In this post, we'll learn how to use TensorFlow.js and Node.js to perform real-time object detection.
TensorFlow.js is an open-source library for machine learning in JavaScript. It allows you to train and run machine learning models in the browser or in a Node.js environment.
Node.js is a JavaScript runtime environment that allows you to run JavaScript code outside of the browser.
We can use TensorFlow.js and Node.js to perform real-time object detection by:
We'll start by training a machine learning model to detect objects in images. For this, we'll use the MobileNet model. MobileNet is a pre-trained model that has been trained on a large dataset of images.
We can use MobileNet to perform object detection by:
Let's see how we can do this in code:
// Load the MobileNet model.
const model = await mobilenet.load();
// Classify the image.
const predictions = await model.classify(image);
// Get the model's predictions.
console.log(predictions);
Once we have trained our model, we can deploy it to a Node.js server. This will allow us to process incoming images and detect objects in real-time.
To do this, we'll need to:
Let's see how we can do this in code:
// Install the TensorFlow.js library.
npm install @tensorflow/tfjs
// Install the Node.js library.
npm install node
// Create a Node.js server.
const tf = require('@tensorflow/tfjs');
const express = require('express');
const app = express();
//Deploy the model to the server.
app.use(express.static(__dirname + '/model'));
// Process incoming images and detect objects in real-time.
app.post('/api/detect', async (req, res) => {
// Classify the image.
const predictions = await model.classify(req.body.image);
// Send the predictions to the client.
res.json(predictions);
});
// Start the server.
app.listen(3000, () => console.log('Server is running on port 3000'));
Once our server is up and running, we can use it to process incoming images and detect objects in real-time.
To do this, we'll need to:
Let's see how we can do this in code:
// Send an image to the server.
const image = 'some-image.jpg';
fetch('/api/detect', {
method: 'POST',
body: JSON.stringify({ image }),
})
.then(res => res.json())
.then(predictions => {
// Use the predictions to detect objects in the image.
console.log(predictions);
});
In this post, we've learned how to use TensorFlow.js and Node.js to perform real-time object detection.