Amazon Rekognition vs OpenCV

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Amazon Rekognition
Amazon Rekognition

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OpenCV
OpenCV

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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.

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What is Amazon Rekognition?

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.

What is OpenCV?

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.
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      What tools integrate with Amazon Rekognition?
      What tools integrate with OpenCV?
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        What are some alternatives to Amazon Rekognition and OpenCV?
        TensorFlow
        TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
        Google Cloud Vision API
        Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API.
        Tesseract OCR
        Tesseract was originally developed at Hewlett-Packard Laboratories Bristol and at Hewlett-Packard Co, Greeley Colorado between 1985 and 1994, with some more changes made in 1996 to port to Windows, and some C++izing in 1998. In 2005 Tesseract was open sourced by HP. Since 2006 it is developed by Google.
        Tesseract.js
        This library supports over 60 languages, automatic text orientation and script detection, a simple interface for reading paragraph, word, and character bounding boxes. Tesseract.js can run either in a browser and on a server with NodeJS.
        scanR
        scanR is a simple OCR API service that supports 32 languages and can extract text from images or PDF files.
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        Decisions about Amazon Rekognition and OpenCV
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        How developers use Amazon Rekognition and OpenCV
        Avatar of ttandon
        ttandon uses OpenCVOpenCV

        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.

        Avatar of ssshake
        ssshake uses OpenCVOpenCV

        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.

        Avatar of Taylor Host
        Taylor Host uses OpenCVOpenCV

        CV glue. Modified libraries for pattern-detection. Some pattern training tasks. HoG matching. Transform

        Avatar of AfricanStockPhoto
        AfricanStockPhoto uses Amazon RekognitionAmazon Rekognition

        The brains behind our auto-tagging - we can automatically detect contents of images using this

        Avatar of Owen Miller
        Owen Miller uses OpenCVOpenCV

        We read people's CAPTCHA images.

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