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 skimagefrom skimage import datacamera = data.camera() # An image with 512 rows# and 512 columnstype(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 skimagefrom skimage import filtersfrom skimage import data# Predefined function to fetch datacoins = data.coins() # way to find thresholdthreshold_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-imageimport os# importing io from skimageimport skimagefrom skimage import io# way to load car image from filefile = os.path.join(skimage.data_dir, 'cc.jpg')cars = io.imread(file)# way to show the input imageio.imshow(cars)io.show() |
Output :
Applications :
- Analysis of Medical images.
- Classification of images for detection.
