Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch)
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.
| 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/.
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
...
{"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"}
{"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, ...}}}
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt