Image Recognition AI App w/ REACT.JS and TENSORFLOW.JS | Beginners Javascript AI

Introduction

In this article, we will guide you through the process of building an object detection application using TensorFlow.js, React.js, and executing it predominantly in the browser. The app allows users to select an image and detects various objects within that image based on a predefined model, specifically the COCO-SSD model, which supports recognition of 80 different classes of objects.

Setting Up the Project

To get started, we will set up a React project using Create React App. This can be achieved by running the following command in your terminal:

npx create-react-app my-object-detection-app

After the setup is complete, navigate into your project directory and launch your text editor. The primary dependencies we need for this project will include TensorFlow.js and the COCO-SSD model, which you can add via your package.json file or through the terminal using npm or yarn.

You may need to add the following dependencies:

(
  "dependencies": {
    "tensorflow/tfjs": "^version",
    "tensorflow/tfjs-converter": "^version",
    "tensorflow/tfjs-backend-cpu": "^version",
    "tensorflow/tfjs-backend-webgl": "^version",
    "@tensorflow-models/coco-ssd": "^version",
    "styled-components": "^version"
  )
}

Make sure to remove the node_modules directory and reinstall dependencies to avoid any installation conflicts.

Building the Object Detection Component

Next, we will create a component named ObjectDetector.jsx. We'll start by importing React and necessary components. This component will handle the UI layout, including an image preview and an upload button. The primary elements include:

• Image Display Container: To show the chosen image.
• Hidden File Input: To trigger the file picker.
• Button: To open the file input dialog.

Our functional component will incorporate the following essential functionalities:

1. Event Handling: To trigger the file selector and load the selected image.
2. Object Detection Logic: Implementing the TensorFlow.js model for object detection.

Image Selection

We’ll implement an openFile function that opens the file input when the button is clicked. Once an image is selected, we will read the image data and render it within an image container.

Running Object Detection

To perform object detection, we need to create a function that invokes the COCO-SSD model. Here's how it works:

• Load the Model: Use cocoSsd.load() to get the detection model.
• Detect Objects: Call the model.detect() method on the image element after it has loaded.
• Render Results: The detection result will provide bounding box coordinates and the class type.

Rendering Bounding Boxes

We will use the predictions array returned from the object detection process to render bounding boxes on the image. Each box will be drawn using a styled component with absolute positioning for accurate placement based on the predicted coordinates.

Enhancements and User Experience

To improve user experience, we can add a loading state that shows a message while the application is processing the image.

With these steps, you will have a functional object detection application using React and TensorFlow.js. This project serves as an excellent introduction to integrating machine learning with web development. You can customize and build on this foundation to explore even more advanced features and functionalities!

Keywords

• React
• TensorFlow.js
• Object Detection
• COCO-SSD
• JavaScript
• Web Development
• Machine Learning

FAQ

Q1: What is TensorFlow.js?
A1: TensorFlow.js is an open-source library that allows you to define, train, and run machine learning models directly in the browser using JavaScript.

Q2: What is the COCO-SSD model?
A2: The COCO-SSD model is a pre-trained object detection model that can recognize various objects in images and videos. It is trained on the COCO dataset and supports 80 different classes.

Q3: Can I run this object detection app on my local machine?
A3: Yes, you can run this application locally on your machine by setting up a React project and installing the required dependencies listed in the article.

Q4: Is TensorFlow.js suitable for production applications?
A4: While TensorFlow.js can be used for production applications, specialized models may require optimization and additional resources to run efficiently in browser environments.

Q5: What kind of objects can be detected using the COCO-SSD model?
A5: The COCO-SSD model can detect a variety of objects, including people, cars, dogs, cats, and many more, due to its extensive training on the COCO dataset comprising 80 classes.