opencv python cuda support
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OpenCV (Open Source Computer Vision Library) is a powerful open-source computer vision and machine learning software library. OpenCV has support for CUDA, which allows developers to accelerate certain operations using NVIDIA GPUs. In this tutorial, we will guide you through the process of setting up OpenCV with CUDA support in Python and provide a simple code example to demonstrate the acceleration.
NVIDIA GPU: Make sure you have an NVIDIA GPU with CUDA support.
CUDA Toolkit: Install the CUDA Toolkit on your system. You can download it from the NVIDIA website.
cuDNN: Optionally, install cuDNN for further GPU acceleration. You can find cuDNN on the NVIDIA cuDNN website.
OpenCV with CUDA Support: Install OpenCV with CUDA support. You can use the following pip command:
Make sure to check the installed OpenCV version with:
Ensure that the version includes "+contrib," as it indicates the installation of OpenCV with CUDA support.
Let's create a simple Python script that loads an image and applies a Gaussian blur using OpenCV with CUDA support.
Save this script in a file, for example, gpu_blur_example.py. Replace input_image.jpg with the path to your input image. Run the script, and it will apply a Gaussian blur to the input image using OpenCV with CUDA support.
Remember that not all OpenCV functions have GPU-accelerated versions, so you should check the OpenCV documentation to verify whether a specific function supports CUDA acceleration.
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