Detection Implementation

Tutorial 2 of 4

Introduction

In this tutorial, we will learn how to implement object detection in an HTML project. The goal is to identify and locate objects within images or videos, which can be extremely useful in applications like security monitoring, retail, and more.

By the end of this tutorial, you will:

Understand what object detection is and why it's important
Be able to use popular libraries and APIs for object detection
Be able to implement object detection in an HTML project

Prerequisites: Basic knowledge of HTML, JavaScript, and a general understanding of APIs.

Step-by-Step Guide

Object detection is a computer vision technique that allows us to identify and locate objects in an image or video. The most common approach is to use pre-trained models that have been trained on large datasets.

There are several libraries and APIs available for object detection. In this tutorial, we will use the TensorFlow.js library, which allows us to run machine learning models in the browser.

First, we need to include the TensorFlow.js library in our HTML file:

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>

Then, we load a pre-trained model. For this tutorial, we'll use the COCO-SSD model, which has been trained to detect 90 different types of objects.

let net;
async function loadModel() {
    net = await cocoSsd.load();
}
loadModel();

Now we can use this model to detect objects in an image or video. For this, we call the detect method on our model and pass the image or video element.

const img = document.getElementById('img');
const predictions = await net.detect(img);

The detect method returns an array of predictions. Each prediction includes the class of the object, a score indicating the confidence of the prediction, and a bounding box specifying the location of the object in the image.

for (let i = 0; i < predictions.length; i++) {
    console.log(`Class: ${predictions[i].class}, Score: ${predictions[i].score}, BBox: ${predictions[i].bbox}`);
}

Code Examples

Here's a complete example of how to use TensorFlow.js for object detection:

<html>
<head>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/coco-ssd"></script>
</head>
<body>
    <img id="img" src="image.jpg">
    <script>
        let net;
        async function loadModel() {
            net = await cocoSsd.load();
            const img = document.getElementById('img');
            const predictions = await net.detect(img);
            for (let i = 0; i < predictions.length; i++) {
                console.log(`Class: ${predictions[i].class}, Score: ${predictions[i].score}, BBox: ${predictions[i].bbox}`);
            }
        }
        loadModel();
    </script>
</body>
</html>

Summary

In this tutorial, we've learned the basics of object detection and how to implement it in an HTML project using TensorFlow.js. We've seen how to load a pre-trained model, how to use it to detect objects in an image, and how to interpret the results.

Next steps could include learning more about TensorFlow.js and its capabilities, or exploring other machine learning models for different tasks.

Practice Exercises

Try modifying the example to detect objects in a video instead of an image. You'll need to use the navigator.mediaDevices.getUserMedia API to access the webcam, and then call the detect method in a loop.
Try using a different pre-trained model. You can find a list of models available in TensorFlow.js here.
Try drawing the bounding boxes on the image. You'll need to create a canvas element, get its context with canvas.getContext('2d'), and then use the fillRect method to draw the boxes.

Good luck and happy coding!