I’m working with a USB 2.0 camera that seems to reliably deliver 100 fps to my opencv Python script running on my desktop. I know I’m being greedy, but here goes:
After perusing this forum, I’ve come up with the following pipeline that delivers 85-95 fps on the Jetson Nano in the same Python script:
Hi,
The decoded YUVs are in NVMM buffer and need to copy to CPU buffer and send to appsink, so there is high CPU usage. Please execute sudo nvpmodel -m 0 and sudo jetson_clcoks. See if it makes performance improvement.
Thank you, DaneLLL, that did make a difference! It is in the 90+ fps range now, averaging about 95 fps.
Please correct me if I’m wrong: setting nvpmodel to mode 0 removed all constraints on the CPU/GPU, and jetson_clocks then sets the clock speed to maximum as mode 0 has no constraints - the Nano is running with the pedal to the metal?
Begs the question: Is there a more efficient pipeline that doesn’t require warp-factor 10?