TypeScript for Computer Vision


Introduction

Computer vision is the field of enabling machines to interpret and understand the visual world. TypeScript can enhance the development process by providing type safety and code organization. In this guide, we'll introduce TypeScript for computer vision and provide a basic example using TypeScript and the TensorFlow.js library to perform image classification.


Prerequisites

Before you begin, make sure you have the following prerequisites:

  • Node.js: You can download it from https://nodejs.org/
  • TypeScript: Install it globally with npm install -g typescript
  • Visual Studio Code (or your preferred code editor)

Getting Started with TypeScript for Computer Vision

Let's create a basic example of image classification using TypeScript and TensorFlow.js.


Step 1: Set Up Your Project

Create a new directory for your project and navigate to it in your terminal:

mkdir computer-vision-app
cd computer-vision-app

Step 2: Initialize a Node.js Project

Initialize a Node.js project and answer the prompts. You can use the default settings for most prompts:

npm init

Step 3: Install Dependencies

Install the required dependencies, including TypeScript and TensorFlow.js:

npm install typescript @tensorflow/tfjs-node --save

Step 4: Create TypeScript Configuration

Create a TypeScript configuration file (tsconfig.json) in your project directory:

{
  "compilerOptions": {
    "target": "ES6",
    "outDir": "./dist",
    "rootDir": "./src"
  }
}

Step 5: Create TypeScript Code

Create a TypeScript file (e.g., app.ts) to perform image classification:

// app.ts
import * as tf from '@tensorflow/tfjs-node';
import * as fs from 'fs';
const loadAndClassifyImage = async (modelPath: string, imagePath: string) => {
    const model = await tf.loadLayersModel(`file://${modelPath}`);
    const imageBuffer = fs.readFileSync(imagePath);
    const image = tf.node.decodeImage(imageBuffer);
    const predictions = model.predict(image);
    const values = predictions.dataSync();
    const topClass = values.indexOf(Math.max(...values));
    return topClass;
};
loadAndClassifyImage('path/to/model.json', 'path/to/image.jpg')
    .then((topClass) => {
        console.log('Predicted class:', topClass);
    });

Step 6: Create an HTML File

Create an HTML file (index.html) for displaying the image and prediction:

<!-- index.html -->
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Image Classification</title>
</head>
<body>
    <h2>Image Classification</h2>
    <div id="app">
        <img src="path/to/image.jpg" alt="Image for classification">
    </div>
    <script src="dist/app.js"></script>
</body>
</html>

Step 7: Include TypeScript in Your HTML

Include the compiled TypeScript code in your HTML file by referencing the generated JavaScript file:

<script src="dist/app.js"></script>

Conclusion

This basic example demonstrates how to use TypeScript with TensorFlow.js to perform image classification. In real computer vision projects, you can work with more complex models, perform object detection, facial recognition, and create custom vision applications. TypeScript ensures that your code is maintainable and well-structured as your computer vision projects become more advanced.