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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

By : Joe Minichino, Joseph Howse
2.1 (7)
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Learning OpenCV 3 Computer Vision with Python (Update)

Learning OpenCV 3 Computer Vision with Python (Update)

2.1 (7)
By: Joe Minichino, Joseph Howse

Overview of this book

OpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the development of all sorts of real-world applications, including security and surveillance. Starting with basic image processing operations, the book will take you through to advanced computer vision concepts. Computer vision is a rapidly evolving science whose applications in the real world are exploding, so this book will appeal to computer vision novices as well as experts of the subject wanting to learn the brand new OpenCV 3.0.0. You will build a theoretical foundation of image processing and video analysis, and progress to the concepts of classification through machine learning, acquiring the technical know-how that will allow you to create and use object detectors and classifiers, and even track objects in movies or video camera feeds. Finally, the journey will end in the world of artificial neural networks, along with the development of a hand-written digits recognition application.
Table of Contents (11 chapters)
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6
6. Retrieving Images and Searching Using Image Descriptors
10
Index

Converting between different color spaces


There are literally hundreds of methods in OpenCV that pertain to the conversion of color spaces. In general, three color spaces are prevalent in modern day computer vision: gray, BGR, and Hue, Saturation, Value (HSV).

  • Gray is a color space that effectively eliminates color information translating to shades of gray: this color space is extremely useful for intermediate processing, such as face detection.

  • BGR is the blue-green-red color space, in which each pixel is a three-element array, each value representing the blue, green, and red colors: web developers would be familiar with a similar definition of colors, except the order of colors is RGB.

  • In HSV, hue is a color tone, saturation is the intensity of a color, and value represents its darkness (or brightness at the opposite end of the spectrum).

A quick note on BGR

When I first started dealing with the BGR color space, something wasn't adding up: the [0 255 255] value (no blue, full green, and full...

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