Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By : Joseph Howse, Joe Minichino

Book Image

Learning OpenCV 4 Computer Vision with Python 3 - Third Edition

By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.

Preface

Who this book is for

What this book covers

To get the most out of this book

Setting Up OpenCV

Setting Up OpenCV

Technical requirements

What's new in OpenCV 4

Choosing and using the right setup tools

Running samples

Finding documentation, help, and updates

Free Chapter

Handling Files, Cameras, and GUIs

Handling Files, Cameras, and GUIs

Technical requirements

Basic I/O scripts

Project Cameo (face tracking and image manipulation)

Cameo – an object-oriented design

Processing Images with OpenCV

Processing Images with OpenCV

Technical requirements

Converting images between different color models

Exploring the Fourier transform

Creating modules

Custom kernels – getting convoluted

Modifying the application

Edge detection with Canny

Contour detection

Detecting lines, circles, and other shapes

Depth Estimation and Segmentation

Depth Estimation and Segmentation

Technical requirements

Creating modules

Capturing frames from a depth camera

Converting 10-bit images to 8-bit

Creating a mask from a disparity map

Modifying the application

Depth estimation with a normal camera

Foreground detection with the GrabCut algorithm

Image segmentation with the Watershed algorithm

Detecting and Recognizing Faces

Detecting and Recognizing Faces

Technical requirements

Conceptualizing Haar cascades

Getting Haar cascade data

Using OpenCV to perform face detection

Swapping faces in the infrared

Retrieving Images and Searching Using Image Descriptors

Retrieving Images and Searching Using Image Descriptors

Technical requirements

Understanding types of feature detection and matching

Detecting Harris corners

Detecting DoG features and extracting SIFT descriptors

Detecting Fast Hessian features and extracting SURF descriptors

Using ORB with FAST features and BRIEF descriptors

Filtering matches using K-Nearest Neighbors and the ratio test

Matching with FLANN

Performing homography with FLANN-based matches

A sample application – tattoo forensics

Building Custom Object Detectors

Building Custom Object Detectors

Technical requirements

Understanding HOG descriptors

Understanding NMS

Understanding SVMs

Detecting people with HOG descriptors

Creating and training an object detector

Tracking Objects

Tracking Objects

Technical requirements

Detecting moving objects with background subtraction

Tracking colorful objects using MeanShift and CamShift

Finding trends in motion using the Kalman filter

Tracking pedestrians

Camera Models and Augmented Reality

Camera Models and Augmented Reality

Technical requirements

Understanding 3D image tracking and augmented reality

Implementing the demo application

Improving the 3D tracking algorithm

Introduction to Neural Networks with OpenCV

Introduction to Neural Networks with OpenCV

Technical requirements

Understanding ANNs

Training a basic ANN in OpenCV

Training an ANN classifier in multiple epochs

Recognizing handwritten digits with an ANN

Using DNNs from other frameworks in OpenCV

Detecting and classifying objects with third-party DNNs

Detecting and classifying faces with third-party DNNs

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Appendix A: Bending Color Space with the Curves Filter

Appendix A: Bending Color Space with the Curves Filter

Formulating a curve

Caching and applying a curve

Designing object-oriented curve filters

Emulating photo films

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Using DNNs from other frameworks in OpenCV

OpenCV can load and use DNNs that have been trained in any of the following frameworks:

Caffe (http://caffe.berkeleyvision.org/)
TensorFlow (https://www.tensorflow.org/)
Torch (http://torch.ch/)
Darknet (https://pjreddie.com/darknet/)
ONNX (https://onnx.ai/)
DLDT (https://github.com/opencv/dldt/)

The Deep Learning Deployment Toolkit (DLDT) is part of Intel's OpenVINO Toolkit (https://software.intel.com/openvino-toolkit/) for computer vision. DLDT provides tools for optimizing DNNs from other frameworks and for converting them into a common format. A collection of DLDT-compatible models is freely available in a repository called the Open Model Zoo (https://github.com/opencv/open_model_zoo/). DLDT, the Open Model Zoo, and OpenCV have some of the same people on their development teams; all three of these projects are sponsored by...