Mahotas – Getting SURF Dense Points
In this article we will see how we can get the speeded up robust dense 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.dense method
Syntax : surf.surf(img, spacing)
Argument : It takes image object and integer as argument
Return : It returns numpy.ndarray i.e descriptors at dense points
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()
dense_img = surf.dense(nuclear, 120 )
print ( "Dense Image" )
imshow(dense_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()
dense_img = surf.dense(gaussian, 80 )
print ( "Dense Image" )
imshow(dense_img)
show()
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Output :
Last Updated :
29 Apr, 2021
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