Libpixel vs OpenCV: What are the differences?
Libpixel: One image, any size, every device. It is a realtime image manipulation service for any images that are available on the internet. It works by fetching images from an existing Image Source, processing them on the fly and responding with the modified image; 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.
Libpixel and OpenCV can be primarily classified as "Image Processing and Management" tools.
Some of the features offered by Libpixel are:
- Width & height manipulation
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
OpenCV is an open source tool with 42.4K GitHub stars and 33.1K GitHub forks. Here's a link to OpenCV's open source repository on GitHub.
What is Libpixel?
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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