The Open Source Computer Vision Library has >2500 algorithms, extensive documentation and sample code for real-time computer vision. It works on Windows, Linux, Mac OS X, Android, iOS in your browser through JavaScript.
Languages: C++, Python, Julia, Javascript
Homepage: https://opencv.org
Q&A forum: https://forum.opencv.org/
Documentation: https://docs.opencv.org
Source code: https://github.com/opencv
Please pay special attention to our tutorials! https://docs.opencv.org/master
Books about the OpenCV are described here: https://opencv.org/books.html
Features
- computer vision
- deep learning
- opencv
Project Samples
License
BSD License
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User Reviews
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A Powerful Computer Vision Library — Great Tools, Hard to Master I use OpenCV regularly for projects that involve image recognition, object tracking, and video analysis. It’s one of the most versatile computer vision libraries available — whether you’re detecting faces in photos, filtering frames in real time, or building custom vision pipelines, there’s usually a tool or function for the job. The fact that it supports multiple languages and runs on many platforms makes it easy to integrate into different systems. That said, it’s not super beginner-friendly. You’ll get much farther with some background in image processing concepts, and even then, reading the docs and community examples becomes part of the workflow. Some parts of the API feel inconsistent or clunky, especially if you’re coming from higher-level ML frameworks designed for ease of use
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Hello my name is Julio Herrera Velutini. I found OpenCV as a powerful and versatile open-source library for computer vision and image processing. Its extensive functionalities, including object detection, facial recognition, and real-time video analysis, make it a go-to tool for developers and researchers.
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Excellent library.
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Good for fast prototyping some image processing with high level API.
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Good