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Learning OpenCV 4 Computer Vision with Python 3

You're reading from   Learning OpenCV 4 Computer Vision with Python 3 Get to grips with tools, techniques, and algorithms for computer vision and machine learning

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781789531619
Length 372 pages
Edition 3rd Edition
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Authors (2):
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Joe Minichino Joe Minichino
Author Profile Icon Joe Minichino
Joe Minichino
Joseph Howse Joseph Howse
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Joseph Howse
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Table of Contents (13) Chapters Close

Preface 1. Setting Up OpenCV 2. Handling Files, Cameras, and GUIs FREE CHAPTER 3. Processing Images with OpenCV 4. Depth Estimation and Segmentation 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Building Custom Object Detectors 8. Tracking Objects 9. Camera Models and Augmented Reality 10. Introduction to Neural Networks with OpenCV 11. Other Book You May Enjoy Appendix A: Bending Color Space with the Curves Filter

Converting 10-bit images to 8-bit

As we noted in the previous section, some of the channels of a depth camera use a range larger than 8 bits for their data. A large range tends to be useful for computations, but inconvenient for display, since most computer monitors are only capable of using an 8-bit range, [0, 255], per channel.

OpenCV's cv2.imshow function re-scales and truncates the given input data in order to convert the image for display. Specifically, if the input image's data type is unsigned 16-bit or signed 32-bit integers, cv2.imshow divides the data by 256 and truncates it to the 8-bit unsigned integer range, [0, 255]. If the input image's data type is 32-bit or 64-bit floating-point numbers, cv2.imshow assumes that the data's range is [0.0, 1.0], so it multiplies the data by 255 and truncates it to the 8-bit unsigned integer range, [0, 255]. By...

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