Firesize vs OpenCV: What are the differences?
Developers describe Firesize as "On the fly image resizing. No code required. Built-in CDN". Firesize is a hosted image processing proxy. It's focused on doing one thing fast: resizing and cropping images on demand. It's a simple url based API allows you to quickly generate images on demand. On the other hand, OpenCV is detailed as "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.
Firesize and OpenCV can be primarily classified as "Image Processing and Management" tools.
Some of the features offered by Firesize are:
- Processing options specified via URL
- URL signing (for security)
- Process images from anywhere (no uploading to Firesize required)
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
Firesize and OpenCV are both open source tools. It seems that OpenCV with 36.3K GitHub stars and 26.6K forks on GitHub has more adoption than Firesize with 92 GitHub stars and 40 GitHub forks.
What is Firesize?
What is OpenCV?
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Why do developers choose Firesize?
What are the cons of using Firesize?
What are the cons of using OpenCV?
What companies use Firesize?
What tools integrate with OpenCV?
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