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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]
OpenCV runs on the desktop operating systems: Windows, Linux, macOS, FreeBSD, NetBSD and OpenBSD as well as mobile operating systems: Android, iOS, Maemo, [19] BlackBerry 10 and QNX. [20] The user can get official releases from SourceForge or take the latest sources from GitHub. [21] OpenCV uses CMake.
opencv.github.io /cvat /about / Computer Vision Annotation Tool (CVAT) is a free, open source , web-based image and video annotation tool used for labeling data for computer vision algorithms. Originally developed by Intel , CVAT is designed for use by a professional data annotation team, with a user interface optimized for computer vision ...
CPython is distributed with a large standard library written in a mixture of C and native Python, and is available for many platforms, including Windows (starting with Python 3.9, the Python installer deliberately fails to install on Windows 7 and 8; [140] [141] Windows XP was supported until Python 3.5) and most modern Unix-like systems ...
pip (also known by Python 3's alias pip3) is a package-management system written in Python and is used to install and manage software packages. [4] The Python Software Foundation recommends using pip for installing Python applications and its dependencies during deployment. [5]
Built on top of OpenCV, a widely used computer vision library, Albumentations provides high-performance implementations of various image processing functions. It also offers a rich set of image transformation functions and a simple API for combining them, allowing users to create custom augmentation pipelines tailored to their specific needs.
Originally developed in MATLAB, [14] it was re-written in Python and released as CellProfiler 2.0 in 2010. [2] Version 3.0, supporting volumetric analysis of 3D image stacks and optional deep learning modules, was released in October 2017. [ 16 ]
Python mahotas, an open source computer vision package which includes an implementation of LBPs. OpenCV 's Cascade Classifiers support LBPs as of version 2. VLFeat , an open source computer vision library in C (with bindings to multiple languages including MATLAB) has an implementation .