Mahotas – Getting SURF Integral

0 0 1 minute read

In this article, we will see how we can get the speeded up robust integral feature of image in mahotas. In computer vision, speeded up robust features (SURF) is a patented local feature detector and descriptor. It can be used for tasks such as object recognition, image registration, classification, or 3D reconstruction. It is partly inspired by the scale-invariant feature transform (SIFT) descriptor. 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 surf.integral method

Syntax : surf.integral(img)
Argument : It takes image object as argument
Return : It returns numpy.ndarray

Example 1 :

Python3

# importing various libraries
import mahotas
import mahotas.demos
import mahotas as mh
import numpy as np
from pylab import imshow, show
from mahotas.features import surf
 
# loading nuclear image
nuclear = mahotas.demos.nuclear_image()
 
# filtering image
nuclear = nuclear[:, :, 0]
 
# adding gaussian filter
nuclear = mahotas.gaussian_filter(nuclear, 4)
 
# showing image
print("Image")
imshow(nuclear)
show()
 
 
# getting Speeded-Up Robust integral feature
i_img = surf.integral(nuclear)
 
# showing image
print("Integral Image")
imshow(i_img)
show()

Output :

Example 2 :

Python3

# importing required libraries
import numpy as np
import mahotas
from pylab import imshow, show
from mahotas.features import surf
  
# loading image
img = mahotas.imread('dog_image.png')
 
    
# filtering the image
img = img[:, :, 0]
     
# setting gaussian filter
gaussian = mahotas.gaussian_filter(img, 5)
  
# showing image
print("Image")
imshow(gaussian)
show()
 
 
# getting Speeded-Up Robust integral feature
i_img = surf.integral(gaussian)
 
# showing image
print("Integral Image")
imshow(i_img)
show()