AI & Automation / Computer Vision for Automation
Detection Implementation
This tutorial will guide you through implementing object detection in your HTML project. We'll use popular libraries and APIs to identify and locate objects in images or videos.
Section overview
4 resourcesExplains how computer vision automates image and video-based processes.
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
detectmethod on our model and pass the image or video element.
const img = document.getElementById('img');
const predictions = await net.detect(img);
- The
detectmethod 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.getUserMediaAPI to access the webcam, and then call thedetectmethod 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 thefillRectmethod to draw the boxes.
Good luck and happy coding!
Need Help Implementing This?
We build custom systems, plugins, and scalable infrastructure.
Related topics
Keep learning with adjacent tracks.
Popular tools
Helpful utilities for quick tasks.
Random Password Generator
Create secure, complex passwords with custom length and character options.
Use toolLatest articles
Fresh insights from the CodiWiki team.
AI in Drug Discovery: Accelerating Medical Breakthroughs
In the rapidly evolving landscape of healthcare and pharmaceuticals, Artificial Intelligence (AI) in drug dis…
Read articleAI in Retail: Personalized Shopping and Inventory Management
In the rapidly evolving retail landscape, the integration of Artificial Intelligence (AI) is revolutionizing …
Read articleAI in Public Safety: Predictive Policing and Crime Prevention
In the realm of public safety, the integration of Artificial Intelligence (AI) stands as a beacon of innovati…
Read articleAI in Mental Health: Assisting with Therapy and Diagnostics
In the realm of mental health, the integration of Artificial Intelligence (AI) stands as a beacon of hope and…
Read articleAI in Legal Compliance: Ensuring Regulatory Adherence
In an era where technology continually reshapes the boundaries of industries, Artificial Intelligence (AI) in…
Read article