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javascript - 如何更改 tmImage 中的相机输入设备？

问题描述

我最近用 Google Teachable Machine 制作了一个简单的图像检测 AI，我做了很多工作，但我有一个问题。我无法更改相机输入设备。我安装了 Iriun 网络摄像头，无论我做什么它都不想切换到其他输入（我更改了 opera gx 相机设置）。当我阻止或移除 Iriun 网络摄像头时，它没有显示任何内容，它要求摄像头权限然后什么也没发生。我使用了谷歌可教机器的示例代码。任何人都可以帮忙吗？

设置相机的部分：

        webcam = new tmImage.Webcam(1280, 720, flip); // width, height, flip
        await webcam.setup(); // request access to the webcam
        await webcam.play();
        window.requestAnimationFrame(loop);

完整代码：

<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/sDyEbFFcX/";

    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 = false; // whether to flip the webcam
        webcam = new tmImage.Webcam(1280, 720, 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>

标签： javascripthtmlmachine-learningwebcam

解决方案

看这个部分：

    // Convenience function to setup a webcam
    const flip = false; // whether to flip the webcam
    webcam = new tmImage.Webcam(1280, 720, flip); // width, height, flip
 -->>   await webcam.setup(); // request access to the webcam
    await webcam.play();
    window.requestAnimationFrame(loop);

    // append elements to the DOM

替换为：

    // Convenience function to setup a webcam
    const flip = false; // 
    webcam = new tmImage.Webcam(1280, 720, flip); // 
    await webcam.setup({ facingMode: "environment" });// <--aca esta la magia
    await webcam.play();
    window.requestAnimationFrame(loop);

    // append elements to the DOM

祝你好运

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