我正在使用 TensorflowJS 对网络摄像头源进行推理。代码可以在 此处 找到。

<script type="text/javascript">
    let model, webcam, labelContainer;

    // 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(500, 500, flip); // width, height, flip
        await webcam.setup(); // request access to the webcam
        await webcam.play();
        window.requestAnimationFrame(loop);

    }

    async function loop() {
        webcam.update(); // update the webcam frame
        window.requestAnimationFrame(loop);
    }


</script>

有没有办法即使在浏览器标签未处于活动状态时也能进行预测？我的意思是未选择标签并且浏览器窗口可能会最小化。