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Raspberry Pi Computer Vision Programming

You're reading from   Raspberry Pi Computer Vision Programming Design and implement computer vision applications with Raspberry Pi, OpenCV, and Python 3

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Product type Paperback
Published in Jun 2020
Publisher Packt
ISBN-13 9781800207219
Length 306 pages
Edition 2nd Edition
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Author (1):
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Ashwin Pajankar Ashwin Pajankar
Author Profile Icon Ashwin Pajankar
Ashwin Pajankar
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Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Introduction to Computer Vision and the Raspberry Pi 2. Chapter 2: Preparing the Raspberry Pi for Computer Vision FREE CHAPTER 3. Chapter 3: Introduction to Python Programming 4. Chapter 4: Getting Started with Computer Vision 5. Chapter 5: Basics of Image Processing 6. Chapter 6: Colorspaces, Transformations, and Thresholding 7. Chapter 7: Let's Make Some Noise 8. Chapter 8: High-Pass Filters and Feature Detection 9. Chapter 9: Image Restoration, Segmentation, and Depth Maps 10. Chapter 10: Histograms, Contours, and Morphological Transformations 11. Chapter 11: Real-Life Applications of Computer Vision 12. Chapter 12: Working with Mahotas and Jupyter 13. Chapter 13: Appendix 14. Other Books You May Enjoy

Creating a negative of an image

In terms of pure mathematics, when we invert the colors of an image, it creates a negative of the image. This inversion operation can be computed by subtracting the color of a pixel from 255. If it is a color image, we invert the color of all the planes. For a grayscale image, we can directly compute the inversion by subtracting it from 255, as follows:

import cv2
img = cv2.imread('/home/pi/book/dataset/4.2.07.tiff', 0)
negative = abs(255 - img)
cv2.imshow('Grayscale', img)
cv2.imshow('Negative', negative)
cv2.waitKey(0)
cv2.destroyAllWindows()

The following is the output of this:

Figure 5.6 – A negative of an image

Figure 5.6 – A negative of an image

Try to find the negative of a color image, we just need to read the image in color mode in the preceding program.

Note:

The negative of a negative will be the original grayscale image. Try this on your own by computing the negative of the negative again for our...

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