imgix vs OpenCV: What are the differences?
imgix: Real-time image resizing service and CDN. imgix is a real-time image processing service and CDN. Resize, crop, and edit images simply by changing their URLs; 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.
imgix and OpenCV can be primarily classified as "Image Processing and Management" tools.
Some of the features offered by imgix are:
- Process Images - Resize, crop, and composite multiple images simply by changing their URLs.
- Optimize Images - Change output formats, device-pixel ratios, and chroma subsampling rates.
- Make Images Responsive - Easily integrate imgix into the latest HTML5 responsive image standards without reprocessing your entire image library.
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
"Image processing on demand" is the primary reason why developers consider imgix over the competitors, whereas "Computer Vision" was stated as the key factor in picking OpenCV.
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.
Lensley, Athento, and Suggestic are some of the popular companies that use OpenCV, whereas imgix is used by Coursera, Product Hunt, and Zillow. OpenCV has a broader approval, being mentioned in 39 company stacks & 39 developers stacks; compared to imgix, which is listed in 55 company stacks and 6 developer stacks.
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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