NQvision 0.1.2

A library to simplify the development of AI-driven object detection and monitoring solutions.

NQvision

NQvision is a powerful library built around Ultralytics models in ONNX format, designed to simplify the development of AI-driven object detection and tracking solutions. It transforms complex computer vision capabilities into an accessible, production-ready solution that revolutionizes how organizations approach real-time monitoring and security.

🚀 Features

Core Capabilities

• ONNX Model Integration: Seamless integration with Ultralytics models
• Real-Time Object Detection: Optimized for immediate recognition and action
• Continuous Object Tracking: Advanced tracking maintaining object identities across frames
• High-Performance Processing: Efficient operation on both CPU and GPU
• Customizable Detection Settings: Adjustable confidence thresholds and tracking configurations
• Scalable Architecture: Handles multiple video feeds simultaneously

Event Management

• Real-Time Event Alerts: Instant notification system for critical detections
• Event Aggregation: Intelligent clustering of detections to reduce false positives
• Customizable Criteria: Configurable detection thresholds and frequency parameters
• High-Confidence Alerts: Aggregated detection within defined time windows
• Scalable Event Management: Suitable for both small setups and enterprise deployments

💫 Key Benefits

Unmatched Flexibility

• Universal Ultralytics Compatibility
• Expanding Architecture Support
• Adaptable Integration with existing security infrastructure

Enterprise-Grade Performance

• Scalable from single-camera setups to city-wide deployments
• Resource-optimized processing
• Built for 24/7 mission-critical environments

Revolutionary Features

• Intelligent Tracking across camera views
• Event Streaming with customizable detection criteria
• Automated Response System
• Multi-Camera Coordination
• Seamless handling of multiple video streams

🎯 Impact

For Developers

• Eliminates the need to develop intricate AI pipelines from scratch
• Provides a ready-to-use framework for advanced surveillance
• Customizable settings and real-time capabilities
• Implement AI detection without deep AI expertise

For Companies

• Accelerate deployment of AI-driven surveillance systems
• Minimize development costs
• Improve system reliability
• Handle complex, large-scale environments
• Event-driven architecture for prompt action on high-risk detections

⚡ Quick Start

Dependencies

To install NQvision Dependencies, follow these steps:

• Install NQvision requirements found in ‘requirements.txt’:

pip install -r requirements.txt

• install onnxruntime :
  • For cpu only inference :

  pip install onnxruntime
  

  • For gpu accelerated inference

  pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/
  
  For CUDA 11.X (default):
  pip install onnxruntime-gpu
  

Verifying the Installation

To verify that NQvision is installed correctly, run the following Python code:

from NQvision.core import NQvisionCore, ModelConfig

# Create a basic configuration
config = ModelConfig(input_size=(640, 640), confidence_threshold=0.4)

# Initialize NQvisionCore (replace with your model path)
detector = NQvisionCore("path/to/model/model.onnx", config)

print("NQvision initialized successfully!")

If you see the success message without any errors, NQvision is installed and ready to use.

🔄 Current Support

• Currently supporting models such as rtlder
• Designed for future expansion
• Regular updates and expanding capabilities

🛠 Integration

Deployment Features

• Rapid deployment: Operational in minutes
• Immediate enhancement of surveillance capabilities
• Minimal training requirements
• Intuitive system for security teams

System Requirements

• Compatible with existing cameras and systems
• Supports both CPU and GPU processing
• Scalable for various deployment sizes

🔮 Future Development

NQvision is designed for continuous evolution, with plans to:

• Adopt additional models and architectures
• Expand ecosystem support
• Regular feature updates
• Enhanced capabilities based on community feedback

📝 License

[License details to be added]

🤝 Contributing

[Contribution guidelines to be added]

📞 Support

[Support]

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Developed by Neuron Q | Making advanced surveillance technology accessible