Depth Estimation Skill: Real-Time Privacy for AI Web Apps

Beyond the Pixel: Spatial Awareness as a Privacy Feature

In the world of computer vision, the tension between security and privacy is a constant struggle. Traditional camera feeds reveal everything—identities, sensitive documents, and personal details. The depth-estimation skill, authored by SharpAI, offers a sophisticated middle ground. By utilizing the state-of-the-art Depth Anything v2 model, this modular skill transforms standard RGB video into colorized depth maps.

This isn't just about cool visuals where near objects appear warm and far objects appear cool. It is about anonymization. By employing the depth_only blend mode, builders can monitor spatial layouts and activity patterns without ever recording or processing a human face or identifiable trait.

What Makes This Skill Special?

The depth-estimation skill is more than a simple wrapper around a model; it is a hardware-optimized transformation engine designed for production environments.

On macOS, it leverages CoreML to run on the Apple Neural Engine (ANE). This is a critical architectural choice: by offloading the depth calculations to the ANE, your GPU remains completely free for rendering or other complex UI tasks. On Linux and Windows, the skill falls back to high-performance PyTorch with CUDA support.

Key Features:

• Privacy Anonymization: Hide visual identity while preserving the physical "intent" of a scene.
• 3D Scene Understanding: Extract spatial layouts from a single 2D camera feed.
• Optimized Backends: Auto-detects hardware to ensure the lowest possible latency.
• HuggingFace Integration: Automatically downloads the latest Depth-Anything-V2-Small weights on first run.

How to Install

Getting started with Depth Estimation in your Lovable project is straightforward. Ensure you have your Python environment ready, then run:

lovable add depth-estimation

Once added, the skill will manage its own dependencies. You can then configure the blend modes (overlay vs depth_only) and opacity via a simple JSON-based command protocol.

Under the Hood: The TransformSkillBase Interface

This skill is built upon the TransformSkillBase protocol, making it incredibly easy to extend. If you are a developer looking to build a custom privacy filter—perhaps a Gaussian blur that only triggers on faces—you can use the same architecture.

Here is how the skill structure looks for developers wanting to peek under the hood:

from transform_base import TransformSkillBase

class MyPrivacySkill(TransformSkillBase):
    def load_model(self, config):
        # Skill handles model fetching and device allocation
        return {"model": "depth_v2_small", "device": "neural_engine"}

    def transform_frame(self, image, metadata):
        # This is where the depth map magic happens
        # BGR in, Depth-mapped BGR out
        return transformed_image

Implementation and Protocol

The skill communicates via standard I/O using JSONL (JSON Lines). This makes it language-agnostic at the application layer while maintaining high-speed processing through Python on the backend.

To update the visualization in real-time, you simply send a config update to the skill's stdin:

{"command": "config-update", "config": {"opacity": 0.8, "blend_mode": "overlay"}}

The skill responds with the transformed frame as a Base64 JPEG, along with performance statistics so you can monitor your frame rates and transformation latency (typically ~12.5ms on modern Apple Silicon).

Use Cases for Builders

1. Privacy-Preserving Security: Deploy a monitoring system in a hospital or private home that only shows spatial movements, ensuring no patient or resident is "seen" in high definition.
2. Augmented Reality: Use the depth data to understand where objects are in a room to correctly occlude virtual 3D elements.
3. Retail Analytics: Track foot traffic and store density without the legal liabilities associated with storing biometric or facial data.

Summary

The depth-estimation skill is a powerful example of how AI can be used to protect privacy rather than invade it. By converting raw data into a spatial depth map, you provide value through understanding without the risks of standard video recording.

Explore the full capabilities and documentation at /skill/depth-estimation.