LA NOSTRA SOLUZIONE

Qui sotto il nostro fiel html + javascript che potete salvare in un file RIFIUTI.HTML, caricarlo nel browser e provare a riconoscere i rifiuti da smaltire per un corretto conferimento

Jv script:

<div>Teachable Machine Image Model</div>

<button type="button" onclick="init()">Start</button>

<div id="webcam-container"></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/image@0.8/dist/teachablemachine-image.min.js"></script>

<script type="text/javascript">

    // More API functions here:

    // https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/image

    // the link to your model provided by Teachable Machine export panel

    const URL = "https://teachablemachine.withgoogle.com/models/sFBex2OZS/";

    let model, webcam, labelContainer, maxPredictions;

    // Load the image model and setup the webcam

    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

        // or files from your local hard drive

        // Note: the pose library adds "tmImage" object to your window (window.tmImage)

        model = await tmImage.load(modelURL, metadataURL);

        maxPredictions = model.getTotalClasses();

        // Convenience function to setup a webcam

        const flip = true; // whether to flip the webcam

        webcam = new tmImage.Webcam(200, 200, flip); // width, height, flip

        await webcam.setup(); // request access to the webcam

        await webcam.play();

        window.requestAnimationFrame(loop);

        // append elements to the DOM

        document.getElementById("webcam-container").appendChild(webcam.canvas);

        labelContainer = document.getElementById("label-container");

        for (let i = 0; i < maxPredictions; i++) { // and class labels

            labelContainer.appendChild(document.createElement("div"));

        }

    }

    async function loop() {

        webcam.update(); // update the webcam frame

        await predict();

        window.requestAnimationFrame(loop);

    }

    // run the webcam image through the image model

    async function predict() {

        // predict can take in an image, video or canvas html element

        const prediction = await model.predict(webcam.canvas);

        for (let i = 0; i < maxPredictions; i++) {

            const classPrediction =

                prediction[i].className + ": " + prediction[i].probability.toFixed(2);

            labelContainer.childNodes[i].innerHTML = classPrediction;

        }

    }

</script>