vision-framework

Vision Framework Detect text, faces, barcodes, objects, and body poses in images and video using on-device computer vision. Patterns target iOS 26+ with Swift 6.2, backward-compatible where noted. See references/vision-requests.md for complete code patterns and references/visionkit-scanner.md for DataScannerViewController integration. Contents Two API Generations Request Pattern (Modern API) Text Recognition (OCR) Face Detection Barcode Detection Document Scanning (iOS 26+) Image Segmentation Object Tracking Other Request Types Core ML Integration VisionKit: DataScannerViewController Common Mistakes Review Checklist References Two API Generations Vision has two distinct API layers. Prefer the modern API for new code. Aspect Modern (iOS 18+) Legacy Pattern let result = try await request.perform(on: image) VNImageRequestHandler + completion handler Request types Swift types — structs and classes ( RecognizeTextRequest , DetectFaceRectanglesRequest ) ObjC classes ( VNRecognizeTextRequest , VNDetectFaceRectanglesRequest ) Concurrency Native async/await Completion handlers or synchronous perform Observations Typed return values Cast results from [Any] Availability iOS 18+ / macOS 15+ iOS 11+ The modern API uses the ImageProcessingRequest protocol. Each request type has a perform(on:orientation:) method that accepts CGImage , CIImage , CVPixelBuffer , CMSampleBuffer , Data , or URL . Most requests are structs; stateful requests for video tracking (e.g., TrackObjectRequest , TrackRectangleRequest , DetectTrajectoriesRequest ) are final classes. Request Pattern (Modern API) All modern Vision requests follow the same pattern: create a request struct, call perform(on:) , and handle the typed result. import Vision func recognizeText ( in image : CGImage ) async throws -> [ String ] { var request = RecognizeTextRequest ( ) request . recognitionLevel = . accurate request . recognitionLanguages = [ Locale . Language ( identifier : "en-US" ) ] let observations = try await request . perform ( on : image ) return observations . compactMap { observation in observation . topCandidates ( 1 ) . first ? . string } } Legacy Pattern (Pre-iOS 18) Use VNImageRequestHandler with completion-based requests when targeting older deployment versions. import Vision func recognizeTextLegacy ( in image : CGImage ) throws -> [ String ] { var recognized : [ String ] = [ ] let request = VNRecognizeTextRequest { request , error in guard let observations = request . results as ? [ VNRecognizedTextObservation ] else { return } recognized = observations . compactMap { $0 . topCandidates ( 1 ) . first ? . string } } request . recognitionLevel = . accurate let handler = VNImageRequestHandler ( cgImage : image ) try handler . perform ( [ request ] ) return recognized } Text Recognition (OCR) Modern: RecognizeTextRequest (iOS 18+) var request = RecognizeTextRequest ( ) request . recognitionLevel = . accurate // .fast for real-time request . recognitionLanguages = [ Locale . Language ( identifier : "en-US" ) , Locale . Language ( identifier : "fr-FR" ) , ] request . usesLanguageCorrection = true request . customWords = [ "SwiftUI" , "Xcode" ] // domain-specific terms let observations = try await request . perform ( on : cgImage ) for observation in observations { guard let candidate = observation . topCandidates ( 1 ) . first else { continue } let text = candidate . string let confidence = candidate . confidence // 0.0 ... 1.0 let bounds = observation . boundingBox // normalized coordinates } Legacy: VNRecognizeTextRequest let request = VNRecognizeTextRequest ( ) request . recognitionLevel = . accurate request . recognitionLanguages = [ "en-US" , "fr-FR" ] request . usesLanguageCorrection = true Key differences: Modern API uses Locale.Language for languages; legacy uses string identifiers. Both support .accurate (best quality) and .fast (real-time suitable) recognition levels. Face Detection Detect face rectangles, landmarks (eyes, nose, mouth), and capture quality. // Modern API let faceRequest = DetectFaceRectanglesRequest ( ) let faces = try await faceRequest . perform ( on : cgImage ) for face in faces { let boundingBox = face . boundingBox // normalized CGRect let roll = face . roll // Measurement let yaw = face . yaw // Measurement } // Landmarks (eyes, nose, mouth contours) var landmarkRequest = DetectFaceLandmarksRequest ( ) let landmarkFaces = try await landmarkRequest . perform ( on : cgImage ) for face in landmarkFaces { let landmarks = face . landmarks let leftEye = landmarks ? . leftEye ? . normalizedPoints let nose = landmarks ? . nose ? . normalizedPoints } Coordinate System Vision uses a normalized coordinate system with origin at the bottom-left. Convert to UIKit (top-left origin) before display: func convertToUIKit ( _ rect : CGRect , imageHeight : CGFloat ) -> CGRect { CGRect ( x : rect . origin . x , y : imageHeight - rect . origin . y - rect . height , width : rect . width , height : rect . height ) } Barcode Detection Detect 1D and 2D barcodes including QR codes. var request = DetectBarcodesRequest ( ) request . symbologies = [ . qr , . ean13 , . code128 , . pdf417 ] let barcodes = try await request . perform ( on : cgImage ) for barcode in barcodes { let payload = barcode . payloadString // decoded content let symbology = barcode . symbology // .qr, .ean13, etc. let bounds = barcode . boundingBox // normalized rect } Common symbologies: .qr , .aztec , .pdf417 , .dataMatrix , .ean8 , .ean13 , .code39 , .code128 , .upce , .itf14 . Document Scanning (iOS 26+) RecognizeDocumentsRequest provides structured document reading with layout understanding beyond basic OCR. Returns DocumentObservation objects with a nested Container structure for paragraphs, tables, lists, and barcodes. var request = RecognizeDocumentsRequest ( ) let documents = try await request . perform ( on : cgImage ) for observation in documents { let container = observation . document // Full text content let fullText = container . text // Structured access to paragraphs for paragraph in container . paragraphs { let paragraphText = paragraph . text } // Tables and lists for table in container . tables { / structured table data / } for list in container . lists { / structured list data / } // Embedded barcodes detected within the document for barcode in container . barcodes { / barcode data / } // Document title if detected if let title = container . title { print ( title ) } } For simpler document camera scanning, use VisionKit's VNDocumentCameraViewController which provides a full-screen camera UI with auto-capture, perspective correction, and multi-page scanning. Image Segmentation Modern: GeneratePersonSegmentationRequest (iOS 18+) var request = GeneratePersonSegmentationRequest ( ) request . qualityLevel = . accurate // .balanced, .fast let mask = try await request . perform ( on : cgImage ) // mask is a PersonSegmentationObservation with a pixelBuffer property let maskBuffer = mask . pixelBuffer // Apply mask using Core Image: CIFilter.blendWithMask() Legacy: VNGeneratePersonSegmentationRequest let request = VNGeneratePersonSegmentationRequest ( ) request . qualityLevel = . accurate // .balanced, .fast request . outputPixelFormat = kCVPixelFormatType_OneComponent8 let handler = VNImageRequestHandler ( cgImage : cgImage ) try handler . perform ( [ request ] ) guard let mask = request . results ? . first ? . pixelBuffer else { return } // Apply mask using Core Image: CIFilter.blendWithMask() Quality levels: .accurate -- best quality, slowest (~1s), full resolution .balanced -- good quality, moderate speed (~100ms), 960x540 .fast -- lowest quality, fastest (~10ms), 256x144, suitable for real-time Instance Segmentation (iOS 18+) Separate masks per person for individual effects. // Modern API (iOS 18+) let request = GeneratePersonInstanceMaskRequest ( ) let observation = try await request . perform ( on : cgImage ) let indices = observation . allInstances for index in indices { let mask = try observation . generateMask ( forInstances : IndexSet ( integer : index ) ) // mask is a CVPixelBuffer with only this person visible } // Legacy API (iOS 17+) let request = VNGeneratePersonInstanceMaskRequest ( ) let handler = VNImageRequestHandler ( cgImage : cgImage ) try handler . perform ( [ request ] ) guard let result = request . results ? . first else { return } let indices = result . allInstances for index in indices { let instanceMask = try result . generateMaskedImage ( ofInstances : IndexSet ( integer : index ) , from : handler , croppedToInstancesExtent : false ) } See references/vision-requests.md for mask composition and Core Image filter integration patterns. Object Tracking Modern: TrackObjectRequest (iOS 18+) TrackObjectRequest is a stateful request that maintains tracking context across frames. Conforms to both ImageProcessingRequest and StatefulRequest . // Initialize with a detected object's bounding box let initialObservation = DetectedObjectObservation ( boundingBox : detectedRect ) var request = TrackObjectRequest ( observation : initialObservation ) request . trackingLevel = . accurate // For each video frame: let results = try await request . perform ( on : pixelBuffer ) if let tracked = results . first { let updatedBounds = tracked . boundingBox let confidence = tracked . confidence } Legacy: VNTrackObjectRequest let trackRequest = VNTrackObjectRequest ( detectedObjectObservation : initialObservation ) trackRequest . trackingLevel = . accurate let sequenceHandler = VNSequenceRequestHandler ( ) // For each frame: try sequenceHandler . perform ( [ trackRequest ] , on : pixelBuffer ) if let result = trackRequest . results ? . first { let updatedBounds = result . boundingBox trackRequest . inputObservation = result } Other Request Types Vision provides additional requests covered in references/vision-requests.md : Request Purpose ClassifyImageRequest Classify scene content (outdoor, food, animal, etc.) GenerateAttentionBasedSaliencyImageRequest Heat map of where viewers focus attention GenerateObjectnessBasedSaliencyImageRequest Heat map of object-like regions GenerateForegroundInstanceMaskRequest Foreground object segmentation (not person-specific) DetectRectanglesRequest Detect rectangular shapes (documents, cards, screens) DetectHorizonRequest Detect horizon angle for auto-leveling photos DetectHumanBodyPoseRequest Detect body joints (shoulders, elbows, knees) DetectHumanBodyPose3DRequest 3D human body pose estimation DetectHumanHandPoseRequest Detect hand joints and finger positions DetectAnimalBodyPoseRequest Detect animal body joint positions DetectFaceCaptureQualityRequest Face capture quality scoring (0–1) for photo selection TrackRectangleRequest Track rectangular objects across video frames TrackOpticalFlowRequest Optical flow between video frames DetectTrajectoriesRequest Detect object trajectories in video All modern request types above are iOS 18+ / macOS 15+. Core ML Integration Run custom Core ML models through Vision for automatic image preprocessing (resizing, normalization, color space conversion). // Modern API (iOS 18+) let model = try MLModel ( contentsOf : modelURL ) let request = CoreMLRequest ( model : . init ( model ) ) let results = try await request . perform ( on : cgImage ) // Classification model if let classification = results . first as ? ClassificationObservation { let label = classification . identifier let confidence = classification . confidence } // Legacy API let vnModel = try VNCoreMLModel ( for : model ) let request = VNCoreMLRequest ( model : vnModel ) { request , error in guard let results = request . results as ? [ VNClassificationObservation ] else { return } let topResult = results . first } let handler = VNImageRequestHandler ( cgImage : cgImage ) try handler . perform ( [ request ] ) For model conversion and optimization, see the coreml skill. VisionKit: DataScannerViewController DataScannerViewController provides a full-screen live camera scanner for text and barcodes. See references/visionkit-scanner.md for complete patterns. Quick Start import VisionKit // Check availability (requires A12+ chip and camera) guard DataScannerViewController . isSupported , DataScannerViewController . isAvailable else { return } let scanner = DataScannerViewController ( recognizedDataTypes : [ . text ( languages : [ "en" ] ) , . barcode ( symbologies : [ . qr , . ean13 ] ) ] , qualityLevel : . balanced , recognizesMultipleItems : true , isHighFrameRateTrackingEnabled : true , isHighlightingEnabled : true ) scanner . delegate = self present ( scanner , animated : true ) { try ? scanner . startScanning ( ) } SwiftUI Integration Wrap DataScannerViewController in UIViewControllerRepresentable . See references/visionkit-scanner.md for the full implementation. Common Mistakes DON'T: Use the legacy VNImageRequestHandler API for new iOS 18+ projects. DO: Use modern struct-based requests with perform(on:) and async/await. Why: Modern API provides type safety, better Swift concurrency support, and cleaner error handling. DON'T: Forget to convert normalized coordinates before drawing bounding boxes. DO: Use VNImageRectForNormalizedRect(::_:) or manual conversion from bottom-left origin to UIKit top-left origin. Why: Vision uses normalized coordinates (0...1) with bottom-left origin; UIKit uses points with top-left origin. DON'T: Run Vision requests on the main thread. DO: Perform requests on a background thread or use async/await from a detached task. Why: Image analysis is CPU/GPU-intensive and blocks the UI if run on the main actor. DON'T: Use .accurate recognition level for real-time camera feeds. DO: Use .fast for live video, .accurate for still images or offline processing. Why: Accurate recognition is too slow for 30fps video; fast recognition trades quality for speed. DON'T: Ignore the confidence score on observations. DO: Filter results by confidence threshold (e.g., > 0.5) appropriate for your use case. Why: Low-confidence results are often incorrect and degrade user experience. DON'T: Create a new VNImageRequestHandler for each frame when tracking objects. DO: Use VNSequenceRequestHandler for video frame sequences. Why: Sequence handler maintains temporal context for tracking; per-frame handlers lose state. DON'T: Request all barcode symbologies when you only need QR codes. DO: Specify only the symbologies you need in the request. Why: Fewer symbologies means faster detection and fewer false positives. DON'T: Assume DataScannerViewController is available on all devices. DO: Check both isSupported (hardware) and isAvailable (user permissions) before presenting. Why: Requires A12+ chip; isAvailable also checks camera access authorization. Review Checklist Uses modern Vision API (iOS 18+) unless targeting older deployments Vision requests run off the main thread (async/await or background queue) Normalized coordinates converted before UI display Confidence threshold applied to filter low-quality observations Recognition level matches use case ( .fast for video, .accurate for stills) Language hints set for text recognition when input language is known Barcode symbologies limited to only those needed DataScannerViewController availability checked before presentation Camera usage description ( NSCameraUsageDescription ) in Info.plist for VisionKit Person segmentation quality level appropriate for use case VNSequenceRequestHandler used for video frame tracking (not per-frame handler) Error handling covers request failures and empty results References Vision request patterns: references/vision-requests.md VisionKit scanner integration: references/visionkit-scanner.md Apple docs: Vision | VisionKit | RecognizeTextRequest | DataScannerViewController