OpenCV with Python Blueprints

Packt Publishing Limited
  • 1. Auflage
  • |
  • erschienen am 19. Oktober 2015
  • |
  • 230 Seiten
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-1-78528-986-6 (ISBN)
Design and develop advanced computer vision projects using OpenCV with PythonAbout This BookProgram advanced computer vision applications in Python using different features of the OpenCV libraryPractical end-to-end project covering an important computer vision problemAll projects in the book include a step-by-step guide to create computer vision applicationsWho This Book Is ForThis book is for intermediate users of OpenCV who aim to master their skills by developing advanced practical applications. Readers are expected to be familiar with OpenCV's concepts and Python libraries. Basic knowledge of Python programming is expected and assumed.What You Will LearnGenerate real-time visual effects using different filters and image manipulation techniques such as dodging and burningRecognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensorLearn feature extraction and feature matching for tracking arbitrary objects of interestReconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniquesTrack visually salient objects by searching for and focusing on important regions of an imageDetect faces using a cascade classifier and recognize emotional expressions in human faces using multi-layer peceptrons (MLPs)Recognize street signs using a multi-class adaptation of support vector machines (SVMs)Strengthen your OpenCV2 skills and learn how to use new OpenCV3 featuresIn DetailOpenCV is a native cross platform C++ Library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. OpenCV has C++/C, Python, and Java interfaces with support for Windows, Linux, Mac, iOS, and Android. Developers using OpenCV build applications to process visual data; this can include live streaming data from a device like a camera, such as photographs or videos. OpenCV offers extensive libraries with over 500 functionsThis book demonstrates how to develop a series of intermediate to advanced projects using OpenCV and Python, rather than teaching the core concepts of OpenCV in theoretical lessons. Instead, the working projects developed in this book teach the reader how to apply their theoretical knowledge to topics such as image manipulation, augmented reality, object tracking, 3D scene reconstruction, statistical learning, and object categorization.By the end of this book, readers will be OpenCV experts whose newly gained experience allows them to develop their own advanced computer vision applications.Style and approachThis book covers independent hands-on projects that teach important computer vision concepts like image processing and machine learning for OpenCV with multiple examples.
  • Englisch
  • Birmingham
  • |
  • Großbritannien
978-1-78528-986-6 (9781785289866)
1785289861 (1785289861)
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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.
  • Cover
  • Copyright
  • Credits
  • About the Author
  • About the Reviewers
  • Table of Contents
  • Preface
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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
  • Chapter 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

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