Hands-On Vision and Behavior for Self-Driving Cars : Explore Visual Perception, Lane Detection, and Object Classification with Python 3 and OpenCV 4.

Cover
Copyright
About PACKT
Contributors
Table of Contents
Preface
Section 1: OpenCV and Sensors and Signals
Chapter 1: OpenCV Basics and Camera Calibration
Technical requirements
Introduction to OpenCV and NumPy
OpenCV and NumPy
Image size
Grayscale images
RGB images
Working with image files
Working with video files
Working with webcams
Manipulating images
Flipping an image
Blurring an image
Changing contrast, brightness, and gamma
Drawing rectangles and text
Pedestrian detection using HOG
Sliding window

Using HOG with OpenCV
Introduction to the camera
Camera terminology
The components of a camera
Considerations for choosing a camera
Strengths and weaknesses of cameras
Camera calibration with OpenCV
Distortion detection
Calibration
Summary
Questions
Chapter 2: Understanding and Working with Signals
Technical requirements
Understanding signal types
Analog versus digital
Serial versus parallel
Universal Asynchronous Receive and Transmit (UART)
Differential versus single-ended
I2C
SPI
Framed-based serial protocols
Understanding CAN

Ethernet and internet protocols
Understanding UDP
Understanding TCP
Summary
Questions
Further reading
Open source protocol tools
Chapter 3: Lane Detection
Technical requirements
How to perform thresholding
How thresholding works on different color spaces
RGB/BGR
HLS
HSV
LAB
YCbCr
Our choice
Perspective correction
Edge detection
Interpolated threshold
Combined threshold
Finding the lanes using histograms
The sliding window algorithm
Initialization
Coordinates of the sliding windows
Polynomial fitting
Enhancing a video

Partial histogram
Rolling average
Summary
Questions
Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks
Chapter 4: Deep Learning with Neural Networks
Technical requirements
Understanding machine learning and neural networks
Neural networks
Neurons
Parameters
The success of deep learning
Learning about convolutional neural networks
Convolutions
Why are convolutions so great?
Getting started with Keras and TensorFlow
Requirements
Detecting MNIST handwritten digits
What did we just load?

Training samples and labels
One-hot encoding
Training and testing datasets
Defining the model of the neural network
LeNet
The code
The architecture
Training a neural network
CIFAR-10
Summary
Questions
Further reading
Chapter 5: Deep Learning Workflow
Technical requirements
Obtaining the dataset
Datasets in the Keras module
Existing datasets
Your custom dataset
Understanding the three datasets
Splitting the dataset
Understanding classifiers
Creating a real-world dataset
Data augmentation
The model
Tuning convolutional layers