docs: Add comprehensive README with app features and LaMa ML model details

README.md

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+# 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.