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====== Bac à sable ======

L'espace de **découverte** du wiki.

<html>
<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 src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/pose@0.8/dist/teachablemachine-pose.min.js"></script>
<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 = "./my_model/";
    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) {
        webcam.update(); // update the webcam frame
        await predict();
        window.requestAnimationFrame(loop);
    }

async function predict() {
        // Prediction #1: run input through posenet
        // estimatePose can take in an image, video or canvas html element
        const { pose, posenetOutput } = await model.estimatePose(webcam.canvas);
        // Prediction 2: run input through teachable machine classification model
        const prediction = await model.predict(posenetOutput);

for (let i = 0; i < maxPredictions; i++) {
            const classPrediction =
                prediction[i].className + ": " + prediction[i].probability.toFixed(2);
            labelContainer.childNodes[i].innerHTML = classPrediction;
        }

// finally draw the poses
        drawPose(pose);
    }

Résumé

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