Most used 5 Python Image Processing Libraries

Today, I would like to share about most used image processing libraries in python. Let’s take a look.

OpenCV

First released in 2000, OpenCV has become a popular library due to its ease of use and readability. It is mostly used in computer vision tasks such as object detection, face detection, face recognition, image segmentation, etc.

Scikit-Image

Scikit-Image is a python-based image processing library that has some parts written in Cython to achieve good performance. It is a collection of algorithms for image processing such as:

• Segmentation,
• Geometric transformations,
• Color space manipulation,
• Analysis,
• Filtering,
• Morphology,
• Feature detection

Pillow/PIL

PIL (Python Imaging Library) is an open-source library for image processing in Python. PIL can perform tasks on an image such as reading, rescaling, saving in different image formats. PIL can be used for Image archives, Image processing, Image display.

NumPy

NumPy(Numerical Python) is a open-source Python library for data manipulation and scientific computing. It is used in the domain of linear algebra, Fourier transforms, matrices, and the data science field. NumPy arrays are faster than Python Lists. And an image is essentially an array of pixel values where each pixel is represented by 1 (greyscale) or 3 (RGB) values. So, NumPy can easily perform tasks such as image cropping, masking, or manipulation of pixel values.

Matplotlib

Matplotlib is mostly used for 2D visualizations, but it can also be leveraged for image processing. Matplotlib is effective in altering images for extracting information out of it although it does not support all the file formats.

This is all for now.

Hope you enjoy that.

By Asahi