What is Scikit-image?

 This latest Blogpost is by our valued guest author, Sriraksha V. Raghavan.

Image analysis, commonly known as computer vision, has made significant advances with the help of AI. However, there are still many challenges to overcome. The use of deep learning and neural networks in the analysis of images by AI is helping to narrow this gap. Currently, AI models are improving in areas such as object detection, image recognition, and generating images from text descriptions.

There are several coding tools and libraries available for these tasks, including OpenCV, TensorFlow, PyTorch, Keras, Caffe, MATLAB, CUDA, Microsoft’s Cognitive Toolkit (CNTK), Scikit-image, Dlib, Mahotas, and others. The choice of tool depends on your specific requirements, such as the complexity of the task and the hardware available.

Meet our guest author Sriraksha, an electrical engineer specializing in image sensors and machine vision. In her free time, she enjoys reading about human visual systems and continental philosophy, and dabbles in writing. You can reach her at Sriraksha.v.raghavan@gmail.com.

One of the popular Python libraries for image processing is scikit-image, an open-source collection of algorithms hosted on GitHub(1). Scikit-image has an advantage over other libraries because of the speed of its algorithms, which is essential for working with large amounts of data.

To get started with scikit-image, some understanding of the NumPy library would be beneficial but not mandatory. The easiest way to install scikit-image is via pip in your command line window(2).

Scikit-image stores images as NumPy ndarrays(n-dimensional), which are arrays of numbers with rows, columns, and dimensions. The dimensions represent the color planes of the image, such as red, green, blue, infrared, x-ray, etc. Scikit-image has various functions and classes for image processing, ranging from simple tasks like changing the color scale to complex ones like image segmentation, feature detection, and image restoration.

The best way to learn scikit-image is to download its pre-existing dataset using the function call ski.data.download_all() in your Python environment. This will give you some image sets that you can manipulate using different scikit-image functions and classes. You can also follow the tutorials available on the scikit-image website(3), which use the same datasets to demonstrate how scikit-image works.

Additionally, some useful forums to discuss scikit-image applications are their Zulip page(4), a community forum where people post queries and share ideas, and their GitHub page(1), where you can access their API and codebase.

1: scikit-image/scikit-image: Image processing in Python (https://en.wikipedia.org/wiki/Scikit-)

2: Installation — scikit-image (https://scikit-image.org/docs/stable/user_guide/install.html)

3: Tutorials — scikit-image (https://scikit-image.org/docs/stable/user_guide/tutorials.html)

4: scikit-image - Zulip Chat Archive (https://zulip.com/help/view-images-and-videos)

If you are new to,

 'pip [https://hodentekhelp.blogspot.com/2018/09/how-do-you-upgrade-pip.html]' 

or 

'Numpy [https://hodentekhelp.blogspot.com/2020/06/what-is-numpy.html]' 

you can find them in this blog in addition to other python items.