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  2. Python Imaging Library - Wikipedia

    en.wikipedia.org/wiki/Python_Imaging_Library

    Python Imaging Library is a free and open-source additional library for the Python programming language that adds support for opening, manipulating, and saving many different image file formats. It is available for Windows, Mac OS X and Linux. The latest version of PIL is 1.1.7, was released in September 2009 and supports Python 1.5.2–2.7. [3]

  3. Tkinter - Wikipedia

    en.wikipedia.org/wiki/Tkinter

    Tkinter is a binding to the Tk GUI toolkit for Python. It is the standard Python interface to the Tk GUI toolkit, [1] and is Python's de facto standard GUI. [2] Tkinter is included with standard Linux, Microsoft Windows and macOS installs of Python. The name Tkinter comes from Tk interface.

  4. Asus Tinker Board - Wikipedia

    en.wikipedia.org/wiki/Asus_Tinker_Board

    RAM access tested using the mbw benchmark is 25% faster than the Raspberry Pi 3. SD card (microSD) access is about twice as fast at 37 MiB/s for buffered reads (compared to typically around 18 MiB/s for the Pi 3 [ 30 ] ) due to the Tinker Board's SDIO 3.0 interface, while cached reads can reach speeds up to 770 MiB/s.

  5. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    The Raspberry Pi single-board computer project has adopted Python as its main user-programming language. LibreOffice includes Python and intends to replace Java with Python. Its Python Scripting Provider is a core feature [ 234 ] since Version 4.0 from 7 February 2013.

  6. Multiple-camera setup - Wikipedia

    en.wikipedia.org/wiki/Multiple-camera_setup

    The multiple-camera setup, multiple-camera mode of production, multi-camera or simply multicam is a method of filmmaking, television production and video production. Several cameras—either film or professional video cameras —are employed on the set and simultaneously record or broadcast a scene.

  7. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words.