OpenCV vs Piio: 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; Piio: Generates responsive images so you don't have to optimize. Piio automatically generates real-time responsive images for your website visitors and delivers them at maximum speed. (lazy loading feature included).
OpenCV and Piio belong to "Image Processing and Management" category of the tech stack.
Some of the features offered by OpenCV are:
- 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
On the other hand, Piio provides the following key features:
- Cross device experience - Don’t worry about multiple resolutions or mobile devices, Piio automatically loads the best image for each device.
- Automatic Responsive Images - We learn from your html and deliver pixel perfect images, they don't only load faster but they'll look better.
- CDN Deliver - With worldwide reach, our CDN provides low latency and faster download times.
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 OpenCV?
What is Piio?
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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