OpenCV vs VPI: Comparison of Computer Vision Libraries

Computer vision (CV) has become an indispensable component of numerous applications, including robotics, autonomous vehicles, medical imaging, and smart surveillance. As demands grow for real-time performance and optimized resource usage, developers have been turning to specialized libraries that can fully harness modern hardware architectures.

In this blog post, we’ll compare two such libraries—OpenCV and VPI (Vision Programming Interface)—with a particular focus on how VPI, a relatively new entrant from NVIDIA, can help you open additional performance on heterogeneous computing platforms.

What is OpenCV?

OpenCV stands for Open Source Computer Vision Library. As its name suggests, it’s an open-source library released under the BSD license. OpenCV has been under active development for over two decades and offers more than 2,500 optimized computer vision and machine learning algorithms.

From classic image processing tasks (like filtering, edge detection, and morphological operations) to advanced deep learning and object recognition algorithms, OpenCV provides an extensive toolkit for both beginners and seasoned professionals.

Key highlights of OpenCV include:

• Wide Language Support: C++, Python, Java, and MATLAB interfaces.
• Rich Ecosystem and Community: Large user base, extensive online tutorials, detailed documentation, and a variety of third-party libraries and bindings.
• Cross-Platform Compatibility: Supports Windows, Linux, macOS, Android, and iOS.
• GPU Acceleration (Optional): OpenCV can use CUDA (when built with CUDA support) to accelerate many operations on NVIDIA GPUs.

What is VPI?

VPI stands for Vision Programming Interface, developed and maintained by NVIDIA. It’s designed to implement and optimize computer vision and image processing algorithms on NVIDIA’s embedded devices (such as Jetson modules) as well as on x86 platforms equipped with discrete GPUs.

VPI’s fundamental value proposition is the ability to access different computing backends seamlessly—such as CPU, GPU, PVA (Programmable Vision Accelerator), VIC (Video and Image Compositor), and OFA (Optical Flow Accelerator)—to make full use of the hardware’s capabilities.

Key highlights of VPI include:

• Multi-Backend Flexibility: CPU, GPU, PVA, VIC, and OFA are supported backends, allowing developers to pick and choose the best-suited accelerator for specific tasks.
• Optimized for NVIDIA Hardware: Especially powerful on Jetson modules for robotics and edge applications, as well as on x86 systems with NVIDIA GPUs.
• Language Support: C/C++ and Python (so far).
• Interoperability with OpenCV: You can seamlessly hand off data between VPI and OpenCV in the same application.

Why Consider VPI Over OpenCV?

OpenCV’s longevity and vast community make it the go-to library for many developers. However, VPI introduces several compelling features—particularly for NVIDIA hardware—that can significantly benefit projects demanding high performance and real-time processing.

1. Seamless Hardware Integration:
   • OpenCV primarily uses CPU by default, and GPU acceleration requires a custom build with CUDA support.
   • VPI natively supports not just CPU and GPU but also specialized accelerators (PVA, VIC, and OFA) on Jetson platforms, giving you more fine-grained control over the execution device.
2. Performance Benchmarks:
   • According to NVIDIA’s official benchmarks, VPI can be up to 20× faster than CPU-only OpenCV implementations and up to 50× faster than GPU-accelerated OpenCV in select operations.
   • These performance gains hinge on offloading tasks to specialized hardware units, which helps free up resources on the CPU and GPU.
3. Ease of Integration with NVIDIA Jetson Devices:
   • Jetson modules, such as Jetson Nano, TX2, Xavier NX, and Orin, are widely used in edge AI and robotics.
   • VPI is specifically optimized for these devices, automatically leveraging hardware accelerators for tasks like optical flow, video encoding/decoding, and more.
4. Future Potential and NVIDIA’s Ecosystem:
   • VPI is still young compared to OpenCV, but it’s evolving rapidly. NVIDIA continuously adds new algorithms and improves performance for its hardware platforms.
   • For projects that rely heavily on NVIDIA’s ecosystem (e.g., TensorRT for inference, DeepStream for video analytics, and CUDA libraries for accelerated computing), VPI integrates neatly into that workflow.

Detailed Comparison: OpenCV vs VPI

1. Language Support

• OpenCV:
  • C++, Python, Java, MATLAB
  • Vibrant community resources and mature documentation.
• VPI:
  • C/C++ and Python
  • Newer library, so fewer language bindings and a smaller community.

2. Hardware Backends

• OpenCV:
  • Primarily CPU, optional GPU acceleration (via CUDA) when built correctly.
• VPI:
  • CPU, GPU, PVA, VIC, OFA, all within a single framework.
  • Specifically tailored for NVIDIA platforms (both Jetson and discrete GPUs on x86).

3. Performance Benchmarks

• OpenCV vs VPI:
  • On NVIDIA’s official benchmarks, VPI shows up to 20× speedup over CPU-only OpenCV and up to 50× speedup compared to GPU-accelerated OpenCV for certain algorithms.
  • Results vary based on the algorithm, image size, and hardware configuration, but the specialized accelerators in VPI tend to offer more pronounced speedups.

4. Community & Documentation

• OpenCV:
  • Extremely large and established user base.
  • Numerous tutorials, online courses, blog posts, and a stack of Q&A forums.
  • Well-developed documentation and a broad ecosystem of third-party extensions.
• VPI:
  • Smaller community, as it’s a newer library.
  • Well-structured official documentation with code examples on NVIDIA’s site.
  • Official NVIDIA forums can provide direct technical support, though community content (like third-party tutorials) is still limited.

Use Cases and Practical Scenarios

OpenCV Use Cases

• General-Purpose Computer Vision: If your project must run on multiple platforms (including non-NVIDIA hardware) and you need a broad range of algorithms—from basic image processing to more advanced tasks like feature tracking, segmentation, and object detection—OpenCV is often the first choice.
• Cross-Platform Applications: If you need to deploy across Windows, macOS, and embedded Linux devices without restricting yourself to NVIDIA GPUs, OpenCV’s portability is a significant advantage.

VPI Use Cases

• NVIDIA Jetson-Based Robotics: If you’re building a robot or autonomous machine on Jetson hardware, VPI’s specialized accelerators let you maximize performance per watt while reducing CPU usage.
• High-Throughput Video Analytics: Tasks such as optical flow, stereo matching, and image warping can see huge speedups by leveraging the PVA, VIC, or OFA accelerators.
• Real-Time Edge AI: For time-critical applications (e.g., real-time object detection or tracking in drones, robotic arms, or industrial inspection systems), the performance gains from VPI can be vital.

Potential Drawbacks of Each Library

OpenCV Drawbacks

• Limited Specialized Accelerator Support: Although OpenCV supports GPU acceleration, it does not have built-in support for the additional specialized hardware units (like PVA or OFA).
• Complex CUDA Build: Enabling GPU acceleration in OpenCV can be somewhat cumbersome, requiring specialized builds and dependencies on the CUDA toolkit.

VPI Drawbacks

• NVIDIA Ecosystem Lock-In: VPI is designed primarily for NVIDIA devices. If your project later needs to migrate to alternative hardware (like AMD or Intel GPUs), you would lose VPI’s acceleration advantages.
• Smaller Community and Fewer Tutorials: Because it’s younger, you might find fewer online code examples, tutorials, and troubleshooting guides compared to OpenCV.
• Language Support Gaps: Currently, VPI offers official support only for C/C++ and Python. If you rely on Java or MATLAB, you’ll need to build your own wrappers or switch languages.

Getting Started With VPI

If you’re curious to try out VPI, NVIDIA provides a range of resources:

1. Official VPI Documentation:
   NVIDIA VPI Documentation includes detailed API references, installation guides, and performance metrics.
2. Sample Projects on GitHub:
   Code samples demonstrating how to integrate VPI in various computer vision tasks can often be found within NVIDIA’s GitHub repositories or embedded computing forums.
3. Developer Forums:
   NVIDIA hosts active discussion forums for Jetson developers and VPI-specific inquiries, offering direct access to NVIDIA engineers and community experts.

Conclusion

OpenCV remains a cornerstone of the computer vision world, offering a comprehensive set of algorithms, a vast community, and cross-platform flexibility. It’s an excellent choice for beginners and for projects that need to run on diverse hardware.

However, NVIDIA’s VPI emerges as a powerful alternative when you need to tap into specialized hardware accelerators on Jetson platforms or NVIDIA discrete GPUs. VPI can provide impressive speedups—often drastically outperforming OpenCV in optimized environments—by leveraging PVA, VIC, and OFA units.

In the end, if you’re working on NVIDIA hardware and seeking the highest possible performance, it’s well worth giving VPI a try. With NVIDIA’s consistent updates, ongoing expansions of supported algorithms, and potential for major performance gains, VPI is poised to become an increasingly important player in the realm of accelerated computer vision.