"On the latest iOS 18 beta 2, the OCR API，the Translate App and Live Text performs very poorly in recognizing Japanese."

I'm looking for a solution to take a picture or point the camera at a piece of clothing and match that image with an image the user has stored in my app. I'm storing the data in a Core Data database as a Binary Data object. Since the user also takes the pictures they store in the database I think I cannot use pre-trained Core ML models. I would like the matching to be done on device if possible instead of going to an external service. That will probably describe the item based on what the AI sees, but then I cannot match the item with the stored images in the app. Does anyone know if this is possible with frameworks as Vision or VisionKit?

I try to use the new VNCalculateImageAestheticsScoresRequest API. Code is compiling and running but delivers the same result for every image Xcode 16 Beta 2 Simulator Did I missing anything ?

I'm seeking insight on why the new VisionOS Barcode Scanning API is categorized as an Enterprise API and restricted only for proprietary and in-house apps. I understand Apple's focus on privacy and I can see how this restriction could make sense for other Enterprise APIs like main camera access and passthrough screen capture. Why is barcode scanning restricted from open apps? What makes barcode scanning more of a risk to privacy versus the unrestricted APIs for object tracking, image tracking, or hand tracking?

Vision pro cannot capture recordings over 16kHz to 24kHz at a sampling rate of 48kHz, why? Or can you tell me how to configure it?Vision pro cannot capture recordings over 16kHz to 24kHz at a sampling rate of 48kHz, why? Or can you tell me how to configure it?

Hi everyone, I'm curious about the capabilities of ARKit's object tracking feature. Specifically, I'd like to know: Is there a size limit for the objects that can be tracked? Can ARKit differentiate between two objects with the same shape but different models (e.g., different colors)? Are objects with single colors and generic shapes (like squares or circles) effectively trackable? Any insights or examples from your experiences would be greatly appreciated! Thanks in advance.

I have created a Hand Action Classification project following the guidelines which causes the Create ML tool to provide the very cryptic "Unexpected Error". The feature extraction phase is fine, with the error occurring during model training after the tool reports completion of the first five training iterations as it moves on to report the next ten. The project is small with 3 training classes and 346 items I have tried to vary the frame rate and action duration, with all augmentations unset, but the error still persists. Can you please confirm how I may get further error diagnostic information so that I can determine why Create ML is unable to work with my training data? Mac OS is Sonoma 14.5 on an iMac 24-inch, M1, 2021. Create ML is Version 5.0 (121.4)

Hello, I want to capture video from Vision Pro in the Vision OS app. I am referring to the (https://developer.apple.com/videos/play/wwdc2024/10139/) Apple video and their code. step like below import ARKit com.apple.developer.arkit.main-camera-access.allow = true in info.plist Do below code func loadCameraFeed() async { // Main Camera Feed Access Example let formats = CameraVideoFormat.supportedVideoFormats(for: .main, cameraPositions:[.left]) let cameraFrameProvider = CameraFrameProvider() var arKitSession = ARKitSession() var pixelBuffer: CVPixelBuffer? await arKitSession.queryAuthorization(for: [.cameraAccess]) do { try await arKitSession.run([cameraFrameProvider]) } catch { return } guard let cameraFrameUpdates = cameraFrameProvider.cameraFrameUpdates(for: formats[0]) else { return } print(cameraFrameUpdates) for await cameraFrame in cameraFrameUpdates { print(cameraFrame) guard let mainCameraSample = cameraFrame.sample(for: .left) else { continue } pixelBuffer = mainCameraSample.pixelBuffer } } I want to convert "pixelBuffer" into video streaming and show it in a frame like iOS. Please guide me on how to achieve my next step. I am blank after this code.

I have created an archive for both iOS and MacOS versions of my app by doing the the following steps Destinations select Build Any Mac (Mac Catalyst, arm64, x86_64) Product > Archive However when doing the same steps for VisionOS I get an error Invalid Run Destination I have selected both destinations, visionOS Simulator and Build any VisionOS simulator device (arm64, x86_64) I am able to run the app and test, now I would like to upload to AppStoreConnect for TestFlight and App Store submission.

I'm playing with the new Vision API for iOS18, specifically with the new CalculateImageAestheticsScoresRequest API. When I try to perform the image observation request I get this error: internalError("Error Domain=NSOSStatusErrorDomain Code=-1 \"Failed to create espresso context.\" UserInfo={NSLocalizedDescription=Failed to create espresso context.}") The code is pretty straightforward: if let image = image { let request = CalculateImageAestheticsScoresRequest() Task { do { let cgImg = image.cgImage! let observations = try await request.perform(on: cgImg) let description = observations.description let score = observations.overallScore print(description) print(score) } catch { print(error) } } } I'm running it on a M2 using the simulator. Is it a bug? What's wrong?

this week i was watching https://developer.apple.com/videos/play/wwdc2024/10105/ with the amazing "configuration" feature to change the color or mesh straight in quick look, but i tried a lot with goarounds but nothing bring me to success how do i write in the usda files? anytiome i overwrite the usda even with just a "{}" inside... Reality composer pro rejects the file to be open again where is the developer man in the tutorial writing the usda? how is the usda compressed in usdz? (none of the compressors i tried accepeted the modified usda file) this is the code it's suggested in the video #usda 1.0 ( defaultPrim = "iPhone" ) def Xform "iPhone" ( variants = { string Color = "Black_Titanium" } prepend variantSets = ["Color"] ) { variantSet "Color" = { "Black_Titanium" { } "Blue_Titanium" { } "Natural_Titanium" { } "White_Titanium" { } } } but i dont understand how to do it with my own files,

HI eveyone i've read that USDZ supports LOD to have 3 meshes with high medium and low polygon detail to be visible depending on the distance from the user to the entity... but i dont know how to use it... any experience or... god bless you... a downloadable file with a sample??? thanks a lot !!!!

The DINO v1/v2 models are particularly interesting to me as they produce embeddings for the detected objects rather than ordinary classification indexes. That makes them so much more useful than the CNN based models. I would like to prepare some of the models posted on Huggingface to run on Apple Silicon, but it seems that the default conversion with TorchScript will not work. The other default conversions I've looked at so far also don't work. Conversion based on an example input doesn't capture enough of the model. I know that some have managed to convert it as I have a demo with a coreml model that seems to work, but I would like to know how to do the conversion myself. Has anyone managed to convert any of the DINOv2 models?

I know that I can use face detect with CoreML, but I'm wandering that is there any to identify the same person between two images like Photos app.

In my app, I'm setting the app language to Japanese. On iPhone and iPad, the Apple sign up button still displays correctly in Japanese: "Appleでサインイン". However, when running on vision Pro, the sign up button displays in English with the text "Sign up with Apple". Is this a vision pro error? The code setting the app language is Japanese: `CFBundleDevelopmentRegion ja_JP Photos displayed on iphone and vision pro. On Iphone: On vision Pro code signup button apple: (signInAppleButton as UIControl).cornerRadius = 2.0 let tapGesture = UITapGestureRecognizer(target: self, action: #selector(handleAuthorizationAppleIDButtonPress)) signInAppleButton.addGestureRecognizer(tapGesture) signInAppleStackView.insertArrangedSubview(signInAppleButton, at: 0) Hope you can help me why Vision Pro displays English text?

The new Mac virtual display feature on visionOS 2 offers a curved/panoramic window. I was wondering if this is simply a property that can be applied to a window, or if it involves an immersive mode or SceneKit/RealityKit?

I'm wondering if it's possible to implement object tracking on Vision Pro using the Vision framework of Apple? I see that the Vision documentation offers a variety of classes for computer vision which have a tag "visionOS", but all the example codes in the documentation are only for iOS, iPadOS or macOS. So can those classes also be used for developing Vision Pro apps? If so, how do they get data feed from the camera of Vision Pro?

Currently, visionos is customizing immersive mode in 360-degree full, and I'm looking for a way to adjust it like Apple's basic immersive mode.

Hi, I have a custom object detection CoreML model and I notice something strange when using the model with the Vision framework. I have tried two different approaches as to how to process an image and do inference on the CoreML model. The first one is using the CoreML "raw": initialising the model, getting the input image ready and using the model's .prediction() function to get the models output. The second one is using Vision to wrap the CoreML model in a VNCoreMLModel, creating a VNCoreMLRequest and using the VNImageRequestHandler to actually perform the model inference. The result of the VNCoreMLRequest is of type VNRecognizedObjectObservation. The issue I now face is in the difference in the output of both methods. The first method gives back the raw output of the CoreML model: confidence and coordinates. The confidence is an array with size equal to the number of classes in my model (3 in my case). The second method gives back the boundingBox, confidence and labels. However here the confidence is only the confidence for the most likely class (so size is equal to 1). But the confidence I get from the second approach is quite different from the confidence I get during the first approach. I can use either one of the approaches in my application. However, I really want to find out what is going on and understand how this difference occurred. Thanks!

I am trying to create demo for spatial meeting using persona also refer apple videos, But not getting clear idea about it. Any one could you please guide me step by step process or any code are appreciated for learning.