In this article we will see how we can edges of the binary image in mahotas with the help of DoG algorithm. In imaging science, difference of Gaussians (DoG) is a feature enhancement algorithm that involves the subtraction of one blurred version of an original image from another, less blurred version of the original.
In order to do this we will use
mahotas.dogmethodSyntax : mahotas.dog(img)
Argument : It takes binary image object as argument
Return : It returns image object
Below is the implementation
# importing required libraries import mahotas as mh import numpy as np from pylab import imshow, show # creating region # numpy.ndarray regions = np.zeros((10, 10), bool) # setting 1 value to the region regions[:3, :3] = 1regions[6:, 6:] = 1 # getting labeled function labeled, nr_objects = mh.label(regions) # showing the image with interpolation = 'nearest' print("Binary Image") imshow(labeled, interpolation ='nearest') show() # getting edges using dog algo dog = mahotas.dog(labeled) # showing image print("Edges using DoG algo") imshow(dog) show() |
Output :
Binary Image
Edges using DoG algo
Another example
# importing required libraries import mahotas as mh import numpy as np from pylab import imshow, show # creating region # numpy.ndarray regions = np.zeros((10, 10), bool) # setting 1 value to the region regions[1, :2] = 1regions[5:8, 6: 8] = 1regions[8, 0] = 1 # getting labeled function labeled, nr_objects = mh.label(regions) # showing the image with interpolation = 'nearest' print("Image") imshow(labeled, interpolation ='nearest') show() # getting edges dog = mahotas.dog(labeled) # showing image print("Edges using DoG algo") imshow(dog) show() |
Output :
Binary Image
Edges using DoG algo
