In this tutorial, we will be discussing how to enhance front-end pupil detection using TensorFlow.js and BlazeFace. Pupil detection is a crucial aspect of many computer vision applications, such as eye tracking, facial recognition, and emotion detection. By using TensorFlow.js and BlazeFace, we can achieve real-time and accurate pupil detection on the front-end without the need for additional libraries or server-side processing.
BlazeFace is a lightweight and efficient face detection model developed by Google, which is specifically designed for real-time face detection in mobile and web applications. By combining BlazeFace with TensorFlow.js, we can leverage the power of deep learning to accurately detect and track pupils in real-time directly in the browser.
To get started, make sure you have the following prerequisites:
- Basic knowledge of HTML, CSS, and JavaScript
- Familiarity with TensorFlow.js and BlazeFace
- A code editor of your choice
Step 1: Set up a new HTML file
First, create a new HTML file in your code editor and name it index.html. In this file, we will set up the structure of our front-end pupil detection application using HTML tags.
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Front-End Pupil Detection with TensorFlow.js and BlazeFace</title>
</head>
<body>
<h1>Front-End Pupil Detection with TensorFlow.js and BlazeFace</h1>
<video id="video" width="640" height="480" style="display: block"></video>
<canvas id="canvas" width="640" height="480" style="display: block"></canvas>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/blazeface"></script>
<script src="script.js"></script>
</body>
</html>In this HTML file, we have set up the basic structure of our application, including a video element for capturing the webcam feed, a canvas element for drawing the detected pupils, and script tags for including TensorFlow.js, BlazeFace, and our custom JavaScript code.
Step 2: Create a new JavaScript file
Next, create a new JavaScript file in your code editor and name it script.js. In this file, we will write the JavaScript code for setting up the webcam feed, initializing the BlazeFace model, and detecting and tracking the pupils in real-time.
// Load the BlazeFace model
async function loadModel() {
const model = await blazeface.load();
return model;
}
// Detect and track pupils in real-time
async function detectPupils(model, video) {
const predictions = await model.estimateFaces(video);
if (predictions.length > 0) {
const canvas = document.getElementById('canvas');
const ctx = canvas.getContext('2d');
ctx.drawImage(video, 0, 0, video.width, video.height);
const face = predictions[0];
if (face.landmarks.length > 0) {
const leftEye = face.landmarks[4];
const rightEye = face.landmarks[5];
ctx.beginPath();
ctx.arc(leftEye[0], leftEye[1], 5, 0, 2 * Math.PI);
ctx.fillStyle = 'red';
ctx.fill();
ctx.beginPath();
ctx.arc(rightEye[0], rightEye[1], 5, 0, 2 * Math.PI);
ctx.fillStyle = 'red';
ctx.fill();
}
}
requestAnimationFrame(() => detectPupils(model, video));
}
// Set up the webcam feed
async function setupCamera() {
const video = document.getElementById('video');
try {
const stream = await navigator.mediaDevices.getUserMedia({ video: {} });
video.srcObject = stream;
} catch (err) {
console.error('Error accessing the camera:', err);
}
}
// Main function
async function main() {
await tf.setBackend('webgl');
setupCamera();
const model = await loadModel();
detectPupils(model, document.getElementById('video'));
}
main();In this JavaScript file, we define three main functions:
loadModel(): This function loads the BlazeFace model using theblazeface.load()method.detectPupils(): This function detects and tracks the pupils in real-time using the BlazeFace model and draws them on the canvas.setupCamera(): This function initializes the webcam feed using thenavigator.mediaDevices.getUserMedia()method.
We then call these functions in the main() function, which sets up the webcam feed, loads the BlazeFace model, and starts detecting and tracking pupils in real-time.
Step 3: Test the application
Now that we have set up our HTML and JavaScript files, we can test the front-end pupil detection application by opening the index.html file in a web browser. You should see the webcam feed and the detected pupils displayed on the canvas in real-time.
By following this tutorial, you have learned how to enhance front-end pupil detection using TensorFlow.js and BlazeFace. You can further customize and optimize the application by experimenting with different configurations, models, and techniques.
I hope this tutorial was helpful and inspiring for your next computer vision project!
