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Learning OpenCV 3 Computer Vision with Python (Update)

You're reading from   Learning OpenCV 3 Computer Vision with Python (Update) Unleash the power of computer vision with Python using OpenCV

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781785283840
Length 266 pages
Edition 1st Edition
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Toc

Table of Contents (11) Chapters Close

Preface 1. Setting Up OpenCV FREE CHAPTER 2. Handling Files, Cameras, and GUIs 3. Processing Images with OpenCV 3 4. Depth Estimation and Segmentation 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Detecting and Recognizing Objects 8. Tracking Objects 9. Neural Networks with OpenCV – an Introduction Index

Basic I/O scripts

Most CV applications need to get images as input. Most also produce images as output. An interactive CV application might require a camera as an input source and a window as an output destination. However, other possible sources and destinations include image files, video files, and raw bytes. For example, raw bytes might be transmitted via a network connection, or they might be generated by an algorithm if we incorporate procedural graphics into our application. Let's look at each of these possibilities.

Reading/writing an image file

OpenCV provides the imread() and imwrite() functions that support various file formats for still images. The supported formats vary by system but should always include the BMP format. Typically, PNG, JPEG, and TIFF should be among the supported formats too.

Let's explore the anatomy of the representation of an image in Python and NumPy.

No matter the format, each pixel has a value, but the difference is in how the pixel is represented...

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