code outline and main code $($in attached image$)$ made by me is given complete the code Full compete it should work test it with any pic

import numpy as np

from scipy.ndimage import convolve, maximum$\_$filter

def derivative$\_$filters$()$:

$"""$ Create derivative filters for x and y direction

Returns:

fx: derivative filter in x direction

fy: derivative filter in y direction

$"""$

fx $=$ np$.$array$([[0\mathrm{.}5,0,-0\mathrm{.}5]])$

fy $=$ fx$.$transpose$()$

return fx$,$ fy

def gauss$\_2$d$($sigma$,$ fsize$)$:

$"""$ Create a $2$D Gaussian filter

Args:

sigma: width of the Gaussian filter

fsize: $($h$,$ w$)$ dimensions of the filter

Returns:

$*$normalized$*$ Gaussian filter as $($h$,$ w$)$ np$.$array

$"""$

m$,$ n $=$ fsize

x $=$ np$.$arange$(-$m $/2+0\mathrm{.}5,$ m $/2)$

y $=$ np$.$arange$(-$n $/2+0\mathrm{.}5,$ n $/2)$

xx$,$ yy $=$ np$.$meshgrid$($x$,$ y$,$ sparse$=$True$)$

g $=$ np$.$exp$(-($xx $**2+$ yy $**2)/(2*$ sigma $**2))$

return g $/$ np$.$sum$($g$)$

def compute$\_$hessian$($img$,$ gauss, fx$,$ fy$)$:

$"""$ Compute elements of the Hessian matrix

Args:

img: numpy array with the image

gauss: Gaussian filter

fx: derivative filter in x direction

fy: derivative filter in y direction

Returns:

I$\_$xx: $($h$,$ w$)$ np$.$array of $2$nd derivatives in x direction

I$\_$yy: $($h$,$ w$)$ np$.$array of $2$nd derivatives in y direction

I$\_$xy: $($h$,$ w$)$ np$.$array of $2$nd derivatives in x$-$y direction

$"""$

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def compute$\_$criterion$($I$\_$xx$,$ I$\_$yy$,$ I$\_$xy$,$ sigma$)$:

$"""$ Compute criterion function

Args:

I$\_$xx: $($h$,$ w$)$ np$.$array of $2$nd derivatives in x direction

I$\_$yy: $($h$,$ w$)$ np$.$array of $2$nd derivatives in y direction

I$\_$xy: $($h$,$ w$)$ np$.$array of $2$nd derivatives in x$-$y direction

sigma: scaling factor

Returns:

criterion: $($h$,$ w$)$ np$.$array of scaled determinant of Hessian matrix

$"""$

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def non$\_$max$\_$suppression$($criterion$,$ threshold$)$:

$"""$ Apply non$-$maximum suppression to criterion values

and return Hessian interest points

Args:

criterion: $($h$,$ w$)$ np$.$array of criterion function values

threshold: criterion threshold

Returns:

rows: $($n$,)$ np$.$array with y$-$positions of interest points

cols: $($n$,)$ np$.$array with x$-$positions of interest points

$"""$

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