In this article we will see how we can get the speeded up robust features 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.surf method
Syntax : surf.surf(img)
Argument : It takes image object as argument
Return : It returns numpy.ndarray
Example 1 :
# 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 Features spoints = surf.surf(nuclear) print("No of points: {}".format(len(spoints))) |
Output :
No of points: 217
Example 2 :
# 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 Features spoints = surf.surf(gaussian) print("No of points: {}".format(len(spoints))) |
Output :
No of points: 364
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