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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.

Advice on OpenCV and scikit-image
Needs advice

I want to make a video template where we can change images and make a new video with template animation text and song but the images are our and the image which is chosen from a user can convert into different shapes just like cartoon or childhood or old age and at the last show both images in the template video. How I can do it in Android Studio?

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Replies (2)
Steve Barnes
Lead Software Tools Engineer at Leonardo UK · | 4 upvotes · 9.5K views

Since I believe Android Studio now supports Python plugins &/or invoking Python scripts I personally would use MoviePy ( as a wrapper around FFMPEG &

ImageMagick ( - this combination will allow you to have a script that takes in an image or video, resizes it to a specified size, adds the text, (either scrolling it in or fading in/out or even with some special effects), then saves the output video. There are example scripts on the MoviePy site on how to do these. While you could do it yourself with FFMPEG & ImageMagick plus command line parameters I personally find using MoviePy much more clear & maintainable.

If you want the end user to be able to supply the image/video then you will need to set up a server somewhere that can take in the POSTed image and return the processed results.

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Hi! I will start by asking questions before giving specific advice, because there is the need to understand better what you want to do, and there will be a number of steps and choices to take, therefore there is not "the one" advice to give here.

First of all, afaiu you want to implement the described video processing on Android?

If that is the case, you will have to create or use native bindings to Java runtime (JNI i.e Android NDK) in order to access FFmpeg or OpenCV or any other C lib itfp. My first advice is therefore, you should check how feasible that is (existing bindings working for your target JRE etc).

Further, i find the description of what you want to produce quite unspecific. I guess parts of what you want to do with either sourced videos or images can be achieved with ffmpeg or opencv based processing.

In the end you just need to start writing some kind of processing steps with either. Once it gets more specific in details you can probably ask more specific questions on how to use these libs or even other tools.

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Pros of OpenCV
Pros of scikit-image
  • 36
    Computer Vision
  • 17
    Open Source
  • 12
  • 9
    Face Detection
  • 9
    Machine Learning
  • 6
    Great community
  • 4
    Realtime Image Processing
  • 2
    Helping almost CV problem
  • 2
    Image Augmentation
  • 6
    More powerful
  • 4
    Anaconda compatibility
  • 1
    Great documentation

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What is OpenCV?

OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.

What is scikit-image?

scikit-image is a collection of algorithms for image processing.

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What companies use OpenCV?
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What are some alternatives to OpenCV and scikit-image?
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
It mainly consists in a (big) single header file CImg.h providing a set of C++ classes and functions that can be used in your own sources, to load/save, manage/process and display generic images.
It is a cross-language, cross-platform application programming interface for rendering 2D and 3D vector graphics. The API is typically used to interact with a graphics processing unit, to achieve hardware-accelerated rendering.
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
It is the open, royalty-free standard for cross-platform, parallel programming of diverse processors found in personal computers, servers, mobile devices and embedded platforms. It greatly improves the speed and responsiveness of a wide spectrum of applications in numerous market categories including gaming and entertainment titles, scientific and medical software, professional creative tools, vision processing, and neural network training and inferencing.
See all alternatives