The VPI computer vision and image processing software library from NVIDIA is ideal for implementing algorithms on computing engines, including central processing units (CPUs), graphics processing units (GPUs), programmable vision accelerator (PVA), Video and Image Compositor (VIC), and Optical Flow Accelerator (OFA). VPI optimizes the algorithms on both NVIDIA Jetson™ modules and x86 devices with discrete GPUs and features an interface that lets developers access multiple computing engines to achieve high image throughput and easily interoperate with OpenCV.
VPI Computer Vision and Image Processing Use Cases for Software Developers
Use VPI to solve the wide range of challenges developers face when working with Jetson embedded devices.
Solving Real-World Challenges
Use VPI to take on questions developers face when working with Jetson embedded devices.
Maximum Computing Performance
Software developers need to maximize computing performance when building and delivering computer vision systems. This is particularly important as computer vision and image processing pipelines are becoming increasingly complex and time-consuming. If software developers aren’t hitting target or desired frames per second (FPS) rate, you should consider VPI for highly optimized algorithms to increase performance, especially to replace non-performant OpenCV algorithms in the processing pipeline. Optimized VPI algorithms include background subtraction, perspective warp, temporal noise reduction, histogram equalization, and lens distortion.
Better Workload Distribution of Computer Vision and Image Processing Pipelines
Coding computer vision pipelines with multiple hardware backends is critical to take full advantage of the device’s compute capacity. VPI lets you experiment with different hardware accelerators for one or multiple processing stages and efficiently program multiple compute engines—including CPU and GPU—through one uniform interface. It also provides a zero-copy mechanism to share memory buffers between supported backends, enabling software developers to optimally and efficiently distribute workload across multiple compute engines.
Python Application Programming Interface (API)
VPI gives software developers the flexibility to develop computer vision and image processing pipelines. In addition to C support, it also offers Python bindings for the algorithms, which lets you use VPI directly in Python scripts.
VPI Performance Benchmarks
VPI computer vision and image processing algorithms are highly optimized. This helps it deliver significantly better performance than other well-known computer vision and image processing libraries, including typical speeds 11X and 7X faster than that of OpenCV on GPUs and CPUs respectively. Read more about VPI performance benchmarks.
VPI Computer Vision and Image Processing Algorithms
VPI library supports algorithms in image processing, computer vision, and feature detector and tracking:
- Image Processing
- Computer Vision
- Feature Detection and Tracking
- Gaussian Pyramid Generator
- Laplacian Pyramid Generator
- Separable and Direct Image Convolution
- Box Image Filter
- Gaussian Image Filter
- Bilateral Image Filter
- Image Rescaling
- Image Flip
- Image Views / Crop
- Image Crop and Scale
- Image Remapping
- Image Histogram
- Image Histogram Equalization
- Erode and Dilate
- Minimum/Maximum Location
- Direct and Inverse Fast Fourier Transform
- Background Subtraction
- Lens Distortion Correction
- Temporal Noise Reduction
- Pyramidal LK Optical Flow
- Dense Optical Flow
- Stereo Disparity
- KLT Bounding Box Tracker
- Harris Corners Detector
- ColorNames Features Detector
- Histogram of Oriented Gradients
Simultaneous Computing Engine Support
VPI lets computer vision software developers use multiple compute engines simultaneously—including VIC, PVA, NVENC, and OFA—through one interface. This means you don’t have to change or use multiple computer vision and image processing libraries. In fact, VPI requires little to no change to existing computer vision and image processing pipelines. The table below summarizes and compares VPI compute engine support to OpenCV.
| Computer Vision & Image Processing Library | ||||||
|---|---|---|---|---|---|---|
| VPI | ||||||
| OpenCV | ||||||
VPI 3.0 Updates
VPI 3.0 is part of NVIDIA JetPack 6.0 Developer Preview (DP) for Jetson devices and is also available for x86 devices with discrete GPUs. It includes new algorithms and other updates
VPI 3.0 highlights include:
- New Algorithms:
• Crop Scaler on GPU: Crops rectangular regions from the input frames and rescales them all to the same dimensions.
• DCF Tracker on GPU and PVA: Multi-object tracker that employs a discriminative correlation filter for visual object tracking.
The VPI support matrix details algorithm-specific backend support.
Visit the JetPack download page
See VPI In Action
Webinars
Introduction to VPI
The Implementing Computer Vision and Image Processing Solutions with VPI webinar overviews the computer vision and image processing software library. This tutorial reviews VPI programming concepts and best practices, how to build a complete and efficient stereo disparity-estimation pipeline, and VPI interoperability with OpenCV input and OpenGL output using computing platform and CUDA.
Watch the overview(Registration required)
New Algorithms and Python Bindings Overview
The Accelerate Computer Vision and Image Processing using VPI 1.1 webinar (Registration Required) discusses the new algorithms and Python support included in VPI-1.1 as part of JetPack 4.6.
See the Python support (Registration required)
VPI and PyTorch Interoperability Demo
The VPI and PyTorch Interoperability Demo (Registration Required) shows how to build a Python-based application to improve object detection using PyTorch without copying data.
Learn how to program with Python (Registration required)
Blogs
Improved Interoperability between VPI and PyTorch
This blog demonstrates how VPI is interoperable with PyTorch and other Pytorch-based libraries. This post shares how to use a PyTorch-based object detection and tracking example on a noisy video.
Read how Interoperability works
Reducing Temporal Noise on Images with VPI
The Reducing Temporal Noise blog demonstrates how to build the VPI pipeline and run the Temporal Noise Reduction (TNR) sample application on Jetson devices, including how to submit and synchronize processing tasks in a video stream.
Learn to build the VPI pipeline
VPI References
Release Notes
Read details about the latest release highlights, new features, application programming interface (API) updates, known issues, and bug fixes.
Review the release notes
Documentation
Learn more about the technical background and installation information, including architecture.
Review documentation
Computer Vision Solutions
Learn more about computer vision technology, image processing solutions, computer vision machine learning, and deep learning models and the solutions NVIDIA provides.
Explore CV solutions
Computer Vision Glossary
Learn more about the field of computer vision and applications of computer vision.
Learn Computer Vision
Additional Resources
- Review VPI Documentation
- Review Release Notes
- Read Blog: Improved Interoperability between VPI and PyTorch
Get started with Vision Programming Interface (VPI).
