Related Articles

Related Articles

Mahotas – Getting Bounding Boxes of Labelled Image
  • Last Updated : 30 Jun, 2020

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.labeled.bbox method

Syntax : mahotas.labeled.bbox(labeled_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

iamge = image[:, :, 0]

Example 1 :

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 nuclear image
f = mahotas.demos.load('nuclear')
  
# setting filter to the image
f = f[:, :, 0]
  
# setting gaussian filter
f = mahotas.gaussian_filter(f, 4)
  
# setting threshold value
f = (f> f.mean())
  
# creating a labeled image
labeled, n_nucleus = mahotas.label(f)
  
  
# showing the labeleed image
print("Labelled Image")
imshow(labeled)
show()
  
# getting bounding boxes
relabeled = mahotas.labeled.bbox(labeled)
  
# showing the image
print("Bounding Boxes")
imshow(relabeled)
show()

chevron_right


Output :

Example 2 :

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing required libraries
import numpy as np
import mahotas
from pylab import imshow, show
   
# 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())
   
# creating a labeled image
labeled, n_nucleus = mahotas.label(gaussian)
    
print("Labelled Image")
# showing the gaussian filter
imshow(labeled)
show()
   
# getting bounding boxes
relabeled = mahotas.labeled.bbox(labeled)
  
# showing the image
print("Bounding Boxes")
imshow(relabeled)
show()

chevron_right


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.




My Personal Notes arrow_drop_up
Recommended Articles
Page :