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Hands-On Vision and Behavior for Self-Driving Cars

You're reading from   Hands-On Vision and Behavior for Self-Driving Cars Explore visual perception, lane detection, and object classification with Python 3 and OpenCV 4

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781800203587
Length 374 pages
Edition 1st Edition
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Authors (2):
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Krishtof Korda Krishtof Korda
Author Profile Icon Krishtof Korda
Krishtof Korda
Luca Venturi Luca Venturi
Author Profile Icon Luca Venturi
Luca Venturi
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: OpenCV and Sensors and Signals
2. Chapter 1: OpenCV Basics and Camera Calibration FREE CHAPTER 3. Chapter 2: Understanding and Working with Signals 4. Chapter 3: Lane Detection 5. Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks
6. Chapter 4: Deep Learning with Neural Networks 7. Chapter 5: Deep Learning Workflow 8. Chapter 6: Improving Your Neural Network 9. Chapter 7: Detecting Pedestrians and Traffic Lights 10. Chapter 8: Behavioral Cloning 11. Chapter 9: Semantic Segmentation 12. Section 3: Mapping and Controls
13. Chapter 10: Steering, Throttle, and Brake Control 14. Chapter 11: Mapping Our Environments 15. Assessments 16. Other Books You May Enjoy

Working with image files

OpenCV provides a very simple way to load images, using imread():

import cv2
image = cv2.imread('test.jpg')

To show the image, you can use imshow(), which accepts two parameters:

  • The name to write on the caption of the window that will show the image
  • The image to be shown

Unfortunately, its behavior is counterintuitive, as it will not show an image unless it is followed by a call to waitKey():

cv2.imshow("Image", image)cv2.waitKey(0)

The call to waitKey() after imshow() will have two effects:

  • It will actually allow OpenCV to show the image provided to imshow().
  • It will wait for the specified amount of milliseconds, or until a key is pressed if the amount of milliseconds passed is <=0. It will wait indefinitely.

An image can be saved on disk using the imwrite() method, which accepts three parameters:

  • The name of the file
  • The image
  • An optional format-dependent parameter:
cv2.imwrite("out.jpg", image)

Sometimes, it can be very useful to combine multiple pictures by putting them next to each other. Some examples in this book will use this feature extensively to compare images.

OpenCV provides two methods for this purpose: hconcat() to concatenate the pictures horizontally and vconcat() to concatenate them vertically, both accepting as a parameter a list of images. Take the following example:

black = np.zeros([50, 50], dtype=np.uint8)white = np.full([50, 50], 255, dtype=np.uint8)cv2.imwrite("horizontal.jpg", cv2.hconcat([white, black]))cv2.imwrite("vertical.jpg", cv2.vconcat([white, black]))

Here's the result:

Figure 1.2 – Horizontal concatenation with hconcat() and vertical concatenation with vconcat()

Figure 1.2 – Horizontal concatenation with hconcat() and vertical concatenation with vconcat()

We could use these two methods to create a chequerboard pattern:

row1 = cv2.hconcat([white, black])row2 = cv2.hconcat([black, white])cv2.imwrite("chess.jpg", cv2.vconcat([row1, row2]))

You will see the following chequerboard:

Figure 1.3 – A chequerboard pattern created using hconcat() in combination with vconcat()

Figure 1.3 – A chequerboard pattern created using hconcat() in combination with vconcat()

After having worked with images, it's time we work with videos.

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Hands-On Vision and Behavior for Self-Driving Cars
Published in: Oct 2020
Publisher: Packt
ISBN-13: 9781800203587
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