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‎.gitattributes

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# Auto detect text files and perform LF normalization
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* text=auto

‎.vscode/launch.json

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{
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// Use IntelliSense to learn about possible attributes.
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// Hover to view descriptions of existing attributes.
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// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
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"version": "0.2.0",
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"configurations": [
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{
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"type": "node",
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"request": "launch",
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"name": "Launch Program",
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"skipFiles": [
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"<node_internals>/**"
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],
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"program": "${file}"
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}
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]
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}

‎about.html

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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<script src="https://cdn.jsdelivr.net/npm/typed.js@2.0.11"></script>
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<link rel="stylesheet" href="https://unpkg.com/blocks.css/dist/blocks.min.css" />
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<title>About yogAI</title>
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<link rel="stylesheet" href="style.css">
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</head>
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<body>
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<div><h1 class= "fixed block" style="width:700px; margin-bottom: 40px; padding:20px; font-size: 100px">About yogAI.</h1></div>
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<p class= "fixed block" style="margin-bottom: 40px; padding:20px; font-size: 24px">Hi you! Welcome to yogAI, developed in 2020 by Cristina Maillo. <br><br> I'd first like to thank my . <br>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed eget orci pretium, placerat ante vitae, finibus ligula. Nulla ac dignissim lacus. Vestibulum lobortis leo lorem. Pellentesque et finibus leo, eu scelerisque metus. Etiam id nulla ac sapien posuere laoreet eget non nibh. Mauris eleifend nunc aliquet nunc ultricies, at scelerisque lectus auctor. Quisque ultrices, purus a dictum scelerisque, urna orci hendrerit est, at vestibulum dolor turpis porttitor odio. Morbi quis lectus sit amet tellus accumsan ultricies in non velit. Sed eget tellus ac ante cursus venenatis. Fusce placerat tristique velit, non tincidunt nisl rutrum nec.</p>
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<div><a href="landingPage.html" style="width: 150px"><button class="block" style="width: 150px; height: 60px; font-size: 30px;">🏠 Back</button></a></div>
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</body>
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<footer>&copy; Copyright & Stuff 2020. By Cristina Maillo. Made With ❤️ & 💧. Please stay home. </footer>
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</html>

‎basic4.json

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‎dataCollection.js

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let video;
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let poseNet;
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let pose;
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let skeleton;
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let yogi;
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let poseLabel = "";
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let targetPose;
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let state = "waiting";
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function keyPressed() {
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if (key == 's') {
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yogi.saveData();
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} else {
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targetPose = key;
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console.log(targetPose);
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setTimeout(function() {
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console.log('collecting');
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state = 'collecting';
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setTimeout(function() {
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console.log('not collecting');
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state = 'waiting';
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}, 5000);
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}, 5000);
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}
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}
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function setup() {
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createCanvas(640, 480);
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video = createCapture(VIDEO);
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video.hide();
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poseNet = ml5.poseNet(video, modelLoaded);
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poseNet.on('pose', gotPoses);
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let options = {
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inputs: 34,
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outputs: 4,
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task: "classification",
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debug: true
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}
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yogi = ml5.neuralNetwork(options);
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}
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function gotPoses(poses) {
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if (poses.length > 0) {
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pose = poses[0].pose;
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skeleton = poses[0].skeleton;
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if (state == 'collecting') {
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let inputs = [];
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for (let i = 0; i < pose.keypoints.length; i++) {
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let x = pose.keypoints[i].position.x;
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let y = pose.keypoints[i].position.y;
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inputs.push(x);
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inputs.push(y);
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}
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let target = [targetPose];
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yogi.addData(inputs, target);
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}}}
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function modelLoaded() {
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console.log('poseNet ready');
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}
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function draw() {
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translate(video.width, 0);
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scale(-1,1);
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image(video, 0, 0, video.width, video.height);
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if (pose) {
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for (let i = 0; i < skeleton.length; i++) {
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let a = skeleton[i][0];
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let b = skeleton[i][1];
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strokeWeight(8);
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stroke(244, 194, 194);
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line(a.position.x, a.position.y, b.position.x, b.position.y);
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}
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}
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}

‎github.svg

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‎index.html

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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Welcome to yogAI</title>
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<link rel="stylesheet" href="style.css">
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<link rel="stylesheet" href="https://unpkg.com/blocks.css/dist/blocks.min.css" />
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</head>
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<body>
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<h1 class="fixed block" style="width:28%; text-align:center; padding-left:20px; padding-right: 350px; margin-bottom: 20px">Hello,</h1>
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<h1 class="fixed block" style="width:1200px; margin-bottom: 50px; padding-left:20px"> welcome to yogAI.</h1>
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<div><a href="instructions.html" style="width: 120px"><li class="block" style="width: 120px; height: 60px; font-size: 30px;">💡Test</li></a></div>
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<div><a href="instructions.html" style="width: 150px"><li class="block" style="width: 150px; height: 60px; font-size: 30px;">📖Learn</li></a></div>
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<div><a href="https://github.com/cris-maillo/yogAI" target="_blank" style="width: 180px"><li class="block" style="width: 180px; height: 60px; font-size: 30px;"><img src="github.svg" alt="GitHub logo" width="30" height="30" style="margin-top:3px"> Git Hub</li></a></div>
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<div><a href="about.html" style="width: 210px"><li class="block" style="width: 210px; height: 60px; font-size: 30px;">😊About Me</li></a></div>
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</body>
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<footer>&copy; Copyright & Stuff 2020. By Cristina Maillo. Made With ❤️ & 💧. Please stay home. </footer>
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</html>

‎instructions.html

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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Instructions</title>
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<link rel="stylesheet" href="style.css">
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<link rel="stylesheet" href="https://unpkg.com/blocks.css/dist/blocks.min.css" />
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</head>
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<body>
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<h1 class="fixed block" style="width:650px; text-align:center; padding-left:20px; padding-right: 350px; margin-bottom: 50px; font-size: 100px">Instructions</h1>
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<div class="buttons">
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<div><li class="fixed block" style="width: 60px; height: 60px; font-size: 30px;">1.</li></div>
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<div><li class="fixed block" style="width: 60px; height: 60px; font-size: 30px;">2.</li></div>
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<div><li class="fixed block" style="width: 60px; height: 60px; font-size: 30px;">3.</li></div>
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<div><li></li></div>
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</div>
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<div class="buttons">
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<div><a href="landingPage.html" style="width: 150px"><button class="block" style="width: 150px; height: 60px; font-size: 30px; margin-top: 350px">🏠 Back</button></a></div>
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<div></div>
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<div><a href="model.html" style="width: 150px"><li class="block" style="width: 150px; height: 60px; font-size: 30px; margin-top:350px">🏁 Start</li></a></div>
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<div></div>
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</div>
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</body>
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<footer>&copy; Copyright & Stuff 2020. By Cristina Maillo. Made With ❤️ & 💧. Please stay home. </footer>
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</html>

‎ml5.min.js

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‎model.html

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<!DOCTYPE html><html lang="en"><head>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<script src="p5.min.js"></script>
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<script src="p5.dom.min.js"></script>
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<script src="p5.min.js"></script>
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<script src="p5.sound.min.js"></script>
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<script src="ml5.min.js"></script>
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<link rel="stylesheet" href="style.css">
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<meta charset="utf-8">
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</head>
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<body>
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<h1 class="fixed block" style="width:300px; padding-left:20px; padding-right: 350px; margin-bottom: 20px; font-size: 40px;">💡Test</h1>
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<script src="modelDeploy.js">
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</script>
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</body>
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<footer>&copy; Copyright & Stuff 2020. By Cristina Maillo. Made With ❤️ & 💧. Please stay home. </footer></html>

‎model/model.json

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{"modelTopology":{"class_name":"Sequential","config":[{"class_name":"Dense","config":{"units":16,"activation":"sigmoid","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense1","trainable":true,"batch_input_shape":[null,34],"dtype":"float32"}},{"class_name":"Dense","config":{"units":4,"activation":"softmax","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"normal","seed":null}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense2","trainable":true}}],"keras_version":"tfjs-layers 1.2.2","backend":"tensor_flow.js"},"weightsManifest":[{"paths":["./model.weights.bin"],"weights":[{"name":"dense_Dense1/kernel","shape":[34,16],"dtype":"float32"},{"name":"dense_Dense1/bias","shape":[16],"dtype":"float32"},{"name":"dense_Dense2/kernel","shape":[16,4],"dtype":"float32"},{"name":"dense_Dense2/bias","shape":[4],"dtype":"float32"}]}]}

‎model/model.weights.bin

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‎model/model_meta.json

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{"data":{"inputMin":[302.3426513671875,150.57106018066406,307.4953918457031,146.5146942138672,296.05572509765625,145.79965209960938,313.5270080566406,154.1967315673828,286.1737365722656,152.5470428466797,327.3584289550781,191.11138916015625,266.07916259765625,193.9165802001953,343.07098388671875,198.33685302734375,225.36016845703125,213.9613800048828,343.88751220703125,194.80796813964844,181.90676879882812,210.48597717285156,280.67218017578125,291.8767395019531,273.5666809082031,285.855712890625,303.8507080078125,351.0625305175781,255.94866943359375,293.529541015625,276.515625,382.5232238769531,245.9301300048828,358.8548278808594],"inputMax":[406.1121826171875,365.42529296875,405.3507385253906,361.3297424316406,404.16778564453125,366.1007385253906,409.6846008300781,360.70269775390625,401.01171875,359.9224853515625,416.2422790527344,392.8089904785156,387.92608642578125,400.4131164550781,411.9314270019531,437.4988708496094,398.0821228027344,431.7273254394531,429.5989074707031,450.0535888671875,418.49114990234375,449.38507080078125,425.494140625,454.1315002441406,394.6825256347656,450.94390869140625,416.5000915527344,500.5633850097656,402.4130554199219,501.7619934082031,423.9959411621094,556.3433227539062,420.876708984375,556.0668334960938],"outputMin":0,"outputMax":1},"meta":{"inputUnits":34,"outputUnits":4,"inputs":{"input0":{"dtype":"number"},"input1":{"dtype":"number"},"input2":{"dtype":"number"},"input3":{"dtype":"number"},"input4":{"dtype":"number"},"input5":{"dtype":"number"},"input6":{"dtype":"number"},"input7":{"dtype":"number"},"input8":{"dtype":"number"},"input9":{"dtype":"number"},"input10":{"dtype":"number"},"input11":{"dtype":"number"},"input12":{"dtype":"number"},"input13":{"dtype":"number"},"input14":{"dtype":"number"},"input15":{"dtype":"number"},"input16":{"dtype":"number"},"input17":{"dtype":"number"},"input18":{"dtype":"number"},"input19":{"dtype":"number"},"input20":{"dtype":"number"},"input21":{"dtype":"number"},"input22":{"dtype":"number"},"input23":{"dtype":"number"},"input24":{"dtype":"number"},"input25":{"dtype":"number"},"input26":{"dtype":"number"},"input27":{"dtype":"number"},"input28":{"dtype":"number"},"input29":{"dtype":"number"},"input30":{"dtype":"number"},"input31":{"dtype":"number"},"input32":{"dtype":"number"},"input33":{"dtype":"number"}},"outputs":{"output0":{"dtype":"string","uniqueValues":["m","t","w","d"],"legend":{"m":[1,0,0,0],"t":[0,1,0,0],"w":[0,0,1,0],"d":[0,0,0,1]}}},"isNormalized":true}}

‎modelDeploy.js

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let video;
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let poseNet;
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let pose;
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let skeleton;
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let yogi;
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let poseLabel
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function setup() {
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createCanvas(640, 480);
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video = createCapture(VIDEO);
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video.hide();
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poseNet = ml5.poseNet(video, modelLoaded);
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poseNet.on('pose', gotPoses);
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let options = {
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inputs: 34,
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outputs: 4,
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task: 'classification',
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debug: true
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}
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yogi = ml5.neuralNetwork(options);
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const modelInfo = {
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model: 'model/model.json',
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metadata: 'model/model_meta.json',
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weights: 'model/model.weights.bin',
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};
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yogi.load(modelInfo, yogiLoaded);
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}
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function yogiLoaded(){
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console.log("Model ready!")
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classifyPose();
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}
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function classifyPose(){
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if (pose) {
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let inputs = [];
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for (let i = 0; i < pose.keypoints.length; i++) {
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let x = pose.keypoints[i].position.x;
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let y = pose.keypoints[i].position.y;
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inputs.push(x);
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inputs.push(y);
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}
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yogi.classify(inputs, gotResult);
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} else {
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setTimeout(classifyPose, 100);
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}
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}
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function gotResult(error, results) {
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if (results[0].confidence > 0.70) {
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if (results[0].label == "m"){
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poseLabel = "Mountain";
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}else{
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if(results[0].label == "t"){
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poseLabel = "Tree";
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}else{
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if(results[0].label == "d"){
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poseLabel = "Downward Dog";
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}else{
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poseLabel = "Warrior II";
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}}}}
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classifyPose();
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}
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function gotPoses(poses) {
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if (poses.length > 0) {
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pose = poses[0].pose;
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skeleton = poses[0].skeleton;
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}
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}
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function modelLoaded() {
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console.log('poseNet ready');
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}
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function draw() {
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push();
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translate(video.width, 0);
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scale(-1,1);
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image(video, 0, 0, video.width, video.height);
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if (pose) {
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for (let i = 0; i < skeleton.length; i++) {
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let a = skeleton[i][0];
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let b = skeleton[i][1];
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strokeWeight(8);
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stroke(244, 194, 194);
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line(a.position.x, a.position.y, b.position.x, b.position.y);
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}
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}
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pop();
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fill(255, 0, 255);
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noStroke();
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if (poseLabel == "Downward Dog"){
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textSize(90);
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}else{
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textSize(150);}
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textAlign(CENTER, CENTER);
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text(poseLabel, width / 2, height / 2);
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}

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