Ussop Visual Inspection Engine

"I am the Sniper King!" — Ussop's precision, now for your production line.

Ussop is a premium, production-ready AI visual inspection system that combines high-speed object detection (Faster R-CNN) with precise edge segmentation (NanoSAM) to automate quality control in industrial manufacturing environments—all engineered to run exceptionally fast on standard CPUs.

🌟 Key Features

✅ CPU-Only — No GPU required, runs on standard industrial PCs ✅ Fast Deployment — Hours to deploy, not months ✅ Precise Segmentation — SAM masks enable accurate measurements ✅ Cost-Effective — 1/3 the price of competitors like Cognex ✅ Industrial Grade — Modbus TCP, MQTT, OPC-UA, REST API ✅ Production Ready — 70+ API endpoints, JWT auth, audit logging ✅ Premium UI/UX — Modern 'OS-in-Browser' TypeScript dashboard with fluid light & dark modes ✅ Real-Time Telemetry — Live WebSockets rendering advanced Chart.js KPI analytics ✅ Multi-Station Fleet — Centralized panoramic overview across all connected production lines ✅ AI Query — Natural language questions about your inspection data ✅ On-Device Retraining — Fine-tune models from active-learning annotations

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💻 Modern Tech Stack

Frontend (OS-in-Browser UI)

• React 18 (Vite + TypeScript)
• Tailwind CSS (Custom Olive Brand Tokens, Fluid Typography, 12px Radii System)
• Radix UI & Phosphor Icons (Accessible, Premium Aesthetics)
• Chart.js (Real-time Analytics, Scatter Plots & Pareto Visualizations)

Backend (Performant Web Engine)

• FastAPI (Async, high-throughput REST + WebSockets)
• SQLAlchemy & Alembic (Robust ORM & Database Migrations)
• PyJWT (Secure RBAC Authentication & Session Management)
• Redis & Prometheus (Caching and Real-time Telemetry)

AI & Computer Vision Core

• PyTorch & OpenVINO (CPU-Optimized Inference Engine)
• Faster R-CNN (Real-time Defect Object Detection)
• NanoSAM (Sub-millisecond Instance Segmentation)
• Transformers/VLM (Natural Language Scene Understanding)

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🏗️ Technical Architecture

graph TD
    classDef frontend fill:#4EA5D9,stroke:#333,stroke-width:2px,color:#fff;
    classDef core fill:#2A4494,stroke:#333,stroke-width:2px,color:#fff;
    classDef ml fill:#F05D5E,stroke:#333,stroke-width:2px,color:#fff;
    classDef storage fill:#2892D7,stroke:#333,stroke-width:2px,color:#fff;
    classDef ext fill:#44CFCB,stroke:#333,stroke-width:2px,color:#fff;

    UI(React SPA Dashboard):::frontend
    API(FastAPI Backend):::core
    
    subgraph Inspection Engine
        Det(Faster-RCNN Detection):::ml
        Seg(NanoSAM Segmentation):::ml
        OV(OpenVINO Optimizer):::ml
    end
    
    subgraph Services
        AL(Active Learning):::core
        VLM(Vision Language Model):::core
        Wkr(Background Workers):::core
    end
    
    subgraph Storage layer
        DB[(PostgreSQL / SQLite)]:::storage
        Cach[(Redis Cache)]:::storage
        FS[(Local Image Storage)]:::storage
    end

    subgraph Hardware & Integrations
        Cam[Cameras / CCTV]:::ext
        PLC[Modbus / OPC-UA / MQTT]:::ext
    end

    UI <-->|REST & WebSockets| API
    Cam -->|Image Streams| API
    API <--> Det
    Det --> Seg
    Seg --> OV
    API <--> AL
    API <--> VLM
    
    API <--> Cach
    API <--> DB
    API <--> FS
    
    Wkr <--> DB
    Wkr <--> Cach
    
    API -->|Hardware Signals| PLC

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🔄 Inspection Workflow

sequenceDiagram
    participant Cam as Camera
    participant API as Server (FastAPI)
    participant ML as Inspector (CV)
    participant DB as Database
    participant UI as Dashboard (WebSockets)
    participant EXT as PLC / OPC-UA

    Cam->>API: 1. Send Frame (Trigger)
    API->>ML: 2. Process Image
    Note over ML: Detect Objects (Faster R-CNN)
    Note over ML: Generate Masks (NanoSAM)
    Note over ML: AI Query Description (VLM)
    ML-->>API: 3. Inspection Results
    API->>DB: 4. Save Record & Images
    API->>EXT: 5. Signal Modbus/OPC-UA/MQTT
    API->>UI: 6. Push Live Update (WS)

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🚀 Quick Start

Option 1: Docker Compose (Recommended)

cp .env.example .env        # fill in secrets
docker compose -f docker/docker-compose.yml up -d
# API:     http://localhost:8080
# UI:      http://localhost:8080
# Grafana: http://localhost:3001  (admin / admin)

Option 2: Python (Development)

# 1. Download models
python scripts/download_models.py

# 2. Install dependencies
pip install -e ".[full]"

# 3. Setup database
python scripts/migrate.py upgrade

# 4. Start API
cd ussop && python run.py

# 5. Start frontend dev server (separate terminal)
cd ussop/frontend && npm install && npm run dev

# Access at http://localhost:8080

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📁 Project Structure

ussop-project/
├── ussop/                      # Production application package
│   ├── api/                    # FastAPI app + 70+ endpoints
│   ├── services/               # Core domain services (inspector, models, etc.)
│   ├── integrations/           # Modbus TCP, MQTT, Webhooks, OPC-UA server
│   ├── models/                 # SQLAlchemy ORM (9 tables)
│   ├── config/                 # Pydantic settings loading
│   ├── frontend/               # React 18 + TypeScript + Vite SPA
│   ├── tests/                  # 437 tests across 13 files — all passing
│   ├── worker.py               # Background workers (batch, training, alerts)
│   └── run.py                  # Application entry point
├── alembic/                    # Database migrations
├── docs/                       # Comprehensive documentation
├── examples/                   # Reference implementations (legacy ML code)
├── scripts/                    # Utility scripts (download_models.py, migrate.py)
├── docker/                     # Docker configuration (Nginx, Prometheus, Grafana)
├── pyproject.toml              # Dependencies (PEP 517)
├── .env.example                # Configuration template
└── README.md                   # This file

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📚 Documentation

Document	Description
API Reference	All REST + WebSocket endpoints
Deployment Guide	Docker + bare-metal setup
Customer Onboarding	8-step go-live guide
Architecture	Technical design
Roadmap	Product roadmap

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⚙️ System Requirements

• Minimum: Intel Core i5-10400 (6+ cores), 16GB RAM, 256GB SSD, Win10/Ubuntu 20.04
• Recommended: Intel Core i7-12700 (12 cores), 32GB RAM, 512GB NVMe SSD, Ubuntu 22.04 LTS

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🔌 Core API Endpoints

Interactive Swagger docs available at http://localhost:8080/docs

Method	Path	Description
POST	/api/v1/inspect	Upload and inspect image
GET	/api/v1/inspections	List inspection history
GET	/api/v1/statistics	Dashboard statistics (cached 30s)
POST	/api/v1/query	Natural language query
POST	/api/v1/models/deploy	Hot-swap inference model
GET	/api/v1/opcua/status	OPC-UA server status
WS	/ws/dashboard	Real-time dashboard push

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🛠️ Testing & Performance

• Inference: < 1s on Intel i5 (MobileNet + NanoSAM)
• Throughput: 30+ inspections/minute
• Accuracy: > 85% mAP (Detection), > 80% IoU (Segmentation)

# Run all tests (437 passing tests)
cd ussop
pytest tests/ -v

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🤝 Contributing

1. Create a branch: git checkout -b feature/name
2. Make changes and test: pytest tests/ -v
3. Commit: git commit -am "Add feature"
4. Push and open a Pull Request

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📄 License

• License: MIT License — See LICENSE file for details.

Ussop v2.0 — Sniper precision. Enterprise ready. "Every defect is a target, and we never miss."