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Depth Estimation — Privacy Transform

Transform camera feeds into colorized depth maps using Depth Anything v2, providing real-time privacy protection for security monitoring.

In privacy mode (depth_only), the scene is fully anonymized — no faces, no clothing, no identifying features — while preserving spatial layout and activity patterns for security awareness.

Privacy Transform Flow Depth Anything v2

How It Works

Camera Frame → Depth Anything v2 → Colorized Depth Map → Aegis Overlay
   (BGR)         (monocular)         (warm=near, cool=far)    (0.5 FPS)

The depth model converts each frame into a distance map where warm colors (red/orange) indicate nearby objects and cool colors (blue/purple) indicate distant ones. This preserves enough spatial information to understand activity (someone approaching, car in driveway, etc.) without revealing identity.

Hardware Support

Auto-detected via HardwareEnv from skills/lib/env_config.py:

Platform Backend Notes
NVIDIA CUDA FP16 on GPU
AMD ROCm PyTorch HIP
Apple Silicon MPS Unified memory, leaves Neural Engine free
Intel OpenVINO CPU + NPU support
CPU PyTorch Fallback, slower

Models

Model Size Speed Quality
depth-anything-v2-small 25MB Fast Good (default)
depth-anything-v2-base 98MB Medium Better
depth-anything-v2-large 335MB Slow Best

Weights are downloaded from HuggingFace Hub on first run and cached locally.

Display Modes

  • depth_only (default) — Full anonymization. Only the depth map is shown.
  • overlay — Depth map blended on top of the original feed (adjustable opacity).
  • side_by_side — Original and depth map shown next to each other.

Setup

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

Integration with Aegis

This skill communicates with Aegis via JSONL over stdin/stdout. Aegis sends frame events, the skill returns transformed frames (base64 JPEG). See SKILL.md for the full protocol specification and the TransformSkillBase interface for building new privacy skills.

Creating New Privacy Skills

Subclass TransformSkillBase and implement two methods:

from transform_base import TransformSkillBase

class MyPrivacySkill(TransformSkillBase):
    def load_model(self, config):
        self.model = load_my_model()
        return {"model": "my-model", "device": self.device}

    def transform_frame(self, image, metadata):
        return self.model.anonymize(image)

if __name__ == "__main__":
    MyPrivacySkill().run()

The base class handles JSONL protocol, performance tracking, hardware detection, rate limiting, and graceful shutdown.