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 :

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# 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 iamge
nuclear = nuclear[:, :, 0]
  
# adding gaussian filter
nuclear = mahotas.gaussian_filter(nuclear, 4)
  
# showing iamge
print("Image")
imshow(nuclear)
show()
  
  
# getting Speeded-Up Robust dense points
dense_img = surf.dense(nuclear, 120)
  
# shwoing image
print("Dense Image")
imshow(dense_img)
show()

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Output :

Example 2 :

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# importing required libraries
import numpy as np
import mahotas
from pylab import imshow, show
from mahotas.features import surf
   
# loading iamge
img = mahotas.imread('dog_image.png')
  
     
# filtering the imagwe
img = img[:, :, 0]
      
# setting gaussian filter
gaussian = mahotas.gaussian_filter(img, 5)
   
# showing iamge
print("Image")
imshow(gaussian)
show()
  
  
# getting Speeded-Up Robust dense points
dense_img = surf.dense(gaussian, 80)
  
# shwoing image
print("Dense Image")
imshow(dense_img)
show()

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Output :




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