OpenVX Programming Guide

OpenVX is the computer vision API adopted by many high-performance processor vendors. It is quickly becoming the preferred way to write fast and power-efficient code on embedded systems. OpenVX Programming Guidebook presents definitive information on OpenVX 1.2 and 1.3, the Neural Network, and other extensions as well as the OpenVX Safety Critical standard.

This book gives a high-level overview of the OpenVX standard, its design principles, and overall structure. It covers computer vision functions and the graph API, providing examples of usage for the majority of the functions. It is intended both for the first-time user of OpenVX and as a reference for experienced OpenVX developers.

• Get to grips with the OpenVX standard and gain insight why various options were chosen
• Start developing efficient OpenVX code instantly
• Understand design principles and use them to create robust code
• Develop consumer and industrial products that use computer vision to understand and interact with the real world

Engineers developing products with a component of computer vision and intelligence in areas such as automotive safety, video surveillance, computational photography, autonomous mobile robots and drones. Computer Engineering students and researchers

1. Introduction

• What is OpenVX and why do we need it?
• How portable is OpenVX
• Graph API with opaque memory model
• OpenVX vs. OpenCV and OpenCL
• What you should know before reading this book

2. Build your first OpenVX program

• With Immediate Mode API
• With Graph API

3. Using the Graph API to write efficient portable code

• Node parameters
• Graph parameters
• Execution model
• Asynchronous execution
• Control flow
• User kernels and nodes
• Examples

4. Building an OpenVX graph

• Linking nodes
• Graph verification
• Parameter validation
• What can be changed at runtime?
• Examples

5. Deploying an OpenVX graph to a target platform

• Exporting and importing objects
• The XML schema extension
• Immutable graphs

6. Basic image transformations

• Data objects: image, matrix
• Convolution
• Region of interest
• Border modes
• Undistortion with Remap
• Image filtering example
• Virtual objects
• Filter stacking example

7. Background subtraction and object detection

• Threshold
• Distribution
• User kernels
• Object detection example

8. Computational photography

• LUT
• Pyramid
• Example

9. Efficient data input/output

• Import an image to OpenVX
• Import a video stream to OpenVX
• Accessing OpenVX data

10. Tracking

• Data structures: keypoint, array
• Delay object
• Optical flow example

11. Use OpenVX for deep neural networks

• Neural Network Extension
• Tensor objects
• How to import a network into OpenVX
• Examples

12. OpenVX safety critical applications

• Feature subsets
• Determinism

13. Using OpenVX with other vision frameworks

• General remarks
• OpenCL
• OpenCV

14. Making the most of your OpenVX code

• Using OpenVX debugging capabilities to understand what is going on
• Profiling your OpenVX code
• Optimization tips