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Mahotas – Computing Linear Binary Patterns
  • Last Updated : 29 Jun, 2020

In this article we will see how we can get the linear binary patterns of image in mahotas. Local binary patterns is a type of visual descriptor used for classification in computer vision. LBP is the particular case of the Texture Spectrum model proposed in 1990. LBP was first described in 1994. For this we are going to use the fluorescent microscopy image from a nuclear segmentation benchmark. We can get the image with the help of command given below

mahotas.demos.nuclear_image()

Below is the nuclear_image

In order to do this we will use mahotas.features.lbp method

Syntax : mahotas.features.lbp(image, radius, points)

Argument : It takes image object and two integers as argument



Return : It returns 1-D numpy ndarray i.e histogram feature

Note : The input of the this should should be the filtered image or 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

iamge = image[:, :, 0]

Example 1 :

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# importing various libraries
import mahotas
import mahotas.demos
import mahotas as mh
import numpy as np
from pylab import imshow, show
import matplotlib.pyplot as plt
  
# loading nuclear image
nuclear = mahotas.demos.nuclear_image()
  
# filtering iamge
nuclear = nuclear[:, :, 0]
  
# adding gaussian filter
nuclear = mahotas.gaussian_filter(nuclear, 4)
  
# setting threshold
threshed = (nuclear > nuclear.mean())
  
# making is labeled image
labeled, n = mahotas.label(threshed)
  
# showing iamge
print("Labelled Image")
imshow(labeled)
show()
  
# Computing Linear Binary Patterns
value = mahotas.features.lbp(labeled, 200, 5)
  
# showing histograph
plt.hist(value)

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Output :

Example 2 :

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# importing required libraries
import numpy as np
import mahotas
from pylab import imshow, show
import matplotlib.pyplot as plt
   
# loading iamge
img = mahotas.imread('dog_image.png')
     
# filtering the imagwe
img = img[:, :, 0]
      
# setting gaussian filter
gaussian = mahotas.gaussian_filter(img, 15)
   
# setting threshold value
gaussian = (gaussian > gaussian.mean())
   
# making is labeled image
labeled, n = mahotas.label(gaussian)
  
# showing iamge
print("Labelled Image")
imshow(labeled)
show()
  
  
# Computing Linear Binary Patterns
value = mahotas.features.lbp(labeled, 200, 5, ignore_zeros = False)
  
  
# showinh histograph
plt.hist(value)

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Output :

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