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OpenCV with Python Blueprints 无水印pdf.pdf
Cover
Copyright
Credits
About the Author
About the Reviewers
www.PacktPub.com
Table of Contents
Preface
1: Fun with Filters
Planning the app
Creating a black-and-white pencil sketch
Implementing dodging and burning in OpenCV
Pencil sketch transformation
Generating a warming/cooling filter
Color manipulation via curve shifting
Implementing a curve filter by using lookup tables
Designing the warming/cooling effect
Cartoonizing an image
Using a bilateral filter for edge-aware smoothing
Detecting and emphasizing prominent edges
Combining colors and outlines to produce a cartoon
Putting it all together
Running the app
The GUI base class
The GUI constructor
Handling video streams
A basic GUI layout
A custom filter layout
Summary
2: Hand Gesture Recognition Using a Kinect Depth Sensor
Planning the app
Setting up the app
Accessing the Kinect 3D sensor
Running the app
The Kinect GUI
Tracking hand gestures in real time
Hand region segmentation
Finding the most prominent depth of the image center region
Applying morphological closing to smoothen the segmentation mask
Finding connected components in a segmentation mask
Hand shape analysis
Determining the contour of the segmented hand region
Finding the convex hull of a contour area
Finding the convexity defects of a convex hull
Hand gesture recognition
Distinguishing between different causes of convexity defects
Classifying hand gestures based on the number of extended fingers
Summary
3: Finding Objects via Feature Matching and Perspective Transforms
Tasks performed by the app
Planning the app
Setting up the app
Running the app
The FeatureMatching GUI
The process flow
Feature extraction
Feature detection
Detecting features in an image with SURF
Feature matching
Matching features across images with FLANN
The ratio test for outlier removal
Visualizing feature matches
Homography estimation
Warping the image
Feature tracking
Early outlier detection and rejection
Seeing the algorithm in action
Summary
4: 3D Scene Reconstruction Using Structure from Motion
Planning the app
Camera calibration
The pinhole camera model
Estimating the intrinsic camera parameters
The camera calibration GUI
Initializing the algorithm
Collecting image and object points
Finding the camera matrix
Setting up the app
The main function routine
The SceneReconstruction3D class
Estimating the camera motion from a pair of images
Point matching using rich feature descriptors
Point matching using optic flow
Finding the camera matrices
Image rectification
Reconstructing the scene
3D point cloud visualization
Summary
5: Tracking Visually Salient Objects
Planning the app
Setting up the app
The main function routine
The Saliency class
The MultiObjectTracker class
Visual saliency
Fourier analysis
Natural scene statistics
Generating a Saliency map with the spectral residual approach
Detecting proto-objects in a scene
Mean-shift tracking
Automatically tracking all players on a soccer field
Extracting bounding boxes for proto-objects
Setting up the necessary bookkeeping for mean-shift tracking
Tracking objects with the mean-shift algorithm
Putting it all together
Summary
6: Learning to Recognize Traffic Signs
Planning the app
Supervised learning
The training procedure
The testing procedure
A classifier base class
The GTSRB dataset
Parsing the dataset
Feature extraction
Common preprocessing
Grayscale features
Color spaces
Speeded Up Robust Features
Histogram of Oriented Gradients
Support Vector Machine
Using SVMs for Multi-class classification
Training the SVM
Testing the SVM
Confusion matrix
Accuracy
Precision
Recall
Putting it all together
Summary
7: Learning to Recognize Emotion in Faces
Planning the app
Face detection
Haar-based cascade classifiers
Pre-trained cascade classifiers
Using a pre-trained cascade classifier
The FaceDetector class
Detecting faces in grayscale images
Preprocessing detected faces
Facial expression recognition
Assembling a training set
Running the screen capture
The GUI constructor
The GUI layout
Processing the current frame
Adding a training sample to the training set
Dumping the complete training set to file
Feature extraction
Preprocessing the dataset
Principal component analysis
Multi-layer perceptrons (MLPs)
The perceptron
Deep architectures
An MLP for facial expression recognition
Training the MLP
Testing the MLP
Running the script
Putting it all together
Summary
Index
OpenCV with Python Blueprints
Design and develop advanced computer vision projects
using OpenCV with Python
Michael Beyeler
BIRMINGHAM - MUMBAI
OpenCV with Python Blueprints
Copyright © 2015 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval
system, or transmitted in any form or by any means, without the prior written
permission of the publisher, except in the case of brief quotations embedded in
critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy
of the information presented. However, the information contained in this book is
sold without warranty, either express or implied. Neither the author, nor Packt
Publishing, and its dealers and distributors will be held liable for any damages
caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the
companies and products mentioned in this book by the appropriate use of capitals.
However, Packt Publishing cannot guarantee the accuracy of this information.
First published: October 2015
Production reference: 1141015
Published by Packt Publishing Ltd.
Livery Place
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Birmingham B3 2PB, UK.
ISBN 978-1-78528-269-0
www.packtpub.com
Credits
Author
Michael Beyeler
Project Coordinator
Suzanne Coutinho
Proofreader
Safis Editing
Indexer
Rekha Nair
Production Coordinator
Melwyn D'sa
Cover Work
Melwyn D'sa
Reviewers
Jia-Shen Boon
Florian LE BOURDAIS
Steve Goldsmith
Rahul Kavi
Scott Lobdell
Vipul Sharma
Commissioning Editor
Akram Hussain
Acquisition Editor
Divya Poojari
Content Development Editor
Zeeyan Pinheiro
Technical Editor
Namrata Patil
Copy Editor
Vikrant Phadke
About the Author
Michael Beyeler is a PhD candidate in the department of computer science at
the University of California, Irvine, where he is 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.
This is his first technical book that, in contrast to his (or any) dissertation, might
actually be read.
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.
I would like to thank Packt Publishing for this great opportunity
and their support, my girlfriend for putting up with my late-night
writing sessions, as well as the technical reviewers, who have
spotted (hopefully) all my glaring errors and helped make this book
a success.
About the Reviewers
Jia-Shen Boon is a researcher in robotics at the University of Wisconsin-Madison,
supervised by Professor Michael Coen. He is a proud son of the sunny city-state of
Singapore. Before coming to Wisconsin, he was a research engineer at DSO National
Labs, where he worked on autonomous underwater vehicles and other unspeakable
things. During his free time, Jia-Shen likes to study the Japanese language and write
about himself in the third person.
Florian LE BOURDAIS hails from France and Germany. While he was growing
up in the lazy south of France, an encounter with music from The Beatles gave him
an early grasp of the English language. One of his earliest childhood memories has
him watching his older German cousin, Dominik, coding a Tetris clone in the family
basement using QBasic. High school and the advent of hand-held calculators led him
to write his first Snake program using the TI-Basic language. After having acquired
a solid background in mathematics and physics, Florian was admitted to one of the
top French engineering schools. He studied mechanical engineering, but interned
as an index-arbitrage trader in Japan during the financial crisis. Keen to come back
to a country he much liked, he specialized in nuclear engineering and was doing an
internship in a Japanese fast-breeder reactor during the Fukushima nuclear crisis.
Coming back to France, Florian was happy to start an engineering job in non-
destructive testing. He specializes in ultrasound inspection methods, with a focus on
phased array transducers, guided waves, and EMATs. He has published more than
10 international conference proceedings. At night, he's a hacker who likes to play
with 3D printers, fermented Korean cabbage, the Raspberry Pi, Japanese characters,
and guitars. He regularly writes a blog about his side projects at http://flothesof.
github.io.
I would like to thank my friends and family for supporting me
throughout this project. Special thanks goes to my favorite machine
learning specialists at the Geeks d'Orléans, as well as Coloc du 1000.
Rahul Kavi is a PhD student at West Virginia University. He holds a master's
degree in computer science. He is pursuing a PhD in the area of distributed machine
learning and computer vision. He is a computer vision and robotics enthusiast.
Rahul has worked on developing prototypes, optimizing computer vision, and
machine learning applications for desktops, mobile devices, and autonomous
robots. He writes blogs on his research interests and part-time projects at www.
developerstation.org. He is a source code contributor to OpenCV.
Vipul Sharma is an engineering undergraduate from Jabalpur Engineering
College. He is an ardent Python enthusiast and was one of the students selected
for Google Summer of Code 2015 under the Python Software Foundation. He has
been actively involved in Python and OpenCV since 2012. A few of his projects on
OpenCV include a motion sensing surveillance camera, hand-gesture recognition,
and solving a Rubik's cube by reading images of its faces in real time. Vipul loves
contributing to open source software and is currently working on Optical Character
Recognition (OCR) using OpenCV. You can check out his projects at https://
github.com/vipul-sharma20.
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