Detect faces in real-time with Tensorflow.js

Detect faces in real-time with TensorFlow.js

If you've ever wanted to create an interactive art installation that counts how many people pass by every day, using a face detection model and a webcam is a fantastic approach. In this guide, we'll show you how to turn on your webcam, capture an image, and feed it into a face detection model using TensorFlow.js to predict where faces appear in the camera's view.

Here’s a step-by-step walkthrough of the process:

1. Turn on the Webcam:

   • To begin, we need to click on the webcam to activate it. This step is crucial because we need a live video feed to perform real-time face detection.

2. Capture an Image:

   • Once the webcam is active, we capture an image. This captured image serves as input for our face detection model.

3. Feed the Image into the Model:

   • We then feed the captured image into our new face detection model. Note that the syntax here might differ from other models you may have used, so be sure to refer to the model’s documentation.

4. Get Predictions:

   • The model processes the image and predicts where faces are located within the camera feed. Typically, this is visualized by drawing a square around each detected face.

5. Draw on Canvas:

   • In the final step, we draw the predicted bounding boxes (the squares around faces) onto a canvas overlay. This visual representation helps us see where the model has detected faces in real time.

Here’s a snippet of what this process could look like in code:

// Pseudocode visualization
const videoElement = document.querySelector('video');
const canvasElement = document.querySelector('canvas');
const model = await tf.loadModel('model-url'); // Load pre-trained face detection model

// Turn on webcam
navigator.mediaDevices.getUserMedia(( video: true ))
  .then(function(stream) (
    videoElement.srcObject = stream;
  ));

videoElement.addEventListener('play', () => (
  const context = canvasElement.getContext('2d');
  setInterval(async () => {
    // Capture image and make predictions
    const predictions = await model.predict(tf.browser.fromPixels(videoElement));
  
    // Draw predictions
    context.clearRect(0, 0, canvasElement.width, canvasElement.height);
    predictions.forEach(prediction => {
      context.beginPath();
      context.rect(prediction.bbox[0], prediction.bbox[1], prediction.bbox[2], prediction.bbox[3]);
      context.stroke();
    ));
  }, 100);
});

By using the steps and code above, you can detect and visualize faces in real-time with TensorFlow.js, creating a foundation for projects like interactive art installations.

Keywords

• Face detection
• TensorFlow.js
• Webcam
• Real-time detection
• Art installation
• Predictions
• Bounding box
• Model feeding

FAQ

Q: What is TensorFlow.js?
A: TensorFlow.js is an open-source library for machine learning in JavaScript, allowing developers to run pre-trained models and create neural networks in the browser or on Node.js.

Q: How do I turn on the webcam for face detection?
A: To turn on the webcam, you need to request access to the user's media devices using navigator.mediaDevices.getUserMedia(( video: true )) and then set the video stream as the source of a video element.

Q: What format does the model require for image input?
A: The format can vary based on the model, but typically, the image should be converted to a tensor before being fed into the model. TensorFlow.js provides utilities for this, such as tf.browser.fromPixels().

Q: How are the predictions visualized?
A: Predictions are visualized by drawing rectangles (bounding boxes) around the detected faces on an HTML canvas overlaid on the video feed.

Q: Can this setup be used for counting people?
A: Yes, by detecting faces consistently over time, you can count how many people pass by a specific area, which is useful for applications like interactive art installations or automated entry counting systems.