OpenCV with Python By Example
图书信息
| 作者 | Prateek Joshi |
| 出版社 | Packt Publishing |
| ISBN | 9781785289873 |
| 出版时间 | 2015-09-22 |
| 字数 | 46.6万 |
| 分类 | 进口书,外文原版书,电脑,网络 |
读书简介
Build real-world computer vision applications and develop cool demos using OpenCV for Python About This Book Learn how to apply complex visual effects to images using geometric transformations and image filters Extract features from an image and use them to develop advanced applications Build algorithms to help you understand the image content and perform visual searches Who This Book Is For This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV-Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on. What You Will Learn Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image Detect and track various body parts such as the face, nose, eyes, ears, and mouth Stitch multiple images of a scene together to create a panoramic image Make an object disappear from an image Identify different shapes, segment an image, and track an object in a live video Recognize an object in an image and build a visual search engine Reconstruct a 3D map from images Build an augmented reality application In Detail Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel. This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications. This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner’s level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples. The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. Style and approach This is a conversational-style book filled with hands-on examples that are really easy to understand. Each topic is explained very clearly and is followed by a programmatic implementation so that the concept is solidified. Each topic contributes to something bigger in the following chapters, which helps you understand how to piece things together to build something big and complex.
目录
OpenCV with Python By Example
Table of Contents
OpenCV with Python By Example
Credits
About the Author
About the Reviewers
www.PacktPub.com
Support files, eBooks, discount offers, and more
Why subscribe?
Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Applying Geometric Transformations to Images
Installing OpenCV-Python
Windows
Mac OS X
Linux (for Ubuntu)
Reading, displaying, and saving images
What just happened?
Loading and saving an image
Image color spaces
Converting between color spaces
What just happened?
Image translation
What just happened?
Image rotation
What just happened?
Image scaling
What just happened?
Affine transformations
What just happened?
Projective transformations
What just happened?
Image warping
Summary
2. Detecting Edges and Applying Image Filters
2D convolution
Blurring
The size of the kernel versus the blurriness
Edge detection
Motion blur
Under the hood
Sharpening
Understanding the pattern
Embossing
Erosion and dilation
Afterthought
Creating a vignette filter
What's happening underneath?
How do we move the focus around?
Enhancing the contrast in an image
How do we handle color images?
Summary
3. Cartoonizing an Image
Accessing the webcam
Under the hood
Keyboard inputs
Interacting with the application
Mouse inputs
What's happening underneath?
Interacting with a live video stream
How did we do it?
Cartoonizing an image
Deconstructing the code
Summary
4. Detecting and Tracking Different Body Parts
Using Haar cascades to detect things
What are integral images?
Detecting and tracking faces
Understanding it better
Fun with faces
Under the hood
Detecting eyes
Afterthought
Fun with eyes
Positioning the sunglasses
Detecting ears
Detecting a mouth
It's time for a moustache
Detecting a nose
Detecting pupils
Deconstructing the code
Summary
5. Extracting Features from an Image
Why do we care about keypoints?
What are keypoints?
Detecting the corners
Good Features To Track
Scale Invariant Feature Transform (SIFT)
Speeded Up Robust Features (SURF)
Features from Accelerated Segment Test (FAST)
Binary Robust Independent Elementary Features (BRIEF)
Oriented FAST and Rotated BRIEF (ORB)
Summary
6. Creating a Panoramic Image
Matching keypoint descriptors
How did we match the keypoints?
Understanding the matcher object
Drawing the matching keypoints
Creating the panoramic image
Finding the overlapping regions
Stitching the images
What if the images are at an angle to each other?
Why does it look stretched?
Summary
7. Seam Carving
Why do we care about seam carving?
How does it work?
How do we define "interesting"?
How do we compute the seams?
Can we expand an image?
Can we remove an object completely?
How did we do it?
Summary
8. Detecting Shapes and Segmenting an Image
Contour analysis and shape matching
Approximating a contour
Identifying the pizza with the slice taken out
How to censor a shape?
What is image segmentation?
How does it work?
Watershed algorithm
Summary
9. Object Tracking
Frame differencing
Colorspace based tracking
Building an interactive object tracker
Feature based tracking
Background subtraction
Summary
10. Object Recognition
Object detection versus object recognition
What is a dense feature detector?
What is a visual dictionary?
What is supervised and unsupervised learning?
What are Support Vector Machines?
What if we cannot separate the data with simple straight lines?
How do we actually implement this?
What happened inside the code?
How did we build the trainer?
Summary
11. Stereo Vision and 3D Reconstruction
What is stereo correspondence?
What is epipolar geometry?
Why are the lines different as compared to SIFT?
Building the 3D map
Summary
12. Augmented Reality
What is the premise of augmented reality?
What does an augmented reality system look like?
Geometric transformations for augmented reality
What is pose estimation?
How to track planar objects?
What happened inside the code?
How to augment our reality?
Mapping coordinates from 3D to 2D
How to overlay 3D objects on a video?
Let's look at the code
Let's add some movements
Summary
Index
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