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.
What is OpenCV?
What is scikit-image?
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What are the cons of using OpenCV?
What are the cons of using scikit-image?
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I used both scikit-image and OpenCV for image processing and cell identification on the backend. Trained to identify malaria cells based on image datasets online. When it comes to quick training for image processing, OpenCV and scikit-image are the two best choices in my opinion. The approach I took to cell detection was template-matching and edge detection based. Both are highly tested and very powerful features of the Scikit Image and OpenCV libraries, and also have great Python interfaces.
I use openCV to serve as "motion capture" logic for my home security cameras. Which means that instead of capturing in a dumb way based on motion, it captures video when it recognizes human faces or bodies. This saves a lot of disk, but at the expense of CPU.
CV glue. Modified libraries for pattern-detection. Some pattern training tasks. HoG matching. Transform