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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

Converting images from RGB to grayscale

In this section, we will use a powerful image-processing library called OpenCV and use it to convert an image to grayscale. We will take a color image of a road, as shown in the following screenshot:

Fig 4.10: Sample image

In the following steps, we will convert the color image into grayscale using the OpenCV library:

  1. First, import the matplotlib (mpimg and pyplot), numpy, and openCV libraries:
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. Next, import the image for the operation:
In[5]: image_color = mpimg.imread('image.jpg')
In[6]: plt.imshow(image_color)

Let's see what our image looks like: 

Fig 4.11: Reading an image using matplotlib
  1. In the preceding screenshot, the image has three channels because it is in an RGB format. Let's check the size of the image. We can see that the value is (515, 763, 3):
In [7]: image_color...
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