# Depth Estimation

Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch)

- Repository: https://github.com/SharpAI/DeepCamera (3124 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-17
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add SharpAI/DeepCamera --skill depth-estimation -g -a claude-code -y`
Codex: `npx skills add SharpAI/DeepCamera --skill depth-estimation -g -a codex -y`
Cursor: `npx skills add SharpAI/DeepCamera --skill depth-estimation -g -a cursor -y`
Gemini CLI: `npx skills add SharpAI/DeepCamera --skill depth-estimation -g -a gemini-cli -y`

## Description

Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch)

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:

```python
from transform_base import TransformSkillBase

class MyPrivacySkill(TransformSkillBase):
    def load_model(self, config):
        # Load your model, return {"model": "...", "device": "..."}
        ...

    def transform_frame(self, image, metadata):
        # Transform BGR image, return BGR image
        ...
```

## Protocol

### Aegis → Skill (stdin)
```jsonl
{"event": "frame", "frame_id": "cam1_1710001", "camera_id": "front_door", "frame_path": "/tmp/frame.jpg", "timestamp": "..."}
{"command": "config-update", "config": {"opacity": 0.8, "blend_mode": "overlay"}}
{"command": "stop"}
```

The rest: https://github.com/SharpAI/DeepCamera/blob/HEAD//SKILL.md