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
Developed by Neuron Q | Making advanced surveillance technology accessible
Metadata
Release files for NQvision 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nqvision-0.1.2.tar.gz | 11.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| NQvision-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 21.3 kB
Release files / nqvision-0.1.2.tar.gz
| Download URL | nqvision-0.1.2.tar.gz |
|---|---|
| Size | 11.2 kB |
| Tags | Source |
|
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No |
| Uploaded via |
twine/5.1.1 CPython/3.9.20
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Release files / NQvision-0.1.2-py3-none-any.whl
| Download URL | NQvision-0.1.2-py3-none-any.whl |
|---|---|
| Size | 10.1 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.9.20
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