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OpenCV

818
863
+ 1
88
TensorFlow

2.7K
2.9K
+ 1
77
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OpenCV vs TensorFlow: What are the differences?

Developers describe OpenCV 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. On the other hand, TensorFlow is detailed as "Open Source Software Library for Machine Intelligence". 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.

OpenCV belongs to "Image Processing and Management" category of the tech stack, while TensorFlow can be primarily classified under "Machine Learning Tools".

"Computer Vision" is the top reason why over 19 developers like OpenCV, while over 16 developers mention "High Performance" as the leading cause for choosing TensorFlow.

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, TensorFlow has a broader approval, being mentioned in 200 company stacks & 135 developers stacks; compared to OpenCV, which is listed in 39 company stacks and 39 developer stacks.

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Pros of OpenCV
Pros of TensorFlow
  • 30
    Computer Vision
  • 17
    Open Source
  • 11
    Imaging
  • 9
    Machine Learning
  • 8
    Face Detection
  • 6
    Great community
  • 4
    Realtime Image Processing
  • 2
    Image Augmentation
  • 1
    Helping almost CV problem
  • 25
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    True Portability
  • 9
    Auto-Differentiation
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful

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Cons of OpenCV
Cons of TensorFlow
    Be the first to leave a con
    • 9
      Hard
    • 6
      Hard to debug
    • 1
      Documentation not very helpful

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    - No public GitHub repository available -

    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.

    What is 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.

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    What companies use OpenCV?
    What companies use TensorFlow?
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    What tools integrate with OpenCV?
    What tools integrate with TensorFlow?

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    What are some alternatives to OpenCV and TensorFlow?
    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
    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 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
    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
    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.
    See all alternatives