# CheapRetouch

A privacy-first iOS photo editor for removing unwanted elements from your photos — powered by on-device machine learning.

![Platform](https://img.shields.io/badge/Platform-iOS%2017.0+-blue)
![Swift](https://img.shields.io/badge/Swift-5.9-orange)

## Features

### 🧑 Person Removal
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.

### 📦 Object Removal  
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.

### ⚡ Wire & Line Removal
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.

## How It Works

CheapRetouch combines Apple's Vision framework for intelligent object detection with an AI-powered inpainting engine:

### Object Detection (Vision Framework)
- **`VNGenerateForegroundInstanceMaskRequest`** — Generates pixel-accurate masks for people and salient foreground objects
- **`VNDetectContoursRequest`** — Detects edges and contours for wire/line detection
- **Tap-based selection** — Simply tap on what you want to remove

### AI-Powered Inpainting (LaMa Model)

The app uses **LaMa (Large Mask Inpainting)**, a state-of-the-art deep learning model optimized for removing objects from images:

- **Model**: `LaMaFP16_512.mlpackage` — A Core ML-optimized neural network running entirely on-device
- **Architecture**: Fourier convolutions that capture both local textures and global image structure
- **Processing**: Runs on the Neural Engine (ANE) for fast, efficient inference
- **Quality**: Produces natural-looking results even for large masked areas

**Technical Details:**
- Input resolution: 512×512 pixels (automatically crops and scales around masked regions)
- Quantization: FP16 for optimal balance of quality and performance
- Fallback: Metal-accelerated exemplar-based inpainting when needed

### Processing Pipeline

```
1. User taps object → Vision generates mask
2. Mask is dilated and feathered for smooth edges
3. Region is cropped and scaled to 512×512
4. LaMa model inpaints the masked area
5. Result is composited back into original image
```

## Privacy

🔒 **100% On-Device Processing**

- No photos leave your device
- No cloud services or network calls
- No analytics or telemetry
- Photo library access via secure PHPicker

## Technical Stack

| Component | Technology |
|-----------|------------|
| UI | SwiftUI + UIKit |
| Object Detection | Vision Framework |
| ML Inference | Core ML (Neural Engine) |
| GPU Processing | Metal |
| Image Pipeline | Core Image |
| Fallback Processing | Accelerate/vImage |

## Requirements

- iOS 17.0 or later
- iPhone or iPad with A14 chip or newer (for optimal Neural Engine performance)

## Performance

| Operation | Target Time | Device |
|-----------|-------------|--------|
| Preview inpaint | < 300ms | iPhone 12+ |
| Full resolution (12MP) | < 4 seconds | iPhone 12+ |
| Full resolution (48MP) | < 12 seconds | iPhone 15 Pro+ |

## Non-Destructive Editing

All edits are stored as an operation stack — your original photos are never modified. Full undo/redo support included.

## License

MIT License — see [LICENSE](LICENSE) for details.
