Google Cloud Vision API vs OpenCV

Google Cloud Vision API
Google Cloud Vision API

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OpenCV
OpenCV

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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 are some alternatives to Google Cloud Vision API 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.
Cloudinary
Cloudinary is a cloud-based service that streamlines websites and mobile applications' entire image and video management needs - uploads, storage, administration, manipulations, and delivery.
imgix
imgix is a real-time image processing service and CDN. Resize, crop, and edit images simply by changing their URLs.
FFMPEG
The universal multimedia toolkit.
Aviary
Aviary's beautiful photo editor is powerful, customizable, and can be plugged into your mobile apps and website in minutes. The best photo editing for your app or website Our 3500+ partners chose Aviary because our editor is powerful, customizable, and integration takes just minutes. Aviary comes preloaded with a ton of intuitive features that your users will love.
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What tools integrate with Google Cloud Vision API?
What tools integrate with 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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