In this article we will see how we can obtain sizes of labelled region in mahotas. Labelled images are integer images where the values correspond to different regions. I.e., region 1 is all of the pixels which have value 1, region two is the pixels with value 2, and so on. By convention, region 0 is the background and often handled differently. We can create a labelled region with the help of mahotas.label method.
In order to do this we will use mahotas.label_size method
Syntax : mahotas.labelled_size(labelled_region)
Argument : It takes numpy.ndarray object as argument i.e labelled region
Return : It returns list of integer
Example 1:
Python3
# importing required librariesimport mahotas as mhimport numpy as npfrom pylab import imshow, show# creating region# numpy.ndarrayregions = np.zeros((10, 10), bool)# setting 1 value in the regionregions[1, 1] = 1regions[6, 6] = 1regions[4, 4] = 1regions[9, 9] = 1# getting labelled functionlabelled, nr_objects = mh.label(regions)# showing the image with interpolation = 'nearest'imshow(labelled, interpolation ='nearest')show()# getting sizes of labelled regionsizes = mh.labelled.labelled_size(labelled)# printing sizesfor i in range(len(sizes)): print("Size of region " + str(i) + " : " + str(sizes[i])) |
Output :
Size of region 0 : 96 Size of region 1 : 1 Size of region 2 : 1 Size of region 3 : 1 Size of region 4 : 1
Example 2:
Python3
# importing required librariesimport mahotas as mhimport numpy as npfrom pylab import imshow, show# creating region# numpy.ndarrayregions = np.zeros((10, 10), bool)# setting 1 value to the regionregions[:3, :3] = 1regions[6:, 6:] = 1# getting labelled functionlabelled, nr_objects = mh.label(regions)# showing the image with interpolation = 'nearest'imshow(labelled, interpolation ='nearest')show()# getting sizes of labelled regionsizes = mh.labelled.labelled_size(labelled)# printing sizesfor i in range(len(sizes)): print("Size of region " + str(i) + " : " + str(sizes[i])) |
Output :
Size of region 0 : 75 Size of region 1 : 9 Size of region 2 : 16

