learning opencv 3 computer vision with python.pdf

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Cover

Table of Contents

About the Authors

Preface

1. Setting Up OpenCV

Choosing and using the right setup tools

Installation on Windows

Using binary installers (no support for depth cameras)

Using CMake and compilers

Installing on OS X

Using MacPorts with ready-made packages

Using MacPorts with your own custom packages

Using Homebrew with ready-made packages (no support for depth cameras)

Using Homebrew with your own custom packages

Installation on Ubuntu and its derivatives

Using the Ubuntu repository (no support for depth cameras)

Building OpenCV from a source

Installation on other Unix-like systems

Installing the Contrib modules

Running samples

Finding documentation, help, and updates

Summary

2. Handling Files, Cameras, and GUIs

Basic I/O scripts

Reading/writing an image file

Converting between an image and raw bytes

Accessing image data with numpy.array

Reading/writing a video file

Capturing camera frames

Displaying images in a window

Displaying camera frames in a window

Project Cameo (face tracking and image manipulation)

Cameo – an object-oriented design

Abstracting a video stream with managers.CaptureManager

Abstracting a window and keyboard with managers.WindowManager

Applying everything with cameo.Cameo

Summary

3. Processing Images with OpenCV 3

Converting between different color spaces

A quick note on BGR

The Fourier Transform

High pass filter

Low pass filter

Creating modules

Edge detection

Custom kernels – getting convoluted

Modifying the application

Edge detection with Canny

Contour detection

Contours – bounding box, minimum area rectangle, and minimum enclosing circle

Contours – convex contours and the Douglas-Peucker algorithm

Line and circle detection

Line detection

Circle detection

Detecting shapes

Summary

4. Depth Estimation and Segmentation

Creating modules

Capturing frames from a depth camera

Creating a mask from a disparity map

Masking a copy operation

Depth estimation with a normal camera

Object segmentation using the Watershed and GrabCut algorithms

Example of foreground detection with GrabCut

Image segmentation with the Watershed algorithm

Summary

5. Detecting and Recognizing Faces

Conceptualizing Haar cascades

Getting Haar cascade data

Using OpenCV to perform face detection

Performing face detection on a still image

Performing face detection on a video

Performing face recognition

Generating the data for face recognition

Recognizing faces

Preparing the training data

Loading the data and recognizing faces

Performing an Eigenfaces recognition

Performing face recognition with Fisherfaces

Performing face recognition with LBPH

Discarding results with confidence score

Summary

6. Retrieving Images and Searching Using Image Descriptors

Feature detection algorithms

Defining features

Detecting features – corners

Feature extraction and description using DoG and SIFT

Anatomy of a keypoint

Feature extraction and detection using Fast Hessian and SURF

ORB feature detection and feature matching

FAST

BRIEF

Brute-Force matching

Feature matching with ORB

Using K-Nearest Neighbors matching

FLANN-based matching

FLANN matching with homography

A sample application – tattoo forensics

Saving image descriptors to file

Scanning for matches

Summary

7. Detecting and Recognizing Objects

Object detection and recognition techniques

HOG descriptors

The scale issue

The location issue

Image pyramid

Sliding windows

Non-maximum (or non-maxima) suppression

Support vector machines

People detection

Creating and training an object detector

Bag-of-words

BOW in computer vision

The k-means clustering

Detecting cars

What did we just do?

SVM and sliding windows

Example – car detection in a scene

Examining detector.py

Associating training data with classes

Dude, where's my car?

Summary

8. Tracking Objects

Detecting moving objects

Basic motion detection

Background subtractors – KNN, MOG2, and GMG

Meanshift and CAMShift

Color histograms

The calcHist function

The calcBackProject function

In summary

Back to the code

CAMShift

The Kalman filter

Predict and update

An example

A real-life example – tracking pedestrians

The application workflow

A brief digression – functional versus object-oriented programming

The Pedestrian class

The main program

Where do we go from here?

Summary

9. Neural Networks with OpenCV – an Introduction

Artificial neural networks

Neurons and perceptrons

The structure of an ANN

Network layers by example

The input layer

The output layer

The hidden layer

The learning algorithms

ANNs in OpenCV

ANN-imal classification

Training epochs

Handwritten digit recognition with ANNs

MNIST – the handwritten digit database

Customized training data

The initial parameters

The input layer

The hidden layer

The output layer

Training epochs

Other parameters

Mini-libraries

The main file

Possible improvements and potential applications

Improvements

Potential applications

Summary

To boldly go…

Index

Learning OpenCV 3 Computer Vision with Python Second Edition

Table of Contents Learning OpenCV 3 Computer Vision with Python Second Edition Credits About the Authors 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 Errata Piracy Questions 1. Setting Up OpenCV Choosing and using the right setup tools Installation on Windows Using binary installers (no support for depth cameras) Using CMake and compilers Installing on OS X Using MacPorts with ready-made packages Using MacPorts with your own custom packages Using Homebrew with ready-made packages (no support for depth cameras) Using Homebrew with your own custom packages

Installation on Ubuntu and its derivatives Using the Ubuntu repository (no support for depth cameras) Building OpenCV from a source Installation on other Unix-like systems Installing the Contrib modules Running samples Finding documentation, help, and updates Summary 2. Handling Files, Cameras, and GUIs Basic I/O scripts Reading/writing an image file Converting between an image and raw bytes Accessing image data with numpy.array Reading/writing a video file Capturing camera frames Displaying images in a window Displaying camera frames in a window Project Cameo (face tracking and image manipulation) Cameo – an object-oriented design Abstracting a video stream with managers.CaptureManager Abstracting a window and keyboard with managers.WindowManager Applying everything with cameo.Cameo Summary 3. Processing Images with OpenCV 3 Converting between different color spaces A quick note on BGR The Fourier Transform High pass filter Low pass filter Creating modules Edge detection

Custom kernels – getting convoluted Modifying the application Edge detection with Canny Contour detection Contours – bounding box, minimum area rectangle, and minimum enclosing circle Contours – convex contours and the Douglas-Peucker algorithm Line and circle detection Line detection Circle detection Detecting shapes Summary 4. Depth Estimation and Segmentation Creating modules Capturing frames from a depth camera Creating a mask from a disparity map Masking a copy operation Depth estimation with a normal camera Object segmentation using the Watershed and GrabCut algorithms Example of foreground detection with GrabCut Image segmentation with the Watershed algorithm Summary 5. Detecting and Recognizing Faces Conceptualizing Haar cascades Getting Haar cascade data Using OpenCV to perform face detection Performing face detection on a still image Performing face detection on a video Performing face recognition Generating the data for face recognition Recognizing faces Preparing the training data

Loading the data and recognizing faces Performing an Eigenfaces recognition Performing face recognition with Fisherfaces Performing face recognition with LBPH Discarding results with confidence score Summary 6. Retrieving Images and Searching Using Image Descriptors Feature detection algorithms Defining features Detecting features – corners Feature extraction and description using DoG and SIFT Anatomy of a keypoint Feature extraction and detection using Fast Hessian and SURF ORB feature detection and feature matching FAST BRIEF Brute-Force matching Feature matching with ORB Using K-Nearest Neighbors matching FLANN-based matching FLANN matching with homography A sample application – tattoo forensics Saving image descriptors to file Scanning for matches Summary 7. Detecting and Recognizing Objects Object detection and recognition techniques HOG descriptors The scale issue The location issue Image pyramid

Sliding windows Non-maximum (or non-maxima) suppression Support vector machines People detection Creating and training an object detector Bag-of-words BOW in computer vision The k-means clustering Detecting cars What did we just do? SVM and sliding windows Example – car detection in a scene Examining detector.py Associating training data with classes Dude, where’s my car? Summary 8. Tracking Objects Detecting moving objects Basic motion detection Background subtractors – KNN, MOG2, and GMG Meanshift and CAMShift Color histograms The calcHist function The calcBackProject function In summary Back to the code CAMShift The Kalman filter Predict and update An example A real-life example – tracking pedestrians

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