Preface

In this book, you learn how to leverage the power of Python, OpenCV, and TensorFlow to solve problems in computer vision. Python is the ideal programming language for rapidly prototyping and developing production-grade code for image processing and computer vision, with its robust syntax and wealth of powerful libraries.

This book will be your practical guide to designing and developing production-grade computer vision projects that tackle real-world problems. You will learn how to set up Anaconda Python for the major OSes with cutting-edge third-party libraries for computer vision, and you will learn state-of-the-art techniques of classifying images and finding and identifying humans within videos. You will gain the expertise required to build your own computer vision projects using Python and its associated libraries by the end of this book.

Who this book is for

Python programmers and machine learning developers who wish to build exciting computer vision projects using the power of machine learning and OpenCV will find this book to be useful. The only prerequisite for this book is that you should have a sound knowledge of Python programming.

What this book covers

Chapter 1, Setting Up an Anaconda Environment, helps you download and install Python 3 and Anaconda along with their additional libraries, and also discusses the basic concepts of Jupyter Notebook.
Chapter 2, Image Captioning with TensorFlow, introduces you to image captioning using the Google Brain im2txt captioning model, which is a pre-defined model. We will also learn the process of retraining the model for our own customized images.
Chapter 3, Reading License Plates with OpenCV, introduces you to reading license plates using the plate utility functions. We learn the process of finding the possible candidates for our license plate characters, which is key to reading license plates.
Chapter 4, Human Pose Estimation with TensorFlow, introduces you to pose estimation using the DeeperCut algorithm and the pre-defined ArtTrack model. You will learn about single-person and multi-person pose detection, and you'll learn how to retrain the model for images and videos.

Chapter 5, Handwritten Digit Recognition with scikit-learn and TensorFlow, helps you acquire and process MNIST digit data. You will learn how to create and train a support vector machine, and also learn about digit classification using TensorFlow.
Chapter 6, Facial Feature Tracking and Classification with dlib, helps you detect facial features from images and videos, which helps us carry out facial recognition.
Chapter 7, Deep Learning Image Classification with TensorFlow, helps you learn image classification using a pre-trained Inception model. The chapter also teaches you how to retrain the model for customized images.

To get the most out of this book

Some programming experience in Python and its packages, such as TensorFlow, OpenCV, and dlib, will help you get the most out of this book.

A powerful GPU with CUDA support is required to retrain the models.

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Download the color images

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Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The word_counts.txt file contains a vocabulary list with the number of counts from our trained model, which our image caption generator is going to need."

A block of code is set as follows:

testfile = 'test_images/dog.jpeg'

figure()
imshow(imread(testfile))

Any command-line input or output is written as follows:

conda install -c menpo dlib

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Click the Download button."

Warnings or important notes appear like this.

Tips and tricks appear like this.

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