60 lines
2.6 KiB
HTML
60 lines
2.6 KiB
HTML
<div>Teachable Machine Image Model</div>
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<button type="button" onclick="init()">Start</button>
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<div id="webcam-container"></div>
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<div id="label-container"></div>
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<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest/dist/tf.min.js"></script>
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<script src="https://cdn.jsdelivr.net/npm/@teachablemachine/image@latest/dist/teachablemachine-image.min.js"></script>
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<script type="text/javascript">
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// More API functions here:
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// https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image
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// the link to your model provided by Teachable Machine export panel
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const URL = "https://teachablemachine.withgoogle.com/models/0z9_XB2UA/";
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let model, webcam, labelContainer, maxPredictions;
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// Load the image model and setup the webcam
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async function init() {
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const modelURL = URL + "model.json";
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const metadataURL = URL + "metadata.json";
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// load the model and metadata
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// Refer to tmImage.loadFromFiles() in the API to support files from a file picker
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// or files from your local hard drive
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// Note: the pose library adds "tmImage" object to your window (window.tmImage)
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model = await tmImage.load(modelURL, metadataURL);
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maxPredictions = model.getTotalClasses();
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// Convenience function to setup a webcam
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const flip = true; // whether to flip the webcam
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webcam = new tmImage.Webcam(200, 200, flip); // width, height, flip
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await webcam.setup(); // request access to the webcam
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await webcam.play();
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window.requestAnimationFrame(loop);
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// append elements to the DOM
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document.getElementById("webcam-container").appendChild(webcam.canvas);
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labelContainer = document.getElementById("label-container");
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for (let i = 0; i < maxPredictions; i++) { // and class labels
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labelContainer.appendChild(document.createElement("div"));
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}
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}
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async function loop() {
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webcam.update(); // update the webcam frame
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await predict();
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window.requestAnimationFrame(loop);
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}
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// run the webcam image through the image model
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async function predict() {
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// predict can take in an image, video or canvas html element
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const prediction = await model.predict(webcam.canvas);
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for (let i = 0; i < maxPredictions; i++) {
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const classPrediction =
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prediction[i].className + ": " + prediction[i].probability.toFixed(2);
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labelContainer.childNodes[i].innerHTML = classPrediction;
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}
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}
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</script>
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