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OpenCV with Python By Example电子书

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22人正在读 | 0人评论 9.8

作       者:Prateek Joshi

出  版  社:Packt Publishing

出版时间:2015-09-22

字       数:46.6万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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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.
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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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