The Python programming language is an ideal platform for rapidly prototyping and developing production-grade codes for image processing and computer vision with its robust syntax and wealth of powerful libraries. Python’s wealth of powerful packages along with its clear syntax make state-of-the art computer vision and machine learning accessible to developers with a variety of backgrounds.
This is a hands-on, practical approach, designed to teach you the skills required to develop computer vision solutions in Python. At the very beginning you will learn how to set up Anaconda Python for the major OS’s with cutting-edge third-party libraries for computer vision. Than you’ll see how to read text from license plates from real-world images using Google’s Tesseract Software & how to track human body poses using “DeeperCut” within TensorFlow. By end of this course, you’ll know the complete insight into basic tools of computer vision and be able to put it into practice.
Contents and Overview
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Computer Vision Projects with Python 3 start by showing you how to set up Anaconda Python for the major OSes with cutting-edge third-party libraries for computer vision. You’ll learn state-of-the-art techniques to classify images and find and identify humans within videos. Next, you’ll understand how to set up Anaconda Python 3 for the major OSes (Windows, Mac, and Linux) and augment it with the powerful vision and machine learning tools OpenCV and TensorFlow, as well as Dlib. You’ll be taken through the handwritten digits classifier and then move on to detecting facial features and finally develop a general image classifier. By the end of this course, you’ll know the basic tools of computer vision and be able to put it into practice.
The second course, Advanced Computer Vision Projects will equip you with the tools and skills to utilize the latest and greatest algorithms in computer vision, making applications that weren’t possible until recent years. In this course, you’ll continue to use TensorFlow and extend it to generate full captions from images. Later, you’ll see how to read text from license plates from real-world images using Google’s Tesseract Software. Finally, you’ll see how to track human body poses using “DeeperCut” within TensorFlow. At the end of this course, you’ll develop an application that can estimate human poses within images and will be able to take on the world with best practices in computer vision with machine learning.
About the Authors:
Matthew Rever is an image processing and computer vision engineer at a major national laboratory. He has years of experience automating the analysis of complex scientific data, as well as the control of sophisticated instruments. He has applied computer vision technology to save a great many hours of valuable human labor. He is also enthusiastic about making the latest developments in computer vision accessible to developers of all backgrounds.
