Computer Vision Projects with OpenCV and Python 3

By : Rever

1 (1)

Buy this Book

Computer Vision Projects with OpenCV and Python 3

1 (1)

By: Rever

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Setting Up an Anaconda Environment

Introducing and installing Python and Anaconda

Installing additional libraries

Exploring Jupyter Notebook

Summary

Image Captioning with TensorFlow

Technical requirements

Introduction to image captioning

Google Brain im2txt captioning model

Running the captioning code on Jupyter

Retraining the captioning model

Summary

Reading License Plates with OpenCV

Identifying the license plate

Plate utility functions

Finding plate characters

Finding and reading license plates with OpenCV

Result analysis

Summary

Human Pose Estimation with TensorFlow

Pose estimation using DeeperCut and ArtTrack

Single-person pose detection

Multi-person pose detection

Retraining the human pose estimation model

Summary

Handwritten Digit Recognition with scikit-learn and TensorFlow

Acquiring and processing MNIST digit data

Creating and training a support vector machine

Introducing TensorFlow with digit classification

Evaluating the results

Summary

Facial Feature Tracking and Classification with dlib

Introducing dlib

Facial landmarks

Finding 68 facial landmarks in images

Faces in videos

Facial recognition

Summary

Deep Learning Image Classification with TensorFlow

Technical requirements

An introduction to TensorFlow

Using Inception for image classification

Retraining with our own images

Speeding up computation with your GPU

Summary

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Facial Feature Tracking and Classification with dlib

In this chapter, we'll learn about dlib and how to locate faces from images and videos with the help of some examples. We will also learn about facial recognition using dlib.

We are going to cover the following topics: