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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 video files

Using videos in OpenCV is very simple; in fact, every frame is an image and can be manipulated with the methods that we have already analyzed.

To open a video in OpenCV, you need to call the VideoCapture() method:

cap = cv2.VideoCapture("video.mp4")

After that, you can call read(), typically in a loop, to retrieve a single frame. The method returns a tuple with two values:

  • A Boolean value that is false when the video is finished
  • The next frame:
ret, frame = cap.read()

To save a video, there is the VideoWriter object; its constructor accepts four parameters:

  • The filename
  • A FOURCC (four-character code) of the video code
  • The number of frames per second
  • The resolution

Take the following example:

mp4 = cv2.VideoWriter_fourcc(*'MP4V')writer = cv2.VideoWriter('video-out.mp4', mp4, 15, (640, 480))

Once VideoWriter has been created, the write() method can be used to add a frame to the video file:

writer.write(image)

When you have finished using the VideoCapture and VideoWriter objects, you should call their release method:

cap.release()
writer.release()

Working with webcams

Webcams are handled similarly to a video in OpenCV; you just need to provide a different parameter to VideoCapture, which is the 0-based index identifying the webcam:

cap = cv2.VideoCapture(0)

The previous code opens the first webcam; if you need to use a different one, you can specify a different index.

Now, let's try manipulating some images.

You have been reading a chapter from
Hands-On Vision and Behavior for Self-Driving Cars
Published in: Oct 2020
Publisher: Packt
ISBN-13: 9781800203587
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