Book Image

Artificial Intelligence with Python - Second Edition

By : Alberto Artasanchez, Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Alberto Artasanchez, Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
24
Other Books You May Enjoy
25
Index

Tracking objects using color spaces

An image can be represented using various color spaces. The RGB color space is probably the most popular color space, but it does not lend itself nicely to applications like object tracking. So, we will be using the HSV color space instead. It is an intuitive color space model that is closer to how humans perceive color. You can learn more about it here:

https://en.wikipedia.org/wiki/HSL_and_HSV

We can convert the captured frame from RGB to the HSV color space, and then use color thresholding to track any given object. We should note that we need to know the color distribution of the object so that we can select the appropriate ranges for thresholding.

Create a new Python file and import the following packages:

import cv2
import numpy as np

Define a function to grab the current frame from the webcam. Start by reading it from the video capture object:

# Define a function to get the current frame from the webcam
def get_frame...