Learning OpenCV 3 Computer Vision with Python
Author(s): Joe Minichino, Joseph Howse
Publisher: Packt Publishing, Year: 2015
ISBN: 1785283847, 978-1785283840
Describe:
Unleash the power of computer vision with Python with OpenCV
About this book
• Create great apps using OpenCV and Python
• Learn about advanced machine learning concepts
• Take advantage of the power of computer vision with this easy-to-follow guide
for whom is this book
This book is intended for beginners in the world of OpenCV and computer vision, as well as OpenCV veterans who want to know what is new in OpenCV 3, this book is useful as a reference for experts and a training guide for beginners, or for anyone who wants to get to know the concepts of classification and detection of objects in simple and understandable terms. Basic knowledge about Python and programming concepts is required, although the book has an easy learning curve from a theory and coding point of view.
What will you learn?
• Install and learn about Python API OpenCV 3
Understand the basics of image processing and video analysis
• Identify and recognize objects in photos and videos
• Face detection and recognition using OpenCV
• Train and use your object's workbooks
• Learn the concepts of machine learning in the context of computer vision
• Working with artificial neural networks using OpenCV
• Developing your computer's real-world vision application
In detail
OpenCV 3 is a sophisticated computer vision library that allows a wide variety of image and video processing operations. Some of the more exciting and futuristic features such as face recognition or object tracking can be easily achieved with OpenCV 3. Learning the basic concepts behind computer vision algorithms and models and an OpenCV API will allow the development of all types of applications in the real world, including security and surveillance.
Starting with basic image processing, the book will take you to advanced computer vision concepts. Computer vision is a science that is rapidly evolving and exploding its applications in the real world, so this book will attract beginners in computer vision as well as subject matter experts wishing to learn the all-new OpenCV 3.0.0. You will build a theoretical foundation for image processing and video analysis, advances in classification concepts through machine learning, gain technical knowledge that will allow you to create and use object and work detectors, and even track objects in movies or feed the camcorder. Finally, the journey will end in the world of artificial neural networks, along with the development of handwriting number recognition application.
Method and approach
This book is a comprehensive guide to the all-new OpenCV 3 with Python for developing real computer vision applications.
Table of Contents
Chapter 1: Setting Up OpenCV 1
Choose and use the correct setup tools 2
Installing on Windows 2
Use dual stabilizers (do not support depth cameras) 3
Use of CMake and compilers 4
Install on OS X 7
Use MacPorts with prepackaged packages 8
Use MacPorts with your custom packs 10
Homebrew use with prepackaged packages (does not support depth cameras) 12
Use Homebrew with your custom packages 13
Installation on Ubuntu and its variants 13
Ubuntu warehouse use (does not support depth cameras) 14
Build OpenCV from Source 14
Installing on other Unix 15-like systems
Installing the contributing units 16
Sample operation 16
Find documents, assistance, and updates 18
Abstract 19
Chapter Two: Processing files, cameras, and the graphical user interface 21
Basic I / O texts 21
Read / write 22 image file
Conversion between image and raw bytes 24
Access image data using numpy.array 26
Read / write video file 28
Capture camera frames 29
Display pictures in window 31
Show camera frames in window 32
Project Cameo (Facial Tracking and Image Processing) 34
Engraving - Object Oriented Design 35
Summarize video streams with CaptureManager 35 managers
Window and keyboard summary with managers. Windows 41
Apply everything in veil embossing 42
Summary 44
Chapter 3: Image Processing with OpenCV 3 45
Conversion between different color spaces 45
A quick note on BGR 46
Fourier transform 46
High pass filter 47
Low pass filter 49
Create 49 units
Edge Detection 49
Custom beads - Twisted 51
Modify the application 53
Edge detection with Canny 55
Contour 56 revealed
Lines - bounding box, minimal rectangle,
The minimum closed circuit is 57
Fonts - convex features and the Douglas-Poker 60 algorithm
Line and Circle Detection 62
Line 62 revealed
Circle Detection 63
Figures 64 revealed
Summary 65
Chapter Four: Estimating Depth and Segmentation 67
Create 67 units
Capture frames from a 68-depth camera
Create a mask from the unevenness map 71
Hide copy operation 72
Depth estimation with regular camera 74
Hash the object using Watershed and GrabCut 80 algorithms
Example of detection provided with GrabCut 82
Image segmentation using Watershed 84 algorithm
Abstract 87
Chapter Five: Face Detection and Recognition 89
90 concepts of Har Falls
Get data for Haar Cascade 91
Use OpenCV to perform facial detection 91
Static face detection 92
Perform a face detection on Video 94
Face Recognition Procedure 97
Generate data for face recognition 98
Face recognition 100
Prepare training data 101
Data download and face recognition 102
Understanding Eigenfaces 103
Facial recognition procedure with Fisherfaces 105
Conduct facial recognition using LBPH 106
Ignore the results with a confidence score of 106
Abstract 107
Chapter Six: Photo Retrieval and Research
Using photo descriptors 109
Feature detection algorithms 109
Fixing features 110
Feature detection - 110 angles
Extract and describe features using DoG and SIFT 113
Anatomy of a major point 116
Extract and discover features with Fast Hessian and SURF 117
ORB feature detection and feature matching 120
Fast 120
Summary 121
Prot force matching 121
Feature matching with ORB 122
Using K-nearest neighbors matching 125
FLANN 126-based matching
Matching FLANN with homography 130
Sample application - forensic tattoo 133
Save image descriptors to file 133
Find 134 matches
Abstract 137
Chapter Seven: Detecting and Identifying Things 139
139 techniques for detecting and identifying objects
HOG 140 descriptor
Scale the number 142
Site issue 142
Non-Max Suppression (or Non-Maximum) 145
Support vector machines 146
People revealed 147
Creation and training of the object detector 149
Bag of words 149
Sagittarius in computer vision 150
Auto detection 153
What did we just do? 155
SVM and sliding windows 160
Example - revealed a car in scene 161
Oh man where is my car? 171
Abstract 175
Chapter Eight: Tracking Things 177
Moving objects detection 177
Basic motion detection 178
Background Developers - KNN, MOG2, and GMG 181
Mincheft and Cam Shift 185
188 colorful charts
CalcHist 189
CalcBackProject 190
In summary 190
Refer to symbol 191
Cam Shift 193
Kalman candidate 194
Expect and update 195
Example 196
Realistic example - tracing infantry 199
Application workflow 200
Brief deviation - functional programming vs. object-oriented programming 200
Infantry class 202
The main program 205
Where do we go from here? 207
Summary 208
Chapter 9: Neural Networks with OpenCV - Introduction 209
Artificial neural networks 209
Neurons and theory 210
Structure of ANN 211
Grid layers by exa
