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Aviary

Stacks21

Followers21

+ 1

Votes2

OpenCV

Stacks335

Followers278

+ 1

Votes67

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Aviary vs OpenCV: What are the differences?

Aviary: The world's best photo editing SDK. 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; 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.

Aviary and OpenCV belong to "Image Processing and Management" category of the tech stack.

Some of the features offered by Aviary are:

Enhance
Effects
Frames

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.

- No public GitHub repository available -

What is 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.

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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Why do developers choose Aviary?

Why do developers choose OpenCV?

2
Nice effects

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21
Computer Vision
15
Open Source
10
Imaging
8
Machine Learning
5
Face Detection
4
Great community
2
Realtime Image Processing

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What tools integrate with Aviary?

What tools integrate with OpenCV?

What are some alternatives to Aviary and OpenCV?

Greenhouse

Greenhouse automatically builds your project for every commit you make and runs all your unit or UI tests on the emulator/simulator or on real hardware. Automatic build distribution is also supported so that your team and clients are always up to date with the latest state of your apps.

Apiary

It takes more than a simple HTML page to thrill your API users. The right tools take weeks of development. Weeks that apiary.io saves.

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.

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Decisions about Aviary and OpenCV

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How developers use Aviary and OpenCV

ttandon uses

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.

ssshake uses

OpenCV

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.