See what the PYNQ-Z1 and the PYNQ Computer Vision overlay are capable of doing with a 720p standard HD video stream. In the video we run a 2D filter and dilate function on the incoming video, first using the Python OpenCV functions (ie. without hardware acceleration), then we test it again with the accelerator IPs running on the FPGA. Without acceleration, we get a frame rate of 5 frames per second. At that frame rate the flicker is obvious. When we switch to the hardware accelerated functions, we get 60 frames per second and we see a very smooth video output.

Below is the Jupyter notebook that I used for the demo:

If you’re wondering, yes it’s a video of Canadian landmarks (my adopted country), so I’m sure you’ll enjoy the video even if you have no interest in computer vision!

In the next video I’ll see what frame rates we can get while running at 1080p (full HD) resolution. According to the PYNQ docs, the PYNQ-Z1 cannot meet the official HDMI spec for 1080p, but we’ll try it anyway and see how it goes.

Jeff is passionate about FPGAs, SoCs and high-performance computing, and has been writing the FPGA Developer blog since 2008. As the owner of Opsero, he leads a small team of FPGA all-stars providing start-ups and tech companies with FPGA design capability that they can call on when needed.

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