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This is an ml5.js example made and shared with ❤️. + * Learn more about the ml5.js project: https://ml5js.org/ + * ml5.js license and Code of Conduct: https://github.com/ml5js/ml5-next-gen/blob/main/LICENSE.md + * + * This example demonstrates hand tracking on an image through ml5.objectDetector. + */ + +let objectDetector; +let video; +let hands = []; + +function preload() { + // Load the image to be detected + img = loadImage("hand.jpg"); + // Load the objectDetector model + objectDetector = ml5.objectDetector(); +} + +function setup() { + createCanvas(640, 480); + // Draw the image + image(img, 0, 0); + // Detect hands in an image + objectDetector.detect(img, gotHands); +} + +function draw() { + // Draw all the hand keypoints + for (let i = 0; i < hands.length; i++) { + let hand = hands[i]; + for (let j = 0; j < hand.keypoints.length; j++) { + let keypoint = hand.keypoints[j]; + fill(0, 255, 0); + noStroke(); + circle(keypoint.x, keypoint.y, 10); + } + } +} + +// Callback function 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This is an ml5.js example made and shared with ❤️. + * Learn more about the ml5.js project: https://ml5js.org/ + * ml5.js license and Code of Conduct: https://github.com/ml5js/ml5-next-gen/blob/main/LICENSE.md + * + * This example demonstrates hand tracking on an image through ml5.handPose. + */ + +let handPose; +let video; +let hands = []; + +function preload() { + // Load the image to be detected + img = loadImage("hand.jpg"); + // Load the handPose model + handPose = ml5.handPose(); +} + +function setup() { + createCanvas(640, 480); + // Draw the image + image(img, 0, 0); + // Detect hands in an image + handPose.detect(img, gotHands); +} + +function draw() { + // Draw all the hand keypoints + for (let i = 0; i < hands.length; i++) { + let hand = hands[i]; + for (let j = 0; j < hand.keypoints.length; j++) { + let keypoint = hand.keypoints[j]; + fill(0, 255, 0); + noStroke(); + circle(keypoint.x, keypoint.y, 10); + } + } +} + +// Callback function for when handPose outputs data +function gotHands(results) { + // save the output to the hands variable + hands = results; +} diff --git a/package.json b/package.json index 63b88db7..469578eb 100644 --- a/package.json +++ b/package.json @@ -56,12 +56,13 @@ "@mediapipe/pose": "^0.5.1675469404", "@mediapipe/selfie_segmentation": "~0.1.0", "@tensorflow-models/body-segmentation": "^1.0.1", + "@tensorflow-models/coco-ssd": "^2.2.3", "@tensorflow-models/face-landmarks-detection": "1.0.5", "@tensorflow-models/hand-pose-detection": "^2.0.0", "@tensorflow-models/mobilenet": "^2.1.0", "@tensorflow-models/pose-detection": "^2.1.0", "@tensorflow-models/speech-commands": "^0.5.4", - "@tensorflow/tfjs": "^4.2.0", + "@tensorflow/tfjs": "^4.20.0", "@tensorflow/tfjs-vis": "^1.5.1", "axios": "^1.3.4", "webpack-merge": "^5.9.0" diff --git a/src/ObjectDetector/index.js b/src/ObjectDetector/index.js new file mode 100644 index 00000000..fff7ee2c --- /dev/null +++ b/src/ObjectDetector/index.js @@ -0,0 +1,273 @@ +// Copyright (c) 2020-2024 ml5 +// +// This software is released under the MIT License. +// https://opensource.org/licenses/MIT + +/* + * HandPose: Palm detector and hand-skeleton finger tracking in the browser + * Ported and integrated from all the hard work by: https://github.com/tensorflow/tfjs-models/tree/master/hand-pose-detection + */ + +import * as tf from "@tensorflow/tfjs"; +import * as cocoSsD from "@tensorflow-models/coco-ssd"; +import callCallback from "../utils/callcallback"; +import handleArguments from "../utils/handleArguments"; +import handleOptions from "../utils/handleOptions"; +import { handleModelName } from "../utils/handleOptions"; +import { mediaReady } from "../utils/imageUtilities"; +import objectRenameKey from "../utils/objectRenameKey"; + +class ObjectDetector { + /** + * An object for configuring HandPose options. + * @typedef {Object} configOptions + * @property {number} maxHands - Optional. The maximum number of hands to detect. Default: 2. + * @property {string} modelType - Optional. The type of model to use: "lite" or "full". Default: "full". + * @property {boolean} flipHorizontal - Optional. Flip the result data horizontally. Default: false. + * @property {string} runtime - Optional. The runtime of the model: "mediapipe" or "tfjs". Default: "mediapipe". + * + * // For using custom or offline models + * @property {string} solutionPath - Optional. The file path or URL to the model. Only used when using "mediapipe" runtime. + * @property {string} detectorModelUrl - Optional. The file path or URL to the hand detector model. Only used when using "tfjs" runtime. + * @property {string} landmarkModelUrl - Optional. The file path or URL to the hand landmark model Only used when using "tfjs" runtime. + */ + + /** + * Creates HandPose. + * @param {configOptions} options - An object containing HandPose configuration options. + * @param {function} callback - A callback to be called when the model is ready. + * @private + */ + constructor(modelName, options, callback) { + this.modelName = this.modelName = handleModelName( + modelName, + ["MediaPipeHands"], + "MediaPipeHands", + "handPose" + ); + this.model = null; + this.config = options; + this.runtimeConfig = {}; + this.detectMedia = null; + this.detectCallback = null; + + // flags for detectStart() and detectStop() + this.detecting = false; // True when detection loop is running + this.signalStop = false; // Signal to stop the loop + this.prevCall = ""; // Track previous call to detectStart() or detectStop() + + this.ready = callCallback(this.loadModel(), callback); + } + + /** + * Loads the model. + * @return {this} the HandPose model. + * @private + */ + async loadModel() { + const pipeline = handPoseDetection.SupportedModels.MediaPipeHands; + //filter out model config options + const modelConfig = handleOptions( + this.config, + { + maxHands: { + type: "number", + min: 1, + max: 2147483647, + integer: true, + default: 2, + }, + runtime: { + type: "enum", + enums: ["mediapipe", "tfjs"], + default: "tfjs", + }, + modelType: { + type: "enum", + enums: ["lite", "full"], + default: "full", + }, + solutionPath: { + type: "string", + default: "https://cdn.jsdelivr.net/npm/@mediapipe/hands", + ignore: (config) => config.runtime !== "mediapipe", + }, + detectorModelUrl: { + type: "string", + default: undefined, + ignore: (config) => config.runtime !== "tfjs", + }, + landmarkModelUrl: { + type: "string", + default: undefined, + ignore: (config) => config.runtime !== "tfjs", + }, + }, + "handPose" + ); + this.runtimeConfig = handleOptions( + this.config, + { + flipHorizontal: { + type: "boolean", + alias: "flipped", + default: false, + }, + }, + "handPose" + ); + + await tf.ready(); + this.model = await handPoseDetection.createDetector(pipeline, modelConfig); + + return this; + } + + /** + * A callback function that handles the handPose detection results. + * @callback gotHands + * @param {Array} results - The detection output. + */ + + /** + * Asynchronously outputs a single hand landmark detection result when called. + * Supports both callback and promise. + * @param {*} [media] - An HMTL or p5.js image, video, or canvas element to run the detection on. + * @param {gotHands} [callback] - Optional. A callback to handle the hand detection result. + * @returns {Promise} The detection result. + */ + async detect(...inputs) { + //Parse out the input parameters + const argumentObject = handleArguments(...inputs); + argumentObject.require( + "image", + "An html or p5.js image, video, or canvas element argument is required for detect()." + ); + const { image, callback } = argumentObject; + + await mediaReady(image, false); + const predictions = await this.model.estimateHands( + image, + this.runtimeConfig + ); + // Modify the prediction result to make it more user-friendly + let result = predictions; + this.renameScoreToConfidence(result); + this.addKeypoints(result); + + if (typeof callback === "function") callback(result); + return result; + } + + /** + * Repeatedly outputs hand predictions through a callback function. + * @param {*} [media] - An HMTL or p5.js image, video, or canvas element to run the prediction on. + * @param {gotHands} [callback] - A callback to handle the hand detection results. + */ + detectStart(...inputs) { + // Parse out the input parameters + const argumentObject = handleArguments(...inputs); + argumentObject.require( + "image", + "An html or p5.js image, video, or canvas element argument is required for detectStart()." + ); + argumentObject.require( + "callback", + "A callback function argument is required for detectStart()." + ); + this.detectMedia = argumentObject.image; + this.detectCallback = argumentObject.callback; + + this.signalStop = false; + if (!this.detecting) { + this.detecting = true; + this.detectLoop(); + } + if (this.prevCall === "start") { + console.warn( + "detectStart() was called more than once without calling detectStop(). Only the latest detectStart() call will take effect." + ); + } + this.prevCall = "start"; + } + + /** + * Stops the detection loop before next detection loop runs. + */ + detectStop() { + if (this.detecting) this.signalStop = true; + this.prevCall = "stop"; + } + + /** + * Calls estimateHands in a loop. + * Can be started by detectStart() and terminated by detectStop(). + * @private + */ + async detectLoop() { + await mediaReady(this.detectMedia, false); + while (!this.signalStop) { + const predictions = await this.model.estimateHands( + this.detectMedia, + this.runtimeConfig + ); + let result = predictions; + // Modify the prediction result to make it more user-friendly + this.renameScoreToConfidence(result); + this.addKeypoints(result); + + this.detectCallback(result); + // wait for the frame to update + await tf.nextFrame(); + } + this.detecting = false; + this.signalStop = false; + } + + /** + * Renames the score key to confidence in the detection results. + * @param {Object[]} hands - The detection results. + */ + renameScoreToConfidence(hands) { + hands.forEach((hand) => { + objectRenameKey(hand, "score", "confidence"); + }); + } + + /** + * Returns a new array of results with named keypoints added. + * @param {Array} hands - the original detection results. + * @return {Array} the detection results with named keypoints added. + * + * @private + */ + addKeypoints(hands) { + const result = hands.map((hand) => { + for (let i = 0; i < hand.keypoints.length; i++) { + let keypoint = hand.keypoints[i]; + let keypoint3D = hand.keypoints3D[i]; + hand[keypoint.name] = { + x: keypoint.x, + y: keypoint.y, + x3D: keypoint3D.x, + y3D: keypoint3D.y, + z3D: keypoint3D.z, + }; + } + return hand; + }); + return result; + } +} + +/** + * Factory function that returns a new HandPose instance. + * @returns {HandPose} A new handPose instance. + */ +const objectDetector = (...inputs) => { + const { string, options = {}, callback } = handleArguments(...inputs); + const instance = new ObjectDetector(string, options, callback); + return instance; +}; + +export default objectDetector; diff --git a/src/index.js b/src/index.js index 09416c1c..3e327b56 100644 --- a/src/index.js +++ b/src/index.js @@ -1,5 +1,6 @@ import neuralNetwork from "./NeuralNetwork"; import handPose from "./HandPose"; +import objectDetector from "./ObjectDetector"; import sentiment from "./Sentiment"; import faceMesh from "./FaceMesh"; import bodyPose from "./BodyPose"; @@ -22,6 +23,7 @@ const withPreload = { neuralNetwork, sentiment, soundClassifier, + objectDetector, }; const ml5 = Object.assign({ p5Utils }, withPreload, { diff --git a/yarn.lock b/yarn.lock index 83e5ed70..c75e9895 100644 --- a/yarn.lock +++ b/yarn.lock @@ -1186,6 +1186,11 @@ dependencies: rimraf "^3.0.2" +"@tensorflow-models/coco-ssd@^2.2.3": + version "2.2.3" + resolved "https://registry.yarnpkg.com/@tensorflow-models/coco-ssd/-/coco-ssd-2.2.3.tgz#3825286569076d6788199c9cb89fb2fa31f7d2f2" + integrity 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-"@tensorflow/tfjs-backend-webgl@4.8.0": - version "4.8.0" - resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-4.8.0.tgz#b361c28660aee3d33554b0b50032a8d78bcd268c" +"@tensorflow/tfjs-backend-webgl@4.20.0": + version "4.20.0" + resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-4.20.0.tgz#920bdda8cce920c183a48d917e34e80cea2e0c43" + integrity sha512-M03fJonJGxm2u3SCzRNA2JLh0gxaAye64SEmGAXOehizowxy42l+lMsPWU8xU7r7mN6PEilBNkuKAf5YJ7Xumg== dependencies: - "@tensorflow/tfjs-backend-cpu" "4.8.0" + "@tensorflow/tfjs-backend-cpu" "4.20.0" "@types/offscreencanvas" "~2019.3.0" "@types/seedrandom" "^2.4.28" - "@types/webgl-ext" "0.0.30" seedrandom "^3.0.5" "@tensorflow/tfjs-backend-webgpu@^4.17.0": @@ -1244,34 +1250,37 @@ dependencies: "@tensorflow/tfjs-backend-cpu" "4.17.0" -"@tensorflow/tfjs-converter@4.8.0": - version "4.8.0" - resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-converter/-/tfjs-converter-4.8.0.tgz#be38e9708eb52a2dcd7e78df44bea831da5970d6" +"@tensorflow/tfjs-converter@4.20.0": + version "4.20.0" + resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-converter/-/tfjs-converter-4.20.0.tgz#e23549330e0bb693db9373efa892e695fd48186b" + integrity sha512-UJ2ntQ1TNtVHB5qGMwB0j306bs3KH1E1HKJ9Dxvrc6PUaivOV+CPKqmbidOFG5LylXeRC36JBdhe+gVT2nFHNw== -"@tensorflow/tfjs-core@4.8.0": - version "4.8.0" - resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-core/-/tfjs-core-4.8.0.tgz#a238007edd3643fc4bcf7ec726c1cea67a29b894" +"@tensorflow/tfjs-core@4.20.0": + version "4.20.0" + resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-core/-/tfjs-core-4.20.0.tgz#6f40757d33e53489a4a165e59ef4768502db12a9" + integrity sha512-m/cc9qDc63al9UhdbXRUYTLGfJJlhuN5tylAX/2pJMLj32c8a6ThGDJYoKzpf32n5g3MQGYLchjClDxeGdXMPQ== dependencies: "@types/long" "^4.0.1" "@types/offscreencanvas" "~2019.7.0" "@types/seedrandom" "^2.4.28" - "@types/webgl-ext" "0.0.30" - "@webgpu/types" "0.1.30" + "@webgpu/types" "0.1.38" long "4.0.0" node-fetch "~2.6.1" seedrandom "^3.0.5" -"@tensorflow/tfjs-data@4.8.0": - version "4.8.0" - resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-data/-/tfjs-data-4.8.0.tgz#6969b90f41ff53ab0dd3b0c933183a5cbe4c52e1" +"@tensorflow/tfjs-data@4.20.0": + version "4.20.0" + resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-data/-/tfjs-data-4.20.0.tgz#4c0e84a0e12e07392c3c854efd5f2a147548776e" + integrity sha512-k6S8joXhoXkatcoT6mYCxBzRCsnrLfnl6xjLe46SnXO0oEEy4Vuzbmp5Ydl1uU2hHr73zL91EdAC1k8Hng/+oA== dependencies: "@types/node-fetch" "^2.1.2" node-fetch "~2.6.1" string_decoder "^1.3.0" -"@tensorflow/tfjs-layers@4.8.0": - version "4.8.0" - resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-layers/-/tfjs-layers-4.8.0.tgz#23b09daa67cd05d732bca67922723368bb7dba81" +"@tensorflow/tfjs-layers@4.20.0": + version "4.20.0" + resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-layers/-/tfjs-layers-4.20.0.tgz#7d00dc3c3cea1ce8e7df67480e2738ab71a0016f" + integrity sha512-SCHZH29Vyw+Y9eoaJHiaNo6yqM9vD3XCKncoczonRRywejm3FFqddg1AuWAfSE9XoNPE21o9PsknvKLl/Uh+Cg== "@tensorflow/tfjs-vis@^1.5.1": version "1.5.1" @@ -1285,16 +1294,17 @@ vega-embed "6.17.0" vega-lite "4.13.1" -"@tensorflow/tfjs@^4.2.0": - version "4.8.0" - resolved "https://registry.yarnpkg.com/@tensorflow/tfjs/-/tfjs-4.8.0.tgz#58b2b8d1bcf4249da7bb2ebecdaf7e8be87f01fd" - dependencies: - "@tensorflow/tfjs-backend-cpu" "4.8.0" - "@tensorflow/tfjs-backend-webgl" "4.8.0" - "@tensorflow/tfjs-converter" "4.8.0" - "@tensorflow/tfjs-core" "4.8.0" - "@tensorflow/tfjs-data" "4.8.0" - "@tensorflow/tfjs-layers" "4.8.0" +"@tensorflow/tfjs@^4.20.0": + version "4.20.0" + resolved "https://registry.yarnpkg.com/@tensorflow/tfjs/-/tfjs-4.20.0.tgz#7835d14d1661d459817595196a4bc7bb0d949b75" + integrity sha512-+ZLfJq2jyIOE2/+yKPoyD/gfy3RZypbfMrlzvBDgodTK5jnexprihhX38hxilh9HPWvWQXJqiUjKJP5ECCikrw== + dependencies: + "@tensorflow/tfjs-backend-cpu" "4.20.0" + "@tensorflow/tfjs-backend-webgl" "4.20.0" + "@tensorflow/tfjs-converter" "4.20.0" + "@tensorflow/tfjs-core" "4.20.0" + "@tensorflow/tfjs-data" "4.20.0" + "@tensorflow/tfjs-layers" "4.20.0" argparse "^1.0.10" chalk "^4.1.0" core-js "3.29.1" @@ -1536,10 +1546,6 @@ version "4.0.5" resolved "https://registry.yarnpkg.com/@types/tough-cookie/-/tough-cookie-4.0.5.tgz#cb6e2a691b70cb177c6e3ae9c1d2e8b2ea8cd304" -"@types/webgl-ext@0.0.30": - version "0.0.30" - resolved "https://registry.yarnpkg.com/@types/webgl-ext/-/webgl-ext-0.0.30.tgz#0ce498c16a41a23d15289e0b844d945b25f0fb9d" - "@types/ws@^8.5.5": version "8.5.5" resolved "https://registry.yarnpkg.com/@types/ws/-/ws-8.5.5.tgz#af587964aa06682702ee6dcbc7be41a80e4b28eb" @@ -1662,9 +1668,10 @@ "@webassemblyjs/ast" "1.11.6" "@xtuc/long" "4.2.2" -"@webgpu/types@0.1.30": - version "0.1.30" - resolved "https://registry.yarnpkg.com/@webgpu/types/-/types-0.1.30.tgz#b6406dc4a1c1e0d469028ceb30ddffbbd2fa706c" +"@webgpu/types@0.1.38": + version "0.1.38" + resolved "https://registry.yarnpkg.com/@webgpu/types/-/types-0.1.38.tgz#6fda4b410edc753d3213c648320ebcf319669020" + integrity sha512-7LrhVKz2PRh+DD7+S+PVaFd5HxaWQvoMqBbsV9fNJO1pjUs1P8bM2vQVNfk+3URTqbuTI7gkXi0rfsN0IadoBA== "@webpack-cli/configtest@^2.1.1": version "2.1.1" @@ -4955,7 +4962,15 @@ string-length@^4.0.1: char-regex "^1.0.2" strip-ansi "^6.0.0" -"string-width-cjs@npm:string-width@^4.2.0", string-width@^4.1.0, string-width@^4.2.0, string-width@^4.2.3: +"string-width-cjs@npm:string-width@^4.2.0": + version "4.2.3" + resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.3.tgz#269c7117d27b05ad2e536830a8ec895ef9c6d010" + dependencies: + emoji-regex "^8.0.0" + is-fullwidth-code-point "^3.0.0" + strip-ansi "^6.0.1" + +string-width@^4.1.0, string-width@^4.2.0, string-width@^4.2.3: version "4.2.3" resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.3.tgz#269c7117d27b05ad2e536830a8ec895ef9c6d010" dependencies: @@ -4983,7 +4998,13 @@ string_decoder@~1.1.1: dependencies: safe-buffer "~5.1.0" -"strip-ansi-cjs@npm:strip-ansi@^6.0.1", strip-ansi@^6.0.0, strip-ansi@^6.0.1: +"strip-ansi-cjs@npm:strip-ansi@^6.0.1": + version "6.0.1" + resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-6.0.1.tgz#9e26c63d30f53443e9489495b2105d37b67a85d9" + dependencies: + ansi-regex "^5.0.1" + +strip-ansi@^6.0.0, strip-ansi@^6.0.1: version "6.0.1" resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-6.0.1.tgz#9e26c63d30f53443e9489495b2105d37b67a85d9" dependencies: @@ -5880,7 +5901,7 @@ wildcard@^2.0.0: version "2.0.1" resolved "https://registry.yarnpkg.com/wildcard/-/wildcard-2.0.1.tgz#5ab10d02487198954836b6349f74fff961e10f67" -"wrap-ansi-cjs@npm:wrap-ansi@^7.0.0", wrap-ansi@^7.0.0: +"wrap-ansi-cjs@npm:wrap-ansi@^7.0.0": version "7.0.0" resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43" dependencies: @@ -5896,6 +5917,14 @@ wrap-ansi@^6.2.0: string-width "^4.1.0" strip-ansi "^6.0.0" +wrap-ansi@^7.0.0: + version "7.0.0" + resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43" + dependencies: + ansi-styles "^4.0.0" + string-width "^4.1.0" + strip-ansi "^6.0.0" + wrap-ansi@^8.1.0: version "8.1.0" resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-8.1.0.tgz#56dc22368ee570face1b49819975d9b9a5ead214" From 3123c2123f138eef68f790bf801654776a386c29 Mon Sep 17 00:00:00 2001 From: douaaz <94295853+douaaz@users.noreply.github.com> Date: Fri, 2 Aug 2024 13:20:06 +0100 Subject: [PATCH 02/13] update objectDetector example and index.js files --- .../sketch.js | 9 ++++++-- src/ObjectDetector/index.js | 21 ++++++++++++++++--- 2 files changed, 25 insertions(+), 5 deletions(-) diff --git a/examples/objectDetector-single-image copy/sketch.js b/examples/objectDetector-single-image copy/sketch.js index 18f0fc00..81fe0102 100644 --- a/examples/objectDetector-single-image copy/sketch.js +++ b/examples/objectDetector-single-image copy/sketch.js @@ -3,7 +3,7 @@ * Learn more about the ml5.js project: https://ml5js.org/ * ml5.js license and Code of Conduct: https://github.com/ml5js/ml5-next-gen/blob/main/LICENSE.md * - * This example demonstrates hand tracking on an image through ml5.objectDetector. + * This example demonstrates object detection on an image through ml5.objectDetector. */ let objectDetector; @@ -13,6 +13,11 @@ let hands = []; function preload() { // Load the image to be detected img = loadImage("hand.jpg"); + + // trying to work around "WebGPU readSync is only available for CPU-resident tensors." + // see https://github.com/ml5js/ml5-next-gen/issues/117 + ml5.setBackend("webgl"); + // Load the objectDetector model objectDetector = ml5.objectDetector(); } @@ -42,4 +47,4 @@ function draw() { function gotHands(results) { // save the output to the hands variable hands = results; -} +} \ No newline at end of file diff --git a/src/ObjectDetector/index.js b/src/ObjectDetector/index.js index fff7ee2c..71c5d924 100644 --- a/src/ObjectDetector/index.js +++ b/src/ObjectDetector/index.js @@ -9,7 +9,8 @@ */ import * as tf from "@tensorflow/tfjs"; -import * as cocoSsD from "@tensorflow-models/coco-ssd"; +import * as handPoseDetection from "@tensorflow-models/hand-pose-detection"; +import * as cocoSsd from "@tensorflow-models/coco-ssd"; import callCallback from "../utils/callcallback"; import handleArguments from "../utils/handleArguments"; import handleOptions from "../utils/handleOptions"; @@ -45,7 +46,8 @@ class ObjectDetector { "MediaPipeHands", "handPose" ); - this.model = null; + this.model = null; // currently still handPose + this.model2 = null; // temp. for cocoSsd this.config = options; this.runtimeConfig = {}; this.detectMedia = null; @@ -120,6 +122,10 @@ class ObjectDetector { await tf.ready(); this.model = await handPoseDetection.createDetector(pipeline, modelConfig); + console.log('Loading cocoSsd'); + this.model2 = await cocoSsd.load(); + console.log('Finished loading cocoSsd'); + return this; } @@ -146,6 +152,8 @@ class ObjectDetector { const { image, callback } = argumentObject; await mediaReady(image, false); + // This is the actual handPose code + /* const predictions = await this.model.estimateHands( image, this.runtimeConfig @@ -154,6 +162,13 @@ class ObjectDetector { let result = predictions; this.renameScoreToConfidence(result); this.addKeypoints(result); + */ + + const predictions = await this.model2.detect(image); + console.log('raw result from cocoSsd', predictions); + + // return an empty array for now + const result = []; if (typeof callback === "function") callback(result); return result; @@ -270,4 +285,4 @@ const objectDetector = (...inputs) => { return instance; }; -export default objectDetector; +export default objectDetector; \ No newline at end of file From f53a371f09c948f9742853977580db03fc3ef601 Mon Sep 17 00:00:00 2001 From: douaaz <94295853+douaaz@users.noreply.github.com> Date: Wed, 7 Aug 2024 11:26:30 +0100 Subject: [PATCH 03/13] update image --- .../Printable Bill.pdf | Bin 0 -> 184195 bytes .../family.jpeg | Bin 0 -> 117939 bytes .../objectDetector-single-image copy/hand.jpg | Bin 17823 -> 0 bytes .../objectDetector-single-image copy/sketch.js | 14 +++----------- 4 files changed, 3 insertions(+), 11 deletions(-) create mode 100644 examples/objectDetector-single-image copy/Printable Bill.pdf create mode 100644 examples/objectDetector-single-image copy/family.jpeg delete mode 100644 examples/objectDetector-single-image copy/hand.jpg diff --git a/examples/objectDetector-single-image copy/Printable Bill.pdf b/examples/objectDetector-single-image copy/Printable Bill.pdf new file mode 100644 index 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+++++------------------------------- 1 file changed, 25 insertions(+), 171 deletions(-) diff --git a/src/ObjectDetector/index.js b/src/ObjectDetector/index.js index 71c5d924..6f69004a 100644 --- a/src/ObjectDetector/index.js +++ b/src/ObjectDetector/index.js @@ -4,50 +4,33 @@ // https://opensource.org/licenses/MIT /* - * HandPose: Palm detector and hand-skeleton finger tracking in the browser - * Ported and integrated from all the hard work by: https://github.com/tensorflow/tfjs-models/tree/master/hand-pose-detection + * ObjectDetector: Object detection using cocoSsd in the browser */ import * as tf from "@tensorflow/tfjs"; -import * as handPoseDetection from "@tensorflow-models/hand-pose-detection"; import * as cocoSsd from "@tensorflow-models/coco-ssd"; import callCallback from "../utils/callcallback"; import handleArguments from "../utils/handleArguments"; import handleOptions from "../utils/handleOptions"; import { handleModelName } from "../utils/handleOptions"; import { mediaReady } from "../utils/imageUtilities"; -import objectRenameKey from "../utils/objectRenameKey"; class ObjectDetector { /** - * An object for configuring HandPose options. + * An object for configuring ObjectDetector options. * @typedef {Object} configOptions - * @property {number} maxHands - Optional. The maximum number of hands to detect. Default: 2. - * @property {string} modelType - Optional. The type of model to use: "lite" or "full". Default: "full". - * @property {boolean} flipHorizontal - Optional. Flip the result data horizontally. Default: false. - * @property {string} runtime - Optional. The runtime of the model: "mediapipe" or "tfjs". Default: "mediapipe". - * - * // For using custom or offline models - * @property {string} solutionPath - Optional. The file path or URL to the model. Only used when using "mediapipe" runtime. - * @property {string} detectorModelUrl - Optional. The file path or URL to the hand detector model. Only used when using "tfjs" runtime. - * @property {string} landmarkModelUrl - Optional. The file path or URL to the hand landmark model Only used when using "tfjs" runtime. + * @property {string} modelType - Optional. The type of model to use. Default: "lite". */ /** - * Creates HandPose. - * @param {configOptions} options - An object containing HandPose configuration options. + * Creates ObjectDetector. + * @param {configOptions} options - An object containing ObjectDetector configuration options. * @param {function} callback - A callback to be called when the model is ready. * @private */ constructor(modelName, options, callback) { - this.modelName = this.modelName = handleModelName( - modelName, - ["MediaPipeHands"], - "MediaPipeHands", - "handPose" - ); - this.model = null; // currently still handPose - this.model2 = null; // temp. for cocoSsd + this.modelName = handleModelName(modelName, ["cocoSsd"], "cocoSsd", "objectDetector"); + this.model = null; this.config = options; this.runtimeConfig = {}; this.detectMedia = null; @@ -63,133 +46,48 @@ class ObjectDetector { /** * Loads the model. - * @return {this} the HandPose model. + * @return {this} the ObjectDetector model. * @private */ async loadModel() { - const pipeline = handPoseDetection.SupportedModels.MediaPipeHands; - //filter out model config options - const modelConfig = handleOptions( - this.config, - { - maxHands: { - type: "number", - min: 1, - max: 2147483647, - integer: true, - default: 2, - }, - runtime: { - type: "enum", - enums: ["mediapipe", "tfjs"], - default: "tfjs", - }, - modelType: { - type: "enum", - enums: ["lite", "full"], - default: "full", - }, - solutionPath: { - type: "string", - default: "https://cdn.jsdelivr.net/npm/@mediapipe/hands", - ignore: (config) => config.runtime !== "mediapipe", - }, - detectorModelUrl: { - type: "string", - default: undefined, - ignore: (config) => config.runtime !== "tfjs", - }, - landmarkModelUrl: { - type: "string", - default: undefined, - ignore: (config) => config.runtime !== "tfjs", - }, - }, - "handPose" - ); - this.runtimeConfig = handleOptions( - this.config, - { - flipHorizontal: { - type: "boolean", - alias: "flipped", - default: false, - }, - }, - "handPose" - ); - await tf.ready(); - this.model = await handPoseDetection.createDetector(pipeline, modelConfig); - - console.log('Loading cocoSsd'); - this.model2 = await cocoSsd.load(); + this.model = await cocoSsd.load(); console.log('Finished loading cocoSsd'); - return this; } /** - * A callback function that handles the handPose detection results. - * @callback gotHands - * @param {Array} results - The detection output. - */ - - /** - * Asynchronously outputs a single hand landmark detection result when called. + * Asynchronously outputs a single object detection result when called. * Supports both callback and promise. * @param {*} [media] - An HMTL or p5.js image, video, or canvas element to run the detection on. - * @param {gotHands} [callback] - Optional. A callback to handle the hand detection result. + * @param {function} [callback] - Optional. A callback to handle the detection result. * @returns {Promise} The detection result. */ async detect(...inputs) { - //Parse out the input parameters const argumentObject = handleArguments(...inputs); - argumentObject.require( - "image", - "An html or p5.js image, video, or canvas element argument is required for detect()." - ); + argumentObject.require("image", "An html or p5.js image, video, or canvas element argument is required for detect()."); const { image, callback } = argumentObject; await mediaReady(image, false); - // This is the actual handPose code - /* - const predictions = await this.model.estimateHands( - image, - this.runtimeConfig - ); - // Modify the prediction result to make it more user-friendly - let result = predictions; - this.renameScoreToConfidence(result); - this.addKeypoints(result); - */ - const predictions = await this.model2.detect(image); + const predictions = await this.model.detect(image); console.log('raw result from cocoSsd', predictions); - // return an empty array for now - const result = []; + const result = predictions; if (typeof callback === "function") callback(result); return result; } /** - * Repeatedly outputs hand predictions through a callback function. + * Repeatedly outputs object predictions through a callback function. * @param {*} [media] - An HMTL or p5.js image, video, or canvas element to run the prediction on. - * @param {gotHands} [callback] - A callback to handle the hand detection results. + * @param {function} [callback] - A callback to handle the object detection results. */ detectStart(...inputs) { - // Parse out the input parameters const argumentObject = handleArguments(...inputs); - argumentObject.require( - "image", - "An html or p5.js image, video, or canvas element argument is required for detectStart()." - ); - argumentObject.require( - "callback", - "A callback function argument is required for detectStart()." - ); + argumentObject.require("image", "An html or p5.js image, video, or canvas element argument is required for detectStart()."); + argumentObject.require("callback", "A callback function argument is required for detectStart()."); this.detectMedia = argumentObject.image; this.detectCallback = argumentObject.callback; @@ -199,9 +97,7 @@ class ObjectDetector { this.detectLoop(); } if (this.prevCall === "start") { - console.warn( - "detectStart() was called more than once without calling detectStop(). Only the latest detectStart() call will take effect." - ); + console.warn("detectStart() was called more than once without calling detectStop(). Only the latest detectStart() call will take effect."); } this.prevCall = "start"; } @@ -215,69 +111,27 @@ class ObjectDetector { } /** - * Calls estimateHands in a loop. + * Calls detect in a loop. * Can be started by detectStart() and terminated by detectStop(). * @private */ async detectLoop() { await mediaReady(this.detectMedia, false); while (!this.signalStop) { - const predictions = await this.model.estimateHands( - this.detectMedia, - this.runtimeConfig - ); - let result = predictions; - // Modify the prediction result to make it more user-friendly - this.renameScoreToConfidence(result); - this.addKeypoints(result); + const predictions = await this.model.detect(this.detectMedia); + const result = predictions; this.detectCallback(result); - // wait for the frame to update await tf.nextFrame(); } this.detecting = false; this.signalStop = false; } - - /** - * Renames the score key to confidence in the detection results. - * @param {Object[]} hands - The detection results. - */ - renameScoreToConfidence(hands) { - hands.forEach((hand) => { - objectRenameKey(hand, "score", "confidence"); - }); - } - - /** - * Returns a new array of results with named keypoints added. - * @param {Array} hands - the original detection results. - * @return {Array} the detection results with named keypoints added. - * - * @private - */ - addKeypoints(hands) { - const result = hands.map((hand) => { - for (let i = 0; i < hand.keypoints.length; i++) { - let keypoint = hand.keypoints[i]; - let keypoint3D = hand.keypoints3D[i]; - hand[keypoint.name] = { - x: keypoint.x, - y: keypoint.y, - x3D: keypoint3D.x, - y3D: keypoint3D.y, - z3D: keypoint3D.z, - }; - } - return hand; - }); - return result; - } } /** - * Factory function that returns a new HandPose instance. - * @returns {HandPose} A new handPose instance. + * Factory function that returns a new ObjectDetector instance. + * @returns {ObjectDetector} A new objectDetector instance. */ const objectDetector = (...inputs) => { const { string, options = {}, callback } = handleArguments(...inputs); @@ -285,4 +139,4 @@ const objectDetector = (...inputs) => { return instance; }; -export default objectDetector; \ No newline at end of file +export default objectDetector; From 53c6b1f675b5563a06eb6c2c9e1792ef6c25d5a5 Mon Sep 17 00:00:00 2001 From: douaaz <94295853+douaaz@users.noreply.github.com> Date: Thu, 8 Aug 2024 01:44:09 +0100 Subject: [PATCH 05/13] add box for detected objects --- .../family.jpeg | Bin 117939 -> 0 bytes .../objects.jpg | Bin 0 -> 185627 bytes .../sketch.js | 54 ++++++++++++++---- 3 files changed, 43 insertions(+), 11 deletions(-) delete mode 100644 examples/objectDetector-single-image copy/family.jpeg create mode 100644 examples/objectDetector-single-image copy/objects.jpg diff --git a/examples/objectDetector-single-image copy/family.jpeg b/examples/objectDetector-single-image copy/family.jpeg deleted file mode 100644 index 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bytes examples/objectDetector-webcam/sketch.js | 94 ++++++++++++++++++ 5 files changed, 117 insertions(+), 2 deletions(-) create mode 100644 examples/objectDetector-webcam/Printable Bill.pdf create mode 100644 examples/objectDetector-webcam/index.html create mode 100644 examples/objectDetector-webcam/objects.jpg create mode 100644 examples/objectDetector-webcam/sketch.js diff --git a/examples/objectDetector-single-image copy/sketch.js b/examples/objectDetector-single-image copy/sketch.js index c2776fb0..e4442ae3 100644 --- a/examples/objectDetector-single-image copy/sketch.js +++ b/examples/objectDetector-single-image copy/sketch.js @@ -42,7 +42,6 @@ function draw() { let w = object.bbox[2]; let h = object.bbox[3]; - // Set the color stroke(object.color.r, object.color.g, object.color.b); noFill(); @@ -69,6 +68,6 @@ function gotObjects(results) { return object; }); - // Redraw the canvas to update the bounding boxes + // Redraw canvas to update the bounding boxes redraw(); } diff --git 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zoXhl)G`8(dMmmVurC3rr;QA2}4!f1wTnKHfTpw7~9DJuM?;AAEqbD+9Ks)u5 + + + + + Object Detection in Dim Light + + + + + + + + + diff --git a/examples/objectDetector-dimLight/sketch.js b/examples/objectDetector-dimLight/sketch.js new file mode 100644 index 00000000..8c9a879b --- /dev/null +++ b/examples/objectDetector-dimLight/sketch.js @@ -0,0 +1,61 @@ +let canvas; +let ctx; +let imageElement; +let objectDetector; + +async function setup() { + + canvas = document.getElementById('canvas'); + ctx = canvas.getContext('2d'); + + + imageElement = new Image(); + imageElement.src = 'dimmy.jpg'; + imageElement.onload = async () => { + + canvas.width = imageElement.width; + canvas.height = imageElement.height; + ctx.drawImage(imageElement, 0, 0); + + + objectDetector = await cocoSsd.load(); + console.log('Object Detector Loaded'); + + + detectObjects(); + }; +} + +async function detectObjects() { + + const results = await objectDetector.detect(canvas); + console.log(results); + + + drawResults(results); +} + +function drawResults(objects) { + + ctx.clearRect(0, 0, canvas.width, canvas.height); + + + ctx.drawImage(imageElement, 0, 0); + + + objects.forEach(object => { + ctx.beginPath(); + ctx.rect(object.bbox[0], object.bbox[1], object.bbox[2], object.bbox[3]); + ctx.lineWidth = 2; + ctx.strokeStyle = 'red'; + ctx.stroke(); + + ctx.font = '16px Arial'; + ctx.fillStyle = 'red'; + ctx.fillText(object.class, object.bbox[0], object.bbox[1] > 10 ? object.bbox[1] - 5 : 10); + }); +} + + +setup(); + diff --git a/examples/objectDetector-single-image copy/index.html b/examples/objectDetector-single-image copy/index.html index 5280c27d..31e16d08 100644 --- a/examples/objectDetector-single-image copy/index.html +++ b/examples/objectDetector-single-image copy/index.html @@ -3,7 +3,7 @@ Learn more about the ml5.js project: https://ml5js.org/ ml5.js license and Code of Conduct: https://github.com/ml5js/ml5-next-gen/blob/main/LICENSE.md - This example demonstrates hand tracking on an image through ml5.handPose. + This example demonstrates detecting objects in an image through ml5.objectDetector. --> From f1535b0633462e5701b89f624c6ccda7d1b38534 Mon Sep 17 00:00:00 2001 From: douaaz <94295853+douaaz@users.noreply.github.com> Date: Mon, 2 Sep 2024 09:09:55 +0800 Subject: [PATCH 08/13] changes --- .../Printable Bill.pdf | Bin .../index.html | 0 .../objects.jpg | Bin .../sketch.js | 0 4 files changed, 0 insertions(+), 0 deletions(-) rename examples/{objectDetector-single-image copy => objectDetector-single-image}/Printable Bill.pdf (100%) rename examples/{objectDetector-single-image copy => objectDetector-single-image}/index.html (100%) rename examples/{objectDetector-single-image copy => objectDetector-single-image}/objects.jpg (100%) rename examples/{objectDetector-single-image copy => objectDetector-single-image}/sketch.js (100%) diff --git a/examples/objectDetector-single-image copy/Printable Bill.pdf b/examples/objectDetector-single-image/Printable Bill.pdf similarity index 100% rename from examples/objectDetector-single-image copy/Printable Bill.pdf rename to examples/objectDetector-single-image/Printable Bill.pdf diff --git a/examples/objectDetector-single-image copy/index.html b/examples/objectDetector-single-image/index.html similarity index 100% rename from examples/objectDetector-single-image copy/index.html rename to examples/objectDetector-single-image/index.html diff --git a/examples/objectDetector-single-image copy/objects.jpg b/examples/objectDetector-single-image/objects.jpg similarity index 100% rename from examples/objectDetector-single-image copy/objects.jpg rename to examples/objectDetector-single-image/objects.jpg diff --git a/examples/objectDetector-single-image copy/sketch.js b/examples/objectDetector-single-image/sketch.js similarity index 100% rename from examples/objectDetector-single-image copy/sketch.js rename to examples/objectDetector-single-image/sketch.js From 23dc069cd0ec1917e665e4b4ea65d21ca6f25567 Mon Sep 17 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zn4B@|rka-?Q_*srGE&xZ9g>e{buF9c_Fbm`wK(gA=1O(5rmoe&k^iq5qd{v;j&G=1 z3dN6GeOv0{`ExxAjt!2(2?nLDB3ex~gp^6?a}_`1*pBnnZ|S3?n33GlBBvWyPhy=s z{ybAPErQGkZ$$gXaVbx3vAULbU(=6LxjZWLuz?A?6Q_^~ns zS+4f@NYe;ie$)B>o1;o`?fuQ?0xwJBW z!>=5aweLupLMp4+8j&u8?XtTX$3EZAqx?Jh?MNIF-))GaRC`Sr7;+ z?EzMb8YwdU%YA{T?0t2Bfe4^i zoTD%XPBC(uQLm5+B62*1JSp<5nZo?DoQ_g5{nZ>LO29Y}0ZziyWmd7v^c2f%PN+{| zrsC7eapLPy2#swaP@rkao06XJ*q+pak1g3wVoE3Ggite(Q#np}Pz5S}%Mu!DVZV`9 z24kg-8gUN^l+8!AqmohheKSrZ>V#ROmYzXU6M%F|gn0_fo>hVZ1cdLs6Lco)p|X|^ ztyYJ&0((ndT0s!6A{7Ti8ay>tGg6PaQy>G%_<;K1$F_xlW~#cQh4{P5aV732nNJcS z47){iG9^BBBV{sUWUMjzEcTW0TCJs6KS>~fs8wxn*AMfBOx_Plp^(x#B{AbbJ+Fum zYP0yP3qxti?Nxbe9^fZSWfLUq?Ht9^hs+Tz1xrHI5qi};6dbUwKAR+dE8`3&XE?fta93d5T1M?vE;f1-d3~i#bgNSRy4w zP&rBPr3Ep<_Ef&{D+~9KiRB3rRf^P$peKW&=6Wc} z0RTyXa0HS0AYk;^W@ffyx|PD9FMDg*j6&=BnZNz10vatmW+TeqzDp18iXbX7TGp*!BY?sJgT)tG1R9xyy`m(gE~;MZ-caB+1N>7PHcsH#tff1SU@Mfr1KS zzEPCg6?T&a9Lt1Tlw{oH3yqxcz0lp(RE)43x+ - - - - - - - - ml5.js handPose Image Example - - - - - - - diff --git a/examples/soundClassifier-speech-command/handPose-single-image copy/sketch.js b/examples/soundClassifier-speech-command/handPose-single-image copy/sketch.js deleted file mode 100644 index f756fa44..00000000 --- a/examples/soundClassifier-speech-command/handPose-single-image copy/sketch.js +++ /dev/null @@ -1,45 +0,0 @@ -/* - * 👋 Hello! This is an ml5.js example made and shared with ❤️. - * Learn more about the ml5.js project: https://ml5js.org/ - * ml5.js license and Code of Conduct: https://github.com/ml5js/ml5-next-gen/blob/main/LICENSE.md - * - * This example demonstrates hand tracking on an image through ml5.handPose. - */ - -let handPose; -let video; -let hands = []; - -function preload() { - // Load the image to be detected - img = loadImage("hand.jpg"); - // Load the handPose model - handPose = ml5.handPose(); -} - -function setup() { - createCanvas(640, 480); - // Draw the image - image(img, 0, 0); - // Detect hands in an image - handPose.detect(img, gotHands); -} - -function draw() { - // Draw all the hand keypoints - for (let i = 0; i < hands.length; i++) { - let hand = hands[i]; - for (let j = 0; j < hand.keypoints.length; j++) { - let keypoint = hand.keypoints[j]; - fill(0, 255, 0); - noStroke(); - circle(keypoint.x, keypoint.y, 10); - } - } -} - -// Callback function for when handPose outputs data -function gotHands(results) { - // save the output to the hands variable - hands = results; -} From 0dbb20e4304255216f6d19e34e07ae073fcb87c5 Mon Sep 17 00:00:00 2001 From: douaaz <94295853+douaaz@users.noreply.github.com> Date: Mon, 2 Sep 2024 09:23:26 +0800 Subject: [PATCH 11/13] changes after yarn run format --- examples/objectDetector-dimLight/sketch.js | 36 ++++++++----------- .../objectDetector-single-image/sketch.js | 4 +-- examples/objectDetector-webcam/sketch.js | 12 +++---- src/ObjectDetector/index.js | 30 ++++++++++++---- 4 files changed, 45 insertions(+), 37 deletions(-) diff --git a/examples/objectDetector-dimLight/sketch.js b/examples/objectDetector-dimLight/sketch.js index 8c9a879b..142f57f4 100644 --- a/examples/objectDetector-dimLight/sketch.js +++ b/examples/objectDetector-dimLight/sketch.js @@ -4,58 +4,50 @@ let imageElement; let objectDetector; async function setup() { - - canvas = document.getElementById('canvas'); - ctx = canvas.getContext('2d'); + canvas = document.getElementById("canvas"); + ctx = canvas.getContext("2d"); - imageElement = new Image(); - imageElement.src = 'dimmy.jpg'; + imageElement.src = "dimmy.jpg"; imageElement.onload = async () => { - canvas.width = imageElement.width; canvas.height = imageElement.height; ctx.drawImage(imageElement, 0, 0); - objectDetector = await cocoSsd.load(); - console.log('Object Detector Loaded'); + console.log("Object Detector Loaded"); - detectObjects(); }; } async function detectObjects() { - const results = await objectDetector.detect(canvas); console.log(results); - drawResults(results); } function drawResults(objects) { - ctx.clearRect(0, 0, canvas.width, canvas.height); - ctx.drawImage(imageElement, 0, 0); - - objects.forEach(object => { + objects.forEach((object) => { ctx.beginPath(); ctx.rect(object.bbox[0], object.bbox[1], object.bbox[2], object.bbox[3]); ctx.lineWidth = 2; - ctx.strokeStyle = 'red'; + ctx.strokeStyle = "red"; ctx.stroke(); - - ctx.font = '16px Arial'; - ctx.fillStyle = 'red'; - ctx.fillText(object.class, object.bbox[0], object.bbox[1] > 10 ? object.bbox[1] - 5 : 10); + + ctx.font = "16px Arial"; + ctx.fillStyle = "red"; + ctx.fillText( + object.class, + object.bbox[0], + object.bbox[1] > 10 ? object.bbox[1] - 5 : 10 + ); }); } - setup(); - diff --git a/examples/objectDetector-single-image/sketch.js b/examples/objectDetector-single-image/sketch.js index e4442ae3..6e547868 100644 --- a/examples/objectDetector-single-image/sketch.js +++ b/examples/objectDetector-single-image/sketch.js @@ -59,11 +59,11 @@ function draw() { // Callback function for when objectDetector outputs data function gotObjects(results) { // Save the output to the objects variable and assign a random color to each object - objects = results.map(object => { + objects = results.map((object) => { object.color = { r: random(255), g: random(255), - b: random(255) + b: random(255), }; return object; }); diff --git a/examples/objectDetector-webcam/sketch.js b/examples/objectDetector-webcam/sketch.js index d61004b0..874e5e7c 100644 --- a/examples/objectDetector-webcam/sketch.js +++ b/examples/objectDetector-webcam/sketch.js @@ -19,7 +19,7 @@ function preload() { ml5.setBackend("webgl"); // Load the objectDetector model - objectDetector = ml5.objectDetector('cocossd', modelReady); + objectDetector = ml5.objectDetector("cocossd", modelReady); } function setup() { @@ -58,9 +58,9 @@ function draw() { // Draw the label with the class name noStroke(); - fill(object.color.r, object.color.g, object.color.b); - textSize(16); - text(object.class, x + 5, y + 15); + fill(object.color.r, object.color.g, object.color.b); + textSize(16); + text(object.class, x + 5, y + 15); } } } @@ -80,11 +80,11 @@ function videoReady() { // Callback function for when objectDetector outputs data function gotObjects(results) { // Save the output to the objects variable and assign a random color to each object - objects = results.map(object => { + objects = results.map((object) => { object.color = { r: random(255), g: random(255), - b: random(255) + b: random(255), }; return object; }); diff --git a/src/ObjectDetector/index.js b/src/ObjectDetector/index.js index 6f69004a..2a88874e 100644 --- a/src/ObjectDetector/index.js +++ b/src/ObjectDetector/index.js @@ -29,7 +29,12 @@ class ObjectDetector { * @private */ constructor(modelName, options, callback) { - this.modelName = handleModelName(modelName, ["cocoSsd"], "cocoSsd", "objectDetector"); + this.modelName = handleModelName( + modelName, + ["cocoSsd"], + "cocoSsd", + "objectDetector" + ); this.model = null; this.config = options; this.runtimeConfig = {}; @@ -52,7 +57,7 @@ class ObjectDetector { async loadModel() { await tf.ready(); this.model = await cocoSsd.load(); - console.log('Finished loading cocoSsd'); + console.log("Finished loading cocoSsd"); return this; } @@ -65,13 +70,16 @@ class ObjectDetector { */ async detect(...inputs) { const argumentObject = handleArguments(...inputs); - argumentObject.require("image", "An html or p5.js image, video, or canvas element argument is required for detect()."); + argumentObject.require( + "image", + "An html or p5.js image, video, or canvas element argument is required for detect()." + ); const { image, callback } = argumentObject; await mediaReady(image, false); const predictions = await this.model.detect(image); - console.log('raw result from cocoSsd', predictions); + console.log("raw result from cocoSsd", predictions); const result = predictions; @@ -86,8 +94,14 @@ class ObjectDetector { */ detectStart(...inputs) { const argumentObject = handleArguments(...inputs); - argumentObject.require("image", "An html or p5.js image, video, or canvas element argument is required for detectStart()."); - argumentObject.require("callback", "A callback function argument is required for detectStart()."); + argumentObject.require( + "image", + "An html or p5.js image, video, or canvas element argument is required for detectStart()." + ); + argumentObject.require( + "callback", + "A callback function argument is required for detectStart()." + ); this.detectMedia = argumentObject.image; this.detectCallback = argumentObject.callback; @@ -97,7 +111,9 @@ class ObjectDetector { this.detectLoop(); } if (this.prevCall === "start") { - console.warn("detectStart() was called more than once without calling detectStop(). Only the latest detectStart() call will take effect."); + console.warn( + "detectStart() was called more than once without calling detectStop(). Only the latest detectStart() call will take effect." + ); } this.prevCall = "start"; } From 603514e508259ff4c6d9145e86e4fd829652ae60 Mon Sep 17 00:00:00 2001 From: douaaz <94295853+douaaz@users.noreply.github.com> Date: Mon, 2 Sep 2024 09:27:30 +0800 Subject: [PATCH 12/13] editing comments --- examples/objectDetector-webcam/index.html | 2 +- examples/objectDetector-webcam/sketch.js | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/objectDetector-webcam/index.html b/examples/objectDetector-webcam/index.html index 5280c27d..fc2704d3 100644 --- a/examples/objectDetector-webcam/index.html +++ b/examples/objectDetector-webcam/index.html @@ -3,7 +3,7 @@ Learn more about the ml5.js project: https://ml5js.org/ ml5.js license and Code of Conduct: https://github.com/ml5js/ml5-next-gen/blob/main/LICENSE.md - This example demonstrates hand tracking on an image through ml5.handPose. + This example demonstrates object detection on a video through ml5.ObjectDetector. --> diff --git a/examples/objectDetector-webcam/sketch.js b/examples/objectDetector-webcam/sketch.js index 874e5e7c..7ad52f51 100644 --- a/examples/objectDetector-webcam/sketch.js +++ b/examples/objectDetector-webcam/sketch.js @@ -3,7 +3,7 @@ * Learn more about the ml5.js project: https://ml5js.org/ * ml5.js license and Code of Conduct: https://github.com/ml5js/ml5-next-gen/blob/main/LICENSE.md * - * This example demonstrates object detection on an image through ml5.objectDetector. + * This example demonstrates object detection on a video through ml5.objectDetector. */ let objectDetector; From 436e505b6fd64fed91396b21551416437de52638 Mon Sep 17 00:00:00 2001 From: douaaz <94295853+douaaz@users.noreply.github.com> Date: Mon, 2 Sep 2024 09:39:16 +0800 Subject: [PATCH 13/13] remove unnecessary code --- src/ObjectDetector/index.js | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/ObjectDetector/index.js b/src/ObjectDetector/index.js index 2a88874e..a7abdacb 100644 --- a/src/ObjectDetector/index.js +++ b/src/ObjectDetector/index.js @@ -76,10 +76,10 @@ class ObjectDetector { ); const { image, callback } = argumentObject; - await mediaReady(image, false); + // await mediaReady(image, false); - const predictions = await this.model.detect(image); - console.log("raw result from cocoSsd", predictions); + // const predictions = await this.model.detect(image); + // console.log("raw result from cocoSsd", predictions); const result = predictions;