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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
OpenCV is one of the best open source libraries available, and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you’re completely new to computer vision, or have a basic understanding of its concepts, Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects.
You’ll begin with the installation of OpenCV and the basics of image processing. Then, you’ll cover user interfaces and get deeper into image processing. As you progress through the book, you'll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters, you'll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module.
By the end of this book, you'll be familiar with the basics of Open CV, such as matrix operations, filters, and histograms, and you'll have mastered commonly used computer vision techniques to build OpenCV projects from scratch.
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Veröffentlichungsjahr: 2018
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Learn OpenCV 4 by Building Projects Second Edition
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Contributors
About the authors
David Millán Escrivá was eight years old when he wrote his first program on an 8086 PC using the BASIC language. He completed his studies in IT from the Universitat Politécnica de Valencia with honors in human-computer interaction supported by computer vision with OpenCV (v0.96). He has a master's degree in artificial intelligence, computer graphics, and pattern recognition, focusing on pattern recognition and computer vision. He also has more than nine years' experience in computer vision, computer graphics, and pattern recognition. He is the author of the Damiles Blog, where he publishes articles and tutorials on OpenCV, computer vision in general, and optical character recognition algorithms.
Vinícius G. Mendonçais a computer graphics university professor at Pontifical Catholic University of Paraná (PUCPR). He started programming with C++ back in 1998, and ventured into the field of computer gaming and computer graphics back in 2006. He is currently a mentor at the Apple Developer Academy in Brazil, working with, and teaching, metal, machine learning and computer vision for mobile devices. He has served as a reviewer on other Pack books, including OpenNI Cookbook, and Mastering OpenCV and Computer Vision with OpenCV 3 and Qt5. In his research, he has used Kinect, OpenNI, and OpenCV to recognize Brazilian sign language gestures. His areas of interest include mobile, OpenGL, image processing, computer vision, and project management.
Prateek Joshi is an artificial intelligence researcher, an author of eight published books, and a TEDx speaker. He has been featured in Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and many more publications. He is the founder of Pluto AI, a venture-funded Silicon Valley start-up building an intelligence platform for water facilities. He graduated from the University of Southern California with a Master's degree specializing in Artificial Intelligence. He has previously worked at NVIDIA and Microsoft Research.
About the reviewers
Marc Amberg is an experienced machine learning and computer vision engineer with a proven history of working in the IT and service industries. He is skilled in Python, C/C++, OpenGL, 3D Reconstruction, and Java. He is a strong engineering professional with a master's degree focused on computer science (Image, Vision, and Interactions) from Université des Sciences et Technologies de Lille (Lille I).
Vincent Kok currently works as a software platform application engineer with Intel in the transportation industry sector. He graduated from the University Sains Malaysia (USM) with a degree in electronic engineering. Currently, he is pursuing his master's degree in embedded system engineering at USM. Vincent actively involves himself with the developer community and regularly participates in the Maker Faire, which is held in different parts of the world. He likes to design electronic hardware kits and gives soldering/arduino classes for beginners during his spare time.
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Table of Contents
Title Page
Copyright and Credits
Learn OpenCV 4 by Building Projects Second Edition
About Packt
Why subscribe?
Packt.com
Contributors
About the authors
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Code in Action
Conventions used
Get in touch
Reviews
Getting Started with OpenCV
Understanding the human visual system
How do humans understand image content?
Why is it difficult for machines to understand image content?
What can you do with OpenCV?
Inbuilt data structures and input/output
Image processing operations
GUI
Video analysis
3D reconstruction
Feature extraction
Object detection
Machine learning
Computational photography
Shape analysis
Optical flow algorithms
Face and object recognition
Surface matching
Text detection and recognition
Deep learning
Installing OpenCV
Windows
Mac OS X
Linux
Summary
An Introduction to the Basics of OpenCV
Technical requirements
Basic CMake configuration file
Creating a library
Managing dependencies
Making the script more complex
Images and matrices
Reading/writing images
Reading videos and cameras
Other basic object types
Vec object type
Scalar object type
Point object type
Size object type
Rect object type
RotatedRect object type
Basic matrix operations
Basic data persistence and storage
Writing to FileStorage
Summary
Learning Graphical User Interfaces
Technical requirements
Introducing the OpenCV user interface
Basic graphical user interface with OpenCV
Adding slider and mouse events to our interfaces
Graphic user interface with Qt
Adding buttons to the user interface
OpenGL support
Summary
Delving into Histogram and Filters
Technical requirements
Generating a CMake script file
Creating the graphical user interface
Drawing a histogram
Image color equalization
Lomography effect
Cartoonize effect
Summary
Automated Optical Inspection, Object Segmentation, and Detection
Technical requirements
Isolating objects in a scene
Creating an application for AOI
Preprocessing the input image
Noise removal
Removing the background using the light pattern for segmentation
Thresholding
Segmenting our input image
The connected components algorithm
The findContours algorithm
Summary
Learning Object Classification
Technical requirements
Introducing machine learning concepts
OpenCV machine learning algorithms
Computer vision and the machine learning workflow
Automatic object inspection classification example
Feature extraction
Training an SVM model
Input image prediction
Summary
Detecting Face Parts and Overlaying Masks
Technical requirements
Understanding Haar cascades
What are integral images?
Overlaying a face mask in a live video
What happened in the code?
Get your sunglasses on
Looking inside the code
Tracking the nose, mouth, and ears
Summary
Video Surveillance, Background Modeling, and Morphological Operations
Technical requirements
Understanding background subtraction
Naive background subtraction
Does it work well?
Frame differencing
How well does it work?
The Mixture of Gaussians approach
What happened in the code?
Morphological image processing
What's the underlying principle?
Slimming the shapes
Thickening the shapes
Other morphological operators
Morphological opening
Morphological closing
Drawing the boundary
Top Hat transform
Black Hat transform
Summary
Learning Object Tracking
Technical requirements
Tracking objects of a specific color
Building an interactive object tracker
Detecting points using the Harris corner detector
Good features to track
Feature-based tracking
Lucas-Kanade method
Farneback algorithm
Summary
Developing Segmentation Algorithms for Text Recognition
Technical requirements
Introducing optical character recognition
Preprocessing stage
Thresholding the image
Text segmentation
Creating connected areas
Identifying paragraph blocks
Text extraction and skewing adjustment
Installing Tesseract OCR on your operating system
Installing Tesseract on Windows
Building the latest library
Setting up Tesseract in Visual Studio
Static linking
Installing Tesseract on Mac
Using the Tesseract OCR library
Creating an OCR function
Sending the output to a file
Summary
Text Recognition with Tesseract
Technical requirements
How the text API works
The scene detection problem
Extremal regions
Extremal region filtering
Using the text API
Text detection
Text extraction
Text recognition
Summary
Deep Learning with OpenCV
Technical requirements
Introduction to deep learning
What is a neural network and how can we learn from data?
Convolutional neural networks
Deep learning in OpenCV
YOLO – real-time object detection
YOLO v3 deep learning model architecture
The YOLO dataset, vocabulary, and model
Importing YOLO into OpenCV
Face detection with SSD
SSD model architecture
Importing SSD face detection into OpenCV
Summary
Further reading
Other Books You May Enjoy
Leave a review – let other readers know what you think
Preface
OpenCV is one of the most popular libraries used to develop computer vision applications. It enables us to run many different computer vision algorithms in real time. It has been around for many years, and it has become the standard library in this field. One of the main advantages of OpenCV is that it is highly optimized and available on almost all platforms.
This book starts off by giving a brief introduction to the various fields in computer vision and the associated OpenCV functionalities in C++. Each chapter contains real-world examples and code samples to demonstrate the use cases. This helps you to easily grasp the topics and understand how they can be applied in real life. To sum up, this is a practical guide on how to use OpenCV in C++ and build various applications using this library.
Who this book is for
This book is for developers who are new to OpenCV and want to develop computer vision applications with OpenCV in C++. A basic knowledge of C++ would be helpful in understanding this book. This book is also useful for people who want to get started with computer vision and understand the underlying concepts. They should be aware of basic mathematical concepts, such as vectors, matrices, and matrix multiplication, in order to get the most out of this book. During the course of this book, you will learn how to build various computer vision applications from scratch using OpenCV.
What this book covers
Chapter 1, Getting Started with OpenCV, covers installation steps on various operating systems and provides an introduction to the human visual system, as well as various topics in computer vision.
Chapter 2, Introduction to OpenCV Basics, discusses how to read/write images and videos in OpenCV, and also explains how to build a project using CMake.
Chapter 3, Learning Graphical User Interface and Basic Filtering, covers how to build a graphical user interface and mouse event detector to build interactive applications.
Chapter 4, Delving into Histograms and Filters, explores histograms and filters and also shows how we can cartoonize an image.
Chapter 5, Automated Optical Inspection, Object Segmentation, and Detection, describes various image pre-processing techniques, such as noise removal, thresholding, and contour analysis.
Chapter 6, Learning Object Classification, deals with object recognition and machine learning, and how to use support vector machines to build an object classification system.
Chapter 7, Detecting Face Parts and Overlaying Masks, discusses face detection and Haar Cascades, and then explains how these methods can be used to detect various parts of the human face.
Chapter 8, Video Surveillance, Background Modeling, and Morphological Operations, explores background subtraction, video surveillance, and morphological image processing, and describes how they are connected to one another.
Chapter 9, Learning Object Tracking, covers how to track objects in a live video using different techniques, such as color-based and feature-based tracking.
Chapter 10, Developing Segmentation Algorithms for Text Recognition, covers optical character recognition, text segmentation, and provides an introduction to the Tesseract OCR engine.
Chapter 11, Text Recognition with Tesseract, delves deeper into the Tesseract OCR engine to explain how it can be used for text detection, extraction, and recognition.
Chapter 12, Deep Learning with OpenCV, explores how to apply deep learning in OpenCV with two commonly used deep learning architectures: YOLO v3 for object detection, and Single Shot Detector for face detection.
To get the most out of this book
A basic knowledge of C++ would be helpful in understanding this book. The examples are built using the following technologies: OpenCV 4.0; CMake 3.3.x or newer; Tesseract; Leptonica (a dependency of Tesseract); Qt (optional); and OpenGL (optional).
Detailed installation instructions are provided in the relevant chapters.
Download the example code files
You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.
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Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781789341225_ColorImages.pdf.
Code in Action
Visit the following link to check out videos of the code being run:http://bit.ly/2Sfrxgu
Get in touch
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Getting Started with OpenCV
Computer vision applications are interesting and useful, but the underlying algorithms are computationally intensive. With the advent of cloud computing, we are getting more processing power to work with.
The OpenCV library enables us to run computer vision algorithms efficiently in real time. It has been around for many years, and has become the standard library in this field. One of the main advantages of OpenCV is that it is highly optimized, and available on almost all platforms.
This book will cover the various algorithms we will be using, why we are using them, and how to implement them in OpenCV.
In this chapter, we are going to learn how to install OpenCV on various operating systems. We will discuss what OpenCV offers out of the box, and the various things that we can do using the inbuilt functions.
By the end of this chapter, you will be able to answer the following questions:
How do humans process visual data, and how do they understand image content?
What can we do with OpenCV, and what are the various modules available in OpenCV that can be used to achieve those things?
How do we install OpenCV on Windows, Linux, and Mac OS X?
Understanding the human visual system
Before we jump into OpenCV functionalities, we need to understand why those functions were built in the first place. It's important to understand how the human visual system works, so that you can develop the right algorithms.
The goal of computer vision algorithms is to understand the content of images and videos. Humans seem to do it effortlessly! So, how do we get machines to do it with the same accuracy?
Let's consider the following diagram:
The human eye captures all the information that comes along the way, such as color, shape, brightness, and so on. In the preceding image, the human eye captures all the information about the two main objects and stores it in a certain way. Once we understand how our system works, we can take advantage of it to achieve what we want.
For example, here are a few things we need to know:
Our visual system is more sensitive to low-frequency content than high-frequency content. Low-frequency content refers to planar regions where pixel values don't change rapidly, and high-frequency content refers to regions with corners and edges where pixel values fluctuate a lot. We can easily see if there are blotches on a planar surface, but it's difficult to spot something like that on a highly-textured surface.
The human eye is more sensitive to changes in brightness than to changes in color.
Our visual system is sensitive to motion. We can quickly recognize if something is moving in our field of vision, even though we are not directly looking at it.
We tend to make a mental note of salient points in our field of vision. Let's say you look at a white table with four black legs, and a red dot at one of the corners of the table surface. When you look at this table, you'll immediately make a mental note that the surface and legs have opposing colors, and that there is a red dot on one of the corners. Our brain is really smart that way! We do this automatically so that we can immediately recognize an object if we encounter it again.
To get an idea of our field of view, let's look at the top view of a human, and the angles at which we see various things:
Our visual system is actually capable of a lot more, but this should be good enough to get us started. You can explore further by reading up on Human Visual System (HVS) models on the web.
How do humans understand image content?
If you look around, you will see a lot of objects. You encounter many different objects every day, and you recognize them almost instantaneously without any effort. When you see a chair, you don't wait for a few minutes before realizing that it is in fact a chair. You just know that it's a chair right away.
Computers, on the other hand, find it very difficult to do this task. Researchers have been working for many years to find out why computers are not as good as we are at this.
To get an answer to that question, we need to understand how humans do it. The visual data processing happens in the ventral visual stream. This ventral visual stream refers to the pathway in our visual system that is associated with object recognition. It is basically a hierarchy of areas in our brain that helps us recognize objects.
Humans can recognize different objects effortlessly, and can cluster similar objects together. We can do this because we have developed some sort of invariance toward objects of the same class. When we look at an object, our brain extracts the salient points in such a way that factors such as orientation, size, perspective, and illumination don't matter.
A chair that is double the normal size and rotated by 45 degrees is still a chair. We can recognize it easily because of the way we process it. Machines cannot do that so easily. Humans tend to remember an object based on its shape and important features. Regardless of how the object is placed, we can still recognize it.
In our visual system, we build up these hierarchical invariances with respect to position, scale, and viewpoint that help us to be very robust. If you look deeper into our system, you will see that humans have cells in their visual cortex that can respond to shapes such as curves and lines.
As we move further along our ventral stream, we will see more complex cells that are trained to respond to more complex objects such as trees, gates, and so on. The neurons along our ventral stream tend to show an increase in the size of the receptive field. This is coupled with the fact that the complexity of their preferred stimuli increases as well.
Why is it difficult for machines to understand image content?
We now understand how visual data enters the human visual system, and how our system processes it. The issue is that we still don't fully understand how our brain recognizes and organizes this visual data. In machine learning, we just extract some features from images, and ask the computers to learn them using algorithms. We still have these variations, such as shape, size, perspective, angle, illumination, occlusion, and so on.
For example, the same chair looks very different to a machine when you look at it from the profile view. Humans can easily recognize that it's a chair, regardless of how it's presented to us. So, how do we explain this to our machines?
One way to do this would be to store all the different variations of an object, including sizes, angles, perspectives, and so on. But this process is cumbersome and time-consuming. Also, it's actually not possible to gather data that can encompass every single variation. The machines would consume a huge amount of memory and a lot of time to build a model that can recognize these objects.
Even with all this, if an object is partially occluded, computers still won't recognize it. This is because they think this is a new object. So when we build a computer vision library, we need to build the underlying functional blocks that can be combined in many different ways to formulate complex algorithms.
OpenCV provides a lot of these functions, and they are highly optimized. So once we understand what OpenCV is capable of, we can use it effectively to build interesting applications.
Let's go ahead and explore that in the next section.
What can you do with OpenCV?
Using OpenCV, you can pretty much do every computer vision task you can think of. Real-life problems require you to use many computer vision algorithms and modules together to achieve the desired result. So, you just need to understand which OpenCV modules and functions to use, in order to get what you want.
Let's look at what OpenCV can do out of the box.
Inbuilt data structures and input/output
One of the best things about OpenCV is that it provides a lot of in-built primitives to handle operations related to image processing and computer vision. If you have to write something from scratch, you will have to define Image, Point, Rectangle, and so on. These are fundamental to almost any computer vision algorithm.
OpenCV comes with all these basic structures out of the box, contained in the core module. Another advantage is that these structures have already been optimized for speed and memory, and so you don't have to worry about the implementation details.
The imgcodecs module handles reading and writing of image files. When you operate on an input image and create an output image, you can save it as a .jpg or a .png file with a simple command.
You will be dealing with a lot of video files when you work with cameras. The videoio module handles everything related to the input and output of video files. You can easily capture a video from the webcam or read a video file in many different formats. You can even save a bunch of frames as a video file by setting properties such as frames per second, frame size, and so on.
Image processing operations
When you write a computer vision algorithm, there are a lot of basic image processing operations that you will use over and over again. Most of these functions are present in the imgproc module. You can do things such as image filtering, morphological operations, geometric transformations, color conversions, drawing on images, histograms, shape analysis, motion analysis, feature detection, and more.
Let's consider the following photo:
The right image is a rotated version of the one on the left. We can carry out this transformation with a single line in OpenCV.
There is another module, called ximgproc, which contains advanced image processing algorithms such as structured forests for edge detection, domain transform filter, adaptive manifold filter, and so on.
GUI
OpenCV provides a module called highgui that handles all the high-level user interface operations. Let's say you are working on a problem, and you want to check what the image looks like before you proceed to the next step. This module has functions that can be used to create windows to display images and/or videos.
There is a waiting function that will wait until you hit a key on your keyboard before it goes on to the next step. There is also a function that can detect mouse events. This is very useful in developing interactive applications.
Using this functionality, you can draw rectangles on those input windows, and then proceed based on the selected region. Consider the following screenshot:
As you can see, we drew a green rectangle on top of the window. Once we have the coordinates of that rectangle, we can operate only on that region.
Video analysis
Video analysis includes tasks such as analyzing the motion between successive frames in a video, tracking different objects in a video, creating models for video surveillance, and so on. OpenCV provides a module called video that can handle all of this.
There is also a module called videostab that deals with video stabilization. Video stabilization is important, as when you are capturing videos by holding the camera in your hands, there's usually a lot of shake that needs correcting. All modern devices use video stabilization to process the video before it's presented to the end user.
3D reconstruction
3D reconstruction is an important topic in computer vision. Given a set of 2D images, we can reconstruct the 3D scene using relevant algorithms. OpenCV provides algorithms that can find the relationship between various objects in those 2D images to compute their 3D positions in its calib3d module.
This module can also handle camera calibration, which is essential for estimating the parameters of the camera. These parameters define how the camera sees the scene in front of it. We need to know these parameters to design algorithms, or else we might get unexpected results.
Let's consider the following diagram:
As we can see here, the same object is captured from multiple positions. Our job is to reconstruct the original object using these 2D images.
Feature extraction
As we discussed earlier, the human visual system tends to extract the salient features from a given scene to remember it for retrieval later. To mimic this, people started designing various feature extractors that can extract these salient points from a given image. Popular algorithms include Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Features From Accelerated Segment Test (FAST).
An OpenCV module called features2d provides functions to detect and extract all these features. Another module called xfeatures2d provides a few more feature extractors, some of which are still in the experimental phase. You can play around with these if you get the chance.
There is also a module called bioinspired that provides algorithms for biologically-inspired computer vision models.
Object detection
Object detection refers to detecting the location of an object in a given image. This process is not concerned with the type of object. If you design a chair detector, it will not tell you whether the chair in a given image is red with a high back, or blue with a low back—it willjust tell you the location of the chair.
Detecting the location of objects is a critical step in many computer vision systems. Consider the following photo:
If you run a chair detector on this image, it will put a green box around all the chairs—but it won't tell you what kind of chair it is.
Object detection used to be a computationally-intensive task because of the number of calculations required to perform the detection at various scales. To solve this, Paul Viola and Michael Jones came up with a great algorithm in their seminal 2001 paper, which you can read at the following link: https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf. They provided a fast way to design an object detector for any object.
OpenCV has modules called objdetect and xobjdetect that provide the framework to design an object detector. You can use it to develop detectors for random items such as sunglasses, boots, and so on.
Machine learning
Machine learning algorithms are used extensively to build computer vision systems for object recognition, image classification, face detection, visual search, and so on.
OpenCV provides a module called ml, which has many machine learning algorithms bundled into it, including a Bayes classifier, k-nearest neighbors (KNN), support vector machines (SVM), decision trees, neural networks, and more.
It also has a module called Fast Approximate Nearest Neighbor Search Library (FLANN), which contains algorithms for fast nearest neighbor searches in large datasets.
Computational photography
Computational photography refers to using advanced image processing techniques to improve the images captured by cameras. Instead of focusing on optical processes and image capture methods, computational photography uses software to manipulate visual data. Applications include high dynamic range imaging, panoramic images, image relighting, and light field cameras.
Let's look at the following image:
Look at those vivid colors! This is an example of a high dynamic range image, and it wouldn't be possible to get this using conventional image capture techniques. To do this, we have to capture the same scene at multiple exposures, register those images with each other, and then blend them nicely to create this image.
The photo and xphoto modules contain various algorithms that provide algorithms pertaining to computational photography. There is also a module called stitching that provides algorithms to create panoramic images.
Shape analysis
The notion of shape is crucial in computer vision. We analyze visual data by recognizing various different shapes in the image. This is actually an important step in many algorithms.
Let's say you are trying to identify a particular logo in an image. You know that it can appear in various shapes, orientations, and sizes. One good way to get started is to quantify the characteristics of the shape of the object.
The shape module provides all the algorithms required to extract different shapes, measure similarity between them, transform the shapes of objects, and more.
Optical flow algorithms
Optical flow algorithms are used in videos to track features across successive frames. Let's say you want to track a particular object in a video. Running a feature extractor on each frame would be computationally expensive; hence, the process would be slow. So, you just extract the features from the current frame, and then track those features in successive frames.
Optical flow algorithms are heavily used in video-based applications in computer vision. The optflow
