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CUDA 11.0 Nvidia Video Codec SDK Nvidia cuDNN Intel Media SDK Intel Math Kernel Libraries (MKL) Intel Threaded Building Blocks (TBB)

pre-build checklist

  1. Where to buy fungal cream

  2. troubleshooting guide
  3. Hamdi Sahloul including Python bindings
  4. DNN cuDNN below
  5. ninja compute capability

  1. Visual Studio 2019
  2. OpenCV OpenCV Contrib OpenCV 4.4.0 OpenCV Contrib 4.4.0
  3. CMake 3.17.1
  4. The CUDA 11.0 Toolkit
  5. Nvidia Video Codec SDK

    Nvidia Video Decoder Support Matrix

  6. Quick Sync download Intel Media SDK
  7. community licensing this issue
  8. Anaconda3

here

  1. Building OpenCV with Visual Studio solution files.
  2. Building OpenCV with the ninja build system to reduce the build time.

Python bindings

choosing the compute-capability including python bindings decreasing the build time with Ninja

  1. 
    

  2. contrib repo
    
    
    
    
    
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    1. Prerequisites Pre-build Checklist step (6)

    2. Verifying OpenCV is CUDA accelerated

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    6 thoughts on “Accelerate OpenCV 4.4.0 – build with CUDA and python bindings

    1. Hi,
      Thanks for your work. Can we have OpenCV 4.4 with Cuda 10.2 + cuDNN 7.6.5? because the dnn performance is not good in Cuda 11.

      1. Hi, is it CUDA 11.0 or cuDNN 8.0.1 which is slow? I see that 8.0.2 has been released, 8.0.1 was a release candidate. Do you have a link showing the perfomance compared to CUDA 10.0/cuDNN 7.6.5?

          1. Thanks for the link. I will wait until the problem is resolved and then upload a build with the fix instead of reverting to CUDA 10.2.

    2. Hi,
      When I run opencv_test_cudaarithm.exe in the install folder, there are several failed test:
      [———-] Global test environment tear-down
      [==========] 11621 tests from 67 test cases ran. (9171 ms total)
      [ PASSED ] 11613 tests.
      [ FAILED ] 8 tests, listed below:
      [ FAILED ] CUDA_Arithm/PolarToCart.Accuracy/0, where GetParam() = (GeForce RTX 2070 SUPER, 128×128, 32FC1, AngleInDegrees(false), whole matrix)
      [ FAILED ] CUDA_Arithm/PolarToCart.Accuracy/1, where GetParam() = (GeForce RTX 2070 SUPER, 128×128, 32FC1, AngleInDegrees(false), sub matrix)
      [ FAILED ] CUDA_Arithm/PolarToCart.Accuracy/2, where GetParam() = (GeForce RTX 2070 SUPER, 128×128, 32FC1, AngleInDegrees(true), whole matrix)
      [ FAILED ] CUDA_Arithm/PolarToCart.Accuracy/3, where GetParam() = (GeForce RTX 2070 SUPER, 128×128, 32FC1, AngleInDegrees(true), sub matrix)
      [ FAILED ] CUDA_Arithm/PolarToCart.Accuracy/8, where GetParam() = (GeForce RTX 2070 SUPER, 113×113, 32FC1, AngleInDegrees(false), whole matrix)
      [ FAILED ] CUDA_Arithm/PolarToCart.Accuracy/9, where GetParam() = (GeForce RTX 2070 SUPER, 113×113, 32FC1, AngleInDegrees(false), sub matrix)
      [ FAILED ] CUDA_Arithm/PolarToCart.Accuracy/10, where GetParam() = (GeForce RTX 2070 SUPER, 113×113, 32FC1, AngleInDegrees(true), whole matrix)
      [ FAILED ] CUDA_Arithm/PolarToCart.Accuracy/11, where GetParam() = (GeForce RTX 2070 SUPER, 113×113, 32FC1, AngleInDegrees(true), sub matrix)

      Is it ok to leave it be?

      1. I would say yes. The reason for the failure is that the difference between the gold standard result and the result on your system exceeds the given tolerance. This is probably due to the version of CUDA and the accelerated libraries which it uses behind the scenes. If you are in any doubt regarding the accuracy of the CUDA algorithms I would compare the GPU and CPU results to convince myself that any differences are within an acceptable range.

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