By Adam Taylor
Several times in this series, we have looked at image processing using the Avnet EVK and the ZedBoard. Along with the basics, we have examined object tracking using OpenCV running on the Zynq SoC’s or Zynq UltraScale+ MPSoC’s PS (processing system) and using HLS with its video library to generate image-processing algorithms for the Zynq SoC’s or Zynq UltraScale+ MPSoC’s PL (programmable logic, see blogs 140 to 148 here).
Xilinx’s reVision is an embedded-vision development stack that provides support for a wide range of frameworks and libraries often used for embedded-vision applications. Most exciting, from my point of view, is that the stack includes acceleration-ready OpenCV functions.
The stack itself is split into three layers. Once we select or define our platform, we will be mostly working at the application and algorithm layers. Let’s take a quick look at the layers of the stack:
- Platform layer: This is the lowest level of the stack and is the one on which the remaining stack layers are built. This layer includes platform definitions of the hardware and the software environment. Should we choose not to use a predefined platform, we can generate a custom platform using Vivado.
- Algorithm layer: Here we create our application using SDSoC and the platform definition for the target hardware. It is within this layer that we can use the acceleration-ready OpenCV functions along with predefined and optimized implementations for Customized Neural Network (CNN) developments such as inference accelerators within the PL.
- Application Development Layer: The highest layer of the stack. Development here is where high-level frameworks such as Caffe and OpenVX are used to complete the application.
As I mentioned above one of the most exciting aspects of the reVISION stack is the ability to accelerate a wide range of OpenCV functions using the Zynq SoC’s or Zynq UltraScale+ MPSoC’s PL. We can group the OpenCV functions that can be hardware-accelerated using the PL into four categories:
- Computation – Includes functions such as absolute difference between two frames, pixel-wise operations (addition, subtraction and multiplication), gradient, and integral operations
- Input Processing – Supports bit-depth conversions, channel operations, histogram equalization, remapping, and resizing.
- Filtering – Supports a wide range of filters including Sobel, Custom Convolution, and Gaussian filters.
- Other – Provides a wide range of functions including Canny/Fast/Harris edge detection, thresholding, SVM, HoG, LK Optical Flow, Histogram Computation, etc.
What is very interesting with these function calls is that we can optimize them for resource usage or performance within the PL. The main optimization method is specifying the number of pixels to be processed during each clock cycle. For most accelerated functions, we can choose to process either one or eight pixels. Processing more pixels per clock cycle reduces latency but increases resource utilization. Processing one pixel per clock minimizes the resource requirements at the cost of increased latency. We control the number of pixels processed per clock in via the function call.
Over the next few blogs, we will look more at the reVision stack and how we can use it. However in the best Blue Peter tradition, the image below shows the result of running a reVision Harris OpenCV acceleration function within the PL when accelerated.
Accelerated Harris Corner Detection in the PL
Code is available on Github as always.
If you want E book or hardback versions of previous MicroZed chronicle blogs, you can get them below.
via Xcell Daily Blog articles http://ift.tt/2fBJIws
March 13, 2017 at 07:59PM