In this article, we will see how we can apply majority filter to the image in mahotas. In Majority filters for each group of pixels considered in the input map, a majority filter assign the predominant (=mostly frequently occurring) value or class name of these to the center pixel in the output map.
In this tutorial we will use “lena” image, below is the command to load it.
mahotas.demos.load('lena')
Below is the lena image
In order to do this we will use mahotas.majority_filter method
Syntax : mahotas.majority_filter(img)
Argument : It takes image object as argument
Return : It returns image object
Note : Input image should be filtered or should be loaded as grey
In order to filter the image we will take the image object which is numpy.ndarray and filter it with the help of indexing, below is the command to do this
image = image[:, :, 0]
Below is the implementation
Python3
# importing required libraries import mahotas import mahotas.demos from pylab import gray, imshow, show import numpy as np import matplotlib.pyplot as plt # loading image img = mahotas.demos.load('lena') # filtering image img = img.max(2) print("Image") # showing image imshow(img) show() # applying majority filter new_img = mahotas.majority_filter(img) # showing image print("Majority Filter") imshow(new_img) show() |
Output :
Image
Majority Filter
Another example
Python3
# importing required libraries import mahotas import numpy as np from pylab import gray, imshow, show import os import matplotlib.pyplot as plt # loading image img = mahotas.imread('dog_image.png') # filtering image img = img[:, :, 0] print("Image") # showing image imshow(img) show() # applying majority filter new_img = mahotas.majority_filter(img) # showing image print("Majority Filter") imshow(new_img) show() |
Output :
Image
Majority Filter
