// Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch)
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| name | depth-estimation |
| description | Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch) |
| version | 1.2.0 |
| category | privacy |
| parameters | [{"name":"model","label":"Depth Model","type":"select","options":["depth-anything-v2-small","depth-anything-v2-base","depth-anything-v2-large"],"default":"depth-anything-v2-small","group":"Model"},{"name":"variant","label":"CoreML Variant (macOS)","type":"select","options":["DepthAnythingV2SmallF16","DepthAnythingV2SmallF16INT8","DepthAnythingV2SmallF32"],"default":"DepthAnythingV2SmallF16","group":"Model"},{"name":"blend_mode","label":"Display Mode","type":"select","options":["depth_only","overlay","side_by_side"],"default":"depth_only","group":"Display"},{"name":"opacity","label":"Overlay Opacity","type":"number","min":0,"max":1,"default":0.5,"group":"Display"},{"name":"colormap","label":"Depth Colormap","type":"select","options":["inferno","viridis","plasma","magma","jet","turbo","hot","cool"],"default":"viridis","group":"Display"},{"name":"device","label":"Device","type":"select","options":["auto","cpu","cuda","mps"],"default":"auto","group":"Performance"}] |
| capabilities | {"live_transform":{"script":"scripts/transform.py","description":"Real-time depth estimation overlay on live feed"}} |
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