https://github.com/bekcodingaddict/opencv-basic-samples

OpenCV (Open Source Computer Vision) is an open-source library of computer vision and image processing functions. It provides a wide range of tools, algorithms, and functions that enable developers to work with images and videos, perform various tasks such as object detection, image recognition, facial analysis, and much more.
https://github.com/bekcodingaddict/opencv-basic-samples

Last synced: 8 months ago
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OpenCV (Open Source Computer Vision) is an open-source library of computer vision and image processing functions. It provides a wide range of tools, algorithms, and functions that enable developers to work with images and videos, perform various tasks such as object detection, image recognition, facial analysis, and much more.

• Host: GitHub
• URL: https://github.com/bekcodingaddict/opencv-basic-samples
• Owner: BekCodingAddict
• Created: 2023-03-29T13:44:22.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-06-03T16:05:58.000Z (over 2 years ago)
• Last Synced: 2025-02-22T07:56:30.419Z (12 months ago)
• Language: Python
• Homepage: https://opencv.org/
• Size: 1.54 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# About OpenCV...

-  OpenCV (Open Source Computer Vision) is an open-source library of computer vision and image processing functions. It provides a      wide range of tools, algorithms, and functions that enable developers to work with images and videos, perform various tasks such as object detection, image recognition, facial analysis, and much more.

  Originally developed by Intel in 1999, OpenCV has become a popular choice for computer vision applications due to its efficiency, portability, and extensive community support. It is written in C++ and offers interfaces for several programming languages, including Python, Java, and MATLAB/Octave.

  OpenCV encompasses a broad range of functionalities, including basic image processing operations such as filtering, blending, and thresholding. It also provides advanced features like feature detection, camera calibration, optical flow estimation, 3D reconstruction, and machine learning algorithms for tasks like object detection and classification.

  The library is widely used in various fields, including robotics, augmented reality, surveillance systems, medical imaging, and autonomous vehicles. Its versatility and ease of use have made it a popular choice for both academic research and industrial applications in the computer vision domain.