Amazon Rekognition vs OpenCV: What are the differences?
Developers describe Amazon Rekognition as "Image Detection and Recognition Powered by Deep Learning". Amazon Rekognition is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications. 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.
Amazon Rekognition can be classified as a tool in the "Image Analysis API" category, while OpenCV is grouped under "Image Processing and Management".
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.
Lensley, Athento, and Suggestic are some of the popular companies that use OpenCV, whereas Amazon Rekognition is used by AfricanStockPhoto, Printiki, and Bunee.io. OpenCV has a broader approval, being mentioned in 39 company stacks & 39 developers stacks; compared to Amazon Rekognition, which is listed in 7 company stacks and 4 developer stacks.
What is Amazon Rekognition?
What is OpenCV?
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What are the cons of using Amazon Rekognition?
What are the cons of using OpenCV?
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What tools integrate with Amazon Rekognition?
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
The brains behind our auto-tagging - we can automatically detect contents of images using this