Building Computer Vision Projects with OpenCV 4 and C++ : Implement Complex Computer Vision Algorithms and Explore Deep Learning and Face Detection.

Print version record.

Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with OpenCV; Understanding the human visual system; How do humans understand image content?; Why is it difficult for machines to understand image content?; What can you do with OpenCV?; Inbuilt data structures and input/output; Image processing operations; GUI; Video analysis; 3D reconstruction; Feature extraction; Object detection; Machine learning; Computational photography; Shape analysis; Optical flow algorithms; Face and object recognition; Surface matching

Text detection and recognitionDeep learning; Installing OpenCV; Windows; Mac OS X; Linux; Summary; Chapter 2: An Introduction to the Basics of OpenCV; Technical requirements; Basic CMake configuration file; Creating a library; Managing dependencies; Making the script more complex; Images and matrices; Reading/writing images; Reading videos and cameras; Other basic object types; Vec object type; Scalar object type; Point object type; Size object type; Rect object type; RotatedRect object type; Basic matrix operations; Basic data persistence and storage; Writing to FileStorage; Summary

Chapter 3: Learning Graphical User InterfacesTechnical requirements; Introducing the OpenCV user interface; Basic graphical user interface with OpenCV; Adding slider and mouse events to our interfaces; Graphic user interface with Qt; Adding buttons to the user interface; OpenGL support; Summary; Chapter 4: Delving into Histogram and Filters; Technical requirements; Generating a CMake script file; Creating the graphical user interface; Drawing a histogram; Image color equalization; Lomography effect; Cartoonize effect; Summary

Chapter 5: Automated Optical Inspection, Object Segmentation, and DetectionTechnical requirements; Isolating objects in a scene; Creating an application for AOI; Preprocessing the input image; Noise removal; Removing the background using the light pattern for segmentation; Thresholding; Segmenting our input image; The connected components algorithm; The findContours algorithm; Summary; Chapter 6: Learning Object Classification; Technical requirements; Introducing machine learning concepts; OpenCV machine learning algorithms; Computer vision and the machine learning workflow

Automatic object inspection classification exampleFeature extraction; Training an SVM model; Input image prediction; Summary; Chapter 7: Detecting Face Parts and Overlaying Masks; Technical requirements; Understanding Haar cascades; What are integral images?; Overlaying a face mask in a live video; What happened in the code?; Get your sunglasses on; Looking inside the code; Tracking the nose, mouth, and ears; Summary; Chapter 8: Video Surveillance, Background Modeling, and Morphological Operations; Technical requirements; Understanding background subtraction; Naive background subtraction

Does it work well?

This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and projects. By taking this Learning Path, you will be able to work on complex projects that involves image processing, motion detection, and image segmentation.

eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - Worldwide