4.12.0.88

OpenCV python wheels built against CUDA 12.9, Nvidia Video Codec SDK 13.0 and cuDNN 9.10.2.

Suitable for all devices of compute capability >= 5.0 with binary compatible code for devices of compute capability 5.0-12.0.

Nvidia GPU Computing Toolkit v12.9 is required for import cv2 to work and cuDNN 9.10.2 for accelerated inference when using the dnn module.

Note Windows OS: This wheel relies on cuDNN being installed in the CUDA Toolkit directory. Therefore you can either download

1. the cuDNN Tarball (Version->Tarball) and extract its contents to your CUDA directory, or

2. the installer (Version->exe (local)) and the add the path to the bin folder inside the cuDNN installation directory to your PATH_TO_PYTHON_DIST/Lib/site-packages/cv2/config.py file. e.g.

   import os

   BINARIES_PATHS = [
   os.path.join('D:/build/opencv/install', 'x64/vc17/bin'),
   os.path.join(os.getenv('CUDA_PATH', 'C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.9'), 'bin')
   os.path.join('C:/Program Files/NVIDIA/CUDNN/v9.10.2/bin/12.9')
   ] + BINARIES_PATHS

Nvidia CMake configuration output

--   NVIDIA CUDA:                   YES (ver 12.9.86, CUFFT CUBLAS NVCUVID NVCUVENC)
--     NVIDIA GPU arch:             86
--     NVIDIA PTX archs:
--
--   cuDNN:                         YES (ver 9.10.2)

Build Summary

Windows Build commands

set "CMAKE_ARGS=-DWITH_CUDA=ON -DCUDA_ARCH_BIN=5.0;5.2;6.0;6.1;7.0;7.5;8.0;8.6;8.9;9.0;10.0;12.0 -DCUDA_ARCH_PTX=12.0 -DENABLE_CUDA_FIRST_CLASS_LANGUAGE=ON"
set ENABLE_CONTRIB=1
python.exe setup.py bdist_wheel --py-limited-api=cp37

Ubuntu 22.04 Build commands

export "CMAKE_ARGS=-DWITH_CUDA=ON -DCUDA_ARCH_BIN=5.0;5.2;6.0;6.1;7.0;7.5;8.0;8.6;8.9;9.0;10.0;12.0 -DCUDA_ARCH_PTX=12.0"
export ENABLE_CONTRIB=1
python setup.py bdist_wheel --py-limited-api=cp37