Q$1.$ Image Segmentation with K$-$mean

Problem Statement:

You are given an image named "youwsuid.png$"$ saved in the "Exam$\_2\_$Image$\_$Folder"

folder, and your task is to perform image segmentation using the K$-$means clustering

algorithm. Implement the K$-$means clustering algorithm in Python and segment the

image into $K$ clusters. Write a Python function

kmeans$\_$image$\_$segmentation$($image$\_$path, K$)$ that takes the path to the image file and

the number of clusters $K$ as input and returns the segmented image.

Instructions:

Implement the K$-$means clustering algorithm from scratch or use libraries like scikit$-$

learn for K$-$means clustering.

You can use any image processing libraries like OpenCV or PIL for image loading

and manipulation.

Ensure that the function kmeans$\_$image$\_$segmentation takes the image path

$($image$\_$path$)$ and the optimized number of clusters $($K$)$ as input arguments.

The output of the function should be the plot that contains both the orignal image

and the segmented image.

Provide necessary comments to explain your code.

You can assume that the image is in RGB format.

You can assume that the image file exists at the provided path. ### Need to submit:

functional $.$ipynb file.

the pdf version of the lipynb file.

the original figure and the segmented figure. The segmented figure should be saved

in "yourwsuid.png$"$ format.