Caire vs OpenCV: What are the differences?
Developers describe Caire as "Content aware image resize library". A content aware image resize library based on the Seam Carving for Content-Aware Image Resizing paper. 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.
Caire and OpenCV can be categorized as "Image Processing and Management" tools.
Some of the features offered by Caire are:
- Customizable command line support
- Support for both shrinking or enlarging the image
- Resize image both vertically and horizontally
On the other hand, OpenCV provides the following key features:
- 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
Caire and OpenCV are both open source tools. It seems that OpenCV with 36.3K GitHub stars and 26.6K forks on GitHub has more adoption than Caire with 8.93K GitHub stars and 311 GitHub forks.
What is Caire?
What is OpenCV?
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What are the cons of using Caire?
What are the cons of using OpenCV?
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What tools integrate with Caire?
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