Depth Estimation

# Depth Estimation (Privacy)

Real-time monocular depth estimation using Depth Anything v2. Transforms camera feeds with colorized depth maps — near objects appear warm, far objects appear cool.

When used for **privacy mode**, the `depth_only` blend mode fully anonymizes the scene while preserving spatial layout and activity, enabling security monitoring without revealing identities.

## Hardware Backends

| Platform | Backend | Runtime | Model |
|----------|---------|---------|-------|
| **macOS** | CoreML | Apple Neural Engine | `apple/coreml-depth-anything-v2-small` (.mlpackage) |
| Linux/Windows | PyTorch | CUDA / CPU | `depth-anything/Depth-Anything-V2-Small` (.pth) |

On macOS, CoreML runs on the Neural Engine, leaving the GPU free for other tasks. The model is auto-downloaded from HuggingFace and stored at `~/.aegis-ai/models/feature-extraction/`.

## What You Get

- **Privacy anonymization** — depth-only mode hides all visual identity
- **Depth overlays** on live camera feeds
- **3D scene understanding** — spatial layout of the scene
- **CoreML acceleration** — Neural Engine on Apple Silicon (3-5x faster than MPS)

## Interface: TransformSkillBase

This skill implements the `TransformSkillBase` interface. Any new privacy skill can be created by subclassing `TransformSkillBase` and implementing two methods: