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Image processing with Scikit-image in Python

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scikit-image is an image processing Python package that works with NumPy arrays which is a collection of algorithms for image processing. Let’s discuss how to deal with images in set of information and its application in the real world. 
Important features of scikit-image : 
 

Simple and efficient tools for image processing and computer vision techniques. 
Accessible to everybody and reusable in various contexts. 
Built on the top of NumPy, SciPy, and matplotlib. 
Open source, commercially usable – BSD license.


Note : Before installing scikit-image, ensure that NumPy and SciPy are pre-installed. Now, the easiest way to install scikit-image is using pip : 
 

pip install -U scikit-image


  
Most functions of skimage are found within submodules. Images are represented as NumPy arrays, for example 2-D arrays for grayscale 2-D images.
Code #1 : 
 

Python3

# Python3 program to process
# images using scikit-image
 
# importing data from skimage
from skimage import data
 
camera = data.camera()
 
# An image with 512 rows
# and 512 columns
type(camera)
 
print(camera.shape)

                    

Output : 
 

numpy.ndarray
(512, 512)


  
Code #2 : skimage.data submodule provides a set of functions returning example images. 
 

Python

# Python3 program to process
# images using scikit-image
 
# importing filters and
# data from skimage
from skimage import filters
from skimage import data
 
# Predefined function to fetch data
coins = data.coins()
 
# way to find threshold
threshold_value = filters.threshold_otsu(coins)
 
print(threshold_value)

                    

Output : 
 

107


  
Code #3 : Load own images as NumPy arrays from image files. 
 

Python

# Python3 program to process
# images using scikit-image
import os
 
# importing io from skimage
import skimage
from skimage import io
 
# way to load car image from file
file = os.path.join(skimage.data_dir, 'cc.jpg')
 
 
cars = io.imread(file)
 
# way to show the input image
io.imshow(cars)
io.show()

                    

Output : 
 


Applications : 
 

  • Analysis of Medical images.
  • Classification of images for detection.


 



Last Updated : 19 Jan, 2023
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