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Computer Vision Projects with OpenCV and Python 3

You're reading from   Computer Vision Projects with OpenCV and Python 3 Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789954555
Length 182 pages
Edition 1st Edition
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Author (1):
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Matthew Rever Matthew Rever
Author Profile Icon Matthew Rever
Matthew Rever
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Table of Contents (9) Chapters Close

Preface 1. Setting Up an Anaconda Environment FREE CHAPTER 2. Image Captioning with TensorFlow 3. Reading License Plates with OpenCV 4. Human Pose Estimation with TensorFlow 5. Handwritten Digit Recognition with scikit-learn and TensorFlow 6. Facial Feature Tracking and Classification with dlib 7. Deep Learning Image Classification with TensorFlow 8. Other Books You May Enjoy

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...

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