Computer Vision Projects with OpenCV and Python 3

Gain a working knowledge of advanced machine learning and explore Python’s powerful tools for extracting data from images and videos

Computer Vision Projects with OpenCV and Python 3

Matthew Rever

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Gain a working knowledge of advanced machine learning and explore Python’s powerful tools for extracting data from images and videos
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Book Details

ISBN 139781789954555
Paperback182 pages

Book Description

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.

What You Will Learn

  • Install and run major Computer Vision packages within Python
  • Apply powerful support vector machines for simple digit classification
  • Understand deep learning with TensorFlow
  • Build a deep learning classifier for general images
  • Use LSTMs for automated image captioning
  • Read text from real-world images
  • Extract human pose data from images

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Book Details

ISBN 139781789954555
Paperback182 pages
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