I suggest to install jtop, it will show a lot of useful information including CPU/GPU load: https://pypi.org/project/jetson-stats/. If GPU is fully loaded when running your code and you are using maximum power (NV Power in jtop should be “MAXN”), you will know you are using GPU efficiently.
I did not try using OpenCV myself yet but in this thread mdegans wrote that “OpenCV installed by default on nano does not have CUDA built in. You can see with print(cv2.getBuildInformaton()) in Python or (or cv2::getBuildInformation() in C/c++ probably. Almost all the fun build options are off.”. In other words, you have to build OpenCV with GPU support to get GPU acceleration in OpenCV.
UPDATE:
I looked around for more information about OpenCV on Jetson Nano since in the near future I planned to use it too, so I decided it is good time to figure out how to get it working with GPU. To check if OpenCV has cuda, run python3 and execute the following code:
import cv
print(cv2.cuda.getCudaEnabledDeviceCount())
If output is zero, then OpenCV needs to be built with GPU support. Here is an article how to install OpenCV 4.5 on Jetson Nano, it contains a lot of details how to install all the requirements to build OpenCV and how to compile it with GPU support.