opencv-python-cuda

Pre-built NVIDIA® CUDA™ enabled OpenCV wheels for Python - batteries included.

This is a fork of the official opencv-python project that ships fully standalone CUDA-enabled OpenCV builds. No CUDA Toolkit, cuDNN, or other NVIDIA SDK needs to be installed - every required runtime library is bundled in the wheel, including hardware video decoding and encoding (NVDEC/NVENC). Once installed, the following should just work:

import cv2
print(cv2.cuda.getCudaEnabledDeviceCount())

See the project homepage for more documentation.

Requirements

• Windows x64 (the only platform packages are currently produced for)
• NVIDIA GPU - Maxwell class (GeForce GTX 900 series) or newer
• NVIDIA driver - the wheel links directly against driver libraries, so an up-to-date driver must be installed for import cv2 to succeed
• Python 3.7 or newer - a single abi3 wheel covers all supported Python versions

Installation

1. Download the latest opencv_python_cuda-*-win_amd64.whl from the Releases page.

2. Install it with pip (or another package manager like uv):

   pip install opencv_python_cuda-<version>-win_amd64.whl
   

3. Import the cv2 package as usual.

Note

If you have a previous manually-installed (not via pip) version of OpenCV (e.g. a cv2 module in the root of Python's site-packages), remove it before installing to avoid conflicts. Also make sure your pip is up to date (19.3 is the minimum supported version): pip install --upgrade pip.

What's included

• All OpenCV modules that can be built with CUDA support
• Hardware-accelerated video decoding and encoding via NVDEC/NVENC (cv2.cudacodec)
• FFmpeg for video I/O
• All required CUDA runtime libraries, bundled in the wheel

Non-free algorithms (e.g. SURF) are excluded - see the FAQ below.

Documentation

• Project homepage - downloads and overview
• CUDA Compatibility Reference - GPU architectures, compute capabilities, and CUDA toolkit support
• Workflow Guide - repository setup, triggering builds, and wheel size strategy

Frequently Asked Questions

For general OpenCV questions, refer to the FAQ in the upstream project.

Q: Why can't I pip install opencv-python-cuda from PyPI?

A: The package is far too large for PyPI and exceeds its project size limits. Download wheels from the Releases page instead.

Q: Why does import cv2 fail with "DLL load failed"?

A: The wheel requires an NVIDIA driver to be installed. On machines without one (or with a very old driver), the bundled CUDA and video codec libraries cannot be loaded.

Q: Why are non-free algorithms excluded?

A: Non-free algorithms such as SURF are patented and cannot be distributed as built binaries. Note that SIFT is included, due to patent expiration as of OpenCV 4.3.0 / 3.4.10. See opencv-python#126 for more info.

Licensing

The opencv-python-cuda package (i.e. the scripts in this repository) is available under the MIT license.

• OpenCV itself is available under the Apache 2 license.
• All wheels are distributed with FFmpeg, licensed under the LGPLv2.1, and redistributable portions of the NVIDIA® CUDA™ SDK under the NVIDIA Software License Agreement (EULA).
• The packages include other binaries as well; the full list of licenses can be found in LICENSE-3RD-PARTY.txt.

By downloading and using the software, you agree to fully comply with the terms and conditions of the CUDA EULA.