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
# 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 dense points dense_img = surf.dense(nuclear, 120 ) # showing image print ( "Dense Image" ) imshow(dense_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 dense points dense_img = surf.dense(gaussian, 80 ) # showing image print ( "Dense Image" ) imshow(dense_img) show() |
Output :