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Applied Deep Learning and Computer Vision for Self-Driving Cars

Applied Deep Learning and Computer Vision for Self-Driving Cars

By : Sumit Ranjan, Dr. S. Senthamilarasu
4.3 (9)
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Applied Deep Learning and Computer Vision for Self-Driving Cars

Applied Deep Learning and Computer Vision for Self-Driving Cars

4.3 (9)
By: Sumit Ranjan, Dr. S. Senthamilarasu

Overview of this book

Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving. By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.
Table of Contents (18 chapters)
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1
Section 1: Deep Learning Foundation and SDC Basics
5
Section 2: Deep Learning and Computer Vision Techniques for SDC
10
Section 3: Semantic Segmentation for Self-Driving Cars
13
Section 4: Advanced Implementations

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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