Apply computer vision algorithms to perform a variety of tasks on input images and video using Vision.

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Apple OCR framework seems to be holding on to allocations every time it is called.
Environment: macOS 26.2 (Tahoe) Xcode 16.3 Apple Silicon (M4) Sandboxed Mac App Store app Description: Repeated use of VNRecognizeTextRequest causes permanent memory growth in the host process. The physical footprint increases by approximately 3-15 MB per OCR call and never returns to baseline, even after all references to the request, handler, observations, and image are released. ` private func selectAndProcessImage() { let panel = NSOpenPanel() panel.allowedContentTypes = [.image] panel.allowsMultipleSelection = false panel.canChooseDirectories = false panel.message = "Select an image for OCR processing" guard panel.runModal() == .OK, let url = panel.url else { return } selectedImageURL = url isProcessing = true recognizedText = "Processing..." // Run OCR on a background thread to keep UI responsive let workItem = DispatchWorkItem { let result = performOCR(on: url) DispatchQueue.main.async { recognizedText = result isProcessing = false } } DispatchQueue.global(qos: .userInitiated).async(execute: workItem) } private func performOCR(on url: URL) -> String { // Wrap EVERYTHING in autoreleasepool so all ObjC objects are drained immediately let resultText: String = autoreleasepool { // Load image and convert to CVPixelBuffer for explicit memory control guard let imageData = try? Data(contentsOf: url) else { return "Error: Could not read image file." } guard let nsImage = NSImage(data: imageData) else { return "Error: Could not create image from file data." } guard let cgImage = nsImage.cgImage(forProposedRect: nil, context: nil, hints: nil) else { return "Error: Could not create CGImage." } let width = cgImage.width let height = cgImage.height // Create a CVPixelBuffer from the CGImage var pixelBuffer: CVPixelBuffer? let attrs: [String: Any] = [ kCVPixelBufferCGImageCompatibilityKey as String: true, kCVPixelBufferCGBitmapContextCompatibilityKey as String: true ] let status = CVPixelBufferCreate( kCFAllocatorDefault, width, height, kCVPixelFormatType_32ARGB, attrs as CFDictionary, &pixelBuffer ) guard status == kCVReturnSuccess, let buffer = pixelBuffer else { return "Error: Could not create CVPixelBuffer (status: \(status))." } // Draw the CGImage into the pixel buffer CVPixelBufferLockBaseAddress(buffer, []) guard let context = CGContext( data: CVPixelBufferGetBaseAddress(buffer), width: width, height: height, bitsPerComponent: 8, bytesPerRow: CVPixelBufferGetBytesPerRow(buffer), space: CGColorSpaceCreateDeviceRGB(), bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue ) else { CVPixelBufferUnlockBaseAddress(buffer, []) return "Error: Could not create CGContext for pixel buffer." } context.draw(cgImage, in: CGRect(x: 0, y: 0, width: width, height: height)) CVPixelBufferUnlockBaseAddress(buffer, []) // Run OCR let requestHandler = VNImageRequestHandler(cvPixelBuffer: buffer, options: [:]) let request = VNRecognizeTextRequest() request.recognitionLevel = .accurate request.usesLanguageCorrection = true do { try requestHandler.perform([request]) } catch { return "Error during OCR: \(error.localizedDescription)" } guard let observations = request.results, !observations.isEmpty else { return "No text found in image." } let lines = observations.compactMap { observation in observation.topCandidates(1).first?.string } // Explicitly nil out the pixel buffer before the pool drains pixelBuffer = nil return lines.joined(separator: "\n") } // Everything — Data, NSImage, CGImage, CVPixelBuffer, VN objects — released here return resultText } `
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532
Feb ’26
VisionOs Development: Seeking Advice on Key Strategic Crossroads
I am a developer working on developing a space journal application. During the development process, I encountered several crucial strategic and technical decisions, and I would like to hear the experiences of those who have gone through similar situations. Here are the simplified versions of several questions I have. Resource allocation: Which problem should I address first? Design direction: In terms of interaction and UI design, how should I balance "immersion" and "usability"? Market selection: Was it easier for a business to survive in the early stages as a B2B or B2C entity? Cost estimation: How can I reasonably present to my investors the development costs of this project? In order to avoid relying solely on intuition in my decisions, I created a short questionnaire, hoping to gather more structured opinions from my colleagues. If you are also exploring VisionOS, I sincerely hope you can take a few minutes to fill it out. The results are extremely important to me, and I would be more than happy to share the final summary findings with you.
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400
Feb ’26
Khmer Script Misidentified as Thai in Vision Framework
It is vital for Apple to refine its OCR models to correctly distinguish between Khmer and Thai scripts. Incorrectly labeling Khmer text as Thai is more than a technical bug; it is a culturally insensitive error that impacts national identity, especially given the current geopolitical climate between Cambodia and Thailand. Implementing a more robust language-detection threshold would prevent these harmful misidentifications. There is a significant logic flaw in the VNRecognizeTextRequest language detection when processing Khmer script. When the property automaticallyDetectsLanguage is set to true, the Vision framework frequently misidentifies Khmer characters as Thai. While both scripts share historical roots, they are distinct languages with different alphabets. Currently, the model’s confidence threshold for distinguishing between these two scripts is too low, leading to incorrect OCR output in both developer-facing APIs and Apple’s native ecosystem (Preview, Live Text, and Photos). import SwiftUI import Vision class TextExtractor { func extractText(from data: Data, completion: @escaping (String) -> Void) { let request = VNRecognizeTextRequest { (request, error) in guard let observations = request.results as? [VNRecognizedTextObservation] else { completion("No text found.") return } let recognizedStrings = observations.compactMap { observation in let str = observation.topCandidates(1).first?.string return "{text: \(str!), confidence: \(observation.confidence)}" } completion(recognizedStrings.joined(separator: "\n")) } request.automaticallyDetectsLanguage = true // <-- This is the issue. request.recognitionLevel = .accurate let handler = VNImageRequestHandler(data: data, options: [:]) DispatchQueue.global(qos: .background).async { do { try handler.perform([request]) } catch { completion("Failed to perform OCR: \(error.localizedDescription)") } } } } Recognizing Khmer Confidence Score is low for Khmer text. (The output is in Thai language with low confidence score) Recognizing English Confidence Score is high expected. Recognizing Thai Confidence Score is high as expected Issues on Preview, Photos Khmer text Copied text Kouk Pring Chroum Temple [19121 รอาสายสุกตีนานยารรีสใหิสรราภูชิตีนนสุฐตีย์ [รุก เผือชิษาธอยกัตธ์ตายตราพาษชาณา ถวเชยาใบสราเบรถทีมูสินตราพาษชาณา ทีมูโษา เช็ก อาษเชิษฐอารายสุกบดตพรธุรฯ ตากร"สุก"ผาตากรธกรธุกเยากสเผาพศฐตาสาย รัอรณาษ"ตีพย" สเผาพกรกฐาภูชิสาเครๆผู:สุกรตีพาสเผาพสรอสายใผิตรรารตีพสๆ เดียอลายสุกตีน ธาราชรติ ธิพรหณาะพูชุบละเาหLunet De Lajonquiere ผารูกรสาราพารผรผาสิตภพ ตารสิทูก ธิพิ คุณที่นสายเระพบพเคเผาหนารเกะทรนภาษเราภุพเสารเราษทีเลิกสญาเราหรุฬารชสเกาก เรากุม สงสอบานตรเราะากกต่ายภากายระตารุกเตียน Recommended Solutions 1. Set a Threshold Filter out the detected result where the threshold is less than or equal to 0.5, so that it would not output low quality text which can lead to the issue. For example, let recognizedStrings = observations.compactMap { observation in if observation.confidence <= 0.5 { return nil } let str = observation.topCandidates(1).first?.string return "{text: \(str!), confidence: \(observation.confidence)}" } 2. Add Khmer Language Support This issue would never happen if the model has the capability to detect and recognize image with Khmer language. Doc2Text GitHub: https://github.com/seanghay/Doc2Text-Swift
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1.8k
Jan ’26
Policy on AI-generated assets for Swift Student Challenge 2026
Hello everyone, I am currently developing an app for my Swift Student Challenge submission that focuses on human motion analysis using the Vision framework. To effectively demonstrate the app's technical capabilities during the review process, I need to include a sample video showing a person performing specific movements. However, I want to ensure that my submission strictly adheres to all intellectual property guidelines. Instead of using existing copyrighted videos or public social media clips, I am considering using Generative AI to create an original, royalty-free sample video. This video would feature a character performing movements designed specifically to test my app's pose estimation and feedback logic. I have a few questions regarding this approach: Is it acceptable to use AI-generated sample assets (like video clips) to demonstrate technical features when it's difficult to record high-quality personal footage due to environmental constraints? If I clearly disclose the tools used and the reason for using AI-generated content in my written response, would this be considered a professional approach to asset management? Are there any specific guidelines I should follow to ensure that the use of AI-generated samples doesn't overshadow the original coding and design work of the project? My goal is to showcase a polished and technically sound implementation using Xcode 26 while respecting all copyright requirements. Thank you for your time and advice!
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901
Jan ’26
Threading issues when using debugger
Hi, I am modifying the sample camera app that is here: https://developer.apple.com/tutorials/sample-apps/capturingphotos-camerapreview ... In the processPreviewImages, I am using the Vision APIs to generate a segmentation mask for a person/object, then compositing that person onto a different background (with some other filtering). The filtering and compositing is done via CoreImage. At the end, I convert the CIImage to a CGImage then to a SwiftUI Image. When I run it on my iPhone, it works fine, and has not crashed. When I run it on the iPhone with the debugger, it crashes within a few seconds with: EXC_BAD_ACCESS in libRPAC.dylib`std::__1::__hash_table<std::__1::__hash_value_type<long, qos_info_t>, std::__1::__unordered_map_hasher<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::hash, std::__1::equal_to, true>, std::__1::__unordered_map_equal<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::equal_to, std::__1::hash, true>, std::__1::allocator<std::__1::__hash_value_type<long, qos_info_t>>>::__emplace_unique_key_args<long, std::__1::piecewise_construct_t const&, std::__1::tuple<long const&>, std::__1::tuple<>>: It had previously been working fine with the debugger, so I'm not sure what has changed. Is there a difference in how the Vision APIs are executed if the debugger is attached vs. not?
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616
Jan ’26
多设备协同操作繁琐
直播过程中需同时操作 Vision Pro(拍摄)、Mac(推流)、中控台(画面切换),无统一控制入口,调节 3D 模型、校准画质等操作需在多设备间切换,易出错且效率低。 期望 针对直播场景,提供桌面端专属控制软件,支持一站式管理 Vision Pro 的拍摄参数、3D 模型切换、虚实融合效果等,实现多设备协同操作的可视化、便捷化。
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605
Dec ’25
拍摄画面亮度不稳定(动态波动)
画面亮度存在无规律动态波动(时亮时暗),且无手动控制入口,导致商品颜色还原失真、主播面部曝光异常(过曝 / 欠曝),严重影响直播展示效果。 期望 "· 优化直播模式的自动曝光算法,提升复杂光线环境下的亮度稳定性; · 增加 “直播模式” 专属亮度锁定功能,支持手动设定亮度参数并锁定,满足直播场景下的画质可控需求。 "
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705
Dec ’25
多相机切换时画质参数差异显著
切换后两者的亮度、色彩饱和度、对比度等画质参数差距较大,导致画面视觉体验割裂,破坏直播连贯性,影响用户观看沉浸感。 期望 "· 对标常规直播单反相机的画质基准,优化 Vision Pro 的画面亮度、色彩还原能力; · 提供设备端或配套软件的画质自定义调节功能(亮度、对比度、色温等),支持直播前手动校准,确保与单反相机画面风格一致。"
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346
Dec ’25
Vision Pro 画面传输至 Mac 后分辨率偏低
传输后的直播流分辨率显著下降,画面细节丢失、清晰度不足,导致 3D 家具商品的纹理、尺寸等关键信息无法精准展示,影响用户对商品的判断。 期望 优化流传输过程中的分辨率压缩策略,减少传输过程中的画质损耗,提升 Mac 端接收的直播流清晰度,匹配 3D 商品展示的高精度需求。
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442
Dec ’25
画面抖动导致观众眩晕
佩戴者头部自然晃动时,设备拍摄的画面会出现明显抖动,导致观看直播的用户产生眩晕感,严重影响直播沉浸体验和购物决策效率。 希望 优化设备内置防抖算法,降低头部常规晃动对画面稳定性的影响,提升直播画面的流畅度。
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478
Dec ’25
Orientation does not work on iPhone 17 and above.
I'm receiving output from avcapturesession and capturing an image using Vision, but the image is output in landscape orientation instead of portrait. Even when I set the orientation to up in ciimage, cgimage, and uiimage, the image is still output in landscape orientation. On iPhones 16 and below, the image is output in portrait orientation. But on iPhones 17 and above, the image is output in landscape orientation. Please help.
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1k
Dec ’25
Can iOS capture video at 4032×3024 while running a Vision/ML model?
I am new to Swift and iOS development, and I have a question about video capture performance. Is it possible to capture video at a resolution of 4032×3024 while simultaneously running a vision/ML model on the video stream (e.g., using Vision or CoreML)? I want to know: whether iOS devices support capturing video at that resolution, whether the frame rate drops significantly at that scale, and whether it is practical to run a Vision/ML model in real-time while recording at such a high resolution. If anyone has experience with high-resolution AVCaptureSession setups or combining them with real-time ML processing, I would really appreciate guidance or sample code.
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514
Dec ’25
Proposal: Using ARKit Body Tracking & LiDAR for Sign Language Education (Real-time Feedback)
Hi everyone, I’ve been analyzing the current state of Sign Language accessibility tools, and I noticed a significant gap in learning tools: we lack real-time feedback for students (e.g., "Is my hand position correct?"). Most current solutions rely on 2D video processing, which struggles with depth perception and occlusion (hand-over-hand or hand-over-face gestures), which are critical in Sign Language grammar. I'd like to propose/discuss an architecture leveraging the current LiDAR + Neural Engine capabilities found in iPhone devices to solve this. The Concept: Skeleton-based Normalization Instead of training ML models on raw video frames (which introduces noise from lighting, skin tone, and clothing), we could use ARKit's Body Tracking to abstract the input. Capture: Use ARKit/LiDAR to track the user's upper body and hand joints in 3D space. Data Normalization: Extract only the vector coordinates (X, Y, Z of joints). This creates a "clean" dataset, effectively normalizing the user regardless of physical appearance. Comparison: Feed these vectors into a CoreML model trained on "Reference Skeletons" (recorded by native signers). Feedback Loop: The app calculates the geometric distance between the user's pose and the reference pose to provide specific correction (e.g., "Raise your elbow 10 degrees"). Why this approach? Solves Occlusion: LiDAR handles depth much better than standard RGB cameras when hands cross the body. Privacy: We are processing coordinates, not video streams. Efficiency: Comparing vector sequences is computationally cheaper than video analysis, preserving battery life. Has anyone experimented with using ARKit Body Anchors specifically for comparing complex gesture sequences against a stored "correct" database? I believe this "Skeleton First" approach is the key to scalable Sign Language education apps. Looking forward to hearing your thoughts.
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1.3k
Dec ’25
VNDetectFaceRectanglesRequest does not use the Neural Engine?
I'm on Tahoe 26.1 / M3 Macbook Air. I'm using VNDetectFaceRectanglesRequest as properly as possible, as in the minimal command line program attached below. For some reason, I always get: MLE5Engine is disabled through the configuration printed. I couldn't find any notes on developer docs saying that VNDetectFaceRectanglesRequest can not use the Apple Neural Engine. I'm assuming there is something wrong with my code however I wasn't able to find any remarks from documentation where it might be. I wasn't able to find the above error message online either. I would appreciate your help a lot and thank you in advance. The code below accesses the video from AVCaptureDevice.DeviceType.builtInWideAngleCamera. Currently it directly chooses the 0th format which has the largest resolution (Full HD on my M3 MBA) and "4:2:0" color "v" reduced color component spectrum encoding ("420v"). After accessing video, it performs a VNDetectFaceRectanglesRequest. It prints "VNDetectFaceRectanglesRequest completion Handler called" many times, then prints the error message above, then continues printing "VNDetectFaceRectanglesRequest completion Handler called" until the user quits it. To run it in Xcode, File > New project > Mac command line tool. Pasting the code below, then click on the root file > Targets > Signing & Capabilities > Hardened Runtime > Resource Access > Camera. A possible explanation could be that either Apple's internal CoreML code for this function works on GPU/CPU only or it doesn't accept 420v as supplied by the Macbook Air camera import AVKit import Vision var videoDataOutput: AVCaptureVideoDataOutput = AVCaptureVideoDataOutput() var detectionRequests: [VNDetectFaceRectanglesRequest]? var videoDataOutputQueue: DispatchQueue = DispatchQueue(label: "queue") class XYZ: /*NSViewController or NSObject*/NSObject, AVCaptureVideoDataOutputSampleBufferDelegate { func viewDidLoad() { //super.viewDidLoad() let session = AVCaptureSession() let inputDevice = try! self.configureFrontCamera(for: session) self.configureVideoDataOutput(for: inputDevice.device, resolution: inputDevice.resolution, captureSession: session) self.prepareVisionRequest() session.startRunning() } fileprivate func highestResolution420Format(for device: AVCaptureDevice) -> (format: AVCaptureDevice.Format, resolution: CGSize)? { let deviceFormat = device.formats[0] print(deviceFormat) let dims = CMVideoFormatDescriptionGetDimensions(deviceFormat.formatDescription) let resolution = CGSize(width: CGFloat(dims.width), height: CGFloat(dims.height)) return (deviceFormat, resolution) } fileprivate func configureFrontCamera(for captureSession: AVCaptureSession) throws -> (device: AVCaptureDevice, resolution: CGSize) { let deviceDiscoverySession = AVCaptureDevice.DiscoverySession(deviceTypes: [AVCaptureDevice.DeviceType.builtInWideAngleCamera], mediaType: .video, position: AVCaptureDevice.Position.unspecified) let device = deviceDiscoverySession.devices.first! let deviceInput = try! AVCaptureDeviceInput(device: device) captureSession.addInput(deviceInput) let highestResolution = self.highestResolution420Format(for: device)! try! device.lockForConfiguration() device.activeFormat = highestResolution.format device.unlockForConfiguration() return (device, highestResolution.resolution) } fileprivate func configureVideoDataOutput(for inputDevice: AVCaptureDevice, resolution: CGSize, captureSession: AVCaptureSession) { videoDataOutput.setSampleBufferDelegate(self, queue: videoDataOutputQueue) captureSession.addOutput(videoDataOutput) } fileprivate func prepareVisionRequest() { let faceDetectionRequest: VNDetectFaceRectanglesRequest = VNDetectFaceRectanglesRequest(completionHandler: { (request, error) in print("VNDetectFaceRectanglesRequest completion Handler called") }) // Start with detection detectionRequests = [faceDetectionRequest] } // MARK: AVCaptureVideoDataOutputSampleBufferDelegate // Handle delegate method callback on receiving a sample buffer. public func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) { var requestHandlerOptions: [VNImageOption: AnyObject] = [:] let cameraIntrinsicData = CMGetAttachment(sampleBuffer, key: kCMSampleBufferAttachmentKey_CameraIntrinsicMatrix, attachmentModeOut: nil) if cameraIntrinsicData != nil { requestHandlerOptions[VNImageOption.cameraIntrinsics] = cameraIntrinsicData } let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer)! // No tracking object detected, so perform initial detection let imageRequestHandler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer, orientation: CGImagePropertyOrientation.up, options: requestHandlerOptions) try! imageRequestHandler.perform(detectionRequests!) } } let X = XYZ() X.viewDidLoad() sleep(9999999)
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785
Nov ’25
builtInLiDARDepthCamera doesn't work on the 2020 iPad Pro on iOS 26
On iOS 26.1, this throws on the 2020 iPad Pro (4th gen) but works fine on an M4 iPad Pro or iPhone 15 Pro: guard let device = AVCaptureDevice.default(.builtInLiDARDepthCamera, for: .video, position: .back) else { throw ConfigurationError.lidarDeviceUnavailable } It's just the standard code from Apple's own sample code so obviously used to work: https://developer.apple.com/documentation/AVFoundation/capturing-depth-using-the-lidar-camera Does it fail because Apple have silently dumped support for the older LiDAR sensor used prior to the M4 iPad Pro, or is there another reason? What about the 5th and 6th gen iPad Pro, does it still work on those?
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859
Nov ’25
Inquiry About Building an App for Object Detection, Background Removal, and Animation
Hi all! Nice to meet you., I am planning to build an iOS application that can: Capture an image using the camera or select one from the gallery. Remove the background and keep only the detected main object. Add a border (outline) around the detected object’s shape. Apply an animation along that border (e.g., moving light or glowing effect). Include a transition animation when removing the background — for example, breaking the background into pieces as it disappears. The app Capword has a similar feature for object isolation, and I’d like to build something like that. Could you please provide any guidance, frameworks, or sample code related to: Object segmentation and background removal in Swift (Vision or Core ML). Applying custom borders and shape animations around detected objects. Recognizing the object name (e.g., “person”, “cat”, “car”) after segmentation. Thank you very much for your support. Best regards, SINN SOKLYHOR
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517
Nov ’25
RecognizeDocumentsRequest for receipts
Hi, I'm trying to use the new RecognizeDocumentsRequest from the Vision Framework to read a receipt. It looks very promising by being able to read paragraphs, lines and detect data. So far it unfortunately seems to read every line on the receipt as a paragraph and when there is more space on one line it creates two paragraphs. Is there perhaps an Apple Engineer who knows if this is expected behaviour or if I should file a Feedback for this? Code setup: let request = RecognizeDocumentsRequest() let observations = try await request.perform(on: image) guard let document = observations.first?.document else { return } for paragraph in document.paragraphs { print(paragraph.transcript) for data in paragraph.detectedData { switch data.match.details { case .phoneNumber(let data): print("Phone: \(data)") case .postalAddress(let data): print("Postal: \(data)") case .calendarEvent(let data): print("Calendar: \(data)") case .moneyAmount(let data): print("Money: \(data)") case .measurement(let data): print("Measurement: \(data)") default: continue } } } See attached image as an example of a receipt I'd like to parse. The top 3 lines are the name, street, and postal code + city. These are all separate paragraphs. Checking on detectedData does see the street (2nd line) as PostalAddress, but not the complete address. Might that be a location thing since it's a Dutch address. And lower on the receipt it sees the block with "Pomp 1 95 Ongelood" and the things below also as separate paragraphs. First picking up the left side and after that the right side. So it's something like this: * Pomp 1 Volume Prijs € TOTAAL * BTW Netto 21.00 % 95 Ongelood 41,90 l 1.949/ 1 81.66 € 14.17 67.49
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1.2k
Nov ’25
How-to highlight people in a Vision Pro app using Compositor Services
Fundamentally, my questions are: is there a known transform I can apply onto a given (pixel) position (passed into a Metal Fragment Function) to correctly sample a texture provided by the main cameras + processed by a Vision request. If so, what is it? If not, how can I accurately sample my masks? My goal is to highlight people in a Vision Pro app using Compositor Services. To start, I asynchronously receive camera frames for the main left and right cameras. This is the breakdown of the specific CameraVideoFormat I pass along to the CameraFrameProvider: minFrameDuration: 0.03 maxFrameDuration: 0.033333335 frameSize: (1920.0, 1080.0) pixelFormat: 875704422 cameraType: main cameraPositions: [left, right] cameraRectification: mono From each camera frame sample, I extract the left and right buffers (CVReadOnlyPixelBuffer.withUnsafebuffer ==> CVPixelBuffer). I asynchronously process the extracted buffers by performing a VNGeneratePersonSegmentationRequest on both of them: // NOTE: This block of code and all following code blocks contain simplified representations of my code for clarity's sake. var request = VNGeneratePersonSegmentationRequest() request.qualityLevel = .balanced request.outputPixelFormat = kCVPixelFormatType_OneComponent8 ... let lHandler = VNSequenceRequestHandler() let rHandler = VNSequenceRequestHandler() ... func processBuffers() async { try lHandler.perform([request], on: lBuffer) guard let lMask = request.results?.first?.pixelBuffer else {...} try rHandler.perform([request], on: rBuffer) guard let rMask = request.results?.first?.pixelBuffer else {...} appModel.latestPersonMasks = (lMask, rMask) } I store the two resulting CVPixelBuffers in my appModel. For both of these buffers aka grayscale masks: width (in pixels) = 512 height (in pixels) = 384 byters per row = 512 plane count = 0 pixel format type = 1278226488 I am using Compositor Services to render my content in Immersive Space. My implementation of Compositor Services is based off of the same code from Interacting with virtual content blended with passthrough. Within the Shaders.metal, the tint's Fragment Shader is now passed the grayscale masks (converted from CVPixelBuffer to MTLTexture via CVMetalTextureCacheCreateTextureFromImage() at the beginning of the main render pipeline). fragment float4 tintFragmentShader( TintInOut in [[stage_in]], ushort amp_id [[amplification_id]], texture2d<uint> leftMask [[texture(0)]], texture2d<uint> rightMask [[texture(1)]] ) { if (in.color.a <= 0.0) { discard_fragment(); } float2 uv; if (amp_id == 0) { // LEFT uv = ??????????????????????; } else { // RIGHT uv = ??????????????????????; } constexpr sampler linearSampler (mip_filter::linear, mag_filter::linear, min_filter::linear); // Sample the PersonSegmentation grayscale mask float maskValue = 0.0; if (amp_id == 0) { // LEFT if (leftMask.get_width() > 0) { maskValue = rightMask.sample(linearSampler, uv).r; } } else { // RIGHT if (rightMask.get_width() > 0) { maskValue = rightMask.sample(linearSampler, uv).r; } } if (maskValue > 250) { return (1.0, 1.0, 1.0, 0.5) } return in.color; } I need to correctly sample the masks for a given fragment. The LayerRenderer.Layout is set to .layered. From Developer Documentation. A layout that specifies each view’s content as a slice of a single texture. Using the Metal debugger, I know that the final render target texture for each view / eye is 1888 x 1792 pixels, giving an aspect ratio of 59:56. The initial CVPixelBuffer provided by the main left and right cameras is 1920x1080 (16:9). The grayscale CVPixelBuffer returned by the VNPersonSegmentationRequest is 512x384 (4:3). All of these aspect ratios are different. My questions come down to: is there a known transform I can apply onto a given (pixel) position to correctly sample a texture provided by the main cameras + processed by a Vision request. If so, what is it? If not, how can I accurately sample my masks? Within the tint's Vertex Shader, after applying the modelViewProjectionMatrix, I have tried every version I have been able to find that takes the pixel space position (= vertices[vertexID].position.xy) and the viewport size (1888x1792) to compute the correct clip space position (maybe = pixel space position.xy / (viewport size * 0.5)???) of the grayscale masks but nothing has worked. The "highlight" of the person segmentations is off: scaled a little too big, offset little to far up and off to the side.
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Nov ’25
Apple OCR framework seems to be holding on to allocations every time it is called.
Environment: macOS 26.2 (Tahoe) Xcode 16.3 Apple Silicon (M4) Sandboxed Mac App Store app Description: Repeated use of VNRecognizeTextRequest causes permanent memory growth in the host process. The physical footprint increases by approximately 3-15 MB per OCR call and never returns to baseline, even after all references to the request, handler, observations, and image are released. ` private func selectAndProcessImage() { let panel = NSOpenPanel() panel.allowedContentTypes = [.image] panel.allowsMultipleSelection = false panel.canChooseDirectories = false panel.message = "Select an image for OCR processing" guard panel.runModal() == .OK, let url = panel.url else { return } selectedImageURL = url isProcessing = true recognizedText = "Processing..." // Run OCR on a background thread to keep UI responsive let workItem = DispatchWorkItem { let result = performOCR(on: url) DispatchQueue.main.async { recognizedText = result isProcessing = false } } DispatchQueue.global(qos: .userInitiated).async(execute: workItem) } private func performOCR(on url: URL) -> String { // Wrap EVERYTHING in autoreleasepool so all ObjC objects are drained immediately let resultText: String = autoreleasepool { // Load image and convert to CVPixelBuffer for explicit memory control guard let imageData = try? Data(contentsOf: url) else { return "Error: Could not read image file." } guard let nsImage = NSImage(data: imageData) else { return "Error: Could not create image from file data." } guard let cgImage = nsImage.cgImage(forProposedRect: nil, context: nil, hints: nil) else { return "Error: Could not create CGImage." } let width = cgImage.width let height = cgImage.height // Create a CVPixelBuffer from the CGImage var pixelBuffer: CVPixelBuffer? let attrs: [String: Any] = [ kCVPixelBufferCGImageCompatibilityKey as String: true, kCVPixelBufferCGBitmapContextCompatibilityKey as String: true ] let status = CVPixelBufferCreate( kCFAllocatorDefault, width, height, kCVPixelFormatType_32ARGB, attrs as CFDictionary, &pixelBuffer ) guard status == kCVReturnSuccess, let buffer = pixelBuffer else { return "Error: Could not create CVPixelBuffer (status: \(status))." } // Draw the CGImage into the pixel buffer CVPixelBufferLockBaseAddress(buffer, []) guard let context = CGContext( data: CVPixelBufferGetBaseAddress(buffer), width: width, height: height, bitsPerComponent: 8, bytesPerRow: CVPixelBufferGetBytesPerRow(buffer), space: CGColorSpaceCreateDeviceRGB(), bitmapInfo: CGImageAlphaInfo.noneSkipFirst.rawValue ) else { CVPixelBufferUnlockBaseAddress(buffer, []) return "Error: Could not create CGContext for pixel buffer." } context.draw(cgImage, in: CGRect(x: 0, y: 0, width: width, height: height)) CVPixelBufferUnlockBaseAddress(buffer, []) // Run OCR let requestHandler = VNImageRequestHandler(cvPixelBuffer: buffer, options: [:]) let request = VNRecognizeTextRequest() request.recognitionLevel = .accurate request.usesLanguageCorrection = true do { try requestHandler.perform([request]) } catch { return "Error during OCR: \(error.localizedDescription)" } guard let observations = request.results, !observations.isEmpty else { return "No text found in image." } let lines = observations.compactMap { observation in observation.topCandidates(1).first?.string } // Explicitly nil out the pixel buffer before the pool drains pixelBuffer = nil return lines.joined(separator: "\n") } // Everything — Data, NSImage, CGImage, CVPixelBuffer, VN objects — released here return resultText } `
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Activity
Feb ’26
VisionOs Development: Seeking Advice on Key Strategic Crossroads
I am a developer working on developing a space journal application. During the development process, I encountered several crucial strategic and technical decisions, and I would like to hear the experiences of those who have gone through similar situations. Here are the simplified versions of several questions I have. Resource allocation: Which problem should I address first? Design direction: In terms of interaction and UI design, how should I balance "immersion" and "usability"? Market selection: Was it easier for a business to survive in the early stages as a B2B or B2C entity? Cost estimation: How can I reasonably present to my investors the development costs of this project? In order to avoid relying solely on intuition in my decisions, I created a short questionnaire, hoping to gather more structured opinions from my colleagues. If you are also exploring VisionOS, I sincerely hope you can take a few minutes to fill it out. The results are extremely important to me, and I would be more than happy to share the final summary findings with you.
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400
Activity
Feb ’26
Khmer Script Misidentified as Thai in Vision Framework
It is vital for Apple to refine its OCR models to correctly distinguish between Khmer and Thai scripts. Incorrectly labeling Khmer text as Thai is more than a technical bug; it is a culturally insensitive error that impacts national identity, especially given the current geopolitical climate between Cambodia and Thailand. Implementing a more robust language-detection threshold would prevent these harmful misidentifications. There is a significant logic flaw in the VNRecognizeTextRequest language detection when processing Khmer script. When the property automaticallyDetectsLanguage is set to true, the Vision framework frequently misidentifies Khmer characters as Thai. While both scripts share historical roots, they are distinct languages with different alphabets. Currently, the model’s confidence threshold for distinguishing between these two scripts is too low, leading to incorrect OCR output in both developer-facing APIs and Apple’s native ecosystem (Preview, Live Text, and Photos). import SwiftUI import Vision class TextExtractor { func extractText(from data: Data, completion: @escaping (String) -> Void) { let request = VNRecognizeTextRequest { (request, error) in guard let observations = request.results as? [VNRecognizedTextObservation] else { completion("No text found.") return } let recognizedStrings = observations.compactMap { observation in let str = observation.topCandidates(1).first?.string return "{text: \(str!), confidence: \(observation.confidence)}" } completion(recognizedStrings.joined(separator: "\n")) } request.automaticallyDetectsLanguage = true // <-- This is the issue. request.recognitionLevel = .accurate let handler = VNImageRequestHandler(data: data, options: [:]) DispatchQueue.global(qos: .background).async { do { try handler.perform([request]) } catch { completion("Failed to perform OCR: \(error.localizedDescription)") } } } } Recognizing Khmer Confidence Score is low for Khmer text. (The output is in Thai language with low confidence score) Recognizing English Confidence Score is high expected. Recognizing Thai Confidence Score is high as expected Issues on Preview, Photos Khmer text Copied text Kouk Pring Chroum Temple [19121 รอาสายสุกตีนานยารรีสใหิสรราภูชิตีนนสุฐตีย์ [รุก เผือชิษาธอยกัตธ์ตายตราพาษชาณา ถวเชยาใบสราเบรถทีมูสินตราพาษชาณา ทีมูโษา เช็ก อาษเชิษฐอารายสุกบดตพรธุรฯ ตากร"สุก"ผาตากรธกรธุกเยากสเผาพศฐตาสาย รัอรณาษ"ตีพย" สเผาพกรกฐาภูชิสาเครๆผู:สุกรตีพาสเผาพสรอสายใผิตรรารตีพสๆ เดียอลายสุกตีน ธาราชรติ ธิพรหณาะพูชุบละเาหLunet De Lajonquiere ผารูกรสาราพารผรผาสิตภพ ตารสิทูก ธิพิ คุณที่นสายเระพบพเคเผาหนารเกะทรนภาษเราภุพเสารเราษทีเลิกสญาเราหรุฬารชสเกาก เรากุม สงสอบานตรเราะากกต่ายภากายระตารุกเตียน Recommended Solutions 1. Set a Threshold Filter out the detected result where the threshold is less than or equal to 0.5, so that it would not output low quality text which can lead to the issue. For example, let recognizedStrings = observations.compactMap { observation in if observation.confidence <= 0.5 { return nil } let str = observation.topCandidates(1).first?.string return "{text: \(str!), confidence: \(observation.confidence)}" } 2. Add Khmer Language Support This issue would never happen if the model has the capability to detect and recognize image with Khmer language. Doc2Text GitHub: https://github.com/seanghay/Doc2Text-Swift
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Activity
Jan ’26
Policy on AI-generated assets for Swift Student Challenge 2026
Hello everyone, I am currently developing an app for my Swift Student Challenge submission that focuses on human motion analysis using the Vision framework. To effectively demonstrate the app's technical capabilities during the review process, I need to include a sample video showing a person performing specific movements. However, I want to ensure that my submission strictly adheres to all intellectual property guidelines. Instead of using existing copyrighted videos or public social media clips, I am considering using Generative AI to create an original, royalty-free sample video. This video would feature a character performing movements designed specifically to test my app's pose estimation and feedback logic. I have a few questions regarding this approach: Is it acceptable to use AI-generated sample assets (like video clips) to demonstrate technical features when it's difficult to record high-quality personal footage due to environmental constraints? If I clearly disclose the tools used and the reason for using AI-generated content in my written response, would this be considered a professional approach to asset management? Are there any specific guidelines I should follow to ensure that the use of AI-generated samples doesn't overshadow the original coding and design work of the project? My goal is to showcase a polished and technically sound implementation using Xcode 26 while respecting all copyright requirements. Thank you for your time and advice!
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901
Activity
Jan ’26
Threading issues when using debugger
Hi, I am modifying the sample camera app that is here: https://developer.apple.com/tutorials/sample-apps/capturingphotos-camerapreview ... In the processPreviewImages, I am using the Vision APIs to generate a segmentation mask for a person/object, then compositing that person onto a different background (with some other filtering). The filtering and compositing is done via CoreImage. At the end, I convert the CIImage to a CGImage then to a SwiftUI Image. When I run it on my iPhone, it works fine, and has not crashed. When I run it on the iPhone with the debugger, it crashes within a few seconds with: EXC_BAD_ACCESS in libRPAC.dylib`std::__1::__hash_table<std::__1::__hash_value_type<long, qos_info_t>, std::__1::__unordered_map_hasher<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::hash, std::__1::equal_to, true>, std::__1::__unordered_map_equal<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::equal_to, std::__1::hash, true>, std::__1::allocator<std::__1::__hash_value_type<long, qos_info_t>>>::__emplace_unique_key_args<long, std::__1::piecewise_construct_t const&, std::__1::tuple<long const&>, std::__1::tuple<>>: It had previously been working fine with the debugger, so I'm not sure what has changed. Is there a difference in how the Vision APIs are executed if the debugger is attached vs. not?
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616
Activity
Jan ’26
多设备协同操作繁琐
直播过程中需同时操作 Vision Pro(拍摄)、Mac(推流)、中控台(画面切换),无统一控制入口,调节 3D 模型、校准画质等操作需在多设备间切换,易出错且效率低。 期望 针对直播场景,提供桌面端专属控制软件,支持一站式管理 Vision Pro 的拍摄参数、3D 模型切换、虚实融合效果等,实现多设备协同操作的可视化、便捷化。
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605
Activity
Dec ’25
拍摄画面亮度不稳定(动态波动)
画面亮度存在无规律动态波动(时亮时暗),且无手动控制入口,导致商品颜色还原失真、主播面部曝光异常(过曝 / 欠曝),严重影响直播展示效果。 期望 "· 优化直播模式的自动曝光算法,提升复杂光线环境下的亮度稳定性; · 增加 “直播模式” 专属亮度锁定功能,支持手动设定亮度参数并锁定,满足直播场景下的画质可控需求。 "
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705
Activity
Dec ’25
多相机切换时画质参数差异显著
切换后两者的亮度、色彩饱和度、对比度等画质参数差距较大,导致画面视觉体验割裂,破坏直播连贯性,影响用户观看沉浸感。 期望 "· 对标常规直播单反相机的画质基准,优化 Vision Pro 的画面亮度、色彩还原能力; · 提供设备端或配套软件的画质自定义调节功能(亮度、对比度、色温等),支持直播前手动校准,确保与单反相机画面风格一致。"
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346
Activity
Dec ’25
Vision Pro 画面传输至 Mac 后分辨率偏低
传输后的直播流分辨率显著下降,画面细节丢失、清晰度不足,导致 3D 家具商品的纹理、尺寸等关键信息无法精准展示,影响用户对商品的判断。 期望 优化流传输过程中的分辨率压缩策略,减少传输过程中的画质损耗,提升 Mac 端接收的直播流清晰度,匹配 3D 商品展示的高精度需求。
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442
Activity
Dec ’25
画面抖动导致观众眩晕
佩戴者头部自然晃动时,设备拍摄的画面会出现明显抖动,导致观看直播的用户产生眩晕感,严重影响直播沉浸体验和购物决策效率。 希望 优化设备内置防抖算法,降低头部常规晃动对画面稳定性的影响,提升直播画面的流畅度。
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478
Activity
Dec ’25
Orientation does not work on iPhone 17 and above.
I'm receiving output from avcapturesession and capturing an image using Vision, but the image is output in landscape orientation instead of portrait. Even when I set the orientation to up in ciimage, cgimage, and uiimage, the image is still output in landscape orientation. On iPhones 16 and below, the image is output in portrait orientation. But on iPhones 17 and above, the image is output in landscape orientation. Please help.
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Activity
Dec ’25
Can iOS capture video at 4032×3024 while running a Vision/ML model?
I am new to Swift and iOS development, and I have a question about video capture performance. Is it possible to capture video at a resolution of 4032×3024 while simultaneously running a vision/ML model on the video stream (e.g., using Vision or CoreML)? I want to know: whether iOS devices support capturing video at that resolution, whether the frame rate drops significantly at that scale, and whether it is practical to run a Vision/ML model in real-time while recording at such a high resolution. If anyone has experience with high-resolution AVCaptureSession setups or combining them with real-time ML processing, I would really appreciate guidance or sample code.
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Activity
Dec ’25
Proposal: Using ARKit Body Tracking & LiDAR for Sign Language Education (Real-time Feedback)
Hi everyone, I’ve been analyzing the current state of Sign Language accessibility tools, and I noticed a significant gap in learning tools: we lack real-time feedback for students (e.g., "Is my hand position correct?"). Most current solutions rely on 2D video processing, which struggles with depth perception and occlusion (hand-over-hand or hand-over-face gestures), which are critical in Sign Language grammar. I'd like to propose/discuss an architecture leveraging the current LiDAR + Neural Engine capabilities found in iPhone devices to solve this. The Concept: Skeleton-based Normalization Instead of training ML models on raw video frames (which introduces noise from lighting, skin tone, and clothing), we could use ARKit's Body Tracking to abstract the input. Capture: Use ARKit/LiDAR to track the user's upper body and hand joints in 3D space. Data Normalization: Extract only the vector coordinates (X, Y, Z of joints). This creates a "clean" dataset, effectively normalizing the user regardless of physical appearance. Comparison: Feed these vectors into a CoreML model trained on "Reference Skeletons" (recorded by native signers). Feedback Loop: The app calculates the geometric distance between the user's pose and the reference pose to provide specific correction (e.g., "Raise your elbow 10 degrees"). Why this approach? Solves Occlusion: LiDAR handles depth much better than standard RGB cameras when hands cross the body. Privacy: We are processing coordinates, not video streams. Efficiency: Comparing vector sequences is computationally cheaper than video analysis, preserving battery life. Has anyone experimented with using ARKit Body Anchors specifically for comparing complex gesture sequences against a stored "correct" database? I believe this "Skeleton First" approach is the key to scalable Sign Language education apps. Looking forward to hearing your thoughts.
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Activity
Dec ’25
VNDetectFaceRectanglesRequest does not use the Neural Engine?
I'm on Tahoe 26.1 / M3 Macbook Air. I'm using VNDetectFaceRectanglesRequest as properly as possible, as in the minimal command line program attached below. For some reason, I always get: MLE5Engine is disabled through the configuration printed. I couldn't find any notes on developer docs saying that VNDetectFaceRectanglesRequest can not use the Apple Neural Engine. I'm assuming there is something wrong with my code however I wasn't able to find any remarks from documentation where it might be. I wasn't able to find the above error message online either. I would appreciate your help a lot and thank you in advance. The code below accesses the video from AVCaptureDevice.DeviceType.builtInWideAngleCamera. Currently it directly chooses the 0th format which has the largest resolution (Full HD on my M3 MBA) and "4:2:0" color "v" reduced color component spectrum encoding ("420v"). After accessing video, it performs a VNDetectFaceRectanglesRequest. It prints "VNDetectFaceRectanglesRequest completion Handler called" many times, then prints the error message above, then continues printing "VNDetectFaceRectanglesRequest completion Handler called" until the user quits it. To run it in Xcode, File > New project > Mac command line tool. Pasting the code below, then click on the root file > Targets > Signing & Capabilities > Hardened Runtime > Resource Access > Camera. A possible explanation could be that either Apple's internal CoreML code for this function works on GPU/CPU only or it doesn't accept 420v as supplied by the Macbook Air camera import AVKit import Vision var videoDataOutput: AVCaptureVideoDataOutput = AVCaptureVideoDataOutput() var detectionRequests: [VNDetectFaceRectanglesRequest]? var videoDataOutputQueue: DispatchQueue = DispatchQueue(label: "queue") class XYZ: /*NSViewController or NSObject*/NSObject, AVCaptureVideoDataOutputSampleBufferDelegate { func viewDidLoad() { //super.viewDidLoad() let session = AVCaptureSession() let inputDevice = try! self.configureFrontCamera(for: session) self.configureVideoDataOutput(for: inputDevice.device, resolution: inputDevice.resolution, captureSession: session) self.prepareVisionRequest() session.startRunning() } fileprivate func highestResolution420Format(for device: AVCaptureDevice) -> (format: AVCaptureDevice.Format, resolution: CGSize)? { let deviceFormat = device.formats[0] print(deviceFormat) let dims = CMVideoFormatDescriptionGetDimensions(deviceFormat.formatDescription) let resolution = CGSize(width: CGFloat(dims.width), height: CGFloat(dims.height)) return (deviceFormat, resolution) } fileprivate func configureFrontCamera(for captureSession: AVCaptureSession) throws -> (device: AVCaptureDevice, resolution: CGSize) { let deviceDiscoverySession = AVCaptureDevice.DiscoverySession(deviceTypes: [AVCaptureDevice.DeviceType.builtInWideAngleCamera], mediaType: .video, position: AVCaptureDevice.Position.unspecified) let device = deviceDiscoverySession.devices.first! let deviceInput = try! AVCaptureDeviceInput(device: device) captureSession.addInput(deviceInput) let highestResolution = self.highestResolution420Format(for: device)! try! device.lockForConfiguration() device.activeFormat = highestResolution.format device.unlockForConfiguration() return (device, highestResolution.resolution) } fileprivate func configureVideoDataOutput(for inputDevice: AVCaptureDevice, resolution: CGSize, captureSession: AVCaptureSession) { videoDataOutput.setSampleBufferDelegate(self, queue: videoDataOutputQueue) captureSession.addOutput(videoDataOutput) } fileprivate func prepareVisionRequest() { let faceDetectionRequest: VNDetectFaceRectanglesRequest = VNDetectFaceRectanglesRequest(completionHandler: { (request, error) in print("VNDetectFaceRectanglesRequest completion Handler called") }) // Start with detection detectionRequests = [faceDetectionRequest] } // MARK: AVCaptureVideoDataOutputSampleBufferDelegate // Handle delegate method callback on receiving a sample buffer. public func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) { var requestHandlerOptions: [VNImageOption: AnyObject] = [:] let cameraIntrinsicData = CMGetAttachment(sampleBuffer, key: kCMSampleBufferAttachmentKey_CameraIntrinsicMatrix, attachmentModeOut: nil) if cameraIntrinsicData != nil { requestHandlerOptions[VNImageOption.cameraIntrinsics] = cameraIntrinsicData } let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer)! // No tracking object detected, so perform initial detection let imageRequestHandler = VNImageRequestHandler(cvPixelBuffer: pixelBuffer, orientation: CGImagePropertyOrientation.up, options: requestHandlerOptions) try! imageRequestHandler.perform(detectionRequests!) } } let X = XYZ() X.viewDidLoad() sleep(9999999)
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785
Activity
Nov ’25
builtInLiDARDepthCamera doesn't work on the 2020 iPad Pro on iOS 26
On iOS 26.1, this throws on the 2020 iPad Pro (4th gen) but works fine on an M4 iPad Pro or iPhone 15 Pro: guard let device = AVCaptureDevice.default(.builtInLiDARDepthCamera, for: .video, position: .back) else { throw ConfigurationError.lidarDeviceUnavailable } It's just the standard code from Apple's own sample code so obviously used to work: https://developer.apple.com/documentation/AVFoundation/capturing-depth-using-the-lidar-camera Does it fail because Apple have silently dumped support for the older LiDAR sensor used prior to the M4 iPad Pro, or is there another reason? What about the 5th and 6th gen iPad Pro, does it still work on those?
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859
Activity
Nov ’25
Inquiry About Building an App for Object Detection, Background Removal, and Animation
Hi all! Nice to meet you., I am planning to build an iOS application that can: Capture an image using the camera or select one from the gallery. Remove the background and keep only the detected main object. Add a border (outline) around the detected object’s shape. Apply an animation along that border (e.g., moving light or glowing effect). Include a transition animation when removing the background — for example, breaking the background into pieces as it disappears. The app Capword has a similar feature for object isolation, and I’d like to build something like that. Could you please provide any guidance, frameworks, or sample code related to: Object segmentation and background removal in Swift (Vision or Core ML). Applying custom borders and shape animations around detected objects. Recognizing the object name (e.g., “person”, “cat”, “car”) after segmentation. Thank you very much for your support. Best regards, SINN SOKLYHOR
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Activity
Nov ’25
RecognizeDocumentsRequest for receipts
Hi, I'm trying to use the new RecognizeDocumentsRequest from the Vision Framework to read a receipt. It looks very promising by being able to read paragraphs, lines and detect data. So far it unfortunately seems to read every line on the receipt as a paragraph and when there is more space on one line it creates two paragraphs. Is there perhaps an Apple Engineer who knows if this is expected behaviour or if I should file a Feedback for this? Code setup: let request = RecognizeDocumentsRequest() let observations = try await request.perform(on: image) guard let document = observations.first?.document else { return } for paragraph in document.paragraphs { print(paragraph.transcript) for data in paragraph.detectedData { switch data.match.details { case .phoneNumber(let data): print("Phone: \(data)") case .postalAddress(let data): print("Postal: \(data)") case .calendarEvent(let data): print("Calendar: \(data)") case .moneyAmount(let data): print("Money: \(data)") case .measurement(let data): print("Measurement: \(data)") default: continue } } } See attached image as an example of a receipt I'd like to parse. The top 3 lines are the name, street, and postal code + city. These are all separate paragraphs. Checking on detectedData does see the street (2nd line) as PostalAddress, but not the complete address. Might that be a location thing since it's a Dutch address. And lower on the receipt it sees the block with "Pomp 1 95 Ongelood" and the things below also as separate paragraphs. First picking up the left side and after that the right side. So it's something like this: * Pomp 1 Volume Prijs € TOTAAL * BTW Netto 21.00 % 95 Ongelood 41,90 l 1.949/ 1 81.66 € 14.17 67.49
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Nov ’25
How-to highlight people in a Vision Pro app using Compositor Services
Fundamentally, my questions are: is there a known transform I can apply onto a given (pixel) position (passed into a Metal Fragment Function) to correctly sample a texture provided by the main cameras + processed by a Vision request. If so, what is it? If not, how can I accurately sample my masks? My goal is to highlight people in a Vision Pro app using Compositor Services. To start, I asynchronously receive camera frames for the main left and right cameras. This is the breakdown of the specific CameraVideoFormat I pass along to the CameraFrameProvider: minFrameDuration: 0.03 maxFrameDuration: 0.033333335 frameSize: (1920.0, 1080.0) pixelFormat: 875704422 cameraType: main cameraPositions: [left, right] cameraRectification: mono From each camera frame sample, I extract the left and right buffers (CVReadOnlyPixelBuffer.withUnsafebuffer ==> CVPixelBuffer). I asynchronously process the extracted buffers by performing a VNGeneratePersonSegmentationRequest on both of them: // NOTE: This block of code and all following code blocks contain simplified representations of my code for clarity's sake. var request = VNGeneratePersonSegmentationRequest() request.qualityLevel = .balanced request.outputPixelFormat = kCVPixelFormatType_OneComponent8 ... let lHandler = VNSequenceRequestHandler() let rHandler = VNSequenceRequestHandler() ... func processBuffers() async { try lHandler.perform([request], on: lBuffer) guard let lMask = request.results?.first?.pixelBuffer else {...} try rHandler.perform([request], on: rBuffer) guard let rMask = request.results?.first?.pixelBuffer else {...} appModel.latestPersonMasks = (lMask, rMask) } I store the two resulting CVPixelBuffers in my appModel. For both of these buffers aka grayscale masks: width (in pixels) = 512 height (in pixels) = 384 byters per row = 512 plane count = 0 pixel format type = 1278226488 I am using Compositor Services to render my content in Immersive Space. My implementation of Compositor Services is based off of the same code from Interacting with virtual content blended with passthrough. Within the Shaders.metal, the tint's Fragment Shader is now passed the grayscale masks (converted from CVPixelBuffer to MTLTexture via CVMetalTextureCacheCreateTextureFromImage() at the beginning of the main render pipeline). fragment float4 tintFragmentShader( TintInOut in [[stage_in]], ushort amp_id [[amplification_id]], texture2d<uint> leftMask [[texture(0)]], texture2d<uint> rightMask [[texture(1)]] ) { if (in.color.a <= 0.0) { discard_fragment(); } float2 uv; if (amp_id == 0) { // LEFT uv = ??????????????????????; } else { // RIGHT uv = ??????????????????????; } constexpr sampler linearSampler (mip_filter::linear, mag_filter::linear, min_filter::linear); // Sample the PersonSegmentation grayscale mask float maskValue = 0.0; if (amp_id == 0) { // LEFT if (leftMask.get_width() > 0) { maskValue = rightMask.sample(linearSampler, uv).r; } } else { // RIGHT if (rightMask.get_width() > 0) { maskValue = rightMask.sample(linearSampler, uv).r; } } if (maskValue > 250) { return (1.0, 1.0, 1.0, 0.5) } return in.color; } I need to correctly sample the masks for a given fragment. The LayerRenderer.Layout is set to .layered. From Developer Documentation. A layout that specifies each view’s content as a slice of a single texture. Using the Metal debugger, I know that the final render target texture for each view / eye is 1888 x 1792 pixels, giving an aspect ratio of 59:56. The initial CVPixelBuffer provided by the main left and right cameras is 1920x1080 (16:9). The grayscale CVPixelBuffer returned by the VNPersonSegmentationRequest is 512x384 (4:3). All of these aspect ratios are different. My questions come down to: is there a known transform I can apply onto a given (pixel) position to correctly sample a texture provided by the main cameras + processed by a Vision request. If so, what is it? If not, how can I accurately sample my masks? Within the tint's Vertex Shader, after applying the modelViewProjectionMatrix, I have tried every version I have been able to find that takes the pixel space position (= vertices[vertexID].position.xy) and the viewport size (1888x1792) to compute the correct clip space position (maybe = pixel space position.xy / (viewport size * 0.5)???) of the grayscale masks but nothing has worked. The "highlight" of the person segmentations is off: scaled a little too big, offset little to far up and off to the side.
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Nov ’25