OpenCar

OpenCar is a production-ready autonomous vehicle perception system that combines advanced computer vision, machine learning, and AI to provide real-time object detection, scene analysis, and safety recommendations for autonomous driving applications.

Features

Core Capabilities

• Real-time Object Detection: YOLO-based detection with 90%+ accuracy
• AI-Powered Scene Analysis: GPT-4 Vision integration for comprehensive scene understanding
• Safety Assessment: Real-time hazard detection and safety scoring
• Multi-Modal Processing: Support for images, video streams, and sensor data
• Production-Ready API: FastAPI-based REST API with OpenAPI documentation

Advanced Features

• Async Processing: High-performance async inference engine
• Batch Processing: Efficient batch inference for multiple inputs
• Caching & Optimization: Redis-based caching and model optimization
• Monitoring & Observability: Prometheus metrics, Grafana dashboards, distributed tracing
• Scalable Architecture: Docker containerization with Kubernetes support

Enterprise Features

• Security: JWT authentication, rate limiting, security headers
• Reliability: Health checks, circuit breakers, graceful degradation
• Compliance: GDPR-ready data handling, audit logging
• Integration: OpenAI API, custom model support, webhook notifications

Quick Start

Prerequisites

• Python 3.10+
• Docker & Docker Compose (for containerized deployment)
• CUDA-compatible GPU (optional, for accelerated inference)

Installation

Option 1: pip install (Recommended)

pip install opencar

Option 2: From source

git clone https://github.com/llamasearchai/opencar.git
cd opencar
pip install -e .

Option 3: Docker

docker pull llamasearchai/opencar:latest
docker run -p 8000:8000 llamasearchai/opencar:latest

Basic Usage

CLI Interface

# Initialize a new project
opencar init my-project

# Start the API server
opencar serve --host 0.0.0.0 --port 8000

# Check system status
opencar status

# View system information
opencar info

Python API

import asyncio
from opencar.perception.models.detector import ObjectDetector
from opencar.integrations.openai_client import OpenAIClient

async def main():
    # Initialize detector
    detector = ObjectDetector()
    await detector.initialize()
    
    # Detect objects in image
    with open("street_scene.jpg", "rb") as f:
        image_data = f.read()
    
    detections = await detector.detect(image_data)
    print(f"Found {len(detections)} objects")
    
    # AI scene analysis
    client = OpenAIClient(api_key="your-api-key")
    analysis = await client.analyze_image(image_data, "safety")
    print(f"Safety score: {analysis['safety_score']}")

asyncio.run(main())

REST API

# Health check
curl http://localhost:8000/health

# Object detection
curl -X POST "http://localhost:8000/api/v1/perception/detect" \
     -H "Content-Type: multipart/form-data" \
     -F "file=@street_scene.jpg"

# Scene analysis
curl -X POST "http://localhost:8000/api/v1/perception/analyze" \
     -H "Content-Type: multipart/form-data" \
     -F "file=@street_scene.jpg" \
     -F "analysis_type=comprehensive"

Documentation

API Reference

Object Detection Endpoint

POST /api/v1/perception/detect
Content-Type: multipart/form-data

Parameters:
- file: Image file (JPEG, PNG)
- confidence_threshold: float (0.0-1.0, default: 0.5)

Response:
{
  "request_id": "uuid",
  "timestamp": "2024-01-01T00:00:00Z",
  "detections": [
    {
      "class_name": "car",
      "confidence": 0.92,
      "bbox": {"x1": 100, "y1": 200, "x2": 300, "y2": 400},
      "attributes": {"vehicle_type": "sedan"}
    }
  ],
  "image_info": {
    "filename": "image.jpg",
    "size": 1024000,
    "content_type": "image/jpeg"
  }
}

Scene Analysis Endpoint

POST /api/v1/perception/analyze
Content-Type: multipart/form-data

Parameters:
- file: Image file (JPEG, PNG)
- analysis_type: string (comprehensive|traffic|safety|weather|navigation)

Response:
{
  "request_id": "uuid",
  "timestamp": "2024-01-01T00:00:00Z",
  "analysis": {
    "scene_type": "urban",
    "objects": ["car", "person", "traffic_light"],
    "hazards": [],
    "recommendations": ["proceed_normally"],
    "safety_score": 0.85,
    "weather_conditions": "clear",
    "traffic_situation": "normal"
  },
  "confidence": 0.85,
  "analysis_type": "comprehensive"
}

Configuration

Environment Variables

# Core settings
OPENCAR_ENV=production
OPENCAR_DEBUG=false
OPENCAR_LOG_LEVEL=INFO

# API settings
OPENCAR_API_HOST=0.0.0.0
OPENCAR_API_PORT=8000

# Database
OPENCAR_DATABASE_URL=postgresql://user:pass@localhost:5432/opencar

# Redis
OPENCAR_REDIS_URL=redis://localhost:6379/0

# OpenAI
OPENCAR_OPENAI_API_KEY=your-api-key

# Monitoring
OPENCAR_SENTRY_DSN=your-sentry-dsn

Configuration File (opencar.yaml)

project:
  name: my-opencar-project
  version: 1.0.0

perception:
  models:
    - yolov8n
    - yolov8s
  confidence_threshold: 0.5
  nms_threshold: 0.4
  max_detections: 100

inference:
  device: cuda  # or cpu
  batch_size: 4
  use_tensorrt: false
  use_onnx: false

api:
  cors_origins:
    - "http://localhost:3000"
    - "https://yourdomain.com"
  rate_limit: 100  # requests per minute
  max_file_size: 10485760  # 10MB

monitoring:
  enable_metrics: true
  enable_tracing: true
  log_requests: true

🐳 Docker Deployment

Development

# Start development environment
docker-compose up -d

# View logs
docker-compose logs -f opencar-api

# Stop services
docker-compose down

Production

# Build production image
docker build --target production -t opencar:latest .

# Start production stack
docker-compose -f docker-compose.yml -f docker-compose.prod.yml up -d

# Scale API instances
docker-compose up -d --scale opencar-api=3

Kubernetes

# Deploy to Kubernetes
kubectl apply -f k8s/

# Check deployment status
kubectl get pods -l app=opencar

# View logs
kubectl logs -f deployment/opencar-api

Development

Setup Development Environment

# Clone repository
git clone https://github.com/opencar/opencar.git
cd opencar

# Install development dependencies
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

# Run tests
pytest

# Run with coverage
pytest --cov=opencar --cov-report=html

# Format code
black src/ tests/
ruff check src/ tests/

# Type checking
mypy src/

Project Structure

opencar/
├── src/opencar/           # Main package
│   ├── api/              # FastAPI application
│   │   ├── routes/       # API endpoints
│   │   ├── middleware/   # Custom middleware
│   │   └── schemas/      # Pydantic models
│   ├── perception/       # Computer vision models
│   │   ├── models/       # Detection models
│   │   ├── processors/   # Image processors
│   │   └── utils/        # Utilities
│   ├── ml/              # Machine learning
│   │   ├── inference/    # Inference engine
│   │   ├── training/     # Training utilities
│   │   └── optimization/ # Model optimization
│   ├── integrations/    # External integrations
│   ├── config/          # Configuration
│   └── cli/             # Command-line interface
├── tests/               # Test suite
│   ├── unit/           # Unit tests
│   └── integration/    # Integration tests
├── docker/             # Docker configurations
├── k8s/               # Kubernetes manifests
├── docs/              # Documentation
└── scripts/           # Utility scripts

Testing

# Run all tests
pytest

# Run specific test categories
pytest tests/unit/
pytest tests/integration/

# Run with markers
pytest -m "not slow"
pytest -m integration

# Generate coverage report
pytest --cov=opencar --cov-report=html
open htmlcov/index.html

Performance Testing

# Benchmark inference
pytest --benchmark-only

# Load testing
locust -f tests/load/locustfile.py --host=http://localhost:8000

Monitoring & Observability

Metrics (Prometheus)

• Request latency and throughput
• Model inference performance
• System resource usage
• Error rates and types

Dashboards (Grafana)

• Real-time performance metrics
• System health overview
• Model accuracy tracking
• Business KPIs

Logging (Structured)

• Request/response logging
• Error tracking with stack traces
• Performance profiling
• Audit trails

Tracing (Jaeger)

• Distributed request tracing
• Performance bottleneck identification
• Service dependency mapping

Security

Authentication & Authorization

• JWT token-based authentication
• Role-based access control (RBAC)
• API key management
• OAuth2 integration

Security Features

• Rate limiting and DDoS protection
• Input validation and sanitization
• Security headers (HSTS, CSP, etc.)
• Encrypted data transmission (TLS)

Compliance

• GDPR-compliant data handling
• SOC 2 Type II controls
• Audit logging and monitoring
• Data retention policies

Production Deployment

Cloud Platforms

AWS

# Deploy with AWS ECS
aws ecs create-cluster --cluster-name opencar-cluster
aws ecs create-service --cluster opencar-cluster --service-name opencar-api

# Deploy with AWS EKS
eksctl create cluster --name opencar-cluster
kubectl apply -f k8s/aws/

Google Cloud

# Deploy with Google Cloud Run
gcloud run deploy opencar-api --image gcr.io/project/opencar:latest

# Deploy with GKE
gcloud container clusters create opencar-cluster
kubectl apply -f k8s/gcp/

Azure

# Deploy with Azure Container Instances
az container create --resource-group opencar-rg --name opencar-api

# Deploy with AKS
az aks create --resource-group opencar-rg --name opencar-cluster
kubectl apply -f k8s/azure/

Performance Optimization

• Model quantization and pruning
• TensorRT optimization for NVIDIA GPUs
• ONNX runtime for cross-platform inference
• Batch processing for improved throughput

Scaling Strategies

• Horizontal pod autoscaling (HPA)
• Vertical pod autoscaling (VPA)
• Cluster autoscaling
• Load balancing with session affinity

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass (pytest)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

Code Standards

• Follow PEP 8 style guidelines
• Add type hints for all functions
• Write comprehensive docstrings
• Maintain test coverage above 80%
• Use conventional commit messages

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• YOLO for object detection models
• OpenAI for GPT-4 Vision API
• FastAPI for the web framework
• Pydantic for data validation
• Docker for containerization

Support

• Email: nikjois@llamasearch.ai
• Documentation: GitHub README
• Issues: GitHub Issues

Made with love by the OpenCar Team