Does this skill support the latest Vision features?

Yes, it includes diagnostics for modern iOS 17+ APIs like foreground instance masks and 3D body pose detection.

How do I prevent my app from freezing during Vision requests?

Ensure Vision requests are performed on a background thread using DispatchQueue.global() rather than the main thread to avoid blocking the UI.

What is the most common cause of Vision detection failure?

Issues are typically environmental (40%), such as lighting or occlusion, or related to confidence thresholds (30%) where the system ignores data below a certain reliability level.

Why are my Vision overlays appearing in the wrong place?

This is usually due to coordinate system mismatches between Vision’s lower-left origin (normalized 0,0 to 1,1) and UIKit’s top-left origin.

Vision Framework Diagnostics

Name: Vision Framework Diagnostics
Author: CharlesWiltgen

byCharlesWiltgen

•

148

Mobile Development

Troubleshoots and resolves Apple Vision framework issues including detection failures, performance bottlenecks, and coordinate mismatches.

About

This skill provides a systematic diagnostic framework for Apple's Vision and VisionKit frameworks across all xOS platforms (iOS, iPadOS, macOS, tvOS, and visionOS). It helps developers identify and fix common pitfalls like low confidence scores, threading errors that freeze the UI, coordinate system mismatches between normalized and image space, and API availability issues. By offering structured decision trees and diagnostic code snippets for hand pose, body pose, text recognition, and barcode detection, it significantly reduces the time spent debugging complex computer vision implementations.

Key Features

Threading analysis to prevent UI freezes during heavy image processing
Coordinate conversion guidance for accurate UI overlay positioning
148 GitHub stars
Systematic troubleshooting for hand, body, and face detection failures
Diagnostic code for verifying confidence scores and API availability
Support for VisionKit, DataScannerViewController, and OCR troubleshooting

Use Cases

Fixing misaligned UI overlays caused by mixing normalized and image coordinates
Debugging why hand poses or subjects aren't detected in low-light environments
Optimizing real-time camera feed performance to prevent frame drops and UI lag

About

Key Features

Threading analysis to prevent UI freezes during heavy image processing
Coordinate conversion guidance for accurate UI overlay positioning
148 GitHub stars
Systematic troubleshooting for hand, body, and face detection failures
Diagnostic code for verifying confidence scores and API availability
Support for VisionKit, DataScannerViewController, and OCR troubleshooting

Use Cases

Fixing misaligned UI overlays caused by mixing normalized and image coordinates
Debugging why hand poses or subjects aren't detected in low-light environments
Optimizing real-time camera feed performance to prevent frame drops and UI lag