Showing 5 open source projects for "source testing unit testing"

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  • 1
    Nuke

    Nuke

    Image loading system

    Nuke ILS provides an efficient way to download and display images in your app. It's easy to learn and use thanks to a clear and concise API. Its architecture enables many powerful features while offering virtually unlimited possibilities for customization. Despite the number of features, the framework is lean and compiles in just under 3 seconds¹. Nuke has an automated test suite 2x the size of the codebase itself, ensuring excellent reliability. Every feature is carefully designed and...
    Downloads: 3 This Week
    Last Update:
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  • 2
    Pillow

    Pillow

    The friendly Python Imaging Library fork

    If you've ever wondered or worried about the future of Python's Imaging Library, it's time to stop. Pillow is here to answer your concerns, and offer you more. Pillow is the friendly fork of the Python Imaging Library or PIL, a library that adds image processing capabilities to your Python interpreter. Why turn to Pillow? Aside from offering extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities, Pillow is setuptools...
    Downloads: 56 This Week
    Last Update:
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  • 3
    Marvin Image Processing Framework
    Marvin is an image processing framework that provides features for image and video frame manipulation, multithreading image processing, image filtering and analysis, unit testing, performance analysis and addition of new features via plug-in.
    Downloads: 0 This Week
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  • 4
    libcrn is document image processing library written in C++11 for Linux, Windows, Mac OsX and Google Android. It is a toolbox that allows to create easily software such as OCRs and layout analysis tools.
    Downloads: 1 This Week
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    MongoDB Atlas runs apps anywhere

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  • 5
    A test suite and benchmark for exact Euclidean distance transform algorithms used in Image Processing and computational geometry. It evaluates the exactness and speed of algorithms for a large number of test cases. Results can be visualized in Scilab.
    Downloads: 0 This Week
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