CODEWITHSUNDEEP
javascriptmachine-learningobject-detectiontensorflow.js
Lukas
Lukas

Reputation: 19

Load own model in TensorFlow.js for object detection

I have a question about using TensorFlow.js to detect objects with webcam. Currently I am using the pre-trained model coco-ssd.

index.html:

<html lang="en">
  <head>
    <title>Multiple object detection using pre trained model in TensorFlow.js</title>
    <meta charset="utf-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <!-- Import the webpage's stylesheet -->
    <link rel="stylesheet" href="style.css">
  </head>  
  <body>
    <h1>Multiple object detection using pre trained model in TensorFlow.js</h1>

    <p>Wait for the model to load before clicking the button to enable the webcam - at which point it will become visible to use.</p>
    
    <section id="demos" class="invisible">

      <p>Hold some objects up close to your webcam to get a real-time classification! When ready click "enable webcam" below and accept access to the webcam when the browser asks (check the top left of your window)</p>
      
      <div id="liveView" class="camView">
        <button id="webcamButton">Enable Webcam</button>
        <video id="webcam" autoplay width="640" height="480"></video>
      </div>
    </section>

    <!-- Import TensorFlow.js library -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js" type="text/javascript"></script>
    <!-- Load the coco-ssd model to use to recognize things in images -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/coco-ssd"></script>
    
    <!-- Import the page's JavaScript to do some stuff -->
    <script src="script.js" defer></script>
  </body>
</html>

script.js:

const video = document.getElementById('webcam');
const liveView = document.getElementById('liveView');
const demosSection = document.getElementById('demos');
const enableWebcamButton = document.getElementById('webcamButton');

// Check if webcam access is supported.
function getUserMediaSupported() {
  return !!(navigator.mediaDevices &&
    navigator.mediaDevices.getUserMedia);
}

// If webcam supported, add event listener to button for when user
// wants to activate it to call enableCam function which we will 
// define in the next step.
if (getUserMediaSupported()) {
  enableWebcamButton.addEventListener('click', enableCam);
} else {
  console.warn('getUserMedia() is not supported by your browser');
}

// Enable the live webcam view and start classification.
function enableCam(event) {
  // Only continue if the COCO-SSD has finished loading.
  if (!model) {
    return;
  }
  
  // Hide the button once clicked.
  event.target.classList.add('removed');  
  
  // getUsermedia parameters to force video but not audio.
  const constraints = {
    video: true
  };

  // Activate the webcam stream.
  navigator.mediaDevices.getUserMedia(constraints).then(function(stream) {
    video.srcObject = stream;
    video.addEventListener('loadeddata', predictWebcam);
  });
}


// Store the resulting model in the global scope of our app.
var model = undefined;

// Before we can use COCO-SSD class we must wait for it to finish
// loading. Machine Learning models can be large and take a moment 
// to get everything needed to run.
// Note: cocoSsd is an external object loaded from our index.html
// script tag import so ignore any warning in Glitch.
cocoSsd.load().then(function (loadedModel) {
  model = loadedModel;
  // Show demo section now model is ready to use.
  demosSection.classList.remove('invisible');
});


var children = [];

function predictWebcam() {
  // Now let's start classifying a frame in the stream.
  model.detect(video).then(function (predictions) {
    // Remove any highlighting we did previous frame.
    for (let i = 0; i < children.length; i++) {
      liveView.removeChild(children[i]);
    }
    children.splice(0);
    
    // Now lets loop through predictions and draw them to the live view if
    // they have a high confidence score.
    for (let n = 0; n < predictions.length; n++) {
      // If we are over 66% sure we are sure we classified it right, draw it!
      if (predictions[n].score > 0.66) {
        const p = document.createElement('p');
        p.innerText = predictions[n].class  + ' - with ' 
            + Math.round(parseFloat(predictions[n].score) * 100) 
            + '% confidence.';
        p.style = 'margin-left: ' + predictions[n].bbox[0] + 'px; margin-top: '
            + (predictions[n].bbox[1] - 10) + 'px; width: ' 
            + (predictions[n].bbox[2] - 10) + 'px; top: 0; left: 0;';

        const highlighter = document.createElement('div');
        highlighter.setAttribute('class', 'highlighter');
        highlighter.style = 'left: ' + predictions[n].bbox[0] + 'px; top: '
            + predictions[n].bbox[1] + 'px; width: ' 
            + predictions[n].bbox[2] + 'px; height: '
            + predictions[n].bbox[3] + 'px;';

        liveView.appendChild(highlighter);
        liveView.appendChild(p);
        children.push(highlighter);
        children.push(p);
      }
    }
    
    // Call this function again to keep predicting when the browser is ready.
    window.requestAnimationFrame(predictWebcam);
  });
}

Now I would like to customize the script to use my own model, which I previously created and trained with Tensorflow for Python. I have already converted it with the converter tfjs_convert into a .json format.

Files of my own model

How do I modify my code so that my own model is now used? I have already tried a few things, but unfortunately have not made any progress.

Upvotes: 1

Views: 1881

Answers (1)

Arquitecto
Arquitecto

Reputation: 119

You can use loadGraphModel from @tensorflow/tfjs-converter to load from Json.

I love this example.

Upvotes: 1

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