OpenCV vs CImg: What are the differences?
OpenCV: 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; CImg: A small and open-source C++ toolkit for image processing. 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.
OpenCV and CImg can be categorized as "Image Processing and Management" tools.
OpenCV and CImg are both open source tools. OpenCV with 38.2K GitHub stars and 28.4K forks on GitHub appears to be more popular than CImg with 701 GitHub stars and 163 GitHub forks.
What is CImg?
What is OpenCV?
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What are the cons of using OpenCV?
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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.
It's a great header-only library that makes it OOP, C++11 intuitive to use image components such as ImageKit and libjpeg.
CV glue. Modified libraries for pattern-detection. Some pattern training tasks. HoG matching. Transform