Alternatives to OpenCV logo

Alternatives to OpenCV

TensorFlow, CImg, OpenGL, PyTorch, and OpenCL are the most popular alternatives and competitors to OpenCV.
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What is OpenCV and what are its top alternatives?

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.
OpenCV is a tool in the Image Processing and Management category of a tech stack.
OpenCV is an open source tool with 53.7K GitHub stars and 44.4K GitHub forks. Here鈥檚 a link to OpenCV's open source repository on GitHub

Top Alternatives to OpenCV

  • TensorFlow

    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. ...

  • CImg

    CImg

    It mainly consists in a (big) single header file CImg.h providing a set of C++ classes and functions that can be used in your own sources, to load/save, manage/process and display generic images. ...

  • OpenGL

    OpenGL

    It is a cross-language, cross-platform application programming interface for rendering 2D and 3D vector graphics. The API is typically used to interact with a graphics processing unit, to achieve hardware-accelerated rendering. ...

  • PyTorch

    PyTorch

    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc. ...

  • OpenCL

    OpenCL

    It is the open, royalty-free standard for cross-platform, parallel programming of diverse processors found in personal computers, servers, mobile devices and embedded platforms. It greatly improves the speed and responsiveness of a wide spectrum of applications in numerous market categories including gaming and entertainment titles, scientific and medical software, professional creative tools, vision processing, and neural network training and inferencing. ...

  • MATLAB

    MATLAB

    Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java. ...

  • FFMPEG

    FFMPEG

    The universal multimedia toolkit.

  • Cloudinary

    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. ...

OpenCV alternatives & related posts

TensorFlow logo

TensorFlow

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Open Source Software Library for Machine Intelligence
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PROS OF TENSORFLOW
  • 23
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    Auto-Differentiation
  • 9
    True Portability
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful
CONS OF TENSORFLOW
  • 8
    Hard
  • 5
    Hard to debug
  • 1
    Documentation not very helpful

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

!

See more
CImg logo

CImg

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A small and open-source C++ toolkit for image processing
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PROS OF CIMG
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      related CImg posts

      OpenGL logo

      OpenGL

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      An environment for developing 2D and 3D graphics applications
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      PROS OF OPENGL
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          PyTorch logo

          PyTorch

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          A deep learning framework that puts Python first
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          PROS OF PYTORCH
          • 11
            Easy to use
          • 9
            Developer Friendly
          • 8
            Easy to debug
          • 5
            Sometimes faster than TensorFlow
          CONS OF PYTORCH
          • 2
            Lots of code

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          Server side

          We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

          • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

          • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

          • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

          Client side

          • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

          • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

          • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

          Cache

          • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

          Database

          • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

          Infrastructure

          • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

          Other Tools

          • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

          • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

          See more
          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M views

          Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

          At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

          TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

          Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

          https://eng.uber.com/horovod/

          (Direct GitHub repo: https://github.com/uber/horovod)

          See more
          OpenCL logo

          OpenCL

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          The open standard for parallel programming of heterogeneous systems
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          PROS OF OPENCL
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            CONS OF OPENCL
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              related OpenCL posts

              MATLAB logo

              MATLAB

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              A high-level language and interactive environment for numerical computation, visualization, and programming
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              PROS OF MATLAB
              • 14
                Simulink
              • 5
                Functions, statements, plots, directory navigation easy
              • 3
                S-Functions
              • 2
                Model based software development
              • 1
                Simple variabel control
              • 1
                REPL
              • 1
                Solve invertible matrix
              CONS OF MATLAB
              • 1
                Parameter-value pairs syntax to pass arguments clunky
              • 0
                Does not support named function arguments
              • 0
                Doesn't allow unpacking tuples/arguments lists with *

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              FFMPEG logo

              FFMPEG

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              The universal multimedia toolkit.
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              PROS OF FFMPEG
              • 4
                Open Source
              CONS OF FFMPEG
                Be the first to leave a con

                related FFMPEG posts

                Shared insights
                on
                FFMPEGFFMPEGOpenCVOpenCV

                Hi Team,

                Could you please suggest which one need to be used in between OpenCV and FFMPEG.

                Thank you in Advance.

                See more
                Cloudinary logo

                Cloudinary

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                An end-to-end image & video management solution for your web and mobile applications
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                PROS OF CLOUDINARY
                • 36
                  Easy setup
                • 30
                  Fast image delivery
                • 26
                  Vast array of image manipulation capabilities
                • 20
                  Free tier
                • 11
                  Heroku add-on
                • 9
                  Reduce development costs
                • 7
                  Amazing support
                • 6
                  Virtually limitless scale
                • 6
                  Great libraries for all languages
                • 6
                  Heroku plugin
                • 5
                  Easy to integrate with Rails
                • 4
                  Cheap
                • 3
                  Shot setup time
                • 3
                  Very easy setup
                • 2
                  Solves alot of image problems.
                • 1
                  Best in the market and includes free plan
                • 1
                  Extremely generous free pricing tier
                • 0
                  Fast image delivery, vast array
                CONS OF CLOUDINARY
                • 2
                  Paid plan is expensive

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