index.js

const classifier = knnClassifier.create();

const webcamElement = document.getElementById('webcam');

let net;

async function app() {
    console.log('Loading mobilenet..');
  
    // Load the model.
    net = await mobilenet.load();
    console.log('Successfully loaded model');
  
    // Create an object from Tensorflow.js data API which could capture image 
    // from the web camera as Tensor.
    const webcam = await tf.data.webcam(webcamElement);
  
    // Reads an image from the webcam and associates it with a specific class
    // index.
    const addExample = async classId => {
      // Capture an image from the web camera.
      const img = await webcam.capture();
  
      // Get the intermediate activation of MobileNet 'conv_preds' and pass that
      // to the KNN classifier.
      const activation = net.infer(img, 'conv_preds');
  
      // Pass the intermediate activation to the classifier.
      classifier.addExample(activation, classId);
  
      // Dispose the tensor to release the memory.
      img.dispose();
    };
  
    // When clicking a button, add an example for that class.
    document.getElementById('class-a').addEventListener('click', () => addExample(0));
    document.getElementById('class-b').addEventListener('click', () => addExample(1));
    document.getElementById('class-c').addEventListener('click', () => addExample(2));
  
    while (true) {
      if (classifier.getNumClasses() > 0) {
        const img = await webcam.capture();
  
        // Get the activation from mobilenet from the webcam.
        const activation = net.infer(img, 'conv_preds');
        // Get the most likely class and confidences from the classifier module.
        const result = await classifier.predictClass(activation);
  
        const classes = ['A', 'B', 'C'];
        document.getElementById('console').innerText = `
          prediction: ${classes[result.label]}\n
          probability: ${result.confidences[result.label]}
        `;
  
        // Dispose the tensor to release the memory.
        img.dispose();
      }
  
      await tf.nextFrame();
    }
  }

app();