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OpenVX Training

A practical and incremental approach to learn OpenVX

This project presents a series of simple examples that aim to introduce basic OpenVX concepts. It was developed as a companion to the OpenVX Training presentation.

Preparing the Project

Dependencies

In order to build the project you'll need the following dependencies:

  • Makefile, C and C++ compilers: In Debian based systems (like Ubuntu) you may run the following commands:
sudo apt install build-essential
  • OpenCV

In Debian based systems (like Ubuntu) you may run the following commands:

sudo apt install libopencv-dev
  • A working OpenVX implementation:

Normally this is already included in your system. If not, you may install the reference implmenentation from Khronos.

Building

The project is based on a simple Makefile. To build it, run the following commands:

make

If your OpenVX installation is on a non-standard location, you may pass in the respective flags as in:

make VX_CFLAGS="-I/non/standard/vx/includes" VX_LDFLAGS="-L/non/standard/vx/lib"

Running Examples

To run the examples, simply invoke the executables produced during the build process as in:

./vx_training_01

Of course, the 00 may be changed to match any example as desired. Refer to the following sections for a description of each example.

Examples Description

The following table summarizes the examples available in the project. They were numbered to, ideally, be consumed in order.

Example Description 1st Arg 2nd Arg
vx_training_01 Creates a context and shows the overall VX development pattern.
vx_training_02 Creates an image and shows how to access the underlying memory in order to write to it. Image path (defaults to lena.png)
vx_training_03 Creates a graph with a Channel Extract node and an output image. Does ot process the graph yet. Image path (defaults to lena.png)
vx_training_04 Verifies and executes the graph. Saves the data from the output image into a PNG file. Image path (defaults to lena.png) Image path (defaults to out.png)
vx_training_05 Adds a second Gaussian Kernel node and connects it to the first one using a virtual image. Image path (defaults to lena.png) Image path (defaults to out.png)
vx_training_06 Adds a third Warp Affine node and shows how to pass in a vx_reference as a parameter. Image path (defaults to lena.png) Image path (defaults to out.png)
vx_training_07 First example in C++. Shows how to continuously process the graph and vary a parameter with each execution. Displays the result in a window. Image path (defaults to lena.png)
vx_training_08 Shows how to enable performance measurements. Image path (defaults to lena.png)
vx_training_09 Modifies the previous example to be executed in a pipelining mode. Image path (defaults to lena.png)
vx_training_10 Modifies the previous example to be executed in a batching mode. Image path (defaults to lena.png)

Questions

If you run into any problem or have any question, please do contact us.