GraphicsMagick vs OpenCV: What are the differences?
Developers describe GraphicsMagick as "The swiss army knife of image processing". GraphicsMagick is the swiss army knife of image processing. Comprised of 267K physical lines (according to David A. Wheeler's SLOCCount) of source code in the base package (or 1,225K including 3rd party libraries) it provides a robust and efficient collection of tools and libraries which support reading, writing, and manipulating an image in over 88 major formats including important formats like DPX, GIF, JPEG, JPEG-2000, PNG, PDF, PNM, and TIFF. On the other hand, OpenCV is detailed 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.
GraphicsMagick and OpenCV can be primarily classified as "Image Processing and Management" tools.
OpenCV is an open source tool with 36.3K GitHub stars and 26.6K GitHub forks. Here's a link to OpenCV's open source repository on GitHub.
What is GraphicsMagick?
What is OpenCV?
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What are the cons of using GraphicsMagick?
What are the cons of using OpenCV?
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What tools integrate with GraphicsMagick?
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