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 two pics

Code Outline:

import numpy as np

import matplotlib.pyplot as plt

def load$\_$pts$\_$features$($path$)$:

$"""$ Load interest points and SIFT features.

Args:

path: path to the file pts$\_$feats.npz

Returns:

pts: coordinate points for two images;

an array $(2,)$ of numpy arrays $($N$1,2),($N$2,2)$

feats: SIFT descriptors for two images;

an array $(2,)$ of numpy arrays $($N$1,128),($N$2,128)$

$"""$

#

# Your code here

#

pts $=[$np$.$empty$((123,2)),$ np$.$empty$((123,2))]$

feats $=[$np$.$empty$((123,128)),$ np$.$empty$((123,128))]$

return pts$,$ feats

def min$\_$num$\_$pairs$()$:

return np$.$random.randint$(1,32)$

def pickup$\_$samples$($pts$1,$ pts$2)$:

$"""$ Randomly select k corresponding point pairs.

Note that here we assume that pts$1$ and pts$2$ have

been already aligned: pts$1[$k$]$ corresponds to pts$2[$k$].$

This function makes use of min$\_$num$\_$pairs$()$

Args:

pts$1$ and pts$2$: point coordinates from Image $1$ and Image $2$

Returns:

pts$1\_$sub and pts$2\_$sub: N$\_$min randomly selected points

from pts$1$ and pts$2$

$"""$

#

#Your code here

#

return None, Non

def compute$\_$homography$($pts$1,$ pts$2)$:

$"""$ Construct homography matrix and solve it by SVD

Args:

pts$1$: the coordinates of interest points in img$1,$ array $($N$,2)$

pts$2$: the coordinates of interest points in img$2,$ array $($M$,2)$

Returns:

H: homography matrix as array $(3,3)$

$"""$

#

# Your code here

#

return np$.$empty$(3,3)$

def transform$\_$pts$($pts$,$ H$)$:

$"""$ Transform pst$1$ through the homography matrix to compare pts$2$ to find inliners

Args:

pts: interest points in img$1,$ array $($N$,2)$

H: homography matrix as array $(3,3)$

Returns:

transformed points, array $($N$,2)$

$"""$

#

# Your code here

#

return np$.$empty$(100,2)$

def count$\_$inliers$($H$,$ pts$1,$ pts$2,$ threshold$=5)$:

$"""$ Count inliers

Tips: We provide the default threshold value, but you're free to test other values

Args:

H: homography matrix as array $(3,3)$

pts$1$: interest points in img$1,$ array $($N$,2)$

pts$2$: interest points in img$2,$ array $($N$,2)$

threshold: scale down threshold

Returns:

number of inliers

$"""$

return np$.$empty$(1)$

def ransac$\_$iters$($w$=0\mathrm{.}5,$ d$=$min$\_$num$\_$pairs$(),$ z$=0\mathrm{.}99)$:

$"""$ Computes the required number of iterations for RANSAC.

Args:

w: probability that any given correspondence is valid

d: minimum number of pairs

z: total probability of success after all iterations

Returns:

minimum number of required iterations

$"""$

return np$.$empty$(1)$

def ransac$($pts$1,$ pts$2)$:

$"""$ RANSAC algorithm

Args:

pts$1$: matched points in img$1,$ array $($N$,2)$

pts$2$: matched points in img$2,$ array $($N$,2)$

Returns:

best homography observed during RANSAC, array $(3,3)$

$"""$

#

# Your code here

#

best$\_$H $=$ np$.$empty$((3,3))$

return best$\_$H

def find$\_$matches$($feats$1,$ feats$2,$ rT$=0\mathrm{.}8)$:

$"""$ Find pairs of corresponding interest points with distance comparsion

Tips: We provide the default ratio value, but you're free to test other values

Args:

feats$1$: SIFT descriptors of interest points in img$1,$ array $($N$,128)$

feats$2$: SIFT descriptors of interest points in img$1,$ array $($M$,128)$

rT: Ratio of similar distances

Returns:

idx$1$: list of indices of matching points in img$1$

idx$2$: list of indices of matching points in img$2$

$"""$

idx$1=[]$

idx$2=[]$

#

# Your code here

#

return idx$1,$ idx$2$

def final$\_$homography$($pts$1,$ pts$2,$ feats$1,$ feats$2)$:

$"""$ re$-$estimate the homography based on all inliers

Args:

pts$1$: the coordinates of interest points in img$1,$ array $($N$,2)$

pts$2$: the coordinates of interest points in img$2,$ array $($M$,2)$

feats$1$: SIFT descriptors of interest points in img$1,$ array $($N$,128)$

feats$2$: SIFT descriptors of interest points in img$1,$ array $($M$,128)$

Returns:

ransac$\_$return: refitted homography matrix from ransac fucation, array $(3,3)$

idxs$1$: list of matched points in image $1$

idxs$2$: list of matched points in image $2$

$"""$

#

# Your code here

#

idxs$1,$ idxs$2=[],[]$

ransac$\_$return $=$ np$.$empty$((3,3))$

return ransac$\_$return, idxs$1,$ idxs$2$pts$\_$array,