Différences

Ci-dessous, les différences entre deux révisions de la page.

--- apprentissage:bac [2023/06/30 09:41] – jbpuel
+++ apprentissage:bac [2023/06/30 09:43] (Version actuelle) – jbpuel
@@ Ligne 1: / Ligne 1: @@
 ====== Bac à sable ======
-L'espace de **découverte** du wiki.
+[[https://teachablemachine.withgoogle.com/models/3HfMC5kbh/]]
-<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);
-    }
-    function drawPose(pose) {
-        if (webcam.canvas) {
-            ctx.drawImage(webcam.canvas, 0, 0);
-            // draw the keypoints and skeleton
-            if (pose) {
-                const minPartConfidence = 0.5;
-                tmPose.drawKeypoints(pose.keypoints, minPartConfidence, ctx);
-                tmPose.drawSkeleton(pose.keypoints, minPartConfidence, ctx);
-            }
-        }
-    }
-</script>
-</html>