Computer vision is a field that broadly includes many interesting applications, from industrial monitoring systems that detect improperly manufactured items to automotive systems that can drive cars. Many of these computer vision systems are implemented or prototyped using OpenCV, a library which contains optimized implementations of many common computer vision functions targeting desktop processors and GPUs. Although many functions in the OpenCV library have been heavily optimized to enable many computer vision applications to run close to real-time, an optimized embedded implementation is often preferable.
This application note presents a design flow enabling OpenCV programs to be retargeted to Zynq devices. The design flow leverages HLS technology in the Vivado Design Suite, along with optimized synthesizable video libraries. The libraries can be used directly, or combined with application-specific code to build a customized accelerator for a particular application. This flow can enable many computer vision algorithms to be quickly implemented with both high performance and low power. The flow also enables a designer to target high data rate pixel processing tasks to the programmable logic, while lower data rate frame-based processing tasks remain on the ARM® cores.
