Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How to gauss-filter (blur) a floating point numpy array** **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 gauss-filter (blur) a floating point numpy array?

**How to gauss-filter (blur) a floating point numpy array?**Here is my approach using only numpy. It is prepared with a simple 3×3 kernel, minor changes could make it work with custom sized kernels.

**gauss-filter (blur) a floating point numpy array**Here is my approach using only numpy. It is prepared with a simple 3×3 kernel, minor changes could make it work with custom sized kernels.

## Method 1

If you have a two-dimensional numpy array `a`

, you can use a Gaussian filter on it directly without using Pillow to convert it to an image first. scipy has a function `gaussian_filter`

that does the same.

from scipy.ndimage.filters import gaussian_filter blurred = gaussian_filter(a, sigma=7)

## Method 2

Here is my approach using only numpy. It is prepared with a simple 3×3 kernel, minor changes could make it work with custom sized kernels.

def blur(a): kernel = np.array([[1.0,2.0,1.0], [2.0,4.0,2.0], [1.0,2.0,1.0]]) kernel = kernel / np.sum(kernel) arraylist = [] for y in range(3): temparray = np.copy(a) temparray = np.roll(temparray, y - 1, axis=0) for x in range(3): temparray_X = np.copy(temparray) temparray_X = np.roll(temparray_X, x - 1, axis=1)*kernel[y,x] arraylist.append(temparray_X) arraylist = np.array(arraylist) arraylist_sum = np.sum(arraylist, axis=0) return arraylist_sum

**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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