Book Image

Hands-On Vision and Behavior for Self-Driving Cars

By : Luca Venturi, Krishtof Korda
Book Image

Hands-On Vision and Behavior for Self-Driving Cars

By: Luca Venturi, Krishtof Korda

Overview of this book

The visual perception capabilities of a self-driving car are powered by computer vision. The work relating to self-driving cars can be broadly classified into three components - robotics, computer vision, and machine learning. This book provides existing computer vision engineers and developers with the unique opportunity to be associated with this booming field. You will learn about computer vision, deep learning, and depth perception applied to driverless cars. The book provides a structured and thorough introduction, as making a real self-driving car is a huge cross-functional effort. As you progress, you will cover relevant cases with working code, before going on to understand how to use OpenCV, TensorFlow and Keras to analyze video streaming from car cameras. Later, you will learn how to interpret and make the most of lidars (light detection and ranging) to identify obstacles and localize your position. You’ll even be able to tackle core challenges in self-driving cars such as finding lanes, detecting pedestrian and crossing lights, performing semantic segmentation, and writing a PID controller. By the end of this book, you’ll be equipped with the skills you need to write code for a self-driving car running in a driverless car simulator, and be able to tackle various challenges faced by autonomous car engineers.
Table of Contents (17 chapters)
1
Section 1: OpenCV and Sensors and Signals
5
Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks
12
Section 3: Mapping and Controls

Edge detection

The next step is detecting the edges, and we will use the green channel for that, as during our experiments, it gave good results. Please be aware that you need to experiment with the images and videos taken from the country where you plan to run the software, and with many different light conditions. Most likely, based on the color of the lines and the colors in the image, you might want to choose a different channel, possibly from another color space; you can convert the image into different color spaces using cvtColor(), for example:

img_hls = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HLS).astype(np.float)

We will stick to green.

OpenCV has several ways to compute edge detection, and we are going to use Scharr, as it performs quite well. Scharr computes a derivative, so it detects the difference in colors in the image. We are interested in the X axis, and we want the result to be a 64-bit float, so our call would be like this:

edge_x = cv2.Scharr(channel, cv2...