Mahotas – Getting SURF Integral
Last Updated :
15 Sep, 2021
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
import mahotas
import mahotas.demos
import mahotas as mh
import numpy as np
from pylab import imshow, show
from mahotas.features import surf
nuclear = mahotas.demos.nuclear_image()
nuclear = nuclear[:, :, 0 ]
nuclear = mahotas.gaussian_filter(nuclear, 4 )
print ( "Image" )
imshow(nuclear)
show()
i_img = surf.integral(nuclear)
print ( "Integral Image" )
imshow(i_img)
show()
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Output :
Example 2 :
Python3
import numpy as np
import mahotas
from pylab import imshow, show
from mahotas.features import surf
img = mahotas.imread( 'dog_image.png' )
img = img[:, :, 0 ]
gaussian = mahotas.gaussian_filter(img, 5 )
print ( "Image" )
imshow(gaussian)
show()
i_img = surf.integral(gaussian)
print ( "Integral Image" )
imshow(i_img)
show()
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Output :
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