Book Image

Applied Deep Learning and Computer Vision for Self-Driving Cars

By : Sumit Ranjan, Dr. S. Senthamilarasu
Book Image

Applied Deep Learning and Computer Vision for Self-Driving Cars

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)
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

Applying bitwise_and

In this section, we will apply bitwise_and and multiply all the bits in the black region of the image by 0000 and the white region by 1111, as shown in the following screenshot:

Fig 5.7: bitwise_and used on the black and white images

The bitwise_and conversion is as follows:

Fig 5.8: The bitwise_and conversion

Now, we will implement bitwise_and using OpenCV:

  1. First, import the required libraries:
In[1]: import cv2
In[2]: import numpy as np
In[3]: import matplotlib.pyplot as plt
  1. Then, write a canny edge detection function:
In[4]: def canny_edge(image):
gray_conversion= cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
blur_conversion = cv2.GaussianBlur(gray_conversion, (5,5),0)
canny_conversion = cv2.Canny(blur_conversion, 50,150)
return canny_conversion
  1. Modify the region-of-interest masking function by adding bitwise_and:
In[5]: def reg_of_interest(image):
image_height = image.shape[0]
polygons...