Mahotas – Riddler-Calvard Method

In this article we will see how we can implement riddler calvard method in mahotas. This is alternative of otsu’s method. The Ridler and Calvard algorithm uses an iterative clustering approach. First a initial estimate of the threshold is to be made (e.g. mean image intensity). Pixels above and below the threshold are assigned to the object and background classes respectively.

In this tutorial we will use “luispedro” image, below is the command to load it.

mahotas.demos.load('luispedro')

Below is the luispedro image

In order to do this we will use mahotas.rc method

Syntax : mahotas.rc(image)



Argument : It takes image object as argument

Return : It returns numpy.float64

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]

Example 1:

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing required librries
import mahotas
import mahotas.demos
import numpy as np
from pylab import imshow, gray, show
from os import path
  
# loading the image
photo = mahotas.demos.load('luispedro')
  
# showing original image
print("Origial Image")
imshow(photo)
show()
  
# loading image as grey
photo = mahotas.demos.load('luispedro', as_grey = True)
  
# converting image type to unit8
# beacuse as_grey returns floating values
photo = photo.astype(np.uint8)
  
# riddler calvard
T_rc = mahotas.rc(photo)
  
# printing otsu value
print("R C value : " + str(T_rc))
  
print("Image threshold using riddler calvard method")
# showing image
# image values should be greater than T_rc value
imshow(photo > T_rc)
show()

chevron_right


Output :

Example 2:

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing required libraries
import mahotas
import numpy as np
from pylab import imshow, show
import os
  
  
# loading iamge
img = mahotas.imread('dog_image.png')
      
  
# setting filter to the image
img = img[:, :, 0]
  
imshow(img)
show()
  
  
# riddler calvard
T_rc = mahotas.rc(img)
  
# printing otsu value
print("R C value : " + str(T_rc))
  
print("Image threshold using riddler calvard method")
# showing image
# image values should be greater than T_rc value
imshow(img > T_rc)
show()

chevron_right


Output :




My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.


Article Tags :

Be the First to upvote.


Please write to us at contribute@geeksforgeeks.org to report any issue with the above content.