In this post, we'll learn how to use TensorFlow.js and Node.js to perform real-time object detection on images.
We'll start by setting up a development environment, then we'll install TensorFlow.js and its dependencies. Next, we'll write a Node.js script to load an image, run object detection on it, and draw the results to the screen. Finally, we'll take a look at some of the resources available for further learning.
Before we get started, we need to set up a development environment. For this post, we'll be using Node.js.
If you don't already have Node.js installed, you can download it from the Node.js website. Once you have Node.js installed, you can create a new project directory and initialize it with the following commands:
mkdir object-detection
cd object-detection
npm init -y
Now that we have a project directory set up, we can install TensorFlow.js and its dependencies. We'll be using the tfjs-node package, which provides an implementation of TensorFlow.js that can be used on Node.js.
To install tfjs-node and its dependencies, run the following command:
npm install @tensorflow/tfjs-node
With TensorFlow.js installed, we can now write a script to perform object detection on images. We'll start by loading an image, then we'll use the tf.data.detectObjects method to run object detection on the image. Finally, we'll draw the results to the screen.
The full script is available here, but we'll walk through the key parts below.
First, we need to load the tfjs-node package:
const tf = require('@tensorflow/tfjs-node');
Next, we'll load the coco-ssd model , which we'll use for object detection. The coco-ssd model is a single-shot detection model that is trained on the Common Objects in Context (COCO) dataset.
const cocoSSD = require('@tensorflow-models/coco-ssd');
With the model loaded, we can now write the code to load an image and run object detection on it. We'll start by loading the image using the tf.node.decodeImage method. This method returns a promise that resolves to a tensor containing the image data.
const image = tf.node.decodeImage(
fs.readFileSync('./test-image.jpg')
);
Once the image is loaded, we can use the tf.data.detectObjects method to run object detection on it. This method returns a promise that resolves to an array of objects, each of which contains information about a detected object, including the class of the object and its location within the image.
const predictions = await cocoSSD.detectObjects(image);
Finally, we'll draw the results to the screen. We'll use the tf.node.drawBoxes method to draw boxes around the detected objects, and the tf.node.writeFile method to write the results to a new image file.
const drawnImage = tf.node.drawBoxes(
image,
predictions.map(prediction => prediction.bbox)
);
tf.node.writeFile('./test-image-output.jpg', drawnImage.toBuffer());
With the script written, we can now run it to perform object detection on an image. To run the script, use the following command:
node index.js
This will run the object detection script on the image test-image.jpg
and write the results to test-image-output.jpg
.