Source: See how ML Kit and ARKit play together from Firebase
We at the Firebase office all enjoyed playing with Hanley Weng’s “CoreML-in-ARKit” project. It displays 3D labels on top of images it detects in the scene. While the on-device detection provides a fast response, we wanted to build a solution that gave you the speed of the on-device model with the accuracy you can get from a cloud-based solution. Well, that’s exactly what we built with our MLKit-ARKit project. Read on to find out more about how we did it!
ML Kit for Firebase is a mobile SDK that extends Google Cloud’s machine learning (ML) expertise into Android and iOS apps in a powerful yet easy-to-use package. It includes easy-to-use Base APIs and also offers the ability to bring your own custom TFLite models.
ARKit is Apple’s framework that combines device motion tracking, camera scene capture, advanced scene processing, and display conveniences to simplify the task of building an AR experience. You can use these technologies to create many kinds of AR experiences using either the back camera or front camera of an iOS device.
In this project we are pushing ARKit frames from the back camera into a queue. ML Kit processes these to find out the objects in that frame.
When the user taps the screen, ML Kit returns the detected label with the highest confidence. We then create a 3D bubble text and add it into the user’s scene.
ML Kit makes ML easy for all mobile developers, whether you have experience in ML or are new to the space. For those with more advanced use cases, ML Kit allows you to bring your own TFLite models, but for more common use cases, you can implement one of the easy-to-use Base APIs. These APIs cover use cases such as text recognition, image labeling, face detection and more, and are backed by models trained by Google Cloud. We’ll be using image labeling in our example.
Base APIs are available in two flavors: On-device and cloud-based. The on-device APIs are free to use and run locally, while the cloud-based ones provide higher accuracy and more precise responses. Cloud-based Vision APIs are free for the first 1000/API calls and paid after that. They provide the power of full-sized models from Google’s Cloud Vision APIs.
We are using the ML Kit on-device image labeling API to get a live feed of results while keeping our frame rate steady at 60fps. When the user taps the screen we fire up an async call to the Cloud image labeling API with the current image. When we get a response from this higher accuracy model, we update the 3D label on the fly. So while we are continuously running the on-device API and using its result as the initial source of information, the higher accuracy Cloud API is called on-demand and its results replaces on-device label eventually.
While the on-device API is real-time with all the processing happening locally, the Cloud Vision API makes a network request to the Google Cloud backend, leveraging a larger, higher accuracy model. Given that we consider this the more precise response, in our app we replace the label provided by the on-device API with the result from Cloud Vision API when it arrives.
1. Clone the project
$ git clone https://github.com/FirebaseExtended/MLKit-ARKit.git
2. Install the pods and open the .xcworkspace file to see the project in Xcode.
$ cd MLKit-ARKit
$ pod install --repo-update
$ open MLKit-ARKit.xcworkspace
GoogleService-Info.plistfile generated as part of adding Firebase to your app.
GoogleService-Info.plistfile to your app, next to
At this point, the app should work using the on-device recognition.
★ The cloud label detection feature is still free for first 1000 uses per month. Click here to see additional pricing details.
At this point, the app should update labels with more precise results from the Cloud Vision API.