ImageBoss vs OpenCV: What are the differences?
What is ImageBoss? On-demand content aware image processing API. Content aware image resizing, cropping, compression, cache and globally deliver. All web development best practices, hassle free in one simple and powerful API.
What is 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.
ImageBoss and OpenCV can be categorized as "Image Processing and Management" tools.
Some of the features offered by ImageBoss are:
- Perfect Thumbnails - ImageBoss identifies the content of your image and, in case we need to crop it to accomodate your dimensions, we make sure that the soul of the image won't be lost on the process.
- Fast delivery, every time - Every time you generate a new image it gets copied to hundreds of servers across the world so your users can download your images at maximum speed.
- Fast image loading - ImageBoss uses the best algorithms available for image manipulation taking the all the juice we can get during calculations and image manipulations. Also, all your PNG and JPG images with progressive scans technology with the best compression techonology available.
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 36.3K GitHub stars and 26.6K GitHub forks. Here's a link to OpenCV's open source repository on GitHub.
What is ImageBoss?
What is OpenCV?
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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