<div align="center"> <h1>DeepCamera — Open-Source AI Camera Skills Platform</h1> <p>DeepCamera's open-source skills give your cameras AI — VLM scene analysis, object detection, person re-identification, all running locally with models like Qwen, DeepSeek, SmolVLM, and LLaVA. Built on proven facial recognition, RE-ID, fall detection, and CCTV/NVR surveillance monitoring, the skill catalog extends these machine learning capabilities with modern AI. All inference runs locally for maximum privacy.</p> <p> <a href="https://join.slack.com/t/sharpai/shared_invite/zt-1nt1g0dkg-navTKx6REgeq5L3eoC1Pqg"> <img src="https://img.shields.io/badge/slack-purple?style=for-the-badge&logo=slack" height=25> </a> <a href="https://github.com/SharpAI/DeepCamera/issues"> <img src="https://img.shields.io/badge/support%20forums-navy?style=for-the-badge&logo=github" height=25> </a> <a href="https://github.com/SharpAI/DeepCamera/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/SharpAI/DeepCamera.svg?style=for-the-badge" height=25> </a> <a href="https://pypi.python.org/pypi/sharpai-hub"> <img alt="Pypi release" src="https://img.shields.io/pypi/v/sharpai-hub.svg?style=for-the-badge" height=25> </a> <a href="https://pepy.tech/project/sharpai-hub"> <img alt="download" src=https://static.pepy.tech/personalized-badge/sharpai-hub?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads height=25> </a> </p> </div>

🛡️ Introducing SharpAI Aegis — Desktop App for DeepCamera

Use DeepCamera's AI skills through a desktop app with LLM-powered setup, agent chat, and smart alerts — connected to your mobile via Discord / Telegram / Slack.

SharpAI Aegis is the desktop companion for DeepCamera. It uses LLM to automatically set up your environment, configure camera skills, and manage the full AI pipeline — no manual Docker or CLI required. It also adds an intelligent agent layer: persistent memory, agentic chat with your cameras, AI video generation, voice (TTS), and conversational messaging via Discord / Telegram / Slack.

📦 Download SharpAI Aegis →

</div> <p align="center"> <a href="https://youtu.be/BtHpenIO5WU"><img src="screenshots/aegis-benchmark-demo.gif" alt="Aegis AI Benchmark Demo — Local LLM home security on Apple Silicon (click for full video)" width="60%"></a> </p>

🗺️ Roadmap

[x] Skill architecture — pluggable SKILL.md interface for all capabilities
[x] Skill Store UI — browse, install, and configure skills from Aegis
[x] AI/LLM-assisted skill installation — community-contributed skills installed and configured via AI agent
[x] GPU / NPU / CPU (AIPC) aware installation — auto-detect hardware, install matching frameworks, convert models to optimal format
[x] Hardware environment layer — shared env_config.py for auto-detection + model optimization across NVIDIA, AMD, Apple Silicon, Intel, and CPU
[ ] Skill development — 19 skills across 10 categories, actively expanding with community contributions

🧩 Skill Catalog

Each skill is a self-contained module with its own model, parameters, and communication protocol. See the Skill Development Guide and Platform Parameters to build your own.

| Category | Skill | What It Does | Status | |----------|-------|--------------|:------:| | Detection | yolo-detection-2026 | Real-time 80+ class detection — auto-accelerated via TensorRT / CoreML / OpenVINO / ONNX | ✅| | Analysis | home-security-benchmark | 143-test evaluation suite for LLM & VLM security performance | ✅ | | Privacy | depth-estimation | Real-time depth-map privacy transform — anonymize camera feeds while preserving activity | ✅ | | Segmentation | sam2-segmentation | Interactive click-to-segment with Segment Anything 2 — pixel-perfect masks, point/box prompts, video tracking | ✅ | | Annotation | dataset-annotation | AI-assisted dataset labeling — auto-detect, human review, COCO/YOLO/VOC export for custom model training | ✅ | | Training | model-training | Agent-driven YOLO fine-tuning — annotate, train, export, deploy | 📐 | | Automation | mqtt · webhook · ha-trigger | Event-driven automation triggers | 📐 | | Integrations | homeassistant-bridge | HA cameras in ↔ detection results out | 📐 |

✅ Ready · 🧪 Testing · 📐 Planned

Registry: All skills are indexed in skills.json for programmatic discovery.

🚀 Getting Started with SharpAI Aegis

The easiest way to run DeepCamera's AI skills. Aegis connects everything — cameras, models, skills, and you.

📷 Connect cameras in seconds — add RTSP/ONVIF cameras, webcams, or iPhone cameras for a quick test
🤖 Built-in local LLM & VLM — llama-server included, no separate setup needed
📦 One-click skill deployment — install skills from the catalog with AI-assisted troubleshooting
🔽 One-click HuggingFace downloads — browse and run Qwen, DeepSeek, SmolVLM, LLaVA, MiniCPM-V
📊 Find the best VLM for your machine — benchmark models on your own hardware with HomeSec-Bench
💬 Talk to your guard — via Telegram, Discord, or Slack. Ask what happened, tell it what to watch for, get AI-reasoned answers with footage.

🎯 YOLO 2026 — Real-Time Object Detection

State-of-the-art detection running locally on any hardware, fully integrated as a DeepCamera skill.

YOLO26 Models

YOLO26 (Jan 2026) eliminates NMS and DFL for cleaner exports and lower latency. Pick the size that fits your hardware:

| Model | Params | Latency (optimized) | Use Case | |-------|--------|:-------------------:|----------| | yolo26n (nano) | 2.6M | ~2ms | Edge devices, real-time on CPU | | yolo26s (small) | 11.2M | ~5ms | Balanced speed & accuracy | | yolo26m (medium) | 25.4M | ~12ms | Accuracy-focused | | yolo26l (large) | 52.3M | ~25ms | Maximum detection quality |

All models detect 80+ COCO classes: people, vehicles, animals, everyday objects.

Hardware Acceleration

The shared env_config.py auto-detects your GPU and converts the model to the fastest native format — zero manual setup:

| Your Hardware | Optimized Format | Runtime | Speedup vs PyTorch | |---------------|-----------------|---------|:------------------:| | NVIDIA GPU (RTX, Jetson) | TensorRT .engine | CUDA | 3-5x | | Apple Silicon (M1–M4) | CoreML .mlpackage | ANE + GPU | ~2x | | Intel (CPU, iGPU, NPU) | OpenVINO IR .xml | OpenVINO | 2-3x | | AMD GPU (RX, MI) | ONNX Runtime | ROCm | 1.5-2x | | Any CPU | ONNX Runtime | CPU | ~1.5x |

Aegis Skill Integration

Detection runs as a parallel pipeline alongside VLM analysis — never blocks your AI agent:

Camera → Frame Governor → detect.py (JSONL) → Aegis IPC → Live Overlay
                5 FPS           ↓
                          perf_stats (p50/p95/p99 latency)

🖱️ Click to setup — one button in Aegis installs everything, no terminal needed
🤖 AI-driven environment config — autonomous agent detects your GPU, installs the right framework (CUDA/ROCm/CoreML/OpenVINO), converts models, and verifies the setup
📺 Live bounding boxes — detection results rendered as overlays on RTSP camera streams
📊 Built-in performance profiling — aggregate latency stats (p50/p95/p99) emitted every 50 frames
⚡ Auto start — set auto_start: true to begin detecting when Aegis launches

📖 Full Skill Documentation →

🔒 Privacy — Depth Map Anonymization

Watch your cameras without seeing faces, clothing, or identities. The depth-estimation skill transforms live feeds into colorized depth maps using Depth Anything v2 — warm colors for nearby objects, cool colors for distant ones.

Camera Frame ──→ Depth Anything v2 ──→ Colorized Depth Map ──→ Aegis Overlay
   (live)          (0.5 FPS)           warm=near, cool=far      (privacy on)

🛡️ Full anonymization — depth_only mode hides all visual identity while preserving spatial activity
🎨 Overlay mode — blend depth on top of original feed with adjustable opacity
⚡ Rate-limited — 0.5 FPS frontend capture + backend scheduler keeps GPU load minimal
🧩 Extensible — new privacy skills (blur, pixelation, silhouette) can subclass TransformSkillBase

Runs on the same hardware acceleration stack as YOLO detection — CUDA, MPS, ROCm, OpenVINO, or CPU.

📖 Full Skill Documentation → · 📖 README →

📊 HomeSec-Bench — How Secure Is Your Local AI?

HomeSec-Bench is a 143-test security benchmark that measures how well your local AI performs as a security guard. It tests what matters: Can it detect a person in fog? Classify a break-in vs. a delivery? Resist prompt injection? Route alerts correctly at 3 AM?

Run it o

DeepCamera

Install / Use

README