axiom-vision-diag（公理视觉诊断）

Vision Framework Diagnostics

Systematic troubleshooting for Vision framework issues: subjects not detected, missing landmarks, low confidence, performance problems, coordinate mismatches, text recognition failures, barcode detection issues, and document scanning problems.

Overview

Core Principle: When Vision doesn't work, the problem is usually:

1. Environment (lighting, occlusion, edge of frame) - 40%
2. Confidence threshold (ignoring low confidence data) - 30%
3. Threading (blocking main thread causes frozen UI) - 15%
4. Coordinates (mixing lower-left and top-left origins) - 10%
5. API availability (using iOS 17+ APIs on older devices) - 5%

Always check environment and confidence BEFORE debugging code.

Red Flags

Symptoms that indicate Vision-specific issues:

Mandatory First Steps

Before investigating code, run these diagnostics:

Step 1: Verify Detection with Diagnostic Code

let request = VNGenerateForegroundInstanceMaskRequest()  // Or hand/body pose
let handler = VNImageRequestHandler(cgImage: testImage)

do {
    try handler.perform([request])

    if let results = request.results {
        print("✅ Request succeeded")
        print("Result count: \(results.count)")

        if let observation = results.first as? VNInstanceMaskObservation {
            print("All instances: \(observation.allInstances)")
            print("Instance count: \(observation.allInstances.count)")
        }
    } else {
        print("⚠️ Request succeeded but no results")
    }
} catch {
    print("❌ Request failed: \(error)")
}

Expected output:

• ✅ Request succeeded, instance count > 0 → Detection working
• ⚠️ Request succeeded, instance count = 0 → Nothing detected (see Decision Tree)
• ❌ Request failed → API availability issue

Step 2: Check Confidence Scores

// For hand/body pose
if let observation = request.results?.first as? VNHumanHandPoseObservation {
    let allPoints = try observation.recognizedPoints(.all)

    for (key, point) in allPoints {
        print("\(key): confidence \(point.confidence)")

        if point.confidence < 0.3 {
            print("  ⚠️ LOW CONFIDENCE - unreliable")
        }
    }
}

Expected output:

• Most landmarks > 0.5 confidence → Good detection
• Many landmarks < 0.3 → Poor lighting, occlusion, or edge of frame

Step 3: Verify Threading

print("🧵 Thread: \(Thread.current)")

if Thread.isMainThread {
    print("❌ Running on MAIN THREAD - will block UI!")
} else {
    print("✅ Running on background thread")
}

Expected output:

• ✅ Background thread → Correct
• ❌ Main thread → Move to DispatchQueue.global()

Decision Tree

Vision not working as expected?
│
├─ No results returned?
│  ├─ Check Step 1 output
│  │  ├─ "Request failed" → See Pattern 1a (API availability)
│  │  ├─ "No results" → See Pattern 1b (nothing detected)
│  │  └─ Results but count = 0 → See Pattern 1c (edge of frame)
│
├─ Landmarks have nil/low confidence?
│  ├─ Hand pose → See Pattern 2 (hand detection issues)
│  ├─ Body pose → See Pattern 3 (body detection issues)
│  └─ Face detection → See Pattern 4 (face detection issues)
│
├─ UI freezing/slow?
│  ├─ Check Step 3 (threading)
│  │  ├─ Main thread → See Pattern 5a (move to background)
│  │  └─ Background thread → See Pattern 5b (performance tuning)
│
├─ Overlays in wrong position?
│  └─ See Pattern 6 (coordinate conversion)
│
├─ Person segmentation missing people?
│  └─ See Pattern 7 (crowded scenes)
│
├─ VisionKit not working?
│  └─ See Pattern 8 (VisionKit specific)
│
├─ Text recognition issues?
│  ├─ No text detected → See Pattern 9a (image quality)
│  ├─ Wrong characters → See Pattern 9b (language/correction)
│  └─ Too slow → See Pattern 9c (recognition level)
│
├─ Barcode detection issues?
│  ├─ Barcode not detected → See Pattern 10a (symbology/size)
│  └─ Wrong payload → See Pattern 10b (barcode quality)
│
├─ DataScannerViewController issues?
│  ├─ Blank screen → See Pattern 11a (availability check)
│  └─ Items not detected → See Pattern 11b (data types)
│
└─ Document scanning issues?
   ├─ Edges not detected → See Pattern 12a (contrast/shape)
   └─ Perspective wrong → See Pattern 12b (corner points)

Diagnostic Patterns

Pattern 1a: Request Failed (API Availability)

Symptom: try handler.perform([request]) throws error

Common errors:

"VNGenerateForegroundInstanceMaskRequest is only available on iOS 17.0 or newer"
"VNDetectHumanBodyPose3DRequest is only available on iOS 17.0 or newer"

Root cause: Using iOS 17+ APIs on older deployment target

Fix:

if #available(iOS 17.0, *) {
    let request = VNGenerateForegroundInstanceMaskRequest()
    // ...
} else {
    // Fallback for iOS 14-16
    let request = VNGeneratePersonSegmentationRequest()
    // ...
}

Prevention: Check API availability in axiom-vision-ref before implementing

Time to fix: 10 min

Pattern 1b: No Results (Nothing Detected)

Symptom: request.results == nil or results.isEmpty

Diagnostic:

// 1. Save debug image to Photos
UIImageWriteToSavedPhotosAlbum(debugImage, nil, nil, nil)

// 2. Inspect visually
// - Is subject too small? (< 10% of image)
// - Is subject blurry?
// - Poor contrast with background?

Common causes:

• Subject too small (resize or crop closer)
• Subject too blurry (increase lighting, stabilize camera)
• Low contrast (subject same color as background)

Fix:

// Crop image to focus on region of interest
let croppedImage = cropImage(sourceImage, to: regionOfInterest)
let handler = VNImageRequestHandler(cgImage: croppedImage)

Time to fix: 30 min

Pattern 1c: Edge of Frame Issues

Symptom: Subject detected intermittently as object moves across frame

Root cause: Partial occlusion when subject touches image edges

Diagnostic:

// Check if subject is near edges
if let observation = results.first as? VNInstanceMaskObservation {
    let mask = try observation.createScaledMask(
        for: observation.allInstances,
        croppedToInstancesContent: true
    )

    let bounds = calculateMaskBounds(mask)

    if bounds.minX < 0.1 || bounds.maxX > 0.9 ||
       bounds.minY < 0.1 || bounds.maxY > 0.9 {
        print("⚠️ Subject too close to edge")
    }
}

Fix:

// Add padding to capture area
let paddedRect = captureRect.insetBy(dx: -20, dy: -20)

// OR guide user with on-screen overlay
overlayView.addSubview(guideBox)  // Visual boundary

Time to fix: 20 min

Pattern 2: Hand Pose Issues

Symptom: VNDetectHumanHandPoseRequest returns nil or low confidence landmarks

Diagnostic:

if let observation = request.results?.first as? VNHumanHandPoseObservation {
    let thumbTip = try? observation.recognizedPoint(.thumbTip)
    let wrist = try? observation.recognizedPoint(.wrist)

    print("Thumb confidence: \(thumbTip?.confidence ?? 0)")
    print("Wrist confidence: \(wrist?.confidence ?? 0)")

    // Check hand orientation
    if let thumb = thumbTip, let wristPoint = wrist {
        let angle = atan2(
            thumb.location.y - wristPoint.location.y,
            thumb.location.x - wristPoint.location.x
        )
        print("Hand angle: \(angle * 180 / .pi) degrees")

        if abs(angle) > 80 && abs(angle) < 100 {
            print("⚠️ Hand parallel to camera (hard to detect)")
        }
    }
}

Common causes:

Fix for parallel hand:

// Detect and warn user
if avgConfidence < 0.4 {
    showWarning("Rotate your hand toward the camera")
}

Time to fix: 45 min

Pattern 3: Body Pose Issues

Symptom: VNDetectHumanBodyPoseRequest skips frames or returns low confidence

Diagnostic:

if let observation = request.results?.first as? VNHumanBodyPoseObservation {
    let nose = try? observation.recognizedPoint(.nose)
    let root = try? observation.recognizedPoint(.root)

    if let nosePoint = nose, let rootPoint = root {
        let bodyAngle = atan2(
            nosePoint.location.y - rootPoint.location.y,
            nosePoint.location.x - rootPoint.location.x
        )

        let angleFromVertical = abs(bodyAngle - .pi / 2)

        if angleFromVertical > .pi / 4 {
            print("⚠️ Person bent over or upside down")
        }
    }
}

Common causes:

Time to fix: 1 hour

Pattern 4: Face Detection Issues

Symptom: VNDetectFaceRectanglesRequest misses faces or returns wrong count

Diagnostic:

if let faces = request.results as? [VNFaceObservation] {
    print("Detected \(faces.count) faces")

    for face in faces {
        print("Face bounds: \(face.boundingBox)")
        print("Confidence: \(face.confidence)")

        if face.boundingBox.width < 0.1 {
            print("⚠️ Face too small")
        }
    }
}

Common causes:

• Face < 10% of image (crop closer)
• Profile view (use face landmarks request instead)
• Poor lighting (increase exposure)

Time to fix: 30 min

Pattern 5a: UI Freezing (Main Thread)

Symptom: App freezes when performing Vision request

Diagnostic (Step 3 above confirms main thread)

Fix:

// BEFORE (wrong)
let request = VNGenerateForegroundInstanceMaskRequest()
try handler.perform([request])  // Blocks UI

// AFTER (correct)
DispatchQueue.global(qos: .userInitiated).async {
    let request = VNGenerateForegroundInstanceMaskRequest()
    try? handler.perform([request])

    DispatchQueue.main.async {
        // Update UI
    }
}

Time to fix: 15 min

Pattern 5b: Performance Issues (Background Thread)

Symptom: Already on background thread but still slow / dropping frames

Diagnostic:

let start = CFAbsoluteTimeGetCurrent()

try handler.perform([request])

let elapsed = CFAbsoluteTimeGetCurrent() - start
print("Request took \(elapsed * 1000)ms")

if elapsed > 0.2 {  // 200ms = too slow for real-time
    print("⚠️ Request too slow for real-time processing")
}

Common causes & fixes:

Fix for real-time camera:

// Skip frames
frameCount += 1
guard frameCount % 3 == 0 else { return }

// OR downscale
let scaledImage = resizeImage(sourceImage, to: CGSize(width: 1280, height: 720))

// OR set lower hand count
request.maximumHandCount = 2  // Instead of default

Time to fix: 1 hour

Pattern 6: Coordinate Conversion

Symptom: UI overlays appear in wrong position

Diagnostic:

// Vision point (lower-left origin, normalized)
let visionPoint = recognizedPoint.location
print("Vision point: \(visionPoint)")  // e.g., (0.5, 0.8)

// Convert to UIKit
let uiX = visionPoint.x * imageWidth
let uiY = (1 - visionPoint.y) * imageHeight  // FLIP Y
print("UIKit point: (\(uiX), \(uiY))")

// Verify overlay
overlayView.center = CGPoint(x: uiX, y: uiY)

Common mistakes:

// ❌ WRONG (no Y flip)
let uiPoint = CGPoint(
    x: axiom-visionPoint.x * width,
    y: axiom-visionPoint.y * height
)

// ❌ WRONG (forgot to scale from normalized)
let uiPoint = CGPoint(
    x: axiom-visionPoint.x,
    y: 1 - visionPoint.y
)

// ✅ CORRECT
let uiPoint = CGPoint(
    x: axiom-visionPoint.x * width,
    y: (1 - visionPoint.y) * height
)

Time to fix: 20 min

Pattern 7: Crowded Scenes (>4 People)

Symptom: VNGeneratePersonInstanceMaskRequest misses people or combines them

Diagnostic:

// Count faces
let faceRequest = VNDetectFaceRectanglesRequest()
try handler.perform([faceRequest])

let faceCount = faceRequest.results?.count ?? 0
print("Detected \(faceCount) faces")

// Person instance segmentation
let personRequest = VNGeneratePersonInstanceMaskRequest()
try handler.perform([personRequest])

let personCount = (personRequest.results?.first as? VNInstanceMaskObservation)?.allInstances.count ?? 0
print("Detected \(personCount) people")

if faceCount > 4 && personCount <= 4 {
    print("⚠️ Crowded scene - some people combined or missing")
}

Fix:

if faceCount > 4 {
    // Fallback: Use single mask for all people
    let singleMaskRequest = VNGeneratePersonSegmentationRequest()
    try handler.perform([singleMaskRequest])

    // OR guide user
    showWarning("Please reduce number of people in frame (max 4)")
}

Time to fix: 30 min

Pattern 8: VisionKit Specific Issues

Symptom: ImageAnalysisInteraction not showing subject lifting UI

Diagnostic:

// 1. Check interaction types
print("Interaction types: \(interaction.preferredInteractionTypes)")

// 2. Check if analysis is set
print("Analysis: \(interaction.analysis != nil ? "set" : "nil")")

// 3. Check if view supports interaction
if let view = interaction.view {
    print("View: \(view)")
} else {
    print("❌ View not set")
}

Common causes:

Fix:

// Ensure analysis is set
let analyzer = ImageAnalyzer()
let analysis = try await analyzer.analyze(image, configuration: config)

interaction.analysis = analysis  // Required!
interaction.preferredInteractionTypes = .imageSubject

Time to fix: 20 min

Pattern 9a: Text Not Detected (Image Quality)

Symptom: VNRecognizeTextRequest returns no results or empty strings

Diagnostic:

let request = VNRecognizeTextRequest()
request.recognitionLevel = .accurate

try handler.perform([request])

if request.results?.isEmpty ?? true {
    print("❌ No text detected")

    // Check image quality
    print("Image size: \(image.size)")
    print("Minimum text height: \(request.minimumTextHeight)")
}

for obs in request.results as? [VNRecognizedTextObservation] ?? [] {
    let top = obs.topCandidates(3)
    for candidate in top {
        print("'\(candidate.string)' confidence: \(candidate.confidence)")
    }
}

Common causes:

Fix for small text:

// Lower minimum text height (default ignores very small text)
request.minimumTextHeight = 0.02  // 2% of image height

Time to fix: 30 min

Pattern 9b: Wrong Characters (Language/Correction)

Symptom: Text is detected but characters are wrong (e.g., "C001" → "COOL")

Diagnostic:

// Check all candidates, not just first
for observation in results {
    let candidates = observation.topCandidates(5)
    for (i, candidate) in candidates.enumerated() {
        print("Candidate \(i): '\(candidate.string)' (\(candidate.confidence))")
    }
}

Common causes:

Fix for codes/serial numbers:

let request = VNRecognizeTextRequest()
request.usesLanguageCorrection = false  // Don't "fix" codes

// Post-process with domain knowledge
func correctSerialNumber(_ text: String) -> String {
    text.replacingOccurrences(of: "O", with: "0")
        .replacingOccurrences(of: "l", with: "1")
        .replacingOccurrences(of: "S", with: "5")
}

Time to fix: 30 min

Pattern 9c: Text Recognition Too Slow

Symptom: Text recognition takes >500ms, real-time camera drops frames

Diagnostic:

let start = CFAbsoluteTimeGetCurrent()
try handler.perform([request])
let elapsed = CFAbsoluteTimeGetCurrent() - start

print("Recognition took \(elapsed * 1000)ms")
print("Recognition level: \(request.recognitionLevel == .fast ? "fast" : "accurate")")
print("Language correction: \(request.usesLanguageCorrection)")

Common causes & fixes:

Fix for real-time:

request.recognitionLevel = .fast
request.usesLanguageCorrection = false
request.regionOfInterest = CGRect(x: 0.1, y: 0.3, width: 0.8, height: 0.4)

// Skip frames
frameCount += 1
guard frameCount % 3 == 0 else { return }

Time to fix: 30 min

Pattern 10a: Barcode Not Detected (Symbology/Size)

Symptom: VNDetectBarcodesRequest returns no results

Diagnostic:

let request = VNDetectBarcodesRequest()
// Don't specify symbologies to detect all types
try handler.perform([request])

if let results = request.results as? [VNBarcodeObservation] {
    print("Found \(results.count) barcodes")
    for barcode in results {
        print("Type: \(barcode.symbology)")
        print("Payload: \(barcode.payloadStringValue ?? "nil")")
        print("Bounds: \(barcode.boundingBox)")
    }
} else {
    print("❌ No barcodes detected")
}

Common causes:

Fix for small barcodes:

// Crop to barcode region for better detection
let croppedHandler = VNImageRequestHandler(
    cgImage: croppedImage,
    options: [:]
)

Time to fix: 20 min

Pattern 10b: Wrong Barcode Payload

Symptom: Barcode detected but payloadStringValue is wrong or nil

Diagnostic:

if let barcode = results.first {
    print("String payload: \(barcode.payloadStringValue ?? "nil")")
    print("Raw payload: \(barcode.payloadData ?? Data())")
    print("Symbology: \(barcode.symbology)")
    print("Confidence: Implicit (always 1.0 for barcodes)")
}

Common causes:

Time to fix: 15 min

Pattern 11a: DataScanner Blank Screen

Symptom: DataScannerViewController shows black/blank when presented

Diagnostic:

// Check support first
print("isSupported: \(DataScannerViewController.isSupported)")
print("isAvailable: \(DataScannerViewController.isAvailable)")

// Check camera permission
let status = AVCaptureDevice.authorizationStatus(for: .video)
print("Camera access: \(status.rawValue)")

Common causes:

Fix:

guard DataScannerViewController.isSupported else {
    showError("Scanning not supported on this device")
    return
}

guard DataScannerViewController.isAvailable else {
    // Request camera access
    AVCaptureDevice.requestAccess(for: .video) { granted in
        // Retry after access granted
    }
    return
}

Time to fix: 15 min

Pattern 11b: DataScanner Items Not Detected

Symptom: DataScanner shows camera but doesn't recognize items

Diagnostic:

// Check recognized data types
print("Data types: \(scanner.recognizedDataTypes)")

// Add delegate to see what's happening
func dataScanner(_ scanner: DataScannerViewController,
                 didAdd items: [RecognizedItem],
                 allItems: [RecognizedItem]) {
    print("Added \(items.count) items, total: \(allItems.count)")
    for item in items {
        switch item {
        case .text(let text): print("Text: \(text.transcript)")
        case .barcode(let barcode): print("Barcode: \(barcode.payloadStringValue ?? "")")
        @unknown default: break
        }
    }
}

Common causes:

Time to fix: 20 min

Pattern 12a: Document Edges Not Detected

Symptom: VNDetectDocumentSegmentationRequest returns no results

Diagnostic:

let request = VNDetectDocumentSegmentationRequest()
try handler.perform([request])

if let observation = request.results?.first {
    print("Document found at: \(observation.boundingBox)")
    print("Corners: TL=\(observation.topLeft), TR=\(observation.topRight)")
} else {
    print("❌ No document detected")
}

Common causes:

Fix: Use VNDocumentCameraViewController for guided user experience with live feedback.

Time to fix: 15 min

Pattern 12b: Perspective Correction Wrong

Symptom: Document extracted but distorted

Diagnostic:

// Verify corner order
print("TopLeft: \(observation.topLeft)")
print("TopRight: \(observation.topRight)")
print("BottomLeft: \(observation.bottomLeft)")
print("BottomRight: \(observation.bottomRight)")

// Check if corners are in expected positions
// TopLeft should have larger Y than BottomLeft (Vision uses lower-left origin)

Common causes:

Fix:

// Scale normalized to image coordinates
func scaled(_ point: CGPoint, to size: CGSize) -> CGPoint {
    CGPoint(x: point.x * size.width, y: point.y * size.height)
}

Time to fix: 20 min

Production Crisis Scenario

Situation: App Store review rejected for "app freezes when tapping analyze button"

Triage (5 min):

1. Confirm Vision running on main thread → Pattern 5a
2. Verify on older device (iPhone 12) → Freezes
3. Check profiling: 800ms on main thread

Fix (15 min):

@IBAction func analyzeTapped(_ sender: UIButton) {
    showLoadingIndicator()

    DispatchQueue.global(qos: .userInitiated).async { [weak self] in
        let request = VNGenerateForegroundInstanceMaskRequest()
        // ... perform request

        DispatchQueue.main.async {
            self?.hideLoadingIndicator()
            self?.updateUI(with: results)
        }
    }
}

Communicate to PM: "App Store rejection due to Vision processing on main thread. Fixed by moving to background queue (industry standard). Testing on iPhone 12 confirms fix. Safe to resubmit."

Quick Reference Table

Resources

WWDC: 2019-234, 2021-10041, 2022-10024, 2022-10025, 2025-272, 2023-10176, 2020-10653

Docs: /vision, /vision/vnrecognizetextrequest, /visionkit

Skills: axiom-vision, axiom-vision-ref