Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How to calculate a Gaussian kernel matrix efficiently in numpy** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

## How to calculate a Gaussian kernel matrix efficiently in numpy?

**How to calculate a Gaussian kernel matrix efficiently in numpy?**I myself used the accepted answer for my image processing, but I find it (and the other answers) too dependent on other modules.

**calculate a Gaussian kernel matrix efficiently in numpy**I myself used the accepted answer for my image processing, but I find it (and the other answers) too dependent on other modules.

## Method 1

I myself used the accepted answer for my image processing, but I find it (and the other answers) too dependent on other modules. Therefore, here is my compact solution:

import numpy as np def gkern(l=5, sig=1.): """\ creates gaussian kernel with side length `l` and a sigma of `sig` """ ax = np.linspace(-(l - 1) / 2., (l - 1) / 2., l) gauss = np.exp(-0.5 * np.square(ax) / np.square(sig)) kernel = np.outer(gauss, gauss) return kernel / np.sum(kernel)

Edit: Changed arange to linspace to handle even side lengths

## Method 2

You may simply gaussian-filter a simple 2D dirac function, the result is then the filter function that was being used:

import numpy as np import scipy.ndimage.filters as fi def gkern2(kernlen=21, nsig=3): """Returns a 2D Gaussian kernel array.""" # create nxn zeros inp = np.zeros((kernlen, kernlen)) # set element at the middle to one, a dirac delta inp[kernlen//2, kernlen//2] = 1 # gaussian-smooth the dirac, resulting in a gaussian filter mask return fi.gaussian_filter(inp, nsig)

**Conclusion**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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