Book Image

Computer Vision Projects with OpenCV and Python 3

By : Matthew Rever
Book Image

Computer Vision Projects with OpenCV and Python 3

By: Matthew Rever

Overview of this book

Python is the ideal programming language for rapidly prototyping and developing production-grade codes for image processing and Computer Vision with its robust syntax and wealth of powerful libraries. This book will help you design and develop production-grade Computer Vision projects tackling real-world problems. With the help of this book, you will learn how to set up Anaconda and Python for the major OSes with cutting-edge third-party libraries for Computer Vision. You'll learn state-of-the-art techniques for classifying images, finding and identifying human postures, and detecting faces within videos. You will use powerful machine learning tools such as OpenCV, Dlib, and TensorFlow to build exciting projects such as classifying handwritten digits, detecting facial features,and much more. The book also covers some advanced projects, such as reading text from license plates from real-world images using Google’s Tesseract software, and tracking human body poses using DeeperCut within TensorFlow. By the end of this book, you will have the expertise required to build your own Computer Vision projects using Python and its associated libraries.
Table of Contents (9 chapters)

Facial landmarks

We're going to learn all about facial landmarks in dlib. Before we can run any code, we need to grab some data that's used for facial features themselves. We'll see what these facial features are and exactly what details we're looking for. This is not included with Python dlib distributions, so you will have to download this. We'll go to the dlib.net/files/ site, where you can see all the source code files; scroll to the bottom and you can see the shape_predictor_68_face_landmarks.dat.bz2 file. Click on it and then save it wherever you keep your Jupyter Notebooks for this book.

Okay, so, what exactly is that? What are these 68 landmarks? Well, these landmarks are a common feature set that was generated by training alpha datasets from something called iBUG (https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/), the intelligent...