OpenCV

OpenCV is a popular open-source computer vision library that provides a vast range of tools and algorithms for image and video processing. It is originally written in C++, but has interfaces for various programming languages, including Python, Java and so on, it’s a cross-platform library, although this book will focus only on Python. OpenCV is designed to be fast and efficient, making it an ideal choice for real-time computer vision applications, and it has become a standard tool for many computer vision projects.

OpenCV is used for image and video processing, object detection, as well as machine learning. The library comes with many built-in mathematical algorithms and is fast enough for real-time video processing. Today it’s widely used for resolving the related problems. OpenCV's versatility and powerful set of tools make it a popular choice for a wide range of computer vision applications in various industries, including healthcare, automotive, security, entertainment, and more. It has a wide range of applications in today’s world, which include but not limited to:

• Object detection and recognition: OpenCV can be used to detect and recognize objects in images and videos, allowing for applications such as security and surveillance systems.
• Facial recognition: OpenCV has powerful facial recognition capabilities, which can be used in applications such as biometric authentication and identity verification.
• Optical character recognition (OCR): OpenCV can be used to recognize text in images, making it a useful tool for applications such as document scanning and image-to-text conversion.
• Video processing: OpenCV can be used for real-time video processing applications, such as video stabilization and object tracking.
• Medical imaging: OpenCV can be used to process and analyze medical images, allowing for applications such as diagnosis and treatment planning.
• Robotics: OpenCV can be used in robotics applications for tasks such as object detection and tracking, as well as navigation and mapping.
• Augmented reality: OpenCV can be used to create augmented reality applications, such as virtual try-on applications for fashion and beauty products.

Python and OpenCV together form a powerful combination for computer vision projects. Python provides an easy-to-learn language that is great for prototyping and experimenting with ideas, while OpenCV provides a comprehensive set of tools for image and video processing. Python's integration with OpenCV makes it easy to write computer vision applications in a high-level language, allowing developers to quickly build and test their ideas.

Python and OpenCV are two essential tools for anyone interested in computer vision. Python's ease-of-use and flexibility, combined with OpenCV's powerful set of tools and algorithms, make it a go-to choice for many computer vision projects.

OpenCV combined with Python can be used to-

• Read, show and save images.
• Read and show videos or webcam videos with the specific libraries.
• User interaction such as keyboard or mouse operations.
• Draw texts and shapes such as circles, rectangles, triangles, etc.
• Detection of colors and shapes from images, such as circles, rectangles, triangles, etc.
• Detection of faces, eyes and human from images or videos.
• Text recognition in images.
• Modify image quality or colors, e.g. blur, warp transform, blend, resize, adjust colors, etc.
• Machine learning methods, including K-Means, K-Nearest Neighbors, Support Vector Machines, Artificial Neural Networks and Convolutional Neural Networks.

The benefits of using OpenCV-

• Open source and free, easy and simple to learn.
• Fast for processing, especially used for video processing, for example detect objects from videos.
• Offers over 2,500 mathematical algorithms, they are efficient enough not only for image but also for video and real-time processing.
• The algorithms and functions are designed to take advantage of hardware acceleration and multi-core systems.