<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/[email protected]/dist/teachablemachine-pose.min.js"></script>
<div>Teachable Machine Pose Model</div>
<button type="button" onclick="init()">Start</button>
<div><canvas id="canvas"></canvas></div>
<div id="label-container"></div>
<script type="text/javascript">
// More API functions here:
// https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/pose
// the link to your model provided by Teachable Machine export panel
const URL = "https://teachablemachine.withgoogle.com/models/meSpq04U7/";
let model, webcam, ctx, labelContainer, maxPredictions;
async function init() {
const modelURL = URL + "model.json";
const metadataURL = URL + "metadata.json";
// load the model and metadata
// Refer to tmImage.loadFromFiles() in the API to support files from a file picker
// Note: the pose library adds a tmPose object to your window (window.tmPose)
model = await tmPose.load(modelURL, metadataURL);
maxPredictions = model.getTotalClasses();
// Convenience function to setup a webcam
const size = 200;
const flip = true; // whether to flip the webcam
webcam = new tmPose.Webcam(size, size, flip); // width, height, flip
await webcam.setup(); // request access to the webcam
await webcam.play();
window.requestAnimationFrame(loop);
// append/get elements to the DOM
const canvas = document.getElementById("canvas");
canvas.width = size; canvas.height = size;
ctx = canvas.getContext("2d");
labelContainer = document.getElementById("label-container");
for (let i = 0; i < maxPredictions; i++) { // and class labels
labelContainer.appendChild(document.createElement("div"));
}
}
async function loop(timestamp)...
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