Image Compression using K-Means Clustering Unsupervised Machine Learning
Now days, we have huge amount of data in form of images and producing lots of images in our day to day life. People upload lots of images daily on social media platforms like Instagram, Facebook, twitter and other cloud storage.
To deal with huge amount of data , we want to store image data as efficiently as possible to maximize image quality and minimize storage space and processing resources, Idea came into existence is image compression.
Formally, image compression is the type of data compression applied to digital images to reduce their cost of storage or transmission.
What is k-means clustering?
First we should have knowledge of k-means clustering. K-means clustering is technique to group similar objects into one cluster. For example,Companies such as Amazon, Netflix, all group their customers on the basis of their interest and their search history etc, and then invite their customers to buy the product. Another example like we are having a dataset that contains the location of people from all over the world, then we can create different clusters according to different states, such that each cluster contains people of a particular state only.
Implementation of Image Compression using K-means
In this application reducing the number of colors required to show the image from multiple unique colors to 64, while preserving the overall appearance quality.
As we know that an image consists of different colours, so while compressing the image using K-Means Clustering we will create clusters of major colours and group all the similar colors in one cluster, forming different clusters for only major colors.
K-means clustering will group similar colors together into ‘k’ clusters (say k=64 in this case) of different colors (RGB values). Therefore, each cluster centroid is the representative of the three dimensional color vector in RGB color space of its respective cluster. You might have guessed by now how smoothly K-means can be applied on the pixel values to get the resultant compressed image. Now, these ‘k’ cluster centroids will replace all the color vectors in their respective clusters. Thus, we need to only store the label for each pixel which tells the cluster to which this pixel belongs. Additionally, we keep the record of color vectors of each cluster center. Following original and reduced image-
Importing necessary libraries-
#data science librires
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
Reading and plotting with the help of matplotlib library-
img = plt.imread("1.jpg")
plt.imshow(img)
plt.axis('off')
plt.show()
Checking data type of an image in system- its always numpy array.
type(img)
Output-numpy.ndarray
Analyzing the properties of image-
print(img.shape)
print(img.size)
Output- (176, 287, 3)
151536
The image shape contains the rows( 176), columns(287) and channels(3) in the image. In our image 3 channels because it is coloured image. If we check for grayscale image then colour channel is only 1.
Image size means total no. of pixels (rows * columns * channels).
Reshape image and Normalize image pixel values-
#3d to 2D image by combining w and h
w,h,d=img.shape
image_array = img.reshape(w*h, d)
image_array.shape
#normalize in the range of (0,1)
img=img/255
Output- (50512, 3)
Convert image from 3D to 2D by combining rows and columns, now image contains only 2 parameters that is number of pixels and number of channels. To normalize image pixel value , divide the image size by 255 because that is the maximum intensity value for RGB individually.
Extracting small subset of image for training model-
from sklearn.utils import shuffle
# fitting model on a small sub sample of the complete image
image_array_sample = shuffle(image_array,random_state=1)[:1000]
image_array_sample.size
Output- 3000
KMeans Model Creation and training-
kmeans=KMeans(n_clusters=6,random_state=1)
kmeans.fit(image_array_sample)
Predicting labels for complete image-
#get labels all centroids on the complete image
labels = kmeans.predict(image_array)
Printing Centroids-
print(kmeans.cluster_centers_)
c=kmeans.cluster_centers_
Recreate Original image according to labels-
#recreate original image according to labels and each pixels
def recreate_image(c,labels,w,h,d):
image=np.zeros((w,h,d))
label_idx=0
#now label each pixels according to the limited labels
for i in range(w):
for j in range(h):
image[i][j]=c[labels[label_idx]]
#print(labels[label_idx])
label_idx+=1
return(image)
Visualizing and comparing the original and compressed images-
plt.figure(1)
plt.axis('off')
plt.title("original")
plt.imshow(img)
plt.show()
plt.figure(2)
plt.axis('off')
plt.title("reduced")
plt.imshow(recreate_image(c,labels,w,h,d))
plt.show()
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