OpenCV vs scikit-image: What are the differences?

OpenCV and scikit-image are two popular libraries used for computer vision and image processing tasks. Let's explore the key differences between them.

1. Supported Languages: OpenCV is primarily written in C++ but provides interfaces for multiple languages, including Python. On the other hand, scikit-image is a Python-only library, which makes it easier for Python developers to work with.

2. Ease of Use and Documentation: OpenCV has been around for a longer time and is widely adopted, which results in extensive documentation and numerous online resources available. This can be beneficial for beginners who are learning computer vision as they can find plenty of examples and tutorials. On the other hand, scikit-image, while also having good documentation, might not have as extensive online resources as OpenCV, but it provides a more streamlined and Pythonic interface, making it easier to understand and use.

3. Functionality and Algorithms: OpenCV is known for its comprehensive set of functionalities and a large number of built-in computer vision and image processing algorithms. It covers a wide range of tasks, including image preprocessing, feature detection and extraction, object recognition, and more. Scikit-image, on the other hand, focuses more on image processing and provides a rich collection of algorithms for tasks like filtering, segmentation, and morphological operations. It may not have as many functionalities as OpenCV, but it offers high-quality implementations of several algorithms.

4. Dependencies and Package Size: OpenCV is a large library that comes with a range of dependencies, making it a bit heavier and more difficult to install and manage than scikit-image. Scikit-image, being a Python-only library, has fewer dependencies and is generally easier to install. This can be an advantage for developers working in Python-based environments where managing dependencies is crucial.

5. Community and Support: OpenCV has a large and active community, which means there are more developers using it and contributing to its development. It also means that there are more third-party libraries and tools built on top of OpenCV. Scikit-image, although having a smaller community compared to OpenCV, still has an active user base and contributors. However, it may not have as extensive third-party support as OpenCV.

6. Performance and Speed: Performance is always an important consideration in computer vision and image processing tasks. OpenCV is known for its optimized C++ implementation, resulting in faster execution and better performance in certain cases. Scikit-image, being a Python library, is built on top of NumPy, which can sometimes introduce performance overhead. However, both libraries have optimized functions, and the actual performance can vary depending on the task and implementation.

In summary, OpenCV has a wider range of functionalities, extensive documentation, and a larger community, while scikit-image provides a more Pythonic interface, easier installation, and focuses more on image processing algorithms.