// Create a camera input

  video = createCapture(VIDEO, {

    video: {

      width: 280,

      height: 280,

      aspectRatio: 1

    } 

  });

  // Load the DoodleNet Image Classification model

  classifier = ml5.imageClassifier('DoodleNet', video);

}

​

function setup() {

  // Create a 'label' and 'confidence' div to hold results

  label = createDiv('Label: ...');

  confidence = createDiv('Confidence: ...');

​

  classifyVideo();

}

​

// Get a prediction for the current video frame

function classifyVideo() {

  classifier.classify(gotResult);

}

​

// A function to run when we get any errors and the results

function gotResult(error, results) {

  // Display error in the console

  if (error) {

    console.error(error);

  }

  // The results are in an array ordered by confidence.

  console.log(results);

  // Show the first label and confidence

  label.html('Label: ' + results[0].label);

  confidence.html('Confidence: ' + nf(results[0].confidence, 0, 2)); // Round the confidence to 0.01

  // Call classifyVideo again

  classifyVideo();

}

​