book

Learning OpenCV 3 Computer Vision with Python (Update)

by Joe Minichino, Joseph Howse

September 2015

Intermediate to advanced

266 pages

5h 38m

English

Packt Publishing

Read now

Unlock full access

Support files, eBooks, discount offers, and moreWhy subscribe?Free access for Packt account holders
What this book covers

Downloading the example codeErrataPiracyQuestions
Choosing and using the right setup toolsInstallation on WindowsUsing binary installers (no support for depth cameras)Using CMake and compilersInstalling on OS XUsing MacPorts with ready-made packagesUsing MacPorts with your own custom packagesUsing Homebrew with ready-made packages (no support for depth cameras)Using Homebrew with your own custom packagesInstallation on Ubuntu and its derivativesUsing the Ubuntu repository (no support for depth cameras)Building OpenCV from a sourceInstallation on other Unix-like systems
Basic I/O scriptsReading/writing an image fileConverting between an image and raw bytesAccessing image data with numpy.arrayReading/writing a video fileCapturing camera framesDisplaying images in a windowDisplaying camera frames in a window
Abstracting a video stream with managers.CaptureManagerAbstracting a window and keyboard with managers.WindowManagerApplying everything with cameo.Cameo
Converting between different color spacesA quick note on BGR
High pass filterLow pass filter
Line detectionCircle detection
Creating modules
Example of foreground detection with GrabCutImage segmentation with the Watershed algorithm
Conceptualizing Haar cascades
Performing face detection on a still imagePerforming face detection on a videoPerforming face recognitionGenerating the data for face recognitionRecognizing facesPreparing the training dataLoading the data and recognizing facesPerforming an Eigenfaces recognitionPerforming face recognition with FisherfacesPerforming face recognition with LBPHDiscarding results with confidence score
Feature detection algorithmsDefining featuresDetecting features – cornersFeature extraction and description using DoG and SIFTAnatomy of a keypointFeature extraction and detection using Fast Hessian and SURFORB feature detection and feature matchingFASTBRIEFBrute-Force matchingFeature matching with ORBUsing K-Nearest Neighbors matchingFLANN-based matchingFLANN matching with homographyA sample application – tattoo forensicsSaving image descriptors to fileScanning for matches
Object detection and recognition techniquesHOG descriptorsThe scale issueThe location issueImage pyramidSliding windowsNon-maximum (or non-maxima) suppressionSupport vector machinesPeople detectionCreating and training an object detectorBag-of-wordsBOW in computer visionThe k-means clustering
What did we just do?SVM and sliding windowsExample – car detection in a sceneExamining detector.pyAssociating training data with classesDude, where's my car?
Detecting moving objectsBasic motion detection
Meanshift and CAMShiftColor histogramsThe calcHist functionThe calcBackProject functionIn summaryBack to the code
Predict and updateAn exampleA real-life example – tracking pedestriansThe application workflowA brief digression – functional versus object-oriented programmingThe Pedestrian classThe main programWhere do we go from here?
Artificial neural networksNeurons and perceptrons
Network layers by exampleThe input layerThe output layerThe hidden layerThe learning algorithms
ANN-imal classificationTraining epochs
MNIST – the handwritten digit databaseCustomized training dataThe initial parametersThe input layerThe hidden layerThe output layerTraining epochsOther parametersMini-librariesThe main file
ImprovementsPotential applications
To boldly go…

Overview

Dive into the world of computer vision by mastering OpenCV 3 with Python. This book takes you on a journey from the basics of image processing to advanced concepts like object detection and artificial neural networks, equipping you with the skills to build real-world applications.

What this Book will help me do

Master the fundamentals of image and video processing with OpenCV.
Learn to detect and recognize faces and objects in images and videos.
Discover techniques for object tracking and depth estimation.
Build and train classifiers for various image recognition tasks.
Create neural network-based applications with OpenCV.

Author(s)

Joe Minichino is an expert in computer vision and has worked on numerous projects utilizing OpenCV for real-world applications. Joseph Howse brings extensive experience in Python programming and machine learning, and co-authoring brings a styles-and-skills complementary approach. Their writing style is approachable and focused on providing practical value.

Who is it for?

This book is ideal for software developers, data scientists, and enthusiasts interested in computer vision. Whether you're a novice looking to learn the basics or an expert aiming to explore the latest features of OpenCV 3, this book has something for you. If you want to implement image processing and analysis in your projects, this is suitable. A Python programming background is expected.