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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 Build autonomous vehicles using deep neural networks and behavior-cloning techniques

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Length 332 pages
Edition 1st Edition
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Authors (3):
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Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Author Profile Icon Dr. S. Senthamilarasu
Dr. S. Senthamilarasu
Balu Nair Balu Nair
Author Profile Icon Balu Nair
Balu Nair
Sumit Ranjan Sumit Ranjan
Author Profile Icon Sumit Ranjan
Sumit Ranjan
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars FREE CHAPTER 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

Detection with the grayscale image

We will start by using OpenCV techniques with the grayscale image:

  1. Start by importing the matplotlib (mpimg and pyplot), numpy, and openCV libraries 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. Next, read the image and convert it into a grayscale image:
In[5]: image_color = mpimg.imread('Image_4.12.jpg')
In[6]: image_gray = cv2.cvtColor(image_color, cv2.COLOR_BGR2GRAY)
In[7]: plt.imshow(image_gray, cmap = 'gray')

We have already seen what the image looks like; it is the grayscale conversion of the color image:

Fig 4.13: Color image to grayscale 
  1. Now we will check the shape of the image, which is (515, 763):
In[8]: image_gray.shape
Out[8]: (515, 763)
  1. Now we will apply a filter to identify the white pixels of the image:
In[9]: image_copy = np.copy(image_gray)

# any value that is not white colour
In[10]: image_copy[ (image_copy...
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