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 :
# 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) |
Output :
Example 2 :
# 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) |
Output :
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