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OpenCV with Python Blueprints, by Michael Beyeler
Download OpenCV with Python Blueprints, by Michael Beyeler
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Design and develop advanced computer vision projects using OpenCV with Python
About This Book- Program advanced computer vision applications in Python using different features of the OpenCV library
- Practical end-to-end project covering an important computer vision problem
- All projects in the book include a step-by-step guide to create computer vision applications
This book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. Readers are expected to be familiar with OpenCV's concepts and Python libraries. Basic knowledge of Python programming is expected and assumed.
What You Will Learn- Generate real-time visual effects using different filters and image manipulation techniques such as dodging and burning
- Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor
- Learn feature extraction and feature matching for tracking arbitrary objects of interest
- Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques
- Track visually salient objects by searching for and focusing on important regions of an image
- Detect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs)
- Recognize street signs using a multi-class adaptation of support vector machines (SVMs)
- Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features
OpenCV is a native cross platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android. Developers using OpenCV build applications to process visual data; this can include live streaming data from a device like a camera, such as photographs or videos. OpenCV offers extensive libraries with over 500 functions
This book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this book teach the reader how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.
By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications.
Style and approachThis book covers independent hands-on projects that teach important computer vision concepts like image processing and machine learning for OpenCV with multiple examples.
- Sales Rank: #1065766 in Books
- Published on: 2015-10
- Released on: 2015-10-19
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .52" w x 7.50" l, .89 pounds
- Binding: Paperback
- 230 pages
About the Author
Michael Beyeler
Michael Beyeler received a PhD in Computer Science from the University of California, Irvine, where he is currently working on computational models of the brain as well as their integration into autonomous brain-inspired robots. His work on vision-based navigation, learning, and cognition has been presented at IEEE conferences and published in international journals. Currently, he is one of the main developers of CARLsim, an open source GPGPU spiking neural network simulator. Michael has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Born and raised in Switzerland, he received a BSc degree in Electrical Engineering and Information Technology, as well as a MSc degree in Biomedical Engineering from ETH Zurich. When he is not "nerding out" on robots, he can be found on top of a snowy mountain, in front of a live band, or behind the piano.
Most helpful customer reviews
1 of 1 people found the following review helpful.
good book to develop computer vision projects using OpenCV in Python
By Nick
This book covers multiple computer vision algorithms used in the industry today. Beyeler focuses on a practical approach by providing detailed Python code to perform image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.
I particularly liked the last chapter on facial expression recognition using Haar Cascade Classifiers and multi-layer perceptrons, where the authors described very clearly their implementation and usage with openCV. Finally, having access to all the source code on Github is great and really useful in order to quickly test the models, and modify them yourself.
I recommend this book for people who already have some experience with computer vision and who want to improve their skills by developing advanced practical applications with OpenCV.
1 of 1 people found the following review helpful.
like object detection
By T.M.
This book shows how to use the OpenCV library to quickly prototype some original applications. The requirements are a PC running Windows or Linux, or a Mac, a Webcam and optionally a Kinect. Every experiment, like object detection, motion tracking and image recognition are explained in detail. After understanding the tips and tricks of each project, one can easily reuse and customize them. This book will be useful for practitioners seeking to add computer vision features to their applications.
2 of 2 people found the following review helpful.
Great book for developing real world OpenCV applications with Python
By Sebastian Montabone
Disclaimer: I received a free copy of the book for reviewing.
OpenCV with Python Blueprints is a book for people that can write Python applications, know what OpenCV is, and want to start developing more interesting computer vision applications.
The emphasis of the book is on the practical side instead of formal theory. It contains explanations about why and how the computer vision techniques work just long enough for you to understand them at a high level. This is particularly useful if you want to experiment with different areas of computer vision in a relatively short amount of time, which is exactly what this book is about.
Each chapter presents a different real world project, which ranges from applying filters to an image in real time to recognizing traffic signs or facial emotions from a video feed. Here is the list of the chapter names for reference:
Chapter 1: Fun with Filters
Chapter 2: Hand Gesture Recognition Using a Kinect
Chapter 3: Finding Objects via Feature Matching and Perspective Transforms
Chapter 4: 3D Scene Reconstruction Using Structure from Motion
Chapter 5: Tracking Visually Salient Objects
Chapter 6: Learning to Recognize Traffic Signs
Chapter 7: Learning to Recognize Emotions on Faces
As you can see from the previous list, you will learn from many different areas of computer vision, such as image processing, machine learning, multiple view geometry, feature detection and tracking, object recognition, and so on.
I like how each chapter starts showing the desired output of the project followed by a section called “Planning the app” where the design of the application is discussed. This is great for understanding the code that follows. The comments about the implementation are very easy to follow as well.
The author does a great job explaining the concepts needed to understand what’s happening in the application without the need of going into too many details. A high level overview is provided so that interested readers can research more about the concepts used if they want to.
This book is great for someone who is at a beginner level in computer vision and wants to get into an intermediate level by learning with real world examples. After finishing this book, readers will have practical knowledge about many interesting applications of computer vision, and will be able to dig deeper into any of these areas depending on their future interests.
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