Blitline vs OpenCV: What are the differences?
Blitline: Image processing in the cloud (cropping, rotating, compositing, filtering). Blitline drastically reduces the amount of work you need to develop an application that does any image processing. Stop rebuilding the same image processing functionality, let us do it for much less than it would cost you to make and support it. Pay for only the image processing time that your jobs use. We believe your images should be YOUR images. We also believe that you should never be "locked in" to using Blitline. The flexibility of the JSON API means you could stub out Blitline later without ever touching your production/deployed code; 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.
Blitline and OpenCV can be categorized as "Image Processing and Management" tools.
Some of the features offered by Blitline are:
- Industrial Strength- If you are a website that needs to manipulate large numbers of photos, Blitline is your economical solution.
- Convenient- If you are importing photos from an external service and need to crop, thumbnail, or process them, we are your answer.
- Versatile- Screenshots, PDFs, animated GIFs, obscure image formats, we are your one stop shop for all of these.
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 Blitline?
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