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In the above program, we are importing the required modules. Imageread = cv.imread('C:/Users/admin/Desktop/images/educba.jpg') Then we are applying Gaussian Blur() function on the image to blur the image and display it as the output on the screen. Then we are reading the image that is to be blurred using imread() function. Resultimage = cv.GaussianBlur(imageread, (7, 7), 0) # applying GaussianBlur() function on the image to blur the image and display it as the output on the screen Imageread = cv.imread('C:/Users/admin/Desktop/images/tree.jpg') # reading the image that is to be blurred using imread() function OpenCV program in python to demonstrate Gaussian Blur() function to read the input image and apply Gaussian blurring on the image and then display the blurred image as the output on the screen. Given below are the examples of OpenCV Gaussian Blur: Example #1

The Gaussian Blur() function blurs the image and returns the blurred image as the output.

Then the image along with the matrix representing the size of the Gaussian kernel and standard deviation of Gaussian kernel is passed as the parameters to the Gaussian Blur() function.The image that is to be blurred is read using imread() function.Gaussian Blurring makes use of a function called Gaussian Blur() function to reduce the clarity of images or to make the images distinct or to remove the noise from the images or to reduce the details from the images.In order to be able to reduce the clarity of images or to make the images distinct or to remove the noise from the images or to reduce the details from the images, we make use of Gaussian blurring.sigmaX is a variable representing the standard deviation of Gaussian kernel in X direction and it is of type double.kernel_size is the matrix representing the size of the kernel.source_image is the image that is to be blurred using Gaussian Blur() function.

GaussianBlur(source_image, kernel_size, sigmaX) Web development, programming languages, Software testing & others

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