Object Detection in the Browser using TensorFlow.js

In this tutorial, we will learn how to perform object detection in the browser using TensorFlow.js. Object detection is a computer vision task that involves identifying and locating objects in an image or video. TensorFlow.js is a JavaScript library for training and deploying machine learning models in the browser.

For this tutorial, we will be using the COCO-SSD model, which is a pre-trained model for object detection on the COCO dataset. The COCO dataset contains images with 80 different object categories.

Set up the HTML structure

First, let’s create the HTML structure for our web page. We will need a <video> element for capturing video from the webcam, and a <canvas> element for displaying the detected objects.

<video id="video" width="640" height="480" autoplay></video>
<canvas id="canvas" width="640" height="480"></canvas>

Load the COCO-SSD model

Next, we need to load the COCO-SSD model using TensorFlow.js. This model will be used for detecting objects in the video stream.

const video = document.getElementById('video');
const canvas = document.getElementById('canvas');
const ctx = canvas.getContext('2d');

navigator.mediaDevices.getUserMedia({ video: true })
    .then(stream => {
        video.srcObject = stream;
        video.play();
    });

video.addEventListener('play', () => {
    setInterval(() => {
        detectObjects(video, ctx);
    }, 1000);
});

async function detectObjects(video, ctx) {
    const model = await cocoSsd.load();

    ctx.drawImage(video, 0, 0, 640, 480);

    const predictions = await model.detect(canvas);

    predictions.forEach(prediction => {
        ctx.beginPath();
        ctx.rect(prediction.bbox[0], prediction.bbox[1], prediction.bbox[2], prediction.bbox[3]);
        ctx.stroke();
        ctx.font = "16px Arial";
        ctx.fillStyle = "white";
        ctx.fillText(prediction.class, prediction.bbox[0], prediction.bbox[1]);
    });
}

Run the model and display predictions

Now that we have loaded the COCO-SSD model and set up the video stream, we can run the model on each frame of the video and display the predictions on the canvas.

The detectObjects function takes the video element and canvas context as input and performs object detection on each frame of the video stream. It draws bounding boxes around the detected objects and displays the class names of the objects.

Conclusion

Congratulations! You have successfully learned how to perform object detection in the browser using TensorFlow.js. Object detection is a powerful computer vision task that can be used for a wide range of applications, such as image recognition, video analysis, and augmented reality. With TensorFlow.js, you can deploy machine learning models directly in the browser, making it easy to build interactive and responsive web applications with advanced AI capabilities.