ImageSelectorButton; 56 | -------------------------------------------------------------------------------- /src/components/staticImage/StaticImage.tsx: -------------------------------------------------------------------------------- 1 | import React, {useEffect, useRef} from "react"; 2 | import ImageCloseButton from "./ImageCloseButton"; 3 | 4 | import "./StaticImage.css"; 5 | 6 | type StaticImageProps = { 7 | predictCanvas: (canvas: HTMLCanvasElement) => void; 8 | image: File; 9 | setImageFile: (image: File | null) => void; 10 | } 11 | 12 | // Component for displaying our selected image file for prediction 13 | function StaticImage({ predictCanvas, image, setImageFile }: StaticImageProps) { 14 | // display our image file on a canvas and call the predict function with that canvas 15 | const canvas = useRef(null); 16 | const display = useRef(null); 17 | 18 | useEffect(() => { 19 | const reader = new FileReader(); 20 | reader.onload = (e) => { 21 | // make an image to draw on the canvas 22 | const img = new Image(); 23 | img.onload = () => { 24 | if (canvas.current) { 25 | // draw the image on our canvas 26 | canvas.current.width = img.width; 27 | canvas.current.height = img.height; 28 | const ctx = canvas.current.getContext("2d"); 29 | if (!!ctx) { 30 | // draw our image on the context 31 | ctx.drawImage(img,0,0); 32 | // drawing is finished, run the prediction! 33 | predictCanvas(canvas.current); 34 | } 35 | } 36 | } 37 | 38 | // load the image from our reader 39 | if (e.target) { 40 | img.src = e.target.result as string; 41 | if (display.current) { 42 | display.current.style.backgroundImage = `url(${e.target.result})`; 43 | } 44 | } 45 | } 46 | // read our image file and process it! 47 | reader.readAsDataURL(image); 48 | }, [image, predictCanvas, display]); 49 | 50 | return ( 51 |

52 | 53 |

55 | ) 56 | } 57 | 58 | export default StaticImage; 59 | -------------------------------------------------------------------------------- /src/Icons/gear.svg: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /src/components/App.tsx: -------------------------------------------------------------------------------- 1 | import React, {useCallback, useEffect, useState} from 'react'; 2 | import Camera from './camera/Camera'; 3 | import Prediction from './prediction/Prediction'; 4 | import ImageSelectorButton from './staticImage/ImageSelectorButton'; 5 | import StaticImage from './staticImage/StaticImage'; 6 | 7 | // @ts-ignore 8 | // eslint-disable-next-line import/no-webpack-loader-syntax 9 | import ModelWorker from "workerize-loader!../model/worker"; 10 | 11 | // create our web worker instance for running the tfjs model without blocking the UI thread 12 | const modelWorker = ModelWorker(); 13 | // the filepaths to our exported signature.json and model.json files (in the public/model folder) 14 | const signatureFile = process.env.PUBLIC_URL + `/model/signature.json`; 15 | const modelFile = process.env.PUBLIC_URL + `/model/model.json`; 16 | 17 | 18 | function App() { 19 | // state for keeping track of our predictions -- map of {label: confidence} from running the model on an image 20 | const [predictions, setPredictions] = useState<{[key: string]: number} | undefined>(undefined); 21 | // state for using a static image from file picker 22 | const [imageFile, setImageFile] = useState(null); 23 | 24 | // useEffect callback to load our model 25 | useEffect(() => { 26 | modelWorker.loadModel(signatureFile, modelFile); 27 | return () => { 28 | modelWorker.disposeModel(); 29 | }; 30 | }, []); 31 | 32 | // function to run the image from an html canvas element through our model 33 | const predictCanvas = useCallback((canvas: HTMLCanvasElement) => { 34 | // get the canvas context 35 | const ctx = canvas.getContext('2d'); 36 | if (ctx) { 37 | // get the pixel data from the full canvas 38 | const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height); 39 | // run the async predict function and set the values to our state 40 | modelWorker.predict(imageData).then((results: {Confidences: {[label: string]: number}}) => { 41 | if (results) { 42 | setPredictions(results.Confidences); 43 | } 44 | }); 45 | } 46 | }, []); 47 | 48 | return ( 49 |

50 | 51 | { 52 | !imageFile ? 53 | : 54 | 55 | } 56 | 57 |

58 | ); 59 | } 60 | 61 | export default App; 62 | -------------------------------------------------------------------------------- /src/components/camera/SourceSelector.tsx: -------------------------------------------------------------------------------- 1 | import React, {useState} from "react"; 2 | import SquareButton from "../SquareButton"; 3 | import BlurContainer from "../BlurContainer"; 4 | import SourceSelectorItem from "./SourceSelectorItem"; 5 | import gear from "../../Icons/gear.svg"; 6 | 7 | import "./SourceSelector.css"; 8 | import check from "../../Icons/check.svg"; 9 | import noCheck from "../../Icons/no-check.svg"; 10 | 11 | 12 | type SourceSelectorProps = { 13 | devices: MediaDeviceInfo[] 14 | deviceId?: string, 15 | setDeviceId: (deviceId: string) => void, 16 | imageFlip: boolean, 17 | setImageFlip: (imageFlip: boolean) => void, 18 | selectorVisible: boolean, 19 | setSelectorVisible: (visible: boolean) => void 20 | } 21 | 22 | // Component for selecting the webcam source and flipping the image horizontally 23 | function SourceSelector({devices, deviceId, setDeviceId, imageFlip, setImageFlip, selectorVisible, setSelectorVisible}: SourceSelectorProps) { 24 | const [hovering, setHover] = useState(false); 25 | 26 | return ( 27 |

e.stopPropagation()} 30 | onMouseLeave={() => {setSelectorVisible(false)}} 31 | > 32 | {setHover(hovering); if (hovering) setSelectorVisible(true);}}> 33 |

34 | 35 | 36 | {devices.map((device, key) => ( 37 | setDeviceId(device.deviceId)} 40 | selected={device.deviceId === deviceId} 41 | key={device.deviceId} 42 | /> 43 | ))} 44 |

45 |

46 |

47 | {"Flip Image"} 48 |

49 |

setImageFlip(!imageFlip)} className="toggle-radio-button"> 50 | {'Flip

51 |

52 |

53 |

54 | 55 |

56 | ) 57 | } 58 | 59 | export default SourceSelector; 60 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ![Web Bootstrap header](assets/header.jpg) 2 | 3 | [Lobe](http://lobe.ai/) is a free, easy to use app that has everything you need to bring your machine learning ideas to life. 4 | Web Bootstrap takes the machine learning model created in Lobe, and adds it to a project in the browser that uses 5 | [React](https://reactjs.org), [Create React App](https://github.com/facebook/create-react-app), [TypeScript](https://www.typescriptlang.org/), and [TensorFlow.js](https://www.tensorflow.org/js). 6 | 7 | ## Get Started 8 | 9 | 1. Clone or download the project on your computer and install [Yarn](https://yarnpkg.com/). Yarn is the software package that will install all the dependencies and make sure the code automatically reloads when changes are made. 10 | 11 | 2. Run `yarn install` to install required dependencies and run `yarn start` to start the server in development mode. This will open a web browser to 12 | `localhost:3000`. By default, this project is using the TensorFlow.js exported model from Lobe found in the `public/model/` folder. 13 | 14 | 3. To use your own model file, open your Lobe project, go to the Use tab, select Export, and click on the TensorFlow.js model file. 15 | When exported, drag the `model.json`, `signature.json`, and all the `*.bin` files to the `public/model/` folder. 16 | 17 | ## Additional Information 18 | 19 | Check out the [Create React App documentation](https://create-react-app.dev/docs/getting-started) 20 | for more information on React and the project structure. 21 | 22 | There are three main components: Camera, Prediction, and StaticImage. 23 | The Camera, which runs in `components/camera/Camera.tsx` is responsible for displaying a live full screen view of the user's selected webcam. 24 | The Prediction component `components/prediction/Prediction.tsx` is the box in the lower left hand corner, and is responsible for displaying the top prediction results and their confidences. 25 | The StaticImage component `components/staticImage/StaticImage.tsx` displays an image selected from the file picker and runs it through the model from a canvas element. 26 | 27 | ### Known Issues 28 | TensorFlow.js on Safari may have problems initializing the WebGL backend for acceleration and will fall back to the CPU. 29 | You can use the WebAssembly (wasm) backend as an alternative to WebGL: 30 | https://www.tensorflow.org/js/guide/platform_environment#wasm_backend 31 | 32 | ## Contributing 33 | 34 | GitHub Issues are for reporting bugs, discussing features and general feedback on the Web Bootstrap project. Be sure to check our documentation, FAQ and past issues before opening any new ones. 35 | 36 | To share your project, get feedback on it, and learn more about Lobe, please visit our community on [Reddit](https://www.reddit.com/r/Lobe/). 37 | We look forward to seeing the amazing projects that can be built, when machine learning is made accessible to you. 38 | -------------------------------------------------------------------------------- /src/components/camera/Camera.tsx: -------------------------------------------------------------------------------- 1 | import React, {useEffect, useState, useRef, useCallback} from "react"; 2 | import Webcam from "react-webcam"; 3 | import SourceSelector from "./SourceSelector"; 4 | import "./Camera.css"; 5 | 6 | type CameraProps = { 7 | predictCanvas: (canvas: HTMLCanvasElement) => void; 8 | predictions?: { [label: string]: number }; 9 | } 10 | 11 | // Our webcam display and capture component 12 | function Camera({ predictCanvas, predictions }: CameraProps) { 13 | const [devices, setDevices] = useState([]); 14 | const [deviceId, setDeviceId] = useState(undefined); 15 | const [imageFlip, setImageFlip] = useState(true); 16 | const webcamRef = useRef(null); 17 | const [selectorVisible, setSelectorVisible] = useState(false); 18 | 19 | // handle any webcam plugged into the computer 20 | // https://github.com/mozmorris/react-webcam#show-all-cameras-by-deviceid 21 | const handleDevices = useCallback( 22 | (mediaDevices: MediaDeviceInfo[]) => { 23 | // find all the webcams 24 | const videoDevices = mediaDevices.filter(({kind}) => kind === "videoinput"); 25 | setDevices(videoDevices); 26 | // set our initial webcam to be the first in the list 27 | if (videoDevices.length > 0) { 28 | setDeviceId(videoDevices[0].deviceId); 29 | } 30 | },[setDevices, setDeviceId] 31 | ); 32 | useEffect(() => { 33 | navigator.mediaDevices.enumerateDevices().then(handleDevices); 34 | }, [handleDevices]); 35 | 36 | // function to grab the current frame drawn on canvas from the webcam 37 | const getCanvas: () => Promise = useCallback(async () => { 38 | let newImage; 39 | if (webcamRef.current) { 40 | newImage = webcamRef.current.getCanvas(); 41 | if (newImage) { 42 | return newImage; 43 | } 44 | } 45 | }, [webcamRef]); 46 | 47 | // helper for waiting in our loop when the camera is loading (getting the image) 48 | const sleep = useCallback((ms: number) => { 49 | return new Promise(function (resolve, reject) { 50 | setTimeout(resolve, ms); 51 | }); 52 | }, []); 53 | 54 | // while we have the webcam mounted, predict frames as fast as we get new predictions back from the model 55 | useEffect(() => { 56 | getCanvas().then(async (canvas: HTMLCanvasElement | undefined) => { 57 | let currentCanvas = canvas; 58 | while (!currentCanvas) { 59 | // if no canvas, wait 500ms and try again 60 | await sleep(500); 61 | currentCanvas = await getCanvas(); 62 | } 63 | if (currentCanvas) { 64 | predictCanvas(currentCanvas); 65 | } 66 | }) 67 | }, [sleep, predictions, deviceId, getCanvas, predictCanvas]) 68 | 69 | return ( 70 |

setSelectorVisible(false)}> 71 | 80 | 93 |

94 | ); 95 | } 96 | 97 | export default Camera; 98 | -------------------------------------------------------------------------------- /src/Icons/gallery.svg: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /src/model/imageClassificationModel.ts: -------------------------------------------------------------------------------- 1 | import * as tf from '@tensorflow/tfjs'; 2 | 3 | const LEGACY_VERSION = -1; 4 | const SUPPORTED_VERSIONS = [LEGACY_VERSION, 1]; // use -1 for legacy Lobe exports without the version property 5 | 6 | class ImageClassificationModel { 7 | signaturePath: string; 8 | signature: any; 9 | modelPath: string; 10 | height: number = 224; 11 | width: number = 224; 12 | outputName: string = ''; 13 | inputKey = "Image"; 14 | outputKey = "Confidences"; 15 | labelKey = "Label"; 16 | labels: string[] = []; 17 | version?: number; 18 | model?: tf.GraphModel; 19 | 20 | constructor(signaturePath: string, modelPath: string) { 21 | /* Construct our model from the path to Lobe's exported signature.json and model.json files */ 22 | this.signaturePath = signaturePath; 23 | this.modelPath = modelPath; 24 | } 25 | 26 | async load() { 27 | /* Load our TensorFlow.js GraphModel if it we haven't yet */ 28 | if (!this.model) { 29 | console.log("Loading our model..."); 30 | const signatureFile = await fetch(this.signaturePath); 31 | this.signature = await signatureFile.json(); 32 | [this.width, this.height] = this.signature.inputs[this.inputKey].shape.slice(1,3); 33 | this.outputName = this.signature.outputs[this.outputKey].name; 34 | this.labels = this.signature.classes[this.labelKey]; 35 | this.version = this.signature.export_model_version || LEGACY_VERSION; 36 | if (!this.version || !SUPPORTED_VERSIONS.includes(this.version)) { 37 | const versionMessage = `The model version ${this.version} you are using for this starter project may not be compatible with the supported versions ${SUPPORTED_VERSIONS}. Please update both this starter project and Lobe to latest versions, and try exporting your model again. If the issue persists, please contact us at lobesupport@microsoft.com`; 38 | console.error(versionMessage); 39 | throw new Error(versionMessage); 40 | } 41 | this.model = await tf.loadGraphModel(this.modelPath); 42 | console.log("Model loaded!"); 43 | } 44 | } 45 | 46 | dispose() { 47 | /* Free up the memory used by the TensorFlow.js GraphModel */ 48 | if (this.model) { 49 | console.log("Clearing our model."); 50 | this.model.dispose(); 51 | this.model = undefined; 52 | } 53 | } 54 | 55 | async predict(imageData: ImageData) { 56 | /* 57 | Given an input image data from a Canvas, 58 | preprocess the image into a tensor with pixel values of [0,1], center crop to a square 59 | and resize to the image input size, then run the prediction! 60 | */ 61 | // load our model if we don't have it yet 62 | if(!this.model) { 63 | await this.load(); 64 | } 65 | // use tf tidy to dispose of intermediate tensors automatically 66 | const confidencesTensor = tf.tidy(() => { 67 | // create a tensor from the canvas image data 68 | const image = tf.browser.fromPixels(imageData); 69 | const [imgHeight, imgWidth] = image.shape.slice(0,2); 70 | // convert image to 0-1 71 | const normalizedImage = tf.div(image, tf.scalar(255)); 72 | // make into a batch of 1 so it is shaped [1, height, width, 3] 73 | const batchImage: tf.Tensor4D = tf.expandDims(normalizedImage); 74 | // center crop and resize 75 | /* 76 | Instead of center cropping, you can use any number of methods for making the image square and the right shape. 77 | You can resize (squeeze or expand height/width to fit), pad with 0's so that the whole image is square and has black bars, 78 | or pad with different pixel values like mirroring. We recommend using the same resize function here that was used during 79 | training or the creation of your dataset. Lobe by default with center crop to the square. 80 | */ 81 | let top = 0; 82 | let left = 0; 83 | let bottom = 1; 84 | let right = 1; 85 | if (imgHeight !== imgWidth) { 86 | // the crops are normalized 0-1 percentage of the image dimension 87 | const size = Math.min(imgHeight, imgWidth); 88 | left = (imgWidth - size) / 2 / imgWidth; 89 | top = (imgHeight - size) / 2 / imgHeight; 90 | right = (imgWidth + size) / 2 / imgWidth; 91 | bottom = (imgHeight + size) / 2 / imgHeight; 92 | } 93 | // center crop our image and resize it to the size found in signature.json 94 | const croppedImage = tf.image.cropAndResize( 95 | batchImage, [[top, left, bottom, right]], [0], [this.height, this.width] 96 | ); 97 | // run the model on our image and await the results as an array 98 | if (this.model) { 99 | return this.model.execute( 100 | {[this.signature.inputs[this.inputKey].name]: croppedImage}, this.outputName 101 | ); 102 | } 103 | }) as (tf.Tensor | undefined); 104 | if (confidencesTensor) { 105 | // grab the array of values from the tensor data 106 | const confidencesArray = await confidencesTensor.data(); 107 | // now that we have the array values, we can dispose the tensor and free memory 108 | confidencesTensor.dispose(); 109 | // return a map of [label]: confidence computed by the model 110 | // the list of labels maps in the same index order as the outputs from the results 111 | return { 112 | [this.outputKey]: this.labels.reduce( 113 | (returnConfidences, label, idx) => { 114 | return {[label]: confidencesArray[idx], ...returnConfidences} 115 | }, {} 116 | ) 117 | } 118 | } 119 | } 120 | } 121 | 122 | export default ImageClassificationModel 123 | -------------------------------------------------------------------------------- /public/model/model.json: -------------------------------------------------------------------------------- 1 | {"format": "graph-model", "generatedBy": "1.15.4", "convertedBy": "TensorFlow.js Converter v2.7.0", "userDefinedMetadata": {"signature": 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