![]() ![]() Here we will analyze the colorspace of our image. image_data = (image / 255.0).reshape(749 * 500, 3) image_data.shapeĬolorspace is the specific organization of colours in physical appearance where any 2 images having the same colour model can have entirely different colorspace. We will divide the image size by 255 because that is the maximum intensity value for RGB individually. We will reshape this image so that it contains only 2 parameters: product of rows and columns i.e. Image size displayed here shows the total no. Here we can see that it contains 3 channels because it is a colored picture, similarly, if we check the shape of grayscale images it will have only 1 channel. The image shape contains the rows, columns and channels in the image. plt.rcParams = (20, 12) image = io.imread('image_dataset/Bird.jpg') labels = plt.axes(xticks=, yticks=) labels.imshow(img) We will also set the axes title as blank. (20,12) for all the images for maintaining consistency. In order to access images, you can have different images stored in a folder and parse the images one at a time. Loading Images Dataset and analyzing the properties of images import os import numpy as np import matplotlib.pyplot as plt import matplotlib.image as image %matplotlib inline ("ggplot") from skimage import io from sklearn.cluster import KMeans from ipywidgets import interact, interactive, fixed, interact_manual, IntSlider import ipywidgets as widgets b. Here we require libraries for Visualization, Compression and creating interactive widgets. K-Means Clustering is defined under the SK-Learn library of python, before using it let us install it by pip install sklearn Implementation of Image Compression using K-Means Clustering data points having similar mean in one cluster.Īs 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. If we go around the statistics behind it then we can see that K-Means Clustering as the name suggests takes in account the arithmetic mean of the data point and form clusters containing the homogenous data i.e. K-Means clustering is a Machine Learning algorithm which works on partitioning data points into predefined distinct clusters in which each data point belongs to only one cluster. For example, if 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. Creating Interactive controls to compress imageĬlustering is a grouping of different data points which are similar to each other and form different groups which contain similar data points.Image Compression using K-Means clustering.Here, we will discuss image compression and demonstrate how image compression can be done using K-Means clustering. There are many ways by which we can compress images, one of which is K-Means Clustering. Images of high quality take a lot of memory while storing, whereas the low image of low quality takes less memory. Image compression is reducing the size that an image takes while storing or transmitting. ![]()
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