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Python Mahotas – Introduction

Last Updated : 09 Jun, 2021
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Mahotas is a computer vision and image processing and manipulation library for Python. A library is a collection of functions and methods that allows you to perform many actions without having to write hundreds of lines of code. Mahotas includes many algorithms that operates with arrays, mahotas currently has over 100 functions for image processing and computer vision and is constantly growing.

Mahotas provides a good solution in finding patterns in the image for example “Where’s Wally Problem” can be solved easily using Mahotas.

How to install Mahotas :  

pip install mahotas

Notable algorithms available in Mahotas :
1. Watershed 
2. Convex points calculations. 
3. Hit & miss, thinning. 
4. Zernike & Haralick, LBP, and TAS features. 
5. Speeded-Up Robust Features (SURF), a form of local features. 
6. Thresholding. 
7. Convolution. 
8. Sobel edge detection. 
9. Spline interpolation 
10. SLIC super pixels.

Example 1: Loading Image  


# importing required libraries
import numpy as np
import mahotas
import pylab
# loading image
img = mahotas.imread('dog_image.png')
# showing the original image

Output : 

Example 2: Creating distance transform


# importing required libraries
import pylab as p
import numpy as np
import mahotas
# creating numpy array of type bool
f = np.ones((256, 256), bool)
# setting false values
f[200:, 240:] = False
f[128:144, 32:48] = False
# f is basically True with the exception of two islands:
# one in the lower-right
# corner, another, middle-left
# creating a distance using numpy array
dmap = mahotas.distance(f)
# showing image

Output : 


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