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How to calculate a Gaussian kernel matrix efficiently in numpy?

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.

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Table of Contents

How to calculate a Gaussian kernel matrix efficiently in numpy?

  1. 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.

  2. 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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