opencv use cuda python

Using OpenCV with CUDA (Compute Unified Device Architecture) in Python can significantly accelerate computer vision tasks by harnessing the power of NVIDIA GPUs. CUDA is a parallel computing platform and application programming interface model created by NVIDIA, and it allows developers to use GPUs for general-purpose processing.
In this tutorial, we'll cover the basics of setting up OpenCV with CUDA in Python and provide a simple code example to demonstrate the acceleration of image processing tasks.
Make sure to install the version of OpenCV that includes CUDA support (+contrib).
Let's create a simple Python script that loads an image, applies Gaussian blur using OpenCV, and compares the processing time with and without CUDA.
The script defines a function apply_gaussian_blur that applies Gaussian blur to an input image using OpenCV with and without CUDA. The processing time for each method is printed.
The main function reads an image, calls the apply_gaussian_blur function, and displays the original image, blurred image without CUDA, and blurred image with CUDA.
Run the script, and you should see the processing time difference between applying Gaussian blur with and without CUDA. This example demonstrates the potential speedup that CUDA can provide for image processing tasks in OpenCV.
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