Object Detection in Augmented Reality - iOS Development

Introduction

Object detection in augmented reality (AR) is a powerful feature that allows developers to create applications that can recognize and interact with real-world objects. In this tutorial, we will cover everything from the basics of object detection to implementing it in an iOS application using ARKit and Core ML.

Prerequisites

Before we start, make sure you have the following installed:

• Xcode 11 or later
• Basic knowledge of Swift programming
• A compatible iOS device with a camera

Setting Up the Project

Open Xcode and create a new project. Choose the "Augmented Reality App" template. Ensure that the language is set to Swift and the content technology is set to "SceneKit".

Integrating Core ML Model

To perform object detection, we need a Core ML model. You can download a pre-trained model or train your own. For this tutorial, we'll use a pre-trained model called YOLOv3.

Download the model and add it to your Xcode project by dragging the .mlmodel file into the project navigator.

Loading the Core ML Model

Next, we will load the Core ML model in our ViewController. Open ViewController.swift and add the following code:

import UIKit
import ARKit
import CoreML
import Vision

class ViewController: UIViewController, ARSCNViewDelegate {
    @IBOutlet var sceneView: ARSCNView!
    var visionRequests = [VNRequest]()
    
    override func viewDidLoad() {
        super.viewDidLoad()
        
        // Set the view's delegate
        sceneView.delegate = self
        
        // Create a new scene
        let scene = SCNScene()
        
        // Set the scene to the view
        sceneView.scene = scene
        
        // Load the Core ML model
        guard let model = try? VNCoreMLModel(for: YOLOv3().model) else {
            fatalError("Could not load model")
        }
        
        // Create a Vision request
        let request = VNCoreMLRequest(model: model, completionHandler: visionRequestDidComplete)
        visionRequests = [request]
        
        // Start the AR session
        let configuration = ARWorldTrackingConfiguration()
        sceneView.session.run(configuration)
    }
    
    func visionRequestDidComplete(request: VNRequest, error: Error?) {
        // Handle the results of the vision request
    }
}
                

Handling Vision Request Results

In the visionRequestDidComplete method, we will handle the results of the Vision request. This method will be called whenever the Vision request completes. Add the following code to the visionRequestDidComplete method:

func visionRequestDidComplete(request: VNRequest, error: Error?) {
    guard let results = request.results as? [VNRecognizedObjectObservation] else {
        return
    }
    
    DispatchQueue.main.async {
        for observation in results {
            let boundingBox = observation.boundingBox
            // Process the bounding box
        }
    }
}
                

Displaying Detected Objects

To visualize the detected objects, we will create bounding boxes around them. Add the following code to draw a bounding box:

func drawBoundingBox(boundingBox: CGRect) {
    let box = SCNBox(width: boundingBox.width, height: boundingBox.height, length: 0.01, chamferRadius: 0)
    let material = SCNMaterial()
    material.diffuse.contents = UIColor.red
    box.materials = [material]
    
    let node = SCNNode(geometry: box)
    node.position = SCNVector3(boundingBox.midX, boundingBox.midY, 0)
    sceneView.scene.rootNode.addChildNode(node)
}
                

Running the App

Now that we have set up object detection, it's time to run the app. Connect your iOS device to your Mac and select it as the target device. Click the "Run" button in Xcode to build and run the app on your device. Point your device's camera at objects to see the bounding boxes drawn around detected objects.

Conclusion

In this tutorial, we covered the basics of object detection in augmented reality using ARKit and Core ML. We set up an AR project in Xcode, integrated a Core ML model, handled Vision request results, and visualized detected objects with bounding boxes. With this knowledge, you can create more complex AR applications that interact with the real world in exciting ways.