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OpenCV

923
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92
scikit-image

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+ 1
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OpenCV vs scikit-image: What are the differences?

Developers describe OpenCV as "Open Source Computer Vision Library". 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. On the other hand, scikit-image is detailed as "Image processing in Python". scikit-image is a collection of algorithms for image processing.

OpenCV and scikit-image can be primarily classified as "Image Processing and Management" tools.

Some of the features offered by OpenCV are:

  • C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android
  • More than 47 thousand people of user community and estimated number of downloads exceeding 7 million
  • Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics

On the other hand, scikit-image provides the following key features:

  • Provides I/O, filtering, morphology, transformations, measurement, annotation, color conversions, test data sets, etc.
  • Written in Python with a well-commented source code
  • Has had 5,709 commits made by 116 contributors representing 29,953 lines of code

"Computer Vision" is the primary reason why developers consider OpenCV over the competitors, whereas "More powerful" was stated as the key factor in picking scikit-image.

OpenCV and scikit-image are both open source tools. It seems that OpenCV with 35.8K GitHub stars and 26.2K forks on GitHub has more adoption than scikit-image with 3.07K GitHub stars and 1.3K GitHub forks.

Advice on OpenCV and scikit-image
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FFMPEGFFMPEG

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)

Since I believe Android Studio now supports Python plugins &/or invoking Python scripts I personally would use MoviePy (https://zulko.github.io/moviepy/) as a wrapper around FFMPEG &

ImageMagick (https://www.imagemagick.org/script/index.php) - 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
  • 33
    Computer Vision
  • 17
    Open Source
  • 11
    Imaging
  • 9
    Machine Learning
  • 9
    Face Detection
  • 6
    Great community
  • 4
    Realtime Image Processing
  • 2
    Image Augmentation
  • 1
    Helping almost CV problem
  • 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?
What companies use scikit-image?
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What are some alternatives to OpenCV and scikit-image?
TensorFlow
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.
CImg
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.
OpenGL
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
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.
OpenCL
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