I am using Apple’s Vision framework with DetectHorizonRequest to detect the horizon in an image. Here is my code: func processHorizonImage(_ ciImage: CIImage) async { let request = DetectHorizonRequest() do { let result = try await request.perform(on: ciImage) print(result) } catch { print(error) } } After calling the perform method, I am getting result as nil. To ensure the request's correctness, I have verified the following: The input CIImage is valid and contains a visible horizon. No errors are being thrown. The relevant frameworks are properly imported. Given that my image contains a clear horizon, why am I still not getting any results? I would appreciate any help or suggestions to resolve this issue. Thank you for your support! This is the image

Hello, I’m working on a program that analyzes video files frame by frame to detect human poses in each frame. However, during the process of reading observations from the stream, the analysis frequently stops with the following error: [LOG_ERROR] /Library/Caches/com.apple.xbs/Sources/MediaAnalysis/VideoProcessing/VCPHumanPoseImageRequest.mm[85]: code -18 [LOG_ERROR] /Library/Caches/com.apple.xbs/Sources/MediaAnalysis/VideoProcessing/VCPHumanPoseImageRequest.mm[178]: code -18 The error was caught and printed using a do-catch block, and here is the output: Error Domain=NSOSStatusErrorDomain Code=-18 "Error: failed to processImage" UserInfo={NSLocalizedDescription=Error: failed to processImage} While the do-catch block helps prevent the app from crashing, the frames following the error cannot be analyzed. I’m hoping to understand the cause of this error, or find a way to skip the problematic frames and continue analyzing the subsequent ones. My development environment is Xcode Version 16.0 (16A242d) and iOS 18.0. Thank you for your help. (Attaching my code below.) let videoProcessor = VideoProcessor(videoURL) let bodyPoseRequest = DetectHumanBodyPoseRequest() let asset = AVURLAsset(url: videoURL) let videoTrack = try await asset.loadTracks(withMediaType: .video).first let bodyPoseStream = try await videoProcessor.addRequest(bodyPoseRequest) videoProcessor.startAnalysis() do { for try await observations in bodyPoseStream { guard let observation = observations.first else { continue } if let timeRange = observation.timeRange { /// do something... } } } catch { print("\(error.localizedDescription)") }

Hey everyone, I've been updating my code to take advantage of the new Vision API for text recognition in macOS 15. I'm noticing some very odd behavior though, it seems like in general the new Vision API consistently produces worse results than the old API. For reference here is how I'm setting up my request. var request = RecognizeTextRequest() request.recognitionLevel = getOCRMode() // generally accurate request.usesLanguageCorrection = !disableLanguageCorrection // generally true request.recognitionLanguages = language.split(separator: ",").map { Locale.Language(identifier: String($0)) } // generally 'en' let observations = try? await request.perform(on: image) as [RecognizedTextObservation] Then I will process the results and just get the top candidate, which as mentioned above, typically is of worse quality then the same request formed with the old API. Am I doing something wrong here?

WWDC 2024 mentioned that the OCR feature from the Vision framework has support for "Korean, Swedish, and Chinese", but the Swedish support does not seem to be available... Running either print(try? VNRecognizeTextRequest().supportedRecognitionLanguages()) or var ocrRequest = RecognizeTextRequest(.revision3) print(ocrRequest.supportedRecognitionLanguages) did not print out Swedish as one of the supported languages, but Korean and Chinese are. Tested on early versions of iOS 18 developer beta, and the latest version of iOS 18.1 (22B5054e).

I would like to offer the functionality that the user aims the camera at a graph (including axes and scales) and the app detects the graph and the app replicates the graph using the image. I have the whole camera setup finished with a AVCaptureSession, VNDetectContoursRequest, VNImageRequestHandler, etc. However, now I get many many results so I guess I will now need to tell the image processing process what I am looking for. i.e. filter the VNContoursObservations. I 'think' I first need to detect two perpendicular lines (the two axes). How do I do that? If I do not see them, I can just ignore that input and wait for the next VNContoursObservation. When I found the axes of the graph, I will need to find the curve (graph) that I need to scan. Any tips on how I can find that curve and turn that curve into a bunch of coordinates? Thanks! Wouter

Hi, we have in our app an immersive space and we taught the palm menu button is not available in immersive spaces, but when I look in the hand and tap the menu button appear. Is it possible to keep it hidden? Because we a have an hand tracking feature in palm and when we try to press a button to overlap the palm it triggers the menu button and then when the user presses again by mistake, it sends the application to the background. This is very important for us because we would like to release this hand-tracking feature as soon as possible. Here is a link with to a video with the problem: https://drive.google.com/file/d/1cfOcdzF19h_mbmpvkVNCJjXEBJecVeJL/view?usp=sharing

Hi everyone, I'm working on an iOS app built in Swift using Xcode, where I'm integrating Roboflow's object detection API to extract items from grocery receipts. My goal is to identify key information (like items, total, tax, etc.) from the images of these receipts. I'm successfully sending images to the Roboflow API and receiving predictions with bounding box data, but when I attempt to extract text from the detected regions (bounding boxes), it appears that the text extraction is failing—no text is being recognized. The issue seems to be that the bounding boxes are either not properly being handled or something is going wrong in the way I process the API response. Here's a brief breakdown of what I'm doing: The image is captured, converted to base64, and sent to the Roboflow API. The API response comes back with bounding boxes for the detected elements (items, date, subtotal, etc.). The problem occurs when I try to extract the text from the image using the bounding box data—it seems like the bounding boxes are being found, but no text is returned. I suspect the issue might be happening because the app’s segue to the results view controller is triggered before the OCR extraction completes, or there might be a problem in my code handling the bounding box response. Response Data: { "inference_id": "77134cce-91b5-4600-a59b-fab74350ca06", "time": 0.09240847699993537, "image": { "width": 370, "height": 502 }, "predictions": [ { "x": 163.5, "y": 250.5, "width": 313.0, "height": 127.0, "confidence": 0.9357666373252869, "class": "Item", "class_id": 1, "detection_id": "753341d5-07b6-42a1-8926-ecbc61128243" }, { "x": 52.5, "y": 417.5, "width": 89.0, "height": 23.0, "confidence": 0.8819760680198669, "class": "Date", "class_id": 0, "detection_id": "b4681149-d538-47b1-8700-d9528bf1daa0" }, ... ] } And the log showing bounding boxes: Prediction: ["width": 313, "y": 250.5, "x": 163.5, "detection_id": 753341d5-07b6-42a1-8926-ecbc61128243, "class": Item, "height": 127, "confidence": 0.9357666373252869, "class_id": 1] No bounding box found in prediction. I've double-checked the bounding box coordinates, and everything seems fine. Does anyone have experience with using OCR alongside object detection APIs in Swift? Any help on how to ensure the bounding boxes are properly processed and used for OCR would be greatly appreciated! Also, would it help to delay the segue to the results view controller until OCR is complete? Thank you!

When I use VNGenerateForegroundInstanceMaskRequest to generate the mask in the simulator by SwiftUI, there is an error "Could not create inference context". Then I add the code to make the vision by CPU: let request = VNGenerateForegroundInstanceMaskRequest() let handler = VNImageRequestHandler(ciImage: inputImage) #if targetEnvironment(simulator) if #available(iOS 18.0, *) { let allDevices = MLComputeDevice.allComputeDevices for device in allDevices { if(device.description.contains("MLCPUComputeDevice")){ request.setComputeDevice(.some(device), for: .main) break } } } else { // Fallback on earlier versions request.usesCPUOnly = true } #endif do { try handler.perform([request]) if let result = request.results?.first { let mask = try result.generateScaledMaskForImage(forInstances: result.allInstances, from: handler) return CIImage(cvPixelBuffer: mask) } } catch { print(error) } Even I force the simulator to run the code by CPU, but it still have the error: "Could not create inference context"

Hi, I'm trying to personalize the Detect animal poses in Vision example (WWDC 23). Detect animal poses in Vision After some tests I saw that the landmarks and connection drawings work only if I do not ignore the safe area, if I ignore it (removing the toggle) or use the app on the iPad the drawings are no longer applied correctly. In the example GeometryReader is used to detect the size of the view: ... ZStack { GeometryReader { geo in AnimalSkeletonView(animalJoint: animalJoint, size: geo.size) } }.frame(maxWidth: .infinity) ... struct AnimalSkeletonView: View { // Get the animal joint locations. @StateObject var animalJoint = AnimalPoseDetector() var size: CGSize var body: some View { DisplayView(animalJoint: animalJoint) if animalJoint.animalBodyParts.isEmpty == false { // Draw the skeleton of the animal. // Iterate over all recognized points and connect the joints. ZStack { ZStack { // left head if let nose = animalJoint.animalBodyParts[.nose] { if let leftEye = animalJoint.animalBodyParts[.leftEye] { Line(points: [nose.location, leftEye.location], size: size) .stroke(lineWidth: 5.0) .fill(Color.orange) } } ... } } } } } // Create a transform that converts the pose's normalized point. struct Line: Shape { var points: [CGPoint] var size: CGSize func path(in rect: CGRect) -> Path { let pointTransform: CGAffineTransform = .identity .translatedBy(x: 0.0, y: -1.0) .concatenating(.identity.scaledBy(x: 1.0, y: -1.0)) .concatenating(.identity.scaledBy(x: size.width, y: size.height)) var path = Path() path.move(to: points[0]) for point in points { path.addLine(to: point) } return path.applying(pointTransform) } } Looking online I saw that it was recommended to change the property cameraView.previewLayer.videoGravity from: cameraView.previewLayer.videoGravity = .resizeAspectFill to: cameraView.previewLayer.videoGravity = .resizeAspect but it doesn't work for me. Could you help me understand where I'm wrong? Thanks!

We have updated our cross-platform applications to support iOS 18 and are in the final stages of releasing versions built with MacCatalyst. After merging the MacCatalyst changes with those for iOS 18, we are now required to build the app using Xcode 16. However, since transitioning to Xcode 16, the app builds successfully but crashes immediately on startup with the following error: dyld[45279]: Symbol not found: _$sSo22VNFaceLandmarkRegion2DC6VisionE16normalizedPointsSaySo7CGPointVGvg Referenced from: <211097A0-6612-3A9A-80B5-AE12915EBA2A> /Users/***/Library/Developer/Xcode/DerivedData/DM_iOS_Apps-gzpzdsacfldxxwclyngreqkbhtey/Build/Products/Debug-maccatalyst/MyApp.app/Contents/Frameworks/Filters_MyApp.framework/Versions/A/Filters_MyApp Expected in: <50DB755E-C83C-3FC7-A0BB-9C4DF9FEA374> /System/Library/Frameworks/Vision.framework/Versions/A/Vision This crash occurs only when building the app with Xcode 16 for MacCatalyst on macOS 14.6.1. On iOS and macOS 15, it functions as expected, and it also worked prior to the iOS 18 changes, which are independent of the Vision framework code, when building with Xcode 15. Here are the environment details where the error occurs: Xcode Version: Xcode 16.0 (16A242d) macOS Version: macOS Sonoma 14.6.1 And the setup where it works: Xcode Version: Xcode 16.0 (16A242d) macOS Version: macOS Sequoia 15.0 Additionally, attempting to implement a workaround using pointsInImage(imageSize:) resulted in a similar issue, where the symbol for this method is also missing. Is this a known issue? Are there any workarounds or fixes available? We have already submitted this issue as feedback (FB15164375), along with a demo project to illustrate the problem.

Hi All, I am trying to build a new iOS app by following https://developer.apple.com/videos/play/wwdc2024/10163/?time=67 When I trying to remove all legacy VN I am getting error, I would appreciate if someone can help me get up to speed with the new Vision API

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?

Hi, I face a problem that I could not scan a specific Code 39 barcode with Vision framework. We have multiple barcode in a label and almost all Code 39 can be scanned, but not for specific one. One more information, regardless the one that is not recognized with Vision can be read by a general barcode scanner. Have anyone faced similar situation? Is there unique condition to make it hard to scan the barcode when using Vision?(size, intensity, etc) Regards,

Currently, the id of my actual visionpro device is different from the xcode that works on my macmini. I added a visionpro id to xcode, but I can't build it with my visionpro. Can I log out of the existing login to xcode and log in with the same ID as visionpro? However, the appleID created for visionpro does not have an Apple Developer membership, so there is no certificate that makes it run on the actual device. How can I add my visionpro ID from the ID with my apple developer membership to run the xcode project app on visionpro? This is the first time this is the first time.

I'm developing 3D Scanner works on iPad. I'm using AVCapturePhoto and Photogrammetry Session. photoCaptureDelegate is like below: extension PhotoCaptureDelegate: AVCapturePhotoCaptureDelegate { func photoOutput(_ output: AVCapturePhotoOutput, didFinishProcessingPhoto photo: AVCapturePhoto, error: Error?) { let fileUrl = CameraViewModel.instance.imageDir!.appendingPathComponent("\(PhotoCaptureDelegate.name)\(id).heic") let img = CIImage(cvPixelBuffer: photo.pixelBuffer!, options: [ .auxiliaryDepth: true, .properties: photo.metadata ]) let depthData = photo.depthData!.converting(toDepthDataType: kCVPixelFormatType_DepthFloat32) let colorSpace = CGColorSpace(name: CGColorSpace.sRGB) let fileData = CIContext().heifRepresentation(of: img, format: .RGBA8, colorSpace: colorSpace!, options: [ .avDepthData: depthData ]) try? fileData!.write(to: fileUrl, options: .atomic) } } But, Photogrammetry session spits warning messages: Sample 0 missing LiDAR point cloud! Sample 1 missing LiDAR point cloud! Sample 2 missing LiDAR point cloud! Sample 3 missing LiDAR point cloud! Sample 4 missing LiDAR point cloud! Sample 5 missing LiDAR point cloud! Sample 6 missing LiDAR point cloud! Sample 7 missing LiDAR point cloud! Sample 8 missing LiDAR point cloud! Sample 9 missing LiDAR point cloud! Sample 10 missing LiDAR point cloud! The session creates a usdz 3d model but scale is not correct. I think the point cloud can help Photogrammetry session to find right scale, but I don't know how to attach point cloud.

Did something change on face detection / Vision Framework on iOS 15? Using VNDetectFaceLandmarksRequest and reading the VNFaceLandmarkRegion2D to detect eyes is not working on iOS 15 as it did before. I am running the exact same code on an iOS 14 and iOS 15 device and the coordinates are different as seen on the screenshot? Any Ideas?