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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

Masking a copy operation


As part of the previous chapter's work, we wrote copyRect() as a copy operation that limits itself to the given rectangles of a source and destination image. Now, we want to apply further limits to this copy operation. We want to use a given mask that has the same dimensions as the source rectangle.

We shall copy only those pixels in the source rectangle where the mask's value is not zero. Other pixels shall retain their old values from the destination image. This logic, with an array of conditions and two arrays of possible output values, can be expressed concisely with the numpy.where() function that we have recently learned.

Let's open rects.py and edit copyRect() to add a new mask argument. This argument may be None, in which case, we fall back to our old implementation of the copy operation. Otherwise, we next ensure that mask and the images have the same number of channels. We assume that mask has one channel but the images may have three channels (BGR). We can...

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