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Applied Deep Learning and Computer Vision for Self-Driving Cars

You're reading from  Applied Deep Learning and Computer Vision for Self-Driving Cars

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Pages 332 pages
Edition 1st Edition
Languages
Authors (2):
Sumit Ranjan Sumit Ranjan
Profile icon Sumit Ranjan
Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Profile icon Dr. S. Senthamilarasu
View More author details

Table of Contents (18) Chapters

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars 3. Dive Deep into Deep Neural Networks 4. Implementing a Deep Learning Model Using Keras 5. Section 2: Deep Learning and Computer Vision Techniques for SDC
6. Computer Vision for Self-Driving Cars 7. Finding Road Markings Using OpenCV 8. Improving the Image Classifier with CNN 9. Road Sign Detection Using Deep Learning 10. Section 3: Semantic Segmentation for Self-Driving Cars
11. The Principles and Foundations of Semantic Segmentation 12. Implementing Semantic Segmentation 13. Section 4: Advanced Implementations
14. Behavioral Cloning Using Deep Learning 15. Vehicle Detection Using OpenCV and Deep Learning 16. Next Steps 17. Other Books You May Enjoy

Color space manipulation 

In this section, we will learn how to manually convert RGB to HSV and RGB to grayscale in an image using the OpenCV computer vision library. 

Some examples of the conversion from RGB to HSV can be seen in the following screenshot:

Fig 4.20: RGB to HSV conversion

In the preceding diagram, we can see how the values of the image formats are different in RGB and HSV. For example, red is represented as (255,0,0) in the RGB format and as (0,100,100) in the HSV format.

Next, we will convert RGB to HSV using Python:

  1. We are going to use the matplotlib (pyplot and mpimg), numpy, and openCV libraries, which can be imported as follows:
In[1]: import matplotlib.image as mpimg
In[2]: import matplotlib.pyplot as plt
In[3]: import numpy as np
In[4]: import cv2
  1. Then we will read and display the image using OpenCV:
In[5]:image = cv2.imread('Test_image.jpg')
  1. Now print and check the dimensions of the image. Because it is a color image, it will...
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