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Computer Vision Projects with OpenCV and Python 3

Computer Vision Projects with OpenCV and Python 3

By : Rever
1 (1)
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Computer Vision Projects with OpenCV and Python 3

Computer Vision Projects with OpenCV and Python 3

1 (1)
By: 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)
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Pose estimation using DeeperCut and ArtTrack

Human pose estimation is the process of estimating the configuration of the body (pose) from an image or video. It includes landmarks (points), which are similar to joints such as the feet, ankles, chin, shoulder, elbows, hands, head, and so on. We will be doing this automatically using deep learning. If you consider a face, the landmarks are relatively rigid or, rather, relatively constant from face to face, such as the relative position of the eyes to the nose, the mouth to the chin, and so forth.

The following photo provides an example:

Although the body structure remains the same, our bodies aren't rigid. So, we need to detect the different parts of our body relative to the other parts. For example, detecting the feet relative to the knee is very challenging compared to facial detection. Also, we can move our hands and feet...

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