Hello!
I am doing some research about using Nvidia GPUs in pattern detection algorithms. In OpenCV there are two versions of Hough Circle Detection algorithm - one for CPU and one for GPU. I want to compare execution times for them.
My basic application is in LabVIEW but to run OpenCV code and custom CUDA code I am using DLL libraries and Call Library Function Node block. I have not any problems with call standard OpenCV function. I also can call my own CUDA function using available LabVIEW block to initialize/deinitialize GPU, allocate/deallocate memory and upload/download data. The main part of code is run as CLFN function. In general I am doing everything like there is described in LabVIEW GPU Analysis Toolkit - Calling Custom GPU Functions.
I have problem when I am trying to use OpenCV functions for GPU from LabVIEW. My application always crash due to some memory access fault. It happens on main OpenCV function line. I suppose it is related to handling data which is already in GPU memory, allocated and uploaded by LabVIEW functions, but which i must convert to OpenCV type GpuMat.
Maybe someone was already doing something like that. If not I can describe whole process but it is quite complicated and it take a lot of time me and this person which will be trying help me... But of course I can do this ;).
Regards,
ksiadz13