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:

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, ...}}}