Using advanced pre-trained Web ML models – Part 2: Use MoveNet for pose estimation in browser

In the previous article, we discussed how to use pre-trained Web ML models to perform advanced tasks in the browser. In this article, we will focus on using MoveNet for pose estimation.

What is MoveNet?

MoveNet is a pre-trained machine learning model that can estimate human poses in real-time. It can be used in a variety of applications, such as fitness tracking, augmented reality, and gesture recognition.

Using MoveNet in the browser

Using MoveNet in the browser is quite straightforward. First, you need to load the MoveNet model using TensorFlow.js, which is a powerful library for machine learning in JavaScript. Once the model is loaded, you can start capturing video from the user’s webcam and feed it into the MoveNet model for pose estimation.

Sample code

Here’s a sample code snippet that demonstrates how to use MoveNet for pose estimation in the browser:

    
      // Load the MoveNet model
      const model = await tf.automl.MoveNet.load();

      // Capture video from the user's webcam
      const video = document.createElement('video');
      const stream = await navigator.mediaDevices.getUserMedia({ video: true });
      video.srcObject = stream;
      video.play();

      // Perform pose estimation
      const predictions = await model.estimatePoses(video);

      // Display the estimated poses on the screen
      for (let i = 0; i < predictions.length; i++) {
        const pose = predictions[i];
        // Draw the pose on the video feed
      }
    
  

Conclusion

Using advanced pre-trained Web ML models like MoveNet can bring a whole new level of interactivity to web applications. With the power of machine learning in the browser, developers can create immersive experiences that were not possible before. Pose estimation is just one example of the many possibilities that Web ML opens up, and we can expect to see even more exciting applications in the future.