Mahotas – Bernsen local thresholding

In this article we will see how we can implement bernsen local thresholding in mahotas. Bernsen’s method is one of locally adaptive binarization methods developed for image segmentation. In this study, Bernsen’s locally adaptive binarization method is implemented and then tested for different gray scale images.

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.thresholding.bernsen method

Syntax : mahotas.thresholding.bernsen(image, constrast_threshold, global_threshold)



Argument : It takes image object and two integer 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]

Example 1:

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# 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')
  
  
# 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)
  
# showing original image
print("Image")
imshow(photo)
show()
  
# bernsen threshold
photo = mahotas.thresholding.bernsen(photo, 7, 200)
  
  
print("Image with bernsen threshold")
  
# showing image
imshow(photo)
show()

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Output :

Example 2:

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# 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]
  
print("Image")
  
# shoing the image
imshow(img)
show()
  
  
# bernsen threshold
img = mahotas.thresholding.bernsen(img, 5, 100)
  
  
print("Image with bernsen threshold")
  
# showing image
imshow(img)
show()

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Output :




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