Scikit-image (also known as skimage) is one of the open-source image-processing libraries for the Python programming language. It provides a powerful toolbox of algorithms and functions for various image processing and computer vision tasks. And it is built on top of popular scientific libraries like NumPy and SciPy.ndimage.
Features of scikit-image
Following are the main features of Scikit Image −
- Scikit-image is an open-source package in Python. This means that it is available free of charge and free of restriction.
- Easy to read and write images of various formats. The library offers multiple plugins and methods to read and write images of various formats, such as JPEG, PNG, TIFF, and more.
- Images in scikit-image are represented by NumPy ndarrays (multidimentional containers). Hence, many common operations can be achieved using standard NumPy methods for manipulating arrays.
- It provides a vast collection of image Processing Algorithms such as filtering, segmentation, feature extraction, morphology, and more.
- And it offers a user-friendly API that simplifies the process of performing image processing tasks.
History of scikit-image
Scikit-image was initially developed by an active, international team of researchers and contributors. It originated from the combination of several existing image processing projects, including scipy.ndimage, matplotlib, and others.
Advantages of scikit-image
scikit-image offers several advantages that make it a valuable tool for image processing tasks −
- Easy Integration with Python”s Scientific Tools − It is built on top of NumPy, SciPy, and other scientific libraries. This enables users to combine image processing with other scientific computing tasks, such as data analysis, machine learning, and visualization.
- Comprehensive Image Processing Tools − scikit-image provides a wide range of tools and algorithms for image processing tasks. It includes comprehensive image filters, morphological operations, image transformations, feature extraction, and more. These tools allow users to perform complex image processing operations with ease and flexibility.
- User-Friendly Visualization − scikit-image includes a simple graphical user interface (GUI) for visualizing results and exploring parameters.
Scikit Image – Environmental setup
To set up the environment for scikit-image, it is recommended to use a package manager such as pip or conda to install scikit-image and its dependencies. pip is the default package manager for Python, while Conda is a popular choice for managing packages in Anaconda environments.
Installing scikit-image using pip
To install scikit-image using pip, just run the below command in your command prompt −
pip install scikit-image
This will download the scikit-image package, wait for download completion. If you see any pip up-gradation error, then just upgrade the pip by the following command −
python -m pip install --upgrade pip
And run “pip install scikit-image” command again, this time it will work.
Installing scikit-image using Conda
If you”re using the Anaconda distribution already in your system then you can directly use the conda package manager to install scikit-image. Following is the command −
conda install scikit-image
If the scikit-image package is already installed on your computer, running the conda install scikit-image command will display the below message −
Collecting package metadata (current_repodata.json): ...working... done Solving environment: ...working... done # All requested packages already installed. Retrieving notices: ...working... done Note: you may need to restart the kernel to use updated packages.
Verification
To check whether scikit-image is already installed or to verify if an installation has been successful, you can execute the following code in a Python shell or Jupyter Notebook −
import skimage
# Check the version of scikit-image
print("scikit-image version:", skimage.__version__)
If the above code executes without any errors, it means that scikit-image is installed successfully and ready to be used.