#080: Integrating TensorFlow.js with AR/VR in Node.js
TensorFlow.js is a powerful tool that allows us to create machine learning models and run them in the browser. This makes it ideal for creating applications that can take advantage of the processing power of the user's device, without the need for a powerful server.
In this post, we'll explore how to use TensorFlow.js to create a simple augmented reality (AR) application. We'll use the Three.js library to render 3D objects in the browser, and the AR.js library to track the user's head and display the 3D objects in the correct position.
We'll start by setting up a new Node.js project. Create a new directory for the project, and run npm init
to create a package.json
file.
We'll need a few libraries for this project. Install the following libraries with the npm
command:
tensorflow
@tensorflow/tfjs-node
three
ar.js
Now we can create the machine learning model that we'll use to generate the 3D objects. We'll use a simple linear regression model for this example.
Create a new file called model.js
and import the @tensorflow/tfjs-node
library.
const tf = require('@tensorflow/tfjs-node');
Next, we'll define some constants that we'll use to train the model. The LEARNING_RATE
constant defines how quickly the model should learn. The BATCH_SIZE
constant defines how many data points to use when training the model. The EPOCHS
constant defines how many times the model should go through the training data.
const LEARNING_RATE = 0.01;
const BATCH_SIZE = 10;
const EPOCHS = 10;
Now we can define the model. We'll use a sequential
model and add a dense
layer. The dense
layer is a fully connected layer that multiplies the input data by a set of weights and adds a bias term.
const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [1] }));
We need to compile the model before we can train it. We'll use the sgd
optimizer, which is a simple gradient descent optimizer. We'll also specify the loss
function that the model should minimize, and the metrics
that we want to track. In this case, we'll track the meanSquaredError
and the meanAbsoluteError
.
model.compile({
optimizer: tf.train.sgd(LEARNING_RATE),
loss: 'meanSquaredError',
metrics: ['meanSquaredError', 'meanAbsoluteError']
});
Now that we've defined the model, we need to train it on some data. We'll use a simple dataset for this example, consisting of 10 points.
const xs = tf.tensor2d([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8], [10, 1]);
const ys = tf.tensor2d([-3, -1, 1, 3, 5, 7, 9, 11, 13, 15], [10, 1]);
We can now train the model. We'll specify the batchSize
, the number of epochs
, and shuffle
the data before each epoch. We'll also specify a validationData
set, which the model will use to evaluate the loss and metrics after each epoch.
model.fit(xs, ys, {
batchSize: BATCH_SIZE,
epochs: EPOCHS,
shuffle: true,
validationData: [xs, ys]
}).then(() => {
// The model is trained!
});
Once the model is trained, we need to save it so that we can use it in the browser. We can use the tf.node.save
function to save the model to a .pb
file.
tf.node.save(model, './model.pb');
Now that we've saved the model, we can load it in the browser. We'll use the tf.loadFrozenModel
function to load the model. This function takes a modelUrl
and a weightsManifestUrl
. The modelUrl
is the path to the .pb
file that we saved in the previous step. The weightsManifestUrl
is the path to a JSON file that contains information about the model's weights.
const modelUrl = './model.pb';
const weightsManifestUrl = './weights_manifest.json';
tf.loadFrozenModel(modelUrl, weightsManifestUrl).then(model => {
// The model is loaded!
});
Now that we've loaded the model, we can use it to generate 3D objects. We'll use the tf.tensor
function to create a tensor from an array of numbers. This tensor will be used as the input to the model.
const input = tf.tensor([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8], [10, 1]);
We can now use the model.predict
method to generate predictions from the input tensor. This method returns a tf.Tensor
object, which we can use to generate the 3D objects.
model.predict(input).then(output => {
// The output tensor can be used to generate 3D objects!
});
Now that we've generated the 3D objects, we need to render them in the browser. We'll use the three.js
library to do this.
First, we need to create a Scene
object. This object will contain the 3D objects that we want to render.
const scene = new THREE.Scene();
Next, we need to create a Camera
object. This object defines the perspective from which we want to view the scene.
const camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 0.1, 1000);
Now we can create the Renderer
object. This object will take the Scene
and Camera
objects and render them to the page.
const renderer = new THREE.WebGLRenderer();
renderer.setSize(window.innerWidth, window.innerHeight);
document.body.appendChild(renderer.domElement);
Finally, we need to create the 3D objects and add them to the Scene
object. We'll use the output
tensor from the previous step to generate the 3D objects.
const geometry = new THREE.Geometry();
for (let i = 0; i < output.shape[0]; i++) {
geometry.vertices.push(new THREE.Vector3(i, output.get(i, 0), 0));
}
const material = new THREE.LineBasicMaterial({ color: 0xff0000 });
const line = new THREE.Line(geometry, material);
scene.add(line);
Now that we've rendered the 3D objects, we need to animate them. We'll use the requestAnimationFrame
function to do this. This function tells the browser to call a specified function before the next repaint.
function animate() {
requestAnimationFrame(animate);
renderer.render(scene, camera);
}
animate();
Now that we've animated the 3D objects, we need to track the user's head so that the 3D objects appear in the correct position. We'll use the ar.js
library to do this.
First, we need to create an ARController
object. This object will track the user's head and display the 3D objects in the correct position.
const arController = new ARController(renderer, {
cameraParametersUrl: './camera_para.dat',
maxDetectionRate: 60,
canvasWidth: 640,
canvasHeight: 480
});
Next, we need to tell the ARController
to track the user's head. We can do this with the loadNFTMarker
method. This method takes a markerUrl
, which is the path to a marker file. We can use the markerUrl
to load a marker file from the nft-marker-files
directory.
arController.loadNFTMarker('./nft-marker-files/hiro.jpg');
Finally, we need to tell the ARController
to start tracking the user's head. We can do this with the startNFTMarkerTracking
method.
arController.startNFTMarkerTracking();
Now that we've tracked the user's head, we can display the 3D objects in the correct position. We'll use the arController.getNFTMarker
method to get the marker that the user is currently looking at. This method returns a THREE.Object3D
object, which we can add to the Scene
object.
const marker = arController.getNFTMarker(0);
scene.add(marker);
That's all there is to it! You should now have a working AR application that renders 3D objects in the correct position relative to the user's head.