Google Cloud Vision API vs OpenCV

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Google Cloud Vision API
Google Cloud Vision API

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OpenCV
OpenCV

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Google Cloud Vision API vs OpenCV: What are the differences?

Google Cloud Vision API: Understand the content of an image by encapsulating powerful machine learning models. 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; 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.

Google Cloud Vision API belongs to "Image Analysis API" category of the tech stack, while OpenCV can be primarily classified under "Image Processing and Management".

Some of the features offered by Google Cloud Vision API are:

  • Powerful Image Analysis
  • Insight From Your Images
  • Detect Inappropriate Content

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.

According to the StackShare community, OpenCV has a broader approval, being mentioned in 39 company stacks & 39 developers stacks; compared to Google Cloud Vision API, which is listed in 25 company stacks and 8 developer stacks.

- No public GitHub repository available -

What is 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.

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 Google Cloud Vision API?
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        What are some alternatives to Google Cloud Vision API and OpenCV?
        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.
        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 Google Cloud Vision API and OpenCV
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        How developers use Google Cloud Vision API 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 Owen Miller
        Owen Miller uses OpenCVOpenCV

        We read people's CAPTCHA images.

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