OpenCV vs VPI: Comparison of Computer Vision Libraries

We will talk about two computer vision libraries: OpenCV vs VPI. The primary focus of this post is to introduce VPI, a relatively new computer vision library for optimized different hardware environments, and compare it with one of the most popular computer vision libraries, OpenCV. We will mention their features, advantages, and disadvantages and why you should give a chance to VPI.

What is OpenCV?

OpenCV name is the abbreviation of Open Source Computer Vision Library. As you can understand from its’ name, it is a computer vision library, open-source and distributed under the BSD license. OpenCV combines more than 2500+ optimized computer vision algorithms in itself, and because of these varieties of algorithms, OpenCV is one of the most popular computer vision libraries.

What is VPI?

VPI name is the abbreviation of Vision Programming Interface. It is also another computer vision and image processing library. VPI is developed and maintained by NVIDIA to implement algorithms for NVIDIA Embedded Devices and other discrete devices. VPI’s value proposition is seamless access to different computing hardware and utilizing the total computing capacity of the computing device.

OpenCV vs VPI Comparison

Language Support

OpenCV has C++, Python, Java, and MATLAB interfaces and supports. ((https://opencv.org/about/)) But you can use VPI with only C/C++ and Python. It has no other interface yet.((https://docs.nvidia.com/vpi/))

Hardware Backend

OpenCV can use as a backend CPU and GPU. In addition to CPU and GPU, VPI can use as backend PVA  (Programming Vision Accelerator), VIC (Video and Image Compositor), and OFA  (Optical Flow Accelerator). VPI optimizes the algorithms on NVIDIA Jetson modules and x86 devices with discrete GPUs. ((https://docs.nvidia.com/vpi/algorithms.html)) You can choose which computing engine to use with VPI’s interface. Also, VPI easily interoperates with OpenCV.((https://developer.nvidia.com/embedded/vpi))

Performance Benchmarks

According to NVIDIA’s OpenCV and VPI benchmarks, VPI is up to 20 times faster than on CPU and up to 50 times faster than on GPU. ((https://docs.nvidia.com/vpi/bench_comparison.html))

Community & Documentation

VPI is a relatively young library and developing day by day. It has well-documented code examples and a support forum. So, VPI has a small community. But OpenCV is older than VPI and has a grown community and documentation with code examples. OpenCV has more tutorials on blogs and courses. In short, beginners can adapt OpenCV ecosystem easier than VPI, but experienced users can use the high potential of VPI more than OpenCV.

Conclusion

Today, we tried to introduce you to VPI and compare OpenCV vs VPI. VPI is relatively new and will be more efficient in the upcoming days. It’s usable on other computing hardware, not limited to CPU and GPU. You can select which hardware to use. Also, it is faster than OpenCV in optimized environments. As a result, I think you should give it a try to VPI library.