NVIDIA® Vision Programming Interface (VPI) is a software library that implements computer vision (CV) and image processing (IP) algorithms on several computing hardware platforms available in NVIDIA embedded and discrete devices.
VPI provides seamless access to computing hardware that must be accessed through different and sometimes incompatible APIs, such as OpenCV and NVIDIA® CUDA® SDK, or for which public APIs do not exist, such as PVA (Programmable Vision Accelerator) and VIC (Video and Image Compositor).
The API is designed to be easy to use, without sacrificing performance. This allows for rapid prototyping and fine tuning, significantly reducing time-to-market.
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Within VPI, the same algorithm is implemented in different backends, such as CPU, GPU, PVA1 and VIC2. The processing pipeline can be set up to utilize the full installed computing capacity of the target device. For example, the GPU can perform inference on one frame while the PVA and VIC preprocess the image in a subsequent frame. The CPU can perform housekeeping tasks such as updating the GUI and postprocessing images without affecting the performance of other tasks.
VPI provides seamless zero-copy memory mapping among the supported device backends, depending on certain memory characteristics. For platforms that support it, zero-copy memory mapping yields a substantial increase in throughput.
VPI supports easy interoperation with existing projects that make use of OpenCV and NVIDIA® CUDA® SDK libraries, among others. This allows for gradual replacement of existing computing tasks with faster VPI equivalents.
Here are some examples of algorithms provided by VPI:
1 PVA backend is only available on Jetson Xavier devices, such as Jetson AGX Xavier and Jetson Xavier NX.
2 VIC backend is only available on Jetson devices.
3 Although the API is designed with safety in mind, VPI library itself is not safety-certified.
NVIDIA® Vision Programming Interface (VPI) is a software library that provides Computer Vision / Image Processing algorithms implemented on several computing engines available in NVIDIA embedded devices like Jetson and in discrete devices like dGPU.
VPI provides a uniform interface for seamless access to multiple compute engines like CPU, GPU Programmable Vision Accelerator (PVA), and Video Image Compositor (VIC). With VPI, a processing pipeline can fully utilize the installed computing capacity of the device with different parts of the pipeline running on different compute engines simultaneously.
VPI algorithms are highly optimized — check out the VPI performance benchmarks. Increase performance of your application by replacing parts of your pipeline containing any non performant OpenCV or Visionworks algorithms with VPI algorithms and by optimally distributing your workload on multiple compute engines with VPI.
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VPI 1.0 is the first production release and is available with JetPack 4.5 to download on Jetson modules and developer kits and is also available for x86.
VPI 1.0 highlights include:
- Support for Pyramidal LK Optical Flow on CPU and GPU
- Supports YUV422 packed color format for VIC
- Enhanced interoperability with OpenCV
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Refer to VPI Release Notes for details.
Refer to installation section in VPI documentation for instructions for installing VPI on Jetson and host PC
KLT Bounding Box Tracker
- Algorithms implemented on multiple compute engines (GPU, CPU, PVA, and VIC)
- Uniform interface to access all supported compute engines
- Seamless, zero-copy memory mapping to achieve high throughput
- Easy interoperability with OpenCV
- Sample applications to illustrate use of VPI algorithms
- Performance benchmarks for every supported algorithm
- Gaussian Pyramid Generator
- Separable Image Convolver
- Box Image Filter
- Gaussian Image Filter
- Bilateral Image Filter
- Image Rescaling
- Fast Fourier Transform
- Inverse Fast Fourier Transform
- Image Format Converter
- Perspective Warp
- Image Remapping
- Lens Distortion Correction
- Temporal Noise Reduction
- Pyramidal LK Optical Flow
- Stereo Disparity
Feature Detector and Tracking
- KLT Bounding Box Tracker
- Harris Corners Detector
- ColorNames Features Detector
- Histogram of Oriented Gradients
Webinars and Blogs
Implementing Computer Vision and Image Processing Solutions with VPI
Learn how to build a complete and efficient stereo disparity-estimation pipeline using VPI that runs on Jetson-family devices. View webinar >
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Reducing Temporal Noise on Images with VPI
Shows how to run the Temporal Noise Reduction (TNR) sample application on the Jetson product family. Read blog >
Vpi Laptops Reviews
- VPI Release Notes
- VPI Documentation