cudawarped's profile - activity

m_image_gpu.data != m_um_ptr implies a reallocation not an error in the resize. Are you 100% that cv::cuda::reisze(um_i

m_image_gpu.data != m_um_ptr implies a reallocation not an error in the resize. Are you 100% that cv::cuda::reisze(um_i

Have you tried resizing with a new destination array? I have never tried using the same array, but I would assume that

For this to work you would need to be able to read from the onboard/usb camera using VideoCapture with FFmpeg. That is

I had the same issue, in the end I just linked directly to ffmpeg, have you tried this?

No it should be there inside the bin folder, are you sure everything built successfully?

If ffmpeg doesn't work then neither will cudacodec as this uses ffmpeg behind the scenes. I take it these are not netwo

There is no GPU version of imread you need to use bool cv::cudacodec::VideoReader::nextFrame(GpuMat & frame,Stream

When you do src = cv::imread(entry.path()) you are performing an assignment of the result of imread() to src. As a r

When you do src = cv::imread(entry.path()) you are performing an assignment of the result of imread() to src. As a r

Did you make sure all the dll's (opencv_world440.dll, opencv_img_hash440.dll, nppc64_10.dll, cudnn64_8.dll) were on the

Did you made sure all the dll's (opencv_world440.dll, opencv_img_hash440.dll, nppc64_10.dll, cudnn64_8.dll) were on the

Make sure the required dll's are on the system/user path. Scroll down to the Troubleshooting section part 3 here to see

Make sure the required dll's are on the system/user path. Scroll down to the Troubleshooting section part 3 herelink tex

Thank you for posting the output. So it is clear the reason you have no python bindings is that they were not built or

So python3 is in the list after To be built not after Disabled or Unavailable? Do you have the bindings file (cv2.cp37-w

You should have a bindings folder. To confirm whether the bindings were built, go tobuild/bin/[Release|Debug] and run

You should have a bindings folder. To confirm that the bindings will be built, go tobuild/bin/[Release|Debug] and run

I think you are passing the arguments in the wrong order see my amended answer.

The python bindings for fastNlMeansDenosingColored are not currently generated. If you change CV_EXPORTS to CV_EXPORTS_

The python bindings for fastNlMeansDenosingColored are not currently generated. If you change CV_EXPORTS to CV_EXPORTS_

The python bindings for fastNlMeansDenosingColored are not currently generated. If you change CV_EXPORTS to CV_EXPORTS_

There are a few periods where the CPU usage goes down however I don't think this is a major cause for concern. I just c

There are a few periods where the CPU usage goes down however I don't think this is a major cause for concern. I just c

Try this

First, there is no advantage that I know of to processing multiple frames at the same time over efficiently processing o

Thanks for the link. I am still unsure what the advantage of opengl would be over cuda in general unless interacting wit

And yes I would use the python tests as a guide.

Hi, the python bindings for CUDA are relatively new Aug 2018. If I remember correctly, before the bindings were updated

First, there is no advantage that I know of to processing multiple frames at the same time over efficiently processing o

Can you link to an example please? Where would OpenGL be used, would you still be using CUDA to access the GPU?

I would say you have two options and both should solve the problem above: Use multiple streams in a single thread to o

From memory that functionality had not been implemented in python (it may have been now, but from your experience it app

Hi, I still can't get your code to run. Can you post the exact code which you are using so I can see there is an issue

I would expect some variance as it is very unlikely that the algorithm will have been implemented in exactly the same wa

What was the magnitude of the difference, which OpenCV and CUDA version were you using did you compile with --use_fast_m

Are you compiling with --use_fast_math enabled? How different are the results, which version of OpenCv and CUDA are you

Which version of OpenCV and CUDA are you using? Additionally I cannot get your code to compile, can you post the exact

From here Including Python bindings Building and installing python support is incredibly simple, the instructions belo

I was hoping that wasn't going to be your question. My answer unfortunately is that: You would need a basic understan

Did you try to use the pre-built binaries i linked to?

Yes, I would remove the build directory first as well. Or you could try the pre-built binaries on the same site as the

OK so what is your question, how to implement cv::undistort in CUDA?

All of the cudamodules apart from cudev are disabled in your build. Disabled: cudaarithm cudabgsegm

Can you run opencv_version_win32.exe and post the output here.

That guide builds OpenCv 4.xx and you have built version 3.xx. Either way, what was the output when you ran "%openCvBui

That guide builds OpenCv 4.xx and you have built version 3.xx. Either way is there any additional error message shown i

You have to compile from src regardless if you want to use the cv::cuda API. Not all functions have an OpenCl implement

Are you trying to find this? How long does it take on the CPU and how does this compare to the time taken by your other