Mahotas – Getting Bounding Boxes of Labelled Image
In this article we will see how we can get the bounding boxes of all the objects in the labelled image in mahotas. 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
mhotas.demos.nuclear_image()
Below is the nuclear_image
In order to do this we will use mahotas.labelled.bbox method
Syntax : mahotas.labelled.bbox(labelled_image)
Argument : It takes numpy.ndarray object as argument i.e labelled image
Return : It returns numpy.ndarray object i.e bounding box image
Note : The input of the this should should be the filtered image object which is labeled
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 :
Python3
# importing required librariesimport mahotasimport numpy as npfrom pylab import imshow, showimport os# loading nuclear imagef = mahotas.demos.load('nuclear')# setting filter to the imagef = f[:, :, 0]# setting gaussian filterf = mahotas.gaussian_filter(f, 4)# setting threshold valuef = (f> f.mean())# creating a labelled imagelabelled, n_nucleus = mahotas.label(f)# showing the labelled imageprint("Labelled Image")imshow(labelled)show()# getting bounding boxesrelabeled = mahotas.labelled.bbox(labelled)# showing the imageprint("Bounding Boxes")imshow(relabelled)show() |
Output :
Example 2 :
Python3
# importing required librariesimport numpy as npimport mahotasfrom pylab import imshow, show # loading imageimg = mahotas.imread('dog_image.png') # filtering the imageimg = img[:, :, 0] # setting gaussian filtergaussian = mahotas.gaussian_filter(img, 15) # setting threshold valuegaussian = (gaussian > gaussian.mean()) # creating a labelled imagelabelled, n_nucleus = mahotas.label(gaussian) print("Labelled Image")# showing the gaussian filterimshow(labelled)show() # getting bounding boxesrelabelled = mahotas.labelled.bbox(labelled)# showing the imageprint("Bounding Boxes")imshow(relabelled)show() |
Output :
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course
