DeepCamera depth-estimation

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

install

source · Clone the upstream repo

git clone https://github.com/SharpAI/DeepCamera

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/SharpAI/DeepCamera "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/transformation/depth-estimation" ~/.claude/skills/sharpai-deepcamera-depth-estimation-184048 && rm -rf "$T"

manifest: skills/transformation/depth-estimation/SKILL.md

source content

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:

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)

{"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"}

Skill → Aegis (stdout)

{"event": "ready", "model": "coreml-DepthAnythingV2SmallF16", "device": "neural_engine", "backend": "coreml"}
{"event": "transform", "frame_id": "cam1_1710001", "camera_id": "front_door", "transform_data": "<base64 JPEG>"}
{"event": "perf_stats", "total_frames": 50, "timings_ms": {"transform": {"avg": 12.5, ...}}}

Setup

python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt