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In this article, let us discuss how to generate a 2-D Gaussian array using NumPy. To create a 2 D Gaussian array using the Numpy python module.

Functions used:

  • numpy.meshgrid()-Itis used to create a rectangular grid out of two given one-dimensional arrays representing the Cartesian indexing or Matrix indexing. 

Syntax:

numpy.meshgrid(*xi, copy=True, sparse=False, indexing='xy')
 

Syntax:

numpy.linspace(start, stop, num = 50, endpoint = True, retstep = False, dtype = None) 
 

  • numpy.exp()-this mathematical function helps the user to calculate the exponential of all the elements in the input array.

Syntax:

numpy.exp(array, out = None, where = True, casting = ‘same_kind’, order = ‘K’, dtype = None)
 

Example 1:

Python
importnumpyasnpdefgaussian_filter(kernel_size,sigma=1,muu=0):# Initializing value of x, y as grid of kernel size in the range of kernel sizex,y=np.meshgrid(np.linspace(-1,1,kernel_size),np.linspace(-1,1,kernel_size))dst=np.sqrt(x**2+y**2)# Normal part of the Gaussian functionnormal=1/(2*np.pi*sigma**2)# Calculating Gaussian filtergauss=np.exp(-((dst-muu)**2/(2.0*sigma**2)))*normalreturngauss# Return the calculated Gaussian filter# Example usage:kernel_size=5gaussian=gaussian_filter(kernel_size)print("Gaussian filter of{} X{}:".format(kernel_size,kernel_size))print(gaussian)

Output
Gaussian filter of 5 X 5:[[0.05854983 0.0851895  0.09653235 0.0851895  0.05854983] [0.0851895  0.12394999 0.14045374 0.12394999 0.0851895 ] [0.09653235 0.14045374 0.15915494 0.14045374 0.09653235]...


Example 2:

Python
importnumpyasnpdefgaussian_filter(kernel_size,sigma=1,muu=0):# Initializing value of x, y as grid of kernel size in the range of kernel sizex,y=np.meshgrid(np.linspace(-2,2,kernel_size),np.linspace(-2,2,kernel_size))dst=np.sqrt(x**2+y**2)# Normal part of the Gaussian functionnormal=1/(2*np.pi*sigma**2)# Calculating Gaussian filtergauss=np.exp(-((dst-muu)**2/(2.0*sigma**2)))*normalreturngauss# Return the calculated Gaussian filter# Example usage:kernel_size=3gaussian=gaussian_filter(kernel_size=kernel_size)print("Gaussian filter of{} X{}:".format(kernel_size,kernel_size))print(gaussian)

Output
Gaussian filter of 3 X 3:[[0.00291502 0.02153928 0.00291502] [0.02153928 0.15915494 0.02153928] [0.00291502 0.02153928 0.00291502]]



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