92 lines
3.4 KiB
Markdown
92 lines
3.4 KiB
Markdown
# CheapRetouch
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A privacy-first iOS photo editor for removing unwanted elements from your photos — powered by on-device machine learning.
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## Features
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### 🧑 Person Removal
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Tap on any person in your photo to instantly remove them. The app uses Apple's Vision framework to generate precise segmentation masks, then fills the removed area seamlessly.
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### 📦 Object Removal
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Remove unwanted foreground objects with a single tap. When automatic detection isn't possible, use the smart brush tool with edge-aware refinement for manual selection.
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### ⚡ Wire & Line Removal
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Easily remove power lines, cables, and other thin linear objects. The app detects contours and automatically selects wire-like shapes, or you can trace them manually with the line brush.
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## How It Works
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CheapRetouch combines Apple's Vision framework for intelligent object detection with an AI-powered inpainting engine:
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### Object Detection (Vision Framework)
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- **`VNGenerateForegroundInstanceMaskRequest`** — Generates pixel-accurate masks for people and salient foreground objects
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- **`VNDetectContoursRequest`** — Detects edges and contours for wire/line detection
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- **Tap-based selection** — Simply tap on what you want to remove
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### AI-Powered Inpainting (LaMa Model)
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The app uses **LaMa (Large Mask Inpainting)**, a state-of-the-art deep learning model optimized for removing objects from images:
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- **Model**: `LaMaFP16_512.mlpackage` — A Core ML-optimized neural network running entirely on-device
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- **Architecture**: Fourier convolutions that capture both local textures and global image structure
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- **Processing**: Runs on the Neural Engine (ANE) for fast, efficient inference
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- **Quality**: Produces natural-looking results even for large masked areas
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**Technical Details:**
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- Input resolution: 512×512 pixels (automatically crops and scales around masked regions)
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- Quantization: FP16 for optimal balance of quality and performance
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- Fallback: Metal-accelerated exemplar-based inpainting when needed
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### Processing Pipeline
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```
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1. User taps object → Vision generates mask
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2. Mask is dilated and feathered for smooth edges
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3. Region is cropped and scaled to 512×512
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4. LaMa model inpaints the masked area
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5. Result is composited back into original image
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```
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## Privacy
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🔒 **100% On-Device Processing**
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- No photos leave your device
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- No cloud services or network calls
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- No analytics or telemetry
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- Photo library access via secure PHPicker
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## Technical Stack
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| Component | Technology |
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|-----------|------------|
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| UI | SwiftUI + UIKit |
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| Object Detection | Vision Framework |
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| ML Inference | Core ML (Neural Engine) |
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| GPU Processing | Metal |
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| Image Pipeline | Core Image |
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| Fallback Processing | Accelerate/vImage |
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## Requirements
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- iOS 17.0 or later
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- iPhone or iPad with A14 chip or newer (for optimal Neural Engine performance)
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## Performance
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| Operation | Target Time | Device |
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|-----------|-------------|--------|
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| Preview inpaint | < 300ms | iPhone 12+ |
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| Full resolution (12MP) | < 4 seconds | iPhone 12+ |
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| Full resolution (48MP) | < 12 seconds | iPhone 15 Pro+ |
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## Non-Destructive Editing
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All edits are stored as an operation stack — your original photos are never modified. Full undo/redo support included.
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## License
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MIT License — see [LICENSE](LICENSE) for details.
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